{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/hep/davide/miniconda3/envs/root_env/lib/ROOT.py:301: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility\n",
" return _orig_ihook( name, *args, **kwds )\n"
]
}
],
"source": [
"import ROOT as r\n",
"import ctypes\n",
"import numpy as np\n",
"from array import array\n",
"import root_numpy as rn\n",
"import matplotlib.pyplot as plt\n",
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"l_flv = ['e','mu']\n",
"data_type = ['MC','small_data']\n",
"tree_name = 'Ds_OfflineTree/DecayTree'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"l_index = 1\n",
"data_index = None "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def find_file_path(l_index=l_index, data_index=data_index): \n",
" return \"/disk/lhcb_data/davide/Rphipi/\"+data_type[data_index]+\"/Ds_phipi_\"+l_flv[l_index]+l_flv[l_index]+\"/Ds_phipi_\"+l_flv[l_index]+l_flv[l_index]+\".root\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data = r.TFile(find_file_path(l_index=l_index, data_index=1))\n",
"MC = r.TFile(find_file_path(l_index=l_index, data_index=0))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ROOT.TTree object (\"DecayTree\") at 0x5578cb4a9110>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_data = data.Get(\"Ds_OfflineTree/DecayTree\")\n",
"t_data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ROOT.TTree object (\"DecayTree\") at 0x5578ca683900>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t_MC = MC.Get(\"Ds_OfflineTree/DecayTree\")\n",
"t_MC"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#Switch on only the branches that you need\n",
"t_data.SetBranchStatus(\"*\",0)\n",
"t_MC.SetBranchStatus(\"*\",0)\n",
"\n",
"branches_needed = [\n",
" \"Ds_ENDVERTEX_CHI2\",\n",
" \"Ds_ENDVERTEX_NDOF\",\n",
" \"Ds_OWNPV_CHI2\",\n",
" \"Ds_OWNPV_NDOF\",\n",
" \"Ds_IPCHI2_OWNPV\",\n",
" \"Ds_IP_OWNPV\",\n",
" \"Ds_DIRA_OWNPV\",\n",
" \"Ds_ConsD_M\",\n",
" l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index],\n",
" l_flv[l_index]+\"_minus_MC15TuneV1_ProbNN\"+l_flv[l_index],\n",
" \"Ds_Hlt1TrackMVADecision_TOS\",\n",
" \"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\",\n",
" \"Ds_Hlt2Phys_TOS\",\n",
" \n",
" \"phi_ENDVERTEX_CHI2\",\n",
" \"phi_ENDVERTEX_NDOF\",\n",
" \"phi_OWNPV_CHI2\",\n",
" \"phi_OWNPV_NDOF\",\n",
" \"phi_IP_OWNPV\",\n",
" \"phi_IPCHI2_OWNPV\",\n",
" \"phi_DIRA_OWNPV\",\n",
" \"phi_M\",\n",
" ] \n",
"\n",
"for branch in branches_needed:\n",
" t_data.SetBranchStatus(branch, 1)\n",
" t_MC.SetBranchStatus(branch, 1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MC event count 49002, data event count 233629\n"
]
}
],
"source": [
"#Count the events in the tuple\n",
"\n",
"MC_count=0\n",
"for event in enumerate(t_MC):\n",
" MC_count+=1\n",
" \n",
"data_count=0\n",
"for event in enumerate(t_data):\n",
" data_count+=1\n",
"print(\"MC event count {0}, data event count {1}\".format(MC_count,data_count))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#Create a dictionary\n",
"\n",
"#dict ={'branch_name'=[branch_value[event]]}\n",
"\n",
"MC_tuple_dict = {}\n",
"\n",
"for branch in branches_needed:\n",
" \n",
" MC_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index, 0),\n",
" treename = tree_name,\n",
" branches = branch,\n",
" start=0,\n",
" stop=MC_count,\n",
" )\n",
" \n",
"data_tuple_dict = {}\n",
"\n",
"for branch in branches_needed:\n",
" \n",
" data_tuple_dict[branch] = rn.root2array(\n",
" \n",
" filenames=find_file_path(l_index, 1),\n",
" treename = tree_name,\n",
" branches = branch,\n",
" start=0,\n",
" stop=data_count,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Check the Ds mass plot\n",
"\n",
"Ds_constrained_mass_MC = np.array([MC_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(MC_tuple_dict[\"Ds_ConsD_M\"]))])\n",
"Ds_constrained_mass_data = np.array([data_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"]))])\n",
"plt.subplot(1,2,1)\n",
"plt.hist(Ds_constrained_mass_MC,bins=70, range=(0,3000));\n",
"plt.subplot(1,2,2)\n",
"plt.hist(Ds_constrained_mass_data,bins=70, range=(0,3000));\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(16,6)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#HLT1 PRESELECTION\n",
"#data_tuple_dict_presel_1={}\n",
"#MC_tuple_dict_presel_1={}\n",
"#\n",
"#for label in branches_needed: \n",
"#\n",
"# data_tuple_dict_presel_1[label] = data_tuple_dict[label][data_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
"# MC_tuple_dict_presel_1[label] = MC_tuple_dict[label][MC_tuple_dict[\"Ds_Hlt1TrackMVADecision_TOS\"]]\n",
"\n",
"#RareCharm D2pi l l HLT2 PRESELECTION\n",
"\n",
"#data_tuple_dict_presel_2={}\n",
"#MC_tuple_dict_presel_2={}\n",
"#\n",
"#for label in branches_needed: \n",
"#\n",
"# data_tuple_dict_presel_2[label] = data_tuple_dict_presel_1[label][data_tuple_dict_presel_1[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]]\n",
"# MC_tuple_dict_presel_2[label] = MC_tuple_dict_presel_1[label][MC_tuple_dict_presel_1[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]]\n",
"#\n",
"##PID preselection\n",
"#\n",
"##MC_PID_indices=np.where(MC_tuple_dict_presel_2[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]>0.4)\n",
"#\n",
"#data_PID_indices_plus=np.where(data_tuple_dict_presel_2[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]>0.4)\n",
"#data_PID_indices_minus=np.where(data_tuple_dict_presel_2[l_flv[l_index]+\"_minus_MC15TuneV1_ProbNN\"+l_flv[l_index]]>0.4)\n",
"#\n",
"#data_PID_indices = np.intersect1d(data_PID_indices_plus,data_PID_indices_minus)\n",
"#\n",
"#data_tuple_dict_presel_3={}\n",
"#MC_tuple_dict_presel_3={}\n",
"#\n",
"#for label in branches_needed: \n",
"#\n",
"# data_tuple_dict_presel_3[label] = data_tuple_dict_presel_2[label][data_PID_indices]\n",
"# MC_tuple_dict_presel_3[label] = MC_tuple_dict_presel_2[label]#[MC_PID_indices]\n",
"#\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#np.float(MC_tuple_dict_presel_1[\"Ds_ConsD_M\"].shape[0])/np.float(MC_tuple_dict[\"Ds_ConsD_M\"].shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#plt.hist([data_tuple_dict[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"]))],alpha=0.2,bins=70,range=(1000,3000));\n",
"#plt.hist([data_tuple_dict_presel_1[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict_presel_1[\"Ds_ConsD_M\"]))],alpha=0.3,bins=70,range=(1000,3000));\n",
"##plt.hist([data_tuple_dict_presel_2[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict_presel_2[\"Ds_ConsD_M\"]))],alpha=0.4,bins=70,range=(1000,3000));\n",
"##plt.hist([data_tuple_dict_presel_3[\"Ds_ConsD_M\"][i][0] for i in range(len(data_tuple_dict_presel_3[\"Ds_ConsD_M\"]))],alpha=0.6,bins=70,range=(1000,3000));\n",
"#fig=plt.gcf()\n",
"#fig.set_size_inches(16,10)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"#data_tuple_dict=data_tuple_dict_presel_1\n",
"#MC_tuple_dict=MC_tuple_dict_presel_1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#Retrieve mc signal and data bkg events\n",
"data_bkg_indices=[]\n",
"MC_sig_indices=[]\n",
"\n",
"\n",
"for i in range(len(data_tuple_dict[\"Ds_ConsD_M\"])):\n",
" #retrieving the Ds reconstructed mass\n",
" \n",
" #if 850<data_tuple_dict[\"phi_M\"][i]<1150:\n",
" Ds_m = data_tuple_dict[\"Ds_ConsD_M\"][i]\n",
" \n",
" #selecting the out of signal regions\n",
" if 0<Ds_m<1850 or 2050 < Ds_m < 2600:\n",
" data_bkg_indices.append(i)\n",
" \n",
"for i in range(len(MC_tuple_dict[\"Ds_ConsD_M\"])):\n",
" \n",
" #retrieving the Ds reconstructed mass\n",
" #if 850<MC_tuple_dict[\"phi_M\"][i]<1150:\n",
" Ds_m = MC_tuple_dict[\"Ds_ConsD_M\"][i]\n",
" \n",
" #selecting the signal regions\n",
" if 1850<Ds_m<2050:\n",
" MC_sig_indices.append(i)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#Create the dict tuples with all MC signal and data bkg events\n",
"\n",
"data_tuple_bkg={}\n",
"MC_tuple_sig ={}\n",
"\n",
"for label in branches_needed: \n",
"\n",
" data_tuple_bkg[label] = data_tuple_dict[label][data_bkg_indices]\n",
" MC_tuple_sig[label] = MC_tuple_dict[label][MC_sig_indices]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(47950,)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MC_tuple_sig[\"Ds_ConsD_M\"].shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def plot_sb_comparison(nbins=None, particle=None, variable=None, \n",
" MC_sig=None, data_bkg=None, \n",
" width_MC=None, width_data=None,):\n",
" #min_plt_range=None, max_plt_range=None):\n",
" \n",
" #Determine maximum between MC and data\n",
" if \"CHI2\" in variable:\n",
" if np.max(MC_sig)>np.max(data_bkg):\n",
" upper_limit=np.max(MC_sig)\n",
" else:\n",
" upper_limit=np.max(data_bkg)\n",
" \n",
" lower_limit=0\n",
" if \"DIRA\" in variable:\n",
" lower_limit=0.99980\n",
" upper_limit=1.\n",
" \n",
" if \"prob\" in variable:\n",
" lower_limit=0.\n",
" upper_limit=1.\n",
" \n",
" if \"Hlt\" in variable:\n",
" lower_limit=0.\n",
" upper_limit=2.\n",
" \n",
" #Create and fill MC Signal histogram\n",
"\n",
" h_mc= r.TH1F(particle+\" \"+variable+\" MC/data comparison\", particle+\" \"+variable+\" MC/data comparison\", nbins, lower_limit, upper_limit)\n",
" \n",
" for i in range(len(MC_sig)):\n",
" h_mc.Fill(MC_sig[i])\n",
" \n",
" n1=h_mc.Integral(\"width\")\n",
" h_mc.Scale(1/n1)\n",
" h_mc.Integral(\"width\");\n",
" \n",
" #Create and fill data bkg histogram\n",
" h_data= r.TH1F(particle+\" \"+variable+\" from data\", particle+\" \"+variable+\" from data\", nbins, lower_limit, upper_limit)\n",
" for i in range(len(data_bkg)):\n",
" h_data.Fill(data_bkg[i])\n",
" \n",
" n2=h_data.Integral(\"width\")\n",
" h_data.Scale(1/n2)\n",
" h_data.Integral(\"width\");\n",
" \n",
" a=[h_mc.GetBinContent(i) for i in range(nbins)]\n",
" b=[h_mc.GetBinCenter(i) for i in range(nbins)]\n",
" c=[h_data.GetBinContent(i) for i in range(nbins)]\n",
" d=[h_data.GetBinCenter(i) for i in range(nbins)]\n",
" plt.title(particle+\" \"+variable+\" Signal MC/ data comparison\", fontsize=20)\n",
" \n",
" plt.bar(b,a,width=width_MC,alpha=0.6, label=\"Signal MC\")\n",
" plt.bar(d,c,width=width_data, alpha=0.4, label=\"Background Data\")\n",
" plt.legend(fontsize=20)\n",
" fig = plt.gcf()\n",
" fig.set_size_inches(16,8)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"#Retrieve data from needed branch\n",
"MC_Ds_endvtx_chi2ratio=MC_tuple_dict[\"Ds_ENDVERTEX_CHI2\"]/MC_tuple_dict[\"Ds_ENDVERTEX_NDOF\"]\n",
"data_Ds_endvtx_chi2ratio=data_tuple_bkg[\"Ds_ENDVERTEX_CHI2\"]/data_tuple_bkg[\"Ds_ENDVERTEX_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"Ds\", variable=\"END VTX CHI2\", \n",
" MC_sig=MC_Ds_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.05, width_data=0.06)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"\n",
"#Retrieve data from needed branch\n",
"MC_Ds_endvtx_chi2ratio=MC_tuple_dict[\"phi_ENDVERTEX_CHI2\"]/MC_tuple_dict[\"phi_ENDVERTEX_NDOF\"]\n",
"data_Ds_endvtx_chi2ratio=data_tuple_bkg[\"phi_ENDVERTEX_CHI2\"]/data_tuple_bkg[\"phi_ENDVERTEX_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70,particle=\"phi\", variable=\"END VTX CHI2\", \n",
" MC_sig=MC_Ds_endvtx_chi2ratio, data_bkg=data_Ds_endvtx_chi2ratio, \n",
" width_MC=0.07, width_data=0.08)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"MC_Ds_ownpv_chi2ratio=MC_tuple_dict[\"Ds_OWNPV_CHI2\"]/MC_tuple_dict[\"Ds_OWNPV_NDOF\"]\n",
"data_Ds_ownpv_chi2ratio=data_tuple_bkg[\"Ds_OWNPV_CHI2\"]/data_tuple_bkg[\"Ds_OWNPV_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\", variable=\"Own PV CHI2\", \n",
" MC_sig=MC_Ds_ownpv_chi2ratio, data_bkg=data_Ds_ownpv_chi2ratio,\n",
" width_MC=0.01, width_data=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#Retrieve data from needed branch\n",
"MC_Ds_ownpv_chi2ratio=MC_tuple_dict[\"phi_OWNPV_CHI2\"]/MC_tuple_dict[\"phi_OWNPV_NDOF\"]\n",
"data_Ds_ownpv_chi2ratio=data_tuple_bkg[\"phi_OWNPV_CHI2\"]/data_tuple_bkg[\"phi_OWNPV_NDOF\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"phi\", variable=\"Own PV CHI2\", \n",
" MC_sig=MC_Ds_ownpv_chi2ratio, data_bkg=data_Ds_ownpv_chi2ratio,\n",
" width_MC=0.009, width_data=0.008)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Ds_DIRA_ownpv=MC_tuple_sig[\"Ds_DIRA_OWNPV\"]\n",
"data_Ds_DIRA_ownpv=data_tuple_bkg[\"Ds_DIRA_OWNPV\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\", variable=\"DIRA Own PV\", \n",
" MC_sig=MC_Ds_DIRA_ownpv, data_bkg=data_Ds_DIRA_ownpv,\n",
" width_MC=0.000001, width_data=0.000001)\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_probNNmu=MC_tuple_sig[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]\n",
"data_probNNmu=data_tuple_bkg[l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index]]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=70, particle=\"Ds\",variable=\"probNN mu\", \n",
" MC_sig=MC_probNNmu, data_bkg=data_probNNmu,\n",
