diff --git a/README.md b/README.md
index 0b166454..6adb8bda 100644
--- a/README.md
+++ b/README.md
@@ -29,12 +29,14 @@ Search for two boosted (high transverse momentum) Higgs bosons (H) decaying to t
     - [BDT Trainings](#bdt-trainings)
     - [Post-Processing](#post-processing-1)
       - [Control plots with resonant and nonresonant samples](#control-plots-with-resonant-and-nonresonant-samples)
+      - [BDT sculpting plots](#bdt-sculpting-plots)
       - [Making separate background and signal templates for scan and bias tests (resonant)](#making-separate-background-and-signal-templates-for-scan-and-bias-tests-resonant)
     - [Create Datacard](#create-datacard)
     - [PlotFits](#plotfits)
   - [Combine](#combine)
     - [CMSSW + Combine Quickstart](#cmssw--combine-quickstart)
     - [Run fits and diagnostics locally](#run-fits-and-diagnostics-locally)
+      - [F-tests locally for non-resonant](#f-tests-locally-for-non-resonant)
     - [Run fits on condor](#run-fits-on-condor)
       - [Making datacards](#making-datacards)
       - [F-tests](#f-tests)
@@ -276,6 +278,9 @@ for year in 2016 2016APV 2017 2018; do python -u postprocessing.py --templates -
 
 Run `postprocessing/bash_scripts/ControlPlots.sh` from inside `postprocessing folder`.
 
+#### BDT sculpting plots
+
+Run `postprocessing/bash_scripts/BDTPlots.sh` from inside `postprocessing folder`.
 
 #### Making separate background and signal templates for scan and bias tests (resonant)
 
@@ -367,6 +372,20 @@ All via the below script, with a bunch of options (see script):
 run_blinded.sh --workspace --bfit --limits
 ```
 
+#### F-tests locally for non-resonant
+
+This will take 5-10 minutes.
+
+```bash
+# automatically make workspaces and do the background-only fit for orders 0 - 3
+run_ftest_nonres.sh --cardstag 23May14 --templatestag $templatestag  # -dl for saving shapes and limits
+# run f-test for desired order
+run_ftest_nonres.sh --cardstag 23May14 --goftoys --ffits --numtoys 100 --seed 444 --order 0
+```
+
+Condor is needed for resonant, see [below](#f-tests).
+
+
 ### Run fits on condor
 
 #### Making datacards
diff --git a/src/HHbbVV/postprocessing/PostProcess.ipynb b/src/HHbbVV/postprocessing/PostProcess.ipynb
index 7fd30fbf..323afecd 100644
--- a/src/HHbbVV/postprocessing/PostProcess.ipynb
+++ b/src/HHbbVV/postprocessing/PostProcess.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
     "import corrections\n",
     "from collections import OrderedDict\n",
     "\n",
-    "from utils import CUT_MAX_VAL\n",
+    "from utils import CUT_MAX_VAL, ShapeVar\n",
     "from hh_vars import (\n",
     "    years,\n",
     "    data_key,\n",
@@ -24,6 +24,7 @@
     "    txbb_wps,\n",
     "    jec_shifts,\n",
     "    jmsr_shifts,\n",
+    "    LUMI,\n",
     ")\n",
     "from postprocessing import nonres_shape_vars\n",
     "\n",
@@ -54,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +78,7 @@
     "# bdt_data_dir = \"/eos/uscms/store/user/cmantill/bbVV/skimmer/Jun10/bdt_data/\"\n",
     "year = \"2018\"\n",
     "\n",
-    "date = \"23Oct12\"\n",
+    "date = \"23Nov7\"\n",
     "plot_dir = f\"../../../plots/PostProcessing/{date}/\"\n",
     "templates_dir = f\"templates/{date}\"\n",
     "_ = os.system(f\"mkdir -p {plot_dir}\")\n",
@@ -93,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,9 +122,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded GluGluToHHTobbVV_node_cHHH1                       : 190955 entries\n",
+      "Loaded GluGluToHHTobbVV_node_cHHH2p45                    : 309045 entries\n",
+      "Loaded GluGluToHHTobbVV_node_cHHH5                       : 45022 entries\n",
+      "Loaded GluGluToHHTobbVV_node_cHHH0                       : 112766 entries\n",
+      "Loaded VBF_HHTobbVV_CV_1_C2V_0_C3_1                      : 529409 entries\n",
+      "Loaded VBF_HHTobbVV_CV_1_5_C2V_1_C3_1                    : 366684 entries\n",
+      "Loaded VBF_HHTobbVV_CV_1_C2V_1_C3_2                      : 20239 entries\n",
+      "Loaded VBF_HHTobbVV_CV_1_C2V_2_C3_1                      : 584319 entries\n",
+      "Loaded VBF_HHTobbVV_CV_1_C2V_1_C3_0                      : 18059 entries\n",
+      "Loaded VBF_HHTobbVV_CV_0_5_C2V_1_C3_1                    : 705871 entries\n",
+      "Loaded QCD_HT300to500                                    : 23 entries\n",
+      "Loaded QCD_HT200to300                                    : 0 entries\n",
+      "Loaded QCD_HT700to1000                                   : 200723 entries\n",
+      "Loaded QCD_HT1000to1500                                  : 124617 entries\n",
+      "Loaded QCD_HT2000toInf                                   : 73623 entries\n",
+      "Loaded QCD_HT1500to2000                                  : 135410 entries\n",
+      "Loaded QCD_HT500to700                                    : 18800 entries\n",
+      "Loaded TTToSemiLeptonic                                  : 565356 entries\n",
+      "Loaded TTToHadronic                                      : 759796 entries\n",
+      "Loaded ST_tW_top_5f_inclusiveDecays                      : 26328 entries\n",
+      "Loaded ST_tW_antitop_5f_inclusiveDecays                  : 19365 entries\n",
+      "Loaded ST_s-channel_4f_leptonDecays                      : 20130 entries\n",
+      "Loaded ST_t-channel_antitop_4f_InclusiveDecays           : 43336 entries\n",
+      "Loaded WJetsToQQ_HT-200to400                             : 0 entries\n",
+      "Loaded ZJetsToQQ_HT-200to400                             : 0 entries\n",
+      "Loaded ZJetsToQQ_HT-400to600                             : 706 entries\n",
+      "Loaded WJetsToQQ_HT-800toInf                             : 135354 entries\n",
+      "Loaded ZJetsToQQ_HT-600to800                             : 76587 entries\n",
+      "Loaded WJetsToQQ_HT-600to800                             : 26048 entries\n",
+      "Loaded ZJetsToQQ_HT-800toInf                             : 299452 entries\n",
+      "Loaded WJetsToQQ_HT-400to600                             : 219 entries\n",
+      "Loaded WW                                                : 596 entries\n",
+      "Loaded ZZ                                                : 966 entries\n",
+      "Loaded WZ                                                : 2289 entries\n",
+      "Loaded JetHT_Run2018A                                    : 376541 entries\n",
+      "Loaded JetHT_Run2018D                                    : 847211 entries\n",
+      "Loaded JetHT_Run2018C                                    : 174689 entries\n",
+      "Loaded JetHT_Run2018B                                    : 175146 entries\n",
+      "\n",
+      "Pre-selection HHbbVV yield: 4.21\n",
+      "Pre-selection ggHH_kl_2p45_kt_1_HHbbVV yield: 2.97\n",
+      "Pre-selection ggHH_kl_5_kt_1_HHbbVV yield: 3.02\n",
+      "Pre-selection ggHH_kl_0_kt_1_HHbbVV yield: 5.53\n",
+      "Pre-selection qqHH_CV_1_C2V_0_kl_1_HHbbVV yield: 37.59\n",
+      "Pre-selection qqHH_CV_1p5_C2V_1_kl_1_HHbbVV yield: 63.62\n",
+      "Pre-selection qqHH_CV_1_C2V_1_kl_2_HHbbVV yield: 0.08\n",
+      "Pre-selection qqHH_CV_1_C2V_2_kl_1_HHbbVV yield: 33.60\n",
+      "Pre-selection qqHH_CV_1_C2V_1_kl_0_HHbbVV yield: 0.22\n",
+      "Pre-selection qqHH_CV_0p5_C2V_1_kl_1_HHbbVV yield: 20.11\n",
+      "Pre-selection QCD yield: 3052764.19\n",
+      "Pre-selection TT yield: 217539.42\n",
+      "Pre-selection ST yield: 15175.48\n",
+      "Pre-selection V+Jets yield: 85869.75\n",
+      "Pre-selection Diboson yield: 1365.25\n",
+      "Pre-selection Data yield: 1573587.00\n"
+     ]
+    }
+   ],
    "source": [
     "filters = postprocessing.new_filters\n",
     "systematics = {year: {}}\n",
@@ -140,12 +203,7 @@
     "events_dict |= utils.load_samples(samples_dir, samples, year, filters, hem_cleaning=False)\n",
     "\n",
     "utils.add_to_cutflow(events_dict, \"BDTPreselection\", \"weight\", cutflow)\n",
-    "\n",
-    "print(\"\")\n",
-    "# print weighted sample yields\n",
-    "for sample in events_dict:\n",
-    "    tot_weight = np.sum(events_dict[sample][\"weight\"].values)\n",
-    "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
+    "cutflow"
    ]
   },
   {
@@ -158,9 +216,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "QCD_SCALE_FACTOR = 1.0727354927990267\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BDTPreselection</th>\n",
+       "      <th>TriggerEffs</th>\n",
+       "      <th>QCD SF</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>QCD</th>\n",
+       "      <td>3.052764e+06</td>\n",
+       "      <td>1.301833e+06</td>\n",
+       "      <td>1.396522e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TT</th>\n",
+       "      <td>2.175394e+05</td>\n",
+       "      <td>1.223041e+05</td>\n",
+       "      <td>1.223041e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ST</th>\n",
+       "      <td>1.517548e+04</td>\n",
+       "      <td>9.009707e+03</td>\n",
+       "      <td>9.009707e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>V+Jets</th>\n",
+       "      <td>8.586975e+04</td>\n",
+       "      <td>4.500155e+04</td>\n",
+       "      <td>4.500155e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diboson</th>\n",
+       "      <td>1.365254e+03</td>\n",
+       "      <td>7.493028e+02</td>\n",
+       "      <td>7.493028e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Data</th>\n",
+       "      <td>1.573587e+06</td>\n",
+       "      <td>1.573587e+06</td>\n",
+       "      <td>1.573587e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HHbbVV</th>\n",
+       "      <td>4.212153e+00</td>\n",
+       "      <td>1.992043e+00</td>\n",
+       "      <td>1.992043e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ggHH_kl_2p45_kt_1_HHbbVV</th>\n",
+       "      <td>2.974578e+00</td>\n",
+       "      <td>1.543273e+00</td>\n",
+       "      <td>1.543273e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ggHH_kl_5_kt_1_HHbbVV</th>\n",
+       "      <td>3.024268e+00</td>\n",
+       "      <td>1.699298e+00</td>\n",
+       "      <td>1.699298e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ggHH_kl_0_kt_1_HHbbVV</th>\n",
+       "      <td>5.525754e+00</td>\n",
+       "      <td>2.500809e+00</td>\n",
+       "      <td>2.500809e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>qqHH_CV_1_C2V_0_kl_1_HHbbVV</th>\n",
+       "      <td>3.759032e+01</td>\n",
+       "      <td>3.068463e+01</td>\n",
+       "      <td>3.068463e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>qqHH_CV_1p5_C2V_1_kl_1_HHbbVV</th>\n",
+       "      <td>6.361566e+01</td>\n",
+       "      <td>5.109147e+01</td>\n",
+       "      <td>5.109147e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>qqHH_CV_1_C2V_1_kl_2_HHbbVV</th>\n",
+       "      <td>7.551329e-02</td>\n",
+       "      <td>4.912744e-02</td>\n",
+       "      <td>4.912744e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>qqHH_CV_1_C2V_2_kl_1_HHbbVV</th>\n",
+       "      <td>3.359757e+01</td>\n",
+       "      <td>2.805713e+01</td>\n",
+       "      <td>2.805713e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>qqHH_CV_1_C2V_1_kl_0_HHbbVV</th>\n",
+       "      <td>2.189970e-01</td>\n",
+       "      <td>1.190405e-01</td>\n",
+       "      <td>1.190405e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>qqHH_CV_0p5_C2V_1_kl_1_HHbbVV</th>\n",
+       "      <td>2.010983e+01</td>\n",
+       "      <td>1.660969e+01</td>\n",
+       "      <td>1.660969e+01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               BDTPreselection   TriggerEffs        QCD SF\n",
+       "QCD                               3.052764e+06  1.301833e+06  1.396522e+06\n",
+       "TT                                2.175394e+05  1.223041e+05  1.223041e+05\n",
+       "ST                                1.517548e+04  9.009707e+03  9.009707e+03\n",
+       "V+Jets                            8.586975e+04  4.500155e+04  4.500155e+04\n",
+       "Diboson                           1.365254e+03  7.493028e+02  7.493028e+02\n",
+       "Data                              1.573587e+06  1.573587e+06  1.573587e+06\n",
+       "HHbbVV                            4.212153e+00  1.992043e+00  1.992043e+00\n",
+       "ggHH_kl_2p45_kt_1_HHbbVV          2.974578e+00  1.543273e+00  1.543273e+00\n",
+       "ggHH_kl_5_kt_1_HHbbVV             3.024268e+00  1.699298e+00  1.699298e+00\n",
+       "ggHH_kl_0_kt_1_HHbbVV             5.525754e+00  2.500809e+00  2.500809e+00\n",
+       "qqHH_CV_1_C2V_0_kl_1_HHbbVV       3.759032e+01  3.068463e+01  3.068463e+01\n",
+       "qqHH_CV_1p5_C2V_1_kl_1_HHbbVV     6.361566e+01  5.109147e+01  5.109147e+01\n",
+       "qqHH_CV_1_C2V_1_kl_2_HHbbVV       7.551329e-02  4.912744e-02  4.912744e-02\n",
+       "qqHH_CV_1_C2V_2_kl_1_HHbbVV       3.359757e+01  2.805713e+01  2.805713e+01\n",
+       "qqHH_CV_1_C2V_1_kl_0_HHbbVV       2.189970e-01  1.190405e-01  1.190405e-01\n",
+       "qqHH_CV_0p5_C2V_1_kl_1_HHbbVV     2.010983e+01  1.660969e+01  1.660969e+01"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "postprocessing.apply_weights(events_dict, year, cutflow)\n",
     "bb_masks = postprocessing.bb_VV_assignment(events_dict)\n",
@@ -170,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -281,6 +499,69 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check BDT Sculpting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cuts = [0, 0.1, 0.5, 0.9, 0.95]\n",
+    "# bdtvars = [\"\", \"TT\", \"VJets\"]\n",
+    "bdtvars = [\"\"]\n",
+    "plot_keys = [\"TT\"]\n",
+    "\n",
+    "shape_var = ShapeVar(\n",
+    "    var=\"bbFatJetParticleNetMass\", label=r\"$m^{bb}_{reg}$ (GeV)\", bins=[20, 50, 250]\n",
+    ")\n",
+    "\n",
+    "for var in bdtvars:\n",
+    "    for key in plot_keys:\n",
+    "        ed_key = {key: events_dict[key]}\n",
+    "        bbm_key = {key: bb_masks[key]}\n",
+    "\n",
+    "        fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
+    "        plt.rcParams.update({\"font.size\": 24})\n",
+    "\n",
+    "        for i, cut in enumerate(cuts):\n",
+    "            sel, _ = utils.make_selection({f\"BDTScore{var}\": [cut, CUT_MAX_VAL]}, ed_key, bbm_key)\n",
+    "            h = utils.singleVarHist(ed_key, shape_var, bbm_key, selection=sel)\n",
+    "\n",
+    "            hep.histplot(\n",
+    "                h[key, ...] / np.sum(h[key, ...].values()),\n",
+    "                yerr=True,\n",
+    "                label=f\"BDTScore >= {cut}\",\n",
+    "                # density=True,\n",
+    "                ax=ax,\n",
+    "                linewidth=2,\n",
+    "                alpha=0.8,\n",
+    "            )\n",
+    "\n",
+    "        ax.set_xlabel(shape_var.label)\n",
+    "        ax.set_ylabel(\"Fraction of Events\")\n",
+    "        ax.legend()\n",
+    "\n",
+    "        hep.cms.label(ax=ax, data=False, year=year, lumi=round(LUMI[year] / 1e3))\n",
+    "        plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -494,13 +775,6 @@
     "    with open(f\"templates/Feb28/{year}_templates.pkl\", \"rb\") as f:\n",
     "        templates_dict[year] = pickle.load(f)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh b/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh
new file mode 100755
index 00000000..25cc2e3b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/bash_scripts/BDTPlots.sh
@@ -0,0 +1,7 @@
+MAIN_DIR="../../.."
+TAG=23Nov7BDTSculpting
+
+for year in 2016APV 2016 2017 2018
+do
+    python postprocessing.py --year $year --data-dir "$MAIN_DIR/../data/skimmer/Feb24/" --signal-data-dir "$MAIN_DIR/../data/skimmer/Jun10/" --bdt-preds-dir "$MAIN_DIR/../data/skimmer/Feb24/23_05_12_multiclass_rem_feats_3/inferences" --no-lp-sf-all-years --sig-samples GluGluToHHTobbVV_node_cHHH1 --bg-keys QCD --bdt-plots --plot-dir "$MAIN_DIR/plots/PostProcessing/$TAG"
+done
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/plotting.py b/src/HHbbVV/postprocessing/plotting.py
index 39226e3c..ab63d887 100644
--- a/src/HHbbVV/postprocessing/plotting.py
+++ b/src/HHbbVV/postprocessing/plotting.py
@@ -6,6 +6,7 @@
 
 from collections import OrderedDict
 import numpy as np
+from pandas import DataFrame
 
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -16,7 +17,11 @@
 hep.style.use("CMS")
 formatter = mticker.ScalarFormatter(useMathText=True)
 formatter.set_powerlimits((-3, 3))
-plt.rcParams.update({"font.size": 20})
+
+# this is needed for some reason to update the font size for the first plot
+fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+plt.rcParams.update({"font.size": 24})
+plt.close()
 
 import hist
 from hist import Hist
@@ -27,6 +32,7 @@
 
 from hh_vars import LUMI, data_key, hbb_bg_keys
 import utils
+from utils import CUT_MAX_VAL
 
 from copy import deepcopy
 
@@ -855,3 +861,51 @@ def ratioTestTrain(
         plt.show()
     else:
         plt.close()
+
+
+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,
+    plotdir: str = "",
+    name: str = "",
+    show: bool = False,
+):
+    """Plot line plots of ``shape_var`` for different cuts on ``cut_var``."""
+    fig, ax = plt.subplots(1, 1, figsize=(12, 12))
+    plt.rcParams.update({"font.size": 24})
+
+    for i, cut in enumerate(cuts):
+        sel, _ = utils.make_selection({cut_var: [cut, CUT_MAX_VAL]}, events_dict, bb_masks)
+        h = utils.singleVarHist(
+            events_dict, shape_var, bb_masks, weight_key=weight_key, selection=sel
+        )
+
+        hep.histplot(
+            h[plot_key, ...] / np.sum(h[plot_key, ...].values()),
+            yerr=True,
+            label=f"BDTScore >= {cut}",
+            # density=True,
+            ax=ax,
+            linewidth=2,
+            alpha=0.8,
+        )
+
+    ax.set_xlabel(shape_var.label)
+    ax.set_ylabel("Fraction of Events")
+    ax.legend()
+
+    hep.cms.label(ax=ax, data=False, year=year, lumi=round(LUMI[year] / 1e3))
+
+    if len(name):
+        plt.savefig(f"{plotdir}/{name}.pdf", bbox_inches="tight")
+
+    if show:
+        plt.show()
+    else:
+        plt.close()
diff --git a/src/HHbbVV/postprocessing/postprocessing.py b/src/HHbbVV/postprocessing/postprocessing.py
index 6917c676..5423ee22 100644
--- a/src/HHbbVV/postprocessing/postprocessing.py
+++ b/src/HHbbVV/postprocessing/postprocessing.py
@@ -14,6 +14,7 @@
 from collections import OrderedDict
 
 import os
+from pathlib import Path
 import sys
 import pickle, json
 import itertools
@@ -376,8 +377,9 @@ def main(args):
     # THWW score vs Top (if not already from processor)
     derive_variables(events_dict)
 
-    if args.plot_dir != "":
-        cutflow.to_csv(f"{args.plot_dir}/preselection_cutflow.csv")
+    # check if args has attribute
+    if hasattr(args, "control_plots_dir"):
+        cutflow.to_csv(f"{args.control_plots_dir}/preselection_cutflow.csv")
 
     print("\nCutflow", cutflow)
 
@@ -401,7 +403,7 @@ def main(args):
             bb_masks,
             sig_keys,
             control_plot_vars,
-            f"{args.plot_dir}/ControlPlots/",
+            args.control_plots_dir,
             args.year,
             bg_keys=args.bg_keys,
             sig_scale_dict={"HHbbVV": 1e5, "VBFHHbbVV": 2e6} | {key: 2e4 for key in res_sig_keys},
@@ -410,6 +412,13 @@ def main(args):
             show=False,
         )
 
+    if args.bdt_plots and not args.resonant and not args.vbf:
