diff --git a/binder/combine.ipynb b/binder/combine.ipynb
index 7fe0f0fcf..3b59453fe 100644
--- a/binder/combine.ipynb
+++ b/binder/combine.ipynb
@@ -115,46 +115,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 130,
    "id": "30a995df",
    "metadata": {},
    "outputs": [],
    "source": [
     "# get lumi\n",
     "import pickle as pkl\n",
-    "with open(\"../combine/templates/v5/hists_templates_Run2.pkl\", \"rb\") as f:    \n",
+    "with open(\"../combine/templates/v6/hists_templates_Run2.pkl\", \"rb\") as f:    \n",
     "    h = pkl.load(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 131,
    "id": "ce58e9a3",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "IndexError",
+     "evalue": "Wrong number of indices for histogram",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[131], line 47\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mutilsCombine\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_hists\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mutilsCombine2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m plot_hists\n\u001b[0;32m---> 47\u001b[0m plot_hists(\u001b[43mh\u001b[49m\u001b[43m[\u001b[49m\u001b[43mregion\u001b[49m\u001b[43m]\u001b[49m[{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSystematic\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnominal\u001b[39m\u001b[38;5;124m\"\u001b[39m}], years, channels,\n\u001b[1;32m     48\u001b[0m             add_data\u001b[38;5;241m=\u001b[39madd_data,\n\u001b[1;32m     49\u001b[0m             logy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     50\u001b[0m             add_soverb\u001b[38;5;241m=\u001b[39madd_soverb,\n\u001b[1;32m     51\u001b[0m             only_sig\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m     52\u001b[0m             mult\u001b[38;5;241m=\u001b[39mmult,\n\u001b[1;32m     53\u001b[0m             outpath\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/Users/fmokhtar/Desktop/AN_2024/combine/\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     54\u001b[0m            save_as\u001b[38;5;241m=\u001b[39mregion,\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m#             text_=\"Signal Regions\",\u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m#            text_=region,\u001b[39;00m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m#            text_=\"Signal region \\n (VBF category)\",\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;66;03m#            text_=\"Signal region \\n\"  + r\"(ggF $p_T$ $\\in$ [250, 300])\",\u001b[39;00m\n\u001b[1;32m     59\u001b[0m            text_\u001b[38;5;241m=\u001b[39mtext,\n\u001b[1;32m     60\u001b[0m \u001b[38;5;66;03m#            text_=r\"Signal region \\n (ggF $p_T$ $\\in$ [450, inf])\",           \u001b[39;00m\n\u001b[1;32m     61\u001b[0m \n\u001b[1;32m     62\u001b[0m             blind_region\u001b[38;5;241m=\u001b[39mblind_region,\n\u001b[1;32m     63\u001b[0m            remove_samples\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWH\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZH\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mttH\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEWKvjets\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQCD\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDYJets\u001b[39m\u001b[38;5;124m\"\u001b[39m],           \n\u001b[1;32m     64\u001b[0m           )\n",
+      "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/hist/basehist.py:400\u001b[0m, in \u001b[0;36mBaseHist.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m    394\u001b[0m     \u001b[38;5;28mself\u001b[39m, index: IndexingExpr\n\u001b[1;32m    395\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m bh\u001b[38;5;241m.\u001b[39maccumulators\u001b[38;5;241m.\u001b[39mAccumulator:\n\u001b[1;32m    396\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    397\u001b[0m \u001b[38;5;124;03m    Get histogram item.\u001b[39;00m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_index_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/boost_histogram/_internal/hist.py:812\u001b[0m, in \u001b[0;36mHistogram.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    811\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m: H, index: IndexingExpr) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m H \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m Accumulator:\n\u001b[0;32m--> 812\u001b[0m     indexes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compute_commonindex\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    814\u001b[0m     \u001b[38;5;66;03m# If this is (now) all integers, return the bin contents\u001b[39;00m\n\u001b[1;32m    815\u001b[0m     \u001b[38;5;66;03m# But don't try *dict!\u001b[39;00m\n\u001b[1;32m    816\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(indexes, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mitems\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mall\u001b[39m(\n\u001b[1;32m    817\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(a, SupportsIndex) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m indexes\n\u001b[1;32m    818\u001b[0m     ):\n",
+      "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/boost_histogram/_internal/hist.py:717\u001b[0m, in \u001b[0;36mHistogram._compute_commonindex\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    714\u001b[0m     indexes \u001b[38;5;241m=\u001b[39m _expand_ellipsis(tuple_index, hist\u001b[38;5;241m.\u001b[39mrank())\n\u001b[1;32m    716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(indexes) \u001b[38;5;241m!=\u001b[39m hist\u001b[38;5;241m.\u001b[39mrank():\n\u001b[0;32m--> 717\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWrong number of indices for histogram\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    719\u001b[0m \u001b[38;5;66;03m# Allow [bh.loc(...)] to work\u001b[39;00m\n\u001b[1;32m    720\u001b[0m \u001b[38;5;66;03m# TODO: could be nicer making a new list via a comprehension\u001b[39;00m\n\u001b[1;32m    721\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(indexes)):  \u001b[38;5;66;03m# pylint: disable=consider-using-enumerate\u001b[39;00m\n\u001b[1;32m    722\u001b[0m     \u001b[38;5;66;03m# Support list of UHI indexers\u001b[39;00m\n",
+      "\u001b[0;31mIndexError\u001b[0m: Wrong number of indices for histogram"
+     ]
     }
    ],
    "source": [
-    "region = \"VBF97\"\n",
-    "text = \"Signal region \"  + r\"(VBF category)\"\n",
+    "# region = \"VBF97\"\n",
+    "# text = \"VBF category\"\n",
     "\n",
     "# region = \"ggF975pt250to300\"\n",
-    "# text = \"Signal region \"  + r\"(ggF $p_T$ $\\in$ [250, 300])\"\n",
+    "# text = r\"ggF $p_T$ $\\in$ [250, 300]\"\n",
     "\n",
     "# region = \"ggF975pt300to450\"\n",
-    "# text = \"Signal region \"  + r\"(ggF $p_T$ $\\in$ [300, 450])\"\n",
+    "# text = r\"ggF $p_T$ $\\in$ [300, 450]\"\n",
     "\n",
-    "# region = \"ggF98pt450toInf\"\n",
-    "# text = \"Signal region \"  + r\"(ggF $p_T$ $\\in$ [450, Inf])\"\n",
+    "region = \"ggF975pt450toInf\"\n",
+    "text = r\"ggF $p_T$ $\\in$ [450, Inf]\"\n",
     "\n",
     "# region = \"TopCR\"\n",
     "# text = \"Top control region\"\n",
@@ -206,6 +210,7 @@
     "#            text_=r\"Signal region \\n (ggF $p_T$ $\\in$ [450, inf])\",           \n",
     "\n",
     "            blind_region=blind_region,\n",
+    "           remove_samples=[\"WH\", \"ZH\", \"ttH\", \"EWKvjets\", \"QCD\", \"DYJets\"],           \n",
     "          )"
    ]
   },
@@ -227,125 +232,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 132,
    "id": "0e93f4ed",
    "metadata": {},
    "outputs": [],
    "source": [
     "# get lumi\n",
     "import pickle as pkl\n",
+    "# with open(\"../combine/templates/v6/hists_templates_Run2.pkl\", \"rb\") as f:\n",
     "with open(\"../combine/templates/v6/hists_templates_Run2.pkl\", \"rb\") as f:\n",
-    "# with open(\"../combine/templates/v3/hists_templates_2016.pkl\", \"rb\") as f:\n",
     "    \n",
     "    h = pkl.load(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "id": "5dce0854",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hist(\n",
-       "  StrCategory(['VBF', 'Data', 'EWKvjets', 'Diboson', 'WH', 'WJetsLNu', 'TTbar', 'SingleTop', 'ggF', 'DYJets', 'QCD', 'WZQQ', 'ttH', 'ZH'], growth=True, name='Sample'),\n",
-       "  StrCategory(['nominal', 'weight_btagSFlightCorrelated_up', 'weight_btagSFlightCorrelated_down', 'weight_btagSFbcCorrelated_up', 'weight_btagSFbcCorrelated_down', 'weight_btagSFlight2016_up', 'weight_btagSFlight2016_down', 'weight_btagSFbc2016_up', 'weight_btagSFbc2016_down', 'weight_btagSFlight2016APV_up', 'weight_btagSFlight2016APV_down', 'weight_btagSFbc2016APV_up', 'weight_btagSFbc2016APV_down', 'weight_btagSFlight2017_up', 'weight_btagSFlight2017_down', 'weight_btagSFbc2017_up', 'weight_btagSFbc2017_down', 'weight_btagSFlight2018_up', 'weight_btagSFlight2018_down', 'weight_btagSFbc2018_up', 'weight_btagSFbc2018_down', 'weight_pileup_up', 'weight_pileup_down', 'weight_pileupIDSF_up', 'weight_pileupIDSF_down', 'weight_isolation_up', 'weight_isolation_down', 'weight_id_up', 'weight_id_down', 'weight_reco_ele_up', 'weight_reco_ele_down', 'weight_L1Prefiring_up', 'weight_L1Prefiring_down', 'weight_trigger_ele_up', 'weight_trigger_ele_down', 'weight_trigger_iso_mu_up', 'weight_trigger_iso_mu_down', 'weight_trigger_noniso_mu_up', 'weight_trigger_noniso_mu_down', 'weight_PSFSR_up', 'weight_PSFSR_down', 'weight_PSISR_up', 'weight_PSISR_down', 'weight_d1kappa_EW_up', 'weight_d1kappa_EW_down', 'weight_d1K_NLO_up', 'weight_d1K_NLO_down', 'weight_d2K_NLO_up', 'weight_d2K_NLO_down', 'weight_d3K_NLO_up', 'weight_d3K_NLO_down', 'weight_W_d2kappa_EW_up', 'weight_W_d2kappa_EW_down', 'weight_W_d3kappa_EW_up', 'weight_W_d3kappa_EW_down', 'weight_Z_d2kappa_EW_up', 'weight_Z_d2kappa_EW_down', 'weight_Z_d3kappa_EW_up', 'weight_Z_d3kappa_EW_down', 'rec_higgs_mUES_up', 'rec_higgs_mUES_down', 'rec_higgs_mJES_up', 'rec_higgs_mJES_down', 'rec_higgs_mJER_up', 'rec_higgs_mJER_down', 'rec_higgs_mJMS_up', 'rec_higgs_mJMS_down', 'rec_higgs_mJMR_up', 'rec_higgs_mJMR_down'], growth=True, name='Systematic'),\n",
-       "  StrCategory(['ParTinclusive9999', 'ParTinclusive9995', 'ParTinclusive999', 'ParTinclusive995', 'ParTinclusive99', 'ParTinclusive985', 'ParTinclusive98', 'ParTinclusive975', 'ParTinclusive97', 'ParTinclusive965', 'ParTinclusive96', 'ParTinclusive955', 'ParTinclusive95', 'inclusive99', 'inclusive985', 'inclusive98', 'inclusive975', 'inclusive97', 'inclusive965', 'inclusive96', 'inclusive955', 'inclusive95', 'VBF985', 'VBF98', 'VBF975', 'VBF97', 'VBF965', 'VBF96', 'VBF955', 'VBF95', 'ggF99', 'ggF985', 'ggF98', 'ggF975', 'ggF97', 'ggF965', 'ggF96', 'ggF955', 'ggF95', 'ggF99pt250to300', 'ggF99pt300to450', 'ggF99pt450toInf', 'ggF985pt250to300', 'ggF985pt300to450', 'ggF985pt450toInf', 'ggF98pt250to300', 'ggF98pt300to450', 'ggF98pt450toInf', 'ggF975pt250to300', 'ggF975pt300to450', 'ggF975pt450toInf', 'ggF97pt250to300', 'ggF97pt300to450', 'ggF97pt450toInf', 'ggF965pt250to300', 'ggF965pt300to450', 'ggF965pt450toInf', 'ggF96pt250to300', 'ggF96pt300to450', 'ggF96pt450toInf', 'ggF975to985', 'ggF97to985', 'ggF965to985', 'ggF96to985', 'ggF955to985', 'ggF95to985', 'ggF945to985', 'ggF94to985', 'ggF97to98', 'ggF965to98', 'ggF96to98', 'ggF955to98', 'ggF95to98', 'ggF945to98', 'ggF94to98', 'ggF965to975', 'ggF96to975', 'ggF955to975', 'ggF95to975', 'ggF945to975', 'ggF94to975', 'ggF96to97', 'ggF955to97', 'ggF95to97', 'ggF945to97', 'ggF94to97', 'SR975to985', 'SR97to985', 'SR965to985', 'SR96to985', 'SR955to985', 'SR95to985', 'SR945to985', 'SR94to985', 'SR97to98', 'SR965to98', 'SR96to98', 'SR955to98', 'SR95to98', 'SR945to98', 'SR94to98', 'SR965to975', 'SR96to975', 'SR955to975', 'SR95to975', 'SR945to975', 'SR94to975', 'SR96to97', 'SR955to97', 'SR95to97', 'SR945to97', 'SR94to97', 'WJetsCR97', 'WJetsCR96', 'WJetsCR95', 'WJetsCR94', 'TopCR'], growth=True, name='Region'),\n",
-       "  Variable([50, 70, 90, 110, 130, 150, 170, 190, 210, 230], name='mass_observable', label='Higgs reconstructed mass [GeV]'),\n",
-       "  storage=Weight()) # Sum: WeightedSum(value=9.90885e+07, variance=7.66672e+08) (WeightedSum(value=1.10401e+08, variance=8.27249e+08) with flow)"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "h"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "id": "f01dd8c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nominal yields: \n",
-      " [0.         0.         0.40519536 0.27844441 0.87791458 3.81288611\n",
-      " 0.50052499 0.18573765 0.        ]\n",
-      "\n",
-      " JMS_up: \n",
-      " [0.         0.         0.23270599 0.68943667 3.31397584 1.45019617\n",
-      " 0.18865078 0.         0.18573765]\n",
-      "\n",
-      "JMS_down: \n",
-      " [0.         0.         0.40519536 0.5169473  3.42164784 1.34252418\n",
-      " 0.18865078 0.         0.18573765]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# nominal\n",
-    "hist = h[{\"Sample\": \"WZQQ\", \"Region\": \"SRggF97pt250to300\"}]\n",
-    "\n",
-    "print(\"nominal yields: \\n\", hist[{\"Systematic\": \"nominal\"}].values())\n",
-    "\n",
-    "print(\"\\n JMS_up: \\n\", hist[{\"Systematic\": \"rec_higgs_mJMS_up\"}].values())\n",
-    "print(\"\\nJMS_down: \\n\", hist[{\"Systematic\": \"rec_higgs_mJMS_down\"}].values())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "166a6940",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h1 = h"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "id": "20aae4c0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hist(\n",
-       "  StrCategory(['nominal', 'weight_btagSFlightCorrelated_up', 'weight_btagSFlightCorrelated_down', 'weight_btagSFbcCorrelated_up', 'weight_btagSFbcCorrelated_down', 'weight_btagSFlight2016_up', 'weight_btagSFlight2016_down', 'weight_btagSFbc2016_up', 'weight_btagSFbc2016_down', 'weight_btagSFlight2016APV_up', 'weight_btagSFlight2016APV_down', 'weight_btagSFbc2016APV_up', 'weight_btagSFbc2016APV_down', 'weight_btagSFlight2017_up', 'weight_btagSFlight2017_down', 'weight_btagSFbc2017_up', 'weight_btagSFbc2017_down', 'weight_btagSFlight2018_up', 'weight_btagSFlight2018_down', 'weight_btagSFbc2018_up', 'weight_btagSFbc2018_down', 'weight_pileup_up', 'weight_pileup_down', 'weight_pileupIDSF_up', 'weight_pileupIDSF_down', 'weight_isolation_up', 'weight_isolation_down', 'weight_id_up', 'weight_id_down', 'weight_reco_ele_up', 'weight_reco_ele_down', 'weight_L1Prefiring_up', 'weight_L1Prefiring_down', 'weight_trigger_ele_up', 'weight_trigger_ele_down', 'weight_trigger_iso_mu_up', 'weight_trigger_iso_mu_down', 'weight_trigger_noniso_mu_up', 'weight_trigger_noniso_mu_down', 'weight_PSFSR_up', 'weight_PSFSR_down', 'weight_PSISR_up', 'weight_PSISR_down', 'weight_d1kappa_EW_up', 'weight_d1kappa_EW_down', 'weight_d1K_NLO_up', 'weight_d1K_NLO_down', 'weight_d2K_NLO_up', 'weight_d2K_NLO_down', 'weight_d3K_NLO_up', 'weight_d3K_NLO_down', 'weight_W_d2kappa_EW_up', 'weight_W_d2kappa_EW_down', 'weight_W_d3kappa_EW_up', 'weight_W_d3kappa_EW_down', 'weight_Z_d2kappa_EW_up', 'weight_Z_d2kappa_EW_down', 'weight_Z_d3kappa_EW_up', 'weight_Z_d3kappa_EW_down', 'rec_higgs_mUES_up', 'rec_higgs_mUES_down', 'rec_higgs_mJES_up', 'rec_higgs_mJES_down', 'rec_higgs_mJER_up', 'rec_higgs_mJER_down', 'rec_higgs_mJMS_up', 'rec_higgs_mJMS_down', 'rec_higgs_mJMR_up', 'rec_higgs_mJMR_down'], growth=True, name='Systematic'),\n",
-       "  StrCategory(['ParTinclusive9999', 'ParTinclusive9995', 'ParTinclusive999', 'ParTinclusive995', 'ParTinclusive99', 'ParTinclusive985', 'ParTinclusive98', 'ParTinclusive975', 'ParTinclusive97', 'ParTinclusive965', 'ParTinclusive96', 'ParTinclusive955', 'ParTinclusive95', 'inclusive99', 'inclusive985', 'inclusive98', 'inclusive975', 'inclusive97', 'inclusive965', 'inclusive96', 'inclusive955', 'inclusive95', 'VBF985', 'VBF98', 'VBF975', 'VBF97', 'VBF965', 'VBF96', 'VBF955', 'VBF95', 'ggF99', 'ggF985', 'ggF98', 'ggF975', 'ggF97', 'ggF965', 'ggF96', 'ggF955', 'ggF95', 'ggF99pt250to300', 'ggF99pt300to450', 'ggF99pt450toInf', 'ggF985pt250to300', 'ggF985pt300to450', 'ggF985pt450toInf', 'ggF98pt250to300', 'ggF98pt300to450', 'ggF98pt450toInf', 'ggF975pt250to300', 'ggF975pt300to450', 'ggF975pt450toInf', 'ggF97pt250to300', 'ggF97pt300to450', 'ggF97pt450toInf', 'ggF965pt250to300', 'ggF965pt300to450', 'ggF965pt450toInf', 'ggF96pt250to300', 'ggF96pt300to450', 'ggF96pt450toInf', 'ggF975to985', 'ggF97to985', 'ggF965to985', 'ggF96to985', 'ggF955to985', 'ggF95to985', 'ggF945to985', 'ggF94to985', 'ggF97to98', 'ggF965to98', 'ggF96to98', 'ggF955to98', 'ggF95to98', 'ggF945to98', 'ggF94to98', 'ggF965to975', 'ggF96to975', 'ggF955to975', 'ggF95to975', 'ggF945to975', 'ggF94to975', 'ggF96to97', 'ggF955to97', 'ggF95to97', 'ggF945to97', 'ggF94to97', 'SR975to985', 'SR97to985', 'SR965to985', 'SR96to985', 'SR955to985', 'SR95to985', 'SR945to985', 'SR94to985', 'SR97to98', 'SR965to98', 'SR96to98', 'SR955to98', 'SR95to98', 'SR945to98', 'SR94to98', 'SR965to975', 'SR96to975', 'SR955to975', 'SR95to975', 'SR945to975', 'SR94to975', 'SR96to97', 'SR955to97', 'SR95to97', 'SR945to97', 'SR94to97', 'WJetsCR97', 'WJetsCR96', 'WJetsCR95', 'WJetsCR94', 'TopCR'], growth=True, name='Region'),\n",
-       "  Variable([50, 70, 90, 110, 130, 150, 170, 190, 210, 230], name='mass_observable', label='Higgs reconstructed mass [GeV]'),\n",
-       "  storage=Weight()) # Sum: WeightedSum(value=52027.2, variance=64.1928) (WeightedSum(value=54593.1, variance=67.3635) with flow)"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "h1[{\"Sample\": \"WH\"}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eacd86cc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 134,
    "id": "186bcb92",
    "metadata": {
     "scrolled": false
@@ -353,7 +255,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -363,8 +265,8 @@
     }
    ],
    "source": [
-    "# region = \"VBF97\"\n",
-    "# text = \"VBF category\"\n",
+    "region = \"VBF97\"\n",
+    "text = \"VBF category\"\n",
     "\n",
     "# region = \"ggF975pt250to300\"\n",
     "# text = r\"ggF $p_T$ $\\in$ [250, 300]\"\n",
@@ -372,8 +274,8 @@
     "# region = \"ggF975pt300to450\"\n",
     "# text = r\"ggF $p_T$ $\\in$ [300, 450]\"\n",
     "\n",
-    "region = \"ggF975pt450toInf\"\n",
-    "text = r\"ggF $p_T$ $\\in$ [450, Inf]\"\n",
+    "# region = \"ggF975pt450toInf\"\n",
+    "# text = r\"ggF $p_T$ $\\in$ [450, Inf]\"\n",
     "\n",
     "# region = \"TopCR\"\n",
     "# text = \"Top control region\"\n",
diff --git a/binder/hists_plots.ipynb b/binder/hists_plots.ipynb
index 9e2399bb1..c71b22197 100644
--- a/binder/hists_plots.ipynb
+++ b/binder/hists_plots.ipynb
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -155,15 +155,15 @@
     "    \"WH\",\n",
     "    \"ZH\",    \n",
     "    \"ttH\",\n",
-    "#     \"QCD\",\n",
-    "#     \"DYJets\",\n",
-    "#     \"WJetsLNu\",\n",
-    "#     \"WZQQ\",\n",
-    "#     \"TTbar\",\n",
-    "#     \"SingleTop\",\n",
-    "#     \"Diboson\",\n",
-    "#     \"EWKvjets\",    \n",
-    "#     \"Data\",\n",
+    "    \"QCD\",\n",
+    "    \"DYJets\",\n",
+    "    \"WJetsLNu\",\n",
+    "    \"WZQQ\",\n",
+    "    \"TTbar\",\n",
+    "    \"SingleTop\",\n",
+    "    \"Diboson\",\n",
+    "    \"EWKvjets\",    \n",
+    "    \"Data\",\n",
     "    \"HTauTau\"\n",
     "]\n",
     "\n",
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 73,
    "metadata": {
     "scrolled": true
    },
@@ -197,325 +197,2816 @@
       "INFO:root:Will fill the VBF dataframe with the remaining 1056 events\n",
       "INFO:root:tot event weight 8.986915818511086 \n",
       "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 69 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 2 events\n",
+      "INFO:root:tot event weight 3.2085493332261086 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1897 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 344 events\n",
+      "INFO:root:tot event weight 105.54188192262322 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
       "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 3905 events\n",
       "INFO:root:Will fill the WH dataframe with the remaining 2401 events\n",
       "INFO:root:tot event weight 1.630915278447007 \n",
       "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 111882 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 23240 events\n",
+      "INFO:root:tot event weight 1254.6445963586352 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 355362 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 27390 events\n",
+      "INFO:root:tot event weight 3164.774857411814 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 23539 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 1157 events\n",
+      "INFO:root:tot event weight 33.682583820996285 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 35 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 4 events\n",
+      "INFO:root:tot event weight 0.05273153687580405 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 122417 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 16910 events\n",
+      "INFO:root:tot event weight 258.7064294663248 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 428 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 36 events\n",
+      "INFO:root:tot event weight 14.102146737996678 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 17205 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 2624 events\n",
+      "INFO:root:tot event weight 1107.4313674662692 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4028 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 468 events\n",
+      "INFO:root:tot event weight 106.63378433505241 \n",
+      "\n",
       "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 4741 events\n",
       "INFO:root:Will fill the ggF dataframe with the remaining 3211 events\n",
       "INFO:root:tot event weight 25.38960951273227 \n",
       "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 154961 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 4933 events\n",
+      "INFO:root:tot event weight 1.8709943629117998 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4009 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 434 events\n",
+      "INFO:root:tot event weight 98.22870354715393 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding SingleElectron_Run2017E samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 22127 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 3467 events\n",
+      "INFO:root:tot event weight 3467.0 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2017B samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 8739 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1225 events\n",
+      "INFO:root:tot event weight 1225.0 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 79 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 1 events\n",