" width_MC=0.01, width_data=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Nz6MwzMy+Nj+eNXKe/hMUiXfudyaoVyKREP1usK+OFv+WYpHr54N4Xyoo73M4N3aB85OgbVL88wT8oTGmFDhAmL//Y4Xgz9WS5Jz7Jhj7cp/8OLvIxBwrzOxx51zSH6z/gDbkse45+Za0lfIf7n7UnrFfN8uP2ZOUPelOuvx4voKoJ/+t/nxJSY0Vy4tzbrOZjZHU1czqywei0c65vI4zYph8t9Ou2vPBLjLJSKJZeSOhc2RMPT40s2/kJ1463jn3dQEOI1mR28JUS6JspMyPsSuCVpNJwY+C4Nhe/pj/YmZjXcytRhIJQuH04CfS0nyp/O0mWku6TjHnKkZkBtlE467zmh001yRAgUhrf3YgNn8v4MnyXXvfk2/d2hSUPUe+RS7uPW/3wTD5cdtXyV9XXytB2Av8FPzem2BXR/4LguH5lQ1EznuiSddyLQ+e28nyY2P/Jz+28Wf5HgeSH1daoHNoZvfLf6Hxi3yQWSHfpd/JX5MnBNuM+0ValHLB7zVJ7Da/Y4+0ZJeLsy5ei2Pkeot3Pea6FqPkd+6zZ1c2s1Ple5eUkh97O1nSRvnr+URJ1yj+ud+c4EuHuJxzk4Ix3nfLf5HSNdj/55L6uT3jtPflHOb1ui20+5IDBytCKXDgaCj/mlwraWlkoXNugaSrzN9Pso58C9xfJA01s93OuafCqOzBKvg2vKt8l7mLnHPbY9Z3VFQodc5tM7Pf5cdqFcRjkk6W1Fl+go52sfvaC/+WD81vyF8riQJlrGnyXU4bmdmf5ANQK/kPyLluqWFmjeU/JEvS4jwa5rvJf6hLtZnyk+RclETZyG1w8m2JDj60jg5mv+wtHzoy96aCzjknaYqZPSR/S6cLlXco3Rj8jvfhNa/lBfVA8PssFzOBjZndpwJ2O03SfyUNlTRQvnX6nuD8JPKR/Afzk82sStCVM1mR28sk2xU+8qVNhQTr4y2/SP75HC3puuhjCb7Y+FucxyRkZqUl/VU+4NZzMfcMNrNG2vN6y0/kS42KSZRN9tiT+WJrXxRk/33lJ/Zr5px7N7qwmXWWD6UpEXRpnhK0UNeT/+L3z/IzKDdxzn2gA+ccAoc8uu8CB4BgTFjkg86YBOMddzrnPnXOPao941fizXS4P0S+kd7bbsRh+lPwe0qcQFpNfrbZWB9LKmdm5xZgP7vkx0r9Wz4Ajg9aw/daMP5svvwH/y+T7U4cfKiOtCx1lQ/KJSS9mCAoR1pJ35GfECTezy5J11vM7SdSZJJ8a9oJlvdtO86W74a8RUneOzUQCYipuH6T3db84Heu8XBBy1zD2OV76U+SvokNpIFU3xJGUnaL9Cj563KH8g7nCsaajpX/DJJvwAt6K0S0lrTYOfe/JKv3mfy12jBBF+smcZZF3iMmxgnXkS8PY+X1nniMfOve9DiB9DD5GZiTNSf43TyJspFrLtHzfmHwe14B9r83jrKY21kFmgS/50ct+5Ok32MDaaCwrt/fnXMfOOf6yt+SzeR7okh7rp/zE1w/++scAoc8QikQMvO3C3lF/j/onxQ1oUkwNizejeMjy8K6IXiky1LKxqbtR5GuoY2jx+YGgXGY4n+ojHSTfi54vnIwsyPj7Sj4QHtL8Pjmkt4ys1L7UHfJdxNtrfgTa+RlpPy4q+u1J3TGm+CogvzEHr9Lauec6xrvR36M5hHyM02mVDDpV2RyqeFm1ixOPc+QDzYm6a8u6hY+ZtbF/P1Acz2XZnaU9kys8kHs+jjlW5vZZfE+kAavzR5Jbusd+W757cysQcy6PkrdRGOrJNW0mHtQmlk3xQ9gqfKI/HXZLN5kYHH0ke+C+mczuz8Yj5qDmVUxfxuiS4O/a8mP8cvVup9I0L19rPxEWH+N2X5jxdxjNRB5j2gSU/5w7XkviJXXe+Jq+W66DaKPM7imnpZvGUzWDPnZd1sEz2kOZnZ40KtG8tfcD5KuDrp1R5frIn+Lmyzn3GcF2P/eGhJVL5m/3Vlk9u8Xo8qtklQyGKKgqPLN5d/7UsL8fWhzXXOK+b816vqpopiJjoKuxn+W/1LslVTVDfijovsusB+Z2QPBP4vId9c7Rf6b9xKSFsqHgOgPdNdJ6m5m70taJP+fXy35sVvb5KfvD8PsoC69gw9qkQ9kT0bGOJrZ49rTxSzSCnSXmV0b/Hu8cy7f2TNTzTn3mZllys9MOcfMpstPFNNKfuzd/+RbfKIfM878DdXvkPSVmY2XH49aQf62EjO0J5zE7s9J6mlmW+W7uk4xs8uCCS/2pv6L5K+Fgj5ulZlNlL92jpD0vnPuqzhFO8uPof1PPuNVh8u3Jtws330zpZxzbwQfup+WNNXMZsq3WO/Snm7sRSTdF2dsdRP5WS5Xmtks+dsa7ZJvBW8hf9uglxK0xsSqIz879Roz+1C+G/Q2+ZmPW8h3654e7C+v49kWHM84STPM7DXtuU/p+fLX0AXaMzZvbw2Vf27mBvv4TX4s6XmS3tSe2ZtTKuiCm/Tr2Tn3fRCUxsl3Oe5u/p6zK+Vv/3GKpEbyn1NGBg+LzPicdCgN3Cl//A+bv29n5D6lbeVb5WN7nEyWHwd+a9DCN1d7ZpxeIH9OY30g30p8XzBEINLNdqDz9/gdLX8v3XlmNll+TGEL+UD6sXxAzJfz97S+Rv6a+3cQLmfJXzcnyfccOEp+YrbtQZfXSfLvO2Plr9/T5F+7ayXdkMx+99E38v9vfWZmk+THi7aTP6cDgiEqEU8GdXvPzF6VH8tbW/5cTdCe7tv7qr+kOsH7/zfyX9jVlr8f62rlbO3vKT9b7n3B9fOR/D1i28m/V16f5BcxAPLiDoD70vDDz6H+oz33Goz87JL/0PKZ/IfZVop/38xz5D9gfiE/i+tO+QldXlXU/QyT2P+yYL818iiTqSTvUxosv1Q+nG6KOq4acfaZ6OeBvTiPNZT8fUrfyqNMWfmws0p+8pLv5VtAKsjP4LgpweOulL9Z/LrguVglf6P2xlFlctynNObxDwbrZksqm8+x5rhPaRLnJu59SmPKRO7f6SR1TFBmUbC+UT77KxZci7sl1SrAc5jvfUpjyteSD1pfBtfa7/IfrIcr6v6GMY85Vn5s2hT54Lc9+PkheL7aFKC+R8l/KB0v6Tv5CWB2yH9wfVf+ljOx9/3MdZ/SqHVN5Ge63iw/Dm2KfDD9d5zXUORcjc/nOY+9r+IN8u8Z2+W/MJokqYES3EtT+3Cf0iTK5rpPacz6dEm3aU9L8g75LtEL5G+V86eosrMkfZ9sPWP2U11+jOiv8l+oZcmH0rjPlXyL5xj54LZV/suqB4P6xj1f8qE1K9h+5HVWLliXJukf8hMc7QiuxcgM0XGfx3yOp7L8l5LfyH9JsiU4Z48o5n6o8mHr1eD8Rl4HIxTcDzumbOQ+pbn+f0l0/eR1zUfOlXwAHS7/nrlN0ueSuiU4tpbyXxxsC14jmfLvq4n2sUB53/c43n1Krwye32+C53e7/Ov7GcW5p7H8bdqGBGW2B9fFJMV5n0xUz719vfHDzx/lx5zLay4CAABQ2Mxsnvx4urKuALOI/lGYWWX5MPWUc65n2PVBcsxsvfyXiKeHXRcABzbGlAIAsB+YWZl4Y4rN7Ab5+xxOJJAm1Er+M0tBu+4CAA4CtJQCALAfmNk5kt6W76r6jXwX6HPkx5T+LKm+c+778GoIpBYtpQCSxURHAADsH9/Lh9Jz5Fv+TH5c7nOS/s85tyLEugEAEBpaSgEAAAAAoWFMKQAAAAAgNKF1361YsaKrUaNGWLsHAAAAABSiTz/9dI1zrlJ+5UILpTVq1FBWVlZYuwcAAAAAFCIzS2oCP7rvAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChKRZ2BQAAAHDo2rZtm9auXavffvtNu3btCrs6APZS0aJFVaZMGVWoUEFpaWkp3TahFAAAAIVi27ZtWr58ucqXL68aNWqoePHiMrOwqwWggJxz2rFjhzZu3Kjly5erevXqKQ2mdN8FAABAoVi7dq3Kly+vihUrqkSJEgRS4CBlZipRooQqVqyo8uXLa+3atSndPqEUAAAAheK3335TRkZG2NUAkEIZGRn67bffUrpNQikAAAAKxa5du1S8ePGwqwEghYoXL57y8eGEUgAAABQauuwCh5bCeE0TSgEAAAAAoSGUAgAAAABCQygFAAAADiAjR46UmWnkyJFhVyUpZqYmTZqEXQ0cxLhPKQAAAELx17Gfh12FPD1y1Wkp2c6uXbs0YsQIjRo1Sp9//rk2btyoUqVKqVq1ajrjjDN09dVXq3Xr1inZ18EgMibRzPT111/r2GOPjVvuggsuUGZmpiTphRdeUJcuXXKV2bx5s4YNG6aJEyfqiy++0Pr161WqVCn96U9/UtOmTXXTTTepVq1ahXUoSBFCKQAAAFBIdu3apcsvv1xvv/22KlWqpFatWunoo4/Wli1btGjRIo0dO1bLli3LEUpbt26tc845R1WqVAmx5oWrWLFi2rlzp55//nkNGDAg1/qvv/5amZmZ2eXimTNnjtq0aaMffvhBVatWVYsWLXTUUUdpy5YtWrBggQYOHKiBAwdqzpw5OvPMMwv7kLAPCKUA9l3WC2HXADg41L0h7BoA2M/GjBmjt99+W/Xr19d7772nMmXK5Fi/ZcsWffzxxzmWlS1bVmXLlt2f1dzvjjzySFWpUkUvvPCCHnroIRUrljOWDB8+XJLUsmVLjRs3LtfjlyxZoksuuUSbNm3SP/7xD/Xu3TvXNpYvX667775bGzduLLwDQUowphQAAAAoJB999JEkqUuXLrkCqSSVKlVKF1xwQY5leY0pnTp1qs477zyVLl1aFSpU0JVXXqkvvvhCXbp0kZlp2bJl2WWXLVsmM1OXLl20bNkytW/fXhUrVlR6errq1q2rt956K9f2N2zYoP79+6tx48Y6/PDDVaxYMVWqVElXXHGFZs+evW8nI0a3bt20atWqXPXYsWOHRo4cqQYNGujkk0+O+9i//OUv2rhxo+655x7dc889uQKpJFWvXl2vvPKKzj333JTWG6lHKAUAAAAKSXp6uiTfHXVfvfLKK2revLnmz5+vtm3bqnv37lq3bp0aNmyob7/9NuHjvv/+e9WvX18//fSTunXrps6dO2vJkiVq1aqVZsyYkaPs4sWLNWDAAJUsWVLXX3+9+vbtqxYtWuiDDz5Qo0aN9Pbbb+/zcUR06NBBpUuXzm4VjZg4caJ+/vlndevWLe7jli5dqvfee0/p6em6++67891PWlpaSuqLwkP3XQAAAKCQtG3bVkOGDNGQIUP0yy+/qHXr1jrrrLN0zDHHFGg7v/32m2699Valp6dr9uzZqlOnTva6v/3tb3HHZUZkZmZq0KBBuvPOO7OXXXfddWrUqJEGDhyYo6X25JNP1qpVq5SRkZFjG6tWrVK9evXUq1cvXXrppQWqeyJlypRR+/btNXLkSK1cuVJVq1aVJA0bNkwZGRlq165d3OOaOXOmJOmss85SuXLlUlIXhItQCmCfjZ3/Q9hVAA4KV9UNuwYA9rdzzz1XL7/8snr16qVRo0Zp1KhRkqQKFSqoUaNGuvHGG9WyZct8tzNhwgStX79ef/7zn3MEUkm6//779dxzz+nXX3+N+9jjjjsuRyCVpPPPP1/HHXecPvnkkxzLY8NoROXKlXXNNddo0KBBWr58uapXr55vnZPRrVs3Pf/88xoxYoTuu+8+ff/993r33XfVvXt3lSpVKu5jfvrpJ0nKDrE4+NF9FwAAAChE7du31/LlyzV16lT169dPl19+uYoWLarx48friiuu0PXXXy/nXJ7bmD9/viQfJmOVKFFCZ599dsLH1q0b/xuxKlWqaN26dbmWz5o1S+3atVO1atWUlpYmM5OZadCgQZKkH35I3ZfRZ599tk477TSNGDFCu3fv1vDhw7V79+6EXXdxaKKlFAAAAChkxYsXV7NmzdSsWTNJ0u7duzVhwgTdcMMNeumll9S6dWtdeeWVCR+/YcMGSUrYXbV8+fIJH5toTGWRIkW0e/fuHMvGjRunNm3aKD09XU2bNtWxxx6r0qVLq0iRIsrMzNT777+vbdu25Xl36uvZAAAgAElEQVSsBdWtWzfdfvvtmjJlil544QWdddZZOuOMMxKWj9wqJ5XhGOEilAIAAAD7WZEiRdS6dWstWrRI/fr10/Tp0/MMpZFutevXr4+7PtHygurXr59Kliypzz//XDVr1syx7s4779T777+fkv1Eu+6663TPPffolltu0Q8//KD77rsvz/INGzaUJGVlZWnDhg2H/O1z/gjovgsAAACEJBI28+u+G2k5/PDDD3Ot2759u+bMmZOS+nz11VeqXbt2rkAqKddMvalSrlw5tWnTRitXrlTp0qXVoUOHPMvXrFlTF198sbZu3aqBAwfmu/1Ut+wi9QilAAAAQCEZM2aM3n333VzdZCVpzZo1eu655yRJjRo1ynM7rVq1UtmyZTVixAgtXLgwx7oHH3ww4SRHBVW5cmV9+eWX+vnnn3MsHzhwoBYsWJCSfcTTv39/jRs3TlOnTo17P9dYTzzxhDIyMvTII49o0KBB2rlzZ64yy5cv1zXXXJPy+6si9ei+CwAAABSSjz/+WEOHDlXlypXVsGFD1axZU8WLF9fSpUs1efJkbdiwQa1atVKbNm3y3E5GRob+9a9/6brrrlODBg3Url07ValSRR999JEWLFighg0baubMmSpSZN/anHr27Kk+ffrojDPOUJs2bVSyZEl9+OGHmj9/vpo3b64pU6bs0/YTqV69eoFm9D3ppJM0depUtWnTRn369NHQoUN10UUX6aijjtLmzZu1cOFCzZo1S2amvn37FkqdkTqEUgAAAKCQ3HnnnapWrZoyMzM1d+5cTZw4Uc45HX744WrQoIE6duyojh07yszy3VanTp1UoUIFPfzww3r11VeVlpamRo0aaebMmXrooYckJb6lS7J69+6ttLQ0PfXUU3rmmWeUkZGh888/XzNnztTEiRMLLZTujXPOOUdLlizRsGHDNHHiRE2aNEnr1q1TqVKldNxxx6l37966+eab43ZFxoHF8uu/Xljq1q3rsrKyQtk3gNQaO6x/2FUADgpXdft72FUA9qvFixfrpJNOCrsahzznnI477jht2bIl+x6eQGFK9rVtZp865/K9SzdjSgEAAICDwIYNG+JO2jNkyBB99913at26dQi1AvYd3XcBAACAg8Ds2bN17bXXqmnTpqpVq5a2bdumWbNmac6cOapWrZoeeOCBsKsI7BVCKQAAAHAQOOGEE9SkSRPNnDlTb7zxhsxMVatW1e233657771XRxxxRNhVBPYKoRQAAAA4CNSsWVOvv/562NUAUo4xpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAACAPxgzU5MmTcKuxkElMzNTZqYHHngg7KoccoqFXQEAAAD8QWW9EHYN8lb3hpRsxsxyLStRooSqVKmixo0bq2/fvjrppJNSsi8cODIzM3XBBRfkWFa8eHGVK1dOxx57rM4991y1b99e9evXT8n+unTpohdffFFLly5VjRo1UrLN/YVQCgAAAOwH999/f/a/t2zZok8++UQvvfSS3njjDc2cOVOnn356iLVDYTnmmGPUpUsXSdL27dv1yy+/aN68eRo8eLAGDx6sq6++WiNHjtRhhx0WbkVDRCgFAAAA9oN43T7vuusuPf744xoyZIhGjhy53+uEwlejRo24z/2CBQvUuXNnvfHGG9q8ebOmTJmy/yt3gGBMKQAAABCSpk2bSpJ+/PHHHMs3bNig/v37q3Hjxjr88MNVrFgxVapUSVdccYVmz56dcHtLlizRjTfeqBo1aigtLU0ZGRk699xzNXTo0KTqM3DgQBUpUkTnnXee1q5dm6M+d9xxh6pWrar09HSdeOKJeuyxx/Tdd9/JzLJbAiO6dOkiM9N3332nJ598UrVr11bJkiVzjGPdvXu3nn32WdWrV0+HHXaYSpcurXr16umZZ57R7t27c2xv2bJlcfcT0aRJk1zdpKPHgC5YsECXXXaZypUrp1KlSqlx48b66KOP4m5r9erVuummm3TkkUeqZMmSOv300/Xiiy8mdf4K4vTTT9d7772nSpUq6e2339b48eNzrH/99dfVtm1b1ahRQyVKlFDp0qV11lln6Yknnsh1fswsu441a9aUmcnMcnTjnTt3rrp3765TTjlFpUqVUnp6uo4//nj17t1b69atS/nxFQQtpQAAAEBIpk2bJkk666yzcixfvHixBgwYoEaNGun6669XqVKltGLFCk2YMEFTpkzRm2++qUsvvTTHYyZNmqS2bdtq27ZtuvTSS9WhQwdt27ZNn376qZ588kn17NkzYT12796tO+64Q08++aSuuuoqjR49Wunp6ZKkrVu36sILL9S8efN0xhlnqFOnTtqwYYMeffRRffDBB3keX8+ePfXRRx/p6quvVsuWLVW0aNHsddddd51efvllVatWTV27dpWZady4cbrttts0c+ZMjR49ukDnMpGsrCw9/vjjatKkiW677TatWLFCr776qi666CItWLBAJ5xwQnbZNWvWqEGDBvruu+/UsGFDNWzYUD/99JNuueUWNWvWLCX1iXbEEUeoe/fu6t+/v0aPHq0rr7wye12/fv2UlpamZs2a6YgjjtCmTZs0bdo09ezZU3PnztV//vOf7LL333+/xo8fr4ULF6pnz54qV66cJGX/lqTnn39eU6ZMUaNGjdSqVSvt2rVL8+bN0z//+U9NmTJFH3/8scqUKZPyY0wGoRQAAADYD6K7cG7dulVz587V9OnT1bRpU/Xt2zdH2ZNPPlmrVq1SRkZGjuWrVq1SvXr11KtXrxyhdM2aNerYsaOcc5oxY4YaNWqU63GJbN26VZ06ddLYsWPVo0cPDR06VEWK7OlQOXDgQM2bN0+dOnXSf/7zn+wWyb/97W+qV69ensf8+eefa9GiRapcuXKO5WPGjNHLL7+sunXrasaMGdnjKfv3768LL7xQL7/8si677DJ17Ngxz+0nY/Lkyfrvf/+rq6++OntZs2bN1LlzZw0dOlRPP/109vJ7771X3333nfr06aOBAwdmL+/Ro4caNmy4z3WJp0mTJurfv78++eSTHMvfeecdVatWLccy55y6d++uYcOGqUePHjr77LMl+Wtr2bJlWrhwoe644464Ex39/e9/1zPPPJOrRXn06NG69tpr9fTTT+uee+5J7cElie67AAAAwH7w4IMPZv88+uijmj59umrWrKkOHTqobNmyOcpmZGTkCqSSVLlyZV1zzTVasmSJli9fnr38xRdf1MaNG9WrV69cgTTyuHjWrl2riy++WOPGjdOjjz6qJ598MkcgjWy7aNGiGjRoUI5AU61aNd199915HvNf//rXuPseMWKEJGnw4ME5JvgpXbq0Bg8eLEkaPnx4nttO1oUXXpgjkEpShw4dlJ6eniMI7tixQ6NHj1a5cuX08MMP5yhft25d3XjjjSmpT6yjjz5akvTLL7/kWB4bSCXfTff222+XJE2dOrVA+6latWrcmaA7deqkihUrFnh7qUQoBQAAAPYD51z2z/bt27O7jt54443q1atXrvKzZs1Su3btVK1aNaWlpWWPExw0aJAk6YcffsguO2fOHElSixYtkq7P6tWrdd5552nu3LkaNWpU3IC5ceNGffvttzr++ON15JFH5lp//vnn57mPc845J+7yefPmKT09XQ0aNIj7mFKlSmn+/PlJHkne4rXmRsboRo+lXLJkibZs2aL69etnd12OVlj3dXXOSZJ27tyZY/mvv/6qvn37qnbt2jrssMOyn//TTjtNUs7nPxk7duzQU089pYYNG6pChQoqWrRo9jbXrFlT4O2lEt13AQAAgP2sePHiqlOnjl5//XUdffTRGjp0qG6//XbVrFlTkjRu3Di1adNG6enpatq0qY499liVLl1aRYoUUWZmpt5//31t27Yte3vr16+XJFWsWDHpOqxatUobN25U1apVE3ZN3bhxo6ScYxOjlS9fPs99xLYAR2zYsEGVKlXK1SorSUWKFFH58uVzTf60t9LS0uIuL1KkiHbt2pWjTpJUoUKFuOUTLd9XkeOMDv3r169XvXr1tHTpUtWvX1+dO3dWhQoVVKxYMa1fv15Dhw7N8fwn45prrtG4ceNUq1YttWrVSpUrV84+N0OGDCnw9lIp31BqZiMkXS7pZ+fcqXHWXyfpbkkmaZuk7s65rFRXFAAAADjUlC5dWieffLJmz56tTz75JDuU9uvXTyVLltTnn3+evSzizjvv1Pvvv59jWSQ0rlmzJul916lTR127dlWXLl3UqFEjTZ8+XbVq1cpRJtKFOBJ6YyVanp+yZctqw4YN2r17d65g6pzT+vXrc3RfjpSJtCrG2rp1617VI7ZOknLMOhwt0fJ9NWPGDEk5J7saPny4li5dqscee0x33XVXjvKfffZZ0rMpR2RlZWncuHFq2bKlxo0bl2PCKUl64okn9rL2qZFM992Rki7NY/1XkhoGgfXvklLT+RsAAAD4A4gEuxIlSmQv++qrr1S7du1cgVTaE2KiRbrJFvRel9dee61eeeUV/fjjj2rUqJG++uqrHOszMjJUq1Ytff3111q9enWux+c3+24iZ5xxhn7//ffsbsfRPv74Y23evFlnnnlm9rJIi2y81tMtW7Zo0aJFe1WPaCeeeKJKlSqlTz75JG7IzczM3Od9xPr555/13HPPSfLPRUTkeWjVqlWux8R7/iVlB814wT2yvcsvvzxXIF24cGHot4TJN5Q65z6QlPBrAefcx865DcGfMyUdnaK6AQAAAIe0qVOnavHixSpevHiO8ZWVK1fWl19+qZ9//jlH+YEDB2rBggW5tnP99dcrIyNDgwcPjhsU85p9t02bNnr99de1Zs0aNW7cOFfA69y5s3bt2qU+ffrkCDwrVqzQY489lvSxRotMGtSnTx9t2bIle/mWLVt05513SpJuuumm7OVlypTRiSeeqA8//FDffPNNjm317dtXmzZt2qt6RCtevLg6deqk9evXq1+/fjnWZWVlZU/OlCoLFy5U06ZNtWbNGl188cU5JmOKTA4VG4QXL16caxKmiMMPP1xS/LGmiba3ceNG3XLLLXt7CCmT6jGl3SVNTPE2AQAAgINe9C1hduzYoS+//FJvvfWWJGnAgAE5xhT27NlTffr00RlnnKE2bdqoZMmS+vDDDzV//nw1b948V4toxYoV9fLLL6tNmza64IIL1Lx5c9WuXVvbt2/XvHnztHz58lxhLtoVV1yhCRMmqHXr1mrSpInee+891alTR5J09913a/z48Ro1apQWLVqkZs2aacOGDXrttdd07rnnatKkSXHHhualY8eOmjBhgl577TWdcsopuvLKK2VmGj9+vJYuXaprrrlGnTp1yvGYu+66SzfddJMaNGig9u3bq2TJkpo2bZo2bdqkOnXqaOHChQWqQzwDBgzQtGnT9Pjjj2vOnDnZ9yl99dVXdckll2jixIJHnWXLlmU/9zt27NCaNWv06aef6tNPP5UktW3bViNGjMgxM+4NN9ygIUOG6LbbbtP06dN13HHHadmyZXrjjTfUokULjR07Ntd+LrroIg0cOFBdu3bV1VdfrZIlS6pcuXLq0aOHGjdurDp16mjMmDH64Ycf1LBhQ61bt07jxo1TjRo1dNRRR+3dCUuRlIVSM2si6SZJCW/gY2Y3S7pZkqpXr56qXQMAAAAHvAcffDD730WLFlWlSpXUokUL9ejRQ02bNs1Rtnfv3kpLS9NTTz2lZ555RhkZGTr//PM1c+ZMTZw4MW433csuu0xZWVl69NFHNW3aNE2dOlWlSpXSqaeeqjvuuCPf+l1yySWaPHmyWrZsqQsuuEBTp05VvXr1VLJkSc2YMUP33XefXn/9dQ0ePFg1a9bUPffco+bNm2vSpElxb1+TnzFjxqhx48YaMWJEdhfWk046Sb1799att96aq/yNN94o55z++c9/6rnnnlP58uXVqlUrDRgwINctX/ZWxYoVNWvWLN1777168803lZWVpRNOOEHPPPOMatSosVeh9Pvvv89+7osVK6by5curVq1a6tWrlzp06BB3duCaNWsqMzNTffv21VtvvSXnnE488UQNHjxYl1xySdxQeskll2jQoEEaNmyYHn/8cW3fvl3HHHOMevTooaJFi+rdd9/VXXfdpbfeektz5sxR1apV1blzZ/Xr10+nnppr6qD9yhINFs5RyKyGpLfiTXQUrK8taZyk5s65r+KViVW3bl2XlcV8SMChYOyw/mFXATgoXNXt72FXAdivFi9erJNOOinsaqAQvfTSS7r++uv17LPPqnv37mFXB/tJsq9tM/vUOVc3v3L7fJ9SM6suaayk65INpAAAAAAOHvEmOVq9erX69++vYsWKqWXLliHUCoeKZG4JM0ZSE0kVzWylpPslFZck59yzku6TdLikp4N+0DuTScMAAAAADg6tW7fW7t27dfbZZ6tMmTJasWKFJkyYoA0bNuiRRx4JfUwiDm75hlLnXId81neV1DVlNQIAAABwQGnfvr1eeukljRw5Ups2bVJGRobOPPNM9ejRQ1dddVXY1cNBLtWz7wIAAAA4xNx+++26/fbbw64GDlH7PKYUAAAAAIC9RSgFAAAAAISGUAoAAAAACA2hFAAAAIXGORd2FQCkUGG8pgmlAAAAKBRFixbVjh07wq4GgBTasWOHihYtmtJtEkoBAABQKMqUKaONGzeGXQ0AKbRx40aVKVMmpdsklAIAAKBQVKhQQevWrdOaNWu0fft2uvICBynnnLZv3641a9Zo3bp1qlChQkq3z31KAQAAUCjS0tJUvXp1rV27VsuWLdOuXbvCrhKAvVS0aFGVKVNG1atXV1paWkq3TSgFAABAoUlLS1OVKlVUpUqVsKsC4ABF910AAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCk28oNbMRZvazmX2RYL2Z2RNm9qWZzTezM1NfTQAAAADAoSiZltKRki7NY/1Vko6RdIqkmyS9sO/VAgAAAAD8EeQbSp1zH0ham0eRyySNct48ScXMrFqqKggAAAAAOHSlYkxpVUkrov5eGSzLxcxuNrMsM8v65ZdfUrBrAAAAAMDBbL9OdOSc+7dzrq5zrm6lSpX2564BAAAAAAegVITSlZKiu+tWDZYBAAAAAJCnVITSyZI6SVIw8+5u59yKvB8CAAAAAIBULL8CZjZGUhNJFc1spaT7JRWXJOfcs5LekHSBmX0pabukGwqttgAAAACAQ0q+odQ51yGf9U7Sn1NWIwAAAADAH8Z+negIAAAAAIBohFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEKTVCg1s0vN7AszW2xmfeOsr2xm08zsSzP7ysxuSX1VAQAAAACHmnxDqZmlSXpWUnNJtSW1MbMzY4r1kJTlnDtZ0nmS/mFmJVNdWQAAAADAoSWZltKzJS1yzq1wzu2Q9Kqky2LKrJRUxsxM0mGS1kjaltKaAgAAAAAOOcmE0qqSVkT9vTJYFm2YpJMl/Sjpc0k9nXO7U1JDAAAAAMAhK1UTHf1V0meSjpJ0uqSnzCwjtpCZ3WxmWWaW9csvv6Ro1wAAAACAg1UyoXSlpGpRf1cNlkU7X9J/nfeNpKXyLac5OOf+7Zyr65yrW6lSpb2tMwAAAADgEJFMKP1E0qlmVtXMiku6RtKUmDLfSrpIkszsSPlAuiyF9QQAAAAAHIKK5VfAObfVzG6VNFU+xI5yzmVFbvvinHtW0kOSRpnZYklFJfVzzq0qxHoDAAAAAA4B+YZSSXLOTZY0OWbZs1H/XiXp4tRWDQAAAABwqEvVREcAAAAAABQYoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCUyzsCgAAAADR/jr287CrABzwHrnqtLCrkDK0lAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNY0oBAABwQKn364SwqwAcBBhTCgAAAADAPiOUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKFJKpSa2aVm9oWZLTazvgnKNDGzuWa20Mw+SG01AQAAAACHomL5FTCzNEnPSjpf0ipJs83sHefcvKgylSX9S9JFzrlVZlaxsCoMAAAAADh0JNNSerakRc65Fc65HZJelXRZTJn2kl5zzq2SJOfcmtRWEwAAAABwKEomlFaVtCLq75XBsmgnSqpiZnPM7HMz65aqCgIAAAAADl35dt9NUhFJtSVdJKmkpDlmNts590V0ITO7WdLNklS9evUU7RoAAAAAcLBKpqV0paRqUX9XDZZFWyFpqnNuc9B19335kJqDc+7fzrm6zrm6lSpV2ts6AwAAAAAOEcmE0k8knWpmVc2suKRrJE2JKTNJUkMzK2ZmpSSdK2lJaqsKAAAAADjU5Nt91zm31cxulTRVPsSOcs5lmdktwfpnnXPzzOxtSZ9JKi7p+ejZeQEAAAAAiCepMaXOucmSJscsezbm74GSBqauagAAAACAQ10y3XcBAAAAACgUhFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAA/r+9+w2x7LzrAP79YdZAWyk22SpmtlXfGkKIW0KxagqC2Sy+qA3G2gasKduEUlBRGxRjwFe+UCRiXUKrgpEYaENbysYkUqRUbMMmjc0mqfFNcVdIs1YI/iEka36+uHftzWR258zkzj53734+cJnz5zkzz5zfnPPwvefcMwDDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAw0wKpVV1Y1WdqKpnq+rO87R7V1Wdqaqbl9dFAAAA1tW2obSqLk9yNMmhJNckubmqrtui3fck+YMkjyy7kwAAAKynKVdKr0/ydHef7O5XkjyQ5PAW7T6e5LNJXlhi/wAAAFhjU0LpRpKTC/On5sv+X1VdleR9Sf5seV0DAABg3S3rQUd/nOQT3f3q+RpV1ZGqOl5Vx0+fPr2kHw0AAMDF6rIJbU4lObAwvzFftuhgkr+pqiS5MslNVXWmuz+32Ki7701yb5IcPHiwd9tpAAAA1sOUUPpYkquraiPJt5PckuT2xQbd/SNnp6vqL5N8cXK2gBcAAAqvSURBVHMgBQAAgM22DaXd/VJV3ZHk4cxu972vu49X1e3z9Uf3uI8AAACsqSlXStPdx5Ic27RsyzDa3b/8xrsFAADApWBZDzoCAACAHRNKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhmUiitqhur6kRVPVtVd26x/taqemre5vGqOrj8rgIAALBuLtuuQVVdnuRokp9M8nySf6yqR7r7iYVmzyV5T3e/WFWHknwqybV70WEAAADWx5Qrpdcnebq7T3b3K0keSHJ4sUF3f627X5zPfiXJVcvtJgAAAOtoSijdSHJyYf7UfNm5fDTJF7ZaUVVHqup4VR0/ffr09F4CAACwlpb6oKOquiHJbUl+a6v13X1vdx/s7oP79+9f5o8GAADgIrTtZ0ozuzJ6YGF+Y77sNarqmiSfTnKou7+znO4BAACwzqZcKX0sydVVtVFV+5LckuShxQZV9Y4kDya5tbufW343AQAAWEfbXint7peq6o4kD2cWYu/r7uNVdft8/dEkdyW5IsknqypJznS3fwsDAADAeU25fTfdfSzJsU3Lji5MfyTJR5bbNQAAANbdUh90BAAAADshlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMMymUVtWNVXWiqp6tqju3WF9VdU9VPVNVX6+q65bfVQAAANbNtqG0qi5PcjTJoSTXJLl5i9D580nemeTHktyW5C+W3E8AAADW0JQrpdcnebq7T3b3K0keSHJ4U5vDSe7rmSeSXFZVB5bcVwAAANbMlFC6keTkwvyp+bKdtgEAAIDXuOxC/rCqOpLkyHz2v6rqny/kz9+FK5P8++hO8BpqsprUZfWoySo68rvqsnrUZDWpy+pRk1VzcYwp75zSaEooPZVk8Vbcjfmyrdp89Txt0t33Jrl3SsdWQVUd7+6Do/vBd6nJalKX1aMmq0ldVo+arCZ1WT1qsnrWqSZTbt99LMnVVbVRVfuS3JLkoU1tjiX5YJLMH4L0anefDAAAAJzHtldKu/ulqrojycOZhdj7uvt4Vd0+X380yWeTvLeqnknycpIP72GfAQAAWBOTPlPa3ccyuxq6uOzownQn+dhyu7YSLppbjS8harKa1GX1qMlqUpfVoyarSV1Wj5qsnrWpSc3yJAAAAFx4Uz5TCgAAAHvikg+lVfW2qnq0qp6qqkeq6vvP0e5b8zZPVtXxnW7PdFP2aVUdqKovV9WJqnquqj6xsO7uqvq3ea2erKqbLuxvsD6q6sb5Pn62qu7cYn1V1T1V9UxVfX3+oLNJ27J7E+py6/z4OVFVj1fVwYV1W57LeGMm1OSGqnpx4bx019Rt2Z0JNfnNhXqcqKr/raq3zdc5TvZAVf15Vb1QVSfOsd6YMsCEuhhTLrAJNVm/MaW7L+lXkj9J8uvz6V9Lcs852n0ryZW73d5ruTVJ8oNJrplPf1+Sf0ly7Xz+7iS/Mfr3uNhfSS6f/90fSLIvyfEk121q8/4kn09SSa5L8k9Tt/Xa07pcn+St8+lDSZ5cWLflucxrz2tyQ5Iv7mZbr72pyab2P5fkSwvzjpO9qctPzceKE+dYb0xZzboYU1avJms3plzyV0qTHE7yV/Pp++bzF3J7Xm/bfdrdz3f3N+bT/5nkG0muumA9vDRcn+Tp7j7Z3a8keSCvr8XhzJ7I3d39RJLLqurAxG3ZnW33bXd/rbtfnM9+JY6NvfZG/t4dK3tjp/v1A0nuvyA9u4R195eT/Md5mhhTBtiuLsaUC2/CsXIuF+2xIpQm+7v7dJLMv779HO06ydlbSj++i+2Zbkf7tKp+OMm7MjtRnvWxqvpmVf11VV2xVx1dcxtJFv/f8Kn5siltpmzL7ux03340yRcW5s91LmP3ptbk3fNbqr5UVdfucFt2ZvJ+rao3Jbkxs39vd5bjZAxjyuozpqyOtRpTJv1LmItdVf1dZrd7bvY7O/g27+7u56vq7Un+tqq+2d2PLqeHl54l1SRV9ZYkn0nyqwvv4v1pkt/P7ER5d5J7knxw152Fi1RV3ZDktiTvWVjsXDbG40kOdPf/VNXPJvlcVf3o6E6RZHbr7j909+JVCccJbGJMWSlrN6ZcEqG0u3/mXOuq6nRV7e/u01W1P8kL5/gez8+/vlBVn8nsytyjSSZtz2stoyZVtS+zd7bv7+4HF7736YU2R5P8/dI6fmk5ldlnEs7amC/bqs1XN7XZN2FbdmdKXVJV1yT5dJJD3f2ds8vPcy5j97atyfxjBmenH66qlzN7Y25SPdmxnezXX8ymW3cdJ8MYU1aUMWW1rOOY4vbd5FiSD82nP5Tkoc0NqurN89t7UlVvzuw2n2embs+OTalJZXZyfLa7/3DTusXbfd+f79aKnXksydVVtTF/A+CWvL4WxzK/Cj1/SuKr3X1y4rbszrb7tqrekeTBJLd293MLy893LmP3ptRk/8L0jyd5S2ZvuDlW9sak/VpVb03y05k9XOfsMsfJOMaUFWRMWT3rOKZcEldKt/F7SR6oql9J8u0kv5AkVfVDST7V3Tcl+YHMLot3kjdl9qHhz59ve96QKTX5iSS3Jnmqqp6cb/fb3X0syR/N39H73iT/mtmtJuxQd79UVXckeTizN7Du6+7jVXX7fP3RzK5Uv7eqnknycpIPn2/bEb/HuplYl7uSXJHkk7P3b3Kmuw/m/OcydmliTT5QVUfmm7yc5Je6+0ySM46V5ZtYkyR5X5JHuvu/FzZ3nOyRqro/s6eGXllVpzIb7/clxpSRJtTFmHKBTajJ2o0p1bPHBwMAAMAF5/ZdAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBh/g+BfYNcL8K5rwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Hlt1TrackMVA_TOS=MC_tuple_sig[\"Ds_Hlt1TrackMVADecision_TOS\"]\n",
"data_Hlt1TrackMVA_TOS=data_tuple_bkg[\"Ds_Hlt1TrackMVADecision_TOS\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=4, particle=\"Ds\",variable=\"Hlt1 Track MVA TOS\", \n",
" MC_sig=MC_Hlt1TrackMVA_TOS, data_bkg=data_Hlt1TrackMVA_TOS,\n",
" width_MC=0.5, width_data=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Hlt2RareCharm_TOS=MC_tuple_sig[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]\n",
"data_Hlt2RareCharm_TOS=data_tuple_bkg[\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\"]\n",
"\n",
"#Plot\n",
"plot_sb_comparison(nbins=4, particle=\"Ds\",variable=\"Hlt2 RareCharm D2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\" TOS\", \n",
" MC_sig=MC_Hlt2RareCharm_TOS, data_bkg=data_Hlt2RareCharm_TOS,\n",
