diff --git a/src/HHbbVV/hh_vars.py b/src/HHbbVV/hh_vars.py
index ae1cb108..dc1414eb 100644
--- a/src/HHbbVV/hh_vars.py
+++ b/src/HHbbVV/hh_vars.py
@@ -259,7 +259,10 @@
     ("lp_sf_pt_extrap_vars", 100),
     ("lp_sf_sys_down", 1),
     ("lp_sf_sys_up", 1),
+    ("lp_sf_np_down", 1),
+    ("lp_sf_np_up", 1),
     ("lp_sf_double_matched_event", 1),
-    ("lp_sf_boundary_quarks", 1),
+    ("lp_sf_inside_boundary_quarks", 1),
+    ("lp_sf_outside_boundary_quarks", 1),
     ("lp_sf_unmatched_quarks", 1),
 ]
diff --git a/src/HHbbVV/processors/bbVVSkimmer.py b/src/HHbbVV/processors/bbVVSkimmer.py
index f4916c83..7df17501 100644
--- a/src/HHbbVV/processors/bbVVSkimmer.py
+++ b/src/HHbbVV/processors/bbVVSkimmer.py
@@ -120,6 +120,7 @@ class bbVVSkimmer(SkimmerABC):
     jecs = hh_vars.jecs
 
     # only the branches necessary for templates and post processing
+    # IMPORTANT: Add Lund plane branches in hh_vars.py
     min_branches = [  # noqa: RUF012
         "ak8FatJetPhi",
         "ak8FatJetEta",
@@ -673,15 +674,16 @@ def process(self, events: ak.Array):
         # Lund plane SFs
         ################
 
+        lp_hist = None
+
         if isSignal and self._systematics and self._lp_sfs:
-            # (var, # columns)
             logging.info("Starting LP SFs and saving: " + str(hh_vars.lp_sf_vars))
 
             if len(skimmed_events["weight"]):
                 genbb = genbb[sel_all]
                 genq = genq[sel_all]
 
-                sf_dicts = []
+                sf_dicts, lp_hists = [], []
                 lp_num_jets = num_jets if self._save_all else 1
 
                 for i in range(lp_num_jets):
@@ -713,7 +715,7 @@ def process(self, events: ak.Array):
 
                     for key, (selector, gen_quarks, num_prongs) in selectors.items():
                         if np.sum(selector) > 0:
-                            selected_sfs[key] = get_lund_SFs(
+                            selected_sfs[key], lp_hist = get_lund_SFs(
                                 year,
                                 events[sel_all][selector],
                                 fatjets[sel_all][selector],
@@ -724,10 +726,13 @@ def process(self, events: ak.Array):
                                 ),  # giving HVV jet index if only doing LP SFs for HVV jet
                                 num_prongs,
                                 gen_quarks[selector],
+                                weights_dict["weight"][sel_all][selector],
                                 trunc_gauss=False,
                                 lnN=True,
                             )
 
+                            lp_hists.append(lp_hist)
+
                     sf_dict = {}
 
                     # collect all the scale factors, fill in 1s for unmatched jets
@@ -743,6 +748,7 @@ def process(self, events: ak.Array):
                     sf_dicts.append(sf_dict)
 
                 sf_dicts = concatenate_dicts(sf_dicts)
+                lp_hist = sum(lp_hists)
 
             else:
                 logging.info("No signal events selected")
@@ -790,7 +796,7 @@ def process(self, events: ak.Array):
         fname = events.behavior["__events_factory__"]._partition_key.replace("/", "_") + ".parquet"
         self.dump_table(pddf, fname)
 
-        return {year: {dataset: {"totals": totals_dict, "cutflow": cutflow}}}
+        return {year: {dataset: {"totals": totals_dict, "cutflow": cutflow, "lp_hist": lp_hist}}}
 
     def postprocess(self, accumulator):
         return accumulator
diff --git a/src/HHbbVV/processors/corrections.py b/src/HHbbVV/processors/corrections.py
index 02d4d1c4..9f196c52 100644
--- a/src/HHbbVV/processors/corrections.py
+++ b/src/HHbbVV/processors/corrections.py
@@ -16,6 +16,7 @@
 
 import awkward as ak
 import correctionlib
+import hist
 import numpy as np
 from coffea import util as cutil
 from coffea.analysis_tools import Weights
@@ -672,7 +673,8 @@ def add_trig_effs(weights: Weights, fatjets: FatJetArray, year: str, num_jets: i
     ratio_sys_down,
     pt_extrap_lookups_dict,
     bratio,
-) = (None, None, None, None, None, None, None)
+    ratio_edges,
+) = (None, None, None, None, None, None, None, None)
 
