{
"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\n",
"\n",
"from architectures.data_processing import *"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"mD=1.870\n",
"mDs=1.970\n",
"mp=0.140"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"meson_index=0\n",
"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",
"meson_ID =['pi','X']\n",
"l_flv = ['e','mu']\n",
"\n",
"data_type = ['MC','data']\n",
"\n",
"def find_file_path(l_index=l_index, meson_index=meson_index, mother_index=mother_index, data_index=data_index): \n",
" if data_index == 0:\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]+\"_\"+l_flv[l_index]+l_flv[l_index]+meson_ID[meson_index]+\".root\"\n",
" else:\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]+\"_phi\"+meson_ID[meson_index]+\"_\"+l_flv[l_index]+l_flv[l_index]+\".root\"\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",
"\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, meson_index=meson_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 0x55d3d5bf21b0>,\n",
" <ROOT.TTree object (\"DecayTree\") at 0x55d3d5a379d0>,\n",
" <ROOT.TTree object (\"DecayTree\") at 0x55d3d544c430>)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_MC_Dplus, t_MC_Ds, t_data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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(data_index=0, mother_index=0, l_index=l_index, meson_index=0):\n",
" t_MC_Ds.SetBranchStatus(branch, 1)\n",
"\n",
"for branch in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0):\n",
" t_MC_Dplus.SetBranchStatus(branch, 1)\n",
" \n",
"for branch in return_branches(data_index=1, mother_index=1, l_index=l_index, meson_index=0): \n",
" t_data.SetBranchStatus(branch, 1)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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(data_index=0, mother_index=0, l_index=l_index, meson_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(data_index=0, mother_index=1, l_index=l_index, meson_index=0)\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(data_index=1, mother_index=0, l_index=l_index, meson_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": 7,
"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_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_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_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_mass_MC,bins=70, range=(1750,2100), density=True);\n",
"plt.hist(Dplus_mass_MC,bins=70, range=(1750,2100), density=True);\n",
"plt.subplot(1,2,2)\n",
"plt.hist(Ds_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": 8,
"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",
"\n",
"for label in return_branches(data_index=1, mother_index=0, l_index=l_index, meson_index=0): \n",
" data_tuple_dict_presel_1[label] = data_tuple_dict[label][data_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
" \n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \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(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \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(data_index=1, mother_index=0, l_index=l_index, meson_index=0):\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",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0):\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(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \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(data_index=1, mother_index=0, l_index=l_index, meson_index=0):\n",
" data_tuple_dict_presel_3[label] = data_tuple_dict_presel_2[label][data_PID_indices]\n",
" \n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \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(data_index=0, mother_index=1, l_index=l_index, meson_index=0):\n",
" MC_Dplus_tuple_dict_presel_3[label] = MC_Dplus_tuple_dict_presel_2[label]#[MC_PID_indices]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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 = 750\n",
" upper_phi_mass = 1150\n",
" \n",
"#Cut on phi mass\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(data_index=1, mother_index=0, l_index=l_index, meson_index=0): \n",
" \n",
" data_tuple_dict_presel_4[label] = data_tuple_dict_presel_3[label][data_indices]\n",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \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(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_dict_presel_4[label] = MC_Dplus_tuple_dict_presel_3[label][MC_Dplus_indices]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": 11,
"metadata": {},
"outputs": [],
"source": [
"#Getting rid of MC Background using BKG_CAT in MC Ds\n",
"#Keeping only low mass and ghost background\n",
"MC_Ds_indices=np.where(MC_Ds_tuple_dict_presel_4[\"Ds_BKGCAT\"]<65)[0]\n",
"\n",
"MC_Ds_tuple_dict_presel_5={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \n",
" MC_Ds_tuple_dict_presel_5[label] = MC_Ds_tuple_dict_presel_4[label][MC_Ds_indices]\n",
" \n",
"#No partially reconstructed\n",
"MC_Ds_indices=np.where(MC_Ds_tuple_dict_presel_5[\"Ds_BKGCAT\"]!=40)\n",
"\n",
"MC_Ds_tuple_dict_presel_6={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \n",
" MC_Ds_tuple_dict_presel_6[label] = MC_Ds_tuple_dict_presel_5[label][MC_Ds_indices]\n",
" \n",
"#No reflection\n",
"MC_Ds_indices=np.where(MC_Ds_tuple_dict_presel_6[\"Ds_BKGCAT\"]!=30)\n",
"\n",
"MC_Ds_tuple_dict_presel_7={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \n",
" MC_Ds_tuple_dict_presel_7[label] = MC_Ds_tuple_dict_presel_6[label][MC_Ds_indices]\n",
"\n",
"#Getting rid of MC Background matching mother IDs\n",
"MC_Ds_indices=np.where(np.abs(MC_Ds_tuple_dict_presel_7[\"pi_MC_MOTHER_ID\"])==431)[0]\n",
"\n",
"MC_Ds_tuple_dict_presel_8={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \n",
" MC_Ds_tuple_dict_presel_8[label] = MC_Ds_tuple_dict_presel_7[label][MC_Ds_indices]\n",
" \n",
"MC_Ds_indices=np.where(np.abs(MC_Ds_tuple_dict_presel_8[l_flv[l_index]+\"_plus_MC_MOTHER_ID\"])==333)\n",
"\n",
"MC_Ds_tuple_dict_presel_9={}\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \n",
" MC_Ds_tuple_dict_presel_9[label] = MC_Ds_tuple_dict_presel_8[label][MC_Ds_indices]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#Getting rid of MC Background using BKG_CAT in MC Dplus\n",
"#Keeping only low mass and ghost background\n",
"MC_Dplus_indices=np.where(MC_Dplus_tuple_dict_presel_4[\"Dplus_BKGCAT\"]<65)[0]\n",
"\n",
"MC_Dplus_tuple_dict_presel_5={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_dict_presel_5[label] = MC_Dplus_tuple_dict_presel_4[label][MC_Dplus_indices]\n",
" \n",
"#No partially reconstructed\n",
"MC_Dplus_indices=np.where(MC_Dplus_tuple_dict_presel_5[\"Dplus_BKGCAT\"]!=40)\n",
"\n",
"MC_Dplus_tuple_dict_presel_6={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_dict_presel_6[label] = MC_Dplus_tuple_dict_presel_5[label][MC_Dplus_indices]\n",
" \n",
"#No reflection\n",
"MC_Dplus_indices=np.where(MC_Dplus_tuple_dict_presel_6[\"Dplus_BKGCAT\"]!=30)\n",
"\n",
"MC_Dplus_tuple_dict_presel_7={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_dict_presel_7[label] = MC_Dplus_tuple_dict_presel_6[label][MC_Dplus_indices]\n",
"\n",
"#Getting rid of MC Background matching mother IDs\n",
"MC_Dplus_indices=np.where(np.abs(MC_Dplus_tuple_dict_presel_7[\"pi_MC_MOTHER_ID\"])==411)[0]\n",
"\n",
"MC_Dplus_tuple_dict_presel_8={}\n",
"\n",
"for label in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_dict_presel_8[label] = MC_Dplus_tuple_dict_presel_7[label][MC_Dplus_indices]\n",
" \n",
"MC_Dplus_indices=np.where(np.abs(MC_Dplus_tuple_dict_presel_8[l_flv[l_index]+\"_plus_MC_MOTHER_ID\"])==333)\n",
"\n",
"MC_Dplus_tuple_dict_presel_9={}\n",