" width_MC=0.5, width_data=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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yq0NkeRVJG4Mvxz6Q/8LnS/nxt2vlh3pI/j2b33lK+JYvzrkN5u/5fK/8+OwLglXrzOwxSUOdn7U3Ure4r5/geW+QdGScw8R7z0px3rcAwkMoBdBK/m/Beu2ZuVLOuYXy9+IsI//B7xz5cY2jg8khHo23s4OB+fsuviXfZfRB59ydIVepII9Jukl+YqOZ2jPB0ZOxBUO4JvPkP9AdGUycsrSAspEZROcm0kXaOfe5md0s3zJ51r5UMgjsV8p3IS5sX5uD3/lNvpLvpCxFdL18IL3DOTc8eoWZNZYPpUnlnFtnZpFbv2yWb8X7soDyv5vZXPlW67NVhFBqZqfIP794k/PkZ5OkKvlMelU9tnDwOr9Xvmtxk6Cbb/T6xvLvg6L4q/w44DOcc7labM2sn3woTUTkS41EWmkjIS3Pc4xZfiDu31ld8a9zbB06ygfSx5xz/xNdMOgmf0+yKuSc+05Sr6DXx4ny7+H/kf8CrpL2DJPI9/UTbFtVe97fAIoZuu8ChzDzU+j/JXg4MZ9voH93zs13zj2gPbf7uORA1TFGJOzk22XVzKrIz3baWn4im4M5kMo5t1h+9slOZna6fLj7qKAAeKCuSdDl7cXg4V/zKxfM0ntb8DBPmC5A5ANkMrogJ7qvZfJdSJtbnNu4yAe0ZIjcDmRKnHXtknSMeJ6Sb8GqpYJbSSMi12tQMBNqvmLG/0bueVqU+/QukO86HW+8Y0acZYfLB42P4wTScvIzwcYq7G/EHyStjg2kgaJcl2XywbRFdJfXfPyffOvu6cF7JVbki5QFRTj+3mobu8D8zN5/kJ9YLhK2D/jr13lLnHOPyM9oLOX+u/a5/OvnjDibny4fYA/EOQSwHxBKgUOU+dtivCT/YfBnRd3yIxjTUyXOZpFlhY2h2l8i3dzijmUKxia+L/+h5R7nXL5B6iDzmHwXytfkP0w/HlsgxGvyV/lu3T3M7O9B61V0varJ3/qhkfy5nxi1rrmZ9Y73QTzYT+QLg48Kq4SZNTB/f8885yBoJYncZ7fAfQXjSF+WbxnK1doTtLzFmyhlb0S6PmfEHOME5TOpT5LMlG/l6iT//i7MRPkJg46VNCUIKLmYv7fnn+RvOxTRSX7m0vxuNRPPuOD3cIu6z6WZVZe/fU6sSPfi5tGBObje/5QPrbE2yHftzW+842pJR1je+wZfrsRbSRX0BnhMfqzoMzGBPXL/0siEXzvlJyCqIn87muhyx0i6Xb5b7PPa/243s5zJ5YIvJkfJd2EdF1Uuv9dvHfmJmZIiGN97RJxV8f6uRSaMeyjm9VBR/vUg+cnUABRDdN8FDgHmbyov+S+iqspP1tNKPggtknR5zIyUV0m60cw+lL/dxTb5CVQuk+/OOerA1DyPOUFdBgYf+CIh9RHn3Cb5Vpt0+e5ppaKed7TJQTfYg8lr8pN9HCU/Ru31OGVCuSbOuZ/MrIP82Lu/SrrSzN6RDwz15WfEjXwZ0DWmtf1I+Q+6j5rZLElfyd/b83D5mTuPlvQfFXAP1ChV5Gf9HG5ms+XHuW0Kjh2Z/GiN/D0SCzNYvnXqrmCc7ifyMw1fLj+O7kLtGW+2t8bJj5N9zMzOkn+eafKT87wtf92SLpjZdWoRymebWVf5QNRR0ndmNkN+zO5u+TqfJd/y+pCUM4b3j/KzUBfFRElXyLd+fWlmUySVldRFvqv4H6ILO+e2m9mTkvpKWmhmU+UD57ny1+sjxbRsO+e2mNmn8pM9jZf/W+AkTXXOfSE/83FrSbPN7GX50HOafPflKSp4PHKs++Rb6C6T9LWZvRns7yj5Waf/In8rFsm/5lpL6h+Ma52pPfcprSypr3Nuufa/ufLnMnKP1nPlu0DPl/RgVLnX5N+Xfw6+rFkoP9a9i/xY3EZJqk977XlPL5Lv8XCk/DnNlpTT9d0596KZdZQ/Z0vMbLL2jC1uIOll59wEASiWCKXAoSHSIpQtP3PiSvlumZMlvRGn2+5Lkg6Tb3FsLd8dcI38h937wwp1waQYneWfTx/57lqS9IL8B6y04PExyn/M0wr5D1gHDefcTjObIH9bg/H5TKYT2jVxzn1hZifJj3e9TD5YHCYfTD+Rn2Dn39G3ugjMkL8NSHv5mTMj9d4s3/3xcUmPRt/eoQBL5e+72UF+Jt/e8q1UW+UD3//K34twbQLP5xczayk/KdAF8sHi/+THef4kH0r3aWyac265mWXIzwp8kXwL+DL5azxd+ymU7o3gthuXBl8+9JbUQj6kmfz5eF/Sc865d4JNIl134315UtBxXBCABwfH6SvfS2OcfACKN6nPAPkvaq6Rvz6/yk941Em+xTled+ur5Ce8ulj+SwuTH5f6hXPuVTPrFtThWu2ZDfhc+cm6Eg6lwfv2PPkx4b0kXS3/Rd9P8q/92VFl1wcT+vxZ/trfFnXs4c65dxM97j4aIH/u+sj/vfxV/sueu2MmVttkZm3kv4g4S35YwbeSHgiWxZu5em+8K//3+kz5a1xRPtjPljQimPwtWnf5GYSv1Z7x90vlW/HHJKlOAEJgeT9DAAAOtKAFtLWk45y/dyFCYGZ3y7eAneecmx52fQ5GZvaJpCOdc2lh1wWJCVqNr5a/PdeKcGsDAHkxphQAQmZm6fItPtMJpAdGvHFsZvYH+dmM12s/3j+3OAvGFJ6hxO9NCgBAoei+CwAhCW6JcpR8V8ZsJfE2CyjUfDP7Wn5Gz+3yY+Qulu9efFV0V0bs4Zz7WXyhDQBIMkIpAITnTvnJgr6X1Duf21Rg/3hWfsKdG+VvM7FBfpKjh5xzmSHWCwCAQw5jSgEAAAAAoaELDgAAAAAgNKF1361Zs6ZLS0sL6/AAAAAAgP1o/vz565xztQorF1ooTUtLU1ZWVliHBwAAAADsR2b2fSLl6L4LAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISmTNgVSMSOHTu0fv16/fbbb9q9e3fY1QGwl0qXLq3KlSurevXqSklJCbs6AAAAOAgc9KF0x44dWrlypapVq6a0tDSVLVtWZhZ2tQAUkXNOu3bt0ubNm7Vy5UrVr1+fYAoAAICDv/vu+vXrVa1aNdWsWVPlypUjkALFlJmpXLlyqlmzpqpVq6b169eHXSUAAAAcBA76UPrbb78pNTU17GoASKLU1FT99ttvYVcDAAAAB4GDPpTu3r1bZcuWDbsaAJKobNmyjA8HAACApGIQSiXRZRcoYXhPAwAAIKJYhFIAAAAAQMlEKAUAAAAAhIZQWkKNHz9eZqbx48eHXZWEmJkyMjLCrgYAAACAA+ygv09pYf78+uKwq1Cg+y/7Y1L2s3v3bo0dO1YvvPCCFi9erM2bN6tixYqqV6+emjZtqs6dO6tTp05JOVZxEBmTaGb65ptvdMwxx8Qt165dO2VmZkqSxo0bp969e+cps3XrVj311FOaOnWqvvzyS23cuFEVK1bUH/7wB7Vv317XXXedGjZsuL+eCgAAAHBIK/ah9FCwe/duXXTRRXrnnXdUq1YtdezYUUcddZS2bdumJUuW6PXXX9eKFStyhdJOnTrpjDPOUJ06dUKs+f5VpkwZ/f7773rmmWc0bNiwPOu/+eYbZWZm5pSLZ+7cuerSpYt+/PFH1a1bVxdccIGOPPJIbdu2TQsXLtTw4cM1fPhwzZ07V6eeeur+fkoAAADAIYdQWgxMnDhR77zzjpo3b673339flStXzrV+27Zt+vTTT3Mtq1KliqpUqXIgq3nAHXHEEapTp47GjRunv/3tbypTJvfL+emnn5YkXXzxxZo0aVKe7ZctW6Zzzz1XW7Zs0T/+8Q8NHDgwzz5WrlypO+64Q5s3b95/TwQAAOSWNS7sGgAHv/Rrwq5B0jCmtBj45JNPJEm9e/fOE0glqWLFimrXrl2uZQWNKZ0+fbrOPPNMVapUSdWrV9ell16qL7/8Ur1795aZacWKFTllV6xYITNT7969tWLFCnXr1k01a9ZU+fLllZ6erjfffDPP/jdt2qShQ4eqbdu2qlGjhsqUKaNatWrpkksu0Zw5c/btZMTo06ePVq9enaceu3bt0vjx49WyZUudeOKJcbf905/+pM2bN+vOO+/UnXfemSeQSlL9+vX10ksvqUWLFkmtNwAAAACPUFoMlC9fXpLvjrqvXnrpJZ1//vn6/PPP1bVrV914443asGGDWrVqpW+//Tbf7b7//ns1b95cP//8s/r06aNevXpp2bJl6tixo2bOnJmr7NKlSzVs2DBVqFBBV199tQYPHqwLLrhAH330kdq0aaN33nlnn59HRPfu3VWpUqWcVtGIqVOnas2aNerTp0/c7ZYvX673339f5cuX1x133FHocVJSUpJSXwAAAAC50X23GOjatatGjRqlUaNGae3aterUqZNOO+00HX300UXaz2+//aabb75Z5cuX15w5c9SkSZOcdX/5y1/ijsuMyMzM1IgRI3TbbbflLLvqqqvUpk0bDR8+PFdL7YknnqjVq1crNTU11z5Wr16tZs2aacCAATrvvPOKVPf8VK5cWd26ddP48eP1ww8/qG7dupKkp556Sqmpqbr88svjPq9Zs2ZJkk477TRVrVo1KXUBAAAAUHS0lBYDLVq00IsvvqgjjjhCL7zwgjp37qy0tDTVqFFDnTp10htvvJHQfqZMmaKNGzfq2muvzRVIJemee+5RjRo18t22UaNGuQKpJLVu3VqNGjXSZ599lmt5ampqnkAqSbVr19YVV1yhZcuWaeXKlQnVORF9+vTJmZ1Y8q267733nnr27KmKFSvG3ebnn3+WpJwQCwAAACAchNJiolu3blq5cqWmT5+uIUOG6KKLLlLp0qU1efJkXXLJJbr66qvlnCtwH59//rkkHyZjlStXTqeffnq+26anp8ddXqdOHW3YsCHP8tmzZ+vyyy9XvXr1lJKSIjOTmWnEiBGSpB9//LHAuhbF6aefrj/+8Y8aO3assrOz9fTTTys7OzvfrrsAAAAADh503y1GypYtqw4dOqhDhw6SpOzsbE2ZMkXXXHONnnvuOXXq1EmXXnppvttv2rRJkvLtrlqtWrV8t81vTGWpUqWUnZ2da9mkSZPUpUsXlS9fXu3bt9cxxxyjSpUqqVSpUsrMzNSHH36oHTt2FPhci6pPnz7q16+fpk2bpnHjxum0005T06ZN8y0fuVVOMsMxAAAAgKIjlBZjpUqVUqdOnbRkyRINGTJEH3zwQYGhNNKlduPGjXHX57e8qIYMGaIKFSpo8eLFatCgQa51t912mz788MOkHCfaVVddpTvvvFM33XSTfvzxR919990Flm/VqpUkKSsrS5s2bSrxt88BAAAADlZ03y0BImGzsO67kZbDjz/+OM+6nTt3au7cuUmpz9dff63GjRvnCaSS8szUmyxVq1ZVly5d9MMPP6hSpUrq3r17geUbNGigc845R9u3b9fw4cML3X+yW3YBAAAAeITSYmDixIl677338nSTlaR169bpiSeekCS1adOmwP107NhRVapU0dixY7Vo0aJc6+677z79+uuvSalv7dq19dVXX2nNmjW5lg8fPlwLFy5MyjHiGTp0qCZNmqTp06fHvZ9rrIcfflipqam6//77NWLECP3+++95yqxcuVJXXHFF0u+vCgAAAMCj+24x8Omnn2r06NGqXbu2WrVqpQYNGqhs2bJavny53n77bW3atEkdO3ZUly5dCtxPamqq/vWvf+mqq65Sy5Ytdfnll6tOnTr65JNPtHDhQrVq1UqzZs1SqVL79l1F//79NWjQIDVt2lRdunRRhQoV9PHHH+vzzz/X+eefr2nTpu3T/vNTv3591a9fP+HyJ5xwgqZPn64uXbpo0KBBGj16tM4++2wdeeSR2rp1qxYtWqTZs2fLzDR48OD9UmcAAADgUEcoLQZuu+021atXT5mZmZo3b56mTp0q55xq1Kihli1bqkePHurRo4fMrNB99ezZU9WrV9ff//53vfzyy0pJSVGbNm00a9Ys/e1vf5OkuLdzKYqBAwcqJSVFjz76qMaMGaPU1FS1bt1as2bN0tSpU/dbKN0bZ5xxhpYtW6annnpKU6dO1VtvvaUNGzaoYsVDBW/jAAAgAElEQVSKatSokQYOHKgbbrghbldkAAAAAPvOChuHuL+kp6e7rKysQsstXbpUJ5xwwgGo0aHNOadGjRpp27ZtOffwBPYn3tsAgHxljQu7BsDBL/2asGtQKDOb75yLf2/JKIwpPcRs2rQp7qQ9o0aN0nfffadOnTqFUCsAAAAAhyq67x5i5syZoyuvvFLt27dXw4YNtWPHDs2ePVtz585VvXr1dO+994ZdRQAAAACHkEJDqZmNlXSRpDXOuZPzKZMhabikcpI2OecKngYWoTnuuOOUkZGhWbNm6bXXXpOZqW7duurXr5/uuusuHX744WFXEQAAAMAhJJGW0vGSHpX0XLyVZlZb0r8kne2cW21mNZNXPSRbgwYN9Oqrr4ZdDQAAAACQlMCYUufcR5LWF1Ckm6RXnHOrg/LrklQ3AAAAAEAJl4yJjo6XVMfM5prZYjPrk4R9AgAAAAAOAcmY6KiUpMaSzpZUQdJcM5vjnPsytqCZ3SDpBkmqX79+Eg4NAAAAACjOktFSukrSdOfc1qDr7ofyITUP59yTzrl051x6rVq1knBoAAAAAEBxloxQ+pakVmZWxswqSmohaVkS9gsAAAAAKOESuSXMREkZkmqa2Q+S7pFUVpKcc4875xaY2TuSvgiWP+OcW7D/qgwAAAAAKCkKDaXOue4JlBkuf59SAAAAAAASlozuuwAAAAAA7BVCKQAAAAAgNIRS7DUzU0ZGRtjVKFYyMzNlZrr33nvDrgoAAABwUEjGfUrDlTUu7BoULP2apOzGzPIsK1eunOrUqaO2bdtq8ODBOuGEE5JyLBw8MjMz1a5du1zLypYtq6pVq+qYY45RixYt1K1bNzVv3jwpx+vdu7eeffZZLV++XGlpaUnZJwAAAFCQ4h9KDzH33HNPzr+3bdumzz77TM8995xee+01zZo1S6ecckqItcP+cvTRR6t3796SpJ07d2rt2rVasGCBRo4cqZEjR6pz584aP368DjvssHArCgAAABQRobSYidft8/bbb9dDDz2kUaNGafz48Qe8Ttj/0tLS4l77hQsXqlevXnrttde0detWTZs27cBXDgAAANgHjCktAdq3by9J+umnn3It37Rpk4YOHaq2bduqRo0aKlOmjGrVqqVLLrlEc+bMyXd/y5Yt07XXXqu0tDSlpKQoNTVVLVq00OjRoxOqz/Dhw1WqVCmdeeaZWr9+fa763Hrrrapbt67Kly+v448/Xg8++KC+++47mVlOS2BE7969ZWb67rvv9Mgjj6hx48aqUKFCrnGs2dnZevzxx9WsWTMddthhqlSpkpo1a6YxY8YoOzs71/5WrFgR9zgRGRkZebpJR48BXbhwoS688EJVrVpVFStWVNu2bfXJJ5/E3dcvv/yi6667TkcccYQqVKigU045Rc8++2xC568oTjnlFL3//vuqVauW3nnnHU2ePDnX+ldffVVdu3ZVWlqaypUrp0qVKum0007Tww8/nOf8mFlOHRs0aCAzk5nl6sY7b9483XjjjTrppJNUsWJFlS9fXscee6wGDhyoDRs2JP35AQAAoOSjpbQEmDFjhiTptNNOy7V86dKlGjZsmNq0aaOrr75aFStW1KpVqzRlyhRNmzZNb7zxhs4777xc27z11lvq2rWrduzYofPOO0/du3fXjh07NH/+fD3yyCPq379/vvXIzs7WrbfeqkceeUSXXXaZJkyYoPLly0uStm/frrPOOksLFixQ06ZN1bNnT23atEkPPPCAPvroowKfX//+/fXJJ5+oc+fOuvjii1W6dOmcdVdddZVefPFF1atXT9dff73MTJMmTdItt9yiWbNmacKECUU6l/nJysrSQw89pIyMDN1yyy1atWqVXn75ZZ199tlauHChjjvuuJyy69atU8uWLfXdd9+pVatWatWqlX7++WfddNNN6tChQ1LqE+3www/XjTfeqKFDh2rChAm69NJLc9YNGTJEKSkp6tChgw4//HBt2bJFM2bMUP/+/TVv3jw9//zzOWXvueceTZ48WYsWLVL//v1VtWpVScr5LUnPPPOMpk2bpjZt2qhjx47avXu3FixYoH/+85+aNm2aPv30U1WuXDnpzxEAAAAlF6G0mInuwrl9+3bNmzdPH3zwgdq3b6/BgwfnKnviiSdq9erVSk1NzbV89erVatasmQYMGJArlK5bt049evSQc04zZ85UmzZt8myXn+3bt6tnz556/fXX1bdvX40ePVqlSu1piB8+fLgWLFignj176vnnn89pkfzLX/6iZs2aFficFy9erCVLlqh27dq5lk+cOFEvvvii0tPTNXPmzJzxlEOHDtVZZ52lF198URdeeKF69OhR4P4T8fbbb+vf//63OnfunLOsQ4cO6tWrl0aPHq3HHnssZ/ldd92l7777ToMGDdLw4cNzlvft21etWrXa57rEk5GRoaFDh+qzzz7Ltfzdd99VvXr1ci1zzunGG2/UU089pb59++r000+X5F9bK1as0KJFi3TrrbfGnejor3/9q8aMGZOnRXnChAm68sor9dhjj+nOO+9M7pMDAABAiUb33WLmvvvuy/l54IEH9MEHH6hBgwbq3r27qlSpkqtsampqnkAqSbVr19YVV1yhZcuWaeXKlTnLn332WW3evFkDBgzIE0gj28Wzfv16nXPOOZo0aZIeeOABPfLII7kCaWTfpUuX1ogRI3IFmnr16umOO+4o8Dn/+c9/jnvssWPHSpJGjhyZa4KfSpUqaeTIkZKkp59+usB9J+qss87KFUglqXv37ipfvnyuILhr1y5NmDBBVatW1d///vdc5dPT03XttdcmpT6xjjrqKEnS2rVrcy2PDaSS76bbr18/SdL06dOLdJy6devGnQm6Z8+eqlmzZpH3BwAAABBKixnnXM7Pzp07c7qOXnvttRowYECe8rNnz9bll1+uevXqKSUlJWec4IgRIyRJP/74Y07ZuXPnSpIuuOCChOvzyy+/6Mwzz9S8efP0wgsvxA2Ymzdv1rfffqtjjz1WRxxxRJ71rVu3LvAYZ5xxRtzlCxYsUPny5dWyZcu421SsWFGff/55gs+kYPFacyNjdKPHUi5btkzbtm1T8+bNc7ouR9tf93V1zkmSfv/991zLf/31Vw0ePFiNGzfWYYcdlnP9//jHP0rKff0TsWvXLj366KNq1aqVqlevrtKlS+fsc926dUXeHwAAAED33WKsbNmyatKkiV599VUdddRRGj16tPr166cGDRpIkiZNmqQuXbqofPnyat++vY455hhVqlRJpUqVUmZmpj788EPt2LEjZ38bN26UJNWsWTPhOqxevVqbN29W3bp18+2aunnzZkm5xyZGq1atWoHHiG0Bjti0aZNq1aqVp1VWkkqVKqVq1arlmfxpb6WkpMRdXqpUKe3evTtXnSSpevXqccvnt3xfRZ5ndOjfuHGjmjVrpuXLl6t58+bq1auXqlevrjJlymjjxo0aPXp0ruufiCuuuEKTJk1Sw4YN1bFjR9WuXTvn3IwaNarI+wMAAAAIpSVApUqVdOKJJ2rOnDn67LPPckLpkCFDVKFCBS1evDhnWcRtt92mDz/8MNeySGhct25dwsdu0qSJrr/+evXu3Vtt2rTRBx98oIYNG+YqE+lCHAm9sfJbXpgqVapo06ZNys7OzhNMnXPauHFjru7LkTKRVsVY27dv36t6xNZJUq5Zh6Plt3xfzZw5U1Luya6efvppLV++XA8++KBuv/32XOW/+OKLhGdTjsjKytKkSZN08cUXa9KkSbkmnJKkhx9+eC9rDwAAgEMZ3XdLiEiwK1euXM6yr7/+Wo0bN84TSKU9ISZapJtsUe91eeWVV+qll17STz/9pDZt2ujrr7/OtT41NVUNGzbUN998o19++SXP9oXNvpufpk2b6r///W9Ot+Non376qbZu3apTTz01Z1mkRTZe6+m2bdu0ZMmSvapHtOOPP14VK1bUZ599FjfkZmZm7vMxYq1Zs0ZPPPGEJH8tIiLXoWPHjnm2iXf9JeUEzXjBPbK/iy66KE8gXbRoEbeEAQAAwF4hlJYA06dP19KlS1W2bNlc4ytr166tr776SmvWrMlVfvjw4Vq4cGGe/Vx99dVKTU3VyJEj4wbFgmbf7dKli1599VWtW7dObdu2zRPwevXqpd27d2vQoEG5As+qVav04IMPJvxco0UmDRo0aJC2bduWs3zbtm267bbbJEnXXXddzvLKlSvr+OOP18cff6z//Oc/ufY1ePBgbdmyZa/qEa1s2bLq2bOnNm7cqCFDhuRal5WVlTM5U7IsWrRI7du317p163TOOefkmowpMjlUbBBeunRpnkmYImrUqCEp/ljT/Pa3efNm3XTTTXv7FAAAAHCIo/tuMRN9S5hdu3bpq6++0ptvvilJGjZsWK4xhf3799egQYPUtGlTdenSRRUqVNDHH3+szz//XOeff36eFtGaNWvqxRdfVJcuXdSuXTudf/75aty4sXbu3KkFCxZo5cqVecJctEsuuURTpkxRp06dlJGRoffff19NmjSRJN1xxx2aPHmyXnjhBS1ZskQdOnTQpk2b9Morr6hFixZ666234o4NLUiPHj00ZcoUvfLKKzrppJN06aWXysw0efJkLV++XFdccYV69uyZa5vbb79d1113nVq2bKlu3bqpQoUKmjFjhrZs2aImTZpo0aJFRapDPMOGDdOMGTP00EMPae7cuTn3KX355Zd17rnnaurUqUXe54oVK3Ku/a5du7Ru3TrNnz9f8+fPlyR17dpVY8eOzTUz7jXXXKNRo0bplltu0QcffKBGjRppxYoVeu2113TBBRfo9ddfz3Ocs88+W8OHD9f111+vzp07q0KFCqpatar69u2rtm3bqkmTJpo4caJ+/PFHtWrVShs2bNCkSZOUlpamI488cu9OGAAAAA5phNJi5r777sv5d+nSpVWrVi1dcMEF6tu3r9q3b5+r7MCBA5WSkqJHH31UY8aMUWpqqlq3bq1Zs2Zp6tSpcbvpXnjhhcrKytIDDzygGTNmaPr06apYsaJOPvlk3XrrrYXW79xzz9Xbb7+tiy++WO3atdP06dPVrFkzVahQQTNnztTdd9+tV199VSNHjlSDBg1055136vzzz9dbb70V9/Y1hZk4caLatm2rsWPH5nRhPeGEEzRw4EDdfPPNecpfe+21cs7pn//8p5544glVq1ZNHTt21LBhw/Lc8mVv1axZU7Nnz9Zdd92lN954Q1lZWTruuOM0ZswYpaWl7VUo/f7773OufZkyZVStWjU1bNhQAwYMUPfu3ePODtygQQNlZmZq8ODBevPNN+Wc0/HHH6+RI0fq3HPPjRtKzz33XI0YMUJPPfWUHnroIe3cuVNHH320+vbtq9KlS+u9997T7bffrjfffFNz585V3bp11atXLw0ZMkQnn3xy0U8WAAAADnmW36Qv+1t6errLysoqtNzSpUt1wgknHIAaISzPPfecrr76aj3++OO68cYbw64ODhDe2wCAfGWNC7sGwMEv/Zqwa1AoM5vvnEsvrBxjSnHAxJvk6JdfftHQoUNVpkwZXXzxxSHUCgAAAECY6L6LA6ZTp07Kzs7W6aefrsqVK2vVqlWaMmWKNm3apPvvv58xiQAAAMAhiFCKA6Zbt2567rnnNH78eG3ZskWpqak69dRT1bdvX1122WVhVw8AAABACAilOGD69eunfv36hV0NAAAAAAcRxpQCAAAAAEJDKAUAAAAAhIZQCgAAAAAITbEIpWHdSxXA/sF7GgAAABEHfSgtXbq0du3aFXY1ACTRrl27VLp06bCrAQAAgIPAQR9KK1eurM2bN4ddDQBJtHnzZlWuXDnsagAAAOAgcNCH0urVq2vDhg1at26ddu7cSbc/oJhyzmnnzp1at26dNmzYoOrVq4ddJQAAABwEDvr7lKakpKh+/fpav369VqxYod27d4ddJQB7qXTp0qpcubLq16+vlJSUsKsDAACAg8BBH0olH0zr1KmjOnXqhF0VAAAAAEASHfTddwEAAAAAJRehFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAITaGh1MzGmtkaM/uykHLNzOx3M+uSvOoBAAAAAEqyRFpKx0s6r6ACZlZa0gOS3k1CnQAAAAAAh4hCQ6lz7iNJ6wsp9idJr0lak4xKAQAAAAAODfs8ptTMjpLUSdKYfa8OAAAAAOBQkoyJjkZJutM5l11YQTO7wcyyzCxr7dq1STg0AAAAAKA4K5OEfaRLesnMJKmmpAvM7Hfn3OTYgs65JyU9KUnp6ekuCccGAAAAABRj+xxKnXMNIv82s/GS3owXSAEAAAAAiFVoKDWziZIyJNU0sx8k3SOprCQ55x7fr7UDAAAAAJRohYZS51z3RHfmnOu9T7UBAAAAABxSkjHREQAAAAAAe4VQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISm0FBqZmPNbI2ZfZnP+qvMbLGZfWlm880sPfnVBAAAAACURIm0lI6XdF4B67+W1Mo5d7Kkv0p6Ogn1AgAAAAAcAsoUVsA595GZpRWw/tOoh7MkHbXv1QIAAAAAHAqSPab0RklTk7xPAAAAAEAJVWhLaaLMLEPSdZJaFVDmBkk3SFL9+vWTdWgAAAAAQDGVlJZSM2ss6RlJHZ1zv+ZXzjn3pHMu3TmXXqtWrWQcGgAAAABQjO1zKDWz+pJel3SVc+7rfa8SAAAAAOBQUWj3XTObKClDUk0z+0HSPZLKSpJz7nFJd0uqIekxM5Ok351z3BYGAAAAAFCoRGbf7V7I+uslXZ+0GgEAAAAADhnJnn0XAAAAAICEJW32XQAAACAZXv/8x7CrABz0LitBAyZpKQUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAUGkrNbKyZrTGzL/NZb2b2sJl9ZWafm9mpya8mAAAAAKAkSqSldLyk8wpYf5mkoyWdJOk6SeP2vVoAAAAAgENBoaHUOfeRpPUFFLlQ0gvOWyCpjJnVS1YFAQAAAAAlVzLGlNaVtCrq8Q/BMgAAAAAACnRAJzoysxvMLMvMstauXXsgDw0AAAAAOAglI5T+ICm6u27dYFkezrknnXPpzrn0WrVqJeHQAAAAAIDiLBmh9G1JPSUpmHk32zm3quBNAAAAAACQyhRWwMwmSsqQVNPMfpB0j6SykuSce1zSa5LamdlXknZKuma/1RYAAAAAUKIUGkqdc90LWe8k/U/SagQAAAAAOGQc0ImOAAAAAACIRigFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDRlwq4AgOLvz68vDrsKQLFw/2V/DLsKAAAcdGgpBQAAAACEhlAKAAAAAAgNoRQAAAAAEJqEQqmZnWdmX5rZUjMbHGd9bTObYWZfmdnXZnZT8qsKAAAAAChpCg2lZpYi6XFJ50tqLKmLmZ0aU6yvpCzn3ImSzpT0DzOrkOzKAgAAAABKlkRaSk+XtMQ5t8o5t0vSy5IujCnzg6TKZmaSDpO0TtKOpNYUAAAAAFDiJBJK60paFfX4h2BZtKcknSjpJ0mLJfV3zmXH7sjMbjCzLDPLWrt27V5WGQAAAABQUiRroqM/S/pC0pGSTpH0qJmlxhZyzj3pnEt3zqXXqlUrSYcGAAAAABRXiYTSHyTVi3pcN1gWrbWkfzvvP5KWy7ecAgAAAACQr0RC6WeSTjazumZWVtIVkqbFlPlW0tmSZGZHyAfSFUmsJwAAAACgBCpTWAHn3HYzu1nSdPkQ+4JzLity2xfn3OOS/ibpBTNbKqm0pCHOudX7sd4AAAAAgBKg0FAqSc65tyW9HbPs8ah/r5Z0TnKrBgAAAAAo6ZI10REAAAAAAEVGKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoSGUAgAAAABCQygFAAAAAISGUAoAAAAACA2hFAAAAAAQGkIpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQikAAAAAIDSEUgAAAABAaAilAAAAAIDQEEoBAAAAAKEhlAIAAAAAQkMoBQAAAACEhlAKAAAAAAgNoRQAAAAAEBpCKQAAAAAgNIRSAAAAAEBoCKUAAAAAgNAQSgEAAAAAoUkolJrZeWb2pZktNbPB+ZTJMLN5ZrbIzD5KbjUBAAAAACVRmcIKmFmKpMcltZa0WtIcM3vXObcgqkxtSf+SdLZzbrWZ1dxfFQYAAAAAlByJtJSeLmmJc26Vc26XpJclXRhTppukV5xzqyXJObcuudUEAAAAAJREiYTSupJWRT3+IVgW7XhJdcxsrpktNrM+yaogAAAAAKDkKrT7boJKSWos6WxJFSTNNbM5zrkvowuZ2Q2SbpCk+vXrJ+nQAAAAAIDiKpGW0h8k1Yt6XDdYFm2VpOnOua1B190P5UNqLs65J51z6c659Fq1au1tnQEAAAAAJUQiofQzSSebWV0zKyvpCknTYsq8JamVmZUxs4qSWkhaltyqAgAAAABKmkK77zrntpvZzZKmy4fYF5xzWWZ2U7D+cefcAjN7R9IXkspKeiZ6dl4AAAAAAOJJaEypc+5tSW/HLHs85vFwScOTVzUAAAAAQEmXSPddAAAAAAD2C0IpAAAAACA0hFIAAAAAQGgIpQAAAACA0BBKAQAAAAChIZQCAAAAAEJDKAUAAAAAhIZQCgAAAAAIDaEUAAAAABAaQimA/2/vfkMsO+86gH9/mBhoK8UmW8XMtupbYwhxQyhWTUEwyeKL2mCsbcCakiaUgoraoBgDvvKFIhHrEloVXImBNjSlbExSipSKbdiksdkkNb4p7gpp1gjBP4Rkzc8X9669mczunJnc2efu3c8HLnP+PGfmmfObcx6+95x7BgAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGCYSaG0qq6vqmNV9WxV3XmWdtdU1amquml5XQQAAGBdbRtKq+qSJIeS3JDkyiQ3VdXVW7T7niR/mOSRZXcSAACA9TTlSum1SZ7u7uPd/WqS+5Mc3KLdJ5J8LskLS+wfAAAAa2xKKN1Icnxh/sR82f+rqsuTvD/Jny+vawAAAKy7ZT3o6E+SfLK7Xztbo6q6raqOVtXRkydPLulHAwAAcL66aEKbE0n2L8xvzJctOpDkb6sqSS5LcmNVneruzy826u57k9ybJAcOHOjddhoAAID1MCWUPpbkiqraSPKdJDcnuX2xQXf/yOnpqvqrJF/cHEgBAABgs21DaXe/XFV3JHk4s9t9D3f30aq6fb7+0B73EQAAgDU15UppuvtIkiOblm0ZRrv7V958twAAALgQLOtBRwAAALBjQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlMID7bIAAAmVSURBVAIAADCMUAoAAMAwQikAAADDXDS6A8D575oXHxzdBThP/PjoDgDAynGlFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIaZFEqr6vqqOlZVz1bVnVusv6Wqnpq3ebyqDiy/qwAAAKybi7ZrUFWXJDmU5KeSPJ/kH6vqke5+YqHZc0ne290vVdUNST6d5Kq96DAAAADrY8qV0muTPN3dx7v71ST3Jzm42KC7v97dL81nv5rk8uV2EwAAgHU0JZRuJDm+MH9ivuxMPpbkC1utqKrbqupoVR09efLk9F4CAACwlpb6oKOqui7JrUl+e6v13X1vdx/o7gP79u1b5o8GAADgPLTtZ0ozuzK6f2F+Y77sdarqyiSfSXJDd7+4nO4BAACwzqZcKX0syRVVtVFVFye5OclDiw2q6l1JHkhyS3c/t/xuAgAAsI62vVLa3S9X1R1JHs4sxB7u7qNVdft8/aEkdyW5NMmnqipJTnW3fwsDAADAWU25fTfdfSTJkU3LDi1MfzTJR5fbNQAAANbdUh90BAAAADshlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDTAqlVXV9VR2rqmer6s4t1ldV3VNVz1TVN6rq6uV3FQAAgHWzbSitqkuSHEpyQ5Irk9y0Rej8hSTvTvJjSW5N8pdL7icAAABraMqV0muTPN3dx7v71ST3Jzm4qc3BJId75okkF1XV/iX3FQAAgDUzJZRuJDm+MH9ivmynbQAAAOB1LjqXP6yqbkty23z2v6rqn8/lz9+Fy5L8++hO8DpqsprUZfWoySq67ffUZfWoyWpSl9WjJqvm/BhT3j2l0ZRQeiLJ4q24G/NlW7X52lnapLvvTXLvlI6tgqo62t0HRveD71KT1aQuq0dNVpO6rB41WU3qsnrUZPWsU02m3L77WJIrqmqjqi5OcnOShza1OZLkQ0kyfwjSa919PAAAAHAW214p7e6Xq+qOJA9nFmIPd/fRqrp9vv5Qks8leV9VPZPklSQf2cM+AwAAsCYmfaa0u49kdjV0cdmhhelO8vHldm0lnDe3Gl9A1GQ1qcvqUZPVpC6rR01Wk7qsHjVZPWtTk5rlSQAAADj3pnymFAAAAPbEBR9Kq+odVfVoVT1VVY9U1fefod23522erKqjO92e6abs06raX1VfqapjVfVcVX1yYd3dVfVv81o9WVU3ntvfYH1U1fXzffxsVd25xfqqqnuq6pmq+sb8QWeTtmX3JtTllvnxc6yqHq+qAwvrtjyX8eZMqMl1VfXSwnnprqnbsjsTavJbC/U4VlX/W1XvmK9znOyBqvqLqnqhqo6dYb0xZYAJdTGmnGMTarJ+Y0p3X9CvJH+a5Dfm07+e5J4ztPt2kst2u73XcmuS5AeTXDmf/r4k/5Lkqvn83Ul+c/Tvcb6/klwy/7vfn+TiJEeTXL2pzQeSPJikklyd5J+mbuu1p3W5Nsnb59M3JHlyYd2W5zKvPa/JdUm+uJttvfamJpva/3ySLy/MO072pi4/PR8rjp1hvTFlNetiTFm9mqzdmHLBXylNcjDJX8+nD8/nz+X2vNG2+7S7n+/ub86n/zPJN5Ncfs56eGG4NsnT3X28u19Ncn/eWIuDmT2Ru7v7iSQXVdX+iduyO9vu2+7+ene/NJ/9ahwbe+3N/L07VvbGTvfrB5Pcd056dgHr7q8k+Y+zNDGmDLBdXYwp596EY+VMzttjRShN9nX3ySSZf33nGdp1ktO3lH5iF9sz3Y72aVX9cJJrMjtRnvbxqvpWVf1NVV26Vx1dcxtJFv/f8In5siltpmzL7ux0334syRcW5s90LmP3ptbkPfNbqr5cVVftcFt2ZvJ+raq3JLk+s39vd5rjZAxjyuozpqyOtRpTJv1LmPNdVX0ps9s9N/vdHXyb93T381X1ziR/V1Xf6u5Hl9PDC8+SapKqeluSzyb5tYV38f4syR9kdqK8O8k9ST60687Ceaqqrktya5L3Lix2Lhvj8ST7u/t/qurnkny+qn50dKdIMrt19x+6e/GqhOMENjGmrJS1G1MuiFDa3T97pnVVdbKq9nX3yaral+SFM3yP5+dfX6iqz2Z2Ze7RJJO25/WWUZOqujizd7bv6+4HFr73yYU2h5L8/dI6fmE5kdlnEk7bmC/bqs3XNrW5eMK27M6UuqSqrkzymSQ3dPeLp5ef5VzG7m1bk/nHDE5PP1xVr2T2xtykerJjO9mvv5RNt+46ToYxpqwoY8pqWccxxe27yZEkH55PfzjJQ5sbVNVb57f3pKremtltPs9M3Z4dm1KTyuzk+Gx3/9GmdYu3+34g360VO/NYkiuqamP+BsDNeWMtjmR+FXr+lMTXuvv4xG3ZnW33bVW9K8kDSW7p7ucWlp/tXMbuTanJvoXpn0jytszecHOs7I1J+7Wq3p7kZzJ7uM7pZY6TcYwpK8iYsnrWcUy5IK6UbuP3k9xfVb+a5DtJfjFJquqHkny6u29M8gOZXRbvJG/J7EPDD55te96UKTX5ySS3JHmqqp6cb/c73X0kyR/P39H73iT/mtmtJuxQd79cVXckeTizN7AOd/fRqrp9vv5QZleq31dVzyR5JclHzrbtiN9j3Uysy11JLk3yqdn7NznV3Qdy9nMZuzSxJh+sqtvmm7yS5Je7+1SSU46V5ZtYkyR5f5JHuvu/FzZ3nOyRqrovs6eGXlZVJzIb7y9OjCkjTaiLMeUcm1CTtRtTqmePDwYAAIBzzu27AAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADD/B9s1rgFwzptKAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"MC_Hlt2Phys_TOS=MC_tuple_sig[\"Ds_Hlt2Phys_TOS\"]\n",
"data_Hlt2Phys_TOS=data_tuple_bkg[\"Ds_Hlt2Phys_TOS\"]\n",
"\n",
"plot_sb_comparison(nbins=4, particle=\"Ds\",variable=\"Hlt2 Phys TOS\", \n",
" MC_sig=MC_Hlt2Phys_TOS, data_bkg=data_Hlt2Phys_TOS,\n",
" width_MC=0.5, width_data=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"#h_data_under.SetLineColor(38)\n",
"#h_mc_under.SetLineColor(46)\n",
"#\n",
"#c1=r.TCanvas(\"c1\",\"c1\",1200,700)\n",
"#r.gStyle.SetOptStat(0)\n",
"#h_mc_under.Draw()\n",
"#h_data_under.Draw(\"same\")\n",
"#\n",
"#legend = r.TLegend(0.9,0.8,0.6,0.9)\n",
"#legend.SetHeader(\"Ds End vertex chi2\")\n",
"#legend.AddEntry(h_mc_under,\"Signal MC\",\"L\")\n",
"#legend.AddEntry(h_data_under,\"data below Ds reco mass MC\",\"L\")\n",
"#legend.Draw()\n",
"#c1.Update()\n",
"#c1.SaveAs(\"/home/hep/davide/Rphipi/plt.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"with open('/disk/lhcb_data/davide/Rphipi/NN/'+l_flv[l_index]+l_flv[l_index]+'/MC_for_NN_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(MC_tuple_sig, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"with open('/disk/lhcb_data/davide/Rphipi/NN/'+l_flv[l_index]+l_flv[l_index]+'/data_for_NN_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(data_tuple_bkg, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
"with open('/disk/lhcb_data/davide/Rphipi/NN_for_selection/'+l_flv[l_index]+l_flv[l_index]+'/data_for_NN_'+l_flv[l_index]+l_flv[l_index]+'.pickle', 'wb') as handle:\n",
" pickle.dump(data_tuple_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini