diff --git a/.codespell-whitelist.txt b/.codespell-whitelist.txt
index a2da5edc..0ee33019 100644
--- a/.codespell-whitelist.txt
+++ b/.codespell-whitelist.txt
@@ -1,3 +1,4 @@
 subjet
 fpr
 tpr
+datas
diff --git a/src/HHbbVV/postprocessing/CreateDatacard.py b/src/HHbbVV/postprocessing/CreateDatacard.py
index 361bcfb8..a6bea068 100644
--- a/src/HHbbVV/postprocessing/CreateDatacard.py
+++ b/src/HHbbVV/postprocessing/CreateDatacard.py
@@ -1163,7 +1163,11 @@ def get_systematics_abcd(channels, channels_dict, channels_summed, rates_dict):
         vals = []
         for channel in channels:
             for _, key in enumerate(all_processes):
-                if key == qcd_data_key or skey not in channel_systs_dict[channel][key]:
+                if (
+                    key == qcd_data_key
+                    or key not in channel_systs_dict[channel]
+                    or skey not in channel_systs_dict[channel][key]
+                ):
                     vals.append("-")
                 else:
                     val, val_down = channel_systs_dict[channel][key][skey]
diff --git a/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb b/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb
index 16d7069a..18a5343d 100644
--- a/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb
+++ b/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,8 +33,8 @@
     "\n",
     "from tqdm import tqdm\n",
     "\n",
-    "import utils\n",
-    "from hh_vars import samples, nonres_samples, res_samples, nonres_sig_keys, res_sig_keys\n",
+    "import utils, postprocessing\n",
+    "from HHbbVV.hh_vars import samples, nonres_samples, res_samples, nonres_sig_keys, res_sig_keys\n",
     "\n",
     "MAIN_DIR = \"../../../\"\n",
     "\n",
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,22 +53,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "# plot_dir = f\"{MAIN_DIR}/plots/TaggerAnalysis/23Aug28\"\n",
-    "plot_dir = f\"{MAIN_DIR}/plots/BDT/23Nov2\"\n",
+    "plot_dir = f\"{MAIN_DIR}/plots/BDT/24Apr3\"\n",
     "os.system(f\"mkdir -p {plot_dir}\")\n",
     "\n",
-    "samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
-    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Mar10_2\"\n",
-    "year = \"2017\""
+    "samples_dir = f\"{MAIN_DIR}/../data/skimmer/24Mar14UpdateData\"\n",
+    "year = \"2018\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,30 +117,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removing 726 events\n",
+      "Loaded GluGluToHHTobbVV_node_cHHH1                       : 167211 entries\n"
+     ]
+    }
+   ],
    "source": [
     "# Both Jet's Regressed Mass above 50, electron veto\n",
-    "filters = [\n",
-    "    [\n",
-    "        (\"('ak8FatJetParticleNetMass', '0')\", \">=\", 50),\n",
-    "        (\"('ak8FatJetParticleNetMass', '1')\", \">=\", 50),\n",
-    "        # (\"('nGoodElectrons', '0')\", \"==\", 0),\n",
-    "    ],\n",
-    "]\n",
-    "\n",
     "events_dict = utils.load_samples(\n",
-    "    samples_dir, {sig_key: samples[sig_key] for sig_key in nonres_sig_keys}, year, filters=filters\n",
-    ")\n",
-    "events_dict |= utils.load_samples(signal_samples_dir, res_samples, year, filters=filters)\n",
-    "# events_dict[\"HHbbVV\"] = pd.read_parquet(\"../../../0-1.parquet\")\n",
-    "# events_dict[\"HHbbVV\"] = utils.load_samples(samples_dir, {sig_key: samples[sig_key]}, year)[\"HHbbVV\"]"
+    "    samples_dir,\n",
+    "    {sig_key: samples[sig_key] for sig_key in nonres_sig_keys},\n",
+    "    year,\n",
+    "    filters=postprocessing.load_filters,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -681,6 +681,102 @@
     "### BDT Plots"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Checking thresholds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{samples_dir}/24_03_07_new_samples_max_depth_5/roc_dict.pkl\", \"rb\") as f:\n",
+    "    roc = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GLdO4ceMy24oPqa8uvXv3lqenZ4lzFY3EcHNzU9++fZ16bseOHfXFF18oJSVF8+fP14033lju96oiv/zyi5544olKXdOnTx8988wzJc4dO3ZMMTExGj58uIKCgjRixAi99dZbio2NrXCqQPHpG7ZGCRRv/89//qPJkyeXmlrTtGnTEruzjBo1Sq+99lqpn5GHh0eJxV1btmxZ5rMqmkryzz//WKej2Kq7vEU9k5KSrIu79uvXT9HR0Taf4enpqSFDhli/btOmTaWeU6S87/Fjjz1mnQp111136eWXX7a56GpgYKCeeuop69fFR8jYek5QUJC+//57m7vmNGvWTIMHD7Z+fe7PCQAAAKjvCDFqGW9vbzVo0MBmW2ZmpourKc3Hx0e9e/cucW7z5s3WdRosFkupEKNr16421yJwRLNmzXTLLbfos88+0/Hjx/XXX3/p/fff1913360LL7ywUjtvzJkzR0ePHq3U82fMmKHFixerZ8+epdpyc3P1008/6YEHHlDHjh0VGRmp2bNnKysry+a9yltXIjk52VpbUFCQZsyYUWZNqamp1uPXX3+9zLVHjh07Zj1u37693bVUpm7p/8IFPz8/denSpUSbyWTSN998o2+//Vbz5s0r9zlFa0xIUq9evcp8jlT27jtFtXp4eKhHjx7W87t379ZHH30kSQoLC9Ps2bPLXXul+FowRTvU2HqOJMXExCg0NLTMexUPIyMiIsrsBwAAANRHhBi1UFnbSiYmJrq4EtvOXRcjNzdXf//9tyRp7969pcIWR9fDqIiHh4d69+6tu+66S++99542b96szMxM/f7775oxY4bNoKG4/Px8/fDDD5V+7nXXXactW7Zoz549euONNzR8+HCba3YcOHBADz30kPr27Wtdc6G44v96f+5LevG2CRMmWNd4ONfJkyeVnJws6ezPpayXYsMwrD+jkJAQBQcHl/m8c0Oq8uo+d9RGenq69u7da207d2RDq1atdM011+iaa65Rp06dynzG4cOH9fPPP0s6uxaMrc9VFGJ4e3uXCCiK5Obm6p9//pEkdevWrcTP6MMPP7QeT58+Xb6+vmXWIpVcD+XcEVEpKSlKSEiQdDZku+6668q9V1FNTZs2VXh4eLl9AQAAgPqGEKMWKuvlu+jlp7qdu0OJdHbxRcn2op62+p8v3t7eGjhwoJ555hlt2bJFK1asKPdfu23Vaw+TyaROnTrpwQcf1M8//6xTp07p559/1oMPPqiQkJASfffs2aORI0eWWATUbDZr69atkqQOHTooMDCwxDXFg4LLL7+8zDqK9ys+/eJciYmJSklJkVR69IRRbFHPNm3alBmiFSnq6+vra90FpMimTZusx2VN8ThXRkaGtm7dqi+++EKzZs3STTfdpG7duun06dOSzoYh546SyMnJsf73oVevXjZH4JQ17cUwDH366afWzzBu3LgKazxz5oz1uEmTJiXaiv8Mxo4da3NKSpHigUefPn0qvfMOAAAAUNeV/bdp1FiXXHKJvvvuu1Lnt23bpjNnzsjPz6/S9/zhhx9sjuQYMGCAunbtWql72RpZ8ccff+iBBx7Qn3/+aVd/VzCZTBo2bJh+/vln9e7dWxkZGaX6nLs9rKN8fX01fPhwDR8+XK+++qp+/PFHPfjggzp48KCks0HGZ599pltvvVWStG/fPusuF7amZNg7vaOidTVs9Tt39MSBAwesW5ZWNJUkKytLe/bskXQ2PDj3hd2edSr279+vBQsW6JdfflFcXFyF29Xaqmnr1q3WgKKs55T1PYyNjbU+89JLL7VrqlPxqTgtWrQo8zlXXXVVufepzLQdAAAAoD4ixKiFbG1jKp391+AffvhBN9xwQ6XuZxiG7rzzTuu/xBe3aNGiSocYLVu2VNu2bUtMkShrJEbTpk0dmve/bdu2Ml8If//9d3Xs2NHue7Vv316DBw+2GQzl5eVVuraKuLu7a9SoUbrwwgvVo0cP6wvzxo0brSFGeaFC8ZERISEhpRbhLM6REOPcfpVZ1HP79u2yWCxl9i0vxMjPz9fjjz+ut956S2azudS1QUFB6tixoy699FIdPXpUCxYssOs5Za2HUdbnKn5tRZ+3SNGoGUmlFqkt/pyKFrC19+cFAAAA1FdMJ6mF+vbta3M3Bkl66623Ktz14ly7d++2GWBIZ9cocMS5U0QSEhK0b9++UoseXnTRRQ4NmW/SpIlSUlJs/ilac6Eyyvp+nrs2xLk++ugjhYaGKjQ0VLNnz67UM1u2bKlRo0ZZvy7+4l7ev8gfOnTIulhneS/FhmFYp2+EhYWV+1nKW/OiMqMDli9fXmZfwzCsAUGrVq1KLG5ZWFioq6++Wm+88YbMZrNMJpNGjhypl156SatXr9aJEyeUmpqqDRs26KWXXiox9aaiHVAqWtTTx8enxAKjxUcknTv1pyy//PKL9bh4yGgYhvV7GxoaWuHvU2UCIwAAAKA+IsSohdzd3XXffffZbFu/fr0WLlxYqfsVzf+39Zxu3bpVuj7J9miROXPmWP+Vvoij62EEBweXuZhl0b/QV0bxl8fiii/YaMu2bduUlJSkpKSkSodHkqxTHiSVWHyyqB43N7dSa6DYGyokJCRYp8OU189isVjv2a5du3LXdLC1C0gRwzD0xRdflFnboUOHdPLkSUmlR2G89NJLWrlypaSza77s2bNHP/zwgx599FENGjSo1DocReFMs2bNFBYWVqqWohCjRYsWNn+G2dnZ2r17t/V5xbcyPXXqlPW4rJ2AiktLS7OO4gkODtall15qbUtISLCOtKkolKhs4AEAAADUR4QYtdQdd9yhhg0b2my76667tHbtWrvus3btWr322ms224YMGaKgoCCH6rMVTnz88celzjm6HoaXl5eGDx9us+3rr78uM5ixZfXq1WUu4FnRGgZpaWnW4+JbftojKytLy5Ytk3T28xRNAyooKND27dslSV26dCm1xklVTBEpbv/+/db1QM590S6+wGj79u0VEBBQ5n3WrVunuLg4SVKjRo1KTRMqaypJYWGh5syZI0ny9PTUDz/8UO50oM2bN1vXErG1+GVKSoqOHDkiqeyRPtu2bStz2kvxLWjt2fFn5syZ1mlHkydPLhGIVGZ6SPHAg6kkAAAAgG2EGLVU48aN9eabb9psy83N1bBhw/TCCy8oJyfHZp+0tDQ99dRTuvLKK5Wfn2+zzz333ONwfV27di0VshTfwUE6O9LDmZe1m266qcy2W265RZMmTVJsbGyZfXJycjRv3jxdffXVJUZEFAkLCytzKkLxPkVWrVpV5vfyXGfOnNHo0aOt00IefPBB6wiInTt3Wl+KbX1/7J1yUHwnEEfDjtjYWOsCo+UFWtnZ2Zo0aVKJuoqHAZJKLOpaPMSIj4+3Tmfq3r17uVOYcnNzNW3atDLrlexbPLS8z1x8DZg//vijzFokac2aNXrrrbcknV3fZerUqXY/pzI1AQAAADiLhT1rsdtvv11Lly4tc0HKJ554QrNmzdJVV12lyMhINWzYUAkJCTp06JA2bNigzMzMMu89fPhw/etf/3K4Nnd3d1100UUl1go4V/fu3e0arl+W66+/Xm+99ZZ10dDiDMPQRx99pI8//lj9+/dX27ZtFR4ergYNGujYsWNKSkrSr7/+anNHkiKvvfZaqRfxc40cOVIvvviipLM7jDzwwAN68803bW7pWVTX6tWr9eSTT1pfkDt37qzp06db+5Q3XcRisWjLli2SpMjIyFJbrxZX3joXZfU7NxRJSkqyHu/Zs0eZmZlq1KhRiT7Z2dm6/fbbraMwbNUt/V+44ObmVuI5ubm51uNTp07JbDbL3d291PXx8fEaN25ciTDE1nbD27Ztsx7bmmoilf89Lj6tZ9myZfrtt99sbk/722+/6brrrrOuz/HWW2+VmvZS/DkVTSdhPQwAAACgYoQYtZjJZNLHH3+skydPat26dTb7ZGVlafHixZW6b/v27fW///3PoQU3i7vkkkvKDTEcXQ+jiJubm+bOnatLL73UutbCuQzD0IYNG2wGHeUZP368rrvuugr7XXLJJbriiiu0YsUKSdJ7772nZcuW6c4771RkZKRatmypvLw8HT58WIcOHdLPP/+sv//+23p9mzZttHz58hJhTnkvs/v27VNWVpYk+9e56NChg11hh8lkKrXmRfHFN7OysnT99dfrnXfeUbt27ZSamqr169fr0UcfLTXi5dy68/LyrOFCly5dSozSiYyMlKenpwoKCnTo0CHdeOONeuihh9S6dWudPHlScXFx+uabb7R48eJSu5asWLFCPXr0UFhYmDX4KBo5IknTpk3TihUrlJqaqu+//9665WvRZ/b391eHDh1K3LN79+665pprrOHgiBEj9PTTT2v48OFq2rSp4uLiNH/+fC1atMg6JWXGjBkaP358ifsU/xm0b99ejRs3Lv3NL4YQAwAAALCDAZdKT083oqOjjc6dOxt+fn5Gz549jeeff97Iz893+J5nzpwxRo0aZUhy+k9UVJQRHx9f5rPCw8NtXjdx4sRSfZcvX17usxYuXFjqmokTJ9rsGx4eXmZNO3fuNFq2bFkln1+SMX78eKOgoMDu7//JkyeN3r17V/o5119/vXHq1KlS9+vZs6chyfDy8jLy8vJKtH3yySfW6994440ya9q9e7e1380331xmv4KCAsPX19eQZHTq1KlUu9lsNrp161aq9gYNGpT4OjIy0oiOjrZ+ffjw4RL32bhxo7XtzjvvLPWc++67z67v2c0332z069evxDk/Pz/DbDZb77VkyZJS1/Xs2dPanpaWZj1/2WWX2fy+HD161GjRokWF9Xh6ehovv/yyYbFYSt1j37591n433nhjmT+Dou9zo0aNDElGREREuX0BAACA+ow1MVwoNzdXffr0UUxMjAIDA3XdddcpJydHTz/9tIYNG1bqX5nt5evrq2+//VZz585Vy5YtHbqHl5eXnn76aW3durXMIfiV1a9fv3JHczi6qOe5unTpol27dunuu+92avRIs2bNtHDhQn366afWf7G3R5MmTbRhwwa98MILJUYu2NKoUSONHz9eGzdu1JdfflnqX+dzc3Ot29D26NGj1LQUe3cmsXd9hd27d1vXTbHVz83NTd98802pERpFox0CAgI0Y8YMbdmyxbrmSbNmzUrtCFLROhWvvPKKJk6cWObPr2/fvvr222+1cOHCUgu6duvWrcS0n1GjRun1119XmzZt5O3trfDw8BJTo4oWKi3rM0tndxnZuXOnbrnlFpvtnp6euuaaa7Rt2zZNmzbNZt2V2Zp2//791uldrIcBAAAAlM1kGA7sCwmHvPnmm3rwwQc1Y8YMzZgxQyaTSWazWVOnTtU777yj9957T3fffbdTz8jOztbcuXP13Xffae3atTYXrCzi7u6u7t276+abb9bEiRNLzee3pU2bNtadH4qbOHGi5s2bV+p89+7drS/lxTVv3lzHjh0r9fJ322236ZNPPinVPzw8XIcPH66wvoMHD2r+/Pn64osvtHfv3gr7+/j46JJLLtHEiRM1duzYUjuBVJbZbNahQ4d0+PBhHT58WCkpKQoICFBQUJC6dOmirl272lzvoaYzDEPr1q3T3r17deLECQUFBalTp0665JJLSuzG4azY2Fht2bJFhw8flru7u1q2bKm+ffuqQ4cO1t8Vs9msH374Qbt371bTpk118cUXq0uXLlVWw7kSEhK0Y8cO7d69W76+vgoNDdWAAQNKbUULAAAA4PwjxHChK6+8UmvXrtXx48dLrIGQk5Ojxo0ba/jw4fr++++r7Hnp6enaunWrUlJSdPz4cZ0+fVoBAQFq3Lix2rRpo549ezq1sGZNl5GRoe3bt+vIkSPKyMhQZmamPDw8rN+DqKgode7cuVKjLgAAAAAA1YcQw4aCggKlpKSoUaNGpXZicEanTp3k4eGhnTt3lmpr3bq1GjZsqN27d1fZ8wAAAAAAqEvq9JoYiYmJcnNzs2uXCUnasGGDrrzySvn4+CgsLEwBAQGKiIjQq6++Wu60DHvNnTtXCxYsKHX+4MGDSkhIUKtWrZx+BgAAAAAAdVWdHke/aNEi2TvQZP78+brjjjtKLa554MAB6zaNy5Ytc2r+/yWXXFLqXFZWlu666y5J0g033ODwvQEAAAAAqOvq7EiMuLg4zZw5066+O3fu1OTJk2U2m9WlSxetWrVK2dnZ2r9/vyZPnixJWrlypaZPn16lNe7evVtDhgzRr7/+qosvvlgTJ06s0vsDAAAAAFCX1JkQw2Kx6MCBA1qyZImmTp2qnj17WrcsrMhzzz2n/Px8BQUF6ddff9Vll10mPz8/tW/fXu+//75uuukmSWd3Fzlx4oTTtebn5+upp55Sjx49tHnzZuuCnt7e3k7fGwAAAACAuqrOTCeJj49XREREpa/LzMzUt99+K+nsNqEtWrQo0W4ymTRt2jQtWrRIOTk5+vbbb63TP6Sz2y9mZ2eX+4xOnTpZjw8fPqwbbrhBmzdvVmBgoF544QXdddddcnOrM3kSAAAAAADnRZ0JMXx8fDRo0KAS57Zv366MjIxyr1u1apV10c5Ro0bZ7NOjRw+FhYUpISFBy5cvLxFiTJkyRcuWLSv3GUXrcqSlpWn48OGKjY3VkCFD9Nlnn6l58+YVfjYAAAAAAFCHQozg4GCtXr26xLnLLrtMv//+e7nXHT58WJLk4eGhAQMG2OxjMpk0ePBgzZ8/39q/yA8//GB3jU899ZRiY2N1++236/3333dqkVAAAAAAAOqbej+H4dixY5KkoKCgckOFohETKSkpDj0nPz9fn332mQICAjRnzhwCDAAAAAAAKqnOjMRwVFEo0bhx43L7BQUFSZKOHz8uwzBkMpkq9ZzExESlpaWpbdu2euyxx8p8xnPPPVfqfIMGDZSbmyt3d3c1a9asUs8trrI1AwAAAABqr6KlDRxx4sQJmc1m+fj4VLgOpCvV+xCjaCSGvSFGQUGB0tLSrF/b6+TJk5KkQ4cOKSYmxmaf8PBwmyFGbm6uLBaLLBaLkpOTK/VcAAAAAAAclZubW90llFDvQwx3d3dJktlsLrdffn6+9biivrb07dvX4RTM3d1dFotFbm5uCg4OdugekuMjMVJSUkrt2uIqPNu1DMNQcnKyWrVqVS0jd+rj97y+PpvfNZ7tKvyu8WxX4XeNZ7sKv2s8uzKcGYlx7NgxWSwW6ztzTeFUiDFq1CjdeuutGjVqlHx8fKqqJpcqCgVOnTpVbr+0tDRJZxcAbdKkyXmvq7hmzZopOTlZwcHBSkpKcumzJSkqKkq7d+92+XN5tuufnZmZqYCAAO3Zs0eNGjVy+fPr4/e8vj6b3zWe7Sr8rvFsV+F3jWe7Cr9rPNtVQkJClJyc7NSSBueDUwt7Llu2TDfeeKOCg4N11113ac2aNVVVl8sUhRhFIUVZikKOli1bys2t3q+HCgAAAACAyzn9Nm4YhjIzMzV37lwNHjxYbdu21dNPP619+/ZVRX3nXfEQIysrq8x+RSMgWrZs6ZK6AAAAAABASU6PxLj55pvVoEEDGYYhwzAUHx+vWbNmKSoqSv369VNMTIx1UcuaKCIiQtLZMOaXX36x2aewsFC//vqrJKl9+/Yuqw0AAAAAAPwfp0KMq666SgsWLFBKSoo+++wzjRo1Sh4eHtZA46+//tLUqVPVqlUrjRkzRl999ZXy8vKqqvYqMXDgQPn6+kqSli5darPPH3/8YZ1uMnLkSJfVBgAAAAAA/k+VLO7g5+encePG6fvvv9fRo0f1/vvva9CgQZLOjnAoLCzUDz/8oHHjxik4OFh333231q1bVxWPdpqvr68mTJggSfr000+1d+/eEu1ms1nPPvusJKlp06YaPXq0y2sEAAAAAABVFGIUFxQUpMmTJ2vVqlWKj4/XK6+8op49e1pHZ2RkZOh///ufBg0apHbt2mnGjBmKi4ur6jIq5amnnlJAQIDy8vI0ePBgffzxx9qxY4dWrFihK6+80jqV5LnnnpO/v3+11lodoqOjeXY9enZ1qq/f8/r67OpUX7/n9fXZ1am+fs/r67OrU339ntfXZ1en+vo9r6/ProlMhjMbx1bC3r17tWjRIn366ac6dOjQ/xXw//c27tu3r2699VaNGzdOQUFBVfLMyy67TL///rvGjh2rr776qty+K1eu1LXXXlvm4p5Tp07V7Nmzq2Uv5qKtbVq1alUtW6yi/ijasisjI6NatuxC/cHvGlyF3zW4Cr9rcBV+1+AqNfU91MMVD8nLy1NsbKyOHDmiU6dOlQgCijKUTZs2adOmTXr00Ud133336emnn5afn58rypMkDRs2TNu3b9err76qn376SUePHlWjRo3Uu3dv3Xfffbr66qtdVktZjh8/rqioKJtt0dHRJHQAAAAAALvFxMQoJibGZtvx48ddXI19zttIjDNnzujHH3/U119/rWXLlik7O9vaVvTIli1bavTo0frnn3/0xx9//F9RJpMuuugi/fzzz/Vy+sa5amoChrqHZB+uwu8aXIXfNbgKv2twFX7X4Co19T20SkdiZGZmaunSpfr666+1fPly5ebmSvq/0EKSWrVqpbFjx+r666/XJZdcYh2VERsbq3fffVcxMTEqLCzUn3/+qZdeekn//e9/q7JEAAAAAABQSzkdYqSmpur777/X119/rV9//VUFBQWSSgcX1113na6//npdfPHFNteV6NChg9544w1NmDBBffr0kSR9/vnnhBgAAAAAAECSkyHG0KFD9fvvv8tisUiqfHBhS+/evdW1a1ft3LlTJ06ccKY8AAAAAABQhzgVYvz2228ymUzW8KJVq1a6/vrrdf3116t///4O7+RRFIoUjcgAAAAAAABwejpJ8REXzgQXxX3wwQcqKChQq1atnL4XAAAAAACoG5wKMdavX6+LLrqoSoKL4i6++OIqvV9tV/T9rervM3Aub29vzZgxQ97e3tVdCuo4ftfgKvyuwVX4XYOr8LsGV6mp76HnbYvVc+Xk5CgrK0vNmjVzxePqlNDQUCUlJSkkJESJiYnVXQ4AAAAAoI6rqe+hblVxk/T0dM2ePVvXXnutWrRoYV3ToriDBw8qODhYUVFReuCBB2rUPrMAAAAAAKDmc3pNjJUrV2r8+PFKS0ursK9hGNq3b5/27dunjz/+WJ9//rlGjBjhbAkAAAAAAKAecGokxv79+3X11VcrLS1NhmHIMIwyF+MMCgrSgAED5ObmJsMwlJWVpbFjx2rbtm3OlAAAAAAAAOoJp9bEGDNmjJYuXSqTyaQ+ffro3XffVc+ePcu95uTJk4qOjtbixYslSQMGDNCaNWscLaFeKJqL5OHhocjISJt9oqOjFR0d7eLKAAAAAAC1VUxMjGJiYmy2xcXFqbCwsMatieFwiHHw4EFFRETIZDKpa9eu2rJlizw87J+dMnToUP32228ymUyKj49XSEiII2XUCzV1QRUAAAAAQN1UU99DHZ5OsnfvXuvx9OnTKxVgSNK0adOsx7t373a0DAAAAAAAUE84HGLs27fPetyjR49KX9+hQwfr8Z49exwtAwAAAAAA1BMOhxju7u7W49TU1Epfn5KSYj22tSUrAAAAAABAcQ6HGG3btrUer1+/vtLX//HHH9bjsLAwR8sAAAAAAAD1hMMhxpAhQ+Tj4yNJmj17tk6dOmX3tWlpaXrzzTclnR3RcfnllztaBgAAAAAAqCccDjEaNGige+65R4ZhKCkpSf3799fSpUtV0WYnK1as0MCBAxUfHy+TyaQJEyYoMDDQ0TIAAAAAAEA94fAWq5KUlZWl/v37a9euXTKZTJKk8PBwjRo1SmFhYQoNDZW3t7cSExMVHx+vFStWaOfOnZIkwzAUFhamzZs3q3nz5lXzaeqomrq1DQAAAACgbqqp76GV2xf1HA0bNtRvv/2ma6+91rouxpEjR/T222+XeU1RZhIZGally5YRYAAAAAAAALs4PJ2kSLNmzbRmzRp9+OGH6tatmwzDKPdPy5Y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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xlim = [0, 0.3]\n",
+    "ylim = [1e-6, 1e-2]\n",
+    "\n",
+    "# plot_thresholds = [0.6, 0.9, 0.96, 0.99, 0.997, 0.998, 0.999]\n",
+    "plot_thresholds = [0.997, 0.998, 0.999, 0.9993, 0.9995, 0.9997, 0.9999]\n",
+    "th_colours = [\n",
+    "    \"#36213E\",\n",
+    "    \"#9381FF\",\n",
+    "    \"#1f78b4\",\n",
+    "    # \"#a6cee3\",\n",
+    "    # \"#32965D\",\n",
+    "    \"#7CB518\",\n",
+    "    \"#EDB458\",\n",
+    "    \"#ff7f00\",\n",
+    "    \"#a70000\",\n",
+    "]\n",
+    "\n",
+    "roc_colours = [\n",
+    "    \"#23CE6B\",\n",
+    "    \"#a70000\",\n",
+    "]\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 24})\n",
+    "\n",
+    "pths = {th: [[], []] for th in plot_thresholds}\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "c = roc_colours[0]\n",
+    "\n",
+    "plt.plot(\n",
+    "    roc[\"tpr\"],\n",
+    "    roc[\"fpr\"],\n",
+    "    # label=roc_labels[inf],\n",
+    "    linewidth=2,\n",
+    "    color=c,\n",
+    ")\n",
+    "\n",
+    "for th in plot_thresholds:\n",
+    "    idx = find_nearest(roc[\"thresholds\"], th)\n",
+    "    pths[th][0].append(roc[\"tpr\"][idx])\n",
+    "    pths[th][1].append(roc[\"fpr\"][idx])\n",
+    "\n",
+    "for k, th in enumerate(plot_thresholds):\n",
+    "    plt.scatter(\n",
+    "        *pths[th],\n",
+    "        marker=\"o\",\n",
+    "        s=40,\n",
+    "        label=f\"BDT Score > {th}\",\n",
+    "        color=th_colours[k],\n",
+    "        zorder=100,\n",
+    "    )\n",
+    "\n",
+    "hep.cms.label(data=False, rlabel=\"\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Signal efficiency\")\n",
+    "plt.ylabel(\"Background efficiency\")\n",
+    "plt.xlim(*xlim)\n",
+    "plt.ylim(*ylim)\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "# plt.savefig(f\"{plot_dir}/roc_bdt.pdf\", bbox_inches=\"tight\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -954,7 +1050,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.11"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/postprocessing/TrainBDT.py b/src/HHbbVV/postprocessing/TrainBDT.py
index adcebef2..aac46672 100644
--- a/src/HHbbVV/postprocessing/TrainBDT.py
+++ b/src/HHbbVV/postprocessing/TrainBDT.py
@@ -61,20 +61,20 @@
     # "VVFatJetParTMD_THWW4q",
     "VVFatJetParTMD_probHWW3q",
     "VVFatJetParTMD_probQCD",
+    "vbf_Mass_jj",
     "VVFatJetParTMD_probHWW4q",
     "VVFatJetParticleNetMass",
     "DijetMass",
     "VVFatJetParTMD_probT",
-    "VVFatJetPtOverDijetPt",
+    "VVFatJetPt",
     "DijetPt",
+    "VVFatJetPtOverDijetPt",
+    "MET_pt",
     "bbFatJetPt",
-    "VVFatJetPt",
     "VVFatJetPtOverbbFatJetPt",
-    "MET_pt",
-    "DijetdEta",
-    "DijetdPhi",  # TODO: current dPhi is buggy
-    "vbf_Mass_jj",
     "vbf_dEta_jj",
+    "DijetdPhi",  # TODO: current dPhi is buggy
+    "DijetdEta",
 ]
 
 
@@ -286,6 +286,8 @@ def main(args):
 
     if args.rem_feats:
         bdtVars = bdtVars[: -args.rem_feats]
+    if len(args.rem_feats_name):
+        bdtVars = [var for var in bdtVars if var not in args.rem_feats_name]
 
     print("BDT features:\n", bdtVars)
 
@@ -500,6 +502,128 @@ def _get_bdt_scores(preds, sig_keys, multiclass):
             return preds[:, : len(sig_keys)] / (preds[:, : len(sig_keys)] + bg_tot)
 
 
+def plot_train_test_shapes(
+    train, test, sig_keys, model_dir, test_size, training_keys, equalize_sig_bg
+):
+    # BDT score shapes
+    bins = [[20, 0, 1], [20, 0.4, 1], [20, 0.8, 1], [20, 0.9, 1], [20, 0.98, 1]]
+
+    if len(sig_keys) == 1:
+        scores = [("BDTScore", "BDT Score")]
+    else:
+        scores = [
+            (f"BDTScore{sig_key}", f"BDT Score {plotting.sample_label_map[sig_key]}")
+            for sig_key in sig_keys
+        ]
+
+    plot_vars = [utils.ShapeVar(*score, tbins) for score in scores for tbins in bins]
+
+    save_model_dir = model_dir / "hists"
+    save_model_dir.mkdir(exist_ok=True, parents=True)
+
+    for year in train:
+        for shape_var in plot_vars:
+            h = Hist(
+                hist.axis.StrCategory(["Train", "Test"], name="Data"),
+                hist.axis.StrCategory(training_keys, name="Sample"),
+                shape_var.axis,
+                storage="weight",
+            )
+
+            for dataset, label in [(train, "Train"), (test, "Test")]:
+                # Normalize the two distributions
+                data_sf = (0.5 / test_size) if label == "Test" else (0.5 / (1 - test_size))
+                for key in training_keys:
+                    # scale signals back down to normal by ~equalizing scale factor
+                    sf = data_sf / 1e6 if (key in sig_keys and equalize_sig_bg) else data_sf
+                    data = dataset[year][dataset[year]["Dataset"] == key]
+                    fill_data = {shape_var.var: data[shape_var.var]}
+                    h.fill(Data=label, Sample=key, **fill_data, weight=data[weight_key] * sf)
+
+            plotting.ratioTestTrain(
+                h,
+                training_keys,
+                shape_var,
+                year,
+                save_model_dir,
+                name=f"{year}_{shape_var.var}_{shape_var.bins[1]}",
+            )
+
+            plotting.ratioTestTrain(
+                h,
+                [key for key in training_keys if key != "QCD"],
+                shape_var,
+                year,
+                save_model_dir,
+                name=f"{year}_{shape_var.var}_{shape_var.bins[1]}_noqcd",
+            )
+
+
+# def plot_mass_shapes(model, bdtVars, multiclass, train, test, sig_keys, model_dir, training_keys):
+def plot_mass_shapes(train, test, sig_keys, model_dir, training_keys):
+    cuts = [0, 0.1, 0.5, 0.9, 0.95]
+
+    shape_var = utils.ShapeVar(
+        var="bbFatJetParticleNetMass", label=r"$m^{bb}_{reg}$ (GeV)", bins=[20, 50, 250]
+    )
+
+    save_model_dir = model_dir / "mass_hists"
+    save_model_dir.mkdir(exist_ok=True, parents=True)
+
+    for data_dict, label in [(train, "train"), (test, "test")]:
+        for key in training_keys:
+            for sig_key in sig_keys:
+                fig, axs = plt.subplots(len(data_dict), 1, figsize=(12, 12 * len(data_dict)))
+                datas = []
+                for i, (year, data) in enumerate(data_dict.items()):
+                    ed_key = {key: data[data["Dataset"] == key]}
+                    datas.append(data[data["Dataset"] == key])
+
+                    plotting.cutsLinePlot(
+                        ed_key,
+                        shape_var,
+                        key,
+                        f"BDTScore{sig_key}",
+                        cuts,
+                        year,
+                        weight_key,
+                        ax=axs[i] if len(data_dict) > 1 else axs,
+                    )
+
+                plt.savefig(
+                    save_model_dir / f"{label}_{key}_BDT{sig_key}Cuts.pdf", bbox_inches="tight"
+                )
+
+                if len(data_dict) > 1:
+                    ed_key = {key: pd.concat(datas, axis=0)}
+                    plotting.cutsLinePlot(
+                        ed_key,
+                        shape_var,
+                        key,
+                        f"BDTScore{sig_key}",
+                        cuts,
+                        "all",
+                        weight_key,
+                        plot_dir=save_model_dir,
+                        name=f"{label}_{key}_BDT{sig_key}Cuts_AllYears",
+                        show=False,
+                    )
+
+                # make plots with artificially increased qcd statistics
+                # if key == qcd_key:
+                #     nsampling = 100
+                #     X = get_X(data_dict, bdtVars)
+                #     X = X.iloc[np.tile(np.arange(len(X)), nsampling)]
+
+                #     rng = np.random.default_rng(seed=42)
+                #     X["VVFatJetParticleNetMass"] = rng.random(len(X)) * 15 + 125
+                #     X["VVFatJetParTMD_probHWW3q"] = rng.random(len(X)) * 0.05 + 0.95
+                #     X["VVFatJetParTMD_probQCD"] = rng.random(len(X)) * 0.025 + 0.025
+
+                #     preds = model.predict_proba(get_X(data, bdtVars))
+                #     preds = _get_bdt_scores(preds, sig_keys, multiclass)
+
+
 def evaluate_model(
     model: xgb.XGBClassifier,
     model_dir: str,
@@ -626,58 +750,12 @@ def evaluate_model(
     plotting.multiROCCurve(rocs, plot_dir=model_dir, name="roc_combined_thresholds")
     plotting.multiROCCurve(rocs, thresholds=[], plot_dir=model_dir, name="roc_combined")
 
