{
"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 = 1\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 0x55d10035d3a0>"
]
},
"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",
" \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 1152x576 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), density=True);\n",
"plt.hist(phi_mass_MC_Dplus,bins=70, alpha=0.7, range=(lower,upper), density=True);\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(16,8)"
]
},
{
"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=\"Ds_ConsD_M\"\n",
"#plt.title('MC', fontsize=12)\n",
"plt.hist([MC_Ds_tuple_dict[label][i][0]/1000 for i in range(len(MC_Ds_tuple_dict[\"Ds_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][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_1[\"Ds_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][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_2[\"Ds_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][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_4[\"Ds_ConsD_M\"]))],alpha=0.7,bins=70,range=(lower_MC,upper_MC), label='MC after cutting on phi mass');\n",
"label=\"Dplus_ConsD_M\"\n",
"plt.hist([MC_Dplus_tuple_dict[label][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][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][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][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/D_s_reco.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": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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X9x//8R/POHJz//33z9y5c/Pwww8nyarpGxMnTsx1112XZHCKx1DXvmTJkkyZMiUf+chHhrWe1vMhlABgWCZOnJgPfOADT2v/oz/6o8yZMyeHHXZYpk6dmv7+/tx6661PO+7EE0/MiSeemGnTpq36xbgmRx11VN7znvdkYGAgK1euzL/8y7/k2GOPzdSpUzN16tQhF656qilTpmS//fbLrrvumpNPPnmNww0HBgZy8sknZ5999snUqVPzhje8IT//+c9z5513rlosa+bMmfnrv/7rJIM3FGeeeWamTJmSXXbZZdXUjKF89rOfzUUXXZTJkydnt912y0033ZTx48fnox/9aHbdddcccMABefWrX73Ga7/vvvvy5je/edXiVmedddawrhsANjQrVqxY9UjQ/fbbL69//etz6qmnJkne+9735vOf/3x222233HzzzatGGAwMDOSRRx7J5MmTc/bZZw953OoOPPDAPPTQQ9lxxx3zkY98ZNXUiqfad999c91116W/vz/z58/PGWeckaVLl2annXbKlClT8uEPf3hY1/XqV786f//3f59ddtkld955Zz74wQ+u8bih7i9OOOGE9Pf3Z/Lkydlzzz3zmte8Ju9///uz7bbbZpdddkl/f3/+7M/+LI8++uiQNbztbW/L/vvvn/7+/gwMDOQTn/hEkuTDH/5wzj777EybNi2/+tWvhrz2v/3bv82UKVPS39+fjTfeOAcffPCwrv25qCfm6mxopk2b1p5YLRRgtLr11luHnCPI0GbPnp0Xv/jFOfHEE7suZZ1a089HVV3XWpvWUUljjvsRYEPnXmPdWLJkSQ455JB1MrJgffNC70eMlAAAAAA6YaFLAEad2bNnd10CADCGTZo0aUyMklgbhBIwXF/+0zW3v3P+yNYBAIxZs+YuXGP7ee/efYQrAVg7TN8AAAAYwzbUdQbp3tr42RFKAABjTlVtXFU3VNXFve2tquryqrqpqi6rqi27rhFgJIwfPz7Lly8XTPCctdayfPnyjB8//gV9jukbAMBY9MEktyZ5SW/7tCQLWmtnVdWHettPfwYuwCgzceLE3HXXXVm2bFnXpbABGj9+fCZOnPiCPkMoAcAzqqq8613vyvnnn58keeyxx/KKV7wie+65Zy6++OIkyYIFC3Lqqafm4YcfzqOPPpr99tsv55xzzrDPcfjhh+eWW27Jn//5n2fLLbfMAQcckFe+8pXr5HrWhbX1CNK5c+ducNe+IaqqiUkOTvI/kvxVr/ngJHv23p+f5JoIJYAxYNy4cXnVq17VdRmMYUIJeKEsgMlIGurn7fkaxs/p5ptvnptvvjkrVqzIZpttlssvvzzbbrvtqv3XXHNNTjjhhFx66aWZNGlSHn/88Zx77rnDLuHuu+/O9ddfn5/85CdJkunTp2fy5MnP6T/mjz/+eDbeeONhH/98+6xrc+fOfc7XzvPyqSQfSbLFam19rbVlSdJaW1ZVE4bqXFXHJDkmSbbffvt1WScAjHrWlADgWc2YMSNf//rXkyTz5s3LkUceuWrf2WefnVNOOSWTJk1Kkmy88cZ573vf+7TP+MEPfpC99tor/f392W233XLLLbckSQ444ID8/Oc/z8DAQM4444wsWrQo73rXuzIwMJAVK1bk+9//fvbaa69MmTIl06dPz1133ZVkMLw44YQTstdee+XTn/70k841e/bsHHXUUdl7773z+7//+6tGbVx11VXZZ5998ra3vS2TJ09Oknz+859Pf39/dt5557znPe/Jo48+msceeyxHHXVUJk+enClTpmTOnDlJkh//+MeZPn16+vv7s8ceezzro75++ctf5sADD8zkyZPT39+f7373u1myZMmqcyfJnDlzMnv27FxwwQVPu/YPf/jD2XnnnTN16tR86EMfGva/F0OrqkOSLG2tXfd8P6O1dm5rbVprbVpfX99arA4Axh4jJQB4VkcccUROP/30HHLIIVm8eHGOPvroXH311UmSxYsX57TTTnvWz9hpp53yve99LxtttFGuuOKKnHTSSbn44otz4YUX5pBDDsmNN96YJLnyyiszZ86cTJs2LY888kiOO+64fOMb30hfX1/mz5+fk046KV/60peSDI52uOaaa9Z4vsWLF2fhwoX57W9/m8mTJ+fQQw9Nklx//fW57bbbst122+WHP/xhvva1r+W6667LuHHj8r73vS9z587N1KlTc88996wKHR544IEkydFHH525c+dmhx12yLXXXpu//Mu/zHe/+90hr/nYY4/NwQcfnOOPPz4rV67Mgw8+mF//+tdrPPawww7LOeecs+raly5dmksuuSS33HJLqmpVDbxgr0tyaFXNSDI+yUuq6vwky6qqrzdKoi/J0k6rBIAxQigBjHme+f7s+vv7s2TJksybNy8zZsx4Xp9xzz335IgjjsiSJUuy0UYbZcWKFc/aZ/Hixbn99tuz//77JxkMISZM+M9R9YcddtiQfd/61rdm0003zaabbpo3vvGNueaaazJhwoTsscce2W677ZIkl19+eW644Ybsvvvgv/WKFSuy9dZb57DDDstPfvKTHH/88TnwwANz0EEH5Z577sl1112Xd7zjHavO8bvf/e4Z6//mN7+Zr3zlK0mSjTbaKC95yUuGDCWeaquttsq4ceMya9aszJgxI295y1uG1Y9n1lo7OcnJSVJV05Oc2FqbWVXnJJmZ5Oze64LOigSAMUQoAcCwHHrooTnxxBNz1VVXZfny5avap0yZkuuvvz477rjjM/b/+Mc/noMOOijHHXdclixZkunTpz/rOVtrmTp16qpRGU+1+eabD9m3qta4vXqf1lpmzZqVM84442n9b7jhhlx66aX5p3/6p1xwwQX55Cc/mb6+vlUjOobjqTUkg+HEypUrV20/9NBDa+y7ySab5Nprr82VV16Zr371q/nMZz6Tb33rW8M+N8/ZqUnmV9XRSX6V5PCO64HnRMAObKisKQHAsBx99NE59dRTM2XKlCe1n3DCCTn99NPzs5/9LEmycuXKfO5zn3ta/xUrVuQVr3hFkuSLX/zikOfZbLPNVo1A6O/vzx133JHrr78+yeCTP2677bZh1XvhhRfmkUceyb333psrr7wye+6559OO2X///fOVr3xl1eiF+++/P3feeeeq0OXtb397zjjjjCxcuDB9fX3p6+vLRRddlGQw0HhiXYyh7LfffqsW/Vy5cmUeeOCBTJgwIXfffXeWL1+eRx99dNVaHU+99gcffDAPPvhgZsyYkbPOOmvV3wFrT2vtqtbaIb33y1trb2qtTem9Dm9ICwDwggglABiWiRMn5gMfePoTEv/oj/4oc+bMyWGHHZapU6emv78/t95669OOO/HEE3PiiSdm2rRpefjhh4c8z1FHHZX3vOc9GRgYyMqVK/Mv//IvOfbYYzN16tRMnTo1V1111bDqnTJlSvbbb7/suuuuOfnkk9f4lISBgYGcfPLJ2WeffTJ16tS84Q1vyM9//vPceeed2XvvvTMwMJCZM2fmr//6r5Mk8+fPz5lnnpkpU6Zkl112WTU1Yyif/exnc9FFF2Xy5MnZbbfdctNNN2X8+PH56Ec/ml133TUHHHBAXv3qV6/x2u+77768+c1vzsDAQPbee++cddZZw7puAIANSbXWuq7heZk2bVpbtGhR12UwljzXRzF6JOgGY30e8nrrrbdmp5126rqMDc7s2bPz4he/OCeeeGLXpaxTa/r5qKrrWmvTOippzHE/wkgb6nfWUNaH32XA2PNc7keMlAAAAAA6YaFLAEad2bNnd10CAADDYKQEAAAA0AmhBMB6bkNd+4d1y88FADAaCCUA1mPjx4/P8uXL/QeUJ2mtZfny5Rk/fnzXpQAAvCDWlABYj02cODF33XVXli1b1nUprGfGjx+fiRMndl0GAMALIpQAWI+NGzcur3rVq7ouAwAA1gnTNwAAAIBOCCUAAACATgglAAAAgE4IJQAAAIBOCCUAAACAToz40zeqakmSB5I8nuSx1tq0qtoqyfwkL0/yyyR/2lr7zUjXBgAAAIycrkZK7NtaG2itTettn5ZkQWttSpIFvW0AAABgFFtfpm8cnOSLvffn97YBAACAUayLUKIlubyqbqqq43ttfa21ZUnSe53QQV0AAADACBrxNSWSvLa1dndVTUjyjaq6bbgdq+qYJMckyfbbb7+u6gMAAABGwIiPlGit3d17XZrkgiS7J1lWVX1J0ntdOkTfc1tr01pr0/r6+kaqZAAAAGAdGNFQoqo2r6r/8sT7JAcm+VGSS5LM7B02M4OLXQIAAACj2EhP39gmydeqqiX5Lxl8DOj/SXJ1kvlVdXSSXyU5fITrAgAAAEbYiIYSrbX/m6R/DbuWJ3nTSNYCAAAAdGt9eSQoAAAAMMYIJQAAAIBOCCUAAACATgglAAAAgE4IJQAAAIBOCCUAAACATgglAAAAgE4IJQAAAIBOCCUAAACATgglAAAAgE4IJQAAAIBOCCUAAACATgglAAAAgE4IJQAAAIBOCCUAAACATgglAAAAgE4IJQAAAIBOCCUAAACATgglAAAAgE4IJQCAMaWqxlfVoqq6sapur6pP1aDZVfXzXvuNVTWj61oBYLTbpOsCAABG2MNJ3tBa+21VjUvynST79vad3Vqb011pADC2CCUAgDGltdaS/La3OS7JxkmWdlcRAIxdpm8AAGNOVW1cVTdmMIy4qrV2c2/X+6vqtqr6UlVtPUTfY3rTPxYtW7ZsxGoGgNFIKAEAjDmttcdbawNJJibZp6r2TfKZJH+YZOckP03yd0P0Pbe1Nq21Nq2vr2/EagaA0UgoAQCMWa21e5N8PclerbVlvbBiZZLPJdm92+oAYPSzpgTAEGbNXbjG9vPe7f8psCGrqpclebi19kBVbZZk/ySfrKoJrbUn1pZ4e5IfdVYkAIwRQgkAYKx5ZZL/XVWVZHySea21i6rq/KrqT7JpkjuSzOqySAAYC4QSAMCY0lpbnGRgDe0zOygHAMY0a0oAAAAAnRBKAAAAAJ0QSgAAAACdEEoAAAAAnRBKAAAAAJ0QSgAAAACdEEoAAAAAnRBKAAAAAJ3YpOsCAACAdWPW3IVrbD/v3buPcCUAa2akBAAAANAJoQQAAADQCaEEAAAA0AmhBAAAANAJoQQAAADQCaEEAAAA0AmhBAAAANAJoQQAAADQiU26LgAAAHiyWXMXdl0CwIgwUgIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADoRCehRFVtXFU3VNXFve2tquryqrqpqi6rqi27qAsAAAAYOV2NlPhgkltX2z4tyYLW2pQkC3rbAAAAwCg24qFEVU1McnCSf1qt+eAkX+y9P7+3DQCw1lXV+KpaVFU3VtXtVfWpGmTkJgCMsC5GSnwqyUeSrFytra+1tixJeq8TOqgLABgbHk7yhtbaQJKdk7w2yb4xchMARtyIhhJVdUiSpa21655n/2N632wsWrZs2VquDgAYC9qg3/Y2xyXZOMnSGLkJACNupEdKvC7JoVW1JMk/J9mvqs5Psqyq+pKk97p0TZ1ba+e21qa11qb19fWNVM0AwCjTW3T7xgzec1zVWrs5wxy56UsSAFh7RjSUaK2d3Fqb2FqblOSIJN9src1MckmSmb3DZmZwyCQAwDrRWnu8N31jYpJ9qmrf59DXlyQAsJZ09fSNpzo1ycFVdVMGh0qe0nE9AMAY0Fq7N8nXk+yVYY7cBADWns5CidbaVa21Q3rvl7fW3tRam9J7/XVXdQEAo1tVvayqtui93yzJ/klujpGbADDiNum6AACAEfbKJP+7qirJ+CTzWmsXVdX3ksyvqqOT/CrJ4V0WCQBjgVACABhTWmuLkwysoX15kjeNfEUAMHatL2tKAAAAAGOMUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAABmL87HAAAgAElEQVQA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6IRQAgAAAOiEUAIAAADohFACAAAA6MQmXRcAMFJmzV3YdQnAeqCqtkvypSRbJdk0yXmttb+pqtlJ/iLJst6hH2utXdJNlQAwNgglAICx5tEkx7XWFlfVFkmur6pLe/vObq3N6bA2ABhThBIAwJjSWrs7yd299w9U1eIk23ZbFQCMTdaUAADGrKqalGT3JN/pNb2/qm6rqi9V1dadFQYAY4RQAgAYk6rqxUkuSHJCa+2+JJ9J8odJdk7y0yR/N0S/Y6pqUVUtWrZs2ZoOAQCGSSgBAIw5VTUuyVeTzGut/WuStNaWtdYeb62tTPK5DI6geJrW2rmttWmttWl9fX0jVzQAjEJCCQBgTKmqSnJekltba2eu1j5htcPenuRHI10bAIw1FroEAMaa1yU5KslNVXVjr+1jSd5ZVf0ZfEzoHUlmdVQfAIwZQgkAYExprX0nSa1h1yUjXQt0ZdbchWtsP+/da5y1BLDOmL4BAAAAdOJ5hxJV1VdVe1bV5muzIAAAAGBsGFYoUVX/o6pOX237wCQ/S/K9JEuqauo6qg8AAAAYpYY7UuKdSf59te2/TfKtJK9NcmOST67lugAAAIBRbrihxLZJ/m+SVNX2SXZJckpr7QdJPpVkr3VTHgAAADBaDTeU+F2Sl/bevyHJva2163rb9yd50douDAAAABjdhvtI0G8n+WhVJcmJSb6+2r4dM7i+BAAAAMCwDXekxPFJNk3yf5I8lOTk1fa9M8l31nJdAAAAwCg3rJESrbWfZXBRyzV5a5IVa60iAAAAYEwY7iNBv1lVOw6x++VJLl17JQEAAABjwXCnb0xP8pIh9m2RwcUvAQAAAIZtuKFEkrSnNlTVxklen+TXa60iAAAAYEwYck2Jqjo1ySm9zZbkmt7TN9bk79dyXQAAAMAo90wLXV6S5J4kleTvkpyZZMlTjnkkyW2ttavXSXUAAADAqDVkKNFaW5hkYZJU1QNJvt5au2ekCgMAAABGt2GtKdFa+19rI5CoqvFVtaiqbqyq26vqUzVoq6q6vKpuqqrLqmrLF3ouAAAAYP023EeCblpVp1TVdVX1i6pa+tQ/wzzfw0ne0FobSLJzktcm2TfJaUkWtNamJFnQ2wYAAABGsWdaU2J1/5hkZpJ/S/L1JI8/n5O11lqS3/Y2xyXZOMnSJAcn2bPXfn6Sa5J84PmcAwAAANgwDDeUeHuSE1prn3mhJ+w9RvS6JH+Y5HOttZurqq+1tixJWmvLqmrCCz0PAACs72bNXdh1CQCdGtb0jSQPJblpbZywtfZ4b/rGxCT7VNW+w+1bVcf01qRYtGzZsrVRDgAAANCR4YYSn09y5No8cWvt3gxOBdkrybKq6kuS3usa16horZ3bWpvWWpvW19e3NssBAAAARthwp2/8Ksm7quobSa5I8run7G+ttc8+24dU1cuSPNxae6CqNkuyf5JPJrkkg2tWnN17XTDMugAAAIAN1HBDiU/1XrdPcsAa9rckzxpKJHllkv9dVZVkfJJ5rbWLqup7SeZX1dEZDEAOH2ZdAAAAwAZqWKFEa2240zye7XMWJxlYQ/vyJG9aG+cAAAAANgxrJWwAAAAAeK6GHUpU1aSq+lRVXVtVP6mqXXrtx1fV69ZdiQAAAMBoNKxQoqr2SLI4yZuTXJ/kVUle1Ns9IclJ66Q6AAAAYNQa7kiJszP4RIydkxyfpFbb9/0ke67lugAAAIBRbrihxG5JPttaaxl80sbq7k3ye2u1KgAAAGDUG24ocV+Slw2x71VJ7l475QAAAABjxXBDiQuTnF5VE1dra1W1RZITk/zrWq8MAAAAGNWGG0qclOShJLcnuaLX9ukk/zfJ40lOWfulAQAAAKPZsEKJ1tpvkuyV5Lgkv8xgMPGLJB9L8rrW2gPrrEIAAABgVNpkuAe21h5Jcl7vDwAAAMALMqyRElX17ap6b1X1reuCAAAAgLFhuGtK/CrJnCQ/r6rLq+roqtpyHdYFAAAAjHLDmr7RWntHVW2e5NAkhyf5TJJ/qKorksxP8jXrSsBTfPlP19z+zvkjWwcAAMB6argjJdJa+21rbV5r7Y+TTEhyTG/X55PcvS6KAwAAAEavYYcSq+uNivhpkv9Icn+SzdZmUQAAAMDo95xCiarao6rOrKo7knw7yRuSfDrJDuuiOAAAAGD0GtaaElX1N0nekeS/Jrk9yReSzG+t/Wgd1gYAAACMYsMKJTIYSHwlyT+31m5ch/UAAAAAY8Rwn77x++u6EACAkVBV2yX5UpKtkmya5LzW2t9U1VYZfKrYy5P8MsmfttZ+012lADD6DXtNiap6SVWdUFXzq+qKqtqh1354Ve287koEAFirHk1yXGttcpLXJPnzqhpIclqSBa21KUkW9LYBgHVoWKFEVf23JD9K8t+TvCTJvkm26O3eM8kp66Q6AIC1rLV2d2ttce/9A0kWJ9k2ycFJvtg77PzeNgCwDg13pMTfJflxBhe6fEuSWm3ft5PsvZbrAgBY56pqUpLdk3wnSV9rbVmS9F4ndFcZAIwNw13ocp8kb22tPVhVGz9ln1/aAMAGp6penOSCJCe01u6rqmfr8kS/Y5IckyTbb7/9uisQAMaA4Y6UeCjJ+CH2vSLJ8rVTDgDAuldV45J8Ncm81tq/9pqXVVVfb39fkqVr6ttaO7e1Nq21Nq2vr29kCgaAUWq4ocTlST5WVZuv1tZ6oyaOS3LJWq8MAGAdqMEhEeclubW1duZquy5JMrP3fmYGF7sEANah4U7f+HCS7yb5aZJvJGlJPppkcpLNkxy5TqoDAFj7XpfkqCQ3VdWNvbaPJTk1yfyqOjrJr5Ic3lF9ADBmDCuUaK3dWVVTk/xVkjdmMJz4r0m+luSs1prpGwDABqG19p08edHu1b1pJGsBgLFuuCMl0lr7TQYfCfrf1105AAAAwFgx3DUlAAAAANYqoQQAAADQCaEEAAAA0IlhrykBY8aX/7TrCgAAAMYEIyUAAACATgw7lKiq/6eqfm9dFgMAAACMHc9lpMQXkmyfJDXolKp6+bopCwAAABjthlxToqoWJLkxyQ97fypJ6+3eKMmpSS5Ocvc6rhEAAAAYhZ5poctvJNk1yYwkO2UwkDinqr6VZGGeHFIAAAAAPCdDhhKttU8/8b6qXpRkRZLrk7w6yVEZDCS+WFXfSHJFa+0b67hWAAAAYBQZck2JqvpAVe1TVVu01h7uNX+htXZkBoOJSjIvyYuTnLPuSwUAAABGk2eavnFIko8neVlV/SyDIyOOqKrNktzUO2ZBa+36dVwjAAAAMAoNOVKitXZAa22bJNsmeV8GR0a8KYNrTfw6gyHFe6vqjb3pHQAAAADD9qyPBG2t3b3aehF/3lrbMsm0DIYU2yWZm+Q366xCAAAAYFR61lBiCLf2Xj/WWtsuyWvWUj0AAADAGPFMa0o8SWtt9QCjJflZkod7+25dYycAAACAIQw7lFhda21lklet5VoAAACAMeT5Tt8AAAAAeEGEEgAAAEAnhBIAAABAJ57XmhIAY9msuQuf1nbeu3fvoBIAANiwGSkBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRiREOJqtquqr5dVTdX1b9X1Um99q2q6vKquqmqLquqLUeyLgAAAGDkbTLC53s0yXGttcVVtUWS66vq0iSzkixorZ1VVR9KclqSD4xwbQDAGFBV/zPJIUmWttYm99pmJ/mLJMt6h32stXZJNxVCd2bNXbjG9vPevfsIVwKMFSM6UqK1dndrbXHv/QNJFifZNsnBSb7YO+z83jYAwLowN8mBa2g/u7U20PsjkACAEdDZmhJVNSnJ7km+k6SvtbYsSXqvE4boc0xVLaqqRcuWLVvTIQAAz6i19u0kv+66DgCgo1Ciql6c5IIkJ7TW7htuv9baua21aa21aX19feuuQABgLHp/Vd1WVV+qqq2HOsiXJACw9ox4KFFV45J8Ncm81tq/9pqXVVVfb39fkqUjXRcAMKZ9JskfJtk5yU+T/N1QB/qSBADWnpF++kYlOS/Jra21M1fbdUmSmb33M5MsGMm6AICxrbW2rLX2eGttZZLPZXCKKQCwjo300zdel+SoJDdV1Y29to8lOTXJ/Ko6Osmvkhw+wnUBAGNYVU1orT0xUvPtSX7UZT0AMFaMaCjRWvtOkhpi95tGshYAYGyqqnlJpid5WVXdlcEvR/atqv4kmya5I4OPKwcA1rGRHikBANCp1tqRa2g+b8QLAQC6eyQoAAAAMLYJJQAAAIBOCCUAAACATlhTAgAA1rFZcxd2XQLAeslICQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBObdF0AwNo2a+7CrksAAACGwUgJAAAAoBNCCQBgTKmq/1lVS6vq5tXatqqqy6vqpqq6rKq27LJGABgrhBIAwFgzN8mBT2k7LcmC1tqUJAt62wDAOiaUAADGlNbat5P8+inNByf5Yu/9+b1tAGAdE0oAACR9rbVlSdJ7nTDUgVV1TFUtqqpFy5YtG7ECAWA0EkoAADwHrbVzW2vTWmvT+vr6ui4HADZoQgkAgGRZVfUlSe91acf1AMCYIJQAAEguSTKz935mBhe7BADWsU26LgAAYCRV1bwk05O8rKruSnJq78/8qjo6ya+SHN5dhWzIZs1d2HUJABsUoQQAMKa01o4cYtebRrQQAMD0DQAAAKAbQgkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE56+AQAAPKOhHnV63rt3H+FKgNHGSAkAAACgE0IJAAAAoBNCCQAAAKATQgkAAACgE0IJAAAAoBOevsHY9eU/7boCAACAMc1ICQAAAKATQgkAAACgE0IJAAAAoBNCCQAAAKATIxpKVNX/rKqlVXXzam1bVdXlVXVTVV1WVVuOZE0AAABAN0Z6pMTcJAc+pe20JAtaa1OSLOhtAwAAAKPciIYSrbVvJ/n1U5oPTvLF3vvze9sAAADAKLc+rCnR11pbliS91wlDHVhVx1TVoqpatGzZshErEAAAAFj7Num6gOeitXZuknOTZNq0aa3jcuD5+fKfrrn9nfNHtg4AAICOrQ8jJZZVVV+S9F6XdlwPAAAAMALWh1DikiQze+9nZnCxSwAAAGCUG9HpG1U1L8n0JC+rqruSnNr7M7+qjk7yqySHj2RNAAAAQDdGNJRorR05xK43jWQdAAAAQPfWh+kbAAAAwBgklAAAAAA6IZQAAAAAOjGia0oAAMBoMGvuwq5LABgVjJQAAAAAOiGUAAAAADph+gbAWjDUMN7z3r37CFcCAAAbDiMlAAAAgE4IJQAAAIBOmL4BAAA8L2uavmjqIvBcGCkBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdEIoAQAAAHRCKAEAAAB0QigBAAAAdGKTrgsAAFhfVNWSJA8keTzJY621ad1WBACjm1ACAODJ9m2t3dN1EQAwFpi+AQAAAHRCKAEA8J9aksur6qaqOn5NB1TVMVW1qKoWLVu2bITLA4DRxfQNAID/9NrW2t1VNSHJN6rqttba5asf0Fo7N8m5STJt2rTWRZGMnFlzF3ZdAsCoZqQEAEBPa+3u3uvSJBck2b3bigBgdBNKAAAkqarNq+q/PPE+yYFJftRtVQAwupm+8f+3d+fRklT1Ace/PxnQsArDlsgEjisiGiQhBhVFcUEIbqisevC44YlETIxo5BiSAzFEjEcwCaImcWMRg+ACgiyKGowSRDYRNQwBhRlQQAWEYbj5495mavp1v+l+r7urqvv7OadOv66q7rq/ulV177t965YkSVK2DXB2RCRgQ+AM4Jx6kyRJ0nSzUUKSJAlIKf0v8LS60yFJ0izx9g1JkiRJklQLGyUkSZIkSVItbJSQJEmSJEm1cEwJSZIkSSPzhv/4Xs/5nzjMJ+xKmsueEpIkSZIkqRY2SkiSJEmSpFp4+4YkSZJmXr9bDiRJ42WjhKTWsgIpSZIktZu3b0iSJEmSpFrYKCFJkiRJkmpho4QkSZIkSaqFY0pIkiRJGrt+Y0F94rDdJpwSSU1iTwlJkiRJklQLe0po+p16QN0pkCRJDeGTmySpWWyUkJqiX+PJwWdMNh2SJEmSNCE2SkiSJGkq2StCkprPMSUkSZIkSVIt7CkhSZIkqTY+lUOabfaUkCRJkiRJtbCnhCRJklrNsSMkqb3sKSFJkiRJkmphTwlJkiRJjeNYE9JssFFCUuO1uVuuFSpJkiSpPxslJEmSGqRXY+asNWS2uTFakjQcGyUkSZI0FHuBqU4ef9J0sVFCkiRJI2EPB9XJxgqpnWyUkCRJaqlhGwH850yS1DQ2SkiSJM04ezhIkupio4QkSZKkqTXu2zq8bURaHBslJEmSGm5UPRnsESGt0bTbn4Z58o4NIZomNkpIkiRJ0oiNohHQhkTNAhslND1OPaDuFEiSJGlKTWMDQdN6i2g22SghNV2/xpaDz5hsOjRSdruUJEmjNo0NJ5p+NkpIagwLUkmSpPbxxxYtho0Sah9v05AkSZImbtgfkEb1g5ONG9PtEXUnQJIkSZIkzSZ7SkiSJEmSWmeYnhj2tmguGyUkSZIkSY01zser2lhRPxslpLbyqRySJEnSothYUT8bJdRcDmg5tXzKhiRJkpps2PqqjRgLZ6OEmsEGCAmwtV6SJEmzxUYJSYtmz4fxs7FCkiSpuexZsXCNaZSIiL2BE4D1gE+mlP6h5iRJ7eRYE5K0YNZHJEmTYCPGGo1olIiIRwInA3sAtwGXRcQFKaUr6k2ZtHBX3nxXz/m7LHv0hFMiSRqE9RFJUlNNcyNGIxolgGcA16aUbgaIiDOAfQErAeMy5K/pfbuOb3BCz/mj+Ie8af/UtyU9/ezSI8/7xnTU+T3ne5tG83hbhzRS1kckSVOhTY0YTWmU2A64ufL+FmDPepIyV78MPWLF0WPbZr9/dIf9R3TY7+/XWHHEit7bvXLI7Y4i/aPaB6MybHrqSv8w273y+Bf3nH9En/VP2ubY3uuP8RwZpVGkv9939DPsvhn2+4ctiPqlZ9hrUb909ivoeqVz2EJx2Gv0sI1uNvDMlEbXRyRJmkaRUqo7DUTEwcBzUkqHl/cHAXumlN7Std6bgTeXt08CfjTipGwJ3DHi72wqY51esxSvsU6nWYoVRh/v9imlrUb4fTPD+shYTFMsYDxNZzzNZjzNVlt9pCk9JW4BllXeb1fmrSWldApwyrgSERGXp5T+aFzf3yTGOr1mKV5jnU6zFCvMXrwNZ31kxKYpFjCepjOeZjOeZqsznkfUsdEevgvsHBHbRcT6wAHAeTWnSZIkzRbrI5IkTVgjekqklH4bEW8Fzic3lHwmpXR5zcmSJEkzxPqIJEmT14hGCYCU0rnAuTUnY2xdMRvIWKfXLMVrrNNplmKF2Yu30ayPjNw0xQLG03TG02zG02y1xdOIgS4lSZIkSdLsacqYEpIkSZIkacbMRKNERPxbRKyMiGv6LP+riLiyTNdExOqI2KIsWx4RV5dljb+vdIBYt42IiyLiuoi4ISIOryzbu8T/w4h49+RSvTCLjLVV+QoDxbs0Is4r8X43InauLJu2vJ0v1lblbUQsi4hLS/7cEBFH9VgnIuLEEu/3I2LXyrLW5O0IYm1N3g4Y644RcVlE3B8R7+xa1pp8VX/juG5HxBYR8bVyLlwQEZtPIpay7QXFM9/5EBHHRMTPYk09bJ+mx1OW9bwetTR/nlTZ/1dGxK8i4siyrJb8GVfZWFf+LCaeJp4/I8ifRp0/i8yftp4/Q9dBxpo/KaWpn4DnALsC1wyw7n7AxZX3y4Et645hVLECxwLHl7+3Au4Cfgd4ZIl1GbA+cDmwa93xjCPWNubrgPGeBPxN+XtH4LLy9zTmbc9Y25i3wLbA08rfmwA/BnbpWmd/4Bwgyn75QRvzdjGxti1vB4x1a2A34DjgnZX5rcpXp3mPg5Fft8tn/qL8/Q7gxBbE0/d8AI6pHv9tyJ/yvuf1qI3507XOesBtwPZ15s9iyosmnj+LjKdx589i4inLGnX+LDaeyjptOn+GroOMM39moqdESulS4JcDrn4QcNoYkzNWA8R6C7BJRASwMXAHcD/wDODalNLNKaVVwBnAvuNO72IsItZWGiDeHYGLy7rXA1tHxGOYzrztF2vrpJRuSyldVf7+NXAV0B3LvuSnAKSU0hXAkohYRsvydpGxtsogsaaUVqaUvges6vp4q/JV/Y3pur0v8Ony92eY4LGx0HgGPPcnbkxlTevyp2udvYCfppRuGk8qBzPGsrGW/FlMPE08f8ZYnrcuf7rWac35s8A6yNjyZyYaJQYVERsCewP/WZmdgE43lSPqSdlIfQzYCfg5cDXw9pTSQ8B2wM2V9W4p89qsX6wwffkKOcZXAkTEHwPbA7/PdOZtv1ihxXkbETuQW62/1bWoXx62Nm8XECu0NG/nibWf1uarhraQ6/ZWKaXbAcrr1hNL7brNd22mzN+BuefDn0XE9RHx2YhYOpmkDmQhZU2r8wc4kLk/ztWaPyMuG2vPnwWWf/N9tm35Aw0+fxaTP7Tr/OmnlvPHRom17Qd8O6VUbWXePaX0dHLL1+sj4oX1JG1k3kNuLfs9YBfgIxGxab1JGpv5Yp22fAX4W2CbiLgOOIrc3WpaH68zX6ytzNuI2Bj4PHBkSunuutMzTouItXV5O0v5qgWZtuv2vPH0OR/+GXg8+UeEnwInTjTF85u2smZd+bMB8FLgzMpnas2fabuGLiaeJp4/01aeLzJ/PH8WwUaJtc1p3Uop3VZeV5Izdbca0jVKewBnlq5HPwFuJJ8ot5DvHerYrsxrs36xTmO+klK6O6V0cEppp5TS/uTWyxuYwrydJ9ZW5m1ErE/uoXVaSumsHqv0y8PW5e0iYm1d3g4Qaz+ty1ctzAKv27dHxFYA5XXlJNM8n/muzf3Oh5TS7Sml1aUn48k06LxeYFnTyvwpXgJckVJaUflMbfkzprKxtvxZTPnXxPNnTOV5K/OnaNv5008t54+NEkVEbAY8lzyASWfeRuWWDiJiI/KtHdfVk8KR+Sm5VZKI2Ib8T/py4LvAzhGxXTmIDwDOqyuRI9Iz1inNVyJis4hYUv4+FPh+6fUzdXnbL9Y25m1EBPAJ4IcppQ/2We1c4JCy/q7AQymlm2lZ3i4m1rbl7YCx9tOqfNXCLfC6fS5waPn7UBp0bMxzbe57PkREtfvv/jTovF5gWdO6/KmsMmdctbryZ4xlYy35s8jyr3HnzxjL89blT2V5286ffuo5f9KERwOtYyIfILeSB/K4BXgDcDhweGWdw4DTuz73WHL3/x+QRy09Foi641lMrOTRWC8EfkhuHX9T5bP7ANeWZe+tO5ZxxdrGfB0w3meWOK8CzgI2n+K87RlrG/MWeDa5++xVwJVl2qcr3iB3AbyuLP+jNubtYmJtW94OGOu25fj+FfnpQLcAm7YtX53mPQ5Gft0GlpLLtqvL6xZNj6ff+VCWfabMvx64AFjWgnj6Xo/amD9l2UbAL4DNur6zlvwZ8Bo6dNlYV/4sJp4mnj+LjKdx588Ijrc2nj9D10HGmT+dA0CSJEmSJGmivH1DkiRJkiTVwkYJSZIkSZJUCxslJEmSJElSLWyUkCRJkiRJtbBRQpIkSZIk1cJGCdUqIo6JiFSZ7o6Ir0bEH9SdtsWIiA1KbLtMeLtbl+3uMMLvPCEilq9jnWo+PhQRd0bE9yLiuIjYdlRpmWf7y8u2j+6x7NmVtO0w7rQMIiLeExHn95j/3Ig4JyJWRsSq8vqViDgwIga+XkfElyLi6nmWfyQi7oqIR0bEfhHx04jYYKHxSFLbWR8Z+Xatj8xdZn1k7nLrIwJslFAz3A3sXqbXA8uAiyJi81pTtTgbAH8DTLQSAGxdtrvDhLcLa/LxmcCB5Gegvxa4OiL+cALb/03ZbreDyrJGiIhHA0cBx3XNPxK4BFgNHAHsBbyN/PzozwLPG2IzpwE7R8ROPba/HvAq4KyU0v0ppS8B9wJvHD4aSZoq1kdGx/rIXNZH1t6O9RE9zEYJNcGDKaXvlOks4FBgKfDSmtM1ERHxO3WnYUSq+Xh+Sun9wNOAW4HTS+EzTl8GdoqInTszKgXeF8e87WG8Ebg1pXRpZ0ZE7AqcAPxdSumVKaUzUkqXppQ+l1I6CHg2cMcQ2ziHXLAf1GPZ84BtyBWFjo8B74iIGDIWSZom1kemg/WRwVgfUWPYKCwAcGoAAAqWSURBVKEm6nTzekx1ZkRsERGnRMSKiPhtRPxXRDyja531Sle0GyLigdLd7LNd67wtIn4cEfdHxE8i4h1dy4+JiDsi4ukR8Z2yresj4kVd670mIq4q27knIq6IiE7r8a/L679Xu+qVKUXEIRHxqYi4C/hS+b4UEW/rlZauedtHxGkljQ9ExHUR8drSFbCz7y7pbHfI/ffoiDg1In4TEbdGxHvnZs/gUkp3Ae8CHg+8cJDPRMQuEXFJ2acPlLx8+wAf/RnwLdb+deL5wMb0qARExLsj4vsRcV+J92sR8bSudZ4fEf9d9td9EXFNRBxQWT7fMdDPa4Gzu+YdAawEju31gZTSZSmlH3Sl7Y0RcW05jm+KiHdV1r+HfFwd0P1d5P2zEri4Mu8cch79yTrSLkmzxPpIV1q65lkf6c36yJr1rY9oIDZKqImWldfbOzMi4pHAhcBzgD8H9gFuAS6MiMdWPvtR4H3AJ4EXAG8BqgXhkcCJwBnAi8p6J0TEu7vSsCHw8bLuviUtZ0bEZuV7nkxu2f0KuXB7KXA6sGn5/PPL67Gs6Qp6a+X7P0C+EL+UPhf+XiJia+Ay4KnkrnR7AR8hd5O8FTikrPpnle0Os/9OL9/5VuB1Zf1eXRCH8XXgQQYoYCL/kvBlctfLV5a0fABYf8Btncba6T2IXBje02PdLYAPkY+DV5O7VF5YyeMtyJWHq8nHwEvIx9dGZfm6joFe8W1D/rXmO12LngNcnFJ6cJAgI+KvgH8p23sh8E/A31UrAiVtT4hKV9WIWJ+8Xz+XUlrdmZ9Sugm4DXjxINuXpBlhfaQP6yPrZH1kDesjWreUkpNTbRNwDLkb2JIyLSMXAvcA21TWewNwP7B9Zd56wPXAieX9juQC/019trVe2dbJXfM/RC50HlVJUwKeVVnnKWXey8r7Q4Db54lr47L+YV3zdyjzT+/xmQS8rdf+qbx/P3AXsFWf7e5cvmfPrvmD7L+nl8++vLLOhsAKYPkg+TjP8luBfx3geHhMScNThjyOlpO7G24FrAJ2I99HeyfwcuBPy/fuMM+x8aiy/uvKvGeVz2zU5zPzHgN9PvOC8p1P6Jp/H/D+rnlROS+WAI8o8zclV1je07X+0cAvgCXlfSf+D1TW6eyHZ/ZI29eALwwTj5OTk9O0TFgfqS6zPmJ9pDrP+ojT2Cd7SqgJlpIv3KuA/wOeC+ybUlpRWecF5Nbcn0XEkohYQr5Ifp3S+k6+N201eRCeXnYs2/p81/zPkS+sT63Muzel9O3K++vL6++W1x8ASyPi3yPihRGx8SCBVnxlyPU7ng+cm1K6fZ1rrm2Q/fdM8i8I53Y+lFK6F7hggWmtGvTewBWUCkNEvLq05A+s7JeLyb9O7F22e17PBEXsGRGXRsS95LjvAx4NPLGs8qMy79TII0J3D3S2kGOgE88veyW/6/3+rDkvVgH/WObvTv515MxOXpb8vJj8a8uTAFJKD5AH93pNxMP3Zh4A3ET+davbLyrpk6RZZH1kcNZH5mF9xPqIhmOjhJrgbnJL8p+QuzeuJnfVq9qS3KVsVdf0FnLBTnn9dSm4eulcxLsL0M77LSrz7quukNZ0LVtS3l9D7na2I7mQ+WVEnDlEoXXngOt1W0ouKIc1yP7bHPhNKTyqhhnQaI6IeBQDpjvl7oIvJu+f/wBui3wf7R8PscnTgdcABwNnp5Tu75GmxwFfJd9rexD52NutpPFRJS13lLRsTK443hERF0TEE8ryxRwD3ZWinwPbdc27qKRpN9buartlef0xa+dlp9K6tLLuacDvA7uXfHgZ+Vex7gpHrzRJ0qyxPjI46yPrZn1kDesjmteSuhMgkUdJvrz8/d8R8RvgsxFxakrpwjL/l+SL3JE9Pt+5yP8C2CQiNuxTEegUvFt1ze+879Va3FdK6Wzg7IjYlHx/30nAycArhvmeitXkbntVG3W9X2jr8SD77y5g44jYoKsisGWPzwzjeeRrTa/W8DlSSlcDLyv3Gz4LOB74ckQ8JqW0aoCv+AI5H15Nvveyl/3Ihd6rUkr3wcP3j651/2VK6ZvAXpFHJH8e8GHyL1lPL8uHPQZuK69LWbtydSnwoohYr1PhTCndCVxe0lbNj85x+iJ6VyZ/VPn7EnLF5kDyr2qbsPYo11VbVNInSbPI+khmfQTrI9ZHNEn2lFATnQZcS36+dcdFwJOBH6eULu+aOiM8X0wuRHs9dghyl8c7yK3JVa8iP3v56jmfGEBK6VcppTOA/wQ6z2HuXLQHHRAJcutzp6sepYvbXl3rXAS8JCKW0lu/7Q6y//6LXFjvU0nDhuTCZkEiPwP7eOAn5IGtBpZSWpVS+jp50KStGLAykvII28eT86PfNjcgVwIeqsx7GdDzcWgppftSSueSBxub86ztPsdAL519/ZSu+SeRK3d/Pc9nOy4j/3K2bY+8vDyl1BlpvfOL2ufIFaKDgR+mrlGzK3YGrhpg+5I0K6yPYH3E+khf1kc0MvaUUOOklFJE/D3514k9Suvwp4DDgW9ExAnAjeT77XYnD+7z4ZTSjyLiFOCkMir0t8itrfunlF6XUlodEccBH4z8WKsLgT2AtwNHp5R+O2gaI+JNwB8C55MrFk8kVya+UGJ4ICJuBF4VEdeSC+d1XWC/CBwWEf9T4nsjcwu+D5G7kl4SEceSKw47ARunlD5Ivgf2PuC1EfErYHX51WeQ/XdFRHwN+Gi5H3EF8E5yV7xBLImIzojWm5T981by4FR7V7qc9hX5EVjHkrsn3kgu/N9Fbm0fuNU8pfS+daxyEfAP5EekfRx4HPBu8q8znbTsSy44v0juzrgMeDPwjbJ83mOgT7pWRsSV5Ptlz6rMvyIi3gn8U0TsQh6N/efkX6aeBWxLHkyKlNJdEXEMcHLkx659k1yheTLw3JRS92O3TiM/4usVrF2xflhEbF+2MYr7dSVpKlgfsT6C9RHrI5qM1IDRNp1md6LPKMnkXxhuAM6rzNuM3F3tZnLBdDt5ZOw9uj7318D/lnVWAJ/u+u4jyC3lD5T13jFgmh4ejZp8Ef9qScOD5Av2icCGlfX3JRdeD5bP7sCa0a7/tMf3bwacCdxLLvCOBv62Oy3A9uRC4s4Sw7XAIZXlrycPHrQ6n+JD7b/NyfdA3lP23fvIo0gvHyAfU5keIhemlwPHkVvQBz0etiEXWjeV/XYnuVB97Do+txw4YZ7lc0a7JhfoPwN+Sx506xnV7yHfm3l2ydsHy/76JGWk8UGOgT5p+Uvgxj7L9iRXOm4vebSSPNDXgUB0rXso8D/kSt+9ZX8f1ed7byzxP77P8j8nnxMxX9qdnJycpnXC+ghd8VkfsT5ifcRpYlOUA0CSNAGlC+ly8i9mF9WcHAAi4irglJTSR+pOiyRJGj/rI2oSx5SQpAlKa+4xfW/daQGIiP3II3p/rO60SJKkybA+oiaxp4SkiYmIRzBPY2jKj+CSJEkaG+sjUrPYU0LSJP0bc59N/vBUBkqSJEkaJ+sjUoPYU0LSxJRCfr5HaV2V1n4muSRJ0khZH5GaxUYJSZIkSZJUC2/fkCRJkiRJtbBRQpIkSZIk1cJGCUmSJEmSVAsbJSRJkiRJUi1slJAkSZIkSbX4f1YgX+TFY4YGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1296x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plt.suptitle('Reconstructed D_s mass', fontsize=15)\n",
"plt.subplot(1,2,1)\n",
"label=\"Ds_ConsD_M\"\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=(1.75,2.10), label='MC after presel cuts',density=True);\n",
"plt.hist(left_h_Dplus,alpha=0.7,bins=70,range=(1.75,2.10), label='MC after presel cuts',density=True);\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",
"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=(1.90,2.1), label='Data after presel cuts',density=True);\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/D_s_reco.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 = 1930\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 \"PT\" 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=0.99980\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",
" \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": 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_Ds_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.05, width_data=0.06)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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p6ccypHjfTnj+BK97jYrfe6Syv/eVq3jxNUYq997dRmxPCrGd2E5sJ7bHIrZX4theZkmxfB+HGvIHe0lkonNunqQLzKyG/Al9mnx/pNHmOz0/UhY7D26UzeVfeKSTvZxz30nqHdRgHyJfu/pH+WBeV75NfjpFTqhGCeY3ilkuYoak6+Sbfpwq6Xvn3LeSZGafSTrNzPaWr+F6w8XvIF/WGslfSIWC495A/kZUyDn3cRD8zwsCykb5Zk6rtDOQllrwoe0y+b5osQOXxIqULdEAAQkHDkjR1fJB81bn3PDoGWZ2uHzgLFPOuVVmFnk8w3r5ms6Eg5w4534zs1ny30acqhQCp5kdKf/64g0gkcg6SfXNrFqcm1+xayK4jwyVb/51RNAUK3r+4fL3mVT8j3xfseOdc0Vqtc2sv3zgTEbkg1cyNdmlve7LQyPFf59jy9BVPmg+6pz7Y/SC5psy3llWBUrhvp3w/El076mi4sbXKEnfexOIHLt43/Rmx1uB2L5LxHZiO7Gd2B6L2F6JY3uZNJ82/+yzPwf/jk+Q2f/mnJvjnLtf/gYpSeeVxf4Dfwl+T3W+70zs/p1z7kvn3MPyo8eV9f5L6//J14QdZ2a148yP3PTnxkyPNME4Xf4mNz1m3uGSLo5Ztrx1iDOtrfyJ/nmcec/J37Aulu8/1ETSS2VUwy/tvGB21aRvsXwzn2PN9weJ1T7OtNL4XfB7cpx5ncpoH/E8JV/Ll6uSa5Ijngx+DzI/WmBC5ke8jIg8FzHZPkeSP69rK36fmI5xpu0pfzP8ME7QrCU/WmKsyIfGROfB7+SbG8Zr5pXK+7JYPnieEN0sKYHSXvflodh1a36k2N/JP14h8oGgws/fJO7bn8ufP8fHWf04+XtPRRzDcpNMfFXq995YkW/M4tW8l9jnjtieELGd2C4R24ntRRHbVXlj+24nxeaHu39Z/iT/Uf7xCZF5x5hZ/TirRabtqm1+MvvPMbOH5Ie73yA/wmFk3qFmtld57n93Bf1cxmnnM80Kmdn+8q9nm3x/mmjvyTcpuSFYNzo4zpC/SQyO+r8i3GNmhe31gyZ/I4J/n42z/PPytf+9tXPQgbHJ7szMWplZ/3jnWFCTdHvw7wclbSd4D16Rrz0rUiMW1E7G68xfGiuC3x1j9nGwEgw8UUbek68J7KZdDA4TGC8/qMUBkiYHN9EizKyWmf1JO99fBdv/yjmX6HEQ8UTOi+HB+RLZfiP5R1zEijQBOzY6qAfv94PygTXWGvlrJVGfmBWS9greh0JmdpGSr0lW8I3No/L9iZ6J+VAhM6tuZo2DZUt73ZeHW8xs36j9V5NvwlZdRa/bROdvM/nBQ8pEivftMcHvB2LOhzry54PkR5+tkkqKrzFSvffGinxoLPKYGTM7QP4RHrHlIrbvArGd2E5sl0Rsj16O2F7JY3tKzafNbGjwZzX5Gp1D5Zt11ZJ/TthFruhIb5dLus7M3pcfxn6T/MAAF8i3+x6VYnlvNN+HxSTlyHdoby9fY7BUUs+g/0lEZ/kL8qOgfOvla8IvkL9hF2nmkkaDJZ0saYD5PhDvaeczzepJ6uecK9JkLmg684V2NieJDo6fyB/rPeWbPBV5XmA5+lrSl2YW/SzD/SW9qTg3gKD/1HvyTXl+k7TAOZfMtxoR9eVHxou8xwvlm1w0lK/dOkS+U//AhFvYabB8Dd7t5vvKfSypmfx7MEO+tjveNzSpeFb+A+ajZnaK/APL8+QHkHhL/rwsc845J9+XJtnld5jZhfLvWVdJ35nZdPl+Xdvly3yKfO30A1JhE8fDJP1visUbL/9twnmSFprZZPnBZHrI99H6XfTCzrnNZvakpH6S5pnZFPmgeLr8+/WBYmr/nXMbzOxT+QFJxso3J3KSpjjnvpAfHfRkSR+Z2SvyN+Zj5M/LySq5z1qsu+RrMS+Q9LWZvRFsbx9JXeS/8RsbLJvydV9OZskfy8hzHE+Xv6/MkfS3qOVek0/KhgQfKOfJD6jTQ/6De+syKk/S923n3Etm1lX+mH1pZpO0s/9ZK0mvOOfGqQooRXyNltK9N47Jkr6RdJWZtZS/9vaRf2/flD++0YjtySG2E9uJ7cR2YrtX+WO7S+75XC7mZ7t8U4Iv5G8GXSVVi7Pe8fIDIiyU71fym/zoja9IOjKZfQfbWRqz/x3yI6Itkr/weinmuWHBeofIjzA3V/7g/yYfSN5Q8LzDJPefH+y3YzLTS/Mj/yHofvkPJluC4/uOpC4lrDMi2P+XceZNC+a9UsIxXZpg3tBUXlfUcciSNEy+z9sWSd8p6KBfwrqXRb2vA1M8ZlnyN91ngnMx8h6vk7/oh0nKTXA+58eZvo98s6+V8k2u5snXJJ8VrHNjvNedoGx9g3X6xkw/WtI/5b/52BiU83r5i9wp5nl72o1nGSaxbLFnGcZZpov8IyiWBMdkc/C+viTpjKjlIs+gPLoU534t+SaS3wXnzVL5AJwVr3zylXl3Bsttk6/lfEH+Q1rc4xW81tflB7nYEfveBOfR59p57f1TPnDGfR938XpqyAf2z4L3eWtQ1uclHVDa615xnhcbNS8v3vlT0jkfdaz2k/9wuTh4f5fLJzU5cbazn/xzGtcGy34paYj8h514+xiqEu4l8cqtFO/b8knkDfIjd24KfubI91OKF5cSnvOJzp/y/FEp42uwbr5SvPcmev2SWsjH5tXy1/ps+Q8rxc47EdtTeX+J7cR2YjuxndheBWK7BQsDpWZm+ZI6OOfK63EcaWVmf5GvJTzDOTct3eWpjMzsY0l7O+fy0l0WJCeoWe8jqZVzbml6S4PSyPR7L9Ir088vYvuuEdurHmJ76ZX1c4qBKiteXwcz+538iKqrFf9ZjaEX9Ds5Xsk/vxAAgApBbC8dYjvCpiwfyQRUdXPM7Gv5pjab5ZvlnCvf1Ody51y8Z8iFnvMPg6eCDQBQGRHbS4HYjrAhKQZ2ek5+UIjr5IeCXyM/EMcDzrn8NJYLAACUDrEdwC7RpxgAAAAAEFo0iwAAAAAAhFZamk83adLE5eXlpWPXAIAMNGfOnFXOudx0l6MqIzYDAMpSVYrNaUmK8/LyVFBQkI5dAwAykJl9n+4yVHXEZgBAWapKsZnm0wAAAACA0CIpBgAAAACEFkkxAAAAACC0SIoBAAAAAKFFUgwAAAAACC2SYgAAAABAaJEUAwAAAABCi6QYAAAAABBaNdJdAKCsbNmyRatXr9avv/6q7du3p7s4AEqpevXqqlevnho1aqSsrKx0FwfAbiA2A5kh02MzSTEywpYtW7Rs2TI1bNhQeXl5qlmzpsws3cUCkCLnnLZt26b169dr2bJlatmyZUYGXyAMiM1AZghDbKb5NDLC6tWr1bBhQzVp0kS1atUi6AJVlJmpVq1aatKkiRo2bKjVq1enu0gASonYDGSGMMRmkmJkhF9//VU5OTnpLgaAMpSTk6Nff/013cUAUErEZiDzZGpsJilGRti+fbtq1qyZ7mIAKEM1a9akDyJQhRGbgcyTqbGZpBgZg2ZZQGbhmgaqPq5jILNk6jVNUgwAQBVlZtlmVmBm88zsGzMbZd5QM1seTJ9nZmelu6wAAFRWjD4NAEDVtUVSB+fcRjOrKWmmpE7BvJHOuQfSVzQAAKoGvikGQmzs2LEyM40dOzbdRUmKmaljx47pLgZQaThvY/BvTUnVJf2cxiIB2E3EZqDi8U0xQmHIhAXpLkKJ7rvgsDLZzvbt2zVmzBi9+OKLWrBggdavX686deqoRYsWOuqoo9S9e3d169atTPZVFUT6vZiZvvnmG+2///5xl+vUqZPy8/MlSc8++6z69u1bbJmNGzfqqaee0pQpU7Rw4UKtXbtWderU0e9+9zt17txZV111lfbbb7/yeilAQmZWXdIcSa0lPe6cW2hmPST90cyuDub1d879EmfdayVdK0ktW7aswFIDxGZiM7EZlQdJMZAhtm/frnPOOUdvv/22cnNz1bVrV+2zzz7atGmTvvzyS02YMEFLly4tEni7deum448/Xs2aNUtjyctXjRo19Ntvv+mZZ57RvffeW2z+N998o/z8/MLl4pk1a5Z69Oih5cuXq3nz5jrrrLO09957a9OmTZo3b56GDx+u4cOHa9asWTr66KPL+yUBRTjntks60swaSJpmZp0k/V3SPZKcpKGSHpLUK866T0p6UpLatGnjKqrMQFgQm+MjNqOyISlOwZAJC8qs1hAoa+PHj9fbb7+tY489Vu+++67q1atXZP6mTZv06aefFplWv3591a9fvyKLWeH22msvNWvWTM8++6zuvvtu1ahR9Lb39NNPS5LOPfdcTZw4sdj6ixcv1umnn64NGzbor3/9qwYOHFhsG8uWLdOtt96q9evXl98LAXbBObfWzN6UdLxz7r3IdDN7XFJ+2gqWBpFvIInZSDdic3zEZlQ29CkGMsTHH38sSerbt2+xoCtJderUUadOnYpMK6nf0rRp03TSSSepbt26atSokc4//3wtXLhQffv2lZlp6dKlhcsuXbpUZqa+fftq6dKluuSSS9SkSRNlZ2erTZs2euONN4ptf926dRo2bJg6dOigxo0bq0aNGsrNzdV5552nTz75ZPcORoxrrrlGK1asKFaObdu2aezYsTrxxBN1yCGHxF33T3/6k9avX6/bbrtNt912W7GgK/lmpy+//LJOOOGEMi03sCtm1sTM6gV/15bUWdJCM9szarHukr5KR/mAsCM2J0ZsRmVCUgxkiOzsbEm+ydHuevnll3XmmWfq888/14UXXqjrrrtOa9asUbt27fTtt98mXO/777/Xscceqx9//FHXXHONevfurcWLF6tr16567733iiy7aNEi3Xvvvapdu7b69OmjwYMH66yzztIHH3yg9u3b6+23397t1xHRs2dP1a1bt7DmOWLKlCn6+eefdc0118Rdb8mSJXr33XeVnZ2tW2+9dZf7ycrKKpPyAinYW9KHZjZf0jxJ051zr0t60My+MLPFks6V9Kd0FhIIK2JzYsRmVCZJN582s2z5Rz3UkFRX0puSbpJ0p6RrJK0MFr3dOfdWGZcTwC5ceOGFGjVqlEaNGqWVK1eqW7duOuaYY7TvvvumtJ1ff/1Vf/jDH5Sdna1PPvlERxxxROG8P//5z3H7/kTk5+drxIgRuvnmmwunXX755Wrfvr2GDx9epDb8kEMO0YoVK5STk1NkGytWrFDbtm1100036Ywzzkip7InUq1dPl1xyicaOHat///vfat68uSTpqaeeUk5Oji666KK4r2vmzJmSpGOOOUYNGjQok7IAZck594WkI+NMvywNxQEQg9icGLEZlUkq3xRHnoV4pKRDJJ2gos9CPDL4ISEG0uCEE07QSy+9pL322ksvvviiunfvrry8PDVu3FjdunXT66+/ntR2Jk+erLVr1+rKK68sEnQl6c4771Tjxo0Trtu6desiQVeSTj75ZLVu3VqfffZZkek5OTnFgq4kNW3aVBdffLEWL16sZcuWJVXmZFxzzTWFI4BKvub8nXfeUa9evVSnTp246/z444+SVBioAQBIBbG5ZMRmVBZJJ8U8CxGo/C655BItW7ZM06ZN0x133KFzzjlH1atX16RJk3TeeeepT58+cq7kAWY///xzST5gxqpVq5aOO+64hOu2adMm7vRmzZppzZo1xaZ/9NFHuuiii9SiRQtlZWXJzGRmGjFihCRp+fLlJZY1Fccdd5wOO+wwjRkzRjt27NDTTz+tHTt2JGyeBQBAWSA2J0ZsRmWR0ujTu/MsRAAVo2bNmurSpYu6dOkiSdqxY4cmT56sK664Qs8//7y6deum888/P+H669atk6SETZIaNmyYcN1E/XaqVaumHTt2FJk2ceJE9ejRQ9nZ2ercubP2339/1a1bV9WqVVN+fr7ef/99bdmypcTXmqprrrlG/fv319SpU/Xss8/qmGOO0VFHHZVw+cjjMMryAwCAssWTIVAVEJsTIzajMkhpoC3n3Pag+XRzSSdHPQuxtXyT6m/ln4VYjJlda2YFZlawcuXKeIsAKAfVqlVTt27dNGjQIEnSjBkzSlw+0mxq7dq1cecnmp6qO+64Q7Vr19bChQs1adIkjRgxQnfffbeGDh1abs+WdWNmAAAgAElEQVQTvPzyy1W7dm1df/31Wr58ua699toSl2/Xrp0kqaCgoPADCQAAu4vYvBOxGZVBqUafds6tlR9o63jn3MogWd4h6XFJbROs86Rzro1zrk1ubm7pSwygVCIBdVdNtCK1sx9++GGxeVu3btWsWbPKpDxff/21Dj/8cLVq1arYvNjRMMtKgwYN1KNHD/373/9W3bp11bNnzxKXb9WqlU477TRt3rxZw4cP3+X2y7r2HACQ2YjNxGZUDkknxTwLEajcxo8fr3feeadYUyhJWrVqlZ544glJUvv27UvcTteuXVW/fn2NGTNG8+fPLzLvrrvu0i+/lE3viKZNm+qrr77Szz8XHZpg+PDhmjdvXpnsI55hw4Zp4sSJmjZtWtxnRsZ66KGHlJOTo/vuu08jRozQb7/9VmyZZcuW6eKLLy7zZzgCAKo2YnNyiM1It1T6FO8t6XkzM0nZksY75143sxfN7HBJtSQtk3RVOZSz0hoyYYEk0Z8Jaffpp59q9OjRatq0qdq1a6dWrVqpZs2aWrJkid566y2tW7dOXbt2VY8ePUrcTk5Ojv7+97/r8ssv14knnqiLLrpIzZo108cff6x58+apXbt2mjlzpqpV273HnA8YMECDBg3SUUcdpR49eqh27dr68MMP9fnnn+vMM8/U1KlTd2v7ibRs2VItW7ZMevmDDz5Y06ZNU48ePTRo0CCNHj1ap556qvbee29t3LhR8+fP10cffSQz0+DBg8ulzACAqonYnBxiM9It6aSYZyECldvNN9+sFi1aKD8/X7Nnz9aUKVPknFPjxo114okn6tJLL9Wll14qX69Vsl69eqlRo0a655579MorrygrK0vt27fXzJkzdffdd0tS3Ec2pGLgwIHKysrSI488oscee0w5OTk6+eSTNXPmTE2ZMqXcAm9pHH/88Vq8eLGeeuopTZkyRW+++abWrFmjOnXqqHXr1ho4cKCuvfbauM3NAADhRWwuP8RmlCXbVR+G8tCmTRtXUFBQ4fvdXfFGuOSb4sph0aJFOvjgg9NdjIznnFPr1q21adOmwucEAuUp2WvbzOY45+I/dwRJITajrBGbKwaxGRUtE2Pz7rWxAJCR1q1bF3dgilGjRum7775Tt27d0lAqAADCi9gMlJ+UnlMMIBw++eQTXXbZZercubP2228/bdmyRR999JFmzZqlFi1aaOjQoekuIgAAoUJsBsoPSTGAYg488EB17NhRM2fO1GuvvSYzU/PmzdW/f3/dfvvt2nPPPXe9EQAAUGaIzUD5ISkGUEyrVq306quvprsYAAAgQGwGyg99igEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFokxQAAAACA0CIpBgAAAACEFkkxAAAAACC0SIoBAAAAAKFFUgygXJmZOnbsmO5iVCn5+fkyMw0dOjTdRQEAZCBic+qIzZmtRroLAFSIgmfTXYKStbmiTDZjZsWm1apVS82aNVOHDh00ePBgHXzwwWWyL1Qe+fn56tSpU5FpNWvWVIMGDbT//vvrhBNO0CWXXKJjjz22TPbXt29fPffcc1qyZIny8vLKZJsAQojYTGzOYMTmqoWkGMhAd955Z+HfmzZt0meffabnn39er732mmbOnKkjjzwyjaVDedl3333Vt29fSdLWrVu1cuVKzZ07VyNHjtTIkSPVvXt3jR07VnvssUd6CwoAIURsDidic9VAUgxkoHhNe2655RY98MADGjVqlMaOHVvhZUL5y8vLi/vez5s3T71799Zrr72mjRs3aurUqRVfOAAIOWJzOBGbqwb6FAMh0blzZ0nSf/7znyLT161bp2HDhqlDhw5q3LixatSoodzcXJ133nn65JNPEm5v8eLFuvLKK5WXl6esrCzl5OTohBNO0OjRo5Mqz/Dhw1WtWjWddNJJWr16dZHy3HjjjWrevLmys7N10EEH6W9/+5u+++47mVlhbWtE3759ZWb67rvv9PDDD+vwww9X7dq1i/SV2rFjhx5//HG1bdtWe+yxh+rWrau2bdvqscce044dO4psb+nSpXH3E9GxY8diTeGi+xnNmzdPZ599tho0aKA6deqoQ4cO+vjjj+Nu66efftJVV12lvfbaS7Vr19aRRx6p5557Lqnjl4ojjzxS7777rnJzc/X2229r0qRJRea/+uqruvDCC5WXl6datWqpbt26OuaYY/TQQw8VOz5mVljGVq1aycxkZkWaas2ePVvXXXedDj30UNWpU0fZ2dk64IADNHDgQK1Zs6bMXx8AVFXEZmIzsbly4JtiICSmT58uSTrmmGOKTF+0aJHuvfdetW/fXn369FGdOnX0ww8/aPLkyZo6dapef/11nXHGGUXWefPNN3XhhRdqy5YtOuOMM9SzZ09t2bJFc+bM0cMPP6wBAwYkLMeOHTt044036uGHH9YFF1ygcePGKTs7W5K0efNmnXLKKZo7d66OOuoo9erVS+vWrdP999+vDz74oMTXN2DAAH388cfq3r27zj33XFWvXr1w3uWXX66XXnpJLVq00NVXXy0z08SJE3XDDTdo5syZGjduXErHMpGCggI98MAD6tixo2644Qb98MMPeuWVV3Tqqadq3rx5OvDAAwuXXbVqlU488UR99913ateundq1a6cff/xR119/vbp06VIm5Ym255576rrrrtOwYcM0btw4nX/++YXz7rjjDmVlZalLly7ac889tWHDBk2fPl0DBgzQ7Nmz9cILLxQue+edd2rSpEmaP3++BgwYoAYNGkhS4W9JeuaZZzR16lS1b99eXbt21fbt2zV37lw9+OCDmjp1qj799FPVq1evzF8jAFQ1xGZiM7G5ciApBjJQdDOdzZs3a/bs2ZoxY4Y6d+6swYMHF1n2kEMO0YoVK5STk1Nk+ooVK9S2bVvddNNNRQLvqlWrdOmll8o5p/fee0/t27cvtl4imzdvVq9evTRhwgT169dPo0ePVrVqOxusDB8+XHPnzlWvXr30wgsvFNb6/vnPf1bbtm1LfM0LFizQl19+qaZNmxaZPn78eL300ktq06aN3nvvvcI+O8OGDdMpp5yil156SWeffbYuvfTSErefjLfeekv/93//p+7duxdO69Kli3r37q3Ro0fr0UcfLZx+++2367vvvtOgQYM0fPjwwun9+vVTu3btdrss8XTs2FHDhg3TZ599VmT6P//5T7Vo0aLINOecrrvuOj311FPq16+fjjvuOEn+3Fq6dKnmz5+vG2+8Me5gHv/zP/+jxx57rFit/bhx43TZZZfp0Ucf1W233Va2Lw4AKjli807E5p2IzZUDzaeBDHTXXXcV/tx///2aMWOGWrVqpZ49e6p+/fpFls3JySkWdCWpadOmuvjii7V48WItW7ascPpzzz2n9evX66abbioWdCPrxbN69Wqddtppmjhxou6//349/PDDRYJuZNvVq1fXiBEjity0W7RooVtvvbXE1zxkyJC4+x4zZowkaeTIkUUGsahbt65GjhwpSXr66adL3HayTjnllCJBV5J69uyp7OzsIsFu27ZtGjdunBo0aKB77rmnyPJt2rTRlVdeWSblibXPPvtIklauXFlkemzQlXxTrP79+0uSpk2bltJ+mjdvHne01V69eqlJkyYpbw8AMgGxeSdi807E5sqBpDiOIRMWaMiEBekuBlBqzrnCn61btxY2D7ryyit10003FVv+o48+0kUXXaQWLVooKyursC/KiBEjJEnLly8vXHbWrFmSpLPOOivp8vz000866aSTNHv2bL344otxg+j69ev17bff6oADDtBee+1VbP7JJ59c4j6OP/74uNPnzp2r7OxsnXjiiXHXqVOnjj7//PMkX0nJ4tWYR/qBRffXWbx4sTZt2qRjjz22sHlatPJ6dqRzTpL022+/FZn+yy+/aPDgwTr88MO1xx57FL7/hx12mKSi738ytm3bpkceeUTt2rVTo0aNVL169cJtrlq1KuXtAUAmIDbvRGzeidhcOdB8GshwNWvW1BFHHKFXX31V++yzj0aPHq3+/furVatWkqSJEyeqR48eys7OVufOnbX//vurbt26qlatmvLz8/X+++9ry5Ythdtbu3atJKlJkyZJl2HFihVav369mjdvnrD50fr16yUV7f8SrWHDhiXuI7aWPWLdunXKzc0tVvMtSdWqVVPDhg2LDXBSWllZWXGnV6tWTdu3by9SJklq1KhR3OUTTd9dkdcZ/cFm7dq1atu2rZYsWaJjjz1WvXv3VqNGjVSjRg2tXbtWo0ePLvL+J+Piiy/WxIkTtd9++6lr165q2rRp4bEZNWpUytsDgExDbCY2RxCbKweSYiAk6tatq0MOOUSffPKJPvvss8LAe8cdd6h27dpasGBB4bSIm2++We+//36RaZHAuGrVqqT3fcQRR+jqq69W37591b59e82YMUP77bdfkWUizcQigT1Woum7Ur9+fa1bt047duwoFnydc1q7dm2RJmqRZSI1t7E2b95cqnLElklSkZE9oyWavrvee+89SUUHdHn66ae1ZMkS/e1vf9Mtt9xSZPkvvvgi6RFLIwoKCjRx4kSde+65mjhxYpFBVSTpoYceKmXpASDzEJuJzcTmyoHm00CIRIJXrVq1Cqd9/fXXOvzww4sFXWnnjTpapClUqs/Tu+yyy/Tyyy/rP//5j9q3b6+vv/66yPycnBztt99++uabb/TTTz8VW39XI1wmctRRR+m///1vYdOyaJ9++qk2btyoo48+unBapNY7Xg31pk2b9OWXX5aqHNEOOugg1alTR5999lncQJ6fn7/b+4j1888/64knnpDk34uIyPvQtWvXYuvEe/8lFQbTeB9OIts755xzigXd+fPn89gHAIhBbC6K2ExsTgeSYiAkpk2bpkWLFqlmzZpF+vA0bdpUX331lX7++eciyw8fPlzz5s0rtp0+ffooJydHI0eOjBsMSxrhskePHnr11Ve1atUqdejQoVgQ6927t7Zv365BgwYVuan/8MMP+tvf/pb0a40WGRhj0KBB2rRpU+H0TZs26eabb5YkXXXVVYXT69Wrp4MOOkgffvih/vWvfxXZ1uDBg7Vhw4ZSlSNazZo11atXL61du1Z33HFHkXkFBQWFA5CUlfnz56tz585atWqVTjvttCIDjkQGQIkN9osWLSo20EhE48aNJcXvz5Roe+vXr9f1119f2pcAABmJ2ExsJjZXDjSfBjJQ9GMftm3bpq+++kpvvPGGJOnee+8t0m9lwIABGjRokI466ij16NFDtWvX1ocffqjPP/9cZ555ZrFa5yZNmuill15Sjx491KlTJ5155pk6/PDDtXXrVs2dO1fLli0rFrCinXfeeZo8ebK6deumjh076t1339URRxwhSbr11ls1adIkvfjii/ryyy/VpUsXrVu3Tv/4xz90wgkn6M0334zb/6gkl156qSZPnqx//OMfOvTQQ3X++efLzDRp0iQtWbJEF198sXr16lVknVtuuUVXXXWVTjzxRF1yySWqXbu2pk+frg0bNuiII47Q/PnzUypDPPfee6+mT5+uBx54QLNmzSp8FuIrr7yi008/XVOmTEl5m0uXLi1877dt26ZVq1Zpzpw5mjNnjiTpwgsv1JgxY4qMPnnFFVdo1KhRuuGGGzRjxgy1bt1aS5cu1WuvvaazzjpLEyZMKLafU089VcOHD9fVV1+t7t27q3bt2mrQoIH69eunDh066IgjjtD48eO1fPlytWvXTmvWrNHEiROVl5envffeu3QHDACqOGLzTsRmYnNlQ1IMZKC77rqr8O/q1asrNzdXZ511lvr166fOnTsXWXbgwIHKysrSI488oscee0w5OTk6+eSTNXPmTE2ZMiVuU6yzzz5bBQUFuv/++zV9+nRNmzZNderU0e9//3vdeOONuyzf6aefrrfeekvnnnuuOnXqpGnTpqlt27aqXbu23nvvPf3lL3/Rq6++qpEjR6pVq1a67bbbdOaZZ+rNN9+M+4iKXRk/frw6dOigMWPGFDZTOvjggzVw4ED94Q9/KLb8lVdeKeecHnzwQT3xxBNq2LChunbtqnvvvbfYYx1Kq0mTJvroo490++236/XXX1dBQYEOPPBAPfbYY8rLyytV4P3+++8L3/saNWqoYcOG2m+//XTTTTepZ8+ecUfgbNWqlfLz8zV48GC98cYbcs7poIMO0siRI3X66afHDbynn366RowYoaeeekoPPPCAtm7dqn333Vf9+vVT9erV9c477+iWW27RG2+8oVmzZql58+bq3bu37rjjDv3+979P/WABQAYgNhdFbCY2VyaWqMN6eWrTpo0rKCio8P0mK/I4pvsuOKzY9HjT4i2LirVo0SIdfPDB6S4GytHzzz+vPn366PHHH9d1112X7uKggiR7bZvZHOdcmwooUsaq7LE5EWJz5UVsznzE5nDKxNhMn2IAlUq8gTx++uknDRs2TDVq1NC5556bhlIBABBexGZkOppPA6hUunXrph07dui4445TvXr19MMPP2jy5Mlat26d7rvvPvq9AABQwYjNyHQkxQAqlUsuuUTPP/+8xo4dqw0bNignJ0dHH320+vXrpwsuuCDdxQMAIHSIzch0JMUAKpX+/furf//+6S4GgEqGfsJA+hCbkenoUwwAAAAACC2SYgAAAABAaJEUAwAAAABCi6QYGSMdz9wGUH64plGWhkxYUNgvGRWH6xjILJl6TZMUIyNUr15d27ZtS3cxAJShbdu2qXr16ukuBoBSIjYDmSdTYzNJMTJCvXr1tH79+nQXA0AZWr9+verVq5fuYgAoJWIzkHkyNTaTFCMjNGrUSGvWrNGqVau0devWjG3aAWQ655y2bt2qVatWac2aNWrUqFG6iwSglIjNQGYIQ2zmOcXICFlZWWrZsqVWr16tpUuXavv27ekuEoBSql69uurVq6eWLVsqKysr3cUBUErEZiBzZHpsJilGxsjKylKzZs3UrFmzdBcFACqEmWVLmikfz+tKelPSTZIaSnpFUlNJP0q62Dm3Jl3lRHgRmwFUBTSfBgCg6toiqYNz7khJh0g6QVInSXdJmuqcO0zS1OB/AAAQB0kxAABVlPM2Bv/WlFRd0s+Szpb0QjD9xeB/AAAQB0lxOeBZiACAimJm1c1snnwynO+cWygp1zm3UpKC33ums4wAAFRmJMUAAFRhzrntQfPp5pJONrNOya5rZteaWYGZFaxcubL8CgkAQCVGUgwAQAZwzq2VH2jreEkrzSxXkoLfPydY50nnXBvnXJvc3NyKKywAAJUISTEAAFWUmTUxs3rB37UldZa0UNJbki4LFrtMfrAtAAAQR9KPZOKxDwAAVDp7S3rezExStqTxzrnXzexjSa+Y2ZWSfpJ0UToLCQBAZZbKc4ojj33YaGY15RPkTpK6yT/24UEzu0n+sQ/9y76oAAAgmnPuC0lHxpn+i6TTKr5EAABUPUk3n+axDwAAAACATJNSn2Ie+wAAAAAAyCQpJcU89gEAAAAAkElKNfo0j30AAAAAAGSCpJNiHvsAAAAAAMg0qYw+zWMfAAAAAAAZJemkmMc+AAAAAAAyTan6FAMAAAAAkAlIigEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAIBQGjJhgYZMWJDuYgAA0oykGAAAAAAQWiTFAAAAAIDQIikGAAAAAIQWSTEAAAAAILRIigEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFokxQAAAACA0Ap9UjxkwgINmbAg3cUAAAAAAKRB6JNiAAAAAEB4kRQDAAAAAEKLpBgAAAAAEFokxQAAAACA0CIpBgAAAACEFkkxAAAAACC0SIoBAAAAAKFFUgwAAAAACC2SYgAAAABAaJEUAwAAAABCi6QYAAAAABBaJMUAAAAAgNAiKQYAAAAAhBZJMQAAAAAgtEiKAQAAAAChRVIMAAAAAAgtkmIAAAAAQGiRFAMAAAAAQoukGAAAAAAQWiTFAAAAAIDQIikGAAAAAIQWSTEAAAAAILRIigEAAAAAoUVSDAAAAAAILZLiCjJkwgINmbAg3cUAAAAAAEQhKQYAAJUKFckAgIpEUgwAAAAACC2SYgAAqigza2FmH5jZQjP72sxuC6YPNbPlZjYv+Dkr3WUFAKCyqpHuAgAAgFLbJqmfc+4LM6snaa6ZTQvmjXTOPZDGsgEAUCWQFAMAUEU551ZIWhH8/auZfSFpn/SWCgCAqoXm0wAAZAAzy5PUVtLMYNIfzWyxmY0zs8ZpKxgAAJUcSTEAAFWcme0h6VVJNzrn1kn6u6TWkg6R9K2khxKsd62ZFZhZwcqVKyusvAAAVCYkxQAAVGFmVlPSa5LGO+cmSJJzbqVzbrtzboekx+W/QS7GOfekc66Nc65Nbm5uxRUaAIBKhKQYAIAqysxM0jOSFjnnRkRN3zNqse6SvqrosgEAUFUknRTz2AcAACqdkyRdLumUmDj8oJl9YWaLJZ0r6U9pLSUAAJVYKqNP89gHAAAqEefcTEkWZ9ZbFV0WAACqqqSTYh77AAAAwmDIhAWSpPsuOCzNJQEAVIRS9SkuzWMfGOESAAAAAFDZpJwUl/axD4xwCQAAAACobFJKinfnsQ8AAAAAAFQ2qYw+zWMfAAAAAAAZJZXRpyOPfVhgZvOCabdLutTMDpdUS9IySVeVbREBAAAAACgfqYw+zWMfAAAAAAAZpVSjTwMAAAAAkAlIigEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFokxQAAAACA0CIpBgAAAACEFkkxAAAAACC0SIoBAAAAAKFFUgwAAAAACC2SYgAAAABAaJEUAwAAAABCi6QYAAAAABBaJMUAAAAAgNAiKQYAAAAAhBZJMQAAAAAgtEiKAQAAAAChRVIMAAAAAAgtkmIAAAAAQGiRFAMAAAAAQoukGAAAAAAQWiTFAAAAAIDQIikGAAAAAIQWSTEAAAAAILRIitNsyIQFGjJhQbqLAQAAAAChRFIMAAAAAAgtkmIAAAAAQGiRFAMAAAAAQoukGAAAAAAQWiTFAAAAAIDQIikGAAAAAIQWSTEAAAAAILRIigEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFokxZXQkAkLNGTCgnQXAwAAAAAyHkkxAAAAACC0SIoBAAAAAKFFUgwAALALdG0CgMxFUgwAAAAACC2SYgAAAABAaJEUAwBQRZlZCzP7wMwWmtnXZnZbML2Rmb1jZgvM7J9m1jDdZQUAoLIiKQYAoOraJqmfc+73ko6RdLWZHSnpLklTnXOHSZoa/A8AAOIgKQYAoIpyzq1wzn0R/P2rpC8k7SPpbEkvBIu9GPwPAADiICkGACADmFmepLaSZkrKdc6tlKTg954J1rnWzArMrGDlypUVVVQAACoVkmIAAKo4M9tD0quSbnTOrUt2Pefck865Ns65Nrm5ueVXQAAAKrGkk2IG8wAAoPIxs5qSXpM03jk3IZi80sxyg/m5kn5OV/kAAKjsUvmmuMoP5jFkwgINmbAg3cUAAKBMmJlJekbSIufciKhZb0m6LPj7Mvn4DAAA4qiR7ILOuRWSVgR//2pm0YN5HBcs9qKkWZL6l3E5AQBAcSdJulzSAjObF0y7XdKdkl4xsysl/STpojSVDwCASi/ppDha1GAeVypmMA8zSziYh6RrJally5al2S0AAIjinJspyRLMPq0iywIAQFWV8kBbDOYBAAAAAMgUKSXFDOYBAAAAAMgkqYw+zWAeAAAAAICMkkqfYgbzAAAAAABklFRGn2YwDwAAAABARkl5oC0AAAAAADIFSTEAAAAAILRIigEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFokxQAAAACA0CIpBgAAAACEFkkxAAAAACC0SIoBAAAAAKFFUgwAAAAACC2SYgAAAABAaJEUAwAAAABCi6QYAAAAABBaJMUAAAAAgNAiKQYAAAAAhBZJMQAAAAAgtEiKAQAAAAChRVIMAAAAAAgtkmIAAAAAQGiRFAMAAAAAQoukGAAAAAAQWiTFAAAApTBkwgINmbAg3cUAAOwmkmIAAAAAQGiRFAMAAAAAQoukGAAAAAAQWiTFAAAAAIDQIikGAAAAAIQWSTEAAAAAILRIigEAAAAAoUVSDAAAAAAILZJiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFokxQAAAACA0CIpBgAAaTNkwgINmbAg3cUAAIQYSTEAAAAAILRIigEAAAAAoUVSDAAAAAAILZLiKoI+VwAAAABQ9kiKAQAAAAChRVIMAAAAAAgtkmIAAAAAQGiRFAMAAAAAQoukGACAKsrMxpjZz2a2MGraUDNbbmbzgp+z0llGAAAqO5JiAACqrrGSzogzfaRz7sjg560KLhMAAFUKSTEAAFWUc+4DSavTXQ4AAKqyGukuAAAAKHN/NLOrJc2R1N8590u6C4QKUPBs0f/bXJGecgBAFZP0N8X0WwIAoEr4u6TWkg6R9K2khxItaGbXmlmBmRWsXLmyosoHAEClkkrz6bGi3xIAAJWac26lc267c26HpMcltS1h2Sedc22cc21yc3MrrpAAAFQiSTefds59YGZ55VcUAACwu8xsT+fcz8G/3SV9lc7yQDRrBoBKriz6FCfVb8nMrpV0rSS1bNmyDHaLtCLAA0Damdl4SR0lNTGzf0u6U1InMztcUi1JyyRdlb4SAgBQ+e1uUvx3SfdIcpKGyvdb6hVvQefck5KelKQ2bdq43dwvAACh55zrGWfyMxVeEBQxZMIC3XfBYSUvROUyAFQau5UUO+cKR+Uws8cl5e9ugbCbCLIAAAAAkLTdek6xme0Z9S/9lgAAAAAAVUrS3xTTbwkAAAAAkGlSGX2afh45aLAAABDbSURBVEtVFU2qAQAAACCushh9GgAAABWJCm8AKDMkxQAAAKkqeFZtf1ke/F1AUgoAVdhuDbQFAAAAAEBVRlIMAAAAAAgtmk9XBeluokW/JQAAKkZ0zCXeAkCF4JtiAAAAAEBokRQDAAAAAEKLpBgAAAAAEFr0KQ4z+i0BAFD5MbYHAJQrvikGAAAAAIQW3xQDAABkMlqGAUCJSIpRFE20AAAAAIQIzacBAAAAAKFFUgwAAAAACC2aT6Ns0W8JAIDKL5XuUsR2ABmOb4oBAADKSsGzavvL5OJJJwCg0uKbYgAAADDYJoDQIilG+aOJFgCgKit4Vm1/WR78XUB8AoAMQ1JclZFAAvj/7d1vqG1pXQfw7y/HBKc/pN5RmD9FBEY40pVzEaFiwoTRiYIRMmt8cRNGIwILqSRIg6Je1BslkEt1DCeGITs0vZgp7ZUIY7bzXB0dzTAmZg6Nc71Z+qYwfXpx9h3PPZ59zj737LPXs876fOBw9lp7rbV/89znrN/81nr2swAAOBFFMcMwRAuAHu29K5w7Bw0FgPVQFPfGEC0AWIsLVx/efSHfAkya2acBAACYLHeKh2SIFgAAwKAUxWtiiBYAAEB/DJ8GAABgshTFAAAATJaieOS2tneO3ggAAIADKYoBAACYLEUxAAAAk2X2aQAAjme2ef2yJ2sAI+ZOMQAAAJOlKAYAAGCyDJ+mf4ZoAQAAp8SdYgAAACZLUQwAAMBkKYoBAE7Z1vZOtrZ3hg4DgAMoigEAAJgsRfEZ5Go0AADAchTFAAAATJaiGAAAgMlSFAMAADBZimIAAAAm66ahAwAAWJnZZi5cnU82OZslGxeHjQeA7imKAQA4udnm9csuSAAjoSgGAM42d48BOISi+CQk2eG4Gg0AAKyAohgARqqq/jzJTyd5trX2ivm6FyV5KMnLkvxHkje11r4yXJRMmovYwAiYfXrVZpu5cPXhXLj68LcnAgBYrQ8kuXvfut9N8mhr7c4kj86XAYAFFMUAMFKttY8m+c99q+9J8sH56wfmywDAAksPnzZEi1HYe3feEC1gms611q4kSWvtSlXdMnRAANCz49wp/kAM0QKAM6Oq7q+qWVXNrly5MnQ4ADCIpYtiQ7QAYBSuVNW5JJn/fnbRhq21S621jdbaxrlz59YWIAD05KTfKb5uiFYSQ7QAYFiPJLlv/vq+7I7kAgAWWNtEW4ZoAcBqVdWDSR5L8vKqerqq3prk3UnuqarHszuC63eGjBEAenfS5xRfqapz84k8jhyileRSkmxsbLQTfi4ATF5r7c0L3vqptQYCACN20jvFhmgBAAAwWsd5JNODSe5K8pKqejq7w7PeneShqvqlJF9K8nOnESQAAACchqWLYkO0AAAAOGvWNtEWAAAA9EZRDAAAwGQpigEAAJgsRTEAAACTpSgGAABgshTFAAAATJaiGAAAgMlSFAMAADBZNw0dAADADZlt5sLVnWQ2SzYuDh3NsW1t7yRJ7j1/68CRDGC2+a3XI/y3A84Wd4onYmt757nkCwAAwC5F8bJmm7lw9eHrr2wCAAAwaoZPc/YZogUAACzgTjEAAACTpSgGAABgshTFAAAATJaiGACgE54WAbB+imIAAAAmS1EMAADAZCmKAQAAmCzPKQYAYHizzeuXNy4OEwcwOe4UAwAAMFnuFDNNrkYDAABxpxgAAIAJUxQDAAAwWYpiAAAAJktRDAAAwGQpiidsa3snW9s7Q4cBAAAwGEUxAAAAk+WRTABA32abuXB1PrJpNvMYPQBWSlG8n8QLAAAwGYZPAwAAMFmKYgAAACZLUQwAAMBkKYoBAACYLEUxAEDntrZ3srW9M3QYAGeSohgAAIDJOpNF8bu2Hs+7th4fOgwAAAA6dyaLYgAAAFjGTUMHAAAAB5ptXr+8cfHb119bB3CDFMVwjcQLAACTY/g0AAAAk6UoBgAAYLIUxQDAqfNkCAB6pSgGAABgshTFAAAATJaiGAAAgMlSFAMAADBZimIAAAAm66ahAwAAVq+qnkzytSTfSPJ/rbWNYSNi1ba2d5Ik956/deBIAMZtJUWxxAsAXfrJ1tqXhw4CAHq2yjvFEi8AAACjYvg0AJxNLclHquqmJJdaa+/bv0FV3Z/k/iS544471hzeArPNXLi6M389SzYuDhsP4zHbvH5Z3wGWtKqi+MjE2yWJlxsl8QL9e01r7ZmquiXJ31XV51trH9m7QWvtUpJLSbKxsdGGCBIAhraq2adf01o7n+S1SS5W1ev2b1BV91fVrKpmV65cWdHHAgAHaa09M//9bJIPJbkwbEQA0KeVFMXLJN7W2qXW2kZrbePcuXOr+FgA4ABVdXNVvfDa6yR3J3li2KgAoE8nLoolXgDozkuTfLyqPpXkcpKPJnl42JAAoE+r+E7xS5P8TVW1JC9M8lAkXgAYTGvt35K8cug4AGAMTlwUS7xnz9b27uRj956/deBIAABOwMSYwBI8kglWReIFAIDRWdXs0wAAADA6imIAAAAmS1EMAHBGbG3vPDc3CADLURQDAAAwWSbaAgBgWvZOjmliTJg8RTGcNokXAAC6Zfg0AAAAk6UoBgAAYLIUxQAAAEyWohgAAIDJUhSzFM89BAAAzqLpzD4928yFq9eKujsHDQUAiNxMX/Y+LSLxxAiYkOkUxazctTvH956/9eD1G2sPaTwkXgAA6ILh0wAAAEyWohgAAIDJUhQDAJxxJssEWExRDAAAwGQpigEAAJgsRTEAwARtbe8YVg0Qj2RiTRY9vok9PKYJAPoiN8MkuFMMAADAZCmKGYxhWwAAwNAUxQAAAEyW7xQDAMCyfM8YzhxFMfRM4gVgjUyMCUyRohgAAE5q74VsF7FhVBTFMEYSLwAArISJtgAAAJgsRTHd8ZgmAABgXRTFAAAATJaimFHY2t5xBxkAAFg5RTEAAACTZfZpOCs80xgA+iI3wyi4UwwAwKEO+gqTrzYBZ4WiGAAAgMlSFAMAADBZimIAAAAmy0RbTMK17zzduzFwIOt2nAk+TAYCAKdPbobuuFPMaJngAwAAOCl3ioHFXKEGgL7IzbByZ68onm3mwtX53cPZzImChZ4bUn3+1oEjGcDehOpvBDhtcvNkHJRbJ51vj0NuhsEYPg0AAMBknb07xQAAdO+gSTDdaQaGoCgGjsd3mQDowNb2jkL5mkW52ZBsWIqiGJYg8S7hoMSrgAaAvsjN8G18p5gzZZ2PaVr2sxZt55FSAAAwPHeKYQ/fW1oDV6gBWCO5fQlyMxOnKAb64HtPANAXuZmJWElRXFV3J/mjJM9L8hettT9cxXGB4zlzV8NduYYbJjczRaeRB89cbj0puZkz6MRFcVW9IMn7k/x4kmeSPFZVH26tffKkx4axkTjX4KTJWDJnAuRmOJx8vWJyMyO3ijvFr07y2dbaU0lSVQ8luSeJxAvHdJznM550RuyD9l/287v8n4nTmP36NIaNSfysh9wMN2DZ/Hac3DyaPHoa5GZGYhVF8W1Jntqz/HSSu1ZwXGBknkvyGwesu8HEv5b/mZhtXn/MVV3hXjZxH7T/cY55nPh9P2wq5GYYodO4YL1sbj5Obl3LMPXTuvu8xtx84LZHfdYR/53v2no8SfIH99556HYcT7XWTnaAql9I8hOttbfPl9+c5K7W2tv2bXd/kvvniy9P8i8n+uDFXpLky6d07NMk7vUS9/qMMeZE3Ot20ri/v7V2blXBjJ3cvDLiXq8xxj3GmBNxr9tU4x5Nbl7FneKnk9y+Z/m2+brrtNYuJbm0gs87VFXNWmsbR2/ZF3Gvl7jXZ4wxJ+Jet7HG3TG5eQXEvV5jjHuMMSfiXjdx9+87VnCMTyR5RVXdVlXPT/KmJI+u4LgAwI2RmwFgSSe+U9xa+5+q+uUkf5/dIvuB1trsxJEBADdEbgaA5a3kOcWttUeSPLKKY63AqQ8DOyXiXi9xr88YY07EvW5jjbtbcvNKiHu9xhj3GGNOxL1u4u7ciSfaAgAAgLFaxXeKAQAAYJS6Loqr6u6q+kxVfa6qfuuA96uq3ltVT1TVdlW96qh9q+pFVfWRqnq8qj5cVd/XS9xVdXtVfXS+7xeq6jf37POeqtqpqsvznzf0Evf8vSfnbXq5qmZ71vfc3i/f056Xq+qrVfWO+Xs9tPcPV9VjVfW/VfXOZfbtpL0PjHsE/fuw9u65fy9q797791vm7faZqvrnqto4at91tDdHu9Fz7mH7dvK3dGDcIzh3HdbePZ+7FrV37+cuubmDuOfv9dy/5eaxaa11+ZPkBUmezO4jJZ6fZJbkVfu2eWOSh5NUklcl+dRR+yZ5X5Jfn7/+tSTv7SjulyV55fz1dyf51yQ/Ol9+T5J39tje8/eeTPKSA47bbXvv2+Z5SZ7J7vPUemnvW5JcSPL7e2MZQf9eFHfv/fvAuEfQvxfG3Xn/fnWS752/fn2Sy0P3bz8r+7eVmzuIe/5ez+cuubmPuHvv33LzeuOebG7u+U7xq5N8trX2VGvt60keSnLPvm3uye6Mmq219skkN1XV7Ufse0+SD85fP3DAMQeLu7X2TGvt00nSWvtakk8nuXXF8a087iOO221779vmtUm+2Fr79xXHt8iRcbfWnm2t/VOSrx9j38Hbe1HcvffvQ9r7MN229z499u9/bK3993zxY/lWXxiyf3M0ubmzc9eiuI84brftvW+bHs9dcvPqyM399e/J5uaei+Lbkjy1Z/np+bpltjls33OttStJMv99ywpjPiymY21TVT+Q3StMH9uz+leq6vNV9ZdV9eJVBbxsTEds05JcGzrxq3u2GUV7J/n5JA/uWzd0e9/Ivj2095E67d+H6bl/L6P3/v22JH+7xL6n3d4cTW7u79wlN6+O3Nxf/z5Mz/17Gb3370nl5p6L4smqqu9K8qEk79hzteZPkvxQkh9J8sUk7x0ovEVe01o7n92rXher6nVDB7SsqvrOJD+T5K/2rO69vUdL/16v3vt3Vd2V5K1JfmOoGGAZzl3r1fu566zRv9er9/49xdzcc1H8dHbHrV9z23zdMtsctu+VqjqXJPPfz64w5sNiWmqbqnp+kr9O8mBrbevaBq21K621b7TWvpnk/dm9ktdN3K21Z+a/n83uSfVafF2399zrk3yytfalays6ae8b2beH9l6o8/69UOf9+yjd9u+qemWSP0vys621q0vse9rtzdHk5v7OXXLz6sjN/fXvhTrv30fptn9PNTf3XBR/Iskrquq2+R/rm5I8um+bR5L8YpLU7qyF32ytPXXEvo8kuW/++r4DjjlY3FVV2e2En2ut/fHeHapq71CENyZ5oqO4b66qF87X35zk7j3xddvee95/c/YNX+mkvW9k3x7a+0Aj6N8HGkH/PkqX/buq7kiyleQtrbUvLLnvabc3R5Ob9+jhb+mQuHs/d8nNqyM399e/j9Jl/550bm4dzPa16CfJG5J8Nsnnkvz2fN3bk7x9/rqyO9TgiSSXk2wctu98/YuT/EOSx+e/X9RL3El+LLvfj/j0fP3lJG+Yv/fAfP3nk3w4ye0dxf2D89g+ld1ZC38vSfXe3vP3bk5yNfOZ9vas76G9X5bdq3BfTfJf89ffM4L+fWDcI+jfi+LuvX8f1k967t9/muQre/rC7LB919Xeflbybys39xF37+cuubmDuEfQv+Xm9cY92dx8rfMAAADA5PQ8fBoAAABOlaIYAACAyVIUAwAAMFmKYgAAACZLUQwAAMBkKYoBAACYLEUxAAAAk6UoBgAAYLL+H1H6Go4tHvIxAAAAAElFTkSuQmCC\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)"
]
},
{
"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",
"MC_Ds_FD_ownpv=MC_Ds_tuple_dict[\"Ds_FD_OWNPV\"]\n",
"data_Ds_FD_ownpv=data_tuple_bkg[\"Ds_FD_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Ds\", variable=\"Ds FD own pv\", \n",
" MC_sig=MC_Ds_FD_ownpv, data_bkg=data_Ds_FD_ownpv, \n",
" width_MC=0.8, width_data=0.8)\n",
"plt.subplot(1,2,2)\n",
"#Retrieve data from needed branch\n",
"MC_Dplus_FD_ownpv=MC_Dplus_tuple_dict[\"Dplus_FD_OWNPV\"]\n",
"data_Ds_FD_ownpv=data_tuple_bkg[\"Ds_FD_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Dplus\", variable=\"Dplus FD own pv\", \n",
" MC_sig=MC_Dplus_FD_ownpv, data_bkg=data_Ds_FD_ownpv, \n",
" width_MC=1, width_data=1)"
]
},
{
"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(2,1,1)\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(2,1,2)\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": 22,
"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=0.005, width_data=0.005)\n",
"plt.subplot(1,2,2)\n",
"#Retrieve data from needed branch\n",
"MC_Ds_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_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": [],
"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": 24,
"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",
"\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=\"Ds\", variable=\"PT\", \n",
" MC_sig=MC_Ds_pT, data_bkg=data_Ds_pT,\n",
" width_MC=.4, width_data=.4)\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=\"Dplus\", variable=\"PT\", \n",
" MC_sig=MC_Dplus_pT, data_bkg=data_Ds_pT,\n",
" width_MC=.4, width_data=.4)"
]
},
{
"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(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=\"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",
"plt.subplot(1,2,2)\n",
"MC_Ds_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_Ds_DIRA_ownpv, data_bkg=data_Ds_DIRA_ownpv,\n",
" width_MC=0.000001, width_data=0.000001)\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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": 28,
"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": 29,
"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": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"13821"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_tuple_bkg[\"Ds_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9639"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_Dplus_tuple_sig[\"Dplus_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5881"
]
},
"execution_count": 32,
"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