diff --git a/binder/combine.ipynb b/binder/combine.ipynb
index 31db32a3a..be1ba6213 100644
--- a/binder/combine.ipynb
+++ b/binder/combine.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 481,
+   "execution_count": 1,
    "id": "ede33f78",
    "metadata": {},
    "outputs": [],
@@ -40,37 +40,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 482,
-   "id": "5a037292",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "../eos/postprocessNov12_2017/SingleElectron_Run2017E/outfiles/0-20.pkl\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "! ls ../eos/postprocessNov12_2017/SingleElectron_Run2017E/outfiles/0-20.pkl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 483,
+   "execution_count": 2,
    "id": "719eb035",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -78,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 484,
+   "execution_count": 3,
    "id": "62c83828",
    "metadata": {},
    "outputs": [
@@ -102,7 +75,7 @@
        "  '2018': 59781.96}}"
       ]
      },
-     "execution_count": 484,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "9766684a",
    "metadata": {
     "scrolled": true
@@ -129,7 +102,7 @@
        "137.64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -142,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 533,
+   "execution_count": 56,
    "id": "30a995df",
    "metadata": {},
    "outputs": [],
@@ -155,13 +128,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 541,
+   "execution_count": 57,
    "id": "ce58e9a3",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -254,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 485,
+   "execution_count": 68,
    "id": "0e93f4ed",
    "metadata": {},
    "outputs": [],
@@ -269,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 486,
+   "execution_count": 69,
    "id": "5dce0854",
    "metadata": {},
    "outputs": [
@@ -284,7 +257,7 @@
        "  storage=Weight()) # Sum: WeightedSum(value=1.76289e+07, variance=1.61408e+08) (WeightedSum(value=1.96522e+07, variance=1.76107e+08) with flow)"
       ]
      },
-     "execution_count": 486,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -773,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 513,
+   "execution_count": 64,
    "id": "eb14d962",
    "metadata": {},
    "outputs": [],
@@ -808,26 +781,35 @@
     "    \n",
     "}\n",
     "\n",
-    "f = uproot.open(\"/Users/fmokhtar/Downloads/fitDiagnosticsAsimov.root\")"
+    "f = uproot.open(\"/Users/fmokhtar/Downloads/v5/fitDiagnosticsAsimov.root\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 514,
+   "execution_count": 65,
    "id": "4017733f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "massbinwidth = 20\n",
+    "massbinwidth = {\n",
+    "    \"SR1\": 10,\n",
+    "    \"SR2\": 10,\n",
+    "    \"SR3\": 10,\n",
+    "    \"SR4\": 10,\n",
+    "    \"CR1\": 10,\n",
+    "    \"CR2\": 10,\n",
+    "}\n",
+    "\n",
+    "\n",
     "def plot_(key=\"shapes_fit_s\", region=\"SR1\", mult=1):\n",
     "\n",
     "    ######################\n",
-    "    nbins = len(list(range(50, 240, massbinwidth)))-1\n",
+    "    nbins = len(list(range(50, 240, massbinwidth[region])))-1\n",
     "    samples = [samples_dict[sample[:-2]] for sample in f[f\"{key}/{region}\"].keys() if \"total\" not in sample]\n",
     "\n",
     "    hf = hist2.Hist(\n",
-    "        hist.axis.StrCategory(samples, name=\"Sample\", growth=True),    \n",
-    "        hist.axis.Regular(nbins, 50, 240, name=\"var\", label=r\"Higgs reconstructed mass [GeV]\")\n",
+    "        hist2.axis.StrCategory(samples, name=\"Sample\", growth=True),    \n",
+    "        hist2.axis.Regular(nbins, 50, 240, name=\"var\", label=r\"Higgs reconstructed mass [GeV]\")\n",
     "    )\n",
     "\n",
     "\n",
@@ -836,10 +818,10 @@
     "            continue\n",
     "\n",
     "        if \"data\" in sample:    \n",
-    "            X = f[f\"{key}/{region}\"][sample].values()[1]*binwidth\n",
+    "            X = f[f\"{key}/{region}\"][sample].values()[1]*massbinwidth[region]\n",
     "        else:\n",
-    "            X = f[f\"{key}/{region}\"][sample].values()*binwidth\n",
-    "\n",
+    "            X = f[f\"{key}/{region}\"][sample].values()*massbinwidth[region]\n",
+    "        \n",
     "        hf[{\"Sample\": samples_dict[sample[:-2]]}] = X\n",
     "\n",
     "\n",
@@ -854,7 +836,7 @@
     "    plot_hists(hf, years, channels,\n",
     "                add_data=add_data,\n",
     "                logy=False,\n",
-    "                add_soverb=add_soverb,\n",
+    "                add_soverb=False,\n",
     "                only_sig=False,\n",
     "                mult=mult,\n",
     "                outpath=f\"/Users/fmokhtar/Desktop/AN_2024/combine/\",\n",
@@ -866,13 +848,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 532,
+   "execution_count": null,
+   "id": "52c44f8e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d42f7d27",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08c4d0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
    "id": "1069d7fc",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -887,13 +893,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 529,
+   "execution_count": 67,
    "id": "7d41beeb",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -908,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 530,
+   "execution_count": 15,
    "id": "3e68d664",
    "metadata": {},
    "outputs": [
@@ -929,33 +935,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 518,
+   "execution_count": 19,
    "id": "8cfba30b",
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyInFileError",
-     "evalue": "not found: 'SR4' (with any cycle number)\n\n    Available keys: 'SR1;1', 'SR2;1', 'SR3;1', 'CR1;1', 'CR2;1', 'SR1/WH;1', 'SR1/ZH;1', 'SR2/WH;1', 'SR2/ZH;1', 'SR3/WH;1', 'SR3/ZH;1', 'SR1/VBF;1', 'SR1/ggF;1', 'SR1/ttH;1', 'SR2/VBF;1', 'SR2/ggF;1', 'SR2/qcd;1', 'SR2/ttH;1'...\n\nin file /Users/fmokhtar/Downloads/fitDiagnosticsAsimov.root",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyInFileError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[518], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshapes_fit_b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mregion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSR4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmult\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[0;32mIn[514], line 6\u001b[0m, in \u001b[0;36mplot_\u001b[0;34m(key, region, mult)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_\u001b[39m(key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes_fit_s\u001b[39m\u001b[38;5;124m\"\u001b[39m, region\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSR1\u001b[39m\u001b[38;5;124m\"\u001b[39m, mult\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m     \u001b[38;5;66;03m######################\u001b[39;00m\n\u001b[1;32m      5\u001b[0m     nbins \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m240\u001b[39m, massbinwidth)))\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[0;32m----> 6\u001b[0m     samples \u001b[38;5;241m=\u001b[39m [samples_dict[sample[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m]] \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mkey\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mregion\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtotal\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m sample]\n\u001b[1;32m      8\u001b[0m     hf \u001b[38;5;241m=\u001b[39m hist2\u001b[38;5;241m.\u001b[39mHist(\n\u001b[1;32m      9\u001b[0m         hist\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mStrCategory(samples, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSample\u001b[39m\u001b[38;5;124m\"\u001b[39m, growth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),    \n\u001b[1;32m     10\u001b[0m         hist\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mRegular(nbins, \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m240\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar\u001b[39m\u001b[38;5;124m\"\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHiggs reconstructed mass [GeV]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     11\u001b[0m     )\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sample \u001b[38;5;129;01min\u001b[39;00m  f[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mregion\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys():\n",
-      "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/uproot/reading.py:2090\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2088\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2089\u001b[0m         last \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m-> 2090\u001b[0m         step \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2092\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, uproot\u001b[38;5;241m.\u001b[39mbehaviors\u001b[38;5;241m.\u001b[39mTBranch\u001b[38;5;241m.\u001b[39mHasBranches):\n\u001b[1;32m   2093\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items[i:])]\n",
-      "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/uproot/reading.py:2107\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step\n\u001b[1;32m   2106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2107\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget()\n",
-      "File \u001b[0;32m~/miniconda3/envs/coffea-env/lib/python3.9/site-packages/uproot/reading.py:2057\u001b[0m, in \u001b[0;36mReadOnlyDirectory.key\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2055\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m last\n\u001b[1;32m   2056\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cycle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2057\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2058\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2059\u001b[0m     )\n\u001b[1;32m   2060\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2061\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2062\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39mcycle, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2063\u001b[0m     )\n",
-      "\u001b[0;31mKeyInFileError\u001b[0m: not found: 'SR4' (with any cycle number)\n\n    Available keys: 'SR1;1', 'SR2;1', 'SR3;1', 'CR1;1', 'CR2;1', 'SR1/WH;1', 'SR1/ZH;1', 'SR2/WH;1', 'SR2/ZH;1', 'SR3/WH;1', 'SR3/ZH;1', 'SR1/VBF;1', 'SR1/ggF;1', 'SR1/ttH;1', 'SR2/VBF;1', 'SR2/ggF;1', 'SR2/qcd;1', 'SR2/ttH;1'...\n\nin file /Users/fmokhtar/Downloads/fitDiagnosticsAsimov.root"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "plot_(key=\"shapes_fit_b\", region=\"SR4\", mult=1)"
+    "plot_(key=\"shapes_fit_s\", region=\"SR4\", mult=10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 525,
+   "execution_count": 17,
    "id": "d92c0447",
    "metadata": {},
    "outputs": [
diff --git a/binder/combine_sig_v6.ipynb b/binder/combine_sig_v6.ipynb
new file mode 100644
index 000000000..ebe416c34
--- /dev/null
+++ b/binder/combine_sig_v6.ipynb
@@ -0,0 +1,742 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Asimov significance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import json\n",
+    "import pickle\n",
+    "import yaml\n",
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "pd.options.mode.chained_assignment = None\n",
+    "import pyarrow.parquet as pq\n",
+    "from sklearn.metrics import auc, roc_curve\n",
+    "from scipy.special import softmax\n",
+    "\n",
+    "import hist as hist2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "\n",
+    "import sys\n",
+    "sys.path\n",
+    "sys.path.append(\"../python/\")\n",
+    "\n",
+    "import utils\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ele': {'Run2': 137640.0,\n",
+       "  '2016APV': 19492.72,\n",
+       "  '2016': 16809.96,\n",
+       "  '2017': 41476.02,\n",
+       "  '2018': 59816.23},\n",
+       " 'mu': {'Run2': 137640.0,\n",
+       "  '2016APV': 19436.16,\n",
+       "  '2016': 16810.81,\n",
+       "  '2017': 41475.26,\n",
+       "  '2018': 59781.96},\n",
+       " 'lep': {'Run2': 137640.0,\n",
+       "  '2016APV': 19436.16,\n",
+       "  '2016': 16810.81,\n",
+       "  '2017': 41475.26,\n",
+       "  '2018': 59781.96}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get lumi\n",
+    "with open(\"../fileset/luminosity.json\") as f:\n",
+    "    luminosity = json.load(f)\n",
+    "    \n",
+    "luminosity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_lumi(years, channels):\n",
+    "    lum_ = 0\n",
+    "    for year in years:\n",
+    "        lum = 0\n",
+    "        for ch in channels:\n",
+    "            lum += luminosity[ch][year] / 1000.0\n",
+    "\n",
+    "        lum_ += lum / len(channels)    \n",
+    "    return lum_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rename_column(ev_dict, old_name, new_name):\n",
+    "    for year in ev_dict:\n",
+    "        for ch in ev_dict[year]:\n",
+    "            for sample in ev_dict[year][ch]:\n",
+    "                df = ev_dict[year][ch][sample]\n",
+    "                df[new_name] = df[old_name]\n",
+    "                \n",
+    "                # drop old column\n",
+    "                df = df[df.columns.drop(list(df.filter(regex=old_name)))]\n",
+    "\n",
+    "# tagger_old = \"fj_ParT_score_finetuned_v2_1-12\"\n",
+    "# tagger_new = \"fj_ParT_score_finetuned_v2_1_12\"\n",
+    "# rename_column(events_dict, tagger_old, tagger_new)                "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Should we categorize?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# at jet pt > 250 and MET cut\n",
+    "\n",
+    "###########\n",
+    "\n",
+    "# WP1\n",
+    "tagger_cuts = [\n",
+    "#     0.95,\n",
+    "#     0.955,\n",
+    "    0.96,\n",
+    "    0.965,\n",
+    "    0.97,\n",
+    "    0.975,\n",
+    "    0.98,\n",
+    "    0.985,\n",
+    "#     0.99,\n",
+    "]\n",
+    "\n",
+    "# with ggF and VBF splitting\n",
+    "sig_ggFandVBF = [\n",
+    "#     ,   # 0.95\n",
+    "#     ,   # 0.955\n",
+    "    1.5753,   # 0.96\n",
+    "    1.60615,   # 0.965    \n",
+    "    1.72512,   # 0.97\n",
+    "    1.79461,   # 0.975    \n",
+    "    1.80901,   # 0.98\n",
+    "    1.7271,   # 0.985\n",
+    "#     0,   # 0.99    \n",
+    "]\n",
+    "\n",
+    "# without ggF and VBF splitting\n",
+    "sig = [\n",
+    "#     ,   # 0.95\n",
+    "#     ,   # 0.955\n",
+    "    1.02247,   # 0.96\n",
+    "    1.06089,   # 0.965    \n",
+    "    1.20645,   # 0.97\n",
+    "    1.45288,   # 0.975    \n",
+    "    1.50477,   # 0.98\n",
+    "    1.44718,   # 0.985 \n",
+    "#   ,   # 0.99\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(exptext: Custom Text(0.0, 1, 'CMS'),\n",
+       " expsuffix: Custom Text(0.0, 1.005, 'Work in Progress'))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_dict = {    \n",
+    "    \"fj_ParT_score\": \"tab:blue\",\n",
+    "    \"fj_ParT_score_finetuned\": \"tab:green\",   \n",
+    "}\n",
+    "\n",
+    "lab_dict = {    \n",
+    "    \"fj_ParT_score\": \"ParT\",\n",
+    "    \"fj_ParT_score_finetuned\": \"ParT-finetuned\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "\n",
+    "years = [\"2018\", \"2017\", \"2016APV\", \"2016\"]\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 10))\n",
+    "\n",
+    "ax.scatter(tagger_cuts, sig_ggFandVBF, marker=\"x\", s=100, label=\"After ggF and VBF categorization\", color=\"tab:green\")\n",
+    "ax.scatter(tagger_cuts, sig, marker=\"x\", s=100, label=\"Before ggF and VBF categorization\", color=\"tab:orange\")\n",
+    "\n",
+    "# ax.plot([0.975, 0.975], [0.3, 2.00251], ':', color='grey')\n",
+    "# ax.plot([0.3, 0.975], [2.00251, 2.00251], ':', color='grey')\n",
+    "\n",
+    "# ax.plot([0.985, 0.985], [0.3, 1.67914], ':', color='grey')\n",
+    "# ax.plot([0.3, 0.985], [1.67914, 1.67914], ':', color='grey')\n",
+    "\n",
+    "ax.set_xlim(0.956, 0.989)\n",
+    "ax.set_ylim(0.88, 2.4)\n",
+    "\n",
+    "# ax.set_ylim(0, 1.4)\n",
+    "ax.legend(loc=\"upper left\")\n",
+    "ax.set_ylabel(\"Asimov significance\")\n",
+    "ax.set_xlabel(f\"ParT-Finetuned > X\")\n",
+    "\n",
+    "# ax.set_xticks(tagger_cuts)\n",
+    "\n",
+    "hep.cms.lumitext(\"%.0f \" % get_lumi(years, channels) + r\"fb$^{-1}$ (13 TeV)\", ax=ax, fontsize=20)\n",
+    "hep.cms.text(\"Work in Progress\", ax=ax, fontsize=15)\n",
+    "# plt.savefig(f\"/Users/fmokhtar/Desktop/AN/significance-cats.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WP1 (ggF and VBF)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# WP1\n",
+    "# reading from combine for WP1\n",
+    "tagger_cuts = [\n",
+    "    0.95,\n",
+    "    0.955,    \n",
+    "    0.96,\n",
+    "    0.965,\n",
+    "    0.97,\n",
+    "    0.975,\n",
+    "    0.98,\n",
+    "    0.985,\n",
+    "#     0.99,\n",
+    "]\n",
+    "sig_vbf = [\n",
+    "    1.29797,   # 0.95\n",
+    "    1.28452,   # 0.955    \n",
+    "    1.41709,   # 0.96\n",
+    "    1.43333,   # 0.965    \n",
+    "    1.45732,   # 0.97\n",
+    "    1.53811,   # 0.975    \n",
+    "    1.5858,   # 0.98\n",
+    "    1.55601,   # 0.985    \n",
+    "#     0, # 0.99\n",
+    "]\n",
+    "sig_ggf_inclusive = [\n",
+    "    0.862802,   # 0.95\n",
+    "    0.940192,   # 0.955    \n",
+    "    0.972933,   # 0.96\n",
+    "    0.986194,   # 0.965    \n",
+    "    1.12346,   # 0.97\n",
+    "    1.24184,   # 0.975    \n",
+    "    1.24618,   # 0.98\n",
+    "    1.20175,   # 0.985    \n",
+    "#     1.02185, # 0.99\n",
+    "]\n",
+    "sig_ggf_combined = [\n",
+    "    1.03403,   # 0.95\n",
+    "    1.13388,   # 0.955    \n",
+    "    1.16589,   # 0.96\n",
+    "    1.1932,   # 0.965    \n",
+    "    1.30952,   # 0.97\n",
+    "    1.36201,   # 0.975    \n",
+    "    1.34024,   # 0.98\n",
+    "    1.31319,   # 0.985    \n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_dict = {    \n",
+    "    \"fj_ParT_score\": \"tab:blue\",\n",
+    "    \"fj_ParT_score_finetuned\": \"tab:green\",   \n",
+    "}\n",
+    "\n",
+    "lab_dict = {    \n",
+    "    \"fj_ParT_score\": \"ParT\",\n",
+    "    \"fj_ParT_score_finetuned\": \"ParT-finetuned\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "\n",
+    "\n",
+    "years = [\"2018\", \"2017\", \"2016APV\", \"2016\"]\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 10))\n",
+    "\n",
+    "for tagger in [\n",
+    "#     \"fj_ParT_score\",\n",
+    "    \"fj_ParT_score_finetuned\",\n",
+    "]:\n",
+    "    ax.scatter(tagger_cuts, sig_ggf_combined, marker=\"x\", s=100, label=\"ggF category (all inclusive in pT)\")\n",
+    "    ax.scatter(tagger_cuts, sig_vbf, marker=\"x\", s=100, label=\"VBF category (all inclusive in pT)\")\n",
+    "\n",
+    "# ax.plot([0.97, 0.97], [0.957, 1.47527], ':', color='grey')\n",
+    "# ax.plot([0.957, 0.97], [1.47527, 1.47527], ':', color='grey')\n",
+    "\n",
+    "# ax.plot([0.985, 0.985], [0.957, 1.31084], ':', color='grey')\n",
+    "# ax.plot([0.957, 0.985], [1.31084, 1.31084], ':', color='grey')\n",
+    "\n",
+    "# ax.set_xlim(0.957, 0.991)\n",
+    "# ax.set_ylim(1., 1.7)\n",
+    "ax.legend()\n",
+    "ax.set_ylabel(\"Asimov significance\")\n",
+    "ax.set_xlabel(\"ParT-Finetuned > X\")\n",
+    "\n",
+    "# ax.set_xticks([0.9, 0.92, 0.94, 0.96, 0.97, 0.98])\n",
+    "\n",
+    "# ax.set_xticks(tagger_cuts)\n",
+    "\n",
+    "\n",
+    "hep.cms.lumitext(\"%.1f \" % get_lumi(years, channels) + r\"fb$^{-1}$ (13 TeV)\", ax=ax, fontsize=20)\n",
+    "hep.cms.text(\"Work in Progress\", ax=ax, fontsize=15)\n",
+    "plt.savefig(f\"/Users/fmokhtar/Desktop/AN/significance-WP1.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_SR1 = 1.97085 # assumes 0.98 for ggF and 0.97 for VBF\n",
+    "WP1 = 0.98\n",
+    "\n",
+    "# WP1\n",
+    "# reading from combine for WP1\n",
+    "tagger_cuts = [\n",
+    "    0.96,\n",
+    "    0.965,\n",
+    "    0.97,\n",
+    "    0.975,\n",
+    "]\n",
+    "sig = [\n",
+    "    2.02862,   # 0.96\n",
+    "    2.02283,   # 0.965\n",
+    "    2.02771,   # 0.97\n",
+    "    2.01843,   # 0.975\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_dict = {    \n",
+    "    \"fj_ParT_score\": \"tab:blue\",\n",
+    "    \"fj_ParT_score_finetuned\": \"tab:green\",   \n",
+    "}\n",
+    "\n",
+    "lab_dict = {    \n",
+    "    \"fj_ParT_score\": \"ParT\",\n",
+    "    \"fj_ParT_score_finetuned\": \"ParT-finetuned\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "\n",
+    "\n",
+    "years = [\"2018\", \"2017\", \"2016APV\", \"2016\"]\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "\n",
+    "for tagger in [\n",
+    "#     \"fj_ParT_score\",\n",
+    "    \"fj_ParT_score_finetuned\",\n",
+    "]:\n",
+    "    ax.scatter(tagger_cuts, sig, marker=\"x\", s=100, label=\"SR1+SR2\", color=color_dict[tagger])\n",
+    "\n",
+    "ax.axhline(sig_SR1, color=\"orange\", linestyle=\"--\", label=rf\"SR1\")\n",
+    "\n",
+    "# ax.plot([0.97, 0.97], [1.9, 2.08796], ':', color='grey')\n",
+    "# ax.plot([0.959, 0.97], [2.08796, 2.08796], ':', color='grey')\n",
+    "\n",
+    "# ax.set_xlim(0.959, 0.982)\n",
+    "# ax.set_ylim(1.9, 2.2)\n",
+    "\n",
+    "\n",
+    "ax.legend(loc=\"upper left\")\n",
+    "ax.set_ylabel(\"Combined Asimov significance\")\n",
+    "ax.set_xlabel(f\"X < ParT-Finetuned < {WP1}\")\n",
+    "\n",
+    "ax.set_xticks([0.96, 0.965, 0.97, 0.975, 0.98])\n",
+    "\n",
+    "# ax.set_xticks(tagger_cuts)\n",
+    "\n",
+    "\n",
+    "hep.cms.lumitext(\"%.1f \" % get_lumi(years, channels) + r\"fb$^{-1}$ (13 TeV)\", ax=ax, fontsize=20)\n",
+    "hep.cms.text(\"Work in Progress\", ax=ax, fontsize=15)\n",
+    "plt.savefig(f\"/Users/fmokhtar/Desktop/AN/significance-WP2.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WP2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP1 = 0.985   # sig=1.50482\n",
+    "sig = [\n",
+    "    1.52971,  # 0.985>tagger>0.80\n",
+    "    1.52975,  # 0.985>tagger>0.81\n",
+    "    1.53179,  # 0.985>tagger>0.82\n",
+    "    1.53182,  # 0.985>tagger>0.83\n",
+    "    1.53539,  # 0.985>tagger>0.84\n",
+    "    1.53678,  # 0.985>tagger>0.85\n",
+    "    1.54087,  # 0.985>tagger>0.86\n",
+    "    1.54509,  # 0.985>tagger>0.87\n",
+    "    1.54951,  # 0.985>tagger>0.88\n",
+    "    1.55866,  # 0.985>tagger>0.89\n",
+    "    1.55811,  # 0.985>tagger>0.90\n",
+    "    1.5534,  # 0.985>tagger>0.91\n",
+    "    1.56035,  # 0.985>tagger>0.92\n",
+    "    1.5756,  # 0.985>tagger>0.93\n",
+    "    1.60867,  # 0.985>tagger>0.94\n",
+    "    1.61182,  # 0.985>tagger>0.95\n",
+    "    1.67882,  # 0.985>tagger>0.96\n",
+    "    1.70185,  # 0.985>tagger>0.965\n",
+    "    1.73103,  # 0.985>tagger>0.97\n",
+    "    1.67332,  # 0.985>tagger>0.975\n",
+    "    1.63166,  # 0.985>tagger>0.98\n",
+    "    1.59202,  # 0.985>tagger>0.9825   \n",
+    "]\n",
+    "tagger_cuts = [\n",
+    "    0.80,\n",
+    "    0.81,\n",
+    "    0.82,\n",
+    "    0.83,\n",
+    "    0.84,\n",
+    "    0.85,\n",
+    "    0.86,\n",
+    "    0.87,\n",
+    "    0.88,\n",
+    "    0.89,\n",
+    "    0.90,\n",
+    "    0.91,\n",
+    "    0.92,\n",
+    "    0.93,\n",
+    "    0.94,\n",
+    "    0.95,\n",
+    "    0.96,\n",
+    "    0.965,\n",
+    "    0.97,\n",
+    "    0.975,\n",
+    "    0.98,\n",
+    "    0.9825,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_dict = {    \n",
+    "    \"fj_ParT_score\": \"tab:blue\",\n",
+    "    \"fj_ParT_score_finetuned\": \"tab:green\",   \n",
+    "}\n",
+    "\n",
+    "lab_dict = {    \n",
+    "    \"fj_ParT_score\": \"ParT\",\n",
+    "    \"fj_ParT_score_finetuned\": \"ParT-finetuned\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "\n",
+    "\n",
+    "years = [\"2018\", \"2017\", \"2016APV\", \"2016\"]\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 10))\n",
+    "\n",
+    "for tagger in [\n",
+    "#     \"fj_ParT_score\",\n",
+    "    \"fj_ParT_score_finetuned\",\n",
+    "]:\n",
+    "    \n",
+    "    ax.scatter(tagger_cuts, sig, marker=\"x\", s=100, label=lab_dict[tagger], color=color_dict[tagger])\n",
+    "\n",
+    "ax.axvline(0.97, color=\"grey\", linestyle=\"--\", label=rf\"WP2=0.97\")\n",
+    "\n",
+    "# ax.set_ylim(0, 1.4)\n",
+    "ax.legend(title=\"Pre-selection\")\n",
+    "ax.set_ylabel(\"SR2 expected significance (combined with SR1)\")\n",
+    "ax.set_xlabel(f\"{WP1} > Tagger > X\")\n",
+    "# ax.set_xticks(tagger_cuts)\n",
+    "\n",
+    "hep.cms.lumitext(\"%.1f \" % get_lumi(years, channels) + r\"fb$^{-1}$ (13 TeV)\", ax=ax, fontsize=20)\n",
+    "hep.cms.text(\"Work in Progress\", ax=ax, fontsize=15)\n",
+    "plt.savefig(f\"/Users/fmokhtar/Desktop/AN/signicance-WP2.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "WP1 = 0.97    # sig=1.3969\n",
+    "sig = [\n",
+    "    1.41648,  # 0.97>tagger>0.80\n",
+    "    1.41568,  # 0.97>tagger>0.81\n",
+    "    1.416,  # 0.97>tagger>0.82\n",
+    "    1.41532,  # 0.97>tagger>0.83\n",
+    "    1.41636,  # 0.97>tagger>0.84\n",
+    "    1.41523,  # 0.97>tagger>0.85\n",
+    "    1.4157,  # 0.97>tagger>0.86\n",
+    "    1.41774,  # 0.97>tagger>0.87\n",
+    "    1.41867,  # 0.97>tagger>0.88\n",
+    "    1.41859,  # 0.97>tagger>0.89\n",
+    "    1.41733,  # 0.97>tagger>0.90\n",
+    "    1.41284,  # 0.97>tagger>0.91\n",
+    "    1.41211,  # 0.97>tagger>0.92\n",
+    "    1.41973,  # 0.97>tagger>0.93\n",
+    "    1.42414,  # 0.97>tagger>0.94\n",
+    "    1.42296,  # 0.97>tagger>0.95\n",
+    "    1.42592,  # 0.97>tagger>0.96\n",
+    "    1.42106,  # 0.97>tagger>0.965\n",
+    "    1.41098,  # 0.97>tagger>0.9675  \n",
+    "]\n",
+    "tagger_cuts = [\n",
+    "    0.80,\n",
+    "    0.81,\n",
+    "    0.82,\n",
+    "    0.83,\n",
+    "    0.84,\n",
+    "    0.85,\n",
+    "    0.86,\n",
+    "    0.87,\n",
+    "    0.88,\n",
+    "    0.89,\n",
+    "    0.90,\n",
+    "    0.91,\n",
+    "    0.92,\n",
+    "    0.93,\n",
+    "    0.94,\n",
+    "    0.95,\n",
+    "    0.96,\n",
+    "    0.965,\n",
+    "    0.9675,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(exptext: Custom Text(0.0, 1, 'CMS'),\n",
+       " expsuffix: Custom Text(0.0, 1.005, 'Work in Progress'))"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_dict = {    \n",
+    "    \"fj_ParT_score\": \"tab:blue\",\n",
+    "    \"fj_ParT_score_finetuned\": \"tab:green\",   \n",
+    "}\n",
+    "\n",
+    "lab_dict = {    \n",
+    "    \"fj_ParT_score\": \"ParT\",\n",
+    "    \"fj_ParT_score_finetuned\": \"ParT-finetuned\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "\n",
+    "\n",
+    "years = [\"2018\", \"2017\", \"2016APV\", \"2016\"]\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12, 10))\n",
+    "\n",
+    "for tagger in [\n",
+    "#     \"fj_ParT_score\",\n",
+    "    \"fj_ParT_score_finetuned\",\n",
+    "]:\n",
+    "    \n",
+    "    ax.scatter(tagger_cuts, sig, marker=\"x\", s=100, label=lab_dict[tagger], color=color_dict[tagger])\n",
+    "\n",
+    "ax.axvline(0.96, color=\"grey\", linestyle=\"--\", label=rf\"WP2=0.96\")\n",
+    "\n",
+    "# ax.set_ylim(0, 1.4)\n",
+    "ax.legend(title=\"Pre-selection\")\n",
+    "ax.set_ylabel(\"SR2 expected significance (combined with SR1)\")\n",
+    "ax.set_xlabel(f\"{WP1} > Tagger > X\")\n",
+    "# ax.set_xticks(tagger_cuts)\n",
+    "\n",
+    "hep.cms.lumitext(\"%.1f \" % get_lumi(years, channels) + r\"fb$^{-1}$ (13 TeV)\", ax=ax, fontsize=20)\n",
+    "hep.cms.text(\"Work in Progress\", ax=ax, fontsize=15)\n",
+    "# plt.savefig(f\"/Users/fmokhtar/Desktop/farakikopku5/soverb_medium_signal_region.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "coffea-env",
+   "language": "python",
+   "name": "coffea-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/binder/hists_plots.ipynb b/binder/hists_plots.ipynb
index f1129ad03..6ed9a9674 100644
--- a/binder/hists_plots.ipynb
+++ b/binder/hists_plots.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,34 +56,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ele': {'Run2': 137640.0,\n",
-       "  '2016APV': 19492.72,\n",
-       "  '2016': 16809.96,\n",
-       "  '2017': 41476.02,\n",
-       "  '2018': 59816.23},\n",
-       " 'mu': {'Run2': 137640.0,\n",
-       "  '2016APV': 19436.16,\n",
-       "  '2016': 16810.81,\n",
-       "  '2017': 41475.26,\n",
-       "  '2018': 59781.96},\n",
-       " 'lep': {'Run2': 137640.0,\n",
-       "  '2016APV': 19436.16,\n",
-       "  '2016': 16810.81,\n",
-       "  '2017': 41475.26,\n",
-       "  '2018': 59781.96}}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# get lumi\n",
     "with open(\"../fileset/luminosity.json\") as f:\n",
@@ -5876,7 +5851,7 @@
     "fig, ax = plt.subplots(figsize=(10, 8))\n",
     "\n",
     "\n",
-    "taggers = [\n",
+    "taggerss = [\n",
     "    \"fj_ParT_score\",\n",
     "    \"fj_ParT_score_finetuned\",\n",
     "#     \"fj_ParT_score_finetuned_v35_1\",\n",
diff --git a/binder/hists_plots_tagger.ipynb b/binder/hists_plots_tagger.ipynb
new file mode 100644
index 000000000..dd8ca1e25
--- /dev/null
+++ b/binder/hists_plots_tagger.ipynb
@@ -0,0 +1,6379 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Making stacked histograms\n",
+    "- processes an `events[year][ch][sample]` object using `make_events_dict()`\n",
+    "- uses `plot_hists()` to make stacked histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "import json\n",
+    "import pickle\n",
+    "import yaml\n",
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "pd.options.mode.chained_assignment = None\n",
+    "import pyarrow.parquet as pq\n",
+    "from sklearn.metrics import auc, roc_curve\n",
+    "from scipy.special import softmax\n",
+    "\n",
+    "import hist as hist2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "\n",
+    "import sys\n",
+    "sys.path\n",
+    "sys.path.append(\"../python/\")\n",
+    "\n",
+    "import utils\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ele': {'Run2': 137640.0,\n",
+       "  '2016APV': 19492.72,\n",
+       "  '2016': 16809.96,\n",
+       "  '2017': 41476.02,\n",
+       "  '2018': 59816.23},\n",
+       " 'mu': {'Run2': 137640.0,\n",
+       "  '2016APV': 19436.16,\n",
+       "  '2016': 16810.81,\n",
+       "  '2017': 41475.26,\n",
+       "  '2018': 59781.96},\n",
+       " 'lep': {'Run2': 137640.0,\n",
+       "  '2016APV': 19436.16,\n",
+       "  '2016': 16810.81,\n",
+       "  '2017': 41475.26,\n",
+       "  '2018': 59781.96}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get lumi\n",
+    "with open(\"../fileset/luminosity.json\") as f:\n",
+    "    luminosity = json.load(f)\n",
+    "    \n",
+    "luminosity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_lumi(years, channels):\n",
+    "    lum_ = 0\n",
+    "    for year in years:\n",
+    "        lum = 0\n",
+    "        for ch in channels:\n",
+    "            lum += luminosity[ch][year] / 1000.0\n",
+    "\n",
+    "        lum_ += lum / len(channels)    \n",
+    "    return lum_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rename_column(ev_dict, old_name, new_name):\n",
+    "    for year in ev_dict:\n",
+    "        for ch in ev_dict[year]:\n",
+    "            for sample in ev_dict[year][ch]:\n",
+    "                df = ev_dict[year][ch][sample]\n",
+    "                df[new_name] = df[old_name]\n",
+    "                \n",
+    "                # drop old column\n",
+    "                df = df[df.columns.drop(list(df.filter(regex=old_name)))]\n",
+    "\n",
+    "# tagger_old = \"fj_ParT_score_finetuned_v2_1-12\"\n",
+    "# tagger_new = \"fj_ParT_score_finetuned_v2_1_12\"\n",
+    "# rename_column(events_dict, tagger_old, tagger_new)                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " # define your regions here\n",
+    "presel = {\n",
+    "        \"mu\": {\n",
+    "            \"lep_fj_dr003\": \"( ( lep_fj_dr>0.03) )\", \n",
+    "            \"lep_fj_dr08\": \"( ( lep_fj_dr<0.8) )\",   \n",
+    "            \"fj_pt250\": \"( ( fj_pt>250) )\",   \n",
+    "            \"dphi<1.57\": \"(abs_met_fj_dphi<1.57)\",\n",
+    "            \"tagger>0.5\": \"fj_ParT_score_finetuned>0.5\",\n",
+    "            \"MET>20\": \"met_pt>20\",\n",
+    "        },\n",
+    "        \"ele\": {\n",
+    "            \"lep_fj_dr003\": \"( ( lep_fj_dr>0.03) )\", \n",
+    "            \"lep_fj_dr08\": \"( ( lep_fj_dr<0.8) )\",   \n",
+    "            \"fj_pt250\": \"( ( fj_pt>250) )\", \n",
+    "            \"dphi<1.57\": \"(abs_met_fj_dphi<1.57)\",\n",
+    "            \"tagger>0.5\": \"fj_ParT_score_finetuned>0.5\",  \n",
+    "            \"MET>20\": \"met_pt>20\",\n",
+    "        },\n",
+    "}\n",
+    "\n",
+    "from make_stacked_hists_tagger import make_events_dict\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "# channels = [\"ele\"]\n",
+    "samples = [\n",
+    "    \"ggF\", \n",
+    "    \"VBF\",  \n",
+    "    \"WH\",\n",
+    "    \"ZH\",    \n",
+    "    \"ttH\",\n",
+    "    \"QCD\",\n",
+    "    \"DYJets\",\n",
+    "    \"WJetsLNu\",\n",
+    "    \"WZQQ\",\n",
+    "    \"TTbar\",\n",
+    "    \"SingleTop\",\n",
+    "    \"Diboson\",\n",
+    "    \"EWKvjets\",    \n",
+    "    \"Data\",\n",
+    "]\n",
+    "\n",
+    "samples_dir = {\n",
+    "    \"2016\":    \"../eos/Feb9_2016\",\n",
+    "    \"2016APV\": \"../eos/Feb9_2016APV\",\n",
+    "    \"2017\":    \"../eos/Feb9_2017\",\n",
+    "    \"2018\":    \"../eos/Feb9_2018\",    \n",
+    "}\n",
+    "\n",
+    "years = [\"2017\", \"2016\", \"2016APV\", \"2018\"]\n",
+    "# years = [\"2018\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events_dict = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 1663 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1663 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1663 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1663 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1663 events\n",
+      "INFO:root:Applying MET>20 selection on 1056 events\n",
+      "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 lep_fj_dr003 selection on 69 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 69 events\n",
+      "INFO:root:Applying fj_pt250 selection on 69 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 69 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 69 events\n",
+      "INFO:root:Applying MET>20 selection on 2 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 lep_fj_dr003 selection on 1897 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1897 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1897 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1897 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1897 events\n",
+      "INFO:root:Applying MET>20 selection on 344 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 289 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 289 events\n",
+      "INFO:root:Applying fj_pt250 selection on 289 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 289 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 289 events\n",
+      "INFO:root:Applying MET>20 selection on 212 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 212 events\n",
+      "INFO:root:tot event weight 8.662704986692063 \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 lep_fj_dr003 selection on 3905 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3905 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3905 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3905 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3905 events\n",
+      "INFO:root:Applying MET>20 selection on 2401 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",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 111882 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 111882 events\n",
+      "INFO:root:Applying fj_pt250 selection on 111882 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 111882 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 111882 events\n",
+      "INFO:root:Applying MET>20 selection on 23240 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 lep_fj_dr003 selection on 355362 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 355362 events\n",
+      "INFO:root:Applying fj_pt250 selection on 355362 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 355362 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 355362 events\n",
+      "INFO:root:Applying MET>20 selection on 27390 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 lep_fj_dr003 selection on 23539 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 23539 events\n",
+      "INFO:root:Applying fj_pt250 selection on 23539 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 23539 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 23539 events\n",
+      "INFO:root:Applying MET>20 selection on 1157 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 lep_fj_dr003 selection on 35 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 35 events\n",
+      "INFO:root:Applying fj_pt250 selection on 35 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 35 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 35 events\n",
+      "INFO:root:Applying MET>20 selection on 4 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 lep_fj_dr003 selection on 122417 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 122417 events\n",
+      "INFO:root:Applying fj_pt250 selection on 122417 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 122417 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 122417 events\n",
+      "INFO:root:Applying MET>20 selection on 16910 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 lep_fj_dr003 selection on 428 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 428 events\n",
+      "INFO:root:Applying fj_pt250 selection on 428 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 428 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 428 events\n",
+      "INFO:root:Applying MET>20 selection on 36 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 lep_fj_dr003 selection on 17205 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17205 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17205 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17205 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17205 events\n",
+      "INFO:root:Applying MET>20 selection on 2624 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 lep_fj_dr003 selection on 4028 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4028 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4028 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4028 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4028 events\n",
+      "INFO:root:Applying MET>20 selection on 468 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 lep_fj_dr003 selection on 4741 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4741 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4741 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4741 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4741 events\n",
+      "INFO:root:Applying MET>20 selection on 3211 events\n",
+      "INFO:root:Will fill the ggF dataframe with the remaining 3211 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 154961 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 154961 events\n",
+      "INFO:root:Applying fj_pt250 selection on 154961 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 154961 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 154961 events\n",
+      "INFO:root:Applying MET>20 selection on 4933 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 lep_fj_dr003 selection on 4009 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4009 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4009 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4009 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4009 events\n",
+      "INFO:root:Applying MET>20 selection on 434 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 lep_fj_dr003 selection on 22127 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 22127 events\n",
+      "INFO:root:Applying fj_pt250 selection on 22127 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 22127 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 22127 events\n",
+      "INFO:root:Applying MET>20 selection on 3467 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 lep_fj_dr003 selection on 8739 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 8739 events\n",
+      "INFO:root:Applying fj_pt250 selection on 8739 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 8739 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 8739 events\n",
+      "INFO:root:Applying MET>20 selection on 1225 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 lep_fj_dr003 selection on 79 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 79 events\n",
+      "INFO:root:Applying fj_pt250 selection on 79 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 79 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 79 events\n",
+      "INFO:root:Applying MET>20 selection on 1 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 lep_fj_dr003 selection on 4611 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4611 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4611 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4611 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4611 events\n",
+      "INFO:root:Applying MET>20 selection on 2818 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 lep_fj_dr003 selection on 23575 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 23575 events\n",
+      "INFO:root:Applying fj_pt250 selection on 23575 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 23575 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 23575 events\n",
+      "INFO:root:Applying MET>20 selection on 3567 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 lep_fj_dr003 selection on 49919 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 49919 events\n",
+      "INFO:root:Applying fj_pt250 selection on 49919 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 49919 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 49919 events\n",
+      "INFO:root:Applying MET>20 selection on 6094 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 lep_fj_dr003 selection on 10649 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 10649 events\n",
+      "INFO:root:Applying fj_pt250 selection on 10649 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 10649 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 10649 events\n",
+      "INFO:root:Applying MET>20 selection on 1593 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 lep_fj_dr003 selection on 95 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 95 events\n",
+      "INFO:root:Applying fj_pt250 selection on 95 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 95 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 95 events\n",
+      "INFO:root:Applying MET>20 selection on 14 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 lep_fj_dr003 selection on 12856 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 12856 events\n",
+      "INFO:root:Applying fj_pt250 selection on 12856 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 12856 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 12856 events\n",
+      "INFO:root:Applying MET>20 selection on 708 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 lep_fj_dr003 selection on 2980 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2980 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2980 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2980 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2980 events\n",
+      "INFO:root:Applying MET>20 selection on 719 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 lep_fj_dr003 selection on 368 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 368 events\n",
+      "INFO:root:Applying fj_pt250 selection on 368 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 368 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 368 events\n",
+      "INFO:root:Applying MET>20 selection on 14 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 lep_fj_dr003 selection on 876 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 876 events\n",
+      "INFO:root:Applying fj_pt250 selection on 876 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 876 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 876 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying MET>20 selection on 223 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 lep_fj_dr003 selection on 530278 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 530278 events\n",
+      "INFO:root:Applying fj_pt250 selection on 530278 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 530278 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 530278 events\n",
+      "INFO:root:Applying MET>20 selection on 16651 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 lep_fj_dr003 selection on 9552 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 9552 events\n",
+      "INFO:root:Applying fj_pt250 selection on 9552 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 9552 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 9552 events\n",
+      "INFO:root:Applying MET>20 selection on 480 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 lep_fj_dr003 selection on 132362 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 132362 events\n",
+      "INFO:root:Applying fj_pt250 selection on 132362 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 132362 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 132362 events\n",
+      "INFO:root:Applying MET>20 selection on 8529 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 8529 events\n",
+      "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 lep_fj_dr003 selection on 261 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 261 events\n",
+      "INFO:root:Applying fj_pt250 selection on 261 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 261 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 261 events\n",
+      "INFO:root:Applying MET>20 selection on 1 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 EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 88 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 88 events\n",
+      "INFO:root:Applying fj_pt250 selection on 88 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 88 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 88 events\n",
+      "INFO:root:Applying MET>20 selection on 15 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 lep_fj_dr003 selection on 70 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 70 events\n",
+      "INFO:root:Applying fj_pt250 selection on 70 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 70 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 70 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 199 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 199 events\n",
+      "INFO:root:Applying fj_pt250 selection on 199 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 199 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 199 events\n",
+      "INFO:root:Applying MET>20 selection on 21 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 lep_fj_dr003 selection on 644 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 644 events\n",
+      "INFO:root:Applying fj_pt250 selection on 644 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 644 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 644 events\n",
+      "INFO:root:Applying MET>20 selection on 85 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 lep_fj_dr003 selection on 1046 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1046 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1046 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1046 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1046 events\n",
+      "INFO:root:Applying MET>20 selection on 47 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 lep_fj_dr003 selection on 862 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 862 events\n",
+      "INFO:root:Applying fj_pt250 selection on 862 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 862 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 862 events\n",
+      "INFO:root:Applying MET>20 selection on 77 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 lep_fj_dr003 selection on 563 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 563 events\n",
+      "INFO:root:Applying fj_pt250 selection on 563 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 563 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 563 events\n",
+      "INFO:root:Applying MET>20 selection on 58 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 lep_fj_dr003 selection on 858 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 858 events\n",
+      "INFO:root:Applying fj_pt250 selection on 858 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 858 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 858 events\n",
+      "INFO:root:Applying MET>20 selection on 176 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 lep_fj_dr003 selection on 311 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 311 events\n",
+      "INFO:root:Applying fj_pt250 selection on 311 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 311 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 311 events\n",
+      "INFO:root:Applying MET>20 selection on 49 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 lep_fj_dr003 selection on 133756 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 133756 events\n",
+      "INFO:root:Applying fj_pt250 selection on 133756 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 133756 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 133756 events\n",
+      "INFO:root:Applying MET>20 selection on 3533 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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 59 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 59 events\n",
+      "INFO:root:Applying fj_pt250 selection on 59 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 59 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 59 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 81980 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 81980 events\n",
+      "INFO:root:Applying fj_pt250 selection on 81980 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 81980 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 81980 events\n",
+      "INFO:root:Applying MET>20 selection on 19916 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 lep_fj_dr003 selection on 523 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 523 events\n",
+      "INFO:root:Applying fj_pt250 selection on 523 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 523 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 523 events\n",
+      "INFO:root:Applying MET>20 selection on 105 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 lep_fj_dr003 selection on 41782 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 41782 events\n",
+      "INFO:root:Applying fj_pt250 selection on 41782 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 41782 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 41782 events\n",
+      "INFO:root:Applying MET>20 selection on 2219 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 lep_fj_dr003 selection on 14945 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 14945 events\n",
+      "INFO:root:Applying fj_pt250 selection on 14945 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 14945 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 14945 events\n",
+      "INFO:root:Applying MET>20 selection on 4397 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 lep_fj_dr003 selection on 475 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 475 events\n",
+      "INFO:root:Applying fj_pt250 selection on 475 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 475 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 475 events\n",
+      "INFO:root:Applying MET>20 selection on 68 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 lep_fj_dr003 selection on 31134 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31134 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31134 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31134 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31134 events\n",
+      "INFO:root:Applying MET>20 selection on 4814 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 lep_fj_dr003 selection on 898 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 898 events\n",
+      "INFO:root:Applying fj_pt250 selection on 898 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 898 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 898 events\n",
+      "INFO:root:Applying MET>20 selection on 49 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 lep_fj_dr003 selection on 1493 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1493 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1493 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1493 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1493 events\n",
+      "INFO:root:Applying MET>20 selection on 258 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 lep_fj_dr003 selection on 10580 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 10580 events\n",
+      "INFO:root:Applying fj_pt250 selection on 10580 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 10580 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 10580 events\n",
+      "INFO:root:Applying MET>20 selection on 6398 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 lep_fj_dr003 selection on 623 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 623 events\n",
+      "INFO:root:Applying fj_pt250 selection on 623 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 623 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 623 events\n",
+      "INFO:root:Applying MET>20 selection on 139 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 lep_fj_dr003 selection on 39 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 39 events\n",
+      "INFO:root:Applying fj_pt250 selection on 39 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 39 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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 lep_fj_dr003 selection on 32684 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 32684 events\n",
+      "INFO:root:Applying fj_pt250 selection on 32684 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 32684 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 32684 events\n",
+      "INFO:root:Applying MET>20 selection on 7971 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 lep_fj_dr003 selection on 772 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 772 events\n",
+      "INFO:root:Applying fj_pt250 selection on 772 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 772 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 772 events\n",
+      "INFO:root:Applying MET>20 selection on 83 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 lep_fj_dr003 selection on 8388 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 8388 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying fj_pt250 selection on 8388 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 8388 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 8388 events\n",
+      "INFO:root:Applying MET>20 selection on 4986 events\n",
+      "INFO:root:Will fill the ZH dataframe with the remaining 4986 events\n",
+      "INFO:root:tot event weight 2.3958541481907996 \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 lep_fj_dr003 selection on 709 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 709 events\n",
+      "INFO:root:Applying fj_pt250 selection on 709 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 709 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 709 events\n",
+      "INFO:root:Applying MET>20 selection on 16 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 lep_fj_dr003 selection on 2479 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2479 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2479 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2479 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2479 events\n",
+      "INFO:root:Applying MET>20 selection on 1759 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 lep_fj_dr003 selection on 79 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 79 events\n",
+      "INFO:root:Applying fj_pt250 selection on 79 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 79 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 79 events\n",
+      "INFO:root:Applying MET>20 selection on 8 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 lep_fj_dr003 selection on 2079 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2079 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2079 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2079 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2079 events\n",
+      "INFO:root:Applying MET>20 selection on 456 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 496 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 496 events\n",
+      "INFO:root:Applying fj_pt250 selection on 496 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 496 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 496 events\n",
+      "INFO:root:Applying MET>20 selection on 363 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 363 events\n",
+      "INFO:root:tot event weight 14.883407015652985 \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 lep_fj_dr003 selection on 5270 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5270 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5270 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5270 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5270 events\n",
+      "INFO:root:Applying MET>20 selection on 3653 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 148852 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 148852 events\n",
+      "INFO:root:Applying fj_pt250 selection on 148852 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 148852 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 148852 events\n",
+      "INFO:root:Applying MET>20 selection on 36873 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 lep_fj_dr003 selection on 375217 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 375217 events\n",
+      "INFO:root:Applying fj_pt250 selection on 375217 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 375217 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 375217 events\n",
+      "INFO:root:Applying MET>20 selection on 32358 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 lep_fj_dr003 selection on 34606 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 34606 events\n",
+      "INFO:root:Applying fj_pt250 selection on 34606 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 34606 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 34606 events\n",
+      "INFO:root:Applying MET>20 selection on 1682 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 lep_fj_dr003 selection on 107 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 107 events\n",
+      "INFO:root:Applying fj_pt250 selection on 107 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 107 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 107 events\n",
+      "INFO:root:Applying MET>20 selection on 15 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 lep_fj_dr003 selection on 165051 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 165051 events\n",
+      "INFO:root:Applying fj_pt250 selection on 165051 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 165051 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 165051 events\n",
+      "INFO:root:Applying MET>20 selection on 30512 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 lep_fj_dr003 selection on 204 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 204 events\n",
+      "INFO:root:Applying fj_pt250 selection on 204 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 204 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 204 events\n",
+      "INFO:root:Applying MET>20 selection on 42 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 lep_fj_dr003 selection on 19527 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 19527 events\n",
+      "INFO:root:Applying fj_pt250 selection on 19527 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 19527 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 19527 events\n",
+      "INFO:root:Applying MET>20 selection on 3785 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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying lep_fj_dr003 selection on 4057 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4057 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4057 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4057 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4057 events\n",
+      "INFO:root:Applying MET>20 selection on 532 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 lep_fj_dr003 selection on 7334 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 7334 events\n",
+      "INFO:root:Applying fj_pt250 selection on 7334 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 7334 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 7334 events\n",
+      "INFO:root:Applying MET>20 selection on 5314 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 lep_fj_dr003 selection on 74923 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 74923 events\n",
+      "INFO:root:Applying fj_pt250 selection on 74923 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 74923 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 74923 events\n",
+      "INFO:root:Applying MET>20 selection on 9688 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 lep_fj_dr003 selection on 3944 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3944 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3944 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3944 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3944 events\n",
+      "INFO:root:Applying MET>20 selection on 499 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 lep_fj_dr003 selection on 48 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 48 events\n",
+      "INFO:root:Applying fj_pt250 selection on 48 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 48 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 48 events\n",
+      "INFO:root:Applying MET>20 selection on 2 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 lep_fj_dr003 selection on 6442 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6442 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6442 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6442 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6442 events\n",
+      "INFO:root:Applying MET>20 selection on 4417 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 lep_fj_dr003 selection on 36910 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 36910 events\n",
+      "INFO:root:Applying fj_pt250 selection on 36910 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 36910 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 36910 events\n",
+      "INFO:root:Applying MET>20 selection on 7131 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 lep_fj_dr003 selection on 125 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 125 events\n",
+      "INFO:root:Applying fj_pt250 selection on 125 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 125 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 125 events\n",
+      "INFO:root:Applying MET>20 selection on 30 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 lep_fj_dr003 selection on 25136 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 25136 events\n",
+      "INFO:root:Applying fj_pt250 selection on 25136 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 25136 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 25136 events\n",
+      "INFO:root:Applying MET>20 selection on 4813 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 lep_fj_dr003 selection on 16447 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16447 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16447 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16447 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16447 events\n",
+      "INFO:root:Applying MET>20 selection on 932 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 lep_fj_dr003 selection on 11271 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 11271 events\n",
+      "INFO:root:Applying fj_pt250 selection on 11271 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 11271 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 11271 events\n",