 
 def _get_lund_lookups(year: str, seed: int = 42, lnN: bool = True, trunc_gauss: bool = False):
@@ -785,16 +787,17 @@ def _np_pad(arr: np.ndarray, target: int = MAX_PT_FPARAMS):
         ratio_sys_down,
         pt_extrap_lookups_dict,
         bratio,
+        ratio_edges,
     )
 
 
 def _get_flat_lp_vars(lds, kt_subjets_pt):
     if len(lds) != 1:
         # flatten and save offsets to unflatten afterwards
-        if type(lds.layout) == ak._ext.ListOffsetArray64:
+        if type(lds.layout) is ak._ext.ListOffsetArray64:
             ld_offsets = lds.kt.layout.offsets
             flat_subjet_pt = ak.flatten(kt_subjets_pt)
-        elif type(lds.layout) == ak._ext.ListArray64:
+        elif type(lds.layout) is ak._ext.ListArray64:
             ld_offsets = lds.layout.toListOffsetArray64(False).offsets
             flat_subjet_pt = kt_subjets_pt
     else:
@@ -820,9 +823,6 @@ def _get_lund_arrays(
     Gets the ``num_prongs`` subjet pTs and Delta and kT per primary LP splitting of fatjets at
     ``fatjet_idx`` in each event.
 
-    Features are flattened (for now), and offsets are saved in ``ld_offsets`` to recover the event
-    structure.
-
     Args:
         events (NanoEventsArray): nano events
         jec_fatjets (FatJetArray): post-JEC fatjets, used to update subjet pTs.
@@ -830,7 +830,7 @@ def _get_lund_arrays(
         num_prongs (int): number of prongs / subjets per jet to reweight
 
     Returns:
-        flat_logD, flat_logkt, flat_subjet_pt, ld_offsets, kt_subjets_vec
+        lds, kt_subjets_vec, kt_subjets_pt: lund declusterings, subjet 4-vectors, JEC-corrected subjet pTs
     """
 
     # jet definitions for LP SFs
@@ -880,6 +880,27 @@ def _get_lund_arrays(
     return lds, kt_subjets_vec, kt_subjets_pt
 
 
+def _get_flat_lund_arrays(events, jec_fatjet, fatjet_idx, num_prongs):
+    """Wrapper for the _get_lund_arrays and _get_flat_lp_vars functions
+
+    returns:    lds - lund declusterings,
+                kt_subjets_vec - subjet 4-vectors,
+                kt_subjets_pt - JEC-corrected subjet pTs,
+                ld_offsets - offsets for jagged structure,
+                flat_logD - flattened log(0.8/Delta),
+                flat_logkt - flattened log(kT/GeV),
+                flat_subjet_pt - flattened JEC-corrected subjet pTs
+    """
+    lds, kt_subjets_vec, kt_subjets_pt = _get_lund_arrays(
+        events, jec_fatjet, fatjet_idx, num_prongs
+    )
+
+    lds_flat = ak.flatten(lds, axis=1)
+    ld_offsets, flat_logD, flat_logkt, flat_subjet_pt = _get_flat_lp_vars(lds_flat, kt_subjets_pt)
+
+    return lds, kt_subjets_vec, kt_subjets_pt, ld_offsets, flat_logD, flat_logkt, flat_subjet_pt
+
+
 def _calc_lund_SFs(
     flat_logD: np.ndarray,
     flat_logkt: np.ndarray,
@@ -969,6 +990,7 @@ def get_lund_SFs(
     fatjet_idx: int | ak.Array,
     num_prongs: int,
     gen_quarks: GenParticleArray,
+    weights: np.ndarray,
     seed: int = 42,
     trunc_gauss: bool = False,
     lnN: bool = True,
@@ -984,6 +1006,8 @@ def get_lund_SFs(
         jec_fatjets (FatJetArray): post-JEC fatjets, used to update subjet pTs.
         fatjet_idx (int | ak.Array): fatjet index
         num_prongs (int): number of prongs / subjets per jet to r
+        gen_quarks (GenParticleArray): gen quarks
+        weights (np.ndarray): event weights, for filling the LP histogram
         seed (int, optional): seed for random smearings. Defaults to 42.
         trunc_gauss (bool, optional): use truncated gaussians for smearing. Defaults to False.
         lnN (bool, optional): use log normals for smearings. Defaults to True.
@@ -995,7 +1019,7 @@ def get_lund_SFs(
     """
 
     # global variable to not have to load + smear LP ratios each time
-    global ratio_smeared_lookups, ratio_lnN_smeared_lookups, ratio_sys_up, ratio_sys_down, pt_extrap_lookups_dict, bratio, lp_year  # noqa: PLW0603
+    global ratio_smeared_lookups, ratio_lnN_smeared_lookups, ratio_sys_up, ratio_sys_down, pt_extrap_lookups_dict, bratio, ratio_edges, lp_year  # noqa: PLW0603
 
     if (
         (lnN and ratio_lnN_smeared_lookups is None)
@@ -1009,6 +1033,7 @@ def get_lund_SFs(
             ratio_sys_down,
             pt_extrap_lookups_dict,
             bratio,
+            ratio_edges,
         ) = _get_lund_lookups(year, seed, lnN, trunc_gauss)
         lp_year = year
 
@@ -1016,16 +1041,38 @@ def get_lund_SFs(
 
     jec_fatjet = jec_fatjets[np.arange(len(jec_fatjets)), fatjet_idx]
 
-    lds, kt_subjets_vec, kt_subjets_pt = _get_lund_arrays(
-        events, jec_fatjet, fatjet_idx, num_prongs
+    # get lund plane declusterings, subjets, and flattened LP vars
+    lds, kt_subjets_vec, kt_subjets_pt, ld_offsets, flat_logD, flat_logkt, flat_subjet_pt = (
+        _get_flat_lund_arrays(events, jec_fatjet, fatjet_idx, num_prongs)
     )
 
-    lds_flat = ak.flatten(lds, axis=1)
-    ld_offsets, flat_logD, flat_logkt, flat_subjet_pt = _get_flat_lp_vars(lds_flat, kt_subjets_pt)
+    ################################################################################################
+    # ---- Fill LP histogram for signal for distortion uncertainty ---- #
+    ################################################################################################
 
-    sfs = {}
+    lp_hist = hist.Hist(
+        hist.axis.Variable(ratio_edges[0], name="subjet_pt", label="Subjet pT [GeV]"),
+        hist.axis.Variable(ratio_edges[1], name="logD", label="ln(0.8/Delta)"),
+        hist.axis.Variable(ratio_edges[2], name="logkt", label="ln(kT/GeV)"),
+    )
+
+    # repeat weights for each LP splitting
+    flat_weights = np.repeat(
+        np.repeat(weights, num_prongs), ak.count(ak.flatten(lds.kt, axis=1), axis=1)
+    )
 
+    lp_hist.fill(
+        subjet_pt=flat_subjet_pt,
+        logD=flat_logD,
+        logkt=flat_logkt,
+        weight=flat_weights,
+    )
+
+    ################################################################################################
     # ---- get scale factors per jet + smearings for stat unc. + syst. variations + pt extrap unc. ---- #
+    ################################################################################################
+
+    sfs = {}
 
     if trunc_gauss:
         sfs["lp_sf"] = _calc_lund_SFs(
@@ -1079,7 +1126,29 @@ def get_lund_SFs(
         pt_extrap_lookups_dict["smeared_params"],
     )
 
+    ################################################################################################
+    # ---- get scale factors after re-clusteing with +/- one prong, for subjet matching uncs. ---- #
+    ################################################################################################
+
+    for shift, nps in [("down", num_prongs - 1), ("up", num_prongs + 1)]:
+        # get lund plane declusterings, subjets, and flattened LP vars
+        _, _, _, np_ld_offsets, np_flat_logD, np_flat_logkt, np_flat_subjet_pt = (
+            _get_flat_lund_arrays(events, jec_fatjet, fatjet_idx, nps)
+        )
+
+        sfs[f"lp_sf_np_{shift}"] = _calc_lund_SFs(
+            np_flat_logD,
+            np_flat_logkt,
+            np_flat_subjet_pt,
+            np_ld_offsets,
+            nps,
+            ratio_lnN_smeared_lookups,
+            [pt_extrap_lookups_dict["params"]],
+        )
+
+    ################################################################################################
     # ---- b-quark related uncertainties ---- #
+    ################################################################################################
 
     if gen_bs is not None:
         assert ak.all(
@@ -1125,7 +1194,9 @@ def get_lund_SFs(
             # weird edge case where b-subjet has no splittings
             sfs["lp_sfs_bl_ratio"] = 1.0
 
+    ################################################################################################
     # ---- subjet matching uncertainties ---- #
+    ################################################################################################
 
     matching_dR = 0.2
     sj_matched = []
@@ -1147,9 +1218,12 @@ def get_lund_SFs(
     sj_matched_idx_mask[~sj_matched] = -1
 
     j_q_dr = gen_quarks.delta_r(jec_fatjet)
-    q_boundary = (j_q_dr > 0.7) * (j_q_dr < 0.9)
-    # events with quarks at the boundary of the jet
-    sfs["lp_sf_boundary_quarks"] = np.array(np.any(q_boundary, axis=1, keepdims=True))
+    # events with quarks at the inside boundary of the jet
+    q_boundary = (j_q_dr > 0.7) * (j_q_dr <= 0.8)
+    sfs["lp_sf_inside_boundary_quarks"] = np.array(np.any(q_boundary, axis=1, keepdims=True))
+    # events with quarks at the outside boundary of the jet
+    q_boundary = (j_q_dr > 0.8) * (j_q_dr <= 0.9)
+    sfs["lp_sf_outside_boundary_quarks"] = np.array(np.any(q_boundary, axis=1, keepdims=True))
 
     # events which have more than one quark matched to the same subjet
     sfs["lp_sf_double_matched_event"] = np.any(
@@ -1162,4 +1236,4 @@ def get_lund_SFs(
     # OLD pT extrapolation uncertainty
     sfs["lp_sf_num_sjpt_gt350"] = np.sum(kt_subjets_vec.pt > 350, axis=1, keepdims=True).to_numpy()
 
-    return sfs
+    return sfs, lp_hist
diff --git a/src/HHbbVV/scale_factors/top_reweighting.ipynb b/src/HHbbVV/scale_factors/top_reweighting.ipynb
index fcff82dd..94fd1a04 100644
--- a/src/HHbbVV/scale_factors/top_reweighting.ipynb
+++ b/src/HHbbVV/scale_factors/top_reweighting.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,14 +36,12 @@
     "import os\n",
     "\n",
     "from HHbbVV.processors import corrections\n",
-    "import correctionlib\n",
-    "\n",
-    "import utils"
+    "import correctionlib"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,48 +51,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_dir = \"../../../plots/ScaleFactors/24Feb26\"\n",
+    "plot_dir = \"../../../plots/ScaleFactors/24Jul22\"\n",
     "_ = os.system(f\"mkdir -p {plot_dir}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15SubJet_nBHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15SubJet_nCHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15_nBHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15_nCHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDBvLV2 in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDCvBV2 in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDCvLV2 in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_nBHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_nCHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch SubJet_nBHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/uscms/home/rkansal/.local/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch SubJet_nCHadrons in <TTree 'Events' (1975 branches) at 0x7f42e469f820>, taking first instance\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "events = nanoevents.NanoEventsFactory.from_root(\n",
     "    # \"/eos/uscms/store/user/lpcpfnano/cmantill/v2_3/2017/HH_gen/GluGluToHHTobbVV_node_cHHH1_TuneCP5_13TeV-powheg-pythia8/GluGluToHHTobbVV_node_cHHH1/221017_221918/0000/nano_mc2017_100.root\",\n",
@@ -118,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -283,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -365,7 +334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -592,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -635,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -680,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -689,27 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<FatJetArray [[FatJet], [FatJet, ... [FatJet]] type='1095 * var * ?fatJet'>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leading_fatjets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -728,7 +677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -764,103 +713,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "45000"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(events)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "60"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "np.sum(top_match_dict[\"top_matched\"])"
+    "print(len(events))\n",
+    "print(np.sum(top_match_dict[\"top_matched\"]))\n",
+    "print(np.sum(top_match_dict[\"w_matched\"]))\n",
+    "print(np.sum(top_match_dict[\"unmatched\"]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "180"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "np.sum(top_match_dict[\"w_matched\"])"
+    "merged_top_events = presel_events[merged_top_jet_match]\n",
+    "had_top_jets = merged_top_events.FatJet[:, 0]\n",
+    "\n",
+    "merged_ak8_pfcands = merged_top_events.FatJetPFCands\n",
+    "merged_ak8_pfcands = merged_ak8_pfcands[\n",
+    "    merged_ak8_pfcands.jetIdx == fatjet_idx[merged_top_jet_match]\n",
+    "]\n",
+    "merged_pfcands = merged_top_events.PFCands[merged_ak8_pfcands.pFCandsIdx]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "855"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "np.sum(top_match_dict[\"unmatched\"])"
+    "merged_pfcands_vector_ptetaphi = ak.Array(\n",
+    "    [\n",
+    "        [{kin_key: cand[kin_key] for kin_key in skim_vars} for cand in event_cands]\n",
+    "        for event_cands in merged_pfcands\n",
+    "    ],\n",
+    "    with_name=\"PtEtaPhiMLorentzVector\",\n",
+    ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 23,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "merged_top_events = presel_events[merged_top_jet_match]\n",
-    "had_top_jets = merged_top_events.FatJet[:, 0]\n",
-    "\n",
-    "merged_ak8_pfcands = merged_top_events.FatJetPFCands\n",
-    "merged_ak8_pfcands = merged_ak8_pfcands[\n",
-    "    merged_ak8_pfcands.jetIdx == fatjet_idx[merged_top_jet_match]\n",
-    "]\n",
-    "merged_pfcands = merged_top_events.PFCands[merged_ak8_pfcands.pFCandsIdx]"
+    "JEC Correction"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -887,22 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "merged_pfcands_vector_ptetaphi = ak.Array(\n",
-    "    [\n",
-    "        [{kin_key: cand[kin_key] for kin_key in skim_vars} for cand in event_cands]\n",
-    "        for event_cands in merged_pfcands\n",
-    "    ],\n",