+        print("\nMaking BDT sculpting plots\n")
+
+        plot_bdt_sculpting(
+            events_dict, bb_masks, args.bdt_sculpting_plots_dir, args.year, show=False
+        )
+
     if args.templates:
         for wps in scan_wps:  # if not scanning, this will just be a single WP
             cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)]) if scan else ""
@@ -453,7 +462,7 @@ def main(args):
             for jshift in jshifts:
                 print(jshift)
                 plot_dir = (
-                    f"{args.plot_dir}/templates/{cutstr}/" f"{'jshifts/' if jshift != '' else ''}"
+                    f"{args.templates_plots_dir}/{cutstr}/" f"{'jshifts/' if jshift != '' else ''}"
                     if args.plot_dir != ""
                     else ""
                 )
@@ -489,8 +498,8 @@ def main(args):
 
 
 def _init(args):
-    if not (args.control_plots or args.templates or args.scan):
-        print("You need to pass at least one of --control-plots, --templates, or --scan")
+    if not (args.control_plots or args.bdt_plots or args.templates):
+        print("You need to pass at least one of --control-plots, --bdt-plots, or --templates")
         return
 
     if args.resonant:
@@ -600,36 +609,54 @@ def _process_samples(args, BDT_sample_order: List[str] = None):
 
 def _make_dirs(args, scan, scan_cuts, scan_wps):
     if args.plot_dir != "":
-        args.plot_dir = f"{args.plot_dir}/{args.year}/"
-        os.system(f"mkdir -p {args.plot_dir}")
+        args.plot_dir = Path(args.plot_dir)
+        args.plot_dir.mkdir(parents=True, exist_ok=True)
 
         if args.control_plots:
-            os.system(f"mkdir -p {args.plot_dir}/ControlPlots/")
-
-        os.system(f"mkdir -p {args.plot_dir}/templates/")
-
-        if scan:
-            for wps in scan_wps:
-                cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)])
-                os.system(f"mkdir -p {args.plot_dir}/templates/{cutstr}/")
-                os.system(f"mkdir -p {args.plot_dir}/templates/{cutstr}/wshifts")
-                os.system(f"mkdir -p {args.plot_dir}/templates/{cutstr}/jshifts")
-        else:
-            os.system(f"mkdir -p {args.plot_dir}/templates/wshifts")
-            os.system(f"mkdir -p {args.plot_dir}/templates/jshifts")
-
-            if args.resonant:
-                os.system(f"mkdir -p {args.plot_dir}/templates/hists2d")
+            args.control_plots_dir = args.plot_dir / "ControlPlots" / args.year
+            args.control_plots_dir.mkdir(parents=True, exist_ok=True)
+
+        if args.bdt_plots and not args.resonant and not args.vbf:
+            args.bdt_sculpting_plots_dir = args.plot_dir / "BDTSculpting"
+            args.bdt_sculpting_plots_dir.mkdir(parents=True, exist_ok=True)
+
+        if args.templates:
+            args.templates_plots_dir = args.plot_dir / "Templates" / args.year
+            args.templates_plots_dir.mkdir(parents=True, exist_ok=True)
+
+            if scan:
+                for wps in scan_wps:
+                    cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)])
+                    (args.templates_plots_dir / f"{cutstr}/wshifts").mkdir(
+                        parents=True, exist_ok=True
+                    )
+                    (args.templates_plots_dir / f"{cutstr}/jshifts").mkdir(
+                        parents=True, exist_ok=True
+                    )
+            else:
+                (args.templates_plots_dir / "wshifts").mkdir(parents=True, exist_ok=True)
+                (args.templates_plots_dir / "jshifts").mkdir(parents=True, exist_ok=True)
+                if args.resonant:
+                    (args.templates_plots_dir / "hists2d").mkdir(parents=True, exist_ok=True)
+
+    elif args.control_plots or args.bdt_plots:
+        print(
+            "You need to pass --plot-dir if you want to make control plots or BDT plots. Exiting."