+      "INFO:root:Applying MET>20 selection on 2085 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 lep_fj_dr003 selection on 3521 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3521 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3521 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3521 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3521 events\n",
+      "INFO:root:Applying MET>20 selection on 863 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 lep_fj_dr003 selection on 328 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 328 events\n",
+      "INFO:root:Applying fj_pt250 selection on 328 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 328 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 328 events\n",
+      "INFO:root:Applying MET>20 selection on 32 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 lep_fj_dr003 selection on 24145 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 24145 events\n",
+      "INFO:root:Applying fj_pt250 selection on 24145 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 24145 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 24145 events\n",
+      "INFO:root:Applying MET>20 selection on 4574 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 12183 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 12183 events\n",
+      "INFO:root:Applying fj_pt250 selection on 12183 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 12183 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 12183 events\n",
+      "INFO:root:Applying MET>20 selection on 2338 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 lep_fj_dr003 selection on 942 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 942 events\n",
+      "INFO:root:Applying fj_pt250 selection on 942 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 942 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 942 events\n",
+      "INFO:root:Applying MET>20 selection on 330 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 lep_fj_dr003 selection on 126181 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 126181 events\n",
+      "INFO:root:Applying fj_pt250 selection on 126181 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 126181 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 126181 events\n",
+      "INFO:root:Applying MET>20 selection on 12666 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 lep_fj_dr003 selection on 13132 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 13132 events\n",
+      "INFO:root:Applying fj_pt250 selection on 13132 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 13132 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 13132 events\n",
+      "INFO:root:Applying MET>20 selection on 780 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 lep_fj_dr003 selection on 105329 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 105329 events\n",
+      "INFO:root:Applying fj_pt250 selection on 105329 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 105329 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 105329 events\n",
+      "INFO:root:Applying MET>20 selection on 8023 events\n",
+      "INFO:root:Will fill the TTbar dataframe with the remaining 8023 events\n",
+      "INFO:root:tot event weight 268.95194575474613 \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 lep_fj_dr003 selection on 149 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 149 events\n",
+      "INFO:root:Applying fj_pt250 selection on 149 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 149 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 149 events\n",
+      "INFO:root:Applying MET>20 selection on 7 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 EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 63 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 63 events\n",
+      "INFO:root:Applying fj_pt250 selection on 63 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 63 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 63 events\n",
+      "INFO:root:Applying MET>20 selection on 13 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 ZJetsToQQ_HT-400to600 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 61 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 61 events\n",
+      "INFO:root:Applying fj_pt250 selection on 61 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 61 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 61 events\n",
+      "INFO:root:Applying MET>20 selection on 8 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 ZZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 89 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 89 events\n",
+      "INFO:root:Applying fj_pt250 selection on 89 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 89 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 89 events\n",
+      "INFO:root:Applying MET>20 selection on 20 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 TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 1348 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1348 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1348 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1348 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1348 events\n",
+      "INFO:root:Applying MET>20 selection on 228 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 QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 1145 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1145 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1145 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1145 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1145 events\n",
+      "INFO:root:Applying MET>20 selection on 147 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 QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 949 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 949 events\n",
+      "INFO:root:Applying fj_pt250 selection on 949 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 949 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 949 events\n",
+      "INFO:root:Applying MET>20 selection on 134 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 QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 458 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 458 events\n",
+      "INFO:root:Applying fj_pt250 selection on 458 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 458 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 458 events\n",
+      "INFO:root:Applying MET>20 selection on 58 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 WJetsToQQ_HT-800toInf samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 712 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 712 events\n",
+      "INFO:root:Applying fj_pt250 selection on 712 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 712 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 712 events\n",
+      "INFO:root:Applying MET>20 selection on 225 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 ZJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 478 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying lep_fj_dr08 selection on 478 events\n",
+      "INFO:root:Applying fj_pt250 selection on 478 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 478 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 478 events\n",
+      "INFO:root:Applying MET>20 selection on 83 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 DYJetsToLL_LHEFilterPtZ-400To650 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 29251 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 29251 events\n",
+      "INFO:root:Applying fj_pt250 selection on 29251 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 29251 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 29251 events\n",
+      "INFO:root:Applying MET>20 selection on 3074 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 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 lep_fj_dr003 selection on 81 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 81 events\n",
+      "INFO:root:Applying fj_pt250 selection on 81 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 81 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 81 events\n",
+      "INFO:root:Applying MET>20 selection on 9 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 WJetsToLNu_HT-600To800 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 107655 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 107655 events\n",
+      "INFO:root:Applying fj_pt250 selection on 107655 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 107655 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 107655 events\n",
+      "INFO:root:Applying MET>20 selection on 30008 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 WJetsToQQ_HT-600to800 samples and should combine them under WZQQ\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 300 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 300 events\n",
+      "INFO:root:Applying fj_pt250 selection on 300 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 300 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 300 events\n",
+      "INFO:root:Applying MET>20 selection on 98 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 WJetsToLNu_HT-2500ToInf samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 56498 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 56498 events\n",
+      "INFO:root:Applying fj_pt250 selection on 56498 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 56498 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 56498 events\n",
+      "INFO:root:Applying MET>20 selection on 5356 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 lep_fj_dr003 selection on 13697 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 13697 events\n",
+      "INFO:root:Applying fj_pt250 selection on 13697 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 13697 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 13697 events\n",
+      "INFO:root:Applying MET>20 selection on 4764 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 lep_fj_dr003 selection on 833 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 833 events\n",
+      "INFO:root:Applying fj_pt250 selection on 833 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 833 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 833 events\n",
+      "INFO:root:Applying MET>20 selection on 145 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 lep_fj_dr003 selection on 1021 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1021 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1021 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1021 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1021 events\n",
+      "INFO:root:Applying MET>20 selection on 119 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 lep_fj_dr003 selection on 1738 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1738 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1738 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1738 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1738 events\n",
+      "INFO:root:Applying MET>20 selection on 359 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 lep_fj_dr003 selection on 12517 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 12517 events\n",
+      "INFO:root:Applying fj_pt250 selection on 12517 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 12517 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 12517 events\n",
+      "INFO:root:Applying MET>20 selection on 8881 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 lep_fj_dr003 selection on 527 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 527 events\n",
+      "INFO:root:Applying fj_pt250 selection on 527 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 527 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 527 events\n",
+      "INFO:root:Applying MET>20 selection on 134 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 lep_fj_dr003 selection on 19 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 19 events\n",
+      "INFO:root:Applying fj_pt250 selection on 19 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 19 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 19 events\n",
+      "INFO:root:Applying MET>20 selection on 10 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 lep_fj_dr003 selection on 41161 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 41161 events\n",
+      "INFO:root:Applying fj_pt250 selection on 41161 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 41161 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 41161 events\n",
+      "INFO:root:Applying MET>20 selection on 11669 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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying lep_fj_dr003 selection on 767 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 767 events\n",
+      "INFO:root:Applying fj_pt250 selection on 767 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 767 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 767 events\n",
+      "INFO:root:Applying MET>20 selection on 121 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 lep_fj_dr003 selection on 10538 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 10538 events\n",
+      "INFO:root:Applying fj_pt250 selection on 10538 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 10538 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 10538 events\n",
+      "INFO:root:Applying MET>20 selection on 7412 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 lep_fj_dr003 selection on 651 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 651 events\n",
+      "INFO:root:Applying fj_pt250 selection on 651 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 651 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 651 events\n",
+      "INFO:root:Applying MET>20 selection on 60 events\n",
+      "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 lep_fj_dr003 selection on 35806 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 35806 events\n",
+      "INFO:root:Applying fj_pt250 selection on 35806 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 35806 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 35806 events\n",
+      "INFO:root:Applying MET>20 selection on 6988 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 lep_fj_dr003 selection on 1514 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1514 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1514 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1514 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1514 events\n",
+      "INFO:root:Applying MET>20 selection on 987 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 lep_fj_dr003 selection on 34 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 34 events\n",
+      "INFO:root:Applying fj_pt250 selection on 34 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 34 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 34 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 2000 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2000 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2000 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2000 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2000 events\n",
+      "INFO:root:Applying MET>20 selection on 384 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 137 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 137 events\n",
+      "INFO:root:Applying fj_pt250 selection on 137 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 137 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 137 events\n",
+      "INFO:root:Applying MET>20 selection on 96 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 96 events\n",
+      "INFO:root:tot event weight 12.071607992117546 \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 lep_fj_dr003 selection on 5304 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5304 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5304 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5304 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5304 events\n",
+      "INFO:root:Applying MET>20 selection on 3319 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 159607 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 159607 events\n",
+      "INFO:root:Applying fj_pt250 selection on 159607 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 159607 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 159607 events\n",
+      "INFO:root:Applying MET>20 selection on 32972 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 lep_fj_dr003 selection on 349864 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 349864 events\n",
+      "INFO:root:Applying fj_pt250 selection on 349864 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 349864 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 349864 events\n",
+      "INFO:root:Applying MET>20 selection on 26759 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 lep_fj_dr003 selection on 31158 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31158 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31158 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31158 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31158 events\n",
+      "INFO:root:Applying MET>20 selection on 1466 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 lep_fj_dr003 selection on 191329 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 191329 events\n",
+      "INFO:root:Applying fj_pt250 selection on 191329 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 191329 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 191329 events\n",
+      "INFO:root:Applying MET>20 selection on 26216 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 lep_fj_dr003 selection on 694 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 694 events\n",
+      "INFO:root:Applying fj_pt250 selection on 694 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 694 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 694 events\n",
+      "INFO:root:Applying MET>20 selection on 84 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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 18076 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 18076 events\n",
+      "INFO:root:Applying fj_pt250 selection on 18076 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 18076 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 18076 events\n",
+      "INFO:root:Applying MET>20 selection on 2602 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 lep_fj_dr003 selection on 5367 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5367 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5367 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5367 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5367 events\n",
+      "INFO:root:Applying MET>20 selection on 636 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 lep_fj_dr003 selection on 4523 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4523 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4523 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4523 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4523 events\n",
+      "INFO:root:Applying MET>20 selection on 3013 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 lep_fj_dr003 selection on 146465 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 146465 events\n",
+      "INFO:root:Applying fj_pt250 selection on 146465 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 146465 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 146465 events\n",
+      "INFO:root:Applying MET>20 selection on 4958 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 lep_fj_dr003 selection on 5196 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5196 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5196 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5196 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5196 events\n",
+      "INFO:root:Applying MET>20 selection on 601 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 lep_fj_dr003 selection on 39 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 39 events\n",
+      "INFO:root:Applying fj_pt250 selection on 39 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 39 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 5887 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5887 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5887 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5887 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5887 events\n",
+      "INFO:root:Applying MET>20 selection on 3672 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 lep_fj_dr003 selection on 51421 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 51421 events\n",
+      "INFO:root:Applying fj_pt250 selection on 51421 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 51421 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 51421 events\n",
+      "INFO:root:Applying MET>20 selection on 6655 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 lep_fj_dr003 selection on 31550 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31550 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31550 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31550 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31550 events\n",
+      "INFO:root:Applying MET>20 selection on 4699 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 lep_fj_dr003 selection on 85 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 85 events\n",
+      "INFO:root:Applying fj_pt250 selection on 85 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 85 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 85 events\n",
+      "INFO:root:Applying MET>20 selection on 19 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 lep_fj_dr003 selection on 17372 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17372 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17372 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17372 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17372 events\n",
+      "INFO:root:Applying MET>20 selection on 948 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 lep_fj_dr003 selection on 3547 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3547 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3547 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3547 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3547 events\n",
+      "INFO:root:Applying MET>20 selection on 821 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 lep_fj_dr003 selection on 376 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 376 events\n",
+      "INFO:root:Applying fj_pt250 selection on 376 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 376 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 376 events\n",
+      "INFO:root:Applying MET>20 selection on 9 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 lep_fj_dr003 selection on 569 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 569 events\n",
+      "INFO:root:Applying fj_pt250 selection on 569 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 569 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 569 events\n",
+      "INFO:root:Applying MET>20 selection on 151 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 151 events\n",
+      "INFO:root:tot event weight 159.66373559188935 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 508019 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 508019 events\n",
+      "INFO:root:Applying fj_pt250 selection on 508019 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 508019 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 508019 events\n",
+      "INFO:root:Applying MET>20 selection on 16281 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 lep_fj_dr003 selection on 12111 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 12111 events\n",
+      "INFO:root:Applying fj_pt250 selection on 12111 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 12111 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 12111 events\n",
+      "INFO:root:Applying MET>20 selection on 658 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 lep_fj_dr003 selection on 125631 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 125631 events\n",
+      "INFO:root:Applying fj_pt250 selection on 125631 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 125631 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 125631 events\n",
+      "INFO:root:Applying MET>20 selection on 8257 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 lep_fj_dr003 selection on 222 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 222 events\n",
+      "INFO:root:Applying fj_pt250 selection on 222 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 222 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 222 events\n",
+      "INFO:root:Applying MET>20 selection on 1 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 EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 1 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 47 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 47 events\n",
+      "INFO:root:Applying fj_pt250 selection on 47 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 47 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 47 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 227 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 227 events\n",
+      "INFO:root:Applying fj_pt250 selection on 227 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 227 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 227 events\n",
+      "INFO:root:Applying MET>20 selection on 27 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 lep_fj_dr003 selection on 629 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 629 events\n",
+      "INFO:root:Applying fj_pt250 selection on 629 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 629 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 629 events\n",
+      "INFO:root:Applying MET>20 selection on 85 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 lep_fj_dr003 selection on 1090 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1090 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1090 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1090 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1090 events\n",
+      "INFO:root:Applying MET>20 selection on 50 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 lep_fj_dr003 selection on 841 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 841 events\n",
+      "INFO:root:Applying fj_pt250 selection on 841 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 841 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 841 events\n",
+      "INFO:root:Applying MET>20 selection on 73 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 lep_fj_dr003 selection on 503 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 503 events\n",
+      "INFO:root:Applying fj_pt250 selection on 503 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 503 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 503 events\n",
+      "INFO:root:Applying MET>20 selection on 63 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 lep_fj_dr003 selection on 753 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 753 events\n",
+      "INFO:root:Applying fj_pt250 selection on 753 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 753 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 753 events\n",
+      "INFO:root:Applying MET>20 selection on 139 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 lep_fj_dr003 selection on 316 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 316 events\n",
+      "INFO:root:Applying fj_pt250 selection on 316 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 316 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 316 events\n",
+      "INFO:root:Applying MET>20 selection on 53 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 lep_fj_dr003 selection on 142537 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 142537 events\n",
+      "INFO:root:Applying fj_pt250 selection on 142537 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 142537 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 142537 events\n",
+      "INFO:root:Applying MET>20 selection on 3866 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 lep_fj_dr003 selection on 65 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 65 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying fj_pt250 selection on 65 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 65 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 65 events\n",
+      "INFO:root:Applying MET>20 selection on 12 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 lep_fj_dr003 selection on 95 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 95 events\n",
+      "INFO:root:Applying fj_pt250 selection on 95 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 95 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 95 events\n",
+      "INFO:root:Applying MET>20 selection on 11 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 lep_fj_dr003 selection on 105226 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 105226 events\n",
+      "INFO:root:Applying fj_pt250 selection on 105226 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 105226 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 105226 events\n",
+      "INFO:root:Applying MET>20 selection on 25506 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 lep_fj_dr003 selection on 437 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 437 events\n",
+      "INFO:root:Applying fj_pt250 selection on 437 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 437 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 437 events\n",
+      "INFO:root:Applying MET>20 selection on 94 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 lep_fj_dr003 selection on 62868 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 62868 events\n",
+      "INFO:root:Applying fj_pt250 selection on 62868 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 62868 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 62868 events\n",
+      "INFO:root:Applying MET>20 selection on 3313 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 lep_fj_dr003 selection on 5869 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5869 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5869 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5869 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5869 events\n",
+      "INFO:root:Applying MET>20 selection on 1703 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 lep_fj_dr003 selection on 502 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 502 events\n",
+      "INFO:root:Applying fj_pt250 selection on 502 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 502 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 502 events\n",
+      "INFO:root:Applying MET>20 selection on 58 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 lep_fj_dr003 selection on 14770 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 14770 events\n",
+      "INFO:root:Applying fj_pt250 selection on 14770 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 14770 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 14770 events\n",
+      "INFO:root:Applying MET>20 selection on 2292 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 lep_fj_dr003 selection on 67956 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 67956 events\n",
+      "INFO:root:Applying fj_pt250 selection on 67956 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 67956 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 67956 events\n",
+      "INFO:root:Applying MET>20 selection on 10528 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 lep_fj_dr003 selection on 963 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 963 events\n",
+      "INFO:root:Applying fj_pt250 selection on 963 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 963 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 963 events\n",
+      "INFO:root:Applying MET>20 selection on 56 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 lep_fj_dr003 selection on 1736 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1736 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1736 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1736 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1736 events\n",