+      "INFO:root:tot event weight 5.845863770488579e-06 \n",
+      "\n",
       "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 4611 events\n",
       "INFO:root:Will fill the WH dataframe with the remaining 2818 events\n",
       "INFO:root:tot event weight 3.0639648039780765 \n",
       "\n",
+      "INFO:root:Finding SingleElectron_Run2017C samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 23575 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 3567 events\n",
+      "INFO:root:tot event weight 3567.0 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 49919 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 6094 events\n",
+      "INFO:root:tot event weight 321.1964993876937 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2017D samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 10649 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1593 events\n",
+      "INFO:root:tot event weight 1593.0 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 95 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 14 events\n",
+      "INFO:root:tot event weight 1.2256430582221585 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017C samples and should combine them under Data\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 12856 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 708 events\n",
+      "INFO:root:tot event weight 2.1241939572744677 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017D samples and should combine them under Data\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2980 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 719 events\n",
+      "INFO:root:tot event weight 119.2902131403267 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 368 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 14 events\n",
+      "INFO:root:tot event weight 0.009104184941790371 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017E samples and should combine them under Data\n",
+      "INFO:root:Finding SingleMuon_Run2017B samples and should combine them under Data\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 876 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 223 events\n",
+      "INFO:root:tot event weight 106.23937430088087 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 530278 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 16651 events\n",
+      "INFO:root:tot event weight 84.73335864083344 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 9552 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 480 events\n",
+      "INFO:root:tot event weight 15.121120131486999 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 132362 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 8529 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:tot event weight 287.2968088322185 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 261 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 1 events\n",
+      "INFO:root:tot event weight 8.520716772407798e-05 \n",
+      "\n",
       "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 1066 events\n",
       "INFO:root:Will fill the HTauTau dataframe with the remaining 31 events\n",
       "INFO:root:tot event weight 0.27913080934634665 \n",
       "\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 88 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 15 events\n",
+      "INFO:root:tot event weight 0.6034503172086644 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 70 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 2.528760533777852 \n",
+      "\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 199 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 21 events\n",
+      "INFO:root:tot event weight 5.116219499039846 \n",
+      "\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 644 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 85 events\n",
+      "INFO:root:tot event weight 12.825322782322713 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1046 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 47 events\n",
+      "INFO:root:tot event weight 0.720810435429701 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 862 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 77 events\n",
+      "INFO:root:tot event weight 23.828022474700724 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 563 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 58 events\n",
+      "INFO:root:tot event weight 788.5696001554697 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 858 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 176 events\n",
+      "INFO:root:tot event weight 18.995476821169355 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 311 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 49 events\n",
+      "INFO:root:tot event weight 6.2965089763931985 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 133756 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 3533 events\n",
+      "INFO:root:tot event weight 16.85189392965157 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 59 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 0.427557922438115 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 81980 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 19916 events\n",
+      "INFO:root:tot event weight 2289.3523730176817 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 523 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 105 events\n",
+      "INFO:root:tot event weight 23.88251194322472 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 41782 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 2219 events\n",
+      "INFO:root:tot event weight 0.8042531204865015 \n",
+      "\n",
       "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 14945 events\n",
       "INFO:root:Will fill the ttH dataframe with the remaining 4397 events\n",
       "INFO:root:tot event weight 7.089861900128192 \n",
       "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 475 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 68 events\n",
+      "INFO:root:tot event weight 5.307326050567182 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2017F samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 31134 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 4814 events\n",
+      "INFO:root:tot event weight 4814.0 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 898 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 49 events\n",
+      "INFO:root:tot event weight 2.533741562343564 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1493 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 258 events\n",
+      "INFO:root:tot event weight 107.50413017884154 \n",
+      "\n",
       "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 10580 events\n",
       "INFO:root:Will fill the ZH dataframe with the remaining 6398 events\n",
       "INFO:root:tot event weight 0.07907732620240267 \n",
       "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 623 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 139 events\n",
+      "INFO:root:tot event weight 53.87354073992252 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 14.561786885056613 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 32684 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 7971 events\n",
+      "INFO:root:tot event weight 3827.990033597426 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 772 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 83 events\n",
+      "INFO:root:tot event weight 88.7148099800954 \n",
+      "\n",
       "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 8388 events\n",
       "INFO:root:Will fill the ZH dataframe with the remaining 4986 events\n",
       "INFO:root:tot event weight 2.3958541481907996 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 709 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 16 events\n",
+      "INFO:root:tot event weight 0.03934717117848666 \n",
       "\n",
+      "INFO:root:Finding SingleMuon_Run2017F samples and should combine them under Data\n",
       "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 2479 events\n",
       "INFO:root:Will fill the VBF dataframe with the remaining 1759 events\n",
       "INFO:root:tot event weight 14.547485642612212 \n",
       "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 79 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 8 events\n",
+      "INFO:root:tot event weight 13.175776825818003 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2079 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 456 events\n",
+      "INFO:root:tot event weight 140.82165023085858 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
       "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 5270 events\n",
       "INFO:root:Will fill the WH dataframe with the remaining 3653 events\n",
       "INFO:root:tot event weight 2.4769612479104888 \n",
       "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 148852 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 36873 events\n",
+      "INFO:root:tot event weight 2012.4104307155308 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 375217 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 32358 events\n",
+      "INFO:root:tot event weight 3724.0755887090013 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 34606 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 1682 events\n",
+      "INFO:root:tot event weight 47.574485167005804 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 107 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 15 events\n",
+      "INFO:root:tot event weight 0.13509135459157331 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 165051 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 30512 events\n",
+      "INFO:root:tot event weight 471.2733368297564 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 204 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 42 events\n",
+      "INFO:root:tot event weight 17.47395852533799 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 19527 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 3785 events\n",
+      "INFO:root:tot event weight 1619.2679909860087 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4057 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 532 events\n",
+      "INFO:root:tot event weight 119.13873452726128 \n",
+      "\n",
       "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 7334 events\n",
       "INFO:root:Will fill the ggF dataframe with the remaining 5314 events\n",
       "INFO:root:tot event weight 41.694901836889436 \n",
       "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 74923 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 9688 events\n",
+      "INFO:root:tot event weight 3.7481244793070205 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3944 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 499 events\n",
+      "INFO:root:tot event weight 110.8292465699145 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding SingleElectron_Run2017E samples and should combine them under Data\n",
+      "INFO:root:Finding SingleElectron_Run2017B samples and should combine them under Data\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 48 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 2 events\n",
+      "INFO:root:tot event weight 1.0654794758463503e-05 \n",
+      "\n",
       "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 6442 events\n",
       "INFO:root:Will fill the WH dataframe with the remaining 4417 events\n",
       "INFO:root:tot event weight 4.808251435649624 \n",
       "\n",
+      "INFO:root:Finding SingleElectron_Run2017C samples and should combine them under Data\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 36910 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 7131 events\n",
+      "INFO:root:tot event weight 393.8330083292873 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2017D samples and should combine them under Data\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 125 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 30 events\n",
+      "INFO:root:tot event weight 2.391456568876033 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017C samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 25136 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 4813 events\n",
+      "INFO:root:tot event weight 4813.0 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 16447 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 932 events\n",
+      "INFO:root:tot event weight 2.945796035245925 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017D samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 11271 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2085 events\n",
+      "INFO:root:tot event weight 2085.0 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3521 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 863 events\n",
+      "INFO:root:tot event weight 166.3957511071817 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 328 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 32 events\n",
+      "INFO:root:tot event weight 0.019658897868787294 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017E samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 24145 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 4574 events\n",
+      "INFO:root:tot event weight 4574.0 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017B samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 12183 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2338 events\n",
+      "INFO:root:tot event weight 2338.0 \n",
+      "\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 942 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 330 events\n",
+      "INFO:root:tot event weight 156.9061381888132 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 126181 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 12666 events\n",
+      "INFO:root:tot event weight 67.49153363283304 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 13132 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 780 events\n",
+      "INFO:root:tot event weight 24.202537554771858 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 105329 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 8023 events\n",
+      "INFO:root:tot event weight 268.95194575474613 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 149 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 7 events\n",
+      "INFO:root:tot event weight 0.0004738240975623906 \n",
+      "\n",
       "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 214 events\n",
       "INFO:root:Will fill the HTauTau dataframe with the remaining 27 events\n",
       "INFO:root:tot event weight 0.23721608271435057 \n",
       "\n",
-      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 13697 events\n",
-      "INFO:root:Will fill the ttH dataframe with the remaining 4764 events\n",
-      "INFO:root:tot event weight 7.596922218937249 \n",
+      "INFO:root:Applying tagger>0.5 selection on 63 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 13 events\n",
+      "INFO:root:tot event weight 0.4961862326150443 \n",
       "\n",
-      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 12517 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 8881 events\n",
-      "INFO:root:tot event weight 0.10921072817480856 \n",
+      "INFO:root:Applying tagger>0.5 selection on 61 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 8 events\n",
+      "INFO:root:tot event weight 3.990668695087072 \n",
       "\n",
-      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 10538 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 7412 events\n",
-      "INFO:root:tot event weight 3.556631539863629 \n",
+      "INFO:root:Applying tagger>0.5 selection on 89 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 20 events\n",
+      "INFO:root:tot event weight 4.855891995995362 \n",
       "\n",
-      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 784 events\n",
-      "INFO:root:Will fill the VBF dataframe with the remaining 502 events\n",
-      "INFO:root:tot event weight 3.7793306140385057 \n",
+      "INFO:root:Applying tagger>0.5 selection on 1348 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 228 events\n",
+      "INFO:root:tot event weight 33.04551539436591 \n",
       "\n",
-      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1040 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 646 events\n",
-      "INFO:root:tot event weight 0.6351195698398571 \n",
+      "INFO:root:Applying tagger>0.5 selection on 1145 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 147 events\n",
+      "INFO:root:tot event weight 2.176181712620247 \n",
       "\n",
-      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 2025 events\n",
-      "INFO:root:Will fill the ggF dataframe with the remaining 1317 events\n",
-      "INFO:root:tot event weight 9.495215429305969 \n",
+      "INFO:root:Applying tagger>0.5 selection on 949 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 134 events\n",
+      "INFO:root:tot event weight 42.30692082891687 \n",
       "\n",
-      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1316 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 789 events\n",
-      "INFO:root:tot event weight 1.1337529963708728 \n",
+      "INFO:root:Applying tagger>0.5 selection on 458 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 58 events\n",
+      "INFO:root:tot event weight 751.59285791284 \n",
       "\n",
-      "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 540 events\n",
-      "INFO:root:Will fill the HTauTau dataframe with the remaining 16 events\n",
-      "INFO:root:tot event weight 0.11469124956936594 \n",
+      "INFO:root:Applying tagger>0.5 selection on 712 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 225 events\n",
+      "INFO:root:tot event weight 22.48638501729169 \n",
       "\n",
-      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 6374 events\n",
-      "INFO:root:Will fill the ttH dataframe with the remaining 1860 events\n",
-      "INFO:root:tot event weight 2.789550388306791 \n",
+      "INFO:root:Applying tagger>0.5 selection on 478 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 83 events\n",
+      "INFO:root:tot event weight 10.711009800389546 \n",
       "\n",
-      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 4524 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 2628 events\n",
-      "INFO:root:tot event weight 0.029334774715989137 \n",
+      "INFO:root:Applying tagger>0.5 selection on 29251 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 3074 events\n",
+      "INFO:root:tot event weight 15.80627273644205 \n",
       "\n",
-      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 3758 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 2154 events\n",
-      "INFO:root:tot event weight 0.9346161176841448 \n",
+      "INFO:root:Applying tagger>0.5 selection on 81 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 9 events\n",
+      "INFO:root:tot event weight 1247.0867325424344 \n",
       "\n",
-      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1076 events\n",
-      "INFO:root:Will fill the VBF dataframe with the remaining 769 events\n",
-      "INFO:root:tot event weight 5.486228882827361 \n",
+      "INFO:root:Applying tagger>0.5 selection on 107655 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 30008 events\n",
+      "INFO:root:tot event weight 3498.1847982265613 \n",
       "\n",
-      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1499 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 1037 events\n",
-      "INFO:root:tot event weight 0.9656476477134773 \n",
+      "INFO:root:Applying tagger>0.5 selection on 300 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 98 events\n",
+      "INFO:root:tot event weight 22.368699792319617 \n",
       "\n",
-      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 3295 events\n"
+      "INFO:root:Applying tagger>0.5 selection on 56498 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 5356 events\n",
+      "INFO:root:tot event weight 1.9222429909962182 \n",
+      "\n",
+      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 13697 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 4764 events\n",
+      "INFO:root:tot event weight 7.596922218937249 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 833 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 145 events\n",
+      "INFO:root:tot event weight 10.888446717295041 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2017F samples and should combine them under Data\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1021 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 119 events\n",
+      "INFO:root:tot event weight 6.2026912148123605 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1738 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 359 events\n",
+      "INFO:root:tot event weight 150.5313089282237 \n",
+      "\n",
+      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 12517 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 8881 events\n",
+      "INFO:root:tot event weight 0.10921072817480856 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 527 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 134 events\n",
+      "INFO:root:tot event weight 53.32865033433669 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 19 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 10 events\n",
+      "INFO:root:tot event weight 22.314708951112173 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 41161 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 11669 events\n",
+      "INFO:root:tot event weight 5672.385490576543 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 767 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 121 events\n",
+      "INFO:root:tot event weight 128.52842283860005 \n",
+      "\n",
+      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 10538 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 7412 events\n",
+      "INFO:root:tot event weight 3.556631539863629 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 651 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Will fill the QCD dataframe with the remaining 60 events\n",
+      "INFO:root:tot event weight 0.1402000024299263 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2017F samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 35806 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 6988 events\n",
+      "INFO:root:tot event weight 6988.0 \n",
+      "\n",
+      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 784 events\n",
+      "INFO:root:Will fill the VBF dataframe with the remaining 502 events\n",
+      "INFO:root:tot event weight 3.7793306140385057 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 16 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016H samples and should combine them under Data\n",
+      "INFO:root:Finding SingleMuon_Run2016F samples and should combine them under Data\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1038 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 154 events\n",
+      "INFO:root:tot event weight 38.33159486765064 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding SingleMuon_Run2016G samples and should combine them under Data\n",
+      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1040 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 646 events\n",
+      "INFO:root:tot event weight 0.6351195698398571 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 42945 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 7869 events\n",
+      "INFO:root:tot event weight 447.62740487732606 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 353131 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 27685 events\n",
+      "INFO:root:tot event weight 1268.8263467592462 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016G samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 17638 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2569 events\n",
+      "INFO:root:tot event weight 2569.0 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 10797 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 529 events\n",
+      "INFO:root:tot event weight 12.724527493098082 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 17 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 60956 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 7490 events\n",
+      "INFO:root:tot event weight 92.96686325885702 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 287 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 31 events\n",
+      "INFO:root:tot event weight 6.084064891375861 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4544 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 591 events\n",
+      "INFO:root:tot event weight 337.3377789837383 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1696 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 197 events\n",
+      "INFO:root:tot event weight 40.37416910851309 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016H samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 19081 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2673 events\n",
+      "INFO:root:tot event weight 2673.0 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2025 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 1317 events\n",
+      "INFO:root:tot event weight 9.495215429305969 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 76597 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2696 events\n",
+      "INFO:root:tot event weight 0.7924362528900938 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016F samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 908 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 134 events\n",
+      "INFO:root:tot event weight 134.0 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1643 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 156 events\n",
+      "INFO:root:tot event weight 34.54749143260378 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding QCD_Pt_120to170 samples and should combine them under QCD\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 85 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
+      "\n",
+      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1316 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 789 events\n",
+      "INFO:root:tot event weight 1.1337529963708728 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 22344 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2571 events\n",
+      "INFO:root:tot event weight 107.784400970733 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 48 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 3 events\n",
+      "INFO:root:tot event weight 0.16195966831181463 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4858 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 257 events\n",
+      "INFO:root:tot event weight 0.9138612481871102 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1476 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 264 events\n",
+      "INFO:root:tot event weight 27.550983575433634 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 419 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 10 events\n",
+      "INFO:root:tot event weight 0.0033997211201333083 \n",
+      "\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1314 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 371 events\n",
+      "INFO:root:tot event weight 46.18018084284402 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 256032 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 7982 events\n",
+      "INFO:root:tot event weight 36.57044592394489 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3760 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 197 events\n",
+      "INFO:root:tot event weight 6.184099992798291 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 64280 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 4372 events\n",
+      "INFO:root:tot event weight 119.90987879607364 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 229 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 2 events\n",
+      "INFO:root:tot event weight 9.910461959693881e-05 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 540 events\n",
+      "INFO:root:Will fill the HTauTau dataframe with the remaining 16 events\n",
+      "INFO:root:tot event weight 0.11469124956936594 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 1.6593925926418993 \n",
+      "\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 60 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 3 events\n",
+      "INFO:root:tot event weight 0.8763711539873902 \n",
+      "\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 712 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 84 events\n",
+      "INFO:root:tot event weight 5.691843630872283 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1136 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 30 events\n",
+      "INFO:root:tot event weight 0.20857167299520366 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3104 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 194 events\n",
+      "INFO:root:tot event weight 7.043954709650892 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1617 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 163 events\n",
+      "INFO:root:tot event weight 353.8884052530665 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 399 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 64 events\n",
+      "INFO:root:tot event weight 5.790193610013779 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 204 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 19 events\n",
+      "INFO:root:tot event weight 1.5407375128094296 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 77675 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2223 events\n",
+      "INFO:root:tot event weight 8.249146351296023 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 37 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 0.19417151267512886 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 135 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 18 events\n",
+      "INFO:root:tot event weight 940.5314919481804 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 31845 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 6981 events\n",
+      "INFO:root:tot event weight 815.3511151578585 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 218 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 42 events\n",
+      "INFO:root:tot event weight 7.8321213377756385 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 25119 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 1150 events\n",
+      "INFO:root:tot event weight 0.27538006565203155 \n",
+      "\n",
+      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 6374 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 1860 events\n",
+      "INFO:root:tot event weight 2.789550388306791 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 312 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 52 events\n",
+      "INFO:root:tot event weight 3.1625068051801124 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1995 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 107 events\n",
+      "INFO:root:tot event weight 1.171963638052854 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 918 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 143 events\n",
+      "INFO:root:tot event weight 43.33722532758602 \n",
+      "\n",
+      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4524 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 2628 events\n",
+      "INFO:root:tot event weight 0.029334774715989137 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 353 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 70 events\n",
+      "INFO:root:tot event weight 18.432402923381186 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 33 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 7.010107765797535 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 11726 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 2623 events\n",
+      "INFO:root:tot event weight 1354.8704364538617 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2072 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 196 events\n",
+      "INFO:root:tot event weight 35.84071644356585 \n",
+      "\n",
+      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3758 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 2154 events\n",
+      "INFO:root:tot event weight 0.9346161176841448 \n",
+      "\n",
+      "INFO:root:Finding WZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 559 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 94 events\n",
+      "INFO:root:tot event weight 9.90028452834145 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 760 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 22 events\n",
+      "INFO:root:tot event weight 0.02138351895645134 \n",
+      "\n",
+      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1076 events\n",
+      "INFO:root:Will fill the VBF dataframe with the remaining 769 events\n",
+      "INFO:root:tot event weight 5.486228882827361 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:Finding SingleMuon_Run2016H samples and should combine them under Data\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 21545 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 4239 events\n",
+      "INFO:root:tot event weight 4239.0 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016F samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 1050 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 196 events\n",
+      "INFO:root:tot event weight 196.0 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1089 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 266 events\n",
+      "INFO:root:tot event weight 59.51544323916119 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding SingleMuon_Run2016G samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 19080 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 3660 events\n",
+      "INFO:root:tot event weight 3660.0 \n",
+      "\n",
+      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1499 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1037 events\n",
+      "INFO:root:tot event weight 0.9656476477134773 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 60046 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 14300 events\n",
+      "INFO:root:tot event weight 811.9567122227203 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 393726 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 36189 events\n",
+      "INFO:root:tot event weight 1614.8000436935854 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016G samples and should combine them under Data\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 16348 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 917 events\n",
+      "INFO:root:tot event weight 22.626079320771286 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 44 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 7 events\n",
+      "INFO:root:tot event weight 0.05974212853173719 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 87207 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 15429 events\n",
+      "INFO:root:tot event weight 189.74707729550846 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 140 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 28 events\n",
+      "INFO:root:tot event weight 6.60736853491677 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5139 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 981 events\n",
+      "INFO:root:tot event weight 529.0407760974691 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1693 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 232 events\n",
+      "INFO:root:tot event weight 49.52251815543907 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016H samples and should combine them under Data\n",
+      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3295 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 2418 events\n",
+      "INFO:root:tot event weight 16.925275037181837 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 41496 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 5423 events\n",
+      "INFO:root:tot event weight 1.7454701303457452 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016F samples and should combine them under Data\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1861 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 266 events\n",
+      "INFO:root:tot event weight 54.53773590646381 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding QCD_Pt_120to170 samples and should combine them under QCD\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 63 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
+      "\n",
+      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1941 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1331 events\n",
+      "INFO:root:tot event weight 1.9560221731662555 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 16283 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 3158 events\n",
+      "INFO:root:tot event weight 139.49615361266495 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 51 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 13 events\n",
+      "INFO:root:tot event weight 1.0502016165232435 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 6663 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 421 events\n",
+      "INFO:root:tot event weight 1.349872117708667 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1875 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 465 events\n",
+      "INFO:root:tot event weight 64.67701930853318 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 354 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 25 events\n",
+      "INFO:root:tot event weight 0.006013814467257813 \n",
+      "\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1426 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 511 events\n",
+      "INFO:root:tot event weight 60.45298912689332 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 59216 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 6193 events\n",
+      "INFO:root:tot event weight 28.357199255544025 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5717 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 348 events\n",
+      "INFO:root:tot event weight 11.032355459144956 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 54013 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 4366 events\n",
+      "INFO:root:tot event weight 118.53726596300933 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 180 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 12 events\n",
+      "INFO:root:tot event weight 0.0003547529189784989 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 110 events\n",
+      "INFO:root:Will fill the HTauTau dataframe with the remaining 13 events\n",
+      "INFO:root:tot event weight 0.12236425876667559 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 35 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 8 events\n",
+      "INFO:root:tot event weight 0.2390181002005834 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 47 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 3.0038324867197836 \n",
+      "\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 32 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 7 events\n",
+      "INFO:root:tot event weight 1.5514581208415898 \n",
+      "\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1629 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 282 events\n",
+      "INFO:root:tot event weight 16.061797119173477 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1233 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 119 events\n",
+      "INFO:root:tot event weight 0.7696628348214445 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3460 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 456 events\n",
+      "INFO:root:tot event weight 17.166569848118534 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1483 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 218 events\n",
+      "INFO:root:tot event weight 436.8446864862646 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 349 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 116 events\n",
+      "INFO:root:tot event weight 10.04179908621353 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 317 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 63 events\n",
+      "INFO:root:tot event weight 5.7503196202343 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 17443 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 1892 events\n",
+      "INFO:root:tot event weight 7.1852425221720555 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 41 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 10 events\n",
+      "INFO:root:tot event weight 0.6688294518406104 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 64 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 10 events\n",
+      "INFO:root:tot event weight 536.0706861279426 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 45011 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 12242 events\n",
+      "INFO:root:tot event weight 1423.8126542448117 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 172 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 64 events\n",
+      "INFO:root:tot event weight 12.185272523289267 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 36656 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 3392 events\n",
+      "INFO:root:tot event weight 0.8075390796565534 \n",
+      "\n",
+      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 6244 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 2240 events\n",
+      "INFO:root:tot event weight 3.1701116428182283 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 547 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 89 events\n",
+      "INFO:root:tot event weight 4.944959511693492 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2212 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 238 events\n",
+      "INFO:root:tot event weight 2.820832054080934 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 925 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 178 events\n",
+      "INFO:root:tot event weight 51.654924054341606 \n",
+      "\n",
+      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5521 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 3881 events\n",
+      "INFO:root:tot event weight 0.042706775637583 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 303 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 66 events\n",
+      "INFO:root:tot event weight 20.518620800207255 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 15401 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 4263 events\n",
+      "INFO:root:tot event weight 2183.8020882724854 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2343 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 347 events\n",
+      "INFO:root:tot event weight 57.051952843765164 \n",
+      "\n",
+      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5153 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 3681 events\n",
+      "INFO:root:tot event weight 1.5606902664097966 \n",
+      "\n",
+      "INFO:root:Finding WZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 408 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 98 events\n",
+      "INFO:root:tot event weight 9.262372170092224 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 743 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 76 events\n",
+      "INFO:root:tot event weight 0.07446929275369178 \n",
+      "\n",
+      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 463 events\n",
+      "INFO:root:Will fill the VBF dataframe with the remaining 275 events\n",
+      "INFO:root:tot event weight 3.251139247546128 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 983 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 162 events\n",
+      "INFO:root:tot event weight 42.93030682946239 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1032 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 596 events\n",
+      "INFO:root:tot event weight 0.4571723943457689 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 50985 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 9558 events\n",
+      "INFO:root:tot event weight 491.9786366000948 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 326351 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 26524 events\n",
+      "INFO:root:tot event weight 1387.9117281853437 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 9112 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 450 events\n",
+      "INFO:root:tot event weight 14.454179794419954 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 57858 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 7206 events\n",
+      "INFO:root:tot event weight 99.94948935755426 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 356 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 31 events\n",
+      "INFO:root:tot event weight 6.993462220001028 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5086 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 687 events\n",
+      "INFO:root:tot event weight 338.0132168875824 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1494 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 176 events\n",
+      "INFO:root:tot event weight 45.01207702683943 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2289 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 1502 events\n",
+      "INFO:root:tot event weight 10.262109534110245 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 73863 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2660 events\n",
+      "INFO:root:tot event weight 0.9481539115304314 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1450 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 144 events\n",
+      "INFO:root:tot event weight 37.06854294582794 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 99 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 1 events\n",
+      "INFO:root:tot event weight 2.5867641876793905e-06 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016F_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1790 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1053 events\n",
+      "INFO:root:tot event weight 1.2882621462193655 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 22574 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2676 events\n",
+      "INFO:root:tot event weight 122.21571214354935 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 45 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 0.47192895178218774 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4576 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 258 events\n",
+      "INFO:root:tot event weight 0.9573434460334022 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016F_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 5483 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 839 events\n",
+      "INFO:root:tot event weight 839.0 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1486 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 294 events\n",
+      "INFO:root:tot event weight 44.95144489915789 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 367 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 2 events\n",
+      "INFO:root:tot event weight 0.0006198940820622112 \n",
+      "\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1126 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 299 events\n",
+      "INFO:root:tot event weight 46.91151873635681 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016D_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 253048 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 7821 events\n",
+      "INFO:root:tot event weight 40.019781497297515 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3721 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 174 events\n",
+      "INFO:root:tot event weight 5.399790640713981 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 56897 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 4101 events\n",
+      "INFO:root:tot event weight 133.78189914233278 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 231 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 1 events\n",
+      "INFO:root:tot event weight 3.373365250943315e-05 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016E_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 8612 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1415 events\n",
+      "INFO:root:tot event weight 1415.0 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 48 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 8 events\n",
+      "INFO:root:tot event weight 0.22056573184014377 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 43 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 9 events\n",
+      "INFO:root:tot event weight 2.9970475800966283 \n",
+      "\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 88 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 9 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:tot event weight 2.2239442717632736 \n",
+      "\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 792 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 123 events\n",
+      "INFO:root:tot event weight 6.513246903323722 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 982 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 32 events\n",
+      "INFO:root:tot event weight 0.2346619533315027 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016D_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 9163 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1450 events\n",
+      "INFO:root:tot event weight 1450.0 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2380 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 165 events\n",
+      "INFO:root:tot event weight 8.48194700656474 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1448 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 150 events\n",
+      "INFO:root:tot event weight 352.7301829939405 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 528 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 93 events\n",
+      "INFO:root:tot event weight 6.853424224667535 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 168 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 20 events\n",
+      "INFO:root:tot event weight 1.9764705495499995 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 78944 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2279 events\n",
+      "INFO:root:tot event weight 9.550596305742802 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016B_ver2_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding SingleMuon_Run2016E_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 31 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 4 events\n",
+      "INFO:root:tot event weight 0.3394752951637252 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 111 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 503.92096842929334 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 31871 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 7087 events\n",
+      "INFO:root:tot event weight 910.7884396070333 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016C_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 5489 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 828 events\n",
+      "INFO:root:tot event weight 828.0 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 345 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 61 events\n",
+      "INFO:root:tot event weight 8.371096544684152 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 27349 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 1309 events\n",
+      "INFO:root:tot event weight 0.30898898503258554 \n",
+      "\n",
+      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5330 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 1564 events\n",
+      "INFO:root:tot event weight 2.972028381989996 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 170 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 20 events\n",
+      "INFO:root:tot event weight 1.763246694954674 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1656 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 80 events\n",
+      "INFO:root:tot event weight 1.1108797964405857 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 856 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 130 events\n",
+      "INFO:root:tot event weight 42.84931153544825 \n",
+      "\n",
+      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4863 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 2820 events\n",
+      "INFO:root:tot event weight 0.03033737458907912 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 344 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 73 events\n",
+      "INFO:root:tot event weight 12.521109465974096 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 44 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 7 events\n",
+      "INFO:root:tot event weight 5.836512977366987 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 12611 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 2831 events\n",
+      "INFO:root:tot event weight 1433.6559491993007 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1703 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 137 events\n",
+      "INFO:root:tot event weight 29.445011059890472 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016C_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3814 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 2193 events\n",
+      "INFO:root:tot event weight 0.9836307308448438 \n",
+      "\n",
+      "INFO:root:Finding WZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 555 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 75 events\n",
+      "INFO:root:tot event weight 8.157948362148234 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 701 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 20 events\n",
+      "INFO:root:tot event weight 0.021417973122376373 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016B_ver2_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 13038 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Will fill the Data dataframe with the remaining 2098 events\n",
+      "INFO:root:tot event weight 2098.0 \n",
+      "\n",
+      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 816 events\n",
+      "INFO:root:Will fill the VBF dataframe with the remaining 571 events\n",
+      "INFO:root:tot event weight 6.562667379565042 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 17 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 7.320821912319281 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1129 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 236 events\n",
+      "INFO:root:tot event weight 61.96234410106999 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1451 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1019 events\n",
+      "INFO:root:tot event weight 0.7641672840405482 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 73854 events\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 17555 events\n",
+      "INFO:root:tot event weight 908.1540576664222 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 368486 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 35751 events\n",
+      "INFO:root:tot event weight 1857.22146116768 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 13314 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 722 events\n",
+      "INFO:root:tot event weight 22.70840025510929 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 85676 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 15354 events\n",
+      "INFO:root:tot event weight 215.15807516813953 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 166 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 25 events\n",
+      "INFO:root:tot event weight 5.737718327067576 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5777 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 1077 events\n",
+      "INFO:root:tot event weight 533.9713323723513 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1625 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 248 events\n",
+      "INFO:root:tot event weight 64.18086986458306 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3645 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 2624 events\n",
+      "INFO:root:tot event weight 17.761303657583582 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 40241 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 5484 events\n",
+      "INFO:root:tot event weight 2.095528193085684 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1603 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 200 events\n",
+      "INFO:root:tot event weight 51.48979800613732 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 64 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 1 events\n",
+      "INFO:root:tot event weight 2.6778853961117857e-06 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016F_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 6255 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1294 events\n",
+      "INFO:root:tot event weight 1294.0 \n",
+      "\n",
+      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2502 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1737 events\n",
+      "INFO:root:tot event weight 2.1074764088480658 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 16434 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 3253 events\n",
+      "INFO:root:tot event weight 151.82652090522313 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 59 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 10 events\n",
+      "INFO:root:tot event weight 0.904508816884345 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 6400 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 446 events\n",
+      "INFO:root:tot event weight 1.6000939478240634 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016F_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1896 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 455 events\n",
+      "INFO:root:tot event weight 54.43834694479649 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 370 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 40 events\n",
+      "INFO:root:tot event weight 0.012329159870299495 \n",
+      "\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1252 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 441 events\n",
+      "INFO:root:tot event weight 69.39297649938673 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016D_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 9752 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2011 events\n",
+      "INFO:root:tot event weight 2011.0 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 58846 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 6365 events\n",
+      "INFO:root:tot event weight 30.314423864516613 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5538 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 365 events\n",
+      "INFO:root:tot event weight 11.950458019229519 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 48708 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 4416 events\n",
+      "INFO:root:tot event weight 143.41610621353942 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 216 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 17 events\n",
+      "INFO:root:tot event weight 0.000526341580543316 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016E_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 47 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 8 events\n",
+      "INFO:root:tot event weight 0.2255347915647299 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 41 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 1.4671371359461285 \n",
+      "\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 37 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 1.5926499925310602 \n",
+      "\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1921 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 397 events\n",
+      "INFO:root:tot event weight 22.390136399998173 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1127 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 133 events\n",
+      "INFO:root:tot event weight 0.9637371603970373 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016D_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2859 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 402 events\n",
+      "INFO:root:tot event weight 20.60757869934001 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1405 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 197 events\n",
+      "INFO:root:tot event weight 454.3148327830763 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 564 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 175 events\n",
+      "INFO:root:tot event weight 12.030856499463741 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 341 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 40 events\n",
+      "INFO:root:tot event weight 3.6321220625338593 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 18221 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 2109 events\n",
+      "INFO:root:tot event weight 8.568219771487309 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016B_ver2_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 13685 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2667 events\n",
+      "INFO:root:tot event weight 2667.0 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016E_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 9134 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1830 events\n",
+      "INFO:root:tot event weight 1830.0 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 11 events\n",
+      "INFO:root:tot event weight 0.7868824567336594 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 62 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 14 events\n",
+      "INFO:root:tot event weight 1118.7858392370035 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 45525 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 12562 events\n",
+      "INFO:root:tot event weight 1614.3874766281344 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016C_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 247 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 88 events\n",
+      "INFO:root:tot event weight 11.554023928847293 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 40241 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 3596 events\n",
+      "INFO:root:tot event weight 0.850045254786217 \n",
+      "\n",
+      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5431 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 1975 events\n",
+      "INFO:root:tot event weight 3.732589714642666 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 404 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 63 events\n",
+      "INFO:root:tot event weight 5.395749904755239 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2046 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 233 events\n",
+      "INFO:root:tot event weight 3.177221172114929 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 974 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 219 events\n",
+      "INFO:root:tot event weight 71.87599100139172 \n",
+      "\n",
+      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 6210 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 4444 events\n",
+      "INFO:root:tot event weight 0.0473427083610343 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 320 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 59 events\n",
+      "INFO:root:tot event weight 10.473257234297044 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 18 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 4.626649610714743 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 16862 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 4711 events\n",
+      "INFO:root:tot event weight 2388.8336313955742 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2049 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 298 events\n",
+      "INFO:root:tot event weight 62.83326665495568 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016C_HIPM samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 5913 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 1221 events\n",
+      "INFO:root:tot event weight 1221.0 \n",
+      "\n",
+      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5289 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 3783 events\n",
+      "INFO:root:tot event weight 1.659757786025952 \n",
+      "\n",
+      "INFO:root:Finding WZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 414 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 139 events\n",
+      "INFO:root:tot event weight 15.064100931037839 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 773 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 60 events\n",
+      "INFO:root:tot event weight 0.06283756000744974 \n",
+      "\n",
+      "INFO:root:Finding SingleElectron_Run2016B_ver2_HIPM samples and should combine them under Data\n",
+      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1514 events\n",
+      "INFO:root:Will fill the VBF dataframe with the remaining 987 events\n",
+      "INFO:root:tot event weight 12.958180850131882 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 34 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 5 events\n",
+      "INFO:root:tot event weight 10.851796020386798 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2000 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 384 events\n",
+      "INFO:root:tot event weight 160.74181268648925 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5304 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 3319 events\n",
+      "INFO:root:tot event weight 2.3098155854888827 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 159607 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 32972 events\n",
+      "INFO:root:tot event weight 1829.8447734867516 \n",
+      "\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 349864 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 26759 events\n",
+      "INFO:root:tot event weight 4401.249786146961 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 31158 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 1466 events\n",
+      "INFO:root:tot event weight 46.428525196833114 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 191329 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 26216 events\n",
+      "INFO:root:tot event weight 372.3458193934917 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 694 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 84 events\n",
+      "INFO:root:tot event weight 30.532490298063834 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 18076 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 2602 events\n",
+      "INFO:root:tot event weight 1294.1621888587229 \n",
+      "\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5367 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 636 events\n",
+      "INFO:root:tot event weight 150.5073798764137 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 4523 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 3013 events\n",
+      "INFO:root:tot event weight 35.21079455021375 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 146465 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 4958 events\n",
+      "INFO:root:tot event weight 2.7933503674191735 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5196 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 601 events\n",
+      "INFO:root:tot event weight 146.63650202162842 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
+      "\n",
+      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5887 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 3672 events\n",
+      "INFO:root:tot event weight 4.395730004285747 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 51421 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 6655 events\n",
+      "INFO:root:tot event weight 476.8456592939931 \n",
+      "\n",
+      "INFO:root:Finding EGamma_Run2018A samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 31550 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 4699 events\n",
+      "INFO:root:tot event weight 4699.0 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 85 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 19 events\n",
+      "INFO:root:tot event weight 2.6951858255270986 \n",
+      "\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 17372 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 948 events\n",
+      "INFO:root:tot event weight 3.1116281698848525 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 3547 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 821 events\n",
+      "INFO:root:tot event weight 174.00157671671468 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 376 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 9 events\n",
+      "INFO:root:tot event weight 0.008543354780463815 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2018A samples and should combine them under Data\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 569 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 151 events\n",
+      "INFO:root:tot event weight 159.66373559188935 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 508019 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 16281 events\n",
+      "INFO:root:tot event weight 127.72701585163429 \n",
+      "\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 12111 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 658 events\n",
+      "INFO:root:tot event weight 21.69701978823379 \n",
+      "\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 125631 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 8257 events\n",
+      "INFO:root:tot event weight 415.0359068572578 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 222 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 1 events\n",
+      "INFO:root:tot event weight 9.028817821232966e-05 \n",
+      "\n",
+      "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1022 events\n",
+      "INFO:root:Will fill the HTauTau dataframe with the remaining 32 events\n",
+      "INFO:root:tot event weight 0.4349046479460872 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 47 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 4.773106038650719 \n",
+      "\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 227 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 27 events\n",
+      "INFO:root:tot event weight 7.111351761134137 \n",
+      "\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 629 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 85 events\n",
+      "INFO:root:tot event weight 18.69068032377287 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1090 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 50 events\n",
+      "INFO:root:tot event weight 1.111936731176413 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 841 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 73 events\n",
+      "INFO:root:tot event weight 33.56592469852006 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 503 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 63 events\n",
+      "INFO:root:tot event weight 1249.9466025623444 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 753 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 139 events\n",
+      "INFO:root:tot event weight 24.371151939269495 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 316 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 53 events\n",
+      "INFO:root:tot event weight 10.342190283207902 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 142537 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 3866 events\n",
+      "INFO:root:tot event weight 27.37685681854143 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 65 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 12 events\n",
+      "INFO:root:tot event weight 1.3334276654258177 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 95 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 11 events\n",
+      "INFO:root:tot event weight 3423.301300341977 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 105226 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 25506 events\n",
+      "INFO:root:tot event weight 3319.5443524789353 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 437 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 94 events\n",
+      "INFO:root:tot event weight 34.623371327760545 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 62868 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 3313 events\n",
+      "INFO:root:tot event weight 1.1798978643393165 \n",
+      "\n",
+      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 5869 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 1703 events\n",
+      "INFO:root:tot event weight 10.11908289452332 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 502 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 58 events\n",
+      "INFO:root:tot event weight 6.804504245995463 \n",
+      "\n",
+      "INFO:root:Finding EGamma_Run2018C samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 14770 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2292 events\n",
+      "INFO:root:tot event weight 2292.0 \n",
+      "\n",
+      "INFO:root:Finding EGamma_Run2018D samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 67956 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 10528 events\n",
+      "INFO:root:tot event weight 10528.0 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 963 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 56 events\n",
+      "INFO:root:tot event weight 4.2503014922875835 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 1736 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 285 events\n",
+      "INFO:root:tot event weight 142.89749322421284 \n",
+      "\n",
+      "INFO:root:Finding EGamma_Run2018B samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 15939 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2423 events\n",
+      "INFO:root:tot event weight 2423.0 \n",
+      "\n",
+      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 14086 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 8513 events\n",
+      "INFO:root:tot event weight 0.10963312801308542 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 885 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 191 events\n",
+      "INFO:root:tot event weight 68.3843385064198 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2018C samples and should combine them under Data\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 58 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 15 events\n",
+      "INFO:root:tot event weight 44.93515339646502 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2018D samples and should combine them under Data\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 43872 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 10561 events\n",
+      "INFO:root:tot event weight 5257.134255558543 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 503 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 51 events\n",
+      "INFO:root:tot event weight 120.92170142329925 \n",
+      "\n",
+      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 8471 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 5052 events\n",
+      "INFO:root:tot event weight 3.416447343560094 \n",
+      "\n",
+      "INFO:root:Finding WZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 601 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 89 events\n",
+      "INFO:root:tot event weight 30.287974879754916 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 707 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 21 events\n",
+      "INFO:root:tot event weight 0.07401378435293611 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2018B samples and should combine them under Data\n",
+      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2336 events\n",
+      "INFO:root:Will fill the VBF dataframe with the remaining 1603 events\n",
+      "INFO:root:tot event weight 21.299008052688436 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-100To200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 58 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 6 events\n",
+      "INFO:root:tot event weight 12.921947429232027 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2134 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 469 events\n",
+      "INFO:root:tot event weight 197.96132602982527 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 7012 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 4940 events\n",
+      "INFO:root:tot event weight 3.506231726843305 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-800To1200 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skipping sample VBFHToWWToLNuQQ_M-125_withDipoleRecoil\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Will fill the ggF dataframe with the remaining 2418 events\n",
-      "INFO:root:tot event weight 16.925275037181837 \n",
+      "INFO:root:Applying tagger>0.5 selection on 210651 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 51745 events\n",
+      "INFO:root:tot event weight 2945.272296727413 \n",
       "\n",
-      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding TTToSemiLeptonic samples and should combine them under TTbar\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1941 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 1331 events\n",
-      "INFO:root:tot event weight 1.9560221731662555 \n",
+      "INFO:root:Applying tagger>0.5 selection on 366309 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 31809 events\n",
+      "INFO:root:tot event weight 5310.201603516829 \n",
       "\n",
-      "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
+      "INFO:root:Finding ST_t-channel_top_4f_InclusiveDecays samples and should combine them under SingleTop\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 110 events\n",
-      "INFO:root:Will fill the HTauTau dataframe with the remaining 13 events\n",
-      "INFO:root:tot event weight 0.12236425876667559 \n",
+      "INFO:root:Applying tagger>0.5 selection on 44464 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 2243 events\n",
+      "INFO:root:tot event weight 72.01512196970234 \n",
       "\n",
-      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:Finding ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 6244 events\n",
-      "INFO:root:Will fill the ttH dataframe with the remaining 2240 events\n",
-      "INFO:root:tot event weight 3.1701116428182283 \n",
+      "INFO:root:Applying tagger>0.5 selection on 125 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 17 events\n",
+      "INFO:root:tot event weight 0.14062953234102116 \n",
       "\n",
-      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:Finding WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5521 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 3881 events\n",
-      "INFO:root:tot event weight 0.042706775637583 \n",
+      "INFO:root:Applying tagger>0.5 selection on 258011 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 48194 events\n",
+      "INFO:root:tot event weight 702.430223777131 \n",
       "\n",
-      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:Finding EWKZ_ZToLL samples and should combine them under EWKvjets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5153 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 3681 events\n",
-      "INFO:root:tot event weight 1.5606902664097966 \n",
+      "INFO:root:Applying tagger>0.5 selection on 322 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 65 events\n",
+      "INFO:root:tot event weight 23.90625481659908 \n",
       "\n",
-      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:Finding WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 463 events\n",
-      "INFO:root:Will fill the VBF dataframe with the remaining 275 events\n",
-      "INFO:root:tot event weight 3.251139247546128 \n",
+      "INFO:root:Applying tagger>0.5 selection on 20709 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 3729 events\n",
+      "INFO:root:tot event weight 1882.912253046287 \n",
       "\n",
-      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1032 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 596 events\n",
-      "INFO:root:tot event weight 0.4571723943457689 \n",
+      "INFO:root:Applying tagger>0.5 selection on 5518 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 789 events\n",
+      "INFO:root:tot event weight 188.80389909610466 \n",
       "\n",
       "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 2289 events\n",
-      "INFO:root:Will fill the ggF dataframe with the remaining 1502 events\n",
-      "INFO:root:tot event weight 10.262109534110245 \n",
+      "INFO:root:Applying tagger>0.5 selection on 6929 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 5048 events\n",
+      "INFO:root:tot event weight 59.73808983107723 \n",
       "\n",
-      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1790 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 1053 events\n",
-      "INFO:root:tot event weight 1.2882621462193655 \n",
+      "INFO:root:Applying tagger>0.5 selection on 74014 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 9878 events\n",
+      "INFO:root:tot event weight 5.613406380718013 \n",
       "\n",
-      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:Finding WJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding ST_tW_antitop_5f_inclusiveDecays samples and should combine them under SingleTop\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5330 events\n",
-      "INFO:root:Will fill the ttH dataframe with the remaining 1564 events\n",
-      "INFO:root:tot event weight 2.972028381989996 \n",
+      "INFO:root:Applying tagger>0.5 selection on 5300 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 671 events\n",
+      "INFO:root:tot event weight 165.46676783073946 \n",
       "\n",
-      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:Finding ZJetsToQQ_HT-200to400 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding QCD_Pt_3200toInf samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 4863 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 2820 events\n",
-      "INFO:root:tot event weight 0.03033737458907912 \n",
+      "INFO:root:Applying tagger>0.5 selection on 27 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
       "\n",
-      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 3814 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 2193 events\n",
-      "INFO:root:tot event weight 0.9836307308448438 \n",
+      "INFO:root:Applying tagger>0.5 selection on 7928 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 5549 events\n",
+      "INFO:root:tot event weight 6.757543073832429 \n",
       "\n",
-      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 816 events\n",
-      "INFO:root:Will fill the VBF dataframe with the remaining 571 events\n",
-      "INFO:root:tot event weight 6.562667379565042 \n",
+      "INFO:root:Applying tagger>0.5 selection on 37414 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 7211 events\n",
+      "INFO:root:tot event weight 515.2342441164876 \n",
       "\n",
-      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding EGamma_Run2018A samples and should combine them under Data\n",
+      "INFO:root:Finding EWKWplus_WToQQ samples and should combine them under EWKvjets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1451 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 1019 events\n",
-      "INFO:root:tot event weight 0.7641672840405482 \n",
+      "INFO:root:Applying tagger>0.5 selection on 102 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 27 events\n",
+      "INFO:root:tot event weight 3.8125351780959464 \n",
       "\n",
-      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:Finding ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 3645 events\n",
-      "INFO:root:Will fill the ggF dataframe with the remaining 2624 events\n",
-      "INFO:root:tot event weight 17.761303657583582 \n",
+      "INFO:root:Applying tagger>0.5 selection on 22094 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 1282 events\n",
+      "INFO:root:tot event weight 4.338565274821944 \n",
       "\n",
-      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 2502 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 1737 events\n",
-      "INFO:root:tot event weight 2.1074764088480658 \n",
+      "INFO:root:Applying tagger>0.5 selection on 4024 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 1005 events\n",
+      "INFO:root:tot event weight 209.39651229349874 \n",
       "\n",
-      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
+      "INFO:root:Finding QCD_Pt_1800to2400 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5431 events\n",
-      "INFO:root:Will fill the ttH dataframe with the remaining 1975 events\n",
-      "INFO:root:tot event weight 3.732589714642666 \n",
+      "INFO:root:Applying tagger>0.5 selection on 332 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 28 events\n",
+      "INFO:root:tot event weight 0.02580891637447663 \n",
       "\n",
-      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
-      "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 6210 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 4444 events\n",
-      "INFO:root:tot event weight 0.0473427083610343 \n",
+      "INFO:root:Finding SingleMuon_Run2018A samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 33219 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 6289 events\n",
+      "INFO:root:tot event weight 6289.0 \n",
       "\n",
-      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:Finding WW samples and should combine them under Diboson\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5289 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 3783 events\n",
-      "INFO:root:tot event weight 1.659757786025952 \n",
+      "INFO:root:Applying tagger>0.5 selection on 580 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 198 events\n",
+      "INFO:root:tot event weight 209.2526491655633 \n",
       "\n",
-      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1514 events\n",
-      "INFO:root:Will fill the VBF dataframe with the remaining 987 events\n",
-      "INFO:root:tot event weight 12.958180850131882 \n",
+      "INFO:root:Applying tagger>0.5 selection on 121629 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 12571 events\n",
+      "INFO:root:tot event weight 102.02684492588332 \n",
       "\n",
-      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding ST_t-channel_antitop_4f_InclusiveDecays samples and should combine them under SingleTop\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5304 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 3319 events\n",
-      "INFO:root:tot event weight 2.3098155854888827 \n",
+      "INFO:root:Applying tagger>0.5 selection on 17238 events\n",
+      "INFO:root:Will fill the SingleTop dataframe with the remaining 1000 events\n",
+      "INFO:root:tot event weight 35.53150616304373 \n",
       "\n",
-      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:Finding TTTo2L2Nu samples and should combine them under TTbar\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 4523 events\n",
-      "INFO:root:Will fill the ggF dataframe with the remaining 3013 events\n",
-      "INFO:root:tot event weight 35.21079455021375 \n",
+      "INFO:root:Applying tagger>0.5 selection on 99305 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 7514 events\n",
+      "INFO:root:tot event weight 382.7636776171581 \n",
       "\n",
-      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding QCD_Pt_2400to3200 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5887 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 3672 events\n",
-      "INFO:root:tot event weight 4.395730004285747 \n",
+      "INFO:root:Applying tagger>0.5 selection on 192 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 11 events\n",
+      "INFO:root:tot event weight 0.0011328267228117265 \n",
       "\n",
       "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 1022 events\n",
-      "INFO:root:Will fill the HTauTau dataframe with the remaining 32 events\n",
-      "INFO:root:tot event weight 0.4349046479460872 \n",
-      "\n",
-      "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
-      "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 5869 events\n",
-      "INFO:root:Will fill the ttH dataframe with the remaining 1703 events\n",
-      "INFO:root:tot event weight 10.11908289452332 \n",
+      "INFO:root:Applying tagger>0.5 selection on 186 events\n",
+      "INFO:root:Will fill the HTauTau dataframe with the remaining 15 events\n",
+      "INFO:root:tot event weight 0.20736040317455673 \n",
       "\n",
-      "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
+      "INFO:root:Finding EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 14086 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 8513 events\n",
-      "INFO:root:tot event weight 0.10963312801308542 \n",
+      "INFO:root:Applying tagger>0.5 selection on 2 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 0 events\n",
+      "INFO:root:tot event weight 0.0 \n",
       "\n",
-      "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
+      "INFO:root:Finding ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
       "INFO:root:---> Using already stored event weight\n"
      ]
     },
@@ -523,39 +3014,87 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:root:Applying tagger>0.5 selection on 8471 events\n",
-      "INFO:root:Will fill the ZH dataframe with the remaining 5052 events\n",
-      "INFO:root:tot event weight 3.416447343560094 \n",
+      "INFO:root:Applying tagger>0.5 selection on 40 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 4 events\n",
+      "INFO:root:tot event weight 2.859350281340426 \n",
       "\n",
-      "INFO:root:Finding VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil samples and should combine them under VBF\n",
+      "INFO:root:Finding ZZ samples and should combine them under Diboson\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 2336 events\n",
-      "INFO:root:Will fill the VBF dataframe with the remaining 1603 events\n",
-      "INFO:root:tot event weight 21.299008052688436 \n",
+      "INFO:root:Applying tagger>0.5 selection on 129 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 29 events\n",
+      "INFO:root:tot event weight 7.477550098032906 \n",
       "\n",
-      "INFO:root:Finding HWminusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding TTToHadronic samples and should combine them under TTbar\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 7012 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 4940 events\n",
-      "INFO:root:tot event weight 3.506231726843305 \n",
+      "INFO:root:Applying tagger>0.5 selection on 1386 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 267 events\n",
+      "INFO:root:tot event weight 58.263111473685974 \n",
       "\n",
-      "INFO:root:Finding GluGluHToWW_Pt-200ToInf_M-125 samples and should combine them under ggF\n",
+      "INFO:root:Finding QCD_Pt_1000to1400 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 6929 events\n",
-      "INFO:root:Will fill the ggF dataframe with the remaining 5048 events\n",
-      "INFO:root:tot event weight 59.73808983107723 \n",
+      "INFO:root:Applying tagger>0.5 selection on 1111 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 112 events\n",
+      "INFO:root:tot event weight 2.489386364915487 \n",
       "\n",
-      "INFO:root:Finding HWplusJ_HToWW_M-125 samples and should combine them under WH\n",
+      "INFO:root:Finding QCD_Pt_600to800 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 7928 events\n",
-      "INFO:root:Will fill the WH dataframe with the remaining 5549 events\n",
-      "INFO:root:tot event weight 6.757543073832429 \n",
+      "INFO:root:Applying tagger>0.5 selection on 863 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 112 events\n",
+      "INFO:root:tot event weight 52.10080179869338 \n",
       "\n",
-      "INFO:root:Finding GluGluHToTauTau samples and should combine them under HTauTau\n",
+      "INFO:root:Finding QCD_Pt_300to470 samples and should combine them under QCD\n",
       "INFO:root:---> Using already stored event weight\n",
-      "INFO:root:Applying tagger>0.5 selection on 186 events\n",
-      "INFO:root:Will fill the HTauTau dataframe with the remaining 15 events\n",
-      "INFO:root:tot event weight 0.20736040317455673 \n",
+      "INFO:root:Applying tagger>0.5 selection on 519 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 76 events\n",
+      "INFO:root:tot event weight 1523.7361813990362 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 625 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 198 events\n",
+      "INFO:root:tot event weight 32.98792131788023 \n",
+      "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 447 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 61 events\n",
+      "INFO:root:tot event weight 11.660070588157266 \n",
+      "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 31696 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 3332 events\n",
+      "INFO:root:tot event weight 23.393862369780088 \n",
+      "\n",
+      "INFO:root:Finding EWKWminus_WToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 60 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 19 events\n",
+      "INFO:root:tot event weight 2.150082702499046 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_170to300 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 63 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 8 events\n",
+      "INFO:root:tot event weight 2323.452619286314 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 137683 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 38321 events\n",
+      "INFO:root:tot event weight 5106.4924233705715 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 239 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 76 events\n",
+      "INFO:root:tot event weight 28.596171778972778 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 84095 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 8013 events\n",
+      "INFO:root:tot event weight 2.8818838699466807 \n",
       "\n",
       "INFO:root:Finding ttHToNonbb_M125 samples and should combine them under ttH\n",
       "INFO:root:---> Using already stored event weight\n",
@@ -563,17 +3102,90 @@
       "INFO:root:Will fill the ttH dataframe with the remaining 1864 events\n",
       "INFO:root:tot event weight 11.134827669975508 \n",
       "\n",
+      "INFO:root:Finding ZJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 779 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 120 events\n",
+      "INFO:root:tot event weight 13.582787088978025 \n",
+      "\n",
+      "INFO:root:Finding EGamma_Run2018C samples and should combine them under Data\n",
+      "INFO:root:Finding EGamma_Run2018D samples and should combine them under Data\n",
+      "INFO:root:Finding QCD_Pt_800to1000 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 992 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 117 events\n",
+      "INFO:root:tot event weight 9.069285724331444 \n",
+      "\n",
+      "INFO:root:Finding EWKWplus_WToLNu samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 2112 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 459 events\n",
+      "INFO:root:tot event weight 235.2361681931916 \n",
+      "\n",
+      "INFO:root:Finding EGamma_Run2018B samples and should combine them under Data\n",
       "INFO:root:Finding GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8 samples and should combine them under ZH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 16684 events\n",
       "INFO:root:Will fill the ZH dataframe with the remaining 11903 events\n",
       "INFO:root:tot event weight 0.15523861158737645 \n",
       "\n",
+      "INFO:root:Finding DYJetsToLL_LHEFilterPtZ-0To50 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 694 events\n",
+      "INFO:root:Will fill the DYJets dataframe with the remaining 172 events\n",
+      "INFO:root:tot event weight 83.60895539922421 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2018C samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 15318 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2865 events\n",
+      "INFO:root:tot event weight 2865.0 \n",
+      "\n",
+      "INFO:root:Finding WJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:Finding SingleMuon_Run2018D samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 71666 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 13676 events\n",
+      "INFO:root:tot event weight 13676.0 \n",
+      "\n",
+      "INFO:root:Finding WJetsToLNu_HT-400To600 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 55015 events\n",
+      "INFO:root:Will fill the WJetsLNu dataframe with the remaining 15701 events\n",
+      "INFO:root:tot event weight 7984.613718347896 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_470to600 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 513 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 79 events\n",
+      "INFO:root:tot event weight 184.65027105422675 \n",
+      "\n",
       "INFO:root:Finding HZJ_HToWW_M-125 samples and should combine them under ZH\n",
       "INFO:root:---> Using already stored event weight\n",
       "INFO:root:Applying tagger>0.5 selection on 10882 events\n",
       "INFO:root:Will fill the ZH dataframe with the remaining 7739 events\n",
       "INFO:root:tot event weight 5.313047148809423 \n",
+      "\n",
+      "INFO:root:Finding WZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 424 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 105 events\n",
+      "INFO:root:tot event weight 36.30950126053608 \n",
+      "\n",
+      "INFO:root:Finding QCD_Pt_1400to1800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying tagger>0.5 selection on 616 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 59 events\n",
+      "INFO:root:tot event weight 0.20449392838619135 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Finding SingleMuon_Run2018B samples and should combine them under Data\n",
+      "INFO:root:Applying tagger>0.5 selection on 16683 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 3206 events\n",
+      "INFO:root:tot event weight 3206.0 \n",
       "\n"
      ]
     }
@@ -1391,7 +4003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1534,7 +4146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -1551,19 +4163,19 @@
       "\n",
       "\\hline\n",
       "\\hline\n",
-      "\\multirow{2}{*}{VBF category} & \\multicolumn{2}{c|}{13} & \\multicolumn{5}{c|}{84} & \\multicolumn{1}{c}{\\multirow{2}{*}{1.263}} \\\\\n",
-      " & 78\\% & 22\\% & 26\\% & 48\\% & 14\\% & 0\\% & 12\\% &  \\\\\n",
+      "\\multirow{2}{*}{VBF category} & \\multicolumn{2}{c|}{13} & \\multicolumn{5}{c|}{75} & \\multicolumn{1}{c}{\\multirow{2}{*}{1.359}} \\\\\n",
+      " & 78\\% & 22\\% & 29\\% & 53\\% & 5\\% & 0\\% & 13\\% &  \\\\\n",
       "\n",
       "\\hline\n",
-      "\\multirow{2}{*}{ggF category $p_T \\in [250, 300]$} & \\multicolumn{2}{c|}{17} & \\multicolumn{5}{c|}{450} & \\multicolumn{1}{c}{\\multirow{2}{*}{0.676}} \\\\\n",
-      " & 22\\% & 78\\% & 23\\% & 63\\% & 3\\% & 1\\% & 10\\% &  \\\\\n",
+      "\\multirow{2}{*}{ggF category $p_T \\in [250, 300]$} & \\multicolumn{2}{c|}{17} & \\multicolumn{5}{c|}{448} & \\multicolumn{1}{c}{\\multirow{2}{*}{0.678}} \\\\\n",
+      " & 22\\% & 78\\% & 23\\% & 64\\% & 2\\% & 1\\% & 10\\% &  \\\\\n",
       "\n",
       "\\hline\n",
-      "\\multirow{2}{*}{ggF category $p_T \\in [300, 450]$} & \\multicolumn{2}{c|}{25} & \\multicolumn{5}{c|}{672} & \\multicolumn{1}{c}{\\multirow{2}{*}{0.931}} \\\\\n",
-      " & 18\\% & 82\\% & 25\\% & 57\\% & 6\\% & 2\\% & 11\\% &  \\\\\n",
+      "\\multirow{2}{*}{ggF category $p_T \\in [300, 450]$} & \\multicolumn{2}{c|}{25} & \\multicolumn{5}{c|}{667} & \\multicolumn{1}{c}{\\multirow{2}{*}{0.938}} \\\\\n",
+      " & 18\\% & 82\\% & 25\\% & 57\\% & 5\\% & 2\\% & 11\\% &  \\\\\n",
       "\n",
       "\\hline\n",
-      "\\multirow{2}{*}{ggF category $p_T \\in [450, \\mathrm{Inf}]$} & \\multicolumn{2}{c|}{7} & \\multicolumn{5}{c|}{169} & \\multicolumn{1}{c}{\\multirow{2}{*}{0.509}} \\\\\n",
+      "\\multirow{2}{*}{ggF category $p_T \\in [450, \\mathrm{Inf}]$} & \\multicolumn{2}{c|}{7} & \\multicolumn{5}{c|}{169} & \\multicolumn{1}{c}{\\multirow{2}{*}{0.511}} \\\\\n",
       " & 19\\% & 81\\% & 19\\% & 57\\% & 8\\% & 2\\% & 14\\% &  \\\\\n",
       "\n",
       "\\hline\n",
@@ -1613,11 +4225,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def make_composition_table_sig(ev, presel, add_soverb=False):\n",
+    "def make_composition_table_sig(ev, presel):\n",
     "\n",
     "    from collections import OrderedDict\n",
     "\n",
@@ -1639,22 +4251,12 @@
     "    print(\"\\\\begin{table}[!ht]\")\n",
     "    print(\"\\\\begin{center}\")\n",
     "    \n",
-    "    if add_soverb:\n",
-    "        print(\"\\\\caption{Event yield contribution from different processes in the search regions for the full Run2 dataset. The last column includes $s/\\sqrt{b}$ in a mass window of 100-150\\GeV in the mass observable (the Higgs reconstructed mass).}\")\n",
-    "    else:\n",
-    "        print(\"\\\\caption{Event yield contribution from different processes in the search regions for the full Run2 dataset.}\")\n",
-    "        \n",
-    "    if add_soverb:\n",
-    "        print(\"\\\\begin{tabular}{c|cc|cccc|c}\")\n",
-    "    else:\n",
-    "        print(\"\\\\begin{tabular}{c|cc|cccc}\")\n",
+    "    print(\"\\\\caption{Event yield contribution from different Higgs processes at pre-selection level and in the signal-like region, defined by a high tagger score, for the full Run2 dataset. The contribution of H(tau-tau) decays is negligible in both regions.}\")\n",
+    "            \n",
+    "    print(\"\\\\begin{tabular}{c|cc|cccc}\")\n",
     "\n",
-    "    if add_soverb:    \n",
-    "        print(\"& \\\\multicolumn{2}{c|}{Higgs Signal yield} & \\\\multicolumn{4}{c|}{Higgs Background yield} & \\\\multicolumn{1}{c}{$s/\\sqrt{b}$} \\\\\\\\\\n\")\n",
-    "        print(\"& VBF & ggF & \\\\ttH & WH & ZH & HTauTau & \\\\\\\\\\n\")        \n",
-    "    else:\n",
-    "        print(\"& \\\\multicolumn{2}{c|}{Higgs Signal yield} & \\\\multicolumn{4}{c}{Higgs Background yield} \\\\\\\\\\n\")\n",
-    "        print(\"& VBF & ggF & \\\\ttH & WH & ZH & HTauTau & \\\\\\\\\\n\")        \n",
+    "    print(\"& \\\\multicolumn{2}{c|}{Higgs Signal yields} & \\\\multicolumn{4}{c}{Higgs Background yields} \\\\\\\\\\n\")\n",
+    "    print(\"& VBF & ggF & \\\\ttH & WH & ZH & HTauTau \\\\\\\\\\n\")        \n",
     "    print(\"\\\\hline\")\n",
     "    print(\"\\\\hline\")\n",
     "\n",
@@ -1706,46 +4308,54 @@
     "                        b += df[\"event_weight\"].sum()\n",
     "                    ######################## soverb end\n",
     "\n",
-    "        texdata = \"\\multirow{2}{*}{\" + region + \"} & \\multicolumn{2}{c|}{\"\n",
-    "        texdata += str(round(tot_sig))\n",
+    "                        \n",
+    "                \n",
+    "#         texdata = \"\\multirow{2}{*}{\" + region + \"} & \\multicolumn{2}{c|}{\"\n",
+    "#         texdata += str(round(tot_sig))\n",
     "        \n",
-    "        if add_soverb:\n",
-    "            texdata += \"} & \\multicolumn{4}{c|}{\"\n",
-    "            texdata += str(round(tot_bkg))\n",
-    "            texdata += \"} & \\multicolumn{1}{c}{\"            \n",
-    "#             texdata += str(round(s/(b**0.5),3))\n",
-    "            texdata += \"\\multirow{2}{*}{\" + str(round(s/(b**0.5),3)) + \"}\"\n",
+    "#         if add_soverb:\n",
+    "#             texdata += \"} & \\multicolumn{4}{c|}{\"\n",
+    "#             texdata += str(round(tot_bkg))\n",
+    "#             texdata += \"} & \\multicolumn{1}{c}{\"            \n",
+    "# #             texdata += str(round(s/(b**0.5),3))\n",
+    "#             texdata += \"\\multirow{2}{*}{\" + str(round(s/(b**0.5),3)) + \"}\"\n",
     "            \n",
-    "            texdata += \"} \\\\\\\\\\n\"\n",
-    "        else:\n",
-    "            texdata += \"} & \\multicolumn{4}{c}{\"\n",
-    "            texdata += str(round(tot_bkg))\n",
-    "            texdata += \"} \\\\\\\\\\n\"\n",
+    "#             texdata += \"} \\\\\\\\\\n\"\n",
+    "#         else:\n",
+    "#             texdata += \"} & \\multicolumn{4}{c}{\"\n",
+    "#             texdata += str(round(tot_bkg))\n",
+    "#             texdata += \"} \\\\\\\\\\n\"        \n",
+    "\n",
+    "#         for sample in sig_dict:\n",
+    "#             texdata += f\" & {(sig_dict[sample]):.0f}\"\n",
+    "#         for sample in bkg_dict:\n",
+    "#             texdata += f\" & {(bkg_dict[sample]):.0f}\"\n",
     "        \n",
-    "#         texdata += \"\\\\cline{2-9} \\n\"\n",
+    "#         if add_soverb:\n",
+    "#             texdata += \" & \"\n",
+    "\n",
+    "#         texdata += \" \\\\\\\\\\n\"\n",
+    "#         print(texdata)\n",
+    "#         print(\"\\\\hline\")\n",
     "        \n",
+    "#         for sample in sig_dict:\n",
+    "#             texdata += f\" & {(100*sig_dict[sample]/tot_sig):.0f}\\%\"\n",
+    "#         for sample in bkg_dict:\n",
+    "#             texdata += f\" & {(100*bkg_dict[sample]/tot_bkg):.0f}\\%\"\n",
+    "        \n",
+    "#         if add_soverb:\n",
+    "#             texdata += \" & \"\n",
+    "\n",
+    "#         texdata += \" \\\\\\\\\\n\"\n",
+    "#         print(texdata)\n",
+    "#         print(\"\\\\hline\")\n",
     "\n",
+    "        texdata = region\n",
     "        for sample in sig_dict:\n",
     "            texdata += f\" & {(sig_dict[sample]):.0f}\"\n",
     "        for sample in bkg_dict:\n",
     "            texdata += f\" & {(bkg_dict[sample]):.0f}\"\n",
     "        \n",
-    "        if add_soverb:\n",
-    "            texdata += \" & \"\n",
-    "\n",
-    "        texdata += \" \\\\\\\\\\n\"\n",
-    "        print(texdata)\n",
-    "        print(\"\\\\hline\")\n",
-    "        \n",
-    "    \n",
-    "        for sample in sig_dict:\n",
-    "            texdata += f\" & {(100*sig_dict[sample]/tot_sig):.0f}\\%\"\n",
-    "        for sample in bkg_dict:\n",
-    "            texdata += f\" & {(100*bkg_dict[sample]/tot_bkg):.0f}\\%\"\n",
-    "        \n",
-    "        if add_soverb:\n",
-    "            texdata += \" & \"\n",
-    "\n",
     "        texdata += \" \\\\\\\\\\n\"\n",
     "        print(texdata)\n",
     "        print(\"\\\\hline\")\n",
@@ -1759,8 +4369,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
+   "execution_count": 65,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1768,30 +4380,18 @@
      "text": [
       "\\begin{table}[!ht]\n",
       "\\begin{center}\n",
-      "\\caption{Event yield contribution from different processes in the search regions for the full Run2 dataset. The last column includes $s/\\sqrt{b}$ in a mass window of 100-150\\GeV in the mass observable (the Higgs reconstructed mass).}\n",
-      "\\begin{tabular}{c|cc|cccc|c}\n",
-      "& \\multicolumn{2}{c|}{Higgs Signal yield} & \\multicolumn{4}{c|}{Higgs Background yield} & \\multicolumn{1}{c}{$s/\\sqrt{b}$} \\\\\n",
-      "\n",
-      "& VBF & ggF & \\ttH & WH & ZH & HTauTau & \\\\\n",
+      "\\caption{Event yield contribution from different Higgs processes at pre-selection level and in the signal-like region, defined by a high tagger score, for the full Run2 dataset. The contribution of H(tau-tau) decays is negligible in both regions.}\n",
+      "\\begin{tabular}{c|cc|cccc}\n",
+      "& \\multicolumn{2}{c|}{Higgs Signal yields} & \\multicolumn{4}{c}{Higgs Background yields} \\\\\n",
       "\n",
-      "\\hline\n",
-      "\\hline\n",
-      "\\multirow{2}{*}{Pre-selection} & \\multicolumn{2}{c|}{293} & \\multicolumn{4}{c|}{109} & \\multicolumn{1}{c}{\\multirow{2}{*}{21.575}} \\\\\n",
-      " & 77 & 216 & 49 & 38 & 20 & 1 &  \\\\\n",
+      "& VBF & ggF & \\ttH & WH & ZH & HTauTau \\\\\n",
       "\n",
       "\\hline\n",
-      "\\multirow{2}{*}{Pre-selection} & \\multicolumn{2}{c|}{293} & \\multicolumn{4}{c|}{109} & \\multicolumn{1}{c}{\\multirow{2}{*}{21.575}} \\\\\n",
-      " & 77 & 216 & 49 & 38 & 20 & 1 &  \\\\\n",
-      " & 26\\% & 74\\% & 45\\% & 35\\% & 19\\% & 1\\% &  \\\\\n",
-      "\n",
       "\\hline\n",
-      "\\multirow{2}{*}{$T_{HWW}^{\\ell\\nu qq} > 0.97$} & \\multicolumn{2}{c|}{75} & \\multicolumn{4}{c|}{20} & \\multicolumn{1}{c}{\\multirow{2}{*}{13.192}} \\\\\n",
-      " & 22 & 54 & 6 & 9 & 5 & 0 &  \\\\\n",
+      "Pre-selection & 77 & 216 & 49 & 38 & 20 & 1 \\\\\n",
       "\n",
       "\\hline\n",
-      "\\multirow{2}{*}{$T_{HWW}^{\\ell\\nu qq} > 0.97$} & \\multicolumn{2}{c|}{75} & \\multicolumn{4}{c|}{20} & \\multicolumn{1}{c}{\\multirow{2}{*}{13.192}} \\\\\n",
-      " & 22 & 54 & 6 & 9 & 5 & 0 &  \\\\\n",
-      " & 29\\% & 71\\% & 32\\% & 44\\% & 24\\% & 0\\% &  \\\\\n",
+      "$T_{HWW}^{\\ell\\nu qq} > 0.97$ & 22 & 54 & 6 & 9 & 5 & 0 \\\\\n",
       "\n",
       "\\hline\n",
       "\\hline\n",
@@ -1821,7 +4421,7 @@
     "\n",
     "}\n",
     "\n",
-    "make_composition_table_sig(events_dict[\"wjetsHT\"], presel, add_soverb=True)"
+    "make_composition_table_sig(events_dict[\"wjetsHT\"], presel)"
    ]
   },
   {
@@ -1854,7 +4454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1877,7 +4477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1942,7 +4542,7 @@
     "    \"ttH\",\n",
     "    \"WH\",\n",
     "    \"ZH\",    \n",
-    "    \"QCD\",\n",
+    "#     \"QCD\",\n",
     "    \"DYJets\",\n",
     "    \"WJetsLNu\",\n",
     "#     \"WJetsLNu_unmatched\",\n",
@@ -1960,11 +4560,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 98,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DYJets, has 1 bins with negative yield.. will set them to 0\n"
+     ]
+    }
+   ],
    "source": [
     "# tagger = \"fj_ParT_score\"\n",
     "tagger = \"fj_ParT_score_finetuned\"\n",
@@ -1976,7 +4584,7 @@
     "    \"Pre-selection\": f\"fj_pt>0\",   # dummy\n",
     "#     \"SR\": f\"({tagger}>{tagger_cut}) & (n_bjets_T==0)\",   # dummy\n",
     "    \n",
-    "#     \"Pre-selection\": f\"{tagger}>0.95\",\n",
+    "#     \"SR\": f\"{tagger}>0.97\",\n",
     "    \n",
     "#     \"Pre-selection\": f\"(fj_ParT_score_finetuned>0.95) & (fj_msoftdrop>10)\",   # dummy    \n",
     "    \n",
@@ -2051,11 +4659,11 @@
     "            \n",
     "                region, sel = list(presel.items())[0]\n",
     "                \n",
-    "                if sample == \"DYJets\":\n",
+    "                if sample == \"WZQQ\":\n",
     "                    continue  \n",
-    "                if sample == \"WJetsLNu\":\n",
+    "                if sample == \"DYJets\":\n",
     "                    df = ev[year][ch][sample]\n",
-    "                    df = pd.concat([df, ev[year][ch][\"DYJets\"]])\n",
+    "                    df = pd.concat([df, ev[year][ch][\"WZQQ\"]])\n",
     "                else:\n",
     "                    df = ev[year][ch][sample]\n",
     "\n",
@@ -2071,7 +4679,7 @@
     "#                 else:\n",
     "#                     df = ev[year][ch][sample]\n",
     "        \n",
-    "#                 df = ev[year][ch][sample]\n",
+    "                df = ev[year][ch][sample]\n",
     "                \n",
     "                df = df.query(sel)\n",
     "                    \n",
@@ -2160,7 +4768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 99,
    "metadata": {
     "scrolled": false
    },
@@ -2174,7 +4782,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -2193,7 +4801,7 @@
     "    if \"SR1\" in region:\n",
     "        mult=5\n",
     "    else:\n",
-    "        mult=1\n",
+    "        mult=10\n",
     "else:\n",
     "    add_soverb=True\n",
     "    blind_region=[90,160]\n",
diff --git a/combine/config_make_templates_diffbins.yaml b/combine/config_make_templates_diffbins.yaml
index b431b76c8..da601d9f5 100644
--- a/combine/config_make_templates_diffbins.yaml
+++ b/combine/config_make_templates_diffbins.yaml
@@ -18,7 +18,7 @@ regions_massbins:
     "VBF97": 20
     "ggF975pt250to300": 10
     "ggF975pt300to450": 10
-    "ggF975pt450toInf": 10
+    "ggF975pt450toInf": 20
 
     "WJetsCR": 10
     "TopCR": 10
diff --git a/combine/make_templates.py b/combine/make_templates.py
index a2e87a75e..05413e039 100644
--- a/combine/make_templates.py
+++ b/combine/make_templates.py
@@ -176,12 +176,32 @@ def get_templates(years, channels, samples, samples_dir, regions_sel, model_path
 
             for sample in os.listdir(samples_dir[year]):
 
-                for key in utils.combine_samples:
+                if "WJetsToLNu_1J" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+                if "WJetsToLNu_2J" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+
+                if "VBFHToWWToLNuQQ_" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+
+                # first: check if the sample is in one of combine_samples_by_name
+                sample_to_use = None
+                for key in utils.combine_samples_by_name:
                     if key in sample:
-                        sample_to_use = utils.combine_samples[key]
+                        sample_to_use = utils.combine_samples_by_name[key]
                         break
-                    else:
-                        sample_to_use = sample
+
+                # second: if not, combine under common label
+                if sample_to_use is None:
+                    for key in utils.combine_samples:
+                        if key in sample:
+                            sample_to_use = utils.combine_samples[key]
+                            break
+                        else:
+                            sample_to_use = sample
 
                 if sample_to_use not in samples:
                     continue
diff --git a/combine/make_templates_diffbins.py b/combine/make_templates_diffbins.py
index db56632b4..5e342a299 100644
--- a/combine/make_templates_diffbins.py
+++ b/combine/make_templates_diffbins.py
@@ -175,12 +175,32 @@ def get_templates(years, channels, samples, samples_dir, regions_sel, regions_ma
 
                 for sample in os.listdir(samples_dir[year]):
 
-                    for key in utils.combine_samples:
+                    if "WJetsToLNu_1J" in sample:
+                        print(f"Skipping sample {sample}")
+                        continue
+                    if "WJetsToLNu_2J" in sample:
+                        print(f"Skipping sample {sample}")
+                        continue
+
+                    if "VBFHToWWToLNuQQ_" in sample:
+                        print(f"Skipping sample {sample}")
+                        continue
+
+                    # first: check if the sample is in one of combine_samples_by_name
+                    sample_to_use = None
+                    for key in utils.combine_samples_by_name:
                         if key in sample:
-                            sample_to_use = utils.combine_samples[key]
+                            sample_to_use = utils.combine_samples_by_name[key]
                             break
-                        else:
-                            sample_to_use = sample
+
+                    # second: if not, combine under common label
+                    if sample_to_use is None:
+                        for key in utils.combine_samples:
+                            if key in sample:
+                                sample_to_use = utils.combine_samples[key]
+                                break
+                            else:
+                                sample_to_use = sample
 
                     if sample_to_use not in samples:
                         continue
diff --git a/combine/templates/v6/hists_templates_Run2.pkl b/combine/templates/v6/hists_templates_Run2.pkl
index 18a3505ca..0be91f1a6 100644
Binary files a/combine/templates/v6/hists_templates_Run2.pkl and b/combine/templates/v6/hists_templates_Run2.pkl differ
diff --git a/combine/templates/v6/make_templates.py b/combine/templates/v6/make_templates.py
index a2e87a75e..05413e039 100644
--- a/combine/templates/v6/make_templates.py
+++ b/combine/templates/v6/make_templates.py
@@ -176,12 +176,32 @@ def get_templates(years, channels, samples, samples_dir, regions_sel, model_path
 
             for sample in os.listdir(samples_dir[year]):
 
-                for key in utils.combine_samples:
+                if "WJetsToLNu_1J" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+                if "WJetsToLNu_2J" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+
+                if "VBFHToWWToLNuQQ_" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+
+                # first: check if the sample is in one of combine_samples_by_name
+                sample_to_use = None
+                for key in utils.combine_samples_by_name:
                     if key in sample:
-                        sample_to_use = utils.combine_samples[key]
+                        sample_to_use = utils.combine_samples_by_name[key]
                         break
-                    else:
-                        sample_to_use = sample
+
+                # second: if not, combine under common label
+                if sample_to_use is None:
+                    for key in utils.combine_samples:
+                        if key in sample:
+                            sample_to_use = utils.combine_samples[key]
+                            break
+                        else:
+                            sample_to_use = sample
 
                 if sample_to_use not in samples:
                     continue
diff --git a/combine/templates/v6/utils.py b/combine/templates/v6/utils.py
index a55397b99..720a257f4 100644
--- a/combine/templates/v6/utils.py
+++ b/combine/templates/v6/utils.py
@@ -12,21 +12,22 @@
 
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -38,9 +39,10 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",  # TODO: check how many events
 }
 
+signals = ["VBF", "ggF"]
+
 # (name in templates, name in cards)
 labels = {
     # sigs
diff --git a/combine/templates/v7/hists_templates_Run2.pkl b/combine/templates/v7/hists_templates_Run2.pkl
index 8ff422943..545d1da63 100644
Binary files a/combine/templates/v7/hists_templates_Run2.pkl and b/combine/templates/v7/hists_templates_Run2.pkl differ
diff --git a/combine/utils.py b/combine/utils.py
index a55397b99..720a257f4 100644
--- a/combine/utils.py
+++ b/combine/utils.py
@@ -12,21 +12,22 @@
 
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -38,9 +39,10 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",  # TODO: check how many events
 }
 
+signals = ["VBF", "ggF"]
+
 # (name in templates, name in cards)
 labels = {
     # sigs
diff --git a/python/make_stacked_hists.py b/python/make_stacked_hists.py
index df811c0d3..312a3505e 100644
--- a/python/make_stacked_hists.py
+++ b/python/make_stacked_hists.py
@@ -107,12 +107,25 @@ def make_events_dict(
                     print(f"Skipping sample {sample}")
                     continue
 
-                for key in utils.combine_samples:
+                if "VBFHToWWToLNuQQ_" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+
+                # first: check if the sample is in one of combine_samples_by_name
+                sample_to_use = None
+                for key in utils.combine_samples_by_name:
                     if key in sample:
-                        sample_to_use = utils.combine_samples[key]
+                        sample_to_use = utils.combine_samples_by_name[key]
                         break
-                    else:
-                        sample_to_use = sample
+
+                # second: if not, combine under common label
+                if sample_to_use is None:
+                    for key in utils.combine_samples:
+                        if key in sample:
+                            sample_to_use = utils.combine_samples[key]
+                            break
+                        else:
+                            sample_to_use = sample
 
                 if sample_to_use not in samples:
                     continue
diff --git a/python/utils.py b/python/utils.py
index bffff9253..9dd5e1d79 100644
--- a/python/utils.py
+++ b/python/utils.py
@@ -17,21 +17,23 @@
 
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -43,8 +45,8 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    "GluGluHToTauTau": "HTauTau",
 }
+
 signals = ["VBF", "ggF"]
 
 
diff --git a/python/utilsAN.py b/python/utilsAN.py
index 72080d208..b9f8f2575 100644
--- a/python/utilsAN.py
+++ b/python/utilsAN.py
@@ -18,21 +18,22 @@
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -44,8 +45,8 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",
 }
+
 signals = ["VBF", "ggF"]
 
 
diff --git a/python/utilsCombine.py b/python/utilsCombine.py
index 3defa8f42..d0d05e963 100644
--- a/python/utilsCombine.py
+++ b/python/utilsCombine.py
@@ -18,21 +18,22 @@
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -44,10 +45,9 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",
 }
 
-signals = ["ggF", "VBF"]
+signals = ["VBF", "ggF"]
 
 
 def get_sum_sumgenweight(pkl_files, year, sample):
diff --git a/python/utilsCombine2.py b/python/utilsCombine2.py
index d9f0be9d3..968016210 100644
--- a/python/utilsCombine2.py
+++ b/python/utilsCombine2.py
@@ -18,21 +18,22 @@
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -44,8 +45,8 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",
 }
+
 signals = ["VBF", "ggF"]
 
 
diff --git a/python/utilsCombine3.py b/python/utilsCombine3.py
index 9bd5e9208..e7bd13e21 100644
--- a/python/utilsCombine3.py
+++ b/python/utilsCombine3.py
@@ -18,21 +18,22 @@
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -44,8 +45,8 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",
 }
+
 signals = ["VBF", "ggF"]
 
 
diff --git a/python/utilsF.py b/python/utilsF.py
index 87d49d42b..0ab24f105 100644
--- a/python/utilsF.py
+++ b/python/utilsF.py
@@ -18,21 +18,22 @@
 warnings.filterwarnings("ignore", message="Found duplicate branch ")
 
 
-# (name of sample, name in templates)
-combine_samples = {
-    # data
-    "SingleElectron_": "Data",
-    "SingleMuon_": "Data",
-    "EGamma_": "Data",
-    # signal
+combine_samples_by_name = {
     "GluGluHToWW_Pt-200ToInf_M-125": "ggF",
     "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil": "VBF",
-    # "VBFHToWWToLNuQQ_M-125_withDipoleRecoil": "VBF",
     "ttHToNonbb_M125": "ttH",
     "HWminusJ_HToWW_M-125": "WH",
     "HWplusJ_HToWW_M-125": "WH",
     "HZJ_HToWW_M-125": "ZH",
     "GluGluZH_HToWW_M-125_TuneCP5_13TeV-powheg-pythia8": "ZH",
+    "GluGluHToTauTau": "HTauTau",
+}
+
+combine_samples = {
+    # data
+    "SingleElectron_": "Data",
+    "SingleMuon_": "Data",
+    "EGamma_": "Data",
     # bkg
     "QCD_Pt": "QCD",
     "DYJets": "DYJets",
@@ -44,8 +45,8 @@
     "ZZ": "Diboson",
     "JetsToQQ": "WZQQ",
     "EWK": "EWKvjets",
-    # "GluGluHToTauTau": "HTauTau",
 }
+
 signals = ["VBF", "ggF"]