+        )
+        sys.exit()
 
     if args.template_dir != "":
+        args.template_dir = Path(args.template_dir)
         if scan:
             for wps in scan_wps:
                 cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)])
-                os.system(
-                    f"mkdir -p {args.template_dir}/{cutstr}/{args.templates_name}/cutflows/{args.year}/"
+                (args.template_dir / f"{cutstr}/{args.templates_name}/cutflows/{args.year}").mkdir(
+                    parents=True, exist_ok=True
                 )
         else:
-            os.system(f"mkdir -p {args.template_dir}/{args.templates_name}/cutflows/{args.year}/")
+            (args.template_dir / f"{args.templates_name}/cutflows/{args.year}").mkdir(
+                parents=True, exist_ok=True
+            )
 
 
 def _check_load_systematics(systs_file: str, year: str):
@@ -652,12 +679,19 @@ def _load_samples(args, samples, sig_samples, cutflow, filters=None):
 
     events_dict = {}
     for d in args.signal_data_dirs:
-        events_dict = {**events_dict, **utils.load_samples(d, sig_samples, args.year, filters)}
+        events_dict = {
+            **events_dict,
+            **utils.load_samples(
+                d, sig_samples, args.year, filters, hem_cleaning=args.hem_cleaning
+            ),
+        }
 
     if args.data_dir:
         events_dict = {
             **events_dict,
-            **utils.load_samples(args.data_dir, samples, args.year, filters),
+            **utils.load_samples(
+                args.data_dir, samples, args.year, filters, hem_cleaning=args.hem_cleaning
+            ),
         }
 
     print("Samples: ", list(events_dict.keys()))
@@ -1167,6 +1201,44 @@ def control_plots(
     return hists
 
 
+def plot_bdt_sculpting(
+    events_dict: Dict[str, pd.DataFrame],
+    bb_masks: Dict[str, pd.DataFrame],
+    plot_dir: str,
+    year: str,
+    weight_key: str = "finalWeight",
+    show: bool = False,
+):
+    """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", "HHbbVV"]
+
+    shape_var = ShapeVar(
+        var="bbFatJetParticleNetMass", label=r"$m^{bb}_{reg}$ (GeV)", bins=[20, 50, 250]
+    )
+
+    for var in bdtvars:
+        for key in plot_keys:
+            ed_key = {key: events_dict[key]}
+            bbm_key = {key: bb_masks[key]}
+
+            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}",
+                show=show,
+            )
+
+
 def check_weights(events_dict):
     # Check for 0 weights - would be an issue for weight shifts
     print(
@@ -1525,6 +1597,7 @@ def save_templates(
     utils.add_bool_arg(parser, "resonant", "for resonant or nonresonant", default=False)
     utils.add_bool_arg(parser, "vbf", "non-resonant VBF or inclusive", default=False)
     utils.add_bool_arg(parser, "control-plots", "make control plots", default=False)
+    utils.add_bool_arg(parser, "bdt-plots", "make bdt sculpting plots", default=False)
     utils.add_bool_arg(parser, "templates", "save m_bb templates using bdt cut", default=False)
     utils.add_bool_arg(
         parser, "overwrite-template", "if template file already exists, overwrite it", default=False
@@ -1554,6 +1627,7 @@ def save_templates(
     )
 
     utils.add_bool_arg(parser, "data", "include data", default=True)
+    utils.add_bool_arg(parser, "hem-cleaning", "perform hem cleaning for 2018", default=None)
     utils.add_bool_arg(
         parser, "HEM2d", "fatjet phi v eta plots to check HEM cleaning", default=False
     )
@@ -1641,4 +1715,8 @@ def save_templates(
     elif args.resonant:
         args.bdt_preds_dir = None
 
+    if args.hem_cleaning is None:
+        # can't do HEM cleaning for non-resonant until BDT is re-inferenced
+        args.hem_cleaning = True if (args.resonant or args.vbf) else False
+
     main(args)