+      "INFO:root:Applying MET>20 selection on 285 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 lep_fj_dr003 selection on 15939 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 15939 events\n",
+      "INFO:root:Applying fj_pt250 selection on 15939 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 15939 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 15939 events\n",
+      "INFO:root:Applying MET>20 selection on 2423 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 lep_fj_dr003 selection on 14086 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 14086 events\n",
+      "INFO:root:Applying fj_pt250 selection on 14086 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 14086 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 14086 events\n",
+      "INFO:root:Applying MET>20 selection on 8513 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 lep_fj_dr003 selection on 885 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 885 events\n",
+      "INFO:root:Applying fj_pt250 selection on 885 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 885 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 885 events\n",
+      "INFO:root:Applying MET>20 selection on 191 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 lep_fj_dr003 selection on 58 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 58 events\n",
+      "INFO:root:Applying fj_pt250 selection on 58 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 58 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 58 events\n",
+      "INFO:root:Applying MET>20 selection on 15 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 15 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 43872 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 43872 events\n",
+      "INFO:root:Applying fj_pt250 selection on 43872 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 43872 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 43872 events\n",
+      "INFO:root:Applying MET>20 selection on 10561 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 lep_fj_dr003 selection on 503 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 503 events\n",
+      "INFO:root:Applying fj_pt250 selection on 503 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 503 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 503 events\n",
+      "INFO:root:Applying MET>20 selection on 51 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 lep_fj_dr003 selection on 8471 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 8471 events\n",
+      "INFO:root:Applying fj_pt250 selection on 8471 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 8471 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 8471 events\n",
+      "INFO:root:Applying MET>20 selection on 5052 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 lep_fj_dr003 selection on 601 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 601 events\n",
+      "INFO:root:Applying fj_pt250 selection on 601 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 601 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 601 events\n",
+      "INFO:root:Applying MET>20 selection on 89 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 707 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 707 events\n",
+      "INFO:root:Applying fj_pt250 selection on 707 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 707 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 707 events\n",
+      "INFO:root:Applying MET>20 selection on 21 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 lep_fj_dr003 selection on 2336 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2336 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2336 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2336 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2336 events\n",
+      "INFO:root:Applying MET>20 selection on 1603 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 lep_fj_dr003 selection on 58 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 58 events\n",
+      "INFO:root:Applying fj_pt250 selection on 58 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 58 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 58 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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 lep_fj_dr003 selection on 2134 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2134 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2134 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2134 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2134 events\n",
+      "INFO:root:Applying MET>20 selection on 469 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 203 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 203 events\n",
+      "INFO:root:Applying fj_pt250 selection on 203 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 203 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 203 events\n",
+      "INFO:root:Applying MET>20 selection on 146 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 146 events\n",
+      "INFO:root:tot event weight 19.16353505220181 \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 lep_fj_dr003 selection on 7012 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 7012 events\n",
+      "INFO:root:Applying fj_pt250 selection on 7012 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 7012 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 7012 events\n",
+      "INFO:root:Applying MET>20 selection on 4940 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 210651 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 210651 events\n",
+      "INFO:root:Applying fj_pt250 selection on 210651 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 210651 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 210651 events\n",
+      "INFO:root:Applying MET>20 selection on 51745 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 TTToSemiLeptonic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 366309 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 366309 events\n",
+      "INFO:root:Applying fj_pt250 selection on 366309 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 366309 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 366309 events\n",
+      "INFO:root:Applying MET>20 selection on 31809 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 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 lep_fj_dr003 selection on 44464 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 44464 events\n",
+      "INFO:root:Applying fj_pt250 selection on 44464 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 44464 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 44464 events\n",
+      "INFO:root:Applying MET>20 selection on 2243 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 ST_s-channel_4f_hadronicDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 125 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 125 events\n",
+      "INFO:root:Applying fj_pt250 selection on 125 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 125 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 125 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying MET>20 selection on 17 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 WJetsToLNu_HT-1200To2500 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 258011 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 258011 events\n",
+      "INFO:root:Applying fj_pt250 selection on 258011 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 258011 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 258011 events\n",
+      "INFO:root:Applying MET>20 selection on 48194 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 EWKZ_ZToLL samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 322 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 322 events\n",
+      "INFO:root:Applying fj_pt250 selection on 322 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 322 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 322 events\n",
+      "INFO:root:Applying MET>20 selection on 65 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 WJetsToLNu_HT-200To400 samples and should combine them under WJetsLNu\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 20709 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 20709 events\n",
+      "INFO:root:Applying fj_pt250 selection on 20709 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 20709 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 20709 events\n",
+      "INFO:root:Applying MET>20 selection on 3729 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 ST_tW_top_5f_inclusiveDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 5518 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5518 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5518 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5518 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5518 events\n",
+      "INFO:root:Applying MET>20 selection on 789 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 lep_fj_dr003 selection on 6929 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6929 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6929 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6929 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6929 events\n",
+      "INFO:root:Applying MET>20 selection on 5048 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 DYJetsToLL_LHEFilterPtZ-650ToInf samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 74014 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 74014 events\n",
+      "INFO:root:Applying fj_pt250 selection on 74014 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 74014 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 74014 events\n",
+      "INFO:root:Applying MET>20 selection on 9878 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 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 lep_fj_dr003 selection on 5300 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5300 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5300 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5300 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5300 events\n",
+      "INFO:root:Applying MET>20 selection on 671 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 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 lep_fj_dr003 selection on 27 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 27 events\n",
+      "INFO:root:Applying fj_pt250 selection on 27 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 27 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 27 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 7928 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 7928 events\n",
+      "INFO:root:Applying fj_pt250 selection on 7928 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 7928 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 7928 events\n",
+      "INFO:root:Applying MET>20 selection on 5549 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 DYJetsToLL_LHEFilterPtZ-100To250 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 37414 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 37414 events\n",
+      "INFO:root:Applying fj_pt250 selection on 37414 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 37414 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 37414 events\n",
+      "INFO:root:Applying MET>20 selection on 7211 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 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 lep_fj_dr003 selection on 102 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 102 events\n",
+      "INFO:root:Applying fj_pt250 selection on 102 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 102 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 102 events\n",
+      "INFO:root:Applying MET>20 selection on 27 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 ST_s-channel_4f_leptonDecays samples and should combine them under SingleTop\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 22094 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 22094 events\n",
+      "INFO:root:Applying fj_pt250 selection on 22094 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 22094 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 22094 events\n",
+      "INFO:root:Applying MET>20 selection on 1282 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 DYJetsToLL_LHEFilterPtZ-50To100 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 4024 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4024 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4024 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4024 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4024 events\n",
+      "INFO:root:Applying MET>20 selection on 1005 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 QCD_Pt_1800to2400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 332 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 332 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying fj_pt250 selection on 332 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 332 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 332 events\n",
+      "INFO:root:Applying MET>20 selection on 28 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 SingleMuon_Run2018A samples and should combine them under Data\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 33219 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 33219 events\n",
+      "INFO:root:Applying fj_pt250 selection on 33219 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 33219 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 33219 events\n",
+      "INFO:root:Applying MET>20 selection on 6289 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 WW samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 580 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 580 events\n",
+      "INFO:root:Applying fj_pt250 selection on 580 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 580 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 580 events\n",
+      "INFO:root:Applying MET>20 selection on 198 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 DYJetsToLL_LHEFilterPtZ-250To400 samples and should combine them under DYJets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 121629 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 121629 events\n",
+      "INFO:root:Applying fj_pt250 selection on 121629 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 121629 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 121629 events\n",
+      "INFO:root:Applying MET>20 selection on 12571 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 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 lep_fj_dr003 selection on 17238 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17238 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17238 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17238 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17238 events\n",
+      "INFO:root:Applying MET>20 selection on 1000 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 TTTo2L2Nu samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 99305 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 99305 events\n",
+      "INFO:root:Applying fj_pt250 selection on 99305 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 99305 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 99305 events\n",
+      "INFO:root:Applying MET>20 selection on 7514 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 QCD_Pt_2400to3200 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 192 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 192 events\n",
+      "INFO:root:Applying fj_pt250 selection on 192 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 192 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 192 events\n",
+      "INFO:root:Applying MET>20 selection on 11 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 EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 2 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 40 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 40 events\n",
+      "INFO:root:Applying fj_pt250 selection on 40 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 40 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 40 events\n",
+      "INFO:root:Applying MET>20 selection on 4 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 ZZ samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 129 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 129 events\n",
+      "INFO:root:Applying fj_pt250 selection on 129 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 129 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 129 events\n",
+      "INFO:root:Applying MET>20 selection on 29 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 TTToHadronic samples and should combine them under TTbar\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 1386 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1386 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1386 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1386 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1386 events\n",
+      "INFO:root:Applying MET>20 selection on 267 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 QCD_Pt_1000to1400 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 1111 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1111 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1111 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1111 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1111 events\n",
+      "INFO:root:Applying MET>20 selection on 112 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 QCD_Pt_600to800 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 863 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 863 events\n",
+      "INFO:root:Applying fj_pt250 selection on 863 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 863 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 863 events\n",
+      "INFO:root:Applying MET>20 selection on 112 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 QCD_Pt_300to470 samples and should combine them under QCD\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 519 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 519 events\n",
+      "INFO:root:Applying fj_pt250 selection on 519 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 519 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 519 events\n",
+      "INFO:root:Applying MET>20 selection on 76 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 lep_fj_dr003 selection on 625 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 625 events\n",
+      "INFO:root:Applying fj_pt250 selection on 625 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 625 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 625 events\n",
+      "INFO:root:Applying MET>20 selection on 198 events\n",
+      "INFO:root:Will fill the WZQQ dataframe with the remaining 198 events\n",
+      "INFO:root:tot event weight 32.98792131788023 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 447 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 447 events\n",
+      "INFO:root:Applying fj_pt250 selection on 447 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 447 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 447 events\n",
+      "INFO:root:Applying MET>20 selection on 61 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 lep_fj_dr003 selection on 31696 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31696 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31696 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31696 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31696 events\n",
+      "INFO:root:Applying MET>20 selection on 3332 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 lep_fj_dr003 selection on 60 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 60 events\n",
+      "INFO:root:Applying fj_pt250 selection on 60 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 60 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 60 events\n",
+      "INFO:root:Applying MET>20 selection on 19 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 lep_fj_dr003 selection on 63 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 63 events\n",
+      "INFO:root:Applying fj_pt250 selection on 63 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 63 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 63 events\n",
+      "INFO:root:Applying MET>20 selection on 8 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 lep_fj_dr003 selection on 137683 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 137683 events\n",
+      "INFO:root:Applying fj_pt250 selection on 137683 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 137683 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 137683 events\n",
+      "INFO:root:Applying MET>20 selection on 38321 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 lep_fj_dr003 selection on 239 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 239 events\n",
+      "INFO:root:Applying fj_pt250 selection on 239 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 239 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 239 events\n",
+      "INFO:root:Applying MET>20 selection on 76 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 lep_fj_dr003 selection on 84095 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 84095 events\n",
+      "INFO:root:Applying fj_pt250 selection on 84095 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 84095 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 84095 events\n",
+      "INFO:root:Applying MET>20 selection on 8013 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 5265 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5265 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5265 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5265 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5265 events\n",
+      "INFO:root:Applying MET>20 selection on 1864 events\n",
+      "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 lep_fj_dr003 selection on 779 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 779 events\n",
+      "INFO:root:Applying fj_pt250 selection on 779 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 779 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 779 events\n",
+      "INFO:root:Applying MET>20 selection on 120 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 lep_fj_dr003 selection on 992 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 992 events\n",
+      "INFO:root:Applying fj_pt250 selection on 992 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 992 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 992 events\n",
+      "INFO:root:Applying MET>20 selection on 117 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 lep_fj_dr003 selection on 2112 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2112 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2112 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2112 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2112 events\n",
+      "INFO:root:Applying MET>20 selection on 459 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 lep_fj_dr003 selection on 16684 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16684 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16684 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16684 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16684 events\n",
+      "INFO:root:Applying MET>20 selection on 11903 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 lep_fj_dr003 selection on 694 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 694 events\n",
+      "INFO:root:Applying fj_pt250 selection on 694 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 694 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 694 events\n",
+      "INFO:root:Applying MET>20 selection on 172 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 lep_fj_dr003 selection on 15318 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 15318 events\n",
+      "INFO:root:Applying fj_pt250 selection on 15318 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 15318 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 15318 events\n",
+      "INFO:root:Applying MET>20 selection on 2865 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 2865 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 71666 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 71666 events\n",
+      "INFO:root:Applying fj_pt250 selection on 71666 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 71666 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 71666 events\n",
+      "INFO:root:Applying MET>20 selection on 13676 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 lep_fj_dr003 selection on 55015 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 55015 events\n",
+      "INFO:root:Applying fj_pt250 selection on 55015 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 55015 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 55015 events\n",
+      "INFO:root:Applying MET>20 selection on 15701 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 lep_fj_dr003 selection on 513 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 513 events\n",
+      "INFO:root:Applying fj_pt250 selection on 513 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 513 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 513 events\n",
+      "INFO:root:Applying MET>20 selection on 79 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 lep_fj_dr003 selection on 10882 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 10882 events\n",
+      "INFO:root:Applying fj_pt250 selection on 10882 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 10882 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 10882 events\n",
+      "INFO:root:Applying MET>20 selection on 7739 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 lep_fj_dr003 selection on 424 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 424 events\n",
+      "INFO:root:Applying fj_pt250 selection on 424 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 424 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 424 events\n",
+      "INFO:root:Applying MET>20 selection on 105 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 lep_fj_dr003 selection on 616 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 616 events\n",
+      "INFO:root:Applying fj_pt250 selection on 616 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 616 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 616 events\n",
+      "INFO:root:Applying MET>20 selection on 59 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 59 events\n",
+      "INFO:root:tot event weight 0.20449392838619135 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2018B samples and should combine them under Data\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 16683 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16683 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16683 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16683 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16683 events\n",
+      "INFO:root:Applying MET>20 selection on 3206 events\n",
+      "INFO:root:Will fill the Data dataframe with the remaining 3206 events\n",
+      "INFO:root:tot event weight 3206.0 \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 lep_fj_dr003 selection on 784 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 784 events\n",
+      "INFO:root:Applying fj_pt250 selection on 784 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 784 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 784 events\n",
+      "INFO:root:Applying MET>20 selection on 502 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 lep_fj_dr003 selection on 16 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 1038 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1038 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1038 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1038 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1038 events\n",
+      "INFO:root:Applying MET>20 selection on 154 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 131 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 131 events\n",
+      "INFO:root:Applying fj_pt250 selection on 131 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 131 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 131 events\n",
+      "INFO:root:Applying MET>20 selection on 92 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 92 events\n",
+      "INFO:root:tot event weight 3.128314201037672 \n",
+      "\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 lep_fj_dr003 selection on 1040 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1040 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1040 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1040 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1040 events\n",
+      "INFO:root:Applying MET>20 selection on 646 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 lep_fj_dr003 selection on 42945 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 42945 events\n",
+      "INFO:root:Applying fj_pt250 selection on 42945 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 42945 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 42945 events\n",
+      "INFO:root:Applying MET>20 selection on 7869 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",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 353131 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 353131 events\n",
+      "INFO:root:Applying fj_pt250 selection on 353131 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 353131 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 353131 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying MET>20 selection on 27685 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 lep_fj_dr003 selection on 17638 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17638 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17638 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17638 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17638 events\n",
+      "INFO:root:Applying MET>20 selection on 2569 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 lep_fj_dr003 selection on 10797 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 10797 events\n",
+      "INFO:root:Applying fj_pt250 selection on 10797 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 10797 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 10797 events\n",
+      "INFO:root:Applying MET>20 selection on 529 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 lep_fj_dr003 selection on 17 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 60956 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 60956 events\n",
+      "INFO:root:Applying fj_pt250 selection on 60956 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 60956 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 60956 events\n",
+      "INFO:root:Applying MET>20 selection on 7490 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 lep_fj_dr003 selection on 287 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 287 events\n",
+      "INFO:root:Applying fj_pt250 selection on 287 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 287 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 287 events\n",
+      "INFO:root:Applying MET>20 selection on 31 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 lep_fj_dr003 selection on 4544 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4544 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4544 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4544 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4544 events\n",
+      "INFO:root:Applying MET>20 selection on 591 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 lep_fj_dr003 selection on 1696 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1696 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1696 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1696 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1696 events\n",
+      "INFO:root:Applying MET>20 selection on 197 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 lep_fj_dr003 selection on 19081 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 19081 events\n",
+      "INFO:root:Applying fj_pt250 selection on 19081 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 19081 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 19081 events\n",
+      "INFO:root:Applying MET>20 selection on 2673 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 lep_fj_dr003 selection on 2025 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2025 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2025 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2025 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2025 events\n",
+      "INFO:root:Applying MET>20 selection on 1317 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 lep_fj_dr003 selection on 76597 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 76597 events\n",
+      "INFO:root:Applying fj_pt250 selection on 76597 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 76597 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 76597 events\n",
+      "INFO:root:Applying MET>20 selection on 2696 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 lep_fj_dr003 selection on 908 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 908 events\n",
+      "INFO:root:Applying fj_pt250 selection on 908 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 908 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 908 events\n",
+      "INFO:root:Applying MET>20 selection on 134 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 lep_fj_dr003 selection on 1643 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1643 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1643 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1643 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1643 events\n",
+      "INFO:root:Applying MET>20 selection on 156 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 lep_fj_dr003 selection on 85 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 85 events\n",
+      "INFO:root:Applying fj_pt250 selection on 85 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 85 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 85 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 1316 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1316 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1316 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1316 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1316 events\n",
+      "INFO:root:Applying MET>20 selection on 789 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 22344 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 22344 events\n",
+      "INFO:root:Applying fj_pt250 selection on 22344 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 22344 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 22344 events\n",
+      "INFO:root:Applying MET>20 selection on 2571 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 lep_fj_dr003 selection on 48 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 48 events\n",
+      "INFO:root:Applying fj_pt250 selection on 48 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 48 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 48 events\n",
+      "INFO:root:Applying MET>20 selection on 3 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 lep_fj_dr003 selection on 4858 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4858 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4858 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4858 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4858 events\n",
+      "INFO:root:Applying MET>20 selection on 257 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 lep_fj_dr003 selection on 1476 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1476 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1476 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1476 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1476 events\n",
+      "INFO:root:Applying MET>20 selection on 264 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 lep_fj_dr003 selection on 419 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 419 events\n",
+      "INFO:root:Applying fj_pt250 selection on 419 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 419 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 419 events\n",
+      "INFO:root:Applying MET>20 selection on 10 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 lep_fj_dr003 selection on 1314 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1314 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1314 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1314 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1314 events\n",
+      "INFO:root:Applying MET>20 selection on 371 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 lep_fj_dr003 selection on 256032 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 256032 events\n",
+      "INFO:root:Applying fj_pt250 selection on 256032 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 256032 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 256032 events\n",
+      "INFO:root:Applying MET>20 selection on 7982 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 lep_fj_dr003 selection on 3760 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3760 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3760 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3760 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3760 events\n",
+      "INFO:root:Applying MET>20 selection on 197 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 lep_fj_dr003 selection on 64280 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 64280 events\n",
+      "INFO:root:Applying fj_pt250 selection on 64280 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 64280 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 64280 events\n",
+      "INFO:root:Applying MET>20 selection on 4372 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 lep_fj_dr003 selection on 229 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 229 events\n",
+      "INFO:root:Applying fj_pt250 selection on 229 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 229 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 229 events\n",
+      "INFO:root:Applying MET>20 selection on 2 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 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 39 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 39 events\n",
+      "INFO:root:Applying fj_pt250 selection on 39 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 39 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 60 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 60 events\n",
+      "INFO:root:Applying fj_pt250 selection on 60 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 60 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 60 events\n",
+      "INFO:root:Applying MET>20 selection on 3 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 lep_fj_dr003 selection on 712 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 712 events\n",
+      "INFO:root:Applying fj_pt250 selection on 712 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 712 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 712 events\n",
+      "INFO:root:Applying MET>20 selection on 84 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 lep_fj_dr003 selection on 1136 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1136 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1136 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1136 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1136 events\n",
+      "INFO:root:Applying MET>20 selection on 30 events\n",
+      "INFO:root:Will fill the QCD dataframe with the remaining 30 events\n",
+      "INFO:root:tot event weight 0.20857167299520366 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 3104 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3104 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3104 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3104 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3104 events\n",
+      "INFO:root:Applying MET>20 selection on 194 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 lep_fj_dr003 selection on 1617 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1617 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1617 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1617 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1617 events\n",
+      "INFO:root:Applying MET>20 selection on 163 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 lep_fj_dr003 selection on 399 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 399 events\n",
+      "INFO:root:Applying fj_pt250 selection on 399 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 399 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 399 events\n",
+      "INFO:root:Applying MET>20 selection on 64 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 lep_fj_dr003 selection on 204 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 204 events\n",
+      "INFO:root:Applying fj_pt250 selection on 204 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 204 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 204 events\n",
+      "INFO:root:Applying MET>20 selection on 19 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 lep_fj_dr003 selection on 77675 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 77675 events\n",
+      "INFO:root:Applying fj_pt250 selection on 77675 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 77675 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 77675 events\n",
+      "INFO:root:Applying MET>20 selection on 2223 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 lep_fj_dr003 selection on 37 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 37 events\n",
+      "INFO:root:Applying fj_pt250 selection on 37 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 37 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 37 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 135 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 135 events\n",
+      "INFO:root:Applying fj_pt250 selection on 135 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 135 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 135 events\n",
+      "INFO:root:Applying MET>20 selection on 18 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 lep_fj_dr003 selection on 31845 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31845 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31845 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31845 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31845 events\n",
+      "INFO:root:Applying MET>20 selection on 6981 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 lep_fj_dr003 selection on 218 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 218 events\n",
+      "INFO:root:Applying fj_pt250 selection on 218 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 218 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 218 events\n",
+      "INFO:root:Applying MET>20 selection on 42 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 lep_fj_dr003 selection on 25119 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 25119 events\n",
+      "INFO:root:Applying fj_pt250 selection on 25119 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 25119 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 25119 events\n",
+      "INFO:root:Applying MET>20 selection on 1150 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 lep_fj_dr003 selection on 6374 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6374 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6374 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6374 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6374 events\n",
+      "INFO:root:Applying MET>20 selection on 1860 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 lep_fj_dr003 selection on 312 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 312 events\n",
+      "INFO:root:Applying fj_pt250 selection on 312 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 312 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 312 events\n",
+      "INFO:root:Applying MET>20 selection on 52 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 lep_fj_dr003 selection on 1995 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1995 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1995 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1995 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1995 events\n",
+      "INFO:root:Applying MET>20 selection on 107 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 lep_fj_dr003 selection on 918 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 918 events\n",
+      "INFO:root:Applying fj_pt250 selection on 918 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 918 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 918 events\n",
+      "INFO:root:Applying MET>20 selection on 143 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 lep_fj_dr003 selection on 4524 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying lep_fj_dr08 selection on 4524 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4524 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4524 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4524 events\n",
+      "INFO:root:Applying MET>20 selection on 2628 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 lep_fj_dr003 selection on 353 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 353 events\n",
+      "INFO:root:Applying fj_pt250 selection on 353 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 353 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 353 events\n",
+      "INFO:root:Applying MET>20 selection on 70 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 lep_fj_dr003 selection on 33 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 33 events\n",
+      "INFO:root:Applying fj_pt250 selection on 33 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 33 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 33 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 11726 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 11726 events\n",
+      "INFO:root:Applying fj_pt250 selection on 11726 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 11726 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 11726 events\n",
+      "INFO:root:Applying MET>20 selection on 2623 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 lep_fj_dr003 selection on 2072 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2072 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2072 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2072 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2072 events\n",
+      "INFO:root:Applying MET>20 selection on 196 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 lep_fj_dr003 selection on 3758 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3758 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3758 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3758 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3758 events\n",
+      "INFO:root:Applying MET>20 selection on 2154 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 lep_fj_dr003 selection on 559 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 559 events\n",
+      "INFO:root:Applying fj_pt250 selection on 559 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 559 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 559 events\n",
+      "INFO:root:Applying MET>20 selection on 94 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 lep_fj_dr003 selection on 760 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 760 events\n",
+      "INFO:root:Applying fj_pt250 selection on 760 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 760 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 760 events\n",
+      "INFO:root:Applying MET>20 selection on 22 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 lep_fj_dr003 selection on 1076 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1076 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1076 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1076 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1076 events\n",
+      "INFO:root:Applying MET>20 selection on 769 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 21545 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 21545 events\n",
+      "INFO:root:Applying fj_pt250 selection on 21545 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 21545 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 21545 events\n",
+      "INFO:root:Applying MET>20 selection on 4239 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 lep_fj_dr003 selection on 1050 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1050 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1050 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1050 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1050 events\n",
+      "INFO:root:Applying MET>20 selection on 196 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 lep_fj_dr003 selection on 1089 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1089 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1089 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1089 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1089 events\n",
+      "INFO:root:Applying MET>20 selection on 266 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 212 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 212 events\n",
+      "INFO:root:Applying fj_pt250 selection on 212 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 212 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 212 events\n",
+      "INFO:root:Applying MET>20 selection on 154 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 154 events\n",
+      "INFO:root:tot event weight 5.2730741298135015 \n",
+      "\n",
+      "INFO:root:Finding SingleMuon_Run2016G samples and should combine them under Data\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 19080 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 19080 events\n",
+      "INFO:root:Applying fj_pt250 selection on 19080 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 19080 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 19080 events\n",
+      "INFO:root:Applying MET>20 selection on 3660 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 lep_fj_dr003 selection on 1499 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1499 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1499 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1499 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1499 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying MET>20 selection on 1037 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 60046 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 60046 events\n",
+      "INFO:root:Applying fj_pt250 selection on 60046 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 60046 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 60046 events\n",
+      "INFO:root:Applying MET>20 selection on 14300 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 lep_fj_dr003 selection on 393726 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 393726 events\n",
+      "INFO:root:Applying fj_pt250 selection on 393726 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 393726 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 393726 events\n",
+      "INFO:root:Applying MET>20 selection on 36189 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 lep_fj_dr003 selection on 16348 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16348 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16348 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16348 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16348 events\n",
+      "INFO:root:Applying MET>20 selection on 917 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 lep_fj_dr003 selection on 44 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 44 events\n",
+      "INFO:root:Applying fj_pt250 selection on 44 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 44 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 44 events\n",
+      "INFO:root:Applying MET>20 selection on 7 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 lep_fj_dr003 selection on 87207 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 87207 events\n",
+      "INFO:root:Applying fj_pt250 selection on 87207 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 87207 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 87207 events\n",
+      "INFO:root:Applying MET>20 selection on 15429 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 lep_fj_dr003 selection on 140 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 140 events\n",
+      "INFO:root:Applying fj_pt250 selection on 140 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 140 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 140 events\n",
+      "INFO:root:Applying MET>20 selection on 28 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 lep_fj_dr003 selection on 5139 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5139 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5139 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5139 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5139 events\n",
+      "INFO:root:Applying MET>20 selection on 981 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 lep_fj_dr003 selection on 1693 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1693 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1693 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1693 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1693 events\n",
+      "INFO:root:Applying MET>20 selection on 232 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 lep_fj_dr003 selection on 3295 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3295 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3295 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3295 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3295 events\n",
+      "INFO:root:Applying MET>20 selection on 2418 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 lep_fj_dr003 selection on 41496 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 41496 events\n",
+      "INFO:root:Applying fj_pt250 selection on 41496 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 41496 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 41496 events\n",
+      "INFO:root:Applying MET>20 selection on 5423 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 lep_fj_dr003 selection on 1861 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1861 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1861 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1861 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1861 events\n",
+      "INFO:root:Applying MET>20 selection on 266 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 lep_fj_dr003 selection on 63 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 63 events\n",
+      "INFO:root:Applying fj_pt250 selection on 63 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 63 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 63 events\n",
+      "INFO:root:Applying MET>20 selection on 0 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 lep_fj_dr003 selection on 1941 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1941 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1941 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1941 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1941 events\n",
+      "INFO:root:Applying MET>20 selection on 1331 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1331 events\n",
+      "INFO:root:tot event weight 1.9560221731662555 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 16283 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16283 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16283 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16283 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16283 events\n",
+      "INFO:root:Applying MET>20 selection on 3158 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 lep_fj_dr003 selection on 51 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 51 events\n",
+      "INFO:root:Applying fj_pt250 selection on 51 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 51 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 51 events\n",
+      "INFO:root:Applying MET>20 selection on 13 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 lep_fj_dr003 selection on 6663 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6663 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6663 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6663 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6663 events\n",
+      "INFO:root:Applying MET>20 selection on 421 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 lep_fj_dr003 selection on 1875 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1875 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1875 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1875 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1875 events\n",
+      "INFO:root:Applying MET>20 selection on 465 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 lep_fj_dr003 selection on 354 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 354 events\n",
+      "INFO:root:Applying fj_pt250 selection on 354 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 354 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 354 events\n",
+      "INFO:root:Applying MET>20 selection on 25 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 lep_fj_dr003 selection on 1426 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1426 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1426 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1426 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1426 events\n",
+      "INFO:root:Applying MET>20 selection on 511 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 lep_fj_dr003 selection on 59216 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 59216 events\n",
+      "INFO:root:Applying fj_pt250 selection on 59216 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 59216 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 59216 events\n",
+      "INFO:root:Applying MET>20 selection on 6193 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 lep_fj_dr003 selection on 5717 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5717 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5717 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5717 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5717 events\n",
+      "INFO:root:Applying MET>20 selection on 348 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 lep_fj_dr003 selection on 54013 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 54013 events\n",
+      "INFO:root:Applying fj_pt250 selection on 54013 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 54013 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 54013 events\n",
+      "INFO:root:Applying MET>20 selection on 4366 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 lep_fj_dr003 selection on 180 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 180 events\n",
+      "INFO:root:Applying fj_pt250 selection on 180 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 180 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 180 events\n",
+      "INFO:root:Applying MET>20 selection on 12 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 EWKZ_ZToQQ samples and should combine them under EWKvjets\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 35 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 35 events\n",
+      "INFO:root:Applying fj_pt250 selection on 35 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 35 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 35 events\n",
+      "INFO:root:Applying MET>20 selection on 8 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",
+      "INFO:root:Applying lep_fj_dr003 selection on 47 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 47 events\n",
+      "INFO:root:Applying fj_pt250 selection on 47 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 47 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 47 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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 lep_fj_dr003 selection on 32 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 32 events\n",
+      "INFO:root:Applying fj_pt250 selection on 32 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 32 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 32 events\n",
+      "INFO:root:Applying MET>20 selection on 7 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 lep_fj_dr003 selection on 1629 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1629 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1629 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1629 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1629 events\n",
+      "INFO:root:Applying MET>20 selection on 282 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 lep_fj_dr003 selection on 1233 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying lep_fj_dr08 selection on 1233 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1233 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1233 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1233 events\n",
+      "INFO:root:Applying MET>20 selection on 119 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 lep_fj_dr003 selection on 3460 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3460 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3460 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3460 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3460 events\n",
+      "INFO:root:Applying MET>20 selection on 456 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 lep_fj_dr003 selection on 1483 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1483 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1483 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1483 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1483 events\n",
+      "INFO:root:Applying MET>20 selection on 218 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 lep_fj_dr003 selection on 349 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 349 events\n",
+      "INFO:root:Applying fj_pt250 selection on 349 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 349 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 349 events\n",
+      "INFO:root:Applying MET>20 selection on 116 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 lep_fj_dr003 selection on 317 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 317 events\n",
+      "INFO:root:Applying fj_pt250 selection on 317 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 317 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 317 events\n",
+      "INFO:root:Applying MET>20 selection on 63 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 lep_fj_dr003 selection on 17443 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17443 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17443 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17443 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17443 events\n",
+      "INFO:root:Applying MET>20 selection on 1892 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 lep_fj_dr003 selection on 41 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 41 events\n",
+      "INFO:root:Applying fj_pt250 selection on 41 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 41 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 41 events\n",
+      "INFO:root:Applying MET>20 selection on 10 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 lep_fj_dr003 selection on 64 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 64 events\n",
+      "INFO:root:Applying fj_pt250 selection on 64 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 64 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 64 events\n",
+      "INFO:root:Applying MET>20 selection on 10 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 lep_fj_dr003 selection on 45011 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 45011 events\n",
+      "INFO:root:Applying fj_pt250 selection on 45011 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 45011 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 45011 events\n",
+      "INFO:root:Applying MET>20 selection on 12242 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 lep_fj_dr003 selection on 172 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 172 events\n",
+      "INFO:root:Applying fj_pt250 selection on 172 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 172 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 172 events\n",
+      "INFO:root:Applying MET>20 selection on 64 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 lep_fj_dr003 selection on 36656 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 36656 events\n",
+      "INFO:root:Applying fj_pt250 selection on 36656 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 36656 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 36656 events\n",
+      "INFO:root:Applying MET>20 selection on 3392 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 lep_fj_dr003 selection on 6244 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6244 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6244 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6244 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6244 events\n",
+      "INFO:root:Applying MET>20 selection on 2240 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 lep_fj_dr003 selection on 547 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 547 events\n",
+      "INFO:root:Applying fj_pt250 selection on 547 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 547 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 547 events\n",
+      "INFO:root:Applying MET>20 selection on 89 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 lep_fj_dr003 selection on 2212 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2212 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2212 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2212 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2212 events\n",
+      "INFO:root:Applying MET>20 selection on 238 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 lep_fj_dr003 selection on 925 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 925 events\n",
+      "INFO:root:Applying fj_pt250 selection on 925 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 925 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 925 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying MET>20 selection on 178 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 lep_fj_dr003 selection on 5521 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5521 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5521 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5521 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5521 events\n",
+      "INFO:root:Applying MET>20 selection on 3881 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 lep_fj_dr003 selection on 303 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 303 events\n",
+      "INFO:root:Applying fj_pt250 selection on 303 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 303 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 303 events\n",
+      "INFO:root:Applying MET>20 selection on 66 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 lep_fj_dr003 selection on 15401 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 15401 events\n",
+      "INFO:root:Applying fj_pt250 selection on 15401 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 15401 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 15401 events\n",
+      "INFO:root:Applying MET>20 selection on 4263 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 lep_fj_dr003 selection on 2343 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2343 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2343 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2343 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2343 events\n",
+      "INFO:root:Applying MET>20 selection on 347 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 lep_fj_dr003 selection on 5153 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5153 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5153 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5153 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5153 events\n",
+      "INFO:root:Applying MET>20 selection on 3681 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 lep_fj_dr003 selection on 408 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 408 events\n",
+      "INFO:root:Applying fj_pt250 selection on 408 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 408 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 408 events\n",
+      "INFO:root:Applying MET>20 selection on 98 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 lep_fj_dr003 selection on 743 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 743 events\n",
+      "INFO:root:Applying fj_pt250 selection on 743 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 743 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 743 events\n",
+      "INFO:root:Applying MET>20 selection on 76 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 lep_fj_dr003 selection on 463 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 463 events\n",
+      "INFO:root:Applying fj_pt250 selection on 463 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 463 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 463 events\n",
+      "INFO:root:Applying MET>20 selection on 275 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 lep_fj_dr003 selection on 983 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 983 events\n",
+      "INFO:root:Applying fj_pt250 selection on 983 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 983 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 983 events\n",
+      "INFO:root:Applying MET>20 selection on 162 events\n",
+      "INFO:root:Will fill the EWKvjets dataframe with the remaining 162 events\n",
+      "INFO:root:tot event weight 42.93030682946239 \n",
+      "\n",
+      "INFO:root:Finding EWKZ_ZToNuNu samples and should combine them under EWKvjets\n",
+      "INFO:root:Finding VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 119 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 119 events\n",
+      "INFO:root:Applying fj_pt250 selection on 119 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 119 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 119 events\n",
+      "INFO:root:Applying MET>20 selection on 82 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 82 events\n",
+      "INFO:root:tot event weight 3.448808820994209 \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 lep_fj_dr003 selection on 1032 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1032 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1032 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1032 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1032 events\n",
+      "INFO:root:Applying MET>20 selection on 596 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 lep_fj_dr003 selection on 50985 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 50985 events\n",
+      "INFO:root:Applying fj_pt250 selection on 50985 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 50985 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 50985 events\n",
+      "INFO:root:Applying MET>20 selection on 9558 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",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 326351 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 326351 events\n",
+      "INFO:root:Applying fj_pt250 selection on 326351 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 326351 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 326351 events\n",
+      "INFO:root:Applying MET>20 selection on 26524 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 lep_fj_dr003 selection on 9112 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 9112 events\n",
+      "INFO:root:Applying fj_pt250 selection on 9112 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying dphi<1.57 selection on 9112 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 9112 events\n",
+      "INFO:root:Applying MET>20 selection on 450 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 lep_fj_dr003 selection on 57858 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 57858 events\n",
+      "INFO:root:Applying fj_pt250 selection on 57858 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 57858 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 57858 events\n",
+      "INFO:root:Applying MET>20 selection on 7206 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 lep_fj_dr003 selection on 356 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 356 events\n",
+      "INFO:root:Applying fj_pt250 selection on 356 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 356 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 356 events\n",
+      "INFO:root:Applying MET>20 selection on 31 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 lep_fj_dr003 selection on 5086 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5086 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5086 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5086 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5086 events\n",
+      "INFO:root:Applying MET>20 selection on 687 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 lep_fj_dr003 selection on 1494 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1494 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1494 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1494 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1494 events\n",
+      "INFO:root:Applying MET>20 selection on 176 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 lep_fj_dr003 selection on 2289 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2289 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2289 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2289 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2289 events\n",
+      "INFO:root:Applying MET>20 selection on 1502 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 lep_fj_dr003 selection on 73863 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 73863 events\n",
+      "INFO:root:Applying fj_pt250 selection on 73863 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 73863 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 73863 events\n",
+      "INFO:root:Applying MET>20 selection on 2660 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 lep_fj_dr003 selection on 1450 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1450 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1450 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1450 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1450 events\n",
+      "INFO:root:Applying MET>20 selection on 144 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 lep_fj_dr003 selection on 99 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 99 events\n",
+      "INFO:root:Applying fj_pt250 selection on 99 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 99 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 99 events\n",
+      "INFO:root:Applying MET>20 selection on 1 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 lep_fj_dr003 selection on 1790 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1790 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1790 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1790 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1790 events\n",
+      "INFO:root:Applying MET>20 selection on 1053 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 lep_fj_dr003 selection on 22574 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 22574 events\n",
+      "INFO:root:Applying fj_pt250 selection on 22574 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 22574 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 22574 events\n",
+      "INFO:root:Applying MET>20 selection on 2676 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 lep_fj_dr003 selection on 45 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 45 events\n",
+      "INFO:root:Applying fj_pt250 selection on 45 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 45 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 45 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 4576 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4576 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4576 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4576 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4576 events\n",
+      "INFO:root:Applying MET>20 selection on 258 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 lep_fj_dr003 selection on 5483 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5483 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5483 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5483 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5483 events\n",
+      "INFO:root:Applying MET>20 selection on 839 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"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 1486 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1486 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1486 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1486 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1486 events\n",
+      "INFO:root:Applying MET>20 selection on 294 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 lep_fj_dr003 selection on 367 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 367 events\n",
+      "INFO:root:Applying fj_pt250 selection on 367 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 367 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 367 events\n",
+      "INFO:root:Applying MET>20 selection on 2 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 lep_fj_dr003 selection on 1126 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1126 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1126 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1126 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1126 events\n",
+      "INFO:root:Applying MET>20 selection on 299 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 lep_fj_dr003 selection on 253048 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 253048 events\n",
+      "INFO:root:Applying fj_pt250 selection on 253048 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 253048 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 253048 events\n",
+      "INFO:root:Applying MET>20 selection on 7821 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 lep_fj_dr003 selection on 3721 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3721 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3721 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3721 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3721 events\n",
+      "INFO:root:Applying MET>20 selection on 174 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 lep_fj_dr003 selection on 56897 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 56897 events\n",
+      "INFO:root:Applying fj_pt250 selection on 56897 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 56897 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 56897 events\n",
+      "INFO:root:Applying MET>20 selection on 4101 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 lep_fj_dr003 selection on 231 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 231 events\n",
+      "INFO:root:Applying fj_pt250 selection on 231 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 231 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 231 events\n",
+      "INFO:root:Applying MET>20 selection on 1 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 lep_fj_dr003 selection on 8612 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 8612 events\n",
+      "INFO:root:Applying fj_pt250 selection on 8612 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 8612 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 8612 events\n",
+      "INFO:root:Applying MET>20 selection on 1415 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 lep_fj_dr003 selection on 48 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 48 events\n",
+      "INFO:root:Applying fj_pt250 selection on 48 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 48 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 48 events\n",
+      "INFO:root:Applying MET>20 selection on 8 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 lep_fj_dr003 selection on 43 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 43 events\n",
+      "INFO:root:Applying fj_pt250 selection on 43 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 43 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 43 events\n",
+      "INFO:root:Applying MET>20 selection on 9 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 lep_fj_dr003 selection on 88 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 88 events\n",
+      "INFO:root:Applying fj_pt250 selection on 88 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 88 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 88 events\n",
+      "INFO:root:Applying MET>20 selection on 9 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 9 events\n",
+      "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 lep_fj_dr003 selection on 792 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 792 events\n",
+      "INFO:root:Applying fj_pt250 selection on 792 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 792 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 792 events\n",
+      "INFO:root:Applying MET>20 selection on 123 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 lep_fj_dr003 selection on 982 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 982 events\n",
+      "INFO:root:Applying fj_pt250 selection on 982 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 982 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 982 events\n",
+      "INFO:root:Applying MET>20 selection on 32 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 lep_fj_dr003 selection on 9163 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 9163 events\n",
+      "INFO:root:Applying fj_pt250 selection on 9163 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 9163 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 9163 events\n",
+      "INFO:root:Applying MET>20 selection on 1450 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 lep_fj_dr003 selection on 2380 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2380 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2380 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2380 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 2380 events\n",
+      "INFO:root:Applying MET>20 selection on 165 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 lep_fj_dr003 selection on 1448 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1448 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1448 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1448 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1448 events\n",
+      "INFO:root:Applying MET>20 selection on 150 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 lep_fj_dr003 selection on 528 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 528 events\n",
+      "INFO:root:Applying fj_pt250 selection on 528 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 528 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 528 events\n",
+      "INFO:root:Applying MET>20 selection on 93 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 lep_fj_dr003 selection on 168 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 168 events\n",
+      "INFO:root:Applying fj_pt250 selection on 168 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 168 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 168 events\n",
+      "INFO:root:Applying MET>20 selection on 20 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 lep_fj_dr003 selection on 78944 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 78944 events\n",
+      "INFO:root:Applying fj_pt250 selection on 78944 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 78944 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 78944 events\n",
+      "INFO:root:Applying MET>20 selection on 2279 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 lep_fj_dr003 selection on 31 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31 events\n",
+      "INFO:root:Applying MET>20 selection on 4 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 lep_fj_dr003 selection on 111 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 111 events\n",
+      "INFO:root:Applying fj_pt250 selection on 111 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 111 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 111 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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 lep_fj_dr003 selection on 31871 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 31871 events\n",
+      "INFO:root:Applying fj_pt250 selection on 31871 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 31871 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 31871 events\n",
+      "INFO:root:Applying MET>20 selection on 7087 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 lep_fj_dr003 selection on 5489 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5489 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5489 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5489 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5489 events\n",
+      "INFO:root:Applying MET>20 selection on 828 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 lep_fj_dr003 selection on 345 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 345 events\n",
+      "INFO:root:Applying fj_pt250 selection on 345 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 345 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 345 events\n",
+      "INFO:root:Applying MET>20 selection on 61 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 lep_fj_dr003 selection on 27349 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 27349 events\n",
+      "INFO:root:Applying fj_pt250 selection on 27349 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 27349 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 27349 events\n",
+      "INFO:root:Applying MET>20 selection on 1309 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 lep_fj_dr003 selection on 5330 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5330 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5330 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5330 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5330 events\n",
+      "INFO:root:Applying MET>20 selection on 1564 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 lep_fj_dr003 selection on 170 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 170 events\n",
+      "INFO:root:Applying fj_pt250 selection on 170 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 170 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 170 events\n",
+      "INFO:root:Applying MET>20 selection on 20 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 lep_fj_dr003 selection on 1656 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1656 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1656 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1656 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1656 events\n",
+      "INFO:root:Applying MET>20 selection on 80 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 lep_fj_dr003 selection on 856 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 856 events\n",
+      "INFO:root:Applying fj_pt250 selection on 856 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 856 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 856 events\n",
+      "INFO:root:Applying MET>20 selection on 130 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 4863 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 4863 events\n",
+      "INFO:root:Applying fj_pt250 selection on 4863 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 4863 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 4863 events\n",
+      "INFO:root:Applying MET>20 selection on 2820 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 lep_fj_dr003 selection on 344 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 344 events\n",
+      "INFO:root:Applying fj_pt250 selection on 344 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 344 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 344 events\n",
+      "INFO:root:Applying MET>20 selection on 73 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 lep_fj_dr003 selection on 44 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 44 events\n",
+      "INFO:root:Applying fj_pt250 selection on 44 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 44 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 44 events\n",
+      "INFO:root:Applying MET>20 selection on 7 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 lep_fj_dr003 selection on 12611 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 12611 events\n",
+      "INFO:root:Applying fj_pt250 selection on 12611 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 12611 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 12611 events\n",
+      "INFO:root:Applying MET>20 selection on 2831 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 lep_fj_dr003 selection on 1703 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1703 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1703 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1703 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1703 events\n",
+      "INFO:root:Applying MET>20 selection on 137 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 lep_fj_dr003 selection on 3814 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3814 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3814 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3814 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3814 events\n",
+      "INFO:root:Applying MET>20 selection on 2193 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 lep_fj_dr003 selection on 555 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 555 events\n",
+      "INFO:root:Applying fj_pt250 selection on 555 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 555 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 555 events\n",
+      "INFO:root:Applying MET>20 selection on 75 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 lep_fj_dr003 selection on 701 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 701 events\n",
+      "INFO:root:Applying fj_pt250 selection on 701 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 701 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 701 events\n",
+      "INFO:root:Applying MET>20 selection on 20 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 lep_fj_dr003 selection on 13038 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 13038 events\n",
+      "INFO:root:Applying fj_pt250 selection on 13038 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 13038 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 13038 events\n",
+      "INFO:root:Applying MET>20 selection on 2098 events\n",
+      "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 lep_fj_dr003 selection on 816 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 816 events\n",
+      "INFO:root:Applying fj_pt250 selection on 816 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 816 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 816 events\n",
+      "INFO:root:Applying MET>20 selection on 571 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 lep_fj_dr003 selection on 17 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 17 events\n",
+      "INFO:root:Applying fj_pt250 selection on 17 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 17 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 17 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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 lep_fj_dr003 selection on 1129 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1129 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1129 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1129 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1129 events\n",
+      "INFO:root:Applying MET>20 selection on 236 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 VBFHToWWToLNuQQ_M-125_withDipoleRecoil samples and should combine them under Diboson\n",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 192 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 192 events\n",
+      "INFO:root:Applying fj_pt250 selection on 192 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 192 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 192 events\n",
+      "INFO:root:Applying MET>20 selection on 140 events\n",
+      "INFO:root:Will fill the Diboson dataframe with the remaining 140 events\n",
+      "INFO:root:tot event weight 5.910329319737765 \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 lep_fj_dr003 selection on 1451 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1451 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1451 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1451 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1451 events\n",
+      "INFO:root:Applying MET>20 selection on 1019 events\n",
+      "INFO:root:Will fill the WH dataframe with the remaining 1019 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 73854 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 73854 events\n",
+      "INFO:root:Applying fj_pt250 selection on 73854 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 73854 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 73854 events\n",
+      "INFO:root:Applying MET>20 selection on 17555 events\n",
+      "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 lep_fj_dr003 selection on 368486 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 368486 events\n",
+      "INFO:root:Applying fj_pt250 selection on 368486 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 368486 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 368486 events\n",
+      "INFO:root:Applying MET>20 selection on 35751 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 lep_fj_dr003 selection on 13314 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 13314 events\n",
+      "INFO:root:Applying fj_pt250 selection on 13314 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 13314 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 13314 events\n",
+      "INFO:root:Applying MET>20 selection on 722 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 lep_fj_dr003 selection on 85676 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 85676 events\n",
+      "INFO:root:Applying fj_pt250 selection on 85676 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 85676 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 85676 events\n",
+      "INFO:root:Applying MET>20 selection on 15354 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 lep_fj_dr003 selection on 166 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 166 events\n",
+      "INFO:root:Applying fj_pt250 selection on 166 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 166 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 166 events\n",
+      "INFO:root:Applying MET>20 selection on 25 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 lep_fj_dr003 selection on 5777 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5777 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5777 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5777 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5777 events\n",
+      "INFO:root:Applying MET>20 selection on 1077 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 lep_fj_dr003 selection on 1625 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1625 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1625 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1625 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1625 events\n",
+      "INFO:root:Applying MET>20 selection on 248 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 lep_fj_dr003 selection on 3645 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 3645 events\n",
+      "INFO:root:Applying fj_pt250 selection on 3645 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 3645 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 3645 events\n",
+      "INFO:root:Applying MET>20 selection on 2624 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 lep_fj_dr003 selection on 40241 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 40241 events\n",
+      "INFO:root:Applying fj_pt250 selection on 40241 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 40241 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 40241 events\n",
+      "INFO:root:Applying MET>20 selection on 5484 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 lep_fj_dr003 selection on 1603 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1603 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1603 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1603 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1603 events\n",
+      "INFO:root:Applying MET>20 selection on 200 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 lep_fj_dr003 selection on 64 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 64 events\n",
+      "INFO:root:Applying fj_pt250 selection on 64 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 64 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 64 events\n",
+      "INFO:root:Applying MET>20 selection on 1 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 lep_fj_dr003 selection on 6255 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6255 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6255 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6255 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6255 events\n",
+      "INFO:root:Applying MET>20 selection on 1294 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 lep_fj_dr003 selection on 2502 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2502 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2502 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2502 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2502 events\n",
+      "INFO:root:Applying MET>20 selection on 1737 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 lep_fj_dr003 selection on 16434 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16434 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16434 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16434 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:Applying tagger>0.5 selection on 16434 events\n",
+      "INFO:root:Applying MET>20 selection on 3253 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 lep_fj_dr003 selection on 59 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 59 events\n",
+      "INFO:root:Applying fj_pt250 selection on 59 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 59 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 59 events\n",
+      "INFO:root:Applying MET>20 selection on 10 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 lep_fj_dr003 selection on 6400 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6400 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6400 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6400 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6400 events\n",
+      "INFO:root:Applying MET>20 selection on 446 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 lep_fj_dr003 selection on 1896 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1896 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1896 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1896 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1896 events\n",
+      "INFO:root:Applying MET>20 selection on 455 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 lep_fj_dr003 selection on 370 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 370 events\n",
+      "INFO:root:Applying fj_pt250 selection on 370 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 370 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 370 events\n",
+      "INFO:root:Applying MET>20 selection on 40 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 lep_fj_dr003 selection on 1252 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1252 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1252 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1252 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1252 events\n",
+      "INFO:root:Applying MET>20 selection on 441 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 lep_fj_dr003 selection on 9752 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 9752 events\n",
+      "INFO:root:Applying fj_pt250 selection on 9752 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 9752 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 9752 events\n",
+      "INFO:root:Applying MET>20 selection on 2011 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 lep_fj_dr003 selection on 58846 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 58846 events\n",
+      "INFO:root:Applying fj_pt250 selection on 58846 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 58846 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 58846 events\n",
+      "INFO:root:Applying MET>20 selection on 6365 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 lep_fj_dr003 selection on 5538 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5538 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5538 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5538 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5538 events\n",
+      "INFO:root:Applying MET>20 selection on 365 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 lep_fj_dr003 selection on 48708 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 48708 events\n",
+      "INFO:root:Applying fj_pt250 selection on 48708 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 48708 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 48708 events\n",
+      "INFO:root:Applying MET>20 selection on 4416 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 lep_fj_dr003 selection on 216 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 216 events\n",
+      "INFO:root:Applying fj_pt250 selection on 216 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 216 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 216 events\n",
+      "INFO:root:Applying MET>20 selection on 17 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 lep_fj_dr003 selection on 47 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 47 events\n",
+      "INFO:root:Applying fj_pt250 selection on 47 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 47 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 47 events\n",
+      "INFO:root:Applying MET>20 selection on 8 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 lep_fj_dr003 selection on 41 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 41 events\n",
+      "INFO:root:Applying fj_pt250 selection on 41 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 41 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 41 events\n",
+      "INFO:root:Applying MET>20 selection on 5 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",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 37 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 37 events\n",
+      "INFO:root:Applying fj_pt250 selection on 37 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 37 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 37 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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 lep_fj_dr003 selection on 1921 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1921 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1921 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1921 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1921 events\n",
+      "INFO:root:Applying MET>20 selection on 397 events\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 1127 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1127 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1127 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1127 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1127 events\n",
+      "INFO:root:Applying MET>20 selection on 133 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 lep_fj_dr003 selection on 2859 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2859 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2859 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2859 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2859 events\n",
+      "INFO:root:Applying MET>20 selection on 402 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 lep_fj_dr003 selection on 1405 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 1405 events\n",
+      "INFO:root:Applying fj_pt250 selection on 1405 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 1405 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 1405 events\n",
+      "INFO:root:Applying MET>20 selection on 197 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 lep_fj_dr003 selection on 564 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 564 events\n",
+      "INFO:root:Applying fj_pt250 selection on 564 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 564 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 564 events\n",
+      "INFO:root:Applying MET>20 selection on 175 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 lep_fj_dr003 selection on 341 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 341 events\n",
+      "INFO:root:Applying fj_pt250 selection on 341 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 341 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 341 events\n",
+      "INFO:root:Applying MET>20 selection on 40 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 lep_fj_dr003 selection on 18221 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 18221 events\n",
+      "INFO:root:Applying fj_pt250 selection on 18221 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 18221 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 18221 events\n",
+      "INFO:root:Applying MET>20 selection on 2109 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 lep_fj_dr003 selection on 13685 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 13685 events\n",
+      "INFO:root:Applying fj_pt250 selection on 13685 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 13685 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 13685 events\n",
+      "INFO:root:Applying MET>20 selection on 2667 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 lep_fj_dr003 selection on 9134 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 9134 events\n",
+      "INFO:root:Applying fj_pt250 selection on 9134 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 9134 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 9134 events\n",
+      "INFO:root:Applying MET>20 selection on 1830 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 lep_fj_dr003 selection on 39 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 39 events\n",
+      "INFO:root:Applying fj_pt250 selection on 39 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 39 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 39 events\n",
+      "INFO:root:Applying MET>20 selection on 11 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 lep_fj_dr003 selection on 62 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 62 events\n",
+      "INFO:root:Applying fj_pt250 selection on 62 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 62 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 62 events\n",
+      "INFO:root:Applying MET>20 selection on 14 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 lep_fj_dr003 selection on 45525 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 45525 events\n",
+      "INFO:root:Applying fj_pt250 selection on 45525 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 45525 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 45525 events\n",
+      "INFO:root:Applying MET>20 selection on 12562 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 lep_fj_dr003 selection on 247 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 247 events\n",
+      "INFO:root:Applying fj_pt250 selection on 247 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 247 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 247 events\n",
+      "INFO:root:Applying MET>20 selection on 88 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 lep_fj_dr003 selection on 40241 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 40241 events\n",
+      "INFO:root:Applying fj_pt250 selection on 40241 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 40241 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 40241 events\n",
+      "INFO:root:Applying MET>20 selection on 3596 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 lep_fj_dr003 selection on 5431 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5431 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5431 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5431 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5431 events\n",
+      "INFO:root:Applying MET>20 selection on 1975 events\n",
+      "INFO:root:Will fill the ttH dataframe with the remaining 1975 events\n",
+      "INFO:root:tot event weight 3.732589714642666 \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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 lep_fj_dr003 selection on 404 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 404 events\n",
+      "INFO:root:Applying fj_pt250 selection on 404 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 404 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 404 events\n",
+      "INFO:root:Applying MET>20 selection on 63 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 lep_fj_dr003 selection on 2046 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2046 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2046 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2046 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2046 events\n",
+      "INFO:root:Applying MET>20 selection on 233 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 lep_fj_dr003 selection on 974 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 974 events\n",
+      "INFO:root:Applying fj_pt250 selection on 974 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 974 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 974 events\n",
+      "INFO:root:Applying MET>20 selection on 219 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 lep_fj_dr003 selection on 6210 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 6210 events\n",
+      "INFO:root:Applying fj_pt250 selection on 6210 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 6210 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 6210 events\n",
+      "INFO:root:Applying MET>20 selection on 4444 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 lep_fj_dr003 selection on 320 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 320 events\n",
+      "INFO:root:Applying fj_pt250 selection on 320 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 320 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 320 events\n",
+      "INFO:root:Applying MET>20 selection on 59 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 lep_fj_dr003 selection on 18 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 18 events\n",
+      "INFO:root:Applying fj_pt250 selection on 18 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 18 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 18 events\n",
+      "INFO:root:Applying MET>20 selection on 6 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 lep_fj_dr003 selection on 16862 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 16862 events\n",
+      "INFO:root:Applying fj_pt250 selection on 16862 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 16862 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 16862 events\n",
+      "INFO:root:Applying MET>20 selection on 4711 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 lep_fj_dr003 selection on 2049 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 2049 events\n",
+      "INFO:root:Applying fj_pt250 selection on 2049 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 2049 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 2049 events\n",
+      "INFO:root:Applying MET>20 selection on 298 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 lep_fj_dr003 selection on 5913 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5913 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5913 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5913 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5913 events\n",
+      "INFO:root:Applying MET>20 selection on 1221 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 lep_fj_dr003 selection on 5289 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 5289 events\n",
+      "INFO:root:Applying fj_pt250 selection on 5289 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 5289 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 5289 events\n",
+      "INFO:root:Applying MET>20 selection on 3783 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 lep_fj_dr003 selection on 414 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 414 events\n",
+      "INFO:root:Applying fj_pt250 selection on 414 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 414 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 414 events\n",
+      "INFO:root:Applying MET>20 selection on 139 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",
+      "INFO:root:---> Using already stored event weight\n",
+      "INFO:root:Applying lep_fj_dr003 selection on 773 events\n",
+      "INFO:root:Applying lep_fj_dr08 selection on 773 events\n",
+      "INFO:root:Applying fj_pt250 selection on 773 events\n",
+      "INFO:root:Applying dphi<1.57 selection on 773 events\n",
+      "INFO:root:Applying tagger>0.5 selection on 773 events\n",
+      "INFO:root:Applying MET>20 selection on 60 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"
+     ]
+    }
+   ],
+   "source": [
+    "taggers = [\n",
+    "    \"v2_nor2\",\n",
+    "#     \"v35_1_1\",\n",
+    "#     \"v35_1_2\",\n",
+    "#     \"v35_2_1\",\n",
+    "#     \"v35_2_4\",\n",
+    "#     \"v35_2_5\",\n",
+    "    \"v35_2_6\",    \n",
+    "]\n",
+    "\n",
+    "for year in years:\n",
+    "    out = make_events_dict([year], channels, samples_dir[year], samples, presel, taggers)\n",
+    "    events_dict = {**events_dict, **out}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "taggerss = [\"fj_ParT_score\"]\n",
+    "for tagger in taggers:\n",
+    "    if \"v2_nor2\" in tagger:\n",
+    "        taggerss.append(f\"fj_ParT_score_finetuned\")        \n",
+    "    else:\n",
+    "        taggerss.append(f\"fj_ParT_score_finetuned_{tagger}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TAGGER: fj_ParT_score\n",
+      "tagger cut 0.9\n",
+      "tagger cut 0.9031034482758621\n",
+      "tagger cut 0.9062068965517242\n",
+      "tagger cut 0.9093103448275862\n",
+      "tagger cut 0.9124137931034483\n",
+      "tagger cut 0.9155172413793103\n",
+      "tagger cut 0.9186206896551724\n",
+      "tagger cut 0.9217241379310345\n",
+      "tagger cut 0.9248275862068965\n",
+      "tagger cut 0.9279310344827586\n",
+      "tagger cut 0.9310344827586207\n",
+      "tagger cut 0.9341379310344827\n",
+      "tagger cut 0.9372413793103448\n",
+      "tagger cut 0.9403448275862069\n",
+      "tagger cut 0.943448275862069\n",
+      "tagger cut 0.9465517241379311\n",
+      "tagger cut 0.9496551724137932\n",
+      "tagger cut 0.9527586206896552\n",
+      "tagger cut 0.9558620689655173\n",
+      "tagger cut 0.9589655172413794\n",
+      "tagger cut 0.9620689655172414\n",
+      "tagger cut 0.9651724137931035\n",
+      "tagger cut 0.9682758620689655\n",
+      "tagger cut 0.9713793103448276\n",
+      "tagger cut 0.9744827586206897\n",
+      "tagger cut 0.9775862068965517\n",
+      "tagger cut 0.9806896551724138\n",
+      "tagger cut 0.9837931034482759\n",
+      "tagger cut 0.9868965517241379\n",
+      "tagger cut 0.99\n",
+      "TAGGER: fj_ParT_score_finetuned\n",
+      "tagger cut 0.9\n",
+      "tagger cut 0.9031034482758621\n",
+      "tagger cut 0.9062068965517242\n",
+      "tagger cut 0.9093103448275862\n",
+      "tagger cut 0.9124137931034483\n",
+      "tagger cut 0.9155172413793103\n",
+      "tagger cut 0.9186206896551724\n",
+      "tagger cut 0.9217241379310345\n",
+      "tagger cut 0.9248275862068965\n",
+      "tagger cut 0.9279310344827586\n",
+      "tagger cut 0.9310344827586207\n",
+      "tagger cut 0.9341379310344827\n",
+      "tagger cut 0.9372413793103448\n",
+      "tagger cut 0.9403448275862069\n",
+      "tagger cut 0.943448275862069\n",
+      "tagger cut 0.9465517241379311\n",
+      "tagger cut 0.9496551724137932\n",
+      "tagger cut 0.9527586206896552\n",
+      "tagger cut 0.9558620689655173\n",
+      "tagger cut 0.9589655172413794\n",
+      "tagger cut 0.9620689655172414\n",
+      "tagger cut 0.9651724137931035\n",
+      "tagger cut 0.9682758620689655\n",
+      "tagger cut 0.9713793103448276\n",
+      "tagger cut 0.9744827586206897\n",
+      "tagger cut 0.9775862068965517\n",
+      "tagger cut 0.9806896551724138\n",
+      "tagger cut 0.9837931034482759\n",
+      "tagger cut 0.9868965517241379\n",
+      "tagger cut 0.99\n",
+      "TAGGER: fj_ParT_score_finetuned_v35_2_6\n",
+      "tagger cut 0.9\n",
+      "tagger cut 0.9031034482758621\n",
+      "tagger cut 0.9062068965517242\n",
+      "tagger cut 0.9093103448275862\n",
+      "tagger cut 0.9124137931034483\n",
+      "tagger cut 0.9155172413793103\n",
+      "tagger cut 0.9186206896551724\n",
+      "tagger cut 0.9217241379310345\n",
+      "tagger cut 0.9248275862068965\n",
+      "tagger cut 0.9279310344827586\n",
+      "tagger cut 0.9310344827586207\n",
+      "tagger cut 0.9341379310344827\n",
+      "tagger cut 0.9372413793103448\n",
+      "tagger cut 0.9403448275862069\n",
+      "tagger cut 0.943448275862069\n",
+      "tagger cut 0.9465517241379311\n",
+      "tagger cut 0.9496551724137932\n",
+      "tagger cut 0.9527586206896552\n",
+      "tagger cut 0.9558620689655173\n",
+      "tagger cut 0.9589655172413794\n",
+      "tagger cut 0.9620689655172414\n",
+      "tagger cut 0.9651724137931035\n",
+      "tagger cut 0.9682758620689655\n",
+      "tagger cut 0.9713793103448276\n",
+      "tagger cut 0.9744827586206897\n",
+      "tagger cut 0.9775862068965517\n",
+      "tagger cut 0.9806896551724138\n",
+      "tagger cut 0.9837931034482759\n",
+      "tagger cut 0.9868965517241379\n",
+      "tagger cut 0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "signals = [\"VBF\", \"ggF\"]\n",
+    "# signals += [\"ttH\", \"WH\", \"ZH\"]\n",
+    "\n",
+    "tagger_cuts = np.linspace(0.9, 0.99, 30)\n",
+    "# tagger_cuts = np.linspace(0.9, 0.975, 20)\n",
+    "\n",
+    "mass_window = [100, 150]\n",
+    "\n",
+    "years = [\"2017\", \"2018\", \"2016\", \"2016APV\"]\n",
+    "# years = [\"2018\"]\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "# channels = [\"mu\"]\n",
+    "\n",
+    "s_over_b = {}\n",
+    "\n",
+    "den, num = 0, 0\n",
+    "for tagger in taggerss:\n",
+    "        \n",
+    "    print(\"TAGGER:\", tagger)\n",
+    "    s_over_b[tagger] = []\n",
+    "\n",
+    "    for tagger_cut in tagger_cuts:\n",
+    "        print(\"tagger cut\", tagger_cut)\n",
+    "        s, b = 0, 0\n",
+    "        for year in years:\n",
+    "            for ch in channels:\n",
+    "                for sample in events_dict[year][ch]:\n",
+    "                    if sample ==\"Data\":\n",
+    "                        continue\n",
+    "                    \n",
+    "                    df = events_dict[year][ch][sample]\n",
+    "                    df = df[df[tagger]>tagger_cut]\n",
+    "\n",
+    "                    # add mass window        \n",
+    "                    df = df[(df[\"rec_higgs_m\"]>=mass_window[0]) & (df[\"rec_higgs_m\"]<=mass_window[1])]\n",
+    "                    df = df[(df[\"n_bjets_T\"]==0)]                      \n",
+    "                        \n",
+    "#                     if sample == \"QCD\":\n",
+    "#                         threshold = 30\n",
+    "#                         den += len(df['event_weight'])\n",
+    "#                         num += sum(df[\"event_weight\"]>threshold)\n",
+    "\n",
+    "#                         df = df[df[\"event_weight\"] < threshold]\n",
+    "\n",
+    "                    ##############################    \n",
+    "                    if sample in signals:\n",
+    "                        s += df[\"event_weight\"].sum()\n",
+    "                    else:\n",
+    "                        b += df[\"event_weight\"].sum()\n",
+    "        if b <= 0:\n",
+    "            b = 1\n",
+    "            \n",
+    "        if s/math.sqrt(b)>3:\n",
+    "            s_over_b[tagger].append(0)\n",
+    "        else:\n",
+    "            s_over_b[tagger].append(s/math.sqrt(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(exptext: Custom Text(0.0, 1, 'CMS'),\n",
+       " expsuffix: Custom Text(0.0, 1.005, 'Work in Progress'))"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "\n",
+    "taggers_to_plot = [\n",
+    "    \"fj_ParT_score\",\n",
+    "    \"fj_ParT_score_finetuned\",\n",
+    "#     \"fj_ParT_score_finetuned_v35_1\",\n",
+    "    \"fj_ParT_score_finetuned_v35_2_6\",\n",
+    "#     \"fj_ParT_score_finetuned_v35_3\",\n",
+    "#     \"fj_ParT_score_finetuned_v35_4\",\n",
+    "#     \"fj_ParT_score_finetuned_v35_5\",   \n",
+    "]\n",
+    "taggers_to_plot = taggerss\n",
+    "\n",
+    "for tagger in taggers_to_plot:   \n",
+    "    if tagger == \"fj_ParT_score_finetuned\":\n",
+    "        lab = \"fj_ParT_score_finetuned (current)\"\n",
+    "    elif tagger == \"fj_ParT_score_finetuned_v35_1_2\":\n",
+    "        lab = \"fj_ParT_score_finetuned (new)\"\n",
+    "    else:\n",
+    "        lab = tagger\n",
+    "    \n",
+    "    ax.scatter(tagger_cuts, s_over_b[tagger], marker=\"x\", s=100, label=lab)\n",
+    "\n",
+    "ax.legend(loc=\"lower left\")\n",
+    "ax.set_ylabel(r\"$s\\sqrt{b}$\")\n",
+    "ax.set_xlabel(\"ParT-Finetuned > X\")\n",
+    "\n",
+    "ax.axvline(0.975, linestyle=\"--\", color=\"grey\")\n",
+    "ax.axvline(0.93, linestyle=\"--\", color=\"grey\")\n",
+    "\n",
+    "# ax.set_xticks(tagger_cuts)\n",
+    "hep.cms.lumitext(\"%.0f \" % get_lumi(years, channels) + r\"fb$^{-1}$ (13 TeV)\", ax=ax, fontsize=20)\n",
+    "hep.cms.text(\"Work in Progress\", ax=ax, fontsize=15)\n",
+    "# plt.savefig(f\"/Users/fmokhtar/Desktop/AN_2024/soverb-new.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fix_neg_yields(h):\n",
+    "    \"\"\"\n",
+    "    Will set the bin yields of a process to 0 if the nominal yield is negative, and will\n",
+    "    set the yield to 0 for the full Systematic axis.\n",
+    "    \"\"\"\n",
+    "    for sample in h.axes[\"samples\"]:\n",
+    "        neg_bins = np.where(h[{\"samples\": sample}].values() < 0)[0]\n",
+    "\n",
+    "        if len(neg_bins) > 0:\n",
+    "            print(f\"{sample}, has {len(neg_bins)} bins with negative yield.. will set them to 0\")\n",
+    "\n",
+    "            sample_index = np.argmax(np.array(h.axes[\"samples\"]) == sample)\n",
+    "\n",
+    "            for neg_bin in neg_bins:\n",
+    "                h.view(flow=True)[sample_index, neg_bin + 1] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars_to_plot = [\n",
+    "#     \"fj_msoftdrop\",        \n",
+    "#     \"rec_W_lnu_m\",\n",
+    "#     \"fj_pt\",\n",
+    "#     \"lep_pt\",\n",
+    "#     \"lep_eta\",\n",
+    "    \n",
+    "#     \"lep_fj_dr\",\n",
+    "#     \"lep_met_mt\",\n",
+    "#     \"met_fj_dphi\",\n",
+    "#     \"met_pt\",    \n",
+    "    \n",
+    "#     \"btag_1a\",\n",
+    "#     \"0btag_1b\",\n",
+    "#     \"1pbtag_1b\",\n",
+    "    \n",
+    "#     \"0btagT_btagSF\",\n",
+    "#     \"1plusbtagT_btagSF\"\n",
+    "    \n",
+    "#     \"rec_higgs_etajet_m\",\n",
+    "#     \"fj_ParT_mass\",\n",
+    "#     \"fj_ParT_score_finetuned\"\n",
+    "    \n",
+    "    \"rec_higgs_m\",    \n",
+    "    # AN\n",
+    "#     \"FirstFatjet_pt\",\n",
+    "#     \"SecondFatjet_pt\",\n",
+    "#     \"fj_pt\",\n",
+    "#     \"lep_pt\",\n",
+    "#     \"NumFatjets\",\n",
+    "#     \"NumOtherJets\",\n",
+    "#     \"lep_fj_dr\",\n",
+    "#     \"met_pt\",\n",
+    "#     \"met_fj_dphi\",\n",
+    "#     \"lep_met_mt\",    \n",
+    "#     \"ht\",\n",
+    "#     \"fj_mass\",\n",
+    "#     \"rec_W_qq_m\",\n",
+    "#     \"rec_W_lnu_m\",    \n",
+    "#     \"fj_lsf3\",\n",
+    "    \n",
+    "#     \"lep_isolation\",\n",
+    "#     \"lep_isolation_ele\",\n",
+    "#     \"lep_isolation_ele_highpt\",\n",
+    "#     \"lep_isolation_ele_lowpt\",\n",
+    "    \n",
+    "#     \"lep_isolation_mu\",\n",
+    "#     \"lep_isolation_mu_highpt\",\n",
+    "#     \"lep_isolation_mu_lowpt\",   \n",
+    "    \n",
+    "#     \"lep_misolation\",\n",
+    "#     \"lep_misolation_highpt\",\n",
+    "#     \"lep_misolation_lowpt\",    \n",
+    "]\n",
+    "\n",
+    "samples_to_plot = [\n",
+    "    \"ggF\", \n",
+    "    \"VBF\",\n",
+    "    \"ttH\",\n",
+    "    \"WH\",\n",
+    "    \"ZH\",    \n",
+    "    \"QCD\",\n",
+    "    \"DYJets\",\n",
+    "    \"WJetsLNu\",\n",
+    "#     \"WJetsLNu_unmatched\",\n",
+    "#     \"WJetsLNu_matched\",\n",
+    "    \"WZQQ\",\n",
+    "    \"TTbar\",\n",
+    "    \"SingleTop\",\n",
+    "    \"Diboson\",\n",
+    "    \"EWKvjets\",\n",
+    "    \"Data\",\n",
+    "]\n",
+    "\n",
+    "# samples_to_plot = [\"QCD\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ttH, has 1 bins with negative yield.. will set them to 0\n",
+      "ZH, has 1 bins with negative yield.. will set them to 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# tagger = \"fj_ParT_score\"\n",
+    "# tagger = \"fj_ParT_score_finetuned\"\n",
+    "tagger = \"fj_ParT_score_finetuned_v35_2_6\"\n",
+    "\n",
+    "tagger_cut = 0.95\n",
+    "# tagger_cut = 0.9\n",
+    "presel = {\n",
+    "#     \"Pre-selection\": f\"fj_pt>0\",   # dummy\n",
+    "#     \"SR\": f\"({tagger}>{tagger_cut}) & (n_bjets_T==0)\",   # dummy\n",
+    "    \n",
+    "    \"SR\": f\"{tagger}>0.95\",\n",
+    "    \n",
+    "#     \"Pre-selection\": f\"(fj_ParT_score_finetuned>0.95) & (fj_msoftdrop>10)\",   # dummy    \n",
+    "    \n",
+    "#     \"Pre-selection\": f\"met_pt>100 & ({tagger}<0.5) & n_bjets_T>0\",   # dummy    \n",
+    "#     \"Pre-selection\": f\"met_pt>100 & n_bjets_T>0\",   # dummy    \n",
+    "\n",
+    "#     \"Pre-selection\": f\"met_pt>100 & n_bjets_L==0 & {tagger}>0.5 & {tagger}<0.97\",   # dummy    \n",
+    "    \n",
+    "#     \"Pre-selection\": f\"met_pt>20\",\n",
+    "\n",
+    "#     \"SR\": f\"({tagger}>0.98) & (n_bjets_T==0)\",    \n",
+    "    \n",
+    "#     \"SR2\": f\"({tagger}>0.97) & ({tagger}<0.98) & (n_bjets_T==0)\",\n",
+    "#     \"WJets CR\": f\"({tagger}<0.97) & ({tagger}>0.50) & (n_bjets_T==0) & (met_pt>100)\",\n",
+    "#     \"Top CR\": f\"({tagger}>0.5) & (n_bjets_T>0)\",\n",
+    "}\n",
+    "\n",
+    "categories_sel = {\n",
+    "    \"VBF\": f\"( (fj_ParT_score_finetuned>{tagger_cut}) & (n_bjets_T==0) ) & ( (mjj>1000) & (deta>3.5) )\",\n",
+    "#     rf\"ggF\": \"(mjj<1000) | (deta<3.5)\",\n",
+    "        \n",
+    "#     r\"ggF pT [250, 300]\": f\"( (fj_ParT_score_finetuned>{tagger_cut}) & (n_bjets_T==0) ) & ( ( (mjj<1000) | (deta<3.5) ) & (fj_pt>250) & (fj_pt<300) )\",\n",
+    "#     f\"ggF pT [300, 450]\": f\"( (fj_ParT_score_finetuned>{tagger_cut}) & (n_bjets_T==0) ) & ( ( (mjj<1000) | (deta<3.5) ) & (fj_pt>300) & (fj_pt<450) )\",\n",
+    "#     r\"ggF pT [450, Inf]\": f\"( (fj_ParT_score_finetuned>{tagger_cut}) & (n_bjets_T==0) ) & ( ( (mjj<1000) | (deta<3.5) ) & (fj_pt>450) )\",\n",
+    "}\n",
+    "\n",
+    "channels = [\"ele\", \"mu\"]\n",
+    "# channels = [\"ele\"]\n",
+    "years = [\"2018\", \"2017\", \"2016\", \"2016APV\"]\n",
+    "# years = [\"2016APV\"]\n",
+    "\n",
+    "num, den = 0, 0\n",
+    "threshold = 0\n",
+    "\n",
+    "# fill histograms\n",
+    "hists = {}\n",
+    "\n",
+    "# ev = events_dict[\"new_correctionsNOTAGGER\"]\n",
+    "\n",
+    "ev = events_dict\n",
+    "# ev = events_dict[\"wjetsNLO\"]\n",
+    "\n",
+    "# ev = events_dict[\"new_correctionsNODPHI\"]\n",
+    "\n",
+    "MET_cut = False\n",
+    "import utilsAN\n",
+    "\n",
+    "massbin = 10\n",
+    "for var in vars_to_plot:\n",
+    "\n",
+    "    if var == \"rec_higgs_m\":\n",
+    "        hists[var] = hist2.Hist(\n",
+    "            hist2.axis.StrCategory([], name=\"samples\", growth=True),\n",
+    "            hist2.axis.Variable(list(range(45, 210, massbin)), name=\"var\", label=r\"Higgs reconstructed mass [GeV]\", overflow=True)\n",
+    "        )    \n",
+    "    elif var == \"fj_ParT_mass\":\n",
+    "        hists[var] = hist2.Hist(\n",
+    "            hist2.axis.StrCategory([], name=\"samples\", growth=True),\n",
+    "            hist2.axis.Variable(list(range(45, 210, massbin)), name=\"var\", label=r\"ParT regressed mass [GeV]\", overflow=True)\n",
+    "        )              \n",
+    "    else:\n",
+    "        hists[var] = hist2.Hist(\n",
+    "            hist2.axis.StrCategory([], name=\"samples\", growth=True),\n",
+    "            utilsAN.axis_dict[var],\n",
+    "#             hist2.axis.Regular(50, 0, 1, name=\"var\", label=r\"tagger\", overflow=True)\n",
+    "            \n",
+    "        )        \n",
+    "        \n",
+    "    for sample in samples_to_plot:\n",
+    "        for year in years:\n",
+    "            for ch in channels:\n",
+    "            \n",
+    "                region, sel = list(presel.items())[0]\n",
+    "                \n",
+    "                if sample == \"DYJets\":\n",
+    "                    continue  \n",
+    "                if sample == \"WJetsLNu\":\n",
+    "                    df = ev[year][ch][sample]\n",
+    "                    df = pd.concat([df, ev[year][ch][\"DYJets\"]])\n",
+    "                else:\n",
+    "                    df = ev[year][ch][sample]\n",
+    "\n",
+    "\n",
+    "#                 if \"WJetsLNu\" in sample:\n",
+    "#                     df = ev[year][ch][\"WJetsLNu\"]\n",
+    "                    \n",
+    "#                     if \"unmatched\" in sample:\n",
+    "#                         df = df[df[\"fj_V_isMatched\"]!=1]\n",
+    "#                     else:\n",
+    "#                         df = df[df[\"fj_V_isMatched\"]==1]\n",
+    "                \n",
+    "#                 else:\n",
+    "#                     df = ev[year][ch][sample]\n",
+    "        \n",
+    "#                 df = ev[year][ch][sample]\n",
+    "                \n",
+    "                df = df.query(sel)\n",
+    "                    \n",
+    "                if \"MET>X\" in region:\n",
+    "                    MET_cut = 100\n",
+    "                    df = df[df[\"met_pt\"]>MET_cut]\n",
+    "                                \n",
+    "                if len(categories_sel)>=1:\n",
+    "                    category, category_sel = list(categories_sel.items())[0]\n",
+    "                    df = df.query(category_sel)\n",
+    "            \n",
+    "                if sample == \"QCD\":\n",
+    "                    threshold = 30\n",
+    "                    den += len(df['event_weight'])\n",
+    "                    num += sum(df[\"event_weight\"]>threshold)\n",
+    "\n",
+    "                    df = df[df[\"event_weight\"] < threshold]\n",
+    "                \n",
+    "                \n",
+    "                if \"lep_isolation_ele\" in var:\n",
+    "                    if ch==\"ele\":\n",
+    "                        \n",
+    "                        if \"highpt\" in var:\n",
+    "                            df = df[(df[\"lep_pt\"]>120)]\n",
+    "                        elif \"lowpt\" in var:\n",
+    "                            df = df[(df[\"lep_pt\"]<120)]                            \n",
+    "\n",
+    "                        x = df[\"lep_isolation\"]\n",
+    "                    else:\n",
+    "                        continue\n",
+    "                        \n",
+    "                elif \"lep_isolation_mu\" in var:\n",
+    "                    if ch==\"mu\":\n",
+    "                        \n",
+    "                        if \"highpt\" in var:\n",
+    "                            df = df[(df[\"lep_pt\"]>55)]\n",
+    "                        elif \"lowpt\" in var:\n",
+    "                            df = df[(df[\"lep_pt\"]<55)] \n",
+    "\n",
+    "                        x = df[\"lep_isolation\"]\n",
+    "\n",
+    "                    else:\n",
+    "                        continue\n",
+    "                    \n",
+    "                elif \"lep_misolation\" in var:\n",
+    "                    if ch == \"mu\":\n",
+    "                        if \"highpt\" in var:\n",
+    "                            df = df[(df[\"lep_pt\"]>55)]\n",
+    "                        elif \"lowpt\" in var:\n",
+    "                            df = df[(df[\"lep_pt\"]<55)] \n",
+    "\n",
+    "                        x = df[\"lep_misolation\"]\n",
+    "                        \n",
+    "                    else:\n",
+    "                        continue\n",
+    "\n",
+    "                else:\n",
+    "                    x = df[var]\n",
+    "                    \n",
+    "#                 if var == \"lep_eta\":\n",
+    "#                     x = np.absolute(df[var])\n",
+    "                \n",
+    "#                 df = df[np.absolute(df[\"lep_eta\"])<0.5]\n",
+    "#                 x = df[var]\n",
+    "                \n",
+    "                w = df[\"event_weight\"]\n",
+    "                \n",
+    "#                 if sample != \"Data\":\n",
+    "# #                     w *= df[\"btag_1a\"]\n",
+    "#                     w *= df[\"0btag_1b\"]\n",
+    "# #                     w *= df[\"1pbtag_1b\"]\n",
+    "\n",
+    "                hists[var].fill(\n",
+    "                    samples=sample,\n",
+    "                    var=x,\n",
+    "                    weight=w,\n",
+    "                )     \n",
+    "try:\n",
+    "    print(f\"Removing {num} out of {den} qcd events by applying event_weight<{threshold} ({(100*num/den):.2f}%)\")\n",
+    "except:\n",
+    "    z=1\n",
+    "    \n",
+    "for var in vars_to_plot:\n",
+    "    fix_neg_yields(hists[var])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Will plot rec_higgs_m histogram\n",
+      "\u001b[1mSR (RUN2):\u001b[0m\n",
+      "Category: VBF\n",
+      "------------------------\n",
+      "\u001b[1ms/sqrt(b) in [100,150]: 1.19\u001b[0m\n",
+      "------------------------\n",
+      "Signal: 10.64\n",
+      "- ggF: 21%\n",
+      "- VBF: 79%\n",
+      "------------------------\n",
+      "Background: 68.19\n",
+      "- QCD: 0%\n",
+      "- DYJets: 6%\n",
+      "- Others: 19%\n",
+      "- TTbar: 26%\n",
+      "- WJetsLNu: 49%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams.update({\"font.size\": 20})\n",
+    "add_data = True\n",
+    "\n",
+    "if \"SR\" in region or (\"Signal region\") in region:\n",
+    "    add_soverb=True\n",
+    "    blind_region=[90,160]\n",
+    "    if \"SR1\" in region:\n",
+    "        mult=10\n",
+    "    else:\n",
+    "        mult=10\n",
+    "else:\n",
+    "    add_soverb=True\n",
+    "    blind_region=[90,160]\n",
+    "    mult=10\n",
+    "    \n",
+    "    \n",
+    "from utils import plot_hists\n",
+    "if len(years) > 1:\n",
+    "    from utilsF import plot_hists\n",
+    "    PATH = f\"/Users/fmokhtar/Desktop/AN_2024/preselection_Run2/\"\n",
+    "    PATH = f\"/Users/fmokhtar/Desktop/AN_2024/SignalRegion/\"\n",
+    "\n",
+    "#     PATH = f\"/Users/fmokhtar/Desktop/AN_2024/sig_region_{list(categories_sel.keys())[0]}_Run2/\"\n",
+    "else:\n",
+    "    from utilsAN import plot_hists\n",
+    "    PATH = f\"/Users/fmokhtar/Desktop/AN_2024/preselection_{years[0]}/\"\n",
+    "#     PATH = f\"/Users/fmokhtar/Desktop/AN_2024/sig_region_{list(categories_sel.keys())[0]}_{years[0]}/\"\n",
+    "\n",
+    "PATH = f\"/Users/fmokhtar/Desktop/AN_2024/lol/\"\n",
+    "# from utilsF import plot_hists\n",
+    "\n",
+    "if not os.path.exists(PATH):\n",
+    "    # Create the directory\n",
+    "    os.makedirs(PATH)    \n",
+    "\n",
+    "plot_hists(hists, years, channels, vars_to_plot,             \n",
+    "            add_data=add_data,\n",
+    "            logy=False,\n",
+    "            add_soverb=add_soverb,\n",
+    "            only_sig=False,\n",
+    "            mult=mult,\n",
+    "            outpath=PATH,\n",
+    "            text_=region,\n",
+    "#             text_=region + f\"\\n {list(categories_sel.keys())[0]} category\",\n",
+    "#             text_=region + f\"\\n category: {list(categories_sel.keys())[0]} \\n Applying qcd event_weight<{threshold}\",\n",
+    "#             text_=region + f\"\\n Applying qcd event_weight<{threshold}\",\n",
+    "\n",
+    "            blind_region=blind_region,\n",
+    "#            save_as=f\"{years[0]}_{channels[0]}\"\n",
+    "#            save_as=f\"{ch}\"\n",
+    "           \n",
+    "          )\n",
+    "# print()\n",
+    "get_soverb(ev, tagger, presel, categories_sel, years, channels, threshold=threshold, MET_cut=MET_cut)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dominant_backgrounds = [\"WJetsLNu\", \"TTbar\", \"QCD\", \"DYJets\"]\n",
+    "def get_soverb(ev, tagger, presel, categories_sel, years, channels, MET_cut=False, misocut=None, threshold=None):\n",
+    "\n",
+    "    mass_window = [100, 150]\n",
+    "\n",
+    "    num_sig = {\n",
+    "        \"ggF\": 0, \n",
+    "#         \"ttH\": 0, \n",
+    "#         \"VH\": 0,\n",
+    "        \"VBF\": 0,\n",
+    "    }\n",
+    "    num_bkg = {\n",
+    "        \"WJetsLNu\": 0, \n",
+    "        \"TTbar\": 0, \n",
+    "        \"QCD\": 0,\n",
+    "        \"DYJets\": 0,\n",
+    "        \"Others\": 0,\n",
+    "    }\n",
+    "\n",
+    "    deno_sig, deno_bkg = 0, 0\n",
+    "    s, b = 0, 0\n",
+    "\n",
+    "    samples = [\n",
+    "        \"ggF\", \n",
+    "        \"VBF\",\n",
+    "        \"ttH\",\n",
+    "        \"WH\",\n",
+    "        \"ZH\",        \n",
+    "        \"QCD\",\n",
+    "        \"DYJets\",\n",
+    "        \"WJetsLNu\",\n",
+    "        \"WZQQ\",\n",
+    "        \"TTbar\",\n",
+    "        \"SingleTop\",\n",
+    "        \"Diboson\",\n",
+    "        \"Data\"\n",
+    "    ]\n",
+    "\n",
+    "    for year in years:\n",
+    "        for ch in channels:\n",
+    "            for sample in samples:\n",
+    "                if sample==\"Data\":\n",
+    "                    continue\n",
+    "\n",
+    "                region, sel = list(presel.items())[0]\n",
+    "\n",
+    "                df = ev[year][ch][sample]\n",
+    "\n",
+    "                df = df.query(sel)  \n",
+    "\n",
+    "                for category, category_sel in categories_sel.items():\n",
+    "                    df = df.query(category_sel)\n",
+    "\n",
+    "                if MET_cut:\n",
+    "                    df = df[df[\"met_pt\"]>MET_cut]\n",
+    "                    \n",
+    "                if threshold:\n",
+    "                    if sample == \"QCD\":\n",
+    "                        df = df[df[\"event_weight\"] < threshold]\n",
+    "    \n",
+    "                ######################## composition start\n",
+    "                if sample in utils.signals:\n",
+    "\n",
+    "                    deno_sig += df[\"event_weight\"].sum()\n",
+    "                    num_sig[sample] += df[\"event_weight\"].sum()\n",
+    "                else:\n",
+    "                    deno_bkg += df[\"event_weight\"].sum()\n",
+    "\n",
+    "                    if sample in dominant_backgrounds:\n",
+    "                        num_bkg[sample] += df[\"event_weight\"].sum()\n",
+    "                    else:\n",
+    "                        num_bkg[\"Others\"] += df[\"event_weight\"].sum()     \n",
+    "                ######################## composition end\n",
+    "\n",
+    "                ######################## soverb start\n",
+    "                df = df[(df[\"rec_higgs_m\"]>=mass_window[0]) & (df[\"rec_higgs_m\"]<=mass_window[1])]\n",
+    "\n",
+    "                if sample in utils.signals:                \n",
+    "                    s += df[\"event_weight\"].sum()\n",
+    "                else:\n",
+    "                    b += df[\"event_weight\"].sum()\n",
+    "                ######################## soverb end\n",
+    "\n",
+    "    num_sig = dict(sorted(num_sig.items(), key=lambda item: item[1]))\n",
+    "    num_bkg = dict(sorted(num_bkg.items(), key=lambda item: item[1]))\n",
+    "\n",
+    "    if len(years) == 4:\n",
+    "        lab = \"RUN2\"\n",
+    "    else:\n",
+    "        lab = \"_\".join(years)\n",
+    "\n",
+    "    print(\"\\033[1m\" + f\"{list(presel.keys())[0]} ({lab}):\" + '\\033[0m')\n",
+    "    if len(list(categories_sel.items()))!=0:\n",
+    "        print(\"Category:\", list(categories_sel.keys())[0])\n",
+    "\n",
+    "    print(\"------------------------\")\n",
+    "\n",
+    "    if \"SR\" in list(presel.keys())[0]:\n",
+    "        print(\"\\033[1m\" + rf\"s/sqrt(b) in [{mass_window[0]},{mass_window[1]}]: {s/math.sqrt(b):.2f}\" + '\\033[0m')\n",
+    "        print(\"------------------------\")\n",
+    "\n",
+    "    print(f\"Signal: {deno_sig:.2f}\")\n",
+    "    for sample in num_sig:\n",
+    "        print(f\"- {sample}: {100*(num_sig[sample]/deno_sig):.0f}%\")\n",
+    "\n",
+    "    print(\"------------------------\")\n",
+    "    print(f\"Background: {deno_bkg:.2f}\")\n",
+    "    for sample in num_bkg:\n",
+    "        if sample==\"\":\n",
+    "            print(\"\\033[1m\" + f\"- {sample}: {100*(num_bkg[sample]/deno_bkg):.0f}%\")\n",
+    "        else:\n",
+    "            print(f\"- {sample}: {100*(num_bkg[sample]/deno_bkg):.0f}%\")             "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "coffea-env",
+   "language": "python",
+   "name": "coffea-env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/combine/config_make_templates.yaml b/combine/config_make_templates.yaml
index f2281b3a1..15c000962 100644
--- a/combine/config_make_templates.yaml
+++ b/combine/config_make_templates.yaml
@@ -16,6 +16,29 @@ samples:
 
 regions_sel:
 
+    "ParTinclusive999": (fj_ParT_score>0.999) & (n_bjets_T==0)
+    "ParTinclusive995": (fj_ParT_score>0.995) & (n_bjets_T==0)
+    "ParTinclusive99": (fj_ParT_score>0.99) & (n_bjets_T==0)
+    "ParTinclusive985": (fj_ParT_score>0.985) & (n_bjets_T==0)
+    "ParTinclusive98": (fj_ParT_score>0.98) & (n_bjets_T==0)
+    "ParTinclusive975": (fj_ParT_score>0.975) & (n_bjets_T==0)
+    "ParTinclusive97": (fj_ParT_score>0.97) & (n_bjets_T==0)
+    "ParTinclusive965": (fj_ParT_score>0.965) & (n_bjets_T==0)
+    "ParTinclusive96": (fj_ParT_score>0.96) & (n_bjets_T==0)
+    "ParTinclusive955": (fj_ParT_score>0.955) & (n_bjets_T==0)
+    "ParTinclusive95": (fj_ParT_score>0.95) & (n_bjets_T==0)
+
+
+    "inclusive985": (fj_ParT_score_finetuned>0.985) & (n_bjets_T==0)
+    "inclusive98": (fj_ParT_score_finetuned>0.98) & (n_bjets_T==0)
+    "inclusive975": (fj_ParT_score_finetuned>0.975) & (n_bjets_T==0)
+    "inclusive97": (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0)
+    "inclusive965": (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0)
+    "inclusive96": (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0)
+    "inclusive955": (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0)
+    "inclusive95": (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0)
+
+
     ### VBF all inclusive
     "VBF985": (fj_ParT_score_finetuned>0.985) & (n_bjets_T==0) & ( (mjj>1000) & (deta>3.5) )
     "VBF98": (fj_ParT_score_finetuned>0.98) & (n_bjets_T==0) & ( (mjj>1000) & (deta>3.5) )
@@ -38,6 +61,10 @@ regions_sel:
     "ggF95": (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
 
     ### ggF pt bins
+    "ggF99pt250to300": (fj_ParT_score_finetuned>0.99) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt<300)
+    "ggF99pt300to450": (fj_ParT_score_finetuned>0.99) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>300) & (fj_pt<450)
+    "ggF99pt450toInf": (fj_ParT_score_finetuned>0.99) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>450)
+
     "ggF985pt250to300": (fj_ParT_score_finetuned>0.985) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt<300)
     "ggF985pt300to450": (fj_ParT_score_finetuned>0.985) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>300) & (fj_pt<450)
     "ggF985pt450toInf": (fj_ParT_score_finetuned>0.985) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>450)
@@ -63,6 +90,68 @@ regions_sel:
     "ggF96pt450toInf": (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>450)
 
 
+    "ggF975to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.975) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF97to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF965to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF96to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF955to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF95to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF945to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF94to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+
+    "ggF97to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF965to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF96to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF955to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF95to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF945to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF94to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+
+
+    "ggF965to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF96to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF955to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF95to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF945to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF94to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+
+    "ggF96to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF955to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF95to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF945to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+    "ggF94to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) )
+
+
+    "SR975to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.975) & (n_bjets_T==0)
+    "SR97to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0)
+    "SR965to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0)
+    "SR96to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0)
+    "SR955to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0)
+    "SR95to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0)
+    "SR945to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0)
+    "SR94to985": (fj_ParT_score_finetuned<0.985) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0)
+
+    "SR97to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0)
+    "SR965to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0)
+    "SR96to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0)
+    "SR955to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0)
+    "SR95to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0)
+    "SR945to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0)
+    "SR94to98": (fj_ParT_score_finetuned<0.98) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0)
+
+
+    "SR965to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.965) & (n_bjets_T==0)
+    "SR96to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0)
+    "SR955to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0)
+    "SR95to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0)
+    "SR945to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0)
+    "SR94to975": (fj_ParT_score_finetuned<0.975) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0)
+
+    "SR96to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.96) & (n_bjets_T==0)
+    "SR955to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.955) & (n_bjets_T==0)
+    "SR95to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.95) & (n_bjets_T==0)
+    "SR945to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.945) & (n_bjets_T==0)
+    "SR94to97": (fj_ParT_score_finetuned<0.97) & (fj_ParT_score_finetuned>0.94) & (n_bjets_T==0)
 
 
 
@@ -72,7 +161,11 @@ regions_sel:
     # "SRggF97pt300to450": (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>300) & (fj_pt<450)
     # "SRggF97pt450toInf": (fj_ParT_score_finetuned>0.97) & (n_bjets_T==0) & ( (mjj<1000) | (deta<3.5) ) & (fj_pt>450)
 
-    "WJetsCR": (fj_ParT_score_finetuned>0.50) & (fj_ParT_score_finetuned<0.95) & (n_bjets_T==0)
+    "WJetsCR97": (fj_ParT_score_finetuned>0.50) & (fj_ParT_score_finetuned<0.97) & (n_bjets_T==0)
+    "WJetsCR96": (fj_ParT_score_finetuned>0.50) & (fj_ParT_score_finetuned<0.96) & (n_bjets_T==0)
+    "WJetsCR95": (fj_ParT_score_finetuned>0.50) & (fj_ParT_score_finetuned<0.95) & (n_bjets_T==0)
+    "WJetsCR94": (fj_ParT_score_finetuned>0.50) & (fj_ParT_score_finetuned<0.94) & (n_bjets_T==0)
+
     "TopCR": (fj_ParT_score_finetuned>0.50) & (n_bjets_T>0)
 
 samples_dir:
diff --git a/python/make_stacked_hists_tagger.py b/python/make_stacked_hists_tagger.py
new file mode 100644
index 000000000..d8bf0355a
--- /dev/null
+++ b/python/make_stacked_hists_tagger.py
@@ -0,0 +1,295 @@
+"""
+Loads the config from `config_make_stacked_hists.yaml`, and postprocesses
+the condor output to make stacked histograms
+
+Author: Farouk Mokhtar
+"""
+
+import argparse
+import glob
+import json
+import logging
+import os
+import pickle as pkl
+import warnings
+
+import hist as hist2
+import numpy as np
+import pandas as pd
+import pyarrow
+import utils
+import yaml
+
+logging.basicConfig(level=logging.INFO)
+
+warnings.filterwarnings("ignore", message="Found duplicate branch ")
+pd.set_option("mode.chained_assignment", None)
+
+
+def make_events_dict(years, channels, samples_dir, samples, presel, taggers=["v2_nor2"]):
+    """
+    Postprocess the parquets by applying preselections, saving an `event_weight` column, and
+    a tagger score column in a big concatenated dataframe.
+
+    Args
+        years [list]: years to postprocess and save in the output (e.g. ["2016APV", "2016"])
+        channels [list]: channels to postprocess and save in the output (e.g. ["ele", "mu"])
+        samples_dir [str]: points to the path of the parquets
+        samples [list]: samples to postprocess and save in the output (e.g. ["HWW", "QCD", "Data"])
+        presel [dict]: selections to apply per ch (e.g. `presel = {"ele": {"pt cut": fj_pt>250}}`)
+
+    Returns
+        a dict() object events_dict[year][channel][samples] that contains big dataframes of procesed events
+
+    """
+
+    logging_ = True
+    if logging_ is False:
+        logger = logging.getLogger()
+        logger.disabled = True
+
+    events_dict = {}
+    for year in years:
+        events_dict[year] = {}
+
+        for ch in channels:
+            events_dict[year][ch] = {}
+
+            # get lumi
+            with open("../fileset/luminosity.json") as f:
+                luminosity = json.load(f)[ch][year]
+
+            for sample in os.listdir(samples_dir):
+                # get a combined label to combine samples of the same process
+
+                if "WJetsToLNu_1J" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+                if "WJetsToLNu_2J" in sample:
+                    print(f"Skipping sample {sample}")
+                    continue
+
+                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
+
+                logging.info(f"Finding {sample} samples and should combine them under {sample_to_use}")
+
+                out_files = f"{samples_dir}/{sample}/outfiles/"
+                if "postprocess" in samples_dir:
+                    parquet_files = glob.glob(f"{out_files}/{ch}.parquet")
+                else:
+                    parquet_files = glob.glob(f"{out_files}/*_{ch}.parquet")
+
+                pkl_files = glob.glob(f"{out_files}/*.pkl")
+
+                if not parquet_files:
+                    logging.info(f"No parquet file for {sample}")
+                    continue
+
+                try:
+                    data = pd.read_parquet(parquet_files)
+                except pyarrow.lib.ArrowInvalid:
+                    # empty parquet because no event passed selection
+                    continue
+
+                if len(data) == 0:
+                    continue
+
+                data = data[data.columns.drop(list(data.filter(regex="weight_mu_")))]
+                data = data[data.columns.drop(list(data.filter(regex="weight_ele_")))]
+                data = data[data.columns.drop(list(data.filter(regex="L_btag")))]
+                data = data[data.columns.drop(list(data.filter(regex="M_btag")))]
+                data = data[data.columns.drop(list(data.filter(regex="T_btag")))]
+                data = data[data.columns.drop(list(data.filter(regex="veto")))]
+                data = data[data.columns.drop(list(data.filter(regex="fj_H_VV_")))]
+                data = data[data.columns.drop(list(data.filter(regex="_up")))]
+                data = data[data.columns.drop(list(data.filter(regex="_down")))]
+
+                data["abs_met_fj_dphi"] = np.abs(data["met_fj_dphi"])
+
+                # get event_weight
+                if sample_to_use != "Data":
+                    event_weight = utils.get_xsecweight(pkl_files, year, sample, False, luminosity)
+
+                    logging.info("---> Using already stored event weight")
+
+                    event_weight *= data[f"weight_{ch}"]
+
+                else:
+                    event_weight = np.ones_like(data["fj_pt"])
+
+                data["event_weight"] = event_weight
+
+                # add finetuned tagger score
+                for modelv in taggers:
+                    import onnx
+                    import onnxruntime as ort
+                    import scipy
+
+                    if "v2_nor2" in modelv:
+                        PATH = "../../weaver-core-dev/experiments_finetuning/v2_nor2/model.onnx"
+                    else:
+                        PATH = f"../../weaver-core-dev/experiments_finetuning/{modelv}/model.onnx"
+
+                    data["met_relpt"] = data["met_pt"] / data["fj_pt"]
+
+                    input_dict = {
+                        "highlevel": data.loc[:, "fj_ParT_hidNeuron000":"fj_ParT_hidNeuron127"].values.astype("float32"),
+                    }
+
+                    onnx_model = onnx.load(PATH)
+                    onnx.checker.check_model(onnx_model)
+
+                    ort_sess = ort.InferenceSession(
+                        PATH,
+                        providers=["AzureExecutionProvider"],
+                    )
+                    outputs = ort_sess.run(None, input_dict)
+
+                    if modelv == "v2_nor2":
+                        data["fj_ParT_score_finetuned"] = scipy.special.softmax(outputs[0], axis=1)[:, 0]
+                    else:
+                        data[f"fj_ParT_score_finetuned_{modelv}"] = scipy.special.softmax(outputs[0], axis=1)[:, 0]
+
+                # use hidNeurons to get the finetuned scores
+                data["fj_ParT_score_finetuned"] = utils.get_finetuned_score(data, modelv="v2_nor2")
+
+                # drop hidNeuron columns for memory
+                data = data[data.columns.drop(list(data.filter(regex="hidNeuron")))]
+
+                # apply selection
+                for selection in presel[ch]:
+                    logging.info(f"Applying {selection} selection on {len(data)} events")
+                    data = data.query(presel[ch][selection])
+
+                logging.info(f"Will fill the {sample_to_use} dataframe with the remaining {len(data)} events")
+                logging.info(f"tot event weight {data['event_weight'].sum()} \n")
+
+                # fill the big dataframe
+                if sample_to_use not in events_dict[year][ch]:
+                    events_dict[year][ch][sample_to_use] = data
+                else:
+                    events_dict[year][ch][sample_to_use] = pd.concat([events_dict[year][ch][sample_to_use], data])
+
+    return events_dict
+
+
+def make_hists_from_events_dict(events_dict, samples_to_plot, vars_to_plot, selections):
+    """
+    Takes an `events_dict` object that was processed by `make_events_dict` and starts filling histograms.
+
+    Args
+        events_dict [dict]: see output of `make_events_dict()`
+        samples_to_plot [list]: which samples to use when plotting
+        vars_to_plot [list]: which variables to plot
+
+    Returns
+        hist.Hist object
+
+    """
+
+    hists = {}
+    for var in vars_to_plot:
+        hists[var] = hist2.Hist(
+            hist2.axis.StrCategory([], name="samples", growth=True),
+            utils.axis_dict[var],
+        )
+
+        for sample in samples_to_plot:
+            for year in events_dict:
+                for ch in events_dict[year]:
+                    df = events_dict[year][ch][sample]
+
+                    for sel, value in selections[ch].items():
+                        df = df.query(value)
+
+                    hists[var].fill(samples=sample, var=df[var], weight=df["event_weight"])
+
+    return hists
+
+
+def main(args):
+    years = args.years.split(",")
+    channels = args.channels.split(",")
+
+    if not os.path.exists(args.outpath):
+        os.makedirs(args.outpath)
+
+    os.system(f"cp config_make_stacked_hists.yaml {args.outpath}/")
+
+    # load config from yaml
+    with open("config_make_stacked_hists.yaml", "r") as stream:
+        config = yaml.safe_load(stream)
+
+    if args.make_events_dict:
+        events_dict = make_events_dict(
+            years,
+            channels,
+            args.samples_dir,
+            config["samples"],
+            config["presel"],
+        )
+        with open(f"{args.outpath}/events_dict.pkl", "wb") as fp:
+            pkl.dump(events_dict, fp)
+    else:
+        try:
+            with open(f"{args.outpath}/events_dict.pkl", "rb") as fp:
+                events_dict = pkl.load(fp)
+        except FileNotFoundError:
+            logging.info("Event dictionary not found. Run command with --make_events_dict option")
+            exit()
+
+    if args.plot_hists:
+        PATH = args.outpath + f"stacked_hists_{args.tag}"
+        if not os.path.exists(PATH):
+            os.makedirs(PATH)
+
+        os.system(f"cp config_make_stacked_hists.yaml {PATH}/")
+
+        logging.info("##### SELECTIONS")
+        for ch in config["sel"]:
+            logging.info(f"{ch} CHANNEL")
+            for sel, value in config["sel"][ch].items():
+                logging.info(f"{sel}: {value}")
+            logging.info("-----------------------------")
+
+        hists = make_hists_from_events_dict(events_dict, config["samples_to_plot"], config["vars_to_plot"], config["sel"])
+
+        utils.plot_hists(
+            years,
+            channels,
+            hists,
+            config["vars_to_plot"],
+            config["add_data"],
+            config["logy"],
+            config["add_soverb"],
+            config["only_sig"],
+            config["mult"],
+            outpath=PATH,
+        )
+
+
+if __name__ == "__main__":
+    # e.g.
+    # python finetuned_make_stacked_hists.py --years 2017 --channels ele,mu --plot_hists --make_events_dict --tag v1
+    # python finetuned_make_stacked_hists.py --years 2017 --channels ele,mu --plot_hists --tag v1
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--years", dest="years", default="2017", help="years separated by commas")
+    parser.add_argument("--channels", dest="channels", default="mu", help="channels separated by commas")
+    parser.add_argument("--samples_dir", dest="samples_dir", default="../eos/Jul21_2017", help="path to parquets", type=str)
+    parser.add_argument("--outpath", dest="outpath", default="hists/", help="path of the output", type=str)
+    parser.add_argument("--tag", dest="tag", default="test/", help="path of the output", type=str)
+    parser.add_argument("--make_events_dict", dest="make_events_dict", help="Make events dictionary", action="store_true")
+    parser.add_argument("--plot_hists", dest="plot_hists", help="Plot histograms", action="store_true")
+
+    args = parser.parse_args()
+
+    main(args)