SigvsBkg_LL-TTvsOther_emu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c157cd32-2e52-4c7e-9b91-05a41aec33bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.5\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CleanJet_eta_1</th>\n",
       "      <th>CleanJet_eta_2</th>\n",
       "      <th>CleanJet_phi_1</th>\n",
       "      <th>CleanJet_phi_2</th>\n",
       "      <th>CleanJet_pt_1</th>\n",
       "      <th>CleanJet_pt_2</th>\n",
       "      <th>Lepton_eta_1</th>\n",
       "      <th>Lepton_eta_2</th>\n",
       "      <th>Lepton_phi_1</th>\n",
       "      <th>Lepton_phi_2</th>\n",
       "      <th>Lepton_pt_1</th>\n",
       "      <th>Lepton_pt_2</th>\n",
       "      <th>Rpt</th>\n",
       "      <th>Zepp_l1</th>\n",
       "      <th>Zepp_l2</th>\n",
       "      <th>Zepp_ll</th>\n",
       "      <th>detajj</th>\n",
       "      <th>detall</th>\n",
       "      <th>dphijj</th>\n",
       "      <th>dphilep1jet1</th>\n",
       "      <th>dphilep1jet2</th>\n",
       "      <th>dphilep1jj</th>\n",
       "      <th>dphilep2jet1</th>\n",
       "      <th>dphilep2jet2</th>\n",
       "      <th>dphilep2jj</th>\n",
       "      <th>dphill</th>\n",
       "      <th>dphilljet</th>\n",
       "      <th>dphilljetjet</th>\n",
       "      <th>dphillmet</th>\n",
       "      <th>dphilmet1</th>\n",
       "      <th>dphilmet2</th>\n",
       "      <th>dr_l1j1</th>\n",
       "      <th>dr_l1j2</th>\n",
       "      <th>dr_l2j1</th>\n",
       "      <th>dr_l2j2</th>\n",
       "      <th>drll</th>\n",
       "      <th>ht</th>\n",
       "      <th>m2ljj30</th>\n",
       "      <th>mT2</th>\n",
       "      <th>mTi</th>\n",
       "      <th>m_l1j1</th>\n",
       "      <th>m_l1j2</th>\n",
       "      <th>m_l2j1</th>\n",
       "      <th>m_l2j2</th>\n",
       "      <th>mcoll</th>\n",
       "      <th>mcollWW</th>\n",
       "      <th>mjj</th>\n",
       "      <th>mll</th>\n",
       "      <th>mtw1</th>\n",
       "      <th>mtw2</th>\n",
       "      <th>phi_miss</th>\n",
       "      <th>proxyW_l1</th>\n",
       "      <th>proxyW_l2</th>\n",
       "      <th>pt_miss</th>\n",
       "      <th>ptll</th>\n",
       "      <th>recoil</th>\n",
       "      <th>yll</th>\n",
       "      <th>isLL</th>\n",
       "      <th>isMIX</th>\n",
       "      <th>isTT</th>\n",
       "      <th>isBKG</th>\n",
       "      <th>isVBS</th>\n",
       "      <th>notLL</th>\n",
       "      <th>notTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.066406</td>\n",
       "      <td>2.819824</td>\n",
       "      <td>0.798096</td>\n",
       "      <td>1.402344</td>\n",
       "      <td>58.501850</td>\n",
       "      <td>40.687500</td>\n",
       "      <td>0.052612</td>\n",
       "      <td>1.257812</td>\n",
       "      <td>-1.661133</td>\n",
       "      <td>-1.083984</td>\n",
       "      <td>59.333496</td>\n",
       "      <td>24.431036</td>\n",
       "      <td>0.608992</td>\n",
       "      <td>-0.324097</td>\n",
       "      <td>0.881104</td>\n",
       "      <td>0.278503</td>\n",
       "      <td>4.886230</td>\n",
       "      <td>1.205200</td>\n",
       "      <td>0.604248</td>\n",
       "      <td>2.459229</td>\n",
       "      <td>3.063477</td>\n",
       "      <td>2.705436</td>\n",
       "      <td>1.882080</td>\n",
       "      <td>2.486328</td>\n",
       "      <td>2.128288</td>\n",
       "      <td>0.577148</td>\n",
       "      <td>2.293723</td>\n",
       "      <td>2.539931</td>\n",
       "      <td>1.459033</td>\n",
       "      <td>1.293528</td>\n",
       "      <td>1.870677</td>\n",
       "      <td>3.246235</td>\n",
       "      <td>4.128238</td>\n",
       "      <td>3.820034</td>\n",
       "      <td>2.936275</td>\n",
       "      <td>1.336266</td>\n",
       "      <td>238.785126</td>\n",
       "      <td>663.045654</td>\n",
       "      <td>54.077595</td>\n",
       "      <td>116.856529</td>\n",
       "      <td>186.453583</td>\n",
       "      <td>208.308685</td>\n",
       "      <td>201.729980</td>\n",
       "      <td>80.825615</td>\n",
       "      <td>34.081371</td>\n",
       "      <td>172.575851</td>\n",
       "      <td>558.146729</td>\n",
       "      <td>53.316471</td>\n",
       "      <td>69.366989</td>\n",
       "      <td>59.446663</td>\n",
       "      <td>-2.954661</td>\n",
       "      <td>69.433067</td>\n",
       "      <td>77.407051</td>\n",
       "      <td>55.831234</td>\n",
       "      <td>80.912895</td>\n",
       "      <td>103.303886</td>\n",
       "      <td>0.426776</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.962158</td>\n",
       "      <td>2.097168</td>\n",
       "      <td>-2.888672</td>\n",
       "      <td>2.117188</td>\n",
       "      <td>149.991165</td>\n",
       "      <td>35.933941</td>\n",
       "      <td>-0.200928</td>\n",
       "      <td>-0.043259</td>\n",
       "      <td>-0.064484</td>\n",
       "      <td>-1.243164</td>\n",
       "      <td>77.783211</td>\n",
       "      <td>56.874996</td>\n",
       "      <td>0.820799</td>\n",
       "      <td>-0.268433</td>\n",
       "      <td>-0.110764</td>\n",
       "      <td>0.189598</td>\n",
       "      <td>4.059326</td>\n",
       "      <td>0.157669</td>\n",
       "      <td>1.277326</td>\n",
       "      <td>2.824188</td>\n",
       "      <td>2.181671</td>\n",
       "      <td>3.035455</td>\n",
       "      <td>1.645508</td>\n",
       "      <td>2.922834</td>\n",
       "      <td>1.856775</td>\n",
       "      <td>1.178680</td>\n",
       "      <td>2.338290</td>\n",
       "      <td>2.549557</td>\n",
       "      <td>1.280928</td>\n",
       "      <td>0.795030</td>\n",
       "      <td>1.973710</td>\n",
       "      <td>3.328359</td>\n",
       "      <td>3.168743</td>\n",
       "      <td>2.527819</td>\n",
       "      <td>3.984142</td>\n",
       "      <td>1.189179</td>\n",
       "      <td>424.800110</td>\n",
       "      <td>722.115173</td>\n",
       "      <td>36.402397</td>\n",
       "      <td>165.162811</td>\n",
       "      <td>303.863617</td>\n",
       "      <td>177.457932</td>\n",
       "      <td>246.787781</td>\n",
       "      <td>147.545578</td>\n",
       "      <td>55.163944</td>\n",
       "      <td>198.715286</td>\n",
       "      <td>556.838928</td>\n",
       "      <td>74.678581</td>\n",
       "      <td>69.710251</td>\n",
       "      <td>128.463547</td>\n",
       "      <td>0.730546</td>\n",
       "      <td>72.027008</td>\n",
       "      <td>128.506714</td>\n",
       "      <td>104.216820</td>\n",
       "      <td>112.543976</td>\n",
       "      <td>173.874069</td>\n",
       "      <td>-0.134309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.612305</td>\n",
       "      <td>4.087891</td>\n",
       "      <td>2.516113</td>\n",
       "      <td>-0.967896</td>\n",
       "      <td>40.722610</td>\n",
       "      <td>32.580414</td>\n",
       "      <td>0.271790</td>\n",
       "      <td>0.554932</td>\n",
       "      <td>1.902100</td>\n",
       "      <td>-0.195648</td>\n",
       "      <td>49.380596</td>\n",
       "      <td>26.431454</td>\n",
       "      <td>0.983751</td>\n",
       "      <td>-0.466003</td>\n",
       "      <td>-0.182861</td>\n",
       "      <td>0.324432</td>\n",
       "      <td>6.700195</td>\n",
       "      <td>0.283142</td>\n",
       "      <td>2.799176</td>\n",
       "      <td>0.614014</td>\n",
       "      <td>2.869995</td>\n",
       "      <td>1.442570</td>\n",
       "      <td>2.711761</td>\n",
       "      <td>0.772247</td>\n",
       "      <td>2.742868</td>\n",
       "      <td>2.097748</td>\n",
       "      <td>1.178385</td>\n",
       "      <td>2.006942</td>\n",
       "      <td>2.991255</td>\n",
       "      <td>2.727559</td>\n",
       "      <td>1.457879</td>\n",
       "      <td>2.948731</td>\n",
       "      <td>4.774882</td>\n",
       "      <td>4.169537</td>\n",
       "      <td>3.616374</td>\n",
       "      <td>2.116770</td>\n",
       "      <td>188.852966</td>\n",
       "      <td>1117.925415</td>\n",
       "      <td>90.210190</td>\n",
       "      <td>117.845978</td>\n",
       "      <td>181.257996</td>\n",
       "      <td>276.182312</td>\n",
       "      <td>166.170395</td>\n",
       "      <td>168.286209</td>\n",
       "      <td>35.217129</td>\n",
       "      <td>165.960342</td>\n",
       "      <td>1039.650757</td>\n",
       "      <td>63.470570</td>\n",
       "      <td>86.703613</td>\n",
       "      <td>43.173630</td>\n",
       "      <td>-1.653527</td>\n",
       "      <td>87.540642</td>\n",
       "      <td>46.859325</td>\n",
       "      <td>39.737881</td>\n",
       "      <td>42.711731</td>\n",
       "      <td>6.865315</td>\n",
       "      <td>0.370764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.061035</td>\n",
       "      <td>2.718262</td>\n",
       "      <td>0.276855</td>\n",
       "      <td>-0.745605</td>\n",
       "      <td>106.404816</td>\n",
       "      <td>72.261391</td>\n",
       "      <td>0.998047</td>\n",
       "      <td>1.562744</td>\n",
       "      <td>-2.758789</td>\n",
       "      <td>3.061523</td>\n",
       "      <td>86.100220</td>\n",
       "      <td>55.490475</td>\n",
       "      <td>0.621377</td>\n",
       "      <td>1.169434</td>\n",
       "      <td>1.734131</td>\n",
       "      <td>1.451782</td>\n",
       "      <td>5.779297</td>\n",
       "      <td>0.564697</td>\n",
       "      <td>1.022461</td>\n",
       "      <td>3.035645</td>\n",
       "      <td>2.013184</td>\n",
       "      <td>2.631210</td>\n",
       "      <td>2.784668</td>\n",
       "      <td>2.476056</td>\n",
       "      <td>3.094082</td>\n",
       "      <td>0.462873</td>\n",
       "      <td>3.067006</td>\n",
       "      <td>2.811744</td>\n",
       "      <td>0.937767</td>\n",
       "      <td>1.118302</td>\n",
       "      <td>0.655429</td>\n",
       "      <td>5.068657</td>\n",
       "      <td>2.648027</td>\n",
       "      <td>5.397565</td>\n",
       "      <td>3.978624</td>\n",
       "      <td>0.730160</td>\n",
       "      <td>409.368164</td>\n",
       "      <td>1925.205200</td>\n",
       "      <td>53.180969</td>\n",
       "      <td>155.018036</td>\n",
       "      <td>741.145020</td>\n",
       "      <td>203.489059</td>\n",
       "      <td>782.901184</td>\n",
       "      <td>143.249649</td>\n",
       "      <td>77.403580</td>\n",
       "      <td>180.723633</td>\n",
       "      <td>1574.967773</td>\n",
       "      <td>50.694679</td>\n",
       "      <td>74.129723</td>\n",
       "      <td>36.110657</td>\n",
       "      <td>2.406095</td>\n",
       "      <td>103.805618</td>\n",
       "      <td>103.368439</td>\n",
       "      <td>56.702801</td>\n",
       "      <td>137.994324</td>\n",
       "      <td>177.526398</td>\n",
       "      <td>1.220858</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.772705</td>\n",
       "      <td>2.803223</td>\n",
       "      <td>-0.822388</td>\n",
       "      <td>2.676758</td>\n",
       "      <td>202.039673</td>\n",
       "      <td>69.501266</td>\n",
       "      <td>1.101318</td>\n",
       "      <td>1.426025</td>\n",
       "      <td>2.102539</td>\n",
       "      <td>-3.007812</td>\n",
       "      <td>67.372704</td>\n",
       "      <td>38.399914</td>\n",
       "      <td>0.184240</td>\n",
       "      <td>0.586060</td>\n",
       "      <td>0.910767</td>\n",
       "      <td>0.748413</td>\n",
       "      <td>4.575928</td>\n",
       "      <td>0.324707</td>\n",
       "      <td>2.784040</td>\n",
       "      <td>2.924927</td>\n",
       "      <td>0.574219</td>\n",
       "      <td>3.100728</td>\n",
       "      <td>2.185425</td>\n",
       "      <td>0.598615</td>\n",
       "      <td>2.009624</td>\n",
       "      <td>1.172834</td>\n",
       "      <td>2.951855</td>\n",
       "      <td>2.776054</td>\n",
       "      <td>1.125174</td>\n",
       "      <td>0.718771</td>\n",
       "      <td>1.891605</td>\n",
       "      <td>4.100635</td>\n",
       "      <td>1.796164</td>\n",
       "      <td>3.874011</td>\n",
       "      <td>5.849018</td>\n",
       "      <td>1.216953</td>\n",
       "      <td>484.852448</td>\n",
       "      <td>1374.014404</td>\n",
       "      <td>44.040474</td>\n",
       "      <td>193.714752</td>\n",
       "      <td>518.392822</td>\n",
       "      <td>137.338638</td>\n",
       "      <td>446.848633</td>\n",
       "      <td>83.816788</td>\n",
       "      <td>29.771696</td>\n",
       "      <td>226.051010</td>\n",
       "      <td>1179.432251</td>\n",
       "      <td>58.687038</td>\n",
       "      <td>59.872330</td>\n",
       "      <td>104.227180</td>\n",
       "      <td>1.383768</td>\n",
       "      <td>115.313995</td>\n",
       "      <td>144.166290</td>\n",
       "      <td>107.538918</td>\n",
       "      <td>89.548050</td>\n",
       "      <td>166.987473</td>\n",
       "      <td>1.219559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83461</th>\n",
       "      <td>-2.250977</td>\n",
       "      <td>2.995605</td>\n",
       "      <td>-1.189941</td>\n",
       "      <td>2.155273</td>\n",
       "      <td>51.149643</td>\n",
       "      <td>30.781250</td>\n",
       "      <td>1.046387</td>\n",
       "      <td>0.910767</td>\n",
       "      <td>2.294434</td>\n",
       "      <td>-1.092285</td>\n",
       "      <td>89.732719</td>\n",
       "      <td>46.114021</td>\n",
       "      <td>2.628179</td>\n",
       "      <td>0.674072</td>\n",
       "      <td>0.538452</td>\n",
       "      <td>0.606262</td>\n",
       "      <td>5.246582</td>\n",
       "      <td>0.135620</td>\n",
       "      <td>2.937970</td>\n",
       "      <td>2.798810</td>\n",
       "      <td>0.139160</td>\n",
       "      <td>2.510710</td>\n",
       "      <td>0.097656</td>\n",
       "      <td>3.035627</td>\n",
       "      <td>0.385756</td>\n",
       "      <td>2.896466</td>\n",
       "      <td>2.555054</td>\n",
       "      <td>2.266954</td>\n",
       "      <td>2.481720</td>\n",
       "      <td>2.237963</td>\n",
       "      <td>1.148756</td>\n",
       "      <td>4.797236</td>\n",
       "      <td>1.954180</td>\n",
       "      <td>3.163251</td>\n",
       "      <td>3.859169</td>\n",
       "      <td>2.899640</td>\n",
       "      <td>285.494141</td>\n",
       "      <td>729.151184</td>\n",
       "      <td>68.101135</td>\n",
       "      <td>223.153152</td>\n",
       "      <td>364.817749</td>\n",
       "      <td>119.894478</td>\n",
       "      <td>226.338196</td>\n",
       "      <td>120.253395</td>\n",
       "      <td>118.253784</td>\n",
       "      <td>200.574905</td>\n",
       "      <td>549.911804</td>\n",
       "      <td>127.986633</td>\n",
       "      <td>140.258545</td>\n",
       "      <td>60.721649</td>\n",
       "      <td>0.056471</td>\n",
       "      <td>164.180710</td>\n",
       "      <td>80.383110</td>\n",
       "      <td>67.716522</td>\n",
       "      <td>46.367916</td>\n",
       "      <td>42.119709</td>\n",
       "      <td>1.000320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83462</th>\n",
       "      <td>1.069824</td>\n",
       "      <td>4.471680</td>\n",
       "      <td>-0.638306</td>\n",
       "      <td>3.089355</td>\n",
       "      <td>58.605942</td>\n",
       "      <td>48.500324</td>\n",
       "      <td>-1.603760</td>\n",
       "      <td>-1.166992</td>\n",
       "      <td>-2.077637</td>\n",
       "      <td>0.935669</td>\n",
       "      <td>60.529278</td>\n",
       "      <td>30.076031</td>\n",
       "      <td>0.640471</td>\n",
       "      <td>-4.374512</td>\n",
       "      <td>-3.937744</td>\n",
       "      <td>4.156128</td>\n",
       "      <td>3.401855</td>\n",
       "      <td>0.436768</td>\n",
       "      <td>2.555524</td>\n",
       "      <td>1.439331</td>\n",
       "      <td>1.116193</td>\n",
       "      <td>0.464646</td>\n",
       "      <td>1.573975</td>\n",
       "      <td>2.153687</td>\n",
       "      <td>2.548659</td>\n",
       "      <td>3.013306</td>\n",
       "      <td>1.314648</td>\n",
       "      <td>0.339963</td>\n",
       "      <td>3.062589</td>\n",
       "      <td>2.937906</td>\n",
       "      <td>0.331974</td>\n",
       "      <td>3.036400</td>\n",
       "      <td>7.975511</td>\n",
       "      <td>2.735095</td>\n",
       "      <td>6.035975</td>\n",
       "      <td>3.044795</td>\n",
       "      <td>278.796082</td>\n",
       "      <td>1361.831177</td>\n",
       "      <td>94.959755</td>\n",
       "      <td>215.842606</td>\n",
       "      <td>225.925507</td>\n",
       "      <td>1129.015747</td>\n",
       "      <td>129.970383</td>\n",
       "      <td>641.664917</td>\n",
       "      <td>91.747078</td>\n",
       "      <td>203.582291</td>\n",
       "      <td>305.073608</td>\n",
       "      <td>87.205757</td>\n",
       "      <td>139.388062</td>\n",
       "      <td>16.318756</td>\n",
       "      <td>1.267643</td>\n",
       "      <td>187.086182</td>\n",
       "      <td>63.102695</td>\n",
       "      <td>81.084534</td>\n",
       "      <td>30.940586</td>\n",
       "      <td>50.299763</td>\n",
       "      <td>-1.457758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83463</th>\n",
       "      <td>-0.164093</td>\n",
       "      <td>-2.865723</td>\n",
       "      <td>1.238281</td>\n",
       "      <td>0.148163</td>\n",
       "      <td>282.123413</td>\n",
       "      <td>54.614861</td>\n",
       "      <td>-1.054199</td>\n",
       "      <td>-0.608765</td>\n",
       "      <td>-0.422424</td>\n",
       "      <td>-1.454102</td>\n",
       "      <td>129.064484</td>\n",
       "      <td>22.920134</td>\n",
       "      <td>0.191988</td>\n",
       "      <td>0.460709</td>\n",
       "      <td>0.906143</td>\n",
       "      <td>0.683426</td>\n",
       "      <td>2.701630</td>\n",
       "      <td>0.445435</td>\n",
       "      <td>1.090118</td>\n",
       "      <td>1.660706</td>\n",
       "      <td>0.570587</td>\n",
       "      <td>1.504444</td>\n",
       "      <td>2.692383</td>\n",
       "      <td>1.602264</td>\n",
       "      <td>2.536121</td>\n",
       "      <td>1.031677</td>\n",
       "      <td>1.799473</td>\n",
       "      <td>1.643211</td>\n",
       "      <td>1.691781</td>\n",
       "      <td>1.830548</td>\n",
       "      <td>0.798871</td>\n",
       "      <td>1.884206</td>\n",
       "      <td>1.899260</td>\n",
       "      <td>2.728857</td>\n",
       "      <td>2.767871</td>\n",
       "      <td>1.123730</td>\n",
       "      <td>856.066101</td>\n",
       "      <td>635.309082</td>\n",
       "      <td>88.887291</td>\n",
       "      <td>407.117249</td>\n",
       "      <td>334.118439</td>\n",
       "      <td>180.247223</td>\n",
       "      <td>163.749924</td>\n",
       "      <td>110.529678</td>\n",
       "      <td>116.150528</td>\n",
       "      <td>222.319336</td>\n",
       "      <td>467.738007</td>\n",
       "      <td>58.955235</td>\n",
       "      <td>323.104797</td>\n",
       "      <td>66.797386</td>\n",
       "      <td>-2.252972</td>\n",
       "      <td>393.685272</td>\n",
       "      <td>85.326691</td>\n",
       "      <td>321.788025</td>\n",
       "      <td>142.198135</td>\n",
       "      <td>335.742310</td>\n",
       "      <td>-0.985715</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83464</th>\n",
       "      <td>-1.416992</td>\n",
       "      <td>2.556641</td>\n",
       "      <td>-0.606567</td>\n",
       "      <td>2.363281</td>\n",
       "      <td>79.337730</td>\n",
       "      <td>64.242027</td>\n",
       "      <td>-1.321289</td>\n",
       "      <td>-0.404663</td>\n",
       "      <td>2.644531</td>\n",
       "      <td>-0.883667</td>\n",
       "      <td>76.088745</td>\n",
       "      <td>55.231785</td>\n",
       "      <td>0.824538</td>\n",
       "      <td>-1.891113</td>\n",
       "      <td>-0.974487</td>\n",
       "      <td>1.432800</td>\n",
       "      <td>3.973633</td>\n",
       "      <td>0.916626</td>\n",
       "      <td>2.969849</td>\n",
       "      <td>3.032087</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>2.650890</td>\n",
       "      <td>0.277100</td>\n",
       "      <td>3.036237</td>\n",
       "      <td>0.877308</td>\n",
       "      <td>2.754987</td>\n",
       "      <td>2.336234</td>\n",
       "      <td>2.936442</td>\n",
       "      <td>1.829450</td>\n",
       "      <td>1.133597</td>\n",
       "      <td>2.394602</td>\n",
       "      <td>3.252507</td>\n",
       "      <td>3.888115</td>\n",
       "      <td>1.049569</td>\n",
       "      <td>4.394541</td>\n",
       "      <td>2.903473</td>\n",
       "      <td>434.471191</td>\n",
       "      <td>796.626587</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>262.293213</td>\n",
       "      <td>156.522232</td>\n",
       "      <td>476.719391</td>\n",
       "      <td>74.067566</td>\n",
       "      <td>275.839325</td>\n",
       "      <td>122.903557</td>\n",
       "      <td>241.624908</td>\n",
       "      <td>531.220337</td>\n",
       "      <td>141.332779</td>\n",
       "      <td>98.476799</td>\n",
       "      <td>145.486954</td>\n",
       "      <td>1.510935</td>\n",
       "      <td>163.226898</td>\n",
       "      <td>148.928711</td>\n",
       "      <td>110.521294</td>\n",
       "      <td>32.486191</td>\n",
       "      <td>106.928024</td>\n",
       "      <td>-0.931171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83465</th>\n",
       "      <td>1.571533</td>\n",
       "      <td>-1.117676</td>\n",
       "      <td>1.427246</td>\n",
       "      <td>-2.002441</td>\n",
       "      <td>171.025955</td>\n",
       "      <td>148.470657</td>\n",
       "      <td>0.758179</td>\n",
       "      <td>1.452148</td>\n",
       "      <td>-0.606445</td>\n",
       "      <td>2.354004</td>\n",
       "      <td>183.018738</td>\n",
       "      <td>87.263596</td>\n",
       "      <td>0.628964</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>1.225220</td>\n",
       "      <td>0.878235</td>\n",
       "      <td>2.689209</td>\n",
       "      <td>0.693970</td>\n",
       "      <td>2.853498</td>\n",
       "      <td>2.033691</td>\n",
       "      <td>1.395996</td>\n",
       "      <td>3.007492</td>\n",
       "      <td>0.926758</td>\n",
       "      <td>1.926740</td>\n",
       "      <td>0.047043</td>\n",
       "      <td>2.960449</td>\n",
       "      <td>1.873314</td>\n",
       "      <td>2.847115</td>\n",
       "      <td>3.130548</td>\n",
       "      <td>2.992261</td>\n",
