diff --git a/src/HHbbVV/postprocessing/PostProcess.ipynb b/src/HHbbVV/postprocessing/PostProcess.ipynb
index b8cda9bb..8c299bc5 100644
--- a/src/HHbbVV/postprocessing/PostProcess.ipynb
+++ b/src/HHbbVV/postprocessing/PostProcess.ipynb
@@ -84,7 +84,7 @@
     "year = \"2018\"\n",
     "bdt_preds_dir = samples_dir / \"24_04_05_k2v0_training_eqsig_vbf_vars_rm_deta/inferences\"\n",
     "\n",
-    "date = \"24May31\"\n",
+    "date = \"24Jun1\"\n",
     "plot_dir = MAIN_DIR / f\"plots/PostProcessing/{date}LPSFs\"\n",
     "templates_dir = f\"templates/{date}\"\n",
     "_ = os.system(f\"mkdir -p {plot_dir}\")\n",
@@ -135,7 +135,13 @@
     "cutflow = pd.DataFrame(index=list(samples.keys()) + list(nonres_samples.keys()))\n",
     "\n",
     "events_dict = postprocessing.load_samples(\n",
-    "    samples_dir, {**nonres_samples, **samples}, year, filters, hem_cleaning=False, variations=False\n",
+    "    # samples_dir, {**nonres_samples, **samples}, year, filters, hem_cleaning=False, variations=False\n",
+    "    samples_dir,\n",
+    "    {**nonres_samples},\n",
+    "    year,\n",
+    "    filters,\n",
+    "    hem_cleaning=False,\n",
+    "    variations=False,\n",
     ")\n",
     "# events_dict |= postprocessing.load_samples(samples_dir, {\"Data\": \"JetHT\"}, year, filters, hem_cleaning=False)\n",
     "\n",
@@ -447,7 +453,13 @@
     "    #     var=\"bbFatJetPt\", label=r\"$p^{bb}_T$ (GeV)\", bins=[20, 300, 2300], significance_dir=\"right\"\n",
     "    # ),\n",
     "    # ShapeVar(var=\"bbFatJetParticleNetMass\", label=r\"$m^{bb}_{reg}$ (GeV)\", bins=[20, 50, 250]),\n",
-    "    ShapeVar(var=\"BDTScore\", label=r\"BDT Score\", bins=[20, 0, 1]),\n",
+    "    ShapeVar(var=\"BDTScore\", label=r\"$BDT_{ggF}$\", bins=[20, 0, 1]),\n",
+    "    ShapeVar(var=\"BDTScore\", label=r\"$BDT_{ggF}$\", bins=[20, 0.9, 1]),\n",
+    "    ShapeVar(var=\"BDTScore\", label=r\"$BDT_{ggF}$\", bins=[20, 0.99, 1]),\n",
+    "    ShapeVar(var=\"BDTScoreVBF\", label=r\"$BDT_{VBF}$\", bins=[20, 0, 1]),\n",
+    "    ShapeVar(var=\"BDTScoreVBF\", label=r\"$BDT_{VBF}$\", bins=[20, 0.9, 1]),\n",
+    "    ShapeVar(var=\"BDTScoreVBF\", label=r\"$BDT_{VBF}$\", bins=[20, 0.99, 1]),\n",
+    "    # ShapeVar(var=\"BDTScore\", label=r\"BDT Score\", bins=[20, 0, 1]),\n",
     "]\n",
     "\n",
     "\n",
@@ -456,8 +468,9 @@
     "    bb_mask = bb_masks[sig_key]\n",
     "    weight = events[\"finalWeight\"].values.squeeze()\n",
     "    weight_lp = weight * events[\"VV_lp_sf_nom\"].values.squeeze()\n",
-    "    weight_lp_sys_up = weight * events[\"VV_lp_sf_sys_up\"].values.squeeze()\n",
-    "    weight_lp_sys_down = weight * events[\"VV_lp_sf_sys_down\"].values.squeeze()\n",
+    "    weight_lp *= weight.sum() / weight_lp.sum()\n",
+    "    # weight_lp_sys_up = weight * events[\"VV_lp_sf_sys_up\"].values.squeeze()\n",
+    "    # weight_lp_sys_down = weight * events[\"VV_lp_sf_sys_down\"].values.squeeze()\n",
     "\n",
     "    for shape_var in control_plot_vars:\n",
     "        h = Hist(\n",
@@ -482,7 +495,8 @@
     "            }\n",
     "        )\n",
     "\n",
-    "        for norm in [True, False]:\n",
+    "        # for norm in [True, False]:\n",
+    "        for norm in [False]:\n",
     "            fig, ax = plt.subplots(figsize=(10, 10))\n",
     "\n",
     "            for l in [\"Pre-LP\", \"Post-LP\"]:\n",
@@ -504,7 +518,8 @@
     "\n",
     "            norm_str = \"_norm\" if norm else \"\"\n",
     "            plt.savefig(\n",
-    "                plot_dir / f\"{year}_{shape_var.var}_{sig_key}_lpsf{norm_str}.pdf\",\n",
+    "                plot_dir\n",
+    "                / f\"{year}_{shape_var.var}_{sig_key}_lpsf{norm_str}_{shape_var.bins[1]}.pdf\",\n",
     "                bbox_inches=\"tight\",\n",
     "            )\n",
     "            plt.show()"