"for label in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_dict_presel_9[label] = MC_Dplus_tuple_dict_presel_8[label][MC_Dplus_indices]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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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=1350\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_presel_9[\"phi_M\"][i] for i in range(len(MC_Ds_tuple_dict_presel_9[\"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_presel_9[\"phi_M\"][i] for i in range(len(MC_Dplus_tuple_dict_presel_9[\"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",
"plt.hist(phi_mass_MC_Ds,bins=70, alpha=0.7,histtype='step', range=(lower,upper),label='phi_M from Ds MC', density=True);\n",
"plt.hist(phi_mass_MC_Dplus,bins=70, alpha=0.5, 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": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1019.451527895175, 10.386124443762935)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"phi_mass_MC_Dplus.mean(), phi_mass_MC_Dplus.std() "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1019.6615397712993, 10.228840405403313)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"phi_mass_MC_Ds.mean(), phi_mass_MC_Ds.std() "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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iooKtOd1pzz335Oijj6ZZs2Z06NCBH/7wh1W/7LLx7LPPUlZWxqWXXkqLFi3o1q0b3/nOd7jnnnuq2hxxxBEMHDgQgDZt2tR6na997Wv069eP5s2bM3bsWBYuXFhjNsbFF19Mu3btaNOmDQ8++CC9evVi2LBhNGvWjP32249TTz2V3//+9wDk5eWxePFiVq9eTbt27apmQEyePJlRo0Zx4IEHAjBkyBDatWu3xfv9+OOPmTp1Ktdeey1FRUVEBP369aN169ZZf04btGzZkubNm7No0SLWrl1LUVERvXv33urrSJLU0HbZZZeq2RNHHXUU++yzDwC9e/dm8ODBm/39ubXjizPOOIN27drRrFkzRo8eTfPmzVm4cGHWsd58882MGzeO3XbbjZYtW/LTn/6URx55pMay25/97Ge0adOmzrFI8+bN+dnPfkbz5s055JBD+PrXv869995bVX/yySfTt29fIoKI4Pbbb+eqq66iffv2tG3blosvvrhq/JOXl8fSpUt55513aN68OYcddhgRkdW4qS633norI0eOrJpZsdtuu7H33ntn/RlVl5eXxzvvvMN7771Hq1atOPTQQz/TdbJhEkOSdnLDhg1j6tSpTJkyZZOlJB06dKBZs2a8//77WV+vpKSEU045hV133ZX27dtz5plnUlpaulX9lyxZQmFhYdXj8ssv58MPP6xq06VLly1ep/qSjLZt29KxY0eWLVtW6zVKSkr461//WuM977rrrqqB1j333MNDDz1E9+7dOeKII6oGTSUlJVx99dU1+r377rtb/LxWrFjB+vXr+fznP5/dh7IZeXl5TJ06leuvv54uXbpw4oknVk3VlSRpe/L+++/TsWNHAJ555hmOOOIIOnToQGFhYdUszbps7fjiv//7v/nCF75Au3btKCws5IMPPtjq8cjJJ59c9fu9d+/etGzZkhUrVlS12dJ4pGPHjnzuc5+rel1cXFznWGT58uWsW7eOPn36VL3ngAEDWLVqFQAXXngh3bt35/jjj2ePPfZg3LhxpJSyGjfV5b333quXsQjAFVdcwSeffMLBBx/MPvvsw29/+9t6uW5tTGJI0k5ujz32oGfPnjz66KObLBtp27Ythx12GPfdd1/W17vkkkvIz8/ntddeY9WqVUybNo3N7b1cefr2/+ncuTO9evVi5cqVVY/Vq1czc+bMrbqvf/3rX1XP165dywcffMBuu+1Wa9vOnTtz/PHH13jP0tLSql/Ahx56KI888gjLly/ntNNO4/TTT6/qN3bs2Br91qxZw5AhQzYbW6dOnWjVqhVvvvnmJnWtWrWq8S1PRUVFjYHIxp8XwMCBA5k9ezZLly7lgAMO2GS6qyRJTW3x4sW8+uqrHHnkkQCcddZZDB48mGXLlrFy5UpGjx5dNV6o7Xfd1owvZs2axY033sgjjzzCqlWrWLlyJZ06daqzfW3v17lzZ2bNmlXjd/zHH39Mt27dsr7nDz74oMYS05KSkjrHIp06daJly5a8+uqrVe+3atWqqjFBu3btmDBhAm+++SYzZsxgwoQJPPbYY9s0buratWutYxHYdDxSPXlT2+fVtWtXbr31VpYsWcItt9zC97//fV5++eUtxvBZmMSQJDF58mRmz55d6y7d48eP5ze/+Q033XRT1S+zv//975vsMbFBWVkZrVu3Jj8/n2XLlnHVVVdt9r07duzI22+/XfX66KOPpqKiguuvv55PPvmElBKvvPJK1f4U2XrooYeYN28e5eXl/OIXv2C//fZjr732qrXtySefzIIFC7j33nspLy+noqKCF154gZdffplPPvmEe+65h7KyMlq0aEFBQUHVL+9zzjmHm266ib/97W9A5TKRP/3pT6xevXqzsbVp04azzjqLCy64gPfff5+UEnPnzmXdunX07t2b0tJS/vjHP1JRUcH48eMpKyur8Xm9++67VZt5LVu2jBkzZrBu3TpatWpVtfmqJEnbg/Xr1zN79mxOPvlkhg4dygEHHABUjhfy8/Np1aoVL7zwAlOnTq3qU1hYSETw1ltvVZVtzfiirKyMZs2aUVhYSHl5OePHj9/sJqAdO3bkww8/5KOPPqoqGzlyJD/5yU9YsmQJULnEdsOml9kqLy9n3LhxlJeX8/zzz/Pggw/WueFlmzZtGDZsGBdeeGHVlxdLly6t2lxz5syZVZ9HQUEBzZs3JyK2adw0fPhwJk2axP/+7/8ClWOKV199FYAvfelLTJs2jU8//ZQXX3yxxjKY2v58HnjgAZYuXQpU7nfSrFmzBhuPmMRQbpp6Ru0PSZ/JnnvuWedmkIcffjhPPPEEDz30EF27diU/P5/hw4dz7LHH1tp+7NixPPfccxQUFDBgwICqE0fqMmLECObNm0dBQQFf//rXadGiBY899hhPPPFE1ZTRoUOHbnbwUZszzzyTMWPGUFhYyKxZs6o2C6tNx44dmTlzJhMnTqRDhw507NiRH/zgB1Xfntx8883svvvutG3blgkTJnDXXXcBlQmXK6+8km9+85vk5+fTvXt3brrppqziu/HGGykuLmbvvfemffv2nH/++VRUVNChQweuu+46hg0bRpcuXWjRokWNpTGnnXYaa9eupX379hx00EFVA6QNp5w8/vjjTJw4cas+K0naUQy6e1CtDzW+QYMGVZ088uMf/5jvfe973HrrrVX1N9xwA2PGjKFdu3b89Kc/rTEbtH379lxwwQX07duXwsJCnnvuua0aXwwcOJDjjjuOz3/+8+yxxx4Am51Bsf/++/O1r32N4uJiCgsLWbJkCT/+8Y858sgjOeSQQ6pOIHvqqae26jPo3Lkzbdu2pWvXrvznf/4nV199ddU+WrW54YYb6NChA71796agoICjjjqqah+Pf/zjH/Tv35+2bdvSt29fvv3tb3PCCSds07jp2GOPZcKECQwfPpz8/HwOPfRQ3njjDQAuu+wy/vGPf9C+fXvGjBlTNQsVav/zeeaZZ/jyl79M27Zt+cpXvsKvfvWrz7y/xpbE5qb47mj69u2b5s2b19RhqD7UlbAYXPuRj1JTWbx4sZssNoHhw4dTXFzMuHHjmjqU7Updfx8jYn5KySNNGonjEanh1JWwePishxs5kobnGGP79uSTTzJ06FBKSkqaOpTtzraOR5yJIUmSJEmScoJJDEmSJEmSlBNaNHUAkiTVtylTpjR1CJIkaSd2zDHHuJSkgTgTQ5IkSZIk5QSTGJIkSZKUg3amQxq0Y6iPv7MmMSRJkiQpx7Rp04YVK1aYyFDOSCmxYsUK2rRps03XcU8MSZIkScoxxcXFlJSUsHz58qYORcpamzZtKC4u3qZrmMSQJEmSpBzTsmVLevbs2dRhSI3O5SSSpEb10ksv0bt3b/Ly8pgwYUJTh9OgevTowaxZs5o6DEmSpB2GMzEkqTFNPaNhrz94etZNe/TowZIlS1iyZAm77LJLVfmBBx7IggULePPNN+nRowcAzz//PGPHjmXOnDmsX7+evfbai5EjR3LuuedudYjjx49nwIABXHPNNQAMHz6c4uJixo0bt9XX2hmNHTuW119/nTvvvLOpQ5EkSWp0zsSQpJ1Yz549ufvuu6teL1y4kDVr1tRoM2fOHI477jgGDhxISUkJq1ev5ne/+x1PPvnkZ3rPkpIS9ttvv20Ju4ZPP/20SftLkiSp8ZjEkKSd2LBhw7j99turXt92222cffbZNdpcdNFFfPe732X06NHk5eUREfTp04fp02uf9fHGG2/Qv39/OnToQLt27TjllFP44IMPADjuuON46qmnGD16NPn5+UyaNIm77rqL8ePHk5+fz6BBgwB46623GDhwIO3bt6dz585cccUVVdcfO3Ysp556KkOHDqWwsJApU6ZsEsPw4cMZNWoUJ5xwAgUFBfTr14/XXnutqj4iuPHGG9l7773p1asXAAsWLKB///4UFBTQvXt3brvttqr2DzzwAF/4whfIy8ujS5cu/OpXv6qqmz59OnvvvTcFBQUceOCBPP/881l99mVlZYwaNYqioiIKCgo47LDDWLt2LU8++eQmG15tWJYyc+ZMLr/8cqZPn05+fj5f+tKXAJg4cSLdunUjLy+P7t27c8cdd2QVgyRJUq4xiSFJO7FDDz2Ujz76iMWLF1NeXs60adMYOnRoVf2aNWuYM2cOp5xyylZdd9y4caxYsYK3336bVatWMWbMGABmz55N//79ueGGGygtLWXkyJEMGTKEH/3oR5SWlvLwww9TXl7OV77yFY488khWrFjB3LlzmTRpEvfff3/V9R955BEGDx7MypUra8Rb3d133824ceNYuXIlxxxzDGeeeWaN+hkzZjB37lwWLVrEypUrOfHEExk5ciSrVq1ixowZXHDBBcyfPx+AESNGcOutt1JWVsYrr7zCgAEDAHj22WcZPXo006ZNY/Xq1Vx44YWcdNJJrF27douf0Xnnncebb77JokWL+Oijj7juuuto1mzzv5YHDBjApZdeyhlnnEFpaSl///vfWblyJRdddBGzZs2irKyM+fPn07dv3y2+vyRJUi4yiSFJO7kNszEef/xxevfuze67715V9+GHH1JRUUFRUVHW19tzzz05+uijadasGR06dOCHP/whTz/9dNb9n332WcrKyrj00ktp0aIF3bp14zvf+Q733HNPVZsjjjiCgQMHAtR51vjXvvY1+vXrR/PmzRk7diwLFy6sMRvj4osvpl27drRp04YHH3yQXr16MWzYMJo1a8Z+++3Hqaeeyu9//3sA8vLyWLx4MatXr6Zdu3ZVMyAmT57MqFGjOPDAAwEYMmQI7dq12+L9fvzxx0ydOpVrr72WoqIiIoJ+/frRunXrrD+nDVq2bEnz5s1ZtGgRa9eupaioiN69e2/1dSRJknKBSQxJ2skNGzaMqVOnMmXKlE2WknTo0IFmzZrx/vvvZ329kpISTjnlFHbddVfat2/PmWeeSWlp6Vb1X7JkCYWFhVWPyy+/nA8//LCqTZcuXbZ4nepLMtq2bUvHjh1ZtmxZrdcoKSnhr3/9a433vOuuu6qWwdxzzz089NBDdO/enSOOOKIqSVFSUsLVV19do9+77767xc9rxYoVrF+/ns9//vPZfSibkZeXx9SpU7n++uvp0qULJ554Ii+99NI2X1eSJGl7ZBJDknZye+yxBz179uTRRx/dZNlI27ZtOeyww7jvvvuyvt4ll1xCfn4+r732GqtWrWLatGmklOpsHxE1Xnfu3JlevXqxcuXKqsfq1auZOXPmVt3Xv/71r6rna9eu5YMPPmC33XartW3nzp05/vjja7xnaWkpv/3tb4HKZTePPPIIy5cv57TTTuP000+v6jd27Nga/dasWcOQIUM2G1unTp1o1aoVb7755iZ1rVq1qrG5akVFRY0EzsafF8DAgQOZPXs2S5cu5YADDuCcc87Z7PtLkiTlKpMYkiQmT57M7NmzycvL26Ru/Pjx/OY3v+Gmm26q+s/13//+9032mNigrKyM1q1bk5+fz7Jly7jqqqs2+94dO3bk7bffrnp99NFHU1FRwfXXX88nn3xCSolXXnmlan+KbD300EPMmzeP8vJyfvGLX7Dffvux11571dr25JNPZsGCBdx7772Ul5dTUVHBCy+8wMsvv8wnn3zCPffcQ1lZGS1atKCgoKAqkXDOOedw00038be//Q2oXCbypz/9idWrV282tjZt2nDWWWdxwQUX8P7775NSYu7cuaxbt47evXtTWlrKH//4RyoqKhg/fjxlZWU1Pq93332XiooKAJYtW8aMGTNYt24drVq1qtp8VZIkaUdkEkOSxJ577lnnZpCHH344TzzxBA899BBdu3YlPz+f4cOHc+yxx9bafuzYsTz33HMUFBQwYMCAqhNH6jJixAjmzZtHQUEBX//612nRogWPPfYYTzzxRNWSlKFDh1Yt7cjWmWeeyZgxYygsLGTWrFlMmzatzrYdO3Zk5syZTJw4kQ4dOtCxY0d+8IMfVG3QefPNN7P77rvTtm1bJkyYwF133QVUJlyuvPJKvvnNb5Kfn0/37t256aabsorvxhtvpLi4mL333pv27dtz/vnnU1FRQYcOHbjuuusYNmwYXbp0oUWLFjWWxpx22mmsXbuW9u3bc9BBB1E6r/xtAAAgAElEQVReXs64ceOqTjl5/PHHmThx4lZ9VpIkSbkiNjfFd0fTt2/fNG/evKYOQ/Vh6hm1lw+u/chHqaksXrzYTRabwPDhwykuLmbcuHFNHcp2pa6/jxExP6XkkSaNxPGI1HAG3V174vzhsx5u5Egkba1sxyPOxJAkSZIkSTnBJIYkSZIkScoJLZo6AEmS6tuUKVOaOgRJkiQ1AGdiSJIkSZKknGASQ5Ia2M60gbK2X/49lCRJOwKTGJLUgNq0acOKFSv8D6SaVEqJFStW0KZNm6YORZIkaZu4J4YkNaDi4mJKSkpYvnx5U4einVybNm0oLi5u6jAkSZK2iUkMSWpALVu2pGfPnk0dhiRJkrRDcDmJJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEna4UREt4h4OiJeiohXI+LiTHnHiHg8IhZGxJ8iokO1PmMiYnGmz4nVyvtExAsRsSgiJkREZMpbR8T0TPu/RESPxr5PSZJ2NiYxJEnSjmg9MDqltD/QBzgnIr4M/AKYkVI6AJiReU1E9AG+AXwRGAD8NiJaZ651K3BOSmlfYA/g5Ez5aGBZ5j2uBCY0yp1JkrQTM4khSZJ2OCmlpSmlFzPPVwMvArsDXwXuyDS7M/OazM/pKaX1KaUS4B9Av4joDjRPKc2vo8+Gaz0IHB4RzRvwtiRJ2umZxJAkSTu0zDKPg4FngaKU0nKAzM9dM82KgXerdSvJlNVVXqNPSqkCWFHtepIkqQGYxJAkSTusiMgHfg+cn1Ja1YRxjIyIeRExb/ny5U0VhiRJOc8khiRJ2iFFREvgD8DdKaX7MsXLI6IoU18E/DtTXgJ0q9a9OFNWV3mNPhHRDOgE1JqhSClNSin1TSn1LSoq2tZbkyRpp2USQ5Ik7XAyJ4hMBhanlK6uVvUoMDTzfCiVm3tuKD8jIlpGRDGwP/B8SukdoCIiDsq0G7JRnw3XOgl4LqX0aYPckCRJAqBFUwcgSZLUAI4AhgELI2JBpuxS4OfA9Ij4NrAMOB0gpTQvIu6ncgPQCmBUSmldpt+3gFsiohUwm8rZHQA3AHdExEtAKTC44W9LkqSdm0kMSZK0w0kpPQtEHdXH19HnMuCyWsrnAV+upfxj4LRtCFOSJG2lRl1OEhHdIuLpiHgpIl6NiIsz5R0j4vGIWBgRf4qIDtX6jImIxZk+J1Yr7xMRL0TEooiYkJk2KkmSJEmSdlCNvSfGemB0Sml/oA9wTkR8GfgFMCOldACV60x/AZWJCuAbwBeBAcBvI6J15lq3AueklPYF9gBObtQ7kSRJkiRJjapRl5OklJYCSzPPV0fEi8DuwFeBQzLN7gSeA76fKZ+eUloPlETEP4B+EfE20DylNL9an68C96Edy9QzmjoCSZIkSdJ2oslOJ4mIHsDBwLNAUUppOUDm566ZZsXAu9W6lWTK6iqXJEmSJEk7qCZJYkREPvB74PyU0qoGfq+RETEvIuYtX17r0e2SJEmSJCkHNHoSIyJaUnk02d0ppQ3LP5ZHRFGmvgj4d6a8BOhWrXtxpqyu8k2klCallPqmlPoWFRXV341IkiRJkqRG1dinkwQwGVicUrq6WtWjwNDM86FUbu65ofyMiGgZEcXA/sDzKaV3gIqIOCjTbki1PpIkSZIkaQfUqBt7AkcAw4CFEbEgU3Yp8HNgekR8G1gGnA6V57JHxP3Ai0AFMCqltC7T71vALRHRCphN5ewOSZIkSZK0g2rs00meBaKO6uPr6HMZcFkt5fOAL9dfdJIkSZIkaXvWZKeTSJIkSZIkbQ2TGJIkSZIkKSeYxJAkSZIkSTnBJIYkSZIkScoJJjEkSZIkSVJOMIkhSZIkSZJygkkMSZIkSZKUE0xiSJIkSZKknGASQ5IkSZIk5QSTGJIkSZIkKSeYxJAkSZIkSTnBJIYkSZIkScoJJjEkSZIkSVJOaNHUAUiSJEnauQ26e1Ct5Q+f9XAjRyJpe+dMDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCSYxJEmSJElSTjCJIUmSJEmScoJJDEmSJEmSlBNMYkiSJEmSpJxgEkOSJEmSJOUEkxiSJEmSJCknmMSQJEmSJEk5wSSGJEmSJEnKCS2aOgCpXk09o/bywdMbNw5JkiRJUr1zJoYkSZIkScoJJjEkSZIkSVJOMIkhSZIkSZJygkkMSZIkSZKUE0xiSJIkSZKknGASQ5IkSZIk5QSTGJIkSZIkKSeYxJAkSZIkSTnBJIYkSZIkScoJJjEkSZIkSVJOMIkhSZIkSZJygkkMSZIkSZKUE0xiSJIkSZKknGASQ5IkSZIk5QSTGJIkSZIkKSeYxJAkSZIkSTnBJIYkSZIkScoJJjEkSZIkSVJOMIkhSZIkSZJyQotsG0ZES2AfYLdM0VLglZTS+oYITJIkSZIkqbrNJjEiogVwOjAMOBpoDUSmOgHrIuJp4A7gHhMakiRJkiSpodS5nCQivg38E7gGeA84H+gP9Ab2zTw/H1gCXA28kekjSZIkSZJU7zY3E+NcKpMUD6aUyuto8xdgUkQ0B74OXALcUr8hSpIkSZIkbSaJkVI6ONuLZJIcf8g8JEmSJEmS6p2nk0iSJEmSpJywzUmMiOgWEd3rIxhJkqT6EhG3RMS/I+KlamVjI+JfEbEg8xhYrW5MRCyOiJci4sRq5X0i4oWIWBQREyIiMuWtI2J6pv1fIqJHY96fJEk7o/qYifFP4M16uI4kSVJ9mgIMqKX8mpTSlzOPR6EyUQF8A/hips9vI6J1pv2twDkppX2BPYCTM+WjgWUppf2BK4EJDXYnkiQJqJ8kxgjAU0kkSdJ2JaX0NPBBls2/CkxPKa1PKZUA/wD6ZWabNk8pzc+0uzPTdkOfOzLPHwQOz2x2LkmSGsg2JzFSSrenlG6rj2AkSZIawXkR8XJE3BURnTJlxcC71dqUZMrqKq/RJ6VUAawAdq3tDSNiZETMi4h5y5cvr787kSRpJ+PGnpIkaWdyI/AFYF/gDRppCUhKaVJKqW9KqW9RUVFjvKUkSTukOo9YrS4i7tlSm5TS6dsejiRJUsNJKVVNg4iIicCTmZclQLdqTYszZXWVV++zNCKaAZ0Ap1lIktSAsp2JUVTLowcwCDgC2KUhgpMkSapPEVF9ucc3gEWZ548CZ0REy4goBvYHnk8pvQNURMRBmXZDgBnV+gzNPD8JeC6l9GmD3oAkSTu5rGZipJSOra08InYD7geuyeY6EXEL8J/AvzM7eRMRY4Hv8H/fXFxabafwMcDZQDnwXymlxzLlfYDfAa2BWcAPUkopmxgkSdLOISLuBo4BdomIEuDnwLER8UWgFfAOlRuUk1KaFxH3Ay8CFcColNK6zKW+BdwSEa2A2cAfMuU3AHdkjnAtBQY3yo1JkrQTyyqJUZeU0rKI+B9gPPBwFl2mUPkL//aNyq9JKV1VvWCjo852A56NiL0zA4pbgW+llOZHxINUHnV237bciyRJ2rGklM6qpXjyZtpfBlxWS/k84Mu1lH8MnLYtMUqSpK1THxt7VlC5tGSLGuGoM0mSJEmStIPKdmPPfWspbg70ovIbiwXbGMd5EXEOMB/4fkppBZUbZ82u1mbDkWbl1H3U2SYiYiQwEqB79+7bGKYkSZIkSWoq2c7EeAlYuNFjAXAvsJ7MetLPqEGPOvNIM0mSJEmSdgzZ7olR28aenwLvpZT+uS0B1PNRZ5IkSZIkaQeV7ekkTzVUABGxa0rp35mXGx91NjEirqVyY88NR52ti4iKiDgopfQ3Ko86u7Oh4pMkSZIkSduHbTqdZGs1wlFnkiRJkiRpB7XNSYyIeA1ollLac0ttG/qoM0mSJEmStOOqj5kYT1M/R7VKkiRJkiTVaZuTGCmlbTmZRJIkSZIkKSvOoJAkSZIkSTlhq2ZiRMSRQC+gzcZ1KaXf1FdQkiRJkiRJG8sqiRER3ak88nRfIAGRqUrVmpnEkCRJkiRJDSbb5STXAu8DXahMYBwMdAUuAl6mcnaGJEmSJElSg8l2OclxwNnA8szrSCktBX4dEUHlLIwTGyA+SZIkSZIkIPskxueA91NKFRHxCbBLtbp5wNj6DkyStsWIKXNrLZ88/OBGjkSSJElSfcl2OcnrwO6Z5wuBM6rVfR1YU59BSZIkSZIkbSzbmRgzgf8A7gWuAO6JiGMydd1xJoYkSZKk7dSguwfVWv7wWQ83ciSStlVWSYyU0n9Ve/6HTALjFKAVMCuldH/DhCdJkiRJklQp25kYNaSUngGeqedYJEmSJEmS6lTnnhgR0W1rLxYRe2xbOJIkSZIkSbXb3MaeCyPi5ojY4lb+EXFURNxO5aafkiRJkiRJ9W5zy0kOBH4OPBsRy4G/AouAD4AAOgH7AYcAHYC7gS82aLSSJEmSJGmnVWcSI6X0JjA8Ii4BzgL+HzAc2C3TZCnwIvA/wPSU0rKGDVWSJEmSJO3MtrixZ0ppKXBN5iFJkiRJktQkNrcnhiRJkiRJ0nbDJIYkSZIkScoJJjEkSZIkSVJOMIkhSZIkSZJygkkMSZIkSZKUEz5zEiMiiiLikIjIq8+AJEmSJEmSapNVEiMiLouIX1Z7PQB4G/gL8FZEfKmB4pMkSZIkSQKyn4kxGHi12usrgT8DhwELgPH1HJckSZIkSVIN2SYxdgf+CRAR3YH9gJ+llJ4HrgUObZjwJEmSJEmSKmWbxFgDtM88PxpYmVKan3n9EdC6vgOTJEmSJEmqrkWW7Z4GLokIgAuBP1ar24fK/TEkSZIkSZIaTLYzMb4HtAIeBD4GxlSrGww8W89xSZIkSZIk1ZDVTIyU0ttUbuJZm5OAtfUWkSRJkiRJUi2yPWJ1dkTsU0d1Z+Cx+gtJkiRJkiRpU9kuJzkGaFdHXQGVm31KkiRJkiQ1mGyTGABp44KIaA4cBXxQbxFJkiRJkiTVos49MSLi58DPMi8T8FzmdJLaXF/PcUmSJEmSJNWwuY09HwXeBwKYAFwNvLVRm0+Al1NKzzRIdJIkSZIkSRl1JjFSSnOBuQARsRr4Y0rp/cYKTJIkSZIkqbpsj1i9raEDkSRJkiRJ2pyskhgR0Qq4BDgJ6FJbv5TSrvUbmiRJkiRJ0v/JKokB/BYYCtwP/BEob7CIJEmSJEmSapFtEuMbwPkppRsbMhhJkiRJkqS6NMuy3cfAwoYMRJIkSZIkaXOyTWLcDJzVkIFIkiRJkiRtTrbLSZYBQyJiJjALWLNRfUop3VSvkUmSJEmSJFWTbRLj2szP7sAJtdQnwCSGJEmSJElqMFklMVJK2S47kSRJkiRJahAmJyRJkiRJUk7IOokRET0i4tqI+GtEvB4R+2XKvxcRRzRciJIkSZIkSVkmMSKiH/AicCLwN6An0DpTvStwcYNEJ0mSJEmSlJHtTIxrgBnAvsD3gKhWNwc4pJ7jkiRJkiRJqiHb00kOAn6cUkoRkTaqWwkU1m9YkpSdEVPmNnUIkiRJkhpJtjMxVgG71FHXE1haP+FIkiRJkiTVLtskxkPALyOiuFpZiogC4ELgvnqPTJIkSZIkqZpskxgXAx8DrwGzMmXXAf8EyoGf1X9okiRJkiRJ/yerJEZK6UPgUGA08B6ViYwlwKXAESml1Q0WoSRJkiRJEtlv7ElK6RNgcuYhSZIkSZLUqLKaiRERT0fEuRFR1NABSZIkSZIk1SbbPTGWAVcB/4qIxyPi2xHRoQHjkiRJkiRJqiHbPTFOA3YFvgmUAjcC70XEIxExLHNKiSRJkiRJUoPJdiYGKaWylNLdKaWTqUxojMxU3QwsbYjgJEmSJEmSNsg6iVFd5jSSN4A3gY+Az9VnUJIkSZIkSRvbqiRGRPSLiKsj4h3gaeBo4Dpgr4YITpIkSZIkaYOsjliNiF8BpwF7AK8BtwLTU0qLGjA2SZIkSZKkKlklMahMYNwDTEspLWjAeCRJkiRJkmqVVRIjpfT5hg5EkiRJkiRpc7LeEyMi2kXE+RExPSJmRcRemfLTI2LfhgtRkiRJkiQp+z0xegGzqTyF5HngWKAgU30IcApwZkMEKEmSJEmSBNnPxJgAvELlxp6DgKhW9zRwZD3HJUmSJEmSVEO2G3v2B05KKZVGRPON6pYDu9ZvWJIkSZIkSTVlOxPjY6BNHXVdgBX1E44kSZIkSVLtsk1iPA5cGhF51cpSZlbGaODReo9MkiRJkiSpmmyXk1wE/C/wBjATSMAlwP5AHnBWg0QnSZIkSZKUkdVMjJTSu8CXgJuBXlQmM/YAHgD6pJSWNliEkiRJn0FE3BIR/46Il6qVdYyIxyNiYUT8KSI6VKsbExGLI+KliDixWnmfiHghIhZFxISIiEx568zR8y9FxF8iokdj3p8kSTujbJeTkFL6MKX005TS4SmlXimlQ1NKP04puR+GJEnaHk0BBmxU9gtgRkrpAGBG5jUR0Qf4BvDFTJ/fRkTrTJ9bgXNSSvtS+SXOyZny0cCylNL+wJVUnuYmSZIaUNZJDEmSpFySUnoa+GCj4q8Cd2Se35l5vaF8ekppfUqpBPgH0C8iugPNU0rz6+iz4VoPAofXcoqbJEmqRyYxJEnSzqQopbQcIPNzwzHxxcC71dqVZMrqKq/RJ6VUQeVpbR47L0lSAzKJIUmS1MAiYmREzIuIecuXL2/qcCRJylnZnk5SLyLiFuA/gX9n1o8SER2B6UBn4D3gjJTSh5m6McDZQDnwXymlxzLlfYDfAa2BWcAPUkqpMe9FUm4aMWVureWThx/cyJFIaiLLI6IopbQ8IoqAf2fKS4Bu1doVZ8rqKq/eZ2lENAM6AbVmKFJKk4BJAH379nXMIknSZ9TYMzGm0LAbbEmSJG3Oo8DQzPOhVI49NpSfEREtI6KYymPkn08pvQNURMRBmXZDNuqz4VonAc+llD5t6BuQJGlnlnUSIyLOjojCbXmzRthgS5IkCYCIuBuYA+wdESURMQL4OfDViFhI5fjhZwAppXnA/cCLwGPAqJTSusylvgXcEhGLqJx98YdM+Q1A18wRrhcD32+cO5Mkaee1NctJbgUOBFZmzkf/KTAppbR0G2OoscFWRFTfYGt2tXYbNtIqp+4NtjYRESOBkQDdu3ffxlAlSVKuSCmdVUfV8XW0vwy4rJbyecCXayn/GDhtW2KUJElbp86ZGBExIyL+JyL+f3t3Hi3LVdcL/PszNwwhjMklPEkgz5EhYoBERAQSwqTxigiSAXCBIMN7oLgeKiAP0EVUnigKLEUUiYq5BAQCgYQACQgoQgLEJBAmJZgoGRjClJBxvz+qDunbp8+9fe4953RXn89nrVr39K7qrr137era91e7dh9bVXdPUknayPtenOT7NyCPe6S19trW2mGttcO2bt066+wAAAAAu2lnIzHenW7kxc8muXu6AMarq+r9Sc7OjkGNPbGWE2wBAAAAC2rFkRittT9rrT2ptfbjSW6dLmjxiSQ/muSV6QIYf19VL6+q8ck6V2MtJ9gCAAAAFtSKIzGq6teSfDLJua21b3XTYOT1rbXzqmpLkmuTbE83KuLVSX5oVzvrJ9g6Isn+VXVJukdSXpzk5Kr6lSSXJXlc0j1/WlVLE2zdmMkTbN0s3bwZbwkAAACw0Hb2OMnPJfmddAGHL6UbeXFsVd0yyfn9Nqe31j4x7c7We4ItBuykY2adAwAAAObczh4neXhr7YAkd07yv9I9TvLQdHNlfC1dUOOZVXVUVd18IzILAAAAbF4rBjGWtNYuba29u3/51Nba7ZMcli6ocVCSE5N8fd1yCAAAAJApghgruLD/9wWttYOS3HeN8gMAAAAw0c7mxNhBa2004NGSfCnJNf26Cye+CQAAAGCNTB3EGNVauzHJ/1zjvAAAAACsaHcfJwEAAADYUIIYAAAAwCAIYgAAAACDIIgBAAAADIIgBgAAADAIghgAAADAIAhiAAAAAIMgiAEAAAAMgiAGAAAAMAiCGAAAAMAgCGIAAAAAgyCIAQAAAAyCIAYAAAAwCIIYAAAAwCAIYgAAAACDIIgBAAAADIIgBgAAADAIghgAAADAIAhiAAAAAIMgiAEAAAAMwpZZZwA2xEnHTE4//uSNzQcAAAC7zUgMAAAAYBAEMQAAAIBBEMQAAAAABkEQAwAAABgEQQwAAABgEAQxAAAAgEEQxAAAAAAGQRADAAAAGARBDAAAAGAQBDEAAACAQRDEAAAAAAZBEAMAAAAYBEEMAAAAYBAEMQAAAIBBEMQAAAAABkEQAwAAABgEQQwAAABgEAQxAAAAgEEQxAAAAAAGQRADAAAAGARBDAAAAGAQBDEAAACAQRDEAAAAAAZBEAMAAAAYBEEMAAAAYBAEMQAAAIBBEMQAAAAABkEQAwAAABgEQQwAAABgEAQxAAAAgEEQxAAAAAAGYcusMwAwjaecePasswAAAMyYkRgAAADAIAhiAAAAAIMgiAEAAAAMgiAGAAAAMAiCGAAAAMAgCGIAAAAAgyCIAQAAAAyCIAYAAAAwCIIYAAAAwCAIYgAAAACDIIgBAAAADIIgBgAAADAIghgAAADAIAhiAAAAAIOwZdYZAJgHTznx7GVpr3vS4TPICQAAsBIjMQAAAIBBEMQAADadqrqoqs6vqnOr6pw+7Q5V9d4+/T1VdfuR7Z9fVRdW1QVV9YiR9PtW1Ser6tNV9cqqqlmUBwA2C0EMAGCzOrK1dmhr7bD+9e8mOb219mNJTu9fp6rum+QxSe6V5JFJ/rKqbt6/5/VJntpau0eSuyZ59EYWAAA2m7kJYqzVHREAgN10dJK/7/9+Q/96Kf3k1tp1rbVLknwqyU9U1V2S7NVa+/iE9wAA62Bughi9tbgjAgCwKy3J0o2SZ/dpW1trVyRJ/+8d+/QDk1w88t5L+rSV0pepqqdV1TlVdc4VV1yxhsUAgM1l3oIY41Z1R2QG+QMAhun+rbV7JzkqyZOr6mHrubPW2mtba4e11g7bunXreu4KABbaPAUx1uKOyDLufAAA41prl/b/Xp7kH5McnuSKqtqaJP2/l/ebX5LkoJG3H9inrZQOAKyTeQpirMsdEXc+AIBRVXWrqtpn6e90j6Z+OslpSZ7Qb/aEdI+ypk8/pqr2rqoDkxyS5GOttf9McmNV3aff7vEj7wEA1sGWWWdgyegdkara4Y5Ia+2KKe+IAADsygFJTqmqlmSfJCcneXuSDyU5uap+JcllSR6XJK21c6rqbUnOS3Jjkme01q7pP+vJSf6mqm6W5Kwkb9nQkgDAJjMXQYz+LkhrrV01ckfkT3LTHZFXZPkdkddU1Z+m64gckuRjG55xAGBwWmv/kW5y8HFfTfLQFd5zQpITJqSfk+TQNc0gALCiuQhiZG3viAAAAAALaC6CGGt5RwQAAABYTPM0sScAAADAiuZiJAabzEnHzDoHAAAADJCRGAAAAMAgCGIAAAAAgyCIAQAAAAyCIAYAAAAwCIIYAAAAwCAIYgAAAACDIIgBAAAADIIgBgAAADAIW2adAQAAYHPYtn3brLMADJyRGAAAAMAgCGIAAAAAgyCIAQAAAAyCIAYAAAAwCIIYAAAAwCAIYgAAAACDIIgBAAAADIIgBgAAADAIghgAAADAIAhiAAAAAIMgiAEAAAAMgiAGAAAAMAiCGAAAAMAgCGIAAAAAgyCIAQAAAAzClllnAGbqpGMmpx9/8sbmAwBggWzbvm3WWQAWlCAGAAAwl1YKhpx63KkbnBNgXnicBAAAABgEIzEAAIDdMqvHRjyuApuXkRgAAADAIAhiAAAAAIMgiAEAAAAMgiAGAAAAMAgm9gTmylNOPHvWWQAAAOaUkRgAAADAIAhiAAAAAIMgiAEAAAAMgiAGAAAAMAiCGAAAAMAgCGIAAAAAg+AnVlk/Jx0z6xwAAACwQAQxAFbwlBPPnpj+uicdvsE5AQDWw7bt2yamn3rcqRucE2BaHicBAAAABkEQAwAAABgEQQwAAABgEAQxAAAAgEEQxAAAAAAGQRADAAAAGARBDAAAAGAQtsw6AwAAAPNk2/ZtE9NPPe7UDc4JMM5IDAAAAGAQBDEAAACAQfA4CUxy0jGT048/eWPzAQAwB1Z6vAJgoxmJAQAAAAyCIAYAAAAwCIIYAAAAwCAIYgAAAACDIIgBAAAADIJfJwFm4iknnj3rLAAAAANjJAYAAAAwCIIYAAAAwCAIYgAAAACDYE4M9txJx8w6B7ChVprP43VPOnyDcwIAbKRt27dNTD/1uFM3OCeweRmJAQAAAAyCIAYAAAAwCIIYAAAAwCCYEwNYVyvNHwEAALBaghhMzwSeK9fB8SdvbD4AAAA2IY+TAAAAAIMgiAEAAAAMgsdJYC14zMTcFwAAwLoTxGA5c1/AblkpkPO6Jx2+wTkBAA+bytIAABXiSURBVIDF5HESAAAAYBCMxNjMjLgAAABgQAQxYD0t4FwZ5r5YPY+ZAADA2hh0EKOqHpnk5Un2SvK3rbU/nHGW5pMRFwCwbvRHWDTbtm+bdRYGZ6U6O/W4Uzc4J7D4BhvEqKqbJ3lNkgcmuTTJR6rqPa21T8w2ZzCFAYzQMOJi/RmhAcOnPwIAG2uwQYwk90vyqdbaxUlSVScnOTrJ4nQajKBgDQlKDIfgBgzK4vdHAGCODDmIcWCSi0deX5LkiA3NwSqDDOdefOXE9EMPut2qth+K1ZRrUetg1V72iFVt/qoDXros7dmXvXDyxhO2XUsr7XdSHjficxbRaoMbswpcrSbYImDDAph9fwSYW5MeM/GICeyZaq3NOg+7paqOT/Kg1toz+tfHJTmitfb0se2eluRp/csfTfLZNczG/km+soaftyjUy3LqZDl1spw6WU6dLLcedXLX1trWNf7MTUF/ZF0oz3xTnvmmPPNrkcqSzLA/MuSRGJckOWjk9YF92g5aa69N8tr1yEBVndNaO2w9PnvI1Mty6mQ5dbKcOllOnSynTuaO/sgaU575pjzzTXnm1yKVJZlteb5vFjtdIx9LckhVHVhVeyc5JsnpM84TALC56I8AwAYa7EiM1tp3q+qZSc5IF4x5Q2vtnBlnCwDYRPRHAGBjDTaIkSSttdOSnDbDLKzLsNAFoF6WUyfLqZPl1Mly6mQ5dTJn9EfWnPLMN+WZb8ozvxapLMkMyzPYiT0BAACAzWXIc2IAAAAAm4ggxgRV9TdVdXlVXbDC+jtV1ZlV9emq+lxVPWNk3SOr6oKqurCqnrdxuV5/e1gvF1XV+VV1blUtzLPCU9TJflV1el8nH6uqQ0bWLWRb2cM6WdR2clBVfbA/3p+rqt+esE1V1Sv7evlkVd1nZN3CtZU1qJOFaytT1sndquojVXVNVT13bN3CtZPNborv00F9b0xRnif25/UFVfXxqjpsZN3cnfNTlOeIqvpGn+dzq+pFI+uGeHx+c6QsF1TVDVV1h37dXB2fRbvuTlmewZw/U5ZnMOfPlOUZ0vlzi6o6p8/P56vqT6uqxraZ7fnTWrOMLUkelOQ+SS5YYf1Lk7ys/3trkiuT3DLJzZNclO6n1vZOck6S+8y6PLOul/71RUn2n3UZZlAnr0ry4v7vuyX5SP/3wraV3a2TBW8nd0pyr/7vWyf5fJJDx7Z5TJK3J6m+/v5tkdvKntTJoraVKevkjkkOT3JCkueOpC9kO9nsyxTfp4P63piiPPdLctv+759Jcu7Iurk756cozxFJ3jkhfZDHZ2zbbUnOmtfjs2jX3SnLM5jzZ8ryDOb8maY8Y9vP+/lTSW7V/713ko8mecjYNjM9f4zEmKC19sEkX9vJJpckuXUfkdo3yVeSXJPuy+NTrbWLW2vXJTk5ydHrnd+Nsgf1srCmqJO7JTmr3/YzSe5YVXfOAreVPaiThdVau7S1dl7/97eSnJdkvMxHp/tVg9Za+0SSLVV1UBa0rexhnSykaeqktXZ5a+3sJNeNvX0h28lmN8X36aC+N3ZVntbaR1tr3+hffjjLvxPmyhTHZyWDPD5jjkuyfR2zs0cW7bo75fVhMOfPlMdnJYM8PmPm/fxprbXv9C/3TrJXksvHNpvp+SOIsXv+Ksk9kvx3kvOT/Hpr7cYkBya5eGS7S/q0zWKlekmSluS9/VCpZ88qgzNwfpJfTJKq+okkd01yl2zutrJSnSSboJ1U1cHp7qR/eGzVSm1i4dvKbtRJsuBtZSd1spKFbydMtMjfG09P8o6R10M95+/fD6s+q6oO7dMGfXyqap8kj0zylpHkuT0+i3bdnfL6MJjzZxflGdz5s6vjM5Tzp6r2qqpz0wUvPtBaG3/MbKbnz6B/YnWGnp8uwnZkkh9M1+g+NNsszYWJ9dJa+2aS+7fWLq2qOyZ5d1V9prX23llmdoP8bpK/qKpPJ7kw3ZCqzf6TQDurk4VuJ1W1b5J/TPKckbslm9oe1MnCthXthM2uqo5I8pQkPz2SPMRz/uNJDmqtXVVVj0hySlX9wKwztQa2Jfnn1troqI25PD6L9n06TXmGdP7sojyDO3+mbG+DOH9aazckObSqbpfkjKo6srX2/lnmaZSRGLvngUne3A+f+UKSL6YbgXBJuud/lhzYp20WK9VLWmuX9v9enu7kPnxmudxArbVvtNaOb63do7X2mHTPs38um7it7KROFrqdVNXe6aLu21trb52wyUptYmHbyh7UycK2lSnqZCUL207YqYX73qiqeyV5XZJHtda+upQ+xHO+tfat1tpV/d9nJLk23bPzgz0+vWMzNhR+Ho/Pol13p7k+DOn82VV5hnb+rOL6PYjzZ0lr7cok70ryk2OrZnr+CGLsnn9PclSSVNUB6f6jflGSjyU5pKoO7BvyMUlOn1UmZ2BivVTVrfqhU6mqW6UbQvXpmeVyA1XVbatqS//3E5J8so+8btq2slKdLHI76eeJeV2SC1trf7zCZqcleXy//X2S3NhauzgL2lb2pE4Wta1MWScrWch2wi4t1PdGVd0lyVuTPLG19rmR9EGe81W1deTv+6abL+zyDPT4JN01PMmD003ot5Q2d8dn0a6705RnSOfPlOUZzPkz7fV7QOfP/lV16/7vWyZ5WJLxx0lmev54nGSCqtqebkbc/avqkiQvTjepSVprr0nye0neUFUXppvo5P8uRdCq6plJzkgXIHpDa23mP5OzVna3XvqhX6dUVUuyT7oJXt4+YReDM0Wd3DPJiVX13SRfSDe8L6217y5qW9ndOklyQBa0nSR5QJInJjm/uucLk+QF6ecC6evlLUmOrO4xm2uTPLlft6htZbfrJIvbVnZZJ1V1p3SPYN0myY1V9Zwk92itfXNB28mmNsX36aC+N6Yoz4uS7Jfkz7v/E+T61tphmdNzforyHFdVT+s3vzbJ8a2165NcP9DjkySPTvKekUn/kvk8Pot23Z2mPEM6f6Ypz5DOn2nKkwzn/Pn+JH/XB2dukW50yalV9YxkPs6fam2zP54PAAAADIHHSQAAAIBBEMQAAAAABkEQAwAAABgEQQwAAABgEAQxAAAAgEEQxGBQquolVdVGlm9U1bur6sdnnbc9UVU368t26Abv9479fg9ew898eVVdtIttRo/jjVX19ao6u6pO6H9Ccl1V1UX9vl84Yd1Pj+Tt4PXOyzSq6vlVdcaE9AdX1dur6vKquq7/911VdWxVTf39XlWnVtX5O1n/6qq6sqpuXlXbqurfq+pmu1segKHTH1nz/eqPLF+nP7J8vf4ISQQxGKZvJLl/vzw5yUFJzqyq2880V3vmZul+j31DOw1J7tjv9+AN3m9y03H8qSTHJnlrbvqN7ftuwP6/3e933HH9urlQVbdL8ttJThhLf06S9ye5IcmzkxyV5FlJvpnkH5IcuYrdbE9ySFXdY8L+90ry2CRvba1d01o7NclVSZ66+tIALBT9kbWjP7Kc/siO+9Ef4XsEMRii61tr/9ovb03yhCT7Jfn5GedrQ1TVLWedhzUyehzPaK39QZJ7Jflykjf2F6v19M4k96iqQ5YSRi6Q71jnfa/GU5N8ubX2waWEqrpPkpcn+b3W2i+21k5urX2wtfam1tpxSX46yVdWsY+3p+sIHDdh3ZFJDkjXsVjyV0l+o6pqlWUBWCT6I4tBf2Q6+iPMDUEMFsHSsLM7jyZW1R2q6rVVdVlVfbeq/qWq7je2zV790LjPVdW1/fC3fxjb5llV9fmquqaqvlBVvzG2/iVV9ZWqundV/Wu/r89U1cPHtntcVZ3X7+c7VfWJqlqKTn+r//f1o0MH+6VV1eOr6u+q6sokp/af16rqWZPyMpZ216ra3ufx2qr6dFU9sR+auFR371/a7yrr73ZVdVJVfbuqvlxVv7P88EyvtXZlkt9K8kNJHjbNe6rq0Kp6f1+n1/bH8teneOt/Jflwdrz78ZAk+2ZCp6GqnldVn6yqq/vyvreq7jW2zUOq6qN9fV1dVRdU1TEj63fWBlbyxCSnjKU9O8nlSV466Q2ttY+01v5tLG9PrapP9e34S1X1WyPbfydduzpm/LPS1c/lSc4aSXt7umP0k7vIO8Bmoj8ylpexNP2RyfRHbtpef4SpCGKwCA7q/71iKaGqbp7kfUkelOTXkvxskkuSvK+qfmDkvX+Z5EVJ/jbJQ5M8PcnohfM5SV6Z5OQkD++3e3lVPW8sD/sk+et+26P7vLy5qm7bf87d00WO35XuYvjzSd6Y5Db9+x/S//vS3DQ09csjn/9H6b64fz4rXCgmqao7JvlIkh9LN7TvqCSvTjds88tJHt9v+r9H9rua+ntj/5nPTPLL/faThkSuxgeSXJ8pLkjV3al4Z7qhoL/Y5+WPkuw95b62Z8f8Hpfu4vmdCdveIckr0rWDX0o3xPN9I8f4Duk6G+enawM/k6593apfv6s2MKl8B6S7G/SvY6selOSs1tr10xSyqn4zyZ/3+3tYkj9J8nujHYc+bz9cI0Nnq2rvdPX6ptbaDUvprbUvJbk0ySOm2T/AJqE/sgL9kV3SH7mJ/gi71lqzWAazJHlJumFpW/rloHQXje8kOWBku6ckuSbJXUfS9krymSSv7F/fLV0H4VdX2Nde/b5eM5b+inQXqVuM5KklecDINvfs0x7Vv358kit2Uq59++2fNJZ+cJ/+xgnvaUmeNal+Rl7/QZIrk2xdYb+H9J9zxFj6NPV37/69vzCyzT5JLkty0TTHcSfrv5zkL6ZoD3fu83DPVbaji9INf9ya5Lokh6d7DvjrSX4hyc/1n3vwTtrGLfrtf7lPe0D/nlut8J6dtoEV3vPQ/jN/eCz96iR/MJZWI+fFliTf16ffJl0H5/lj278wyVeTbOlfL5X/j0a2WaqHn5qQt/cmedtqymOxWCyLskR/ZHSd/oj+yGia/ohl3RcjMRii/dJ90V+X5D+TPDjJ0a21y0a2eWi6aPF/VdWWqtqS7kv1A+mj++merbsh3aRDk9yt39c/jqW/Kd0X8Y+NpF3VWvvnkdef6f/9H/2//5Zkv6p6fVU9rKr2naagI961yu2XPCTJaa21K3a55Y6mqb+fSneH4rSlN7XWrkrynt3M66hpn228LH0Ho6p+qb9TMLW+Xs5Kd/fjkf1+T5+YoaojquqDVXVVunJfneR2SX6k3+SzfdpJ1c2YPT6x2+60gaXyfG1S9sdePyY3nRfXJfl/ffr90919efPSseyP51np7ub8aJK01q5NN5nZ46q+92zpMUm+lO7u2bivjuQPYDPSH5me/shO6I/oj7A6ghgM0TfSRap/Mt1wyxvSDR0ctX+6IW7XjS1PT9cRSP/vt/oL3SRLX/rjF9yl13cYSbt6dIN201C3Lf3rC9INg7tbuovS16rqzau4yH19yu3G7Zfuwrpa09Tf7ZN8u7/YjFrNBE7LVNUtMmW+Wzd88RHp6ufEJJdW9xzwT6xil29M8rgkxyc5pbV2zYQ8/WCSd6d7Vvi4dG3v8D6Pt+jz8pU+L/