       "      <td>0.330475</td>\n",
       "      <td>2.190307</td>\n",
       "      <td>2.338298</td>\n",
       "      <td>0.934416</td>\n",
       "      <td>5.057926</td>\n",
       "      <td>3.040699</td>\n",
       "      <td>678.337341</td>\n",
       "      <td>987.080994</td>\n",
       "      <td>119.422691</td>\n",
       "      <td>407.202240</td>\n",
       "      <td>336.290680</td>\n",
       "      <td>415.727631</td>\n",
       "      <td>112.958656</td>\n",
       "      <td>424.245178</td>\n",
       "      <td>270.082367</td>\n",
       "      <td>326.527466</td>\n",
       "      <td>652.645874</td>\n",
       "      <td>267.144196</td>\n",
       "      <td>253.910645</td>\n",
       "      <td>28.919601</td>\n",
       "      <td>2.684479</td>\n",
       "      <td>272.474579</td>\n",
       "      <td>142.145096</td>\n",
       "      <td>88.558395</td>\n",
       "      <td>98.446266</td>\n",
       "      <td>9.941506</td>\n",
       "      <td>0.986386</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55644 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CleanJet_eta_1  CleanJet_eta_2  CleanJet_phi_1  CleanJet_phi_2  \\\n",
       "0           -2.066406        2.819824        0.798096        1.402344   \n",
       "1           -1.962158        2.097168       -2.888672        2.117188   \n",
       "2           -2.612305        4.087891        2.516113       -0.967896   \n",
       "3           -3.061035        2.718262        0.276855       -0.745605   \n",
       "4           -1.772705        2.803223       -0.822388        2.676758   \n",
       "...               ...             ...             ...             ...   \n",
       "83461       -2.250977        2.995605       -1.189941        2.155273   \n",
       "83462        1.069824        4.471680       -0.638306        3.089355   \n",
       "83463       -0.164093       -2.865723        1.238281        0.148163   \n",
       "83464       -1.416992        2.556641       -0.606567        2.363281   \n",
       "83465        1.571533       -1.117676        1.427246       -2.002441   \n",
       "\n",
       "       CleanJet_pt_1  CleanJet_pt_2  Lepton_eta_1  Lepton_eta_2  Lepton_phi_1  \\\n",
       "0          58.501850      40.687500      0.052612      1.257812     -1.661133   \n",
       "1         149.991165      35.933941     -0.200928     -0.043259     -0.064484   \n",
       "2          40.722610      32.580414      0.271790      0.554932      1.902100   \n",
       "3         106.404816      72.261391      0.998047      1.562744     -2.758789   \n",
       "4         202.039673      69.501266      1.101318      1.426025      2.102539   \n",
       "...              ...            ...           ...           ...           ...   \n",
       "83461      51.149643      30.781250      1.046387      0.910767      2.294434   \n",
       "83462      58.605942      48.500324     -1.603760     -1.166992     -2.077637   \n",
       "83463     282.123413      54.614861     -1.054199     -0.608765     -0.422424   \n",
       "83464      79.337730      64.242027     -1.321289     -0.404663      2.644531   \n",
       "83465     171.025955     148.470657      0.758179      1.452148     -0.606445   \n",
       "\n",
       "       Lepton_phi_2  Lepton_pt_1  Lepton_pt_2       Rpt   Zepp_l1   Zepp_l2  \\\n",
       "0         -1.083984    59.333496    24.431036  0.608992 -0.324097  0.881104   \n",
       "1         -1.243164    77.783211    56.874996  0.820799 -0.268433 -0.110764   \n",
       "2         -0.195648    49.380596    26.431454  0.983751 -0.466003 -0.182861   \n",
       "3          3.061523    86.100220    55.490475  0.621377  1.169434  1.734131   \n",
       "4         -3.007812    67.372704    38.399914  0.184240  0.586060  0.910767   \n",
       "...             ...          ...          ...       ...       ...       ...   \n",
       "83461     -1.092285    89.732719    46.114021  2.628179  0.674072  0.538452   \n",
       "83462      0.935669    60.529278    30.076031  0.640471 -4.374512 -3.937744   \n",
       "83463     -1.454102   129.064484    22.920134  0.191988  0.460709  0.906143   \n",
       "83464     -0.883667    76.088745    55.231785  0.824538 -1.891113 -0.974487   \n",
       "83465      2.354004   183.018738    87.263596  0.628964  0.531250  1.225220   \n",
       "\n",
       "        Zepp_ll    detajj    detall    dphijj  dphilep1jet1  dphilep1jet2  \\\n",
       "0      0.278503  4.886230  1.205200  0.604248      2.459229      3.063477   \n",
       "1      0.189598  4.059326  0.157669  1.277326      2.824188      2.181671   \n",
       "2      0.324432  6.700195  0.283142  2.799176      0.614014      2.869995   \n",
       "3      1.451782  5.779297  0.564697  1.022461      3.035645      2.013184   \n",
       "4      0.748413  4.575928  0.324707  2.784040      2.924927      0.574219   \n",
       "...         ...       ...       ...       ...           ...           ...   \n",
       "83461  0.606262  5.246582  0.135620  2.937970      2.798810      0.139160   \n",
       "83462  4.156128  3.401855  0.436768  2.555524      1.439331      1.116193   \n",
       "83463  0.683426  2.701630  0.445435  1.090118      1.660706      0.570587   \n",
       "83464  1.432800  3.973633  0.916626  2.969849      3.032087      0.281250   \n",
       "83465  0.878235  2.689209  0.693970  2.853498      2.033691      1.395996   \n",
       "\n",
       "       dphilep1jj  dphilep2jet1  dphilep2jet2  dphilep2jj    dphill  \\\n",
       "0        2.705436      1.882080      2.486328    2.128288  0.577148   \n",
       "1        3.035455      1.645508      2.922834    1.856775  1.178680   \n",
       "2        1.442570      2.711761      0.772247    2.742868  2.097748   \n",
       "3        2.631210      2.784668      2.476056    3.094082  0.462873   \n",
       "4        3.100728      2.185425      0.598615    2.009624  1.172834   \n",
       "...           ...           ...           ...         ...       ...   \n",
       "83461    2.510710      0.097656      3.035627    0.385756  2.896466   \n",
       "83462    0.464646      1.573975      2.153687    2.548659  3.013306   \n",
       "83463    1.504444      2.692383      1.602264    2.536121  1.031677   \n",
       "83464    2.650890      0.277100      3.036237    0.877308  2.754987   \n",
       "83465    3.007492      0.926758      1.926740    0.047043  2.960449   \n",
       "\n",
       "       dphilljet  dphilljetjet  dphillmet  dphilmet1  dphilmet2   dr_l1j1  \\\n",
       "0       2.293723      2.539931   1.459033   1.293528   1.870677  3.246235   \n",
       "1       2.338290      2.549557   1.280928   0.795030   1.973710  3.328359   \n",
       "2       1.178385      2.006942   2.991255   2.727559   1.457879  2.948731   \n",
       "3       3.067006      2.811744   0.937767   1.118302   0.655429  5.068657   \n",
       "4       2.951855      2.776054   1.125174   0.718771   1.891605  4.100635   \n",
       "...          ...           ...        ...        ...        ...       ...   \n",
       "83461   2.555054      2.266954   2.481720   2.237963   1.148756  4.797236   \n",
       "83462   1.314648      0.339963   3.062589   2.937906   0.331974  3.036400   \n",
       "83463   1.799473      1.643211   1.691781   1.830548   0.798871  1.884206   \n",
       "83464   2.336234      2.936442   1.829450   1.133597   2.394602  3.252507   \n",
       "83465   1.873314      2.847115   3.130548   2.992261   0.330475  2.190307   \n",
       "\n",
       "        dr_l1j2   dr_l2j1   dr_l2j2      drll          ht      m2ljj30  \\\n",
       "0      4.128238  3.820034  2.936275  1.336266  238.785126   663.045654   \n",
       "1      3.168743  2.527819  3.984142  1.189179  424.800110   722.115173   \n",
       "2      4.774882  4.169537  3.616374  2.116770  188.852966  1117.925415   \n",
       "3      2.648027  5.397565  3.978624  0.730160  409.368164  1925.205200   \n",
       "4      1.796164  3.874011  5.849018  1.216953  484.852448  1374.014404   \n",
       "...         ...       ...       ...       ...         ...          ...   \n",
       "83461  1.954180  3.163251  3.859169  2.899640  285.494141   729.151184   \n",
       "83462  7.975511  2.735095  6.035975  3.044795  278.796082  1361.831177   \n",
       "83463  1.899260  2.728857  2.767871  1.123730  856.066101   635.309082   \n",
       "83464  3.888115  1.049569  4.394541  2.903473  434.471191   796.626587   \n",
       "83465  2.338298  0.934416  5.057926  3.040699  678.337341   987.080994   \n",
       "\n",
       "              mT2         mTi      m_l1j1       m_l1j2      m_l2j1  \\\n",
       "0       54.077595  116.856529  186.453583   208.308685  201.729980   \n",
       "1       36.402397  165.162811  303.863617   177.457932  246.787781   \n",
       "2       90.210190  117.845978  181.257996   276.182312  166.170395   \n",
       "3       53.180969  155.018036  741.145020   203.489059  782.901184   \n",
       "4       44.040474  193.714752  518.392822   137.338638  446.848633   \n",
       "...           ...         ...         ...          ...         ...   \n",
       "83461   68.101135  223.153152  364.817749   119.894478  226.338196   \n",
       "83462   94.959755  215.842606  225.925507  1129.015747  129.970383   \n",
       "83463   88.887291  407.117249  334.118439   180.247223  163.749924   \n",
       "83464    0.000006  262.293213  156.522232   476.719391   74.067566   \n",
       "83465  119.422691  407.202240  336.290680   415.727631  112.958656   \n",
       "\n",
       "           m_l2j2       mcoll     mcollWW          mjj         mll  \\\n",
       "0       80.825615   34.081371  172.575851   558.146729   53.316471   \n",
       "1      147.545578   55.163944  198.715286   556.838928   74.678581   \n",
       "2      168.286209   35.217129  165.960342  1039.650757   63.470570   \n",
       "3      143.249649   77.403580  180.723633  1574.967773   50.694679   \n",
       "4       83.816788   29.771696  226.051010  1179.432251   58.687038   \n",
       "...           ...         ...         ...          ...         ...   \n",
       "83461  120.253395  118.253784  200.574905   549.911804  127.986633   \n",
       "83462  641.664917   91.747078  203.582291   305.073608   87.205757   \n",
       "83463  110.529678  116.150528  222.319336   467.738007   58.955235   \n",
       "83464  275.839325  122.903557  241.624908   531.220337  141.332779   \n",
       "83465  424.245178  270.082367  326.527466   652.645874  267.144196   \n",
       "\n",
       "             mtw1        mtw2  phi_miss   proxyW_l1   proxyW_l2     pt_miss  \\\n",
       "0       69.366989   59.446663 -2.954661   69.433067   77.407051   55.831234   \n",
       "1       69.710251  128.463547  0.730546   72.027008  128.506714  104.216820   \n",
       "2       86.703613   43.173630 -1.653527   87.540642   46.859325   39.737881   \n",
       "3       74.129723   36.110657  2.406095  103.805618  103.368439   56.702801   \n",
       "4       59.872330  104.227180  1.383768  115.313995  144.166290  107.538918   \n",
       "...           ...         ...       ...         ...         ...         ...   \n",
       "83461  140.258545   60.721649  0.056471  164.180710   80.383110   67.716522   \n",
       "83462  139.388062   16.318756  1.267643  187.086182   63.102695   81.084534   \n",
       "83463  323.104797   66.797386 -2.252972  393.685272   85.326691  321.788025   \n",
       "83464   98.476799  145.486954  1.510935  163.226898  148.928711  110.521294   \n",
       "83465  253.910645   28.919601  2.684479  272.474579  142.145096   88.558395   \n",
       "\n",
       "             ptll      recoil       yll  isLL  isMIX  isTT  isBKG  isVBS  \\\n",
       "0       80.912895  103.303886  0.426776   0.0    0.0   0.0    1.0    0.0   \n",
       "1      112.543976  173.874069 -0.134309   0.0    0.0   0.0    1.0    0.0   \n",
       "2       42.711731    6.865315  0.370764   0.0    0.0   0.0    1.0    0.0   \n",
       "3      137.994324  177.526398  1.220858   0.0    0.0   0.0    1.0    0.0   \n",
       "4       89.548050  166.987473  1.219559   0.0    0.0   0.0    1.0    0.0   \n",
       "...           ...         ...       ...   ...    ...   ...    ...    ...   \n",
       "83461   46.367916   42.119709  1.000320   0.0    0.0   0.0    1.0    0.0   \n",
       "83462   30.940586   50.299763 -1.457758   0.0    0.0   0.0    1.0    0.0   \n",
       "83463  142.198135  335.742310 -0.985715   0.0    0.0   0.0    1.0    0.0   \n",
       "83464   32.486191  106.928024 -0.931171   0.0    0.0   0.0    1.0    0.0   \n",
       "83465   98.446266    9.941506  0.986386   0.0    0.0   0.0    1.0    0.0   \n",
       "\n",
       "       notLL  notTT  \n",
       "0        0.0    0.0  \n",
       "1        0.0    0.0  \n",
       "2        0.0    0.0  \n",
       "3        0.0    0.0  \n",
       "4        0.0    0.0  \n",
       "...      ...    ...  \n",
       "83461    0.0    0.0  \n",
       "83462    0.0    0.0  \n",
       "83463    0.0    0.0  \n",
       "83464    0.0    0.0  \n",
       "83465    0.0    0.0  \n",
       "\n",
       "[55644 rows x 64 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(pd. __version__)\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "df = pd.read_pickle('https://pandora.infn.it/public/296f0d/dl/dataset_pol_vbs_full2018v9_VBS_WW_2j_emu_dnn_SigBkg.pkl')\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['notLL'] = df['isTT'] + df['isMIX']\n",
    "df['notTT'] = df['isLL'] + df['isMIX']\n",
    "\n",
    "df_SvB = df[((df['isTT'] + df['isMIX'] + df['isLL']) == 0) & ((df['isVBS'] + df['isBKG']) == 1)]\n",
    "df_SvB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1c30b9d-7706-4fda-8bce-99ffc4a93c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc26e027-8929-4c05-8995-bc477450a479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.0-rc1\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Input, Activation, Dense, Dropout, InputLayer\n",
    "from tensorflow.keras.constraints import max_norm\n",
    "from tensorflow.keras import initializers\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.utils import shuffle\n",
    "import copy\n",
    "print(tf.__version__)\n",
    "from tensorflow.python.client import device_lib\n",
    "tf.config.run_functions_eagerly(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52abbb0d-f65b-408f-9465-36991164434b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57\n",
      "[[-2.06640625e+00  2.81982422e+00  7.98095703e-01 ...  8.09128952e+01\n",
      "   1.03303886e+02  4.26775962e-01]\n",
      " [-1.96215820e+00  2.09716797e+00 -2.88867188e+00 ...  1.12543976e+02\n",
      "   1.73874069e+02 -1.34309053e-01]\n",
      " [ 3.86865234e+00 -1.00723267e-01 -2.09472656e+00 ...  4.88177223e+01\n",
      "   3.23812408e+01  8.20660770e-01]\n",
      " ...\n",
      " [-1.64093018e-01 -2.86572266e+00  1.23828125e+00 ...  1.42198135e+02\n",
      "   3.35742310e+02 -9.85714853e-01]\n",
      " [-1.41699219e+00  2.55664062e+00 -6.06567383e-01 ...  3.24861908e+01\n",
      "   1.06928024e+02 -9.31171119e-01]\n",
      " [ 1.57153320e+00 -1.11767578e+00  1.42724609e+00 ...  9.84462662e+01\n",
      "   9.94150639e+00  9.86385882e-01]] [[0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " ...\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import cm\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.dpi'] = 150\n",
    "\n",
    "nclasses = 2\n",
    "features = df.columns[:len(df.columns)-7]\n",
    "\n",
    "NDIM = len(features)\n",
    "print(NDIM)\n",
    "for col in df.columns:\n",
    "    df[col]    = np.hstack(df[col])\n",
    "'''\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "df.dropna(inplace=True)\n",
    "df = shuffle(df)\n",
    "'''\n",
    "# Perform the splitting and define training and validation datasets\n",
    "msk    = np.random.rand(len(df_SvB)) < 0.8\n",
    "\n",
    "df_train    = df_SvB[msk]\n",
    "df_val      = df_SvB[~msk]\n",
    "\n",
    "X    = df_train.values[:,0:NDIM]\n",
    "Y    = df_train[['isVBS', 'isBKG']].values\n",
    "X_val = df_val.values[:,0:NDIM]\n",
    "Y_val = df_val[['isVBS', 'isBKG']].values\n",
    "\n",
    "print(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bd8aa6e-8ec9-462c-937d-0430fcdec8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleNeuralNetwork ( tf.Module ):\n",
    "    def __init__ (self, nEpochs, learning_rate, N_NODES, n_layers, n_features, n_outputsC=nclasses):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.optimizer  = tf.optimizers.Adam (self.learning_rate)\n",
    "        self.nEpochs = nEpochs\n",
    "        self.N_NODES = N_NODES\n",
    "        self.n_layers = n_layers\n",
    "        self.n_features = n_features\n",
    "        self.n_outputsC = n_outputsC\n",
    "        self.weights = self.build (self.n_features, self.N_NODES)\n",
    "        \n",
    "                                \n",
    "    # Define the structure of the model\n",
    "    def build (self, n_input, N_NODES):\n",
    "        # initializer = initializers.Ones()\n",
    "\n",
    "        # Classifier model\n",
    "        self.model1 = Sequential()\n",
    "        self.model1.add(Dense (self.N_NODES, activation = 'relu', input_dim  = n_input))\n",
    "        for i in range(self.n_layers):\n",
    "            self.model1.add(Dense (self.N_NODES, activation = 'relu'))\n",
    "        if self.n_outputsC > 1:\n",
    "            self.model1.add(Dense (self.n_outputsC, activation = 'softmax',input_dim = self.N_NODES))\n",
    "        else:\n",
    "            self.model1.add(Dense (self.n_outputsC, activation = 'sigmoid',input_dim = self.N_NODES))\n",
    "        \n",
    "        return self.model1.weights\n",
    "     \n",
    "    # Performs the epochs loop and the actual training.\n",
    "    # Monitors the training and validation loss functions, both for the classifier and the adversary.\n",
    "    # Returns the classifier categorical accuracy.\n",
    "    def fit (self, X, Y, X_val, Y_val, show_loss = False):\n",
    "        losses = []\n",
    "        losses_val = []\n",
    "\n",
    "        self.means = np.mean ( X, axis = 0)\n",
    "        self.sigmas = np.std ( X, axis = 0)\n",
    "\n",
    "        for iEpoch in tqdm.tqdm(range(self.nEpochs)):\n",
    "                l, l_val = self._train (X, Y, X_val, Y_val)\n",
    "                losses.append ( l )\n",
    "                losses_val.append ( l_val )\n",
    "\n",
    "        losses = np.array(losses)               \n",
    "        losses_val = np.array(losses_val)\n",
    "               \n",
    "        plt.plot (losses, color = \"c\", label='Training set')\n",
    "        plt.plot (losses_val, color ='tab:blue', label = \"Validation set\")\n",
    "        plt.xlabel (\"Epoch\"); plt.ylabel (\"Loss\")\n",
    "        plt.legend(frameon=False)\n",
    "        plt.show()\n",
    "         \n",
    "        ca = tf.keras.metrics.CategoricalAccuracy()\n",
    "        if self.n_outputsC <= 1:\n",
    "            ca = tf.keras.metrics.BinaryAccuracy()\n",
    "        ca.update_state(Y, self.predict_proba(X))\n",
    "        \n",
    "        return ca.result().numpy()\n",
    "\n",
    "    def save_weights(self, model_name):\n",
    "        self.model1.save_weights(model_name+'_weights_1')\n",
    "    \n",
    "    def load_weights(self, model_name):\n",
    "        self.model1.load_weights(model_name+'_weights_1')\n",
    "        \n",
    "    def save_model(self, model_name):\n",
    "        self.model1.save(\"saved_models/\"+model_name+\"_1\")\n",
    "\n",
    "    def reset_optimizers(self):\n",
    "        self.optimizer  = tf.optimizers.Adam (self.learning_rate)\n",
    "        \n",
    "    def set_epochs(self, epochs):\n",
    "        self.nEpochs = epochs\n",
    "        \n",
    "    # Applies a pre-processing to the input features and returns the classifier representation.\n",
    "    #@tf.function\n",
    "    def predict_proba (self, X):\n",
    "        ppX = (X - self.means)/self.sigmas\n",
    "        return  tf.clip_by_value ( self.model1 (ppX) , 1e-7, 1. - 1e-7 )\n",
    "\n",
    "\n",
    "    @tf.function\n",
    "    def _train (self, X, Y, X_val, Y_val):\n",
    "        Y_true = tf.cast (Y, tf.float32)\n",
    "        Y_true_val = tf.cast (Y_val, tf.float32)\n",
    "\n",
    "        with tf.GradientTape() as gt:\n",
    "            #gt.watch ( self.weightsC )\n",
    "            Y_hat = self.predict_proba (X)\n",
    "            Y_hat_val = self.predict_proba (X_val) #N3(N1(x)) validation set\n",
    "            \n",
    "            ## Training set\n",
    "            # Use the categorical cross-entropy as loss function for the classifier\n",
    "            cce = 0.\n",
    "            cce_val = 0.\n",
    "            bce = 0.\n",
    "            bce_val = 0.\n",
    "            loss = 0.\n",
    "            loss_val = 0.\n",
    "            \n",
    "            if self.n_outputsC > 1:\n",
    "                cce = tf.keras.losses.CategoricalCrossentropy()\n",
    "                loss = tf.reduce_mean ( cce( Y_true, Y_hat ) )\n",
    "            \n",
    "                ## Validation set\n",
    "                cce_val = tf.keras.losses.CategoricalCrossentropy()\n",
    "                loss_val = tf.reduce_mean (cce_val( Y_true_val, Y_hat_val ) )\n",
    "            else:\n",
    "                bce = tf.keras.losses.BinaryCrossentropy()\n",
    "                loss = tf.reduce_mean ( bce( Y_true, Y_hat ) )\n",
    "            \n",
    "                ## Validation set\n",
    "                bce_val = tf.keras.losses.BinaryCrossentropy()\n",
    "                loss_val = tf.reduce_mean (bce_val( Y_true_val, Y_hat_val ) )\n",
    "            \n",
    "            # Compute the gradient of the overall loss with respect to the classifier weights\n",
    "            gradients = gt.gradient ( loss, self.weights )\n",
    "\n",
    "        # Apply the gradients\n",
    "        self.optimizer.apply_gradients ( zip(gradients, self.weights) )\n",
    "        \n",
    "        return loss, loss_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "270faf23-c06e-46c2-b801-40b74b8ee17b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-28 09:38:35.735094: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:35.746371: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:35.746999: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:35.748529: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-28 09:38:35.749169: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:35.749791: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:35.750281: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:36.629004: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:36.629753: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:36.630368: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:939] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-06-28 09:38:36.630989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13789 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n"