-    # BDT score shapes
-    bins = [[20, 0, 1], [20, 0.4, 1], [20, 0.8, 1], [20, 0.9, 1], [20, 0.98, 1]]
-
-    if len(sig_keys) == 1:
-        scores = [("BDTScore", "BDT Score")]
-    else:
-        scores = [
-            (f"BDTScore{sig_key}", f"BDT Score {plotting.sample_label_map[sig_key]}")
-            for sig_key in sig_keys
-        ]
-
-    plot_vars = [utils.ShapeVar(*score, tbins) for score in scores for tbins in bins]
-
-    save_model_dir = model_dir / "hists"
-    save_model_dir.mkdir(exist_ok=True, parents=True)
-
-    for year in train:
-        for shape_var in plot_vars:
-            h = Hist(
-                hist.axis.StrCategory(["Train", "Test"], name="Data"),
-                hist.axis.StrCategory(training_keys, name="Sample"),
-                shape_var.axis,
-                storage="weight",
-            )
-
-            for dataset, label in [(train, "Train"), (test, "Test")]:
-                # Normalize the two distributions
-                data_sf = (0.5 / test_size) if label == "Test" else (0.5 / (1 - test_size))
-                for key in training_keys:
-                    # scale signals back down to normal by ~equalizing scale factor
-                    sf = data_sf / 1e6 if (key in sig_keys and equalize_sig_bg) else data_sf
-                    data = dataset[year][dataset[year]["Dataset"] == key]
-                    fill_data = {shape_var.var: data[shape_var.var]}
-                    h.fill(Data=label, Sample=key, **fill_data, weight=data[weight_key] * sf)
-
-            plotting.ratioTestTrain(
-                h,
-                training_keys,
-                shape_var,
-                year,
-                save_model_dir,
-                name=f"{year}_{shape_var.var}_{shape_var.bins[1]}",
-            )
-
-            plotting.ratioTestTrain(
-                h,
-                [key for key in training_keys if key != "QCD"],
-                shape_var,
-                year,
-                save_model_dir,
-                name=f"{year}_{shape_var.var}_{shape_var.bins[1]}_noqcd",
-            )
+    plot_mass_shapes(train, test, sig_keys, model_dir, training_keys)
+    print("Made mass plots")
+    plot_train_test_shapes(
+        train, test, sig_keys, model_dir, test_size, training_keys, equalize_sig_bg
+    )
+    print("Made histograms")
 
     # temporarily save train and test data as pickles to iterate on plots
     with (model_dir / "train.pkl").open("wb") as f:
@@ -815,7 +893,12 @@ def do_inference(
         "--n-estimators", default=10000, help="max number of trees to keep adding", type=int
     )
 
-    parser.add_argument("--rem-feats", default=0, help="remove N lowest importance feats", type=int)
+    parser.add_argument(
+        "--rem-feats", default=0, help="remove N lowest importance training feats", type=int
+    )
+    parser.add_argument(
+        "--rem-feats-name", default=[], help="remove training features by name", type=str, nargs="*"
+    )
 
     """
     Slightly worse to use a single tagger score
diff --git a/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh b/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh
index 61ed909e..94d79957 100755
--- a/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh
+++ b/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh
@@ -1,22 +1,28 @@
 #!/bin/bash
-# shellcheck disable=SC2086,SC2043
+# shellcheck disable=SC2086,SC2043,SC2206
 
 ####################################################################################################
 # BDT Sculpting plots
 # Author: Raghav Kansal
 ####################################################################################################
 
+years=("2016APV" "2016" "2017" "2018")
+
 MAIN_DIR="../../.."
 data_dir="$MAIN_DIR/../data/skimmer/24Mar14UpdateData"
-bdt_preds_dir="$data_dir/24_03_07_new_samples_max_depth_5/inferences"
+bdt_preds_dir="$data_dir/24_04_03_k2v0_training_eqsig_vbf_vars/inferences"
 TAG=""
 
 
-options=$(getopt -o "" --long "tag:" -- "$@")
+options=$(getopt -o "" --long "year:,tag:" -- "$@")
 eval set -- "$options"
 
 while true; do
     case "$1" in
+        --year)
+            shift
+            years=($1)
+            ;;
         --tag)
             shift
             TAG=$1
@@ -41,9 +47,10 @@ if [[ -z $TAG ]]; then
   exit 1
 fi
 
-for year in 2016APV 2016 2017 2018
+for year in "${years[@]}"
 do
-    python postprocessing.py --year $year --data-dir $data_dir --bdt-preds-dir $bdt_preds_dir --no-lp-sf-all-years \
-    --sig-samples GluGluToHHTobbVV_node_cHHH1 --bg-keys QCD TT "Z+Jets" \
+    echo $year
+    python postprocessing.py --year $year --data-dir $data_dir --bdt-preds-dir $bdt_preds_dir \
+    --sig-samples GluGluToHHTobbVV_node_cHHH1 qqHH_CV_1_C2V_0_kl_1_HHbbVV --bg-keys QCD TT "Z+Jets" --no-data \
     --bdt-plots --plot-dir "$MAIN_DIR/plots/PostProcessing/$TAG"
 done
diff --git a/src/HHbbVV/postprocessing/bash_scripts/NonresTemplatesScan.sh b/src/HHbbVV/postprocessing/bash_scripts/NonresTemplatesScan.sh
index 81314e33..2c35aa54 100755
--- a/src/HHbbVV/postprocessing/bash_scripts/NonresTemplatesScan.sh
+++ b/src/HHbbVV/postprocessing/bash_scripts/NonresTemplatesScan.sh
@@ -10,7 +10,7 @@ years=("2016APV" "2016" "2017" "2018")
 
 MAIN_DIR="../../.."
 data_dir="$MAIN_DIR/../data/skimmer/24Mar14UpdateData"
-bdt_preds_dir="$data_dir/24_04_03_k2v0_training_eqsig_vbf_vars/inferences"
+bdt_preds_dir="$data_dir/24_04_05_k2v0_training_eqsig_vbf_vars_rm_deta/inferences"
 # sig_samples="HHbbVV qqHH_CV_1_C2V_0_kl_1_HHbbVV"
 sig_samples="qqHH_CV_1_C2V_0_kl_1_HHbbVV"
 TAG=""
@@ -28,7 +28,7 @@ while true; do
             ;;
         --bdt)
             # bdt_cut="--nonres-bdt-wp 0.99 0.997 0.998 0.999 0.9997 0.9999"
-            bdt_cut="--nonres-bdt-wp 0.997 0.998 0.999 0.9997 0.9999"
+            bdt_cut="--nonres-bdt-wp 0.99 0.997 0.998 0.999 0.9997 0.9999"
             ;;
         --txbb)
             txbb_cut="--nonres-txbb-wp MP HP"
diff --git a/src/HHbbVV/postprocessing/plotting.py b/src/HHbbVV/postprocessing/plotting.py
index fc3ecb55..20d41310 100644
--- a/src/HHbbVV/postprocessing/plotting.py
+++ b/src/HHbbVV/postprocessing/plotting.py
@@ -486,16 +486,14 @@ def ratioHistPlot(
 
     if year == "all":
         hep.cms.label(
-            "Work in Progress",
+            "Preliminary",
             data=True,
             lumi=f"{np.sum(list(LUMI.values())) / 1e3:.0f}",
             year=None,
             ax=ax,
         )
     else:
-        hep.cms.label(
-            "Work in Progress", data=True, lumi=f"{LUMI[year] / 1e3:.0f}", year=year, ax=ax
-        )
+        hep.cms.label("Preliminary", data=True, lumi=f"{LUMI[year] / 1e3:.0f}", year=year, ax=ax)
 
     if axrax is None:
         if len(name):
@@ -615,7 +613,7 @@ def ratioLinePlot(
     if title is not None:
         ax.set_title(title, y=1.08)
 
-    hep.cms.label("Work in Progress", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax)
+    hep.cms.label("Preliminary", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax)
     if len(name):
         plt.savefig(name, bbox_inches="tight")
 
@@ -886,9 +884,7 @@ def plot_HEM2d(hists2d: list[Hist], plot_keys: list[str], year: str, name: str,
         for i in range(2):
             ax = axs[j][i]
             hep.hist2dplot(hists2d[i][key, ...], cmap="turbo", ax=ax)
-            hep.cms.label(
-                "Work in Progress", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax
-            )
+            hep.cms.label("Preliminary", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax)
             ax.set_title(key, y=1.07)
             ax._children[0].colorbar.set_label("Events")
 
@@ -989,19 +985,25 @@ def ratioTestTrain(
 
 def cutsLinePlot(
     events_dict: dict[str, DataFrame],
-    bb_masks: dict[str, DataFrame],
     shape_var: utils.ShapeVar,
     plot_key: str,
     cut_var: str,
     cuts: list[float],
     year: str,
     weight_key: str,
+    bb_masks: dict[str, DataFrame] = None,
     plot_dir: str = "",
     name: str = "",
+    ax: plt.Axes = None,
     show: bool = False,
 ):
     """Plot line plots of ``shape_var`` for different cuts on ``cut_var``."""
-    fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+        in_ax = False
+    else:
+        in_ax = True
+
     plt.rcParams.update({"font.size": 24})
 
     for _i, cut in enumerate(cuts):
@@ -1024,7 +1026,19 @@ def cutsLinePlot(
     ax.set_ylabel("Fraction of Events")
     ax.legend()
 
-    hep.cms.label(ax=ax, data=False, year=year, lumi=round(LUMI[year] / 1e3))
+    if year == "all":
+        hep.cms.label(
+            "Preliminary",
+            data=True,
+            lumi=f"{np.sum(list(LUMI.values())) / 1e3:.0f}",
+            year=None,
+            ax=ax,
+        )
+    else:
+        hep.cms.label("Preliminary", data=True, lumi=f"{LUMI[year] / 1e3:.0f}", year=year, ax=ax)
+
+    if in_ax:
+        return
 
     if len(name):
         plt.savefig(f"{plot_dir}/{name}.pdf", bbox_inches="tight")
diff --git a/src/HHbbVV/postprocessing/postprocessing.py b/src/HHbbVV/postprocessing/postprocessing.py
index 95a4e707..9e880da9 100644
--- a/src/HHbbVV/postprocessing/postprocessing.py
+++ b/src/HHbbVV/postprocessing/postprocessing.py
@@ -1100,7 +1100,7 @@ def apply_trigger_weights(events_dict: dict[str, pd.DataFrame], year: str, cutfl
 
 def qcd_sf(events_dict: dict[str, pd.DataFrame], cutflow: pd.DataFrame):
     """Applies a QCD scale factor."""
-    if qcd_key not in events_dict:
+    if qcd_key not in events_dict or data_key not in events_dict:
         return
 
     trig_yields = cutflow.iloc[:, -1]
@@ -1261,7 +1261,6 @@ def load_bdt_preds(
     i = 0
     for sample, num_events in sample_order_dict.items():
         if sample in events_dict:
-            print(sample)
             events = events_dict[sample]
             assert num_events == len(
                 events
@@ -1591,8 +1590,8 @@ def plot_bdt_sculpting(
     """Plot bb jet mass for different BDT score cuts."""
     cuts = [0, 0.1, 0.5, 0.9, 0.95]
     # bdtvars = ["", "TT", "VJets"]
-    bdtvars = [""]
-    plot_keys = ["QCD", "TT", "Z+Jets", "HHbbVV"]
+    bdtvars = ["", "VBF"]
+    plot_keys = ["QCD", "TT", "Z+Jets", "HHbbVV", "qqHH_CV_1_C2V_0_kl_1_HHbbVV"]
 
     shape_var = ShapeVar(
         var="bbFatJetParticleNetMass", label=r"$m^{bb}_{reg}$ (GeV)", bins=[20, 50, 250]
@@ -1605,15 +1604,15 @@ def plot_bdt_sculpting(
 
             plotting.cutsLinePlot(
                 ed_key,
-                bbm_key,
                 shape_var,
                 key,
                 f"BDTScore{var}",
                 cuts,
                 year,
                 weight_key,
-                plot_dir,
-                f"{year}_BDT{var}Cuts_{shape_var.var}_{key}",
+                bb_masks=bbm_key,
+                plot_dir=plot_dir,
+                name=f"{year}_BDT{var}Cuts_{shape_var.var}_{key}",
                 show=show,
             )
 
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2016APV_templates.pkl
new file mode 100644
index 00000000..17f4a746
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2016_templates.pkl
new file mode 100644
index 00000000..21ca73f7
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2017_templates.pkl
new file mode 100644
index 00000000..095e1df0
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2018_templates.pkl
new file mode 100644
index 00000000..8d444009
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
new file mode 100644
index 00000000..ba9a26ee
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
@@ -0,0 +1,16 @@
+,Pre-selection,QCD SF,LP SF,bbFatJetPt >= 300,VVFatJetPt >= 300,0.8 ≤ bbFatJetParticleNetMD_Txbb < 0.9883,0 ≤ nGoodElectronsHbb < 0.9,0 ≤ nGoodMuonsHbb < 0.9
+qqHH_CV_1_C2V_0_kl_1_HHbbVV,9.060969753010587,9.060969753010587,9.060969753010587,9.041934826728792,9.02728294983494,4.130999527744988,4.122292180871064,4.10058515870925
+QCD,717986.216814939,956785.8146709627,956785.8146709627,948406.4124347997,941617.267942029,869523.3038219259,867657.9790364413,864309.9524441543
+TT,63126.80979180276,63126.80979180276,63126.80979180276,62714.21430804499,62290.703474200425,57849.38865600453,51692.7750387919,45855.75882626402
+ST,5417.824558008516,5417.824558008516,5417.824558008516,5380.176293677084,5350.773696634727,4975.144333231859,4551.820250132849,4096.554038402913
+W+Jets,9487.889178263631,9487.889178263631,9487.889178263631,9424.292575831972,9375.118673797197,8761.032580281351,8739.245520711138,8712.0092431092
+Z+Jets,12055.822300436781,12055.822300436781,12055.822300436781,11968.016139487008,11904.514452147456,8780.64329542228,8757.428162246923,8705.421191878318
+Diboson,544.751536187187,544.751536187187,544.751536187187,541.5192000426408,539.5255957808083,411.0222919937146,392.2938498674006,372.65305188907155
+ggFHbb,120.74204174793252,120.74204174793252,120.74204174793252,120.02707196890958,119.27175261578469,64.79382636813872,64.74865084736501,64.46713244012037
+VBFHbb,32.24173303425971,32.24173303425971,32.24173303425971,32.089487243194796,31.921442997500897,16.484571753235592,16.437195753462237,16.323889119902795
+ZHbb,32.70020301080715,32.70020301080715,32.70020301080715,32.51666448179822,32.40029638484181,16.65360867894448,16.612144920406166,16.450482111492907
+WHbb,56.014320595270185,56.014320595270185,56.014320595270185,55.69337399997595,55.51504919284625,30.95485250830875,30.88751958901922,30.681005896115494
+ggZHbb,4.8805631912185055,4.8805631912185055,4.8805631912185055,4.852821845932874,4.827078996358681,2.9184107343100223,2.910521083705739,2.8845698425856767
+ttHbb,156.94119216557863,156.94119216557863,156.94119216557863,155.9939482895008,154.97672213467678,125.70638645514036,110.94553172707145,95.85122594771495
+HWW,80.56791059341468,80.56791059341468,80.56791059341468,80.1332628058381,79.59897023669294,73.51495857472764,60.99742349867558,50.51093923412037
+Data,1047903.0,1047903.0,1047903.0,1047903.0,1047903.0,978656.0,968276.0,956637.0
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
new file mode 100644
index 00000000..4f87dade
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
@@ -0,0 +1,16 @@
+,Pre-selection,QCD SF,LP SF,bbFatJetPt >= 300,VVFatJetPt >= 300,BDTScoreVBF >= 0.997,bbFatJetParticleNetMD_Txbb >= 0.9883,0 ≤ nGoodElectronsHbb < 0.9,0 ≤ nGoodMuonsHbb < 0.9
+qqHH_CV_1_C2V_0_kl_1_HHbbVV,9.060969753010587,9.060969753010587,9.060969753010587,9.041934826728792,9.02728294983494,2.5567167579490215,1.5110400902228016,1.508923360026765,1.5034163502571642
+QCD,717986.216814939,956785.8146709627,956785.8146709627,948406.4124347997,941617.267942029,38.04225382762026,3.299597004415339,3.299597004415339,3.299597004415339
+TT,63126.80979180276,63126.80979180276,63126.80979180276,62714.21430804499,62290.703474200425,7.673517493151092,0.188206223343644,0.188206223343644,0.188206223343644
+ST,5417.824558008516,5417.824558008516,5417.824558008516,5380.176293677084,5350.773696634727,0.5290174880805439,0.02891612414660004,0.02891612414660004,0.02891612414660004
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv
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index 00000000..3fe6e041
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..4022f237
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.997_lepton_veto_Hbb/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
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new file mode 100644
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index 00000000..fbda694f
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
new file mode 100644
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9997_lepton_veto_Hbb/lpsfs.csv
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index 00000000..78f54ba5
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
new file mode 100644
index 00000000..71606e57
--- /dev/null
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new file mode 100644
index 00000000..c988344a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..318ac885
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/lpsfs.csv
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new file mode 100644
index 00000000..6e50fee8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.9999_lepton_veto_Hbb/systematics.json
@@ -0,0 +1 @@
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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index 00000000..0222420a
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv
new file mode 100644
index 00000000..f6e9dffc
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/pass_cutflow.csv
new file mode 100644
index 00000000..1475edc8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/pass_cutflow.csv
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index 00000000..43a5f805
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
new file mode 100644
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/fail_cutflow.csv
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index 00000000..5edb1788
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new file mode 100644
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv
new file mode 100644
index 00000000..d6d4ed51
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..cf53b60f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_HP_bdt_0.99_lepton_veto_Hbb/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
new file mode 100644
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+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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new file mode 100644
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.997_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..4022f237
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
new file mode 100644
index 00000000..572d38ba
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv
new file mode 100644
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new file mode 100644
index 00000000..5485f222
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..fbda694f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.998_lepton_veto_Hbb/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9997_lepton_veto_Hbb/lpsfs.csv
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index 00000000..78f54ba5
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
new file mode 100644
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new file mode 100644
index 00000000..1554a5a6
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new file mode 100644
index 00000000..3de7a15b
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..318ac885
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.9999_lepton_veto_Hbb/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
new file mode 100644
index 00000000..8af19666
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv
new file mode 100644
index 00000000..2da67b78
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+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..43a5f805
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/systematics.json b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/systematics.json
new file mode 100644
index 00000000..397ae3a5
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.999_lepton_veto_Hbb/systematics.json
@@ -0,0 +1 @@
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
new file mode 100644
index 00000000..0b9fa1ab
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
new file mode 100644
index 00000000..9f0779c7
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2016APV/fail_cutflow.csv
new file mode 100644
index 00000000..2da67b78
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/fail_cutflow.csv
new file mode 100644
index 00000000..e96e50e1
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/fail_cutflow.csv
@@ -0,0 +1,16 @@
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/pass_cutflow.csv
new file mode 100644
index 00000000..b9903e5a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2017/pass_cutflow.csv
@@ -0,0 +1,16 @@
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/fail_cutflow.csv
new file mode 100644
index 00000000..1554a5a6
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/fail_cutflow.csv
@@ -0,0 +1,16 @@
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv
new file mode 100644
index 00000000..a0d25cef
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/cutflows/2018/pass_cutflow.csv
@@ -0,0 +1,16 @@
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/lpsfs.csv b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/lpsfs.csv
new file mode 100644
index 00000000..cf53b60f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/lpsfs.csv
@@ -0,0 +1,2 @@
+,SF,sj_matching_unc,sj_pt_unc,stat_unc,syst_unc
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diff --git a/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/systematics.json b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/systematics.json
new file mode 100644
index 00000000..6ca69974
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/24Apr8VBFBDTScanNodEta/txbb_MP_bdt_0.99_lepton_veto_Hbb/systematics.json
@@ -0,0 +1 @@
+{"2016APV": {"pass": {"trig_total": 140412.13735940278, "trig_total_err": 3706.8705677465405}, "fail": {"trig_total": 2123840.4120881082, "trig_total_err": 3412.828580253716}}, "qqHH_CV_1_C2V_0_kl_1_HHbbVV": {"lp_sf": 0.9554741068155351, "lp_sf_unc": 0.31541843017565563, "lp_sf_uncs": {"syst_unc": 0.13317300147803565, "stat_unc": 0.03584213038629429, "sj_pt_unc": 0.010309318508302266, "sj_matching_unc": 0.28348332828064565}}, "2016": {"pass": {"trig_total": 121162.58868913878, "trig_total_err": 3304.4677125149924}, "fail": {"trig_total": 1803363.1432624753, "trig_total_err": 3177.4210941042647}}, "2017": {"pass": {"trig_total": 283043.80999701266, "trig_total_err": 6043.755699362849}, "fail": {"trig_total": 1796975.0127041284, "trig_total_err": 1992.6443753794715}}, "2018": {"pass": {"trig_total": 236237.8540227354, "trig_total_err": 5130.059011956928}, "fail": {"trig_total": 1420149.0884094285, "trig_total_err": 1475.6992719148072}}}
diff --git a/src/HHbbVV/postprocessing/utils.py b/src/HHbbVV/postprocessing/utils.py
index f14a0431..ced2f99f 100644
--- a/src/HHbbVV/postprocessing/utils.py
+++ b/src/HHbbVV/postprocessing/utils.py
@@ -417,7 +417,7 @@ def blindBins(h: Hist, blind_region: list, blind_sample: str = None, axis=0):
 def singleVarHist(
     events_dict: dict[str, pd.DataFrame],
     shape_var: ShapeVar,
-    bb_masks: dict[str, pd.DataFrame],
+    bb_masks: dict[str, pd.DataFrame] = None,
     weight_key: str = "finalWeight",
     selection: dict = None,
 ) -> Hist:
@@ -446,12 +446,13 @@ def singleVarHist(
 
     for sample in samples:
         events = events_dict[sample]
+        bb_mask = None if bb_masks is None else bb_masks[sample]
         if sample == data_key and (var.endswith(("_up", "_down"))):
             fill_var = "_".join(var.split("_")[:-2])
         else:
             fill_var = var
 
-        fill_data = {var: get_feat(events, fill_var, bb_masks[sample])}
+        fill_data = {var: get_feat(events, fill_var, bb_mask)}
         weight = events[weight_key].to_numpy().squeeze()
 
         if selection is not None:
@@ -569,7 +570,7 @@ def _var_selection(
 def make_selection(
     var_cuts: dict[str, list[float]],
     events_dict: dict[str, pd.DataFrame],
-    bb_masks: dict[str, pd.DataFrame],
+    bb_masks: dict[str, pd.DataFrame] = None,
     weight_key: str = "finalWeight",
     prev_cutflow: dict = None,
     selection: dict[str, np.ndarray] = None,
@@ -611,7 +612,7 @@ def make_selection(
     cutflow = {}
 
     for sample, events in events_dict.items():
-        bb_mask = bb_masks[sample]
+        bb_mask = None if bb_masks is None else bb_masks[sample]
         if sample not in cutflow:
             cutflow[sample] = {}