-    "    with_name=\"PtEtaPhiMLorentzVector\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -916,36 +804,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "#--------------------------------------------------------------------------\n",
-      "#                         FastJet release 3.4.0\n",
-      "#                 M. Cacciari, G.P. Salam and G. Soyez                  \n",
-      "#     A software package for jet finding and analysis at colliders      \n",
-      "#                           http://fastjet.fr                           \n",
-      "#\t                                                                      \n",
-      "# Please cite EPJC72(2012)1896 [arXiv:1111.6097] if you use this package\n",
-      "# for scientific work and optionally PLB641(2006)57 [hep-ph/0512210].   \n",
-      "#                                                                       \n",
-      "# FastJet is provided without warranty under the GNU GPL v2 or higher.  \n",
-      "# It uses T. Chan's closest pair algorithm, S. Fortune's Voronoi code,\n",
-      "# CGAL and 3rd party plugin jet algorithms. See COPYING file for details.\n",
-      "#--------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "UserWarning: dcut and exclusive jets for jet-finders other than kt, C/A or genkt with p>=0 should be interpreted with care.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# cluster first with kT\n",
     "kt_clustering = fastjet.ClusterSequence(merged_pfcands_vector_ptetaphi, ktdef)\n",
@@ -964,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -983,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -998,20 +859,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [104, 176, 313, 147, ... 141, 197, 454] type='60 * ?float64'>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "closest_sjidx = np.argmin(subjet_bs_dr, axis=1).to_numpy()\n",
     "kt_subjets_pt[np.arange(len(kt_subjets_pt)), closest_sjidx]"
@@ -1019,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1035,40 +885,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[74.4, 170, 104], ... 36.4, 454, 55.4]] type='60 * 3 * ?float64'>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kt_subjets_pt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [104, 176, 313, 147, ... 141, 197, 454] type='60 * ?float64'>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kt_subjets_pt[np.arange(len(kt_subjets_pt)), closest_sjidx]"
    ]
@@ -1165,44 +993,6 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "f = uproot.open(\"../corrections/lp_ratios/ratio_2017.root\")\n",
-    "f.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f[\"ratio_sys_tot_down\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f[\"pt_extrap\"][\"func_16_12\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fold = uproot.open(\"../corrections/lp_ratios/old/ratio_june9.root\")\n",
-    "fold.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "f = uproot.open(\"../corrections/lp_ratios/ratio_2017.root\")\n",
     "# f = uproot.open(\"../corrections/ratio_june9.root\")\n",
@@ -1218,211 +1008,9 @@
     "ratio_nom_errs[zero_vals] = 0\n",
     "\n",
     "ratio_shape = f[\"ratio_nom\"].values().shape\n",
-    "bl_ratio = f[\"h_bl_ratio\"].to_numpy()[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [2.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [0.        , 1.840388  , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [1.4767264 , 1.3192567 , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [1.0331354 , 1.4990366 , 1.9544313 , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ]],\n",
-       "\n",
-       "       [[0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        ...,\n",
-       "        [0.        , 0.53056574, 2.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.42190078, 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ],\n",
-       "        [0.        , 0.        , 0.        , ..., 0.        ,\n",
-       "         0.        , 0.        ]]], dtype=float32)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bl_ratio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        ...,\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ]],\n",
-       "\n",
-       "       [[1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        ...,\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ]],\n",
-       "\n",
-       "       [[1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        ...,\n",
-       "        [0.81918097, 1.6875561 , 0.16781662, ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.6282549 , 3.4878435 , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ]],\n",
-       "\n",
-       "       [[1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        ...,\n",
-       "        [0.20374994, 1.        , 1.4421521 , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 0.6945209 , 2.0556872 , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [6.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ]],\n",
-       "\n",
-       "       [[1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        ...,\n",
-       "        [0.2418148 , 1.459049  , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.5244532 , 0.805354  , 1.6780305 , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [4.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ]],\n",
-       "\n",
-       "       [[1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        ...,\n",
-       "        [1.        , 1.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [0.11765861, 2.4962053 , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ],\n",
-       "        [1.        , 2.        , 1.        , ..., 1.        ,\n",
-       "         1.        , 1.        ]]], dtype=float32)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ratio_nom"
+    "bl_ratio = f[\"h_bl_ratio\"].to_numpy()[0]\n",
+    "\n",
+    "n_LP_sf_toys = 100"
    ]
   },
   {
@@ -1438,16 +1026,6 @@
     "_ = plt.colorbar()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# max_pt_bin = ratio_nom_edges[0][-2]\n",
-    "n_LP_sf_toys = 100"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1455,7 +1033,11 @@
    "outputs": [],
    "source": [
     "plt.figure(figsize=(10, 6))\n",
-    "plt.imshow(ratio_nom[5].T[::-1], extent=[-1, 8, -5, 6.9], cmap=\"turbo\")\n",
+    "plt.imshow(\n",
+    "    ratio_nom[5].T[::-1],\n",
+    "    extent=[-ratio_edges[1][0], ratio_edges[1][-1], ratio_edges[2][0], ratio_edges[2][-1]],\n",
+    "    cmap=\"turbo\",\n",
+    ")\n",
     "plt.xlabel(r\"ln$(0.8/\\Delta)$\")\n",
     "plt.ylabel(r\"ln$(k_T/GeV)$\")\n",
     "_ = plt.colorbar()"
@@ -1463,24 +1045,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# maxp = 0\n",
-    "# for i in range(ratio_shape[1]):\n",
-    "#     for j in range(ratio_shape[2]):\n",
-    "#         if len(pt_extrap_params[i][j]) > maxp:\n",
-    "#             maxp = len(pt_extrap_params[i][j])\n",
-    "\n",
-    "# maxp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "2\n",
+      "2\n",
+      "2\n"
+     ]
+    }
+   ],
    "source": [
     "pt_extrap_lookups_dict = {\"params\": [], \"errs\": [], \"sys_up_params\": [], \"sys_down_params\": []}\n",
     "max_fparams = 4  # max order (+1) of functions\n",
@@ -1545,9 +1123,52 @@
     "\n",
     "# produces array of shape ``[n_sf_toys, subjet_pt bins, ln(0.8/Delta) bins, ln(kT/GeV) bins]``\n",
     "ratio_nom_smeared = ratio_nom + (ratio_nom_errs * rand_noise)\n",
-    "ratio_smeared_lookups = [\n",
-    "    dense_lookup(ratio_nom_smeared[i], ratio_nom_edges) for i in range(n_sf_toys)\n",
-    "]"
+    "ratio_smeared_lookups = [dense_lookup(ratio_nom_smeared[i], ratio_edges) for i in range(n_sf_toys)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _get_flat_lp_vars(lds, kt_subjets_pt):\n",
+    "    if len(lds) != 1:\n",
+    "        # flatten and save offsets to unflatten afterwards\n",
+    "        if type(lds.layout) == ak._ext.ListOffsetArray64:\n",
+    "            ld_offsets = lds.kt.layout.offsets\n",
+    "            flat_subjet_pt = ak.flatten(kt_subjets_pt)\n",
+    "        elif type(lds.layout) == ak._ext.ListArray64:\n",
+    "            ld_offsets = lds.layout.toListOffsetArray64(False).offsets\n",
+    "            flat_subjet_pt = kt_subjets_pt\n",
+    "    else:\n",
+    "        # edge case of single subjet...\n",
+    "        ld_offsets = [0]\n",
+    "        flat_subjet_pt = kt_subjets_pt\n",
+    "\n",
+    "    # repeat subjet pt for each lund declustering\n",
+    "    flat_subjet_pt = np.repeat(flat_subjet_pt, ak.count(lds.kt, axis=1)).to_numpy()\n",
+    "    flat_logD = np.log(0.8 / ak.flatten(lds).Delta).to_numpy()\n",
+    "    flat_logkt = np.log(ak.flatten(lds).kt).to_numpy()\n",
+    "\n",
+    "    return ld_offsets, flat_logD, flat_logkt, flat_subjet_pt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lds_flat = ak.flatten(lds, axis=1)\n",
+    "ld_offsets, flat_logD, flat_logkt, flat_subjet_pt = _get_flat_lp_vars(lds_flat, kt_subjets_pt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fill LP histogram for signal"
    ]
   },
   {
@@ -1556,20 +1177,563 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# save offsets to recover awkward structure later\n",
-    "ld_offsets = lds.kt.layout.offsets\n",
-    "flat_logD = np.log(0.8 / ak.flatten(lds).Delta).to_numpy()\n",
-    "flat_logkt = np.log(ak.flatten(lds).kt).to_numpy()\n",
-    "# repeat subjet pt for each lund declustering\n",
-    "flat_subjet_pt = np.repeat(ak.flatten(kt_subjets_pt), ak.count(lds.kt, axis=1)).to_numpy()"
+    "import hist\n",
+    "\n",
+    "lp_hist = hist.Hist(\n",
+    "    hist.axis.Variable(ratio_edges[0], name=\"subjet_pt\", label=\"Subjet pT [GeV]\"),\n",
+    "    hist.axis.Variable(ratio_edges[1], name=\"logD\", label=\"ln(0.8/Delta)\"),\n",
+    "    hist.axis.Variable(ratio_edges[2], name=\"logkt\", label=\"ln(kT/GeV)\"),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weight = merged_top_events.genWeight.to_numpy()\n",
+    "flat_weight = np.repeat(np.repeat(weight, num_prongs), ak.count(lds_flat.kt, axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hist(\n",
+       "  Variable([10, 65, 110, 175, 240, 300, 99999], name='subjet_pt', label='Subjet pT [GeV]'),\n",
+       "  Variable([-1, -0.55, -0.1, 0.35, 0.8, 1.25, 1.7, 2.15, 2.6, 3.05, 3.5, 3.95, 4.4, 4.85, 5.3, 5.75, 6.2, 6.65, 7.1, 7.55, 8], name='logD', label='ln(0.8/Delta)'),\n",
+       "  Variable([-5, -4.40461, -3.80922, -3.21384, -2.61845, -2.02306, -1.42767, -0.832286, -0.236898, 0.35849, 0.953878, 1.54927, 2.14465, 2.74004, 3.33543, 3.93082, 4.5262, 5.12159, 5.71698, 6.31237, 6.90776], name='logkt', label='ln(kT/GeV)'),\n",
+       "  storage=Double()) # Sum: 435622.09045410156 (435925.44845581055 with flow)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lp_hist.fill(\n",
+    "    subjet_pt=flat_subjet_pt,\n",
+    "    logD=flat_logD,\n",
+    "    logkt=flat_logkt,\n",
+    "    weight=flat_weight,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<html>\n",
+       "<div style=\"display:flex; align-items:center;\">\n",
+       "<div style=\"width:290px;\">\n",
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"-20 -270 290 290\">\n",
+       "<text text-anchor=\"middle\" x=\"0\" y=\"13\" style=\"fill:currentColor;\">\n",
+       "-1\n",
+       "</text>\n",
+       "<text text-anchor=\"middle\" x=\"250\" y=\"13\" style=\"fill:currentColor;\">\n",
+       "8\n",
+       "</text>\n",
+       "<text text-anchor=\"middle\" x=\"-10\" y=\"0\" style=\"fill:currentColor;\">\n",
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+       "</text>\n",
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+       "</text>\n",
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+       "<rect x=\"212.50000264909534\" y=\"-250.0\" width=\"12.5\" height=\"12.5\" opacity=\"0.0\" fill=\"currentColor\" stroke-width=\"0.1\"/>\n",
+       "<rect x=\"224.99999735090466\" y=\"-250.0\" width=\"12.5\" height=\"12.5\" opacity=\"0.0\" fill=\"currentColor\" stroke-width=\"0.1\"/>\n",
+       "<rect x=\"237.50000529819064\" y=\"-250.0\" width=\"12.5\" height=\"12.5\" opacity=\"0.0\" fill=\"currentColor\" stroke-width=\"0.1\"/>\n",
+       "</svg>\n",
+       "</div>\n",
+       "<div style=\"flex=grow:1;\">\n",
+       "Variable([-1, -0.55, -0.1, 0.35, 0.8, 1.25, 1.7, 2.15, 2.6, 3.05, 3.5, 3.95, 4.4, 4.85, 5.3, 5.75, 6.2, 6.65, 7.1, 7.55, 8], name='logD', label='ln(0.8/Delta)')<br/>\n",
+       "Variable([-5, -4.40461, -3.80922, -3.21384, -2.61845, -2.02306, -1.42767, -0.832286, -0.236898, 0.35849, 0.953878, 1.54927, 2.14465, 2.74004, 3.33543, 3.93082, 4.5262, 5.12159, 5.71698, 6.31237, 6.90776], name='logkt', label='ln(kT/GeV)')<br/>\n",
+       "<hr style=\"margin-top:.2em; margin-bottom:.2em;\"/>\n",
+       "Double() Σ=39436.54022216797\n",
+       "\n",
+       "</div>\n",
+       "</div>\n",
+       "</html>"
+      ],
+      "text/plain": [
+       "Hist(\n",
+       "  Variable([-1, -0.55, -0.1, 0.35, 0.8, 1.25, 1.7, 2.15, 2.6, 3.05, 3.5, 3.95, 4.4, 4.85, 5.3, 5.75, 6.2, 6.65, 7.1, 7.55, 8], name='logD', label='ln(0.8/Delta)'),\n",
+       "  Variable([-5, -4.40461, -3.80922, -3.21384, -2.61845, -2.02306, -1.42767, -0.832286, -0.236898, 0.35849, 0.953878, 1.54927, 2.14465, 2.74004, 3.33543, 3.93082, 4.5262, 5.12159, 5.71698, 6.31237, 6.90776], name='logkt', label='ln(kT/GeV)'),\n",
+       "  storage=Double()) # Sum: 39436.54022216797"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lp_hist[0, ...]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Testing summing over histograms\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "lp_hist2 = deepcopy(lp_hist)\n",
+    "lp_hist2.values()[:] = 1\n",
+    "lp_hist2"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Hist(\n",
+       "  Variable([10, 65, 110, 175, 240, 300, 99999], name='subjet_pt', label='Subjet pT [GeV]'),\n",
+       "  Variable([-1, -0.55, -0.1, 0.35, 0.8, 1.25, 1.7, 2.15, 2.6, 3.05, 3.5, 3.95, 4.4, 4.85, 5.3, 5.75, 6.2, 6.65, 7.1, 7.55, 8], name='logD', label='ln(0.8/Delta)'),\n",
+       "  Variable([-5, -4.40461, -3.80922, -3.21384, -2.61845, -2.02306, -1.42767, -0.832286, -0.236898, 0.35849, 0.953878, 1.54927, 2.14465, 2.74004, 3.33543, 3.93082, 4.5262, 5.12159, 5.71698, 6.31237, 6.90776], name='logkt', label='ln(kT/GeV)'),\n",
+       "  storage=Double()) # Sum: 438022.09045410156 (438628.80645751953 with flow)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sum([lp_hist, lp_hist2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "pT Extrapolation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
    "outputs": [],
    "source": [
+    "max_pt_bin = ratio_edges[0][-2]\n",
     "high_pt_sel = flat_subjet_pt > max_pt_bin\n",
     "hpt_logD = flat_logD[high_pt_sel]\n",
     "hpt_logkt = flat_logkt[high_pt_sel]\n",
@@ -1581,11 +1745,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
-    "clip_max, clip_min = 10, 0.1\n",
+    "clip_max, clip_min = 5.0, 1 / 5.0\n",
     "pt_lookup = pt_extrap_lookups_dict[\"params\"]\n",
     "params = pt_lookup(hpt_logD, hpt_logkt)\n",
     "pt_extrap_vals = np.maximum(np.minimum(np.sum(params * sj_pt_orders, axis=1), clip_max), clip_min)"
@@ -1803,7 +1967,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.9.15"
   },
   "orig_nbformat": 4,
   "vscode": {