     ]
    }
   ],
   "source": [
    "dnn = SimpleNeuralNetwork(500, learning_rate=0.0001, N_NODES=50, n_layers=8, n_features=X.shape[1], n_outputsC=2)\n",
    "dnn.save_weights(\"SigVsBkg_emu_RDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4878e7f1-b129-4343-a100-f1de1e3fc119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(44457, 57) (44457, 2)\n",
      "[[-2.06640625e+00  2.81982422e+00  7.98095703e-01 ...  8.09128952e+01\n",
      "   1.03303886e+02  4.26775962e-01]\n",
      " [-1.96215820e+00  2.09716797e+00 -2.88867188e+00 ...  1.12543976e+02\n",
      "   1.73874069e+02 -1.34309053e-01]\n",
      " [ 3.86865234e+00 -1.00723267e-01 -2.09472656e+00 ...  4.88177223e+01\n",
      "   3.23812408e+01  8.20660770e-01]\n",
      " ...\n",
      " [-1.64093018e-01 -2.86572266e+00  1.23828125e+00 ...  1.42198135e+02\n",
      "   3.35742310e+02 -9.85714853e-01]\n",
      " [-1.41699219e+00  2.55664062e+00 -6.06567383e-01 ...  3.24861908e+01\n",
      "   1.06928024e+02 -9.31171119e-01]\n",
      " [ 1.57153320e+00 -1.11767578e+00  1.42724609e+00 ...  9.84462662e+01\n",
      "   9.94150639e+00  9.86385882e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, Y.shape)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03845452-4fd3-4f29-b16f-b0d2ee0ef2c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:22<00:00, 21.97it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = dnn.fit (X.astype(np.float32), Y.astype(np.float32), X_val.astype(np.float32), Y_val.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe6f8ce9-8957-4250-9622-e570e00551d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JKAUwvzrPocAPgZvaviBJU4YJJkmSJEmafB4JrBjnsd+kJJheEBFvatVjKCJ2AR4FLAV+3Lg9Mw9r2H8a8BHgXcBnI+J7mbmi2vY8SnJpGbB/Yw+niNgOeCVw7zivSWt7D/Ax4B/9DkQCE0ySJEmSppZ5wGC/g2gwG1jUzQYz88oOjv1zRFwOPBp4FnB6i11rvZe+286wtap31AeAt1PqY80HLq82P79afr7Z8Llq+NX727sCtSMzbwRu7HccUo0DwiVJkiRNJYNk3jKpXhOQ8IqIrGq4NK7fKyLOiIjFEXFvRNwUEb+PiI9FxGZ1u55cLV/Sov0ADqneNhse11SViFpWva3vsFAbC9rVwuYRsUdEfCci/lld740RcXZEvLrN4/eNiM9HxBURcWtE3B0RV1b3a4sWx7R7j4mI/SPiF3Xx/Ssizo+Io7pw+aNd24nV52RBw/o5VayLIuKOiFgWEVdHxDci4vETHVc7ImLniDg5Iq6NiHsi4paIuDwiPh0RW9XtFxFxSPUZuDoi7oyI26vn8fpoUuQuIo6u7sthEfHYiPhJRNwWEUsj4rsRsXW136YRcWxEXFfF8OeIOLhJewuq9k6MiK2q5c3VZ+myiHjZOK5/m+pzeU117qUR8cOI2GusbU0m9mCSJEnSOmf33Rcxffrsnp5zxYpBLr54Xk/PqfVLRBwAnEGpa/R74EJgC+DhlGFr/wPcUe3+bUrx7mdHxMzMXNbQ3AJKIfBrWhXsbhHDQym9l1YAf6vbdH21fFlEfC0z71jr4DGKiCOA4ykdIy4Ffk3pLbYL8AngK2008wlK7agrgLOB+wG7Ue7XsyNiz/pYx3KPI+INwOeBVcAFlJpTsynDG48GjhnflY9fRGwO/A54KOWZ/IJSoP0hwIuBaynX1TcR8VjgfMqzuAL4AbAJ8DDgCMr9r/XM2ojyWR6i9BK8jPL52wv4AvB44LAWp9qD8rz+DPyM8txfAOxaJdp+QSlwXvtcPQn4bkQ8MzN/1qS9WcBvq5jOpRSxfzJwUkQ8LDOPbvP6nwD8qDr+qurnOcAzgP0i4iWZ+X/ttDXZmGCSJEnSOmf69NkW19a66B2UZMvBmXlq/YaI2J3yJRwow6ci4mzg6ZQv1V9taKs2PO5k2lD13Hk08Klq1Zcy87a6XU6gfNHfDbg2Ik6lJGcuBf6Smc1mphvpfE+sznUHcFBmnl23bUPKdbXjGODC+gRbRGwEfBZ4DfA24IN1+7d9jyl1rhLYMzMvqdsvKMmKfjiYklw6k3LfhuvimgM8oE9x1XszJbn0jsz8ZP2GiHgEq3vIQUmOHQT8qFbvq9pvDqVu2Msj4oTM/HWT87wWeF1m/k91zPTqmKdRPps3AQ/LzDur7a+k/J78FyUh1egASlLqoLpjdgfOAd4fEWdm5mUjXXhE3B84Fbg/cGhmfqtu2+OAnwNfjYhzsvSOnFIcIidJkiRJU0Mta/rLxg2ZeXFm3t6wujb07dD6lRFxP1bXTGqZYKqGBWVEJHA7ZUa6nYA3AW9pOP8F1XmGqjhfC3wDWAjcGBEfjYiZo11gnXdTehF9uD65VJ1rZWauVZS8mcz8SWPvrcy8t4p/JfDchkPGco/nALfVJ5eq/TIzz20nvglQi/+c+uQSQGbekpl/7kNMjUa6x1dWtaVq71dm5hn1yaVq/S2UIuew9jOsOb+WXKqOWQF8rnr7CEry6c66/U+kDHd9QpWMajQMvKn+mMy8mNKTahrw+hZx1Dsc2Ar4dH1yqWrrEuBDwGY0/M5OFSaYJEmSJGlquLRafjMidm9Wf6bB6cCdwBMjYpu69c8GZgK/zcy/NT2yOKnu9R3gImBT4APAfo07Z+YplCFHhwJfpwxNSkqvmXcDl0TE3FFirvVQWlC9/fJo+7fR3oMj4rVVfZ8TIuJE4EvAcsrQt3pjuceXAltGxNciYn6ncXZJLf53RsSLqyFzk00txi9U9Y1GHVkVEY+OiCMj4gsR8fXqGb6u2tz4DGt+3mTdtdXyusy8un5DZq4CFgPTKUPmGl2emVc1WX9Ktdx3pGuo1HrendZi+2+q5aSolTVWDpGTJEmSpKnhv4BHUYbqHADcGhHnU4ZDnZyZ99TvnJl3RsTplITPf1JqMsHq3hEjFvfOzMMa10XEYyi1hs6MiJ0bv3BXvTu+Vb2IiAcAr6AkpXYAPgyMVqB7ANgYWJqZt46y74gi4m3AxyhJg3aM5R6/gVIv6HDg8Ii4mXJvTgO+XyUsRovv3ZTeNPWuzMyPtRnvGjLz7Ij4FKWH1inAyoi4jDK064TMvHak43vkE8A+lCTir4A7IuIiSi2iExuGM86g9Cw6ZO1m7tMqifbPJuvuGGFb/faNmmxb3OKY66rlg1psr7ddtbygjKRsqbdFBLvEHkySJEmSNAVk5vXA4yjFgD9HKeJ8AKXY9RURMdDksFoS6SUAETELeCalSPeYCwln5h+A/6V0VnjdKLuTmTdXyZJ3V6ueNdZzjldE7Al8EriLUh9qO+B+mRmZGawuJH2fsdzjzLwCmEepEfQV4N/ACym9vX5TJUdGsx/w8obXWr3DxiIz30YpNP4uSgJnZ+C9wJUR8fyRju2FzPw38BRKj59jKcW7nwJ8GrgqIup7JL2Nklz6E+Vz+wBgRvX8dqr2aZWpGW6xfrRtE6mWg/k+a/YQbHyd3fToSc4eTJIkSZI0RWTmSsrQn58DRMS2lALbT6EkFI5sOORsSiLlURGxK/AEYAZwZmYOMT5/r5athiY1c061bKdnxiBwNzArIrZoKCY+FgdVy/dm5kn1GyJiY+CBzQ4ayz2uejSdUb2ohsp9m3KfXwV8caQAM3PBmK6oTVXPsmOBY6uaW2+k9Bz6EqXIdF9VRd/Pr15UQyc/TUkmfZiSqIPVz/CQzFzY0MzDJj7SNWw7yvp/tdHGDZTE2Mcy89LRdp5q7MEkSZIkSVNUZi5m9dC3nZtsX8XqGjEvqV4wyvC4UdS+2NeGE9VmThvJDtWy1dCk+1Qxn1u9fc1Yg6uzZbW8ocm2F9C650tjPCPe44Z9F1KKPo+6b69k5j2ZeRwl0TinnTpYvZaZS4Cjq7f1922kZ/jCJusm0qMbelfVvLhant9GG7+olgeNuNcUZYJJkiRJ0lQym4g5k+rVo3opEfHWiGjW62b/anl9i0NryaTDgb2B24CzxhnDY1id9Kmfye2rEfG+iFirDk1E7EQZqgZlaFA7Pk4pEP7eiHhyQ3sbRsT+zQ9bQ62I8yvrZwWLiHmsThg1xtrWPY6ITSLizRGxRcPx01g9xK3V85gwEXFgNTSwcf1jKcPL7qA8/76pCq4/tMmmZp/j2jN8bUMbBwMvm4DwRjIN+FxEbFIXx2MpvcOS0jtsNP8LLAGOjIjXNBaRrz7bz4iISZGcHCuHyEmSJEmaShb1O4A+Ogo4LiL+CPyV0gNnV2BHYClwXLODMvPyiPgzq3uGfC8z7x3tZNVMXTUzKEOB9qR80T6LNXtBDVASWMdExEJKYmBVdczu1TG/p0zDPqrMPC8ijqQM8zonIi6hXPNsyjVvBGwxSjNfB95OqaF0VURcDMwCnkQZ0vZ41h721O49ngF8ptr3Ukqh5xnVtW5Tve94BrxxWAAcERH/BP5AqQv1IEq9o2nAUZm5vMlxp0dEq8/EjzKz/rntFhG/HSGG12fmZSNsfy3wpYhYBPwFWEkpdL4rcA/wwbp9j6Uk7D4WES+gfK4eTqmTdRzwjhHO020/rGK8JiJ+TZmJ8SmUAvL/nZmXjNZAZt4WEc+l/P78L/C+6nfzVsqQzd0on+uDKLMwTikmmCRJkjS1DTep1To02Pt/6a4cXHtds9ik8XsT5cv2YykFj6H09jgeOD4zRxp+9k1W99o5uc3zvbzu52FKz5dfV22dmJn1H/A3UL6AP4NS+PqpwGaUL86/Ar5HmcVsRZvnJjOPi4jfAW+l9LzalVKf6U+sHvY30vFDEbE75bqfBDyHUj/q/ZTkxDVNDmv3Ht9RXfNTq7h2AZYD/wC+Cnw+M5e2e61ddCIlYfNESgJtJnATpbfZZzKzVfHoR4/Q5pUN7zcH9hhh//uPEuP7gQOrNp5KSczdQLlvx9XPTJiZv46IfSh1mR5DSfT9CXg+cBm9TTANURKsH6d8zu9PSXh/OjNPbLeRzPxtRDyK8rl+FuWzCWUI43nA6cAvuxd270SprbX+iYiF8+bNm7dwYWOdMEmSJE0ly2/8CxdeNW+NdXsdCDOWNd9/wuKYCReesea6vXZaxIytHtmzGObPn8+iRYsWZeb8np10IpXhZ0v6HcY4zSXzln4HIakzEbGAkiQ9KTMP62swk5w1mCRJkiRJktQRh8hJkiRJmqyGgEk341WbhvodgCT1kgkmSZIkSZNTqfHjMDNJmgJMMEmSJGndc8H5MHfH3p5zydVwyz69PackaUJl5rmU2QQ1ChNMkiRJWvfMmgVz5vT2nCsH7WsjSVpvWeRbkiRJkiRJHTHBJEmSJEmSpI6YYJIkSZIkSVJHTDBJkiRJkiSpI11LMEXE2yLitIj4a0Qsi4h7I2JxRHwjIh41jva2jIjPVG3U2vp0RGzRrZglSZIkSZLUuW72YPov4JnAUuBs4EfAPcBLgUsj4tntNhQRs4HfA28GVgJnALcDRwC/i4hZXYxbkiRJkiRJHdiwi209F7g0M++pXxkRrwe+AHw1IrbOzJVttPVpYAfgNOBFtWMi4rPAm4DjgcO6F7okSZIkSZLGq2sJpsy8oMX6L0bE24DtgXnAFSO1ExFbAYcAy4HXNySk3gm8GDg0Io7MzCVdCV6SJEnSpBPHxDRgoN9xjNNQHpXD/Q5Cknqlmz2YRrKiWi5vY9/9KEP3fpOZN9dvyMx7I+Is4HBgf+DEbgYpSZIkaVIZAKbqfyrPBW7pdxCS1CsTPotcRLwU2An4a/Uaza7V8rIW22vrd+kwNEmSJEmalCIiI+K6cRw3LSKur47ft439j6z2PbXh3I2vFRHxr4g4NSL2GqG950XEjyNiSXXMYEQsjIhvRsSrImLGWK+pHRFxbhXndhPR/mQUESdW17yggzauq3vGh4yw3+PrPw9Ntq+1PiJeVq3/U0RMb9HunhGxqvqczBnvdaxrxvv7329d78EUEe8E5gObAo+sfv4XcEhmrmqjiYdUyxtabK+t37bNeBa22LR9O8dLkiRJ0lSRmcMR8W3gSOBQ4DejHHJotfxmk20n1f28OaUzwPOAgyLi0Mz8dv3OEfE1ymgTgD8A5wFB+U54aPX6IXBT2xekXnoJcEqLbYe2WN9SZn6j6nDyNMrn8cP126uk01coHV/enpn2+JviJmKI3DOAp9a9Xwy8LDMvbfP4zarlXS2231ktNx9HbJIkSZI0FTyS1aVGxuqblC/0L4iIN2Vm01IlEbEL8CjKTOA/btyemYc17D8N+AjwLuCzEfG9zFxRbXseJbm0DNg/My9sOHY74JXAveO8Jq3tPcDHgH90oa0/AM+IiNmZOVi/ISI2BF4ELKJ01NhoDO3+P+DPwPsi4ruZWT+q6V3AzsAvM/OkpkdrSul6gikznwYQEVtQ/rD6AHBeRLwvMz880rETITPnN1tf9Wya1+NwJEmSJHVmHjA46l69NZvy5btrMvPKDo79c0RcDjwaeBZweotda71SvtsqCdXQ7nBEfAB4O6U+1nzg8mrz86vl5xuTS9Wx1wHvb+8K1I7MvBG4sUvNfQs4jpJI+kLDtqdTaop9GjhqLI1m5rURcRRwLPBl4MkAEbET8D7gbuC1nQSuyWPCinxn5m3AbyJif+Ai4EMR8fPMvHiUQ++olpu02L5ptby98yglSZIkTTGDedTkGkoTx0T32yz1bBZn5nYN6/ei9E56DPBA4FZKD5ZzgP/OzNr3qZMpCaaX0CTBFBFBmb0bmg+Payozl0fEMkqCqf77ZK1+TlefTUTsAbwV2JeSyFtKSeZ9JzO/0sbx+1KSJk8EtgHuRxllcwbwsep7a+Mx7d5jqu+7b6UkPmcDQ8C1wC8y85jxXHO7IuJE4OXAkzPz3Lr1cyhJwOdQStCsAm4GfktJAP6+SXNnUpJHh7J2gulQIClJqDElmCqfonzWFkTEK4ETKMmmjYB3ZeY1Y2ms3ecTEfejfP6fQ+n8shWlB90VwBcz8ztN2j6R6p5SPt8fqM5zN/AD4B2ZuSwi5gIfAp5N+V24Ajiy/jlU7R0GfB04hjL88CPAAsrn8HLgw5m5Vu/BUa7/kZTeX0+lJP5uA34FfCgzW5UI6okJL/JddZn8P8rY2wPaOKTWvW/rFttr6xd3GJokSZIkTRkRcQClptJzKD1XTqMMbZpF+cI5u273b1MSC8+OiJlNmltA+W51TbMeRyPE8FDKF+oVwN/qNl1fLV8WEZutdeA4RMQRwIWUBFHtev9MGVb1iTab+QRlaN7dwNnV6/6U+3V+Y6xjuccR8QbgR5RkxN+AU6v4tgWOHuPldkVEbA78jhLrZsAvgJ9TkjAvpszG3szdlGvdMyLuq1dc3Z/nAudn5riG4mXmSuDVlM/jJyg9l55ISbAcP5a2xvg7sB3wVeBxwHWUBNHlwJ7AKRFx9AinOgj4KSWP8VNKYupVwA8iYjalE80zqlguB3YHfhoRj2rR3vaU5/IYyvO4BHgC8MOIeEVbFw9ExIHV9b6c0pPzTODvwAuB30fEE9ttayJMWA+mBrUurO1Uhf9jtdytxfba+is6ikiSJEmSppZ3UDoJHJyZp9ZviIjdKb1ngDJ8KiLOpgxvegHli3a92vC4k9s5cZVoeDSlNwrAlxp6/5wAHEb5vnZtNSvdhcClwF8yc62Zx0Y53xOrc90BHJSZZ9dt25ByXe04BrgwM5fVHb8R8FngNcDbgA/W7d/2Pab0oklgz8y8pG6/AJ7UZnzddjDwUEri4aDMHK6Law7wgBGOPZmSuHgJq+/JQZTRRW19TlrJzEsj4tOUnlUfpCSbXl0ln8ZiLM/nFuA/gLPrP39VkvQc4P0RcWI1fLPRG4HnZOaPqmM2p3yen0QpXn8hcHhdDbIPURJn76Dcw0aHAt8AXlm75oh4NqUn3eer0V7/HOnCqzpmJ1OSu8/OzF/WbduP8sxPjogd2hnyOhEmvAdTpfbL1U7Xt58Cw8C+Vbez+1R/EBxA+TCOqRuZJEmSJE1xtf+w/2Xjhsy8ODMby4jUhr6tMQNYNXSoVjOpZeKgYVr62ym9NXYC3gS8peH8F1TnGarifC3lC/VC4MaI+GiLnlStvJvSe+TD9cml6lwr2x1WlJk/qU8uVevureJfSemdU28s93gOcFt9cqnaLxuHSvVQLf5z6pNLAJl5S2b+eYRjz6H0CnpJ3bpDKb13vteF2I6nJOQATmq8b21q+/lk5lBm/rIxuZmZf6fMaDeN1qOsvl1LLlXH3E6Z8Q5Kz78315JLleMo19YqsXgH8Jb6hFpm/hD4PiWB104vprdQSga9pz65VLX1U+BLlGGgz2qjrQnRlQRTROwdEftVswrUr58eEW8CXkrpcvd/ddveGBFXRsRH64+pCpWdAswAvlhlp2uOpXygTs7MJd2IXZIkSZKmiNrM3N+MiN0bv381cTplFu4nRsQ2deufDcwEfpuZf2t6ZHFS3es7lGFBm1Lq0uzXuHNmnkIZHnYope7Mnylfuh9ASRhd0tiJoJnqO+CC6u2XR9u/jfYeHBGvjYhPR8QJVZ2dLwHLgYc37D6We3wpsGVEfC0imk4u1Qe1+N8ZES+uet60pUpInQLsWF37Ayl1fn6cmbd2IbZjKElDKDPWjWdm+LH+DhAR+0TE+yLiSxHx9er5v6Da3Pj8a37eZN211fKSxvtRJTGXUuo8NW2vxT08pVru2/ICVqv12jutxfbfVMvHt9HWhOjWELmHU/4AGYyISylZ69msLqR1D3BYZl5fd8xsSva72QN4C2Vc5POBKyPiEsoMBTsDf6V0Y5QkSZKk9cl/Ub5jHVC9bo2I86mGxmTmPfU7Z+adEXE6JeHzn8DHq021Hk0jFvfOzMMa10XEYyhDhM6MiJ0z86rGc1KKQX+r2v8BlN4ZHwB2oPQcefUo1zkAbAws7TSxERFvAz4GTG/zkLHc4zdQhjgdDhweETdT7s1pwPczc1Ub8b0beETD6isz82NtxruGzDw7Ij5F+U59CrAyIi6j1GI6ITOvHel4So+2t1F6MS0GNqDD4XFw35DHV1J6SP0OOJDyWXjzGJtq+/lUPeZOA54yQnutklzNhqvdMcK22vaBFtta1ZC+rlo+qMX2etvVzl9GYbY0e6SNE6lbQ+TOo1RDvwrYhZIN3JuSwfsc8KjM/G67jWXmICXr9jlKT6aDKBn2zwKPz8ylXYpbkiRJkqaE6j/sH0cpLvw5SmHtAyhDd66IiGZfbmtJpJcARMQs4JmUOi7/12T/0WL4A/C/lM4Kr2tj/5urZMm7q1U9G74TEXsCnwTuotSH2g64X2ZGZgYl2bGGsdzjzLyCMnvcQdX2f1OKLX+HMqP6jDbC3I9Ss6f+tVbvsLHIzLcBtZnGfkXpqPFeSueN549y7B+Av1AKgr+MMkPZj0Y6ZjRVqZsvU3ovvYkyfPJW4A1V3aS2jfF34OOU5NJ5lB5xs4ENq2f/jFp4LU413GL9aNsmUi1/c9Ior9/1JTq61IOpGsP43jEeczQjVNavkkhvZuwZTUmSJElaJ1U1XH5evYiIbSkFtp9CSSgc2XDI2ZREyqMiYlfKzFUzgDMzc4jx+Xu1bDW8qJlzqmU7vSsGKSVWZkXEFg3FxMfioGr53sw8qX5DRGxMmeJ+LWO5x1WPmTOqF9VQuW9T7vOrgC+OFGBmLhjTFbWp6ll2LHBsVXPrjZQZ3L5Eme1uJCdTehc9APhKVbOqE++jjF46s1aYOyKOpCSFvhIRjxtLse8xPJ+DKPWbn5OZ/25o5mHjv5xx2XaU9f9qo40bKLPRvb2D390J1atZ5CRJkiSpG2bHMSMOD+mHvg1JyczFEVHrqbFzk+2rIuIUVg97ekK1acThcaOofTmvDRkiImKUmeJ2qJYjzpQF98V8LqWn1WsoiZLx2LJa3tBk2wto3XulMZ4R73HDvgsj4guUXl4j7tsrVRLsuGq44FYRMXeUmsbfBt5KuT/f6OTcEbEzJelzO2VIYc3XKD2k9q3O9YnxnmOE57Ml8O8mySUoPc166ektkqUvrpbnt9HGLygJpoNYe1bIScEEkyRJkqa04Vx7tMLQPUvZ8M5behrHynvWruLQLDZ1bFG/A+iXiHgrcEpm3tSwaf9qeT3NfZOSYDocmEUZ9nTWOGN4DCXpA2vO7P3ViPg7pc7PvxqO2YkyVA3KrFnt+DhlqNh7I+LizPxVXXsbAk9vYya5q6vlKyPix3VTys9jdT2qNbR7jyNiE0oPpW/UJw2qotP71e/bSxFxIHBTZv62Yf1jKT2S7qA8/5Yy8zpWz9bWSSzTKL2UplN63dyX6MvMjIj/B1wOHB0R36vOO1qbY/kduBqYHxEvysz6CcfeCjx5HJfUic2A4yPiNbXeWhHxTEqi625KTevRfJIyhPK4iFiamWsU+66GIh5AKd7fLKk64UwwSZIkaUq79d7b1lr3yFP2YdmKtfedSDOnwxl7rbnu1ntvazmlkDQOR1G+XP6RMvlRALsCO1Lq3x7X7KDMvDwi/szq3h3fa2fYUzXbVs0MynCePSm1YM5izV5QA5QE1jERsZDy5X5Vdczu1TG/Bz7UzoVm5nnVMKpjgXOqiZ/+SukttiuwEbDFKM18HXg75Uv3VRFxMSXB9iTKkLbHs/bQpXbv8QzgM9W+l1KKNc+ornWb6n3HM+CNwwLgiIj4J/AHSl2oB1F6Ck0DjsrM5T2K5fWUz8vvgC80bszMv0TExygF4L/I6iTRSMbyO/BRynC/70TEGyg92XalFFX/FKXnVK98C3gesCAifkeZ7OyJlPjf3E5CKDP/FhGHUHqYnRoRf6PUy7oTeDCwG2WWx8fQvNfehOtWkW9JkiRJ0sR6E6WA9CaU4WP7ASuB44FdMvOvIxxbnwxqd1aw+sLTL6J8Mf81ZTawA6tp7WveQJkd7jTKzGNPpXyhfhil0PRrgX1aDFdqKjOPoySDTgceAhxMSZL9iZI4Gu34IUrC59uU5M9zKF/E3w8c0uKwdu/xHdU1n0Xp7fMcyhCtWylJkMf2qU7OiZSeLv+iJNCeDzyU0tvsaZl5fC+CiIitKROBrQRe3fBZqfcRSjLymRHxojaabvt3IDO/RSkq/1vg0dX+/6I8pzPHflUd+RtleOoVlALjj6/iOiAz2x7ulpk/oEys9kUggf+gXONcymfxhfSxl2eMPEx23RURC+fNmzdv4cKF/Q5FkiRJHbjx7xdw1eJ91lh34IVMih5MO217Pls9dO+exTB//nwWLVq0KDPn9+ykEyiOiTnASLViJrO5eVT2dpympEklIg6j9KQ7pprobJ3mEDlJkiRJk9UQ5X/mp6JJOcuTJE0UE0ySJEla51xw4FnM3XaPnp5zyeLfccuSA3p6znVdHpXDgL2AJGkKMMEkSZKkdc6sjbdkzqYdT4I0Jis33tJMiCRpvWWCSZIkSZIkqcsy80RK4fX1grPISZIkSZIkqSP2YJIkSdL4DQ/DUJ9rGS+9tb/nlyRJJpgkSZLUgaEhmNvnSb4eCJzS3xAkSVrfOUROkiRJkiRJHTHBJEmSJEmSpI44RE6SJEnjNpzDDG3S3xiW9vn8kiTJBJMkSZI6MHTPUuYe2d8YZk6HM9ZauUXvA5EkaT1mgkmSJEnrnmlWglgXnHtuTAMG+h3HOA0tWJDD/Q5CknrFBJMkSZKkyWoAWNLvIMZpLnBLv4OQpF7xv3YkSZIkaZKJiIyI68Zx3LSIuL46ft829j+y2vfUhnM3vlZExL8i4tSI2GuE9p4XET+OiCXVMYMRsTAivhkRr4qIGWO9pnZExLlVnNtNRPuTUUScWF3zgnEce12L59zN19Fdv+gpaH36bNqDSZIkSV216MXnM3vrHXt2vpUrBrnq8nlrrNty41k9O780mWTmcER8GzgSOBT4zSiHHFotv9lk20l1P28O7Ao8DzgoIg7NzG/X7xwRXwMOr97+ATgPCGB+dZ5DgR8CN7V9QZoo3wdmN6x7NOUZXwOc37Bta+CGJu28vFqeCtzRsO3yjiLUlGOCSZIkSV01e+NZzNl0Ts/Ot3z52uumhR31NeU9ElgxzmO/SUkwvSAi3pSZTX5LICJ2AR4FLAV+3Lg9Mw9r2H8a8BHgXcBnI+J7mbmi2vY8SnJpGbB/Zl7YcOx2wCuBe8d5TVrbe4CPAf8Y64GZ+Y7GdVWPo12B8xuffSsRUUswvSMzrxtrHFq3mGCSJEmSNJXMAwb7HUSD2cCibjaYmVd2cOyfI+JySo+UZwGnt9i11nvpu62SUA3tDkfEB4C3U+pjzWd1L5XnV8vPNyaXqmOvA97f3hWoHZl5I3Bjv+OQakwwSZIkSZpKBhcsyElVPPvcc6PrbUZEAoszc7uG9XtReic9BnggcCulB8s5wH9nZm2Y0smUBNNLaJJgiogADqneNhse11RmLo+IZZQEU/33yVq3xa4+m4jYA3grsC8lkbeUksz7TmZ+pY3j9wVeBDwR2Aa4H7AYOAP4WGbe1uSYdu8xEbF/Fd+8Kr4h4FrgF5l5zHiuuV0RcSJliNqTM/PcuvVzKEnA5wAPAVYBNwO/pSQAfz+RcY1FRGwLvBt4KmUY3j2UpNn5wPGZeVXdvs+iJDKfADwY2AD4G/B/wCcz896Gtg8Dvg4cQ/mMfxR4CjADuAB4e2YuiogNKc/7MMr9ugH4VGZ+oaG97YC/U4Z+Pgf4EGXI6Jxq/ZeBz2S2P3tkRMwC3gk8F3gosBy4tLr2H7bbzmRh32FJkiRJmgIi4gBKTaXnUL6En0apdTSLMmytvqbOtymJhWdHxMwmzS2gfKG/plmPoxFieCglubSC8uW+5vpq+bKI2Kzd9kY51xHAhZQEUe16/wzsDHyizWY+QRmadzdwdvW6P+V+nd8Y61jucUS8AfgR8GTKvTi1im9b4OgxXm5XRMTmwO8osW4G/AL4OSVJ9mJg/37E1UxEbANcBry2WvVjSvLmXuDVlERSva9REkxLgZ9QntM2wIeBH0fEBi1O9VDg95TPzS+B64D9gHMj4oGUelRHAguBc6s2Px8Rr27R3kaUZOPLqnZ/QXnmxwMntHPtABGxI6UH4LuBjYGfAZcAewBnRcRawxgnO3swSZIkSdLU8A5KJ4GDM/PU+g0RsTul9wxQhk9FxNnA04EXAF9taKs2PO7kdk5cJWIeDXyqWvWlht4/J1B6gOwGXFvNSnchpTfGXzIz2zlP3fmeWJ3rDuCgzDy7btuGlOtqxzHAhZm5rO74jYDPAq8B3gZ8sG7/tu8xJSmRwJ6ZeUndfgE8qc34uu1gSkLlTMp9u683TdWz6QF9iquZV1ESd5/PzDfVb4iIhwDTG/b/f8DPM/Puuv02pyRTn03prfeNJud5GaVW1X9lZlbPp/Z5PRsYBh6eWXpGRsRTKYmo9wLNesntCVxRHTNYHbM98Gvg5RFxRmaeMdKFV8mw71OSWUdSemANV9t2oCQFPxYRP83MP4/U1mRiDyZJkiRJmhpqw9B+2bghMy/OzNsbVteGvh1avzIi7sfqmkktE0z1U84Dt1N6jOwEvAl4S8P5L6jOM1TF+VrKl/2FwI0R8dEWPalaeTdlBroP1yeXqnOtzMy1ipI3k5k/qU8uVevureJfSRmaVG8s93gOcFt9cqnaL+uHrPVYLf5zGodqZeYtkyxZMdK9/kdmXtOw7gf1yaVq3e2UIYqw9rOsuRb4QC3JWS1ridJ5wFtqyaVq+9mUXmvbVsPimnlHLblUHXMNZcgcwBtbHFPvAEqB/VMz8xP1zyoz/0YZ4rgBpSfXlGEPJkmSJEmaGi6lzC73zYj4EHDpKPVeTgfuBJ4YEdtkZm0Y27OBmcBvqy+zrZxU9/NGlGFAewAfoExl/5P6nTPzlIg4EziQUlNnd0oh8AdQEkYHR8TemblkpIuseigtqN5+eaR92xERD6Z8oX8EZXhcraPFcuDhDbuP5R5fCuwTEV+j1MxZ2GmsXXBptXxnRNwM/KhJ4nGyqMX6kYhYBfwyM+8Z6YCIeDhlmN8OwKaUZ1krgtb4LGvOrc12WOfaarmCMiyu0bWUGlxbUYbU1Vuamb9ocswpwJeAvSJi2ii/m7UeeKe12P6bavn4EdqYdEwwSZIkadwyh5nZMIhh5aqlLF/euxrMK1ZMtgnFpAnzX5ReDwdUr1sj4nzKcKiTG7+cZ+adEXE6pWfRfwIfrzbVejSNWNy72VT1EfEYSp2cMyNi5/oizLVzAt+qXkTEA4BXUJJSO1Dq5YzWK2OAUpNmaWbeOsq+I4qIt1GGRzUOt2plLPf4DZRi4YcDh1cJnfMoSYPvZ+aqNuJ7NyXxVe/KzPxYm/GuITPPjohPUXponQKsjIjLKHWCTsjMa0c6vsdOpCRaXgicBdwTERcDP6XEelNtx2pY23GU3kqtqupv3mL9PxtXZOYdpUluavGcaoXcN2qybXGzk2Tmsoi4DdgC2JI1h1M22q5afisivjXCfrNH2DbpmGCSJEnSuK0avo0z9lpz3VWL92nxz29JncjM6yPicZSZsJ5NqfNTS4QcGRFPyMzGL7XfpCSUXgJ8vJq16pmUnhv/N44Y/hAR/0upVfQ6GobKNdn/ZkotmbuAzwDPGus5xysi9gQ+CSwDjqD0VLmpNttYRPyL0kOlPt6273FmXhER8ygFo/en9Lp6YfW6KCIWZObyUcLcj7XrNZ1HSYqNS2a+rXpGzwWeBuxN6QlzZEQc0lhbql+qxM6LIuJjlFifQukhty/w7ojYr64A/Yso9bKupySZLgJuycwVETGDUhi8VeJppJ5Ebc/41mW1XnQ/pczw18qU+h8UE0ySJEmSNEVk5kpKAeCfw33TvJ9A+XL+LkrB4HpnU2ZDe1RE7EqZmWsGcGaTZFS7/l4tWw1JauacatlOj4xByqxvsyJii4Zi4mNxULV8b2bWD/cjIjYGHtjsoLHc46pH0xnVi4iYTyk6/QRKEesvjhRgZi4Y0xW1qepZdixwbFVz642UGfW+RJntbtLIzD9Qah4dHRH3p8zA91bg06weIlZ7lq/LzB81NPGwHoRZ7yHNVlaxb0H57N42Shs3VMuvTpaEXzdY5FuSJEmSpqjMXMzqoW87N9m+ijJUCkovppdUP484PG4UtS/0tWFEtSFMI9mhWq41XKlRFfO51dvXjDW4OltWyxuabHsBrXu8NMYz4j1u2Hch8IV29u2VzLwnM4+jJBrnRMTcfsfUSmb+G3gPZXa++vs30rN84UTH1WCgmmmu0Yur5UVtDI+s1XA6aMS9phh7MEmSJEmaSmafe25beYFe6kmdlIh4K3BKfW2ayv7V8nqa+yZleNHhlGnhb6PUvBlPDI9hddKnfia3r0bE3ym1c/7VcMxOlKFqUKZmb8fHKcPH3hsRF2fmr+ra2xB4ehszyV1dLV8ZET+uFXquhrV9vNkB7d7jiNiE0kPpG/U9rCJiWhX3ffv2UkQcSBkG+NuG9Y+lFFu/g9F71/RERLwU+EOTme2eSUn+1d+/q4H/AF4TEW+szQgXEfsC7+xFvA2Oi4in1XoBRsRDKXXGYHWCcSSnAouAl0TEVcCxtaGbVXsB7AX3zdA4JZhgkiRJUlftsPVZzH3IHn2NYfr0gb6eXxNqUb8D6KOjKF9s/wj8lfIlfFdgR2AppQjyWjLz8oj4M6t7hHyv/stsKxFxYt3bGZRZ5PakjIQ5izV7QQ1QEljHRMRCSkJgVXXM7tUxv2f1VO4jyszzIuJIyjCvcyLiEso1z6Zc80aU4Ugj+TpluvcDgKuqAtKzKDWPzqAMv9q24Zh27/EMSk2p4yLiUspMYzOqa92met/xDHjjsAA4IiL+SRl29m/gQZS6RtOAo9qoC9Urzwe+ERHXAH+iDC17KKUO0zDwvrp9PwscBrweWBARVwAPBvahJC/f0buw+S3lWf8tIs6hFJB/KrAJpRB8q5nh7pOZK6tk4M+ADwJvrK5pCeUz/mhgLmWooAkmSZIkrZ822GBLZsyY0+8wpHXRmyi9Yx5L6eUBpZfH8cDxmTnS8LNvsrrXzsltnu/ldT8PU3q+/Lpq68SGadjfAPwQeAYwj/KFezPgVuBXwPcovZsap4tvKTOPi4jfUb5k701J9AxSkhGnjHRsdfxQROxOue4nAc+h1I96PyVRdE2Tw9q9x3dU1/zUKq5dgOXAP4CvAp/PzKXtXmsXnQisBJ5ISaDNBG6i9Db7TGae3YeYWjmeMuRtb0oCbFPgX5Ti85/MzEtqO2bm1VXx9WMpCajnAFcB/y8zvxIRvUww3Uv5jHwEOJCSEPo78BVK3ai2ZOZfqx6BbwSeR0nebkh5Xn+gzFz43S7GPeGi6lm23omIhfPmzZu3cOHCfociSZI0Zd349wvKrHF1dtr2fLZ66N59iqh/JsO9mD9/PosWLVqUmfN7dtIJdO65MYfyP/pT0dwFC/KWfgchqTsiYjtKIum8iSrOPtVZ5FuSJEmSJEkdcYicJEmSpMlqiFKHZCoa6ncAktRLJpgkSZIkTUoLFuQw4DAzSZoCTDBJkiRJkiSNIDOvo8wqqBaswSRJkiRJkqSOmGCSJEmSJElSR0wwSZIkSZIkqSMmmCRJkiRJktQRE0ySJEmSJEnqiAkmSZIkSZIkdcQEkyRJkiRJkjqyYb8DUO8ND8PQUL+jWNvAAEwz5SlJkiRJ0pRjgmk9NDQEc+f2O4q1LVkCc+b0OwpJkiRNFhFMAwb6Hcc4DWUy3O8gJKlXTDBJkiRJmqwGgCX9DmKc5gK39DsISeqVjgckRcQmEXFgRHwtIq6KiHsi4s6I+GNEfCAiNhtje9dFRI7wekSnMUuSJEnSZFZ997luHMdNi4jrq+P3bWP/I6t9T204d+NrRUT8KyJOjYi9RmjveRHx44hYUh0zGBELI+KbEfGqiJgx1mtqR0ScW8W53US0PxlFxInVNS/ooI1m379XVc/vJxHxrFHOfViL7c+LiOURsTIiXtpk+yYR8eaI+HlE3BgR90bE7RGxqGr7ORGxwXiva10UEdtV9/zcfsfSSjd6MP0n8JXq578AZwL3B/YCjgEOiYgnZeZY/+fhpBbrl40rSkmSJElax2XmcER8GzgSOBT4zSiHHFotv9lkW/13ss2BXYHnAQdFxKGZ+e36nSPia8Dh1ds/AOcBAcyvznMo8EPgprYvSL1yKnBH9fMmwCOB/YD9IuI9mfmxdhuKiIOBUyjP/qWZeUrD9r2B7wFbAfcAFwP/AjYCtgdeXr0WUT47miK6kWBaAXwZ+HRm/qW2MiK2An4EPAb4NCUR1bbMPKwLsalNixbB7Nm9O9/gIMyb17vzSZIkSVPMIynftcbjm5QE0wsi4k2ZubzZThGxC/AoYCnw48btjd/JImIa8BHgXcBnI+J7mbmi2vY8SnJpGbB/Zl7YcOx2wCuBe8d5TVrbe4CPAf/oQlvvyMzr6ldExOuBLwAfjIivZ+bNozUSES8EvlW9/c/M/G7D9t2AsynJpE8A/52Z/27YZxvgbcBrx3kt6pOOE0yZeRJNehtl5o0R8QbgQuB5ETGj1R9s6r/Zsy2wLUmSpClhHjDY7yAazKb0tuiazLyyg2P/HBGXA48GngWc3mLXWu+l77bzXa3qHfUB4O2U+ljzgcurzc+vlp9vTC5Vx14HvL+9K1A7MvNG4MYJbP+LEfFuYBvgCcAZI+0fES8GTgaGgUMy89SG7dOq7RsB78/M/25x3uuBt0bEyR1fhHpqoieF/2O13IipO/uDJEmSpMljMJNbJtOLCUh4tarBFBF7RcQZEbG4qltzU0T8PiI+1lD/tvbl/CUt2g/gkOpts+FxTVWJqFrZkvoOC7X/ru5qYfOI2CMivhMR/6yu98aIODsiXt3m8ftGxOcj4oqIuDUi7o6IK6v7tUWLY9q9x0TE/hHxi7r4/hUR50fEUV24/NGurWkNpoiYU8W6KCLuiIhlEXF1RHwjIh4/xtPUSt2M2DklIv6T1cmlFzYmlyr7U3rm/QP46GgnzsxLxxJou88iIraIiDdFxM/qnvFQRPw0Iv6jRdv31fiKiBdFxMURcVd1rmOjqi0WEdtHxClRaljdFRG/qnoKNrZ3dNXeYdVn/GcRcVtE/Lu6hj3Hcu1Vm3tExPeq35HlEXFDRHw1Ih4y1rbGa6ITTA+rliso3S7bFhHvjIj/iYjPRMRrIsL+NZIkSZLWWxFxAKWm0nMoPVdOo9Q6mkUZtlZf9OLbwCrg2RExs0lzC4CtgWua9TgaIYaHUjoPrAD+Vrfp+mr5ssYkzHhFxBGUETEvYvX1/hnYmTK8qh2foAzNu5syNOtsSs3gdwHnN0kYtX2PqxE7PwKeTLkXp1bxbQscPcbL7YqI2Bz4HSXWzYBfAD8HbgVeTEnytNvW/YEdq7d/GWG/Q4FvUD5vz8/MM1rs+sxq+b3MXNVuHO0Y47PYE/gs5dquovTwuwp4OvCziDic1o6gJNJuA34KzADeCXwlIh4O/JbSc/CcKo4FwK8i4gEt2tsL+DXld/EnVRxPA86LiKe3cenAfcMZL6TUSFtM6W02RPnsXxIRj2y3rU50owbTSI6olj/NzLGOtT224f2noowfPqELcUmSJEnSVPMOSieBg5sMP9qd8oUSuK9kydmUL80vAL7a0FZteFxbw5CqRMyjgU9Vq76UmbfV7XICcBiwG3BtlFnpLgQuBf6SmdnOeerO98TqXHcAB2Xm2XXbNqRcVzuOAS7MzPsmi4qIjSgJhtdQav18sG7/tu8xpc5VAntm5iV1+wXwpDbj67aDgYdSJt86KDOH6+KaA7RKdFC338aUnkYfpRR3PzMzF7bY/UWUZ7ECeF5mrlXLq86u1fIPo8UwDmN5FlcBT8jM39avjIjHUBJDn4qI72bmHaztVdWxl1THPJAyTPSlwO6U37P/ysyszn1Ste31QLNeba8GPkwZMphVm68DvgicGBHbZ+bdI1141dvps5SE6HPre35FxCurmL5OSaxNqAnrwRQR+1OyZSsY21jbMylZt20p1et3Bo6nDLP7akQ8d4xxLGz2olSnlyRJkqSpojaq45eNGzLz4sy8vWF1bejbofUrI+J+rK6Z1DLBFHVT1wO3U3r27AS8CXhLw/kvqM4zVMX5WkqvloXAjRHx0RY9qVp5N2UWsg/XJ5eqc60cJZFRv+9P6pNL1bp7q/hXAo3fL8dyj+cAt9UnNKr9MjPPbSe+CVCL/5z65BJAZt6SmX9ucdzf6571XZTE4JMpvX9eOML59qPkFT7bxjOplc1pOqQ0Ir5WDfurf+0zSps1bT+LzPx7Y3KpWv8HSlHz+1OuvZlP158jM2+i9BYMSs7iA7VEUbU8rtq1VcJxMXB0fQI2M79E6YW2Fat/T0fybmAD4LWNwwoz82uUHMseVQJtQk1ID6aIeATlD6oA3pmZfxzlkPtk5psbVi0E3h4RV1Jmq/s48INuxSpJkiRJU8SllJ4l34yIDwGXNiYRGpwO3Ak8MSK2qYonAzwbmAn8NjP/1vLoNSdz2ojSCWAP4APANZQhPffJzFMi4kzgQOCplB4d8ym9Zt4NHBwRe2fmEkZQ9VBaUL398kj7tiMiHgwcADyCkjyodbRYDjy8Yfex3ONLgX0i4mvA8SP08umlWoLhnRFxM/CjJonHZk6l9BaDkifYmlLY+22U4Y+tRhJdAOxNKcp9fmaeOe7I4eWUREm9c4Hz2zh2TM8iIjagfEb3oiRyNqo2Pbxh2ejnTdZdW4u1Nqtik21btWjv1Mxc2WT9KZTftX0ZOQk8jXIddwE/a7Fbbcjn45mY3mP36XqCqfrl/SmwJeXBfqZLTX8N+G9gp4jYrnEKxVYyc36LOBdSZqCQJEmSpKngv4BHUZIlBwC3RsT5lB4KJ2fmPfU7Z+adEXE6pWfRf1L+sx5W92gasbh3Zh7WuK7qBXEecGZE7JyZVzWekzJN/beq/R8AvIKSlNqBMhxotALdA8DGwNLMvHWUfUcUEW8DPgZMb/OQsdzjN1Bq3RwOHF4ldM6j1G36fjt1hqLM0vaIhtVXZubH2ox3DZl5dkR8itJD6xRgZURcRqnFdEJmXtvi0Hc0fseOiO0o9YG+FhHXZeY5TY77KuX7/4eA70bEsxp7nNWpDS+c3WxjZt6Xn4iI/wH+X4t2mmn7WUTE1sAPWT1kr5nNW6z/Z5N1d7Talpl3lJFy9yWwGi1usf66avmgFttrZlNqbQEsr8410r4TqqtD5CJiFiWjty1ljN87utV2lTW+pnrbKvsnSZIkSeukqgfS44BnAJ+j9Cw5APgKcEVENJu5u5ZEegnc953tmZRSJv83jhj+APwvpbPC69rY/+YqWfLuatWzxnrO8apq03yS0rvjMGA74H6ZGZkZlJo1axjLPc7MKyidFg6qtv+bMpzsO8BvoppZbBT7UXru1L/2G/vVrnENb6P0wnoX8CtK2Zn3AldGRDtDrmrtXMfqpGTjSKP6/f6bUkN5I+AHEfGEFrvWRjZ1fajWGJ/FVynJpVMpvYS2ADaoPhO1pFarTM1IPQZH2jZRajmdOyg9Dkd6TXgPu671YKqKvv2E8lBPA1491kJubdiyWt7Z5XYlSZIkadKrhtP8vHoREdtShi89hZJQOLLhkLMpiZRHRcSulGFPMyiFm4cYn79Xy1bDiJqp9X5ppxfFIGXWt1kRsUVDMfGxOKhavjcz64f71QpZP7DZQWO5x1WPpjOqFxExn1KT5wmUgtBfHCnAzFwwpitqU9Wz7Fjg2Krm1hspM+p9iZJYaVdbzzoz31XNYPc64McR8eTMvLxht59Qil2/ICLe1e2Z5Np5FhGxKfAfwM3Ai5rE8LBuxtSGbUdZ/69Rjh8E7qEkt14xATmYMelKD6aqCv8PKGP6fgYc0u0PS/Xh2ImSfb6ym21LkiRJmjJmRzBnMr3owdCTVjJzMat7mezcZPsqylApKL2YXlL9POLwuFHUvoTfN8tWjDI2hzI8DpoPMVpDFfO51dvXjDW4OrUOCjc02fYCWvdSaYxnxHvcsO9CSqHoUfftlcy8JzOPoyQa50TE3DEcvtazHsEbKIXdtwB+XtVmrvdj4C/AQ4D3jCGGcWnxLGZS8iA3NuYsImI6q5OSvfK8qh5UoxdXyxHrT1XJ0HMptcWe2t3Qxq7jBFN1M06hZHN/Q5macPkox7wxIq6MiI82rN8/Ip7SZP9dgO9R/gD46mjtS5IkSVpnLQKWTLLXogm94kpEvLWaFr3R/tXy+ibbYHUy6XBKQebbgLPGGcNjWJ30qZ817KsR8b6IWKtmTETsRBmqBvD9Nk/1ccq08++NiDVm9IqIDatZy0dzdbV8ZZU8qB0/j9UJo8ZY27rHEbFJRLw5IrZoOH4aq4e4tXoeEyYiDqyGBjaufyyl2PodlOffTlvbsbq31qiz9lW9Zw6njGiaA/yyaqO2fRh4KXAv8KGIOLbZzILVMMSd2omx2n8sz2IJsAzYOSL2rtt3A8pnYsd2z9sl2wFH1a+IiNdQel3dTHu9zT5M6cH09YhY0LgxIjaLiMOrXnsTqhtD5N7I6izfIKXbWbP93pGZtekIZ1M+MI21lB4PHBURiynjM++iZEx3q2I9l9VjdyVJkiRpfXIUcFxE/BH4K+U/4HelfCleyuop0deQmZdHxJ9Z3Yvje5l572gni4gT697OoAzb2ZPSUeEs1uwFNUBJLhxTTah0NbCqOmb36pjfU4pBjyozz4uIIynDvM6JiEso1zybcs0bUXrKjOTrwNspNZSuioiLgVmUKePPoHz/bByi1O49ngF8ptr3UkpR5hnVtW5Tve94BrxxWAAcERH/pMwY9m9Koeh9Kc/gqBYdNo6LiGazyM2gPLdPNjlmLZm5KiIOoYxw2g84OyL2zcx/VdsvjYinUTqQvBN4c0T8jjIU7H7VeXelFGW/ErikjdO2/Swyc2VEHEtJypwXEedQnuselATcFyg9sXrlK8C7I+J5wBWUnn67U2qkHZaZd43WQGaeHxFvAD4P/Kr6Xb+6amM74NGU35fTKENPJ0w3Ekxb1v08UneyoykJqJH8jPIB2J2SWZ9J+YU4nzILwde7PfROkiRJkqaIN1G+tD+WUqgbSs+M4ykzeI80/OybrO6103La8wYvr/t5mNLz5ddVWydWPVJq3kCZmesZlLq8T6XMbnUrpdD09yizmDVO495SZh5XJR/eSvl+uCvlO+WfWD3sb6TjhyJid8p1P4kyVfvfgfdTEkXXNDms3Xt8R3XNT63i2gVYDvyDUkT685m5tN1r7aITgZXAEykJtJnATZQeSJ8ZYYa3+uLfCdwOXAZ8F/jCWEYRZebyKmHyE8p9/0VEPKnW4aRKiGxPqYv0HEri8wmUnk3/rM75feCH1RCw0YzpWWTmRyLiBspMe3tTki7nU2Y63K3d6+ySCymJ0A8Bz6YkNM8GPpCZF7bbSGb+T0T8lnJNC6q27qLcz29RkkvLuhl4M9HnGlB9ExEL582bN2/hwgkvpD7p3HILzG0YdbtkCcyZs37FIEmSOnfj3y/gqsX7rLFup23PZ6uH7t3iiHXXZLgX8+fPZ9GiRYsyc37PTjqBqvpGS/odxzjNzeSWfgchafKJiKMpveVekZkn9jea7unaLHKSJEmS1GVDwFgKEk8m452hTZKmJBNMkiRJkialTIbBXkCSNBV0PIucJEmSJEmS1m8mmCRJkiRJknokM4/OzFiX6i+BCSZJkiRJkiR1yASTJEmSJEmSOmKCSZIkSZIkSR0xwSRJkiRJkqSOmGCSJEmSJElSR0wwSZIkSZIkqSMmmCRJkiRJktQRE0ySJEmSJEnqiAkmSZIkSZIkdcQEkyRJkiRJkjpigkmSJEmSJEkdMcEkSZIkSZKkjmzY7wCkmsHBfkdQDAzANFOvkiRJkiS1zQSTJo158/odQbFkCcyZ0+8oJEmSJEmaOuynIUmSJEmSpI6YYJIkSZIkSVJHTDBJkiRJkiSpI9ZgUl8MDJRaR/02ODh5aj9JkiRJkjRVmWBSX0ybZiFtSZIkSZLWFQ6RkyRJkiRJUkdMMEmSJEmSJKkjDpHroeFhGBrqdxSl7pAkSZIkSVK3mGDqoaEhmDu331FIkiRJkiR1l0PkJEmSJEmS1BETTJIkSZIkSeqICSZJkiRJkiR1xBpMfbZoEcye3e8oYGCg3xFIkiRJkqSpygRTn82eDXPm9DsKSZIkSZKk8XOInCRJkiRJkjpigkmSJEmSJEkdMcEkSZIkSZKkjphgkiRJkiRJUkdMMEmSJEmSJKkjJpgkSZIkSZLUERNMkiRJkiRJ6ogJJkmSJEmSJHXEBJMkSZIkSZI6YoJJkiRJkiRJHTHBJEmSJEmSpI6YYJIkSZIkSVJHTDBJkiRJkiSpIyaYJEmSJEmS1BETTJIkSZIkSeqICSZJkiRJkiR1xASTJEmSJEmSOmKCSZIkSZIkSR3pOMEUEZtExIER8bWIuCoi7omIOyPijxHxgYjYbBxtbhkRn4mIxRFxb7X8dERs0Wm8kiRJkiRJ6q5u9GD6T+B04HBgFXAm8BvgocAxwMURMbfdxiJiNvB74M3ASuAM4HbgCOB3ETGrCzFLkiRJkiSpS7qRYFoBfBmYl5nzMvOFmbkfsBPwB+ARwKfH0N6ngR2A04CdMvNFmbkz8DlgR+D4LsQsSZIkSZKkLuk4wZSZJ2Xm/8vMvzSsvxF4Q/X2eRExY7S2ImIr4BBgOfD6zFxZt/mdwC3AoWPpESVJkiRJkqSJNdFFvv9YLTcCBtrYfz9KTL/JzJvrN2TmvcBZwAbA/t0MUpIkSZIkSeM30Qmmh1XLFcDSNvbftVpe1mJ7bf0unQQlSZIkSZKk7pnoBNMR1fKnVQ+k0TykWt7QYntt/bYdRSVJkiRJkqSu2XCiGo6I/YFXUnovvb/Nwzarlne12H5ntdx8DHEsbLFp+3bbkCRJkiRJUmsT0oMpIh4BnAwE8M7M/OMoh0iSJEmSJGmK6noPpoh4MPBTYEvg+Mz8zBgOv6NabtJi+6bV8vZ2G8zM+c3WVz2b5rUfmiRJkiRJkprpag+miJgF/JxSI+nrwDvG2MQ/quXWLbbX1i8ee3SSJEmSJEmaCF1LMEXEZsBPKL2CTgNenZk5xmZqQ+l2a7G9tv6KsUcoSZIkSZKkidCVBFNEbAT8AHg88DPgkMxcNY6mfgoMA/tGxNwm5zgAWAX8uLOIJUmSJEmS1C0dJ5giYgPgFOApwG+A52Xm8lGOeWNEXBkRH61fn5k3Vm3NAL4YEfU1oo4F5gAnZ+aSTuOWJEmSJElSd3SjyPcbgYOqnwcpiaFm+70jMwern2cDOwFbNdnvLcCewPOBKyPiEmA+sDPwV+BtXYhZkiRJkiRJXdKNBNOWdT8f1HIvOJqSgBpRZg5GxOOr/Q+s2rwZ+CxwVGbeNs44JUmSJEmSNAE6TjBl5tGUZFDXjsnMpcCbq5ckSZIkSZImsa7NIidJkiRJkqT1kwkmSZIkSZIkdcQEkyRJkiRJkjpigkmSJEmSJEkdMcEkSZIkSZKkjnQ8i5wkSZJ6L3OYFSuG+h0Gq1bd2u8QJEnSJGCCSZIkaQpasWKICy+c2+8wJEmSAIfISZIkSZIkqUMmmCRJkiRJktQRE0ySJEmSJEnqiDWYpAaDg/2OAAYGYJrpX0nSGO2++yKmT5/d03MO/fNqHnnKPmusu/qhW/Q0BkmS1H8mmKQG8+b1OwJYsgTmzOl3FJKkqWb69NnMmNHbv0A23GCQZSvWXBfh/5JIkrS+8W9/SZIkSZIkdcQEkyRJkiRJkjpigkmSJEmSJEkdsQaT1msDA6XeUT8NDk6Ouk+SJEmSJI2XCSat16ZNs5i2JEmSJEmdcoicJEmSJEmSOmKCSZIkSZIkSR1xiJwkSdIUNLxq5Vrrhv55NRtuMNjTOAZvvKan55MkSZOTCSZJkqQp6Nabrl1r3SNP2YdlK/oQjCRJWu85RE6SJEmSJEkdMcEkSZIkSZKkjphgkiRJkiRJUkeswSRJkrSOuOApJzP3Qbv1OwwGHvzwfocgSZJ6zASTJEnSOmLWA7ZjzkMe2e8wJEnSesghcpIkSZIkSeqICSZJkiRJkiR1xASTJEmSJEmSOmKCSZIkSZIkSR0xwSRJkiRJkqSOmGCSJEmSJElSR0wwSZIkSZIkqSMmmCRJkiRJktQRE0ySJEmSJEnqiAkmSZIkSZIkdWTDfgcgaW2Dg/2OAAYGYJopaEmSJElSG0wwSZPQvHn9jgCWLIE5c/odhSRJkiRpKrB/giRJkiRJkjpigkmSJEmSJEkdMcEkSZIkSZKkjliDSeqzgYFS76ifBgcnR90nSZIkSdLUZIJJ6rNp0yymLUmSJEma2hwiJ0mSJEmSpI6YYJIkSZIkSVJHTDBJkiRJkiSpIyaYJEmSJEmS1BETTJIkSZIkSeqICSZJkiRJkiR1xASTJEmSJEmSOtKVBFNEPDYi3h0Rp0XEDRGREZHjbOu62vEtXo/oRsySJEmSJEnqjg271M77ged2qa2ak1qsX9bl80iSJEmSJKkD3UowXQRcAVxcva4DNuqkwcw8rOOoJEmSJEmSNOG6kmDKzI/Xv4+IbjQrSZIkSZKkKcAi35IkSZIkSepIt4bIdV1EvBPYHrgXWAicnpm39DcqSZIkSZIkNZq0CSbg2Ib3n4qIN2XmCX2JRpIkSZIkSU1NxgTTmcCvgEuBW4CHAYcDRwBfjYihzPxBu41FxMIWm7bvNFBJkiRJkiRNwgRTZr65YdVC4O0RcSXwZeDjQNsJJkmSJEmSJE2sSZdgGsHXgP8GdoqI7TLzunYOysz5zdZXPZvmdS88SZIkSZKk9dOUmUUuM4eBa6q3W/UzFkmSJEmSJK02lXowAWxZLe/saxSSJGn9NjwMQ0P9jWHprf09vyRJUp0pk2CKiPnATsBdwJV9DkeSJK3PhoZg7tz+xvBA4JT+hiBJklTTlyFyEfHGiLgyIj7asH7/iHhKk/13Ab4HBPDVzFzeo1AlSZIkSZI0iq70YIqIZwHvr1s1o1r/27p1H8rMH1U/z6b0RmqspfR44KiIWAz8kdJb6WHAblWs5wLv7kbMkiRJkiRJ6o5uDZGbA+zRZP0eDfuM5mfANsDuwN7ATODfwPnAt4CvZ+aqzkKVJEmSJElSN3UlwZSZJwInjmH/o4Gjm6y/CLioGzFJkiT11KJFMHt27853x9WweJ81183confnlyRJqjNlinxLkiRNarNnw5x2Omx3yfRBWNywblpfymtKkiT1p8i3JEmSJEmS1h0mmCRJkiRJktQRE0ySJEmSJEnqiDWYJEmSxihzmBUzG1auHITlvYth1cqlvTuZJEnSKEwwSZIkjdGKVUu58IyGlVfN60cokiRJk4JD5CRJkiRJktQRE0ySJEmSJEnqiAkmSZKkMRrO4X6HIEmSNKlYg0mSJGmMbr33trXWvfxiWLai97HUu/pxs/obgCRJWm+ZYJIkSeqCZSv6n2CKsHO6JEnqD/8VIkmSJEmSpI6YYJIkSZIkSVJHHCInSZLUBRcceBZzt92jrzEMbDLQ1/NLkqT1lwkmSU0NDvY7gmJgAKbZ11LSFDBr4y2Zs+mcfochSZLUFyaYJDU1b16/IyiWLIE5fl+TJEmSpEnNfgGSJEmSJEnqiAkmSZIkSZIkdcQEkyRJkiRJkjpiDSZJDAyUWkf9Njg4eWo/SZIkSZLaZ4JJEtOmWUhbkiRJkjR+DpGTJEmSJElSR0wwSZIkSZIkqSMmmCRJkiRJktQRE0ySJEmSJEnqiAkmSZIkSZIkdcQEkyRJkiRJkjpigkmSJEmSJEkdMcEkSZIkSZKkjphgkiRJkiRJUkdMMEmSJEmSJKkjJpgkSZIkSZLUERNMkiRJkiRJ6ogJJkmSJEmSJHXEBJMkSZIkSZI6YoJJkiRJkiRJHdmw3wFIkiSNxXAOM3TXUF9jWHr3rX09vyRJ0mRjgkmSJE0pQ3cNMfe4uX2NYeZ0OGOvvoYgSZI0qThETpIkSZIkSR0xwSRJkiRJkqSOmGCSJEmSJElSR6zBJEmSprxFr1/E7E1m9+x8K5dczVWL91lj3ZYbbdGz80uSJE02JpgkSdKUkjnMzOlrrttiOmutm0grmpxrWtgxXJIkrb9MMEmSpCll1cqla83gdtXl8/oTjCRJkgBrMEmSJEmSJKlDJpgkSZIkSZLUERNMkiRJkiRJ6og1mCRJ0pS3w87nM/f+O/buhEOD8Mg16z5Nv3pW784vSZI0yZhgkiRJU94GG85ixow5vTvhhsCyhnXOIidJktZjJpgkSdLUMjy89rpbl8KKW3oXw+Bg784lSZI0BXQlwRQRjwX+A3h89XowQGbGONvbEjgaOBB4IHATcDpwdGbe1nHAkiRp6lp229rr9tqn/GtBkiRJfdGtHkzvB57bjYYiYjZwEbADcC1wBjAfOAJ4ZkQ8ITOXduNckiRJkiRJ6ly3igVcBHwIeA6wFXBvB219mpJcOg3YKTNflJk7A58DdgSO7yxUSZIkSZIkdVNXejBl5sfr30eMa2QcEbEVcAiwHHh9Zq6s2/xO4MXAoRFxZGYuGWe4kqaQyVDmZGAAplm7V2I4hxm6a6jfYbD07lv7HYIkSZIaTLYi3/tRelX9JjNvrt+QmfdGxFnA4cD+wIm9D09Sr82bN/o+E23JEpjTw8mppMlq6K4h5h43t99hMHM6nLFXw8ofngUP2aMv8dxnYKC/55ckSeqjyZZg2rVaXtZi+2WUBNMuvQlHkiRNCbO2NBMsSZLUR5MtwfSQanlDi+219du222BELGyxaft225AkSZIkSVJrk62qyGbV8q4W2++slpv3IBZJkiRJkiS1YbL1YOq6zJzfbH3Vs2kSVHeRVDMwUOod9dPg4OSo+yRNFYtev4jZm8zu6TlXLrmaqxbvs8a6LTfaoqcxSJIkaU2TLcF0R7XcpMX2Tavl7T2IRVKPTZtmCRVpqpm9yWzmbNrbX9zl91t7eslpMdk6ZUuSJK1fJtu/xv5RLbdusb22fnEPYpEkSZIkSVIbJluC6Y/VcrcW22vrr+hBLJIkSZIkSWrDZEsw/RQYBvaNiLn1GyJiI+AAYBXw4z7EJkmSJEmSpCb6UoMpIt4IvBE4PTPfU1ufmTdGxCnAS4AvRsSLM3NltflYYA5wUmb2uQywJEnroeHhtdcNDbae+3WiLF3a4xNKkiRpNF1JMEXEs4D3162aUa3/bd26D2Xmj6qfZwM7AVs1ae4twJ7A84ErI+ISYD6wM/BX4G3diFmSJI3RrU0SO4+c1/sE00zgjB6fU5IkSSPqVg+mOcAeTdbv0bDPqDJzMCIeDxwNHAgcBNwMfBY4KjNv6yRQSZI0PpnDzJy+5rqV94fl05vvP1FWzOzt+SRJkjS6riSYMvNE4MQx7H80JYHUavtS4M3VS5IkTQKrhm/jjL3WXHfVXs33lSRJ0vplshX5liRJkiRJ0hRjgkmSJE19W87qdwSSJEnrNRNMkiRp6pvmP2kkSZL6qVtFviVJ0gQazmGG7hrqawxL7751rXU7bH0Wcx/SbJ6P3po+faDfIUiSJK3XTDBJkjQFDN01xNzj5vY1hpnTWavI9wYbbMmMGW1NFCtJkqR1mAkmSRrF4GC/I4CBAUcASZIkSZq8TDBJ0ijmzet3BLBkCcyxk4gkSZKkScr/D5ckSZIkSVJH7MEkSdIUkDnMzOlrrrvgFecza+NZPYth1eA1/O2GA9ZYt+VGW/Ts/JIkSZq8TDBJkjQFrFq5dK0C27dctQ+39Cec+0wLO0NLkiTJBJMkrWFgoNQ76qfBwclR90l1hodhaKi/MdyxtL/nlyRJkkZggkmS6kybZjFtNTE0BHPn9jeGBwKn9DcESZIkqRX7tUuSJEmSJKkj9mCSJGmK2mHrs5j7kD16d8KhQXjkmuM3p1/duyLjkiRJmrxMMEmSNEVtsAxmLOvhCW8DGs9nkW9JkiRhgkmSpPFZtAhmz+7d+f7xO7j9gDXXPfsAuKl3IUiSJEmtmGCSJGkUwznM0CYNKzcFGtdNoKUbA7f37nySJEnSWJhgkqQpYHCw3xEUAwNlpr1eGc5hhu4a6t0JW1iy9Cr2fm/DypPnNd13osycDift3tNTSpIkSW0zwSRJU8C83uYyWlqyBObM6d35hu4aYu5xc3t3whZmTocz9up3FJIkSdLkZYJJkqSp6sLzYbMd+xvDwEB/zy9JkqRJwQSTJElT1ZazYIsedimTJEmSWnBuYUmSpqgtN57V7xAkSZIkwB5MkjTpDAyUWkf9Njg4eWo/1Vv0+kXM3mR2T8+5csnVXLV4nzXWPW77XzPjAY/oaRyNpk93eJokSZImBxNMklQnc5gVK/o/a9nMmWu+nz59gIj1sNPp8PBaq2bfDb0eFLb8rrXXzdhwNjNmODxNkiRJAhNMkrSGFSuGuPDC/s9a1mivvZasn8mMW5euve6R86BJwmdCzQTO6PE5JUmSpCnEBJMkqanMYRpL9S1fPsjy5dmzGFauWsrM6Q3r7g/Lpzfff6KsmDn6PpIkSdL6zASTJNUZzrWHZE0G/YhrxYqlwJq1ji6++JFsscVgT+M4Y68131+1V/P9JEmSJPXPeljQQ5Jau/XuJkOyJoHJGpckSZIkgT2YJGlUL78Ylq3o3flmToeTdu/d+VpqUmBbdbac1e8IJEmSpEnDBJMkjWLZit4mmCaNZbcBk6/g+aQxzU7AkiRJUo0JJkmaAlatXMry5bf09JwrVt261rpHvxEedEfvYhjaGB75pjXX/eWQ8xl48I69C6KF6dMH+h2CJEmSNGmYYJKkUVzwivOZe//eJTSW/PtqbrlqnzXW/e3P+/C3nkVQ3HbbbGDNpNYdPe7Nddv0tc+34QazmDFjTu+CkCRJkjQqE0ySGM5hhu4a6ncYk8LSO9eeIW3WPTCnh39arrynMa0zeez1Krh9Rr+jkCRJkjTZmGCSxNBdQ8w9rv+1dgK4//T+xtC0wPZe+8BNPQzigcApPTzfVGNxbUmSJGnSMcEkadK4/3Q4Y69+R6FJz+LakiRJ0qRjgkmSJpkNbocDL+x3FDB859rrbl8BOEROkiRJUgMTTFK/DQ/DUJ/rH929dt0h9U9kbwtpt9QkhvMP+iE77vKw3sdSZ2ATZ2+TJEmSJhsTTFK/DQ3B3D7XP9oEOHLNVYtefD6zt+7tVPArl1zNVYv3GX3HHtvyF7+GBzyidye84Wo4c/LdBwDumg139XkGt40pBbskSZIkTRommLRemwyzp+VdS1j1wDXXbXkPTMvexbBy41Lcut7A/bZgzqa9TSQsv9/aPal2fzlMX9bTMNYy/eodYU4P78Uk7lG2z9O273cILFnS28chSZIkaXQmmLRemwyzp82cDmdMghnDzmh4v2r4tj5Esbbpy2BGnxNMRG+LSg/cbxZLjm1Y+ZdFMDC7p3EMXn0r8z7R01NOWpNhJGszAwPWPJckSdLkYIJJUlMrh/7O8vv1djr4FYPXrL3ygvNhbm+H6q1loLc1f6bFNObc1bBy49nQ4x5l3M9xaDWTYSRrM/bmkiRJ0mRhgklSU9fc8VKuuarfUQCzZvkNGmCwD8Pmlt4K9LbXlCRJkqSpyQSTJE0F8+b1/JQDBEto6L216C8wu3dJp8HBvly6JEmSpDEywSQ1WPT6RczepHdfoJcs/h23LDlgjXUvv7i309RvMR1O3L1359PUMI1kDg09p2Yn9LlDWT86c/XjnJIkSdJUYoJJajB7k9k9nT1t5cZbckvDumUreptgkqaSydKjadGinnbmsjeXJEmSJjUTTJL49wo48MI11/3lkPMZ6HGR72amz314v0OQmpo92/JgkiRJUo0JJkkka/eY2nDujszo9axlKgYGyvRgk1GPZ9STJEmSNDWYYNL6bXh47XVDg9A4RfwE2vLOtWP4yyHns+HcHXsXRBMDm5hI6Jtp0+waI0mSJGlKMcGk9dutS9de98h5PU0wTZsJnLHmuoH7zbL3kISduSRJkqSpwgSTJGnSsjOXJEmSNDVM61ZDEbFxRHwwIq6OiHsi4l8RcUJEPHiM7VwXETnC6xHdilmSJEmSJEmd60oPpoi4H3AOsCdwI/ADYDvgFcCzI2LPzLx2jM2e1GL9svHGKUmSJEmSpO7r1hC591GSSxcBT8/MOwAi4m3AJ4ETgAVjaTAzD+tSbNLYnH8+bN3DAtsrB+GqeWuu23JW784vSZIkSVKHOk4wRcQM4I3V2zfUkksAmXl8RLwceFJEPDYzL+30fNKEG5jV26Ivy5usm9a10auSJEmSJE24bnyL3RuYCVyTmX9osv371fKALpxLkiRJkiRJk0w3hsjtWi0va7G9tn6XsTQaEe8EtgfuBRYCp2fmLeOKUGohc5iZ09dct3LVUpYv791HbcWKwZ6dS9K6ZdA/Pu4zMGDnT0mSpH7qRoLpIdXyhhbba+u3HWO7xza8/1REvCkzTxhLIxGxsMWm7ccYj9ZBq4Zv44y91lx31eJ9YHF/4pGksZg3b/R91hdLlvR2dLMkSZLW1I0E02bV8q4W2++slpu32d6ZwK+AS4FbgIcBhwNHAF+NiKHM/ME4Y9UkkTnMihVD/Q6DlauW9jsESZIkSZKmvG7NItc1mfnmhlULgbdHxJXAl4GPA20nmDJzfrP1Vc8m/++3T1bcewsX/vaB/Q5DkiRJkiR1QTeqFdRmjdukxfZNq+XtHZ7na8ASYKeI2K7DttRvt9pzSJIkSZKkdUU3ejD9o1pu3WJ7bX1HVW0yczgirgHmAlsB13XSntTK9lufyQMesmdfY5g+faCv55c0+QwMlDpDKsXNrT8lSZI0uXQjwfTHarlbi+219Vd04VxbVss7R9xL6sCGG8xixgwrxUqaXKZNs4i1JEmSJq9uJJguAJYB20fEozPz8obtB1fLszo5SUTMB3aiFBO/spO21H/DObzWuh1eCxss620cSzeBvV+55rqrH7pFb4OQJEmSJGmK6zjBlJnLI+LzwHuBL0TE0zPzToCIeBuwC3BeZl5aOyYi3gi8ETg9M99Tt35/4J7MPKf+HBGxC/AdIICvZubyTuNWf916721rrdv5ZbBsRe9joeGcMWt2H4KQJEmSJGnq6tYscv8NPA3YC/hrRPwG2BbYA7gFOLxh/9mU3khbNax/PHBURCymDL27C3gYZZjdhsC5wLu7FLPU3LRu1L6XJPXS4GC/Iyh1svwrRJIkra+6kmDKzHsi4snAe4D/BA4ElgInAu/PzBvabOpnwDbA7sDewEzg38D5wLeAr2fmqm7EvF4bHoahof7GsPTW/p5fkrROmQxFv5cssU6WJElaf3WrBxOZeTfwgeo12r5HA0c3WX8RcFG3YlILQ0Mwd25/Y3ggcEp/Q5AkSZIkSd3RtQSTpo7hHGZok/7GsLTJ+S848CzmbrtH74NpMLDJQL9DkCRJkiRpSjHBtB4aumcpc4/sbwwzp8MZDetmbbwlczZ1bIEkSZIkSVONCSZJkjSlDAyUekf9NDg4Oeo+SZIkTRYmmDR5zNyi3xFIkqaAadMspi1JkjTZmGDS5OHczpKkKWxwsN8RlN5d/nUqSZL6wQSTAFj04vOZvfWOPTvfyhWDXHX5mmMLttx4Vs/OL0lSt02GIXNLlti7S5Ik9YcJJgEwe+NZPS2wvXz52uumhf/lKklSJ265pd8RFPakkiRp/WOCSZIkaR0xf36/IygWLux/TyqTXJIk9ZYJJkmSJHXVZEh0OVxQkqTeMsEkSZI0RgMDJYHRT7fcMjkSOZIkSWCCab2UOczM6WuuW7lqKcuX965ww4oVk2CqHUmSxmnaNHvHSJIk1TPBtB5aNXwbZ+y15rqrFu8Di/sTjyRJGrvJ0IsK7EklSZIKE0ySJElT0GTpRTUZEl2DgzBvXn9jkCRpfWeCSZIkSeM2WRJdkiSpv5y8VZIkSZIkSR2xB5MA2GHrs5j7kD36GsP06QN9Pb8kSZIkSRofE0wCYIMNtmTGDPu3S5IkSZKksXOInCRJkiRJkjpigkmSJEmSJEkdMcEkSZIkSZKkjphgkiRJkiRJUkdMMEmSJEmSJKkjJpgkSZIkSZLUERNMkiRJkiRJ6ogJJkmSJEmSJHXEBJMkSZIkSZI6YoJJkiRJkiRJHTHBJEmSJEmSpI6YYJIkSZIkSVJHNux3AOuV4WHWyukNDgLZ2ziW3trb80mSJEmSpHWaCaZeWroUmL3munmPBAZ7G8cDgVN6e0pJkiRJkrTucoicJEmSJEmSOmKCSZIkSZIkSR0xwSRJkiRJkqSOWIOp386/AHbcsrfnvONqWLzPmutmbtHbGCRJkibQYI9LXAIMDW3IbbetWW9zeJvexyFJUj+YYOqh4Rxea93gJsAmvY1j6comK6fZmU2SJK075s3rx1n3AG5ZY82fLv4dD+5HKJIk9ZgJph4aumcpMHeNdXt8f2+mbdrb/2KbOR1O2r2np5QkSZIkSeswE0w9NDy8bK1139gdttii97FIkiRJkiR1i+OiJEmSJEmS1BF7MEmSJGlKGxiAJUv6HQX85fLLeNLTd+t3GJIk9YUJJgGw5caz+h2CJEnSuEybBnPm9DsKuHnLFf0OQZKkvjHB1GcP3epbPPIxj+l3GEyfPtDvECRJkiRJ0hRlgqnPNthgJjNmTIL/cpMkSZIkSRoni3xLkiRJkiSpIyaYJEmSJEmS1BETTJIkSZIkSeqICSZJkiRJkiR1xASTJEmSJEmSOmKCSZIkSZIkSR0xwSRJkiRJkqSOdC3BFBEbR8QHI+LqiLgnIv4VESdExIPH0daWEfGZiFgcEfdWy09HxBbdileSJEmSJEnd0ZUEU0TcDzgHeD+wGfAD4HrgFcAfIuJhY2hrNvB74M3ASuAM4HbgCOB3ETGrGzFLkiRJkiSpO7rVg+l9wJ7ARcCOmfmizNwDeDswBzhhDG19GtgBOA3YqWprZ+BzwI7A8V2KWZIkSZIkSV3QcYIpImYAb6zeviEz76hty8zjgSuAJ0XEY9toayvgEGA58PrMXFm3+Z3ALcChETG307glSZIkSZLUHd3owbQ3MBO4JjP/0GT796vlAW20tV8V028y8+b6DZl5L3AWsAGw//jDlSRJkiRJUjd1I8G0a7W8rMX22vpdetyWJEmSJEmSemDDLrTxkGp5Q4vttfXb9rgtACJiYYtNj7jmmmuYP39+u0117N677wY2XmPdgS+8m4023rj5AZIkSZoyJsO/9a655hqAbXp2QkmSKt1IMG1WLe9qsf3Oarl5j9sazfC9995756JFi67vQltjtX21vOaav/fh7Oqn+559X6NQP/js118++/WXz3791c9/621D639LS5I0YbqRYJrUMrN3XZTaVOtVNRlj08Ty2a+/fPbrL5/9+stnv/7y2UuS1kfdqMFUmzVukxbbN62Wt/e4LUmSJEmSJPVANxJM/6iWW7fYXlu/uMdtSZIkSZIkqQe6kWD6Y7XcrcX22voretyWJEmSJEmSeqAbCaYLgGXA9hHx6CbbD66WZ7XR1k+BYWDfiJhbvyEiNgIOAFYBPx53tJIkSZIkSeqqjhNMmbkc+Hz19gsRUauTRES8DdgFOC8zL61b/8aIuDIiPtrQ1o3AKcAM4IsRUV+E/FhgDnByZi7pNG5JkiRJkiR1R2Rm541E3A84F9gDuBH4DbBt9f4WYM/MvLZu/6OBo4CTMvOwhrZmA7+lTO96DXAJMB/YGfhr1dbSjoOWJEmSJElSV3RjiByZeQ/wZOBDwF3AgZQE04nAbvXJpTbaGgQeD3yO0pPpIGAm8Fng8SaXJEmSJEmSJpeu9GCSJEmSJEnS+qsrPZgkSZIkSZK0/jLBJEmSJEmSpI6YYJIkSZIkSVJHTDBJkiRJkiSpIyaYJEmSJEmS1BETTF0QERtHxAcj4uqIuCci/hURJ0TEg8fR1pYR8ZmIWBwR91bLT0fEFhMQujrUjWcfEVtExH9GxCkR8feIWB4Rt0fE7yLiiIiYPpHXoPHp5u99Q7sPj4i7IyIj4pfdilfd0+1nHxHbRcT/VL//90bEYERcFBHv7Hbs6kyX/77/j4j4UUTcEhErImIoIn4eEQdNROwan4h4bES8OyJOi4gbqj+bxz0Fs//OkyStyyJz3H9HCoiI+wG/AvYEbgR+A2wHPB64BdgzM69ts63ZwEXADsC1wCXA/Op1NfCEzFza5UvQOHXr2UfEfwPvBRK4nPKs5wB7AxsB5wPPyMy7un4RGpdu/t43aftXwJOAAM7OzKd1I2Z1R7effUQ8E/g+sDFwGfBXYAB4FHBnZu7Qzfg1fl3++/4twKcof+5fBFwPbAM8gfK7/5HMfG93r0DjERFnAM9tXJ+ZMY62/HeeJGmdZg+mzr2P8o/Ni4AdM/NFmbkH8HZKkuCEMbT1aco/Ok4Ddqra2hn4HLAjcHw3A1fHuvXs7wSOBbbLzN0y88WZ+VTKF8x/APtU59Lk0c3f+/tExCuBBcBXuhSnuq9rzz4iHkH58/5OYJ/MfFxmHpKZTwceDLy469GrE1159hExB/gYsAJ4cmbuXf25vzfl9/9e4D0R8bAJuAaN3UXAh4DnAFtRns94fRr/nSdJWofZg6kDETEDWALMBHbLzD80bP8jsAvwuMy8dJS2tgJuAFYCD8nMm+u2bUT5381ZwIMyc0lXL0Rj1s1nP8p5DgG+DVyXmQ/tIGR1yUQ9+4h4APAXyv9of4TSU8IeTJNIt599RPwYeCbwrMz88QSErC7p8t/3zwbOAn6Wmfs12f4DSjLjRZn53S5dgrokIu4BNhprDyb/nSdJWh/Yg6kze1P+sXlN4z82K9+vlge00dZ+lOfxm/p/dABk5r2Uf4xuAOw//nDVRd189iP5Y7V8UIftqHsm6tl/hjJM6vUdxKaJ1bVnHxHbAM8ArjW5NCV08/e+3R4wQ23up6nBf+dJktZ5Jpg6s2u1vKzF9tr6XXrcliZer55XbYjETR22o+7p+rOPiP2BF1Hqrvytg9g0sbr57BdQ/g6+MCI2jIgXVoV/Px8Rr42ILTuMVd3VzWf/e+A24CkR8aT6DRHxREri8a+UGk9ad/jvPEnSOm/DfgcwxT2kWt7QYntt/bY9bksTr1fP64hq+YMO21H3dPXZR8SmwBeBq4CPdxaaJlg3n/28ankHJZGwZ8P2D0fEwZn5q7GFqAnStWefmcuqemvfBn4VERdWx28N7AVcALwsM5d3FrImGf+dJ0la59mDqTObVctWs3vdWS0373FbmngT/rwi4rXA0yj/0/2x8bajruv2s/9vyheK1/qFctLr5rOv9VB6FfAI4D8p9Vd2Ak6ufj49Ih48vlDVZV39vc/M0yj1t4Yow+9eVC1vB34O/HPckWqy8t95kqR1ngkmaRKKiH0pNXkSODwz/9XnkDQBIuJxwJuBb2TmuX0OR71V+/t3Q+D/ZeYpmXlrZl6dmS8FLqbU/LEm1zooIt4O/BL4NWVI1GbV8hzgg5RZxiRJkqYUE0yduaNabtJi+6bV8vYet6WJN2HPKyJ2pgyJmwEckZmnjz08TaCuPPuI2BD4CqWH2ju6Epkm2kT8mX8H8L0m279eLZ/UZJt6r2vPPiIWAMcBlwMvyMw/Zeadmfkn4OBq/bMi4pkdxKvJx3/nSZLWedZg6sw/quXWLbbX1i/ucVuaeBPyvCLioZThEVsCR2fm58YXniZQt5791sCjKQXcvxexxozXW1TLx0bEuQCZuWBsYWoCdPP3vrbPPzIzm2y/rlrObS80TbBuPvuXVsvTM3O4fkNmroqI0yh/NjwR+MkY49Tk5b/zJEnrPBNMnalNIb9bi+219Vf0uC1NvK4/r4jYCvgFsBXwmcw8ZvzhaQJ1+9k/sHo1swX2YJlMuvnsa1Pdt5otbla1vKPFdvVWN599LZGwrMX22npnEly3+O88SdI6zyFynbmA8g/B7SPi0U22H1wtz2qjrZ8Cw8C+EbHG/1hHxP9v7/6j5aqqA45/N7CCUsQqUEItJVE0WkEWFPkZBCtLSSCCaESE1vBDkbZYYFFjtSouBG1VlgtRKgZNTZAWiPyObVpUDFgEAWHJavilQWj4jSACxoTs/nHOkMll5r15b+YlL/D9rDXrvrn33HvOzH3vZbLfPvtsDMwAngUWjnq0GqRB3nvqkuT/CbyGMjXmxAGMUWNjIPc+M5dmZnR6AG+tza5q26d1b5A/9z+mFHieGBFTOhxvBRZv7nBMa98g7/0DdbtLl+NvrtulvQ5O6wU/50mSXvAMMPWhrvh0Vn361brcOAARcRKlYOfVmXlj2/6/jYglEfG5xrXuB86n1N35Wq3P0vLPwJbA/Mx8aGxejUZikPc+IjYBrgR2AC4APthlyozGgUHee61fBvw7fyVwBhD1Wpu1nbMfMItS5P/rY/RyNAID/rm/pG4Pj4gD2w9ExEGUFQVXAdbfWw/5OU+S9GLmFLn+fZaylPyewJ0RsZiy5PhuwMPAUY32W1CWod66w7VOAHYH3g0siYifAm8EtgfuBE4ag/Fr9AZ1708D9qD85XIlcG6jHg8AmTlrgGNXfwb5c6/1yyDv/Rco2Wr7AXdExHW1/e7AhsAnMvP6sXgRGpVB3ftLKIXdZwKX13/rfwlMZnVW0ycy8/YxeA0aoYg4APhk264Jdf91bftOzcwr69d+zpMkvWiZwdSnzPwd5T8IpwJPAwdTPnDOBXbOzF+M4FqPALsCX6F8gHkXZZnqM4FdM/OxQY5d/RngvW/V2diQ8pfrD3R5aJwY5M+91i8D/p2/ApgOzAYeAd5ByWS8GpiRmacPcuzqz6Dufc1QPRQ4GvgRsB3l3/tJlOlR07z348qWlCBi69H6C1D7vi17uZCf8yRJL3ThTBxJkiRJkiT1wwwmSZIkSZIk9cUAkyRJkiRJkvpigEmSJEmSJEl9McAkSZIkSZKkvhhgkiRJkiRJUl8MMEmSJEmSJKkvBpgkSZIkSZLUFwNMkiRJkiRJ6osBJkmSJEmSJPXFAJMkSZIkSZL6YoBJkiRJkiRJfTHAJEkNEZERsXQU520QEffW8/fuof1Ha9sFjb6bjxURsSwiFkTEnkNc75CIWBgRD9VzHomI2yJiXkQcExETenwd+3YYw8qIeCAiLo2It/b2joxeRMyq/Z4yHvvp9D0SEZPq/h829rfez7n9jleSJEkarwwwSdKAZOYq4Dv16RE9nNJqM6/DsX9te1wGPA0cAlwTEe9vNo6Ic4EFwDTgPuAS4IeU3/NHAN8AXtnbK3nOg21juAh4HHgncFVEHDfCa6khIk6pgadZ63oskiRJUr82WtcDkKRx6A3AilGeOw/4KDAzIo7PzN93ahQRbwJ2AB4DFjaPZ+asRvsNgNOB2cCZEXFhZq6oxw4BjgKeAKZn5o8b504CjgaWj/C1LGkfR0QE8CngFOBLEbEgMx8a4TVfKEbyPXJ9bf/E2A1HkiRJWrfMYJKkhsxckpl3j/LcnwM/A14BHDBE01b20gXdglCN666iBHdWApsDb2w7/O66PasZXKrnLs3MT2bmr4d/BUOOIYFTgbuBlwJv7+d667ORfI9k5tO1/f1jPS5JkiRpXTHAJEkN3WowRcSeEXFJRNwTEctrTaLrI+LzEbFpW9P5dXt4l+sHcFh92ml6XEc1ENXKgmnPQN2ybh/u9VqjVQNdt9Sn28CaNYYiYmJEzImI+2rdphNa50bEHrWG08P1/VsaEV+LiD8eqs+ImFLrTz0aEU9FxLURMb1L2wMi4psR8b8R8Zva/paI+HhEbDzAfnqu09WpBlM999P16bca9a72jYiT69enD3HdRbXNmNfEkiRJkoZjgEmSehARM4DFlBpE9wPfBW6m1DWaDWzR1vw7wLPAgRHx8g6X2xf4E+DuThlHQ4xhMiV7aQVwV9uhe+v2rxqBrrHysrptTrnbEriBkrn1P8D3KLWjiIgjWP3+3U55/5YDxwE3RcTru/T1GuAnwE7AIuCnwB7AFRFxZIf251Iyuh6r/S+mBMJOAxZGxIYD6qdfF7E6UHcta9bcegCYS3l/joyI501nr98L+wF3ZuYPxmB8kiRJ0ogYYJKk3pxM+Z05MzN3z8zDMnNaZm4H7Ao82mpYp0JdBWwMzOxwrdb0uPkdjj1PRGwaEVOBC+quszPz8bYm3wRWATsDv4iIsyPiLyPiz2q21MBExB8Bu9WntzYOT6cEmCZn5szMnJGZ50TENsA5tc1BmTk1Mw+j1CX6MrAV3TO5jgAuBV5X3/N9KEGqVcBZEfGqRvtjgYmZuVdmHpqZ+wPbAlcAf0GXrLJR9NOXzDyZUogdYE5mzmp7LMnMRyhF2ycCB3a4xNFAAHMGOS5JkiRptAwwSVJvWtPQ/rt5IDNvyMwnG7tbAZM1VpOLiJewumZS1wBT+5Qp4ElKJs4U4HjghEb/19Z+Hq3j/DDwbeA24P6I+FyXTKqeRcRLImI3yop2m1GykJqZM8uB4zPzd439x1BqNl2QmZe1jXsV8DFgGbBLROzVoevfAidk5sq2866gZABtAqyRXZSZl2bmM419TwIn1qcHdXmJI+pnLfmXuv1g+86ahTWLksk2d+0OSZIkSerMVeQkqTc3UjJu5kXEqcCNNUDSzcXAU8BbImKbzGxNYzsQeDlwXWbe1fXsMlWqZWNKFs5ulELfd1Omfz0nM8+PiMuAg4G3AW+mFALfihLEeU9E7DXCVd/2qQGupruAgzPz2cb+mzLz/zq037tuz2seyMzlEXEh8He13bWNJou6FCc/Hzi07drPiYjXUrKptgP+gPLHlFYm12s7XGtU/Yy1zFwcEbcB+ze+h6YDrwIuehGv4idJkqRxxgCTJPXm48AOwIz6+HVEXEPJ6JnfzNrJzKci4mJKZtH7gX+qh1oZTUMW987MWc19EbETcDVwWURsn5m3N/ukBHHOq+23omTefIoSbDmNRjbMMB4E/qN+vZKSIXUdcEVmrujQ/lddrtMq4r20y/HW/k7T0O4Z5pznCoTX6YBfpGQrdZsa+LIu+3vuZy37OnAmcBTwmbqvdQ+/sU5GJEmSJHXgFDlJ6kHNHtkFeAfwFUph7RmU/+TfGhGbdzitFUQ6HCAiXglMo0xt+vdRjOFmSsBhI0px7OHaP5iZn6dkMEEpvj0SS9rqAh2TmbMz8+IuwSWA5tS4XnXKkhqNQ4GTgPuA91ACVhMyMyhZYNA98DRefZtSKP2oiNigrrg3nRL4+q91OTBJkiSpnQEmSepRZq7MzEWZ+ZHM3BGYBHyfMu1qdodTrqKsOLdDROwIvBeYAHwvMx/t0L4Xv6zbblO9Ovl+3W4xZKuxs6xut+1yfFLddppe1+2c1v5lbfveVbfHZeaCzFzWFgx79TBjHEk/a01mPgH8G/CnlODmkcCGlMLggwrMSZIkSX0zwCRJo5SZ97B66tv2HY4/S6nhAyWLqbWC2ZDT44bRCpT8trWjh5XitqvbTgGctWFx3R7WPBARE1i90t7i5nHg7RHxhx32v69ur2nb94q6va9D+/cOM8aR9DMov6/b4aart4p9H0tZPe5Z4FtjMB5JkiRp1AwwSVIPIuLEiJjY4dD0ur23wzFYHUw6CtgLeBy4fJRj2An4UH26sO3QnIj4xzp9qnnOFOBL9elFo+l3AM4FngHeFxHPTdOLiA2A0ylT2W6sq+E1bQqcEREbtZ03jRIweoY1Ay131O2H2oNuEbE38PfDjHEk/QxKKytqylCNMvMG4CbKCniTgSszc51kVEmSJEndWORbknrzaeCLEXELcCells+OwOuAxyjFpZ8nM38WET9ndYbThZm5fLjOImJu29MJlKlau1P+MHA5a2ZBbU4tAl1XHbuDkuWyLWU1uQ2A64FTe3mhg5aZv4qIY4G5wOURcS0lILczJbjyIKuLnzedBxwC7BsRPwG2Bt5Cef8/kpnt2UpnArOAv67tb6UEr6ZSgmwnDzHMkfQzKIsodatOjIjtKQGnBL7QLOBOyWI6p359DpIkSdI4YwaTJPXmeEotnE0ohbr3p6ysdgbwpsy8c4hz24NB83vs7wNtj0OB1wM/okyROjgzV7W1/RvKymLfpdTneRslWPJq4AfAh4GpmfmbHvseuMycB+wNXAG8gVKE+6XA2cCfZ+aSLqfeBewB3EqpQbQrZSW7GZk5p9HHHZRC7JdT6k29k5KZdGxmDpfB1HM/g1KzkA6q/UylBAmPpgS3mlp1tO5j9cp+kiRJ0rgR1giVJGl8i4h/oEwn/ExmnrKOhyNJkiQ9jwEmSZLGsYjYDFhCmQo52fpLkiRJGo+swSRJ0jgUEUcC+1BqQW0NfNngkiRJksYrazBJkjQ+7UOpwbUp8FXgY+t2OJIkSVJ3TpGTJEmSJElSX8xgkiRJkiRJUl8MMEmSJEmSJKkvBpgkSZIkSZLUFwNMkiRJkiRJ6osBJkmSJEmSJPXFAJMkSZIkSZL6YoBJkiRJkiRJfTHAJEmSJEmSpL4YYJIkSZIkSVJfDDBJkiRJkiSpLwaYJEmSJEmS1BcDTJIkSZIkSeqLASZJkiRJkiT1xQCTJEmSJEmS+vL/DyPW/ShlZc8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_predict_train = dnn.predict_proba(X)\n",
    "Y_predict_val = dnn.predict_proba(X_val)\n",
    "\n",
    "axis = np.linspace(0,1,20)\n",
    "\n",
    "targets = ['isLL', 'isMIX', 'isTT', 'isBKG']\n",
    "predictions = ['isVBS']\n",
    "colors = ['r', 'g', 'y', 'b']\n",
    " \n",
    "Y_targets = {}\n",
    "Y_predictions = {}\n",
    "\n",
    "for target in targets:\n",
    "    #Y_targets[target] = dnn.predict_proba( df_val[ df_val[target]==1 ].values[:,0:NDIM] )\n",
    "    if target != 'isBKG':\n",
    "        Y_targets[target] = dnn.predict_proba( df[ df[target]==1 ].values[:,0:NDIM] )\n",
    "    else:\n",
    "        Y_targets[target] = dnn.predict_proba( df_val[ df_val[target]==1 ].values[:,0:NDIM] )\n",
    "    Y_predictions[target] = {}\n",
    "    for iprediction, prediction in enumerate(predictions):\n",
    "        Y_predictions[target][prediction] = Y_targets[target][:,iprediction]\n",
    "\n",
    "for prediction in predictions:\n",
    "    for target, mycolor in zip(targets, colors):\n",
    "        plt.hist(Y_predictions[target][prediction].numpy(), bins = axis, label = prediction + ' class - ' + target + ' sample', histtype='step', color=mycolor,  density=True, linewidth=2 )\n",
    "    plt.title('Validation dataset')\n",
    "    plt.xlabel(prediction + ' Probability')\n",
    "    plt.legend(frameon=False, bbox_to_anchor=(1.05, 1.))\n",
    "    #plt.yscale('log')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db374a53-c7bd-4337-9216-68f5e143c8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools\n",
    "#from matplotlib.backends.backend_pdf import PdfPages\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, fontsize = 16)\n",
    "    plt.yticks(tick_marks, classes, fontsize = 16)\n",
    "\n",
    "    thresh = cm.max() / 1.2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\",fontsize=10)\n",
    "\n",
    "    plt.xlabel(\"Predicted label\", fontsize=16)\n",
    "    plt.ylabel(\"True label\", fontsize=16)\n",
    "\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f3ec098a-fc62-4ab3-87a8-7ee81376b653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier categorical accuracy =  0.7669616519174042\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from scipy.stats import ks_2samp, anderson_ksamp\n",
    "\n",
    "X = df_val.values[:,0:NDIM]\n",
    "Y_true = df_val[['isVBS', 'isBKG']].values\n",
    "Y_pred = dnn.predict_proba(X)\n",
    "\n",
    "# The classifier categorical accuracy is a good and simple metrics to evaluate the overall classification performance\n",
    "Y_true_max = np.argmax(Y_true, axis=1)\n",
    "Y_pred_max = np.argmax(Y_pred, axis=1)\n",
    "print(\"Classifier categorical accuracy = \", accuracy_score(Y_true_max, Y_pred_max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca1fc441-c2f5-4a0d-a4b2-7a451354a1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = confusion_matrix(Y_true_max, Y_pred_max, normalize=\"true\")\n",
    "C = np.around(C, decimals=3)\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(C, classes=['isVBS', 'isBKG'], title='')\n",
    "plt.show(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d364ee44-a77c-4a0b-9a4c-845fdf4aa08e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83466\n"
     ]
    }
   ],
   "source": [
    "X    = df.values[:,0:NDIM]\n",
    "print(len(dnn.predict_proba(X)[...,0]))\n",
    "df['DNN_SigVsBkg'] = dnn.predict_proba(X)[...,0].numpy().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af3362d5-95ce-44db-bc16-b545f6e919c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83466\n",
      "5264\n",
      "6753\n",
      "6716\n",
      "6794\n"
     ]
    }
   ],
   "source": [
    "print(len(df))\n",
    "print(len(df[(df['DNN_SigVsBkg'] > 0.5) & (df['isBKG'] == 1)]))\n",
    "print(len(df[(df['DNN_SigVsBkg'] > 0.5) & (df['isLL'] == 1)]))\n",
    "print(len(df[(df['DNN_SigVsBkg'] > 0.5) & (df['isMIX'] == 1)]))\n",
    "print(len(df[(df['DNN_SigVsBkg'] > 0.5) & (df['isTT'] == 1)]))\n",
    "\n",
    "df_other = df[((df['isTT'] + df['isMIX'] + df['isLL']) == 1) & (df['DNN_SigVsBkg'] > 0.5)]\n",
    "\n",
    "df_notLL = df_other[df_other['notLL'] == 1].sample(n=int(len(df_other[df_other['notLL'] == 1])/2))\n",
    "df_notTT= df_other[df_other['notTT'] == 1].sample(n=int(len(df_other[df_other['notTT'] == 1])/2))\n",
    "\n",
    "df_LL = pd.concat([df_notLL, df_other[df_other['isLL'] == 1]])\n",
    "df_TT = pd.concat([df_notTT, df_other[df_other['isTT'] == 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6a579013-794d-4daf-9141-a0d48c1d8a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CleanJet_eta_1</th>\n",
       "      <th>CleanJet_eta_2</th>\n",
       "      <th>CleanJet_phi_1</th>\n",
       "      <th>CleanJet_phi_2</th>\n",
       "      <th>CleanJet_pt_1</th>\n",
       "      <th>CleanJet_pt_2</th>\n",
       "      <th>Lepton_eta_1</th>\n",
       "      <th>Lepton_eta_2</th>\n",
       "      <th>Lepton_phi_1</th>\n",
       "      <th>Lepton_phi_2</th>\n",
       "      <th>Lepton_pt_1</th>\n",
       "      <th>Lepton_pt_2</th>\n",
       "      <th>Rpt</th>\n",
       "      <th>Zepp_l1</th>\n",
       "      <th>Zepp_l2</th>\n",
       "      <th>Zepp_ll</th>\n",
       "      <th>detajj</th>\n",
       "      <th>detall</th>\n",
       "      <th>dphijj</th>\n",
       "      <th>dphilep1jet1</th>\n",
       "      <th>dphilep1jet2</th>\n",
       "      <th>dphilep1jj</th>\n",
       "      <th>dphilep2jet1</th>\n",
       "      <th>dphilep2jet2</th>\n",
       "      <th>dphilep2jj</th>\n",
       "      <th>dphill</th>\n",
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       "      <th>dphilljetjet</th>\n",
       "      <th>dphillmet</th>\n",
       "      <th>dphilmet1</th>\n",
       "      <th>dphilmet2</th>\n",
       "      <th>dr_l1j1</th>\n",
       "      <th>dr_l1j2</th>\n",
       "      <th>dr_l2j1</th>\n",
       "      <th>dr_l2j2</th>\n",
       "      <th>drll</th>\n",
       "      <th>ht</th>\n",
       "      <th>m2ljj30</th>\n",
       "      <th>mT2</th>\n",
       "      <th>mTi</th>\n",
       "      <th>m_l1j1</th>\n",
       "      <th>m_l1j2</th>\n",
       "      <th>m_l2j1</th>\n",
       "      <th>m_l2j2</th>\n",
       "      <th>mcoll</th>\n",
       "      <th>mcollWW</th>\n",
       "      <th>mjj</th>\n",
       "      <th>mll</th>\n",
       "      <th>mtw1</th>\n",
       "      <th>mtw2</th>\n",
       "      <th>phi_miss</th>\n",
       "      <th>proxyW_l1</th>\n",
       "      <th>proxyW_l2</th>\n",
       "      <th>pt_miss</th>\n",
       "      <th>ptll</th>\n",
       "      <th>recoil</th>\n",
       "      <th>yll</th>\n",
       "      <th>isLL</th>\n",
       "      <th>isMIX</th>\n",
       "      <th>isTT</th>\n",
       "      <th>isBKG</th>\n",
       "      <th>isVBS</th>\n",
       "      <th>notLL</th>\n",
       "      <th>notTT</th>\n",
       "      <th>DNN_SigVsBkg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70917</th>\n",
       "      <td>-3.092285</td>\n",
       "      <td>1.499023</td>\n",
       "      <td>2.187500</td>\n",
       "      <td>1.440918</td>\n",
       "      <td>157.166031</td>\n",
       "      <td>52.063175</td>\n",
       "      <td>-1.562988</td>\n",
       "      <td>-0.656494</td>\n",
       "      <td>-0.072418</td>\n",
       "      <td>-2.857422</td>\n",
       "      <td>96.386803</td>\n",
       "      <td>57.625023</td>\n",
       "      <td>0.678796</td>\n",
       "      <td>-0.766357</td>\n",
       "      <td>0.140137</td>\n",
       "      <td>0.313110</td>\n",
       "      <td>4.591309</td>\n",
       "      <td>0.906494</td>\n",
       "      <td>0.746582</td>\n",
       "      <td>2.259918</td>\n",
       "      <td>1.513336</td>\n",
       "      <td>2.080887</td>\n",
       "      <td>1.238263</td>\n",
       "      <td>1.984846</td>\n",
       "      <td>1.417295</td>\n",
       "      <td>2.785004</td>\n",
       "      <td>2.703020</td>\n",
       "      <td>2.523989</td>\n",
       "      <td>0.939242</td>\n",
       "      <td>1.382344</td>\n",
       "      <td>1.402660</td>\n",
       "      <td>2.728732</td>\n",
       "      <td>3.415568</td>\n",
       "      <td>5.602170</td>\n",
       "      <td>4.808532</td>\n",
       "      <td>2.928818</td>\n",
       "      <td>530.825073</td>\n",
       "      <td>1072.307251</td>\n",
       "      <td>19.612677</td>\n",
       "      <td>348.181213</td>\n",
       "      <td>304.772980</td>\n",
       "      <td>327.348541</td>\n",
       "      <td>314.234222</td>\n",
       "      <td>169.695938</td>\n",
       "      <td>228.121277</td>\n",
       "      <td>291.495880</td>\n",
       "      <td>892.626770</td>\n",
       "      <td>162.492233</td>\n",
       "      <td>162.029800</td>\n",
       "      <td>126.814606</td>\n",
       "      <td>-1.454762</td>\n",
       "      <td>272.820129</td>\n",
       "      <td>142.813248</td>\n",
       "      <td>167.584045</td>\n",
       "      <td>46.917835</td>\n",
       "      <td>198.921967</td>\n",
       "      <td>-1.217005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.808055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59459</th>\n",
       "      <td>0.581299</td>\n",
       "      <td>-3.280762</td>\n",
       "      <td>1.704102</td>\n",
       "      <td>-1.308350</td>\n",
       "      <td>225.053024</td>\n",
       "      <td>135.194000</td>\n",
       "      <td>0.511841</td>\n",
       "      <td>-1.573486</td>\n",
       "      <td>0.362244</td>\n",
       "      <td>-0.467712</td>\n",
       "      <td>36.858833</td>\n",
       "      <td>18.419266</td>\n",
       "      <td>0.022314</td>\n",
       "      <td>1.861572</td>\n",
       "      <td>-0.223755</td>\n",
       "      <td>0.818909</td>\n",
       "      <td>3.862061</td>\n",
       "      <td>2.085327</td>\n",
       "      <td>3.012451</td>\n",
       "      <td>1.341858</td>\n",
       "      <td>1.670593</td>\n",
       "      <td>1.152786</td>\n",
       "      <td>2.171814</td>\n",
       "      <td>0.840637</td>\n",
       "      <td>1.982742</td>\n",
       "      <td>0.829956</td>\n",
       "      <td>1.610918</td>\n",
       "      <td>1.421846</td>\n",
       "      <td>1.930716</td>\n",
       "      <td>2.199776</td>\n",
       "      <td>1.369820</td>\n",
       "      <td>1.343654</td>\n",
       "      <td>4.144239</td>\n",
       "      <td>3.059391</td>\n",
       "      <td>1.903013</td>\n",
       "      <td>2.244419</td>\n",
       "      <td>526.447998</td>\n",
       "      <td>1343.913086</td>\n",
       "      <td>70.342552</td>\n",
       "      <td>135.631653</td>\n",
       "      <td>117.120461</td>\n",
       "      <td>471.837585</td>\n",
       "      <td>204.428085</td>\n",
       "      <td>106.199913</td>\n",
       "      <td>16.623631</td>\n",
       "      <td>176.293900</td>\n",
       "      <td>1230.585449</td>\n",
       "      <td>68.053810</td>\n",
       "      <td>87.136017</td>\n",
       "      <td>43.726074</td>\n",
       "      <td>-1.837532</td>\n",
       "      <td>90.733963</td>\n",
       "      <td>74.377235</td>\n",
       "      <td>64.846382</td>\n",
       "      <td>51.129742</td>\n",
       "      <td>66.961327</td>\n",
       "      <td>-0.264890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.903923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65929</th>\n",
       "      <td>2.535156</td>\n",
       "      <td>-1.600830</td>\n",
       "      <td>-1.598877</td>\n",
       "      <td>1.245605</td>\n",
       "      <td>133.824417</td>\n",
       "      <td>53.164108</td>\n",
       "      <td>1.101562</td>\n",
       "      <td>0.198730</td>\n",
       "      <td>-0.389038</td>\n",
       "      <td>-2.561523</td>\n",
       "      <td>63.651546</td>\n",
       "      <td>31.910093</td>\n",
       "      <td>0.285485</td>\n",
       "      <td>0.634399</td>\n",
       "      <td>-0.268433</td>\n",
       "      <td>0.182983</td>\n",
       "      <td>4.135986</td>\n",
       "      <td>0.902832</td>\n",
       "      <td>2.844482</td>\n",
       "      <td>1.209839</td>\n",
       "      <td>1.634644</td>\n",
       "      <td>1.024448</td>\n",
       "      <td>0.962646</td>\n",
       "      <td>2.476056</td>\n",
       "      <td>1.148037</td>\n",
       "      <td>2.172485</td>\n",
       "      <td>0.686486</td>\n",
       "      <td>0.501095</td>\n",
       "      <td>2.824813</td>\n",
       "      <td>2.301459</td>\n",
       "      <td>1.809241</td>\n",
       "      <td>1.875873</td>\n",
       "      <td>3.158320</td>\n",
       "      <td>2.526969</td>\n",
       "      <td>4.211015</td>\n",
       "      <td>2.352615</td>\n",
       "      <td>399.748535</td>\n",
       "      <td>776.867737</td>\n",
       "      <td>76.063339</td>\n",
       "      <td>228.930252</td>\n",
       "      <td>179.216339</td>\n",
       "      <td>226.318878</td>\n",
       "      <td>200.145462</td>\n",
       "      <td>115.193047</td>\n",
       "      <td>15.702890</td>\n",
       "      <td>162.733032</td>\n",
       "      <td>677.865295</td>\n",
       "      <td>90.181892</td>\n",
       "      <td>157.723129</td>\n",
       "      <td>96.157379</td>\n",
       "      <td>1.912421</td>\n",
       "      <td>186.766861</td>\n",
       "      <td>96.924858</td>\n",
       "      <td>117.198364</td>\n",
       "      <td>52.634521</td>\n",
       "      <td>69.154572</td>\n",
       "      <td>0.791606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.783956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72296</th>\n",
       "      <td>1.422607</td>\n",
       "      <td>-2.786621</td>\n",
       "      <td>-0.685425</td>\n",
       "      <td>2.348145</td>\n",
       "      <td>404.592499</td>\n",
       "      <td>114.317879</td>\n",
       "      <td>0.659424</td>\n",
       "      <td>0.015083</td>\n",
       "      <td>2.346680</td>\n",
       "      <td>-1.586426</td>\n",
       "      <td>445.382507</td>\n",
       "      <td>78.496254</td>\n",
       "      <td>0.755875</td>\n",
       "      <td>1.341431</td>\n",
       "      <td>0.697090</td>\n",
       "      <td>1.019260</td>\n",
       "      <td>4.209229</td>\n",
       "      <td>0.644341</td>\n",
       "      <td>3.033569</td>\n",
       "      <td>3.032104</td>\n",
       "      <td>0.001465</td>\n",
       "      <td>2.989767</td>\n",
       "      <td>0.901001</td>\n",
       "      <td>2.348615</td>\n",
       "      <td>0.943338</td>\n",
       "      <td>2.350080</td>\n",
       "      <td>3.108937</td>\n",
       "      <td>3.131911</td>\n",
       "      <td>2.828755</td>\n",
       "      <td>2.970899</td>\n",
       "      <td>0.620819</td>\n",
       "      <td>3.126677</td>\n",
       "      <td>3.446045</td>\n",
       "      <td>1.671205</td>\n",
       "      <td>4.830154</td>\n",
       "      <td>2.436811</td>\n",
       "      <td>1173.684937</td>\n",
       "      <td>2435.389160</td>\n",
       "      <td>324.226959</td>\n",
       "      <td>578.783264</td>\n",
       "      <td>912.235962</td>\n",
       "      <td>1223.763062</td>\n",
       "      <td>316.836487</td>\n",
       "      <td>401.437958</td>\n",
       "      <td>461.371277</td>\n",
       "      <td>494.615082</td>\n",
       "      <td>1791.726929</td>\n",
       "      <td>366.175690</td>\n",
       "      <td>421.634705</td>\n",
       "      <td>54.264511</td>\n",
       "      <td>-0.965607</td>\n",
       "      <td>444.925842</td>\n",
       "      <td>54.281044</td>\n",
       "      <td>100.518684</td>\n",
       "      <td>394.193298</td>\n",
       "      <td>300.151855</td>\n",
       "      <td>0.558945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.908375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70920</th>\n",
       "      <td>-1.737305</td>\n",
       "      <td>3.809082</td>\n",
       "      <td>0.166656</td>\n",
       "      <td>1.801758</td>\n",
       "      <td>324.955566</td>\n",
       "      <td>38.819939</td>\n",
       "      <td>0.965088</td>\n",
       "      <td>0.387634</td>\n",
       "      <td>-2.032715</td>\n",
       "      <td>1.817383</td>\n",
       "      <td>114.886208</td>\n",
       "      <td>58.149158</td>\n",
       "      <td>0.529581</td>\n",
       "      <td>-0.070801</td>\n",
       "      <td>-0.648254</td>\n",
       "      <td>0.359528</td>\n",
       "      <td>5.546387</td>\n",
       "      <td>0.577454</td>\n",
       "      <td>1.635101</td>\n",
       "      <td>2.199371</td>\n",
       "      <td>2.448713</td>\n",
       "      <td>2.318936</td>\n",
       "      <td>1.650726</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>1.531162</td>\n",
       "      <td>2.433088</td>\n",
       "      <td>2.690584</td>\n",
       "      <td>2.810149</td>\n",
       "      <td>0.468256</td>\n",
       "      <td>0.959469</td>\n",
       "      <td>1.473619</td>\n",
       "      <td>3.484273</td>\n",
       "      <td>4.774043</td>\n",
       "      <td>2.690774</td>\n",
       "      <td>3.421484</td>\n",
       "      <td>2.500674</td>\n",
       "      <td>778.057373</td>\n",
       "      <td>2043.885498</td>\n",
       "      <td>143.352966</td>\n",
       "      <td>323.671753</td>\n",
       "      <td>777.200684</td>\n",
       "      <td>289.505768</td>\n",
       "      <td>405.216370</td>\n",
       "      <td>254.384308</td>\n",
       "      <td>282.466492</td>\n",
       "      <td>344.249573</td>\n",
       "      <td>1798.984741</td>\n",
       "      <td>160.613968</td>\n",
       "      <td>153.676437</td>\n",
       "      <td>159.167786</td>\n",
       "      <td>-2.992184</td>\n",
       "      <td>226.931168</td>\n",
       "      <td>165.737137</td>\n",
       "      <td>241.246521</td>\n",
       "      <td>80.216133</td>\n",
       "      <td>314.915924</td>\n",
       "      <td>0.768749</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.945333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55638</th>\n",
       "      <td>-1.153320</td>\n",
       "      <td>1.540771</td>\n",
       "      <td>-2.131836</td>\n",
       "      <td>-2.070312</td>\n",
       "      <td>235.171143</td>\n",
       "      <td>114.415413</td>\n",
       "      <td>-0.848755</td>\n",
       "      <td>1.215820</td>\n",
       "      <td>1.506836</td>\n",
       "      <td>0.788208</td>\n",
       "      <td>334.497192</td>\n",
       "      <td>33.498402</td>\n",
       "      <td>0.416436</td>\n",
       "      <td>-1.042480</td>\n",
       "      <td>1.022095</td>\n",
       "      <td>0.010193</td>\n",
       "      <td>2.694092</td>\n",
       "      <td>2.064575</td>\n",
       "      <td>0.061523</td>\n",
       "      <td>2.644513</td>\n",
       "      <td>2.706037</td>\n",
       "      <td>2.664646</td>\n",
       "      <td>2.920044</td>\n",
       "      <td>2.858521</td>\n",
       "      <td>2.899911</td>\n",
       "      <td>0.718628</td>\n",
       "      <td>2.705746</td>\n",
       "      <td>2.725879</td>\n",
       "      <td>1.524074</td>\n",
       "      <td>1.585307</td>\n",
       "      <td>0.866679</td>\n",
       "      <td>3.651396</td>\n",
       "      <td>4.301840</td>\n",
       "      <td>3.760251</td>\n",
       "      <td>2.876931</td>\n",
       "      <td>2.186069</td>\n",
       "      <td>916.897461</td>\n",
       "      <td>1153.941650</td>\n",
       "      <td>212.150146</td>\n",
       "      <td>455.254425</td>\n",
       "      <td>552.030518</td>\n",
       "      <td>700.726318</td>\n",
       "      <td>318.219116</td>\n",
       "      <td>125.398819</td>\n",
       "      <td>507.944580</td>\n",
       "      <td>536.029236</td>\n",
       "      <td>590.004517</td>\n",
       "      <td>269.947052</td>\n",
       "      <td>299.907959</td>\n",
       "      <td>55.955212</td>\n",
       "      <td>-0.078471</td>\n",
       "      <td>351.912720</td>\n",
       "      <td>102.677315</td>\n",
       "      <td>132.524811</td>\n",
       "      <td>360.387177</td>\n",
       "      <td>389.747437</td>\n",
       "      <td>-0.564144</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55639</th>\n",
       "      <td>-3.743164</td>\n",
       "      <td>0.514893</td>\n",
       "      <td>-0.576904</td>\n",
       "      <td>2.270508</td>\n",
       "      <td>104.235992</td>\n",
       "      <td>82.054703</td>\n",
       "      <td>-1.188477</td>\n",
       "      <td>2.070312</td>\n",
       "      <td>3.004883</td>\n",
       "      <td>1.624756</td>\n",
       "      <td>76.944870</td>\n",
       "      <td>28.181719</td>\n",
       "      <td>0.253528</td>\n",
       "      <td>0.425659</td>\n",
       "      <td>3.684448</td>\n",
       "      <td>2.055054</td>\n",
       "      <td>4.258057</td>\n",
       "      <td>3.258789</td>\n",
       "      <td>2.847412</td>\n",
       "      <td>2.701398</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>2.835040</td>\n",
       "      <td>2.201660</td>\n",
       "      <td>0.645752</td>\n",
       "      <td>1.454913</td>\n",
       "      <td>1.380127</td>\n",
       "      <td>3.025798</td>\n",
       "      <td>2.510640</td>\n",
       "      <td>2.858856</td>\n",
       "      <td>2.534456</td>\n",
       "      <td>2.368602</td>\n",
       "      <td>4.399503</td>\n",
       "      <td>1.854932</td>\n",
       "      <td>6.216415</td>\n",
       "      <td>1.684140</td>\n",
       "      <td>3.538991</td>\n",
       "      <td>369.485748</td>\n",
       "      <td>1348.370361</td>\n",
       "      <td>49.499893</td>\n",
       "      <td>328.573456</td>\n",
       "      <td>344.460968</td>\n",
       "      <td>163.113510</td>\n",
       "      <td>993.591431</td>\n",
       "      <td>88.793961</td>\n",
       "      <td>95.524620</td>\n",
       "      <td>199.770462</td>\n",
       "      <td>789.160889</td>\n",
       "      <td>235.965881</td>\n",
       "      <td>147.921600</td>\n",
       "      <td>86.890663</td>\n",
       "      <td>-0.743846</td>\n",
       "      <td>177.235519</td>\n",
       "      <td>144.458282</td>\n",
       "      <td>78.068459</td>\n",
       "      <td>86.813774</td>\n",
       "      <td>24.792519</td>\n",
       "      <td>-0.018384</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.835519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55640</th>\n",
       "      <td>-3.791992</td>\n",
       "      <td>4.085938</td>\n",
       "      <td>-1.271973</td>\n",
       "      <td>0.086639</td>\n",
       "      <td>69.944092</td>\n",
       "      <td>31.950378</td>\n",
       "      <td>0.638062</td>\n",
       "      <td>0.429688</td>\n",
       "      <td>0.341248</td>\n",
       "      <td>-2.769531</td>\n",
       "      <td>501.502380</td>\n",
       "      <td>115.429169</td>\n",
       "      <td>25.903683</td>\n",
       "      <td>0.491089</td>\n",
       "      <td>0.282715</td>\n",
       "      <td>0.386902</td>\n",
       "      <td>7.877930</td>\n",
       "      <td>0.208374</td>\n",
       "      <td>1.358612</td>\n",
       "      <td>1.613220</td>\n",
       "      <td>0.254608</td>\n",
       "      <td>1.226380</td>\n",
       "      <td>1.497559</td>\n",
       "      <td>2.856171</td>\n",
       "      <td>1.884399</td>\n",
       "      <td>3.110779</td>\n",
       "      <td>1.604010</td>\n",
       "      <td>1.217170</td>\n",
       "      <td>2.877326</td>\n",
       "      <td>2.868117</td>\n",
       "      <td>0.304290</td>\n",
       "      <td>4.714643</td>\n",
       "      <td>3.457264</td>\n",
       "      <td>4.479426</td>\n",
       "      <td>4.639598</td>\n",
       "      <td>3.117750</td>\n",
       "      <td>1109.396729</td>\n",
       "      <td>3201.487305</td>\n",
       "      <td>285.464661</td>\n",
       "      <td>1047.870117</td>\n",
       "      <td>1716.912964</td>\n",
       "      <td>687.926331</td>\n",
       "      <td>741.122986</td>\n",
       "      <td>387.314819</td>\n",
       "      <td>497.502930</td>\n",
       "      <td>532.741943</td>\n",
       "      <td>2428.071045</td>\n",
       "      <td>483.755768</td>\n",
       "      <td>876.886963</td>\n",
       "      <td>64.360344</td>\n",
       "      <td>-3.073821</td>\n",
       "      <td>922.722656</td>\n",
       "      <td>112.226982</td>\n",
       "      <td>390.570801</td>\n",
       "      <td>386.144379</td>\n",
       "      <td>102.425438</td>\n",
       "      <td>0.598931</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.998958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55642</th>\n",
       "      <td>-1.738037</td>\n",
       "      <td>3.506348</td>\n",
       "      <td>-1.678955</td>\n",
       "      <td>1.187988</td>\n",
       "      <td>74.346756</td>\n",
       "      <td>45.876911</td>\n",
       "      <td>1.355469</td>\n",
       "      <td>-0.124817</td>\n",
       "      <td>0.074463</td>\n",
       "      <td>-1.181885</td>\n",
       "      <td>63.812317</td>\n",
       "      <td>25.024496</td>\n",
       "      <td>0.468181</td>\n",
       "      <td>0.471313</td>\n",
       "      <td>-1.008972</td>\n",
       "      <td>0.268829</td>\n",
       "      <td>5.244385</td>\n",
       "      <td>1.480286</td>\n",
       "      <td>2.866943</td>\n",
       "      <td>1.753418</td>\n",
       "      <td>1.113525</td>\n",
       "      <td>1.362489</td>\n",
       "      <td>0.497070</td>\n",
       "      <td>2.369873</td>\n",
       "      <td>0.106141</td>\n",
       "      <td>1.256348</td>\n",
       "      <td>1.432338</td>\n",
       "      <td>1.041409</td>\n",
       "      <td>2.615683</td>\n",
       "      <td>2.294603</td>\n",
       "      <td>2.732235</td>\n",
       "      <td>3.555876</td>\n",
       "      <td>2.422028</td>\n",
       "      <td>1.688064</td>\n",
       "      <td>4.336087</td>\n",
       "      <td>1.941560</td>\n",
       "      <td>288.244690</td>\n",
       "      <td>916.869812</td>\n",
       "      <td>45.374245</td>\n",
       "      <td>203.999268</td>\n",
       "      <td>326.589935</td>\n",
       "      <td>151.596756</td>\n",
       "      <td>80.632843</td>\n",
       "      <td>212.333572</td>\n",
       "      <td>46.561543</td>\n",
       "      <td>168.867645</td>\n",
       "      <td>808.247803</td>\n",
       "      <td>79.953590</td>\n",
       "      <td>129.608551</td>\n",
       "      <td>87.170731</td>\n",
       "      <td>2.369066</td>\n",
       "      <td>166.112839</td>\n",
       "      <td>87.347855</td>\n",
       "      <td>79.184219</td>\n",
       "      <td>75.405815</td>\n",
       "      <td>40.348640</td>\n",
       "      <td>0.897303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.881798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55643</th>\n",
       "      <td>-2.638184</td>\n",
       "      <td>0.110016</td>\n",
       "      <td>0.711426</td>\n",
       "      <td>-2.693848</td>\n",
       "      <td>277.162048</td>\n",
       "      <td>175.776321</td>\n",
       "      <td>-1.376953</td>\n",
       "      <td>0.099640</td>\n",
       "      <td>-1.156982</td>\n",
       "      <td>0.549805</td>\n",
       "      <td>47.937496</td>\n",
       "      <td>43.284531</td>\n",
       "      <td>0.042591</td>\n",
       "      <td>-0.112869</td>\n",
       "      <td>1.363724</td>\n",
       "      <td>0.625427</td>\n",
       "      <td>2.748199</td>\n",
       "      <td>1.476593</td>\n",
       "      <td>2.877912</td>\n",
       "      <td>1.868408</td>\n",
       "      <td>1.536865</td>\n",
       "      <td>2.271403</td>\n",
       "      <td>0.161621</td>\n",
       "      <td>3.039533</td>\n",
       "      <td>0.564616</td>\n",
       "      <td>1.706787</td>\n",
       "      <td>1.073410</td>\n",
       "      <td>1.476405</td>\n",
       "      <td>2.184669</td>\n",
       "      <td>1.389671</td>\n",
       "      <td>3.096458</td>\n",
       "      <td>2.254252</td>\n",
       "      <td>2.138465</td>\n",
       "      <td>2.742590</td>\n",
       "      <td>3.243669</td>\n",
       "      <td>2.256867</td>\n",
       "      <td>666.860779</td>\n",
       "      <td>1077.547363</td>\n",
       "      <td>108.450798</td>\n",
       "      <td>232.553192</td>\n",
       "      <td>242.530502</td>\n",
       "      <td>197.352432</td>\n",
       "      <td>403.537537</td>\n",
       "      <td>174.999344</td>\n",
       "      <td>164.583847</td>\n",
       "      <td>233.529099</td>\n",
       "      <td>927.836975</td>\n",
       "      <td>100.601738</td>\n",
       "      <td>98.207870</td>\n",
       "      <td>145.716476</td>\n",
       "      <td>-2.546653</td>\n",
       "      <td>150.579590</td>\n",
       "      <td>145.897446</td>\n",
       "      <td>122.700363</td>\n",
       "      <td>60.074482</td>\n",
       "      <td>100.857300</td>\n",
       "      <td>-0.670706</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.908858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13508 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CleanJet_eta_1  CleanJet_eta_2  CleanJet_phi_1  CleanJet_phi_2  \\\n",
       "70917       -3.092285        1.499023        2.187500        1.440918   \n",
       "59459        0.581299       -3.280762        1.704102       -1.308350   \n",
       "65929        2.535156       -1.600830       -1.598877        1.245605   \n",
       "72296        1.422607       -2.786621       -0.685425        2.348145   \n",
       "70920       -1.737305        3.809082        0.166656        1.801758   \n",
       "...               ...             ...             ...             ...   \n",
       "55638       -1.153320        1.540771       -2.131836       -2.070312   \n",
       "55639       -3.743164        0.514893       -0.576904        2.270508   \n",
       "55640       -3.791992        4.085938       -1.271973        0.086639   \n",
       "55642       -1.738037        3.506348       -1.678955        1.187988   \n",
       "55643       -2.638184        0.110016        0.711426       -2.693848   \n",
       "\n",
       "       CleanJet_pt_1  CleanJet_pt_2  Lepton_eta_1  Lepton_eta_2  Lepton_phi_1  \\\n",
       "70917     157.166031      52.063175     -1.562988     -0.656494     -0.072418   \n",
       "59459     225.053024     135.194000      0.511841     -1.573486      0.362244   \n",
       "65929     133.824417      53.164108      1.101562      0.198730     -0.389038   \n",
       "72296     404.592499     114.317879      0.659424      0.015083      2.346680   \n",
       "70920     324.955566      38.819939      0.965088      0.387634     -2.032715   \n",
       "...              ...            ...           ...           ...           ...   \n",
       "55638     235.171143     114.415413     -0.848755      1.215820      1.506836   \n",
       "55639     104.235992      82.054703     -1.188477      2.070312      3.004883   \n",
       "55640      69.944092      31.950378      0.638062      0.429688      0.341248   \n",
       "55642      74.346756      45.876911      1.355469     -0.124817      0.074463   \n",
       "55643     277.162048     175.776321     -1.376953      0.099640     -1.156982   \n",
       "\n",
       "       Lepton_phi_2  Lepton_pt_1  Lepton_pt_2        Rpt   Zepp_l1   Zepp_l2  \\\n",
       "70917     -2.857422    96.386803    57.625023   0.678796 -0.766357  0.140137   \n",
       "59459     -0.467712    36.858833    18.419266   0.022314  1.861572 -0.223755   \n",
       "65929     -2.561523    63.651546    31.910093   0.285485  0.634399 -0.268433   \n",
       "72296     -1.586426   445.382507    78.496254   0.755875  1.341431  0.697090   \n",
       "70920      1.817383   114.886208    58.149158   0.529581 -0.070801 -0.648254   \n",
       "...             ...          ...          ...        ...       ...       ...   \n",
       "55638      0.788208   334.497192    33.498402   0.416436 -1.042480  1.022095   \n",
       "55639      1.624756    76.944870    28.181719   0.253528  0.425659  3.684448   \n",
       "55640     -2.769531   501.502380   115.429169  25.903683  0.491089  0.282715   \n",
       "55642     -1.181885    63.812317    25.024496   0.468181  0.471313 -1.008972   \n",
       "55643      0.549805    47.937496    43.284531   0.042591 -0.112869  1.363724   \n",
       "\n",
       "        Zepp_ll    detajj    detall    dphijj  dphilep1jet1  dphilep1jet2  \\\n",
       "70917  0.313110  4.591309  0.906494  0.746582      2.259918      1.513336   \n",
       "59459  0.818909  3.862061  2.085327  3.012451      1.341858      1.670593   \n",
       "65929  0.182983  4.135986  0.902832  2.844482      1.209839      1.634644   \n",
       "72296  1.019260  4.209229  0.644341  3.033569      3.032104      0.001465   \n",
       "70920  0.359528  5.546387  0.577454  1.635101      2.199371      2.448713   \n",
       "...         ...       ...       ...       ...           ...           ...   \n",
       "55638  0.010193  2.694092  2.064575  0.061523      2.644513      2.706037   \n",
       "55639  2.055054  4.258057  3.258789  2.847412      2.701398      0.734375   \n",
       "55640  0.386902  7.877930  0.208374  1.358612      1.613220      0.254608   \n",
       "55642  0.268829  5.244385  1.480286  2.866943      1.753418      1.113525   \n",
       "55643  0.625427  2.748199  1.476593  2.877912      1.868408      1.536865   \n",
       "\n",
       "       dphilep1jj  dphilep2jet1  dphilep2jet2  dphilep2jj    dphill  \\\n",
       "70917    2.080887      1.238263      1.984846    1.417295  2.785004   \n",
       "59459    1.152786      2.171814      0.840637    1.982742  0.829956   \n",
       "65929    1.024448      0.962646      2.476056    1.148037  2.172485   \n",
       "72296    2.989767      0.901001      2.348615    0.943338  2.350080   \n",
       "70920    2.318936      1.650726      0.015625    1.531162  2.433088   \n",
       "...           ...           ...           ...         ...       ...   \n",
       "55638    2.664646      2.920044      2.858521    2.899911  0.718628   \n",
       "55639    2.835040      2.201660      0.645752    1.454913  1.380127   \n",
       "55640    1.226380      1.497559      2.856171    1.884399  3.110779   \n",
       "55642    1.362489      0.497070      2.369873    0.106141  1.256348   \n",
       "55643    2.271403      0.161621      3.039533    0.564616  1.706787   \n",
       "\n",
       "       dphilljet  dphilljetjet  dphillmet  dphilmet1  dphilmet2   dr_l1j1  \\\n",
       "70917   2.703020      2.523989   0.939242   1.382344   1.402660  2.728732   \n",
       "59459   1.610918      1.421846   1.930716   2.199776   1.369820  1.343654   \n",
       "65929   0.686486      0.501095   2.824813   2.301459   1.809241  1.875873   \n",
       "72296   3.108937      3.131911   2.828755   2.970899   0.620819  3.126677   \n",
       "70920   2.690584      2.810149   0.468256   0.959469   1.473619  3.484273   \n",
       "...          ...           ...        ...        ...        ...       ...   \n",
       "55638   2.705746      2.725879   1.524074   1.585307   0.866679  3.651396   \n",
       "55639   3.025798      2.510640   2.858856   2.534456   2.368602  4.399503   \n",
       "55640   1.604010      1.217170   2.877326   2.868117   0.304290  4.714643   \n",
       "55642   1.432338      1.041409   2.615683   2.294603   2.732235  3.555876   \n",
       "55643   1.073410      1.476405   2.184669   1.389671   3.096458  2.254252   \n",
       "\n",
       "        dr_l1j2   dr_l2j1   dr_l2j2      drll           ht      m2ljj30  \\\n",
       "70917  3.415568  5.602170  4.808532  2.928818   530.825073  1072.307251   \n",
       "59459  4.144239  3.059391  1.903013  2.244419   526.447998  1343.913086   \n",
       "65929  3.158320  2.526969  4.211015  2.352615   399.748535   776.867737   \n",
       "72296  3.446045  1.671205  4.830154  2.436811  1173.684937  2435.389160   \n",
       "70920  4.774043  2.690774  3.421484  2.500674   778.057373  2043.885498   \n",
       "...         ...       ...       ...       ...          ...          ...   \n",
       "55638  4.301840  3.760251  2.876931  2.186069   916.897461  1153.941650   \n",
       "55639  1.854932  6.216415  1.684140  3.538991   369.485748  1348.370361   \n",
       "55640  3.457264  4.479426  4.639598  3.117750  1109.396729  3201.487305   \n",
       "55642  2.422028  1.688064  4.336087  1.941560   288.244690   916.869812   \n",
       "55643  2.138465  2.742590  3.243669  2.256867   666.860779  1077.547363   \n",
       "\n",
       "              mT2          mTi       m_l1j1       m_l1j2      m_l2j1  \\\n",
       "70917   19.612677   348.181213   304.772980   327.348541  314.234222   \n",
       "59459   70.342552   135.631653   117.120461   471.837585  204.428085   \n",
       "65929   76.063339   228.930252   179.216339   226.318878  200.145462   \n",
       "72296  324.226959   578.783264   912.235962  1223.763062  316.836487   \n",
       "70920  143.352966   323.671753   777.200684   289.505768  405.216370   \n",
       "...           ...          ...          ...          ...         ...   \n",
       "55638  212.150146   455.254425   552.030518   700.726318  318.219116   \n",
       "55639   49.499893   328.573456   344.460968   163.113510  993.591431   \n",
       "55640  285.464661  1047.870117  1716.912964   687.926331  741.122986   \n",
       "55642   45.374245   203.999268   326.589935   151.596756   80.632843   \n",
       "55643  108.450798   232.553192   242.530502   197.352432  403.537537   \n",
       "\n",
       "           m_l2j2       mcoll     mcollWW          mjj         mll  \\\n",
       "70917  169.695938  228.121277  291.495880   892.626770  162.492233   \n",
       "59459  106.199913   16.623631  176.293900  1230.585449   68.053810   \n",
       "65929  115.193047   15.702890  162.733032   677.865295   90.181892   \n",
       "72296  401.437958  461.371277  494.615082  1791.726929  366.175690   \n",
       "70920  254.384308  282.466492  344.249573  1798.984741  160.613968   \n",
       "...           ...         ...         ...          ...         ...   \n",
       "55638  125.398819  507.944580  536.029236   590.004517  269.947052   \n",
       "55639   88.793961   95.524620  199.770462   789.160889  235.965881   \n",
       "55640  387.314819  497.502930  532.741943  2428.071045  483.755768   \n",
       "55642  212.333572   46.561543  168.867645   808.247803   79.953590   \n",
       "55643  174.999344  164.583847  233.529099   927.836975  100.601738   \n",
       "\n",
       "             mtw1        mtw2  phi_miss   proxyW_l1   proxyW_l2     pt_miss  \\\n",
       "70917  162.029800  126.814606 -1.454762  272.820129  142.813248  167.584045   \n",
       "59459   87.136017   43.726074 -1.837532   90.733963   74.377235   64.846382   \n",
       "65929  157.723129   96.157379  1.912421  186.766861   96.924858  117.198364   \n",
       "72296  421.634705   54.264511 -0.965607  444.925842   54.281044  100.518684   \n",
       "70920  153.676437  159.167786 -2.992184  226.931168  165.737137  241.246521   \n",
       "...           ...         ...       ...         ...         ...         ...   \n",
       "55638  299.907959   55.955212 -0.078471  351.912720  102.677315  132.524811   \n",
       "55639  147.921600   86.890663 -0.743846  177.235519  144.458282   78.068459   \n",
       "55640  876.886963   64.360344 -3.073821  922.722656  112.226982  390.570801   \n",
       "55642  129.608551   87.170731  2.369066  166.112839   87.347855   79.184219   \n",
       "55643   98.207870  145.716476 -2.546653  150.579590  145.897446  122.700363   \n",
       "\n",
       "             ptll      recoil       yll  isLL  isMIX  isTT  isBKG  isVBS  \\\n",
       "70917   46.917835  198.921967 -1.217005   0.0    0.0   1.0    0.0    1.0   \n",
       "59459   51.129742   66.961327 -0.264890   0.0    1.0   0.0    0.0    1.0   \n",
       "65929   52.634521   69.154572  0.791606   0.0    0.0   1.0    0.0    1.0   \n",
       "72296  394.193298  300.151855  0.558945   0.0    0.0   1.0    0.0    1.0   \n",
       "70920   80.216133  314.915924  0.768749   0.0    0.0   1.0    0.0    1.0   \n",
       "...           ...         ...       ...   ...    ...   ...    ...    ...   \n",
       "55638  360.387177  389.747437 -0.564144   1.0    0.0   0.0    0.0    1.0   \n",
       "55639   86.813774   24.792519 -0.018384   1.0    0.0   0.0    0.0    1.0   \n",
       "55640  386.144379  102.425438  0.598931   1.0    0.0   0.0    0.0    1.0   \n",
       "55642   75.405815   40.348640  0.897303   1.0    0.0   0.0    0.0    1.0   \n",
       "55643   60.074482  100.857300 -0.670706   1.0    0.0   0.0    0.0    1.0   \n",
       "\n",
       "       notLL  notTT  DNN_SigVsBkg  \n",
       "70917    1.0    0.0      0.808055  \n",
       "59459    1.0    1.0      0.903923  \n",
       "65929    1.0    0.0      0.783956  \n",
       "72296    1.0    0.0      0.908375  \n",
       "70920    1.0    0.0      0.945333  \n",
       "...      ...    ...           ...  \n",
       "55638    0.0    1.0      0.788395  \n",
       "55639    0.0    1.0      0.835519  \n",
       "55640    0.0    1.0      0.998958  \n",
       "55642    0.0    1.0      0.881798  \n",
       "55643    0.0    1.0      0.908858  \n",
       "\n",
       "[13508 rows x 65 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_LL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be847e8c-9b7e-4377-88d6-573fc207d768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-3.09228516e+00  1.49902344e+00  2.18750000e+00 ...  4.69178352e+01\n",
      "   1.98921967e+02 -1.21700537e+00]\n",
      " [ 5.81298828e-01 -3.28076172e+00  1.70410156e+00 ...  5.11297417e+01\n",
      "   6.69613266e+01 -2.64889568e-01]\n",
      " [ 1.42260742e+00 -2.78662109e+00 -6.85424805e-01 ...  3.94193298e+02\n",
      "   3.00151855e+02  5.58944702e-01]\n",
      " ...\n",
      " [-3.79199219e+00  4.08593750e+00 -1.27197266e+00 ...  3.86144379e+02\n",
      "   1.02425438e+02  5.98931074e-01]\n",
      " [-1.73803711e+00  3.50634766e+00 -1.67895508e+00 ...  7.54058151e+01\n",
      "   4.03486404e+01  8.97303402e-01]\n",
      " [-2.63818359e+00  1.10015869e-01  7.11425781e-01 ...  6.00744820e+01\n",
      "   1.00857300e+02 -6.70705855e-01]] [[0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " ...\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    df_LL[col] = np.hstack(df_LL[col])\n",
    "    df_TT[col] = np.hstack(df_TT[col])\n",
    "'''\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "df.dropna(inplace=True)\n",
    "df = shuffle(df)\n",
    "'''\n",
    "# Perform the splitting and define training and validation datasets\n",
    "msk_LL = np.random.rand(len(df_LL)) < 0.8\n",
    "msk_TT = np.random.rand(len(df_TT)) < 0.8\n",
    "\n",
    "df_train_LL = df_LL[msk_LL]\n",
    "df_train_TT = df_TT[msk_TT]\n",
    "df_val_LL   = df_LL[~msk_LL]\n",
    "df_val_TT   = df_TT[~msk_TT]\n",
    "\n",
    "X_LL = df_train_LL.values[:,0:NDIM]\n",
    "Y_LL = df_train_LL[['isLL', 'notLL']].values\n",
    "#Y_LL = df_train_LL[['isLL']].values\n",
    "X_val_LL = df_val_LL.values[:,0:NDIM]\n",
    "Y_val_LL = df_val_LL[['isLL', 'notLL']].values\n",
    "#Y_val_LL = df_val_LL[['isLL']].values\n",
    "\n",
    "X_TT = df_train_TT.values[:,0:NDIM]\n",
    "Y_TT = df_train_TT[['isTT', 'notTT']].values\n",
    "#Y_TT = df_train_TT[['isTT']].values\n",
    "X_val_TT = df_val_TT.values[:,0:NDIM]\n",
    "Y_val_TT = df_val_TT[['isTT', 'notTT']].values\n",
    "#Y_val_TT = df_val_TT[['isTT']].values\n",
    "\n",
    "#X_val = df_val.values[:,0:NDIM+1]\n",
    "#Y_val = df_val.values[:,NDIM+1:NDIM+1+nclasses] # isLL, isMIX, isTT, isBKG\n",
    "print(X_LL,Y_LL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c21764e-e053-40e7-a288-9e7fe2763a48",
   "metadata": {},
   "outputs": [],
   "source": [
    "dnnLL = SimpleNeuralNetwork(200, learning_rate=0.0001, N_NODES=50, n_layers=8, n_features=X_LL.shape[1], n_outputsC=2)\n",
    "dnnLL.save_weights(\"LLvsOther_emu_RDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "99f11e98-eafd-4202-8ec6-6044171a86cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10827, 57) (10827, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X_LL.shape, Y_LL.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "63f73e91-f4f2-44ef-8baa-b762ce41b801",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [00:05<00:00, 38.60it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = dnnLL.fit (X_LL.astype(np.float32), Y_LL.astype(np.float32), X_val_LL.astype(np.float32), Y_val_LL.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66ab7ce2-9739-480f-88e6-2730bda10ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_predict_train_LL = dnnLL.predict_proba(X_LL)\n",
    "Y_predict_val_LL = dnnLL.predict_proba(X_val_LL)\n",
    "\n",
    "axis = np.linspace(0,1,20)\n",
    "\n",
    "plt.hist(dnnLL.predict_proba( df_val_LL[ df_val_LL['isLL']==1 ].values[:,0:NDIM] ).numpy()[...,0], bins = axis, label = 'isLL class - isLL sample', histtype='step', color='r',  density=True, linewidth=2 )\n",
    "plt.hist(dnnLL.predict_proba( df_val_LL[ df_val_LL['isLL']==0 ].values[:,0:NDIM] ).numpy()[...,0], bins = axis, label = 'isLL class - isnotLL sample', histtype='step', color='b',  density=True, linewidth=2 )\n",
    "plt.title('Validation dataset')\n",
    "plt.xlabel('isLL Probability')\n",
    "plt.legend(frameon=False, bbox_to_anchor=(1.05, 1.))\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ac511b3c-889a-41a0-8484-76841bf345c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier categorical accuracy =  0.669899291309213\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from scipy.stats import ks_2samp, anderson_ksamp\n",
    "\n",
    "X_LL = df_val_LL.values[:,0:NDIM]\n",
    "Y_true_LL = df_val_LL[['isLL', 'notLL']].values\n",
    "Y_pred_LL = dnnLL.predict_proba(X_LL)\n",
    "\n",
    "# The classifier categorical accuracy is a good and simple metrics to evaluate the overall classification performance\n",
    "Y_true_max_LL = np.argmax(Y_true_LL, axis=1)\n",
    "Y_pred_max_LL = np.argmax(Y_pred_LL, axis=1)\n",
    "print(\"Classifier categorical accuracy = \", accuracy_score(Y_true_max_LL, Y_pred_max_LL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "21632233-f418-4238-af28-48a0d7c934ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = confusion_matrix(Y_true_max_LL, Y_pred_max_LL, normalize=\"true\")\n",
    "C = np.around(C, decimals=3)\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(C, classes=['isLL', 'notLL'], title='')\n",
    "plt.show(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "86c893f8-a7b7-441f-b144-e66a18bb4906",
   "metadata": {},
   "outputs": [],
   "source": [
    "dnnTT = SimpleNeuralNetwork(210, learning_rate=0.0001, N_NODES=50, n_layers=8, n_features=X_TT.shape[1], n_outputsC=2)\n",
    "dnnTT.save_weights(\"TTvsOther_emu_RDF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c4e300d-5fa1-4e71-a5ef-267d5beb01e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 210/210 [00:05<00:00, 38.81it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = dnnTT.fit (X_TT.astype(np.float32), Y_TT.astype(np.float32), X_val_TT.astype(np.float32), Y_val_TT.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c961815e-a9f8-42ae-996e-6679723ba022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_predict_train_TT = dnnTT.predict_proba(X_TT)\n",
    "Y_predict_val_TT = dnnTT.predict_proba(X_val_TT)\n",
    "\n",
    "axis = np.linspace(0,1,20)\n",
    "\n",
    "plt.hist(dnnTT.predict_proba( df_val_TT[ df_val_TT['isTT']==1 ].values[:,0:NDIM] ).numpy()[...,0], bins = axis, label = 'isTT class - isTT sample', histtype='step', color='r',  density=True, linewidth=2 )\n",
    "plt.hist(dnnTT.predict_proba( df_val_TT[ df_val_TT['isTT']==0 ].values[:,0:NDIM] ).numpy()[...,0], bins = axis, label = 'isTT class - isnotTT sample', histtype='step', color='b',  density=True, linewidth=2 )\n",
    "plt.title('Validation dataset')\n",
    "plt.xlabel('isTT Probability')\n",
    "plt.legend(frameon=False, bbox_to_anchor=(1.05, 1.))\n",
    "#plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5948ad04-3997-4577-890b-0cd619f9351c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier categorical accuracy =  0.679002726918582\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from scipy.stats import ks_2samp, anderson_ksamp\n",
    "\n",
    "X_TT = df_val_TT.values[:,0:NDIM]\n",
    "Y_true_TT = df_val_TT[['isTT', 'notTT']].values\n",
    "Y_pred_TT = dnnTT.predict_proba(X_TT)\n",
    "\n",
    "# The classifier categorical accuracy is a good and simple metrics to evaluate the overall classification performance\n",
    "Y_true_max_TT = np.argmax(Y_true_TT, axis=1)\n",
    "Y_pred_max_TT = np.argmax(Y_pred_TT, axis=1)\n",
    "print(\"Classifier categorical accuracy = \", accuracy_score(Y_true_max_TT, Y_pred_max_TT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "68a8bade-814c-414d-8c1d-fd64024cac1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = confusion_matrix(Y_true_max_TT, Y_pred_max_TT, normalize=\"true\")\n",
    "C = np.around(C, decimals=3)\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(C, classes=['isTT', 'notTT'], title='')\n",
    "plt.show(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1234dc6-62fb-4667-bdc4-46e3d3a06b91",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2681\n",
      "2681\n",
      "       CleanJet_eta_1  CleanJet_eta_2  CleanJet_phi_1  CleanJet_phi_2  \\\n",
      "65929        2.535156       -1.600830       -1.598877        1.245605   \n",
      "56774       -3.569336        3.067383       -1.486084        0.841431   \n",
      "73752        2.403320       -1.788086       -3.137695       -0.072617   \n",
      "65413        1.079346       -1.475342       -2.329590       -1.080811   \n",
      "64848        2.161133       -2.320312       -2.862305        1.224854   \n",
      "...               ...             ...             ...             ...   \n",
      "55607       -1.851562        3.745605       -1.714355        0.733032   \n",
      "55608       -2.174805        1.967773        2.919922       -0.133667   \n",
      "55632        3.305664       -0.553223       -1.116455        2.641113   \n",
      "55637       -3.242188        4.319336       -1.834473       -0.822876   \n",
      "55638       -1.153320        1.540771       -2.131836       -2.070312   \n",
      "\n",
      "       CleanJet_pt_1  CleanJet_pt_2  Lepton_eta_1  Lepton_eta_2  Lepton_phi_1  \\\n",
      "65929     133.824417      53.164108      1.101562      0.198730     -0.389038   \n",
      "56774      67.427391      51.092205     -1.452637      2.044434     -1.866211   \n",
      "73752      98.303955      50.026268     -1.126465      0.911133      0.093460   \n",
      "65413     242.033112     106.643234      1.643555     -0.582886      1.286377   \n",
      "64848     101.381371      61.885746     -1.282715      0.307190     -1.754639   \n",
      "...              ...            ...           ...           ...           ...   \n",
      "55607     165.994385      79.647423      1.716797      1.496826     -3.049316   \n",
      "55608     468.020416     450.303162     -1.032227      1.896484      2.945312   \n",
      "55632     127.487320     120.138176      1.768799      0.518188      1.648438   \n",
      "55637     124.837799      43.997906     -2.273926     -0.646973     -0.726440   \n",
      "55638     235.171143     114.415413     -0.848755      1.215820      1.506836   \n",
      "\n",
      "       Lepton_phi_2  Lepton_pt_1  Lepton_pt_2        Rpt   Zepp_l1   Zepp_l2  \\\n",
      "65929     -2.561523    63.651546    31.910093   0.285485  0.634399 -0.268433   \n",
      "56774     -0.266724    70.450722    37.562996   0.768165 -1.201660  2.295410   \n",
      "73752     -2.256836    74.690331    22.744308   0.345436 -1.434082  0.603516   \n",
      "65413      1.213135   184.612869    36.790405   0.263141  1.841553 -0.384888   \n",
      "64848      1.146484   122.672966    89.156967   1.743233 -1.203125  0.386780   \n",
      "...             ...          ...          ...        ...       ...       ...   \n",
      "55607      1.582031   102.800598    46.249138   0.359612  0.769775  0.549805   \n",
      "55608      2.630371    67.758385    30.780602   0.009896 -0.928711  2.000000   \n",
      "55632      1.103027    57.663784    39.798496   0.149838  0.392578 -0.858032   \n",
      "55637      2.179199   357.299316   337.370422  21.946289 -2.812500 -1.185547   \n",
      "55638      0.788208   334.497192    33.498402   0.416436 -1.042480  1.022095   \n",
      "\n",
      "        Zepp_ll    detajj    detall    dphijj  dphilep1jet1  dphilep1jet2  \\\n",
      "65929  0.182983  4.135986  0.902832  2.844482      1.209839      1.634644   \n",
      "56774  0.546875  6.636719  3.497070  2.327515      0.380127      2.707642   \n",
      "73752  0.415283  4.191406  2.037598  3.065079      3.052030      0.166077   \n",
      "65413  0.728333  2.554688  2.226440  1.248779      2.667218      2.367188   \n",
      "64848  0.408173  4.481445  1.589905  2.196027      1.107666      2.979492   \n",
      "...         ...       ...       ...       ...           ...           ...   \n",
      "55607  0.659790  5.597168  0.219971  2.447388      1.334961      2.500837   \n",
      "55608  0.535645  4.142578  2.928711  3.053589      0.025391      3.078979   \n",
      "55632  0.232727  3.858887  1.250610  2.525617      2.764893      0.992676   \n",
      "55637  1.999023  7.561523  1.626953  1.011597      1.108032      0.096436   \n",
      "55638  0.010193  2.694092  2.064575  0.061523      2.644513      2.706037   \n",
      "\n",
      "       dphilep1jj  dphilep2jet1  dphilep2jet2  dphilep2jj    dphill  \\\n",
      "65929    1.024448      0.962646      2.476056    1.148037  2.172485   \n",
      "56774    1.234455      1.219360      1.108154    0.365032  1.599487   \n",
      "73752    3.130835      0.880859      2.184219    0.802054  2.350296   \n",
      "65413    3.018794      2.740461      2.293945    3.092036  0.073242   \n",
      "64848    1.763893      2.274396      0.078369    1.618169  2.901123   \n",
      "...           ...           ...           ...         ...       ...   \n",
      "55607    1.787602      2.986799      0.848999    2.843746  1.651838   \n",
      "55608    1.139207      0.289551      2.764038    0.824266  0.314941   \n",
      "55632    2.348511      2.219482      1.538086    2.893921  0.545410   \n",
      "55637    0.861458      2.269513      3.002075    2.516088  2.905640   \n",
      "55638    2.664646      2.920044      2.858521    2.899911  0.718628   \n",
      "\n",
      "       dphilljet  dphilljetjet  dphillmet  dphilmet1  dphilmet2   dr_l1j1  \\\n",
      "65929   0.686486      0.501095   2.824813   2.301459   1.809241  1.875873   \n",
      "56774   0.115966      0.738362   2.793994   2.297901   2.385797  2.150561   \n",
      "73752   2.962251      2.883446   2.309750   2.040846   1.892043  4.785368   \n",
      "65413   2.679383      3.030959   0.656768   0.668933   0.595691  3.659720   \n",
      "64848   1.639579      2.295806   1.284631   1.816544   1.084579  3.617597   \n",
      "...          ...           ...        ...        ...        ...       ...   \n",
      "55607   1.770518      2.223158   1.893368   2.328924   0.677086  3.809896   \n",
      "55608   0.072565      1.041252   2.741843   2.643888   2.958829  1.142860   \n",
      "55632   2.543408      2.569996   1.928727   2.150212   1.604801  3.163319   \n",
      "55637   2.323379      2.076804   1.595305   2.810651   0.094989  1.471484   \n",
      "55638   2.705746      2.725879   1.524074   1.585307   0.866679  3.651396   \n",
      "\n",
      "        dr_l1j2   dr_l2j1   dr_l2j2      drll           ht      m2ljj30  \\\n",
      "65929  3.158320  2.526969  4.211015  2.352615   399.748535   776.867737   \n",
      "56774  5.268956  5.744671  1.508122  3.845499   362.720703  1945.613281   \n",
      "73752  0.682147  1.732783  3.472261  3.110578   323.264648   802.732666   \n",
      "65413  3.915494  3.913299  2.461435  2.227645   635.564636  1054.973267   \n",
      "64848  3.154993  4.416729  2.628671  3.308219   447.566101  1092.592285   \n",
      "...         ...       ...       ...       ...          ...          ...   \n",
      "55607  4.292112  4.698709  2.403707  1.666420   490.388397  2129.625732   \n",
      "55608  4.298851  4.081573  2.764957  2.945596  1180.379395  3914.484131   \n",
      "55632  2.525310  3.563162  1.874468  1.364367   483.040131   988.411865   \n",
      "55637  6.593967  4.779613  5.803161  3.330123  1051.636353  5074.486816   \n",
      "55638  4.301840  3.760251  2.876931  2.186069   916.897461  1153.941650   \n",
      "\n",
      "              mT2          mTi      m_l1j1       m_l1j2      m_l2j1  \\\n",
      "65929   76.063339   228.930252  179.216339   226.318878  200.145462   \n",
      "56774   95.619888   376.930054  177.025528   580.749268  832.366028   \n",
      "73752    0.001170   182.278137  515.280090    44.107468   88.036797   \n",
      "65413   58.363331   312.587585  432.090790   689.447693  258.106323   \n",
      "64848   91.945587   351.359375  615.517395   197.965851  266.448730   \n",
      "...           ...          ...         ...          ...         ...   \n",
      "55607   54.264477   307.298462  773.120789   277.091614  483.747681   \n",
      "55608  119.693954   338.175568  217.396439   821.909485  904.668457   \n",
      "55632  108.100677   240.899185  223.472794   253.317398  298.849274   \n",
      "55637  289.686981  1298.765991  308.273529  3383.374512  788.631775   \n",
      "55638  212.150146   455.254425  552.030518   700.726318  318.219116   \n",
      "\n",
      "            m_l2j2       mcoll     mcollWW          mjj         mll  \\\n",
      "65929   115.193047   15.702890  162.733032   677.865295   90.181892   \n",
      "56774    66.666862  104.873444  192.005188  1622.458862  295.990479   \n",
      "73752   135.650070   67.249184  187.388107   579.286072  125.071785   \n",
      "65413   129.424149  398.093323  454.717957   566.525330  223.887192   \n",
      "64848   256.557434  301.825836  343.583435   749.780334  277.674988   \n",
      "...            ...         ...         ...          ...         ...   \n",
      "55607   175.230988   99.416725  202.669937  1893.748047  102.516487   \n",
      "55608   232.004135  280.571228  342.175781  3700.762939  187.494644   \n",
      "55632   124.638618    3.713211  165.566605   868.647217   68.906868   \n",
      "55637  1469.544067  824.845276  840.764160  3248.959229  933.517578   \n",
      "55638   125.398819  507.944580  536.029236   590.004517  269.947052   \n",
      "\n",
      "             mtw1        mtw2  phi_miss   proxyW_l1   proxyW_l2     pt_miss  \\\n",
      "65929  157.723129   96.157379  1.912421  186.766861   96.924858  117.198364   \n",
      "56774  129.570084   96.385185  2.119073  171.570633  158.206909   71.574043   \n",
      "73752   97.388481   51.141788  2.134306  118.672203   59.154694   43.699440   \n",
      "65413   72.186607   28.808365  0.617444  214.274643   40.888966   65.484970   \n",
      "64848  148.679291   82.969147  0.061905  197.108139   86.593292   72.469070   \n",
      "...           ...         ...       ...         ...         ...         ...   \n",
      "55607  182.218140   44.189281  0.904945  264.685638  117.746445   95.696831   \n",
      "55608  182.773148  126.573288 -0.693985  209.166656  188.302368  131.213562   \n",
      "55632  137.317062   93.249390 -0.501774  208.408875   99.247185  105.651459   \n",
      "55637  511.449890   23.921661  2.084211  887.276367  167.567078  188.130814   \n",
      "55638  299.907959   55.955212 -0.078471  351.912720  102.677315  132.524811   \n",
      "\n",
      "             ptll      recoil       yll  isLL  isMIX  isTT  isBKG  isVBS  \\\n",
      "65929   52.634521   69.154572  0.791606   0.0    0.0   1.0    0.0    1.0   \n",
      "56774   78.882515   26.995203  0.001066   0.0    1.0   0.0    0.0    1.0   \n",
      "73752   60.891083   45.089287 -0.543498   0.0    0.0   1.0    0.0    1.0   \n",
      "65413  221.321014  276.093506  1.131108   0.0    0.0   1.0    0.0    1.0   \n",
      "64848   41.865593   93.366600 -0.592763   0.0    1.0   0.0    0.0    1.0   \n",
      "...           ...         ...       ...   ...    ...   ...    ...    ...   \n",
      "55607  109.257385  120.275063  1.648398   1.0    0.0   0.0    0.0    1.0   \n",
      "55608   97.492386   56.162697  0.081233   1.0    0.0   0.0    0.0    1.0   \n",
      "55632   93.983902  114.183365  1.245546   1.0    0.0   0.0    0.0    1.0   \n",
      "55637   84.125671  204.192657 -1.479716   1.0    0.0   0.0    0.0    1.0   \n",
      "55638  360.387177  389.747437 -0.564144   1.0    0.0   0.0    0.0    1.0   \n",
      "\n",
      "       notLL  notTT  DNN_SigVsBkg    DNN_LL    DNN_TT  \n",
      "65929    1.0    0.0      0.783956  0.559171  0.626170  \n",
      "56774    1.0    1.0      0.965528  0.732645  0.161980  \n",
      "73752    1.0    0.0      0.669277  0.583842  0.540125  \n",
      "65413    1.0    0.0      0.574626  0.382765  0.376061  \n",
      "64848    1.0    1.0      0.661974  0.247384  0.693116  \n",
      "...      ...    ...           ...       ...       ...  \n",
      "55607    0.0    1.0      0.936746  0.260986  0.665577  \n",
      "55608    0.0    1.0      0.998179  0.809820  0.097179  \n",
      "55632    0.0    1.0      0.563586  0.583539  0.342231  \n",
      "55637    0.0    1.0      0.985114  0.995291  0.394148  \n",
      "55638    0.0    1.0      0.788395  0.106477  0.808125  \n",
      "\n",
      "[2681 rows x 67 columns]\n",
      "       CleanJet_eta_1  CleanJet_eta_2  CleanJet_phi_1  CleanJet_phi_2  \\\n",
      "4           -1.772705        2.803223       -0.822388        2.676758   \n",
      "11           1.317871       -4.582031       -1.584961        1.491455   \n",
      "39           3.522461       -3.313965       -0.613159        2.713867   \n",
      "64           1.144287       -2.574219       -1.495361        1.861816   \n",
      "68           3.281738       -4.096680       -2.347168        1.082520   \n",
      "...               ...             ...             ...             ...   \n",
      "83454       -2.627930        0.599609       -0.184845       -2.418457   \n",
      "83459       -2.026855        3.253906       -2.402344        1.454834   \n",
      "83460       -0.834106        2.685059        1.628174       -2.746094   \n",
      "83461       -2.250977        2.995605       -1.189941        2.155273   \n",
      "83465        1.571533       -1.117676        1.427246       -2.002441   \n",
      "\n",
      "       CleanJet_pt_1  CleanJet_pt_2  Lepton_eta_1  Lepton_eta_2  Lepton_phi_1  \\\n",
      "4         202.039673      69.501266      1.101318      1.426025      2.102539   \n",
      "11        101.038025      94.866104     -1.010498     -1.590332      0.322693   \n",
      "39        205.036591      77.703690      0.237122      1.539551      2.350098   \n",
      "64        144.442429     140.436874     -1.401123     -0.437195     -1.361328   \n",
      "68        176.970276      46.323917      1.863281      1.138184      0.660034   \n",
      "...              ...            ...           ...           ...           ...   \n",
      "83454     411.385712     381.445374     -1.287598     -1.757324      2.855469   \n",
      "83459      45.631340      40.802193     -1.181396      0.830200      0.772095   \n",
      "83460     456.183105     116.797783     -1.006836      0.755371     -0.369995   \n",
      "83461      51.149643      30.781250      1.046387      0.910767      2.294434   \n",
      "83465     171.025955     148.470657      0.758179      1.452148     -0.606445   \n",
      "\n",
      "       Lepton_phi_2  Lepton_pt_1  Lepton_pt_2       Rpt   Zepp_l1   Zepp_l2  \\\n",
      "4         -3.007812    67.372704    38.399914  0.184240  0.586060  0.910767   \n",
      "11         2.486816    46.751190    24.824713  0.121082  0.621582  0.041748   \n",
      "39        -2.335449    86.019882    22.896854  0.123624  0.132874  1.435303   \n",
      "64        -2.594238    78.228027    16.318880  0.062933 -0.686157  0.277771   \n",
      "68        -0.067245    57.055382    56.650059  0.394268  2.270752  1.545654   \n",
      "...             ...          ...          ...       ...       ...       ...   \n",
      "83454     -0.086731   329.594971    56.889927  0.119491 -0.273438 -0.743164   \n",
      "83459      0.179291    67.708321    40.211052  1.462314 -1.794922  0.216675   \n",
      "83460     -1.878906   222.016647    49.550236  0.206470 -1.932312 -0.170105   \n",
      "83461     -1.092285    89.732719    46.114021  2.628179  0.674072  0.538452   \n",
      "83465      2.354004   183.018738    87.263596  0.628964  0.531250  1.225220   \n",
      "\n",
      "        Zepp_ll    detajj    detall    dphijj  dphilep1jet1  dphilep1jet2  \\\n",
      "4      0.748413  4.575928  0.324707  2.784040      2.924927      0.574219   \n",
      "11     0.331665  5.899902  0.579834  3.076416      1.907654      1.168762   \n",
      "39     0.784088  6.836426  1.302429  2.956159      2.963257      0.363770   \n",
      "64     0.204193  3.718506  0.963928  2.926008      0.134033      3.060041   \n",
      "68     1.908203  7.378418  0.725098  2.853498      3.007202      0.422485   \n",
      "...         ...       ...       ...       ...           ...           ...   \n",
      "83454  0.508301  3.227539  0.469727  2.233612      3.040314      1.009260   \n",
      "83459  0.789124  5.280762  2.011597  2.426008      3.108747      0.682739   \n",
      "83460  1.051208  3.519165  1.762207  1.908918      1.998169      2.376099   \n",
      "83461  0.606262  5.246582  0.135620  2.937970      2.798810      0.139160   \n",
      "83465  0.878235  2.689209  0.693970  2.853498      2.033691      1.395996   \n",
      "\n",
      "       dphilep1jj  dphilep2jet1  dphilep2jet2  dphilep2jj    dphill  \\\n",
      "4        3.100728      2.185425      0.598615    2.009624  1.172834   \n",
      "11       1.137763      2.211408      0.995361    2.981298  2.164124   \n",
      "39       3.074147      1.722290      1.233869    1.611400  1.597638   \n",
      "64       1.467826      1.098877      1.827131    0.234916  1.232910   \n",
      "68       3.106171      2.279922      1.149765    2.378891  0.727280   \n",
      "...           ...           ...           ...         ...       ...   \n",
      "83454    2.203299      0.098114      2.331726    1.137687  2.942200   \n",
      "83459    2.044080      2.581635      1.275543    2.636884  0.592804   \n",
      "83460    2.256237      2.776105      0.867188    2.518038  1.508911   \n",
      "83461    2.510710      0.097656      3.035627    0.385756  2.896466   \n",
      "83465    3.007492      0.926758      1.926740    0.047043  2.960449   \n",
      "\n",
      "       dphilljet  dphilljetjet  dphillmet  dphilmet1  dphilmet2   dr_l1j1  \\\n",
      "4       2.951855      2.776054   1.125174   0.718771   1.891605  4.100635   \n",
      "11      2.467070      1.697179   2.686503   3.037266   1.081796  3.010057   \n",
      "39      3.058082      2.947192   0.845168   0.583321   2.180960  4.424291   \n",
      "64      0.048011      1.285782   2.712869   2.530825   2.519450  2.548937   \n",
      "68      2.644919      2.743887   1.765949   1.403666   2.130946  3.324949   \n",
      "...          ...           ...        ...        ...        ...       ...   \n",
      "83454   2.999186      2.244426   1.180959   1.222086   1.720113  3.322649   \n",
      "83459   2.955694      2.262824   2.580237   2.798981   2.206177  3.285097   \n",
      "83460   2.214455      2.472522   1.151989   1.368275   0.140637  2.005621   \n",
      "83461   2.555054      2.266954   2.481720   2.237963   1.148756  4.797236   \n",
      "83465   1.873314      2.847115   3.130548   2.992261   0.330475  2.190307   \n",
      "\n",
      "        dr_l1j2   dr_l2j1   dr_l2j2      drll           ht      m2ljj30  \\\n",
      "4      1.796164  3.874011  5.849018  1.216953   484.852448  1374.014404   \n",
      "11     3.757906  5.003700  3.152936  2.240455   296.022888  1956.206177   \n",
      "39     3.569670  2.626446  7.003728  2.061255   465.343201  3983.301758   \n",
      "64     3.429987  1.925777  4.941993  1.565000   433.544098  1046.231445   \n",
      "68     5.974916  3.129357  5.359641  1.026987   417.723145  3849.618408   \n",
      "...         ...       ...       ...       ...          ...          ...   \n",
      "83454  5.601414  0.876117  3.315431  2.979460  1497.734375  2461.748047   \n",
      "83459  4.487543  3.850663  2.738861  2.097126   272.462677   825.685852   \n",
      "83460  4.390436  3.850461  2.115587  2.319954  1117.777222  1860.976074   \n",
      "83461  1.954180  3.163251  3.859169  2.899640   285.494141   729.151184   \n",
      "83465  2.338298  0.934416  5.057926  3.040699   678.337341   987.080994   \n",
      "\n",
      "              mT2         mTi      m_l1j1       m_l1j2      m_l2j1  \\\n",
      "4       44.040474  193.714752  518.392822   137.338638  446.848633   \n",
      "11      27.129892  117.390221  228.726257   393.219757  222.147308   \n",
      "39       3.958272  143.185120  712.307495   469.958069  192.178009   \n",
      "64      89.341599  177.558640  350.462830   247.827637   99.920036   \n",
      "68      66.263412  229.942245  254.250610  1009.819214  316.554230   \n",
      "...           ...         ...         ...          ...         ...   \n",
      "83454    0.117974  528.164185  907.864441   846.473938  141.147064   \n",
      "83459  138.122208  239.422348  121.686691   478.462067  187.737747   \n",
      "83460    9.060924  486.585449  539.925354  1038.613037  398.776367   \n",
      "83461   68.101135  223.153152  364.817749   119.894478  226.338196   \n",
      "83465  119.422691  407.202240  336.290680   415.727631  112.958656   \n",
      "\n",
      "           m_l2j2       mcoll     mcollWW          mjj         mll  \\\n",
      "4       83.816788   29.771696  226.051010  1179.432251   58.687038   \n",
      "11     211.357483   49.315384  177.082474  1876.847412   63.405373   \n",
      "39     476.569611  106.838234  205.021881  3855.958984   88.801819   \n",
      "64     146.227356   46.840839  171.305450   936.685669   54.660000   \n",
      "68     700.405334   31.991850  210.184494  3625.203857   58.401474   \n",
      "...           ...         ...         ...          ...         ...   \n",
      "83454  511.810333  238.089798  332.173126  2040.066895  280.130951   \n",
      "83459  133.329666   14.641098  161.448288   607.496094  127.280487   \n",
      "83460  182.874207  721.588501  739.285889  1356.057495  254.187988   \n",
      "83461  120.253395  118.253784  200.574905   549.911804  127.986633   \n",
      "83465  424.245178  270.082367  326.527466   652.645874  267.144196   \n",
      "\n",
      "             mtw1        mtw2  phi_miss   proxyW_l1   proxyW_l2     pt_miss  \\\n",
      "4       59.872330  104.227180  1.383768  115.313995  144.166290  107.538918   \n",
      "11      72.959816   27.412489 -2.714573   82.496513   54.355755   28.542833   \n",
      "39      45.785252   72.855217  1.766776   49.541439  100.806061   73.686234   \n",
      "64     124.110268   56.582920  1.169497  158.638641   58.078705   54.117878   \n",
      "68      87.630318  118.344261  2.063700  169.898209  143.517624   80.723518   \n",
      "...           ...         ...       ...         ...         ...         ...   \n",
      "83454  247.811356  136.002579  1.633382  387.338104  224.618042  141.513733   \n",
      "83459  143.317749  100.049355 -2.026887  169.767029  110.913910   78.109764   \n",
      "83460  311.319824   16.350344 -1.738270  404.706360   91.469231  273.229431   \n",
      "83461  140.258545   60.721649  0.056471  164.180710   80.383110   67.716522   \n",
      "83465  253.910645   28.919601  2.684479  272.474579  142.145096   88.558395   \n",
      "\n",
      "             ptll      recoil       yll  isLL  isMIX  isTT  isBKG  isVBS  \\\n",
      "4       89.548050  166.987473  1.219559   0.0    0.0   0.0    1.0    0.0   \n",
      "11      38.783005   18.171227 -1.213794   0.0    0.0   0.0    1.0    0.0   \n",
      "39      88.419243  147.968857  0.543502   0.0    0.0   0.0    1.0    0.0   \n",
      "64      85.042900   42.301411 -1.221254   0.0    0.0   0.0    1.0    0.0   \n",
      "68     106.270157  120.343681  1.501971   0.0    0.0   0.0    1.0    0.0   \n",
      "...           ...         ...       ...   ...    ...   ...    ...    ...   \n",
      "83454  274.063965  353.009705 -1.358262   0.0    0.0   0.0    1.0    0.0   \n",
      "83459  103.525406   55.927727 -0.372782   0.0    0.0   0.0    1.0    0.0   \n",
      "83460  230.450302  423.053680 -0.609158   0.0    0.0   0.0    1.0    0.0   \n",
      "83461   46.367916   42.119709  1.000320   0.0    0.0   0.0    1.0    0.0   \n",
      "83465   98.446266    9.941506  0.986386   0.0    0.0   0.0    1.0    0.0   \n",
      "\n",
      "       notLL  notTT  DNN_SigVsBkg    DNN_LL    DNN_TT  \n",
      "4        0.0    0.0      0.844411  0.620403  0.166751  \n",
      "11       0.0    0.0      0.540175  0.673677  0.634153  \n",
      "39       0.0    0.0      0.910292  0.579576  0.298732  \n",
      "64       0.0    0.0      0.850533  0.751953  0.300828  \n",
      "68       0.0    0.0      0.535465  0.563373  0.733364  \n",
      "...      ...    ...           ...       ...       ...  \n",
      "83454    0.0    0.0      0.970081  0.083196  0.592442  \n",
      "83459    0.0    0.0      0.704532  0.816225  0.415527  \n",
      "83460    0.0    0.0      0.799760  0.159825  0.760041  \n",
      "83461    0.0    0.0      0.633890  0.335482  0.457470  \n",
      "83465    0.0    0.0      0.627475  0.225844  0.721732  \n",
      "\n",
      "[5264 rows x 67 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_176/3038950175.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_val_LL['DNN_LL'] = dnnLL.predict_proba(X_val_LL)[...,0].numpy().tolist()\n",
      "/tmp/ipykernel_176/3038950175.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_val_LL['DNN_TT'] = dnnTT.predict_proba(X_val_LL)[...,0].numpy().tolist()\n",
      "/tmp/ipykernel_176/3038950175.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_val_bkg['DNN_LL'] = dnnLL.predict_proba(X_val_bkg)[...,0].numpy().tolist()\n",
      "/tmp/ipykernel_176/3038950175.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_val_bkg['DNN_TT'] = dnnTT.predict_proba(X_val_bkg)[...,0].numpy().tolist()\n"
     ]
    }
   ],
   "source": [
    "print(len(dnnLL.predict_proba(X_val_LL)[...,0]))\n",
    "print(len(dnnTT.predict_proba(X_val_LL)[...,0]))\n",
    "df_val_LL['DNN_LL'] = dnnLL.predict_proba(X_val_LL)[...,0].numpy().tolist()\n",
    "df_val_LL['DNN_TT'] = dnnTT.predict_proba(X_val_LL)[...,0].numpy().tolist()\n",
    "df_val_bkg = df[(df['DNN_SigVsBkg'] > 0.5) & (df['isBKG'] == 1)]\n",
    "X_val_bkg = df_val_bkg.values[:,0:NDIM]\n",
    "df_val_bkg['DNN_LL'] = dnnLL.predict_proba(X_val_bkg)[...,0].numpy().tolist()\n",
    "df_val_bkg['DNN_TT'] = dnnTT.predict_proba(X_val_bkg)[...,0].numpy().tolist()\n",
    "print(df_val_LL)\n",
    "print(df_val_bkg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a93cc097-ea4a-4f84-a256-9703427ff3a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LL = df_val_LL[df_val_LL['isLL'] == 1]\n",
    "mix = df_val_LL[df_val_LL['isMIX'] == 1]\n",
    "TT = df_val_LL[df_val_LL['isTT'] == 1]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "h_isLL = ax.hist2d(LL['DNN_LL'].values, LL['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isLL[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1f0cd41d-696b-4645-8343-fff9ea287e41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "h_isTT = ax.hist2d(TT['DNN_LL'].values, TT['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isTT[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6afe52fe-8b78-4b8b-bb16-5ad97e7bdf04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "h_isMIX = ax.hist2d(mix['DNN_LL'].values, mix['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isMIX[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "72de65a9-e950-497a-9b1b-f190ff3c6bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "h_isBKG = ax.hist2d(df_val_bkg['DNN_LL'].values, df_val_bkg['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isBKG[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff3b2c7c-47e9-4261-878a-e5b2f6dc9404",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_176/3595125263.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_val_TT['DNN_LL'] = dnnLL.predict_proba(X_val_TT)[...,0].numpy().tolist()\n",
      "/tmp/ipykernel_176/3595125263.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_val_TT['DNN_TT'] = dnnTT.predict_proba(X_val_TT)[...,0].numpy().tolist()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_val_TT['DNN_LL'] = dnnLL.predict_proba(X_val_TT)[...,0].numpy().tolist()\n",
    "df_val_TT['DNN_TT'] = dnnTT.predict_proba(X_val_TT)[...,0].numpy().tolist()\n",
    "\n",
    "LL = df_val_TT[df_val_TT['isLL'] == 1]\n",
    "mix = df_val_TT[df_val_TT['isMIX'] == 1]\n",
    "TT = df_val_TT[df_val_TT['isTT'] == 1]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "h_isLL = ax.hist2d(LL['DNN_LL'].values, LL['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isLL[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c09e08d4-55cb-4265-ba47-5d5ef43c2d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "h_isTT = ax.hist2d(TT['DNN_LL'].values, TT['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isTT[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bac8cc9a-7ec8-47be-97e2-7b4262e3433d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "h_isMIX = ax.hist2d(mix['DNN_LL'].values, mix['DNN_TT'].values, density=True)\n",
    "fig.colorbar(h_isMIX[3], ax=ax)\n",
    "plt.ylabel('DNN TT probability')\n",
    "plt.xlabel('DNN LL probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c51de0ef-f2c1-420c-bf18-f5e2e3cdffb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "with open('generated_code_dnn_SigVsBkg_emu.h', 'w') as f:\n",
    "\n",
    "\n",
    "    print (\"\"\"\n",
    "    // Select all (Ctrl+A), Copy (Ctrl+C), paste to an empty text file (Ctrl+V) and save that file as 'generated_code.h'\n",
    "    // \n",
    "    // Auto-generated header file. Assumes img to be a floating point array\n",
    "    // of 64 elements (corresponding to an 8x8 b&w image)\n",
    "\n",
    "    #include <math.h>\n",
    "\n",
    "    float norma_VBS;\n",
    "    \"\"\", file=f)\n",
    "    \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnn.model1.layers):\n",
    "      print(type(layer))\n",
    "#       if type(layer) is not Dropout and type(layer) is not Input :\n",
    "      if type(layer) is Dense:\n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "        print (\"inline float activation_VBS_%d (float x, float n) \" % iLayer_tmp, file=f);\n",
    "        activation =  layer.get_config()['activation'] \n",
    "        if activation == 'sigmoid':\n",
    "          print (\"{ return 1./(1 + exp(-x)); }\", file=f)\n",
    "        elif activation == 'tanh':\n",
    "          print (\"{ return tanh(x);}\")\n",
    "        elif activation == 'relu':\n",
    "          print (\"{ return x > 0 ? x : 0;}\", file=f)\n",
    "        elif activation == 'linear':\n",
    "          print (\"{ return x;}\")\n",
    "        elif activation == 'softmax':\n",
    "          print (\"{ return exp(x)/n;  }\", file=f)\n",
    "        else:\n",
    "          raise KeyError (\"Unexpected activation %s\"%activation)\n",
    "      \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    \n",
    "    print (\"\"\"\n",
    "    float guess_VBS (const float *img, int flag)\n",
    "    {\n",
    "    \"\"\", file=f)\n",
    "\n",
    "    max_out = 100\n",
    "    for iLayer, layer in enumerate(dnn.model1.layers):\n",
    "      if type(layer) is Dense:\n",
    "        iLayer_tmp+=1\n",
    "        print (\"  // Declare the arrays in the stack\", file=f)\n",
    "        kernel, bias = layer.get_weights()\n",
    "        \n",
    "        print (type (dnn.means))\n",
    "        print (type(bias))\n",
    "        \n",
    "         \n",
    "        max_out = max(kernel.shape[1], max_out)\n",
    "        #print (\"  //\",bias.shape)\n",
    "        kernel_values = \"{%s}\"%(',\\n   '.join([\"{%s}\"%(','.join([\"%18.13f\"%x for x in row])) for row in kernel]))\n",
    "        bias_values   = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in bias]))\n",
    "        print (\"  const float kernel_%d[%d][%d] = \\n  %s;\" % (iLayer_tmp, kernel.shape[0], kernel.shape[1],kernel_values), file=f)\n",
    "        print (\"  const float bias_%d[%d] = %s;\" % (iLayer_tmp, bias.shape[0], bias_values), file=f)\n",
    "      \n",
    "    print (\"  float buffer_in[%d];\" % max_out, file=f)\n",
    "    print (\"  float buffer_out[%d];\" % max_out, file=f)\n",
    "\n",
    "    print (\"  unsigned int i,j,c; \", file=f)\n",
    "    \n",
    "\n",
    "    mean_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnn.means]))\n",
    "    sigma_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnn.sigmas]))\n",
    "    \n",
    "\n",
    "    print (\" float mean[%d] = %s;\" % (max_out, mean_values), file=f)\n",
    "    print (\" float sigma[%d] = %s;\" % (max_out, sigma_values), file=f)\n",
    "\n",
    "    print (\"\\n\\n\\n\", file=f)\n",
    "    print (\"  // Load the input in the buffer\", file=f)\n",
    "    print (\"  for (c = 0; c < 64; ++c) \\n  buffer_in[c] = (img[c]-mean[c])/sigma[c];\", file=f)\n",
    "\n",
    "\n",
    "\n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnn.model1.layers):\n",
    "      if type(layer) is Dense: \n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "\n",
    "        if iLayer != len(dnn.model1.layers)-1:\n",
    "            \n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_VBS = 0;\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_VBS_{iLayer_tmp}(buffer_out[c], norma_VBS);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_VBS = 0;\n",
    "\n",
    "            for(c=0;c<{n_out};++c)\n",
    "              norma_VBS+=exp(buffer_out[c]);\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_VBS_{iLayer_tmp}(buffer_out[c], norma_VBS);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "      \n",
    "    last_kernel, last_bias = dnn.model1.layers[-1].get_weights()\n",
    "    print (\"\"\"\n",
    "      //i = 0;\n",
    "      //for (c = 0; c < {n_out}; ++c)\n",
    "      //  if (buffer_in[c] > buffer_in[i])\n",
    "      //    i = c;\n",
    "      \n",
    "      if (flag==0) //vbf\n",
    "        return buffer_in[0];\n",
    "      else if (flag==1) //bkg\n",
    "        return buffer_in[1];\n",
    "         \n",
    "      \n",
    "      //return i;\n",
    "    \"\"\".format(n_out = last_kernel.shape[1]), file=f)\n",
    "\n",
    "    print (\"}\", file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4c523d50-4f12-4c56-858f-0800889fe9d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "with open('generated_code_dnn_emu_LLVsOther.h', 'w') as f:\n",
    "\n",
    "\n",
    "    print (\"\"\"\n",
    "    // Select all (Ctrl+A), Copy (Ctrl+C), paste to an empty text file (Ctrl+V) and save that file as 'generated_code.h'\n",
    "    // \n",
    "    // Auto-generated header file. Assumes img to be a floating point array\n",
    "    // of 64 elements (corresponding to an 8x8 b&w image)\n",
    "\n",
    "    #include <math.h>\n",
    "\n",
    "    float norma_LL;\n",
    "    \"\"\", file=f)\n",
    "    \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnnLL.model1.layers):\n",
    "      print(type(layer))\n",
    "#       if type(layer) is not Dropout and type(layer) is not Input :\n",
    "      if type(layer) is Dense:\n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "        print (\"inline float activation_LL_%d (float x, float n) \" % iLayer_tmp, file=f);\n",
    "        activation =  layer.get_config()['activation'] \n",
    "        if activation == 'sigmoid':\n",
    "          print (\"{ return 1./(1 + exp(-x)); }\", file=f)\n",
    "        elif activation == 'tanh':\n",
    "          print (\"{ return tanh(x);}\")\n",
    "        elif activation == 'relu':\n",
    "          print (\"{ return x > 0 ? x : 0;}\", file=f)\n",
    "        elif activation == 'linear':\n",
    "          print (\"{ return x;}\")\n",
    "        elif activation == 'softmax':\n",
    "          print (\"{ return exp(x)/n;  }\", file=f)\n",
    "        else:\n",
    "          raise KeyError (\"Unexpected activation %s\"%activation)\n",
    "      \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    \n",
    "    print (\"\"\"\n",
    "    float guess_LL (const float *img, int flag)\n",
    "    {\n",
    "    \"\"\", file=f)\n",
    "\n",
    "    max_out = 100\n",
    "    for iLayer, layer in enumerate(dnnLL.model1.layers):\n",
    "      if type(layer) is Dense:\n",
    "        iLayer_tmp+=1\n",
    "        print (\"  // Declare the arrays in the stack\", file=f)\n",
    "        kernel, bias = layer.get_weights()\n",
    "        \n",
    "        print (type (dnnLL.means))\n",
    "        print (type(bias))\n",
    "        \n",
    "         \n",
    "        max_out = max(kernel.shape[1], max_out)\n",
    "        #print (\"  //\",bias.shape)\n",
    "        kernel_values = \"{%s}\"%(',\\n   '.join([\"{%s}\"%(','.join([\"%18.13f\"%x for x in row])) for row in kernel]))\n",
    "        bias_values   = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in bias]))\n",
    "        print (\"  const float kernel_%d[%d][%d] = \\n  %s;\" % (iLayer_tmp, kernel.shape[0], kernel.shape[1],kernel_values), file=f)\n",
    "        print (\"  const float bias_%d[%d] = %s;\" % (iLayer_tmp, bias.shape[0], bias_values), file=f)\n",
    "      \n",
    "    print (\"  float buffer_in[%d];\" % max_out, file=f)\n",
    "    print (\"  float buffer_out[%d];\" % max_out, file=f)\n",
    "\n",
    "    print (\"  unsigned int i,j,c; \", file=f)\n",
    "    \n",
    "\n",
    "    mean_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnnLL.means]))\n",
    "    sigma_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnnLL.sigmas]))\n",
    "    \n",
    "\n",
    "    print (\" float mean[%d] = %s;\" % (max_out, mean_values), file=f)\n",
    "    print (\" float sigma[%d] = %s;\" % (max_out, sigma_values), file=f)\n",
    "\n",
    "    print (\"\\n\\n\\n\", file=f)\n",
    "    print (\"  // Load the input in the buffer\", file=f)\n",
    "    print (\"  for (c = 0; c < 64; ++c) \\n  buffer_in[c] = (img[c]-mean[c])/sigma[c];\", file=f)\n",
    "\n",
    "\n",
    "\n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnnLL.model1.layers):\n",
    "      if type(layer) is Dense: \n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "\n",
    "        if iLayer != len(dnnLL.model1.layers)-1:\n",
    "            \n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_LL = 0;\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_LL_{iLayer_tmp}(buffer_out[c], norma_LL);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_LL = 0;\n",
    "\n",
    "            for(c=0;c<{n_out};++c)\n",
    "              norma_LL+=exp(buffer_out[c]);\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_LL{iLayer_tmp}(buffer_out[c], norma_LL);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "      \n",
    "    last_kernel, last_bias = dnnLL.model1.layers[-1].get_weights()\n",
    "    print (\"\"\"\n",
    "      //i = 0;\n",
    "      //for (c = 0; c < {n_out}; ++c)\n",
    "      //  if (buffer_in[c] > buffer_in[i])\n",
    "      //    i = c;\n",
    "      \n",
    "      if (flag==0) //vbf\n",
    "        return buffer_in[0];\n",
    "      else if (flag==1) //bkg\n",
    "        return buffer_in[1];\n",
    "         \n",
    "      \n",
    "      //return i;\n",
    "    \"\"\".format(n_out = last_kernel.shape[1]), file=f)\n",
    "\n",
    "    print (\"}\", file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "72c6e0df-9dac-4ae1-b71f-7a1c7d662fce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "with open('generated_code_dnn_TTVsOther.h', 'w') as f:\n",
    "\n",
    "\n",
    "    print (\"\"\"\n",
    "    // Select all (Ctrl+A), Copy (Ctrl+C), paste to an empty text file (Ctrl+V) and save that file as 'generated_code.h'\n",
    "    // \n",
    "    // Auto-generated header file. Assumes img to be a floating point array\n",
    "    // of 64 elements (corresponding to an 8x8 b&w image)\n",
    "\n",
    "    #include <math.h>\n",
    "\n",
    "    float norma_TT;\n",
    "    \"\"\", file=f)\n",
    "    \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnnTT.model1.layers):\n",
    "      print(type(layer))\n",
    "#       if type(layer) is not Dropout and type(layer) is not Input :\n",
    "      if type(layer) is Dense:\n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "        print (\"inline float activation_TT_%d (float x, float n) \" % iLayer_tmp, file=f);\n",
    "        activation =  layer.get_config()['activation'] \n",
    "        if activation == 'sigmoid':\n",
    "          print (\"{ return 1./(1 + exp(-x)); }\", file=f)\n",
    "        elif activation == 'tanh':\n",
    "          print (\"{ return tanh(x);}\")\n",
    "        elif activation == 'relu':\n",
    "          print (\"{ return x > 0 ? x : 0;}\", file=f)\n",
    "        elif activation == 'linear':\n",
    "          print (\"{ return x;}\")\n",
    "        elif activation == 'softmax':\n",
    "          print (\"{ return exp(x)/n;  }\", file=f)\n",
    "        else:\n",
    "          raise KeyError (\"Unexpected activation %s\"%activation)\n",
    "      \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    \n",
    "    print (\"\"\"\n",
    "    float guess_TT (const float *img, int flag)\n",
    "    {\n",
    "    \"\"\", file=f)\n",
    "\n",
    "    max_out = 100\n",
    "    for iLayer, layer in enumerate(dnnTT.model1.layers):\n",
    "      if type(layer) is Dense:\n",
    "        iLayer_tmp+=1\n",
    "        print (\"  // Declare the arrays in the stack\", file=f)\n",
    "        kernel, bias = layer.get_weights()\n",
    "        \n",
    "        print (type (dnnTT.means))\n",
    "        print (type(bias))\n",
    "        \n",
    "         \n",
    "        max_out = max(kernel.shape[1], max_out)\n",
    "        #print (\"  //\",bias.shape)\n",
    "        kernel_values = \"{%s}\"%(',\\n   '.join([\"{%s}\"%(','.join([\"%18.13f\"%x for x in row])) for row in kernel]))\n",
    "        bias_values   = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in bias]))\n",
    "        print (\"  const float kernel_%d[%d][%d] = \\n  %s;\" % (iLayer_tmp, kernel.shape[0], kernel.shape[1],kernel_values), file=f)\n",
    "        print (\"  const float bias_%d[%d] = %s;\" % (iLayer_tmp, bias.shape[0], bias_values), file=f)\n",
    "      \n",
    "    print (\"  float buffer_in[%d];\" % max_out, file=f)\n",
    "    print (\"  float buffer_out[%d];\" % max_out, file=f)\n",
    "\n",
    "    print (\"  unsigned int i,j,c; \", file=f)\n",
    "    \n",
    "\n",
    "    mean_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnnTT.means]))\n",
    "    sigma_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnnTT.sigmas]))\n",
    "    \n",
    "\n",
    "    print (\" float mean[%d] = %s;\" % (max_out, mean_values), file=f)\n",
    "    print (\" float sigma[%d] = %s;\" % (max_out, sigma_values), file=f)\n",
    "\n",
    "    print (\"\\n\\n\\n\", file=f)\n",
    "    print (\"  // Load the input in the buffer\", file=f)\n",
    "    print (\"  for (c = 0; c < 64; ++c) \\n  buffer_in[c] = (img[c]-mean[c])/sigma[c];\", file=f)\n",
    "\n",
    "\n",
    "\n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnnTT.model1.layers):\n",
    "      if type(layer) is Dense: \n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "\n",
    "        if iLayer != len(dnnTT.model1.layers)-1:\n",
    "            \n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_TT = 0;\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_TT_{iLayer_tmp}(buffer_out[c], norma_TT);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_TT = 0;\n",
    "\n",
    "            for(c=0;c<{n_out};++c)\n",
    "              norma_TT+=exp(buffer_out[c]);\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_TT_{iLayer_tmp}(buffer_out[c], norma_TT);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "      \n",
    "    last_kernel, last_bias = dnnTT.model1.layers[-1].get_weights()\n",
    "    print (\"\"\"\n",
    "      //i = 0;\n",
    "      //for (c = 0; c < {n_out}; ++c)\n",
    "      //  if (buffer_in[c] > buffer_in[i])\n",
    "      //    i = c;\n",
    "      \n",
    "      if (flag==0) //vbf\n",
    "        return buffer_in[0];\n",
    "      else if (flag==1) //bkg\n",
    "        return buffer_in[1];\n",
    "         \n",
    "      \n",
    "      //return i;\n",
    "    \"\"\".format(n_out = last_kernel.shape[1]), file=f)\n",
    "\n",
    "    print (\"}\", file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d4d6ba-3206-4c98-89ec-24dffc5708cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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