um62h+pareU1U/3K/fkzYw3on67yQHjqWd2efp8Ow49Hf//t/PZ8djudTJ3W9k2+1J7pLk/v1xeFS6u27jHZRJeQLYbPRHpqc/smv6IzfRH2Gntsw6A7Abrm+tndP//dGq+naSf6iqk1pr7+vTv5buS/E5E96/dFH4apJbV9U+K3Qcli7UW8fSl15PikavqLV2SpJTquo26Z5PfFWS1yR59Go+Z8QN6YYRjrrV2OvdjU5PU39XJtm3qm421nHYf8J7VuPIdN9Nk6Lty7TWzk/yqP55yQckeVmSd1bVnVtr103xEW9Ldxx+Kd2zo5NsS3eRfGxr7erke8+/7vD8aGvtQ0mOqm7G9iOT/Fm6O2X37tevtg1c2v+7X3bsjH0wycOraq+lDmpr7etJzunzNno8ltrpwzO58/nZkb/fn64jdGy6u3a3zo6zgI+6w0j+ADYj/ZGO/kj0R/RH2EhGYrAItif5VLrfF19yZpK7J/l8a+2csWVpBuyz0l10J/2MU9INwfxKumj1qMem++3r85e9YwqttW+21k5O8pYkS7+DvfQlP+0EUEkX3V4aOph+yN1RY9ucmeRnqmq/TLbSfqepv39Jd3H/2ZE87JPu4rRbqvsN8pcl+UK6ibym1lq7rrX2gXSTRG3NlJ2X1s1A/rJ0x2Olfd4sXafhxpG0RyWZ+PNyrbWrW2unpZtcbdlvna/QBiZZqut7jqW/Kl1n8AU7ee+Sj6S7M3enCcfynNba0kz0S3fs3pSuA3V8kgvb2KziIw5Jct4U+wfYLPRHoj+iP7Ii/RHWjJEYDF5rrVXV76e7+/HAPvr8d0mekeSfqurlSb6Y7nnB+6ebzOjPWmufrarXJnlVP2v2h9NFcx/TWvvl1toNVXVCkj+u7mfC3pfkgUl+PckLW2vfnTaPVfWrSe6b5Ix0HZEfSdf5eFtfhmur6otJHltVn0p3Md/VF/I7kjypqj7el++pWX6hfEW6oa3vr6qXputo3CPJvq21P073DO/VSZ5YVd9MckN/V2ma+vtEVb03yV/2z1NeluS56YYGTmNLVS3N+H3rvn6emW4yrkeODIFdUXU/KfbSdMMlv5ius/Bb6aL5U0flW2sv2sUmZyb5w3Q/OffXSX4wyfPS3f1ZysvR6S6070g3vPKgJE9L8k/9+p22gRXydXlVnZvued+3jqR/oqqem+RPqurQdLPV/3e6O18PSHKndJNnpbV2ZVW9JMlrqvsZuw+l6wDdPcmDW2vjP2O2Pd1Ppj06O3bEv6eq7trvYy2eNwZYCPoj+iPRH9EfYWO0OZhd1GKZdskKs0inu4PxuSSnj6TdNt3wuYvTXciuSDdz+APH3veCJP/Rb3NZkr8f++xnp4vEX9tv9xtT5ul7s3Wn+9J/d5+H69N9wb8yyT4j2x+d7mJ3ff/eg3PTbOA/N+Hzb5vkzUmuSneBfGGS3x3PS5K7pruofL0vw6eSPH5k/ZPTTZZ0Q/eVsKr6u326Zzi/09fdi9LNsn3RFMex9cuN6S6+5yQ5IV2Eftr2cEC6i9yX+nr7erqL8A/s4n0XJXn5TtYvmw08XQfgv5J8N90kY/cb/Zx0z5ae0h/b6/v6+tv0M7FP0wZWyMv/SfLFFdYdka6TckV/jC5PN7HZsUlqbNsnJPl4uk7iVX19//YKn/vFvvw/tML6X0t3TtTO8m6xWCyLukR/JGPl0x/RH9EfsWzYUn0DAGAO9UNaL0p3R+7MGWcnSVJV5yV5bWvt1bPOCwCw/vRHmCfmxACYY+2mZ2R/Z9Z5SZKq2pZuxvO/mnVeAICNoT/CPDESA5hbVfV92UmwtXU/aQYAsG70R2C+GIkBzLO/yfLfhv/e0k8MBQCwnvRHYI4YiQHMrb5TsLOfJjuv7fib8AAAa0p/BOaLIAYAAAAwCB4nAQAAAAZBEAMAAAAYBEEMAAAAYBAEMQAAAIBBEMQAAAAABuH/A14USXXvAm3TAAAAAElFTkSuQmCC\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 = 1.7\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_presel_9[label][i][0]/1000 for i in range(len(MC_Ds_tuple_dict_presel_9[\"Ds_ConsD_M\"]))]\n",
"label=\"Dplus_ConsD_M\"\n",
"left_h_Dplus=[MC_Dplus_tuple_dict_presel_9[label][i][0]/1000 for i in range(len(MC_Dplus_tuple_dict_presel_9[\"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_presel_4[label][i][0]/1000 for i in range(len(data_tuple_dict_presel_4[\"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/'+l_flv[l_index]+'_dataMC_after_presel.png', format='png', dpi=100)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-11284.10784068, -3191.47331083, -4319.60960101, ...,\n",
" 4002.90405006, 1450.30552012, 2983.92342003])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_Dplus_tuple_dict_presel_9[\"Dplus_PX\"]"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [],
"source": [
"def get_boost_d_to_lab(data, mass, mother_index=mother_index):\n",
" \n",
" p_x = np.array(data[mother_ID[mother_index]+\"_PX\"])/1000\n",
" p_y = np.array(data[mother_ID[mother_index]+\"_PY\"])/1000\n",
" p_z = np.array(data[mother_ID[mother_index]+\"_PZ\"])/1000\n",
" \n",
" p_s = np.array([ p_x, p_y, p_z ])\n",
" \n",
" mod_p_s = np.sqrt(np.power(p_s,2).sum(axis=0))\n",
" \n",
" E_s = np.sqrt(np.power(mod_p_s,2)+np.power(mD,2))\n",
" \n",
" beta_X=p_x/E_s\n",
" beta_Y=p_y/E_s\n",
" beta_Z=p_z/E_s\n",
" \n",
" beta = mod_p_s/E_s\n",
" beta_vec= np.array([beta_X,beta_Y,beta_Z])\n",
"\n",
" return E_s, p_x, p_y, p_z, beta, beta_vec"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [],
"source": [
"Dplus_Es, Dplus_p_x_s, Dplus_p_y_s, Dplus_p_z_s, Dplus_betas, Dplus_beta_vecs = get_E_beta(MC_Dplus_tuple_dict_presel_9, mD, mother_index=1)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.09567449673232613"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dplus_beta_vecs[:,0][0]"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"b = r.TVector3(Dplus_beta_vecs[:,0][0], Dplus_beta_vecs[:,0][1], Dplus_beta_vecs[:,0][2])"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9943688868860036"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.z()"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"v = r.TLorentzVector(Dplus_p_x_s[0], Dplus_p_y_s[0], Dplus_p_z_s[0], Dplus_Es[0])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-11.284107840680841"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Dplus_p_x_s[0]"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-11.284107840680841"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v.X()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"l = r.TLorentzRotation(b)"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [],
"source": [
"v1=l.VectorMultiplication(v)"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14875.647585213312"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v1.E()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def costhetal(data, l_index=l_index, meson_index=meson_index):\n",
" \n",
" p_e = np.array([data[l_flv[l_index]+\"_plus_PX\"], data[l_flv[l_index]+\"_plus_PY\"], data[l_flv[l_index]+\"_plus_PZ\"]])\n",
" \n",
" p_pi = np.array([data[\"pi_PX\"], data[\"pi_PY\"], data[\"pi_PZ\"]])\n",
" \n",
" num = (p_e*p_pi).sum(axis=0)\n",
" \n",
" den = np.sqrt((p_e*p_e).sum(axis=0))*np.sqrt((p_pi*p_pi).sum(axis=0))\n",
" \n",
" return num/den"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(np.concatenate((costhetal(MC_Dplus_tuple_dict),costhetal(MC_Ds_tuple_dict)),axis=0),bins=70,alpha=0.7,density=True, label='MC');\n",
"plt.hist(costhetal(data_tuple_dict),bins=70,alpha=0.3, density=True, label='data');\n",
"plt.legend(fontsize='15');\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(18,8)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"MC_Dplus_tuple_dict=MC_Dplus_tuple_dict_presel_9\n",
"MC_Ds_tuple_dict=MC_Ds_tuple_dict_presel_9\n",
"data_tuple_dict=data_tuple_dict_presel_4"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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": 19,
"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=1910\n",
"\n",
" lower_Ds_mass = 1910\n",
" upper_Ds_mass = 2020\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",
"MC_Ds_mass_for_fit= np.array([MC_Ds_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(MC_Ds_tuple_dict[\"Ds_ConsD_M\"]))])\n",
"MC_Dplus_mass_for_fit=np.array([MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"][i][0] for i in range(len(MC_Dplus_tuple_dict[\"Dplus_ConsD_M\"]))])\n",
"\n",
"for label in return_branches(data_index=1, mother_index=0, l_index=l_index, meson_index=0): \n",
"\n",
" data_tuple_bkg[label] = data_tuple_dict[label][data_bkg_indices]\n",
"\n",
"for label in return_branches(data_index=0, mother_index=0, l_index=l_index, meson_index=0): \n",
" \n",
" MC_Ds_tuple_sig[label] = MC_Ds_tuple_dict[label][MC_Ds_sig_indices]\n",
" \n",
"for label in return_branches(data_index=0, mother_index=1, l_index=l_index, meson_index=0): \n",
" MC_Dplus_tuple_sig[label] = MC_Dplus_tuple_dict[label][MC_Dplus_sig_indices]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 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_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": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_mu_endvtx_ownpv_chi2=MC_Ds_tuple_dict[l_flv[l_index]+\"_plus_OWNPV_CHI2\"]/MC_Ds_tuple_dict[\"Ds_ENDVERTEX_NDOF\"]\n",
"data_mu_endvtx_ownpv_chi2=data_tuple_bkg[l_flv[l_index]+\"_plus_OWNPV_CHI2\"]/data_tuple_bkg[\"Ds_ENDVERTEX_NDOF\"]\n",
"\n",
"plot_sb_comparison(nbins=70,particle=\"e\", variable=\"own pv CHI2\", \n",
" MC_sig=MC_mu_endvtx_ownpv_chi2, data_bkg=data_mu_endvtx_ownpv_chi2, \n",
" width_MC=0.5, width_data=0.6)"
]
},
{
"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",
"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": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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F3I/Qsp+SfbZ3RMSZZD8utwa2SSl9PaX0p4i4Ebg4IjbO34fjgPfT+DEEa2tU/n9hLtm9OZ8lG3Cl/v90hx0PETGB7P/pTWQ/8rYkG3DiHsi6JUXEj4BvRMRK4I9kn/vHaN0PDaktjM0ZYzPG5hbqGJuNzd03NqcCjPzU2S+ywFdL9qE+SzYC4X2UjeBGhZHsKBsBkLUfifCuknrzyUciLFv+jrzumWXtqF/H22RdOWrJguiwdrwfCTiuhXoVRyIkOwP8FrB1hXmH5+v+YAvr7k32xfgoWV/75WSj8Z0M9Cl7H9v7cPImRzBsoW2fIDsz+SLZAAOvkHVd2q+544XsrPi3ye4neJ1sJLpjqTzy3MRKx2jJ9DvIRgRcQRZEzgWOL93n5t6fkvVU3F4rP+f3AFeSDWe/Kt+vX5KP5Jj/fWErj7nNyLo8vZnvz+nl2yX7oXdNXmcZ2ZnFieX7SPYj5+9k92SkvKwn2XPNXiX70Xc92Y+sZvc9X3Yw2RDzi/P9fA74Ysn8fmT3qbxE9t1RC3y4bB3zKfu/TIXRQ8s/s5LP58h831/P3+dvANFRxwONRyLcjiz4vkB2fC8huzepuqR+j7wNC8jOnD8FHNncMdva482Xr/oXxuZK74exuem2GZuTsblkvrG5G8XmyBu6QYuI2WQHSk1LdaWOlJ8BnZhSemdnt6Uj5fe8LCUbMr+5UQjVjMgeuP43YFJK6ebObY20fhmb1VmMzWqOsXn9s0uwtJ7kAyEcSnZfSB0wHjiB9j9QvLBSNqrgxp3dDkmSmmNslorPhFVaf94CDiB7aHQfsuH9v0w23LkkSVr/jM1SwdklWJIkSZJUSD7WRpIkSZJUSCaskiRJkqRCKuQ9rEOGDElbbbVVZzdDktQNPPzwwy+nlKo7ux1dnbFZktRR2hKbC5mwbrXVVtTW1nZ2MyRJ3UBE/L2z29AdGJslSR2lLbHZLsGSJEmSpEIyYZUkSZIkFZIJqyRJkiSpkExYJUmSJEmFZMIqSZIkSSqkQo4SLKnjvfbaayxevJhVq1Z1dlOkDlNVVcXQoUPZZJNNOrspktRmxmZ1Rx0dm01YpQ3Aa6+9xksvvcTw4cPp27cvEdHZTZLWWkqJt956i0WLFgGYtErqUozN6o7WRWy2S7C0AVi8eDHDhw+nX79+BkR1GxFBv379GD58OIsXL+7s5khSmxib1R2ti9hswiptAFatWkXfvn07uxnSOtG3b1+700nqcozN6s46Mja3mLBGxJYRcW9EPBERz0bElyvUiYi4KCKeiog/RsQHSuaNz5d9OiK+0iGtltRmnr1Vd+WxLamr8vtL3VVHHtutucK6CjgxpbQDMAo4LiJ2LqvzMeCdwPuAY4Ef5w3tDcwCDgR2Ag4rTWYlSZIkSWpKiwlrSunFlNJj+d+vA48Bw8uqTQCuSplHgJ4RsSWwO/BkSmlBSmkVMCevK0mSJElSs9p0D2tEbAXsCvyubNYIYEHJ9MK8rKlySWqzmTNn8r73vY/evXvTv39/dtppJ0466aSG+c8//zwRwc0339xpbZw9ezYRwRtvvNFknfnz5xMRbLTRRixYsGCN+ccddxwRwZgxY9aY9+ijjzJ58mSGDRtGr1692GKLLTjyyCN56KGHOnI3JElqFWOzsXlda/VjbSJiAHAd8IWU0qsd3ZCImAZMAxg5cmRHr15SBTNueLxTtnvWx3Zs+zJnncUZZ5zBjBkzGDt2LKtWreKBBx7gZz/7WUOdd7zjHdx///1st912HdncdaZ///7MmTOHL33pSw1lK1eu5IYbbmDAgAFr1L/hhhuYMmUK++67L9/73vcYPnw4ixYt4uqrr+ZDH/oQr7zyyvpsviRpHTA2dy5jc/G0KmGNiCrgeuCalNINFaosBLYE/pBPj8jLqvJyysrXkFK6FLgUoKamJrWmXZI2HDNnzuSkk07iG9/4RkPZAQccwNe+9rWG6d69e7PHHnt0RvPaZdKkSVx77bWNguLtt9/O6tWrGTNmDK+//npD+QsvvMBRRx3F1KlTG84U15s6dWqnnrmWJG2YjM3G5vWhNaMEB3AF8HRK6fwmqs0DjszrfwB4O6W0AHgQ2CEiRuRJ72Tg1g5puaQNypIlSxg4cOAa5aXBoVK3o3//+99Mnz6dgQMHMnjwYE499VQuvPDCRsvVdwOaP38+hx9+OP3792fYsGGcd955jbZ13333MX78eAYPHkyvXr3YZpttmDVrVrv3acqUKTz88MM899xzDWXXXnsthxxyCL17925U9/LLL2flypWcf/75FUfemzhxYrvbIUlSexibjc3rQ2vuYd0b+CQwLiIezV8fiYgTIuKEvM71wKKIeAq4EjgaIKW0ApgO3E42WNMvU0q1Hb4Xkrq9nXfemR/84AdcffXVbepec9JJJ3HVVVdx9tlnc91117F48WLOP7/yubdp06axxx57cOutt3LwwQdz2mmnMX/+/Ib5Cxcu5IMf/CDXXnstd911F8cccwwnn3wy11xzTbv2aeutt2a33XZrWH758uXcdNNNTJ06dY2699xzDzU1NQwZMqRd25IkqaMZm43N60OLXYJTSr8Dmn2QTkopAZ9rYt48siuwktRuF198MQcffDCf+MQniAi22WYbDjnkEE499VQGDx5ccZnFixdz5ZVXcsEFF3DCCdn5tTFjxjBq1CgWLlzz7oSjjjqKU045BYC9996bG2+8kRtvvLFhgIXJkyc31H377bfZa6+9WLBgAZdddlnFQNYaU6ZM4YorruC///u/ufnmm+nTpw/7778/l19+eaN6ixYtYpdddmnXNiRJWheMzcbm9aFNowRLUmcZNWoUzz33HL/4xS/47Gc/S69evTjnnHPYddddWbZsWcVlHnzwQVavXs2kSZMayiKiyS46BxxwQMPfPXr04L3vfS///Oc/G8qWLl3Ksccey9ChQ+nRowdVVVX86Ec/4tlnn233fh1xxBE8/fTTPP7441x77bUceuih9OxZ+VyiD5iXJBWJsdnYvD5scAnrjBse77TR1yStnT59+nDYYYcxc+ZMHnvsMf73f/+Xv/3tb2uc8az3r3/9C4BBgwY1Km/qrG+/fv0aTffo0YO6urqG6cmTJ3Pbbbfx7W9/m9/+9rc89NBDHHfccaxYsaLd+zR8+HBGjx7NJZdcwq233sqUKVOarPePf/yj3duRiszYLHVdxmZj87q2wSWskrqPT3ziEwwdOrTRwAil6geCWLp0aaPy8unW+Ne//sWvf/1rvvWtbzFt2jRGjx5NTU0NG2209l+jU6ZM4eKLL2bQoEHsu+++FeuMGTOG2traJs9YS5JUBMZmdTQTVkldwuLFi9coe+WVV3j11VfZYostKi6z22670aNHj0YjE6aU2jXM/MqVK4HGXX/efPNNfvWrX7V5XeUOP/xwJk2axIwZM5oMssceeyxVVVWNhtkvdcstt6x1OyRJagtjs7F5fWjVc1glqbPtuOOOHHzwwXzoQx9i2LBhLFq0iPPOO49evXpx1FFHVVxm6NChHH300Xzta1+jd+/ebLvttsyePZslS5a0+Z6ToUOHsuOOO/LNb36TTTbZhH79+nH22WfTp0+fhoDZXkOGDGHu3LnN1tliiy2YPXs2U6dOZeHChRxzzDENDye/9tpruffeez3DK0lar4zNxub1wYRV2oCd9bEdO7sJrfbVr36VG2+8keuvv55XX32VzTbbjNGjR3PllVfyrne9q8nlfvCDH9CzZ09OO+00NtpoIz75yU8yffp0zj333Da3Yc6cORx77LFMnTqVYcOGceKJJ7J8+XJmzpy5NrvWaoceeigPPPAAZ511Fp///OdZtmwZ1dXVjBs3jrvuumu9tEGStG4Zm9vG2Nz9RfZEmmKpqalJtbXr5nGt9YM6dKUvA2ltPf3002y//fad3YzCmDBhAm+88Qb33HNPZzdFHaS5YzwiHk4p1aznJnU7xmapYxmbGzM2dz8dFZu9wiqpW5s/fz61tbWMGjWK1atX8/Of/5x58+bxi1/8orObJknSBsnYrLYwYZXUrfXr149rr72W008/ndWrV/Pe976X2bNnc9hhh3V20yRJ2iAZm9UWJqySurXddtuNddWNUZIktZ2xWW3hY20kSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJEmSpEIyYZXUJZx55plERMOrqqqKbbfdlgsvvHCdbG/MmDEbxPPgbr75ZiKC559/vsk68+fPb/Teb7zxxmy33XYcf/zx/OlPf2rXdn/+858ze/bs9jVaklQIxuZ1w9jcmM9hlTZktT/unO3WHN2uxTbddFNuu+02AP79739zyy23cPLJJzNgwACOO+64jmyhKrj66qvZeuutWb58Of/3f//Hj3/8Y2pqapg1axbHHntsm9b185//nJdffplPf/rT66axktRVGZvVBhtCbDZhldRl9OzZkz322KNher/99uO+++7jxhtv7FJBccWKFfTp06ezm9FmO+20EzvssAMA48aN4/jjj+eYY45h+vTp7LfffrznPe/p5BZKktY3Y3Pn2hBis12CJXVpAwYMoGfP/5x7e/PNN5k2bRrvfve7qaqqYsiQIRx11FEsW7as0XKrV6/mrLPOYptttqFXr14MHTqUI488ssntvPrqq+y99968//3vZ8mSJQC88sorTJkyhf79+7PFFltwzjnn8KUvfYmtttqqYbnZs2cTETz44IOMGTOGvn37ct555wHw8ssvc9RRRzF48GD69evHmDFjqK2tbbTdiGDmzJmNys4880yGDBmyxjYef/xxDjjgAPr27cs73/lOrrrqqkbLpZQ488wzGTp0KBtvvDGf+tSneO2111rxLle20UYb8b3vfY8ePXpw+eWXN5RfeeWV7LHHHgwYMIC+ffuy1157MX/+/Ib5n/70p7n++uu55557GroynXnmmQDcdNNNjBkzhoEDB9K7d2/e//73c/3117e7jZKk9c/YbGzuSCaskrqUuro66urqWL58Ob/4xS+4++67+ehHP9owf/ny5fTp04fzzjuP+fPn88Mf/pBHHnmEqVOnNlrPZz7zGb75zW9y1FFHcdddd3HJJZcQERW3uWzZMvbff39WrlzJ3XffTXV1NQCTJ0/mnnvu4ZJLLuGnP/0p9957L3PmzKm4jk984hMcccQR3HHHHRx00EHU1dUxfvx47r77bmbOnMncuXOpqqpi7NixPPfcc+16bz75yU9yxBFHcOuttzJq1CiOPvroRus655xz+Pa3v83JJ5/MzTffzKBBgzjttNPata16m222GTU1NfzhD39oKHvxxRc59thjmTdvHjfddBP/9V//xYEHHshf//pXAP77v/+bsWPHsssuu3D//fdz//33N5yFX7RoEYceeii//OUvue222zjwwAOZPHky991331q1U5K07hibm2ZsXnt2CZbUZSxdupSqqqpGZSeccAKf+tSnGqarq6u56KKLGqbr6up417vexe67784//vEPRo4cyTPPPMMVV1zBpZdeyvHHH99QtzS41luyZAn7778/AwYM4NZbb2WTTTYB4JFHHuHOO+/kxhtv5KCDDgJg9OjRbL311vTo0WON9Xzxi1/khBNOaJieO3cuDz/8MPfff39DV6p99tmH97znPZx33nlccsklbX5/Tj311IYz0bvssgtDhgzhlltu4fOf/zx1dXWcd955nHjiicyYMQPIum395S9/YdGiRW3eVqkRI0bw6KOPNkx/9atfbfh79erVjB07lj/84Q9cddVVnH766bz73e9m0KBBvP322426kQFMnz694e+3336bffbZhyeeeIIrrriCvffee63aKUnqeMbm5hmb155XWCV1GZtuuikPPfQQDz30EH/4wx+YNWsWc+bMafQlDDBr1ize97730bNnT6qqqth9990BePbZZwG4++676dGjR7PdjABeeukl9ttvPwYPHswdd9zREBABfv/739OzZ0/Gjx/fUNanTx/233//iuuaMGFCo+n77ruP4cOHNwoKffv2ZeLEifzud79rxbuxpgMOOKDh70033ZRhw4bxz3/+E4A///nPLFu2rCGA1zvkkEPata1SKaVG04899hjjx49DlctQAAAgAElEQVRnk002afgMnnzyyYb3vzl///vfOfzwwxk0aBA9evSgqqqKW265pVXLSpLWP2Nz84zNa88rrJK6jJ49e1JTU9Mwvfvuu1NXV8fnP/95Tj75ZKqrq7nqqquYPn06J598MhdeeCEDBw7khRde4JBDDmHFihVAdjZ44403pl+/fs1u76mnnmLZsmWceuqp9O/fv9G8f/3rXwwYMIBevXo1Kh88eHDFdW222WaNpl955ZWG7kulqqur17inp7XK96dHjx7U1dU1bK9S+0rvt2mvRYsWsfnmmwPZ/UTjxo3jve99L5dffjkjR46kZ8+eTJs2reH9b8rq1av58Ic/TETw/e9/n3e/+9306tWLM844g5deemmt2ylJ6njG5uYZm9eeCaukLm3bbbdl9erVPPvss1RXV3Pdddexzz77cMEFFzTUuf/++xstM3jwYF5//XWWL1/ebGCsv5dj2rRpDBkyhEmTJjXMGzhwIG+88QYrV65sFBiXLl3aqnZvttlmDQNElFqyZAmDBg1qmO7RowerV69uVOfNN99s1TZKDRw4sGL7Xn755Tavq9Qrr7xCbW0tX/jCF4DsDPnSpUt58MEH2XrrrRvqtabNjz/+OH/+85/59a9/zbhx4xrK6wO7JKlrMDa3jrG5dewSLKlLe/rppwEaziKuXLlyjQEarrnmmkbT48aNY/Xq1WuUV/K1r32NU045hcMPP5zf/OY3DeV77bUXdXV1Dc+eg2xI/DvvvLNV7R49ejSLFi3iwQcfbLT8LbfcwujRoxvK3vGOdzTqcpNS4te//nWrtlFqu+22Y9CgQdx0002NyufOndvmddV7++23Ofnkk1m9enXDs95WrlwJ0OgzeOSRR9boNtSrVy9WrVrVqKzSsi+88AL33ntvu9soSVr/jM2tY2xuHa+wSuoy6urqGka8W716NY8++iinn34648aNa3jO2P77788pp5zCd77zHUaPHs0dd9zBvHnzGq1n2223Zdq0aZx00kksXryY0aNHs2zZMq6//np++tOfrrHds88+m9dff52DDz6YO++8kz322IMPfOAD7L///nzmM5/h9ddfZ9iwYVxwwQX07NmTjTZq+VzghAkTGDVqFEcccQTnnHMOgwYN4rvf/S6vvvoqp556akO9gw46iNmzZzNq1Cje9a53cfnll7frzGvPnj055ZRTOP3009l8883Za6+9mDt3Ln/84x9bvY7HHnuMN954gxUrVvDss8/y4x//mNraWi655JKG93/06NH07t2b448/ni9/+cssWbKEM844g+HDhzda13bbbcevfvUrbr75ZoYNG8YWW2zBTjvtRHV1Naeccgrf+ta3WLlyJWeccQbDhg1r8/52dxFxJTARWJxS2qHC/FOB+hvBegLbA9UppWUR8TzwOrAaqEsp1ZQvL0mtZWw2Nq9rXmGV1GW8+uqr7Lnnnuy5557su+++nHXWWRx55JHccMMNDXX+3//7f3zuc5/j3HPPZdKkSTzzzDNcffXVa6zrRz/6EV//+te57LLLGDduHNOmTVtjgIJSM2fO5NBDD+XAAw/kT3/6EwBz5sxh3333Zdq0aXz84x9n991356CDDmo0AERTevbsya233sq+++7LCSecwEEHHcSKFSv4zW9+0+gh3//zP//DRz7yEU488USmTp3K9ttvz9FHH92Wt63BjBkzmDFjBhdccAETJ05k8eLFnHvuua1e/sgjj2TPPfdkwoQJnH/++eywww7U1tZyzDHHNNTZYostuPbaa3n++eeZOHEiZ599Nt///vfXeHD5Zz/7WUaPHs3kyZPZddddufTSS+nTpw+//OUvqaur46Mf/SinnXYaX/ziF9lvv/3atb/d3GxgfFMzU0rnpZR2TintDMwA7kkpld6ANTafb7Iqaa0Ym43N61o0dxB0lpqamlT+gN6OMuOGxwE462M7rpP1S0X09NNPs/3223d2M7q9t99+m5133plddtmFn/zkJ53dnA1Kc8d4RDzcHROziNgKuLnSFdayej8D7k4pXZZPPw/UpJTadDnA2Cx1LGPz+mFs7jwdFZvtEixJ7XTdddfx0ksvseOOO/Lmm29y2WWX8eSTT3L55Zd3dtMkACKiH9mV2BNLihNwZ0T0BC5NKf2gmeWnAdMARo4cuS6bKkkdwtjc/ZiwSlI79enTh4svvpjnnnuOiGDHHXfkV7/6FbvttltnN02qNwm4r6w78J4ppRcjYihwW0Q8k1KqOCJJSulS4FLIrrCu++ZK0toxNnc/JqyS1E4TJ05k4sSJnd0MqTlTgEZDbqaUXsz/XRwR1wG7Aq0bQlOSCs7Y3P046JIkSd1QRGwK7AfcWFLWP+8mTET0J+su/FTntFCSpJZ5hVWSpC4mIq4BxgBDImIhcAZQBZBSmpVX+yhwR0qp9MnwmwNzIyIB/YA5lCS0kiQVjQmrtIFIKa3x0G6pOyjiaPfrWkppaivqzCZ7/E1p2V+BndZNqyS1lbFZ3VVHxma7BEsbgKqqKt56663Oboa0Trz11ltUVVV1djMkqU2MzerOOjI2m7BKG4ChQ4eyaNEili9fvkFejVL3lFJi+fLlLFq0iKFDh3Z2cySpTYzN6o7WRWy2S7C0Adhkk00AeOGFF1i1alUnt0bqOFVVVWy++eYNx7gkdRXGZnVXHR2bTVilDcQmm2zij3pJkgrE2Cy1zC7BkiRJkqRC8gqrJEnqVLsurX+yzo6d2g5JUvF4hVWSJEmSVEgmrJIkSZKkQmqxS3BEXAlMBBanlHaoMP9U4MiS9W0PVKeUlkXE88DrwGqgLqVU01ENlyRJkiR1b625wjobGN/UzJTSeSmlnVNKOwMzgHtSSstKqozN55usSpIkSZJarcWENaV0L7CspXq5qcA1a9UiSZIkSZLowHtYI6If2ZXY60uKE3BnRDweESd11LYkSZIkSd1fRz7WZhJwX1l34D1TSi9GxFDgtoh4JqV0Z6WFI2IaMA1g5MiRHdgsSZIkSVJX1JGjBE+hrDtwSunF/N/FwHXArk0tnFK6NKVUk1Kqqa6u7sBmSZIkSZK6og5JWCNiU2A/4MaSsv55N2Eioj9Zd+GnOmJ7kiRJkqTurzWPtbkGGAMMiYiFwBlAFUBKaVZe7aPAHSmlN0sW3RyYGxEJ6AfMoSShlSRJkiSpOS0mrCmlqa2oM5vs8TelZX8FdmpvwyRJkiRJG7aOvIdVkiRJkqQOY8IqSZIkSSokE1ZJkiRJUiGZsEqSJEmSCsmEVZIkSZJUSC2OEtzd7Lq0/sk6O3ZqOyRJkiRJzfMKqyRJkiSpkExYJUmSJEmFZMIqSZIkSSokE1ZJkiRJUiGZsEqSJEmSCsmEVZIkSZJUSCaskiRJkqRCMmGVJEmSJBWSCaskSZIkqZBMWCVJkiRJhWTCKkmSJEkqJBNWSZIkSVIhmbBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJEmSpEIyYZUkqYuJiCsjYnFEPNHE/DER8WpEPJq/Ti+ZNz4inoiIpyPiK+uv1ZIktV3Pzm6AJElqs9nATOCnzdT5bUppYmlBRPQGZgH7AC8C90fEHSmlR9ZVQyVJWhteYZUkqYtJKd0LLGvHorsDT6aUFqSUVgFzgAkd2jhJkjqQCaskSd3TnnnX399ExM552QhgQUmdhXlZRRExLSJqI6J2yZIl67KtkiRVZMIqSVL38zCwZUppB+AcYG5EtDnmp5QuTSnVpJRqqqurO7yRkiS1xHtYJUnqZlJKr5f8fXtErASGkV1R3bKk6oi8TJKkQvIKqyRJ3UxEVJf8PQoYACwGHgR2iIgREVEFTAZu7ZxWSpLUMq+wSpLUxUTENcAYYEhELATOAKoAUkqzgKkRMS2vvhL4eEqpDqiLiOnA7WQnra9KKdWu7/ZLktRaJqySJHUxKaWpLcy/CLioiXnzgHnrol2SJHU0uwRLkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYXUYsIaEVdGxOKIeKKJ+WMi4tWIeDR/nV4yb3xEPBERT0fEVzqy4ZIkSZKk7q01z2GdDcwEftpMnd+mlCaWFkREb2AWsA/wInB/RNyRUnqknW2VJEmSJG1AWrzCmlK6F1jWjnXvDjyZUlqQUloFzAEmtGM9kiRJkqQNUEfdw7pn3vX3NxGxc142AlhQUmdhXiZJkiRJUota0yW4JQ8DW6aUlkfEh4G5EbF1W1cSEdOAaQAjR47sgGZJkiRJkrqytb7CmlJ6PaW0PP/7dmAlMIzsiuqWJVVH5GVNrefSlFJNSqmmurp6bZslSZIkSeri1jphjYjqkr9HAQOAxcCDwA4RMSIiqoDJwK1ruz1JkiRJ0oahxS7BEXENMAYYEhELgTOAKoCU0ixgat6dF7Krqx9PKdUBdRExHbidLDG+KqVU2/G7IEmSJEnqjlpMWFNKU1uYfxFwURPz5gHz2tc0SZIkSdKGrKNGCZYkSZIkqUOZsEqSJEmSCsmEVZIkSZJUSCaskiRJkqRCMmGVJEmSJBWSCaskSZIkqZBMWCVJkiRJhWTCKkmSJEkqJBNWSZIkSVIhmbBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJEmSpEIyYZUkSZIkFZIJqyRJkiSpkExYJUmSJEmFZMIqSZIkSSokE1ZJkiRJUiGZsEqSJEmSCsmEVZIkSZJUSCaskiRJkqRCMmGVJEmSJBWSCaskSZIkqZBMWCVJkiRJhWTCKklSFxMRV0bE4oh4oon5n4yIxyPiiYh4OCJqSuY9n897NCJq11+rJUlqOxNWSZK6ntnA+GbmPwuMTintAHwduLxs/tiU0s4ppZo1F5UkqTh6dnYDJElS26SU7o2IrZqZ/0DJ5O+A4eu6TZIkrQteYZUkqXv7DHBTyXQC7sy7BZ/U3IIRMS0iaiOidsmSJeu0kZIkVeIVVkmSuqmIGAMcC4wuKd4zpfRiRAwFbouIZ1JKd1ZaPqV0KXApQE1NTVrX7ZUkqZxXWCVJ6oYiYifgCuDglNLS+vKU0ov5v4uB64BdO6eFkiS1zIRVkqRuJiJGAjcAn0wpPVtS3j8i+tX/TTZw01Od00pJklpml2BJkrqYiLgGGAMMiYiFwBlAFUBKaRZwOjAY+FFEANTlIwJvDsyNiAT0A+YAN673HZAkqZVMWCVJ6mJSSlNbmH8ccFyF8r8CO62rdkmS1NHsEixJkiRJKiQTVkmSJElSIZmwSpIkSZIKqcWENSKujIjFEfFEE/M/mT98/ImIeDgiakrmPZ/PezQiajuy4ZIkSZKk7q01V1hnkw1735RngdEppR2ArwOXl80fm1LaOR+dUJIkSZKkVmlxlOCU0r0RsVUz8x8omfwdMHztmyVJkiRJ2tB19D2snwFuKplOwJ15t+CTOnhbkiRJkqRurMOewxoRY4BjgdElxXumlF6MiKHAbRHxTErpziaWnwZMAxg5cmRHNUuSJEmS1EV1yBXWiNgJuAI4OKW0tL48pfRi/u9i4Dpg16bWkVK6NKVUk1Kqqa6u7ohmSZIkSZK6sLVOWCNiJHAD8MmU0rMl5f0jol/932QDNz21ttuTJEmSJG0YWuwSHBHXAGOAIRGxEDgDqAJIKc0CTgcGAz+KCIC6fETgzYG5EZGAfsAc4MZ1sA+SJEmSpG6oNaMET21h/nHAcRXK/wrs1P6mSZIkSZI2ZB09SrAkSZIkSR3ChFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJEmSpEIyYZUkSZIkFZIJqyRJkiSpkExYJUmSJEmFZMIqSZIkSSokE1ZJkiRJUiGZsEqSJEmSCsmEVZIkSZJUSCaskiRJkqRCMmGVJEmSJBWSCaskSZIkqZBMWCVJkiRJhWTCKkmSJEkqJBNWSZIkSVIhmbBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJHUxEXFlRCyOiCeamB8RcVFEPBURf4yID5TMGx8RT0TE0xHxlfXXakmS2s6EVZKkrmc2ML6Z+R8D3gm8DzgW+DFARPQGZgEHAjsBh5Ums5IkFY0JqyRJXUxK6V5gWTNVJgBXpcwjQM+I2BLYHXgypbQgpbQKmJPXlSSpkExYJUnqfkYAC0qmF+ZlTZVLklRIJqySJKmiiJgWEbURUbtkyZLObo4kaQNkwipJUvezENiyZHpEXtZUeUUppUtTSjUppZrq6up10lBJkppjwipJUvczDzgSIB9U6e2U0gLgQWCHiBgREVXAZODWzmumJEnN69nZDZAkSW0TEdcAY4AhEbEQOAOoAkgpzQKuB8ZGxFPASuDofN6KiJgO3E520vqqlFLt+t8DSZJax4RVkqQuJqU0tYX5CfhcE/PmkV2BlSSp8OwSLEmSJEkqpBYT1oi4MiIWR8QTTcyPiLgoIp6KiD+WPoA8IsZHxBMR8XREfKUjGy5JkiRJ6t5ac4V1NjC+mfkfA94JvA84FvgxQET0BmYBBwI7AYeVJrOSJEmSJDWnxYQ1pXQvsKyZKhPIBm1IKaVHgJ4RsSWwO/BkSmlBSmkVMCevK0mSJElSizriHtYRwIKS6YV5WVPlkiRJkiS1qDCDLkXEtIiojYjaJUuWdHZzJEmSJEmdrCMS1oXAliXTI/KypsorSildmlKqSSnVVFdXd0CzJEmSJEldWUckrPOAIwHyQZXeTiktAB4EdoiIERFRBUwGbu2A7UmSJEmSNgA9W6oQEdcAY4AhEbEQOAOoAkgpzQKuB8ZGxFPASuDofN6KiJgO3E6WGF+VUqpdFzshSZIkSep+WkxYU0pTW5ifgM81MW8e2RVYSZIkSZLapDCDLkmSJEmSVMqEVZIkSZJUSCaskiRJkqRCMmGVJEmSJBWSCaskSZIkqZBMWCVJkiRJhWTCKkmSJEkqJBNWSZIkSVIhmbBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJEmSpEIyYZUkSZIkFZIJqyRJkiSpkExYJUmSJEmFZMIqSZIkSSokE1ZJkiRJUiGZsEqSJEmSCsmEVZIkSZJUSCaskiRJkqRCMmGVJEmSJBWSCaskSZIkqZBMWCVJkiRJhWTCKkmSJEkqJBNWSZIkSVIhmbBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEldUESMj4gnIuLpiPhKhfmnRsSj+euJiFgdEYPyec9HxOP5vNr133pJklqnZ2c3QJIktU1E9AZmAfsALwL3R8QdKaVH6uuklM4DzsvrTwJOTiktK1nN2JTSy+ux2ZIktZlXWCVJ6np2B55MKS1IKa0C5gATmqk/FbhmvbRMkqQOZMIqSVLXMwJYUDK9MC9bQ0T0A8YD15cUJ+DOvFvwSeuslZIkrSW7BEuS1L1NAu4r6w68Z0rpxYgYCtwWEc+klO4sXzAipgHTAEaOHLl+WitJUgmvsEqS1PUsBLYsmR6Rl1UyhbLuwCmlF/N/FwPXAbtWWjCldGlKqSalVFNdXb3WjZYkqa1MWCVJ6noeBHaIiBERUQVMBm4trxQRmwL7ATeWlPXPuwkTEf3Jugs/tV5aLUlSG7WqS3BEjAe+C/QAfpJSOrts/qnAkSXr3B6oTikti4jngdeB1UBdSqmmg9ouSdIGKaW0IiKmA7eTnXy+KqVUGxEn5PNn5VU/CtyRUnqzZPHNgbkRkYB+ZAM23YgkSQXUYsLq0PmSJBVPSmkeMK+sbFbZ9GxgdlnZX4Gd1nHzJEnqEK3pEuzQ+ZIkSZKk9a41CatD50uSJEmS1ruOfqyNQ+dLkiRJkjpEa66wOnS+JEmSJGm9a03C6tD5kiRJkqT1rsUuwQ6dL0mSJEnqDK26h9Wh8yVJkiRJ61trugRLkiRJkrTembBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwpqbccPjzLjh8c5uhiRJkiQpZ8IqSZIkSSokE1ZJkiRJUiGZsEqSJEmSCsmEVZIkSZJUSCaskiRJkqRCMmGVJEmF5Aj+kiQTVkmSJElSIZmwSpIkSZIKyYRVkiRJklRIJqySJEmSpEIyYZUkSZIkFVLPzm6AJElSJbsuvTH/a8dObYckqfN4hVWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYXkKME5RyKUJEmSpGLxCqskSZIkqZBMWCVJkiRJhWTCKkmSJEkqJBNWSZIkSVIhmbBKkiRJkgrJhFWSJEmSVEgmrJIkSZKkQjJhlSRJkiQVkgmrJEmSJKmQTFglSZIkSYVkwipJkiRJKiQTVkmSJElSIZmwSpIkSZIKyYRVkqQuKCLGR8QTEfF0RHylwvwxEfFqRDyav05v7bKSJBVFz85ugCRJapuI6A3MAvYBXgTuj4g7UkqPlFX9bUppYjuXlSSp03mFtRkzbnicGTc83tnNkCSp3O7AkymlBSmlVcAcYMJ6WFaSpPXKhFWSpK5nBLCgZHphXlZuz7zr728iYuc2LitJUqezS7AkSd3Tw8CWKaXlEfFhYG5EbN2WFUTENGAawMiRI9dBEyVJal6rrrA6sIMkSYWyENiyZHpEXtYgpfR6Sml5/vftwEpgWGuWLVnHpSmlmpRSTXV1dQc2X5Kk1mnxCqsDO0iSVDgPAjtExAjgJWAycEJphYioTiktyf8eBQwAFgPLWlpWkqSiaE2X4IbBGQAion5whtYknWuzrCRJqiCltCIipgO3k/WWuiqlVBsRJ+TzZwFT8y69kF1d/XhKqQ6oq7Ts+t8LSZJa1pqEtdLgDGMq1NszIp4gO3v7xZTSo21YVpIktUFKaR4wr6xsVsnfFwEXtXZZSZKKqKMGXXJgB0mSJElSh2rNoEsO7CBJkv5/e/cfK1lZ33H8/VEoFaRSZdHqglptqBWoP3b9TYPaKrBazaYp9QdVgkWJbWprgm5VFENL0vqjaazdghQSIZYWb8EiCFj+sEaQrqAsv8S22rK0G5YlUm2lBf32j5mLw2Xu7syduXPOnPt+JZM7c+acM98H7s73fp/nOc+RJGnmRilYH1rYIcm+9BZnuGJwhyTrBp4PLuyw12MlSZIkSRpmr1OCXdhBkiRJktSEka5hdWEHSZIkSdKsjTIlWJIkSZKkmbNglSRJkiS1kgWrJEmSJKmVLFglSZIkSa1kwSpJkiRJaiULVkmSJElSK1mwSpIkSZJayYJVkiRJktRKFqySJEmSpFayYJUkSZIktZIFqyRJkiSplSxYJUnS3NiysJ0tC9ubDkOSNCMWrJIkSZKkVtqn6QAkSZJGtXH3pf1nRzYahyRpNhxhlSRJkiS1kgWrJEmSJKmVLFglSZIkSa3kNax74HUykiRJktQcR1glSZIkSa1kwSpJkiRJaiULVkmSJElSK1mwSpIkSZJayYJVkiRJktRKFqySJEmSpFayYJUkSZIktZIFqyRJkiSplSxYJUnSXNuysJ0tC9ubDkOStAosWCVJkiRJrWTBKkmSJElqJQtWSZIkSVIrWbBKkiRJklrJglWSJEmS1Er7NB2AJEnSJDbuvrT/7MhG45AkTZ8jrJIkSZKkVrJglSRJkiS1kgWrJEmSJKmVLFjHte283kOSJEmStKpcdGlMCzfeBcDmDQ0HIkmSJEkd5wirJEmSJKmVLFglSZIkSa1kwSpJkiRJaiULVkmS5lCSY5PcnOS2JO8d8v6JSbb39/lakg0D732n/97Xk2ybbeSSJI3ORZckSZozSfYDtgJHAzuBa5NcVVU3DOx2B/CyqrovyXHAp4DnDLz/8qq6Z2ZBS5K0Ao6wSpI0f14I3FJVd1bVA8BFwKbBHarqq1V1X//ll4GnzDhGSZImNlLB6rQjSZJaZT1w58DrHf1ty3k78LmB1wVc3c/Pv7MK8bXCloXtbFnY3nQYkqQJ7HVKsNOOJEmaX0mOAU4GXjaw+cVVtTPJIcAXktxeVVcPOfYU4BSAww47bBbhSpL0MKOMsDrtSJKkdtkBHDrwen1/28MkOQo4F3hdVe1e3F5VO/s/7wYuBjYO+5CqOruqNlTVhnXr1k0x/NnYuPtSNu6+tOkwJEkTGKVgncm0oySnJNmWZNuuXbtGCEuSpDXreuCIJOuT7AucAFwxuEOSw4AF4MSqumNg+wFJ9l98DhwL3DqzyCVJGsNUVwmeZNpRVZ0NnA2wYcOGmmZckiR1SVXdn+RU4Ep6nc8XVNW2JO/ov78VOB14AvDJJAAPVtUG4InAJUkK2J/ezCmHISVJrTRKwTrutKPjlpt2lGRx2tEjCta5t+283s8NJzUbhyRpTaiqy4HLl2zbOvD8bcDbhhz3r8BRqx6gJElTMMqUYKcdSZIkSZJmbq8jrE47kiRJkiQ1YaRrWJ12JEmSumDxvqxnbT6y4UgkSaOY6qJLa9nCjXcBsHlDw4FIkiRJUkeMcg2rJEmSJEkzZ8EqSZIkSWolC1ZJkiRJUitZsEqSJEmSWsmCVZIkSZLUSq4SLEmS1oyNuxdvB+9tbSRpHjjCupq2ndd7SJIkSZLG5gjrKvLerJIktd+WhehOpPUAAA0DSURBVO0AnLX5yJG2S5Jmx4JVkiStaU4TlqT2ckqwJEmSJKmVLFhnzetaJUmSJGkkFqySJEmSpFayYJUkSZIktZIFqyRJkiSplVwlWJIkaQhXD5ak5jnCKkmSJElqJQvWtnD1YEmSJEl6GAtWSZIkSVIrWbBKkiRJklrJRZdaYuHGuwDYvKHhQCRJ0vIWL9/ZcFKzcUjSGuEIqyRJkiSplRxhbTN7cSVJahVnREnSbDnCKkmSNAlX+pekVWPBOm9MipIkSZLWCKcEt5jTjiRJkiStZY6wdtiWhe1sWdjedBiSJEmStCKOsM6Yo6aSJEmSNBoLVkmSpAnYGS1Jq8eCVZIkadoGF0gcvD2dt6yTpLF4DWtHLJxz5iNWD964+1I27r60oYgkSdJSCzfe9dCIrCRp7yxYJUmSJEmtZME6ZybtmXXlYEmSZmPkfO091iVpWV7Dusb8eIrwkT/euNx1NpIkqRnmZkkCHGHVntjjK0lSuwzLzeZrSR1mwarxmBQlSZIkzYgFqyRJUgtNtJqwHcySOsKCVYBJUZKkpqzWrW6GndPFFyXNGxdd0lgWk9/mDSPse86ZvX1/6/2rGZIkSRrR0MUXJanFHGFV4+ztlSSpQeMs5OSsKkkz5girZmsxyQ0s0b9sb++QfYdukyRJKzbW7Klh+5qbJa0iR1g1FaNeezPOdTrD9l2t63wkSZoH85IHnT0laVocYdWyxulxbdRyPbuj9vjaMyxJmmNtzNfLzZ4aeX0Lc7OkvpFGWJMcm+TmJLclee+Q95Pkz5LcmuTGJM8b9VhppoZcezNWb7XX7khqCXOzVtNMR3LHyK0L55z5UNG7x+PN11Jn7HWENcl+wFbgaGAncG2Sq6rqhoHdNgNPBZ4NPBc4D/jFEY+VVsXEPc6TjtCOeg3uOL3I9jhLwtysdpk03w47ftLravd4rS2sfm42X0tTM8qU4BcCt1TVnQBJLgI2AYOJbRNwQVUVcEOSfZIcCjx9hGOliUy8WMQMP39k285j4ca72Pzcpzws2Y282MUySXnoVKxxEu2MCu7F657O2uxtF6RlmJulFXgot46wH4xY8I5qtTq4h2wf69aCExbX5myttlEK1vXAnQOvdwDHjLDP+hGPldQ1YyS/YUl14pWjV+OPgnH+UBnj+JET/aS99Y7kd425WZqScTu+lxa8Yx+/4ZHblh6/Kp3xy3y3D/38UTu4WSZnj1FEL5xz5iM66CfOzRMeP2luHtbWUbeN+1lrQXodr3vYIXkj8EtV9Y7+6zcAx1TV2wf2uQo4vaqu67++EvgQvV7cPR47cI5TgFP6Lw8HvjlZ04Y6GLhnFc7blK61B7rXpq61B7rXpq61B7rXpknb89SqWjetYNrA3NxqXWsPdK9NXWsPdK9NXWsPdK9NM8vNo4yw7gAOHXi9vr9t2D7XLdln3xGOBaCqzgbOHiGeFUuyrapatIbeZLrWHuhem7rWHuhem7rWHuhem7rWnikxN7dU19oD3WtT19oD3WtT19oD3WvTLNszyirB1wNHJFmfZF/gBOCKJftcDrwJoL8K4Y/618aMcqwkSRqPuVmStCbsdYS1qu5PcipwJb0C94Kq2pbkHf33twKfBV6e5Fbg/4CT9nTs6jRFkqS1wdwsSVorRpkSTFVdTq+ndnDb1oHnBbxz1GMbtKrTmhrQtfZA99rUtfZA99rUtfZA99rUtfZMhbm5tbrWHuhem7rWHuhem7rWHuhem2bWnr0uuiRJkiRJUhNGuYZVkiRJkqSZWxMFa5Jjk9yc5LYk7206nkklOTTJl/ptuiPJe5qOaRqSPDrJjUkuazqWaUhyUJK/TXJTktuTvKTpmCaR5Iwk30ryzSSfTXJA0zGNK8lfJbk7yc0D2x6f5Ook25NcleSnm4xxHMu052P977rbknw+ycFNxjiuYW0aeO/dSWre2qThzM3zwdzcbubm9jE3T1/nC9Yk+wFbgeOAo4Bf66+WOM8eAH67qo4Ang+8LclzGo5pGn4XuK3pIKboHOCSqjoKOAK4peF4VizJM4HfBI6qqsOBHwJvaDaqFTkfOHbJtjOAK6rqSHorpZ4x66AmcD6PbM9lwJFV9SzgZmCEu5G3yvk8sk0kORR4FfDvsw5I02dunivm5pYyN7fW+Zibp6rzBSvwQuCWqrqzqh4ALgI2NRzTRKpqZ1Xd1H/+PeAm4CnNRjWZJOvp/X/5VNOxTEOSJwDPraoLAarqwaq6r+GwJnEvvT/GHpNkH2B/5rBwqKov0WvLoE3Ap/vPL2COvh+GtaeqrqmqB/svv8ycfTcs8/8I4OPAaYALL3SDuXkOmJtbz9zcQubm6VsLBet64M6B1zv62zohydOAjfR++efZn9L7hf9R04FMyc8Bu/rTjm5J8ukkBzYd1EpV1b3AR+glwv8E7quqq5qNamrWVdUugP7PQxqOZ5pOAT7XdBCTSvI64K6q+kbTsWhqzM3zwdzcYubmuWVuHtNaKFg7K8ljgYuBd81zD2GS1wB3V9XXmo5lih5F74+Vj1TVs+n1Sn2g2ZBWLskzgN8Dng48GTggyZubjUp7kuR9wIP0eqbnVpL9gT8ATm86FmkU5uZWMzerUebmlVkLBesO4NCB1+v72+Zakn3p3RT+M1W10HQ8E3op8KtJvgP8NfCKJHP9D5neyMFdVfXV/uuLgXm+lukFwFeqald/+t4C8LKGY5qWXUnWAfR/3t1wPBNL8hbgtcCbav7vXfYMen+MfaP/HbEeuCHJkxqNSpMyN7efubn9zM1zxNy8cmuhYL0eOCLJ+n4iOYHexdtzK0mAc4HbquqjTcczqaraUlXrq+ppwG8A11TVXPcQVtWdwD1JDu9veiVwe4MhTepfgBcl2b//+/fK/rYuuBxY/H17M/P//XAs8B7gtVX1P03HM6mq2l5Vh1TV0/rfETuA51XVzoZD02TMzS1nbp4L5uY5YW6eTOcL1qq6HzgVuJLeAgh/V1Xbmo1qYi8FTqTX2/n1/uP4poPSI5wMXJjkVuBFzNcKdw9TVdfT64m+Cfgm8BjgzxsNagWSfAa4Fjg8yY4kJwMfBDYl2U5vUYe5mXq6THs+ARwIXN3/btjaaJBjWqZN6hhzsxpkbm4Zc3P7NZ2bM/8j0pIkSZKkLur8CKskSZIkaT5ZsEqSJEmSWsmCVZIkSZLUShaskiRJkqRWsmCVJEmSJLWSBaskSZIkqZUsWCVJkiRJrWTBKk0oyRuTbEvyvSTfTfIPSV6R5ENJLhiyfyV5Zv/5Q/skOSTJxUnuTvLfSf4pydFDjv/LJKckeWv/XKcteX9HkmMGzv9AP7b7ktyR5BNJfmbJMQcl+YskO5N8P8n2JCct2ec7SX7Qf3/x8eSJ/wNKkjRl5mapOyxYpQkk+X3go8D7gYOAdcDHgWNXcLoDgGuAZwE/BWwFLkvyuCX7HQdc3n9+L3BakgP3cN6LqurAqnoccDxwIPC1xcSY5CeALwKHAM+pqscC7wT+sN++Qa+tqscOPP5jBe2UJGnVmJvNzeoWC1ZphfrJ6sPAyVX1har6YVU9UFWXVdVpezt+qar6dlV9sqp29891LvAA8AsDn3kU8N2q2tHfdBtwLbA0eS33Gf8MvBX4N+Dd/c0nAk8C3lRVO/v7fQl4O/DhJD81blskSWqCuVnqHgtWaeVeDAT4wmqcvJ8ADwC+NbD5eODzS3b9APCuJI8f5bxVVcClwOKUpl8BPl9V9y/Z9XJ63xEvHjN0SZKaYm6WOsaCVVq5JwD3VtWP9rDPr/evnXnoMcqJ+z2nFwIfrqp7Bt7axI+nHAFQVV8HrgbeM0bs9wCLSfRg4O6lO1TVD+lNazp4YPMlA225ZIzPkyRpFszNUsdYsEortxt4fJI9/Tv6m6o6aPCxt5MmeQzw98B1VXXWwPaDgJ8HvjLksNOBU5M8ccTYD6aX8KCXIA8ZEsej6SXOwaT8+oG2vH7Ez5IkaVbMzVLHWLBKK3dt/+erp3XCJPsBlwA76F2nMujVwDX93tWHqarbgQXgfSN8RoDXAf/Y3/RF4PgkP7lk1+OBAq4bpw2SJDXI3Cx1jAWrtEJVdR+93tNPJXlVkkcl2TfJcUn+eNzzJdkXuBj4AfCWIdOZhl0jM+gM4CR6KyIu9xk/C5wLPB34WH/zp4FdwIVJntTf72h6KyF+qN9OSZJaz9wsdY8FqzSBqvoocBrwR8B99JLLu1lyLcuIXgK8BngV8N2B+6kd3e95fTV7WESiqr5NL8EdsOStE/rn+S/gSuB+4PmLy95X1f8Cv0xvetE3knyfXkL8YFX9yQraIUlSY8zNUrektyiZpDZL8gLgE1X1gqZjkSRJ5mZpVhxhlebHB5sOQJIkPYy5WVpljrBKkiRJklrJEVZJkiRJUitZsEqSJEmSWsmCVZIkSZLUShaskiRJkqRWsmCVJEmSJLWSBaskSZIkqZX+H1Pn6NIo17SbAAAAAElFTkSuQmCC\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": 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": [
"#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": 26,
"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": 30,
"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=.03, width_data=.03)\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": 31,
"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": 32,
"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": 33,
"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": 34,
"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": 35,
"metadata": {},
"outputs": [],
"source": [
"with open('/disk/lhcb_data/davide/Rphipi/NN_for_training/'+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_for_training/'+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/MC/for_fit/'+l_flv[l_index]+l_flv[l_index]+'/MC_Ds_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(MC_Ds_mass_for_fit, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
" \n",
"with open('/disk/lhcb_data/davide/Rphipi/MC/for_fit/'+l_flv[l_index]+l_flv[l_index]+'/MC_Dplus_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(MC_Dplus_mass_for_fit, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"\n",
"with open('/disk/lhcb_data/davide/Rphipi/NN_for_training/'+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": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25059"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_tuple_bkg[\"Ds_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2020"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_Dplus_tuple_sig[\"Dplus_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1245"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_Ds_tuple_sig[\"Ds_ConsD_M\"].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini