From 080c0e9da00fa531c54a0482fec6b72f8f2876e7 Mon Sep 17 00:00:00 2001
From: Cristina Mantilla Suarez <cmantill@users.noreply.github.com>
Date: Tue, 21 May 2024 16:52:36 -0500
Subject: [PATCH] Implement trigger and TT weights (#162)

* add extra hlt for 2023 and remove fatjet arguments from weight function

* all signal samples and get rid of lep vh decays

* fix bug in cutflow and no more rescaling for hh4b

* add bdtmin to cutflow, add option for pt threshold 2nd jet, move mass cut upper to 220

* add

* add trigger weights

* add tt

* fix merge

* save wshift templates

* style: pre-commit fixes

* update

* style: pre-commit fixes

---------

Co-authored-by: Cristina Mantilla <cmantill@uaf-2.t2.ucsd.edu>
Co-authored-by: Javier Duarte <jduarte@ucsd.edu>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 src/HH4b/boosted/TrainBDT.py                  |   6 +
 .../data/fatjet_triggereff_2018_combined.pkl  | Bin 128694 -> 0 bytes
 ...et_triggereff_2022EE_combined_nodijet.json | 299 ----------------
 .../fatjet_triggereff_2022EE_ptmsd_QCD.json   |   1 +
 .../fatjet_triggereff_2022EE_ptmsd_TTbar.json |   1 +
 .../fatjet_triggereff_2022EE_txbb_QCD.json    |   1 +
 .../fatjet_triggereff_2022EE_txbb_TTbar.json  |   1 +
 .../fatjet_triggereff_2022EE_txbbv11_QCD.json |   1 +
 ...atjet_triggereff_2022EE_txbbv11_TTbar.json |   1 +
 .../fatjet_triggereff_2022_ptmsd_QCD.json     |   1 +
 .../fatjet_triggereff_2022_ptmsd_TTbar.json   |   1 +
 .../data/fatjet_triggereff_2022_txbb_QCD.json |   1 +
 .../fatjet_triggereff_2022_txbb_TTbar.json    |   1 +
 .../fatjet_triggereff_2022_txbbv11_QCD.json   |   1 +
 .../fatjet_triggereff_2022_txbbv11_TTbar.json |   1 +
 .../fatjet_triggereff_2023BPix_ptmsd_QCD.json |   1 +
 ...atjet_triggereff_2023BPix_ptmsd_TTbar.json |   1 +
 .../fatjet_triggereff_2023BPix_txbb_QCD.json  |   1 +
 ...fatjet_triggereff_2023BPix_txbb_TTbar.json |   1 +
 ...atjet_triggereff_2023BPix_txbbv11_QCD.json |   1 +
 ...jet_triggereff_2023BPix_txbbv11_TTbar.json |   1 +
 .../fatjet_triggereff_2023_ptmsd_QCD.json     |   1 +
 .../fatjet_triggereff_2023_ptmsd_TTbar.json   |   1 +
 .../data/fatjet_triggereff_2023_txbb_QCD.json |   1 +
 .../fatjet_triggereff_2023_txbb_TTbar.json    |   1 +
 .../fatjet_triggereff_2023_txbbv11_QCD.json   |   1 +
 .../fatjet_triggereff_2023_txbbv11_TTbar.json |   1 +
 src/HH4b/corrections/data/ttbarcorr_2022.json |   1 +
 src/HH4b/corrections/data/ttbarcorr_2023.json |   1 +
 src/HH4b/corrections/trigger_weight.py        | 257 ++++++++++++++
 src/HH4b/corrections/ttbar_weight.py          |  38 +++
 src/HH4b/plotting.py                          | 323 +++---------------
 .../postprocessing/CombineTemplates.ipynb     |  12 +-
 src/HH4b/postprocessing/CreateDatacard.py     |   2 +
 src/HH4b/postprocessing/PostProcess.py        | 247 +++++++++++---
 src/HH4b/postprocessing/corrections.py        | 169 +++++----
 src/HH4b/postprocessing/postprocessing.py     |  24 +-
 37 files changed, 715 insertions(+), 688 deletions(-)
 delete mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2018_combined.pkl
 delete mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_combined_nodijet.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_TTbar.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_QCD.json
 create mode 100644 src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_TTbar.json
 create mode 100644 src/HH4b/corrections/data/ttbarcorr_2022.json
 create mode 100644 src/HH4b/corrections/data/ttbarcorr_2023.json
 create mode 100644 src/HH4b/corrections/trigger_weight.py
 create mode 100644 src/HH4b/corrections/ttbar_weight.py

diff --git a/src/HH4b/boosted/TrainBDT.py b/src/HH4b/boosted/TrainBDT.py
index fa5e9660..c35998fc 100644
--- a/src/HH4b/boosted/TrainBDT.py
+++ b/src/HH4b/boosted/TrainBDT.py
@@ -150,8 +150,11 @@ def preprocess_data(
     training_keys = train_keys.copy()
     print("Training keys ", training_keys)
 
+    print("events dict ", events_dict.keys())
+
     for key in training_keys:
         if key not in events_dict:
+            print(f"removing {key}")
             training_keys.remove(key)
 
     print("Train keys ", training_keys)
@@ -1090,6 +1093,8 @@ def main(args):
     else:
         years = args.year
 
+    print(years)
+
     X_train = OrderedDict()
     X_test = OrderedDict()
     y_train = OrderedDict()
@@ -1108,6 +1113,7 @@ def main(args):
                 samples_run3[year].pop(key)
 
     for year in years:
+        print("loading ", year)
         events_dict_years[year] = load_run3_samples(
             args.data_path,
             year,
diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2018_combined.pkl b/src/HH4b/corrections/data/fatjet_triggereff_2018_combined.pkl
deleted file mode 100644
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_combined_nodijet.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_combined_nodijet.json
deleted file mode 100644
index 1ba5ebb3..00000000
--- a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_combined_nodijet.json
+++ /dev/null
@@ -1,299 +0,0 @@
-{
-  "schema_version": 2,
-  "corrections": [
-    {
-      "name": "fatjet_triggereff_2022EE_MC",
-      "description": "MC efficiency for trigger soup: combined_nodijet",
-      "version": 1,
-      "inputs": [
-        {
-          "name": "systematic",
-          "type": "string",
-          "description": "Systematic variation"
-        },
-        {
-          "name": "pt",
-          "type": "real",
-          "description": "Jet transverse momentum (NanoAODv11 nominal value)"
-        },
-        {
-          "name": "xbb",
-          "type": "real",
-          "description": "Jet Xbb (xbb/xbb+qcd) (NanoAODv11 nominal value)"
-        }
-      ],
-      "output": {
-        "name": "weight",
-        "type": "real",
-        "description": "Jet MC trigger efficiency"
-      },
-      "data": {
-        "nodetype": "category",
-        "input": "systematic",
-        "content": [
-          {
-            "key": "nominal",
-            "value": {
-              "nodetype": "multibinning",
-              "inputs": ["pt", "xbb"],
-              "edges": [
-                [
-                  250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0,
-                  475.0, 500.0, 550.0, 600.0, 800.0, 1000.0
-                ],
-                [0.0, 0.8, 0.9, 0.95, 0.98, 1.0]
-              ],
-              "content": [
-                0.21493854266281393, 0.5757102181499004, 0.6069364161849711,
-                0.6107088637159874, 0.6343192040343465, 0.32611861667209124,
-                0.8172577075505687, 0.8395017313486531, 0.8425563503846673,
-                0.8475392019872691, 0.376494925576367, 0.8529640176028993,
-                0.8798248015329866, 0.8903390100270572, 0.893978102189781,
-                0.41958864353312303, 0.8734161793372319, 0.8963356490941289,
-                0.906790182408557, 0.915603128859613, 0.46362856645090395,
-                0.8899193548387097, 0.9170323419470177, 0.9295965645835688,
-                0.9365, 0.5122639023104629, 0.9072917967286802,
-                0.9320417287630403, 0.9459567654123299, 0.9564095210782908,
-                0.5895892351274787, 0.9212481426448736, 0.944477540807816,
-                0.9630714169513583, 0.9724517906336089, 0.8189256492351477,
-                0.9629922818127845, 0.9688955672426747, 0.9776723967595337,
-                0.9795333606221859, 0.95862920654523, 0.9849340866290018,
-                0.9917618423516196, 0.9886304909560724, 0.9926931106471816,
-                0.9816711320329519, 0.9951986754966887, 0.9953681131155534,
-                0.9960668633235005, 0.9986082115518441, 0.9898560453494443,
-                0.9973790802954491, 0.9976798143851509, 0.9982621648460774, 1.0,
-                0.993998476770754, 0.9985169491525424, 0.9984718826405868,
-                0.9991628296358309, 1.0, 0.9960670162652209, 0.9985406194259769,
-                0.999023199023199, 0.9993425378040762, 1.0, 0.9986213811254543,
-                0.9988304093567252, 1.0, 1.0, 1.0
-              ],
-              "flow": 1.0
-            }
-          },
-          {
-            "key": "stat_up",
-            "value": {
-              "nodetype": "multibinning",
-              "inputs": ["pt", "xbb"],
-              "edges": [
-                [
-                  250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0,
-                  475.0, 500.0, 550.0, 600.0, 800.0, 1000.0
-                ],
-                [0.0, 0.8, 0.9, 0.95, 0.98, 1.0]
-              ],
-              "content": [
-                0.21493854266281393, 0.5757102181499004, 0.6069364161849711,
-                0.6107088637159874, 0.6343192040343465, 0.32611861667209124,
-                0.8172577075505687, 0.8395017313486531, 0.8425563503846673,
-                0.8475392019872691, 0.376494925576367, 0.8529640176028993,
-                0.8798248015329866, 0.8903390100270572, 0.893978102189781,
-                0.41958864353312303, 0.8734161793372319, 0.8963356490941289,
-                0.906790182408557, 0.915603128859613, 0.46362856645090395,
-                0.8899193548387097, 0.9170323419470177, 0.9295965645835688,
-                0.9365, 0.5122639023104629, 0.9072917967286802,
-                0.9320417287630403, 0.9459567654123299, 0.9564095210782908,
-                0.5895892351274787, 0.9212481426448736, 0.944477540807816,
-                0.9630714169513583, 0.9724517906336089, 0.8189256492351477,
-                0.9629922818127845, 0.9688955672426747, 0.9776723967595337,
-                0.9795333606221859, 0.95862920654523, 0.9849340866290018,
-                0.9917618423516196, 0.9886304909560724, 0.9926931106471816,
-                0.9816711320329519, 0.9951986754966887, 0.9953681131155534,
-                0.9960668633235005, 0.9986082115518441, 0.9898560453494443,
-                0.9973790802954491, 0.9976798143851509, 0.9982621648460774, 1.0,
-                0.993998476770754, 0.9985169491525424, 0.9984718826405868,
-                0.9991628296358309, 1.0, 0.9960670162652209, 0.9985406194259769,
-                0.999023199023199, 0.9993425378040762, 1.0, 0.9986213811254543,
-                0.9988304093567252, 1.0, 1.0, 1.0
-              ],
-              "flow": 1.0
-            }
-          },
-          {
-            "key": "stat_dn",
-            "value": {
-              "nodetype": "multibinning",
-              "inputs": ["pt", "xbb"],
-              "edges": [
-                [
-                  250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0,
-                  475.0, 500.0, 550.0, 600.0, 800.0, 1000.0
-                ],
-                [0.0, 0.8, 0.9, 0.95, 0.98, 1.0]
-              ],
-              "content": [
-                0.21493854266281393, 0.5757102181499004, 0.6069364161849711,
-                0.6107088637159874, 0.6343192040343465, 0.32611861667209124,
-                0.8172577075505687, 0.8395017313486531, 0.8425563503846673,
-                0.8475392019872691, 0.376494925576367, 0.8529640176028993,
-                0.8798248015329866, 0.8903390100270572, 0.893978102189781,
-                0.41958864353312303, 0.8734161793372319, 0.8963356490941289,
-                0.906790182408557, 0.915603128859613, 0.46362856645090395,
-                0.8899193548387097, 0.9170323419470177, 0.9295965645835688,
-                0.9365, 0.5122639023104629, 0.9072917967286802,
-                0.9320417287630403, 0.9459567654123299, 0.9564095210782908,
-                0.5895892351274787, 0.9212481426448736, 0.944477540807816,
-                0.9630714169513583, 0.9724517906336089, 0.8189256492351477,
-                0.9629922818127845, 0.9688955672426747, 0.9776723967595337,
-                0.9795333606221859, 0.95862920654523, 0.9849340866290018,
-                0.9917618423516196, 0.9886304909560724, 0.9926931106471816,
-                0.9816711320329519, 0.9951986754966887, 0.9953681131155534,
-                0.9960668633235005, 0.9986082115518441, 0.9898560453494443,
-                0.9973790802954491, 0.9976798143851509, 0.9982621648460774, 1.0,
-                0.993998476770754, 0.9985169491525424, 0.9984718826405868,
-                0.9991628296358309, 1.0, 0.9960670162652209, 0.9985406194259769,
-                0.999023199023199, 0.9993425378040762, 1.0, 0.9986213811254543,
-                0.9988304093567252, 1.0, 1.0, 1.0
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_QCD.json
new file mode 100644
index 00000000..e8de038b
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_TTbar.json
new file mode 100644
index 00000000..8ff9f111
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_ptmsd_TTbar.json
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_QCD.json
new file mode 100644
index 00000000..89c241ed
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_TTbar.json
new file mode 100644
index 00000000..a56667d0
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbb_TTbar.json
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_QCD.json
new file mode 100644
index 00000000..a1313dde
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_TTbar.json
new file mode 100644
index 00000000..ee7d65f8
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022EE_txbbv11_TTbar.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_QCD.json
new file mode 100644
index 00000000..157dbf19
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_TTbar.json
new file mode 100644
index 00000000..ea28338f
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022_ptmsd_TTbar.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_QCD.json
new file mode 100644
index 00000000..b94759a6
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_QCD.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2022_txbb","description":"mc Trigger efficiency for trigger soup: txbb","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger 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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_TTbar.json
new file mode 100644
index 00000000..e174d5a2
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbb_TTbar.json
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_QCD.json
new file mode 100644
index 00000000..1f8103b5
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_TTbar.json
new file mode 100644
index 00000000..2c12c050
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2022_txbbv11_TTbar.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2022_txbbv11","description":"mc Trigger efficiency for trigger soup: txbbv11","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger 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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_QCD.json
new file mode 100644
index 00000000..a475f39c
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_TTbar.json
new file mode 100644
index 00000000..c686eff6
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_ptmsd_TTbar.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2023BPix_ptmsd","description":"mc Trigger efficiency for trigger soup: ptmsd","version":1,"inputs":[{"name":"pt","type":"real","description":"Jet transverse momentum"},{"name":"msd","type":"real","description":"Jet softdrop mass"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger 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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_QCD.json
new file mode 100644
index 00000000..dd63c6dd
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_QCD.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2023BPix_txbb","description":"mc Trigger efficiency for trigger soup: txbb","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger efficiency"},"data":{"nodetype":"category","input":"systematic","content":[{"key":"nominal","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.007232851314654976,0.1207576862389157,0.18647722878657885,0.23179912699399965,0.25033202989268194,0.31581140934879987,0.3845027388426402,0.42697940139648854,0.48926017324121424,0.5299394847877235,0.5747916686209416,0.6034790701448413,0.7071541437832414,0.7509616773389216,0.8115552298857186,0.7965040706178029,0.8557712852706403,0.9012199629780233,0.928709792167852,0.9497543418581766],"flow":1.0}},{"key":"stat_up","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010199115291500127,0.0015019102930327757,0.007925783666852487,0.003960405786169174,0.006851216772917039,0.006879758278639039,0.008419900192755866,0.021135872207581455,0.01166593330920403,0.019685484169173793,0.01852164553986136,0.041626944966406676,0.02085952079379706,0.03434357005987936,0.014840763784032346,0.05965907543391375,0.019383001165462713,0.015973526212554486,0.016105455773290697,0.020790569519275],"flow":1.0}},{"key":"stat_dn","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010199115291500127,0.0015019102930327757,0.007925783666852487,0.003960405786169174,0.006851216772917039,0.006879758278639039,0.008419900192755866,0.021135872207581455,0.01166593330920403,0.019685484169173793,0.01852164553986136,0.041626944966406676,0.02085952079379706,0.03434357005987936,0.014840763784032346,0.05965907543391375,0.019383001165462713,0.015973526212554486,0.016105455773290697,0.020790569519275],"flow":1.0}}]}},{"name":"fatjet_triggereffdata_2023BPix_txbb","description":"data Trigger efficiency for trigger soup: txbb","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet data trigger efficiency"},"data":{"nodetype":"category","input":"systematic","content":[{"key":"nominal","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.013433322317682829,0.1276443100071743,0.17733811500181326,0.21819893316598682,0.26244541484716155,0.30397007360926753,0.36227018385291765,0.39069855017887406,0.45488149597738636,0.49029493319889084,0.5221112696148359,0.5805650419124495,0.6378348214285714,0.702152414194299,0.7362338545207342,0.7904974489795918,0.8151898734177215,0.8428919012860618,0.8928312816799421,0.9278937381404174],"flow":1.0}},{"key":"stat_up","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010123314964642314,0.0016706120088800563,0.002900527621677147,0.004084222419344774,0.005379266444214935,0.006915929493577946,0.008882480533361088,0.010114612519649983,0.011995855914141896,0.013571683241752598,0.015057772221417192,0.01687859391562389,0.017072816093012064,0.018644982629131078,0.020844460919083593,0.021244624663944365,0.023133580198088286,0.023236244644646246,0.024735962521462783,0.033637579202603074],"flow":1.0}},{"key":"stat_dn","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010123314964642314,0.0016706120088800563,0.002900527621677147,0.004084222419344774,0.005379266444214935,0.006915929493577946,0.008882480533361088,0.010114612519649983,0.011995855914141896,0.013571683241752598,0.015057772221417192,0.01687859391562389,0.017072816093012064,0.018644982629131078,0.020844460919083593,0.021244624663944365,0.023133580198088286,0.023236244644646246,0.024735962521462783,0.033637579202603074],"flow":1.0}}]}}]}
diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_TTbar.json
new file mode 100644
index 00000000..0e88eb8e
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbb_TTbar.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_QCD.json
new file mode 100644
index 00000000..9e6b18d7
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_QCD.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2023BPix_txbbv11","description":"mc Trigger efficiency for trigger soup: txbbv11","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger 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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_TTbar.json
new file mode 100644
index 00000000..d42848d5
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023BPix_txbbv11_TTbar.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2023BPix_txbbv11","description":"mc Trigger efficiency for trigger soup: txbbv11","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger 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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_QCD.json
new file mode 100644
index 00000000..a059e1b9
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_QCD.json
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_TTbar.json
new file mode 100644
index 00000000..62cf2b05
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023_ptmsd_TTbar.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2023_ptmsd","description":"mc Trigger efficiency for trigger soup: ptmsd","version":1,"inputs":[{"name":"pt","type":"real","description":"Jet transverse momentum"},{"name":"msd","type":"real","description":"Jet softdrop mass"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger 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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_QCD.json
new file mode 100644
index 00000000..f3f8fef6
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_QCD.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"fatjet_triggereffmc_2023_txbb","description":"mc Trigger efficiency for trigger soup: txbb","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet mc trigger efficiency"},"data":{"nodetype":"category","input":"systematic","content":[{"key":"nominal","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.007232851314654976,0.1207576862389157,0.18647722878657885,0.23179912699399965,0.25033202989268194,0.31581140934879987,0.3845027388426402,0.42697940139648854,0.48926017324121424,0.5299394847877235,0.5747916686209416,0.6034790701448413,0.7071541437832414,0.7509616773389216,0.8115552298857186,0.7965040706178029,0.8557712852706403,0.9012199629780233,0.928709792167852,0.9497543418581766],"flow":1.0}},{"key":"stat_up","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010199115291500127,0.0015019102930327757,0.007925783666852487,0.003960405786169174,0.006851216772917039,0.006879758278639039,0.008419900192755866,0.021135872207581455,0.01166593330920403,0.019685484169173793,0.01852164553986136,0.041626944966406676,0.02085952079379706,0.03434357005987936,0.014840763784032346,0.05965907543391375,0.019383001165462713,0.015973526212554486,0.016105455773290697,0.020790569519275],"flow":1.0}},{"key":"stat_dn","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010199115291500127,0.0015019102930327757,0.007925783666852487,0.003960405786169174,0.006851216772917039,0.006879758278639039,0.008419900192755866,0.021135872207581455,0.01166593330920403,0.019685484169173793,0.01852164553986136,0.041626944966406676,0.02085952079379706,0.03434357005987936,0.014840763784032346,0.05965907543391375,0.019383001165462713,0.015973526212554486,0.016105455773290697,0.020790569519275],"flow":1.0}}]}},{"name":"fatjet_triggereffdata_2023_txbb","description":"data Trigger efficiency for trigger soup: txbb","version":1,"inputs":[{"name":"txbb","type":"real","description":"Jet TXbb"},{"name":"systematic","type":"string","description":"Systematic variation"}],"output":{"name":"weight","type":"real","description":"Jet data trigger efficiency"},"data":{"nodetype":"category","input":"systematic","content":[{"key":"nominal","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.013433322317682829,0.1276443100071743,0.17733811500181326,0.21819893316598682,0.26244541484716155,0.30397007360926753,0.36227018385291765,0.39069855017887406,0.45488149597738636,0.49029493319889084,0.5221112696148359,0.5805650419124495,0.6378348214285714,0.702152414194299,0.7362338545207342,0.7904974489795918,0.8151898734177215,0.8428919012860618,0.8928312816799421,0.9278937381404174],"flow":1.0}},{"key":"stat_up","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010123314964642314,0.0016706120088800563,0.002900527621677147,0.004084222419344774,0.005379266444214935,0.006915929493577946,0.008882480533361088,0.010114612519649983,0.011995855914141896,0.013571683241752598,0.015057772221417192,0.01687859391562389,0.017072816093012064,0.018644982629131078,0.020844460919083593,0.021244624663944365,0.023133580198088286,0.023236244644646246,0.024735962521462783,0.033637579202603074],"flow":1.0}},{"key":"stat_dn","value":{"nodetype":"binning","input":"txbb","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.00010123314964642314,0.0016706120088800563,0.002900527621677147,0.004084222419344774,0.005379266444214935,0.006915929493577946,0.008882480533361088,0.010114612519649983,0.011995855914141896,0.013571683241752598,0.015057772221417192,0.01687859391562389,0.017072816093012064,0.018644982629131078,0.020844460919083593,0.021244624663944365,0.023133580198088286,0.023236244644646246,0.024735962521462783,0.033637579202603074],"flow":1.0}}]}}]}
diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_TTbar.json
new file mode 100644
index 00000000..15a70d1b
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbb_TTbar.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_QCD.json b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_QCD.json
new file mode 100644
index 00000000..20352d00
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_QCD.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_TTbar.json b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_TTbar.json
new file mode 100644
index 00000000..991d814d
--- /dev/null
+++ b/src/HH4b/corrections/data/fatjet_triggereff_2023_txbbv11_TTbar.json
@@ -0,0 +1 @@
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diff --git a/src/HH4b/corrections/data/ttbarcorr_2022.json b/src/HH4b/corrections/data/ttbarcorr_2022.json
new file mode 100644
index 00000000..61b16561
--- /dev/null
+++ b/src/HH4b/corrections/data/ttbarcorr_2022.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"Tau3OverTau2","version":0,"inputs":[{"name":"xaxis","type":"real","description":"xaxis"}],"output":{"name":"weight","type":"real"},"data":{"nodetype":"binning","input":"xaxis","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.0,0.0,0.0,0.6168749752119087,0.38939666462177613,0.6540345969861341,0.8130034861521819,0.8232380008733856,0.80857344628639,0.9228789546300515,0.976589525806759,1.0093721673028306,1.0922555530200015,1.100082487039008,1.119558606307026,1.11808154156482,1.0432653203971105,0.9180914184522364,0.8359427505325836,0.957340608338002],"flow":"clamp"}},{"name":"Xbb","version":0,"inputs":[{"name":"xaxis","type":"real","description":"xaxis"}],"output":{"name":"weight","type":"real"},"data":{"nodetype":"binning","input":"xaxis","edges":[0.0,0.009999999776482582,0.09000000357627869,0.18000000715255737,0.27000001072883606,0.36000001430511475,0.44999998807907104,0.5400000214576721,0.6299999952316284,0.7200000286102295,0.7900000214576721,0.8500000238418579,0.9100000262260437,0.9399999976158142,0.9700000286102295,1.0],"content":[0.7880398393562502,1.1435109416780362,1.086128885762138,1.0278547923535586,0.9714792302429641,0.9742959046338915,0.9666221255871995,0.9467313978590316,0.9584705415050221,0.9893214095703733,1.0844436702387923,1.1223211939920719,1.1347035026121455,1.1659065337298058,1.1650369433623373],"flow":"clamp"}},{"name":"PTJJ","version":0,"inputs":[{"name":"xaxis","type":"real","description":"xaxis"}],"output":{"name":"weight","type":"real"},"data":{"nodetype":"binning","input":"xaxis","edges":[0.0,30.0,80.0,140.0,240.0,350.0,500.0,700.0,1000.0],"content":[0.92748666533527,1.021892793168479,1.0082211040548181,0.997014392101844,1.0497907533322275,1.0307098980591127,0.7858688285934999,0.5449755948685895],"flow":"clamp"}}]}
diff --git a/src/HH4b/corrections/data/ttbarcorr_2023.json b/src/HH4b/corrections/data/ttbarcorr_2023.json
new file mode 100644
index 00000000..d4e3870b
--- /dev/null
+++ b/src/HH4b/corrections/data/ttbarcorr_2023.json
@@ -0,0 +1 @@
+{"schema_version":2,"corrections":[{"name":"Tau3OverTau2","version":0,"inputs":[{"name":"xaxis","type":"real","description":"xaxis"}],"output":{"name":"weight","type":"real"},"data":{"nodetype":"binning","input":"xaxis","edges":[0.0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],"content":[0.0,0.0,1.1443394949711005,1.0791725518541246,0.7804590479378803,0.8433929781481829,0.8215885080118138,0.8852970379147735,0.9680610855771213,1.032041702206101,1.0123429233308854,1.0982472633374496,1.1562157368459185,1.1189828272246987,1.1284240864260218,1.0839357754839458,0.9827257634161055,0.8801316851154978,0.7466615968389546,0.7090557993060628],"flow":"clamp"}},{"name":"Xbb","version":0,"inputs":[{"name":"xaxis","type":"real","description":"xaxis"}],"output":{"name":"weight","type":"real"},"data":{"nodetype":"binning","input":"xaxis","edges":[0.0,0.009999999776482582,0.10000000149011612,0.20999999344348907,0.3100000023841858,0.4000000059604645,0.5,0.6000000238418579,0.699999988079071,0.800000011920929,0.8700000047683716,0.9200000166893005,0.9599999785423279,1.0],"content":[0.8371500758387456,1.0594760160715764,1.1028271657081041,1.0406177027159629,1.0063499558233893,0.9248778883658831,0.9452432382648285,0.9643222999131915,1.0138836286878052,1.038578717493114,1.1323969962391296,1.169833916630135,1.1552766400471006],"flow":"clamp"}},{"name":"PTJJ","version":0,"inputs":[{"name":"xaxis","type":"real","description":"xaxis"}],"output":{"name":"weight","type":"real"},"data":{"nodetype":"binning","input":"xaxis","edges":[0.0,50.0,100.0,150.0,250.0,400.0,550.0,1000.0],"content":[0.8508039329188849,1.010036939070987,1.0826686132931487,1.1257363614371256,1.2078068729282008,1.3340898168641595,0.9837438344099854],"flow":"clamp"}}]}
diff --git a/src/HH4b/corrections/trigger_weight.py b/src/HH4b/corrections/trigger_weight.py
new file mode 100644
index 00000000..9462d959
--- /dev/null
+++ b/src/HH4b/corrections/trigger_weight.py
@@ -0,0 +1,257 @@
+"""
+Convert ROOT files with trigger SFs derived by Armen to correctionlib
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+import uproot
+from correctionlib import schemav2
+
+
+def get_corr_pt_msd(eff, eff_unc_up, eff_unc_dn, year, label, trigger_label, edges):
+    def multibinning(eff):
+        return schemav2.MultiBinning(
+            nodetype="multibinning",
+            inputs=["pt", "msd"],
+            edges=edges,
+            content=list(eff.flatten()),
+            flow=1.0,  # SET FLOW TO 1.0
+        )
+
+    corr = schemav2.Correction(
+        name=f"fatjet_triggereff{label}_{year}_{trigger_label}",
+        description=f"{label} Trigger efficiency for trigger soup: {trigger_label}",
+        version=1,
+        inputs=[
+            schemav2.Variable(
+                name="pt",
+                type="real",
+                description="Jet transverse momentum",
+            ),
+            schemav2.Variable(
+                name="msd",
+                type="real",
+                description="Jet softdrop mass",
+            ),
+            schemav2.Variable(
+                name="systematic",
+                type="string",
+                description="Systematic variation",
+            ),
+        ],
+        output=schemav2.Variable(
+            name="weight", type="real", description=f"Jet {label} trigger efficiency"
+        ),
+        data=schemav2.Category(
+            nodetype="category",
+            input="systematic",
+            content=[
+                {"key": "nominal", "value": multibinning(eff)},
+                {"key": "stat_up", "value": multibinning(eff_unc_up)},
+                {"key": "stat_dn", "value": multibinning(eff_unc_dn)},
+            ],
+        ),
+    )
+    return corr
+
+
+def get_corr_txbb(eff, eff_unc_up, eff_unc_dn, year, label, trigger_label, edges):
+    def singlebinning(eff):
+        return schemav2.Binning(
+            nodetype="binning",
+            input="txbb",
+            edges=edges,
+            content=list(eff.flatten()),
+            flow=1.0,  # SET FLOW TO 1.0
+        )
+
+    corr = schemav2.Correction(
+        name=f"fatjet_triggereff{label}_{year}_{trigger_label}",
+        description=f"{label} Trigger efficiency for trigger soup: {trigger_label}",
+        version=1,
+        inputs=[
+            schemav2.Variable(
+                name="txbb",
+                type="real",
+                description="Jet TXbb",
+            ),
+            schemav2.Variable(
+                name="systematic",
+                type="string",
+                description="Systematic variation",
+            ),
+        ],
+        output=schemav2.Variable(
+            name="weight", type="real", description=f"Jet {label} trigger efficiency"
+        ),
+        data=schemav2.Category(
+            nodetype="category",
+            input="systematic",
+            content=[
+                {"key": "nominal", "value": singlebinning(eff)},
+                {"key": "stat_up", "value": singlebinning(eff_unc_up)},
+                {"key": "stat_dn", "value": singlebinning(eff_unc_dn)},
+            ],
+        ),
+    )
+    return corr
+
+
+def main(args):
+
+    region = args.region
+
+    corr_pt_msd = {
+        "2022": {
+            "fname": f"data/trigger/2022/PT_Mass_2dSF_{region}.root",
+            "data": "Eff_Data_PreEE",
+            "mc": "Eff_MC_PreEE",
+        },
+        "2022EE": {
+            "fname": f"data/trigger/2022/PT_Mass_2dSF_{region}.root",
+            "data": "Eff_Data_PostEE",
+            "mc": "Eff_MC_PostEE",
+        },
+        "2023": {
+            "fname": f"data/trigger/2023/PT_Mass_SF_{region}/PT_Mass_2dSF_PreBPix.root",
+            "data": "Eff_Data_ETA0",
+            "mc": "Eff_MC_ETA0",
+        },
+        "2023BPix": {
+            "fname": f"data/trigger/2023/PT_Mass_SF_{region}/PT_Mass_2dSF_PostBPix.root",
+            "data": "Eff_Data_ETA0",
+            "mc": "Eff_MC_ETA0",
+        },
+    }
+
+    corr_txbb = {
+        "2022": {
+            "fname": f"data/trigger/2022/BTG_SF_{region}_PreEE.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+        "2022EE": {
+            "fname": f"data/trigger/2022/BTG_SF_{region}_PostEE.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+        "2023": {
+            "fname": f"data/trigger/2023/Online_BTG_{region}/BTG_SF.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+        "2023BPix": {
+            "fname": f"data/trigger/2023/Online_BTG_{region}/BTG_SF.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+    }
+
+    corr_txbb_v11 = {
+        "2022": {
+            "fname": f"data/trigger/BTG_SFs_TXbb/{region}/2022/BTG_SF_TXbb_PreEE.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+        "2022EE": {
+            "fname": f"data/trigger/BTG_SFs_TXbb/{region}/2022/BTG_SF_TXbb_PostEE.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+        "2023": {
+            "fname": f"data/trigger/BTG_SFs_TXbb/{region}/2023/BTG_SF_TXbb_PreBPix.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+        "2023BPix": {
+            "fname": f"data/trigger/BTG_SFs_TXbb/{region}/2023/BTG_SF_TXbb_PostBPix.root",
+            "data": "BTG_Eff_Data",
+            "mc": "BTG_Eff_MC",
+        },
+    }
+
+    for year, corr_items in corr_pt_msd.items():
+        f = uproot.open(corr_items["fname"])
+        corrs = {}
+        for key in ["data", "mc"]:
+            h = f[corr_items[key]].to_hist()
+            edges = [
+                h.axes[0].edges,
+                h.axes[1].edges,
+            ]
+
+            corr = get_corr_pt_msd(
+                h.values(),
+                np.sqrt(h.variances()),
+                np.sqrt(h.variances()),
+                year,
+                key,
+                "ptmsd",
+                edges,
+            )
+
+            # rich.print(corr)
+            corrs[key] = corr
+
+        cset = schemav2.CorrectionSet(schema_version=2, corrections=[corrs["mc"], corrs["data"]])
+        path = Path(f"data/fatjet_triggereff_{year}_ptmsd_{region}.json")
+        with path.open("w") as fout:
+            fout.write(cset.json(exclude_unset=True))
+
+    for year, corr_items in corr_txbb.items():
+        f = uproot.open(corr_items["fname"])
+        corrs = {}
+        for key in ["data", "mc"]:
+            h = f[corr_items[key]].to_hist()
+            edges = h.axes.edges[0]
+            corr = get_corr_txbb(
+                h.values(),
+                np.sqrt(h.variances()),
+                np.sqrt(h.variances()),
+                year,
+                key,
+                "txbb",
+                edges,
+            )
+            # rich.print(corr)
+            corrs[key] = corr
+
+        cset = schemav2.CorrectionSet(schema_version=2, corrections=[corrs["mc"], corrs["data"]])
+        path = Path(f"data/fatjet_triggereff_{year}_txbb_{region}.json")
+        with path.open("w") as fout:
+            fout.write(cset.json(exclude_unset=True))
+
+    for year, corr_items in corr_txbb_v11.items():
+        f = uproot.open(corr_items["fname"])
+        corrs = {}
+        for key in ["data", "mc"]:
+            h = f[corr_items[key]].to_hist()
+            edges = h.axes.edges[0]
+            corr = get_corr_txbb(
+                h.values(),
+                np.sqrt(h.variances()),
+                np.sqrt(h.variances()),
+                year,
+                key,
+                "txbbv11",
+                edges,
+            )
+            # rich.print(corr)
+            corrs[key] = corr
+
+        cset = schemav2.CorrectionSet(schema_version=2, corrections=[corrs["mc"], corrs["data"]])
+        path = Path(f"data/fatjet_triggereff_{year}_txbbv11_{region}.json")
+        with path.open("w") as fout:
+            fout.write(cset.json(exclude_unset=True))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("--region", required=True, choices=["QCD", "TTbar"], type=str)
+    args = parser.parse_args()
+
+    main(args)
diff --git a/src/HH4b/corrections/ttbar_weight.py b/src/HH4b/corrections/ttbar_weight.py
new file mode 100644
index 00000000..03b3a6e5
--- /dev/null
+++ b/src/HH4b/corrections/ttbar_weight.py
@@ -0,0 +1,38 @@
+"""
+Convert ROOT files with trigger SFs derived by Armen to correctionlib
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import correctionlib.convert
+import uproot
+from correctionlib import schemav2
+
+
+def main():
+
+    for year in ["2022", "2023"]:
+        corrs = {}
+        for corr_key in ["Tau3OverTau2", "Xbb", "PTJJ"]:
+            f = uproot.open(f"data/tt_corr/TTbar_Offline_SFs_{year}/{corr_key}_SF.root")
+            sfhist = f["SF"].to_hist()
+
+            sfhist.name = corr_key
+            sfhist.label = "weight"
+            corr = correctionlib.convert.from_histogram(sfhist)
+            corr.data.flow = "clamp"
+            print(corr)
+
+            corrs[corr_key] = corr
+        cset = schemav2.CorrectionSet(
+            schema_version=2, corrections=[corrs["Tau3OverTau2"], corrs["Xbb"], corrs["PTJJ"]]
+        )
+        path = Path(f"data/ttbarcorr_{year}.json")
+        with path.open("w") as fout:
+            fout.write(cset.json(exclude_unset=True))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/HH4b/plotting.py b/src/HH4b/plotting.py
index aba8df60..86f8584c 100644
--- a/src/HH4b/plotting.py
+++ b/src/HH4b/plotting.py
@@ -5,7 +5,6 @@
 from copy import deepcopy
 from pathlib import Path
 
-import hist
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import matplotlib.ticker as mticker
@@ -17,7 +16,7 @@
 from numpy.typing import ArrayLike
 from tqdm import tqdm
 
-from .hh_vars import LUMI, data_key, hbb_bg_keys, sig_keys
+from .hh_vars import LUMI, data_key
 
 plt.style.use(hep.style.CMS)
 hep.style.use("CMS")
@@ -145,282 +144,73 @@
 ]
 
 
-def plot_hists(
-    hists,
-    vars_to_plot,
-    luminosity=None,  # float (fb)
-    add_data=True,
-    add_data_over_mc=True,
-    mult_factor=1,  # multiplicative factor for signal
-    logy=True,
-    density=False,
-    stack=True,
-    show=False,
-    bbox_to_anchor=(1.05, 1),
-    energy=13.6,
+def sigErrRatioPlot(
+    h: Hist,
+    sig_key: str,
+    wshift: str,
+    xlabel: str,
+    title: str = None,
+    plot_dir: str = None,
+    name: str = None,
+    show: bool = False,
+    ylim: list = None,
 ):
-    if add_data_over_mc and not add_data:
-        add_data_over_mc = False
-    if density:
-        add_data_over_mc = False
-
-    for var in vars_to_plot:
-        if var not in hists:
-            print(f"{var} not stored in hists")
-            continue
+    fig, (ax, rax) = plt.subplots(
+        2, 1, figsize=(12, 14), gridspec_kw={"height_ratios": [3, 1], "hspace": 0}, sharex=True
+    )
 
-        print(f"Will plot {var} histogram")
-        h = hists[var]
-
-        samples = [h.axes[0].value(i) for i in range(len(h.axes[0].edges))]
-
-        signal_labels = [label for label in samples if label in sig_keys]
-        signal = [h[{"Sample": label}] for label in signal_labels]
-        signal_mult = [s * mult_factor for s in signal]
-
-        bkg_labels = [
-            label
-            for label in samples
-            if (label and label not in signal_labels and (label not in ["data"]))
-        ]
-        bkg = [h[{"Sample": label}] for label in bkg_labels]
-
-        if add_data_over_mc:
-            fig, (ax, rax) = plt.subplots(
-                nrows=2,
-                ncols=1,
-                figsize=(8, 8),
-                gridspec_kw={"height_ratios": (4, 1), "hspace": 0.07},
-                sharex=True,
-            )
-        else:
-            fig, ax = plt.subplots(figsize=(8, 8))
-            rax = None
-        plt.subplots_adjust(hspace=0)
-
-        # plot data
-        if add_data:
-            data = h[{"Sample": "data"}]
-            hep.histplot(
-                data,
-                ax=ax,
-                histtype="errorbar",
-                color="k",
-                capsize=4,
-                yerr=True,
-                label="Data",
-                **data_err_opts,
-            )
+    nom = h[f"{sig_key}_{wshift}", :].values()
+    hep.histplot(
+        h[f"{sig_key}_{wshift}", :],
+        histtype="step",
+        label=sig_key,
+        yerr=False,
+        color="k",
+        ax=ax,
+        linewidth=2,
+    )
 
-        # plot bkg
-        # accumulate values and bins (little trick to avoid having error bars at the end)
-        bkg_hists = []
-        bkg_bins = []
-        for h in bkg:
-            hist_values, bins = h.to_numpy()
-            bkg_hists.append(hist_values)
-            bkg_bins.append(bins)
-
-        if stack:
-            bkg_args = {
-                "histtype": "fill",
-                "edgecolor": "black",
-            }
-        else:
-            bkg_args = {
-                "histtype": "step",
-            }
+    for skey, shift in [("Up", "up"), ("Down", "down")]:
+        if f"{sig_key}_{wshift}_{shift}" not in h.axes[0]:
+            continue
 
+        colour = {"up": "#81C14B", "down": "#1f78b4"}[shift]
         hep.histplot(
-            bkg,
+            h[f"{sig_key}_{wshift}_{shift}", :],
+            histtype="step",
+            yerr=False,
+            label=f"{sig_key} {skey}",
+            color=colour,
             ax=ax,
-            stack=stack,
-            edges=True,
-            sort="yield",
-            w2method=None,
-            linewidth=1,
-            density=density,
-            label=[label_by_sample[bkg_label] for bkg_label in bkg_labels],
-            color=[color_by_sample[bkg_label] for bkg_label in bkg_labels],
-            **bkg_args,
+            linewidth=2,
         )
 
-        # sum all the background
-        tot = bkg[0].copy()
-        for i, b in enumerate(bkg):
-            if i > 0:
-                tot = tot + b
-
-        tot_val = tot.values()
-        tot_val_zero_mask = tot_val == 0
-        tot_val[tot_val_zero_mask] = 1
-
-        tot_err = np.sqrt(tot_val)
-        tot_err[tot_val_zero_mask] = 0
-
-        # plot bkg uncertainty
-        # print(tot.values().shape)
-        # print(tot.axes[0].edges.shape)
-        if not density:
-            ax.stairs(
-                values=tot.values() + tot_err,
-                baseline=tot.values() - tot_err,
-                edges=tot.axes[0].edges,
-                **ps,
-                label="Stat. unc.",
-            )
-
-        # plot signal
-        if len(signal) > 0:
-            # tot_signal = None
-
-            for i, sig in enumerate(signal_mult):
-                lab_sig_mult = f"{mult_factor} * {label_by_sample[signal_labels[i]]}"
-                if mult_factor == 1:
-                    lab_sig_mult = f"{label_by_sample[signal_labels[i]]}"
-                # print(lab_sig_mult)
-                hep.histplot(
-                    sig,
-                    ax=ax,
-                    label=lab_sig_mult,
-                    linewidth=1,
-                    density=density,
-                    color=color_by_sample[signal_labels[i]],
-                )
-
-                # if tot_signal is None:
-                #     tot_signal = signal[i].copy()
-                # else:
-                #     tot_signal = tot_signal + signal[i]
-
-            # plot the total signal (w/o scaling)
-            # hep.histplot(tot_signal, ax=ax, label="Total signal", linewidth=3, color="tab:red")
-
-            # add MC stat errors for total signal
-            # ax.stairs(
-            #    values=tot_signal.values() + np.sqrt(tot_signal.values()),
-            #     baseline=tot_signal.values() - np.sqrt(tot_signal.values()),
-            #    edges=sig.axes[0].edges,
-            #   **errps,
-            # )
-
-        # plot data/mc ratio
-        if add_data_over_mc:
-            data_val = data.values()
-            data_val[tot_val_zero_mask] = 1
-            yerr = ratio_uncertainty(data_val, tot_val, "poisson")
-
-            hep.histplot(
-                data_val / tot_val,
-                tot.axes[0].edges,
-                yerr=yerr,
-                ax=rax,
-                histtype="errorbar",
-                color="k",
-                capsize=4,
-            )
-            rax.grid()
-
-        ax.set_ylabel("Events")
-        ax.set_xlabel("")
-
-        if rax is not None:
-            rax.set_xlabel(
-                f"{h.axes[-1].label}"
-            )  # assumes the variable to be plotted is at the last axis
-            rax.set_ylabel("Data/MC", fontsize=20)
-        else:
-            ax.set_xlabel(f"{h.axes[-1].label}")
-
-        if luminosity:
-            hep.cms.lumitext(
-                "%.1f " % luminosity + r"fb$^{-1}$" + f"({energy} TeV)", ax=ax, fontsize=20
-            )
-            hep.cms.text("Internal", ax=ax, fontsize=15)
-
-        # add legend
-        handles, labels = ax.get_legend_handles_labels()
-
-        # get total yield of backgrounds per label
-        first_key = next(iter(hists.keys()))
-        # (sort by yield after pre-sel)
-        order_dic = {}
-        for bkg_label in bkg_labels:
-            bkg_yield = hists[first_key][{"Sample": bkg_label}].sum().value
-            order_dic[label_by_sample[bkg_label]] = bkg_yield
-
-        summ = [order_dic[label] for label in labels[: len(bkg_labels)]]
-
-        # get indices of labels arranged by yield
-        order = []
-        for _ in range(len(summ)):
-            order.append(np.argmax(np.array(summ)))
-            summ[np.argmax(np.array(summ))] = -100
-
-        # print(labels)
-        # print(labels[-1])
-        if add_data:
-            legend_handles = [handles[-1]] + [handles[i] for i in order] + handles[len(bkg) : -1]
-            legend_labels = [labels[-1]] + [labels[i] for i in order] + labels[len(bkg) : -1]
-            loc = "upper left"
-        else:
-            legend_handles = [handles[i] for i in order] + handles[len(bkg) :]
-            legend_labels = [labels[i] for i in order] + labels[len(bkg) :]
-            loc = "best"
-
-        ax.legend(
-            [legend_handles[idx] for idx in range(len(legend_handles))],
-            [legend_labels[idx] for idx in range(len(legend_labels))],
-            bbox_to_anchor=bbox_to_anchor,
-            loc=loc,
+        hep.histplot(
+            h[f"{sig_key}_{wshift}_{shift}", :] / nom,
+            histtype="step",
+            label=f"{sig_key} {skey}",
+            color=colour,
+            ax=rax,
         )
 
-        if logy:
-            ax.set_yscale("log")
-            ax.set_ylim(1e-1)
-
-        outpath = Path("plots")
-        if not outpath.exists():
-            outpath.mkdir(parents=True)
-
-        if show:
-            plt.show()
-        else:
-            plt.close()
-
-        plt.savefig(f"{outpath}/{var}.pdf", bbox_inches="tight")
-
-
-def _combine_hbb_bgs(hists, bg_keys):
-    # skip this if no hbb bg keys specified
-    if len(set(bg_keys) & set(hbb_bg_keys)) == 0:
-        return hists, bg_keys
-
-    # combine all hbb backgrounds into a single "Hbb" background for plotting
-    hbb_hists = []
-    for key in hbb_bg_keys:
-        if key in bg_keys:
-            hbb_hists.append(hists[key, ...])
-            bg_keys.remove(key)
-
-    if "Hbb" not in bg_keys:
-        bg_keys.append("Hbb")
-
-    hbb_hist = sum(hbb_hists)
-
-    # have to recreate hist with "Hbb" sample included
-    h = Hist(
-        hist.axis.StrCategory(list(hists.axes[0]) + ["Hbb"], name="Sample"),
-        *hists.axes[1:],
-        storage="weight",
-    )
-
-    for i, sample in enumerate(hists.axes[0]):
-        h.view()[i] = hists[sample, ...].view()
+    ax.legend()
+    ax.set_ylim(0)
+    ax.set_ylabel("Events")
+    ax.set_title(title, y=1.08)
 
-    h.view()[-1] = hbb_hist
+    rax.set_ylim([0, 2])
+    if ylim is not None:
+        rax.set_ylim(ylim)
+    rax.set_xlabel(xlabel)
+    rax.legend()
+    rax.set_ylabel("Variation / Nominal")
+    rax.grid(axis="y")
 
-    return h, bg_keys
+    plt.savefig(f"{plot_dir}/{name}.pdf", bbox_inches="tight")
+    if show:
+        plt.show()
+    else:
+        plt.close()
 
 
 def sigErrRatioPlot(
@@ -631,7 +421,6 @@ def ratioHistPlot(
 
     # copy hists and bg_keys so input objects are not changed
     hists, bg_keys = deepcopy(hists), deepcopy(bg_keys)
-    # hists, bg_keys = _combine_hbb_bgs(hists, bg_keys)
 
     if bg_order is None:
         bg_order = bg_order_default
diff --git a/src/HH4b/postprocessing/CombineTemplates.ipynb b/src/HH4b/postprocessing/CombineTemplates.ipynb
index c780c8b7..e83e7740 100644
--- a/src/HH4b/postprocessing/CombineTemplates.ipynb
+++ b/src/HH4b/postprocessing/CombineTemplates.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,13 +185,15 @@
    "source": [
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "sig_key = \"hh4b\"\n",
+    "# sig_key = \"hh4b\"\n",
     "# sig_key = \"vbfhh4b-k2v0\"\n",
+    "sig_key = \"ttbar\"\n",
     "\n",
     "for rname, region in selection_regions.items():\n",
     "    plot_dir = main_plot_dir / year / \"wshifts\"\n",
     "    plot_dir.mkdir(exist_ok=True, parents=True)\n",
-    "    for wshift in list((jecs).keys()):\n",
+    "    # for wshift in list((jecs).keys()):\n",
+    "    for wshift in [\"ttbarSF\", \"trigger\"]:\n",
     "        if wshift in jecs:\n",
     "            # adding jshift-ed histograms into the same histogram\n",
     "            cjshift_templates = [templates[rname]]\n",
diff --git a/src/HH4b/postprocessing/CreateDatacard.py b/src/HH4b/postprocessing/CreateDatacard.py
index 6d41be43..d378133f 100644
--- a/src/HH4b/postprocessing/CreateDatacard.py
+++ b/src/HH4b/postprocessing/CreateDatacard.py
@@ -234,6 +234,8 @@
     # ),
     # TODO: separate into individual
     "JES": Syst(name="CMS_scale_j", prior="shape", samples=sig_keys),  # TODO: update to all_mc
+    "ttbarSF": Syst(name=f"{CMS_PARAMS_LABEL}_ttbar_sf", prior="shape", samples=["ttbar"]),
+    "trigger": Syst(name=f"{CMS_PARAMS_LABEL}_trigger", prior="shape", samples=all_mc),
     # "txbb": Syst(
     #     name=f"{CMS_PARAMS_LABEL}_PNetHbbScaleFactors_correlated",
     #     prior="shape",
diff --git a/src/HH4b/postprocessing/PostProcess.py b/src/HH4b/postprocessing/PostProcess.py
index ee278016..b656f97b 100644
--- a/src/HH4b/postprocessing/PostProcess.py
+++ b/src/HH4b/postprocessing/PostProcess.py
@@ -7,6 +7,7 @@
 from pathlib import Path
 from typing import Callable
 
+import corrections
 import hist
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -23,6 +24,7 @@
     Region,
     combine_run3_samples,
     load_run3_samples,
+    weight_shifts,
 )
 from HH4b.utils import ShapeVar, check_get_jec_var, get_var_mapping, singleVarHist
 
@@ -165,71 +167,82 @@ def bdt_roc(events_combined: dict[str, pd.DataFrame], plot_dir: str, legacy: boo
         plt.close()
 
 
-def load_process_run3_samples(args, year, bdt_training_keys, control_plots, plot_dir):
+def load_process_run3_samples(args, year, bdt_training_keys, control_plots, weight_plots, plot_dir):
     legacy_label = "Legacy" if args.legacy else ""
 
-    events_dict = load_run3_samples(
-        f"{args.data_dir}/{args.tag}",
-        year,
-        args.legacy,
-        samples_run3,
-        reorder_txbb=True,
-        txbb=f"bbFatJetPNetTXbb{legacy_label}",
-    )
-
-    cutflow = pd.DataFrame(index=list(events_dict.keys()))
-    cutflow_print = pd.DataFrame(index=list(events_dict.keys()))
-    cutflow_dict = {
-        key: OrderedDict(
-            [("Skimmer Preselection", np.sum(events_dict[key]["finalWeight"].to_numpy()))]
-        )
-        for key in events_dict
-    }
-
     # define BDT model
     bdt_model = xgb.XGBClassifier()
     bdt_model.load_model(fname=f"../boosted/bdt_trainings_run3/{args.bdt_model}/trained_bdt.model")
+
     # get function
     make_bdt_dataframe = importlib.import_module(
         f".{args.bdt_config}", package="HH4b.boosted.bdt_trainings_run3"
     )
 
-    # inference and assign score
+    # make histograms to validate weights
+    h_weights = hist.Hist(
+        hist.axis.StrCategory([], name="samp", growth=True),
+        hist.axis.Regular(30, 0, 1.5, name="evweight", label="evweight"),
+    )
+    h_mass = hist.Hist(
+        hist.axis.StrCategory([], name="samp", growth=True),
+        hist.axis.Regular(16, 60, 220, name="h2mass", label="Jet 2 mass (GeV)"),
+    )
+
+    # define cutflows
+    samples_year = list(samples_run3[year].keys())
+    cutflow = pd.DataFrame(index=samples_year)
+    cutflow_dict = {}
+
+    # region in which QCD trigger weights were extracted
+    trigger_region = "QCD"
+
     events_dict_postprocess = {}
-    for key in events_dict:
+    for key in samples_year:
+        samples_to_process = {year: {key: samples_run3[year][key]}}
 
+        events_dict = load_run3_samples(
+            f"{args.data_dir}/{args.tag}",
+            year,
+            args.legacy,
+            samples_to_process,
+            reorder_txbb=True,
+            txbb=f"bbFatJetPNetTXbb{legacy_label}",
+        )[key]
+
+        cutflow_dict[key] = OrderedDict(
+            [("Skimmer Preselection", np.sum(events_dict["finalWeight"].to_numpy()))]
+        )
+
+        # inference and assign score
         jshifts = [""] + hh_vars.jec_shifts if key in hh_vars.syst_keys else [""]
 
         bdt_events = {}
         for jshift in jshifts:
             bdt_events[jshift] = make_bdt_dataframe.bdt_dataframe(
-                events_dict[key], get_var_mapping(jshift)
+                events_dict, get_var_mapping(jshift)
             )
             preds = bdt_model.predict_proba(bdt_events[jshift])
             add_bdt_scores(bdt_events[jshift], preds, jshift)
         bdt_events = pd.concat([bdt_events[jshift] for jshift in jshifts], axis=1)
-        bdt_events["H1Pt"] = events_dict[key]["bbFatJetPt"].to_numpy()[:, 0]
-        bdt_events["H2Pt"] = events_dict[key]["bbFatJetPt"].to_numpy()[:, 1]
-        bdt_events["H1Msd"] = events_dict[key]["bbFatJetMsd"].to_numpy()[:, 0]
-        bdt_events["H2Msd"] = events_dict[key]["bbFatJetMsd"].to_numpy()[:, 1]
-        bdt_events["H1TXbb"] = events_dict[key][f"bbFatJetPNetTXbb{legacy_label}"].to_numpy()[:, 0]
-        bdt_events["H2TXbb"] = events_dict[key][f"bbFatJetPNetTXbb{legacy_label}"].to_numpy()[:, 1]
-        bdt_events["H1PNetMass"] = events_dict[key][f"bbFatJetPNetMass{legacy_label}"].to_numpy()[
-            :, 0
-        ]
-        bdt_events["H2PNetMass"] = events_dict[key][f"bbFatJetPNetMass{legacy_label}"].to_numpy()[
-            :, 1
-        ]
-        bdt_events["H1TXbbNoLeg"] = events_dict[key]["bbFatJetPNetTXbb"].to_numpy()[:, 0]
-        bdt_events["H2TXbbNoLeg"] = events_dict[key]["bbFatJetPNetTXbb"].to_numpy()[:, 1]
+        bdt_events["H1Pt"] = events_dict["bbFatJetPt"][0]
+        bdt_events["H2Pt"] = events_dict["bbFatJetPt"][1]
+        bdt_events["H1Msd"] = events_dict["bbFatJetMsd"][0]
+        bdt_events["H2Msd"] = events_dict["bbFatJetMsd"][1]
+        bdt_events["H1TXbb"] = events_dict[f"bbFatJetPNetTXbb{legacy_label}"][0]
+        bdt_events["H2TXbb"] = events_dict[f"bbFatJetPNetTXbb{legacy_label}"][1]
+        bdt_events["H1PNetMass"] = events_dict[f"bbFatJetPNetMass{legacy_label}"][0]
+        bdt_events["H2PNetMass"] = events_dict[f"bbFatJetPNetMass{legacy_label}"][1]
+        bdt_events["H1TXbbNoLeg"] = events_dict["bbFatJetPNetTXbb"][0]
+        bdt_events["H2TXbbNoLeg"] = events_dict["bbFatJetPNetTXbb"][1]
 
         # add HLTs
         bdt_events["hlt"] = np.any(
             np.array(
                 [
-                    events_dict[key][trigger].to_numpy()[:, 0]
+                    events_dict[trigger].to_numpy()[:, 0]
                     for trigger in postprocessing.HLTs[year]
-                    if trigger in events_dict[key]
+                    if trigger in events_dict
                 ]
             ),
             axis=0,
@@ -237,15 +250,87 @@ def load_process_run3_samples(args, year, bdt_training_keys, control_plots, plot
 
         # weights
         # finalWeight: includes genWeight, puWeight
-        # FIXME: genWeight taken only as sign for HH sample...
-        bdt_events["weight"] = events_dict[key]["finalWeight"].to_numpy()
+        nominal_weight = events_dict["finalWeight"].to_numpy()
+
+        nevents = len(events_dict["bbFatJetPt"][0])
+        trigger_weight = np.ones(nevents)
+        if key != "data":
+            trigger_weight, trigger_weight_up, trigger_weight_dn = corrections.trigger_SF(
+                year, events_dict, f"PNetTXbb{legacy_label}", trigger_region
+            )
+            h_weights.fill(f"{key}_trigger", trigger_weight)
+            h_weights.fill(f"{key}_trigger_up", trigger_weight_up)
+            h_weights.fill(f"{key}_trigger_down", trigger_weight_dn)
+
+            h_mass.fill(
+                f"{key}_trigger", bdt_events[args.mass], weight=nominal_weight * trigger_weight
+            )
+            h_mass.fill(
+                f"{key}_trigger_up",
+                bdt_events[args.mass],
+                weight=nominal_weight * trigger_weight_up,
+            )
+            h_mass.fill(
+                f"{key}_trigger_down",
+                bdt_events[args.mass],
+                weight=nominal_weight * trigger_weight_dn,
+            )
+
+        # tt corrections
+        ttbar_weight = np.ones(nevents)
+        if key == "ttbar":
+            ptjjsf = corrections.ttbar_SF(year, bdt_events, "PTJJ", "HHPt")
+            tau32sf = corrections.ttbar_SF(
+                year, bdt_events, "Tau3OverTau2", "H1T32"
+            ) * corrections.ttbar_SF(year, bdt_events, "Tau3OverTau2", "H2T32")
+            txbbsf = corrections.ttbar_SF(year, bdt_events, "Xbb", "H1TXbb") * corrections.ttbar_SF(
+                year, bdt_events, "Xbb", "H2TXbb"
+            )
+
+            ttbar_weight = ptjjsf * txbbsf * tau32sf
+
+            h_weights.fill(f"{key}_ptjj", ptjjsf)
+            h_weights.fill(f"{key}_tau32", tau32sf)
+            h_weights.fill(f"{key}_txbb", txbbsf)
+            # h_mass.fill(f"{key}_ptjj",  bdt_events[args.mass], weight=nominal_weight * trigger_weight * ptjjsf)
+            # h_mass.fill(f"{key}_tau32",  bdt_events[args.mass], weight=nominal_weight * trigger_weight * tau32sf)
+            # h_mass.fill(f"{key}_txbb",  bdt_events[args.mass], weight=nominal_weight * trigger_weight * txbbsf)
+            h_mass.fill(
+                f"{key}_ttsf",
+                bdt_events[args.mass],
+                weight=nominal_weight * trigger_weight * ptjjsf * txbbsf,
+            )
+            h_mass.fill(
+                f"{key}_ttsf_down", bdt_events[args.mass], weight=nominal_weight * trigger_weight
+            )
 
-        ## Add TTBar Weight here TODO: does this need to be re-measured for legacy PNet Mass?
-        # if key == "ttbar" and not args.legacy:
-        #    bdt_events["weight"] *= corrections.ttbar_pTjjSF(year, events_dict, "bbFatJetPNetMass")
+            h_mass.fill(
+                f"{key}_ttsftau32",
+                bdt_events[args.mass],
+                weight=nominal_weight * trigger_weight * ptjjsf * txbbsf * tau32sf,
+            )
+            h_mass.fill(
+                f"{key}_ttsftau32_down",
+                bdt_events[args.mass],
+                weight=nominal_weight * trigger_weight,
+            )
+
+        bdt_events["weight_ttbar"] = ttbar_weight
+
+        # FIXME: genWeight taken only as sign for HH sample...
+        bdt_events["weight_nottbar"] = nominal_weight * trigger_weight
+        bdt_events["weight"] = nominal_weight * trigger_weight * ttbar_weight
+        if key != "data":
+            bdt_events["weight_triggerUp"] = nominal_weight * trigger_weight_up * ttbar_weight
+            bdt_events["weight_triggerDown"] = nominal_weight * trigger_weight_dn * ttbar_weight
+        if key == "ttbar":
+            bdt_events["weight_ttbarSFUp"] = (
+                nominal_weight * trigger_weight * ttbar_weight * ttbar_weight
+            )
+            bdt_events["weight_ttbarSFDown"] = nominal_weight * trigger_weight
 
         # add selection to testing events
-        bdt_events["event"] = events_dict[key]["event"].to_numpy()[:, 0]
+        bdt_events["event"] = events_dict["event"][0]
         if (
             args.training_years is not None
             and year in args.training_years
@@ -367,6 +452,18 @@ def load_process_run3_samples(args, year, bdt_training_keys, control_plots, plot
             bdt_events["weight"][mask_bin1 & mask_mass].to_numpy()
         )
 
+        if key == "ttbar":
+            h_mass.fill(
+                f"{key}_ttsftau32bin1",
+                bdt_events[args.mass][mask_bin1],
+                weight=(bdt_events["weight_ttbar"] * bdt_events["weight_nottbar"])[mask_bin1],
+            )
+            h_mass.fill(
+                f"{key}_ttsftau32bin1_down",
+                bdt_events[args.mass][mask_bin1],
+                weight=bdt_events["weight_nottbar"][mask_bin1],
+            )
+
         cutflow_dict[key]["VBF & Bin 1 overlap"] = np.sum(
             bdt_events["weight"][
                 (bdt_events["H2TXbb"] > args.txbb_wps[0])
@@ -396,6 +493,10 @@ def load_process_run3_samples(args, year, bdt_training_keys, control_plots, plot
             ]
         if "bdt_score_vbf" in bdt_events:
             columns += [check_get_jec_var("bdt_score_vbf", jshift) for jshift in jshifts]
+        if key == "ttbar":
+            columns += ["weight_ttbarSFUp", "weight_ttbarSFDown"]
+        if key != "data":
+            columns += ["weight_triggerUp", "weight_triggerDown"]
         columns = list(set(columns))
 
         if control_plots:
@@ -429,12 +530,56 @@ def load_process_run3_samples(args, year, bdt_training_keys, control_plots, plot
         for key in events_dict_postprocess:
             events_dict_postprocess[key] = events_dict_postprocess[key][columns]
 
-    for cut in cutflow_dict[key]:
-        cutflow[cut] = [cutflow_dict[key][cut] for key in events_dict]
-        cutflow_print[cut] = [f"{cutflow_dict[key][cut]:.2f}" for key in events_dict]
+    # make plots of weights
+    if weight_plots:
+        plotting.sigErrRatioPlot(
+            h_weights,
+            "hh4b",
+            "trigger",
+            "Event weight",
+            plot_dir=plot_dir,
+            name=f"{year}_sig_trigger{trigger_region}",
+            show=False,
+            ylim=[0, 2],
+        )
+        plotting.sigErrRatioPlot(
+            h_mass,
+            "hh4b",
+            "trigger",
+            "Jet 2 Mass (GeV)",
+            plot_dir=plot_dir,
+            name=f"mass_{year}_sig_trigger{trigger_region}",
+            show=False,
+            ylim=[0.9, 1.1],
+        )
+        for key in ["ptjj", "tau32", "txbb"]:
+            plotting.sigErrRatioPlot(
+                h_weights,
+                "ttbar",
+                key,
+                "Event weight",
+                plot_dir=plot_dir,
+                name=f"{year}_ttbar_{key}",
+                show=False,
+                ylim=[0, 2],
+            )
+        for key in ["ttsf", "ttsftau32", "ttsftau32bin1"]:
+            plotting.sigErrRatioPlot(
+                h_mass,
+                "ttbar",
+                key,
+                "Jet 2 Mass (GeV)",
+                plot_dir=plot_dir,
+                name=f"mass_{year}_ttbar_{key}",
+                show=False,
+                ylim=[0.5, 1.5],
+            )
+
+    for cut in cutflow_dict["hh4b"]:
+        cutflow[cut] = [cutflow_dict[key][cut].round(2) for key in events_dict_postprocess]
 
     print("\nCutflow")
-    print(cutflow_print)
+    print(cutflow)
     return events_dict_postprocess, cutflow
 
 
@@ -771,6 +916,7 @@ def postprocess_run3(args):
             year,
             bdt_training_keys,
             args.control_plots,
+            args.weight_plots,
             plot_dir,
         )
 
@@ -939,8 +1085,10 @@ def postprocess_run3(args):
             systematics={},
             template_dir=templ_dir,
             bg_keys=bg_keys_combined,
-            plot_dir=f"{templ_dir}/{year}",
+            plot_dir=Path(f"{templ_dir}/{year}"),
             weight_key="weight",
+            weight_shifts=weight_shifts,
+            plot_shifts=True,
             show=False,
             energy=13.6,
             jshift=jshift,
@@ -1045,6 +1193,7 @@ def postprocess_run3(args):
 
     run_utils.add_bool_arg(parser, "bdt-roc", default=False, help="make BDT ROC curve")
     run_utils.add_bool_arg(parser, "control-plots", default=False, help="make control plots")
+    run_utils.add_bool_arg(parser, "weight-plots", default=False, help="make weight plots")
     run_utils.add_bool_arg(parser, "fom-scan", default=False, help="run figure of merit scans")
     run_utils.add_bool_arg(parser, "fom-scan-bin1", default=True, help="FOM scan for bin 1")
     run_utils.add_bool_arg(parser, "fom-scan-bin2", default=True, help="FOM scan for bin 2")
diff --git a/src/HH4b/postprocessing/corrections.py b/src/HH4b/postprocessing/corrections.py
index 38e10d8f..f47dde3b 100644
--- a/src/HH4b/postprocessing/corrections.py
+++ b/src/HH4b/postprocessing/corrections.py
@@ -1,68 +1,121 @@
 from __future__ import annotations
 
+import correctionlib
 import numpy as np
 import pandas as pd
-from coffea.lookup_tools.dense_lookup import dense_lookup
-
-from HH4b.utils import makeHH
-
-TT_SF_2022 = np.array(
-    [
-        # lower bin edge, nom, up, down
-        (-1000000, 1),
-        (0, 0.886178),
-        (35, 1.02858),
-        (75, 1.04224),
-        (130, 1.05555),
-        (200, 1.0296),
-        (315, 0.845703),
-        (450, 0.699666),
-        (700, 0.439261),
-        (1000, 1),
-        (1000000, 1),
-    ]
-)
-
-TT_SF_2023 = np.array(
-    [
-        (-1000000, 1),
-        (0, 0.876845),
-        (50, 0.984064),
-        (100, 0.99184),
-        (150, 1.17205),
-        (250, 1.36115),
-        (450, 1.13521),
-        (750, 1),
-        (1000000, 1),
-    ]
-)
-
-# TODO: update to include up/down variations like so
-# tt_sfs_lookups = {
-#     "nom": {
-#         "2022": dense_lookup(TT_SF_2022[:, 1][:-1], TT_SF_2022[:, 0]),
-#         "2023": dense_lookup(TT_SF_2023[:, 1][:-1], TT_SF_2023[:, 0]),
-#     },
-#     "up": {
-#         "2022": dense_lookup(TT_SF_2022[:, 2][:-1], TT_SF_2022[:, 0]),
-#         "2023": dense_lookup(TT_SF_2023[:, 2][:-1], TT_SF_2023[:, 0]),
-#     }
-# }
-
-tt_sfs_lookups = {
-    "2022": dense_lookup(TT_SF_2022[:, 1][:-1], TT_SF_2022[:, 0]),
-    "2023": dense_lookup(TT_SF_2023[:, 1][:-1], TT_SF_2023[:, 0]),
-}
-
-
-def ttbar_pTjjSF(year: str, events_dict: dict[str, pd.DataFrame], mass: str = "bbFatJetPNetMass"):
-    """Apply ttbar recoil scale factors"""
+
+
+def ttbar_SF(year: str, events_dict: dict[str, pd.DataFrame], corr: str, branch: str):
+    # corr: PTJJ, Tau3OverTau2, Xbb
+    # branch: HHPt, H1T32, H2T32, H1TXbb, H2TXbb
+    """Apply ttbar scale factors"""
     year_ = None
     if "2022" in year:
         year_ = "2022"
     elif "2023" in year:
         year_ = "2023"
-    hh = makeHH(events_dict, "ttbar", mass)
-    lookup = tt_sfs_lookups[year_]
-    sfs = lookup(hh.pt)
+    tt_sf = correctionlib.CorrectionSet.from_file(f"../corrections/data/ttbarcorr_{year_}.json")[
+        corr
+    ]
+    input_var = events_dict[branch]
+    sfs = tt_sf.evaluate(input_var)
+    # replace zeros with 1
+    sfs[sfs == 0] = 1.0
+
     return sfs
+
+
+def _load_trig_effs(year: str, label: str, region: str):
+    return correctionlib.CorrectionSet.from_file(
+        f"../corrections/data/fatjet_triggereff_{year}_{label}_{region}.json"
+    )
+
+
+def trigger_SF(year: str, events_dict: dict[str, pd.DataFrame], pnet_str: str, region: str):
+    """
+    Evaluate trigger Scale Factors
+    """
+
+    triggereff_ptmsd = _load_trig_effs(year, "ptmsd", region)
+    # triggereff_btag = _load_trig_effs(year, "txbb", region)
+    triggereff_btag = _load_trig_effs(year, "txbbv11", region)
+
+    eff_data = triggereff_ptmsd[f"fatjet_triggereffdata_{year}_ptmsd"]
+    eff_mc = triggereff_ptmsd[f"fatjet_triggereffmc_{year}_ptmsd"]
+    eff_data_btag = triggereff_btag[f"fatjet_triggereffdata_{year}_txbbv11"]
+    eff_mc_btag = triggereff_btag[f"fatjet_triggereffmc_{year}_txbbv11"]
+
+    eff_data_per_jet = {}
+    eff_mc_per_jet = {}
+
+    for jet in range(2):
+        pt = events_dict["bbFatJetPt"][jet]
+        msd = events_dict["bbFatJetMsd"][jet]
+        xbb = events_dict[f"bbFatJet{pnet_str}"][jet]
+
+        num_ev = pt.shape[0]
+        # TODO: add matching to trigger objects
+        # for now, assuming both are matched
+        matched = np.ones(num_ev)
+
+        eff_data_per_jet[jet] = {}
+        eff_mc_per_jet[jet] = {}
+
+        for var in ["nominal", "stat_up", "stat_dn"]:
+            eff_data_val = np.zeros(num_ev)
+            eff_data_btag_val = np.zeros(num_ev)
+            eff_mc_val = np.zeros(num_ev)
+            eff_mc_btag_val = np.zeros(num_ev)
+
+            eff_data_all = eff_data.evaluate(pt, msd, var)
+            eff_data_btag_all = eff_data_btag.evaluate(xbb, var)
+            eff_mc_all = eff_mc.evaluate(pt, msd, var)
+            eff_mc_btag_all = eff_mc_btag.evaluate(xbb, var)
+
+            # replace zeros (!) should belong to unmatched...
+            eff_data_all[eff_data_all == 0] = 1.0
+            eff_data_btag_all[eff_data_btag_all == 0] = 1.0
+            eff_mc_all[eff_mc_all == 0] = 1.0
+            eff_mc_btag_all[eff_mc_btag_all == 0] = 1.0
+
+            eff_data_val[matched == 1] = eff_data_all[matched == 1]
+            eff_data_btag_val[matched == 1] = eff_data_btag_all[matched == 1]
+            eff_mc_val[matched == 1] = eff_mc_all[matched == 1]
+            eff_mc_btag_val[matched == 1] = eff_mc_btag_all[matched == 1]
+
+            eff_data_per_jet[jet][var] = eff_data_val * eff_data_btag_val
+            eff_mc_per_jet[jet][var] = eff_mc_val * eff_mc_btag_val
+
+    tot_eff_data = 1 - (1 - eff_data_per_jet[0]["nominal"]) * (1 - eff_data_per_jet[1]["nominal"])
+    tot_eff_mc = 1 - (1 - eff_mc_per_jet[0]["nominal"]) * (1 - eff_mc_per_jet[1]["nominal"])
+
+    if np.any(tot_eff_data == 0):
+        print("Warning: eff data has 0 values")
+    if np.any(tot_eff_mc == 0):
+        print("Warning: eff mc has 0 values")
+
+    sf = tot_eff_data / tot_eff_mc
+
+    # unc on eff: (1 - z): dz
+    # z = x * y = (1-eff_1)(1-eff_2)
+    # dz = z * sqrt( (dx/x)**2 + (dy/y)**2 )
+    for var in ["up"]:
+        dx_data = eff_data_per_jet[0][f"stat_{var}"]
+        dy_data = eff_data_per_jet[1][f"stat_{var}"]
+        x_data = 1 - eff_data_per_jet[0]["nominal"]
+        y_data = 1 - eff_data_per_jet[1]["nominal"]
+        z = x_data * y_data
+        dz = z * np.sqrt((dx_data / x_data) ** 2 + (dy_data / y_data) ** 2)
+        unc_eff_data = dz
+
+        dx_mc = eff_mc_per_jet[0][f"stat_{var}"]
+        dy_mc = eff_mc_per_jet[1][f"stat_{var}"]
+        x_mc = 1 - eff_mc_per_jet[0]["nominal"]
+        y_mc = 1 - eff_mc_per_jet[1]["nominal"]
+        z = x_mc * y_mc
+        dz = z * np.sqrt((dx_mc / x_mc) ** 2 + (dy_mc / y_mc) ** 2)
+        unc_eff_mc = dz
+
+        unc_sf = sf * np.sqrt((unc_eff_data / tot_eff_data) ** 2 + (unc_eff_mc / tot_eff_mc) ** 2)
+
+    return sf, sf + unc_sf, sf - unc_sf
diff --git a/src/HH4b/postprocessing/postprocessing.py b/src/HH4b/postprocessing/postprocessing.py
index b28639ea..a46522b3 100644
--- a/src/HH4b/postprocessing/postprocessing.py
+++ b/src/HH4b/postprocessing/postprocessing.py
@@ -137,12 +137,13 @@ class Region:
 ]
 
 weight_shifts = {
-    "pileup": Syst(samples=sig_keys + bg_keys, label="Pileup"),
+    "ttbarSF": Syst(samples=["ttbar"], label="ttbar SF", years=years + ["2022-2023"]),
+    "trigger": Syst(samples=sig_keys + bg_keys, label="Trigger", years=years + ["2022-2023"]),
+    # "pileup": Syst(samples=sig_keys + bg_keys, label="Pileup"),
     # "PDFalphaS": Syst(samples=sig_keys, label="PDF"),
     # "QCDscale": Syst(samples=sig_keys, label="QCDscale"),
     # "ISRPartonShower": Syst(samples=sig_keys_ggf + ["vjets"], label="ISR Parton Shower"),
     # "FSRPartonShower": Syst(samples=sig_keys_ggf + ["vjets"], label="FSR Parton Shower"),
-    # "top_pt": ["ttbar"],
 }
 
 
@@ -356,7 +357,7 @@ def get_templates(
     systematics: dict,  # noqa: ARG001
     template_dir: str = "",
     bg_keys: list[str] = bg_keys,
-    plot_dir: str = "",
+    plot_dir: Path = "",
     prev_cutflow: pd.DataFrame | None = None,
     weight_key: str = "weight",
     plot_sig_keys: list[str] | None = None,
@@ -521,12 +522,12 @@ def get_templates(
                     "bg_err_mcstat": True,
                     "reweight_qcd": False,
                 }
-
-                plot_name = (
-                    f"{plot_dir}/"
-                    f"{'jshifts/' if do_jshift else ''}"
-                    f"{rname}_region_{shape_var.var}"
-                )
+                if do_jshift:
+                    plot_dir_jshifts = plot_dir / "jshifts"
+                    plot_dir_jshifts.mkdir(exist_ok=True, parents=True)
+                    plot_name = plot_dir_jshifts / f"{rname}_region_{shape_var.var}"
+                else:
+                    plot_name = plot_dir / f"{rname}_region_{shape_var.var}"
 
                 plotting.ratioHistPlot(
                     **plot_params,
@@ -536,7 +537,10 @@ def get_templates(
                 )
 
             if not do_jshift and plot_shifts:
-                plot_name = f"{plot_dir}/wshifts/" f"{rname}_region_{shape_var.var}"
+                plot_dir_wshifts = plot_dir / "wshifts"
+                plot_dir_wshifts.mkdir(exist_ok=True, parents=True)
+
+                plot_name = plot_dir_wshifts / f"{rname}_region_{shape_var.var}"
 
                 for wshift, wsyst in weight_shifts.items():
                     if wsyst.samples == [sig_key]: