diff --git a/.github/workflows/auto-approve.yml b/.github/workflows/auto-approve.yml
new file mode 100644
index 00000000..3397abaa
--- /dev/null
+++ b/.github/workflows/auto-approve.yml
@@ -0,0 +1,14 @@
+name: Auto approve
+
+on: pull_request_target
+
+jobs:
+  auto-approve:
+    runs-on: ubuntu-latest
+    permissions:
+      pull-requests: write
+    if: github.actor == 'pre-commit-ci[bot]'
+    steps:
+      - uses: hmarr/auto-approve-action@v3
+        with:
+          review-message: "Auto approved automated PR"
diff --git a/.gitignore b/.gitignore
index f236b0cc..92a490e5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,6 +4,7 @@
 !*.ipynb
 !figure.png
 !requirements.txt
+!environment.yml
 
 !src
 src/*
@@ -36,6 +37,11 @@ src/HHbbVV/postprocessing/*.pkl
 src/HHbbVV/postprocessing/cards
 src/HHbbVV/postprocessing/templates_old
 src/HHbbVV/postprocessing/*.txt
+src/HHbbVV/postprocessing/condor_templates
+src/HHbbVV/postprocessing/outs
+
+!src/HHbbVV/resonant_studies
+!src/HHbbVV/resonant_studies/**
 
 !src/HHbbVV/scale_factors
 !src/HHbbVV/scale_factors/**
@@ -46,11 +52,21 @@ src/HHbbVV/postprocessing/*.txt
 !src/HHbbVV/combine
 !src/HHbbVV/combine/**
 
+!src/HHbbVV/tagger
+!src/HHbbVV/tagger/**
+
+!src/HHbbVV/VBF_binder
+!src/HHbbVV/VBF_binder/**
+
 !data
 !data/*
 !data/*/**
 
+!paper
+!paper/**
 
+!inference_scans
+!inference_scans/**
 
 !.gitignore
 !.github
@@ -62,6 +78,8 @@ src/HHbbVV/postprocessing/*.txt
 
 **/.ipynb_checkpoints
 **/*test*
+!**/*F_test*
+!**/*ftest*
 **/__pycache__
 **/.DS_Store
 .DS_Store
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e03def5b..bb72bd60 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,5 +1,6 @@
 repos:
-  - repo: https://github.com/psf/black
-    rev: "23.1.0"
+  - repo: https://github.com/psf/black-pre-commit-mirror
+    rev: "23.9.1"
     hooks:
       - id: black-jupyter
+        args: [--line-length=100]  
diff --git a/AnalysisPlan.md b/AnalysisPlan.md
index a35670c8..ddd5ffd9 100644
--- a/AnalysisPlan.md
+++ b/AnalysisPlan.md
@@ -2,54 +2,54 @@
 
 - [Analysis Plan](#analysis-plan)
   - [TODOs](#todos)
-    - [Trigger Efficiencies](#trigger-efficiencies)
     - [Incorporate remaining systematics](#incorporate-remaining-systematics)
       - [Skimmer](#skimmer)
       - [Shapes / Values](#shapes--values)
       - [Datacard](#datacard)
-    - [Full Run 2 and all samples](#full-run-2-and-all-samples)
-      - [Update processor](#update-processor)
-      - [Update post-processing](#update-post-processing)
-    - [Scans](#scans)
-    - [TTbar corrections](#ttbar-corrections)
+    - [Update processor](#update-processor)
+    - [New LP SFs](#new-lp-sfs)
+    - [Nonresonant](#nonresonant)
     - [Statistical tests of fits](#statistical-tests-of-fits)
-    - [Resonant X-\>HY sensitivity](#resonant-x-hy-sensitivity)
+    - [Resonant X-\>HY](#resonant-x-hy)
+    - [Semi-leptonic ttbar selection skimmer](#semi-leptonic-ttbar-selection-skimmer)
+    - [Resolved WW veto](#resolved-ww-veto)
+    - [TTbar corrections](#ttbar-corrections)
   - [Plan](#plan)
     - [Feb 13 - 17](#feb-13---17)
     - [Feb 20 - 24](#feb-20---24)
-    - [Feb 27 - 3](#feb-27---3)
+    - [Feb 27 - Mar 3](#feb-27---mar-3)
+    - [Mar](#mar)
+    - [Apr](#apr)
+    - [Apr 24 - 28](#apr-24---28)
+    - [Jun 5 - 9](#jun-5---9)
   - [In progress:](#in-progress)
   - [~Completed:](#completed)
     - [Preliminary 2017 cut-based signal and background yields estimate](#preliminary-2017-cut-based-signal-and-background-yields-estimate)
-    - [Preliminary 2017 trigger scale factor measurements](#preliminary-2017-trigger-scale-factor-measurements)
+    - [Trigger scale factor measurements](#trigger-scale-factor-measurements)
     - [Processor for skimming nano files](#processor-for-skimming-nano-files)
+      - [Update 2/23](#update-223)
     - [Triton/SONIC inference server](#tritonsonic-inference-server)
     - [BDT Training](#bdt-training)
     - [Tagger](#tagger)
     - [Fits, combine](#fits-combine)
-    - [Samples generation](#samples-generation)
+    - [Full Run 2 and all UL samples](#full-run-2-and-all-ul-samples)
     - [Lund plane scale factors](#lund-plane-scale-factors)
+    - [Post-processing](#post-processing)
+      - [Update post-processing](#update-post-processing)
 
 
 ## TODOs
 
-### Trigger Efficiencies
-
- - [x] Measure for all years
- - [x] **Update selection**
- - [x] Check if binning in VV tagger is necessary (probably not since only btag is in the trigger)
- - [ ] Investigate high unc.
-
 
 ### Incorporate remaining systematics
 
-
 #### Skimmer
  - [x] Pileup
  - [x] JES/R http://cds.cern.ch/record/2792322/files/DP2021_033.pdf
-   - [ ] Need to update to latest
+   - [x] Need to update to latest
  - [x] JMS/R http://cds.cern.ch/record/2256875/files/JME-16-003-pas.pdf
    - [ ] Need UL mSD and regressed mass corrections
+ - [ ] Top pt
  - Theory
    - [x] pdf uncertainties
    - [x] scale variation
@@ -60,11 +60,13 @@
 #### Shapes / Values
  - [x] Pileup
  - [x] ParticleNet Xbb
+   - [ ] Split up uncertainties
  - [x] JES/R
  - [x] JMS/R
  - [x] Trigger SFs
    - [x] Stat. Unc.
    - [ ] Correlated Syst.
+ - [ ] Top pt
  - Theory
    - [x] pdf uncertainties
    - [x] parton shower weights
@@ -76,82 +78,102 @@
  - [x] MC Stats
  - [x] Lumi
  - [x] Pileup
- - [ ] Trigger SFs
-   - [ ] Stat.
+ - [x] Trigger SFs
+   - [x] Stat.
    - [ ] Correlated Syst.
  - [x] ParticleNet Xbb
+   - [ ] Separate uncertainties
  - [x] JES/R
  - [x] JMS/R
+ - [ ] Top pT
  - Theory
-   - [x] pdf uncertainties
+   - [x] BR
+   - [ ] pdf uncertainties
+     - [x] rate
+     - [ ] shape?
+   - [x] QCD scale
+   - [ ] alpha_s (for single Higgs)
    - [x] parton shower weights
    - [ ] scale variation?
    - [ ] W k factor??
 
 
-### Full Run 2 and all samples
+### Update processor
 
-- [x] JetHT
-- [x] QCD
-- [x] TTbar
-- [x] ST
-- [x] W, Z+jets
-- [x] Diboson
-- [x] HHbbWW (all kL)
-- Need xsecs for:
-  https://docs.google.com/spreadsheets/d/1XQQsN4rl3xGDa35W516TwKyadccpfoT7M1mFGxZ4UjQ/edit#gid=1223976475
-  - [x] VBF HHbbWW (kL = 1, k2V = 1)
-  - [x] HH4b (kL = 1) (Pre-UL)
-  - [x] HWW (ggF, VH, VBF, ttH)
-  - [x] Hbb (ggF, VBF, VH, ttH)
+- [ ] New e, mu, b-tag jets selections from VHbb
+- [ ] Re-run with VV regressed mass for Dijet variables
+- [ ] FatJet ID
+- [ ] Look at ID scale factors
+- [ ] New LP Method
 
-#### Update processor
 
-- [x] FatJet selections
-- [x] Modify JECs code to save only variations of pT
-- [x] Add JMS/R
-- [x] Regressed mass cut
-- [x] Add e, mu, b-tag jets
-- [x] Add tagger vars
-- [x] Dijet variables
-  - [ ] Re-run with VV regressed mass for Dijet variables
+### New LP SFs
 
+ - [ ] Update LP method
 
-#### Update post-processing
 
- - [x] Check control plots for all years
- - [x] Re-train BDT for all years
- - [x] Templates, systematics for all years
- - [ ] Update datacard with all years
+### Nonresonant
 
+- [x] BDT ROC
+  - [x] Try multi-class BDT
+  - [x] Try equalizing background weights
+  - [x] Optimize hyperparams
+  - [x] Trim features
+  - [x] Check sculpting
+- [x] Scan Txbb, BDT WPs
+- [x] Run over all kL and k_2V
+  - [x] Re-run VBF with gen selection
+- [x] HH inference
+- [ ] Theory uncertainties
 
-### Scans
 
- - kL
- - C2?
+### Statistical tests of fits
 
+ - [x] GoF
+ - [x] F-test
+ - [ ] Impacts
 
-### TTbar corrections
 
- - Tagger efficiency
- - Recoil
- - JMS
- - Regressed mass
- - BDT
- - Check VBF?
+### Resonant X->HY
 
+ - [x] Control plots
+ - [x] Preliminary signal region
+ - [x] Validation region
+ - [x] 2D fit, xUL
+   - [x] GoF
+   - [x] F-test
+ - [x] Run over all signals
+   - [ ] TRSM, NMSSM exclusions
+ - [x] Scan Txbb, THWW working points
 
-### Statistical tests of fits
 
- - f-test
- - GoF
- - Impacts
+### Semi-leptonic ttbar selection skimmer
+
+ - [ ] Update LP
+ - [x] JECs for AK4 Jets
+ - [x] DeepJet Btag
+ - [x] B-tag scale factors
+ - [x] muon ID SFs
+ - [x] Save regressed mass
+ - [x] Jet ID SFs (not required) https://twiki.cern.ch/twiki/bin/view/CMS/JetID13TeVUL
+ - [ ] PFNano on SingleMuon to get regressed mass
+
+
+### Resolved WW veto
+
+ - [ ] Test veto on 2 AK8 W-tagged jets
+
 
+### TTbar corrections
 
-### Resonant X->HY sensitivity
+May not be necessary given low yield
 
- - Run on 1-2 mass points
- - Check selection, fits, sensitivity
+- Tagger efficiency
+- Recoil
+- JMS
+- Regressed mass
+- BDT
+- Check VBF?
 
 
 ## Plan
@@ -166,7 +188,7 @@ Raghav:
    - [ ] e, mu 4 vectors 
    - [x] ak4 b jets (medium btag) - outside bb jet
    - [x] check control plots on num_x
- - [ ] Check vetoes on these
+ - [x] Check vetoes on these
 
 Cristina
  - [x] Systematics
@@ -183,10 +205,42 @@ Cristina:
  - [ ] Goodness-of-fit, f-test, impacts 
 
 
-### Feb 27 - 3
+### Feb 27 - Mar 3
 
 Raghav:
-- [ ] Full run 2, all samples
+- [x] Full run 2, all samples
+
+
+### Mar
+- [x] Wrap up nonresonant for now with full run 2 limits and GoF
+- [x] Develop resonant analysis strategy
+- [x] Plots, selections for subset of signals
+- [ ] Get 2D fit working
+
+
+### Apr
+- [x] Get 2D fit working
+- [x] Upper limits for subset of signals
+- [x] Upper limits for all signals!
+- [ ] Complete v1 of AN
+- [ ] Higher mass ParT training samples
+
+
+### Apr 24 - 28
+- [x] Complete v1 of AN
+- [x] B2G Workshop talk
+- [x] F-test
+- [x] Start WP Scan
+
+
+### Jun 5 - 9
+- [ ] Update semi-leptonic ttbar processor
+- [x] Twiki
+- [x] Respond to comments
+- [ ] Samples
+  - [x] Nanogen
+- [x] Skimming VBF
+- [ ] Runnning over kL, k2V
 
 
 ## In progress:
@@ -204,9 +258,14 @@ Raghav:
  - background estimation from data sidebands
  - with AK8 and AK15 mass-decorrelated ParticleNet Hbb taggers + NOT mass-decorrelated AK8 ParticleNet H4q tagger
 
-### Preliminary 2017 trigger scale factor measurements
+### Trigger scale factor measurements
  - Measured for AK8, AK15, and hybrid jets, single-jet 2D (mass, pT binned) efficiencies (applied assuming prob. of each fat jet passing trigger is independent) and 3D (jet 1 mass, jet 1 pt, jet 2 pt binned)  efficiencies ([processors](https://github.com/rkansal47/HHbbVV/blob/main/processors/JetHTTriggerEfficienciesProcessor.py))
- - Tentatively decided to use hybrid case which gave the highest preselection yield and should increase with a better HWW tagger
+ - Decided on AK8 only - ~same sensitivity, significantly easier practically
+
+- [x] Measure for all years
+- [x] **Update selection**
+- [x] Check if binning in VV tagger is necessary (probably not since only btag is in the trigger)
+- [x] Investigate high unc.
 
 ### Processor for skimming nano files
 https://github.com/rkansal47/HHbbVV/blob/main/processors/bbVVSkimmer.py
@@ -217,10 +276,16 @@ https://github.com/rkansal47/HHbbVV/blob/main/processors/bbVVSkimmer.py
    - Inference via triton server running on SDSC
    - Lund plane scale factors for skimmer
    - Saving flat skimmed data to parquet files, and metadata (total events, cutflow) to pickles
- - TODO:
-   - Add Txbb tagger pre-selection cut
-   - Apply trigger SFs
-   - Add remaining systematics
+
+#### Update 2/23
+
+- [x] FatJet selections
+- [x] Modify JECs code to save only variations of pT
+- [x] Add JMS/R
+- [x] Regressed mass cut
+- [x] Add e, mu, b-tag jets
+- [x] Add tagger vars
+- [x] Dijet variables
 
 ### Triton/SONIC inference server
 https://github.com/rkansal47/sonic-models
@@ -238,25 +303,32 @@ HWW tagger + mass regression development ([Zichun's tagger repo](https://github.
 ### Fits, combine
 
 
-### Samples generation
+### Full Run 2 and all UL samples
 
-Currently have for UL 2017:
-
- - Data: JetHT RunB-F
- - QCD HT 300-inf
- - TT hadronic, TT semileptonic, T Mtt
- - ST s-channel leptonic, ST t-channel, Inclusive, ST tW top + antitop no fully hadronic
- - ggF HWW inclusive
- - VV, V+jets
+- [x] JetHT
+- [x] QCD
+- [x] TTbar
+- [x] ST
+- [x] W, Z+jets
+- [x] Diboson
+- [x] HHbbWW (all kL)
+- Need xsecs for:
+  https://docs.google.com/spreadsheets/d/1XQQsN4rl3xGDa35W516TwKyadccpfoT7M1mFGxZ4UjQ/edit#gid=1223976475
+  - [x] VBF HHbbWW (kL = 1, k2V = 1)
+  - [x] HH4b (kL = 1) (Pre-UL)
+  - [x] HWW (ggF, VH, VBF, ttH)
+  - [x] Hbb (ggF, VBF, VH, ttH)
 
-1) Requesting UL samples
-    - Requesting HH signal samples ggF and VBF for bbWW all-hadronic
-    - TODO: Request UL ggH (and VBF?) signal samples for HtoWW inclusive
+### Lund plane scale factors
 
-2) Need to use `RunIISummer20UL17` in PFNano for next production
+ - Implemented and validated for top jets in control region
+ - Implemented and measured for nonresonant signal
 
+### Post-processing
 
-### Lund plane scale factors
+#### Update post-processing
 
- - Implemented and validated for top jets in control region
- - Implemented and measured for nonresonant signal
\ No newline at end of file
+- [x] Check control plots for all years
+- [x] Re-train BDT for all years
+- [x] Templates, systematics for all years
+- [x] Update datacard with all years
\ No newline at end of file
diff --git a/PrintParquet.py b/PrintParquet.py
index 2d9506ae..0628d7fa 100644
--- a/PrintParquet.py
+++ b/PrintParquet.py
@@ -16,7 +16,7 @@ def print_parquet(filename, branches):
         ],
     ]
     events = pd.read_parquet(filename, filters=filters)
-    print(events.columns.to_numpy())
+    # print(events.columns.to_numpy())
     if len(branches) > 0:
         for b in branches:
             if b in events.columns:
diff --git a/README.md b/README.md
index c6f8117a..4aa5db03 100644
--- a/README.md
+++ b/README.md
@@ -14,21 +14,38 @@ Search for two boosted (high transverse momentum) Higgs bosons (H) decaying to t
   - [Instructions for running coffea processors](#instructions-for-running-coffea-processors)
     - [Coffea-Casa](#coffea-casa)
     - [Condor](#condor)
+      - [Setup](#setup)
       - [TODO: instructions for lpcjobqueue (currently quite buggy)](#todo-instructions-for-lpcjobqueue-currently-quite-buggy)
   - [Processors](#processors)
     - [JetHTTriggerEfficiencies](#jethttriggerefficiencies)
     - [bbVVSkimmer](#bbvvskimmer)
     - [TaggerInputSkimmer](#taggerinputskimmer)
     - [TTScaleFactorsSkimmer](#ttscalefactorsskimmer)
+  - [Condor Scripts](#condor-scripts)
+    - [Check jobs](#check-jobs)
+    - [Combine pickles](#combine-pickles)
   - [Post Processing](#post-processing)
     - [BDT Pre-Processing](#bdt-pre-processing)
     - [BDT Trainings](#bdt-trainings)
     - [Post-Processing](#post-processing-1)
+      - [Control plots with resonant and nonresonant samples](#control-plots-with-resonant-and-nonresonant-samples)
+      - [Making separate background and signal templates for scan (resonant)](#making-separate-background-and-signal-templates-for-scan-resonant)
     - [Create Datacard](#create-datacard)
     - [PlotFits](#plotfits)
+  - [Combine](#combine)
+    - [CMSSW + Combine Quickstart](#cmssw--combine-quickstart)
+    - [Run fits and diagnostics locally](#run-fits-and-diagnostics-locally)
+    - [Run fits on condor](#run-fits-on-condor)
+      - [Making datacards](#making-datacards)
+      - [F-tests](#f-tests)
+      - [Impacts](#impacts)
+      - [Signal injection tests](#signal-injection-tests)
   - [Misc](#misc)
     - [Command for copying directories to PRP in background](#command-for-copying-directories-to-prp-in-background)
-    - [Get all running job names:](#get-all-running-job-names)
+    - [Command for copying res samples to my laptop](#command-for-copying-res-samples-to-my-laptop)
+    - [Get all running condor job names:](#get-all-running-condor-job-names)
+    - [Crab data jobs recovery](#crab-data-jobs-recovery)
+
 
 ## Instructions for running coffea processors
 
@@ -43,6 +60,24 @@ General note: Coffea-casa is faster and more convenient, however still somewhat
 
 ### Condor
 
+#### Setup
+
+To submit to condor, all you need is python >= 3.7.
+
+For testing locally, it is recommended to use miniconda/mamba (**mamba is way faster!**):
+
+```bash
+# Download the setup bash file from here https://github.com/conda-forge/miniforge#mambaforge
+# e.g. wget https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh
+# Install: (the mamba directory can end up taking O(1-10GB) so make sure the directory you're using allows that quota)
+./Mambaforge-Linux-x86_64.sh  # follow instructions in the installation
+mamba create -n bbVV python=3.9
+mamba activate bbVV
+pip install coffea "tritonclient[all]" pyyaml
+mamba install -c conda-forge xrootd=5.4.0  # need openssl v1.1 for lxplus and UCSD t2, hence pinning xrootd version.
+```
+
+
 Manually splits up the files into condor jobs.
 
 ```bash
@@ -78,7 +113,7 @@ Applies a muon pre-selection and accumulates 4D ([Txbb, Th4q, pT, mSD]) yields b
 To test locally:
 
 ```bash
-python -W ignore src/run.py --year 2018 --processor trigger --sample SingleMu2017 --subsamples SingleMuon_Run2018B --starti 0 --endi 1
+python -W ignore src/run.py --year 2018 --processor trigger --sample SingleMu2018 --subsamples SingleMuon_Run2018B --starti 0 --endi 1
 ```
 
 And to submit all:
@@ -154,8 +189,6 @@ Applies cuts for a semi-leptonic ttbar control region, as defined for the [JMAR
 
 Lund plane scale factors are calculated for top-matched jets in semi-leptonic ttbar events.
 
-Local testing:
-
 To test locally:
 ```bash
 python -W ignore src/run.py --year 2018 --processor ttsfs --sample TTbar --subsamples TTToSemiLeptonic --starti 0 --endi 1
@@ -173,6 +206,33 @@ python src/condor/submit.py --year 2018 --tag $TAG --sample TTbar --subsamples T
 ```
 
 
+## Condor Scripts
+
+### Check jobs
+
+Check that all jobs completed by going through output files:
+
+```bash
+for year in 2016APV 2016 2017 2018; do python src/condor/check_jobs.py --tag $TAG --processor trigger (--submit) --year $year; done
+```
+
+nohup version:
+
+(Do `condor_q | awk '{ print $9}' | grep -o '[^ ]*\.sh' > running_jobs.txt` first to get a list of jobs which are running.)
+
+```bash
+nohup bash -c 'for year in 2016APV 2016 2017 2018; do python src/condor/check_jobs.py --year $year --tag '"${TAG}"' --processor skimmer --submit --check-running; done' &> tmp/submitout.txt &
+```
+
+### Combine pickles
+
+Combine all output pickles into one:
+
+```bash
+for year in 2016APV 2016 2017 2018; do python src/condor/combine_pickles.py --tag $TAG --processor trigger --r --year $year; done
+```
+
+
 ## Post Processing
 
 In `src/HHbbVV/postprocessing':
@@ -181,36 +241,86 @@ In `src/HHbbVV/postprocessing':
 ### BDT Pre-Processing
 
 ```bash
-python BDTPreProcessing.py --data-dir "../../../../data/skimmer/Feb24/" --plot-dir "../../../plots/BDTPreProcessing/$TAG/" --year "2017" --bdt-data (--control-plots)
+python BDTPreProcessing.py --data-dir "../../../../data/skimmer/Feb24/" --signal-data-dir "../../../../data/skimmer/Jun10/" --plot-dir "../../../plots/BDTPreProcessing/$TAG/" --year "2017" --bdt-data (--control-plots)
 ```
 
 
 ### BDT Trainings
 
 ```bash
-python TrainBDT.py --model-dir testBDT --data-path "../../../../data/skimmer/Feb24/" (--year 2017 or --all-years) (--test)
+python TrainBDT.py --model-dir testBDT --data-path "../../../../data/skimmer/Feb24/" --year "all" (--test)
 ```
 
+Inference-only:
+```bash
+python TrainBDT.py  --data-path "../../../../data/skimmer/Feb24/bdt_data" --year "all" --inference-only --model-dir "../../../../data/skimmer/Feb24/23_05_12_multiclass_rem_feats_3"
+```
 
 ### Post-Processing
 
 ```bash
-python postprocessing.py --templates --year "2017" --template-dir "templates/$TAG/" --plot-dir "../../../plots/PostProcessing/$TAG/" --data-dir "../../../../data/skimmer/Feb24/" (--control-plots)
+python postprocessing.py --templates --year "2017" --template-dir "templates/$TAG/" --plot-dir "../../../plots/PostProcessing/$TAG/" --data-dir "../../../../data/skimmer/Feb24/" (--resonant --signal-data-dir "" --control-plots)
 ```
 
-All years:
+All years (non-resonant):
 ```bash
-for year in 2016APV 2016 2017 2018; do python postprocessing.py --templates --year $year --template-dir "templates/$TAG/" --plot-dir "../../../plots/PostProcessing/$TAG/" --data-dir "../../../../data/skimmer/Feb24/"; done 
+for year in 2016 2016APV 2017 2018; do python -u postprocessing.py --templates --year $year --template-dir "templates/Jun14" --data-dir "../../../../data/skimmer/Feb24" --signal-data-dir "../../../../data/skimmer/Jun10" --bdt-preds-dir "../../../../data/skimmer/Feb24/23_05_12_multiclass_rem_feats_3/inferences"; done
 ```
 
+Scan (non-resonant):
+```bash
+for year in 2016 2016APV 2017 2018; do python -u postprocessing.py --templates --year $year --template-dir "templates/$TAG/" --data-dir "../../../../data/skimmer/Feb24/" --old-processor --nonres-txbb-wp "LP" "MP" "HP" --nonres-bdt-wp 0.995 0.998 0.999 --no-do-jshifts; done
+```
+
+#### Control plots with resonant and nonresonant samples
+
+Run `postprocessing/bash_scripts/ControlPlots.sh` from inside `postprocessing folder`.
+
+
+#### Making separate background and signal templates for scan (resonant)
+
+Background and data:
+
+```bash
+nohup bash -c 'for year in 2016APV 2016 2017 2018; do python -u postprocessing.py --templates --year $year --template-dir "/eos/uscms/store/user/rkansal/bbVV/templates/$TAG/" --data-dir "/eos/uscms/store/user/rkansal/bbVV/skimmer/Feb24" --resonant --sig-samples "" --res-txbb-wp LP MP HP --res-thww-wp 0.4 0.6 0.8 0.9 0.94 0.96 0.98 --no-do-jshifts --templates-name backgrounds --old-processor; done' &> outs/bgout.txt & 
+```
+
+Signal:
+
+```bash
+nohup bash -c 'for sample in NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150 NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250; do for year in 2016APV 2016 2017 2018; do python -u postprocessing.py --templates --year $year --template-dir "/eos/uscms/store/user/rkansal/bbVV/templates/$TAG/" --data-dir "/eos/uscms/store/user/rkansal/bbVV/skimmer/Feb24" --signal-data-dir "/eos/uscms/store/user/rkansal/bbVV/skimmer/Apr11" --resonant --sig-samples $sample --bg-keys "" --res-txbb-wp LP MP HP --res-thww-wp 0.4 0.6 0.8 0.9 0.94 0.96 0.98 --no-do-jshifts --templates-name $sample --no-data; done; done' &> outs/sigout.txt & 
+```
+
+
 ### Create Datacard
 
 Need `root==6.22.6`, and `square_coef` branch of https://github.com/rkansal47/rhalphalib installed (`pip install -e . --user` after checking out the branch). `CMSSW_11_2_0` recommended.
 
 ```bash
-python3 postprocessing/CreateDatacard.py --templates-dir templates/$TAG --model-name $TAG
+python3 postprocessing/CreateDatacard.py --templates-dir templates/$TAG --model-name $TAG (--resonant)
+```
+
+Or from separate templates for background and signal:
+
+```bash
+python3 -u postprocessing/CreateDatacard.py --templates-dir "/eos/uscms/store/user/rkansal/bbVV/templates/23Apr30Scan/txbb_HP_thww_0.96" \
+--sig-separate --resonant --model-name $sample --sig-sample $sample
+```
+
+Scan (non-resonant):
+```bash
+for txbb_wp in "LP" "MP" "HP"; do for bdt_wp in 0.994 0.99 0.96 0.9 0.8 0.6 0.4; do python3 -u postprocessing/CreateDatacard.py --templates-dir templates/23May13NonresScan/txbb_${txbb_wp}_bdt_${bdt_wp} --model-name 23May13NonresScan/txbb_${txbb_wp}_bdt_${bdt_wp} --no-do-jshifts --nTF 0; done; done
 ```
 
+Datacards with different orders of TFs for F-tests:
+
+Use the `src/HHbbVV/combine/F_test_res.sh` script.
+
+
+Datacards (and background-only fits) for bias tests:
+
+`src/HHbbVV/combine/biastests.sh --cardstag $cardstag --templatestag $templatestag`
+
 
 ### PlotFits
 
@@ -218,6 +328,91 @@ python3 postprocessing/CreateDatacard.py --templates-dir templates/$TAG --model-
 python PlotFits.py --fit-file "cards/test_tied_stats/fitDiagnosticsBlindedBkgOnly.root" --plots-dir "../../../plots/PostFit/09_02/"
 ```
 
+## Combine
+
+### CMSSW + Combine Quickstart
+```bash
+cmsrel CMSSW_11_2_0
+cd CMSSW_11_2_0/src
+cmsenv
+git clone -b py3 https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
+git clone -b v2.0.0 https://github.com/cms-analysis/CombineHarvester.git CombineHarvester
+scramv1 b clean; scramv1 b
+```
+
+New version:
+
+```bash
+cmsrel CMSSW_11_3_4
+cd CMSSW_11_3_4/src
+cmsenv
+# need my fork until regex for float parameters is merged into the main repo
+git clone -b regex-float-parameters https://github.com/rkansal47/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
+git clone -b v2.0.0 https://github.com/cms-analysis/CombineHarvester.git CombineHarvester
+# Important: this scram has to be run from src dir
+scramv1 b clean; scramv1 b
+```
+
+I also add this to my .bashrc for convenience:
+
+```
+export PATH="$PATH:/uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine"
+
+csubmit() {
+    local file=$1; shift;
+    python3 "/uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_${file}.py" "$@"
+}
+```
+
+### Run fits and diagnostics locally
+
+All via the below script, with a bunch of options (see script):
+
+```bash
+run_blinded.sh --workspace --bfit --limits
+```
+
+### Run fits on condor
+
+#### Making datacards
+
+Can run over all the resonant signals (default) or scan working points for a subset of signals (`--scan`)
+
+```bash
+csubmit cards --test --scan --resonant --templates-dir 23Apr30Scan
+```
+
+#### F-tests
+
+Generate toys and fits for F-tests (after making cards and b-only fits for the testing order AND testing order + 1!)
+
+```bash
+csubmit f_test --tag 23May2 --cards-tag 23May2 --low1 0 --low2 0
+```
+
+#### Impacts
+
+```bash
+csubmit impacts --tag 23May2 (--local [if you want to run them locally])
+```
+
+This was also output a script to collect all the impacts after the jobs finish.
+
+#### Signal injection tests
+
+For resonant, use scripts inside the `src/HHbbVV/combine/` directory and run from one level above the sample datacard folders (e.g. `/uscms/home/rkansal/hhcombine/cards/biastests/23Jul17ResClipTFScale1`).
+
+Submitting 1000 toys for each sample and `r` value + more toys for samples with high fit failure rates:
+
+```bash
+submit_bias_res_loop.sh $seed $TAG
+```
+
+Moving the outputs to a `bias/$TAG` dir after (uses the last digit of the seed - so make sure different runs use different last digits!):
+
+```bash
+mv_bias_outputs.sh [last-digit-of-seed] $TAG
+```
 
 ## Misc
 
@@ -232,9 +427,49 @@ mkdir ../copy_logs
 for i in *; do echo $i && sleep 3 && (nohup sh -c "krsync -av --progress --stats $i/root/ $HWWTAGGERDEP_POD:/hwwtaggervol/training/$FOLDER/$i" &> ../copy_logs/$i.txt &) done
 ```
 
+### Command for copying res samples to my laptop
+
+```bash
+for sample in 'NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150'; do for year in 2016APV 2016 2017 2018; do rsync -avP rkansal@cmslpc-sl7.fnal.gov:eos/bbVV/skimmer/Apr11/$year/$sample data/skimmer/Apr11/$year/; done; done
+```
 
-### Get all running job names:
+
+### Get all running condor job names:
 
 ```bash
 condor_q | awk '{ print $9}' | grep -o '[^ ]*\.sh'
-```
\ No newline at end of file
+```
+
+
+### Crab data jobs recovery
+
+Kill each task:
+
+```bash
+dataset=SingleMuon
+
+for i in {B..F}; do crab kill -d crab/pfnano_v2_3/crab_pfnano_v2_3_2016_${dataset}_Run2016${i}*HIPM; done
+for i in {F..H}; do crab kill -d crab/pfnano_v2_3/crab_pfnano_v2_3_2016_${dataset}_Run2016$i; done
+for i in {B..F}; do crab kill -d crab/pfnano_v2_3/crab_pfnano_v2_3_2017_${dataset}_Run2017$i; done
+for i in {A..D}; do crab kill -d crab/pfnano_v2_3/crab_pfnano_v2_3_2018_${dataset}_Run2018$i; done
+```
+
+Get a crab report for each task:
+
+```bash
+for i in {B..F}; do crab report -d crab/pfnano_v2_3/crab_pfnano_v2_3_2016_${dataset}_Run2016${i}*HIPM; done
+for i in {F..H}; do crab report -d crab/pfnano_v2_3/crab_pfnano_v2_3_2016_${dataset}_Run2016$i; done
+for i in {B..F}; do crab report -d crab/pfnano_v2_3/crab_pfnano_v2_3_2017_${dataset}_Run2017$i; done
+for i in {A..D}; do crab report -d crab/pfnano_v2_3/crab_pfnano_v2_3_2018_${dataset}_Run2018$i; done
+```
+
+Combine the lumis to process:
+
+```bash
+mkdir -p recovery/$dataset/
+# shopt extglob
+# jq -s 'reduce .[] as $item ({}; . * $item)' crab/pfnano_v2_3/crab_pfnano_v2_3_2016_${dataset}_*HIPM/results/notFinishedLumis.json > recovery/$dataset/2016APVNotFinishedLumis.json
+for year in 2016 2017 2018; do jq -s 'reduce .[] as $item ({}; . * $item)' crab/pfnano_v2_3/crab_pfnano_v2_3_${year}_${dataset}_*/results/notFinishedLumis.json > recovery/$dataset/${year}NotFinishedLumis.json; done
+```
+
+Finally, add these as a lumimask for the recovery task.
\ No newline at end of file
diff --git a/data/indexpfnano.py b/data/indexpfnano.py
index b984dbc8..84d799c4 100644
--- a/data/indexpfnano.py
+++ b/data/indexpfnano.py
@@ -23,9 +23,9 @@ def get_subfolders(parent):
 
 folders_to_index = [
     #
-    "/store/user/lpcpfnano/dryu/v2_2_1/2016/SingleMu2016",
-    "/store/user/lpcpfnano/dryu/v2_2_1/2017/SingleMu2017",
-    "/store/user/lpcpfnano/dryu/v2_2/2018/SingleMu2018",
+    # "/store/user/lpcpfnano/rkansal/v2_3/2016/SingleMu2016",
+    # "/store/user/lpcpfnano/rkansal/v2_3/2017/SingleMu2017",
+    "/store/user/lpcpfnano/rkansal/v2_3/2018/SingleMu2018",
     #
     "/store/user/lpcpfnano/cmantill/v2_3/2016/JetHT2016",
     "/store/user/lpcpfnano/cmantill/v2_3/2017/JetHT2017",
@@ -73,11 +73,12 @@ def get_subfolders(parent):
     #
     "/store/user/lpcpfnano/cmantill/v2_2/2017/HWWPrivate",
     #
-    "/store/user/lpcpfnano/cmantill/v2_3/2017/Hgg/",
+    # "/store/user/lpcpfnano/cmantill/v2_3/2017/Hgg/",
     #
-    "/store/user/lpcpfnano/cmantill/v2_3/2017/XHYPrivate",
-    "/store/user/lpcpfnano/ammitra/v2_3/2017/XHYPrivate",
-    "/store/user/lpcpfnano/rkansal/v2_3/2017/XHYPrivate",
+    "/store/user/lpcpfnano/rkansal/v2_3/2016/XHY",
+    "/store/user/lpcpfnano/rkansal/v2_3/2016APV/XHY",
+    "/store/user/lpcpfnano/ammitra/v2_3/2017/XHY",
+    "/store/user/lpcpfnano/cmantill/v2_3/2018/XHY",
     #
     "/store/user/lpcpfnano/rkansal/v2_3/2016/Diboson/",
     "/store/user/lpcpfnano/rkansal/v2_3/2016APV/Diboson/",
@@ -119,9 +120,11 @@ def get_subfolders(parent):
     "/store/user/lpcpfnano/cmantill/v2_3/2018/SingleTop/ST_t-channel_top_4f_InclusiveDecays_TuneCP5_13TeV-powheg-madspin-pythia8/ST_t-channel_top_4f_InclusiveDecays/220808_150919/0000/nano_mc2018_17.root"
 ]
 
+ignore_subsamples = ["SingleMuon_Run2016B_ver1_HIPM"]
+
 for pyear in ["2016", "2016APV", "2017", "2018"]:
-    # if pyear != "2017":
-    #    continue
+    if pyear != "2018":
+        continue
     print(pyear)
     index = {}
     for f1 in folders_to_index:
@@ -164,9 +167,13 @@ def get_subfolders(parent):
                 if "ext1" in subsample_short:
                     print("   Ext1")
 
-                if year == "2016" and subsample_short.endswith("HIPM"):
+                if subsample_short in ignore_subsamples:
+                    print(f"Ignoring {subsample_short}")
                     continue
 
+                # if year == "2016" and subsample_short.endswith("HIPM"):
+                #     continue
+
                 # skip non-PSWeights files, and rename PSWeights ones
                 if year == "2018" and subsample_short.startswith("QCD"):
                     if not subsample_short.endswith("_PSWeights_madgraph"):
diff --git a/data/pfnanoindex_2016.json b/data/pfnanoindex_2016.json
index d328a45b..7da19caf 100644
--- a/data/pfnanoindex_2016.json
+++ b/data/pfnanoindex_2016.json
@@ -169,6 +169,19 @@
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH1_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH1_preUL/230217_204700/0000/nano_mc2016_4.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH1_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH1_preUL/230217_204700/0000/nano_mc2016_5.root"
       ],
+      "GluGluToHHTo4B_node_cHHH2p45_preUL": [
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-10.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-11.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-12.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-13.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-14.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-19.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-2.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-3.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-4.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-6.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-9.root"
+      ],
       "GluGluToHHTo4B_node_cHHH5_preUL": [
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH5_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_204627/0000/nano_mc2016_1.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH5_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_204627/0000/nano_mc2016_2.root",
@@ -1087,7 +1100,57 @@
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_194717/0000/nano_mc2016post_2.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_194717/0000/nano_mc2016post_3.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_194717/0000/nano_mc2016post_5.root",
-        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_194717/0000/nano_mc2016post_6.root"
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_194717/0000/nano_mc2016post_6.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-1.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-11.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-12.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-13.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-14.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-15.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-16.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-2.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-3.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-4.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-5.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-6.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-7.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173707/0000/nano_mc2016post_1-8.root",
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+      ]
+    },
     "ZJetsToQQ": {
       "ZJetsToQQ_HT-200to400": [
         "/store/user/lpcpfnano/cmantill/v2_3/2016/ZJetsToQQ/ZJetsToQQ_HT-200to400_TuneCP5_13TeV-madgraphMLM-pythia8/ZJetsToQQ_HT-200to400/221111_224643/0000/nano_mc2016post_1-1.root",
diff --git a/data/pfnanoindex_2016APV.json b/data/pfnanoindex_2016APV.json
index d531b68a..7dc20e41 100644
--- a/data/pfnanoindex_2016APV.json
+++ b/data/pfnanoindex_2016APV.json
@@ -161,6 +161,19 @@
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       ],
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+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-6.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_204643/0000/nano_mc2016_1-9.root"
+      ],
       "GluGluToHHTo4B_node_cHHH5_preUL": [
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         "/store/user/lpcpfnano/cmantill/v2_3/2016/HH/GluGluToHHTo4B_node_cHHH5_TuneCUETP8M1_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_204627/0000/nano_mc2016_2.root",
@@ -994,7 +1007,80 @@
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-        "/store/user/lpcpfnano/cmantill/v2_3/2016APV/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_173332/0000/nano_mc2016pre_3.root"
+        "/store/user/lpcpfnano/cmantill/v2_3/2016APV/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_173332/0000/nano_mc2016pre_3.root",
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     "ZJetsToQQ": {
       "ZJetsToQQ_HT-200to400": [
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diff --git a/data/pfnanoindex_2017.json b/data/pfnanoindex_2017.json
index da45b140..ebd1e042 100644
--- a/data/pfnanoindex_2017.json
+++ b/data/pfnanoindex_2017.json
@@ -169,6 +169,16 @@
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       ],
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+        "/store/user/lpcpfnano/cmantill/v2_3/2017/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_204811/0000/nano_mc2017_2.root"
+      ],
       "GluGluToHHTobbVV_node_cHHH0": [
         "/store/user/lpcpfnano/cmantill/v2_3/2017/HH/GluGluToHHTobbVV_node_cHHH0_TuneCP5_13TeV-powheg-pythia8/GluGluToHHTobbVV_node_cHHH0/220808_163755/0000/nano_mc2017_1-1.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2017/HH/GluGluToHHTobbVV_node_cHHH0_TuneCP5_13TeV-powheg-pythia8/GluGluToHHTobbVV_node_cHHH0/220808_163755/0000/nano_mc2017_1-2.root",
@@ -2706,7 +2716,106 @@
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+        "/store/user/lpcpfnano/ammitra/v2_3/2017/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90/230323_174628/0000/nano_mc2017_7.root",
+        "/store/user/lpcpfnano/ammitra/v2_3/2017/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90/230323_174628/0000/nano_mc2017_8.root",
+        "/store/user/lpcpfnano/ammitra/v2_3/2017/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90/230323_174628/0000/nano_mc2017_9.root"
       ]
     },
     "ZJetsToQQ": {
diff --git a/data/pfnanoindex_2018.json b/data/pfnanoindex_2018.json
index cddcde4f..cfe60b76 100644
--- a/data/pfnanoindex_2018.json
+++ b/data/pfnanoindex_2018.json
@@ -171,6 +171,7 @@
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-11.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-12.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-13.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-2.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-3.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-6.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH2p45_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH2p45_preUL/230217_205019/0000/nano_mc2018_1-7.root",
@@ -182,6 +183,8 @@
       ],
       "GluGluToHHTo4B_node_cHHH5_preUL": [
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_205002/0000/nano_mc2018_1-1.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_205002/0000/nano_mc2018_1-2.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_205002/0000/nano_mc2018_1-3.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_205002/0000/nano_mc2018_1-4.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_205002/0000/nano_mc2018_1-5.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HH/GluGluToHHTo4B_node_cHHH5_TuneCP5_PSWeights_13TeV-powheg-pythia8/GluGluToHHTo4B_node_cHHH5_preUL/230217_205002/0000/nano_mc2018_1-6.root",
@@ -1893,7 +1896,105 @@
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_195802/0000/nano_mc2018_2.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_195802/0000/nano_mc2018_4.root",
         "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_195802/0000/nano_mc2018_5.root",
-        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_195802/0000/nano_mc2018_6.root"
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125/230120_195802/0000/nano_mc2018_6.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-1.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-10.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-11.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-12.root",
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+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-14.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-15.root",
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+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-17.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-18.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-2.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-20.root",
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+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-22.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-23.root",
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+        "/store/user/lpcpfnano/cmantill/v2_3/2018/HWW/GluGluZH_HToWW_ZTo2L_M-125_TuneCP5_13TeV-powheg-pythia8/GluGluZH_HToWW_ZTo2L_M-125_ext1/230316_173503/0000/nano_mc2018_1-28.root",
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+        "/store/user/lpcpfnano/cmantill/v2_3/2018/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80/230323_135221/0000/nano_mc2018_6.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80/230323_135221/0000/nano_mc2018_7.root",
+        "/store/user/lpcpfnano/cmantill/v2_3/2018/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80/230323_135221/0000/nano_mc2018_9.root"
+      ]
+    },
     "ZJetsToQQ": {
       "ZJetsToQQ_HT-200to400": [
         "/store/user/lpcpfnano/cmantill/v2_3/2018/ZJetsToQQ/ZJetsToQQ_HT-200to400_TuneCP5_13TeV-madgraphMLM-pythia8/ZJetsToQQ_HT-200to400/221111_224810/0000/nano_mc2018_1-1.root",
diff --git a/data/xhy/check_pfnanos.ipynb b/data/xhy/check_pfnanos.ipynb
new file mode 100644
index 00000000..d3e5a034
--- /dev/null
+++ b/data/xhy/check_pfnanos.ipynb
@@ -0,0 +1,71 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year_users = {\n",
+    "    \"2016\": \"rkansal\",\n",
+    "    \"2016APV\": \"rkansal\",\n",
+    "    \"2017\": \"ammitra\",\n",
+    "    \"2018\": \"cmantill\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for year, user in year_users.items():\n",
+    "    print(year)\n",
+    "    pfnano_samples = [\n",
+    "        sample.split(\"_TuneCP5_13TeV-madgraph-pythia8\")[0]\n",
+    "        for sample in os.listdir(f\"/eos/uscms/store/group/lpcpfnano/{user}/v2_3/{year}/XHY\")\n",
+    "    ]\n",
+    "\n",
+    "    with open(f\"parsed_miniaod_{year}.yaml\", \"r\") as f:\n",
+    "        mini_samples = list(yaml.safe_load(f)[\"samples\"][\"XHY\"][\"datasets\"].keys())\n",
+    "\n",
+    "    for sample in mini_samples:\n",
+    "        if sample not in pfnano_samples:\n",
+    "            print(sample)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/xhy/filter_samples.ipynb b/data/xhy/filter_samples.ipynb
new file mode 100644
index 00000000..1cedc4bc
--- /dev/null
+++ b/data/xhy/filter_samples.ipynb
@@ -0,0 +1,149 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"parsed_miniaod_2017.yaml\", \"r\") as f:\n",
+    "    files = yaml.safe_load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = list(files[\"samples\"][\"XHY\"][\"datasets\"].keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(samples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mxmy(sample):\n",
+    "    mY = int(sample.split(\"-\")[-1])\n",
+    "    mX = int(sample.split(\"NMSSM_XToYHTo2W2BTo4Q2B_MX-\")[1].split(\"_\")[0])\n",
+    "\n",
+    "    return (mX, mY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def filters(sample):\n",
+    "    mX, mY = mxmy(sample)\n",
+    "    return (mY <= 250) and (mX >= 600) and (mY != 70) and (mY != 90) and (mX != 2500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_samples = [sample for sample in samples if filters(sample)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates = os.listdir(\"/eos/uscms/store/user/rkansal/bbVV/templates/Apr11\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mps = [mxmy(sample) for sample in templates if sample.startswith(\"NMSSM\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/xhy/miniaod.txt b/data/xhy/miniaod.txt
new file mode 100644
index 00000000..9bc673f7
--- /dev/null
+++ b/data/xhy/miniaod.txt
@@ -0,0 +1,1824 @@
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+/NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
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\ No newline at end of file
diff --git a/data/xhy/parse_das.ipynb b/data/xhy/parse_das.ipynb
new file mode 100644
index 00000000..f23d7626
--- /dev/null
+++ b/data/xhy/parse_das.ipynb
@@ -0,0 +1,78 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"./miniaod.txt\", \"r\") as f:\n",
+    "    samples = f.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year_selectors = {\n",
+    "    \"2016APV\": \"UL16MiniAODAPVv2\",\n",
+    "    \"2016\": \"UL16MiniAODv2\",\n",
+    "    \"2017\": \"UL17MiniAODv2\",\n",
+    "    \"2018\": \"UL18MiniAODv2\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_dict = {year: {} for year in year_selectors}\n",
+    "\n",
+    "for sample in samples:\n",
+    "    for year, selector in year_selectors.items():\n",
+    "        if selector in sample:\n",
+    "            key = \"_\".join(sample[1:].split(\"_\")[:4])\n",
+    "            sample_dict[year][key] = sample[:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "\n",
+    "for year, year_samples in sample_dict.items():\n",
+    "    with open(f\"./parsed_miniaod_{year}.yaml\", \"w\") as f:\n",
+    "        yaml.dump({\"samples\": {\"XHY\": {\"datasets\": year_samples}}}, f)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/xhy/parsed_miniaod.yaml b/data/xhy/parsed_miniaod.yaml
new file mode 100644
index 00000000..9fc8afc8
--- /dev/null
+++ b/data/xhy/parsed_miniaod.yaml
@@ -0,0 +1,1828 @@
+'2016':
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
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+  NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
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+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+  NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSI
diff --git a/data/xhy/parsed_miniaod_2016.yaml b/data/xhy/parsed_miniaod_2016.yaml
new file mode 100644
index 00000000..2df63e38
--- /dev/null
+++ b/data/xhy/parsed_miniaod_2016.yaml
@@ -0,0 +1,459 @@
+samples:
+  XHY:
+    datasets:
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
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+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
diff --git a/data/xhy/parsed_miniaod_2016APV.yaml b/data/xhy/parsed_miniaod_2016APV.yaml
new file mode 100644
index 00000000..57b63a1c
--- /dev/null
+++ b/data/xhy/parsed_miniaod_2016APV.yaml
@@ -0,0 +1,459 @@
+samples:
+  XHY:
+    datasets:
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-800: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-1000: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-800: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-900: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1000: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1200: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
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+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM
diff --git a/data/xhy/parsed_miniaod_2017.yaml b/data/xhy/parsed_miniaod_2017.yaml
new file mode 100644
index 00000000..3db8c55e
--- /dev/null
+++ b/data/xhy/parsed_miniaod_2017.yaml
@@ -0,0 +1,459 @@
+samples:
+  XHY:
+    datasets:
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-800: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-1000: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-800: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-900: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1000: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1200: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-800: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
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+      NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
diff --git a/data/xhy/parsed_miniaod_2018.yaml b/data/xhy/parsed_miniaod_2018.yaml
new file mode 100644
index 00000000..8142c133
--- /dev/null
+++ b/data/xhy/parsed_miniaod_2018.yaml
@@ -0,0 +1,459 @@
+samples:
+  XHY:
+    datasets:
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-800: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-1000: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-350: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-450: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
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+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM
+      NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90: /NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSI
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 00000000..46e36c57
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,14 @@
+name: bbVV
+channels:
+  - conda-forge
+dependencies:
+  - python=3.9
+  - coffea=0.7.21
+  - xrootd=5.4.0
+  - pip
+  - pip:
+      - fastjet=3.4.0.1  # pre awkward v2, but doesn't work on mac!
+      - pre-commit
+      - black
+      - pyyaml
+      - tritonclient[all]
\ No newline at end of file
diff --git a/inference_scans/README.md b/inference_scans/README.md
new file mode 100644
index 00000000..bf5ea510
--- /dev/null
+++ b/inference_scans/README.md
@@ -0,0 +1,60 @@
+# HH Inference scans
+
+For HH limits, kl/c2v scans, etc. Adapted from https://github.com/LPC-HH/inference_scans.
+
+- [HH Inference scans](#hh-inference-scans)
+  - [Setup](#setup)
+
+
+## Setup
+
+1.  Make a fresh area on lxplus, for example:
+
+```bash
+mkdir ~/work/HH
+```
+
+2. Setup inference as follows
+  - Set up the environment in a new folder with a clean environment (no `cmsenv`, ETC.)
+  - `v1` is the name of the environment here.
+
+```bash
+git clone ssh://git@gitlab.cern.ch:7999/hh/tools/inference.git
+cd inference
+source setup.sh v1
+```
+
+TODO: update this with better datacard dirs and setting up card repos.
+
+3. Add the following to the `.setups/v1.sh` file 
+```bash
+export DHI_DATACARDS_RUN2="/afs/cern.ch/user/r/rkansal/work/hh/datacards_run2"
+export DHI_DATA="/afs/cern.ch/user/r/rkansal/work/hh/inference/data"
+export DHI_STORE="/eos/user/r/rkansal/bbVV/inference"
+export Cbbww4q="bbww_hadronic/v1"
+export Cbbww4qInject="$Cbbww4q<$DHI_DATACARDS_RUN2/$Cbbww4q/inject.json"
+export VERSION="dev"
+export UNBLINDED="False"
+```
+
+4. Checkout this repo outside of `inference`
+
+```bash
+cd ..
+git clone https://github.com/rkansal47/HHbbVV.git
+```
+
+
+5. Finally, from the `inference_scans` directory in `screen` or `tmux`, run the desired script:
+
+```bash
+cd inference
+source setup.sh v1 # required before running a scan
+cd ../HHbbVV/inference_scans/
+source run_likelihood_scan_2d_kl_c2v.sh
+``` 
+
+Note! Need to make persistent screens to log off lxplus while running: 
+https://hsf-training.github.io/analysis-essentials/shell-extras/persistent-screen.html
+
+Also keep track of the specific lxplus node.
\ No newline at end of file
diff --git a/inference_scans/nuisance_renames.py b/inference_scans/nuisance_renames.py
new file mode 100644
index 00000000..56c314b2
--- /dev/null
+++ b/inference_scans/nuisance_renames.py
@@ -0,0 +1,11 @@
+def rename_nuisance(nuisance: str):
+    if nuisance.startswith("CMS_bbWW_hadronic_"):
+        nuisance = nuisance.split("CMS_bbWW_hadronic_")[1]
+
+    if nuisance.startswith("tf_dataResidual_bbFatJetParticleNetMass_"):
+        return "TF_" + nuisance.split("tf_dataResidual_bbFatJetParticleNetMass_")[1]
+
+    if nuisance.startswith("lp_sf"):
+        return "Lund plane SF"
+
+    return nuisance
diff --git a/inference_scans/run_impacts.sh b/inference_scans/run_impacts.sh
new file mode 100755
index 00000000..b1e18b85
--- /dev/null
+++ b/inference_scans/run_impacts.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+law run PlotPullsAndImpacts \
+    --version "$VERSION" \
+    --datacards "$Cbbww4qInject" \
+    --file-types "pdf,png" \
+    --pois r \
+    --PullsAndImpacts-workflow "htcondor" \
+    --PullsAndImpacts-tasks-per-job 10 \
+    --PullsAndImpacts-custom-args="--rMin -40 --rMax 40" \
+    --parameters-per-page 40 \
+    --order-by-impact \
+    --labels "nuisance_renames.py" \
+    --skip-parameters "*dataResidual_Bin*" \
+    --campaign run2 \
+    --page 0 \
+    --pull-range 3 \
+    --unblinded $UNBLINDED \
+    --remove-output 0,a,y
+    # --print-command 2 \
+    # --use-snapshot True \  # for (fit to data) after unblinding
+    # --Snapshot-workflow "local" \
\ No newline at end of file
diff --git a/inference_scans/run_upper_limits_point_r.sh b/inference_scans/run_upper_limits_point_r.sh
new file mode 100755
index 00000000..e9894563
--- /dev/null
+++ b/inference_scans/run_upper_limits_point_r.sh
@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+
+# export DHI_CMS_POSTFIX="Supplementary"
+law run PlotUpperLimitsAtPoint \
+    --version "$VERSION" \
+    --multi-datacards "$Cbbww4qInject" \
+    --datacard-names "bbVV" \
+    --file-types "pdf,png" \
+    --pois r \
+    --show-parameters kl,kt,C2V,CV \
+    --UpperLimits-workflow "htcondor" \
+    --UpperLimits-tasks-per-job 1 \
+    --x-log \
+    --campaign run2 \
+    --unblinded $UNBLINDED \
+    --h-lines 1 \
+    --save-hep-data True \
+    --remove-output 0,a,y
+    # --use-snapshot True \  # for (fit to data) after unblinding
+    # --Snapshot-workflow "local" \
\ No newline at end of file
diff --git a/inference_scans/run_upper_limits_r_1d_c2v.sh b/inference_scans/run_upper_limits_r_1d_c2v.sh
new file mode 100755
index 00000000..6bfa6e05
--- /dev/null
+++ b/inference_scans/run_upper_limits_r_1d_c2v.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+
+# export DHI_CMS_POSTFIX="Supplementary"
+law run PlotUpperLimits \
+    --version "$VERSION" \
+    --datacards "$Cbbww4qInject" \
+    --xsec fb \
+    --pois r \
+    --scan-parameters C2V,-1,3,10 \
+    --UpperLimits-workflow "htcondor" \
+    --UpperLimits-tasks-per-job 1 \
+    --file-types "png,pdf" \
+    --campaign run2 \
+    --unblinded $UNBLINDED \
+    --y-log \
+    --show-parameters "kt,C2V,CV" \
+    --br bbww \
+    --save-ranges \
+    --remove-output 0,a,y \
+    --save-hep-data True \
+    --frozen-groups signal_norm_xsbr
+    # --use-snapshot True \  # for (fit to data) after unblinding
+    # --Snapshot-workflow "local" \
+    # --scan-parameters kl,-3,5,8 \
\ No newline at end of file
diff --git a/inference_scans/run_upper_limits_r_1d_kl.sh b/inference_scans/run_upper_limits_r_1d_kl.sh
new file mode 100755
index 00000000..4a8ca1a3
--- /dev/null
+++ b/inference_scans/run_upper_limits_r_1d_kl.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+
+# export DHI_CMS_POSTFIX="Supplementary"
+law run PlotUpperLimits \
+    --version "$VERSION" \
+    --datacards "$Cbbww4qInject" \
+    --xsec fb \
+    --pois r \
+    --scan-parameters kl,-30,-7,4:kl,-6,7,14:kl,15,30,4 \
+    --UpperLimits-workflow "htcondor" \
+    --UpperLimits-tasks-per-job 1 \
+    --file-types "png,pdf" \
+    --campaign run2 \
+    --unblinded $UNBLINDED \
+    --y-log \
+    --show-parameters "kt,C2V,CV" \
+    --br bbww \
+    --save-ranges \
+    --remove-output 0,a,y \
+    --save-hep-data True \
+    --frozen-groups signal_norm_xsbr
+    # --scan-parameters kl,-30,-12,7:kl,-7,-5,3:kl,-2,7,19:kl,10,30,6 \
+    # --use-snapshot True \  # for (fit to data) after unblinding
+    # --Snapshot-workflow "local" \
\ No newline at end of file
diff --git a/paper/latex_tables.ipynb b/paper/latex_tables.ipynb
new file mode 100644
index 00000000..69e80d4b
--- /dev/null
+++ b/paper/latex_tables.ipynb
@@ -0,0 +1,329 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "import json\n",
+    "import numpy as np\n",
+    "from copy import deepcopy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "years = [\"2016\", \"2017\", \"2018\"]\n",
+    "years_vfp = [\"2016APV\", \"2016\", \"2017\", \"2018\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"samples/JetHT.yaml\", \"r\") as f:\n",
+    "    samples = yaml.safe_load(f)[\"samples\"]\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "for year in years:\n",
+    "    datasets = list(samples[f\"JetHT{year}\"][\"datasets\"].values())\n",
+    "    for i in range(len(datasets)):\n",
+    "        datasets[i] = datasets[i].replace(\"_\", \"\\_\")\n",
+    "\n",
+    "    lend = len(datasets)\n",
+    "    lines.append(rf\"\\multirow{{{lend}}}{{*}}{{ {year} }} & {datasets[0]} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    for dataset in datasets[1:]:\n",
+    "        lines.append(rf\" & {dataset} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    lines.append(r\"\\hline\" + \"\\n\")\n",
+    "\n",
+    "# remove trailing \"\\\\\" and \"\\hline\"\n",
+    "lines = lines[:-1]\n",
+    "lines[-1] = lines[-1][:-4]\n",
+    "\n",
+    "with open(\"tables/datasets_jetht.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"samples/SingleMuon.yaml\", \"r\") as f:\n",
+    "    samples = yaml.safe_load(f)[\"samples\"]\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "for year in years:\n",
+    "    datasets = list(samples[f\"SingleMu{year}\"][\"datasets\"].values())\n",
+    "    for i in range(len(datasets)):\n",
+    "        datasets[i] = datasets[i].replace(\"_\", \"\\_\")\n",
+    "\n",
+    "    lend = len(datasets)\n",
+    "    lines.append(rf\"\\multirow{{{lend}}}{{*}}{{ {year} }} & {datasets[0]} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    for dataset in datasets[1:]:\n",
+    "        lines.append(rf\" & {dataset} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    lines.append(r\"\\hline\" + \"\\n\")\n",
+    "\n",
+    "# remove trailing \"\\\\\" and \"\\hline\"\n",
+    "lines = lines[:-1]\n",
+    "lines[-1] = lines[-1][:-4]\n",
+    "\n",
+    "with open(\"tables/datasets_singlemuon.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "MC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_xsec(xsec):\n",
+    "    if isinstance(xsec, str):\n",
+    "        xsec = eval(xsec)\n",
+    "\n",
+    "    return xsec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"samples/MC_bg.yaml\", \"r\") as f:\n",
+    "    samples = yaml.safe_load(f)[\"samples\"]\n",
+    "\n",
+    "with open(\"../data/xsecs.json\", \"r\") as f:\n",
+    "    xsecs = json.load(f)\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "for sample in np.sort(list(samples.keys())):\n",
+    "    for key, dataset in samples[sample][\"datasets\"].items():\n",
+    "        dname = dataset.split(\"/\")[1].replace(\"_\", \"\\_\")\n",
+    "        lines.append(rf\" & {dname} & {process_xsec(xsecs[key]):.2f} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    lend = len(samples[sample][\"datasets\"])\n",
+    "    lines[-lend] = rf\"\\multirow{{{lend}}}{{*}}{{{sample}}}\" + lines[-lend]\n",
+    "    lines.append(r\"\\hline\" + \"\\n\")\n",
+    "\n",
+    "# remove trailing \"\\\\\" and \"\\hline\"\n",
+    "lines = lines[:-1]\n",
+    "lines[-1] = lines[-1][:-4]\n",
+    "\n",
+    "with open(\"tables/datasets_mcbg.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"samples/MC_sig.yaml\", \"r\") as f:\n",
+    "    samples = yaml.safe_load(f)[\"samples\"]\n",
+    "\n",
+    "with open(\"../data/xsecs.json\", \"r\") as f:\n",
+    "    xsecs = json.load(f)\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "for sample in np.sort(list(samples.keys())):\n",
+    "    for key, dataset in samples[sample][\"datasets\"].items():\n",
+    "        key = key.split(\"_pn4q\")[0]\n",
+    "        cl = key.split(\"cHHH\")[1].split(\"_\")[0].replace(\"p\", \".\")\n",
+    "        sm = \"SM\" if cl == \"1\" else \"BSM\"\n",
+    "        pname = rf\"ggHH {sm} ($\\kappa_{{\\lambda}} = {cl}$)\"\n",
+    "        dname = dataset.split(\"/\")[1].replace(\"_\", \"\\_\")\n",
+    "        lines.append(rf\"{pname} & {dname} & {process_xsec(xsecs[key]) * 1000:.2f} \\\\\" + \"\\n\")\n",
+    "\n",
+    "with open(\"tables/datasets_mcsig_nonres.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mxmy(sample):\n",
+    "    mY = int(sample.split(\"-\")[-1])\n",
+    "    mX = int(sample.split(\"NMSSM_XToYHTo2W2BTo4Q2B_MX-\")[1].split(\"_\")[0])\n",
+    "\n",
+    "    return (mX, mY)\n",
+    "\n",
+    "\n",
+    "with open(\"../data/xhy/parsed_miniaod_2017.yaml\", \"r\") as f:\n",
+    "    samples = yaml.safe_load(f)[\"samples\"][\"XHY\"][\"datasets\"]\n",
+    "\n",
+    "mps = [[*mxmy(sample)] for sample in samples]\n",
+    "mxs = np.sort(np.unique(np.array(mps)[:, 0]))\n",
+    "mys = np.sort(np.unique(np.array(mps)[:, 1]))\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "line = r\"$M_Y$ [\\gev] & \" + \" & \".join(mxs.astype(str)) + r\"\\\\\" + \"\\n\"\n",
+    "lines.append(line)\n",
+    "\n",
+    "for my in mys:\n",
+    "    line = rf\"{my}\"\n",
+    "    for mx in mxs:\n",
+    "        line += \" & \"\n",
+    "        if [mx, my] in mps:\n",
+    "            line += r\"$\\checkmark$ \"\n",
+    "    line += r\"\\\\\" + \"\\n\"\n",
+    "    lines.append(line)\n",
+    "\n",
+    "# remove trailing \"\\\\\\n\"\n",
+    "lines[-1] = lines[-1][:-3]\n",
+    "\n",
+    "with open(\"tables/datasets_mcsig_res.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Triggers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from triggers import HLTs\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "for year in years:\n",
+    "    hlts = deepcopy(HLTs[year])\n",
+    "    for i in range(len(hlts)):\n",
+    "        hlts[i] = hlts[i].replace(\"_\", \"\\_\")\n",
+    "\n",
+    "    lend = len(hlts)\n",
+    "    lines.append(rf\"\\multirow{{{lend}}}{{*}}{{ {year} }} & {hlts[0]} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    for hlt in hlts[1:]:\n",
+    "        lines.append(rf\" & {hlt} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    lines.append(r\"\\hline\" + \"\\n\")\n",
+    "\n",
+    "# remove trailing \"\\\\\" and \"\\hline\"\n",
+    "lines = lines[:-1]\n",
+    "lines[-1] = lines[-1][:-4]\n",
+    "\n",
+    "with open(\"tables/hlts.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from triggers import muon_HLTs\n",
+    "\n",
+    "lines = []\n",
+    "\n",
+    "for year in years:\n",
+    "    hlts = deepcopy(muon_HLTs[year])\n",
+    "    for i in range(len(hlts)):\n",
+    "        hlts[i] = hlts[i].replace(\"_\", \"\\_\")\n",
+    "\n",
+    "    lend = len(hlts)\n",
+    "    lines.append(rf\"\\multirow{{{lend}}}{{*}}{{ {year} }} & {hlts[0]} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    for hlt in hlts[1:]:\n",
+    "        lines.append(rf\" & {hlt} \\\\\" + \"\\n\")\n",
+    "\n",
+    "    lines.append(r\"\\hline\" + \"\\n\")\n",
+    "\n",
+    "# remove trailing \"\\\\\" and \"\\hline\"\n",
+    "lines = lines[:-1]\n",
+    "lines[-1] = lines[-1][:-4]\n",
+    "\n",
+    "with open(\"tables/hlts_singlemuon.tex\", \"w\") as f:\n",
+    "    f.writelines(lines)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "15adc7883e707560d0d9727709639b8fe3f3cff1f197d2d643742923ff23a29c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/paper/samples/JetHT.yaml b/paper/samples/JetHT.yaml
new file mode 100644
index 00000000..ed650ad0
--- /dev/null
+++ b/paper/samples/JetHT.yaml
@@ -0,0 +1,30 @@
+samples:
+  JetHT2016: 
+    year: 2016
+    datasets: 
+      JetHT_Run2016B_ver1_HIPM: /JetHT/Run2016B-ver1_HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016B_ver2_HIPM: /JetHT/Run2016B-ver2_HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016C_HIPM: /JetHT/Run2016C-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016D_HIPM: /JetHT/Run2016D-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016E_HIPM: /JetHT/Run2016E-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016F_HIPM: /JetHT/Run2016F-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016F: /JetHT/Run2016F-UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016G: /JetHT/Run2016G-UL2016_MiniAODv2-v2/MINIAOD
+      JetHT_Run2016H: /JetHT/Run2016H-UL2016_MiniAODv2-v2/MINIAOD
+
+  JetHT2017: 
+    year: 2017
+    datasets: 
+      JetHT_Run2017B: /JetHT/Run2017B-UL2017_MiniAODv2-v1/MINIAOD
+      JetHT_Run2017C: /JetHT/Run2017C-UL2017_MiniAODv2-v1/MINIAOD
+      JetHT_Run2017D: /JetHT/Run2017D-UL2017_MiniAODv2-v1/MINIAOD
+      JetHT_Run2017E: /JetHT/Run2017E-UL2017_MiniAODv2-v1/MINIAOD
+      JetHT_Run2017F: /JetHT/Run2017F-UL2017_MiniAODv2-v1/MINIAOD
+
+  JetHT2018: 
+    year: 2018
+    datasets: 
+      JetHT_Run2018A: /JetHT/Run2018A-UL2018_MiniAODv2-v1/MINIAOD
+      JetHT_Run2018B: /JetHT/Run2018B-UL2018_MiniAODv2-v1/MINIAOD
+      JetHT_Run2018C: /JetHT/Run2018C-UL2018_MiniAODv2-v1/MINIAOD
+      JetHT_Run2018D: /JetHT/Run2018D-UL2018_MiniAODv2-v1/MINIAOD
\ No newline at end of file
diff --git a/paper/samples/MC_bg.yaml b/paper/samples/MC_bg.yaml
new file mode 100644
index 00000000..9fd26869
--- /dev/null
+++ b/paper/samples/MC_bg.yaml
@@ -0,0 +1,86 @@
+samples:
+  QCD:
+    datasets:
+      # QCD_Pt_170to300:   /QCD_Pt_170to300_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_300to470:   /QCD_Pt_300to470_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_470to600:   /QCD_Pt_470to600_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_600to800:   /QCD_Pt_600to800_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_800to1000:  /QCD_Pt_800to1000_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_1000to1400: /QCD_Pt_1000to1400_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_1400to1800: /QCD_Pt_1400to1800_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_1800to2400: /QCD_Pt_1800to2400_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_2400to3200: /QCD_Pt_2400to3200_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # QCD_Pt_3200toInf:  /QCD_Pt_3200toInf_TuneCP5_13TeV_pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT50to100   : /QCD_HT50to100_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT100to200  : /QCD_HT100to200_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT200to300  : /QCD_HT200to300_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT300to500  : /QCD_HT300to500_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT500to700  : /QCD_HT500to700_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT700to1000 : /QCD_HT700to1000_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT1000to1500: /QCD_HT1000to1500_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT1500to2000: /QCD_HT1500to2000_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      QCD_HT2000toInf : /QCD_HT2000toInf_TuneCP5_PSWeights_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+
+  V+Jets:
+    datasets:
+      WJetsToQQ_HT-400to600: /WJetsToQQ_HT-400to600_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      WJetsToQQ_HT-600to800: /WJetsToQQ_HT-600to800_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      WJetsToQQ_HT-800toInf: /WJetsToQQ_HT-800toInf_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      ZJetsToQQ_HT-400to600: /ZJetsToQQ_HT-400to600_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      ZJetsToQQ_HT-600to800: /ZJetsToQQ_HT-600to800_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      ZJetsToQQ_HT-800toInf: /ZJetsToQQ_HT-800toInf_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+
+  TTbar:
+    datasets:
+      TTToHadronic         : /TTToHadronic_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      TTToSemiLeptonic     : /TTToSemiLeptonic_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      # TTToSemiLeptonic_ext1: /TTToSemiLeptonic_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-PUForMUOVal_106X_mc2017_realistic_v9_ext1-v2/MINIAODSIM
+
+  SingleTop:
+    datasets: 
+      ST_t-channel_top_4f_InclusiveDecays: /ST_t-channel_top_4f_InclusiveDecays_TuneCP5_13TeV-powheg-madspin-pythia8/RunIISummer20UL17MiniAOD-106X_mc2017_realistic_v6-v1/MINIAODSIM
+      ST_t-channel_antitop_4f_InclusiveDecays: /ST_t-channel_antitop_4f_InclusiveDecays_TuneCP5_13TeV-powheg-madspin-pythia8/RunIISummer20UL17MiniAOD-106X_mc2017_realistic_v6-v1/MINIAODSIM
+      ST_tW_antitop_5f_inclusiveDecays: /ST_tW_antitop_5f_inclusiveDecays_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAOD-106X_mc2017_realistic_v6-v1/MINIAODSIM
+      ST_tW_top_5f_inclusiveDecays: /ST_tW_top_5f_inclusiveDecays_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAOD-106X_mc2017_realistic_v6-v1/MINIAODSIM
+  
+  Diboson:
+    datasets:
+      WW: /WW_TuneCP5_13TeV-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      WZ: /WZ_TuneCP5_13TeV-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      ZZ: /ZZ_TuneCP5_13TeV-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+
+  "Hbb (inclusive)":
+    datasets:
+      GluGluHToBB: /GluGluHToBB_Pt-200ToInf_M-125_TuneCP5_MINLO_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      VBFHToBB: /VBFHToBB_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      ttHToBB: /ttHTobb_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      WplusH_HToBB_WToQQ: /WplusH_HToBB_WToQQ_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      WminusH_HToBB_WToQQ: /WminusH_HToBB_WToQQ_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      ZH_HToBB_ZToQQ: /ZH_HToBB_ZToQQ_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      ggZH_HToBB_ZToQQ: /ggZH_HToBB_ZToQQ_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+
+  "HWW (inclusive)":
+    datasets:
+      GluGluHToWW_Pt-200ToInf_M-125: /GluGluHToWW_Pt-200ToInf_M-125_TuneCP5_MINLO_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      HWminusJ_HToWW_M-125: /HWminusJ_HToWW_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      HWplusJ_HToWW_M-125: /HWplusJ_HToWW_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      HZJ_HToWW_M-125: /HZJ_HToWW_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      # HZJ_HToWW_M-125_ext1: /HZJ_HToWW_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9_ext1-v2/MINIAODSIM
+      ttHToNonbb_M125: /ttHToNonbb_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+  
+  HH:
+    datasets:
+      VBF_HHTobbVV_CV_1_C2V_1_C3_1: /VBF_HHTobbVV_CV_1_C2V_1_C3_1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+      GluGluToHHTo4B_node_cHHH1_preUL: /GluGluToHHTo4B_node_cHHH1_TuneCP5_PSWeights_13TeV-powheg-pythia8/RunIIFall17MiniAODv2-PU2017_12Apr2018_94X_mc2017_realistic_v14-v1/MINIAODSIM
+  
+  #     GluGluToHHTobbVV_node_cHHH1_pn4q: /GluGluToHHTobbVV_node_cHHH1_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+  #     GluGluToHHTobbVV_node_cHHH2p45_pn4q: /GluGluToHHTobbVV_node_cHHH2p45_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+  #     GluGluToHHTobbVV_node_cHHH0_pn4q: /GluGluToHHTobbVV_node_cHHH0_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+  #     GluGluToHHTobbVV_node_cHHH5_pn4q: /GluGluToHHTobbVV_node_cHHH5_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+  #     
+  #     VBF_HHTobbVV_CV_1_C2V_2_C3_1: /VBF_HHTobbVV_CV_1_C2V_2_C3_1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+  #     VBF_HHTobbVV_CV_1_C2V_1_C3_2: /VBF_HHTobbVV_CV_1_C2V_1_C3_2_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+  #     VBF_HHTobbVV_CV_1_C2V_1_C3_0: /VBF_HHTobbVV_CV_1_C2V_1_C3_0_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+  #     VBF_HHTobbVV_CV_1_C2V_0_C3_1: /VBF_HHTobbVV_CV_1_C2V_0_C3_1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+  #     VBF_HHTobbVV_CV_1_5_C2V_1_C3_1: /VBF_HHTobbVV_CV_1_5_C2V_1_C3_1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
+  #     VBF_HHTobbVV_CV_0_5_C2V_1_C3_1: /VBF_HHTobbVV_CV_0_5_C2V_1_C3_1_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM
\ No newline at end of file
diff --git a/paper/samples/MC_sig.yaml b/paper/samples/MC_sig.yaml
new file mode 100644
index 00000000..3208669a
--- /dev/null
+++ b/paper/samples/MC_sig.yaml
@@ -0,0 +1,7 @@
+samples:
+  HWW:
+    datasets:
+      GluGluToHHTobbVV_node_cHHH1_pn4q: /GluGluToHHTobbVV_node_cHHH1_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      GluGluToHHTobbVV_node_cHHH0_pn4q: /GluGluToHHTobbVV_node_cHHH0_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      GluGluToHHTobbVV_node_cHHH2p45_pn4q: /GluGluToHHTobbVV_node_cHHH2p45_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
+      GluGluToHHTobbVV_node_cHHH5_pn4q: /GluGluToHHTobbVV_node_cHHH5_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v1/MINIAODSIM
\ No newline at end of file
diff --git a/paper/samples/SingleMuon.yaml b/paper/samples/SingleMuon.yaml
new file mode 100644
index 00000000..38902b07
--- /dev/null
+++ b/paper/samples/SingleMuon.yaml
@@ -0,0 +1,38 @@
+samples:
+  SingleMu2016: 
+    year: 2016
+    pset: $CMSSW_BASE/src/PhysicsTools/PFNano/test/ak15/nano_data_2016_UL_NANO.py
+    lumimask: https://cms-service-dqmdc.web.cern.ch/CAF/certification/Collisions16/13TeV/Legacy_2016/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt
+    datasets: 
+      /SingleMuon/Run2016B_ver1_HIPM: /SingleMuon/Run2016B-ver1_HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016B_ver2_HIPM: /SingleMuon/Run2016B-ver2_HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016C_HIPM     : /SingleMuon/Run2016C-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016D_HIPM     : /SingleMuon/Run2016D-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016E_HIPM     : /SingleMuon/Run2016E-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016F_HIPM     : /SingleMuon/Run2016F-HIPM_UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016F          : /SingleMuon/Run2016F-UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016G          : /SingleMuon/Run2016G-UL2016_MiniAODv2-v2/MINIAOD
+      /SingleMuon/Run2016H          : /SingleMuon/Run2016H-UL2016_MiniAODv2-v2/MINIAOD
+
+  SingleMu2017: 
+    year: 2017
+    pset: $CMSSW_BASE/src/PhysicsTools/PFNano/test/ak15/nano_data_2017_UL_NANO.py
+    lumimask: https://cms-service-dqmdc.web.cern.ch/CAF/certification/Collisions17/13TeV/Legacy_2017/Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt
+    datasets: 
+      SingleMuon_Run2017B : /SingleMuon/Run2017B-UL2017_MiniAODv2-v1/MINIAOD
+      SingleMuon_Run2017C : /SingleMuon/Run2017C-UL2017_MiniAODv2-v1/MINIAOD
+      SingleMuon_Run2017D : /SingleMuon/Run2017D-UL2017_MiniAODv2-v1/MINIAOD
+      SingleMuon_Run2017E : /SingleMuon/Run2017E-UL2017_MiniAODv2-v1/MINIAOD
+      SingleMuon_Run2017F : /SingleMuon/Run2017F-UL2017_MiniAODv2-v1/MINIAOD
+      SingleMuon_Run2017G : /SingleMuon/Run2017G-UL2017_MiniAODv2-v1/MINIAOD
+      #SingleMuon_Run2017H: /SingleMuon/Run2017H-UL2017_MiniAODv2-v1/MINIAOD
+
+  SingleMu2018: 
+    year: 2018
+    pset: $CMSSW_BASE/src/PhysicsTools/PFNano/test/ak15/nano_data_2018_UL_NANO.py
+    lumimask : https://cms-service-dqmdc.web.cern.ch/CAF/certification/Collisions18/13TeV/Legacy_2018/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt
+    datasets: 
+      SingleMuon_Run2018A: /SingleMuon/Run2018A-UL2018_MiniAODv2-v2/MINIAOD
+      SingleMuon_Run2018B: /SingleMuon/Run2018B-UL2018_MiniAODv2-v2/MINIAOD
+      SingleMuon_Run2018C: /SingleMuon/Run2018C-UL2018_MiniAODv2-v2/MINIAOD
+      SingleMuon_Run2018D: /SingleMuon/Run2018D-UL2018_MiniAODv2-v3/MINIAOD
\ No newline at end of file
diff --git a/paper/tables/datasets_jetht.tex b/paper/tables/datasets_jetht.tex
new file mode 100644
index 00000000..45052da7
--- /dev/null
+++ b/paper/tables/datasets_jetht.tex
@@ -0,0 +1,20 @@
+\multirow{9}{*}{ 2016 } & /JetHT/Run2016B-ver1\_HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016B-ver2\_HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016C-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016D-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016E-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016F-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016F-UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016G-UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /JetHT/Run2016H-UL2016\_MiniAODv2-v2/MINIAOD \\
+\hline
+\multirow{5}{*}{ 2017 } & /JetHT/Run2017B-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2017C-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2017D-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2017E-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2017F-UL2017\_MiniAODv2-v1/MINIAOD \\
+\hline
+\multirow{4}{*}{ 2018 } & /JetHT/Run2018A-UL2018\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2018B-UL2018\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2018C-UL2018\_MiniAODv2-v1/MINIAOD \\
+ & /JetHT/Run2018D-UL2018\_MiniAODv2-v1/MINIAOD
\ No newline at end of file
diff --git a/paper/tables/datasets_mcbg.tex b/paper/tables/datasets_mcbg.tex
new file mode 100644
index 00000000..64b80e09
--- /dev/null
+++ b/paper/tables/datasets_mcbg.tex
@@ -0,0 +1,45 @@
+\multirow{3}{*}{Diboson} & WW\_TuneCP5\_13TeV-pythia8 & 75.83 \\
+ & WZ\_TuneCP5\_13TeV-pythia8 & 27.56 \\
+ & ZZ\_TuneCP5\_13TeV-pythia8 & 12.14 \\
+\hline
+\multirow{2}{*}{HH} & VBF\_HHTobbVV\_CV\_1\_C2V\_1\_C3\_1\_TuneCP5\_13TeV-madgraph-pythia8 & 0.00 \\
+ & GluGluToHHTo4B\_node\_cHHH1\_TuneCP5\_PSWeights\_13TeV-powheg-pythia8 & 0.01 \\
+\hline
+\multirow{5}{*}{HWW (inclusive)} & GluGluHToWW\_Pt-200ToInf\_M-125\_TuneCP5\_MINLO\_13TeV-powheg-pythia8 & 0.10 \\
+ & HWminusJ\_HToWW\_M-125\_TuneCP5\_13TeV-powheg-jhugen727-pythia8 & 0.12 \\
+ & HWplusJ\_HToWW\_M-125\_TuneCP5\_13TeV-powheg-jhugen727-pythia8 & 0.19 \\
+ & HZJ\_HToWW\_M-125\_TuneCP5\_13TeV-powheg-jhugen727-pythia8 & 0.20 \\
+ & ttHToNonbb\_M125\_TuneCP5\_13TeV-powheg-pythia8 & 0.21 \\
+\hline
+\multirow{7}{*}{Hbb (inclusive)} & GluGluHToBB\_Pt-200ToInf\_M-125\_TuneCP5\_MINLO\_13TeV-powheg-pythia8 & 0.27 \\
+ & VBFHToBB\_M-125\_TuneCP5\_13TeV-powheg-pythia8 & 2.25 \\
+ & ttHTobb\_M125\_TuneCP5\_13TeV-powheg-pythia8 & 0.29 \\
+ & WplusH\_HToBB\_WToQQ\_M-125\_TuneCP5\_13TeV-powheg-pythia8 & 0.34 \\
+ & WminusH\_HToBB\_WToQQ\_M-125\_TuneCP5\_13TeV-powheg-pythia8 & 0.21 \\
+ & ZH\_HToBB\_ZToQQ\_M-125\_TuneCP5\_13TeV-powheg-pythia8 & 0.33 \\
+ & ggZH\_HToBB\_ZToQQ\_M-125\_TuneCP5\_13TeV-powheg-pythia8 & 0.03 \\
+\hline
+\multirow{9}{*}{QCD} & QCD\_HT50to100\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 248600000.00 \\
+ & QCD\_HT100to200\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 27990000.00 \\
+ & QCD\_HT200to300\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 1712000.00 \\
+ & QCD\_HT300to500\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 347700.00 \\
+ & QCD\_HT500to700\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 30330.00 \\
+ & QCD\_HT700to1000\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 6412.00 \\
+ & QCD\_HT1000to1500\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 1118.00 \\
+ & QCD\_HT1500to2000\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 108.50 \\
+ & QCD\_HT2000toInf\_TuneCP5\_PSWeights\_13TeV-madgraph-pythia8 & 21.94 \\
+\hline
+\multirow{4}{*}{SingleTop} & ST\_t-channel\_top\_4f\_InclusiveDecays\_TuneCP5\_13TeV-powheg-madspin-pythia8 & 113.40 \\
+ & ST\_t-channel\_antitop\_4f\_InclusiveDecays\_TuneCP5\_13TeV-powheg-madspin-pythia8 & 67.93 \\
+ & ST\_tW\_antitop\_5f\_inclusiveDecays\_TuneCP5\_13TeV-powheg-pythia8 & 32.51 \\
+ & ST\_tW\_top\_5f\_inclusiveDecays\_TuneCP5\_13TeV-powheg-pythia8 & 32.45 \\
+\hline
+\multirow{2}{*}{TTbar} & TTToHadronic\_TuneCP5\_13TeV-powheg-pythia8 & 303.85 \\
+ & TTToSemiLeptonic\_TuneCP5\_13TeV-powheg-pythia8 & 306.14 \\
+\hline
+\multirow{6}{*}{V+Jets} & WJetsToQQ\_HT-400to600\_TuneCP5\_13TeV-madgraphMLM-pythia8 & 277.00 \\
+ & WJetsToQQ\_HT-600to800\_TuneCP5\_13TeV-madgraphMLM-pythia8 & 59.06 \\
+ & WJetsToQQ\_HT-800toInf\_TuneCP5\_13TeV-madgraphMLM-pythia8 & 28.75 \\
+ & ZJetsToQQ\_HT-400to600\_TuneCP5\_13TeV-madgraphMLM-pythia8 & 114.50 \\
+ & ZJetsToQQ\_HT-600to800\_TuneCP5\_13TeV-madgraphMLM-pythia8 & 25.41 \\
+ & ZJetsToQQ\_HT-800toInf\_TuneCP5\_13TeV-madgraphMLM-pythia8 & 12.91
\ No newline at end of file
diff --git a/paper/tables/datasets_mcsig_nonres.tex b/paper/tables/datasets_mcsig_nonres.tex
new file mode 100644
index 00000000..8ec3de0d
--- /dev/null
+++ b/paper/tables/datasets_mcsig_nonres.tex
@@ -0,0 +1,4 @@
+ggHH SM ($\kappa_{\lambda} = 1$) & GluGluToHHTobbVV\_node\_cHHH1\_TuneCP5\_13TeV-powheg-pythia8 & 4.02 \\
+ggHH BSM ($\kappa_{\lambda} = 0$) & GluGluToHHTobbVV\_node\_cHHH0\_TuneCP5\_13TeV-powheg-pythia8 & 9.02 \\
+ggHH BSM ($\kappa_{\lambda} = 2.45$) & GluGluToHHTobbVV\_node\_cHHH2p45\_TuneCP5\_13TeV-powheg-pythia8 & 1.70 \\
+ggHH BSM ($\kappa_{\lambda} = 5$) & GluGluToHHTobbVV\_node\_cHHH5\_TuneCP5\_13TeV-powheg-pythia8 & 11.80 \\
diff --git a/paper/tables/datasets_mcsig_res.tex b/paper/tables/datasets_mcsig_res.tex
new file mode 100644
index 00000000..717319df
--- /dev/null
+++ b/paper/tables/datasets_mcsig_res.tex
@@ -0,0 +1,32 @@
+$M_Y$ [\gev] & 240 & 280 & 300 & 320 & 360 & 400 & 500 & 600 & 700 & 800 & 900 & 1000 & 1200 & 1400 & 1600 & 1800 & 2000 & 2200 & 2400 & 2500 & 2600 & 2800 & 3000 & 3500 & 4000\\
+60 & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+70 & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+80 & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+90 & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+100 & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+125 &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+150 &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+190 &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+250 &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+300 &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+350 &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+400 &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+450 &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+500 &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+600 &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+700 &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+800 &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+900 &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1000 &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1100 &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1200 &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1300 &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1400 &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1600 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+1800 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+2000 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+2200 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+2400 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+2500 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+2600 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$  & $\checkmark$ \\
+2800 &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  &  & $\checkmark$  & $\checkmark$  & $\checkmark$ 
\ No newline at end of file
diff --git a/paper/tables/datasets_singlemuon.tex b/paper/tables/datasets_singlemuon.tex
new file mode 100644
index 00000000..ff342b6f
--- /dev/null
+++ b/paper/tables/datasets_singlemuon.tex
@@ -0,0 +1,21 @@
+\multirow{9}{*}{ 2016 } & /SingleMuon/Run2016B-ver1\_HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016B-ver2\_HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016C-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016D-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016E-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016F-HIPM\_UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016F-UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016G-UL2016\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2016H-UL2016\_MiniAODv2-v2/MINIAOD \\
+\hline
+\multirow{6}{*}{ 2017 } & /SingleMuon/Run2017B-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /SingleMuon/Run2017C-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /SingleMuon/Run2017D-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /SingleMuon/Run2017E-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /SingleMuon/Run2017F-UL2017\_MiniAODv2-v1/MINIAOD \\
+ & /SingleMuon/Run2017G-UL2017\_MiniAODv2-v1/MINIAOD \\
+\hline
+\multirow{4}{*}{ 2018 } & /SingleMuon/Run2018A-UL2018\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2018B-UL2018\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2018C-UL2018\_MiniAODv2-v2/MINIAOD \\
+ & /SingleMuon/Run2018D-UL2018\_MiniAODv2-v3/MINIAOD
\ No newline at end of file
diff --git a/paper/tables/hlts.tex b/paper/tables/hlts.tex
new file mode 100644
index 00000000..01f4ce9f
--- /dev/null
+++ b/paper/tables/hlts.tex
@@ -0,0 +1,36 @@
+\multirow{13}{*}{ 2016 } & AK8DiPFJet250\_200\_TrimMass30\_BTagCSV\_p20 \\
+ & AK8DiPFJet280\_200\_TrimMass30\_BTagCSV\_p20 \\
+ & AK8PFHT600\_TrimR0p1PT0p03Mass50\_BTagCSV\_p20 \\
+ & AK8PFHT700\_TrimR0p1PT0p03Mass50 \\
+ & AK8PFJet360\_TrimMass30 \\
+ & AK8PFJet450 \\
+ & PFJet450 \\
+ & PFHT800 \\
+ & PFHT900 \\
+ & PFHT1050 \\
+ & PFHT750\_4JetPt50 \\
+ & PFHT750\_4JetPt70 \\
+ & PFHT800\_4JetPt50 \\
+\hline
+\multirow{12}{*}{ 2017 } & PFJet450 \\
+ & PFJet500 \\
+ & AK8PFJet400 \\
+ & AK8PFJet450 \\
+ & AK8PFJet500 \\
+ & AK8PFJet360\_TrimMass30 \\
+ & AK8PFJet380\_TrimMass30 \\
+ & AK8PFJet400\_TrimMass30 \\
+ & AK8PFHT750\_TrimMass50 \\
+ & AK8PFHT800\_TrimMass50 \\
+ & PFHT1050 \\
+ & AK8PFJet330\_PFAK8BTagCSV\_p17 \\
+\hline
+\multirow{9}{*}{ 2018 } & PFJet500 \\
+ & AK8PFJet500 \\
+ & AK8PFJet360\_TrimMass30 \\
+ & AK8PFJet380\_TrimMass30 \\
+ & AK8PFJet400\_TrimMass30 \\
+ & AK8PFHT750\_TrimMass50 \\
+ & AK8PFHT800\_TrimMass50 \\
+ & PFHT1050 \\
+ & AK8PFJet330\_TrimMass30\_PFAK8BTagCSV\_p17\_v
\ No newline at end of file
diff --git a/paper/tables/hlts_singlemuon.tex b/paper/tables/hlts_singlemuon.tex
new file mode 100644
index 00000000..c5770ea0
--- /dev/null
+++ b/paper/tables/hlts_singlemuon.tex
@@ -0,0 +1,9 @@
+\multirow{3}{*}{ 2016 } & IsoMu24 \\
+ & IsoTkMu24 \\
+ & Mu50 \\
+\hline
+\multirow{2}{*}{ 2017 } & IsoMu27 \\
+ & Mu50 \\
+\hline
+\multirow{2}{*}{ 2018 } & IsoMu24 \\
+ & Mu50
\ No newline at end of file
diff --git a/paper/triggers.py b/paper/triggers.py
new file mode 100644
index 00000000..af1f3cf2
--- /dev/null
+++ b/paper/triggers.py
@@ -0,0 +1,61 @@
+muon_HLTs = {
+    "2016": ["IsoMu24", "IsoTkMu24", "Mu50"],
+    "2017": ["IsoMu27", "Mu50"],
+    "2018": ["IsoMu24", "Mu50"],
+}
+
+HLTs = {
+    "2016": [
+        "AK8DiPFJet250_200_TrimMass30_BTagCSV_p20",
+        "AK8DiPFJet280_200_TrimMass30_BTagCSV_p20",
+        #
+        "AK8PFHT600_TrimR0p1PT0p03Mass50_BTagCSV_p20",
+        "AK8PFHT700_TrimR0p1PT0p03Mass50",
+        #
+        "AK8PFJet360_TrimMass30",
+        "AK8PFJet450",
+        "PFJet450",
+        #
+        "PFHT800",
+        "PFHT900",
+        "PFHT1050",
+        #
+        "PFHT750_4JetPt50",
+        "PFHT750_4JetPt70",
+        "PFHT800_4JetPt50",
+    ],
+    "2017": [
+        "PFJet450",
+        "PFJet500",
+        #
+        "AK8PFJet400",
+        "AK8PFJet450",
+        "AK8PFJet500",
+        #
+        "AK8PFJet360_TrimMass30",
+        "AK8PFJet380_TrimMass30",
+        "AK8PFJet400_TrimMass30",
+        #
+        "AK8PFHT750_TrimMass50",
+        "AK8PFHT800_TrimMass50",
+        #
+        "PFHT1050",
+        #
+        "AK8PFJet330_PFAK8BTagCSV_p17",
+    ],
+    "2018": [
+        "PFJet500",
+        #
+        "AK8PFJet500",
+        #
+        "AK8PFJet360_TrimMass30",
+        "AK8PFJet380_TrimMass30",
+        "AK8PFJet400_TrimMass30",
+        "AK8PFHT750_TrimMass50",
+        "AK8PFHT800_TrimMass50",
+        #
+        "PFHT1050",
+        #
+        "AK8PFJet330_TrimMass30_PFAK8BTagCSV_p17_v",
+    ],
+}
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 1fff7f9b..00000000
--- a/requirements.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-jetnet>=0.2.1.post2
-coffea>=0.7.20
-awkward>=1.10.2
-matplotlib
-mplhep
-hist
-black
-pre-commit
-tritonclient[grpc]
-tritonclient[http]
-# fastjet
\ No newline at end of file
diff --git a/src/HHbbVV/VBF_binder/VBFVectorTesting.ipynb b/src/HHbbVV/VBF_binder/VBFVectorTesting.ipynb
new file mode 100644
index 00000000..2cc24218
--- /dev/null
+++ b/src/HHbbVV/VBF_binder/VBFVectorTesting.ipynb
@@ -0,0 +1,811 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we test vector mehtods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(0.37454012, 0.15599452, 0.02058449, 0.18340451)\n",
+      " (0.95071431, 0.05808361, 0.96990985, 0.30424224)\n",
+      " (0.73199394, 0.86617615, 0.83244264, 0.52475643)\n",
+      " (0.59865848, 0.60111501, 0.21233911, 0.43194502)\n",
+      " (0.15601864, 0.70807258, 0.18182497, 0.29122914)]\n",
+      "[(0.61185289, 0.78517596, 0.60754485, 0.80839735)\n",
+      " (0.13949386, 0.19967378, 0.17052412, 0.30461377)\n",
+      " (0.29214465, 0.51423444, 0.06505159, 0.09767211)\n",
+      " (0.36636184, 0.59241457, 0.94888554, 0.68423303)\n",
+      " (0.45606998, 0.04645041, 0.96563203, 0.44015249)]\n",
+      "[(0.12203823, 0.66252228, 0.96958463, 0.92187424)\n",
+      " (0.49517691, 0.31171108, 0.77513282, 0.0884925 )\n",
+      " (0.03438852, 0.52006802, 0.93949894, 0.19598286)\n",
+      " (0.9093204 , 0.54671028, 0.89482735, 0.04522729)\n",
+      " (0.25877998, 0.18485446, 0.59789998, 0.32533033)]\n",
+      "[(0.38867729, 0.54269608, 0.77224477, 0.72900717)\n",
+      " (0.27134903, 0.14092422, 0.19871568, 0.77127035)\n",
+      " (0.82873751, 0.80219698, 0.00552212, 0.07404465)\n",
+      " (0.35675333, 0.07455064, 0.81546143, 0.35846573)\n",
+      " (0.28093451, 0.98688694, 0.70685734, 0.11586906)]\n",
+      "\n",
+      "Time taken: 0.005919 seconds\n",
+      "difference: [( 0.00000000e+00,  0.00000000e+00,  2.77555756e-17, -2.77555756e-17)\n",
+      " ( 0.00000000e+00,  0.00000000e+00, -2.22044605e-16, -1.92296269e-16)\n",
+      " (-1.11022302e-16, -1.11022302e-16, -2.22044605e-16, -2.48253415e-16)\n",
+      " ( 1.11022302e-16,  1.11022302e-16,  2.22044605e-16, -2.48253415e-16)\n",
+      " ( 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import vector\n",
+    "import numpy as np\n",
+    "import time\n",
+    "\n",
+    "start_time = time.time()\n",
+    "\n",
+    "# Generating dummy data for vbf1\n",
+    "np.random.seed(42)\n",
+    "vbf1 = vector.array(\n",
+    "    {\n",
+    "        \"pt\": np.random.rand(5),\n",
+    "        \"phi\": np.random.rand(5),\n",
+    "        \"eta\": np.random.rand(5),\n",
+    "        \"M\": np.random.rand(5),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Generating dummy data for vbf2\n",
+    "vbf2 = vector.array(\n",
+    "    {\n",
+    "        \"pt\": np.random.rand(5),\n",
+    "        \"phi\": np.random.rand(5),\n",
+    "        \"eta\": np.random.rand(5),\n",
+    "        \"M\": np.random.rand(5),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Generating dummy data for VVJet\n",
+    "VVJet = vector.array(\n",
+    "    {\n",
+    "        \"pt\": np.random.rand(5),\n",
+    "        \"phi\": np.random.rand(5),\n",
+    "        \"eta\": np.random.rand(5),\n",
+    "        \"M\": np.random.rand(5),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "# Generating dummy data for bbJet\n",
+    "bbJet = vector.array(\n",
+    "    {\n",
+    "        \"pt\": np.random.rand(5),\n",
+    "        \"phi\": np.random.rand(5),\n",
+    "        \"eta\": np.random.rand(5),\n",
+    "        \"M\": np.random.rand(5),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "end_time = time.time()\n",
+    "elapsed_time = end_time - start_time\n",
+    "\n",
+    "print(vbf1)\n",
+    "print(vbf2)\n",
+    "print(VVJet)\n",
+    "print(bbJet)\n",
+    "print(f\"\\nTime taken: {elapsed_time:.6f} seconds\")\n",
+    "\n",
+    "system_4momentum = vbf1 + vbf2 + VVJet + bbJet\n",
+    "\n",
+    "# Boost using boost_p4\n",
+    "vbf1_boosted_p4 = vbf1.boostCM_of_p4(system_4momentum)\n",
+    "# vbf1_boosted_p4 = vbf1.boost_p4(-system_4momentum)\n",
+    "\n",
+    "# Boost using to_beta3\n",
+    "vbf1_boosted_beta = vbf1.boost(-system_4momentum.to_beta3())\n",
+    "\n",
+    "# Compare the results\n",
+    "difference = vbf1_boosted_p4 - vbf1_boosted_beta\n",
+    "\n",
+    "print(f\"difference: {difference}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(MomentumObject4D(px=nan, py=nan, pz=nan, mass=100),\n",
+       " MomentumObject4D(px=nan, py=nan, pz=nan, mass=50),\n",
+       " MomentumObject4D(px=0.0, py=0.0, pz=0.0, mass=100),\n",
+       " MomentumObject4D(px=0.0, py=0.0, pz=0.0, mass=50))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import vector\n",
+    "\n",
+    "# Define two particles A and B with momentum along z-axis\n",
+    "particle_A = vector.obj(pt=0, eta=0, phi=0, M=100)\n",
+    "particle_B = vector.obj(pt=0, eta=0, phi=0, M=50)\n",
+    "\n",
+    "# Calculate the 4-momentum of the system (sum of A and B)\n",
+    "system_4momentum = particle_A + particle_B\n",
+    "\n",
+    "# Boost both particles to the center of mass frame using boost_p4 method\n",
+    "particle_A_boosted_p4 = particle_A.boost_p4(-system_4momentum)\n",
+    "particle_B_boosted_p4 = particle_B.boost_p4(-system_4momentum)\n",
+    "\n",
+    "# Boost both particles to the center of mass frame using to_beta3 method and then boost\n",
+    "system_beta3 = system_4momentum.to_beta3()\n",
+    "particle_A_boosted_beta3 = particle_A.boost(system_beta3)\n",
+    "particle_B_boosted_beta3 = particle_B.boost(system_beta3)\n",
+    "\n",
+    "particle_A_boosted_p4, particle_B_boosted_p4, particle_A_boosted_beta3, particle_B_boosted_beta3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we test how eta changes when we boost. if we boost a particle together will its eta double?? lets test it. also test how it changes under CM operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_675063/3765333115.py:22: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  gamma = 1.0 / np.sqrt(1.0 - np.sum(beta_vector**2))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\nimport vector\\n\\n# Generating the particles\\nmassive_particles = vector.obj(pt=massive_pt, phi=massive_phi, eta=massive_eta, m=massive_m)\\nsmaller_particles = vector.obj(pt=smaller_pt, phi=smaller_phi, eta=smaller_eta, m=smaller_m)\\nall_particles = vector.concatenate([massive_particles, smaller_particles])\\n\\n# Calculating the boost vector\\ntotal_4momentum = massive_particles[0] + massive_particles[1]\\nboost_vector = -total_4momentum.to_beta3()\\n\\n# Applying the boost\\nboosted_particles_vector = all_particles.boost(boost_vector)\\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def to_four_momentum(pt, phi, eta, m):\n",
+    "    px = pt * np.cos(phi)\n",
+    "    py = pt * np.sin(phi)\n",
+    "    pz = pt * np.sinh(eta)\n",
+    "    E = np.sqrt(m**2 + px**2 + py**2 + pz**2)\n",
+    "    return E, px, py, pz\n",
+    "\n",
+    "\n",
+    "def lorentz_transform(p, gamma, beta):\n",
+    "    E, px, py, pz = p\n",
+    "    bp = beta[0] * px + beta[1] * py + beta[2] * pz\n",
+    "    factor = gamma * E - bp\n",
+    "    E_prime = gamma * E - bp\n",
+    "    px_prime = px + beta[0] * (factor + E / (gamma + 1))\n",
+    "    py_prime = py + beta[1] * (factor + E / (gamma + 1))\n",
+    "    pz_prime = pz + beta[2] * (factor + E / (gamma + 1))\n",
+    "    return E_prime, px_prime, py_prime, pz_prime\n",
+    "\n",
+    "\n",
+    "def boost_particles(particles, beta_vector):\n",
+    "    gamma = 1.0 / np.sqrt(1.0 - np.sum(beta_vector**2))\n",
+    "    return np.array([lorentz_transform(p, gamma, beta_vector) for p in particles])\n",
+    "\n",
+    "\n",
+    "# Particle data\n",
+    "massive_pt = np.array([200, 250])\n",
+    "massive_phi = np.array([0, 0])\n",
+    "massive_eta = np.array([1, 1.1])\n",
+    "massive_m = np.array([100, 100])\n",
+    "\n",
+    "smaller_pt = np.array([30, 40, 20, 25])\n",
+    "smaller_phi = np.array([0, 0, 0, 0])\n",
+    "smaller_eta = np.array([1.5, -1.3, 2, -2.2])\n",
+    "smaller_m = np.array([10, 10, 10, 10])\n",
+    "\n",
+    "# Manual boost\n",
+    "massive_particles = np.array(\n",
+    "    [\n",
+    "        to_four_momentum(pt, phi, eta, m)\n",
+    "        for pt, phi, eta, m in zip(massive_pt, massive_phi, massive_eta, massive_m)\n",
+    "    ]\n",
+    ")\n",
+    "smaller_particles = np.array(\n",
+    "    [\n",
+    "        to_four_momentum(pt, phi, eta, m)\n",
+    "        for pt, phi, eta, m in zip(smaller_pt, smaller_phi, smaller_eta, smaller_m)\n",
+    "    ]\n",
+    ")\n",
+    "all_particles = np.concatenate([massive_particles, smaller_particles])\n",
+    "boost_vector = (\n",
+    "    -(massive_particles[0] + massive_particles[1])[:3] / massive_particles[0][0]\n",
+    ")  # px, py, pz divided by energy\n",
+    "boosted_particles_manual = boost_particles(all_particles, boost_vector)\n",
+    "\n",
+    "# Vector library boost (pseudo code for reference)\n",
+    "\"\"\"\n",
+    "import vector\n",
+    "\n",
+    "# Generating the particles\n",
+    "massive_particles = vector.obj(pt=massive_pt, phi=massive_phi, eta=massive_eta, m=massive_m)\n",
+    "smaller_particles = vector.obj(pt=smaller_pt, phi=smaller_phi, eta=smaller_eta, m=smaller_m)\n",
+    "all_particles = vector.concatenate([massive_particles, smaller_particles])\n",
+    "\n",
+    "# Calculating the boost vector\n",
+    "total_4momentum = massive_particles[0] + massive_particles[1]\n",
+    "boost_vector = -total_4momentum.to_beta3()\n",
+    "\n",
+    "# Applying the boost\n",
+    "boosted_particles_vector = all_particles.boost(boost_vector)\n",
+    "\"\"\"\n",
+    "\n",
+    "# Visualization code would follow here, comparing `boosted_particles_manual` with `boosted_particles_vector`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Momentum sum before boost: [1094.647799    565.            0.         -141.81896375]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Momentum sum after boost: [9.26777312e+02 2.84217094e-14 0.00000000e+00 1.42108547e-14]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def to_four_momentum(pt, phi, eta, m):\n",
+    "    px = pt * np.cos(phi)\n",
+    "    py = pt * np.sin(phi)\n",
+    "    pz = pt * np.sinh(eta)\n",
+    "    E = np.sqrt(m**2 + px**2 + py**2 + pz**2)\n",
+    "    return E, px, py, pz\n",
+    "\n",
+    "\n",
+    "def lorentz_transform(p, boost_vector):\n",
+    "    gamma = 1 / np.sqrt(1 - np.dot(boost_vector, boost_vector))\n",
+    "    E, px, py, pz = p\n",
+    "    E_prime = gamma * (E - np.dot(p[1:], boost_vector))\n",
+    "    spatial_part = (\n",
+    "        np.array(p[1:])\n",
+    "        + (gamma - 1)\n",
+    "        * np.dot(p[1:], boost_vector)\n",
+    "        / np.dot(boost_vector, boost_vector)\n",
+    "        * boost_vector\n",
+    "        - gamma * E * boost_vector\n",
+    "    )\n",
+    "    return E_prime, *spatial_part\n",
+    "\n",
+    "\n",
+    "def get_eta(px, pz):\n",
+    "    pt = np.sqrt(px**2)\n",
+    "    theta = np.arccos(pz / np.sqrt(px**2 + pz**2))\n",
+    "    eta = -np.log(np.tan(theta / 2))\n",
+    "    return eta\n",
+    "\n",
+    "\n",
+    "def plot_particles(particles, title=\"\"):\n",
+    "    colors = [\"b\", \"g\", \"r\", \"c\", \"m\", \"y\"]\n",
+    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "    for idx, particle in enumerate(particles):\n",
+    "        ax.quiver(\n",
+    "            0,\n",
+    "            0,\n",
+    "            particle[3],\n",
+    "            particle[1],\n",
+    "            angles=\"xy\",\n",
+    "            scale_units=\"xy\",\n",
+    "            scale=5,\n",
+    "            color=colors[idx],\n",
+    "        )\n",
+    "    ax.set_xlim(-100, 100)\n",
+    "    ax.set_ylim(-100, 100)\n",
+    "    ax.axhline(0, color=\"black\", linewidth=0.5)\n",
+    "    ax.axvline(0, color=\"black\", linewidth=0.5)\n",
+    "    ax.set_ylabel(\"x (transverse) [m]\")\n",
+    "    ax.set_xlabel(\"z (along beam) [m]\")\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "    # Adding labels\n",
+    "    labels = []\n",
+    "    for idx, particle in enumerate(particles):\n",
+    "        pt = np.sqrt(particle[1] ** 2)\n",
+    "        eta = get_eta(particle[1], particle[3])\n",
+    "        mass = np.sqrt(particle[0] ** 2 - (particle[1] ** 2 + particle[2] ** 2 + particle[3] ** 2))\n",
+    "        label = f\"Particle {idx+1} (color {colors[idx]}): pt: {pt:.2f} GeV, eta: {eta:.2f}, m: {mass:.2f} GeV\"\n",
+    "        labels.append(label)\n",
+    "    props = dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5)\n",
+    "    ax.text(\n",
+    "        0.05,\n",
+    "        0.95,\n",
+    "        \"\\n\".join(labels),\n",
+    "        transform=ax.transAxes,\n",
+    "        fontsize=8,\n",
+    "        verticalalignment=\"top\",\n",
+    "        bbox=props,\n",
+    "    )\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Define particles\n",
+    "particles = [\n",
+    "    to_four_momentum(200, 0, 1, 100),\n",
+    "    to_four_momentum(250, 0, -1.1, 100),\n",
+    "    to_four_momentum(30, 0, 1.5, 10),\n",
+    "    to_four_momentum(40, 0, -1.3, 10),\n",
+    "    to_four_momentum(20, 0, 2, 10),\n",
+    "    to_four_momentum(25, 0, -2.2, 10),\n",
+    "]\n",
+    "\n",
+    "# Print momentum sum before boost\n",
+    "momentum_sum_before = np.sum(particles, axis=0)\n",
+    "print(\"Momentum sum before boost:\", momentum_sum_before)\n",
+    "\n",
+    "# Plot original particles\n",
+    "plot_particles(particles, title=\"Before Boost\")\n",
+    "\n",
+    "# Determine boost vector for all 6 particles\n",
+    "total_E = np.sum([p[0] for p in particles])\n",
+    "total_px = np.sum([p[1] for p in particles])\n",
+    "total_py = np.sum([p[2] for p in particles])\n",
+    "total_pz = np.sum([p[3] for p in particles])\n",
+    "\n",
+    "boost_vector = np.array([total_px / total_E, total_py / total_E, total_pz / total_E])\n",
+    "\n",
+    "# Apply boost to particles\n",
+    "boosted_particles = [lorentz_transform(p, boost_vector) for p in particles]\n",
+    "\n",
+    "# Print momentum sum after boost\n",
+    "momentum_sum_after = np.sum(boosted_particles, axis=0)\n",
+    "print(\"Momentum sum after boost:\", momentum_sum_after)\n",
+    "\n",
+    "# Plot boosted particles\n",
+    "plot_particles(boosted_particles, title=\"After Boost\")\n",
+    "\n",
+    "print(system_4vec)\n",
+    "print(particles)\n",
+    "print(boosted_particles)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using vector package to do the same computation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1do+k6Xh7UbDHsvFTY9l3QF/r626eFc1rC0K93t/0G2JI7NIOY1gnCovOzmX3z+fCMZvrNyB86ReB9B+SQO9BifQelMjLb3gew67dBi4bF0ePAYn0GJDIiq8Cqt3OE/+MoPuARPpdnMDAYQn8uKri4ap/PhdObId29B+SQP8hCdxwa+sa6ykpUZj9SCSd+7alz0Wu7c28rTUpqbWf/8+XBzJwWEKV5a++GcqV18TW+traFBZp+NcrYaf9+sqefCaCPhclus/D0i8Ca2y76Q8f+l2cQNf+bRk1oQ3HjlfcZSko1HDDra3p2r8tvQYm8ticiLNSX33Vds3WVndt6041799hJPdpS3Kftjz5TES911Vms8Ezz4fTfUAivQYmcsElCVw1I4btO421Ht/mP32IT26H3e65/MuvA+g7ONFjmaIoqKpa6/aEEOJ8IgG6mVr83nE2r0tl5WfpPPnPCHb+Vfsfy8rsdkhN0/POohCP5SuXHqNTB9tZrtTllhsL2bT2yFnd5jMvRGC21C9At4m189Vn6WzdcIQ136fx9qIQ1q73BaCsTGHytbE8/VguO34/wtYNR7hoYHm127nownI2rUnlj19Seeu1TK69KYbS0ooapl9lYvO6VDavS+XDtzOq3YaqwoRpbSg3a9iy/ghbfj3CprWpjL28hMMptQfo8WOKSU3TsWu351jUDxcHM3NGUb3ORXXOZoB+4O58tvx6hM3rUlmx5Bh33t+K3DxtlXZOJ9xwW2v+/Ww2f21OYdSIUh58NMq9/ta7o+nZ3cxfm1PYtvEId99ecFbqq6+artna6q7rmCr7ZYMvn34RxJ+/HGH7xiP8tNqfb3/0r3NdlTrvas22nUbW/ZDGto2ua+mOmwvZf6D28cr9+5qJCHfw/c+e2130UTAzZxTWcXaEEOL8JgG6mUuIs9Oxg5UDB/W8Mj+UQcPj6T8kgUHD4/ltk4+7XceeSTz6dAQXjYjnb3e25q7Zrdh/0ED/IQnunsuOPZPcvVbHjuuYfkMMfS5KpO/gRJ6eG15l3zYbPDbHtc3+QxK45sbWFBRWf0kNH1pGVKTD6+MzhnXiqbkRXHCJq0fvk89cvZmzHmgFwLDRrn1n51QNaJUNurCc6Fau/QcHOenUwUpqmiusLvk8iAH9zFx0oSs0a7UQGVF9rZdfVoqvr6snrlsXC6oKOdWEw9qsXutHapqeV1/Icm9Lo4GrJhUzfGgZUPO5NRjg6ikmPvio4u7B5j99yM3TMnpkSZ37/mOLD6MmtGHgsAQuuCSBL1a4etrveiCa4hIN/YckuHu4a7ueahMS7HT/f0mpgoqC01m13ZZtPuh0KkMvdp33W2YW8s0P/pjNCgcP69my1Yf7ZlWE5pM/v9ocSdMRldiep+eGM2BoAl36tWXDb748+Gik+w7EX7vr9yBcTddsbXXXtu5Uny0P5NppJvz9VYxGlRuuLWLpF0F1rqvswCE9X34TwNuvZxIaUnGShw8tY8qVFUMuXno9lItGxDNgaAJjr2pD6lFXr/jMGUV8uLjiWsrI1LJ2vR/XTDXV6xwJIcT5SgJ0M7drt4F9+w1072bh2mkmNqxKY/O6VF5+Lptb7o72aJufr2X9T2l8+HYGb7yYRcf2VjavS2XZx8eqbPfG21vTp5eZLb8e4c/1R5h1W2GVNi+9Hoa/n8qvP7v22a2Llafmen+bffzUWP7cWnMPuoLKprWpfPVZOvf/vRVH0nTMfykLgNXfuvYdFengq+/8uf2eVnXub89eA79v9mXYJa6wumefAaNBZeL0WPoPSeCmO6LJya07FH+4OJi2iTYS4irugS9bGUi/ixMYNaENa37xrfZ1W3f40LOHGX0tnc21ndsbrytiyedB2E7cLPhgcTAzphdR1zOmhUUa7ry/FR+8lcHG1al8uyydvz8RxbHjOt54KZPAACeb16WycXUqQK3X05xnw6sMAarsjbdC6HZBWy68NJE3X86sNogeTdcR36bi3AUGqgQFOjmeqWPPPgOxMXbumt2KCy9NYPSVbdi2o353WYpMWnr3svD7mlQeujefsVPaMPaKEjavS2XG9CKeeaHiGu0/JIHjGd69Aaqt7trWVd2Onvi4ijs+CfE2jqbr6lxX2fYdPrRrayUstJp3KCcs+TyQAwcNrPshjd/XpHL1FBP3POj6PblmqolVa/zd1/tHS4IZPbKE8LCatyeEEEKmsWu2rv1bDL4+Tvx8Vd5+PZMO7Wz8tNqP518KJy9fi06nsv+AkfJyxd3Led01pnrNIV1SovDrb758/flR97LqemRXfhtAkUnL8hPjha1WhYR474eArFxaNcBXduP1rqEJSYk2Bg8qY/0GPxLjq/aQjbuilHFXlNa6rfRjOibPiOWNFzNpE+sKOnaHwuq1fqz7MY2Y1nae+GcEd89uxZIPj9e4ndVr/Zj7QjjfLDvqPqe33FjIw7Pz0Othw2++TL0+hl9XpXoE7Oqs3+jL/X+PoqRUw/TJJp56NK/Wc9uti5WEeBvffB/AqBGlfL48kHU/pta6D4CNm3xJOaJn/NQ2Hsv3HzTQNtFapf22HcYar6enHs2rdV933VbIXbcVsmOXkZm3tWbEpaVehTKHXWHzFh/+8UQOb76cxfc/+TPp6lj2bztc6xsPAB8fJxPGuHrj+/Y2E+DvdPcI9+9rZsnnFT25m9fVfd6ak0MpeqbfEEO5WcPA/uW8Mz+Tld8E8MdWHy681HVnweGo+EcgItzB5SNKWPxpENFh8OHHwbz8XFZjlS+EEM2GBOhmavF7x+nZ3eL+3mqFaTfE8uOXR+nXx4zJpCEysQMWa0WADvA/u71KqgovP5fFZcPKzup263K6HyRzPEPLFVe24ZHZeUyeWDHcIS7WxiUXlxEb4wq610wxMfaqNjVthnW/+nLrXdEs+8RzzHjlIQaDLiynZ3cLW7b6kBDnObSiV3czC98JwWYDvR4GDyxn87pU/vlcuPthtbrO7cwZRXz4cTBl5Rq6drbUa+y6qkKXZCtrf0irsu5Imuc/BfW5nuqjRzcLMa3trFvvx6Txnuchro2dtEq9qsXFCkUmDTHRdgoKNMS2truD7+WXlWK1KqQe1dM+qfZjNRoq6tNqVHyMlb7Xgt1+Zp9EVFvd+fnaGtdV3Y6NtKMV7wZS0/TEnei9rm1dZT17mDmUYqCgUENoiJN2bW1sXpfKfz4OYuW3rjdfqqrwf/flc/PM6sfIz5xRxMNPRjHx8jgsZsU9jEgIIUTNZAhHC2E2a7BaFeLauMLFm++E1No+KNBJkan6H39AgMrgQWW8Mr/iobLqhjSMH13CawtCKStzBZKyMoXde87+By3858QYzSNpOn7d6MdFA11/4AMDHDUew6kyMrVcPjGOB+/J57qrPXuvr5pUzB9bfDCd2NZ3P/nTvZulus3wywZfbrq9NZ8vPkaPU9qkH6sITgcO6dmxy0jXLlW3M3xoGW1i7TzwSBTl5RVhrrSs4ljqOrfTrixm/QZfXpkfWuXhwZvuiObLr6vOIjLwgnKOpOlZtaZi5pDtO41Yra7rodysYD3REe3t9VTZnr0VdR5K0bN9p5HkTlV7uPv0MmOzKe6hLu98EMKYUaX4+Kj06WUhMNDpfjh2858+qCrEnbhrMGpiGzb/Wb8x2Wdb7XXXvO5UkycUs/jTIEpLFSwWhQ8XBzPlSlOd6yrr0M7GuCtKuO2eaI+ZQjyupTHFvPNBCPkFrmU2Gx7DYUZcWobJpOHjL0Zx/TVFaOSvghBC1El6oFuIoCAnTz+Wy+ARCYSHO5gyqfaHgLp3tdAl2UrvQYm0TbRVGQe9aEEG9/+9Fb0GJqLXq4y7ooQnH/G8bf/gvflYLOEMvizB3Sv84L15dOlcNSxNmBbLzl2uP9q9ByXSPsnGT1+5hoiMnxrLU4/k0rd39aHV4YALLkmgtEzDS89lkRjvClH3zSpg9JVx+Pk6+eaLdH7/w4dvvgtg4WtVb0H/Y14ER4/peeOtUN54yzWF3V23FXDDtSbi29j5+wP5XHJ5PBoNxLS28ebLrm0cz9AyYVob963+2+6JxmJVuOWuivHAixZmuMcon3yITKuFV1/IomP7qr2ligIrl6bz1LMR9L4oEX8/lcAAJ20Trfzfffn1OrdBQU7Gjynhy68DmDzBc37eP7f6MOvWqjNWhIY4WbEknYefjOTvT0RisynEtbHz+UfHCAt1MmOaib4XJxLgr7JxdWqt19OcZ8Np3drOrTdW7dV85OlIjqTq0etVdDp45fksOp8I0G8vCiYjQ8dTj+ah0cAHCzOY9UA0ZotCTLSd9xdkuM/Re29mcMe9rSg3azAanSz58DhGo4rDATt3GYmNOfMZY/oPSeDLT9OJaV11iFJN12xtdde2Djyv9UsGlzNlUjF9TkwZN2VSMWNGuYYg1bbuVO/Oz+C5F13Xik6rEhriICLCwYP3uK6lq6cUk5evZeT4OMDVA3/DtUX06mFx13z9NUU8++8Yrr/28BmeUSGEOD8oqkzu6RWTyURwcDBFRUUEBTX8B5wsWvQ+/tpSJo4b2eD7aoqMYZ3ISjngMbODqFlOrpbrb2nNd8vTG7uUBvPHFh/e/SC42jdK4vQt+WRJlY/yPun5l99j5JjJDBgw4BxXJYQQp6eh85r0QDcD8h5H1FdkhKNFh2eAfn3M9Otjbuwyzivyb5AQQniSAN3EBQYGkZNe82wQLZ0lf19jlyDEea2wyITdqRIYWPMnSgohxPlGHhdp4pKTk8nIKWLr9r9wVvdpFEII0UDMZgur/rcBvdGfdu3aNXY5QgjRZEgPdBPXtWtX+vS/iG9+3siqtZvw96v+wzmEEOJM/LrhVwoqPafoVJ2YistAY2TKtGswGuv3QTZCCHE+kADdxCmKwoQJE7jgggs4ePAg5eXljV2SEKIF+uSL7/hbjwvd3yuKQkhICMnJyefkgWkhhGhOJEA3A4qiEBMTQ0xMTGOXIoRooebPn8+oUaMauwwhhGgWZAy0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFXWMXIOpms9nYs2cPBw8epKysrLHLEUK0QMeOpfPRRx/V2sbf35+OHTuSnJyMVqs9R5UJIUTTIwG6ibPZbCxe/BGH9/9FTKtQAvx9G7skIUQLNKBnEpiza1yvonI8u4ytf2ygc7c+TJ06VUK0EOK8JQG6iduxYwcpB3Zz3fTRJMTFNnY5QogWymktZurkMXW223fgMJ99uZr9vXrRuXPnc1CZEEI0PTIGuok7cOAAcTHhEp6FEE1Cpw5JRIYFcuDAgcYuRQghGo0E6CauvLyMwAC/xi5DCCHcAgN8KS8vb+wyhBCi0cgQjmZAURSP7zsuGoZBa8BX54PVYeP2HtdwR89rvdrmEVM6P6au59bu093Lxn95K/8a8jCdQpNqfN3NPz1Mj4jO3NP7hnrv63hJFrf8/CippmMYtQbahyTwxqVziPQL86rmU7229UOmdhxNtH9kvV+jqiqXL5/J1uzdZN++GXCdi84fjqRbeEd3uyWjX6NdSHyV1y/Yvph3di1Bq2ixOx38rdsU7up1PQA5Zfnc+vOjpBUfx+a0079Vd94YNgdfnU+V7dgcNp7/4y0+3f8NWkWLQaMnPiiGJwbcRc/Imm+Lb87cweSv7+TwTWvQaSp+fb889BP/+O11/rx2Zb3Pxan++dvrPNTvVnx0xtPeRnXe2PYf3tv1GYoCCgqz+97MNcnjq2172RfXkVp8nGBDIAAzOk/k3t4zAbjlp0fYkv0XGkWDXqPjmYtmMyxu4FmttTbPb17If/es4GBhKp+OeZ0J7Ua412WX5XHTj3/ncNFRjFoDr136JBfH9q9z3am+SfkfD69/AYfTSbeIjrw7Yh5BxoA6151q6f5veGXLIgotxQQbA/DV+XB3r+uZ1H5UjcdnVW1EvzWAVVf9l66Vfheyy/Lo9MEI9s78iVZ+EUDVf5OEEOJ8IwG6mVp8xcv0jOxMqukY/T6ewODYfnSP6FSv19qddlJNx3hn5xKPAL1ywtsNUqtWo+XRC+7kopi+ADy8/gUe+fUF3r3suTPa7hvbPuSSNhd4FaBf3foBScHxbM3e7bE8UO/P5mtW1Pn6a5LHu9+smCwl9Fk8jsEx/egV1YXn/lhI+5AElo9fiMPpYMLK2/hw9zJu73FNle3c8vOjlNjKWDdlCaE+wQCsStvA/oKUWgN0/+geRPiG8v2RdYxNGuZevuivz5nZ9ar6nIIaPbNpPnf3vuGsB+gu4R1YM+Vjgo2BHC3OYMAnkxgQ3avaNygA/7r4EY9w6l4+5BFCjEEAbMvezeXLb+T4rRvRKOfmRtqwuEFM6TiG235+tMq6xze8yIDonnw98V3+yNrJ1K/vYt/Mn9Fr9bWuq6zEWsrtPz/OT5P/S3JYEveu+QfPbn6T5wb/X63rTvX+rs94eev7LB39Op3D2wOwr+AwXx/+X63HZ1D0TO80lg93L+OFix92L/9o7wpGxF/kDs9CCCEkQDd7CUGxdAxty4GCFFalbWDp/m+wOe3oNTpeuuQxLmzdG3D1Wl/VcTRr03+nfUgCW7L/Is10nP4fTyQusDXLxi2g46JhfDZ2Pj0jO3OsJIvZa+eyvzAFBYVxScN5euC9Hvu2OWw8/dtrrEn/DavDRoeQROYPm+MOhCe18ovw+ON7QaseLNixuF7H13HRMK7scDlrjv5GkbWEm7tNY3bfvzH39/kcL83h2u/ux1fnw7uXzas1eALszjvAV4dX8faIZ/niwPf12v+pgo2B7v8vtZdhc9rd3ysolNjKcKpOrE4b5XYzsQHRVbZxoPAIXx76mUM3/c/jXA2PH+TR7qUt7/HFge+xOx1E+oYxf9gcEoJimdnlKj7cvcwdoDNKs1mbvon36vGG5EDhER5cN4+csjwsDit/6zaVO3vOYNbqpwAY9vm1aBUt30x8j9VHN/DGtv9iddhw4uTpC+/1CO31VbmXOC6wNa38IkkvyagxQNfkZHgGKLIW1/t1HRcN4+rk8aw5+hvpJZn8vf/tGLV63tu1lMzSHJ65aDZTO9b98Fz/6B41rvv8wPfsvv4HAPq16k7rgCjWHdvM8PhBta6r7IfUX+gZ2ZnkMNcdoNu7X8OYFX/jucH/V+u6Uz3z+xssGP6MOzwDdApNolPfijtLP6b+wrxNCyi3m9FqtMwdNBuAG7texdgVNzN30Gx3wP/P7uXMvWh2nedHCCHOJxKgm7ldufvYl3+Y7pHJXBx7Aff1uRGA3zO2cfPPj7Dzuu/cbfPLC1k/dSmKorA2/XceXDevxl7XG394iBHxF7FkzGuAa3jCqV7a8h7+el9+nfYZAM9uepOnNr7Ka5c+WWO9DqeDBTsWMzZpuHvZ7T8/zpikYYyrIZxll+WxcfoX5JkLufCTKxkU05vHBsziw91fuHviAf7M2smc316vtifd5rBxx6onWDhiLlpN1R7LUns5g5ZchUN1Mj5pOA/3vx2tpvopupYd+J5//P4GhwpT+ceg++kV1QWARy+4g+nf3EP8u4Mpt5uZ1mlstce0PXsP7ULiCfMJqfE8Ldn3FQcKjrBuyhK0Gi2L93zJPWv+wZfj3+Ka5PE8/dur5JTlE+kXxkd7VjC67VDCfUNr3B64zv31389m0ch/kRyWRJmtnIuXTuOC6J7MHzaHd3d9yuqrFruD6mXxg5nWcSyKonDElM6QpdO5LH4wRp2Bt3cuIaM0m6cuvKfWfZ5qVdoGCi1F9GvVvcY2j294kTm/vUrnsPb8c9ADJAXHudc99uuLfHHgewotJpaMfrXevc+ltnLWTl3CwcJU+i4ez8P9b2fd1E/5I2snE7681R2g67oWq5NXXoDNYfO4E5IQGMvR4oxa150qrfg48UExFe2CYskoy8HutNe6rvJQnuyyPI6VZnFBLWH/cNFRnvn9Db6e8B5BxgAOFqYy/PMZPON3Gz0jOxMb0Ipvj6xlQrsR/J6xjSKLicsThtT7fAghxPlAAnQzdbLn1U/nw9sj5tIhJJGfUtfz/OaF5JmL0Gm07C9Iodxudo/Bva7LpHqNXSyxlvJrxha+nviue1l145VXHl5FkaWY5Qd/BMDqsJEQVPNsIaqqcveaOYQYg7n7xLhhgIUjnqm1npldJqMoChG+oUxofxmr0zYysHWfKu36tupe4zCUZzbNZ0L7y+gc1o4jpnSPda39oki5aS1RfuHkmwu59rsHeHnrIh7se3O127qyw+Vc2eFyjpjSmfr13YxuO5ROoUks3f8tyWHt+W7SIsrs5Uz+6k7e3/UZN3WbUuvxHSpMY/q391BuNzOwdW/euWweKw+t4o/snVy4ZDIADtXhbh/hG8rlCUNYvPdL7utzIx/uXsbLlzxe6z4A9heksDvvINd9/4B7WbGtlD35B6sNtEdM6dzww0McK8lCp9GSby4ixZROcliSx9Cf+tqVu49bf36Uj654GX999Q/Gvj/yBeICW6OqKgt2LGbSytvZft037vVzL5rN3ItmsyptA4/++m/WTPkYg9ZQ576ndLgCgPYhCfjojO6xwH2jupFvKaLQYiLEGFTntdgcjfzienLNBVgdNnZd/z0/pv7CocI0hn8xw91GoyjkOYoAmNn1Kj7c/QUT2o3gg91fMKPzxBrfTAohxPlKAnQzVbnnFcDqsDLt23v48coP6deqOyZLCZFv9cPisLoDdEANoeV0qarKy5c8zmUJg+vV/v61z5BenMnnY984o3Grp/MA0y/HNnO0OIOF2xdjdzowWUvouGgYv077nEi/MKJ04QCE+YQws8uVLNn3dY0B+qTEoDb0j+7Btylr6BSaxFs7PuHN4XPQarQEGgKY1H4Ua4/9XiVA94zqzKHCNArMRYT6BNMuJJ7N16zgP7uXsfLwKsD1oRX/1+9Wbu42rdp9z+w6mYfXv0D/6B5YHNYqwwGqo6IS5hNcr7HeADO+n83cQQ9wZYfLAYh+awAWh6XW1xRaTFz2hevNUWJQGz4b+wYAe/IOMumrO3hrxFz3WPjqxAW2Blw/4zt7zuDh9S+QV15QpXd9ePwg7lv7T3bl7adPVLc6j6XyuG6tosFHZ3DvR0HB7nTU9NI6hfuGotPoyCzNcfc0pxYfIy6wda3rThUfGMOqtA3u71NNx2jtF4lOo6t1XWVRfuHE+rdic9YORiZcDMCPk//DEVM6F3w8CXD93g6PH8R/Ln/R47VLPlkCwPROY3n81xc5XHSULw58z4bpn5/2uRFCiJZKprFrIcx2K1aHzf2H+c0dtX8kb5AhoMZxpAEGfwbH9OOVLYvcy6obwjG+3Qhe2/YhZTbXdFZltnJ251U/N+z9a5/hUFEan415o149hpX9d89yAPLNhaw89DOXxl0IQKAhgCJL/cbCrr5qMQduXM3+G1ezespiggwB7L9xNZF+YWSX5WFz2ACw2K2sOPSTe1jGqfbkHXT/f05ZPmvSf3M/vNk2uA0/pq4HXENGfkpbT9ewDlW20SEkkXFJw7ht1eMUWkzu5aW2imnBxieN4J2dn5JvLnRvb1ulBx9HxF+EyVLCQ+vmcX3nSR5vSB7/9UXe3F71598xtC2BhgA+3P2Fe9nBwlT3PgL1/h7ns9BSRGJwGwA+3ruSAktRteekshBjEJuvWcHma1ZUhOf8Q0xYeSvzh81hRPxFNb7W7rSTVZbr/n75wR9o5RdBuG8oNoeNg4Wp7nWbM3eQU5ZP26C4Wo/5XJncYRTv7HQF0D+ydnK8JIshJ2baqG1dZSMTBrMtZzd78w8DsHDnx0zpOLrOdad6dMCdPLTuOXdbwP07CnBZwmBWH93Iztx97mWbM3e4/z/EGMTYpGHM+O5+ekYm0z4kwfsTIoQQLZz0QLcQQcYAnh54L4M/nUq4TwhT6ngoqntEJ7qEtaf3R+NoG9yGZeMWeKxfNPJ57l/7DL0+Goteo2Nc0jCePGW864N9b8bisDJ46VQUlBPLbqFLuGdo3HB8C29u/4hOoUkMXjoV8OydrGvcaYRvGBd+ciVF1hJu73Gte/jGrJ7XccfqJ/DT+fLuZfOwO+01joGuzYbjfzLnt9fRajTYnQ6GthnAw/1ud6/v//FEvhz/FjEBrXh9+3/49difGLR6VFTu7nWDOxS+OOQx7vrfU/RZPA6H08mFrXtxz4kp2E717mXzeG7zQgZ/OhWdRkeoMYgI3zB3r/fVyePIMxcwcplrukC708ENXa50B3uNouH6LpN4dtMCPhn9qse2d+Tuo09U1yr71Gl0LB+3kAfXPctrWz/EoTqJ8AnhwxM9kff1uZHRK27CT+fLNxPf48UhjzH923sJMQQxNG4A8YEVY3C9GQP9wNq5FFlLeOzXF3nsV9e+5l40m5EJF3uMW7c4rExceRsWhxWNoiHcJ5Qvxr4JgM1p5+afHqbIUoJOo8Vf78sno191P4RZ0zF7q7Zrcd6mBbyzcwk55fn8lXeA+9f8k9+vXk6kXxhzL3qQG3/4P7p8OAqDVs+ikS+4H8Krbd2c316jtX8Ut3afTqAhgAXDn2HKN7OwOx10De/gfjC0tnWnurnbNPx1ftz440MUWUqI9A3FR2fk1aGuZxPahyTw4ah/M2v1k5TZzVgdNnpFdmY0FaF+ZpfJjFo+k/cve/6Mz6kQQrREiqqqamMX0ZyYTCaCg4MpKioiKCio7hecoUWL3sdfW8rEcSMbfF9NUeWZQUTdHE4HFy+dxvppS8/Z9G6N7Xw85oaw5JMlTL+6fmPbP/nsK3yC45g2rfohRkII0dgaOq9JD3QTpygKDoezscsQzYRWoz3vxqyej8fc2JxOVT5MRQhxXpPumiYuPDyCzOw8ztcbBftvXC29z0I0IU6nk6zsfMLCzuyTRIUQojlrUT3QiYmJpKamVll+5513Mn/+fIYOHcratWs91t12220sXLjwXJXotW7duvHH77/y7Y9ruLB/LwID/KXnRwhx1tkdDmw2W43rVVWlyFTCrxv/xGxX6Nat7tlPhBCipWpRAXrz5s04HBXTUe3atYvLLruMKVMqphG75ZZb+Mc//uH+3s/v7E7tdra1bduWMeOv5Mfvv2Xrzi9AleEcQoizb/369RzJKK29kaLB6BvApMnTiI6u+imbQghxvmhRAToyMtLj++eee4527dpxySWXuJf5+fk1u3/4L7jgAnr16kVaWhrl5eV1v0AIIby0bsOfTLl6Zq1t/Pz8iI+PR6/Xn5uihBCiiWpRAboyq9XKRx99xAMPPOAx5GHx4sV89NFHREdHM27cOJ544olae6EtFgsWS8WHR5hMphrbNiSDwUD79u0bZd9CiJYvKCiI7t1r/oh1IYQQFVpsgF6xYgWFhYXMnDnTveyaa64hISGBmJgYduzYwd///nf27dvHsmXLatzOvHnzmDNnzjmoWAghhBBCNActdh7oUaNGYTAY+Oqrr2pss3r1aoYPH87Bgwdp165dtW2q64GOi4s7Z/NACyHEuTB+/HhWrlzZ2GUIIcRZIfNAn4bU1FR+/vnnWnuWAQYMGABQa4A2Go0YjcazXqMQQgghhGieWuQ80IsWLSIqKooxY2r/OOtt27YB0Lp163NQlRBCCCGEaAlaXA+00+lk0aJF3HDDDeh0FYd36NAhPv74Y0aPHk14eDg7duzg/vvvZ8iQIfTo0aMRKxZCCCGEEM1JiwvQP//8M2lpadx0000eyw0GAz///DOvvPIKpaWlxMXFMXnyZB5//PFGqlQIIYQQQjRHLS5Ajxw5stqPvY6Li6vyKYRCCCGEEEJ4q0WOgRZCCCGEEKKhSIAWQgghhBDCCxKghRBCCCGE8IIEaCGEEEIIIbzQ4h4ibIlMJhMbNmzgwP59mM1ljV2OEKKFUBSF4OAwunXvXu3D10IIIaonAbqJKykp4f333qW8OIfkjgkEBoShKEpjlyWEaAGcTifZ2fn88M1ysrIyG7scIYRoNiRAN3Hbt2/HVJDB7TdNJST47H+WuxBC/Ll1J9/98BP5+fmEhYU1djlCCNHkyRjoJi4lJYX42CgJz0KIBtO9aycC/IwcOXKksUsRQohmQQJ0E2ezWfH1MTZ2GUKIFsxgMKDX67DZbI1dihBCNAsSoJuBU8c863sOQ3/B5eiGTER/4Rg07y72fqNp6WgWLfFYpJt6Kxw4XOvLtLMeRrPgQ+/2VVqGbsRUdBdPcH1ddTOkpXtbcdVannsdzJZ6tVW27EQ3ajr62F7oZsyq97oqcvLQXXUz+n6j0A0ah7Jhs3uVbsRUdEMmur4GjcMQlozy177qt5Odi/auR9H3HuE6J4PHo33gKcgvqHX3mtfeRXfDPVWWax+ei/bhZ2qvvTbVXA+nSzvrEfT9R7mO6/KrUbbsrLlxYRHaWx90tR84Fu2cF92rlD+2o7t4gmvdhBvgeNZZqa++dDfcg77LxRjCkqHI5LHOm9o0/16Avs9l6PtchvaZl+u9zoPNhub5N9APuALdwLHoLpmEbsYslJ17aj0G5c8d6JMHg93uufzrn9ANHl/ra4UQQtRMAnQzZX/vZezrVmD77G20/3y55qBW7YvtKGnHqgQm+9K3oUPSWa4U8PXBvvx97L98if2XL3EOG4zukWfPeLPaF+aDpX4BWm0ViePZR3HMfdirdVX2OedF1H49sf3xA443nkV3y4NwotfO/vNS7OtWYF+3AsffZ+Hs3AG1a6eqGykrRz92Bmp8LLY/fnCdl9Wfo3ZLRsnIrnX/zmkTUFb94hm0rVY0n3+Fc8ZVddZfk+quh9PlHHsZto3fYP/lSxz334ruxntrbKu7+zHU7p2xbf4B+8avcdx+/YmNONHd9hCOZx/BtvkHnCOGoHv0zK8ZbzhunI5t7YqqK7yoTdmwGc0X32D75UtsG79BWb0e5cc1da47lfauR9Hs3IPthyXYN36Nfe1yHDdfi3IgpdZjUPv2QA0PRfl5nef2Pvr8jK4XIYQ430mAbu7iYlE7tIWDKWjmL0I3/CpXD+jwq1A2bXU30/cchvbpf6MbMQXtnQ+jm/00ysEUV9tr7nC3cfdoHc9Cd8M96C4a5+odnftq1X3bbGjnvIhuxBR0QyaivfE+KCyq2k6jgcAA1/+rKhSXQD1nEjGEJaOd+wq6Syah7z8KzWdfAbh6awHd6GvRDZkIOXm1byg2GrVvDzAYvFt36qF8+T2OG6e7DqVPd9TWUSi/bq7STvvRFzUGFM3nX6MGB+P8v1mg1Z44UAPOm66uCNzFJWjvfcJ1bgePR3vfE2C1QqtI1KGD0Cz9qmJ736xCjY9F7ZZcZ/3Kql/QXXENukuvRDdiCsovvwFUez1on3i+4noaM6POuxMnqVcMA53r+WS1Xy/IyK7SAwrA4VSUrbtwzrqxYlmrSFed2/4CnRb14gsBcM6chvLD/+q+45CWjj6xP9q5r6IbeiX6fqNQftuC9tF57jsDyu799TuOoYMgMrzKcm9q0yz/Due08eDvB0YDzmsno/nimzrXeTh0BM03P2N/fS6EBHvU57xydMW+Xn/Pdb0MvdJ1l+foMVd9M65Cu3hZxfYys1HWb8I5dVy9zoMQQoiqJEA3c8rufSj7D6N2S8Y5bQL2VZ+7ekCfexzd3Y96Ns4vxP7TUhxv/xv7i0+jtm/r6jH9eEGV7epufwhnr67Yf/0K+/qVOG67rkobzevvofr5Yv/5M+zrVqB26Vh90D65zUk3ok8ejObLH7C/8GTF8qm3omyt5TY/Cva1y7F99i7avz8Daek4XpoDgP3bxdjXrYDIcJTvVqO95/Faz9cZyS9w9TafCHkAalwsSnqGZ7v0DJQNm3FOrf4WubJjtyuw10L7xPOoA/u6zu0vX4JTRbPwvwA4ZkxG83FFINIsrjmsezhyFO3zb2D/9G3s/1uG/e1/o7v1IbBYq70eHPfe4r6enDdd7XHXQHvP4yjfra5zl9qF/0G9bIg7UHuch32HUGNaoZ39tCvQX3kTyo7drpXpx1HbxFQ0DgxwfWXW3kMPoJiKXdfummU47r0F3ZSbcV5xqes4pk903bk4QTdkImR4OTTEi9qU9OMQV9FWja+4XmpbV5lmxx7UtvEQGlJjSZrPv0I5eAT7D0uwr1mGc8o4dA/+AwDn1PEoazZAbr6r7ZIVOEcOhbBQb45aCCFEJTKNXTOl+9v9qD4+ruERr8+Fdokoq9ejfWkh5BeBTuu6vVtuBl8fAJzXTKpfz29JKcpvW3B+/m7FsoiqU1tpvl2FYipG89WPAChWG2p8bI2btS9fBE4nmhcXon1pIY5/u3qR7UvfrrUcx/UnwmFiHOqgfmg2/IEzvk2VduoVw3BcMayuo2twmk+WuwJKeP0CimbZt2heeRvFVIzj/ttw3jAVzberUDdvQ/PmBwAoZjNOrev9rnrZJSgPPIWy/S/UiHCUTVtwvl/L+NmT+1n1C0pKGrqxMyotVCD9ePXt1/yK5u2PoKQMxen0uLvgeK3u8daapSvRfPkdtq8/qr6B3Y6yZSeOJ+5HffkfKD+tQ3f17di2rapz27VRfYyoY0a4/r93N/D3c/cWq327w+cVvff2dSvOaF+NIiUN3Q33uK6J/r1xzJ+H8s0qNFt3olw62dXG4ahoHx6Kc8QQNJ9+iXPWjWg/Xob9uQZ8oymEEOcBCdDNlP29l1G7d65YYLWiu+Ee7F9+iNqnO5hKMCT2c932PxGg8fc7u0WoKvbnHkcdNrj+r9FocF4/BX3/Ue4A7bXG+iCZsFBXT2pWTsVQg6PHcLZpXdFGVV0BpZZjU7t3RrP4C/f3zitH47xyNNpZD0NZuXs79g9fg/Ztq25Aq8U5fSKaj5ehRkbgHD0CggLrrl9VcQ4dhOOdF6uuO7UXNv042v97Btuqz6BtPMpf+1zDOOpJs+xbtC/Mx7ZiEURFVN+oTWto3aoi3F42BKw2OHoc2sS4emhPKi4BUzFER9W988pDcTQaMFb6XqsFu6Pqa7zhRW1qmxjX8ZygpB1DPXG91LauMmePzmhT0lxvYEKCoW089nUr0Hy8DM23rjcbiqriuO9WnDOnVVuyc8ZktE++4LrzYba6hqcIIYQ4bTKEo6UwW8Fqc/8B1rxTQ6/fSYEBKKbi6tcF+Lt6eucvqlh24vZvZc7RI9Au+LAi9JWVo+w5UHV7WTkevZea5d+hdqnm4boauMdvpqWjbPwD58C+AKgB/q7gcg45J4xCe+JhO2XLTpSMLNSL+rvXK+t+A7sD9dKLat7GVWNR8gvRvLjQs6ewzFzRZvQItK++WzF2uLAIDqe61ztOjJfVfrwM54zJHtvXTZyJ8ueOqvsdNhjN2o0eD5y6251yPSimYtDrXG8UVLXu66kSzfLv0D77Krbl70PloQ6nUHt1Qw0McNej/LnDNUY+tjVqr65gs7vHaGs++BR11KVwYkrHmo7xXKirtsqcE0ah+XQllJaBxeoabnNi3HJt6zy0S8R5xTB09zzuORvIyd87wDlmBJoPPoWCQtcCm61iOAygXnoRiqkE7WPzXHeiNPJPvxBCnAnpgW4pggJwPHYv+hFTUcNDcE4aU2tztWsn1OT26AaNg8Q2VcZB2xc8j+7vz6AZOBb0OtfwiEc8p09z3nszisWK7rKp7l5h5723oHbu4NFOSc9wPfTncLgCUtt47G/9y71eN/VWHI/cjdq7e/XFOpzoLpmEUlaG47nH4cTwDcesG9FfeROqry/2L95D+WM7mu9WVz+84MBh9BNvdA1pMZvRd70ExwO34fzbNbWuU7buRDvvdfcwE8dTD6K7/f/Q9xuFqtdjX/gC6PXu3Wg++rzugOLvh+3r/6L950vo+45EDQ4CHyNq9844x13m2s/ch9H+40V0Qya5hlnodDiefhA1KcG1jXaJqMntUTKyUS+6oNK5cqDs2osa06rqfpMSsL/9b7T3PwnlZhSrDWePzjjeebHa68E5aTT6QWNRw0JcvdyVaO95HOcVw1wPDJ5Ce9tDEBWB7tqKKQHtKxZBWCiaRUtQMrJxPHoPKAqON+ehvfcJMJvBaHD1up/oMbYvfAHtA0+hWCyo0VHYF7xQ9zF6STdkIvZP34LWVbelm3Ybyq69AK7zkJSA/av/gkZTc214Xs/q4AE4J12B/sSUcc5JV7jCNtS67lSO+fPQvrgQ/WVTUbU6CAmCiDAc99zseu2UcZBfgG78Da4X2B04r70StUcX1/caDc5rJqH59wLsH9T8nIIQQoj6UVRVVRu7iObEZDIRHBxMUVERQUEN/+mAixa9j7+2lInjRjb4vpoiQ1gy1pRNIJ/EWC/Klp1oPvi0XmOUm6vz4Rgbw/gpf2PO3BcZMGBAY5cihBBnrKHzmvRAN3EajQabrZopwISohtqnO44+NfTktxDnwzGea06nE4fDifbktIpCCCFqJQPhmriYmFiOHs/BXt08uucBa/5e6X0WooEdSTtGWbmF6Ojoxi5FCCGaBemBbuJ69uzJbxt+YfGSlfTt05XAkx9IIoQQZ8jpVMnOzmXDpu2UWxzExtY8DaUQQogKEqCbuKioKGZcfyM/fP8dX363ERVnY5ckhGhBdDoDHTp1J7r1NpTGmiJSCCGaGQnQzUDbtm25/Y47KS0txWw21/0CIYSoB0VRCAgIwGAw8MknnzR2OUII0WxIgG5G/P398ff3b+wyhBBCCCHOa/IQoRBCCCGEEF6QAC2EEEIIIYQXJEALIYQQQgjhBQnQQgghhBBCeEECtBBCCCGEEF6QAC2EEEIIIYQXJEALIYQQQgjhBQnQQgghhBBCeEECtBBCCCGEEF6QAC2EEEIIIYQX5KO8mxGLxYLFYmnsMoQQzZyPjw8Gg6GxyxBCiGZLAnQzcPToUX788QfSjhxCVdXGLkcI0cxpNFoSk9pz+eVXEB0d3djlCCFEsyMBuonLzc3lPx++T1iAhtHD+xMQ4N/YJQkhmjFVVSkuLuGPLX/xwaJ3uXPWPQQFBTV2WUII0axIgG7itm3bhsZRznVXXyu3XIUQZ03nTu157a3F7Nq1i0GDBjV2OUII0azIQ4RN3PHjx4lv00rCsxDirPLz8yU2OoLjx483dilCCNHsSIBu4hwOO3q93CgQQpx9Op0Wh8PR2GUIIUSzIwG6Geq4O5tue7Ppvy+HnntzWJBb6vU2jljtvH3K68Yfzmef2V7r625OK+S1HO/3d9I/Mosxbs9ge7nttLdx0ms5pWTa6vfH/4jVzmUH84jcmUn/fTm1tn0iw0T3vdn025fDwP25/GiqmPnknvQi+u/LcX8F7cjgjRrOh01VeSazmO57s+m1N4cL9uVwVUp+nce+ucxK/F9Z2E95YPTLIjN966i9Lv/MLMbsbLgHUbNtDuL+yuKqlPwa21x2MI+Oe7Ld5/DVnBL3uuezSui2Nxuf7Rl8WWRusDprUtv+s20Oxh7Op8uebHrvy+GXkppnxPnGZKb73my67Mlm6pF8TA5nvdadamlBOYP259JlTzYD9+cy7GAuywvLaz0Gs1Mlelcmf51ynWXbHITuzCSrnr8zQgghaiYBuplanBDK5k6RrEwK5cmMYnZ6EUjtqkqq1cE7eWUey1cmhdHJp+F6uzeXWfmjzEaCXntWtvdGTilZ9prDR2VBGg1PRwfyYUJInW0v8jewqWMkf3SK5K24YK5NLaD0RMh5rU0wmztFsrlTJF8lhaEAV4X4VLudW9IK2VZuY137CLYlR7KpUyR3RPizv443Kf39DEToNHxv8gxoi/LKmBnmV6/jrckzWSWYG3Aml1npRYwOMtbZ7l8xQe7zeG9kgHv5sEADK9uGcbF/4wxZqm3/j2cUM8BPz+7OUbwTF8wNaYXYqjmXJQ4ntx8t4rPEMHZ3jqK1TsuzWSV1rjvV+3ll/DOrmPfig9ndOYqNHSNY0CaEw9baA7CPRmF6iC8fFngG7Y8KyhkRYKDVWfr9E0KI85mMDWjmEgw6Ohp1HLDYWVViYWmBGZuqolcUXooN4sITQaDj7myuCvFhbYmV9kYtW8ptpFkd9N+XQ5xBy7K2YXTcnc1nbUPp6avnmM3B7GMm9lvsKMC4IB+ebh3osW+bqvJ0RjFrSqxYVZUORh3z2wQTqqv6vqzMqXJfuokliaEMP5hX7+PruDubK0N8WFNiocihcnO4H7OjApibWcxxu4NrUwvwVRTejQ+hp6++xu2E6TRcFGBgbS29hiddHlQRiLv56FCBHIcTf63ncX1UUM5lgUaiqwkkByx2vjRZONQ5yuN8DA/0DJcvZZfwRaEZOyqROi3z2wSRYNAxM8yPD/PLGBvsqiXD5mBtqZX34kPqrP+Axc6Dx0zk2J1YVJW/hftxZ4Q/s9KLABh2MA8t8E1SGKtLrLyRW4rVqeIEno4OdO/TW4vyykg06Ojuq2PlafYe9/c7veDccXc2V4f6sqbEQrrNwd+jAjBqFN7LKyPT5uSZ1oFMDfU9o/1/XmRmd3IkAP38DLTWaVlXYq3yM/2h2EJPXz3JJ96M3h7hx5jD+TwXE1TrulM9k1XMgjbBdPapuK47+ejo5FPxhuNHk4V5WcWUqypaFObGBDI0wMiN4X6MPZzP3NaB6BUFgP/klzM3JrDKfoQQQnhPAnQzt6vcxj6Lne6+ei4OMHDfid6830ut3Hy0kJ3JUe62+Q4n6zuEoygKa0ssPHjMxOZOkdVu98bUQkYEGlmSGApAjr1qr9dL2aX4axV+7RgBwLNZxTyVWcxrbYKrtH3kuIlbI/yIM1QNm7cfLWRMkA/jaghu2XYnGztEkOdQuXB/DoP8DTwWHciH+eUsTgh1B+c/y6zMySxhZVJYbafMKx/ml9PWoK221/yD/DKea1399F/by220M2gJq+bNxElLCso5YLGzrkM4WkVhcX4Z96Sb+DIpjGtCfXk6s5gcu4NInZaP8ssZHWgkvJbtAThUletTC1kUH0Kyj44yp8rFB3K5wE/P/DbBvJtXxur24YSceDNwWaCRaSE+KIrCEaudIQfyuCzQiFGj8HZuKRl2J09F1x26Uix23skrY1X7cD6rY4gBwOMZJuZkFtPZqOOfrQNJMp75P0WlTpW1HSI4aLHTd18OD7cKYF2HCP4oszLhcIE7QNd1vVUnz+7Epqoeb5YSDFqOVtMbnGZ1EO/RTkeGzYldVWtdpzsRdME13OKYzckFtQT6wxY7z2QV83VSGEFaDQctdoYfzGN/5yh6+uqJ1Wv41mRhQrAPv5daKXI4uTyw7rsDQggh6iYBupk62fPqp1F4Oy6YDkYdPxVbeD6rkDyHEx2w3+Kg3Kniq3H9Yb4u1A+l0h/pmpQ4nPxaauXrSkE0Ulc1QK4sMlPkdLK80NXbaFVdoeJUPxdbSLM5eDWsarAGWBgXUms9M8N8URSFCJ3ChGAfVhdbGFjNLfa+foazGp5XF1uYm1XMN0nhVc7b+hIrJQ6VK+oxXAHgkMXO9CMFlKsqA/0MvBMfwsoiM3+U27hwfy4AlaNYhE7D5YFGFheUc19kAB8WlPFybPXnr7L9Fju7zTauSy1wLyt2quwx2+lXTRg7YrVzQ2YJx2wOdLjeZKVYHST76Lg1on5zjquqym1Hi3glNsh9rdXm/fgQ4gxaVFVlQV4Zk1IK2J5c/Rs5b0w5MZSmvVGHj0ZhUrArMPf11ZPvcFLocBKi1dR5vTVVIw/mketwYlVVdiVH8WOxhUMWh8cdHQ2QZnPQwVhxF2NCsA8f5JcxI8wXbT1+/4UQQtRNAnQzVbnnFcDqVJl2pIAf24XRz8+AyeEkclcWFlXFF9cfzQDt2f3jqQIvxwZzWR29WmtKLGwrs9FxdzYA6TYHEw7n80ab4NMaLnAuMsC6Egu3Hi1kWdvqx4XXFUh6+uo5ZHVQYHcSqtPQzqhjc6dI/pNf5h7eoAL/FxXAzeHVj2ueGebHwxkm+vsasDhheEDdwxtUIEyrqfHOwqlmpBYyt3UgV4a4wmb0rkwsdYyRLnQ4uexEaEs0aHk3PoSdZhszUgsBKHE6KXOqjDqUxw/twqu8/uRdCEVRuDPCn4ePm8izO+vsXa+LT6XwrgV8TmxOURQUwH4GQ7/DdRp0ikKmzeHuhU61Oqq9oxJv0LKqxOr+PtVqp7Xe9fra1lUWpdcSq9ewuczGyBNv0n5sH84Rq50L9rnecKnA8EAD/0kIrbbm6aG+PJ5RzGGLnS8KzWw4cadICCHEmZOHCFsIs6piVVXiTvxxf7OOmTmCNBqKapiNIUCrYXCAgVcqzS5R3RCO8cFGXssppezEdsqcKrvNVR9mfKZ1ECldW7G/SxT7u0TRRq/ly6Sweofn/+a7hgTk252sLLJwaYArUARqFYpqmcHgdP1SYuGmtEI+TwyjRzXjqk0OJ8uKzNxQywN9HYw6xgUZuS29kMJKNZZWOufjg314J6+U/BMPQtpUlW1lFedvRKDrjdBDx01cH+aLplLIejzDVO3PuKNRR6BW4cP8igdED1rs7n0EajzPWaHDSaLB9Qbh44IyChx1p8yQEwF9c6dIPmsbRrBWQ0a3aPfP97mYIEYEGqsNz3ZV9ZgFYnlhOa102nqF55qO+VyZHOzjfvD2jzIrx20OhlTzpmZkoJFt5Tb2nnhYdGFuGVNOvEGpbd2pHm0VyEPHTe62gPt3DVzDb1aXWD0eIN5cVhHOQ7QaxgYZmZFaSE9fPe3PwjAZIYQQLvIvagsRpHXNMjH4QB7hOqXGP8ondffV0cWoo/e+HNqeeIiwskXxIdx/zESvvTnoFRgX7MOTp4yFfTAqAEtWCYMP5KJUWtbFp+aH+apT15jUCJ2GC/fnUORQuT3Czz18Y1aEP3ccLcJP43qI0K6qNY6BLnOqdNubjcUJRU4nSbuzuCbUl2daB3H8RI/4yV7b244WYVHhlqOFHuej24kwvbTQTB9fPR3qCCTvxoXwXLbr/OhQCNUqROg0PBjlGqd+dagveXYnIw+5enPtKtwQ5ksvP9d+NIrC9WF+PJtVwieJIR7b3lFup0814V6nKCxvG8aDx028llOKQ3Wdv5Ozj9wX6c/oQ/n4aRS+SQrjxdggpqcWEKJRGBpo9Bif680Y6NpUHptucapMTCnAoqpocPXsftG2ogd1XlYx7+SVkWN38tdRG/cfU/i9YwSROm2Nx+yt2q632vY/t3UgN6YV0mVPNgZFYVFCiPsBvTmZxbTWabg1wp9ArYYFbYKZciQfuwpdfXTuhz9rW3eqm8P98Nco3JhWSJHDSaROg49G4dUTzxi0N+r4MD6EWelFlDldb6B7+er5T0JFqJ8Z7seoQ/m8H1f38B8hhBD1p6hqA85p1QKZTCaCg4MpKioiKKj6B8jOpkWL3sdPU8Kk8aMafF9NUeWZQYSLQ1W5+EAe6zuEe/RKt2Tn4zGfC5989hU+wXFMmzaN8ePHs3LlysYuSQghzoqGzmsyhKOJ0+n0WKxn/qEjouXQKgobOkacV0HyfDzmc8FstqLXy5tTIYTwlgToJi4xMZHUo1kUF1f/YQst3f4uUdL7LEQDyC8o5HhWPvHx8Y1dihBCNDsyBrqJ69WrF5t+38h7/1lG185JBAb412sqOiGEqI6qqhQWFbN7XwrhrdrQtWvXxi5JCCGaHQnQTVxgYCA3/e0W1q9fz779eykvS2/skoQQzZyfvz/d+1zEkCFD8PWt+xMahRBCeJIA3QyEhoYybtw4YFxjlyKEEEIIcd6TMdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBd0jV2AqB+73c6xY8cwm82NXYoQogUxGo3ExsY2dhlCCNGstKgA/fTTTzNnzhyPZZ06dWLv3r0AmM1mZs+ezZIlS7BYLIwaNYo333yTVq1aNUa59bZ161a+/+5rykuLQXU2djlCiJZEUTD6BlBQUNDYlQghRLPRogI0QNeuXfn555/d3+t0FYd4//3388033/DZZ58RHBzMXXfdxZVXXsmvv/7aGKXWS1paGiu+WEq3Tm24oN9QggIDGrskIUQLUlJaxh9bd/LVyhIOHjxI+/btG7skIYRo8lpcgNbpdERHR1dZXlRUxHvvvcfHH3/MsGHDAFi0aBGdO3fmt99+48ILLzzXpdbLjh07CPLXMX7McBRFaexyhBAtjL+/H6NHDmXp5yvYvn27BGghhKiHFvcQ4YEDB4iJiSEpKYlrr72WtLQ0AP78809sNhsjRoxwt01OTiY+Pp6NGzfWuD2LxYLJZPL4Opdyc3OIiY6Q8CyEaDCKotAqIpS83JzGLkUIIZqFFhWgBwwYwAcffMD333/PggULSElJ4eKLL6a4uJjMzEwMBgMhISEer2nVqhWZmZk1bnPevHkEBwe7v+Li4hr4KDypqopW6/lj+rNnNlsuyGbbkBy2XphDxrulXm/XnGYnc5Hn63ZPzaf8gL3W1x2YVcjxBd7vb0NYBtsuymHbENeXaaPV622cKu25YpxmtV5tc78od52vQa6vY2+UuNcVrbfwW0yGu7ZtQ3JwlFe/3fJDdv6alMe2i3PYOjCH3GXl7nWHHyry2MbG1hlkvFX9uXLaVI4+X8zWAdlsHZjD9kty2Dsjn9KdtlqPo/hPK5uTs1DtnvXlfW1m2+AzCz/enM/a5P9oZvulOWyMziDlkaJa2xb8ZGb70Bz3zyb7kzL3uuItVnZensu2i13ri9ZZzrg2b2R9VMa2i3LYEJlR5Zp3lKnsv7mALX2z2dI/m9wvy2vYChT/YWXbxTls6Z/Nrgl5WI476rXuVIX/s7BrTB5/9s5m+7BcdozIJfODshrbn7T1whzyf/B8+NhpVdnUIYuS7RXXm0ajwemUZyyEEKI+WtQQjiuuuML9/z169GDAgAEkJCSwdOlSfH19T2ubjzzyCA888ID7e5PJdM5DdHU6vReKf3c95qN2tg/OJWigAf+u+nq9VrWrWNIcZC4qI/pGf/fyLkvDGqpcALp9G44u+Oy9Z0t/oYSYO/zR+NTdO2+I1dLlszAMrbTYTU52XJpLQC89wYONAPi019FrXWSd2zk4q5Coa/xodb0ftlwH24flEXihAWOMlqR/BbvbWbMcbOmdTfhEn+q3c1chzlKV7j9EoAtxnZPCNRbKD9jx717zzzGwrwF9uIaCny2EXV6x7eyPyoia4Vdn/bXx5nzWxjdJR/vXQ8j70oyjpOZApqoqB24rpOtX4fh31WNOs7N1QA7hY33QBCjsu66A9vNDCBlqpPyg641L701RaH3Pzd2YgF56Or4fwrFXqr4JOv5GCYpRoc+fUZhT7ey4LI/gi43owzyvb9XpOsZ2rwQTfLGRY6+XcORRE50+CK113akKV1s4cFchnRaFEjTAAIDlmIOs/9QdoKNm+JK9uJywURXXS/53ZowxGgJ61u/fDCGEEJ5aVIA+VUhICB07duTgwYNcdtllWK1WCgsLPXqhs7Kyqh0zfZLRaMRoNJ6Dak+PT5wO3w46yg/aKVpjIWeZGdWmougVkuYFEXiB64/tnz2zCZ/kg2m9FZ8kLaXbbViOOtg2JAdjGy2dPw7jz57ZJH/kCuaW4w5SHjFhPmgHBcKu8CH+sUCPfTttKkefLaboFytOq4pvOx3tXg52B8KzYUNYBm1mB1DwoxlHmUrc/wUSOcWXQw+4ejZ3jc4DLXT5IgxDpLbG7QRdaHD/vy5Ig28HHZa0mnv7alL6l53Qy1zXgz5Ci383HXnLy4mZ5flwZ/aSckKGGTG0qlpT+SE7+d9Y6LszyuNchQz1vM6OvV5C3pdmVLuKPkJL0stB+MTpiJrhR/biMneAtmY6KFpvpf2bIXXWX37ITsqjJmy5TlSLSqsb/Gh9i3+157NorZWMhaWoNhXVCfGPBXqE9pr4tnf9s5L3dT2mXFTAXuQK2Y5iFX2YBsWoYM9XseU53efEt70OXbCGwp/NhI+r/c3wrnF5+PfUU7rVhjnNTtR0PwIv0JP+UgnW405a3+pX5edVHf9uJ8JlNZdz7nIz7V9zvWHySdARfJGB/K/NtLre801MyTYbig6CL3YdR/RMPzY9m4XTrFK6u+Z1p76JOfqvYuIeCnCHZwBjrJb4Ryp+J4u3WEmdU4yjWAWHSuz9AURM9CVymi9Hny/BludEH+46mOzF5Wf8hksIIc5nLTpAl5SUcOjQIa677jr69u2LXq9n1apVTJ48GYB9+/aRlpbGwIEDG7nS01e620b5fjv+3fQEX2RwB4PizVYO3lVI79+j3G3t+U66/xSOoigUrbeQ8qipxl7XA7cXEnKpkeQPXb1httyqYfP466Vo/BR6/BwBuP7Ip80t9uiJreyviXmodggeYiT+0QC0/q4/5run5hP/SAABvQ3Vvg6g59pIzEfs7BiWS+AAPe1eCibrgzKPXu3878zkf2em/WshtZ6zsr02ijdbSXqxok7zEQfbh+aAViHqGl9a/82/2tcG9NST81k5sfcEYD5ip3iTFWN81ZCcvbiMxH8EVbuN0h02fNpq0YfW/EYj5/Nyyg/a6f5DOIpWIfvTMg4/aKLLp2FETvUl7dlibLkO9BFaspeUEzqyau/nqVSHyv5bCumwMAS/jjocZSo7R+YS0Lf68xkyzEjEZB8URcGcZmfnyDxCthvRGBUyF5VizXAS/2hgrfusjaIodHwvlH3XF6Dx0+AodNLpP6FoDAqacAVDKy25y8uJmORL8RYr5Qft9X7TYznqoOvKMBzFKn/2zMZe5Eu3b8OxZjjZOiCHqBl+6II1pD1bjKG1xuNOTL22f8yBMa7i526M12JJr1qbNd2BsU1FO22gBm2gBmumo9Z1Pome/zSX7rDR9vnqf6/A9Sbk8P1FdP40DEO0Fluek+1Dcwi8wHV3JORSIzlLy4m5wx/LcQemjVY6vh3i1TELIYSo0KIC9IMPPsi4ceNISEjg+PHjPPXUU2i1Wq6++mqCg4P529/+xgMPPEBYWBhBQUHcfffdDBw4sMnOwFGbfX8rQOOjoPVVaPd6ML7tdBSutpD+UiG2fCeKDsoPOHCUq+5b3lHX+NXrYURHiZPi36x0+bxiSIc+ompIzP/WjN3kJO8rV0+jaqXaMAnQd0cUxjZaHKVODs82ceSpYtr92xUI6ho6EnW9q8fRJ1FH0CADpg1WfOKrXrphV/gQdkXtPaSWYw72zigg6cVgjLGuWv176Om3KwpdkAbLMQd7puWjD9MQMalqT2f7N4M58nixq+c+TkvwECOK1vOcmjZacZSo7p7quphT7Oy9oQCnWSWwv4EO80PI/8ZMyVYb2y/NdTWqlM304RpCRxjJ+dTV8539cRlJz9Ucrk4qP2CnfK+N/TdXzPfrKFEp32cnsE/VNy+WVDsHbi3BctyBogN7gRNzqgO/jjqvA2d1VLtK+osldPpPKMGDjBRvsbL32gJ6rY9EH64heXEoqU8Xc+yVEnyTdQRdaEDR1W/4RsR4HxStgi5EwSdRS+hII4qiYIzRog/XYElzoOuuOaM3AI1p/60FlO21Y8t20ntTJMWbrJiPONg9Nd+jnfmgHWOMllbX+ZL6j2Ji7vAnZ0k5YZcbz+qdIiGEON+0qACdnp7O1VdfTV5eHpGRkQwePJjffvuNyEhXL+vLL7+MRqNh8uTJHh+k0hydHAN9ktOqsveGArp+GUZgHwN2k5NNiVmoVhVOBGit/9kdO6qqkPRcMCHD6g6KJ3vatP4aom/y49D9tT9cVqvTPAxrhoPdV+bRZrbr1vZJuqCKIGGM1RIx2RfTb9ZqA7RPvI7k/1SMUd19VT5+l3r+GmV9VEbUdN8qwfok/x56zCkO7IVOdCEafNq6xl9nf1xG/rcnhj2oEHtfANEzq7/NHjXDj9QnTQT0NeA0Q/DQmnvv3VTQhWrqNdYbYP/NhcQ/GUjEBNd52JSUiWo584cMTyrdacOa6SB4kOv6CexjwBCjpXSHjZBLjfh303u8ids6IBvf5Pr9k6VUHgKhVTyGRChaqjyE6S1jrBbLUQeGaNd1bUlzEHJp1d8DQxvPnmlHsROHyenqJc531rjuVP7d9ZRssRLQw/U73/Ft1zW4ISwDVNfvol+yju4/RFRbb8gwI4fuK6Jkq5XsT8pI+nfdb7iEEELUrEV1QSxZsoTjx49jsVhIT09nyZIltGvXzr3ex8eH+fPnk5+fT2lpKcuWLat1/HNz4jSrqFbVHVQz36l9pgxtoAaHqfoQoQ3QEDTIwPH5FduobghH+GgjxxeU4ihzbcdRplK2p+osEvZCp7uN6lTJXV6Of4/6P7yUvdg1w4E5zY5po5WggYYTdSrYTfWbNcCa6eCviXnE3BNA1NV+VdapzhPHUOyk4AdzjQ/yWbMr2hasslC2z0bEVRVB225ykrfSTNS1NY8v9W2nI+wKIwfvKXSP/wXc5wggbIwPWR+UYitwrXfaVEp2VJzbkEtdb5JSHjMRdY0viqYiIKb+w0RGNT9/3w46tAEKWYsrHjwrP2x37+PU82kvdOKT4AqsOUvLsBeevfAMrgc7rVlOyvbZ3LVYUuz4dnDt05pZcc1lfViGxl9D8BDXzz7jnVJS/3Fup5SsLHyCj3sGDHOqnaJfrYSNqXr3I6CXHqcNin5xzSCS+UEZoaN80Pgota47VZsHA0j/VwnFmytmr3GUVvysgi4wYE5zULimYqaS0p02nFbXz0zRKkRd48ehB00nhlHV4w2XEEKIGrWoHujzmS5IQ/xjgewYkYc+XKm297Qy/646/JJ1bB2Ug0+i6yHCyjosCOHw301sHZiDoj/xEOEjnre7Y+8NwGkpYedlue5e4dh7A/Dr7Bk+y/fbXQ+pKaDaXeOIE+dVjA+ucwy0A7ZfkoOjTKXtc8Hu4Rsxs/zZfWU+Gl+FLl+EUfKHrcYx0GnzirEcc5LxVql7arnWt/nT6lo/8r4yk/l+GYrOVV/4BB+irnWdP2uGg93T8t29tgXfWzj2aglowRCtpfOnYR6zQuQuMxPQU49vu9p/tdrPDyH9xRJ2XJbrHmqgj9AQe49rDHvkFF/s+U7+Gp8HuOqKutbX3QOpaFyBKP3fJXT6wPN4S3fZ8a9mdgVFp9B5SRgpj5rIWFCK6nANB+nwdgiEVj2fbecFsW9mAbpghaCLjRgqjdetbQx04VoLB+8sxFGsogJ5K80k/TuYsCt8PMapG6K0tHs5mP03Fbreyjuh7QvB7jeBWR+WkfN5Oajg29HV839yCFLZPjs+CTU/NFpftY2Bzv64jLS5xdiLVPK/MXP8jRKSPwkjoIee2Lv9OXh3EX/2yUbRQtLzQe4H9CqfG0Wj0HFhCIceKMJpUTFEa+mwIMT186hl3alCR/jQ7lU48pQJa6brYUCNQaHt80FoAxQUretne+RJE0eeMKHaXHd9kj+quFsSda0v6S+VEPdwgMwrL4QQZ0hRVfXsdiu1cCaTieDgYIqKiggKqv4hsbNp0aL38deWMnHcyAbfV1O0ISyDC1JandXp71oy1aGyY2QePX4K9+iVbml2XpFLl6VhaAPlujhbHn7sH3TsfhG333FnY5cihBBnrKHzmvRANwPyHkfUl6JV6Lmq+nGwLUn371r+MQohhGi6JEA3cT4+vpQUnL8frzsov3VjlyDEeaGs3IzPaX7glBBCnG/k/mcT16FDB44ez+XY8azGLkUI0UJlZedyODWDDh06NnYpQgjRLEgPdBPXvXt3tm3byoeffEVcTAQB/r7yAJAQ4qxQVZXSMjNHj+eQW1BGnz59GrskIYRoFiRAN3FGo5Hrr7+BHTt2cODAAcrM5Y1dkhCiBfEJjuCKvkPZtGU3vjKEQwgh6kUCdDNgMBjo168f/fr1a+xShBAtlFZ75tMCCiHE+ULGQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXtA1dgGifnJycjh48CBms7mxSxFCtEC5ubmsWbOGkJAQOnbsiJ+fX2OXJIQQTZYE6CZOVVW+/fZbft/4CzrFga+vD4rS2FUJIVqaVmE+bPr1Z0rLzOgM/ky/ZgYdOnRo7LKEEKJJkgDdxO3du5ffN6zlskv60rd3N3Q6+ZEJIc6+qGAd06+eTklJKd/8sIZPlyzm//7+KAaDobFLE0KIJkfGQDdxe/bsoVV4IAP695LwLIRocAEB/owcPhiruYRDhw41djlCCNEkSYBu4kymIsLDghq7DCHEeSQ0JBidRqG4uLixSxFCiCZJujSbOFVV0Wg93+f8uXsYisaARvFBVW1ER1xD64hrvdqu2ZpOoWk90RHT3ct2H76VtjEP4+uTVOPrDqQ9jL9vZ2Iib/Bqf3Z7EYeP/ZOSsp0oip6woEtJiJnt1TZOlZb5Om2ibkWjMdbZNq/oJ45mvg5oUFUbYcEjiI++D0VRyM5fRsqxZzEa2gCg0wbRrf1/qt1OcdkOUo7NRXVacaoWosKuJDbqZvf6jNyPycz9CEXRAhp6dFhabX1WWy5pGS9RVLoJrcYfUAn070189H3odaE1Hsex7HcpLttBcuJrHstTjs1FRSUp9vE6z0V1qrsezpb0rIVkF6zAbEmlU+LrhAePqLZdgekXUjNedH9vs+dh0EfSs+MyAPYeuYfi0q3Y7Dlc0G0TOu25fWNZ2/6LS7dzKP1JnE4zBkM0HeJfwKhvVe12jmYtIDvfdUwRIaNJaH1/vdZV5lRtHMt6i9zCbwAtGkWP0RBDXPRd+Pt2rvEYist2sDflTvp1WYOiVPzzn1f0E9FJbwIVP39FUVBVtc7zIoQQ5yMJ0M1Up4SX8fftjNl6jO37JhDk3w9/3071eq2q2rFYj5GZt8QjMHVJeruhyuXg0ccI9O9Nx4R/A2C15ZzxNtOz5hMTeQMa6g7QIQEDCes4HEXR4HRa2XXwWvL9uhEefBkAwQEDSG47v87tHDr6JPHR9xAWPAybvZCt+0YTGjQUP5/25BetIrfgK7p3+BSdNhCbPd8jpJzkcJaz69AMIkPG0SfuBxRFi9NpJTv/C6y27FoDdGToBI5mzcdmL3C3czqt5BR8Rdd2H9RZf02qux7OluDAQUSEjOHg0UdrbRcadDGhQRe7v99z+DaCAga4v48On05S7FP8sfuis15jfdS0f1V1ciDtIdrF/YPggAs5lv0eR449S6fEV6tso6hkM7kF39Cr45coio6dB68m0L83YUFDa113qoNpj+J0ltG9/RJ0umAACos3UG5OqTVAB/r1QK8LpcC0jrDgYe7l2XmfU1rY5zTPjBBCnH8kQDdzPoZYfI1tKbekUFSygZyCb1BVO4qiIynWFVrB1WsdHjIaU8nv+BgTKC3/C4v1ONv2TcRoaE3ntgv4c/cwktvOx9+3MxZbFinH5mK2pAAKYUHDiW99r8e+naqNoxmvUVTyG07Vhq8xkXZt5rj/oJ9UbkmlpHwXnSr1mhr0kfU6vg3bk2kTdTsFprU4nGXERd9FZOg4DqU/BcCug9cCWrokvYdBH17jdrTagEp1W3GqVsD76UwUFOwOk2s7znI0ih6d1nW8x7LfIy56FjptIAB6XVi128gt+BqdNpi46FnuZRqNgeiIq93fOxwlpBx/nrLyvThVC4F+PWkb+wQGfSQhAYPIKfiKmMjrAcg3rcJoiMXfN7nO+gtMv5CetQCnakZBS0LMbIIDLuRw+tNVrocjx5+nqGQzqmpHpw2gXZt/1Hp3oiaBfj28fo3VlkVRyW+0i3vWvSwkcJDX2zFb09m+bxKtI2ZQULwWh6OU9vHzyCv8gaKS31Fx0DH+Rfx9O9a5rZr2X1L+F4qiJTjgQgCiw6exKfNVnE5LlbsPeYXfERk6Hq3WNUVcVNhkcgu+ISxoaK3rKiu3HCHf9DN9O//P43ft1PqOZb9HXuH3qDjQ68JIajMHH0MsUWFXkZ2/zB2grbZsiko3UVp0d53nQAghhIuMgW7mSsv3UW45jL9vMpGhE+jZ8XN6dVpBUuzjVXr87I5CundYSseEf5PU5ml8jG3p1WkFndsuqLLdA6kPEeDblV6dvqJXp5W0jryuSpvj2e+h0frSo+Nn9Oq0Aj/fjqRlVu11KzcfwqBvxeH0p9m+/0r+OnQTJWW73et3H76VkrKdtRylQs9Oy+mS9C4px57BbE2nXZs5AHRrv5henVZg0IeTX7Sag0drHsJgKt3Ctn3j2PzXIIIDLiQsaHildX+ybd9Edh6YTm7h9zVuo338s6RlvsYfuy9l697LiY++3/1moNxyiJKyXew8cDXb908mI6f6YSAl5bvrDJVHjj9PkH9fenT8jJ4dv0RFJSP3v4ArWJ28zQ+Qnf8FrcKuqnV7AGbLUY5mvUHnpLfp2XEZHRL+zf7Uh3A6rdVeD7FRt7ivp+jwq0k5XhFmDx59nPyi1XXu83Rl5y8nJGhIrW+K6svhLMbfrys9Oy4jNuoWdh++mbDgS+nVaQVRoRNJz6q487Bt30Sstiyvtm+1Hseoj3F/r9UGoNUGYLVlV2lrsR3HaKho62OIxWrLqHNdZaXle/AxxKPXhdRYU07BV5RbjtC9wxJ6dlxGRMg4Dqf/A4DI0PEUlmzAZs8HIDt/BaGBQ3E6ZN5nIYSoL+mBbqb2pd6PRvFBq/GhXdxcfI2JFBavJz1rITZHEQpayi0pOJxmtBofAKJCJ6HUYxJph6OU4tItdEl6172sut7U/KJV2J3F5BX+CICq2jAaYqu0U1U7JWU7SWh9P+3i/kGBaR17U26nT5dVaBR9nUNHosJd4dDHGEeQfz9MJX/gE9amSruw4GEet6VPFeTfh16dvsJmz2ffkXswlf5BcEB/QoMuJTzkCrQaX8rMh9h9+G8Y9dEE+veqso307HdIaH0/kaHjMFuOsuvQdQT4dcPPpz2qasdsPUa39h9hd5jYdfA6jMY4woIurfX4cgu+JT37bRzOYtpE3Uar8KnkmVZRXLaN4zkfAJzoMXa93w0NuoRD6U9RUvYXel04ptItdEx4udZ9ABQU/4LZksaugzPcyxQULLbj1bYvLP6VjNyPcDjLQHVidxS517WPe6bO/Z0uVVXJzl9G29jHzsr2NIrRPe46wK8bWo2fu7c4wK87OQVfudv26rTirOzzXDJb0th75B6cqplAv950iJ9HftEqSsp3sn3/5BOtHO72el0ooYFDyCn4kpjIG8kuWHZi7Hx6o9QvhBDNkQToZurkGOiTnE4re4/cQ9d2HxLo1x27o4RNu/qhqlbAFaBP3ho+W04+tBYSOLjWdkZDawz6Vu7QEho0BKdqw2I9jq8x4TT2fGafJKPXhRESOIS8wu8JDujvMebYz6cdoYFDMJVuqRKgbfYC8ot+olPCS4Ar0Af69aS4dAt+Pu0xGGKIDBmDomhdISVoCCWl26sE6ADfzmTlf+H+PiJ0NBGhozmQ9jAOZ7lroarSKfE1fI1tqx69oiUqbCLZ+cvQ6yMIDx7hHjZSO5WQwEF0THixyppTe10t1uOkHHuGHh0+w8cYT2n5Po/gXZPC4g0cOf6C67hCLqdNq9vrUZcnU+kmnKqlzuuqvhSl8jzGGjSVvlfQolYKl6fDYIjxeBPicJTgcBRj0EdVaWvUx2CxVrQ1W49h0Leuc11lrmcf0rDbi9DpgvExxtOr0wqy85eRX7TqRCuV2KhbiQ6fVm3NUWGTSc14gQDfHjidVoIDBgFLT+PohRDi/CRDOFoIp2p19QCf+IObmftRre21mgAczuqnqNJq/QkK6MfxnEXuZSdv91YWHjyC4zkfukOfw1lOmflAlXb+vt3QagMoLd8HuGYCANVda11ODlcwW9Mxlf5BUEDfE8fgj91Rv2m2ysyHUVWnq05HCQXFa90PXVoqhUerLZeikt8J8O1SZRs6bRBajR9Fxb8BrkBdXLYDPx/Xp7VFhoyhoPgX1z6cZopKNuFXzbjkiJCx2O2FpGctRFUrwpvTWfEx7WHBIziW/S6qagdcs5iUW1Ld66PCJpNb+A3Z+cuICptMZX8dmnniHHsKCRxMYclG988BcLc79XqwO4pRFB16fSSqqtZ5PVXsYxC9Oq2gV6cVpxWewTUkxXW3RFvv19R0zOdCgG9XnKqdohLXdZGZ9ymhQZdWO/tKeMgocgpW4nCUuR8cjQgdXee6ynyNiYQFDeNg+uPu8fhAxZsvXNdPVt6n2OyFgOt5hcrDpkICL8LuKCHl+DyiwiahKPKnQAghvCE90C2EThtAfPS97DgwFb0uhIiQMbW29/fthJ+xPVv3jcPH0KbKOOgO8c9z+NgzbN07FkXRERY8jPjoezzaxEbdjDPLys4DUznZKxwbdYs7UJ6kKAod4uZxKP0JnE4zimKgU+JraDSunsDdh28lPvpuAvy6V1+s6mT7vkk4nGW0jX0cnxPTzcVE3sjuQzeh0fjSJek9Ssq2k29aXe3wgrzCb8kt/A5F0aHiJDx4JFFhUwDIzF1MftHqEzNmOGkdeQPBga7e8pKynaRlvk6XpLdRFC0dE17mSMYLqMcdqKqdmMjr3Q9qxkTeyKH0J9m6dwygEB4ykoiQy6vUotX60a39f0nNeIkte0ei0wSh0Rjx8+3snhWkbczDpGa8yLb9k1BQUBQdCa0fdPfY+xoT8fNpj9WWTZD/BRWnSnVQWr632inUfI0JdIz/t3u6NVW14e/bmcCEF6u9HiJCRrNt31h02hDCTpl67uDRxwkLqn3IzElHsxaQlbcEmz2fsqMHSDn2T3p2XI5eF0Za5msYdFHu2T/sjmLyin6iV8eVVbaz+/BtlJn3ArBt71h8jAl0a//fWo/ZW9v2TaRL0lsYqtlWTftXFA0d41/gUPpTOJ0WDPooOsS/UOl1Fdd3cMAAIkKuYNv+8QBEhFzhvkNR27pTtY+bR3r2QnYcmIqCDp02CL0uzD2lYmToOOz2Av465JpuUlUdRIVdSYCf642homiICptEetaCamcLEUIIUTtFlYk+vWIymQgODqaoqIigoIafh3bRovfx15YycdzIBt9XU7Rhe3KjzPnbXBWX7SQr79MGHaPc1JyPx9wQlnyyhOlXV0xj+PzL7zFyzGQGDBhQy6uEEKJpaui8Jj3QzYC8xxH1FejXncCaevJbqPPxmM8F+XdHCCFqJgPfmrjAwCDyC0x1N2yhBvXcK73PQpxjhUUm7E6VwMD6PJwqhBDnHwnQTVxycjIZOUVs3f4XTqezscsRQrRwZrOFVf/bgN7oT7t27Rq7HCGEaJJkCEcT17VrV/r0v4hvft7IqrWb8PfzbeyShBAt0K8bfiWvxImpuAw0RqZMuwajsepMIkIIISRAN3mKojBhwgQuuOACDh48SHl5ed0vEkIIL33yxXfc3OsiQkJCSE5OPicPSQshRHMlAboZUBSFmJgYYmJi6m4shBCnYf78+YwceX7O9iOEEN6SMdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBckQAshhBBCCOEFCdBCCCGEEEJ4QQK0EEIIIYQQXpAALYQQQgghhBdaVICeN28e/fv3JzAwkKioKCZOnMi+ffs82gwdOhRFUTy+br/99kaqWAghhBBCNDctKkCvXbuWWbNm8dtvv/HTTz9hs9kYOXIkpaWlHu1uueUWMjIy3F8vvPBCI1UshBBCCCGaG11jF3A2ff/99x7ff/DBB0RFRfHnn38yZMgQ93I/Pz+io6PPdXlCCCGEEKIFaFE90KcqKioCICwszGP54sWLiYiIoFu3bjzyyCOUlZXVuA2LxYLJZPL4EkIIIYQQ568W1QNdmdPp5L777uOiiy6iW7du7uXXXHMNCQkJxMTEsGPHDv7+97+zb98+li1bVu125s2bx5w5c85V2UIIIYQQoolTVFVVG7uIhnDHHXfw3XffsX79etq0aVNju9WrVzN8+HAOHjxIu3btqqy3WCxYLBb39yaTibi4OIqKiggKCmqQ2oUQ4lwbP348K1eubOwyhBDirDCZTAQHBzdYXmuRPdB33XUXX3/9NevWras1PAMMGDAAoMYAbTQaMRqNDVKnEEIIIYRoflpUgFZVlbvvvpvly5ezZs0a2rZtW+drtm3bBkDr1q0buDohhBBCCNEStKgAPWvWLD7++GO+/PJLAgMDyczMBCA4OBhfX18OHTrExx9/zOjRowkPD2fHjh3cf//9DBkyhB49ejRy9UIIIYQQojloUQF6wYIFgOvDUipbtGgRM2fOxGAw8PPPP/PKK69QWlpKXFwckydP5vHHH2+EaoUQQgghRHPUogJ0Xc9DxsXFsXbt2nNUjRBCCCGEaIla9DzQQgghhBBCnG0SoIUQQgghhPCCBGghhBBCCCG8IAFaCCGEEEIIL0iAFkIIIYQQwgv1moWjT58+Xm1UURRWrlxJbGzsaRUlhBBCCCFEU1WvAL1t2zZmz55NQEBAnW1VVeW5557DYrGccXFCCCGEEEI0NfWeB/qhhx4iKiqqXm1ffPHF0y5ICCGEEEKIpqxeATolJYXIyMh6b3T37t3ExMScdlFCCCGEEEI0VfUK0AkJCV5tNC4u7rSKEUIIIYQQoqk7rY/yNpvN7Nixg+zsbJxOp8e68ePHn5XChBBCCCGEaIq8DtDff/89119/Pbm5uVXWKYqCw+E4K4UJIYQQQgjRFHk9D/Tdd9/NlClTyMjIwOl0enxJeBZCCCGEEC2d1wE6KyuLBx54gFatWjVEPUIIIYQQQjRpXgfoq666ijVr1jRAKUIIIYQQQjR9Xo+BfuONN5gyZQq//PIL3bt3R6/Xe6y/5557zlpxQgghhBBCNDVeB+hPPvmEH3/8ER8fH9asWYOiKO51iqJIgBZCCCGEEC2a1wH6scceY86cOTz88MNoNF6PABFCCCGEEKJZ8zoBW61Wpk2bJuFZCCGEEEKcl7xOwTfccAOffvppQ9QihBBCCCFEk+f1EA6Hw8ELL7zADz/8QI8ePao8RPjSSy+dteKEEEIIIYRoarwO0Dt37qR3794A7Nq1y2Nd5QcKhRBCCCGEaIm8DtD/+9//GqIOIYQQQgghmgV5ElAIIYQQQggv1CtAX3nllZhMpnpv9NprryU7O/u0ixJCCCGEEKKpqtcQji+//JKcnJx6bVBVVb766iv++c9/EhUVdUbFCSGEEEII0dTUK0CrqkrHjh0buhYhhBBCCCGavHoF6NN5cDA2Ntbr1wghhBBCCNHU1StAX3LJJQ1dhxBCCCGEEM2CzMIhhBBCCCGEFyRACyGEEEII4QUJ0EIIIYQQQnhBArQQQgghhBBekAAthBBCCCGEF+o1C8dJe/bsYcmSJfzyyy+kpqZSVlZGZGQkvXv3ZtSoUUyePBmj0dhQtQohhBBCCNHo6tUDvWXLFkaMGEHv3r1Zv349AwYM4L777uOf//wnM2bMQFVVHnvsMWJiYnj++eexWCwNXbcQQgghhBCNol490JMnT+ahhx7i888/JyQkpMZ2Gzdu5NVXX+XFF1/k0UcfPVs1CiGEEEII0WTUK0Dv378fvV5fZ7uBAwcycOBAbDbbGRcmhBBCCCFEU1SvAF2f8Hwm7YUQ1VNVlYySDA4XHOZo0VGmdJ2CTuPVowtCCCGEOMtO6y/x5s2b+d///kd2djZOp9Nj3UsvvXRWChPifFFuKyelMIXDBYfdX4cKDnG44DApBSmU28sBmDd8noRnIYQQognw+q/xs88+y+OPP06nTp1o1aoViqK411X+fyGEi6qqZJdmu0PxqSH5ePHxOrcxNHEoDw166BxUK4QQQoi6eB2gX331Vd5//31mzpzZAOUI0bxtOraJTcc2VQnJZbay095mqE8o/530X7Qa7VmsVAghhBCny+sArdFouOiiixqiFiGavS6RXViftp5Pdn1CZknmWdnm7IGzifSLPCvbEkIIIcSZU1RVVb15wQsvvMDx48d55ZVXGqikps1kMhEcHExRURFBQUGNXY5oosx2M4u2LuK5X58jrSjtjLenUTQkhSbROaKz6yvS9d/kiGSCfYLPQsXifDd+/HhWrlzZ2GUIIcRZ0dB5zesA7XQ6GTNmDPv376dLly5VZtxYtmzZWS2wqZEALbxhc9hYvHMxz/7yLAfyDzTIPloHtHYH6pPhOjkimdYBreW5BFFvEqCFEC1JQ+c1r4dw3HPPPfzvf//j0ksvJTw8XP5AC1ELvVbPzF4zua7HdXy2+zPm/jKXXdm7zuo+MkoyyCjJYHXKao/lwcZgkiOSq4TrtiFtZTy1EEIIcQa87oEODAxkyZIljBkzpqFqatKkB1qcCafq5Kt9XzH3l7lsPr65UWowaA10DO9YZThIx/CO+Op9G6Um0fikB1oI0ZI0uR7osLAw2rVrd9YLEeJ8oFE0TEiewPhO4/np8E/M/WUu61LX1dh+dIfRdI3syp7cPezJ2UNKYQpO1Vlj+/qwOqzsyt5VpSdcQSExJLFKj3XniM6E+oae0T6FEEKIlsTrHuhFixbx/fffs2jRIvz8/BqqriZLeqDF2bYudR1zf5nLj4d+rLJuSMIQ1s5c6/7ebDezP28/e3P3sidnjytY5+5hX+4+LA5Lg9UY5R9VJVR3juxMbGCsDONqIaQHWpxtx49DVBTo5POfRCNocg8R9u7dm0OHDqGqKomJiVUeItyyZctZLbCpkQAtGsrmY5uZ+8tcvtz3pXuZRtGQOTuTSP/ap7FzOB0cKTzCntw9VcJ1obmwwWoOMAS4xlmfEq7bhbWTT01sZiRAi7Ptyy/h6quhZ0/o1w/69nV9de4soVo0vCY3hGPixIlnvQghBPSP7c+K6SvYmbWTZ9c/y9K/lrrGTO//ipt631Tra7UaLe3C2tEurB1jO451L1dVlazSLPbknAjWJ0L1npw9HCs+dsY1l1hL+OP4H/xx/A+P5XqNnvZh7asMB+kU3gl/g/8Z71cI0fRNmACjRsGKFfDbbxXLfX2hV6+qoVorzzaLZsTrHujznfRAi3Nlf95+nlv/HIXmQpZNO/vTQ5osJndvdeVwfSj/EA7Vcdb3d1JCcEJFr3Wl4SARfhENtk9RN+mBFg3h6FHo0gVKSmpv5+dXNVQnJ0uoFqevyQ3hON9JgBbnWmZJJtEB0edsfxa7hYP5B9091XvzKkJ2ub28wfYb4Rfh/nCYyuE6LjgOjaJpsP0KFwnQoqG88grcf7/3r/Pzg969XWH6ZLDu1ElCtaifJjeEw+Fw8PLLL7N06VLS0tKwWq0e6/Pz889acUIIzml4BjDqjHSN6krXqK4ey52qk7SiNPf4anevdc4e8srzzni/uWW5/JL2C7+k/eKx3E/vR3JEcpWx1u3D2mPQGs54v0KIM2O1QlYWZGZW/3XsNEeLlZXBr7+6vk7y93eF6nvugauuAnmGWTQWrwP0nDlzePfdd5k9ezaPP/44jz32GEeOHGHFihU8+eSTDVGjEKIJ0CgaEkMSSQxJ5IoOV3isyynNcYfpyuH6bHyMeZmtjC0ZW9iS4fmAslbRusdZJ4cne3y8eaAx8Iz3K8T5zOmEvLyaQ3Hlr3PVbxYYCNdfD7fdBt27n5t9ClETr4dwtGvXjtdee40xY8YQGBjItm3b3Mt+++03Pv7444aqtUmQIRxC1F+JtYR9ufuqhOsD+QewO+0Ntt82QW3cvdWVP40xyj9Kpt2rgQzhaPlUFYqL6xeKs7PB0XCPQnilXz9XaJ4+HQICGrsa0Vw0uSEcmZmZdD/x1i8gIICioiIAxo4dyxNPPHF2qxNCNGsBhgD6xvSlb0xfj+U2h41DBYc8pts7+UBjqa30jPebbkon3ZTOT4d/8lge6hPq0VN9cjhIYkiijLMWzZbFUvsQispf5Q33GMNZ5ecH117rCs59+9bdXohzzesA3aZNGzIyMoiPj6ddu3b8+OOP9OnTh82bN2M0GhuiRiFEC6PX6t3jmicxyb1cVVXSTekePdYnw3V2afYZ77fAXMCGoxvYcHSDx3IfnQ+dwjtVCdcdwzti1Mm/a+LcczggN7fuQJyVBQUFjV1tzTQa14eplJfDif62WnXvDrff7grPwcENX58Qp8vrAD1p0iRWrVrFgAEDuPvuu5kxYwbvvfceaWlp3H86j9kKIcQJiqIQFxxHXHAcI9uN9FiXV5bn8eDiyXCdWpiKyplNJmS2m9metZ3tWds9lmsUDUmhSR4PL54M18E+8tddeEdVwWSq/xAKp7OxK65ZaChER9f81aqV678REa4Q3blzzQHaaIRp01zB+cIL5cFA0Tyc8TR2GzduZOPGjXTo0IFx48adrbqaLBkDLUTTUmYrY3/efo9QvSdnDwfyD2B1WOvewGlqHdDa44NiTo61bh3QulmOs5Yx0KevvLz6IRTVLTObG7vamvn61h6KK4djb244//orDB5cdXmnTq7QfP31EBZ29o5DCGiCY6BPNXDgQAYOHHg2ahFCCK/56f3oFd2LXtG9PJbbnXZSClKq9FjvydlDsbX4jPebUZJBRkkGq1NWeywPNgZ7PLh4MlwnhSah1cgEts2FwwE5OfXrLa7P0ITGotVW9AZX10Nc+SswsGF6f999t+L/9XqYPNk1tvmSS6S3WTRfp9UD/d///peFCxeSkpLCxo0bSUhI4JVXXqFt27ZMmDChIepsMqQHWojmTVVVMkoyKkJ1pXCdWZLZYPs1aA10DO/oMRzk5DhrX71vg+23vs6HHmhVhcLCunuJMzNd4bkpD6EIC6tfb3F4uGsIRWMxmaB1a1ctt94KN97oGhMtRENrcj3QCxYs4Mknn+S+++5j7ty5OE7McxMSEsIrr7zS4gO0EKJ5UxSFmMAYYgJjGJ403GNdobnQPRtI5R7rlMIUnOqZpSmrw8qu7F3syt7lWQ8KiSGJHj3WJ8dah/l6d1/b5nTyu8mEv1ZL78DzZy7ssrL6z0JhbbhRPWfMz68ibFbXQ3zyKyrKuyEUjSk1FZYvhxEjGjfIC3G2ed0D3aVLF5599lkmTpxIYGAg27dvJykpiV27djF06FByc3MbqtYmQXqghTj/mO1m9uftrxKu9+Xuw+KwNNh+o/yjqvRYd47sTGxgrHucdUp5OT/k5/NDQQGrCwpwqCqHL7yQKIN3n9LY1Hqg7XbXg3T1CcYmU2NXWzOdrvYwXPlL5jgW4uxpcj3QKSkp9O7du8pyo9FIaemZz98qhBBNjY/Ohx6tetCjVQ+P5Q6ngyOFR6qdHaTQXHjG+80uzSa7NJu1qWsrFmp88IkcSGD0cMyB3SjWes4G8nhCgtfh+VxRVdeUa/XpKc7NdbVvqsLD6xeKw8Kk51WIlsjrAN22bVu2bdtGQkKCx/Lvv/+ezp07n7XChBCiqdNqtLQLa0e7sHaM6TjGvVxVVbJKs9iTU/Gx5icD9rHiY17uRYGA9hDaH8L6QVA3zBo91U7mYDOx9PvJ7A5v69Fz3Sm8E/4G/zM51FqVltZ/CIXN1mBlnLGAgPqF4qgo18NwQojzl9cB+oEHHmDWrFmYzWZUVWXTpk188sknzJs3j3crP2rbxM2fP59//etfZGZm0rNnT15//fX/b+/O47Iq8/+Pv29WUQRk10QFzTUtl2JccikVG0dzphwzUyu1MtNcvqVMM7k0jpZpi+2L6K8szSnL0jI0cTK1MRW3EBVRcwEUBVQQBK7fH+Y93YHIUZAbfD0fj/uh5zrXOffnPp1u3xyucx3ddtttFV0WgCrAZrMp1DtUod6h6hbezWFdVm6WfSjIb8N10skkFZhfn53sXkuq1U7yv1Wq1VbyKOVY6EMLtSdtq/akbS2yqp5vvWKHgwRWDyx2V+fPXxhCUZoHeZwuxaQm9+jfOqj6+km3lu6zlBE3t9JPzcYQCgCldUWzcCxcuFBTpkxRUlKSJKlOnTqaOnWqhg0bVuYFlofFixdryJAheuuttxQZGamXX35ZS5YsUWJiooIvc3swY6ABlLXcwkKtOXlCS44la3XmaR3Mv8IZRvPPSIV5UmG+ZM7/+udvXsW03+B2RvU9zuvQyFg9f/7/9F+3IG0801hbMsJ1XmU3FOQurdAK9dYq3amZmqTVulPSlc9hFhR0+Yd4hIZeeOAHQyiA609557WrepBKdna2zpw5c9nQ6WwiIyN166236rXXXpMkFRYWKiwsTKNHj9akSZNK3JYADaAs7MvO1oqTJ7Xy5EnFZWQo+xrOmVZNOeqkdYrSSrXRFh1QA836s4fezHhLNp2Xn+JVSxt0Voe1X0Hao8YOryO6QUbWUqmHcpWqEPnpwqTJP6mtZmqSlurPKtSF+bFr1izd1eKgIIZQACiZ091EeFFaWpoSExMlXfh1ZVBQUJkVVZ7y8vK0efNmRUdH29tcXFzUvXt3bdiwoUj/3Nxc5eb+7y77LGe+3RtApRHo7q5abm5yt9lUcA3ulrOpUK20XVFaqS5aq+rKsa+LULJq2m6UJBm565Ru1alfh1qEaI9a6AcFaLa8lSSbpGx5aa9uLBKs96ixTirA4X09PC4GX09tPdpX3Q5/IElqp836t/orJ+xGnRn5tKo/Olg1/CvJ3GwArnuWA/Tp06f1+OOP6+OPP1bhr1dMXF1dNWDAAL3++uvy9fW9zB4q1okTJ1RQUKCQkBCH9pCQEO3evbtI/xkzZmjq1KlF2gcMGCB3LoEAKAN3GKO0vDwdy8tTal6eCspw357KUbCOK0jHVU25WidpXTH99uUe0jN6poQ9hchFvvLQKXnopNy0QzZtt68N+vVV6OquAi9vybuGXGp6y9WnxoXBxTVq6CVzXC8d/t1uf9kr/W2ENPUJqWFDqX79CwOXAeAqnC/nO5Ytf0sNHz5cW7du1fLly+2P8N6wYYOefPJJPfroo1q0aFGZF1mRoqOjNX78ePtyVlaWwsLCtHjxYoZwAChzOQUF+vbUKX16/LiWnTihzILSx+lOvr5ys9nkWpCpZnnfqvX5rxRRuP3yG0p65qkCTd8yTSos3ePGXXVGAfpRAVovf/0od/06jWnBeenMqQuv3z/YsaThfrm50s8/S0ePSk88IY0Zc2GsBgBcgYtDOMqL5QD91VdfaeXKlerUqZO9LSoqSu+++6569epVpsWVh8DAQLm6uio1NdWhPTU1VaGhoUX6e3p6yrOyPPIJQKXn5eqquwMDdXdgoPIKC/XdqVP69MQJfX7ihE6UcEXFRQV6uPpO3Za/XCcyP5cxpXvAS7VqDRUQ9IBq2L5V2F9dlB6Xq+yUy3/nFchbabpTabpTNuXLV9sUqB8UqPWqptTiN0pLu+T+UnWHArRebhkZ0j//Kc2eLQ0bJk2YIDVoUKrPAgDXiuV7kwMCAopN9L6+vqpVq1aZFFWePDw81LZtW61evdreVlhYqNWrV9uvqAOAM/BwcVGvgAC926SJjrVvrzU336wnbrhBdX7zoJRw7dejekuf6K8KPzZEx48vvmx4dnX1Ue3aI9S69TpFRu7VjQ2nqFpooBp+3E23HYvSbXtuU8SsCPne7luqfyWM3JShttqnMdqoRdqkd5WsB3VajVXa0d1Zaq6fNE+ndMuFhpwc6bXXpEaNpMGDpZ07S9weAK4ly7NwvPPOO1qyZIk++OAD+xXblJQUDR06VH/5y1/06KOPlkuhZWnx4sUaOnSo3n77bd122216+eWX9cknn2j37t1Fxkb/HrNwAKho53LTtOnQPJ1M+0C+538u5VYu8vfvqZCQoQoMvFuurl4Oay/1KO+843k6ueKkTnxxQidXnlRhtrXZQjx0XIFar0D9ID/Fy0XFX0U/qj7aowvD5eroc0Xobbn9/nExf/qTNGmS1LGjpRoAXH+cYhq71q1by2b733yde/fuVW5ururVqydJOnTokDw9PXXjjTdqy5YtZV5keXjttdfsD1K55ZZb9OqrryoyMvKy2xGgAVSEwsJcpacvV0rKAp08uULG5Jdquxo1blJIyFCFhAySp2ftS/a7VID+rYJzBcpYnaETy04ofVm68lLyLH0GV2XLX/9VoH6QvzbKXWfs6zJ0k+I1175cTUfVRC+olrYV3VGnTlJ0tHTXXZLtyueSBlB1OUWALm4WikuZPHnyVRXk7AjQAK4VY4xOn96klJQFSktbpPz8k6XaztUtUKEh9ys0dKi8vR0vgFxKaQK0Q22FRqd/Oq0TX5zQiS9OKHtXdqm3vaBAftqmgF+vTrvpjH7Ql0V61dFSRegdx6vRNWtKN94o3XvvhTHSHmX3wBcAVYNTBGj8DwEagBXGGB079p5q1x4mm610t52cO3dYqakfKjV1gbKzi06vWRybzV0BAX0UGjpU/v695OJiLVRaDdC/l5OUY78ynfF9hqzOxVdDSTqrBpKKzgJSzTdbTR/LlN+fwi4E5+BgrjwDKJHTPkgFAFCy/PzT2rPnUWVkxKlOnREl9i0oOKvjx5cqNXWBTp1aLZXy9ruaNW9VaOhQBQffJ3f3gMtvUE68GnopbFyYwsaF6Xz6eaV/na70L9J18puTKjhz+TR9Vg0vue5cZnXFP19dN5wNUcTMQLkSngFUMAI0AJSDM2d2aNeue5WTs0c1a95abB9jCpWZ+b1SUhbo+PElKig4U2y/3/PwqKOQkMEKDR2qGjWalWXZZcI9wF2hD4Qq9IFQFcZ8qFN//1Tpbl10Ir2J8s56XX4Hl3DktSNKX5GupjFN5dfZr+wKBgCLCNAAUIaMMUpJmae9e59QYeGFcbuennUd+mRn71Nq6gdKTf1/OnfuQKn26+LipcDAvyg0dIhq1bpTNlvpHnhS0VweHKSAhx5QgKQbjdHpzaeV/kW6Tiw7obPbz1re37n95xTfJV43jLlBEf+KkGuNynEcAFQtBGgAKCMFBWe1Z89IpaZ+4NDu6VlX+fmZSkv7RCkpC5SV9UOp9+nr21mhoUMVFHSv3Nwq4X0XvxluYbPZ5NPORz7tfBT+XLhyknOU/mW6Uhak6MyW0l19v+jIq0eUvvzXq9G3+5Vx0QBQMgI0AJSBs2d/1q5d/ZWdXXRe5vT0L3Xs2Lv2K9KXU61ahEJDhygkZLC8vCLKulSn4RXupaD+QToy98gVbX8u6XdXo6tzNRrAtWE5QI8fP77YdpvNpmrVqqlRo0a6++675e/vf9XFAUBlkJLy/7Rnz0gVFhY/lVtphmm4uvooOPivCgkZKl/fjqWaeq6yO59+Xtt6bFPOvpwr34mRjrxyRCeXn1STmCby6+RXZvUBwKVYDtBbt27Vli1bVFBQoCZNmkiS9uzZI1dXVzVt2lRvvPGGJkyYoHXr1ql58+ZlXjAAOIuCgmzt3TtaKSnzrnAPLqpVq4dCQ4cqMLBfkacDVmX5mfnaFrXtCuaPLl7OvhzFd45X3SfrKnx6OFejAZQrywH64tXlmJgY+7x6mZmZGj58uDp16qQRI0bo/vvv17hx47Ry5coyLxgAnEF2dqJ27bpXZ8/utLxt9erNFRo6VCEhD8jTs045VOf80r9Ol297X/lE+kjmwoNZZCQV/u/vV9KWvSdb+/+2X+FTw+XmyyhFAOXD8oNUbrjhBsXGxha5urxr1y717NlTR44c0ZYtW9SzZ0+dOHGiTIt1BjxIBUBq6kdKTHxEhYXWZpEICXlAdeuOlbd3G6cbonG1D1IBAGdS3nmtdI/F+o3MzEylpaUVaT9+/LiysrIkSX5+fsrLy7v66gDAiRQUnFNi4qNKSBhkOTxL0unTW+XhUdvpwjMAwBrLAfruu+/Www8/rKVLl+rw4cM6fPiwli5dqmHDhqlfv36SpP/+979q3LhxWdcKABUmO3uftm5tr2PH3rmKfezS1q23KydnfxlWBgC41iwPEHv77bc1btw43XfffcrPz7+wEzc3DR06VC+99JIkqWnTpnrvvffKtlIAqCBpaUuUmDhMBQWnr3pf587t19atnXTzzbGqUaNFGVQHALjWLAdob29vvfvuu3rppZe0f/+FqygRERHy9va297nlllvKrEAAqCiFhbnat2+Cjh59vZRb2OTm5is3t1pyc/P7zavocn5+RnmWDgAoR1d8i7K3t7datWpVlrUAgNMoKDir5OR/KDf3kEJCBv8uBDsGYnf3C8uurjVls1keGQcAqGQsB+izZ89q5syZWr16tdLS0lRYWOiw/uJVaQCozFxda6hRozkVXQYAwAlZDtDDhw/X2rVrNXjwYNWuzd3kAAAAuL5YDtBff/21li9fro4dO5ZHPQAAAIBTszxYr1atWvL39y+PWgAAAACnZzlAP/fcc3r22WeVnZ1dHvUAAAAATs3yEI7Zs2crKSlJISEhatCggdzd3R3Wb9mypcyKAwAAAJyN5QB98WmDAAAAwPXIcoCePHlyedQBAAAAVApX/CCVzZs3KyEhQZLUokULtW7dusyKAgAAAJyV5QCdlpam++67T3FxcfLz85MkZWRkqFu3blq0aJGCgoLKukYAAADAaViehWP06NE6ffq0du3apZMnT+rkyZPauXOnsrKyNGbMmPKoEQAAAHAalq9Af/PNN1q1apWaNWtmb2vevLlef/119ezZs0yLAwAAAJyN5SvQhYWFRaaukyR3d3cVFhaWSVEAAACAs7IcoO+44w49+eSTOnr0qL3tyJEjGjdunO68884yLQ4AAABwNpYD9GuvvaasrCw1aNBADRs2VMOGDRUeHq6srCzNnTu3PGoEAAAAnIblMdBhYWHasmWLVq1apd27d0uSmjVrpu7du5d5cQAAAICzuaJ5oG02m3r06KEePXqUdT0AAACAUytVgH711VdLvUOmsgMAAEBVVqoA/dJLL5VqZzabjQANAACAKq1UATo5Obm86wAAAAAqBcuzcAAAAADXsyu6ifDw4cNatmyZDh06pLy8PId1c+bMKZPCAAAAAGdkOUCvXr1affv2VUREhHbv3q2bbrpJBw4ckDFGbdq0KY8aAQAAAKdheQhHdHS0/u///k87duxQtWrV9Omnn+qXX35Rly5d1L9///KoEQAAAHAalgN0QkKChgwZIklyc3NTTk6OvL29NW3aND3//PNlXiAAAADgTCwH6Bo1atjHPdeuXVtJSUn2dSdOnCi7ygAAAAAnZHkM9B/+8AetW7dOzZo10x//+EdNmDBBO3bs0GeffaY//OEP5VEjAAAA4DQsB+g5c+bozJkzkqSpU6fqzJkzWrx4sW688UZm4AAAAECVZzlAR0RE2P9eo0YNvfXWW2VaEAAAAODMSjUG2hhT3nUAAAAAlUKpAnSLFi20aNGiIg9N+b29e/dq5MiRmjlzZpkUBwAAADibUg3hmDt3riZOnKjHH39cPXr0ULt27VSnTh1Vq1ZNp06d0s8//6x169Zp165deuKJJzRy5MjyrhsAAACoEKUK0Hfeead++uknrVu3TosXL9bChQt18OBB5eTkKDAwUK1bt9aQIUM0aNAg1apVq7xrBgAAACqMpZsIO3XqpE6dOpVXLQAAAIDTs/wgFQAAAOB6RoAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABZYD9B133KGpU6cWaT916pTuuOOOMikKAAAAcFaWprGTpLi4OO3YsUNbt27VwoULVaNGDUlSXl6e1q5dW+YFAgAAAM7kioZwrFq1SikpKfrDH/6gAwcOlHFJAAAAgPO6ogBdu3ZtrV27Vi1bttStt96quLi4Mi4LAAAAcE6WA7TNZpMkeXp66qOPPtKTTz6pXr166Y033ijz4gAAAABnY3kMtDHGYfnvf/+7mjVrpqFDh5ZZUQAAAICzshygk5OTFRQU5NB2zz33qGnTpvrpp5/KrDAAAADAGVkO0PXr1y+2vUWLFmrRosVVFwQAAAA4Mx6kAgAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAuqTIA+cOCAhg0bpvDwcHl5ealhw4aaPHmy8vLyHPrYbLYir40bN1Zg5QAAAKhMLM/C4ax2796twsJCvf3222rUqJF27typESNG6OzZs3rxxRcd+q5atcphxpCAgIBrXS4AAAAqqSoToHv16qVevXrZlyMiIpSYmKg333yzSIAOCAhQaGjotS4RAAAAVUCVGcJRnMzMTPn7+xdp79u3r4KDg9WpUyctW7asxH3k5uYqKyvL4QUAAIDrV5UN0Pv27dPcuXP16KOP2tu8vb01e/ZsLVmyRMuXL1enTp3Ur1+/EkP0jBkz5Ovra3+FhYVdi/IBAADgpGzGGFPRRZRk0qRJev7550vsk5CQoKZNm9qXjxw5oi5duqhr16567733Stx2yJAhSk5O1vfff1/s+tzcXOXm5tqXs7KyFBYWpszMTPn4+Fj4JADgvPr27XvZ38gBQGWRlZUlX1/fcstrTj8GesKECXrwwQdL7BMREWH/+9GjR9WtWzd16NBB77zzzmX3HxkZqdjY2Euu9/T0lKenZ6nrBQAAQNXm9AE6KChIQUFBpep75MgRdevWTW3btlVMTIxcXC4/QiU+Pl61a9e+2jIBAABwnXD6AF1aR44cUdeuXVW/fn29+OKLOn78uH3dxRk3FixYIA8PD7Vu3VqS9Nlnn2nevHmXHeYBAAAAXFRlAnRsbKz27dunffv2qW7dug7rfjvM+7nnntPBgwfl5uampk2bavHixbr33nuvdbkAAACopJz+JkJnU96D0gGgInATIYCqpLzzWpWdxg4AAAAoDwRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYAEBGgAAALCAAA0AAABYQIAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABQRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYAEBGgAAALCAAA0AAABYQIAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABQRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAgioVoBs0aCCbzebwmjlzpkOf7du36/bbb1e1atUUFhamF154oYKqBQAAQGXkVtEFlLVp06ZpxIgR9uWaNWva/56VlaWePXuqe/fueuutt7Rjxw49/PDD8vPz0yOPPFIR5QIAAKCSqXIBumbNmgoNDS123cKFC5WXl6d58+bJw8NDLVq0UHx8vObMmUOABgAAQKlUqSEckjRz5kwFBASodevWmjVrlvLz8+3rNmzYoM6dO8vDw8PeFhUVpcTERJ06darY/eXm5iorK8vhBQAAgOtXlboCPWbMGLVp00b+/v5av369oqOjdezYMc2ZM0eSlJKSovDwcIdtQkJC7Otq1apVZJ8zZszQ1KlTy794AAAAVApOfwV60qRJRW4M/P1r9+7dkqTx48era9euatWqlR577DHNnj1bc+fOVW5u7hW/f3R0tDIzM+2vX375paw+GgAAACohp78CPWHCBD344IMl9omIiCi2PTIyUvn5+Tpw4ICaNGmi0NBQpaamOvS5uHypcdOenp7y9PS0XjgAAACqJKcP0EFBQQoKCrqibePj4+Xi4qLg4GBJUvv27fXMM8/o/Pnzcnd3lyTFxsaqSZMmxQ7fAAAAAH7P6YdwlNaGDRv08ssva9u2bdq/f78WLlyocePG6YEHHrCH4/vvv18eHh4aNmyYdu3apcWLF+uVV17R+PHjK7h6AAAAVBZOfwW6tDw9PbVo0SJNmTJFubm5Cg8P17hx4xzCsa+vr7799luNGjVKbdu2VWBgoJ599lmmsAMAAECpVZkA3aZNG23cuPGy/Vq1aqXvv//+GlQEAACAqqjKDOEAAAAArgUCNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYAEBGgAAALCAAA0AAABYQIAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABQRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYAEBGgAAALCAAA0AAABYQIAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABQRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYEGVCdBxcXGy2WzFvjZt2iRJOnDgQLHrN27cWMHVAwAAoLJwq+gCykqHDh107Ngxh7Z//OMfWr16tdq1a+fQvmrVKrVo0cK+HBAQcE1qBAAAQOVXZQK0h4eHQkND7cvnz5/XF198odGjR8tmszn0DQgIcOgLAAAAlFaVGcLxe8uWLVN6eroeeuihIuv69u2r4OBgderUScuWLStxP7m5ucrKynJ4AQAA4PpVZQP0+++/r6ioKNWtW9fe5u3trdmzZ2vJkiVavny5OnXqpH79+pUYomfMmCFfX1/7Kyws7FqUDwAAACdlM8aYii6iJJMmTdLzzz9fYp+EhAQ1bdrUvnz48GHVr19fn3zyie65554Stx0yZIiSk5P1/fffF7s+NzdXubm59uWsrCyFhYUpMzNTPj4+Fj4JADivvn37XvY3cgBQWWRlZcnX17fc8prTj4GeMGGCHnzwwRL7REREOCzHxMQoICBAffv2vez+IyMjFRsbe8n1np6e8vT0LFWtAAAAqPqcPkAHBQUpKCio1P2NMYqJidGQIUPk7u5+2f7x8fGqXbv21ZQIAACA64jTB2irvvvuOyUnJ2v48OFF1i1YsEAeHh5q3bq1JOmzzz7TvHnz9N57713rMgEAAFBJVbkA/f7776tDhw4OY6J/67nnntPBgwfl5uampk2bavHixbr33nuvcZUAAACorJz+JkJnU96D0gGgInATIYCqpLzzWpWdxg4AAAAoDwRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYAEBGgAAALCAAA0AAABYQIAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABQRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAAgI0AAAAYAEBGgAAALCAAA0AAABYQIAGAAAALCBAAwAAABYQoAEAAAALCNAAAACABQRoAAAAwAICNAAAAGABARoAAACwgAANAAAAWECABgAAACwgQAMAAAAWEKABAAAACwjQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAgkoToKdPn64OHTqoevXq8vPzK7bPoUOH1Lt3b1WvXl3BwcF66qmnlJ+f79AnLi5Obdq0kaenpxo1aqT58+eXf/EAAACoMipNgM7Ly1P//v01cuTIYtcXFBSod+/eysvL0/r167VgwQLNnz9fzz77rL1PcnKyevfurW7duik+Pl5jx47V8OHDtXLlymv1MQAAAFDJ2YwxpqKLsGL+/PkaO3asMjIyHNq//vpr/elPf9LRo0cVEhIiSXrrrbc0ceJEHT9+XB4eHpo4caKWL1+unTt32re77777lJGRoW+++aZU75+VlSVfX19lZmbKx8enzD4XAFSkvn37atmyZRVdBgCUifLOa25lvscKsmHDBrVs2dIeniUpKipKI0eO1K5du9S6dWtt2LBB3bt3d9guKipKY8eOveR+c3NzlZuba1/OzMyUdOE/DABUFefPn+d7DUCVcfH7rLyuE1eZAJ2SkuIQniXZl1NSUkrsk5WVpZycHHl5eRXZ74wZMzR16tQi7WFhYWVVOgA4BV9f34ouAQDKVHp6erl8t1VogJ40aZKef/75EvskJCSoadOm16iioqKjozV+/Hj7ckZGhurXr69Dhw7xj40FWVlZCgsL0y+//MLQl1LimF0Zjpt1HLMrw3GzjmN2ZThu1mVmZqpevXry9/cvl/1XaICeMGGCHnzwwRL7RERElGpfoaGh+u9//+vQlpqaal938c+Lbb/t4+PjU+zVZ0ny9PSUp6dnkXZfX19O4ivg4+PDcbOIY3ZlOG7WccyuDMfNOo7ZleG4WefiUj7zZVRogA4KClJQUFCZ7Kt9+/aaPn260tLSFBwcLEmKjY2Vj4+Pmjdvbu+zYsUKh+1iY2PVvn37MqkBAAAAVV+lmcbu0KFDio+P16FDh1RQUKD4+HjFx8frzJkzkqSePXuqefPmGjx4sLZt26aVK1fq73//u0aNGmW/gvzYY49p//79evrpp7V792698cYb+uSTTzRu3LiK/GgAAACoRCrNTYTPPvusFixYYF9u3bq1JGnNmjXq2rWrXF1d9dVXX2nkyJFq3769atSooaFDh2ratGn2bcLDw7V8+XKNGzdOr7zyiurWrav33ntPUVFRpa7D09NTkydPLnZYBy6N42Ydx+zKcNys45hdGY6bdRyzK8Nxs668j1mlmwcaAAAAqEiVZggHAAAA4AwI0AAAAIAFBGgAAADAAgI0AAAAYAEBugTTp09Xhw4dVL16dfn5+RXb59ChQ+rdu7eqV6+u4OBgPfXUU8rPz3foExcXpzZt2sjT01ONGjXS/Pnzy794JxEXFyebzVbsa9OmTZKkAwcOFLt+48aNFVx9xWnQoEGR4zFz5kyHPtu3b9ftt9+uatWqKSwsTC+88EIFVescDhw4oGHDhik8PFxeXl5q2LChJk+erLy8PIc+nGtFvf7662rQoIGqVaumyMjIIg+lup7NmDFDt956q2rWrKng4GD169dPiYmJDn26du1a5Jx67LHHKqhi5zBlypQix+S3TxU+d+6cRo0apYCAAHl7e+uee+4p8qCz601x3/s2m02jRo2SxHl20X/+8x/16dNHderUkc1m0+eff+6w3hijZ599VrVr15aXl5e6d++uvXv3OvQ5efKkBg0aJB8fH/n5+WnYsGH2aZFLiwBdgry8PPXv318jR44sdn1BQYF69+6tvLw8rV+/XgsWLND8+fP17LPP2vskJyerd+/e6tatm+Lj4zV27FgNHz5cK1euvFYfo0J16NBBx44dc3gNHz5c4eHhateunUPfVatWOfRr27ZtBVXtHKZNm+ZwPEaPHm1fl5WVpZ49e6p+/fravHmzZs2apSlTpuidd96pwIor1u7du1VYWKi3335bu3bt0ksvvaS33npLf/vb34r05Vz7n8WLF2v8+PGaPHmytmzZoptvvllRUVFKS0ur6NKcwtq1azVq1Cht3LhRsbGxOn/+vHr27KmzZ8869BsxYoTDOXW9/0ArSS1atHA4JuvWrbOvGzdunL788kstWbJEa9eu1dGjR/WXv/ylAquteJs2bXI4XrGxsZKk/v372/twnklnz57VzTffrNdff73Y9S+88IJeffVVvfXWW/rxxx9Vo0YNRUVF6dy5c/Y+gwYN0q5duxQbG6uvvvpK//nPf/TII49YK8TgsmJiYoyvr2+R9hUrVhgXFxeTkpJib3vzzTeNj4+Pyc3NNcYY8/TTT5sWLVo4bDdgwAATFRVVrjU7q7y8PBMUFGSmTZtmb0tOTjaSzNatWyuuMCdTv35989JLL11y/RtvvGFq1aplP8+MMWbixImmSZMm16C6yuOFF14w4eHh9mXOtaJuu+02M2rUKPtyQUGBqVOnjpkxY0YFVuW80tLSjCSzdu1ae1uXLl3Mk08+WXFFOaHJkyebm2++udh1GRkZxt3d3SxZssTelpCQYCSZDRs2XKMKnd+TTz5pGjZsaAoLC40xnGfFkWSWLl1qXy4sLDShoaFm1qxZ9raMjAzj6elpPv74Y2OMMT///LORZDZt2mTv8/XXXxubzWaOHDlS6vfmCvRV2LBhg1q2bKmQkBB7W1RUlLKysrRr1y57n+7duztsFxUVpQ0bNlzTWp3FsmXLlJ6eroceeqjIur59+yo4OFidOnXSsmXLKqA65zJz5kwFBASodevWmjVrlsPQoA0bNqhz587y8PCwt0VFRSkxMVGnTp2qiHKdUmZmpvz9/Yu0c65dkJeXp82bNzt8R7m4uKh79+7X7XfU5WRmZkpSkfNq4cKFCgwM1E033aTo6GhlZ2dXRHlOZe/evapTp44iIiI0aNAgHTp0SJK0efNmnT9/3uG8a9q0qerVq8d596u8vDx9+OGHevjhh2Wz2eztnGclS05OVkpKisO55evrq8jISPu5tWHDBvn5+Tn8Frx79+5ycXHRjz/+WOr3qjRPInRGKSkpDuFZkn05JSWlxD5ZWVnKycmRl5fXtSnWSbz//vuKiopS3bp17W3e3t6aPXu2OnbsKBcXF3366afq16+fPv/8c/Xt27cCq604Y8aMUZs2beTv76/169crOjpax44d05w5cyRdOK/Cw8MdtvntuVerVq1rXrOz2bdvn+bOnasXX3zR3sa55ujEiRMqKCgo9jtq9+7dFVSV8yosLNTYsWPVsWNH3XTTTfb2+++/X/Xr11edOnW0fft2TZw4UYmJifrss88qsNqKFRkZqfnz56tJkyY6duyYpk6dqttvv107d+5USkqKPDw8itxbFBISYv+383r3+eefKyMjQw8++KC9jfPs8i6eP8V9p/02lwUHBzusd3Nzk7+/v6Xz77oL0JMmTdLzzz9fYp+EhASHmx1Q1JUcx8OHD2vlypX65JNPHPoFBgZq/Pjx9uVbb71VR48e1axZs6pUqLFyzH57PFq1aiUPDw89+uijmjFjxnX3KNcrOdeOHDmiXr16qX///hoxYoS9/Xo511A+Ro0apZ07dzqM5ZXkMHayZcuWql27tu68804lJSWpYcOG17pMp3DXXXfZ/96qVStFRkaqfv36+uSTT667C0dX4v3339ddd92lOnXq2Ns4z5zLdRegJ0yY4PATXXEiIiJKta/Q0NAid6tfvIs4NDTU/ufv7yxOTU2Vj49Ppf4SuZLjGBMTo4CAgFIFlcjISPsNFFXF1Zx7kZGRys/P14EDB9SkSZNLnlfS/869qsLqcTt69Ki6deumDh06lOqmyqp4rpVWYGCgXF1diz2Xqtp5dLWeeOIJ+81Gv/0NWnEiIyMlXfgtCMHmAj8/PzVu3Fj79u1Tjx49lJeXp4yMDIer0Jx3Fxw8eFCrVq267JVlzrOiLp4/qampql27tr09NTVVt9xyi73P72+Szs/P18mTJy2df9ddgA4KClJQUFCZ7Kt9+/aaPn260tLS7L8OiI2NlY+Pj5o3b27vs2LFCoftYmNj1b59+zKpoaJYPY7GGMXExGjIkCFyd3e/bP/4+HiHk78quJpzLz4+Xi4uLvbzrH379nrmmWd0/vx5+/GMjY1VkyZNqtzwDSvH7ciRI+rWrZvatm2rmJgYubhc/jaPqniulZaHh4fatm2r1atXq1+/fpIuDFNYvXq1nnjiiYotzkkYYzR69GgtXbpUcXFxRYZOFSc+Pl6SrtvzqjhnzpxRUlKSBg8erLZt28rd3V2rV6/WPffcI0lKTEzUoUOHKv2/jWUhJiZGwcHB6t27d4n9OM+KCg8PV2hoqFavXm0PzFlZWfrxxx/tM6q1b99eGRkZ2rx5s30Gpu+++06FhYX2H0pK5WrvgKzKDh48aLZu3WqmTp1qvL29zdatW83WrVvN6dOnjTHG5Ofnm5tuusn07NnTxMfHm2+++cYEBQWZ6Oho+z72799vqlevbp566imTkJBgXn/9dePq6mq++eabivpYFWLVqlVGkklISCiybv78+eajjz4yCQkJJiEhwUyfPt24uLiYefPmVUClFW/9+vXmpZdeMvHx8SYpKcl8+OGHJigoyAwZMsTeJyMjw4SEhJjBgwebnTt3mkWLFpnq1aubt99+uwIrr1iHDx82jRo1Mnfeeac5fPiwOXbsmP11EedaUYsWLTKenp5m/vz55ueffzaPPPKI8fPzc5hd6Ho2cuRI4+vra+Li4hzOqezsbGOMMfv27TPTpk0zP/30k0lOTjZffPGFiYiIMJ07d67gyivWhAkTTFxcnElOTjY//PCD6d69uwkMDDRpaWnGGGMee+wxU69ePfPdd9+Zn376ybRv3960b9++gquueAUFBaZevXpm4sSJDu2cZ/9z+vRpex6TZObMmWO2bt1qDh48aIwxZubMmcbPz8988cUXZvv27ebuu+824eHhJicnx76PXr16mdatW5sff/zRrFu3ztx4441m4MCBluogQJdg6NChRlKR15o1a+x9Dhw4YO666y7j5eVlAgMDzYQJE8z58+cd9rNmzRpzyy23GA8PDxMREWFiYmKu7QdxAgMHDjQdOnQodt38+fNNs2bNTPXq1Y2Pj4+57bbbHKY3ut5s3rzZREZGGl9fX1OtWjXTrFkz869//cucO3fOod+2bdtMp06djKenp7nhhhvMzJkzK6hi5xATE1Ps/6+/vU7AuVa8uXPnmnr16hkPDw9z2223mY0bN1Z0SU7jUufUxe/xQ4cOmc6dOxt/f3/j6elpGjVqZJ566imTmZlZsYVXsAEDBpjatWsbDw8Pc8MNN5gBAwaYffv22dfn5OSYxx9/3NSqVctUr17d/PnPf3b4Yfd6tXLlSiPJJCYmOrRznv3PmjVriv1/cujQocaYC1PZ/eMf/zAhISHG09PT3HnnnUWOZ3p6uhk4cKDx9vY2Pj4+5qGHHrJfHC0tmzHGXOGVcgAAAOC6wzzQAAAAgAUEaAAAAMACAjQAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0ABQBt5//3317NmzosuodO677z7Nnj37sv1sNptsNpv8/Pyu+j0bNGhg319GRsZV7w/A9YcnEQLAVTp37pwiIiK0ZMkSdezYUZL04IMPKiMjQ59//nnFFufkdu7cqc6dOys5OVm+vr6X7Gez2RQTE6M//vGPCg4Ovqr3PH78uL7//nvdc889OnXqVJmEcgDXF65AA8BV+ve//y0fHx97eLbi/Pnz5VDRtVEWtd90001q2LChPvzww8v29fPzu+rwLElBQUHy9/e/6v0AuH4RoAHgV8ePH1doaKj+9a9/2dvWr18vDw8PrV69+pLbLVq0SH369LEvT5kyRQsWLNAXX3xhHyoQFxenAwcOyGazafHixerSpYuqVaumhQsXKj09XQMHDtQNN9yg6tWrq2XLlvr4448d3qNr164aM2aMnn76afn7+ys0NFRTpkyxrzfGaMqUKapXr548PT1Vp04djRkzRpL0t7/9TZGRkUXqvvnmmzVt2jT78nvvvadmzZqpWrVqatq0qd544w37ukvVfvDgQfXp00e1atVSjRo11KJFC61YscK+3c6dO3XXXXfJ29tbISEhGjx4sE6cOOFQR58+fbRo0aJLHt9LmTJlim655RbNmzdP9erVk7e3tx5//HEVFBTohRdeUGhoqIKDgzV9+nTL+waAEhkAgN3y5cuNu7u72bRpk8nKyjIRERFm3LhxJW7j6+trFi1aZF8+ffq0+etf/2p69epljh07Zo4dO2Zyc3NNcnKykWQaNGhgPv30U7N//35z9OhRc/jwYTNr1iyzdetWk5SUZF599VXj6upqfvzxR/s+u3TpYnx8fMyUKVPMnj17zIIFC4zNZjPffvutMcaYJUuWGB8fH7NixQpz8OBB8+OPP5p33nnHGGPMzp07jSSzb98++/4utu3du9cYY8yHH35oateuba/r008/Nf7+/mb+/PnGGHPJ2nv37m169Ohhtm/fbpKSksyXX35p1q5da4wx5tSpUyYoKMhER0ebhIQEs2XLFtOjRw/TrVs3h+P39ddfGw8PD3Pu3LlLHmNJZunSpQ5tkydPNt7e3ubee+81u3btMsuWLTMeHh4mKirKjB492uzevdvMmzfPSDIbN2502HbNmjVGkjl16lSJ/20BoDgEaAD4nccff9w0btzY3H///aZly5YlBrtTp04ZSeY///mPQ/vQoUPN3Xff7dB2MYS+/PLLl62hd+/eZsKECfblLl26mE6dOjn0ufXWW83EiRONMcbMnj3bNG7c2OTl5RW7v5tvvtlMmzbNvhwdHW0iIyPtyw0bNjQfffSRwzbPPfecad++fYm1t2zZ0kyZMqXY93zuuedMz549Hdp++eUXI8kkJiba27Zt22YkmQMHDhS7H2MuHaCrV69usrKy7G1RUVGmQYMGpqCgwN7WpEkTM2PGDIdtCdAArgZDOADgd1588UXl5+dryZIlWrhwoTw9PS/ZNycnR5JUrVq1Uu+/Xbt2DssFBQV67rnn1LJlS/n7+8vb21srV67UoUOHHPq1atXKYbl27dpKS0uTJPXv3185OTmKiIjQiBEjtHTpUuXn59v7Dho0SB999JGkC8M9Pv74Yw0aNEiSdPbsWSUlJWnYsGHy9va2v/75z38qKSmpxNrHjBmjf/7zn+rYsaMmT56s7du329dt27ZNa9ascdhn06ZNJclhv15eXpKk7OzsUh7B/2nQoIFq1qxpXw4JCVHz5s3l4uLi0HbxOAFAWSBAA8DvJCUl6ejRoyosLNSBAwdK7BsQECCbzaZTp06Vev81atRwWJ41a5ZeeeUVTZw4UWvWrFF8fLyioqKUl5fn0M/d3d1h2WazqbCwUJIUFhamxMREvfHGG/Ly8tLjjz+uzp0722/0GzhwoBITE7VlyxatX79ev/zyiwYMGCBJOnPmjCTp3XffVXx8vP21c+dObdy4scTahw8frv3792vw4MHasWOH2rVrp7lz59r326dPH4d9xsfHa+/evercubN9HydPnpR04eY+q4o7JiUdJwAoC24VXQAAOJO8vDw98MADGjBggJo0aaLhw4drx44dl5z9wcPDQ82bN9fPP//sMA+0h4eHCgoKSvWeP/zwg+6++2498MADkqTCwkLt2bNHzZs3t1S7l5eX+vTpoz59+mjUqFFq2rSpduzYoTZt2qhu3brq0qWLFi5cqJycHPXo0cP+mUJCQlSnTh3t37/fflXairCwMD322GN67LHHFB0drXfffVejR49WmzZt9Omnn6pBgwZyc7v0Pzc7d+5U3bp1FRgYaPm9AaAicAUaAH7jmWeeUWZmpl599VVNnDhRjRs31sMPP1ziNlFRUVq3bp1DW4MGDbR9+3YlJibqxIkTJU75duONNyo2Nlbr169XQkKCHn30UaWmplqqe/78+Xr//fe1c+dO7d+/Xx9++KG8vLxUv359e59BgwZp0aJFWrJkSZGgPHXqVM2YMUOvvvqq9uzZox07digmJkZz5swp8X3Hjh2rlStXKjk5WVu2bNGaNWvUrFkzSdKoUaN08uRJDRw4UJs2bVJSUpJWrlyphx56yOGHi++//56H0ACoVAjQAPCruLg4vfzyy/rggw/k4+MjFxcXffDBB/r+++/15ptvXnK7YcOGacWKFcrMzLS3jRgxQk2aNFG7du0UFBSkH3744ZLb//3vf1ebNm0UFRWlrl27KjQ0VP369bNUu5+fn95991117NhRrVq10qpVq/Tll18qICDA3ufee+9Venq6srOzi+x/+PDheu+99xQTE6OWLVuqS5cumj9/vsLDw0t834KCAo0aNUrNmjVTr1691LhxY/v0d3Xq1NEPP/yggoIC9ezZUy1bttTYsWPl5+dnH6N87tw5ff755xoxYoSlzwsAFYknEQJAGejfv7/atGmj6Ojoii6lUnnzzTe1dOlSffvttyX2s9lsWrp0qeUfLC4lLi5O3bp140mEAK4IV6ABoAzMmjVL3t7eFV1GpePu7m6/6fByBg4cqLp16171e7Zo0UJ33XXXVe8HwPWLK9AAAKe3b98+SZKrq+tlh5VczsGDB+1j0iMiIhymvAOA0iBAAwAAABbwYzcAAABgAQEaAAAAsIAADQAAAFhAgAYAAAAsIEADAAAAFhCgAQAAAAsI0AAAAIAFBGgAAADAgv8PTwSwaetp8fUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MomentumObject4D(pt=565.0, phi=0.0, eta=-0.24844329419577088, mass=926.7773116469677)\n",
+      "[MomentumObject4D(pt=200, phi=0, eta=1, mass=100), MomentumObject4D(pt=250, phi=0, eta=-1.1, mass=100), MomentumObject4D(pt=30, phi=0, eta=1.5, mass=10), MomentumObject4D(pt=40, phi=0, eta=-1.3, mass=10), MomentumObject4D(pt=20, phi=0, eta=2, mass=10), MomentumObject4D(pt=25, phi=0, eta=-2.2, mass=10)]\n",
+      "-3.141592653589793\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import vector\n",
+    "\n",
+    "\n",
+    "def plot_particles(particles, title=\"\"):\n",
+    "    colors = [\"b\", \"g\", \"r\", \"c\", \"m\", \"y\"]\n",
+    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "    for idx, particle in enumerate(particles):\n",
+    "        ax.quiver(\n",
+    "            0,\n",
+    "            0,\n",
+    "            particle.pz,\n",
+    "            particle.px,\n",
+    "            angles=\"xy\",\n",
+    "            scale_units=\"xy\",\n",
+    "            scale=5,\n",
+    "            color=colors[idx],\n",
+    "        )\n",
+    "    ax.set_xlim(-100, 100)\n",
+    "    ax.set_ylim(-100, 100)\n",
+    "    ax.axhline(0, color=\"black\", linewidth=0.5)\n",
+    "    ax.axvline(0, color=\"black\", linewidth=0.5)\n",
+    "    ax.set_xlabel(\"x (transverse) [m]\")\n",
+    "    ax.set_ylabel(\"z (along beam) [m]\")\n",
+    "    ax.set_title(title)\n",
+    "\n",
+    "    # Adding colored labels\n",
+    "    for idx, particle in enumerate(particles):\n",
+    "        label = f\"Particle {idx+1}: pt: {particle.pt:.2f} GeV, eta: {particle.eta:.2f}, m: {particle.M:.2f} GeV\"\n",
+    "        props = dict(boxstyle=\"round\", facecolor=\"wheat\", alpha=0.5)\n",
+    "        ax.text(\n",
+    "            0.05,\n",
+    "            0.95 - 0.05 * idx,\n",
+    "            label,\n",
+    "            transform=ax.transAxes,\n",
+    "            fontsize=8,\n",
+    "            verticalalignment=\"top\",\n",
+    "            bbox=props,\n",
+    "            color=colors[idx],\n",
+    "        )\n",
+    "\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Define particles using pt, eta, phi, and m\n",
+    "particles = [\n",
+    "    vector.obj(pt=200, eta=1, phi=0, M=100),\n",
+    "    vector.obj(pt=250, eta=-1.1, phi=0, M=100),\n",
+    "    vector.obj(pt=30, eta=1.5, phi=0, M=10),\n",
+    "    vector.obj(pt=40, eta=-1.3, phi=0, M=10),\n",
+    "    vector.obj(pt=20, eta=2, phi=0, M=10),\n",
+    "    vector.obj(pt=25, eta=-2.2, phi=0, M=10),\n",
+    "]\n",
+    "\n",
+    "# Plot original particles\n",
+    "plot_particles(particles, title=\"Before Boost\")\n",
+    "\n",
+    "# Compute the combined system four-momentum\n",
+    "system_4vec = sum(particles, vector.obj(pt=0, eta=0, phi=0, M=0))\n",
+    "\n",
+    "# Compute the boost to the center of mass frame\n",
+    "boost_vector = system_4vec\n",
+    "\n",
+    "# Apply the boost to each particle\n",
+    "boosted_particles = [particle.boostCM_of_p4(boost_vector) for particle in particles]\n",
+    "\n",
+    "# Plot boosted particles\n",
+    "plot_particles(boosted_particles, title=\"After Boost\")\n",
+    "\n",
+    "print(system_4vec)\n",
+    "print(particles)\n",
+    "print(boosted_particles[5].phi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have implemented both, we need to confirm that our system momuentum calculation is correct by checking. sometyhing is off about the mass. Then we should alse try to do everything without having to convert into 4momentum. THen we can implement it and test to make sure its doing the same as the vector method and if it actually provides a speed up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "doting [ 0.57515279  0.         -0.11384546] 0.3437615249040406\n",
+      "[  4.29383884   0.         273.77821941]\n",
+      "(4.293838836820981, 0.0, 4.848345862873094, 100.00000000000007)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def to_four_momentum(pt, phi, eta, m):\n",
+    "    px = pt * np.cos(phi)\n",
+    "    py = pt * np.sin(phi)\n",
+    "    pz = pt * np.sinh(eta)\n",
+    "    E = np.sqrt(m**2 + px**2 + py**2 + pz**2)\n",
+    "    return E, px, py, pz\n",
+    "\n",
+    "\n",
+    "def lorentz_transform(p, boost_vector):\n",
+    "    gamma = 1 / np.sqrt(1 - np.dot(boost_vector, boost_vector))\n",
+    "    print(\"doting\", boost_vector, np.dot(boost_vector, boost_vector))\n",
+    "    E, px, py, pz = p\n",
+    "    E_prime = gamma * (E - np.dot(p[1:], boost_vector))\n",
+    "    spatial_part = (\n",
+    "        np.array(p[1:])\n",
+    "        + (gamma - 1)\n",
+    "        * np.dot(p[1:], boost_vector)\n",
+    "        / np.dot(boost_vector, boost_vector)\n",
+    "        * boost_vector\n",
+    "        - gamma * E * boost_vector\n",
+    "    )\n",
+    "    print(spatial_part)\n",
+    "    return E_prime, *spatial_part\n",
+    "\n",
+    "\n",
+    "def from_four_momentum(E, px, py, pz):\n",
+    "    pt = np.sqrt(px**2 + py**2)\n",
+    "    phi = np.arctan2(py, px)\n",
+    "    theta = np.arccos(pz / np.sqrt(px**2 + py**2 + pz**2))\n",
+    "    eta = -np.log(np.tan(theta / 2))\n",
+    "    m = np.sqrt(E**2 - (px**2 + py**2 + pz**2))\n",
+    "    return pt, phi, eta, m\n",
+    "\n",
+    "\n",
+    "def boost_particle_to_system_frame(particles, other_particle):\n",
+    "    # Convert given particles to four-momentum\n",
+    "    particles_4momentum = [to_four_momentum(*particle) for particle in particles]\n",
+    "\n",
+    "    # Compute the total 4-momentum of the system\n",
+    "    total_E = sum(p[0] for p in particles_4momentum)\n",
+    "    total_px = sum(p[1] for p in particles_4momentum)\n",
+    "    total_py = sum(p[2] for p in particles_4momentum)\n",
+    "    total_pz = sum(p[3] for p in particles_4momentum)\n",
+    "    boost_vector = np.array([total_px / total_E, total_py / total_E, total_pz / total_E])\n",
+    "\n",
+    "    # Convert other particle to four-momentum\n",
+    "    other_particle_4momentum = to_four_momentum(*other_particle)\n",
+    "\n",
+    "    # Boost the other particle\n",
+    "    boosted_other_particle_4momentum = lorentz_transform(other_particle_4momentum, boost_vector)\n",
+    "\n",
+    "    # Convert the boosted particle back to (pt, phi, eta, m) coordinates\n",
+    "    return from_four_momentum(*boosted_other_particle_4momentum)\n",
+    "\n",
+    "\n",
+    "# Define particles\n",
+    "particles = [\n",
+    "    (200, 0, 1, 100),\n",
+    "    (250, 0, -1.1, 100),\n",
+    "    (30, 0, 1.5, 10),\n",
+    "    (40, 0, -1.3, 10),  # (20, 0, 2, 10),(25, 0, -2.2, 10)\n",
+    "]\n",
+    "\n",
+    "# Define other particle\n",
+    "other_particle = (200, 0, 1, 100)  # (25, 0, -2.2, 10)\n",
+    "\n",
+    "# Boost other particle to the frame of the particle system\n",
+    "boosted_other_particle = boost_particle_to_system_frame(particles, other_particle)\n",
+    "print(boosted_other_particle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Method 1 results:\n",
+      "[ 0.57515279  0.         -0.11384546]\n",
+      "4.293838836820981 0.0 273.77821941033505\n",
+      "Cos(Theta) for vbf1: 0.9998770344139399\n",
+      "Cos(Theta) for vbf2: -0.9983078101890982\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import time\n",
+    "\n",
+    "\n",
+    "def to_four_momentum(pt, phi, eta, m):\n",
+    "    px = pt * np.cos(phi)\n",
+    "    py = pt * np.sin(phi)\n",
+    "    pz = pt * np.sinh(eta)\n",
+    "    E = np.sqrt(m**2 + px**2 + py**2 + pz**2)\n",
+    "    return E, px, py, pz\n",
+    "\n",
+    "\n",
+    "def compute_spatial_part(E, px, py, pz, boost_vector, boost_vector_dot_product):\n",
+    "    gamma = 1.0 / np.sqrt(1.0 - boost_vector_dot_product)\n",
+    "\n",
+    "    # Dot product of momentum and boost vector\n",
+    "    p_dot_v = px * boost_vector[0] + py * boost_vector[1] + pz * boost_vector[2]\n",
+    "\n",
+    "    # Lorentz transformation for the spatial components\n",
+    "    px_prime = (\n",
+    "        px\n",
+    "        + (gamma - 1) * p_dot_v * boost_vector[0] / boost_vector_dot_product\n",
+    "        - gamma * E * boost_vector[0]\n",
+    "    )\n",
+    "    py_prime = (\n",
+    "        py\n",
+    "        + (gamma - 1) * p_dot_v * boost_vector[1] / boost_vector_dot_product\n",
+    "        - gamma * E * boost_vector[1]\n",
+    "    )\n",
+    "    pz_prime = (\n",
+    "        pz\n",
+    "        + (gamma - 1) * p_dot_v * boost_vector[2] / boost_vector_dot_product\n",
+    "        - gamma * E * boost_vector[2]\n",
+    "    )\n",
+    "\n",
+    "    return px_prime, py_prime, pz_prime\n",
+    "\n",
+    "\n",
+    "# Generate example data for a single event\n",
+    "vbf1 = {\"pt\": 200, \"eta\": 1, \"phi\": 0, \"M\": 100}\n",
+    "vbf2 = {\"pt\": 250, \"eta\": -1.1, \"phi\": 0, \"M\": 100}\n",
+    "VVJet = {\"pt\": 30, \"eta\": 1.5, \"phi\": 0, \"M\": 10}\n",
+    "bbJet = {\"pt\": 40, \"eta\": -1.3, \"phi\": 0, \"M\": 10}\n",
+    "\n",
+    "start_time = time.time()\n",
+    "\n",
+    "# Method 1\n",
+    "E1, px1, py1, pz1 = to_four_momentum(vbf1[\"pt\"], vbf1[\"phi\"], vbf1[\"eta\"], vbf1[\"M\"])\n",
+    "E2, px2, py2, pz2 = to_four_momentum(vbf2[\"pt\"], vbf2[\"phi\"], vbf2[\"eta\"], vbf2[\"M\"])\n",
+    "EVV, pxVV, pyVV, pzVV = to_four_momentum(VVJet[\"pt\"], VVJet[\"phi\"], VVJet[\"eta\"], VVJet[\"M\"])\n",
+    "Ebb, pxbb, pybb, pzbb = to_four_momentum(bbJet[\"pt\"], bbJet[\"phi\"], bbJet[\"eta\"], bbJet[\"M\"])\n",
+    "\n",
+    "total_E = EVV + Ebb + E1 + E2\n",
+    "total_px = pxVV + pxbb + px1 + px2\n",
+    "total_py = pyVV + pybb + py1 + py2\n",
+    "total_pz = pzVV + pzbb + pz1 + pz2\n",
+    "CM_boost_vector = np.array([total_px / total_E, total_py / total_E, total_pz / total_E])\n",
+    "boost_vector_dot_product = (\n",
+    "    CM_boost_vector[0] ** 2 + CM_boost_vector[1] ** 2 + CM_boost_vector[2] ** 2\n",
+    ")\n",
+    "\n",
+    "px1_boosted, py1_boosted, pz1_boosted = compute_spatial_part(\n",
+    "    E1, px1, py1, pz1, CM_boost_vector, boost_vector_dot_product\n",
+    ")\n",
+    "px2_boosted, py2_boosted, pz2_boosted = compute_spatial_part(\n",
+    "    E2, px2, py2, pz2, CM_boost_vector, boost_vector_dot_product\n",
+    ")\n",
+    "\n",
+    "theta1 = np.arccos(pz1_boosted / np.sqrt(px1_boosted**2 + py1_boosted**2 + pz1_boosted**2))\n",
+    "theta2 = np.arccos(pz2_boosted / np.sqrt(px2_boosted**2 + py2_boosted**2 + pz2_boosted**2))\n",
+    "\n",
+    "print(\"Method 1 results:\")\n",
+    "print(CM_boost_vector)\n",
+    "print(px1_boosted, py1_boosted, pz1_boosted)\n",
+    "print(\"Cos(Theta) for vbf1:\", np.cos(theta1))\n",
+    "print(\"Cos(Theta) for vbf2:\", np.cos(theta2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Method 2 results:\n",
+      "Cos(Theta) for vbf1: 0.9998770344139398\n",
+      "Cos(Theta) for vbf2: -0.9983078101890981\n"
+     ]
+    }
+   ],
+   "source": [
+    "import vector\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "# Define a function to convert pt, eta, phi, mass to a vector object\n",
+    "def to_vector(pt, eta, phi, mass):\n",
+    "    return vector.obj(pt=pt, eta=eta, phi=phi, mass=mass)\n",
+    "\n",
+    "\n",
+    "# Generate example data for a single event\n",
+    "vbf1 = to_vector(200, 1, 0, 100)\n",
+    "vbf2 = to_vector(250, -1.1, 0, 100)\n",
+    "VVJet = to_vector(30, 1.5, 0, 10)\n",
+    "bbJet = to_vector(40, -1.3, 0, 10)\n",
+    "\n",
+    "# Create a combined 4-momentum for the system\n",
+    "system_4vec = vbf1 + vbf2 + VVJet + bbJet\n",
+    "\n",
+    "# Boost the vectors into the center-of-mass frame of the system\n",
+    "j1_CMF = vbf1.boostCM_of_p4(system_4vec)\n",
+    "j2_CMF = vbf2.boostCM_of_p4(system_4vec)\n",
+    "\n",
+    "# Calculate the theta values using the boosted eta\n",
+    "thetab1 = 2 * np.arctan(np.exp(-j1_CMF.eta))\n",
+    "thetab2 = 2 * np.arctan(np.exp(-j2_CMF.eta))\n",
+    "\n",
+    "print(\"Method 2 results:\")\n",
+    "print(\"Cos(Theta) for vbf1:\", np.cos(thetab1))\n",
+    "print(\"Cos(Theta) for vbf2:\", np.cos(thetab2))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cofenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/VBF_binder/VBFgenInfoTests.ipynb b/src/HHbbVV/VBF_binder/VBFgenInfoTests.ipynb
new file mode 100644
index 00000000..a44a88e5
--- /dev/null
+++ b/src/HHbbVV/VBF_binder/VBFgenInfoTests.ipynb
@@ -0,0 +1,4553 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7b82bb87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 16})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cc28e06",
+   "metadata": {},
+   "source": [
+    "In this file we will be testing our selection process using the vbf generator information. We will first be investigating whether gen jets make sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "42e7add7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsEta</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsPhi</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsMass</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsPt</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsChildren</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">lp_sf_sys_down</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">lp_sf_sys_up</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">lp_sf_double_matched_event</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">lp_sf_unmatched_quarks</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">lp_sf_num_sjpt_gt350</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
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+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.646484</td>\n",
+       "      <td>-1.164062</td>\n",
+       "      <td>-2.265625</td>\n",
+       "      <td>1.332031</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>634.00</td>\n",
+       "      <td>588.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.013158e+00</td>\n",
+       "      <td>1.756670e-01</td>\n",
+       "      <td>5.813827</td>\n",
+       "      <td>5.763737</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.253906</td>\n",
+       "      <td>-0.190430</td>\n",
+       "      <td>2.023438</td>\n",
+       "      <td>-1.207031</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>109.25</td>\n",
+       "      <td>425.00</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.621220e-01</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.646291</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.319336</td>\n",
+       "      <td>0.796875</td>\n",
+       "      <td>-1.656250</td>\n",
+       "      <td>1.042969</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>646.00</td>\n",
+       "      <td>554.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.953031e-21</td>\n",
+       "      <td>1.416126e-10</td>\n",
+       "      <td>5.274429</td>\n",
+       "      <td>3.219763</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.257812</td>\n",
+       "      <td>0.925781</td>\n",
+       "      <td>1.949219</td>\n",
+       "      <td>-0.402344</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>435.00</td>\n",
+       "      <td>304.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.056727e-01</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>0.587374</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.724609</td>\n",
+       "      <td>0.662109</td>\n",
+       "      <td>1.964844</td>\n",
+       "      <td>-0.525391</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>461.00</td>\n",
+       "      <td>361.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>23</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.437584e-24</td>\n",
+       "      <td>2.414727e-02</td>\n",
+       "      <td>601.768127</td>\n",
+       "      <td>1.070159</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>0.072754</td>\n",
+       "      <td>3.882812</td>\n",
+       "      <td>1.691406</td>\n",
+       "      <td>-0.413086</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>486.00</td>\n",
+       "      <td>49.75</td>\n",
+       "      <td>5</td>\n",
+       "      <td>23</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000e+00</td>\n",
+       "      <td>5.982143e-42</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.583039</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>571</th>\n",
+       "      <td>-0.120117</td>\n",
+       "      <td>0.640625</td>\n",
+       "      <td>2.671875</td>\n",
+       "      <td>-0.479492</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>403.00</td>\n",
+       "      <td>411.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.933920e-01</td>\n",
+       "      <td>2.933717e-01</td>\n",
+       "      <td>7.860237</td>\n",
+       "      <td>1.031016</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>572</th>\n",
+       "      <td>0.890625</td>\n",
+       "      <td>-0.015869</td>\n",
+       "      <td>0.773438</td>\n",
+       "      <td>-2.445312</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>442.00</td>\n",
+       "      <td>357.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.317262e-01</td>\n",
+       "      <td>1.186648e-15</td>\n",
+       "      <td>1.136776</td>\n",
+       "      <td>68.272476</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>-0.190430</td>\n",
+       "      <td>-0.373047</td>\n",
+       "      <td>-2.484375</td>\n",
+       "      <td>1.152344</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>431.00</td>\n",
+       "      <td>386.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.479136e-01</td>\n",
+       "      <td>1.080385e-10</td>\n",
+       "      <td>1.354620</td>\n",
+       "      <td>0.102750</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>574</th>\n",
+       "      <td>0.816406</td>\n",
+       "      <td>0.475586</td>\n",
+       "      <td>0.424805</td>\n",
+       "      <td>-2.781250</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>413.00</td>\n",
+       "      <td>379.00</td>\n",
+       "      <td>5</td>\n",
+       "      <td>23</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.576050e-09</td>\n",
+       "      <td>2.144067e-02</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.855242</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>575 rows × 388 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    GenHiggsEta           GenHiggsPhi           GenHiggsMass         \\\n",
+       "              0         1           0         1            0      1   \n",
+       "0     -0.646484 -1.164062   -2.265625  1.332031        125.0  125.0   \n",
+       "1      1.253906 -0.190430    2.023438 -1.207031        125.0  125.0   \n",
+       "2      0.319336  0.796875   -1.656250  1.042969        125.0  125.0   \n",
+       "3      0.257812  0.925781    1.949219 -0.402344        125.0  125.0   \n",
+       "4      0.724609  0.662109    1.964844 -0.525391        125.0  125.0   \n",
+       "..          ...       ...         ...       ...          ...    ...   \n",
+       "570    0.072754  3.882812    1.691406 -0.413086        125.0  125.0   \n",
+       "571   -0.120117  0.640625    2.671875 -0.479492        125.0  125.0   \n",
+       "572    0.890625 -0.015869    0.773438 -2.445312        125.0  125.0   \n",
+       "573   -0.190430 -0.373047   -2.484375  1.152344        125.0  125.0   \n",
+       "574    0.816406  0.475586    0.424805 -2.781250        125.0  125.0   \n",
+       "\n",
+       "    GenHiggsPt         GenHiggsChildren      ... lp_sf_sys_down                \\\n",
+       "             0       1                0   1  ...              0             1   \n",
+       "0       634.00  588.00                5  24  ...   1.013158e+00  1.756670e-01   \n",
+       "1       109.25  425.00               24   5  ...   2.621220e-01  1.000000e+00   \n",
+       "2       646.00  554.00                5  24  ...   6.953031e-21  1.416126e-10   \n",
+       "3       435.00  304.00                5  24  ...   1.056727e-01  1.000000e+00   \n",
+       "4       461.00  361.00                5  23  ...   7.437584e-24  2.414727e-02   \n",
+       "..         ...     ...              ...  ..  ...            ...           ...   \n",
+       "570     486.00   49.75                5  23  ...   1.000000e+00  5.982143e-42   \n",
+       "571     403.00  411.00                5  24  ...   8.933920e-01  2.933717e-01   \n",
+       "572     442.00  357.00                5  24  ...   1.317262e-01  1.186648e-15   \n",
+       "573     431.00  386.00                5  24  ...   1.479136e-01  1.080385e-10   \n",
+       "574     413.00  379.00                5  23  ...   3.576050e-09  2.144067e-02   \n",
+       "\n",
+       "    lp_sf_sys_up            lp_sf_double_matched_event       \\\n",
+       "               0          1                          0    1   \n",
+       "0       5.813827   5.763737                        0.0  0.0   \n",
+       "1       0.646291   1.000000                        0.0  1.0   \n",
+       "2       5.274429   3.219763                        0.0  0.0   \n",
+       "3       0.587374   1.000000                        0.0  1.0   \n",
+       "4     601.768127   1.070159                        0.0  0.0   \n",
+       "..           ...        ...                        ...  ...   \n",
+       "570     1.000000   1.583039                        1.0  0.0   \n",
+       "571     7.860237   1.031016                        0.0  0.0   \n",
+       "572     1.136776  68.272476                        0.0  0.0   \n",
+       "573     1.354620   0.102750                        0.0  0.0   \n",
+       "574     0.000000   0.855242                        0.0  0.0   \n",
+       "\n",
+       "    lp_sf_unmatched_quarks      lp_sf_num_sjpt_gt350       \n",
+       "                         0    1                    0    1  \n",
+       "0                      0.0  0.0                  0.0  0.0  \n",
+       "1                      1.0  1.0                  0.0  1.0  \n",
+       "2                      0.0  1.0                  1.0  1.0  \n",
+       "3                      0.0  1.0                  0.0  1.0  \n",
+       "4                      0.0  1.0                  1.0  0.0  \n",
+       "..                     ...  ...                  ...  ...  \n",
+       "570                    1.0  0.0                  1.0  1.0  \n",
+       "571                    0.0  0.0                  0.0  0.0  \n",
+       "572                    0.0  0.0                  0.0  0.0  \n",
+       "573                    0.0  1.0                  0.0  0.0  \n",
+       "574                    0.0  0.0                  0.0  0.0  \n",
+       "\n",
+       "[575 rows x 388 columns]"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_parquet(\"0-2.parquet\")\n",
+    "\n",
+    "df.columns.values.tolist()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5c8862d",
+   "metadata": {},
+   "source": [
+    "bbSkimmer modified gives us the two VBF jets which we will be able to access and manipulate. it also gives us the results of cuts on the events (there needs to be atleast 2 VBF jets). We can apply these filters and graph the kinematic variables. We can also record the before and after applying these cuts. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "3cbe320d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(575, 388)\n",
+      "thing 0    7746.732114\n",
+      "dtype: float64 0    579.744052\n",
+      "dtype: float64\n",
+      "tsts 0    1702.294919\n",
+      "dtype: float64 0    127.395054\n",
+      "dtype: float64\n",
+      "0    63.697527\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply lepton veto selections ($N_\\mu =0$ and $N_e=0$)\n",
+    "print(np.shape(df))\n",
+    "df_em = df[(df[(\"nGoodMuons\", 0)] == 0) & (df[(\"nGoodElectrons\", 0)] == 0)]\n",
+    "\n",
+    "# 2 vbf jets\n",
+    "# df_vbf = df[ (df[('nGoodVBFJetsUnsorted', 0)] >= 2)]\n",
+    "\n",
+    "# lepton veto and 2 vbf jets\n",
+    "# df_sorted_Rand = df[(df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJetsUnsorted', 0)] >= 2)& (df[('nGoodJets', 0)] == 0)]\n",
+    "df_sorted_pt = df[\n",
+    "    (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "    & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "    & (df[(\"nGoodVBFJetsSortedpt\", 0)] >= 2)\n",
+    "    & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "]\n",
+    "# df_sorted_M = df[(df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJetsSortedM', 0)] >= 2)& (df[('nGoodJets', 0)] == 0)]\n",
+    "# df_sorted_eta = df[(df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJetsSortedeta', 0)] >= 2)& (df[('nGoodJets', 0)] == 0)]\n",
+    "\n",
+    "\n",
+    "# generate all variables that are needed for the ak8 jet selections in VBF HH4b paper.\n",
+    "# df[('DijetDeltaPhi', 0)] = np.abs(df[('ak8FatJetPhi', 0)] - df[('ak8FatJetPhi', 1)])\n",
+    "# df[('DijetDeltaEta', 0)] = np.abs(df[('ak8FatJetEta', 0)] - df[('ak8FatJetEta', 1)])\n",
+    "# print(np.shape(df)[0],np.shape(df_sorted_Rand)[0],np.shape(df_sorted_pt)[0],np.shape(df_sorted_M)[0],np.shape(df_sorted_eta)[0])\n",
+    "print(\"thing\", np.sum(df[\"weight\"]), np.sum(df[\"weight_noxsec\"]))\n",
+    "print(\"tsts\", np.sum(df_sorted_pt[\"weight\"]), np.sum(df_sorted_pt[\"weight_noxsec\"]))\n",
+    "df_sorted_pt\n",
+    "print(np.sum(df_sorted_pt[\"weight_noxsec\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ebb9216",
+   "metadata": {},
+   "source": [
+    "Graphing the variables for our matched jets: These jets have had vbf selections applied but not the HVV tagging yet. file doesn't have output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eda2e379",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sorting method: Rand\n",
+      "Number of events: 2917\n",
+      "| Index | Jet | dR to gen quark 1 | dR to gen quark 2 |\n",
+      "| --- | --- | --- | --- |\n",
+      "| 3 | Jet 1 | 1.05 | 3.55 |\n",
+      "| 3 | Jet 2 | 0.33 | 4.92 |\n",
+      "| 5 | Jet 1 | 0.02 | 4.27 |\n",
+      "| 5 | Jet 2 | 2.45 | 1.82 |\n",
+      "| 10 | Jet 1 | 6.16 | 0.23 |\n",
+      "| 10 | Jet 2 | 0.27 | 6.11 |\n",
+      "| 12 | Jet 1 | 3.08 | 6.13 |\n",
+      "| 12 | Jet 2 | 2.79 | 5.41 |\n",
+      "| 13 | Jet 1 | 0.03 | 2.60 |\n",
+      "| 13 | Jet 2 | 2.72 | 0.11 |\n",
+      "| 14 | Jet 1 | 4.33 | 0.01 |\n",
+      "| 14 | Jet 2 | 0.16 | 4.19 |\n",
+      "| 16 | Jet 1 | 0.07 | 8.31 |\n",
+      "| 16 | Jet 2 | 6.06 | 2.45 |\n",
+      "| 18 | Jet 1 | 0.76 | 6.75 |\n",
+      "| 18 | Jet 2 | 6.30 | 0.07 |\n",
+      "| 19 | Jet 1 | 7.69 | 0.06 |\n",
+      "| 19 | Jet 2 | 0.11 | 7.81 |\n",
+      "| 20 | Jet 1 | 0.04 | 6.91 |\n",
+      "| 20 | Jet 2 | 6.95 | 0.21 |\n",
+      "\n",
+      "Number of events with exactly 0 dR scores < 0.4: 579\n",
+      "Number of events with exactly 1 dR score < 0.4: 1157\n",
+      "Number of events with exactly 2 dR scores < 0.4: 1181\n",
+      "\n",
+      "Sorting method: pt\n",
+      "Number of events: 1452\n",
+      "| Index | Jet | dR to gen quark 1 | dR to gen quark 2 |\n",
+      "| --- | --- | --- | --- |\n",
+      "| 10 | Jet 1 | 6.16 | 0.23 |\n",
+      "| 10 | Jet 2 | 0.27 | 6.11 |\n",
+      "| 16 | Jet 1 | 0.07 | 8.31 |\n",
+      "| 16 | Jet 2 | 6.06 | 2.45 |\n",
+      "| 18 | Jet 1 | 6.30 | 0.07 |\n",
+      "| 18 | Jet 2 | 0.76 | 6.75 |\n",
+      "| 19 | Jet 1 | 7.69 | 0.06 |\n",
+      "| 19 | Jet 2 | 0.11 | 7.81 |\n",
+      "| 20 | Jet 1 | 0.04 | 6.91 |\n",
+      "| 20 | Jet 2 | 6.95 | 0.21 |\n",
+      "| 27 | Jet 1 | 0.21 | 4.89 |\n",
+      "| 27 | Jet 2 | 5.34 | 0.46 |\n",
+      "| 36 | Jet 1 | 4.16 | 2.02 |\n",
+      "| 36 | Jet 2 | 0.73 | 4.06 |\n",
+      "| 43 | Jet 1 | 5.07 | 0.06 |\n",
+      "| 43 | Jet 2 | 0.15 | 5.15 |\n",
+      "| 53 | Jet 1 | 6.57 | 0.15 |\n",
+      "| 53 | Jet 2 | 0.11 | 6.53 |\n",
+      "| 63 | Jet 1 | 0.06 | 7.90 |\n",
+      "| 63 | Jet 2 | 7.71 | 0.13 |\n",
+      "\n",
+      "Number of events with exactly 0 dR scores < 0.4: 51\n",
+      "Number of events with exactly 1 dR score < 0.4: 400\n",
+      "Number of events with exactly 2 dR scores < 0.4: 1001\n",
+      "\n",
+      "Sorting method: M\n",
+      "Number of events: 1712\n",
+      "| Index | Jet | dR to gen quark 1 | dR to gen quark 2 |\n",
+      "| --- | --- | --- | --- |\n",
+      "| 10 | Jet 1 | 6.16 | 0.23 |\n",
+      "| 10 | Jet 2 | 0.27 | 6.11 |\n",
+      "| 16 | Jet 1 | 0.07 | 8.31 |\n",
+      "| 16 | Jet 2 | 6.06 | 2.45 |\n",
+      "| 18 | Jet 1 | 6.30 | 0.07 |\n",
+      "| 18 | Jet 2 | 0.76 | 6.75 |\n",
+      "| 19 | Jet 1 | 7.69 | 0.06 |\n",
+      "| 19 | Jet 2 | 0.11 | 7.81 |\n",
+      "| 20 | Jet 1 | 0.04 | 6.91 |\n",
+      "| 20 | Jet 2 | 6.95 | 0.21 |\n",
+      "| 27 | Jet 1 | 0.21 | 4.89 |\n",
+      "| 27 | Jet 2 | 5.34 | 0.46 |\n",
+      "| 28 | Jet 1 | 0.01 | 3.41 |\n",
+      "| 28 | Jet 2 | 5.17 | 2.24 |\n",
+      "| 36 | Jet 1 | 4.16 | 2.02 |\n",
+      "| 36 | Jet 2 | 0.73 | 4.06 |\n",
+      "| 43 | Jet 1 | 5.07 | 0.06 |\n",
+      "| 43 | Jet 2 | 0.15 | 5.15 |\n",
+      "| 53 | Jet 1 | 6.57 | 0.15 |\n",
+      "| 53 | Jet 2 | 0.11 | 6.53 |\n",
+      "\n",
+      "Number of events with exactly 0 dR scores < 0.4: 111\n",
+      "Number of events with exactly 1 dR score < 0.4: 594\n",
+      "Number of events with exactly 2 dR scores < 0.4: 1007\n",
+      "\n",
+      "Sorting method: eta\n",
+      "Number of events: 1733\n",
+      "| Index | Jet | dR to gen quark 1 | dR to gen quark 2 |\n",
+      "| --- | --- | --- | --- |\n",
+      "| 10 | Jet 1 | 6.16 | 0.23 |\n",
+      "| 10 | Jet 2 | 0.27 | 6.11 |\n",
+      "| 16 | Jet 1 | 0.07 | 8.31 |\n",
+      "| 16 | Jet 2 | 6.06 | 2.45 |\n",
+      "| 18 | Jet 1 | 6.30 | 0.07 |\n",
+      "| 18 | Jet 2 | 0.76 | 6.75 |\n",
+      "| 19 | Jet 1 | 7.69 | 0.06 |\n",
+      "| 19 | Jet 2 | 0.11 | 7.81 |\n",
+      "| 20 | Jet 1 | 0.04 | 6.91 |\n",
+      "| 20 | Jet 2 | 6.95 | 0.21 |\n",
+      "| 27 | Jet 1 | 0.21 | 4.89 |\n",
+      "| 27 | Jet 2 | 5.34 | 0.46 |\n",
+      "| 28 | Jet 1 | 0.01 | 3.41 |\n",
+      "| 28 | Jet 2 | 5.17 | 2.24 |\n",
+      "| 36 | Jet 1 | 4.16 | 2.02 |\n",
+      "| 36 | Jet 2 | 0.73 | 4.06 |\n",
+      "| 43 | Jet 1 | 5.07 | 0.06 |\n",
+      "| 43 | Jet 2 | 0.15 | 5.15 |\n",
+      "| 53 | Jet 1 | 6.57 | 0.15 |\n",
+      "| 53 | Jet 2 | 0.11 | 6.53 |\n",
+      "\n",
+      "Number of events with exactly 0 dR scores < 0.4: 146\n",
+      "Number of events with exactly 1 dR score < 0.4: 654\n",
+      "Number of events with exactly 2 dR scores < 0.4: 933\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from PIL import Image\n",
+    "import io\n",
+    "\n",
+    "\n",
+    "# Define a function to calculate delta R\n",
+    "def deltaR(eta1, phi1, eta2, phi2):\n",
+    "    deta = eta1 - eta2\n",
+    "    dphi = np.abs(phi1 - phi2)\n",
+    "    dphi = np.where(dphi < np.pi, dphi, 2 * np.pi - dphi)\n",
+    "    return np.sqrt(deta**2 + dphi**2)\n",
+    "\n",
+    "\n",
+    "# Calculate delta R for each sorting method and add it to the DataFrame\n",
+    "for df, method in zip(\n",
+    "    [df_sorted_Rand, df_sorted_pt, df_sorted_M, df_sorted_eta], [\"Rand\", \"pt\", \"M\", \"eta\"]\n",
+    "):\n",
+    "    df[\"deltaR_1_to_0\"] = deltaR(\n",
+    "        df[(\"vbfetaGen\", 0)],\n",
+    "        df[(\"vbfphiGen\", 0)],\n",
+    "        df[(\"vbfetaSorted\" + method, 0)],\n",
+    "        df[(\"vbfphiSorted\" + method, 0)],\n",
+    "    )\n",
+    "    df[\"deltaR_1_to_1\"] = deltaR(\n",
+    "        df[(\"vbfetaGen\", 1)],\n",
+    "        df[(\"vbfphiGen\", 1)],\n",
+    "        df[(\"vbfetaSorted\" + method, 0)],\n",
+    "        df[(\"vbfphiSorted\" + method, 0)],\n",
+    "    )\n",
+    "    df[\"deltaR_2_to_0\"] = deltaR(\n",
+    "        df[(\"vbfetaGen\", 0)],\n",
+    "        df[(\"vbfphiGen\", 0)],\n",
+    "        df[(\"vbfetaSorted\" + method, 1)],\n",
+    "        df[(\"vbfphiSorted\" + method, 1)],\n",
+    "    )\n",
+    "    df[\"deltaR_2_to_1\"] = deltaR(\n",
+    "        df[(\"vbfetaGen\", 1)],\n",
+    "        df[(\"vbfphiGen\", 1)],\n",
+    "        df[(\"vbfetaSorted\" + method, 1)],\n",
+    "        df[(\"vbfphiSorted\" + method, 1)],\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Define a function to create the required table\n",
+    "def display_table(df, method):\n",
+    "    # Initialize a string to store the table\n",
+    "    table_string = f\"Sorting method: {method}\\n\"\n",
+    "    table_string += f\"Number of events: {len(df)}\\n\"\n",
+    "    table_string += \"| Index | Jet | dR to gen quark 1 | dR to gen quark 2 |\\n\"\n",
+    "    table_string += \"| --- | --- | --- | --- |\\n\"\n",
+    "\n",
+    "    # Add each row to the table string\n",
+    "    for index in df.index[:10]:\n",
+    "        table_string += f\"| {index} | Jet 1 | {df['deltaR_1_to_0'][index]:.2f} | {df['deltaR_1_to_1'][index]:.2f} |\\n\"\n",
+    "        table_string += f\"| {index} | Jet 2 | {df['deltaR_2_to_0'][index]:.2f} | {df['deltaR_2_to_1'][index]:.2f} |\\n\"\n",
+    "\n",
+    "    # Calculate the summary information\n",
+    "    dR_cols = [\"deltaR_1_to_0\", \"deltaR_1_to_1\", \"deltaR_2_to_0\", \"deltaR_2_to_1\"]\n",
+    "    counts = (df[dR_cols] < 0.4).sum(axis=1).value_counts()\n",
+    "\n",
+    "    # Add the summary information to the table string\n",
+    "    table_string += f\"\\nNumber of events with exactly 0 dR scores < 0.4: {counts.get(0, 0)}\\n\"\n",
+    "    table_string += f\"Number of events with exactly 1 dR score < 0.4: {counts.get(1, 0)}\\n\"\n",
+    "    table_string += f\"Number of events with exactly 2 dR scores < 0.4: {counts.get(2, 0)}\\n\"\n",
+    "\n",
+    "    # Display the table string\n",
+    "    print(table_string)\n",
+    "\n",
+    "\n",
+    "# Display tables for each DataFrame\n",
+    "display_table(df_sorted_Rand, \"Rand\")\n",
+    "display_table(df_sorted_pt, \"pt\")\n",
+    "display_table(df_sorted_M, \"M\")\n",
+    "display_table(df_sorted_eta, \"eta\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20a68cca",
+   "metadata": {},
+   "source": [
+    "Defining some data that we used to study how vbf jet matching at different selection stages. R1 vs R2 graphs (looking at fatjet exclusion cut effects)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "ef72be48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test the functions with a sample string\n",
+    "log_string = \"\"\"\n",
+    "WARNING:root:[928, 4122, 4381, 9600, 0.6, 0.6]\n",
+    "WARNING:root:[929, 4121, 4381, 9600, 0.6, 0.7]\n",
+    "WARNING:root:[929, 4121, 4381, 9600, 0.6, 0.8]\n",
+    "WARNING:root:[929, 4121, 4381, 9600, 0.6, 0.9]\n",
+    "WARNING:root:[930, 4121, 4380, 9600, 0.6, 1.0]\n",
+    "WARNING:root:[933, 4120, 4378, 9600, 0.6, 1.1]\n",
+    "WARNING:root:[933, 4121, 4377, 9600, 0.6, 1.2]\n",
+    "WARNING:root:[934, 4121, 4376, 9600, 0.6, 1.3]\n",
+    "WARNING:root:[937, 4119, 4375, 9600, 0.6, 1.4]\n",
+    "WARNING:root:[940, 4118, 4373, 9600, 0.6, 1.5]\n",
+    "WARNING:root:[943, 4115, 4373, 9600, 0.6, 1.6]\n",
+    "WARNING:root:[943, 4121, 4367, 9600, 0.6, 1.7]\n",
+    "WARNING:root:[947, 4120, 4365, 9600, 0.6, 1.8]\n",
+    "WARNING:root:[951, 4120, 4361, 9600, 0.6, 1.9]\n",
+    "WARNING:root:[930, 4122, 4379, 9600, 0.7, 0.6]\n",
+    "WARNING:root:[931, 4121, 4379, 9600, 0.7, 0.7]\n",
+    "WARNING:root:[931, 4121, 4379, 9600, 0.7, 0.8]\n",
+    "WARNING:root:[931, 4121, 4379, 9600, 0.7, 0.9]\n",
+    "WARNING:root:[932, 4121, 4378, 9600, 0.7, 1.0]\n",
+    "WARNING:root:[935, 4120, 4376, 9600, 0.7, 1.1]\n",
+    "WARNING:root:[935, 4121, 4375, 9600, 0.7, 1.2]\n",
+    "WARNING:root:[936, 4121, 4374, 9600, 0.7, 1.3]\n",
+    "WARNING:root:[939, 4119, 4373, 9600, 0.7, 1.4]\n",
+    "WARNING:root:[942, 4118, 4371, 9600, 0.7, 1.5]\n",
+    "WARNING:root:[945, 4115, 4371, 9600, 0.7, 1.6]\n",
+    "WARNING:root:[945, 4121, 4365, 9600, 0.7, 1.7]\n",
+    "WARNING:root:[949, 4120, 4363, 9600, 0.7, 1.8]\n",
+    "WARNING:root:[953, 4120, 4359, 9600, 0.7, 1.9]\n",
+    "WARNING:root:[931, 4123, 4377, 9600, 0.8, 0.6]\n",
+    "WARNING:root:[932, 4122, 4377, 9600, 0.8, 0.7]\n",
+    "WARNING:root:[932, 4122, 4377, 9600, 0.8, 0.8]\n",
+    "WARNING:root:[932, 4122, 4377, 9600, 0.8, 0.9]\n",
+    "WARNING:root:[933, 4122, 4376, 9600, 0.8, 1.0]\n",
+    "WARNING:root:[936, 4121, 4374, 9600, 0.8, 1.1]\n",
+    "WARNING:root:[936, 4122, 4373, 9600, 0.8, 1.2]\n",
+    "WARNING:root:[937, 4122, 4372, 9600, 0.8, 1.3]\n",
+    "WARNING:root:[940, 4120, 4371, 9600, 0.8, 1.4]\n",
+    "WARNING:root:[943, 4119, 4369, 9600, 0.8, 1.5]\n",
+    "WARNING:root:[946, 4116, 4369, 9600, 0.8, 1.6]\n",
+    "WARNING:root:[946, 4122, 4363, 9600, 0.8, 1.7]\n",
+    "WARNING:root:[950, 4121, 4361, 9600, 0.8, 1.8]\n",
+    "WARNING:root:[954, 4121, 4357, 9600, 0.8, 1.9]\n",
+    "WARNING:root:[932, 4123, 4376, 9600, 0.9, 0.6]\n",
+    "WARNING:root:[933, 4122, 4376, 9600, 0.9, 0.7]\n",
+    "WARNING:root:[933, 4122, 4376, 9600, 0.9, 0.8]\n",
+    "WARNING:root:[933, 4122, 4376, 9600, 0.9, 0.9]\n",
+    "WARNING:root:[934, 4122, 4375, 9600, 0.9, 1.0]\n",
+    "WARNING:root:[937, 4121, 4373, 9600, 0.9, 1.1]\n",
+    "WARNING:root:[937, 4122, 4372, 9600, 0.9, 1.2]\n",
+    "WARNING:root:[938, 4122, 4371, 9600, 0.9, 1.3]\n",
+    "WARNING:root:[941, 4120, 4370, 9600, 0.9, 1.4]\n",
+    "WARNING:root:[944, 4119, 4368, 9600, 0.9, 1.5]\n",
+    "WARNING:root:[947, 4116, 4368, 9600, 0.9, 1.6]\n",
+    "WARNING:root:[947, 4122, 4362, 9600, 0.9, 1.7]\n",
+    "WARNING:root:[951, 4121, 4360, 9600, 0.9, 1.8]\n",
+    "WARNING:root:[955, 4121, 4356, 9600, 0.9, 1.9]\n",
+    "WARNING:root:[935, 4121, 4375, 9600, 1.0, 0.6]\n",
+    "WARNING:root:[936, 4120, 4375, 9600, 1.0, 0.7]\n",
+    "WARNING:root:[936, 4120, 4375, 9600, 1.0, 0.8]\n",
+    "WARNING:root:[936, 4120, 4375, 9600, 1.0, 0.9]\n",
+    "WARNING:root:[937, 4120, 4374, 9600, 1.0, 1.0]\n",
+    "WARNING:root:[940, 4119, 4372, 9600, 1.0, 1.1]\n",
+    "WARNING:root:[940, 4120, 4371, 9600, 1.0, 1.2]\n",
+    "WARNING:root:[941, 4120, 4370, 9600, 1.0, 1.3]\n",
+    "WARNING:root:[944, 4118, 4369, 9600, 1.0, 1.4]\n",
+    "WARNING:root:[947, 4117, 4367, 9600, 1.0, 1.5]\n",
+    "WARNING:root:[950, 4114, 4367, 9600, 1.0, 1.6]\n",
+    "WARNING:root:[950, 4120, 4361, 9600, 1.0, 1.7]\n",
+    "WARNING:root:[954, 4119, 4359, 9600, 1.0, 1.8]\n",
+    "WARNING:root:[958, 4119, 4355, 9600, 1.0, 1.9]\n",
+    "WARNING:root:[937, 4121, 4373, 9600, 1.1, 0.6]\n",
+    "WARNING:root:[938, 4120, 4373, 9600, 1.1, 0.7]\n",
+    "WARNING:root:[938, 4120, 4373, 9600, 1.1, 0.8]\n",
+    "WARNING:root:[938, 4120, 4373, 9600, 1.1, 0.9]\n",
+    "WARNING:root:[939, 4120, 4372, 9600, 1.1, 1.0]\n",
+    "WARNING:root:[942, 4119, 4370, 9600, 1.1, 1.1]\n",
+    "WARNING:root:[942, 4120, 4369, 9600, 1.1, 1.2]\n",
+    "WARNING:root:[943, 4120, 4368, 9600, 1.1, 1.3]\n",
+    "WARNING:root:[946, 4118, 4367, 9600, 1.1, 1.4]\n",
+    "WARNING:root:[949, 4117, 4365, 9600, 1.1, 1.5]\n",
+    "WARNING:root:[952, 4114, 4365, 9600, 1.1, 1.6]\n",
+    "WARNING:root:[952, 4120, 4359, 9600, 1.1, 1.7]\n",
+    "WARNING:root:[956, 4119, 4357, 9600, 1.1, 1.8]\n",
+    "WARNING:root:[960, 4119, 4353, 9600, 1.1, 1.9]\n",
+    "WARNING:root:[939, 4120, 4372, 9600, 1.2, 0.6]\n",
+    "WARNING:root:[940, 4119, 4372, 9600, 1.2, 0.7]\n",
+    "WARNING:root:[940, 4119, 4372, 9600, 1.2, 0.8]\n",
+    "WARNING:root:[940, 4119, 4372, 9600, 1.2, 0.9]\n",
+    "WARNING:root:[941, 4119, 4371, 9600, 1.2, 1.0]\n",
+    "WARNING:root:[944, 4118, 4369, 9600, 1.2, 1.1]\n",
+    "WARNING:root:[944, 4119, 4368, 9600, 1.2, 1.2]\n",
+    "WARNING:root:[945, 4119, 4367, 9600, 1.2, 1.3]\n",
+    "WARNING:root:[948, 4117, 4366, 9600, 1.2, 1.4]\n",
+    "WARNING:root:[951, 4116, 4364, 9600, 1.2, 1.5]\n",
+    "WARNING:root:[954, 4113, 4364, 9600, 1.2, 1.6]\n",
+    "WARNING:root:[954, 4119, 4358, 9600, 1.2, 1.7]\n",
+    "WARNING:root:[958, 4118, 4356, 9600, 1.2, 1.8]\n",
+    "WARNING:root:[962, 4118, 4352, 9600, 1.2, 1.9]\n",
+    "WARNING:root:[944, 4119, 4368, 9600, 1.3, 0.6]\n",
+    "WARNING:root:[945, 4118, 4368, 9600, 1.3, 0.7]\n",
+    "WARNING:root:[945, 4118, 4368, 9600, 1.3, 0.8]\n",
+    "WARNING:root:[945, 4118, 4368, 9600, 1.3, 0.9]\n",
+    "WARNING:root:[946, 4118, 4367, 9600, 1.3, 1.0]\n",
+    "WARNING:root:[949, 4117, 4365, 9600, 1.3, 1.1]\n",
+    "WARNING:root:[949, 4118, 4364, 9600, 1.3, 1.2]\n",
+    "WARNING:root:[950, 4118, 4363, 9600, 1.3, 1.3]\n",
+    "WARNING:root:[953, 4116, 4362, 9600, 1.3, 1.4]\n",
+    "WARNING:root:[956, 4115, 4360, 9600, 1.3, 1.5]\n",
+    "WARNING:root:[959, 4112, 4360, 9600, 1.3, 1.6]\n",
+    "WARNING:root:[959, 4118, 4354, 9600, 1.3, 1.7]\n",
+    "WARNING:root:[963, 4117, 4352, 9600, 1.3, 1.8]\n",
+    "WARNING:root:[967, 4117, 4348, 9600, 1.3, 1.9]\n",
+    "WARNING:root:[944, 4124, 4363, 9600, 1.4, 0.6]\n",
+    "WARNING:root:[945, 4123, 4363, 9600, 1.4, 0.7]\n",
+    "WARNING:root:[945, 4123, 4363, 9600, 1.4, 0.8]\n",
+    "WARNING:root:[945, 4123, 4363, 9600, 1.4, 0.9]\n",
+    "WARNING:root:[946, 4123, 4362, 9600, 1.4, 1.0]\n",
+    "WARNING:root:[949, 4122, 4360, 9600, 1.4, 1.1]\n",
+    "WARNING:root:[949, 4123, 4359, 9600, 1.4, 1.2]\n",
+    "WARNING:root:[950, 4123, 4358, 9600, 1.4, 1.3]\n",
+    "WARNING:root:[953, 4121, 4357, 9600, 1.4, 1.4]\n",
+    "WARNING:root:[956, 4120, 4355, 9600, 1.4, 1.5]\n",
+    "WARNING:root:[959, 4117, 4355, 9600, 1.4, 1.6]\n",
+    "WARNING:root:[959, 4123, 4349, 9600, 1.4, 1.7]\n",
+    "WARNING:root:[963, 4122, 4347, 9600, 1.4, 1.8]\n",
+    "WARNING:root:[967, 4122, 4343, 9600, 1.4, 1.9]\n",
+    "WARNING:root:[947, 4123, 4361, 9600, 1.5, 0.6]\n",
+    "WARNING:root:[948, 4122, 4361, 9600, 1.5, 0.7]\n",
+    "WARNING:root:[948, 4122, 4361, 9600, 1.5, 0.8]\n",
+    "WARNING:root:[948, 4122, 4361, 9600, 1.5, 0.9]\n",
+    "WARNING:root:[949, 4122, 4360, 9600, 1.5, 1.0]\n",
+    "WARNING:root:[952, 4121, 4358, 9600, 1.5, 1.1]\n",
+    "WARNING:root:[952, 4122, 4357, 9600, 1.5, 1.2]\n",
+    "WARNING:root:[953, 4122, 4356, 9600, 1.5, 1.3]\n",
+    "WARNING:root:[956, 4120, 4355, 9600, 1.5, 1.4]\n",
+    "WARNING:root:[959, 4119, 4353, 9600, 1.5, 1.5]\n",
+    "WARNING:root:[962, 4116, 4353, 9600, 1.5, 1.6]\n",
+    "WARNING:root:[962, 4122, 4347, 9600, 1.5, 1.7]\n",
+    "WARNING:root:[966, 4121, 4345, 9600, 1.5, 1.8]\n",
+    "WARNING:root:[970, 4121, 4341, 9600, 1.5, 1.9]\n",
+    "WARNING:root:[954, 4120, 4357, 9600, 1.6, 0.6]\n",
+    "WARNING:root:[955, 4119, 4357, 9600, 1.6, 0.7]\n",
+    "WARNING:root:[955, 4119, 4357, 9600, 1.6, 0.8]\n",
+    "WARNING:root:[955, 4119, 4357, 9600, 1.6, 0.9]\n",
+    "WARNING:root:[956, 4119, 4356, 9600, 1.6, 1.0]\n",
+    "WARNING:root:[959, 4118, 4354, 9600, 1.6, 1.1]\n",
+    "WARNING:root:[959, 4119, 4353, 9600, 1.6, 1.2]\n",
+    "WARNING:root:[960, 4119, 4352, 9600, 1.6, 1.3]\n",
+    "WARNING:root:[963, 4117, 4351, 9600, 1.6, 1.4]\n",
+    "WARNING:root:[966, 4116, 4349, 9600, 1.6, 1.5]\n",
+    "WARNING:root:[969, 4113, 4349, 9600, 1.6, 1.6]\n",
+    "WARNING:root:[969, 4119, 4343, 9600, 1.6, 1.7]\n",
+    "WARNING:root:[974, 4116, 4342, 9600, 1.6, 1.8]\n",
+    "WARNING:root:[978, 4116, 4338, 9600, 1.6, 1.9]\n",
+    "WARNING:root:[958, 4120, 4354, 9600, 1.7, 0.6]\n",
+    "WARNING:root:[959, 4119, 4354, 9600, 1.7, 0.7]\n",
+    "WARNING:root:[959, 4119, 4354, 9600, 1.7, 0.8]\n",
+    "WARNING:root:[959, 4119, 4354, 9600, 1.7, 0.9]\n",
+    "WARNING:root:[960, 4119, 4353, 9600, 1.7, 1.0]\n",
+    "WARNING:root:[963, 4118, 4351, 9600, 1.7, 1.1]\n",
+    "WARNING:root:[963, 4119, 4350, 9600, 1.7, 1.2]\n",
+    "WARNING:root:[964, 4119, 4349, 9600, 1.7, 1.3]\n",
+    "WARNING:root:[967, 4117, 4348, 9600, 1.7, 1.4]\n",
+    "WARNING:root:[970, 4116, 4346, 9600, 1.7, 1.5]\n",
+    "WARNING:root:[973, 4113, 4346, 9600, 1.7, 1.6]\n",
+    "WARNING:root:[973, 4119, 4340, 9600, 1.7, 1.7]\n",
+    "WARNING:root:[978, 4116, 4339, 9600, 1.7, 1.8]\n",
+    "WARNING:root:[982, 4116, 4335, 9600, 1.7, 1.9]\n",
+    "WARNING:root:[965, 4121, 4347, 9600, 1.8, 0.6]\n",
+    "WARNING:root:[966, 4120, 4347, 9600, 1.8, 0.7]\n",
+    "WARNING:root:[966, 4120, 4347, 9600, 1.8, 0.8]\n",
+    "WARNING:root:[966, 4120, 4347, 9600, 1.8, 0.9]\n",
+    "WARNING:root:[967, 4120, 4346, 9600, 1.8, 1.0]\n",
+    "WARNING:root:[970, 4119, 4344, 9600, 1.8, 1.1]\n",
+    "WARNING:root:[970, 4120, 4343, 9600, 1.8, 1.2]\n",
+    "WARNING:root:[971, 4120, 4342, 9600, 1.8, 1.3]\n",
+    "WARNING:root:[974, 4118, 4341, 9600, 1.8, 1.4]\n",
+    "WARNING:root:[977, 4117, 4339, 9600, 1.8, 1.5]\n",
+    "WARNING:root:[980, 4114, 4339, 9600, 1.8, 1.6]\n",
+    "WARNING:root:[980, 4120, 4333, 9600, 1.8, 1.7]\n",
+    "WARNING:root:[985, 4117, 4332, 9600, 1.8, 1.8]\n",
+    "WARNING:root:[989, 4117, 4328, 9600, 1.8, 1.9]\n",
+    "WARNING:root:[972, 4120, 4342, 9600, 1.9, 0.6]\n",
+    "WARNING:root:[973, 4119, 4342, 9600, 1.9, 0.7]\n",
+    "WARNING:root:[973, 4119, 4342, 9600, 1.9, 0.8]\n",
+    "WARNING:root:[973, 4119, 4342, 9600, 1.9, 0.9]\n",
+    "WARNING:root:[974, 4119, 4341, 9600, 1.9, 1.0]\n",
+    "WARNING:root:[977, 4118, 4339, 9600, 1.9, 1.1]\n",
+    "WARNING:root:[977, 4119, 4338, 9600, 1.9, 1.2]\n",
+    "WARNING:root:[978, 4119, 4337, 9600, 1.9, 1.3]\n",
+    "WARNING:root:[981, 4116, 4337, 9600, 1.9, 1.4]\n",
+    "WARNING:root:[984, 4115, 4335, 9600, 1.9, 1.5]\n",
+    "WARNING:root:[987, 4112, 4335, 9600, 1.9, 1.6]\n",
+    "WARNING:root:[987, 4118, 4329, 9600, 1.9, 1.7]\n",
+    "WARNING:root:[992, 4115, 4328, 9600, 1.9, 1.8]\n",
+    "WARNING:root:[996, 4115, 4324, 9600, 1.9, 1.9]\n",
+    "\"\"\"\n",
+    "\n",
+    "log_string_filtered = \"\"\"\n",
+    "WARNING:root:[4159, 4952, 489, 9600, 0.6, 0.6]\n",
+    "WARNING:root:[4153, 4954, 493, 9600, 0.6, 0.7]\n",
+    "WARNING:root:[4153, 4953, 494, 9600, 0.6, 0.8]\n",
+    "WARNING:root:[4149, 4957, 494, 9600, 0.6, 0.9]\n",
+    "WARNING:root:[4146, 4956, 498, 9600, 0.6, 1.0]\n",
+    "WARNING:root:[4146, 4957, 497, 9600, 0.6, 1.1]\n",
+    "WARNING:root:[4146, 4955, 499, 9600, 0.6, 1.2]\n",
+    "WARNING:root:[4147, 4955, 498, 9600, 0.6, 1.3]\n",
+    "WARNING:root:[4148, 4953, 499, 9600, 0.6, 1.4]\n",
+    "WARNING:root:[4149, 4951, 500, 9600, 0.6, 1.5]\n",
+    "WARNING:root:[4149, 4951, 500, 9600, 0.6, 1.6]\n",
+    "WARNING:root:[4147, 4957, 496, 9600, 0.6, 1.7]\n",
+    "WARNING:root:[4150, 4956, 494, 9600, 0.6, 1.8]\n",
+    "WARNING:root:[4148, 4960, 492, 9600, 0.6, 1.9]\n",
+    "WARNING:root:[4159, 4947, 494, 9600, 0.7, 0.6]\n",
+    "WARNING:root:[4153, 4949, 498, 9600, 0.7, 0.7]\n",
+    "WARNING:root:[4153, 4948, 499, 9600, 0.7, 0.8]\n",
+    "WARNING:root:[4149, 4952, 499, 9600, 0.7, 0.9]\n",
+    "WARNING:root:[4146, 4951, 503, 9600, 0.7, 1.0]\n",
+    "WARNING:root:[4146, 4952, 502, 9600, 0.7, 1.1]\n",
+    "WARNING:root:[4146, 4950, 504, 9600, 0.7, 1.2]\n",
+    "WARNING:root:[4147, 4950, 503, 9600, 0.7, 1.3]\n",
+    "WARNING:root:[4148, 4948, 504, 9600, 0.7, 1.4]\n",
+    "WARNING:root:[4149, 4946, 505, 9600, 0.7, 1.5]\n",
+    "WARNING:root:[4149, 4946, 505, 9600, 0.7, 1.6]\n",
+    "WARNING:root:[4147, 4952, 501, 9600, 0.7, 1.7]\n",
+    "WARNING:root:[4150, 4951, 499, 9600, 0.7, 1.8]\n",
+    "WARNING:root:[4148, 4955, 497, 9600, 0.7, 1.9]\n",
+    "WARNING:root:[4157, 4949, 494, 9600, 0.8, 0.6]\n",
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+    "WARNING:root:[4133, 4957, 510, 9600, 1.5, 0.8]\n",
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+    "WARNING:root:[4136, 4945, 519, 9600, 1.6, 1.6]\n",
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+    "WARNING:root:[4136, 4955, 509, 9600, 1.7, 0.8]\n",
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+    "WARNING:root:[4133, 4951, 516, 9600, 1.7, 1.3]\n",
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+    "WARNING:root:[4136, 4946, 518, 9600, 1.7, 1.6]\n",
+    "WARNING:root:[4134, 4952, 514, 9600, 1.7, 1.7]\n",
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+    "WARNING:root:[4141, 4948, 511, 9600, 1.8, 0.8]\n",
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+    "WARNING:root:[4137, 4946, 517, 9600, 1.8, 1.1]\n",
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+    "WARNING:root:[4138, 4944, 518, 9600, 1.8, 1.3]\n",
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+    "WARNING:root:[4141, 4939, 520, 9600, 1.8, 1.5]\n",
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+    "WARNING:root:[4139, 4946, 515, 9600, 1.8, 1.7]\n",
+    "WARNING:root:[4143, 4944, 513, 9600, 1.8, 1.8]\n",
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+    "WARNING:root:[4151, 4944, 505, 9600, 1.9, 0.6]\n",
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+    "WARNING:root:[4146, 4943, 511, 9600, 1.9, 0.8]\n",
+    "WARNING:root:[4143, 4945, 512, 9600, 1.9, 0.9]\n",
+    "WARNING:root:[4142, 4941, 517, 9600, 1.9, 1.0]\n",
+    "WARNING:root:[4142, 4942, 516, 9600, 1.9, 1.1]\n",
+    "WARNING:root:[4142, 4940, 518, 9600, 1.9, 1.2]\n",
+    "WARNING:root:[4143, 4940, 517, 9600, 1.9, 1.3]\n",
+    "WARNING:root:[4144, 4937, 519, 9600, 1.9, 1.4]\n",
+    "WARNING:root:[4146, 4934, 520, 9600, 1.9, 1.5]\n",
+    "WARNING:root:[4146, 4934, 520, 9600, 1.9, 1.6]\n",
+    "WARNING:root:[4144, 4941, 515, 9600, 1.9, 1.7]\n",
+    "WARNING:root:[4148, 4939, 513, 9600, 1.9, 1.8]\n",
+    "WARNING:root:[4147, 4942, 511, 9600, 1.9, 1.9]\n",
+    "\"\"\"\n",
+    "\n",
+    "log_string_filtered_full = \"\"\"\n",
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+    "WARNING:root:[217, 983, 390, 1590, 1.1, 1.9]\n",
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+    "WARNING:root:[216, 993, 390, 1599, 1.2, 0.7]\n",
+    "WARNING:root:[216, 992, 390, 1598, 1.2, 0.8]\n",
+    "WARNING:root:[214, 991, 390, 1595, 1.2, 0.9]\n",
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+    "WARNING:root:[214, 991, 393, 1598, 1.2, 1.2]\n",
+    "WARNING:root:[214, 989, 392, 1595, 1.2, 1.3]\n",
+    "WARNING:root:[214, 987, 394, 1595, 1.2, 1.4]\n",
+    "WARNING:root:[214, 986, 395, 1595, 1.2, 1.5]\n",
+    "WARNING:root:[215, 985, 395, 1595, 1.2, 1.6]\n",
+    "WARNING:root:[215, 987, 392, 1594, 1.2, 1.7]\n",
+    "WARNING:root:[215, 985, 391, 1591, 1.2, 1.8]\n",
+    "WARNING:root:[214, 986, 389, 1589, 1.2, 1.9]\n",
+    "WARNING:root:[217, 987, 388, 1592, 1.3, 0.6]\n",
+    "WARNING:root:[217, 989, 392, 1598, 1.3, 0.7]\n",
+    "WARNING:root:[217, 988, 392, 1597, 1.3, 0.8]\n",
+    "WARNING:root:[215, 987, 392, 1594, 1.3, 0.9]\n",
+    "WARNING:root:[215, 987, 396, 1598, 1.3, 1.0]\n",
+    "WARNING:root:[215, 991, 394, 1600, 1.3, 1.1]\n",
+    "WARNING:root:[215, 987, 395, 1597, 1.3, 1.2]\n",
+    "WARNING:root:[215, 985, 394, 1594, 1.3, 1.3]\n",
+    "WARNING:root:[215, 983, 396, 1594, 1.3, 1.4]\n",
+    "WARNING:root:[215, 982, 397, 1594, 1.3, 1.5]\n",
+    "WARNING:root:[216, 981, 397, 1594, 1.3, 1.6]\n",
+    "WARNING:root:[216, 983, 394, 1593, 1.3, 1.7]\n",
+    "WARNING:root:[216, 981, 393, 1590, 1.3, 1.8]\n",
+    "WARNING:root:[215, 982, 391, 1588, 1.3, 1.9]\n",
+    "WARNING:root:[215, 985, 389, 1589, 1.4, 0.6]\n",
+    "WARNING:root:[215, 987, 393, 1595, 1.4, 0.7]\n",
+    "WARNING:root:[215, 986, 393, 1594, 1.4, 0.8]\n",
+    "WARNING:root:[213, 985, 393, 1591, 1.4, 0.9]\n",
+    "WARNING:root:[213, 984, 398, 1595, 1.4, 1.0]\n",
+    "WARNING:root:[213, 988, 396, 1597, 1.4, 1.1]\n",
+    "WARNING:root:[213, 984, 397, 1594, 1.4, 1.2]\n",
+    "WARNING:root:[213, 982, 396, 1591, 1.4, 1.3]\n",
+    "WARNING:root:[213, 980, 398, 1591, 1.4, 1.4]\n",
+    "WARNING:root:[213, 978, 399, 1590, 1.4, 1.5]\n",
+    "WARNING:root:[214, 977, 399, 1590, 1.4, 1.6]\n",
+    "WARNING:root:[214, 979, 396, 1589, 1.4, 1.7]\n",
+    "WARNING:root:[214, 977, 395, 1586, 1.4, 1.8]\n",
+    "WARNING:root:[213, 978, 393, 1584, 1.4, 1.9]\n",
+    "WARNING:root:[215, 988, 389, 1592, 1.5, 0.6]\n",
+    "WARNING:root:[215, 990, 393, 1598, 1.5, 0.7]\n",
+    "WARNING:root:[215, 989, 393, 1597, 1.5, 0.8]\n",
+    "WARNING:root:[213, 988, 394, 1595, 1.5, 0.9]\n",
+    "WARNING:root:[213, 987, 399, 1599, 1.5, 1.0]\n",
+    "WARNING:root:[213, 991, 398, 1602, 1.5, 1.1]\n",
+    "WARNING:root:[213, 987, 399, 1599, 1.5, 1.2]\n",
+    "WARNING:root:[213, 985, 398, 1596, 1.5, 1.3]\n",
+    "WARNING:root:[213, 983, 400, 1596, 1.5, 1.4]\n",
+    "WARNING:root:[213, 981, 401, 1595, 1.5, 1.5]\n",
+    "WARNING:root:[214, 980, 401, 1595, 1.5, 1.6]\n",
+    "WARNING:root:[214, 982, 398, 1594, 1.5, 1.7]\n",
+    "WARNING:root:[214, 979, 397, 1590, 1.5, 1.8]\n",
+    "WARNING:root:[213, 980, 395, 1588, 1.5, 1.9]\n",
+    "WARNING:root:[215, 989, 390, 1594, 1.6, 0.6]\n",
+    "WARNING:root:[215, 991, 394, 1600, 1.6, 0.7]\n",
+    "WARNING:root:[215, 990, 394, 1599, 1.6, 0.8]\n",
+    "WARNING:root:[213, 989, 395, 1597, 1.6, 0.9]\n",
+    "WARNING:root:[213, 988, 400, 1601, 1.6, 1.0]\n",
+    "WARNING:root:[213, 992, 399, 1604, 1.6, 1.1]\n",
+    "WARNING:root:[213, 988, 400, 1601, 1.6, 1.2]\n",
+    "WARNING:root:[213, 986, 399, 1598, 1.6, 1.3]\n",
+    "WARNING:root:[213, 984, 401, 1598, 1.6, 1.4]\n",
+    "WARNING:root:[213, 982, 402, 1597, 1.6, 1.5]\n",
+    "WARNING:root:[214, 981, 402, 1597, 1.6, 1.6]\n",
+    "WARNING:root:[214, 983, 399, 1596, 1.6, 1.7]\n",
+    "WARNING:root:[214, 980, 398, 1592, 1.6, 1.8]\n",
+    "WARNING:root:[213, 981, 396, 1590, 1.6, 1.9]\n",
+    "WARNING:root:[217, 988, 389, 1594, 1.7, 0.6]\n",
+    "WARNING:root:[217, 990, 393, 1600, 1.7, 0.7]\n",
+    "WARNING:root:[217, 989, 393, 1599, 1.7, 0.8]\n",
+    "WARNING:root:[214, 988, 394, 1596, 1.7, 0.9]\n",
+    "WARNING:root:[214, 987, 399, 1600, 1.7, 1.0]\n",
+    "WARNING:root:[214, 991, 398, 1603, 1.7, 1.1]\n",
+    "WARNING:root:[214, 987, 399, 1600, 1.7, 1.2]\n",
+    "WARNING:root:[214, 984, 398, 1596, 1.7, 1.3]\n",
+    "WARNING:root:[214, 982, 400, 1596, 1.7, 1.4]\n",
+    "WARNING:root:[214, 980, 401, 1595, 1.7, 1.5]\n",
+    "WARNING:root:[215, 979, 401, 1595, 1.7, 1.6]\n",
+    "WARNING:root:[215, 981, 398, 1594, 1.7, 1.7]\n",
+    "WARNING:root:[215, 977, 397, 1589, 1.7, 1.8]\n",
+    "WARNING:root:[214, 978, 395, 1587, 1.7, 1.9]\n",
+    "WARNING:root:[217, 987, 391, 1595, 1.8, 0.6]\n",
+    "WARNING:root:[217, 988, 395, 1600, 1.8, 0.7]\n",
+    "WARNING:root:[217, 987, 395, 1599, 1.8, 0.8]\n",
+    "WARNING:root:[214, 986, 396, 1596, 1.8, 0.9]\n",
+    "WARNING:root:[214, 985, 401, 1600, 1.8, 1.0]\n",
+    "WARNING:root:[214, 989, 400, 1603, 1.8, 1.1]\n",
+    "WARNING:root:[214, 985, 401, 1600, 1.8, 1.2]\n",
+    "WARNING:root:[214, 982, 400, 1596, 1.8, 1.3]\n",
+    "WARNING:root:[214, 980, 402, 1596, 1.8, 1.4]\n",
+    "WARNING:root:[214, 978, 403, 1595, 1.8, 1.5]\n",
+    "WARNING:root:[215, 977, 403, 1595, 1.8, 1.6]\n",
+    "WARNING:root:[215, 979, 399, 1593, 1.8, 1.7]\n",
+    "WARNING:root:[215, 975, 398, 1588, 1.8, 1.8]\n",
+    "WARNING:root:[214, 976, 396, 1586, 1.8, 1.9]\n",
+    "WARNING:root:[216, 988, 390, 1594, 1.9, 0.6]\n",
+    "WARNING:root:[216, 989, 394, 1599, 1.9, 0.7]\n",
+    "WARNING:root:[216, 988, 394, 1598, 1.9, 0.8]\n",
+    "WARNING:root:[213, 987, 395, 1595, 1.9, 0.9]\n",
+    "WARNING:root:[213, 985, 400, 1598, 1.9, 1.0]\n",
+    "WARNING:root:[213, 989, 399, 1601, 1.9, 1.1]\n",
+    "WARNING:root:[213, 985, 400, 1598, 1.9, 1.2]\n",
+    "WARNING:root:[213, 982, 399, 1594, 1.9, 1.3]\n",
+    "WARNING:root:[213, 980, 401, 1594, 1.9, 1.4]\n",
+    "WARNING:root:[213, 978, 402, 1593, 1.9, 1.5]\n",
+    "WARNING:root:[214, 977, 402, 1593, 1.9, 1.6]\n",
+    "WARNING:root:[214, 979, 398, 1591, 1.9, 1.7]\n",
+    "WARNING:root:[214, 975, 397, 1586, 1.9, 1.8]\n",
+    "WARNING:root:[213, 976, 395, 1584, 1.9, 1.9]\n",
+    "\"\"\"\n",
+    "\n",
+    "log_string_filtered_full2 = \"\"\"\n",
+    "WARNING:root:[247, 981, 371, 1599, 0.6, 0.6]\n",
+    "WARNING:root:[247, 977, 376, 1600, 0.6, 0.7]\n",
+    "WARNING:root:[246, 976, 377, 1599, 0.6, 0.8]\n",
+    "WARNING:root:[245, 978, 377, 1600, 0.6, 0.9]\n",
+    "WARNING:root:[244, 978, 379, 1601, 0.6, 1.0]\n",
+    "WARNING:root:[245, 976, 380, 1601, 0.6, 1.1]\n",
+    "WARNING:root:[245, 976, 381, 1602, 0.6, 1.2]\n",
+    "WARNING:root:[244, 976, 382, 1602, 0.6, 1.3]\n",
+    "WARNING:root:[245, 978, 379, 1602, 0.6, 1.4]\n",
+    "WARNING:root:[246, 980, 379, 1605, 0.6, 1.5]\n",
+    "WARNING:root:[246, 979, 380, 1605, 0.6, 1.6]\n",
+    "WARNING:root:[247, 979, 380, 1606, 0.6, 1.7]\n",
+    "WARNING:root:[248, 976, 379, 1603, 0.6, 1.8]\n",
+    "WARNING:root:[247, 974, 378, 1599, 0.6, 1.9]\n",
+    "WARNING:root:[248, 980, 373, 1601, 0.7, 0.6]\n",
+    "WARNING:root:[248, 976, 378, 1602, 0.7, 0.7]\n",
+    "WARNING:root:[247, 975, 379, 1601, 0.7, 0.8]\n",
+    "WARNING:root:[246, 977, 379, 1602, 0.7, 0.9]\n",
+    "WARNING:root:[245, 977, 381, 1603, 0.7, 1.0]\n",
+    "WARNING:root:[246, 975, 382, 1603, 0.7, 1.1]\n",
+    "WARNING:root:[246, 975, 383, 1604, 0.7, 1.2]\n",
+    "WARNING:root:[245, 975, 384, 1604, 0.7, 1.3]\n",
+    "WARNING:root:[246, 977, 381, 1604, 0.7, 1.4]\n",
+    "WARNING:root:[247, 979, 381, 1607, 0.7, 1.5]\n",
+    "WARNING:root:[247, 978, 382, 1607, 0.7, 1.6]\n",
+    "WARNING:root:[248, 978, 382, 1608, 0.7, 1.7]\n",
+    "WARNING:root:[249, 975, 381, 1605, 0.7, 1.8]\n",
+    "WARNING:root:[248, 972, 380, 1600, 0.7, 1.9]\n",
+    "WARNING:root:[248, 981, 373, 1602, 0.8, 0.6]\n",
+    "WARNING:root:[248, 977, 378, 1603, 0.8, 0.7]\n",
+    "WARNING:root:[247, 976, 379, 1602, 0.8, 0.8]\n",
+    "WARNING:root:[246, 978, 379, 1603, 0.8, 0.9]\n",
+    "WARNING:root:[245, 978, 381, 1604, 0.8, 1.0]\n",
+    "WARNING:root:[246, 976, 382, 1604, 0.8, 1.1]\n",
+    "WARNING:root:[246, 976, 383, 1605, 0.8, 1.2]\n",
+    "WARNING:root:[245, 976, 384, 1605, 0.8, 1.3]\n",
+    "WARNING:root:[246, 978, 381, 1605, 0.8, 1.4]\n",
+    "WARNING:root:[247, 980, 381, 1608, 0.8, 1.5]\n",
+    "WARNING:root:[247, 979, 382, 1608, 0.8, 1.6]\n",
+    "WARNING:root:[248, 979, 382, 1609, 0.8, 1.7]\n",
+    "WARNING:root:[249, 976, 381, 1606, 0.8, 1.8]\n",
+    "WARNING:root:[248, 973, 380, 1601, 0.8, 1.9]\n",
+    "WARNING:root:[250, 982, 378, 1610, 0.9, 0.6]\n",
+    "WARNING:root:[250, 978, 383, 1611, 0.9, 0.7]\n",
+    "WARNING:root:[249, 977, 384, 1610, 0.9, 0.8]\n",
+    "WARNING:root:[248, 979, 384, 1611, 0.9, 0.9]\n",
+    "WARNING:root:[247, 979, 386, 1612, 0.9, 1.0]\n",
+    "WARNING:root:[248, 978, 387, 1613, 0.9, 1.1]\n",
+    "WARNING:root:[248, 978, 388, 1614, 0.9, 1.2]\n",
+    "WARNING:root:[246, 978, 389, 1613, 0.9, 1.3]\n",
+    "WARNING:root:[247, 980, 386, 1613, 0.9, 1.4]\n",
+    "WARNING:root:[248, 981, 386, 1615, 0.9, 1.5]\n",
+    "WARNING:root:[248, 980, 387, 1615, 0.9, 1.6]\n",
+    "WARNING:root:[248, 980, 386, 1614, 0.9, 1.7]\n",
+    "WARNING:root:[249, 977, 385, 1611, 0.9, 1.8]\n",
+    "WARNING:root:[248, 974, 384, 1606, 0.9, 1.9]\n",
+    "WARNING:root:[248, 982, 382, 1612, 1.0, 0.6]\n",
+    "WARNING:root:[248, 978, 388, 1614, 1.0, 0.7]\n",
+    "WARNING:root:[247, 977, 389, 1613, 1.0, 0.8]\n",
+    "WARNING:root:[246, 979, 389, 1614, 1.0, 0.9]\n",
+    "WARNING:root:[245, 979, 391, 1615, 1.0, 1.0]\n",
+    "WARNING:root:[246, 978, 392, 1616, 1.0, 1.1]\n",
+    "WARNING:root:[246, 978, 392, 1616, 1.0, 1.2]\n",
+    "WARNING:root:[244, 978, 393, 1615, 1.0, 1.3]\n",
+    "WARNING:root:[245, 980, 390, 1615, 1.0, 1.4]\n",
+    "WARNING:root:[246, 981, 390, 1617, 1.0, 1.5]\n",
+    "WARNING:root:[246, 980, 391, 1617, 1.0, 1.6]\n",
+    "WARNING:root:[246, 980, 390, 1616, 1.0, 1.7]\n",
+    "WARNING:root:[247, 977, 389, 1613, 1.0, 1.8]\n",
+    "WARNING:root:[246, 974, 388, 1608, 1.0, 1.9]\n",
+    "WARNING:root:[249, 983, 383, 1615, 1.1, 0.6]\n",
+    "WARNING:root:[249, 978, 389, 1616, 1.1, 0.7]\n",
+    "WARNING:root:[248, 977, 390, 1615, 1.1, 0.8]\n",
+    "WARNING:root:[247, 979, 390, 1616, 1.1, 0.9]\n",
+    "WARNING:root:[246, 979, 392, 1617, 1.1, 1.0]\n",
+    "WARNING:root:[247, 978, 393, 1618, 1.1, 1.1]\n",
+    "WARNING:root:[247, 978, 393, 1618, 1.1, 1.2]\n",
+    "WARNING:root:[245, 978, 394, 1617, 1.1, 1.3]\n",
+    "WARNING:root:[246, 980, 391, 1617, 1.1, 1.4]\n",
+    "WARNING:root:[247, 981, 391, 1619, 1.1, 1.5]\n",
+    "WARNING:root:[247, 980, 392, 1619, 1.1, 1.6]\n",
+    "WARNING:root:[247, 980, 391, 1618, 1.1, 1.7]\n",
+    "WARNING:root:[248, 977, 390, 1615, 1.1, 1.8]\n",
+    "WARNING:root:[247, 974, 389, 1610, 1.1, 1.9]\n",
+    "WARNING:root:[249, 978, 383, 1610, 1.2, 0.6]\n",
+    "WARNING:root:[249, 971, 390, 1610, 1.2, 0.7]\n",
+    "WARNING:root:[248, 970, 391, 1609, 1.2, 0.8]\n",
+    "WARNING:root:[247, 971, 391, 1609, 1.2, 0.9]\n",
+    "WARNING:root:[246, 971, 394, 1611, 1.2, 1.0]\n",
+    "WARNING:root:[247, 971, 395, 1613, 1.2, 1.1]\n",
+    "WARNING:root:[247, 971, 395, 1613, 1.2, 1.2]\n",
+    "WARNING:root:[245, 971, 396, 1612, 1.2, 1.3]\n",
+    "WARNING:root:[246, 973, 393, 1612, 1.2, 1.4]\n",
+    "WARNING:root:[247, 973, 393, 1613, 1.2, 1.5]\n",
+    "WARNING:root:[247, 972, 394, 1613, 1.2, 1.6]\n",
+    "WARNING:root:[247, 972, 393, 1612, 1.2, 1.7]\n",
+    "WARNING:root:[248, 969, 392, 1609, 1.2, 1.8]\n",
+    "WARNING:root:[247, 966, 391, 1604, 1.2, 1.9]\n",
+    "WARNING:root:[248, 978, 385, 1611, 1.3, 0.6]\n",
+    "WARNING:root:[248, 971, 392, 1611, 1.3, 0.7]\n",
+    "WARNING:root:[247, 970, 393, 1610, 1.3, 0.8]\n",
+    "WARNING:root:[246, 971, 393, 1610, 1.3, 0.9]\n",
+    "WARNING:root:[245, 972, 396, 1613, 1.3, 1.0]\n",
+    "WARNING:root:[246, 972, 397, 1615, 1.3, 1.1]\n",
+    "WARNING:root:[246, 972, 397, 1615, 1.3, 1.2]\n",
+    "WARNING:root:[244, 972, 397, 1613, 1.3, 1.3]\n",
+    "WARNING:root:[245, 974, 394, 1613, 1.3, 1.4]\n",
+    "WARNING:root:[246, 974, 394, 1614, 1.3, 1.5]\n",
+    "WARNING:root:[246, 973, 395, 1614, 1.3, 1.6]\n",
+    "WARNING:root:[246, 973, 394, 1613, 1.3, 1.7]\n",
+    "WARNING:root:[247, 970, 393, 1610, 1.3, 1.8]\n",
+    "WARNING:root:[246, 967, 392, 1605, 1.3, 1.9]\n",
+    "WARNING:root:[248, 976, 385, 1609, 1.4, 0.6]\n",
+    "WARNING:root:[248, 969, 392, 1609, 1.4, 0.7]\n",
+    "WARNING:root:[247, 968, 393, 1608, 1.4, 0.8]\n",
+    "WARNING:root:[246, 969, 393, 1608, 1.4, 0.9]\n",
+    "WARNING:root:[245, 970, 396, 1611, 1.4, 1.0]\n",
+    "WARNING:root:[246, 970, 397, 1613, 1.4, 1.1]\n",
+    "WARNING:root:[246, 970, 397, 1613, 1.4, 1.2]\n",
+    "WARNING:root:[244, 970, 397, 1611, 1.4, 1.3]\n",
+    "WARNING:root:[245, 972, 394, 1611, 1.4, 1.4]\n",
+    "WARNING:root:[246, 972, 394, 1612, 1.4, 1.5]\n",
+    "WARNING:root:[246, 972, 395, 1613, 1.4, 1.6]\n",
+    "WARNING:root:[246, 972, 394, 1612, 1.4, 1.7]\n",
+    "WARNING:root:[247, 969, 393, 1609, 1.4, 1.8]\n",
+    "WARNING:root:[246, 966, 392, 1604, 1.4, 1.9]\n",
+    "WARNING:root:[247, 976, 385, 1608, 1.5, 0.6]\n",
+    "WARNING:root:[247, 969, 392, 1608, 1.5, 0.7]\n",
+    "WARNING:root:[246, 968, 393, 1607, 1.5, 0.8]\n",
+    "WARNING:root:[245, 968, 393, 1606, 1.5, 0.9]\n",
+    "WARNING:root:[244, 969, 396, 1609, 1.5, 1.0]\n",
+    "WARNING:root:[245, 970, 397, 1612, 1.5, 1.1]\n",
+    "WARNING:root:[245, 970, 397, 1612, 1.5, 1.2]\n",
+    "WARNING:root:[243, 970, 397, 1610, 1.5, 1.3]\n",
+    "WARNING:root:[244, 972, 394, 1610, 1.5, 1.4]\n",
+    "WARNING:root:[245, 970, 394, 1609, 1.5, 1.5]\n",
+    "WARNING:root:[245, 970, 395, 1610, 1.5, 1.6]\n",
+    "WARNING:root:[245, 970, 394, 1609, 1.5, 1.7]\n",
+    "WARNING:root:[246, 967, 393, 1606, 1.5, 1.8]\n",
+    "WARNING:root:[245, 964, 392, 1601, 1.5, 1.9]\n",
+    "WARNING:root:[244, 976, 386, 1606, 1.6, 0.6]\n",
+    "WARNING:root:[244, 969, 393, 1606, 1.6, 0.7]\n",
+    "WARNING:root:[243, 968, 394, 1605, 1.6, 0.8]\n",
+    "WARNING:root:[242, 968, 394, 1604, 1.6, 0.9]\n",
+    "WARNING:root:[242, 968, 397, 1607, 1.6, 1.0]\n",
+    "WARNING:root:[243, 969, 398, 1610, 1.6, 1.1]\n",
+    "WARNING:root:[243, 969, 398, 1610, 1.6, 1.2]\n",
+    "WARNING:root:[241, 969, 398, 1608, 1.6, 1.3]\n",
+    "WARNING:root:[242, 971, 395, 1608, 1.6, 1.4]\n",
+    "WARNING:root:[243, 969, 395, 1607, 1.6, 1.5]\n",
+    "WARNING:root:[243, 969, 396, 1608, 1.6, 1.6]\n",
+    "WARNING:root:[243, 969, 395, 1607, 1.6, 1.7]\n",
+    "WARNING:root:[244, 966, 394, 1604, 1.6, 1.8]\n",
+    "WARNING:root:[243, 963, 393, 1599, 1.6, 1.9]\n",
+    "WARNING:root:[244, 976, 389, 1609, 1.7, 0.6]\n",
+    "WARNING:root:[244, 969, 396, 1609, 1.7, 0.7]\n",
+    "WARNING:root:[243, 968, 397, 1608, 1.7, 0.8]\n",
+    "WARNING:root:[242, 968, 397, 1607, 1.7, 0.9]\n",
+    "WARNING:root:[242, 968, 400, 1610, 1.7, 1.0]\n",
+    "WARNING:root:[243, 969, 401, 1613, 1.7, 1.1]\n",
+    "WARNING:root:[243, 969, 401, 1613, 1.7, 1.2]\n",
+    "WARNING:root:[241, 969, 401, 1611, 1.7, 1.3]\n",
+    "WARNING:root:[242, 971, 398, 1611, 1.7, 1.4]\n",
+    "WARNING:root:[243, 969, 398, 1610, 1.7, 1.5]\n",
+    "WARNING:root:[243, 969, 399, 1611, 1.7, 1.6]\n",
+    "WARNING:root:[243, 969, 398, 1610, 1.7, 1.7]\n",
+    "WARNING:root:[244, 966, 397, 1607, 1.7, 1.8]\n",
+    "WARNING:root:[243, 963, 396, 1602, 1.7, 1.9]\n",
+    "WARNING:root:[245, 976, 390, 1611, 1.8, 0.6]\n",
+    "WARNING:root:[245, 969, 397, 1611, 1.8, 0.7]\n",
+    "WARNING:root:[244, 968, 398, 1610, 1.8, 0.8]\n",
+    "WARNING:root:[243, 968, 398, 1609, 1.8, 0.9]\n",
+    "WARNING:root:[243, 968, 401, 1612, 1.8, 1.0]\n",
+    "WARNING:root:[244, 969, 402, 1615, 1.8, 1.1]\n",
+    "WARNING:root:[244, 969, 402, 1615, 1.8, 1.2]\n",
+    "WARNING:root:[242, 969, 402, 1613, 1.8, 1.3]\n",
+    "WARNING:root:[243, 971, 399, 1613, 1.8, 1.4]\n",
+    "WARNING:root:[244, 969, 399, 1612, 1.8, 1.5]\n",
+    "WARNING:root:[244, 969, 400, 1613, 1.8, 1.6]\n",
+    "WARNING:root:[244, 968, 399, 1611, 1.8, 1.7]\n",
+    "WARNING:root:[244, 965, 398, 1607, 1.8, 1.8]\n",
+    "WARNING:root:[243, 962, 397, 1602, 1.8, 1.9]\n",
+    "WARNING:root:[244, 977, 387, 1608, 1.9, 0.6]\n",
+    "WARNING:root:[244, 970, 393, 1607, 1.9, 0.7]\n",
+    "WARNING:root:[243, 969, 394, 1606, 1.9, 0.8]\n",
+    "WARNING:root:[242, 969, 394, 1605, 1.9, 0.9]\n",
+    "WARNING:root:[242, 969, 397, 1608, 1.9, 1.0]\n",
+    "WARNING:root:[243, 969, 398, 1610, 1.9, 1.1]\n",
+    "WARNING:root:[243, 969, 398, 1610, 1.9, 1.2]\n",
+    "WARNING:root:[241, 969, 399, 1609, 1.9, 1.3]\n",
+    "WARNING:root:[242, 970, 396, 1608, 1.9, 1.4]\n",
+    "WARNING:root:[243, 967, 396, 1606, 1.9, 1.5]\n",
+    "WARNING:root:[243, 967, 397, 1607, 1.9, 1.6]\n",
+    "WARNING:root:[243, 967, 396, 1606, 1.9, 1.7]\n",
+    "WARNING:root:[243, 964, 395, 1602, 1.9, 1.8]\n",
+    "WARNING:root:[242, 961, 394, 1597, 1.9, 1.9]\n",
+    "WARNING:HHbbVV.processors.bbVVSkimmer:9600\n",
+    "WARNING:root:Percentage of events with 0 true values: 5.77% | Total: 9600 | Count: 554 | Percentage of events with 1 true values: 38.77% | Total: 9600 | Count: 3722 | Percentage of events with 2 true values: 53.31% | Total: 9600 | Count: 5118\n",
+    "WARNING:root:[273, 995, 379, 1647, 0.6, 0.6]\n",
+    "WARNING:root:[273, 995, 380, 1648, 0.6, 0.7]\n",
+    "WARNING:root:[273, 995, 381, 1649, 0.6, 0.8]\n",
+    "WARNING:root:[273, 997, 383, 1653, 0.6, 0.9]\n",
+    "WARNING:root:[275, 997, 384, 1656, 0.6, 1.0]\n",
+    "WARNING:root:[275, 996, 384, 1655, 0.6, 1.1]\n",
+    "WARNING:root:[276, 997, 385, 1658, 0.6, 1.2]\n",
+    "WARNING:root:[277, 995, 385, 1657, 0.6, 1.3]\n",
+    "WARNING:root:[275, 996, 387, 1658, 0.6, 1.4]\n",
+    "WARNING:root:[275, 997, 385, 1657, 0.6, 1.5]\n",
+    "WARNING:root:[275, 995, 385, 1655, 0.6, 1.6]\n",
+    "WARNING:root:[275, 994, 383, 1652, 0.6, 1.7]\n",
+    "WARNING:root:[275, 993, 382, 1650, 0.6, 1.8]\n",
+    "WARNING:root:[275, 994, 382, 1651, 0.6, 1.9]\n",
+    "WARNING:root:[274, 995, 380, 1649, 0.7, 0.6]\n",
+    "WARNING:root:[274, 995, 381, 1650, 0.7, 0.7]\n",
+    "WARNING:root:[274, 995, 382, 1651, 0.7, 0.8]\n",
+    "WARNING:root:[274, 997, 384, 1655, 0.7, 0.9]\n",
+    "WARNING:root:[276, 997, 385, 1658, 0.7, 1.0]\n",
+    "WARNING:root:[276, 996, 385, 1657, 0.7, 1.1]\n",
+    "WARNING:root:[277, 997, 386, 1660, 0.7, 1.2]\n",
+    "WARNING:root:[278, 995, 386, 1659, 0.7, 1.3]\n",
+    "WARNING:root:[276, 996, 388, 1660, 0.7, 1.4]\n",
+    "WARNING:root:[276, 997, 386, 1659, 0.7, 1.5]\n",
+    "WARNING:root:[276, 995, 386, 1657, 0.7, 1.6]\n",
+    "WARNING:root:[276, 994, 384, 1654, 0.7, 1.7]\n",
+    "WARNING:root:[276, 993, 383, 1652, 0.7, 1.8]\n",
+    "WARNING:root:[276, 993, 383, 1652, 0.7, 1.9]\n",
+    "WARNING:root:[273, 996, 383, 1652, 0.8, 0.6]\n",
+    "WARNING:root:[273, 996, 385, 1654, 0.8, 0.7]\n",
+    "WARNING:root:[273, 996, 386, 1655, 0.8, 0.8]\n",
+    "WARNING:root:[273, 998, 388, 1659, 0.8, 0.9]\n",
+    "WARNING:root:[275, 998, 389, 1662, 0.8, 1.0]\n",
+    "WARNING:root:[275, 997, 389, 1661, 0.8, 1.1]\n",
+    "WARNING:root:[276, 998, 390, 1664, 0.8, 1.2]\n",
+    "WARNING:root:[277, 996, 390, 1663, 0.8, 1.3]\n",
+    "WARNING:root:[275, 997, 392, 1664, 0.8, 1.4]\n",
+    "WARNING:root:[275, 998, 390, 1663, 0.8, 1.5]\n",
+    "WARNING:root:[275, 996, 390, 1661, 0.8, 1.6]\n",
+    "WARNING:root:[275, 995, 388, 1658, 0.8, 1.7]\n",
+    "WARNING:root:[275, 994, 387, 1656, 0.8, 1.8]\n",
+    "WARNING:root:[275, 994, 387, 1656, 0.8, 1.9]\n",
+    "WARNING:root:[277, 992, 387, 1656, 0.9, 0.6]\n",
+    "WARNING:root:[277, 992, 389, 1658, 0.9, 0.7]\n",
+    "WARNING:root:[277, 992, 390, 1659, 0.9, 0.8]\n",
+    "WARNING:root:[277, 994, 392, 1663, 0.9, 0.9]\n",
+    "WARNING:root:[279, 994, 393, 1666, 0.9, 1.0]\n",
+    "WARNING:root:[279, 993, 393, 1665, 0.9, 1.1]\n",
+    "WARNING:root:[280, 994, 394, 1668, 0.9, 1.2]\n",
+    "WARNING:root:[281, 992, 394, 1667, 0.9, 1.3]\n",
+    "WARNING:root:[279, 993, 396, 1668, 0.9, 1.4]\n",
+    "WARNING:root:[279, 994, 394, 1667, 0.9, 1.5]\n",
+    "WARNING:root:[279, 992, 394, 1665, 0.9, 1.6]\n",
+    "WARNING:root:[279, 991, 392, 1662, 0.9, 1.7]\n",
+    "WARNING:root:[279, 990, 391, 1660, 0.9, 1.8]\n",
+    "WARNING:root:[279, 990, 391, 1660, 0.9, 1.9]\n",
+    "WARNING:root:[276, 995, 390, 1661, 1.0, 0.6]\n",
+    "WARNING:root:[276, 995, 392, 1663, 1.0, 0.7]\n",
+    "WARNING:root:[276, 995, 393, 1664, 1.0, 0.8]\n",
+    "WARNING:root:[276, 996, 395, 1667, 1.0, 0.9]\n",
+    "WARNING:root:[278, 997, 396, 1671, 1.0, 1.0]\n",
+    "WARNING:root:[278, 996, 396, 1670, 1.0, 1.1]\n",
+    "WARNING:root:[279, 996, 397, 1672, 1.0, 1.2]\n",
+    "WARNING:root:[280, 994, 397, 1671, 1.0, 1.3]\n",
+    "WARNING:root:[278, 995, 399, 1672, 1.0, 1.4]\n",
+    "WARNING:root:[277, 996, 397, 1670, 1.0, 1.5]\n",
+    "WARNING:root:[277, 994, 397, 1668, 1.0, 1.6]\n",
+    "WARNING:root:[277, 993, 395, 1665, 1.0, 1.7]\n",
+    "WARNING:root:[277, 992, 394, 1663, 1.0, 1.8]\n",
+    "WARNING:root:[277, 992, 394, 1663, 1.0, 1.9]\n",
+    "WARNING:root:[276, 998, 390, 1664, 1.1, 0.6]\n",
+    "WARNING:root:[275, 998, 392, 1665, 1.1, 0.7]\n",
+    "WARNING:root:[275, 998, 393, 1666, 1.1, 0.8]\n",
+    "WARNING:root:[276, 998, 394, 1668, 1.1, 0.9]\n",
+    "WARNING:root:[278, 999, 395, 1672, 1.1, 1.0]\n",
+    "WARNING:root:[277, 998, 395, 1670, 1.1, 1.1]\n",
+    "WARNING:root:[278, 998, 396, 1672, 1.1, 1.2]\n",
+    "WARNING:root:[279, 995, 396, 1670, 1.1, 1.3]\n",
+    "WARNING:root:[277, 996, 398, 1671, 1.1, 1.4]\n",
+    "WARNING:root:[277, 997, 396, 1670, 1.1, 1.5]\n",
+    "WARNING:root:[277, 996, 396, 1669, 1.1, 1.6]\n",
+    "WARNING:root:[277, 995, 394, 1666, 1.1, 1.7]\n",
+    "WARNING:root:[277, 994, 393, 1664, 1.1, 1.8]\n",
+    "WARNING:root:[277, 994, 393, 1664, 1.1, 1.9]\n",
+    "WARNING:root:[276, 1002, 391, 1669, 1.2, 0.6]\n",
+    "WARNING:root:[275, 1003, 393, 1671, 1.2, 0.7]\n",
+    "WARNING:root:[275, 1003, 394, 1672, 1.2, 0.8]\n",
+    "WARNING:root:[276, 1002, 395, 1673, 1.2, 0.9]\n",
+    "WARNING:root:[278, 1002, 396, 1676, 1.2, 1.0]\n",
+    "WARNING:root:[277, 1001, 396, 1674, 1.2, 1.1]\n",
+    "WARNING:root:[278, 1001, 397, 1676, 1.2, 1.2]\n",
+    "WARNING:root:[279, 998, 397, 1674, 1.2, 1.3]\n",
+    "WARNING:root:[277, 999, 399, 1675, 1.2, 1.4]\n",
+    "WARNING:root:[277, 999, 397, 1673, 1.2, 1.5]\n",
+    "WARNING:root:[278, 998, 397, 1673, 1.2, 1.6]\n",
+    "WARNING:root:[278, 997, 395, 1670, 1.2, 1.7]\n",
+    "WARNING:root:[278, 996, 394, 1668, 1.2, 1.8]\n",
+    "WARNING:root:[278, 997, 394, 1669, 1.2, 1.9]\n",
+    "WARNING:root:[276, 1003, 392, 1671, 1.3, 0.6]\n",
+    "WARNING:root:[275, 1004, 394, 1673, 1.3, 0.7]\n",
+    "WARNING:root:[275, 1004, 395, 1674, 1.3, 0.8]\n",
+    "WARNING:root:[276, 1003, 396, 1675, 1.3, 0.9]\n",
+    "WARNING:root:[278, 1003, 397, 1678, 1.3, 1.0]\n",
+    "WARNING:root:[277, 1001, 397, 1675, 1.3, 1.1]\n",
+    "WARNING:root:[278, 1001, 398, 1677, 1.3, 1.2]\n",
+    "WARNING:root:[279, 998, 398, 1675, 1.3, 1.3]\n",
+    "WARNING:root:[277, 999, 400, 1676, 1.3, 1.4]\n",
+    "WARNING:root:[277, 999, 398, 1674, 1.3, 1.5]\n",
+    "WARNING:root:[278, 998, 398, 1674, 1.3, 1.6]\n",
+    "WARNING:root:[278, 997, 396, 1671, 1.3, 1.7]\n",
+    "WARNING:root:[278, 995, 395, 1668, 1.3, 1.8]\n",
+    "WARNING:root:[279, 996, 395, 1670, 1.3, 1.9]\n",
+    "WARNING:root:[275, 1001, 392, 1668, 1.4, 0.6]\n",
+    "WARNING:root:[274, 1002, 394, 1670, 1.4, 0.7]\n",
+    "WARNING:root:[274, 1002, 395, 1671, 1.4, 0.8]\n",
+    "WARNING:root:[275, 1001, 396, 1672, 1.4, 0.9]\n",
+    "WARNING:root:[277, 1000, 397, 1674, 1.4, 1.0]\n",
+    "WARNING:root:[276, 998, 397, 1671, 1.4, 1.1]\n",
+    "WARNING:root:[277, 998, 398, 1673, 1.4, 1.2]\n",
+    "WARNING:root:[278, 995, 398, 1671, 1.4, 1.3]\n",
+    "WARNING:root:[276, 996, 400, 1672, 1.4, 1.4]\n",
+    "WARNING:root:[276, 996, 398, 1670, 1.4, 1.5]\n",
+    "WARNING:root:[277, 996, 398, 1671, 1.4, 1.6]\n",
+    "WARNING:root:[277, 995, 396, 1668, 1.4, 1.7]\n",
+    "WARNING:root:[277, 993, 395, 1665, 1.4, 1.8]\n",
+    "WARNING:root:[278, 994, 395, 1667, 1.4, 1.9]\n",
+    "WARNING:root:[276, 1003, 393, 1672, 1.5, 0.6]\n",
+    "WARNING:root:[275, 1004, 395, 1674, 1.5, 0.7]\n",
+    "WARNING:root:[275, 1004, 396, 1675, 1.5, 0.8]\n",
+    "WARNING:root:[276, 1003, 397, 1676, 1.5, 0.9]\n",
+    "WARNING:root:[278, 1002, 398, 1678, 1.5, 1.0]\n",
+    "WARNING:root:[277, 1000, 398, 1675, 1.5, 1.1]\n",
+    "WARNING:root:[278, 1000, 399, 1677, 1.5, 1.2]\n",
+    "WARNING:root:[279, 997, 399, 1675, 1.5, 1.3]\n",
+    "WARNING:root:[277, 998, 401, 1676, 1.5, 1.4]\n",
+    "WARNING:root:[277, 998, 399, 1674, 1.5, 1.5]\n",
+    "WARNING:root:[278, 998, 399, 1675, 1.5, 1.6]\n",
+    "WARNING:root:[278, 996, 397, 1671, 1.5, 1.7]\n",
+    "WARNING:root:[278, 994, 396, 1668, 1.5, 1.8]\n",
+    "WARNING:root:[279, 995, 396, 1670, 1.5, 1.9]\n",
+    "WARNING:root:[276, 1004, 392, 1672, 1.6, 0.6]\n",
+    "WARNING:root:[275, 1005, 394, 1674, 1.6, 0.7]\n",
+    "WARNING:root:[275, 1005, 395, 1675, 1.6, 0.8]\n",
+    "WARNING:root:[276, 1004, 396, 1676, 1.6, 0.9]\n",
+    "WARNING:root:[278, 1005, 397, 1680, 1.6, 1.0]\n",
+    "WARNING:root:[277, 1003, 397, 1677, 1.6, 1.1]\n",
+    "WARNING:root:[278, 1002, 398, 1678, 1.6, 1.2]\n",
+    "WARNING:root:[279, 998, 398, 1675, 1.6, 1.3]\n",
+    "WARNING:root:[277, 999, 400, 1676, 1.6, 1.4]\n",
+    "WARNING:root:[277, 999, 398, 1674, 1.6, 1.5]\n",
+    "WARNING:root:[278, 999, 398, 1675, 1.6, 1.6]\n",
+    "WARNING:root:[279, 997, 396, 1672, 1.6, 1.7]\n",
+    "WARNING:root:[279, 995, 395, 1669, 1.6, 1.8]\n",
+    "WARNING:root:[280, 997, 395, 1672, 1.6, 1.9]\n",
+    "WARNING:root:[276, 1006, 394, 1676, 1.7, 0.6]\n",
+    "WARNING:root:[275, 1007, 395, 1677, 1.7, 0.7]\n",
+    "WARNING:root:[275, 1007, 396, 1678, 1.7, 0.8]\n",
+    "WARNING:root:[276, 1006, 397, 1679, 1.7, 0.9]\n",
+    "WARNING:root:[278, 1007, 398, 1683, 1.7, 1.0]\n",
+    "WARNING:root:[277, 1005, 398, 1680, 1.7, 1.1]\n",
+    "WARNING:root:[278, 1004, 399, 1681, 1.7, 1.2]\n",
+    "WARNING:root:[279, 1000, 399, 1678, 1.7, 1.3]\n",
+    "WARNING:root:[277, 1001, 401, 1679, 1.7, 1.4]\n",
+    "WARNING:root:[277, 1001, 399, 1677, 1.7, 1.5]\n",
+    "WARNING:root:[278, 1001, 399, 1678, 1.7, 1.6]\n",
+    "WARNING:root:[279, 999, 397, 1675, 1.7, 1.7]\n",
+    "WARNING:root:[279, 997, 396, 1672, 1.7, 1.8]\n",
+    "WARNING:root:[280, 999, 396, 1675, 1.7, 1.9]\n",
+    "WARNING:root:[276, 1004, 395, 1675, 1.8, 0.6]\n",
+    "WARNING:root:[275, 1005, 396, 1676, 1.8, 0.7]\n",
+    "WARNING:root:[275, 1005, 397, 1677, 1.8, 0.8]\n",
+    "WARNING:root:[276, 1004, 398, 1678, 1.8, 0.9]\n",
+    "WARNING:root:[278, 1005, 399, 1682, 1.8, 1.0]\n",
+    "WARNING:root:[277, 1003, 399, 1679, 1.8, 1.1]\n",
+    "WARNING:root:[278, 1002, 399, 1679, 1.8, 1.2]\n",
+    "WARNING:root:[279, 998, 399, 1676, 1.8, 1.3]\n",
+    "WARNING:root:[277, 999, 401, 1677, 1.8, 1.4]\n",
+    "WARNING:root:[277, 999, 399, 1675, 1.8, 1.5]\n",
+    "WARNING:root:[278, 999, 399, 1676, 1.8, 1.6]\n",
+    "WARNING:root:[279, 997, 397, 1673, 1.8, 1.7]\n",
+    "WARNING:root:[279, 995, 396, 1670, 1.8, 1.8]\n",
+    "WARNING:root:[280, 997, 396, 1673, 1.8, 1.9]\n",
+    "WARNING:root:[274, 1002, 394, 1670, 1.9, 0.6]\n",
+    "WARNING:root:[273, 1003, 395, 1671, 1.9, 0.7]\n",
+    "WARNING:root:[273, 1003, 396, 1672, 1.9, 0.8]\n",
+    "WARNING:root:[274, 1002, 397, 1673, 1.9, 0.9]\n",
+    "WARNING:root:[276, 1003, 398, 1677, 1.9, 1.0]\n",
+    "WARNING:root:[275, 1001, 398, 1674, 1.9, 1.1]\n",
+    "WARNING:root:[276, 1000, 398, 1674, 1.9, 1.2]\n",
+    "WARNING:root:[277, 996, 398, 1671, 1.9, 1.3]\n",
+    "WARNING:root:[275, 997, 400, 1672, 1.9, 1.4]\n",
+    "WARNING:root:[275, 997, 398, 1670, 1.9, 1.5]\n",
+    "WARNING:root:[276, 997, 398, 1671, 1.9, 1.6]\n",
+    "WARNING:root:[277, 995, 396, 1668, 1.9, 1.7]\n",
+    "WARNING:root:[277, 993, 395, 1665, 1.9, 1.8]\n",
+    "WARNING:root:[278, 995, 395, 1668, 1.9, 1.9]\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "5cf675df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mega_log_string_filtered = \"\"\"\n",
+    "WARNING:root:[269, 938, 385, 1592, 0.6, 0.6]\n",
+    "WARNING:root:[270, 938, 387, 1595, 0.6, 0.7]\n",
+    "WARNING:root:[270, 938, 388, 1596, 0.6, 0.8]\n",
+    "WARNING:root:[271, 937, 391, 1599, 0.6, 0.9]\n",
+    "WARNING:root:[272, 938, 391, 1601, 0.6, 1.0]\n",
+    "WARNING:root:[270, 939, 389, 1598, 0.6, 1.1]\n",
+    "WARNING:root:[271, 939, 390, 1600, 0.6, 1.2]\n",
+    "WARNING:root:[271, 941, 388, 1600, 0.6, 1.3]\n",
+    "WARNING:root:[271, 938, 388, 1597, 0.6, 1.4]\n",
+    "WARNING:root:[270, 939, 387, 1596, 0.6, 1.5]\n",
+    "WARNING:root:[270, 938, 388, 1596, 0.6, 1.6]\n",
+    "WARNING:root:[270, 940, 386, 1596, 0.6, 1.7]\n",
+    "WARNING:root:[270, 939, 386, 1595, 0.6, 1.8]\n",
+    "WARNING:root:[269, 939, 385, 1593, 0.6, 1.9]\n",
+    "WARNING:root:[269, 934, 387, 1590, 0.7, 0.6]\n",
+    "WARNING:root:[270, 934, 389, 1593, 0.7, 0.7]\n",
+    "WARNING:root:[270, 934, 390, 1594, 0.7, 0.8]\n",
+    "WARNING:root:[271, 933, 393, 1597, 0.7, 0.9]\n",
+    "WARNING:root:[272, 934, 394, 1600, 0.7, 1.0]\n",
+    "WARNING:root:[270, 935, 392, 1597, 0.7, 1.1]\n",
+    "WARNING:root:[271, 935, 393, 1599, 0.7, 1.2]\n",
+    "WARNING:root:[271, 937, 391, 1599, 0.7, 1.3]\n",
+    "WARNING:root:[271, 934, 391, 1596, 0.7, 1.4]\n",
+    "WARNING:root:[271, 935, 390, 1596, 0.7, 1.5]\n",
+    "WARNING:root:[271, 934, 391, 1596, 0.7, 1.6]\n",
+    "WARNING:root:[271, 935, 389, 1595, 0.7, 1.7]\n",
+    "WARNING:root:[271, 934, 389, 1594, 0.7, 1.8]\n",
+    "WARNING:root:[270, 934, 388, 1592, 0.7, 1.9]\n",
+    "WARNING:root:[270, 936, 388, 1594, 0.8, 0.6]\n",
+    "WARNING:root:[271, 936, 390, 1597, 0.8, 0.7]\n",
+    "WARNING:root:[271, 936, 391, 1598, 0.8, 0.8]\n",
+    "WARNING:root:[272, 935, 394, 1601, 0.8, 0.9]\n",
+    "WARNING:root:[273, 936, 395, 1604, 0.8, 1.0]\n",
+    "WARNING:root:[271, 937, 393, 1601, 0.8, 1.1]\n",
+    "WARNING:root:[272, 937, 394, 1603, 0.8, 1.2]\n",
+    "WARNING:root:[272, 939, 392, 1603, 0.8, 1.3]\n",
+    "WARNING:root:[272, 936, 392, 1600, 0.8, 1.4]\n",
+    "WARNING:root:[272, 937, 391, 1600, 0.8, 1.5]\n",
+    "WARNING:root:[272, 935, 392, 1599, 0.8, 1.6]\n",
+    "WARNING:root:[272, 935, 390, 1597, 0.8, 1.7]\n",
+    "WARNING:root:[272, 934, 390, 1596, 0.8, 1.8]\n",
+    "WARNING:root:[271, 934, 389, 1594, 0.8, 1.9]\n",
+    "WARNING:root:[271, 940, 397, 1608, 0.9, 0.6]\n",
+    "WARNING:root:[272, 940, 399, 1611, 0.9, 0.7]\n",
+    "WARNING:root:[272, 940, 400, 1612, 0.9, 0.8]\n",
+    "WARNING:root:[273, 939, 402, 1614, 0.9, 0.9]\n",
+    "WARNING:root:[274, 939, 403, 1616, 0.9, 1.0]\n",
+    "WARNING:root:[272, 940, 401, 1613, 0.9, 1.1]\n",
+    "WARNING:root:[273, 939, 402, 1614, 0.9, 1.2]\n",
+    "WARNING:root:[273, 941, 400, 1614, 0.9, 1.3]\n",
+    "WARNING:root:[273, 938, 400, 1611, 0.9, 1.4]\n",
+    "WARNING:root:[273, 939, 399, 1611, 0.9, 1.5]\n",
+    "WARNING:root:[273, 937, 400, 1610, 0.9, 1.6]\n",
+    "WARNING:root:[273, 937, 398, 1608, 0.9, 1.7]\n",
+    "WARNING:root:[273, 936, 398, 1607, 0.9, 1.8]\n",
+    "WARNING:root:[272, 936, 397, 1605, 0.9, 1.9]\n",
+    "WARNING:root:[269, 944, 401, 1614, 1.0, 0.6]\n",
+    "WARNING:root:[270, 942, 403, 1615, 1.0, 0.7]\n",
+    "WARNING:root:[270, 942, 404, 1616, 1.0, 0.8]\n",
+    "WARNING:root:[271, 940, 406, 1617, 1.0, 0.9]\n",
+    "WARNING:root:[272, 940, 407, 1619, 1.0, 1.0]\n",
+    "WARNING:root:[270, 941, 405, 1616, 1.0, 1.1]\n",
+    "WARNING:root:[271, 940, 406, 1617, 1.0, 1.2]\n",
+    "WARNING:root:[271, 942, 404, 1617, 1.0, 1.3]\n",
+    "WARNING:root:[271, 939, 404, 1614, 1.0, 1.4]\n",
+    "WARNING:root:[271, 940, 403, 1614, 1.0, 1.5]\n",
+    "WARNING:root:[271, 938, 404, 1613, 1.0, 1.6]\n",
+    "WARNING:root:[271, 938, 402, 1611, 1.0, 1.7]\n",
+    "WARNING:root:[271, 937, 401, 1609, 1.0, 1.8]\n",
+    "WARNING:root:[270, 937, 400, 1607, 1.0, 1.9]\n",
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+    "WARNING:root:[215, 989, 393, 1597, 1.5, 0.8]\n",
+    "WARNING:root:[213, 988, 394, 1595, 1.5, 0.9]\n",
+    "WARNING:root:[213, 987, 399, 1599, 1.5, 1.0]\n",
+    "WARNING:root:[213, 991, 398, 1602, 1.5, 1.1]\n",
+    "WARNING:root:[213, 987, 399, 1599, 1.5, 1.2]\n",
+    "WARNING:root:[213, 985, 398, 1596, 1.5, 1.3]\n",
+    "WARNING:root:[213, 983, 400, 1596, 1.5, 1.4]\n",
+    "WARNING:root:[213, 981, 401, 1595, 1.5, 1.5]\n",
+    "WARNING:root:[214, 980, 401, 1595, 1.5, 1.6]\n",
+    "WARNING:root:[214, 982, 398, 1594, 1.5, 1.7]\n",
+    "WARNING:root:[214, 979, 397, 1590, 1.5, 1.8]\n",
+    "WARNING:root:[213, 980, 395, 1588, 1.5, 1.9]\n",
+    "WARNING:root:[215, 989, 390, 1594, 1.6, 0.6]\n",
+    "WARNING:root:[215, 991, 394, 1600, 1.6, 0.7]\n",
+    "WARNING:root:[215, 990, 394, 1599, 1.6, 0.8]\n",
+    "WARNING:root:[213, 989, 395, 1597, 1.6, 0.9]\n",
+    "WARNING:root:[213, 988, 400, 1601, 1.6, 1.0]\n",
+    "WARNING:root:[213, 992, 399, 1604, 1.6, 1.1]\n",
+    "WARNING:root:[213, 988, 400, 1601, 1.6, 1.2]\n",
+    "WARNING:root:[213, 986, 399, 1598, 1.6, 1.3]\n",
+    "WARNING:root:[213, 984, 401, 1598, 1.6, 1.4]\n",
+    "WARNING:root:[213, 982, 402, 1597, 1.6, 1.5]\n",
+    "WARNING:root:[214, 981, 402, 1597, 1.6, 1.6]\n",
+    "WARNING:root:[214, 983, 399, 1596, 1.6, 1.7]\n",
+    "WARNING:root:[214, 980, 398, 1592, 1.6, 1.8]\n",
+    "WARNING:root:[213, 981, 396, 1590, 1.6, 1.9]\n",
+    "WARNING:root:[217, 988, 389, 1594, 1.7, 0.6]\n",
+    "WARNING:root:[217, 990, 393, 1600, 1.7, 0.7]\n",
+    "WARNING:root:[217, 989, 393, 1599, 1.7, 0.8]\n",
+    "WARNING:root:[214, 988, 394, 1596, 1.7, 0.9]\n",
+    "WARNING:root:[214, 987, 399, 1600, 1.7, 1.0]\n",
+    "WARNING:root:[214, 991, 398, 1603, 1.7, 1.1]\n",
+    "WARNING:root:[214, 987, 399, 1600, 1.7, 1.2]\n",
+    "WARNING:root:[214, 984, 398, 1596, 1.7, 1.3]\n",
+    "WARNING:root:[214, 982, 400, 1596, 1.7, 1.4]\n",
+    "WARNING:root:[214, 980, 401, 1595, 1.7, 1.5]\n",
+    "WARNING:root:[215, 979, 401, 1595, 1.7, 1.6]\n",
+    "WARNING:root:[215, 981, 398, 1594, 1.7, 1.7]\n",
+    "WARNING:root:[215, 977, 397, 1589, 1.7, 1.8]\n",
+    "WARNING:root:[214, 978, 395, 1587, 1.7, 1.9]\n",
+    "WARNING:root:[217, 987, 391, 1595, 1.8, 0.6]\n",
+    "WARNING:root:[217, 988, 395, 1600, 1.8, 0.7]\n",
+    "WARNING:root:[217, 987, 395, 1599, 1.8, 0.8]\n",
+    "WARNING:root:[214, 986, 396, 1596, 1.8, 0.9]\n",
+    "WARNING:root:[214, 985, 401, 1600, 1.8, 1.0]\n",
+    "WARNING:root:[214, 989, 400, 1603, 1.8, 1.1]\n",
+    "WARNING:root:[214, 985, 401, 1600, 1.8, 1.2]\n",
+    "WARNING:root:[214, 982, 400, 1596, 1.8, 1.3]\n",
+    "WARNING:root:[214, 980, 402, 1596, 1.8, 1.4]\n",
+    "WARNING:root:[214, 978, 403, 1595, 1.8, 1.5]\n",
+    "WARNING:root:[215, 977, 403, 1595, 1.8, 1.6]\n",
+    "WARNING:root:[215, 979, 399, 1593, 1.8, 1.7]\n",
+    "WARNING:root:[215, 975, 398, 1588, 1.8, 1.8]\n",
+    "WARNING:root:[214, 976, 396, 1586, 1.8, 1.9]\n",
+    "WARNING:root:[216, 988, 390, 1594, 1.9, 0.6]\n",
+    "WARNING:root:[216, 989, 394, 1599, 1.9, 0.7]\n",
+    "WARNING:root:[216, 988, 394, 1598, 1.9, 0.8]\n",
+    "WARNING:root:[213, 987, 395, 1595, 1.9, 0.9]\n",
+    "WARNING:root:[213, 985, 400, 1598, 1.9, 1.0]\n",
+    "WARNING:root:[213, 989, 399, 1601, 1.9, 1.1]\n",
+    "WARNING:root:[213, 985, 400, 1598, 1.9, 1.2]\n",
+    "WARNING:root:[213, 982, 399, 1594, 1.9, 1.3]\n",
+    "WARNING:root:[213, 980, 401, 1594, 1.9, 1.4]\n",
+    "WARNING:root:[213, 978, 402, 1593, 1.9, 1.5]\n",
+    "WARNING:root:[214, 977, 402, 1593, 1.9, 1.6]\n",
+    "WARNING:root:[214, 979, 398, 1591, 1.9, 1.7]\n",
+    "WARNING:root:[214, 975, 397, 1586, 1.9, 1.8]\n",
+    "WARNING:root:[213, 976, 395, 1584, 1.9, 1.9]\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "1318782f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def parse_log_string(log_string):\n",
+    "    lines = log_string.split(\"\\n\")\n",
+    "    data = []\n",
+    "    for line in lines:\n",
+    "        # Match the bracketed list using a regular expression\n",
+    "        match = re.search(r\"\\[(.*?)\\]\", line)\n",
+    "        if match:\n",
+    "            # Extract the list and convert it to numbers\n",
+    "            values = list(map(float, match.group(1).split(\", \")))\n",
+    "            # Extract count[2], R1, and R2, and calculate count[2]/9600\n",
+    "            data.append([(values[1] + values[2]) / 9600, values[4], values[5]])\n",
+    "    return np.array(data)\n",
+    "\n",
+    "\n",
+    "def parse_mega_log_string(mega_log_string):\n",
+    "    # Split the mega log string into individual logs\n",
+    "    log_strings = mega_log_string.strip().split(\"\\n\\n\")\n",
+    "    data = {}\n",
+    "    for log_string in log_strings:\n",
+    "        lines = log_string.split(\"\\n\")\n",
+    "        for line in lines:\n",
+    "            # Match the bracketed list using a regular expression\n",
+    "            match = re.search(r\"\\[(.*?)\\]\", line)\n",
+    "            if match:\n",
+    "                # Extract the list and convert it to numbers\n",
+    "                values = list(map(float, match.group(1).split(\", \")))\n",
+    "                # Extract count[2], R1, and R2, and calculate count[2]/9600\n",
+    "                r1, r2, count = values[4], values[5], (values[1] + values[2]) / 9600\n",
+    "                # Store the values in the data dictionary\n",
+    "                if (r1, r2) in data:\n",
+    "                    data[(r1, r2)].append(count)\n",
+    "                else:\n",
+    "                    data[(r1, r2)] = [count]\n",
+    "    # Average the values for each (R1, R2) pair and convert to a NumPy array\n",
+    "    averaged_data = []\n",
+    "    for key in data:\n",
+    "        averaged_data.append([sum(data[key]) / len(data[key]), key[0], key[1]])\n",
+    "    return np.array(averaged_data)\n",
+    "\n",
+    "\n",
+    "def parse_log_file(file_path):\n",
+    "    with open(file_path, \"r\") as file:\n",
+    "        lines = file.readlines()\n",
+    "\n",
+    "    data = []\n",
+    "\n",
+    "    for line in lines:\n",
+    "        # Match the bracketed list using a regular expression\n",
+    "        match = re.search(r\"\\[(.*?)\\]\", line)\n",
+    "        if match:\n",
+    "            # Extract the list and convert it to numbers\n",
+    "            values = list(map(float, match.group(1).split(\", \")))\n",
+    "            # Append the values to the data list\n",
+    "            data.append(values)\n",
+    "\n",
+    "    return np.array(data)\n",
+    "\n",
+    "\n",
+    "def plot_heatmap(data, title=\"Heatmap of percent unfiltered events with 2 matched vbf jets\"):\n",
+    "    # Create a 2D array for the heatmap\n",
+    "    heatmap_data = np.zeros((len(np.unique(data[:, 1])), len(np.unique(data[:, 2]))))\n",
+    "    for row in data:\n",
+    "        # Map the values of R1 and R2 to indices in the heatmap array\n",
+    "        r1_idx = np.where(np.unique(data[:, 1]) == row[1])[0][0]\n",
+    "        r2_idx = np.where(np.unique(data[:, 2]) == row[2])[0][0]\n",
+    "        # Set the heatmap value to count[2]/9600\n",
+    "        heatmap_data[r1_idx, r2_idx] = row[0]\n",
+    "\n",
+    "    # Define grid\n",
+    "    x = np.unique(data[:, 2])\n",
+    "    y = np.unique(data[:, 1])\n",
+    "    # Adjust the coordinates to represent the corners of the cells\n",
+    "    x_grid = np.concatenate([x - 0.05, [x[-1] + 0.05]])\n",
+    "    y_grid = np.concatenate([y - 0.05, [y[-1] + 0.05]])\n",
+    "\n",
+    "    # Create the heatmap\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.pcolormesh(x_grid, y_grid, heatmap_data, cmap=\"viridis\", shading=\"auto\")\n",
+    "    plt.colorbar(label=\"# events w 1 match + 2 matched vbf jets /num events passing ak8 selection\")\n",
+    "    plt.xlabel(\"R1\")\n",
+    "    plt.ylabel(\"R2\")\n",
+    "    plt.title(title)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# data = parse_log_string(log_string)\n",
+    "# plot_heatmap(data,title= 'Heatmap of percent unfiltered events with 1 or 2 matched vbf jets')\n",
+    "# data = parse_log_string(log_string_filtered)\n",
+    "# plot_heatmap(data,title= 'Heatmap of percent vbf selection (no dijet formed) filtered events with 1 or 2 matched vbf jets')\n",
+    "data = parse_log_string(log_string_filtered_full)\n",
+    "plot_heatmap(\n",
+    "    data, title=\"Heatmap of percent events with 1 or 2 matched vbf jets from vbf selected events\"\n",
+    ")\n",
+    "# data = parse_log_string(log_string_filtered_full2)\n",
+    "# plot_heatmap(data,title= 'Heatmap of percent events with 1 or 2 matched vbf jets from vbf selected events')\n",
+    "\n",
+    "# data = parse_mega_log_string(mega_log_string_filtered)\n",
+    "# plot_heatmap(data,title= 'Heatmap of percent events with 1 or 2 matched vbf jets from vbf selected events averaged over several samples')\n",
+    "\n",
+    "data = parse_log_file(\"heatmap_selection_success_with_correct_Higgs.txt\")\n",
+    "plot_heatmap(\n",
+    "    data,\n",
+    "    title=\"Heatmap of percent events with 1 or 2 matched vbf jets from vbf selected events averaged over several samples with identified Higgs\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "data = parse_log_file(\"heatmap_fatjet_exclusion_ak8selections_normalized_selected.txt\")\n",
+    "plot_heatmap(\n",
+    "    data,\n",
+    "    title=\"Heatmap of percent unfiltered events with 1 or 2 matched vbf jets (normalized to events w 2 passing ak8 jets)\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aee0be81",
+   "metadata": {},
+   "source": [
+    "Two functions where I visualize data I collected related to the R2 connection with HVV reconstructed pt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ba6bc788",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def plot_data(data):\n",
+    "    # Extract the pt, R2, and match information\n",
+    "    pt = data[:, 0]\n",
+    "    R2 = data[:, 1]\n",
+    "    match = data[:, 2].astype(bool)\n",
+    "\n",
+    "    # Define bins\n",
+    "    pt_bins = np.linspace(np.min(pt), np.max(pt), 50)\n",
+    "    R2_bins = np.concatenate((np.array([0.55]), np.arange(0.65, np.max(R2) + 0.1, 0.1)))\n",
+    "\n",
+    "    # Create 2D histograms\n",
+    "    plt.figure(figsize=(10, 7))\n",
+    "\n",
+    "    # Unsuccessful matches\n",
+    "    hist_unsuccessful, _, _, img = plt.hist2d(\n",
+    "        pt[~match],\n",
+    "        R2[~match],\n",
+    "        bins=[pt_bins, R2_bins],\n",
+    "        cmap=\"Reds\",\n",
+    "        alpha=0.5,\n",
+    "        label=\"Unsuccessful match\",\n",
+    "    )\n",
+    "    plt.colorbar(img, label=\"Count\")\n",
+    "    plt.xlabel(\"VVJet $p_T$\")\n",
+    "    plt.ylabel(\"R2\")\n",
+    "    plt.title(\"Unsuccessful Matches\")\n",
+    "    plt.show()\n",
+    "\n",
+    "    # Successful matches\n",
+    "    plt.figure(figsize=(10, 7))\n",
+    "    hist_successful, _, _, img = plt.hist2d(\n",
+    "        pt[match],\n",
+    "        R2[match],\n",
+    "        bins=[pt_bins, R2_bins],\n",
+    "        cmap=\"Blues\",\n",
+    "        alpha=0.5,\n",
+    "        label=\"Successful match\",\n",
+    "    )\n",
+    "    plt.colorbar(img, label=\"Count\")\n",
+    "    plt.xlabel(\"VVJet $p_T$\")\n",
+    "    plt.ylabel(\"R2\")\n",
+    "    plt.title(\"Successful Matches\")\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Use the function\n",
+    "plot_data(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e40b1fd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fP918AwYM0KlTp3Ty5El99NFH6tq1q8qVK6d//vlHCxcuVNOmTTN9jhIA4BYSJKAISlvLkdW/GJ8/f978vytUqGDzeMqWLauePXtqzpw5OnHihA4dOmReg/TBBx8oLi4uy+fKaM3PfffdJyn9gvU7RUVFadOmTeaH1KaNiY6ONn85t+b777/Xpk2bFBERkW6u0NDQLMdtjYuLiwYMGKAFCxbojz/+UEhIiPmWrTfffNNqda1EiRJq37693n33Xe3du1d//fWXXnrpJUnSnj17tGXLllzFdLvM1lfZStOmTXX//ffrxo0b2rp1q6Ts3V63atUq/fTTT3JyctKBAwe0efNmvf766/Lx8VHt2rXl4OCgxMTEbMeVlZ+nq1evatOmTdq0aZNu3ryZ7TluV6tWLfNatNz8XJlMJjVq1Ehjx47Vpk2bdO7cOQUFBal06dKKiYnRjBkzchUnABQHJEhAEZS2buL06dOZfvFP88svv0i69YU9u7fSWZOUlKS2bduqTZs26XZwS9OyZUvNnTtXkpSSkmJe73G7jJKmjBKAtFuLvvvuuwwrHwsXLlT37t315ptvymQyqX79+ua+lStXWh1z7tw5+fj4qHv37uZdx9Lm2rVrl65evWp13I0bN9SuXTs1b95cu3btMp+rTZs2atOmjY4cOZLueJPJpE6dOmn69OmSblUu0nZdGzFihNq0aaP33nvPYp5atWpp5syZql27tiTpP//5j9V4MmPtvU5OTraofOWFO2+zu3Dhgvbs2aOSJUuqR48edx3/888/S5Jat26t1q1bWz3mp59+ynZcaZ/x4cOHM0xYvv32W3Xv3l0DBw5Mt9FGTpQoUUL333+/pFvVtIxs2bJFzZs3V8eOHc3Vwvnz56tNmzZWE8ry5ctrxIgR5vc4Jz8fAFDckCABRVDaug3DMDRlypRMjz1w4IDWr18vSfL19bXJYv+SJUsqMjJSP/30k+bPn2+1EnJ7gpD213rpf7cabd261WLc+fPnNWvWLKtz9u3bVyaTSVeuXNHEiRMtxl6+fFnz58+XJHXo0ME8V6dOnSRJ06ZNs9h2WZJ5W+WKFSvK09NTkuTn5ycHBwdduHDB6lzSrcrYvn379Ndff5m/uLu5uemXX37RTz/9pODgYKvXkfa+uLq6mhf5JyQk6KefftLcuXOtJrxRUVHmKuDtSV9mbr+lK61yc7uFCxfq9OnTWTpXbqV9ed+0aZO+/vprGYYhb29vubq63nVs2g5358+fV0pKikX/zp07tXTpUknK9PbDpKSkdP9O2z0vJSVFr7/+ukUVKjExUbNnz5YktW/fXiVKlLhrrHeTdsvcv//9bx0+fNii//Lly3rjjTd05MgR1a9f3zyns7OzfvrpJ61du1bHjh2zem1pOy9m9ecDAIq1fHjWEoB8lpKSYrRt29b88Mhx48YZsbGx6Y5JTk42goODDXd3d/MDTc+cOWNxLmXwoNE0f/31l9WHzL7wwgvm9kGDBhk7d+40Tpw4Yezbt8949913jfLlyxuSDG9v73TnGzVqlHnc8OHDjd9//92IiIgwVq1aZdxzzz1GxYoVzQ/LvDOmMWPGmMc+9dRTxtq1a43ffvvNWL16tdGiRQtDklGhQgUjLCzMPObnn382SpQoYUgyatWqZcydO9c4dOiQsWvXLmPYsGHm86U93DTNq6++au578sknjTVr1hhHjhwxvv/+e8Pf39/c99FHH6Ub5+PjY+579dVXjb179xonTpwwdu3aZbz++utGyZIlDUnGkCFDzGO++uor85h27doZ33zzjXH06FHj0KFDxvz5843777/fkGRUrlzZuHjxYpY+v8TERPODc11cXIzg4GDj3LlzxtGjR40JEyYYJpPJaNq0aZ4+KDZNSkqKUa9ePUOS+edi3rx5FuOtPSh2w4YN5mt8+umnjV27dhnHjh0zNm3aZAwdOtQoWbKkYTKZzMcsW7bMiIqKMo/v2LGjIcnw8vIyLly4kO73ZNasWeZxXl5exldffWX89ttvxqZNm4xOnToZkowSJUoYP//8c7bem0mTJll9kPM///xj1KlTx/xQ4IkTJxo7duwwfv31VyM4ONj8YN1KlSoZp0+fNo/7888/zQ8Yrl27trFgwQLjl19+MY4cOWKsW7fOeOyxx8wPc87o9xgA8D8kSEARFR4ebv7SKckoU6aM0bJlS6Nnz55Gu3btjKpVq5r7nJ2djY0bN1o9T04TpEuXLhn33nuvuc/aq169ekZkZGS680VHRxs1atSwenyZMmWMbdu2Ga1bt7Ya082bN42ePXtmOF/ZsmWNNWvWWFzD8uXLzYmJtdeAAQOM5OTkdGPi4+ON/v37Z3p9I0eONFJSUtKNCwsLM1xcXDId17p1a+PatWvmMSkpKUbv3r0zHVOmTBlj9+7d2fr8bk+87nw99NBDxsGDB/MlQTIMwxg3bpx5bgcHh3RJTBprCVJKSorRo0ePTH/Gdu3aZZQqVcrcNmnSJPP4ESNGpDv+9qQlOTnZGDlyZIbnLlmypPHpp59m+73JKEEyDMM4efKk+Q8A1l4VKlQwfvjhB4txn376aaY/H5KMjz/+OMOYAAD/Q4IEFGGXLl0yXnzxRaNMmTIZfml68sknjePHj2d4jpwmSIZhGFevXjWmT59utGnTxqhRo4ZRqlQpo06dOkbHjh2NefPmGTdv3rR6zqioKOPFF180GjZsaDg5ORnu7u7G008/bY4zowTJMG59Yf7666+Nxx57zHBxcTFKly5tNG/e3HjuueeMs2fPZnidp06dMoYMGWLUqlXLHOeTTz5pfPvttxmOSUlJMb755huja9euRtWqVY1SpUoZ9evXN3r16mXs27cvw3EXL140xo8fb7Rs2dJwc3MznJycjHr16hk+Pj7GypUrjaSkJIsxycnJxqpVq4zHHnvMqFu3ruHk5GRUq1bNaN26tfHWW29ZTSgM4+6f3+7du40uXboY1atXN5ydnY0GDRoYkydPNq5fv26cOnUq3xKk//u//zPH2qlTJ6vjrSVIhmEYCQkJxmeffWa0bNnSqFixolGuXDnj4YcfNmbOnGlcv37dMAzDWLVqlVG9enWjQYMGxoYNG8xjIyMjje7duxvly5c3ypcvbzzzzDMW83733XdG9+7djapVqxpOTk5G48aNjQEDBhgnTpywODa3CZJh3KokTZ061WjevLlRrlw5o0KFCkbLli2NV1991YiOjs7wvD///LPRp08fo1GjRkbZsmWNSpUqGZ6ensaQIUOsxgoAsM5kGDZ6CAkAu3X9+nWFhobq9OnT+uuvv+Tm5qb69eurQYMG2XqYJQAAQFHHJg1AMVCmTBl5enqqb9++ev311zVo0CC1bduW5AgAgCIoLCxMAQEB8vDwkIeHh/z9/TN9bEGaq1ev6pVXXlGjRo3M3x2mTp2qhIQEi2NXr14tHx8fVapUSa1bt9b7779vseGNJIWEhKhHjx6qXLmyPD099cYbb+j69eu2uMw8QwUJAAAAKCJiY2Pl6empK1euyN/fX5L05ZdfqmLFivrtt98y3CE0JSVFbdq00aFDhzRw4EA98MADOnTokNavXy9vb299//33cnC4VVtZvXq1/Pz81Lx5c/Xo0UP/93//py1btmjkyJGaM2eO+Zz79+9Xhw4dVKdOHfXr109//PGHVqxYoa5du+rbb7+1yc65eaJg7/ADAAAAYCszZswwJBk7duwwt23bts2QZHzyyScZjlu1apUhWe6+mna+pUuXGoZxa/1t06ZNjUaNGhlXrlwxtw0YMMAoWbJkurWS3bt3N9zc3Ixz586Z28aPH29IMn777TebXG9e4BY7AAAAoIgIDg5W8+bN5e3tbW7z8fFR48aNtXz58gzH7d+/X5I0bNiwdO0vvviiSpUqpR9//FHSref1HTt2TMOGDVO5cuUk3Xro98svv6ykpCStWrVKkhQdHa2tW7dqwIABql69uvl8Y8aMkaRMYyloJEgAAABAEXDz5k0dOXLE/BD023l7e+vQoUNW1wlJt9YfNWnSxPwA7jSJiYkymUyKjo6WJB08eFCSLOZ48MEHVbFiRXP/4cOHlZSUZHGcm5ubmjZtaj7OHpUs6AAAAAAA5F5kZKQkpavYpElri4mJUbVq1Sz658+fb/Wc69atU3x8vJo2bZrpHA4ODnJ3d1dUVFSWYvn777+zdE0FgQQpVdmyZXXz5k2VKFFCbm5uBR0OAAAA7hAVFaXk5GQ5Ozvr2rVrBR1OOi1bttSFCxeyfLyRy33STCaTqlWrpv/7v/8zt6UlJS4uLhbHp7VFRUVZTZCsWbt2rZ577jmVKVNGL7zwQro5KlWqZHWOOxOkjGI5dOhQlmIoCCRIqW7evKmUlBSlpKQoIiKioMMBAABABm7evFnQIVi4cOFCgX+HTNsVzlrylZKSIunWLXN3c+7cOY0dO1Zff/21nJ2dtWLFCtWqVSvdHNakpKSYz3+3WLISR0EhQUpVokQJpaSkyMHBQdWrWN/+MKsMw9C56IuqUbWylR+inGxnaP0vDJEX4+Re2TIrz/k81kVejJV7ZWvvSU7+8mE9roznsI1bn0mMalStksEvdnavJeP317bvV0ZzZPbZZ1d+fCbWr91IMXQu5qJqVKksk8MdcZhst0QyMuai3KtYeeaTDZ9ykPn7ZZufL9vOkcFZMvtMcqJECavNkdExcq9axfqY5OTczytb/57kcB7HUrk+v5Fi6Nz586pRvbptPhNJSrR8ponN368Mrj0yMkru7nl7p0Zez2E3n4kNfr6ku7xfVuK6Kytx2WqO8zGxSklJUYkM/ttiDxxMJrmUK5/pMcZt/zcn4q5etZp4pN0FdenSJcsxcXGSdNfq0cqVKzV8+HBdunRJrVu31qJFi/TAAw9YnaNq1aoWc9SpUydLsWS1iiVJQ4YMUdOmTfXyyy9netxnn32m//73v5o+fXqWz20NCVIqNzc3RUREqHoVV53dtiZX57p89Zpc2nfViXXBqlCubPrOnOz3nsGXuCa9AnR83ZcZDMrmPJn8B75Jz2d1fP1Sy47Uv0RkSwbX36SXv46vC87y8ZnPYfnF+vLVa3Jp20UnNiy3/EwkKSmbf8VwyPjLe4bXkpMvfSnWxzTxG6rjqxZaduQkqShh/T8DGV6HJJXM5v9junHDavPlq9fk4v2UTqxaaPm5VMpBcpbR+9W1n45vXmnZkaO/XmXw++g7UMc3LLM+5Kb1689QKWfrc/QcqOPrM5gjwTZ/Tb189ZpcOj2lE6utfCY5YKpZ12p74w5ddOLHbVb7jIi/cj2vlMnviY1lNo+DZ7tcn//y5cuqWMVdp479pgoVKuT6fJKUcmSvRZut36+Mrv2BZi108uivNpunIOawl8/EFj9fUubvl7W47sZaXLaao3bX/oqIirHr5RAu5cpryStv5Okcg2Z+qItXLlu0u7u7S7pVAbpT2u1/mb1377//vsaPH69y5cpp4cKFGjRokPnZR9bmuD1BSklJUVRUlNq0aZOlWLKTIC1ZskSPP/74XROkHTt2aMeOHSRIAAAAACRnZ2d5enoqJCTEoi8kJEQtWrSQo6Oj1bG7du3S+PHjVbduXX377bdq3Lix1ePSEqCQkBB5enqa2w8fPqzLly+rVatWkm7taleyZEmFhITI19fXfFxkZKSOHz9+12RnyJAh6f597Ngxi7bbXbp0Sdu2bVOZMmUyPW9WkCABAAAANmSy4VKH7PL399err76qH374QZ07d5Ykbdu2TSdPntSnn36a4bi5c+dKurVrXUbJkSR5enqqadOmmjdvnp5//nmVK1dOhmFo1qxZcnR01IABAyTdqlQ98cQT+vrrrzVu3DjzbnaffPKJJGnw4MGZXseSJUvM/9tkMikiIiJdW0ZefPHFux5zNzlKkBISEvTTTz/p119/Vbly5dS+fXvdf//9GR6/Y8cORUREyN/fP8eBAgAAAMjcoEGDNGvWLPXt21cBAQFKTk5WcHCwPD09zd/FIyIitH79ejVs2NCcRB04cEBVqlTR3r17tXev5W2PaceaTCZNmDBBfn5+8vLyUo8ePfTzzz9r69atmjJliqpU+d/60jfffFPt27dXhw4d1K9fP505c0YrVqzQ4MGDzduGZ2Tnzp2Sbq0j9/b21sMPP6wPP/ww0zEuLi5q1qxZtt4va7KdIP3666969tlnderUKXObyWTSoEGDFBQUpFKlLBfmffzxx9q+fTsJEgAAAIq8gqsfSa6urtq9e7cmTpyodevWyWQyqWfPnpoxY4YqVqwoSQoNDdWoUaMUEBBgTpCio6N148YNjRo1yup5bz+2b9++WrlypebNm6dPPvlE9evX1+zZsy3GPvLII9q2bZtmzZqloKAg1axZUxMmTNCkSZPueh0dOnQw/28vLy+1adMmXVteylaCFBMTo3/961+KjY1Vu3bt9Nhjjyk6OlrLly/X4sWLde7cOW3evDnT7f8AAAAA5J26detq6VIrG2yl6tixo8UueNevX8/WHH5+fvLz87vrcY899pgee+yxbJ37Trt27crV+OzKVoI0depUxcbG6rXXXktX4nr99dfl7e2tbdu26ZNPPrnroivYRmC/p/Jpnp55P4df3s+RX/LjWgL79Mj7OfLhc88vgQP65P0cT+fD596vV57PkV8CBw3M+zny4fckP+fJa/l1HSNHDCsSc+QHPhPgf+Li4rR582b9+eefmR6XdgtgbpiMbDzGt0WLFjp79qwuXLhgsQPGoUOH1KZNG5UpU0anT582L8SSpCeeeELbt29Xso2ebZEX7rnnHkVERKimWxWbbfMdt2dznm7znTnbbfOdIRtu822z46VMt/mO278tz7f5zpANt/nOkA23+c6Ujbf5jgv5Jk+3+c6QDbf5zpSNtvnOlI23+Y7baeUzyYGMtvnOjK22+bYHttzm+5+YyDzdUtrWbLUFtT2yl88kP97j/IgrJ9t816xZU+Hh4dkNLU+lfZesXL6Clr46Lk/nenbGB7p45bJdvg95ITQ0VN7e3jp37pzV5z/dzmQy5TrnyNY3o9DQ0Ay3B2zVqpWGDBmihQsXasKECVqwYEGuAgMAAACAt99+WxEREapbt66GDRtmfsZSXslWglS9enWdP38+w/53331Xq1at0uLFi+Xn5ycfH59cBwgAAAAUJqzGt629e/eqbNmy2r9/f54nR5KUrftxHnjgAf3111/65ptvrPa7u7vr448/lmEYGjhwoP773//aIkYAAAAAxdSlS5fk6emZL8mRlM0E6e2335Z0a9eKwYMHKzg4WCdOnEh3zPPPPy8fHx/FxMSoc+fO+uabb3Qjg/UHAAAAQFFiyqdXcdKkSZN8Lbxk6xa7Vq1aae7cuXr99df15ZdfKjg4WEOHDtW8efPSHbdu3Tr5+flpy5Yt6t27t00DBgAAAIq6jT8f0KafD1rti7t2NZ+jKVhvv/22fH19FRwcnC/PVc329lXPP/+8evXqpTVr1ujPP//U/fffb3FMmTJltGHDBq1fv16LFi3S77//rqioKJsEDAAAANgzW1R4fB9+RL4PP2K175kZHyjmymUbzFI4dO/eXUuXLtWoUaO0Z88e9e3bV3Xr1pWTk5PV42vXrp2r+XKwv69UuXJlDRuW+X72JUqUUJ8+fdSnz63nj8TGxuZkKgAAAADFWIkStx4vYhiGFi1apEWLFmV4rMlkUlJSUq7my1GCFBoaqp9++kkXLlxQ/fr11bVrV3PgkpSUlKR//vlHsbGxio6O1n/+8x+9+eabunDhQq6CLSycSjlq4rAAOZWy3A4dBcOplKMmDh/MZ2JnnEo5auJzz/K52BGnUo6a+DyfiT1xcnLSpLffyvAvpch/fCbIlClnj3HM7hzFSfv27WXK8zf1f7KdIE2aNEnvvfeeUm57SGjjxo21bds2OTo66oUXXtDWrVuVkJBg7jcMI18vqqA5lSqlScMHF3QYuI1TqVKaFDi0oMPAHZxKldKkF/L+XmJkHZ+J/XFyctLkiW8XdBi4DZ8JkL927dqVr/NlK0HaunWr3nnnHUlSy5YtVbduXf355586fPiwhgwZotKlS+ubb75RiRIlVKdOHVWoUEGlS5dWo0aN1KRJkzy5AFuLvBinJr0CrPYF9ntKgf165nNEAAAAxUfQqg0KWrPRal/kxbh8jiZnik9ZoGjKVoI0a9YsmUwmBQUFpVuD9MUXX2jEiBEymUz617/+paVLl8rNzc3mweYH98ouOr7uy4IOAwAAoFgK9PNVoJ+v1b7aXfsrIiomnyNCQTt79my2js/XTRpOnz6t6tWrW2zQ8MILL2jatGmKiIjQJ598UmiTIwAAACC3TNSQbKpOnTrZWq6TnJycq/mylSCFh4fr0UcftWg3mUy69957FRERofr16+cqIAAAAABI4+XlZTVBSkhIUFhYmM6fPy8HBwc9++yzqlSpUq7ny1aClJKSorJly1rtK126tCSl280OAAAAKG6oH9nW3TZpOHz4sEaMGKH/+7//0759+3I9n0OuzwAAAAAABeShhx7Spk2bFBERobfeeivX5yNBAgAAAGzIZMrbFyy5ubmpbdu2WrduXa7PRYIEAAAAoNBLSEhQbGxsrs+T7QfFxsbGavfu3VbbJWnPnj0yDMPqWC8vr+xOBwAAABQqtijyrP/pgDb8fNBqX9zVqzaYoWgJCQlRSEiI7rvvvlyfK9sJ0qFDh9SpU6cM+zt27Gi13WQyKSkpKbvTAQAAAMVOz9aPqGfrR6z29f34fcVcvpzPERUcb2/vTPsvXryoEydOyDAMPfvss7meL1sJUu3atbO1BzkAAAAA5MbddrGTJEdHRw0ePFivvfZarufLVoIUFhaW6wkBAACAooxygm399ddfdz2mRo0acnR0tMl82b7FDgAAAADyi4eHR77OR4IEAAAA2IhJeV9BKu4VqsjISP3xxx9KSkpS/fr1Va1aNZuen22+AQAAANi1pKQkff7556pWrZpq1Kih9u3bq1OnTqpZs6aqVaumzz//3GYbwlFBAgAAAGyIPc1sr0ePHtq2bZsMw9ADDzygevXqycHBQWfOnNGJEyc0evRobd26VZs2bcr1XFSQAAAAANituXPn6rvvvtMDDzygn3/+WcePH9eGDRu0fv16HTt2TD/99JMaNGigrVu3av78+bmejwQJAAAAsCFTHr+Km3Xr1qlEiRLauHGjWrZsadHfqlUrbdy4USaTSatWrcr1fCRIAAAAAOzW0aNH1bhxY9WtWzfDY+677z41adJER48ezfV8rEECAAAAbMhULOs8eSs5Ofmux6SkpKhEiRK5nosE6Q6RF+PUpFeA1b5AP18F9uuZvjG7q/CMHARl5GBQdn8vU3ISWDGWklLQEViXnIPdW4wcXIuRzf90ODllf46E+OyPSUzI3vElbfNAOQCA7QSt2qCgNRut9kVejMvnaGAPmjVrppCQEP3+++9q2LCh1WN+//13nTx5Uo899liu5yNBuoN7ZRcdX7ukoMMAAAAolgL9fBXo52u1r3bX/oqIisnniLKP+pFtDRgwQD/88IN69Oih+fPnq0OHDun6d+/ereeff16GYah///65no8ECQAAALAza386oHU/HbDaF3v1aj5HU7AGDRqkzZs3a+3atfL29tY999yjevXqSZL++usvnT17VoZhqHfv3ho0aFCu5yNBAgAAAGzIFhWkPq0fUZ/Wj1jt8/3oPUVfvmyDWQqPVatWaenSpZo6dar+/PNP/fe//zX33XvvvZowYYKeffZZm8xFggQAAADArplMJvn7+8vf31/Xrl3TH3/8IUmqV6+eypYta9O5SJAAAAAAG8ruHl7InrJly6pZs2Z5dn6egwQAAADALp0+fVrvvPOOYmNjLfoaNmyoZ599Vl999ZVN5yRBAgAAAGzIlMev4iA+Pl6DBw9Wo0aNNHnyZN28edPimAsXLmj58uV69tln9fjjjysyMtImc5MgAQAAALArTzzxhIKDg1WmTBkNHDhQlSpVsjhm+/btmjhxoipXrqzt27frqaeessncJEgAAAAA7MaGDRu0a9cuNWrUSMeOHdOXX36pMmXKWBz38MMPa/LkyTpx4oQefvhh/fzzz1q5cmWu5ydBAgAAAGyIW+xyZ/78+TKZTFqwYIHq1Klz1+OrVq2q+fPnS5KWL1+e6/lJkAAAAADYjdDQUNWqVUtt2rTJ8pimTZvKw8NDZ86cyfX8hSZBMgxDdevW1bJly7J0/PXr1zVhwgQ98MADKlu2rDw9PTVr1iwlJSXlcaQAAAAorvK6elQcqkhnz55V7dq1sz3unnvuUVhYWK7nLzQJ0qZNm7J8wSkpKerZs6fee+89tWzZUhMnTtS9996rV155RYGBgXkbKAAAAIAcq1Spkv773/9me1xYWJjVtUrZZdcPir1+/bo2bdqkPXv2aNGiRVket3PnTm3fvl0fffSRxo4da24fOXKkgoKC9MYbb6hevXp5ETIAAACKOR4Umzv169fX3r17FR0drapVq2ZpTGRkpMLDw9WqVatcz2/XCdLZs2fVr1+/bI87cuSIJKlr167p2rt27aqgoCAdPXqUBAkAAAB2a9XBA1pz8IDVvotXr+ZzNPnrqaee0p49ezRmzJgsPwT2pZdekslk0pNPPpnr+e06Qapfv75u3LghSdq9e7e6dOmSpXH33nuvJOnMmTNq1KiRuT1t0RbJEQAAAPKKyQarhPq1aat+bdpa7Xvyw2mKunw513PYqxdeeEEzZszQypUrVaNGDU2ePFnlypWzemx8fLzeffddrVy5UhUqVNDIkSNzPb9dJ0gODg5ydnaWJJUqVSrL47y9vdWsWTONHDlSSUlJeuCBB/TTTz9p0qRJ6ty5sxo3bpxXIQMAAADIhbJly2rFihV6/PHHNWvWLH355ZcaPny4mjZtqnvvvVfOzs76448/dOrUKf373/9WeHi4eVvwKlWq5Hp+u06QcqpChQpat26dmjRpol69epnbq1evrrVr16pEiRIZjjUMQ5evXsv6ZHfcZOpUylFO2UjmAAAAiov4+HjFx8ena0vJxvcuI8WwdUh5giVIudeuXTvt2rVL/v7++v333/Xee+9ZPc4wDFWuXFkLFy5Ujx49bDJ3kUyQ/vjjD3l5ealKlSoaM2aM7rnnHv3yyy+aM2eOfHx8tGPHDpUtW9bq2HPRF+Xi1S3Hc08cFqBJwwfneDwAAEBR9f6HH2nKu9MKOgwUEi1bttSxY8e0ceNGbdmyRadPn1ZoaKiuXr2q+vXrq0GDBmrbtq0CAgIyvAUvJ4pkgjRq1ChdvXpVJ0+eVM2aNSVJTz/9tLy8vNS9e3fNmTNHb7zxhtWxNapW1om1X2Z9MisVJAAAAFh6843X9MpLo9O1pRyzvhGBNY37DtW5mIu2Dgt2rESJEurZs6d69uyZb3MWyQRp9+7d6ty5szk5StO1a1dVrFhRe/fuzTBBMplMqlDOenUpgwG5CRUAAKDYcHJykpOTU7q2lGx87zI58L0Lea9IJkiurq5KSEiwaE9JSVFCQoJcXFwKICoAAAAUBwX99/OwsDBNmjRJu3btkiR16NBBU6dOVZ06dbJ8jsGDB6tEiRJasGBBuvbY2Fhdv34907HVqlVTyZIllZiYqMjISKvHlC1b1m6/kxfJBKldu3Zat26djh8/riZNmpjbFy9erBs3bqhdu3YFGB0AAACQN2JjY9W+fXtduXJF/v7+kqQvv/xSu3bt0m+//SZXV9e7niM8PFzr1q1T3759LfpeeeUVffll5stR/vrrL9WpU0f79u1Tp06drB4zcuRIzZkzJwtXlP8KfYJ0+fJlBQcHq2bNmuZ7E6dPn65t27apTZs2Gjx4sO655x4dPnxYq1ev1qOPPqohQ4YUcNQAAAAoqgqygLRkyRKFh4drx44d8vb2liR169ZNXbp00dKlSzVmzJgMx27fvl0HDx7UvHnzdDmD5yy99NJL6tOnj0W7YRh69dVXVbp0adWoUUOSFBoaKulWgnZnYpadalZ+K/QJUmxsrEaNGqUOHTqYE6R77rlHx48f19tvv62tW7fq3Llzuv/++zVlyhS9/vrrKlmy0F82AAAAYCE4OFjNmzc3J0eS5OPjo8aNG2v58uWZJkj+/v4Z3hKXpnnz5mrevLlF+9KlS3X27FkdPnzY/PzS06dPq3Tp0ho4cKAcHBxydkEFoNBkCh07dpRhWO59X6dOHavt1atX18KFC/MjNAAAAMCsoCpIN2/e1JEjR/Tyyy9b9Hl7e+uzzz5TUlJShsWCsLAw8/8uXbp0lucNDw/XqFGjNH36dDVu3Njcfvr0aTVo0EAODg7mdUtlypTJ8nkLSuFJ5QAAAIAiICEpSVdv3szxy0ptQJLM1Z/q1atb9KW1xcTEZBiXs7Oz+ZUd48aNU/Xq1TVy5Mh07aGhoUpOTlbbtm1VtmxZlS9fXk2bNtWWLVuydf78VmgqSAAAAEBhcLcK0qJdOzU35Aebz5uWIFnbHS6tLSoqStWqVbPZnEePHtVXX32l1atXq0SJEub25ORknTlzRomJiXr66ac1bNgwxcXFac6cOeratavWrFmj3r17Wz1ncHBwrmJK25wip0iQAAAAgHw0tGMnPduufY7HPzVrhqKvWG6iYErdX9za8pOUlBRJUmJiYo7ntebtt99WixYt1KtXr3TtV69e1QsvvCBPT089//zz5vbBgwerYcOGevnll9WrVy9zzLcbNGiQ1fa7MQxDJpOJBAkAAAAoTEqVLKlSudg0LKPcwc3NTZJ06dIli764uDhJsmn16MyZM/r222/1xRdfWCQ0FStWtLqNd8WKFeXv76/p06fr/Pnz5h3vbjdx4kSL8+3cuVO7d+9WyZIl9a9//Uv33XefTCaT/vzzT23fvl2JiYkKDAzUE088kevrIkECAAAAbKigNmlwd3eXJJ07d86i78KFC5L+l0TZwoIFC+Tk5CQ/P79sjfPw8JAkRUdHW02QJk+enO7f+/fv17vvvqu2bdvq66+/Vq1atdL1nzt3Ts8884y++OIL9e/fP3sXYQWbNAAAAABFgLOzszw9PRUSEmLRFxISohYtWsjR0dEmcyUmJmrx4sXy9fVVpUqVLPo3btyobt266fTp0xZ9YWFhMplMql+/fpbm+vTTT1WiRAlt2LDBIjmSpBo1amjt2rUqWbKkJkyYkO1ruRMJEgAAAGAzJplMefvKrEbl7++vo0eP6ocf/rcJxLZt23Ty5EkNHjzYZld54MABRUVFycfHx2p/w4YNtXnzZr3zzjvp1kRFRkZq0aJF6tKlS5a3Ej948KA8PT1VuXLlDI9xdXWVp6enDh06lL0LsYJb7AAAAIAiYtCgQZo1a5b69u2rgIAAJScnKzg4WJ6enubNCyIiIrR+/Xo1bNhQnTt3ztE827dvlyQ9+uijVvvr16+vF198UXPmzNH58+fVuXNnRUdHa+nSpUpJSdFnn32W5blu3ryp8+fP3/W4iIgIVaxYMcvnzQgVJAAAAKCIcHV11e7du9WtWzetW7dOGzduVM+ePbVjxw5z8hAaGqpRo0Zp2bJlOZ5n+/btcnV1VYMGDTI8ZubMmfrkk090/vx5TZ06VevXr9fjjz+u48eP67777svyXC1btlRERIRWrFiR4THLly9XRESEHnzwwWxdhzVUkAAAAIAipG7dulq6dGmG/R07drS6Ffjt7tb/888/3zUOR0dHjRkzRmPGjLnrsZkZOXKktmzZosGDB+vIkSMaOnSo6tatK0n666+/tGDBAs2ePVsODg4aPXp0ruaSqCABAAAANmPKp1dx8sQTT+jdd99VYmKipk+frgYNGsjZ2VnOzs5q0KCBpk+fruTkZH3wwQc5vmXwdlSQ7hB5MU5Neg+y2hfo56vAfk+lb8w8ubYi2wOku2TwtpGDOUw5yK+zO42Rkv05cpL2Z/c9zsHDy2CHblzL/pjSZWwfRxFm/PeP7A9y4G93QHEWtGqDgtZstNoXeTEun6OBvRg/frx69eqlDz74QMeOHdOZM2dUqlQp3XvvvWrRooVee+011atXzyZzkSDdwb2yi46vXVzQYQAAABRLgX6+CvTztdpXu2t/RUTF5HNEsBcNGzbUkiVL8nweEiQAAAAAhUJ8fLwOHz6smJgYVahQQR07dlRiYqLNnu8kkSABAAAANsWN+LZ3+fJlTZgwQV988YUSExMlSV26dFHHjh31zDPPyMHBQXPmzFGVKlVyPRcJEgAAAGBnvjqwX18f3G+1L+bqlXyOpmDduHFDTzzxhA4ePKjKlSurU6dOWr16tbm/QoUKWrRokY4cOaKff/5Z5cuXz9V8JEgAAACAnRnwSFsNeKSt1T7v96cp8vI/+RxRwZk7d64OHDggHx8frVy5UhUrVkyXIM2fP19Vq1bVhx9+qM8++0zjx4/P1XxsFQQAAADAbi1dulTOzs5atmyZ+WG3tzOZTHrnnXdUtWpVrVmzJtfzkSABAAAANsQzkGwrNDRUTZs2zXR9UcmSJdWoUSP98UcOHi9xBxIkAAAAAHbL3d1dly9fvutxMTExcnFxyfV8JEgAAACADZlMefsqbpo3b67Tp0/rp59+yvCYEydO6Pfff1ezZs1yPR8JEgAAAAC79corr8jBwUF9+vTRtm3bZBhGuv6ff/5Zffv2lWEYCgwMzPV8JEgAAAAA7Fbbtm31wQcf6Pz583ryySfl6uoqk8mkPXv2yNXVVY888oh+//13jR07Vo8//niu5yNBAgAAAGyKbRps7dVXX9Wvv/6qHj16qGzZsjIMQ9euXVOJEiXk7e2tH3/8UR9++KFN5uI5SAAAAADsXtOmTbV+/XpJ0rVr15SYmKhKlSrZfB4SJAAAAMCGimeNJ3+VLVs2z87NLXYAAAAA7NaQIUMUGBioCxcuZHrcE088oZIlc1//IUECAAAAYLeWLFmiL774Qq1atdKhQ4cyPfbOHe5yglvsAAAAADuz/MA+fXVgv9W+mKtX8jmagufq6qoLFy7Iy8tL8+bN07PPPptnc5EgAQAAADZkizVIAx95VAMfedRqn9d77yry8j82mKXwaNWqld544w316dNHgwYN0pEjRzR9+nQ5ONj+hjgSpDtEXoxTk96DrfYF+vkqsN9T+RsQAABAMRK0aoOC1my02hd5MS6fo4E96dChgw4dOqSnnnpKs2bN0rFjx7RixQq5uLjYdB4SpDu4V3bR8bWLCzoMAACAYinQz1eBfr5W+2p37a+IqJh8jij72MUu79SpU0f79+/X4MGDtXr1aj388MPauHGjGjVqZLM52KQBAAAAQKFRpkwZrVy5UtOmTdOff/6pRx55RBs3Wq865gQJEgAAAIBC580339SGDRskST179tTBgwdtcl4SJAAAAACFUrdu3XTw4EHdd999+ucf22xcwRokAAAAwIZMLEKyqb/++kulS5fOsL9hw4b6+eefNWfOHCUlJeV6PhIkAAAAAHbLw8PjrsdUrFhRb731lk3mI0ECAAAAYDemTp2q++67TwMGDJAkBQcHZ2u8v79/ruYnQQIAAABgNyZPnqzHH3/cnCANGjRIpizct2gYhkwmEwkSAAAAYE9MPAkpV7y8vNSsWTPzvydOnJilBMlWSJAAAAAA2I1du3al+/fkyZPzdX4SJAAAAMBGTKmvvJ4DeYcECQAAALAzS/fv07ID+6z2RV+9ks/R5K/du3fnaryXl1euxpMgAQAAAHbm2baP6tm2j1rte3TaO7pw2TYPRbVHHTt2zNWao+Tk5FzNT4IEAAAAwG74+/vn66YMdyJBAgAAAGA3lixZUqDzkyABAAAANsQmCgVj06ZNKlWqlHx8fHJ1HhIkAAAAAIXapUuXNGrUKKWkpOjvv//O1bkcbBQTAAAAAOSJ6dOnq169eipRooTVV+XKlfX333+rTp06uZ6LChIAAAAAu7V8+XKNGzdOklS9enXFxMQoMTFR99xzjwzDUEREhCTJ29tbX3zxRa7nI0G6Q+TFODXpPdhqX6CfrwL7PZW/AeWYkaeH3xqTkv0xDtksWqbkYA4jBxeT3Wsxlcj+HMiehJvZH2OiKA4AhV3Qqg0KWrPRal/kxbh8jiYHTFKeb8BWzBY5zZ8/XyaTSZs3b9bjjz+unTt3qnPnzlq/fr0eeugh/f333+rZs6cSEhJ077335no+EqQ7uFd20fG1iws6DAAAgGIp0M9XgX6+Vvtqd+2viKiYfI4IBe306dNq0KCBHn/8cUlShw4dVKFCBR0+fFgPPfSQPDw8tHTpUjVt2lSfffaZRo8enav5+HMrAAAAYEOmPH4VN7Gxsapevbr53w4ODmrZsqUOHTpkbmvcuLEaNGigb7/9NtfzkSABAAAAsFvu7u7mdUZpmjVrppCQkHRtrq6uOnz4cK7nI0ECAAAAYLcaN26s0NBQrV+/3tzWrFkzhYWF6T//+Y8kKTk5WUeOHJGzs3Ou52MNEgAAAGBngvfvU/D+fVb7oq9cyedoCtaYMWP03XffqU+fPho6dKjmzZunVq1ayTAMPffccxo2bJh27typ69ev67HHHsv1fCRIAAAAgA2ZbLBSKKBtOwW0bWe1r/W0qbrwzz+5nqOw6NKli77++mtNnjxZ58+flyQ98MADeu6557RgwQLt379fhmGofPnymjp1aq7nI0ECAAAAYNf69eunfv36KeW2R8DMnTtXHTp00P79+1WjRg3179+fbb4BAAAAFB8Otz1T08HBQc8884yeeeYZ285h07MBAAAAQCFGBQkAAACwoeL4rKK8ZBiGPv/8c61du1Znz56VYRgZHmsymfTHH3/kaj4SJAAAAAB2a/r06Ro/fnymiZEtFZpb7AzDUN26dbVs2bIsj9myZYseeeQRlS1bVvfff7/ee+89JSUl5WGUAAAAQMEKCwtTQECAPDw85OHhIX9/f4WFhWXrHIMHD9Zzzz1nte/q1asKDw+3+rp+/Xq6Y0NCQtSjRw9VrlxZnp6eeuONNyyOuZuFCxdKkt566y1FRUUpJSUl01duFZoEadOmTdn6YFetWqWuXbvq6tWrevXVV9W0aVO99dZbGj9+fN4FCQAAgGLPlMevzMTGxqp9+/basGGDfH195evrqw0bNsjLy0uxsbFZij88PFzr1q3LsP/jjz9WrVq1rL62bNliPm7//v3q0qWLTp06pREjRuiBBx7Q9OnT5efnl61q0Llz59SgQQO98847qlKlSpbH5ZRd32J3/fp1bdq0SXv27NGiRYuyPC45OVmvvvqqmjVrpv3796ts2bKSpIEDB2rmzJl65ZVXVK1atbwKGwAAACgQS5YsUXh4uHbs2CFvb29JUrdu3dSlSxctXbpUY8aMyXDs9u3bdfDgQc2bN0+XL1/O8LjQ0FDVqFFDX3zxhUXfQw89ZP7fH3zwgVxdXbV7925Vr15dknTvvffqvffe09GjR+Xp6Zmla2ratKkSEhKydKwt2HWCdPbsWfXr1y/b43744QeFh4dr9uzZ5uRIkkaMGKFNmzZp37596t27ty1DBQAAAG4pwF0agoOD1bx5c3NyJEk+Pj5q3Lixli9fnmmC5O/vr8jIyLvOcfr0aTVr1kzdunXL8Jjo6Ght3bpVL774ojk5kqQxY8bovffe0/Lly7OcID311FN6++239csvv+jBBx/M0pjcsOsEqX79+rpx44Ykaffu3erSpUuWxm3YsEGOjo7y8fFJ1/7oo4/q0qVLtg4TAAAAKHA3b97UkSNH9PLLL1v0eXt767PPPlNSUpJKlrSeAty+nKV06dJWjzEMQ6dPn1a7du0kSZcuXVK5cuUsznn48GElJSWpU6dO6drd3NzUtGlTHTx4MMvX9cYbb+jUqVN6/PHHNXXqVLVv317ly5fP8PjatWtn+dzW2HWC5ODgIGdnZ0lSqVKlsjwuIiJCNWvWVLly5SRJly9fVtmyZVWiRIk8iRMAAABIU1AFpLTqz+0VmzRpbTExMRkuNUn73p2Z6OhoXb58WadOndK9996rv/76S87OzmrXrp0+++wzNWzYMEux/P3331m7KN1adnPp0iXFxMRo5MiRmR5rMplyvSmbXSdIOXXhwgVVqVJFa9as0fjx4xUaGipnZ2c99thjmj17turVq5fhWMMwdPnqtWzMlv5XwKmUo5yykcwBAAAUF/Hx8YqPj0/XlpKN711GSv5s85zX4pOSlJCLL/EZbXCQlpS4uLhY9KW1RUVF5Wot/unTpyVJO3fu1JgxY9S8eXP95z//0cyZM9WyZUudOHFCHh4ed43l0KFDWZ7z5Zdf1rfffitJatSokapUqSKTKe/S0CKZIMXGxurChQsaMmSIRo0apaZNm+r06dOaOXOm2rdvrxMnTlj9sCTpXPRFuXh1z/HcE18I0KThg3I8HgAAoKh6/8OPNOXdaQUdRoGbE7JDs77fbvPzpiUN1hKotO2vExMTczWHs7OzRo4cqT59+qhjx47m9ieffFJt2rTRhAkTFBwcfNdYshPHunXr5ODgoK1bt6pz5865ij8rimSCVLlyZZ05c0a7du1Shw4dzO2PPvqoOnfurKCgIL311ltWx9aoWlkn1i7JxmyWFSQAAABYevON1/TKS6PTtaUcO5Dl8Y37DtW5mIu2Divfvej9mF7w6nD3AzPQ8aMPFWlllzk3NzdJsrrmPi4uTpJyvZNzy5Yt1bJlS4v21q1b6+GHHzavLbpbLNmJ49q1a3rwwQfzJTmSimiCVK1aNVWvXj1dciTdWpxWvnx5/frrrxmONZlMqlCubIb9VkbkMEoAAIDixcnJSU5OTunaUrLxvcvkUDi+d5nu8v3QuaSjnEvm/I/qGd1e5u7uLunWc4PudOHCBUn/S1zygoeHh/kWvLvFkp0EqVmzZll+hpMtFJoHxWZH48aNdfXqVYsn6SYnJyshIUGurq4FFBkAAACQN5ydneXp6amQkBCLvpCQELVo0UKOjrm722natGny8/NTcnKyRV9YWJgaNWokSXrwwQdVsmRJi1giIyN1/PhxtWrVKstzTpkyRX/88YeWLVuWq9izqkhWkAYNGqT33ntP8+fP17Bhw8ztCxcuVHx8vNq3b1+A0QEAAKCoMknKw/0DzHNkxN/fX6+++qp++OEH8y1p27Zt08mTJ/Xpp5/mem43NzetXr1aXbp00dChQ83tO3bs0KFDh/TBBx+Yj3viiSf09ddfa9y4cebd7D755BNJ0uDBg7M8p4uLi8aOHauAgABt3rxZ7dq1y3Sbb39//xxc2f8U+gTp8uXLCg4OVs2aNdWzZ09J0v3336+nn35aw4cP186dO9W8eXMdPXpUX3/9tTp27KhnnnmmgKMGAAAAbG/QoEGaNWuW+vbtq4CAACUnJys4OFienp7mxCEiIkLr169Xw4YNs72uZ+DAgZo3b56GDRumH3/8UU2aNNF//vMfLV26VA899FC6ZzC9+eabat++vTp06KB+/frpzJkzWrFihQYPHqymTZtmec42bdrIZDLJMAytXLlSq1atsnqcYRgymUwkSLGxsRo1apQ6dOhgTpAkafHixapfv762bNmiTZs2qW7dupo8ebLGjRsnB4cieWchAAAA7EEBLpVydXXV7t27NXHiRK1bt04mk0k9e/bUjBkzVLFiRUlSaGioRo0apYCAgGwnSKVLl9bWrVv1zjvvaOPGjVq9erXuv/9+vfbaa5owYUK6Z5c+8sgj2rZtm2bNmqWgoCDVrFlTEyZM0KRJk7I158SJE/N0W+87mYyMNlIvZu65555bD5h1q6Kz31nPSq3Lhw8rJx9RdsPKr5+C7Cand6wjy5Kc/AKlWN5HmymHHDx0OCefY3bjysn7lZM/GJTI5t9WcvKQ5uQcPB/ClM1rSUzI/hyly2R/zM0b2Tu+1N0f1Gch4Wb2x+SH/PqZtFMOnu0KOgSrUo7szfM57PXa7VVOPpP8eI/zI67szFG7a39FRMWoZs2aCg8Pz25oeSrtu2T1ihV1eGL2EoDsemjqFJ3/5x+7fB+KgqLz/4UAAAAAO2DK41dx88orr+jzzz/Pt/lIkAAAAADYrYULF2rmzJn5Nh8JEgAAAGBLlJBsql+/fvr777919OjRfJmPBAkAAACA3fr888/Vr18/+fn5adu2bcrrLRQK/S52AAAAgD0xFccyTx7y9fVVSkqK/vjjDz355JNycHBQ1apV5exsuamRyWTSH3/8kav5SJAAAAAAO7No714t3md9h7/oK1fyOZqC9d1336X7d3Jysi5cuJBn85EgAQAAAHZmSLt2GtLO+pbpLaZM1vl//snniApOSk4eGZELJEgAAACALXGHXaFGggQAAACgULh06ZL+85//6I8//lBSUpLq16+vhg0bqlKlSjabgwQJAAAAsCEKSLYXFxen9957T3PmzFFCQkK6PkdHR40aNUpvvfWWTRIlEiQAAAAAdispKUldunTR4cOH5ejoqCeeeEL16tWTg4ODzpw5o++//14zZ87Unj17tG/fPpUoUSJX85Eg3SHyYpya9B5ktS+wr68C/Z5K35jdPxHk158Usrs9fB7vJ2+WnJS943MSlykfHu+Vk8WCJv6elC13/HUoS5wst/sEABQuQas2KGjNRqt9kRfj8jmaHOL/5dvURx99pP/7v/+Tl5eXvvrqK9WoUSNdf3h4uPr376/9+/drxowZev3113M1HwnSHdwru+j46sUFHQYAAECxFOjnq0A/X6t9tbv2V0RUTD5HhIK2bds2OTk5ae3atapcubJF/z333KM1a9aodu3a2rx5MwkSAAAAYE94UKxtHT16VE2bNrWaHKVxd3dXs2bNdOzYsVzPlw/3IgEAAABAzpQrV06XLl2663GXL19W+fLlcz0fCRIAAABgQyZT3r6Km2bNmumPP/7Q/v37Mzxm//79Cg0NVbNmzXI9HwkSAAAAALs1bNgwGYahnj176ssvv1RiYqK5LykpScHBwerVq5dMJpOGDRuW6/lIkAAAAADYre7du+vll19WdHS0hgwZojJlyujee+/VvffeqzJlymjw4MGKiorSmDFj1K1bt1zPR4IEAAAAwK7NmDFDO3fuVKdOneTi4qKwsDCFhYWpUqVK6tSpk3bu3KmZM2faZC52sQMAAABsyBbrhBbu3qOFe/Za7Yu+ciX3ExRCHTp0UIcOHSTd2pBBkipUqGDzeUiQAAAAADsz1Ku9hnq1t9rXdMIknf/nn3yOyL7kRWKUhgQJAAAAsBWT8n6ruSK+k11wcHCuxvv7++dqPAkSAAAAALsxaNAgmXKYZJpMJhIkAAAAwJ4U8QJPnhs9enS2EqSzZ89q/fr1NpufBAkAAACA3fjkk0+ydJxhGJo7d64WLVokSapTp47mz5+f6/nZ5hsAAABAoRIaGqqOHTvqxRdf1LVr1/Tyyy/r+PHjeuyxx3J9bipIAAAAgC1xj12eSU5O1scff6wpU6bo5s2batKkiRYsWKCHH37YZnOQIAEAAACwe7/99puGDh2q3377TSVLltSUKVM0btw4OTo62nQeEiQAAADAhigg2VZ8fLymTp2qjz76SElJSWrTpo0WLlyoRo0a5cl8JEgAAAAA7NLevXv13HPPKTQ0VGXKlNGMGTM0cuRIOTjk3VYKJEh3iLwYpyZ9B1vtC+zrq0C/p/I3IAAAgGIkaNUGBa3ZaLUv8mJcPkeTfbeeE5u3NaTiUKG6evWqxo0bp3//+98yDEM+Pj764osv5OHhkedzkyDdwb2yi46vXlzQYQAAABRLgX6+CvTztdpXu2t/RUTF5HNEyG9bt27V8OHDFR4eLldXV82aNUsDBw7Mt/lJkAAAAAA7M//H3Vq4e4/VvqjLV/I5mvzVtWtXcxWuQYMGWrdundatW5elsSaTSWvXrs3V/CRIAAAAgJ15voOXnu/gZbWv8VsTdO7SP/kcUf4yDEOStH///myNs8XtjSRIAAAAgA3l8RKkIm/nzp0FOj8JEgAAAAC70aFDhwKdP+/2xwMAAACAQoYECQAAAABScYsdAAAAYEOsQSrcqCABAAAAQCoqSAAAAIDNmPKhhESJKi9RQQIAAACAVFSQAAAAABuivlO4UUECAAAAgFRUkAAAAAAbYhe7wo0KEgAAAACkooIEAAAA2Jl5O3dr3o97rPZFXb6cz9EULyRIAAAAgJ15oZOXXujkZbWvwRsTdO7SpfwNqBjhFjsAAAAASEUFCQAAALAhE7s0FGokSHeIvBinJn0HW+0L7OurQD/f9I1GdmfIwS+Mke1J8mf7FCMl+2OKc1zFWXx8QUcAACgkglZtUNCajVb7Ii/G5XM0KI5IkO7gXtlFx1cvKugwAAAAiqVAPyt/kE5Vu2t/RUTF5HNEKG5YgwQAAAAUIWFhYQoICJCHh4c8PDzk7++vsLCwbJ1j8ODBeu6556z2paSk6N///rdatmyp8uXLq169eho6dKgiIyMtjgsPD7f6iomx30SXBAkAAACwIZMpb1+ZiY2NVfv27bVhwwb5+vrK19dXGzZskJeXl2JjY7MUf3h4uNatW5dh/+uvv67AwECVL19e48aNk4+Pj7766is1a9ZMUVFR5uPOnj2rWrVqWX0NGjQoS7EUBG6xAwAAAIqIJUuWKDw8XDt27JC3t7ckqVu3burSpYuWLl2qMWPGZDh2+/btOnjwoObNm6fLGTxrKTw8XJ9++qm6deumDRs2yMHhVr1l0KBBevTRRzV+/HgtWLBAkhQaGipJmj59uho1apTuPG5ubrm+1rxCggQAAAAUEcHBwWrevLk5OZIkHx8fNW7cWMuXL880QfL397e4Te5Ohw8fVmJiooYNG2ZOjiSpdevW8vLy0o8//mhuO336tPm87u7uOb2kfMctdgAAAEARcPPmTR05ckSdOnWy6PP29tahQ4eUlJSU4fiwsDDduHFDN27cyPCYixcvqmbNmrrvvvss+pKSkhQdHW3+9+nTp1WpUiW5ubkpPj5eV65cyeYVFQwSJAAAAMBWTLeeg5SXr4yeGpNW/alevbpFX1pbZpsjODs7m18ZGTJkiMLDw9WwYcN07X/99Zf27dunpk2bmttCQ0NVrlw5PfnkkypTpowqVKig+++/X8HBwTJy8hibfMItdgAAAEA+ik9MVHwmlZy7ySi5SEuQXFxcLPrS2qKiolStWrUcz23NqVOn5OPjo5SUFL3yyivm9tOnTys8PFxNmjTRF198oYSEBC1cuFABAQGKiYlJd6w9IUECAAAAbOkuO819tHW73tu4xfbTpm5xZy2BSklJkSQlJibabL4bN27oww8/1AcffKD4+Hi99dZb6tmzpzmGrl27qmbNmho7dqx5vdLzzz+vli1bauLEiXruuedUoUIFm8VjKyRIAAAAQD567UkfjfbxvvuBGWjx9js6f+kfi/a0neEuXbpk0RcXFydJNqse/fbbbxowYIBOnTqlmjVrKigoSD169DD3m0wmzZ4922Kco6Ojhg8frsDAQB0/flxt27a1STy2RIIEAAAA2NBdCkhydnSUs6Njzs+fwcOQ0naKO3funEXfhQsXJNlme+1du3apS5cuSkhI0NixYzVp0iSVK1cuy+M9PDwkKd2GDvak0GzSYBiG6tatq2XLluVo/Ntvv62SJckHAQAAUDQ5OzvL09NTISEhFn0hISFq0aKFHHORmEm3KlG9evVSyZIltXr1an300UdWk6OffvpJ3bp10549eyz6wsLCJMni2Uj2otAkSJs2bTK/mdl16tQpTZ8+3bYBAQAAANaYTHn7yoS/v7+OHj2qH374wdy2bds2nTx5UoMHD871pa1cuVJxcXF677331KdPnwyPa9SokX744QdNmDAh3dbiV69e1WeffabGjRurXr16uY4nL9h1SeX69evatGmT9uzZo0WLFuXoHIZhaMSIEUpMTFSJEiVsHCEAAABgPwYNGqRZs2apb9++CggIUHJysoKDg+Xp6Sl/f39JUkREhNavX6+GDRuqc+fO2Tr/gQMHJElXrlzRnDlzLPrLly+vgIAAVahQQdOmTdPYsWP1yCOP6KmnntK1a9e0YsUKnT9/Xt9//73dfje36wTp7Nmz6tevX67OsXTpUu3evVtNmjTRqVOnbBQZAAAAYH9cXV21e/duTZw4UevWrZPJZFLPnj01Y8YMVaxYUdKt5xONGjVKAQEB2U6Q0tYNTZgwwWq/h4eHAgICJEmvvPKKqlSpok8//VQffPCBKlasqIcfflhbtmyxeI6SPbHrBKl+/frmJ/nu3r1bXbp0ydb42NhYvfrqqxo2bJhu3LhBggQAAIC8d7ddGvJY3bp1tXTp0gz7O3bseNcHtWbUv2VL1rcnN5lMCggIMCdMhYVdr0FycHAwP823VKlS2R4/btw4mUwmTZs2LQ+iAwAAAFDU2HUFKTf279+v+fPn68svv5Srq2uWxxkphi5fvZaNmdL/icCplKOccpDMAQAAFHXx8fGKj49P15aSje9dRkrmVQ/7UcAlJORKkUyQEhMTNXz4cHl5eenZZ5/N1thzMRfl0tE3x3NPfN5fk4YVrjIiAABAfnj/w4805V3u7IF9K5IJ0uzZs3Xq1Cn99ttvGT5IKyM1qlTWiTXZ2THPsoIEAAAAS2++8ZpeeWl0uraUYweyPL5x36E6F3PR1mHZHgWkQq3IJUhxcXGaNGmSnn32WVWsWFHh4eGSpGvXrskwDIWHh8vBwUE1atSwOt7kYFKFcmWzMSO/AQAAAFnh5OQkJyendG0p2fjeZXLgexfyXpFLkP755x9dv35dixcv1uLFiy36a9WqpcqVKysmJqYAogMAAECRl807mGBfilyC5Obmpm+//daifdasWdq5c6c2btwoR0dugwMAAID9mvvDLn2xY5fVvqh/LudvMMVMoU+QLl++rODgYNWsWVM9e/ZUmTJl1K1bN4vj1qxZIwcHB6t9AAAAgC2YZJsC0oh/ddSIf3W02ldvzDhFxF3K/SSwyq6fg5QVsbGxGjVqlGbPnl3QoQAAAAAo5ApNBSmjJ/7WqVPnrk8ClqQlS5ZoyZIleRAZAAAAcDvWIBVmhb6CBAAAAAC2UmgqSAAAAEChQAGpUKOCBAAAAACpqCABAAAAtsRzkAo1KkgAAAAAkIoKEgAAAGBLFJAKNSpIAAAAAJCKBAkAAAAAUnGLHQAAAGBDJu6xK9RIkAAAAAA78+/tOzX3+51W+yL/uZzP0RQvJEgAAACArZhkk00aRnTppBFdOlntu/fF1xQReyn3k8AqEqQ7RF6MU5O+Q6z2BfbpoUA/3/SN2d3n3jByEFUOxmR3iJGS/TnyQ07erpwM4nkFAADYhaBVGxS0ZqPVvsiLcfkcDYojEqQ7uFd20fFVCws6DAAAgGIp0M/X8g/SqWp37a+IqJh8jii7TPnwh1f+sJuX2MUOAAAAAFJRQQIAAABsiQJPoUYFCQAAAABSkSABAAAAQCoSJAAAAABIxRokAAAAwIZMPD6kUKOCBAAAAACpqCABAAAAtkQBqVCjggQAAAAAqaggAQAAALbEGqRCjQQJAAAAsDP//m6H/v1diNW+yEv/5HM0xQsJEgAAAGBnRjz+mEY8/pjVvrrDXlVEbFw+R1R8sAYJAAAAAFJRQQIAAABsiTVIhRoVJAAAAABIRYIEAAAAAKm4xQ4AAACwJe6wK9RIkO4QeTFOTfyGWu0L7NNDgX6++RwRAABA8RG0aoOC1my02hd5kZ3bkPdIkO7gXtlFx1ctLOgwAAAAiqVAP98M/yBdu2t/RUTF5HNE2WeihFSosQYJAAAAAFJRQQIAAABsiQJSoUYFCQAAAABSUUECAAAAbMWkvH9QLBWqPEWCBAAAANiZoC0/aO7WHVb7Ii/9k8/RFC8kSAAAAIAt2aDCE9i1swK7drbaV2foS4pgy/M8wxokAAAAAEhFBQkAAACwKRYJFWZUkAAAAAAgFRUkAAAAwIbyehM75C0qSAAAAACQigQJAAAAsCWTKW9fdxEWFqaAgAB5eHjIw8ND/v7+CgsLy9YlDB48WM8995zVPsMwNHfuXHXo0EEVK1aUl5eX5s6dK8MwLI5dvXq1fHx8VKlSJbVu3Vrvv/++kpKSshVLfiNBAgAAAIqI2NhYtW/fXhs2bJCvr698fX21YcMGeXl5KTY2NkvnCA8P17p16zLs//jjjzVixAgZhqFXXnlFhmFoxIgRmjFjRrrjVq9eLT8/P0VHR2vMmDGqUqWKxo8fr5deeik3l5jnWIMEAAAA2FIBrkFasmSJwsPDtWPHDnl7e0uSunXrpi5dumjp0qUaM2ZMhmO3b9+ugwcPat68ebp8+bLVYxISEvTBBx+oU6dO2r59u0qWLKnx48erc+fO+vDDDzVmzBg5OjrKMAy98847atSokfbs2aNy5crJMAwNHDhQX3zxhSZPnqwqVarkyXuQW1SQAAAAgCIiODhYzZs3NydHkuTj46PGjRtr+fLlmY719/fXpEmTFBERkeExW7ZsUWxsrMaMGaOSJW/VWhwdHTVq1CjFxMTo+++/lyQdOXJEx44d07Bhw1SuXDlJkslk0ssvv6ykpCStWrUqt5eaZ0iQAAAAgCLg5s2bOnLkiDp16mTR5+3trUOHDmW6/icsLEw3btzQjRs3Mjzm4MGDkmQxR1pCltaf0XEPPvigKlasaO63RyRIAAAAgE2Z8vhlXWRkpCSpevXqFn1pbTExMRmOd3Z2Nr8ym6NMmTKqUKFCunYXFxeVKlVKUVFRmcbi4OAgd3d383H2iDVIAAAAQD6KT0xUfGJijsenWNktTvpfUuLi4mLRl9YWFRWlatWq5XjuyMhIq+c3mUxycXGxSJAqVapkNRYSpEIk8mKcmvgNtdoX2KeHAv180zcaKfkQVU5Y/8Wx1eGScrYAMYNf6EInJQefe4kSto/DYo4c/Ern5GfYXrfnvHE9e8eX5D+BAGBvglZtUNCajVb7Ii/G5XM0OXSX70gfrN6od75eb/tpU7cAt7bddkrqd5fEXCRmaXNYO3/aHGnnN2WyHfntx9kjvh3cwb2yi46vWljQYQAAABRLgX6+ln+QTlW7a39FRGV8i1hhMc6vh17u+USOxzce/rrOx16yaHdzc5MkXbpk2RcXdyu5zE31KG0Oa+c3DENxcXHm898eS9WqVS1iqVOnTq7iyEskSAAAAIAt3eVhrk6lSsmpVKkcn94hg/O7u7tLks6dO2fRd+HCBUn/S1xyyt3dXdevX9c///yjihUrmttjY2OVlJRkTpBuj+X2BCklJUVRUVFq06ZNruLIS2zSAAAAABQBzs7O8vT0VEhIiEVfSEiIWrRoIUdHx1zNkZbY3DlH2r9btWqV6XGHDx/W5cuXzcfZIxIkAAAAoIjw9/fX0aNH9cMPP5jbtm3bppMnT2rw4MG5Pv+TTz6pypUra/bs2eYtwxMTE/XZZ5/Jzc1NTzxx69ZBT09PNW3aVPPmzdPVq1cl3boNb9asWXJ0dNSAAQNyHUte4RY7AAAAoIgYNGiQZs2apb59+yogIEDJyckKDg6Wp6en/P39JUkRERFav369GjZsqM6dO2fr/KVKldK4ceP02muvqXPnzurUqZO+//577du3T4sXLzZXqEwmkyZMmCA/Pz95eXmpR48e+vnnn7V161ZNmTJFVapUsfm12woVJAAAAMBmTLfWIOXlK5Nt8lxdXbV7925169ZN69at08aNG9WzZ0/t2LHDvGYoNDRUo0aN0rJly3J0ha+++qqCgoKUnJysmTNnysHBQV999ZUGDRqU7ri+fftq5cqVcnV11SeffKKYmBjNnj1bEyZMyNG8+YUKEgAAAFCE1K1bV0uXLs2wv2PHjhlu1Z0ms36TyaQRI0ZoxIgRd43Fz89Pfn5+dz3OnpAgAQAAALZ0l13sYN+4xQ4AAAAAUpEgAQAAAEAqEiQAAAAASFUoEiTDMFS3bt0s77SRkJCgd955R82aNVPZsmXVqFEjvfLKK7py5UoeRwoAAIBizaS838WOJU55qlBs0rBp0yaFhYVl+fgBAwZo7dq16tGjh/r3769Tp05p9uzZ+u6773T48GGVLl0674IFAAAAcilo4zb9e9N2q32RcZfyN5hixm4TpOvXr2vTpk3as2ePFi1alOVxhw4d0tq1azVy5EjNmTPH3P7UU0+pd+/emjFjht5+++28CBkAAACwicAeXRTYo4vVPo+BgYqIic3niIoPu73F7uzZs+rXr5/mzJmj69evZ3nc/v37JUnDhw9P196rVy/VrVtXP/74o03jBAAAAFB02G2CVL9+fd24cUM3btzQtm3bsjzu0qVLqlmzpurWrZuu3TAMJScnKzo62tahAgAAAP+T12uQkKfsNkFycHCQs7OznJ2dVapUqSyPmzRpksLDw1W2bNl07Xv37tXZs2fVtGlTW4cKAAAAoIiw2zVItrR37151795dDg4OGj16dKbHGimGLl+9luO5nEo5yikbCR0AAEBxER8fr/j4+HRtKdn43mWkGLYOCbBQpBOkS5cuacKECQoKCpIkBQUFqVWrVpmOORdzUS6dnsrxnBOff1aTXvDP8XgAAICi6v0PP9KUd6cVdBh5j9vgCrUimyCFhITI399fERERatiwoRYsWKBHH330ruNqVKmsE6sX5nhep1KOOR4LAABQlL35xmt65aX0d/OkHDuQ5fGN+w7VuZiLtg4LSKdIJkjLly/XwIEDVbJkSX344Yd66aWXsryOyeRgUoVyZe9+IAAAALLFyclJTk5O6dpSsvG9y+RAZQZ5r8glSKdPn9aQIUNUpUoVrV27Vl5eXgUdEgAAAIBCosglSIsWLVJCQoIWLFhAcgQAAID8xxqkQq1QJ0iXL19WcHCwatasqZ49e0qSDhw4IEdHR4WFhWnOnDkWY24/FgAAAABuV6gTpNjYWI0aNUodOnQwJz3R0dFKTEzUSy+9ZHXM7ccCAAAANkcFqVArFAlSx44dZRiW+97XqVPHov3kyZP5FRYAAACAIqZQJEgAAABAcRK0Yav+vWGb1b7IuH/yOZrihQQJAAAAsDOBvk8o0PcJq30eT7+giJjYfI6o+CBBAgAAAGyJNUiFmkNBBwAAAAAA9oIECQAAAABSkSABAAAAQCrWIAEAAAA2ZGINUqFGBQkAAAAAUlFBAgAAAGzGlA+72FGhyktUkAAAAAAgFQkSAAAAAKTiFrs7RF6MUxO/oVb7Avv0UGBf31zOYORgTE7KqNmdp5iXalNS8vZ4STn67FnkaX9iL2Z/TJkyto8DgP4zc062xzR45cU8iAS2FLRqg4LWbLTaF3kxLp+jQXFEgnQH98ouOr5yYUGHAQAAUCwF+vkq0M/6H6Rrd+2viKiYfI4IxQ0JEgAAAGBLNrgDJGj9FgV9s8VqX2TcpVyfHxkjQQIAAADsTGDPJxXY80mrfbX7DFVETA5u+UaWkCABAAAAtmJS3q8hZolynmIXOwAAAABIRYIEAAAAAKlIkAAAAAAgFWuQAAAAAFviOYaFGhUkAAAAAEhFggQAAAAAqUiQAAAAACAVa5AAAAAAW2INUqFGBQkAAAAAUlFBAgAAAGyJClKhRgUJAAAAAFJRQQIAAADsTNDaTQpav9lqX2TspfwNppghQQIAAADsTGDvbgrs3c1qX+2eAYqIvpjPERUfJEgAAACALbEGqVAjQbpD5MU4Nek31GpfYJ8eCuzrm88RAQAAFB9BqzYoaM1Gq32RF+PyORoURyRId3Cv7KLjKxcWdBgAAADFUqCfrwL9rP9BunbX/oqIisnniFDckCABAAAAtsQtdoUaCRIAAABQhISFhWnSpEnatWuXJKlDhw6aOnWq6tSpk+m4a9euaerUqfruu+8UHh6uRx99VC+99JK8vb3Nx0RHRys+Pj7T89xzzz2SpBs3bujiReubSVSsWFHly5fP+kXlIxIkAAAAoIiIjY1V+/btdeXKFfn7+0uSvvzyS+3atUu//fabXF1drY4zDEN+fn7asmWL+vfvr+7du2vFihXq0qWLdu/erUceeUSS1LdvX/3444+ZxmAYhiRp5cqVGjx4sNVjPvroI40dOzanl5mnSJAAAACAImLJkiUKDw/Xjh07zJWfbt26qUuXLlq6dKnGjBljddyvv/6qLVu2aOLEiZoyZYokacSIEWrevLmmT5+u9evXS5Lef/99q1WhxMREPf/883r44YfNbaGhoSpZsqTWrVsn0x23HTZq1Mgm15sXSJAAAAAAWyrANUjBwcFq3rx5utvifHx81LhxYy1fvjzDBCk4OFgODg4aPXq0ua1mzZrq16+f5s+fr7i4OLm4uJgrSXd65513ZDKZtGjRInPb6dOndd9996l79+42urr84VDQAQAAAADIvZs3b+rIkSPq1KmTRZ+3t7cOHTqkpKQkq2MPHjwoT09PVa5c2WJcQkKCfv311wzn/e233zR16lQtXLhQ1apVM7efPn1aDRs2lCRduXJFCQkJObmsfEeCBAAAANiM6VYFKS9fsl6hioyMlCRVr17doi+tLSbG+jbpkZGRmY6LioqyOs4wDL344ovy9vZWjx49zO0pKSk6c+aMoqOj1bRpU1WoUEHlypVTmzZtdPDgwYzfPjvALXYAAABAPopPSFB8YmKOxxtGitX2tATJxcXFoi+tLSoqKl2V5/axjz76aKbjrNmyZYv27dunQ4cOpWs/d+6crl+/rn379mn48OF64403FB4erlmzZunRRx/VgQMH0q1XsickSAAAAEA++uDLlZq6cLnNz5u2EULaLnK3S0m5lVQlZpCYmUymbI9LSUnR+PHj1bNnT7Vs2TJdX0JCgkaOHClvb2/16tXL3P7000+rUaNGGjt2rHbv3p3FK8tfJEgAAABAPhoX0E8vD+h19wMz0Ljf8zoXE2vR7ubmJkm6dOmSRV9cXJwkWa0epY3N7ridO3fq6NGj+uijjyz67r33Xs2ZM8eivU6dOurRo4c2bNig5ORklShRwmo8BYkECQAAALClu+xi5+TkJCcnp1yc3vo2Au7u7pJu3d52pwsXLkj6XxJlbWxm46wlSPPnz1eNGjX02GOPZS3wVB4eHoqPj9fVq1dVsWLFbI3ND2zSAAAAABQBzs7O8vT0VEhIiEVfSEiIWrRoIUdHR6tj27RpoyNHjlhs4hASEiJHR0d5enqma4+JidG6des0cOBAq1WgBQsWqFu3blarUmFhYapevbpdJkcSCRIAAABgY6Y8fmXM399fR48e1Q8//GBu27Ztm06ePKnBgwdnOs4wDM2ePdvcFh4erlWrVql79+6qUqVKuuO3bNmixMRE+fj4WD2fh4eHNm/erBkzZqRrP3nypDZs2CBfX99Mr6MgcYsdAAAAUEQMGjRIs2bNUt++fRUQEKDk5GQFBwfL09NT/v7+kqSIiAitX79eDRs2VOfOnSVJLVq0UNeuXTVt2jSFhYWpdu3aWrFihZKSkjRhwgSLebZv364SJUqodevWVuN47LHH1L17d7377rs6ceKEWrdurf/+97/68ssvVa1aNU2bNi3v3oRcooIEAAAA2EpeF4/uUkRydXXV7t271a1bN61bt04bN25Uz549tWPHDvMtbaGhoRo1apSWLVv2v7BNJq1cuVJjx47Vb7/9prlz5+qBBx7Qjh071Lx583RzpKSk6Pvvv5enp6fKlStnNQ4HBwd9/fXXmjRpko4fP67Jkyfrhx9+kL+/v44cOSJXV9esvqP5jgoSAAAAUITUrVtXS5cuzbC/Y8eOVrf0Llu2rKZPn67p06dnen4HBwfzM5cyU7ZsWU2ePFmTJ0++67H2hAQJAAAAsKW77GIH+8YtdgAAAACQigrSHSIvxqlJv6FW+wL79FBg3x65myD1acTZ4pCDB2hldx6HHOTKRg7+OpLdIUYO3i/+apP3kqw/hTtDJa1vKYoMxF3M/piyZW0fB3Jt03uzsnV8t/Ev51EkQOERtGqDgtZstNoXeTEun6NBcUSCdAf3yi46vnJBQYcBAABQLAX6+SrQz/oW0LW79ldEVIzVPvvCH2sLM26xAwAAAIBUVJAAAAAAW7LB7f5Bq75R0OoNVvu41TBvkSABAAAAdibQ7ykF+j1lta/2k/0Kya2GhRMJEgAAAGBLLEEq1FiDBAAAAACpqCABAAAANkUJqTCjggQAAAAAqaggAQAAALbEQ+sLNSpIAAAAAJCKChIAAABgS1SQCrVCU0EyDEN169bVsmXLsnR8UlKS3n//fbVu3VqVKlWSj4+PVq9encdRAgAAACjMCk2CtGnTJoWFhWX5+DFjxmj8+PGqWrWqxowZo8jISPn5+WnNmjV5FyQAAACAQs2ub7G7fv26Nm3apD179mjRokVZHhcZGakvvvhC/v7+WrJkiUwmk8aOHauWLVtq2rRp6tOnTx5GDQAAAKCwsusE6ezZs+rXr1+2x3399ddKTk7WK6+8IlPqPaDly5fX888/r9dee00nT57UAw88YOtwAQAAUOyZ8mENEmuc8pJd32JXv3593bhxQzdu3NC2bduyPO7gwYNydXVVs2bN0rV7e3ub+wEAAADgTnZdQXJwcJCzs7MkqVSpUlkeFxkZqWrVqpmrR2mqV68uSYqKirJdkAAAAMDtbFBBClqxTkEr11vti7wYm+vzI2N2nSDlVGRkpFxdXS3aXVxcJGWeIBkphi5fvZbjuZ1KOcopG8kcAABAcREfH6/4+Ph0bSnZ+N5lpBi2DsluBT7dS4FP97LaV/tfvRURFZ3PERUfRTJBMplMMgzLX6CUlBRJUmJiYoZjz8VclIt3zxzPPfG5gZr0gn+OxwMAABRV73/4kaa8O62gw8gHrBEqzIpkguTm5ma1ShQXFydJqlatWoZja1SprBOrFuR4bqdSjjkeCwAAUJS9+cZreuWl0enaUo4dyPL4xn2H6lzMRVuHBaRTJBMkd3d3HTlyRCkpKXJw+N8+FBcuXJCUeYJkcjCpQrmyeR4jAABAcePk5CQnJ6d0bSnZ+N5lcqAyg7xn17vY5VSbNm0UFxenI0eOpGsPCQmRJLVq1aogwgIAAEBxYMrjF/JUkUyQ+vfvrxIlSmjmzJnmtUiXL1/WggUL9OCDD1ps/w0AAAAAUhG4xe7y5csKDg5WzZo11bPnrc0V3N3dNXz4cH3++ee6fPmymjdvrm+++UZhYWH69ttvCzhiAAAAFG2UeQqzQl9Bio2N1ahRozR79ux07bNmzdK7776rc+fOafbs2XJzc9OmTZvk4+NTQJECAAAAsHeFpoLUsWNHq1t316lTx2q7o6Oj3nrrLb311lv5ER4AAABwiw0eFIuCU+grSAAAAABgK4WmggQAAADYvfzYaY4CVZ6iggQAAAAAqaggAQAAADaV+xJP0NdrFfT1Wqt9kTGxuT4/MkaCBAAAANiZwP69Fdi/t9W+2o/5KiIyOp8jKj5IkAAAAABbYhe7Qo01SAAAAACQigoSAAAAYEtUkAo1KkgAAAAAkIoECQAAAABSkSABAAAAQCrWIN0h8mKcmvg9Z7UvsE83BfbpkbsJDCMng3I3Z5amyK+48iEnT0nJ/hgH/lYAAIA9CFq1QUFrNlrti7wYl8/R5BBrkAo1EqQ7uLu66PiKeQUdBgAAQLEU6OerQD9fq321u/ZXRFRMPkeE4oY/mwMAAABAKipIAAAAgM2Y8uEWO27hy0tUkAAAAAAgFRUkAAAAwKao8BRmJEgAAACAnQlavkpBX62x2hcZczGfoyleSJAAAAAAW7JBASlwoJ8CB/pZ7avt1VURkVG5nwRWsQYJAAAAAFJRQQIAAABsiQfFFmpUkAAAAAAgFRUkAAAAwKaoIBVmVJAAAAAAIBUJEgAAAGBLJlPevu4iLCxMAQEB8vDwkIeHh/z9/RUWFnbXcdeuXdMbb7whT09PVa5cWT169FBISIjFcZcuXVJ4eLjVV0JCQrpjV69eLR8fH1WqVEmtW7fW+++/r6SkpCy/lQWBBAkAAAAoImJjY9W+fXtt2LBBvr6+8vX11YYNG+Tl5aXY2NgMxxmGIT8/P02fPl2NGzfWiBEjdPLkSXXp0kUHDhxId+xLL72kWrVqWX0dPXrUfNzq1avl5+en6OhojRkzRlWqVNH48eP10ksv5dXl2wRrkAAAAIAiYsmSJQoPD9eOHTvk7e0tSerWrZu6dOmipUuXasyYMVbH/frrr9qyZYsmTpyoKVOmSJJGjBih5s2ba/r06Vq/fr352NDQUDVv3lzvvPOOxXnuu+8+SbcSrnfeeUeNGjXSnj17VK5cORmGoYEDB+qLL77Q5MmTVaVKFVtfvk1QQQIAAACKiODgYDVv3tycHEmSj4+PGjdurOXLl2c6zsHBQaNHjza31axZU/369dOWLVsUFxdnbj99+rTatGmjbt26WbwqVaokSTpy5IiOHTumYcOGqVy5cpIkk8mkl19+WUlJSVq1apWNr9x2SJAAAAAAWyqgNUg3b97UkSNH1KlTJ4s+b29vHTp0KMP1PwcPHjSvPbpzXEJCgn799VdJUlxcnGJiYtSwYUMZhqG4uDilpKRYPZ8ki1gefPBBVaxY0dxvj0iQAAAAgHwUH5+gy1eu5vhlGIbV80ZGRkqSqlevbtGX1hYTE5Ph2MzGRUVFSbp1e50k/fjjj6pWrZpcXV1VsWJF9enTRxEREXeNxcHBQe7u7ubz2SPWIN0hMjZOTZ5+wWpfYJ9uCuzTI58jAgAAKD6CVm1Q0JqNVvsiL8ZZbbcrJt11p7kPvlikqZ9+YfOp05ISFxcXi760tqioKFWrVs3q2EcffTTTcdKt2+skKSQkRC+99JLq16+vw4cP67PPPtOBAwd06tQpVahQwRxL2i13d56TBKkQcXd10fEV8wo6DAAAgGIp0M9XgX6+Vvtqd+2viCjrFZDCZNzwoXp5yLM5Ht/Yp6fORUVbtJtSEzNrFaa02+ASExOtntNkMmVpXNWqVTVy5Ei98MILatasmSRpwIABat26tfr166ePP/5YU6dONcdiTUpKSoZx2AMSJAAAAMCmMq8gOTk5ycnJKednd7B+fjc3N0m3nlN0p7RNFqxVj9LGZmVcly5d1KVLF4vj+vbtq9GjR5vXFt0eS9WqVS3OWadOHatx2APWIAEAAABFgLu7uyTp3LlzFn0XLlyQ9L/ExdrYzMZllFilMZlMql27tqKjozONJSUlJcPb/OwFCRIAAABQBDg7O8vT01MhISEWfSEhIWrRooUcHR2tjm3Tpo2OHDlisYlDSEiIHB0d5enpKUkaM2aMRowYYTHeMAz9/fffatSokfl8aeNvd/jwYV2+fFmtWrXK/gXmExIkAAAAwJZMefzKhL+/v44ePaoffvjB3LZt2zadPHlSgwcPznScYRiaPXu2uS08PFyrVq1S9+7dzQ91LVeunObOnasdO3akG7948WJFRUXJ1/fW+jFPT081bdpU8+bN09WrVyXdSqJmzZolR0dHDRgwIPMLKUCsQQIAAACKiEGDBmnWrFnq27evAgIClJycrODgYHl6esrf31+SFBERofXr16thw4bq3LmzJKlFixbq2rWrpk2bprCwMNWuXVsrVqxQUlKSJkyYYD7/yy+/rOXLl6tbt24aPHiwPDw89Ouvv2rlypXq3r27/Pz8JN265W7ChAny8/OTl5eXevTooZ9//llbt27VlClTzAmXPaKCBAAAANhUwZWQXF1dtXv3bnXr1k3r1q3Txo0b1bNnT+3YsUMVK1aUdOtZRqNGjdKyZcv+F7HJpJUrV2rs2LH67bffNHfuXD3wwAPasWOHmjdvbj6uSpUq+vHHH9WvXz99++23mjp1qs6cOaOPP/5Y69evT7d7Xd++fbVy5Uq5urrqk08+UUxMjGbPnp0u4bJHVJAAAACAIqRu3bpaunRphv0dO3a0uqV32bJlNX36dE2fPj3T83t4eGjJkiVZisXPz89cVSosSJAAAAAAW7rLg2Jh37jFDgAAAABSUUECAAAAbMaUDxUkKlR5iQoSAAAAAKQiQQIAAACAVNxiBwAAANiZoOCvFRT8tdW+yOiYfI6meCFBAgAAAGzJBmuQAgMGKDBggNW+2q29FXEhMtdzwDpusQMAAACAVFSQAAAAAJtil7nCjAoSAAAAAKSiggQAAADYUp4/Bwl5iQTpDpGxcWry9AtW+wL7dFNgnx7pGw0j74NKScn7OfJLclL2jnegyAkAQHEStGqDgtZstNoXeTEun6NBcUSCdAd3VxcdXzGvoMMAAAAolgL9fBXo52u1r3bX/oqIKgxbXFNBKsz48zwAAAAApCJBAgAAAIBU3GIHAAAA2BJ32BVqVJAAAAAAIBUVJAAAAMCW2Oa7UCNBAgAAAOxM0JLlCvpyudW+yJjCsJNf4UWCBAAAANhU7itIgYMGKnDQQKt9tVu2V8SFyFzPAetYgwQAAAAAqaggAQAAALbEGqRCjQoSAAAAAKSy+wQpLCxMAQEB8vDwkIeHh/z9/RUWFnbXcbGxsXrxxRd13333qVy5cmrVqpWWLl0qwzDyPmgAAAAAhZJd32IXGxur9u3b68qVK/L395ckffnll9q1a5d+++03ubq6Wh138+ZNdejQQaGhoRo8eLBq166t77//Xv7+/vrvf/+r8ePH5+dlAAAAACgk7DpBWrJkicLDw7Vjxw55e3tLkrp166YuXbpo6dKlGjNmjNVxy5cv1/Hjx7V69Wr16dNHkjRu3Dh169ZNkydP1ssvv6zSpUvn23UAAACgmDAp79cgscQpT9n1LXbBwcFq3ry5OTmSJB8fHzVu3FjLl1vfF16Sjhw5Iknq2rWruc1kMqlr165KTEzU77//nndBAwAAACi07DZBunnzpo4cOaJOnTpZ9Hl7e+vQoUNKSkqyOvbee++VJJ05cyZd+5kzZ2QymVSnTh2bxwsAAABIplsVpLx8UULKU3abIEVG3nr4VfXq1S360tpiMniKcO/evVWrVi0988wz+v777/X7779rzpw5+vzzzzV48GC5uLjkXeAAAAAACi27XYOUliBZS2bS2qKiolStWjWL/lq1aunrr79Wu3bt5OPjY2739PTUF198kem8hpGiy9euZT3QO3bFc3J0lFOpUlkfDwAAUEzEx8crPj4+XVvK1ax/7zJSCstuxFR4CjO7TZBMqYvbrG3LnZKSIklKTEy0Ovann37Sk08+qUaNGmnEiBGqXLmy9uzZo/nz56tPnz5as2aNSpa0funnYmLl8ljvHMc9cegzmvTcwByPBwAAKKre//AjTXl3WkGHAWTKbhMkNzc3SdKlS5cs+uLi4iTJavXIMAwNHjxYrq6u+vnnn1WuXDlJ0oABA9S4cWONGjVKa9euVb9+/azOW6OKq06snJ/1QK1UkAAAAGDpzTde0ysvjU7XlnLsQJbHN+47VOdiLto6LNujgFSo2W2C5O7uLkk6d+6cRd+FCxck/S+Jut3Fixd16tQpjRkzxpwcpXnmmWc0atQo7d27N8MEyWRyUIWyZbMeKA+eBQAAyBInJyc5OTmla0spl/XvXSaH4pN5BC0KVtDiZVb7IqOj8zma4sVuEyRnZ2d5enoqJCTEoi8kJEQtWrSQo5VqTbly5VSyZEklJCRY9KXd88omDQAAAMg7uU/kAocEKHBIgNW+2i0eUcT5C7meA9bZ7S52kuTv76+jR4/qhx9+MLdt27ZNJ0+e1ODBg62OcXZ2VqtWrbR69WqdP38+Xd+cOXMkSe3atcu7oAEAAAAUWnZbQZKkQYMGadasWerbt68CAgKUnJys4OBgeXp6yt/fX5IUERGh9evXq2HDhurcubMkKSgoSG3atDEf5+rqqj179ui7777T008/rX/9618FeVkAAAAA7JRdV5BcXV21e/dudevWTevWrdPGjRvVs2dP7dixQxUrVpQkhYaGatSoUVq27H/3aDZv3lxHjx5Vx44dtXr1ar333nuKjo7WnDlztGzZMvMOeQAAAIDN5fmDYpGX7LqCJEl169bV0qVLM+zv2LGj1a3A69evr1WrVuVlaAAAAACKGLtPkAAAAIDChSpPYWbXt9gBAAAAQH6iggQAAADYEuuECjUqSAAAAACQigoSAAAAYEMUkAo3KkgAAAAAkIoKEgAAAGBTlJAKMxIkAAAAwM58vnCJghYFW+2LjI7J52iKFxIkAAAAwJZssAhp5HODNfK5wVb7ajVtqYjzF3I9B6xjDRIAAAAApKKCBAAAANgUa5AKMxKkO0TGxqnJ0y9Y7Qvs002BfXrkc0SSDCP7Y7Jb2k1Jyfs5cjqPPcrJZ1Kcxd/M/hhHR9vHAQCwe0GrNihozUarfZEX4/I5GhRHJEh3cHd10fEV8wo6DAAAgGIp0M9XgX6+Vvtqd+2viKhCsEEBBaRCjTVIAAAAAJCKBAkAAACwGdOtZQh5+bpLiSosLEwBAQHy8PCQh4eH/P39FRYWdtfIr127pjfeeEOenp6qXLmyevTooZCQEIvjEhIS9M4776hZs2YqW7asGjVqpFdeeUVXrlxJd1xiYqLCw8OtvuLi7Pd2SRIkAAAAoIiIjY1V+/bttWHDBvn6+srX11cbNmyQl5eXYmNjMxxnGIb8/Pw0ffp0NW7cWCNGjNDJkyfVpUsXHThwIN2xAwYM0MSJE1W3bl29/fbbatWqlWbPnq3WrVvrxo0b5uP27dunWrVqWX1NmDAhz96D3GINEgAAAFBELFmyROHh4dqxY4e8vb0lSd26dVOXLl20dOlSjRkzxuq4X3/9VVu2bNHEiRM1ZcoUSdKIESPUvHlzTZ8+XevXr5ckHTp0SGvXrtXIkSM1Z84c8/innnpKvXv31owZM/T2229LkkJDQyVJX375pVxdXdPNV6dOHZtety2RIAEAAAA2VXC7NAQHB6t58+bm5EiSfHx81LhxYy1fvjzDBCk4OFgODg4aPXq0ua1mzZrq16+f5s+fr7i4OLm4uGj//v2SpOHDh6cb36tXL9WtW1c//vijOUE6ffq0SpcurYEDB8rBofDcuFZ4IgUAAACQoZs3b+rIkSPq1KmTRZ+3t7cOHTqkpKQkq2MPHjxoXnt057iEhAT9+uuvkqRLly6pZs2aqlu3brrjDMNQcnKyoqOjzW2nT59WgwYN5ODgoOvXr+v69eu5vcR8QYIEAAAA2IpJeb9JQwYFqsjISElS9erVLfrS2mJirG+THhkZmem4qKgoSdKkSZMUHh6usmXLpjtu7969Onv2rJo2bWpuCw0NVXJystq2bauyZcuqfPnyatq0qbZs2ZL5e1jAuMUOAAAAyEfx8fGKj0/I8XgjJcVqe1qC5OLiYtGX1hYVFaVq1apZHfvoo49mOi4je/fuVffu3dPdopecnKwzZ84oMTFRTz/9tIYNG6a4uDjNmTNHXbt21Zo1a9S7d++7XGnBIEECAAAA8tH7sz7V1A9n2Py8JtOt0pJhGBZ9KalJVWJiYoZjszvu0qVLmjBhgoKCgiRJQUFBatWqlSTp6tWreuGFF+Tp6annn3/ePGbw4MFq2LChXn75ZfXq1cscsz0hQQIAAADy0Zsvj9YrgcPvfmAGHmjdTucuRFq0u7m5SbqVuNwp7blD1qpHaWOzMy4kJET+/v6KiIhQw4YNtWDBgnQVqIoVK6bb5e72dn9/f02fPl3nz59XjRo1rF9kASJBAgAAAGwq86qIk5OznJycc372DHaEc3d3lySdO3fOou/ChQuS/pdEWRub2bjbE6Tly5dr4MCBKlmypD788EO99NJLKlWqVJbj9/DwkCRFR0fbZYLEJg0AAABAEeDs7CxPT0+FhIRY9IWEhKhFixZydHS0OrZNmzY6cuSIxSYOISEhcnR0lKenp6RbO9MNGTJEVapU0Y4dO/T6669bTY42btyobt266fTp0xZ9YWFhMplMql+/fk4uM8+RIAEAAAC2ZMrjVyb8/f119OhR/fDDD+a2bdu26eTJkxo8eHCm4wzD0OzZs81t4eHhWrVqlbp3764qVapIkhYtWqSEhAQtWLBAXl5eGZ6vYcOG2rx5s9555510a5siIyO1aNEidenSRaVLl878YgoIt9gBAAAARcSgQYM0a9Ys9e3bVwEBAUpOTlZwcLA8PT3l7+8vSYqIiND69evVsGFDde7cWZLUokULde3aVdOmTVNYWJhq166tFStWKCkpSRMmTDCf/8CBA3J0dFRYWJjVNUY1a9ZUz549Vb9+fb344ouaM2eOzp8/r86dOys6OlpLly5VSkqKPvvss/x5Q3KABAkAAACwqYLbmc3V1VW7d+/WxIkTtW7dOplMJvXs2VMzZsxQxYoVJd16PtGoUaMUEBBgTpBMJpNWrlypKVOmaOvWrdqyZYvatm2rKVOmqHnz5ubzR0dHKzExUS+99JLV+Tt06KCePXtKkmbOnKn77rtP8+bN09SpU1WtWjU9/vjj+uCDD+xy7VEaEiQAAACgCKlbt66WLl2aYX/Hjh2tbuldtmxZTZ8+XdOnT89w7MmTJ7Mch6Ojo8aMGaMxY8ZkeYw9YA0SAAAAAKSignSHyNg4NXn6Bat9gX26KbBPj3yOCAAAoPgIWrVBQWs2Wu2LvBiXz9GgOCJBuoO7q4uOr5hX0GEAAAAUS4F+vgr087XaV7trf0VExVjtsyumgluDhNzjFjsAAAAASEWCBAAAAACpuMUOAAAAsDOfz1+ooPmLrPZFRkXnczTFCwkSAAAAYGdGPj9UI58farWvVqNmijh3Pp8jKj64xQ4AAAAAUpEgAQAAAEAqbrEDAAAAbIltvgs1KkgAAAAAkIoECQAAAABSkSABAAAAQCrWIAEAAAA2xRqkwowKEgAAAACkooIEAADw/+3de1hU1foH8O/gcFW5OyClaCpqiiAd8vITEFIsb2hJipro0Uql0mPHLE0S02PHtDLzkpp6NE0j78e7h7SjibcQvHYyIwOSSwOIF4YZZv3+gNk5zgyOMjOO+P08zzw+z9rvmrX2fseZeVl79iayJC4gPdS4gkRERERERFSNK0hERERERBbFJaSHGVeQiIiIiIiIqnEFiYiIiIjIzixatgKLl60wui2/oNDGs3m0sEAiIiIiIrIzSa+MQdIrY4xua9K6HXLzfrfxjB4dLJDukK8sRvshLxvdNv6Ffhg/qJ9+oxD3NoDMRuekVlbaZpx7pdHcW7z8Pl6iWu2997HFGGr1vfdxdb33PkR2pPLX3HvuU695EyvMhIgeFou/3obF32w3ui3/j2Ibz+Z+yGzwfY+/cbImFkh38PP2xNmvlj3oaRARERE9ksa/GIfxL8YZ3da0TwJyC4psPCN61PAiDURERERERNVYIBEREREREVXjKXZERERERBYk42+EHmpcQSIiIiIiIqrGAomIiIiIiKgaT7EjIiIiIrIknmH3UOMKEhERERERUTWuIBERERERWRSXkB5mXEEiIiIiIiKqxhUkIiIiIiI7s+jz5Vi0bLnRbfkFBTaezaPF7leQsrOzkZiYiMDAQAQGBmLEiBHIzs6+az8hBNasWYPQ0FC4urqiXbt2WLZsGYQQ1p80EREREVEtJL36Ms6fOm704adQPOjp1Wl2XSAplUpERERg27ZtiIuLQ1xcHLZt24bIyEgolcoa+86bNw+JiYlwdXXFO++8A39/f7z66qtYsmSJjWZPRERERI8kmcy6D7Iquz7FbvXq1cjJycF//vMfxMTEAAD69u2LXr16Ye3atZgwYYLRfqWlpUhOTkZsbCx27twJuVwOrVaLqKgoTJ06FS+//DIcHR1tuStERERERPQQsOsVJN0pcrriCABiY2PRrl07rFu3zmS/DRs2oLy8HHPmzIFcXlUDOjg4YPz48QCA06dPW3XeRERERET0cLLbAqm8vByZmZmIjo422BYTE4MTJ05Ao9EY7btt2zYEBASgY8eOeu0JCQkoKSlBeHi4VeZMREREREQPN7stkPLz8wEAjRs3NtimaysqKjLaNzc3F0FBQZBVn6NZWloKrVZrpZkSEREREd1OZuUHWZPd/gZJVyB5eXkZbNO1FRQUwN/f32D71atX0apVKyxcuBDz58/Hr7/+igYNGiAuLg7z58+Hn5+fyXGFELh244b5E73jonjOjo5wduLvm4iIiIjupFKpoFKp9Nq0183/3iW0vBoxWZ/dFki61R9jl+XWrQap1WqjfZVKJfbt24fvvvsOEydOxBNPPIEffvgBn376Kc6cOYMTJ07AycnJaN+8IiW8egy673kn/3Uo3hsz/L77ExEREdVVc/75IVJmzX7Q07A+LvI81Oy2QFJUX9+9pKTEYFtxcTEAGF09AgAfHx8UFxfjxIkTaN26NQBgyJAhaN26NcaMGYPU1FQMGzbMaN8AX2+c27DM/IkaWUEiIiIiIkPvTJmMSRPf0GvTnjlqdv928aORV/SHpadFpMduf4OkOw0uLy/PYNvVq1cB/FlE3cnf3x9PPfWUVBzpDBgwAACQkZFhclyZTAb3+vXv4eGm93B2coSqQo2UFV9CVWF8hYtsT1WhRsrK9cyJnWFe7I+qogIpy9ZAVVHxoKdC1VQqFWbMnGVwWhI9OMzJ/XN2doa7u7v+o0F9sx8yBy7NkPXZbYHk4uKCkJAQpKWlGWxLS0tDx44dTd7LqF27digrKzNoLy8vBwB4e3tbdrJ3UKnVmLlyPVQmTgEk21Op1Zi56ivmxM4wL/ZHVaHGzOVrWbTaEZVKhZRZs/ll3I4wJ0R1m90WSAAwYsQIZGVl4cCBA1Lb3r17cf78eYwaNcpkv1GjRuHs2bPYs2ePXvtnn30GAIiIiLDOhImIiIiIeBW7h5rd/gYJAEaOHImPP/4Y8fHxSExMRGVlJdasWYOQkBCMGDECQNUlvbds2YI2bdqgR48eAKruk9S1a1f069cPiYmJaNGiBY4cOYKdO3ciMTGRBRIRERERERll1wWSt7c3vvvuOyQnJ2Pz5s2QyWQYOHAg5s+fDw8PDwDATz/9hNdffx2JiYlSgeTg4ICdO3ciJSUFaWlp2LBhA1q3bo2FCxdi3LhxD3KXiIiIiKgus9Aiz6Kln2PRUuMXDssvKKj9AGSSXRdIANC8eXOsXbvW5Pbu3bsbvRS4p6cnPv74Y2tOjYiIiIjIKpLGvoqksa8a3fZ4iyDk5hpeyIwsw65/g0RERERERGRLdr+CRERERET0cOGFFB5mXEEiIiIiIiKqxgLpIbb4mx22GWfTv60/xpadVh/DVmxyvL7eVifGsJXFm6z/f8UWr+HFW3ZZfQxbWbLnP1Yfw1av4bryf8VW+7FoyVKrj7Hu6BGrj2ELdSknthiDyFJYID3EbPGlDwAWb7ZFgVR3vvjZ5Hh9s71OjGErtinyrf8aXry17vw/WbLX8Cbglmar13Bd+b9iq/1YtORzq4+x/uj3Vh/DFupSTmwxBv0pOzsbiYmJCAwMRGBgIEaMGIHs7Oy79rtx4wamTJmCkJAQ+Pj4oH///khLM3y/1mg0mDNnDjp16gRPT0/ExsYiNTXVIE4IgaVLlyIqKgoeHh6IjIzE0qVLjV5gzZ6wQCIiIiIisiSZzLqPGiiVSkRERGDbtm2Ii4tDXFwctm3bhsjISCiVSpP9hBB48cUXMXfuXLRr1w7jxo3D+fPn0atXLxw9elQvdsKECZg6dSoaNWqECRMmID8/Hy+++CK++eYbvbh58+Zh3LhxEEJg0qRJEEJg3LhxmD9//v0fWxtggUREREREVEesXr0aOTk52Lx5Mz799FN8+umnSE1NxW+//VbjrXMyMjKwa9cuJCcnY/369Zg1axYOHToET09PzJ07V4rLz8/H559/jhEjRmDHjh1ISUnB4cOHERQUhNmzZ0txFRUV+OCDDxAdHY20tDS89957SEtLQ2RkJP75z39CrVZb9TjUBgskIiIiIqI6Ys2aNQgNDUVMTIzUFhsbi3bt2mHdunU19nNwcMAbb7whtT322GMYPHgwdu3aheLiYgDAV199hcrKSkyaNAmy6tWshg0b4uWXX8bp06dx/vx5AMCuXbugVCoxYcIEyOVVF852dHTE66+/jqKiIuzfv9/i+24pLJCIiIiIiOqA8vJyZGZmIjo62mBbTEwMTpw4AY1GY7Rvenq69NujO/tVVFQgIyNDivP29kaHDh0M4nTbb//3zrncGWePWCAREREREVmUzMoP4/Lz8wEAjRs3NtimaysqKjLZt6Z+BQUFUpy/v7+0elRTnJubG9zd3fXivLy84OTkJMXZI94otpouSb//UYym/YfX6rl0V+Zol/CKwYvHkvKVJbWeq9njDBhh/TEG/dVqzy/lZPg4q+YEsPDxMjHX/D+K0bRPgmXGMKHGMSx09Zma83I/eTI+r3xlCZrGvWS44X5eCyb2vcbX8L2OozUxRnEJmg4abbyPg2Ve16J67HbxoyGzxHNqKo0255eUovnYN433kder/biwzf+Tu41Tfqvinp7LZflnBm26nLQNDrVMTgBAbTgvix8vRyejzfn5BXi8eQuLDKEpu2G0veh6GaL+McvoNvnCj2s9rt3kxMQxvlc15sTIvO7KyLwsNcbvRVUXGbDnL9e//34Vjz9xl9e4QK2u5Ha1uhC6k65A8vLyMtimaysoKIC/v7/Rvv/3f/9XYz9dnLe3t1lxxuYhk8ng5eVl1zlkgVStsrLqQ1yr1SK38A+LPGdekekrhViKpeZqD+PYYgxb5ASw0fEqMP4XoIdtDKDu/F+xyRhFtvk/n2eDcXKVxdYfw0avYYuNU3bN5Ka833+3zBg1sNnxys2z+hj510qNbzDVfh+Ykwc3hu57mz3SarU2OZ7G6P7YaKz40mq1AGDy4ggymcysfrWN08Xa80UaWCBVc3FxQXl5OerVqweFQvGgp0NEREREdygoKEBlZSVcXFwe9FQMGFuVqUlt7wUkk8kMxtR9hy0pKTGI111kwdQ8FQqFWf0UCoXR1R9jccaeTwiB4uLiez5etsQCqdqNG8aX6YmIiIiI7ubkyZMPegrw8/MDAOTlGa5gXb16FQBMLgT4+fnV2E9X0Pj5+SEzMxNarRYODg41xt28eROlpaXw8PCQ4pRKJTQajV0XSLxIAxERERFRHeDi4oKQkBCkpaUZbEtLS0PHjh3h6OhotG/nzp2RmZlpcBGHtLQ0ODo6IiQkRIorLi5GZmamQRwAhIeHS3G3t5uKs0cskIiIiIiI6ogRI0YgKysLBw4ckNr27t2L8+fPY9SoUTX2E0JgwYIFUltOTg6+/vpr9OvXD76+vgCAhIQE1KtXDx999JF0muC1a9ewYsUKhIWFSZf/7t27N3x8fLBgwQLp0uJqtRoLFy6EQqHAc889Z/F9txSZqO0JkEREREREZBeUSiVCQkJw/fp1JCYmorKyEmvWrEHz5s1x6NAheHh4IDc3F1u2bEGbNm3Qo0cPAFW/DerXrx927dqFYcOGoWnTptiwYQOUSiW+/fZbhIaGSmO89tprWLRoEfr374/Q0FBs3boVFy9exI4dOxAbGyvFzZs3D5MnT0ZUVBSio6Oxf/9+HDlyBKtWrcLIkSNtfGTMxwKJiIiIiKgO+eWXX5CcnIxDhw5BJpMhOjoa8+fPl24Ce/DgQURHRyMxMRGrV6+W+t24cQMpKSnYvXs38vLy0LVrV6SkpCAsLEzv+dVqNebOnYutW7fip59+Qnh4ON566y307NlTL04IgaVLl2L9+vXIyspCSEgIxo0bh4QE69+CoTZYIBEREREREVXjb5CIiIiIiIiqsUAy0+XLlxEfH4/AwEC4u7uja9eu+Prrr/WuYa/RaDBnzhx06tQJnp6eiI2NRWpqqsFz6ZYbo6Ki4OHhgcjISCxdurTW18N/lN26dQstW7bEmDFj9Npv3LiBKVOmICQkBD4+Pujfv7/RK7uYmzuqmUqlwvTp09GsWTPUr18fXbt2xeHDh/ViioqKMH78eLRu3Rr+/v6Ij483uBIOYH7uqGYXLlzAwIED0bhxY/j4+CA2Nhbp6el6McyJbQgh0Lx5c3z55ZcG2yz9XsXPGfPUlJPr169j0qRJaNu2Ldzc3BASEoKZM2eioqLCIDY1NRWxsbHw9PREp06dMGfOHOlH6bdLS0tD//794ePjg5CQEEyZMgU3b960yr4RUS0Iuqvc3FzRsGFD0bBhQzFx4kQxc+ZMER4eLgCIlJQUKW78+PECgOjTp49ITk4WHTp0EABEamqq3vPNnTtXABARERFixowZolu3bgKA+PDDD229a3XG9OnTBQAxevRoqU2r1YrevXsLACIhIUFMmzZNtGjRQsjlcvH999/r9Tc3d2RaZWWl6Nu3rwAgXnjhBZGSkiJatWol6tWrJ06fPi2EEEKlUonQ0FDh7OwsRo8eLSZPniz8/f2Fu7u7uHTpkvRc95I7Mu1///ufcHNzE40bNxZTp04VM2bMEK1atRIymUx89913QgjmxJa2b98uAIi1a9fqtVvjvYqfM+YxlZPKykrpc3748OHiH//4hxg4cKAAIGJiYkRlZaUU+/XXXwsAIjQ0VCQnJ0u5TEpK0nvOI0eOCLlcLlq2bCmmTZsmhgwZIuVSq9XaZH+JyDwskMyg+0A6fvy41KbRaERcXJyQy+Xi8uXL4urVq6JevXpixIgR0hvdtWvXRFBQkAgNDZX6qVQq4e3tLaKjo4VarRZCCFFRUSEiIyOFr6+vqKiosO3O1QEXL14UTk5OBgXSqVOnBACRnJwsteXk5AhfX18xYMAAqc3c3FHN9u3bJwCI999/X2orKCgQnp6eok+fPkIIITZt2iQAiJUrV0oxmZmZwtnZWUycOFFqMzd3VLPhw4cLV1dX8euvv0ptJSUlwt/fX3Tr1k0IwZxY240bN8TGjRvFa6+9Jtzc3Ix+Gbf0exU/Z2pmTk50Rc+dBeX8+fP14rVarQgODhZt27YVZWVlUtvQoUOFXC4XhYWFUt9+/foJhUIh8vLypLapU6cKANIfkYjIPrBAMkNoaKh46qmnDNoPHTokAIgvv/xSfPzxx0bf5D788EMBQJw7d04IIcSWLVsEALF161a9uNTUVAFA7Ny503o7UgdptVoRHR0tQkJCDAqkCRMmCAcHB1FUVKTXJykpSTg5OQmlUimEEGbnjmo2ZMgQ4enpafDla/z48SIgIEBUVFSIuLg44e3tLTQajV5Mnz59hL+/v/RlztzcUc2Cg4PF008/bdAeHx8v3N3dhRCCObGyCxcuCAB6jzu/jFv6vYqfMzUzJycTJ04UAMS1a9f02lUqlXBychJjxowRQgiRkZEhAIhPPvlEL+7EiRMCgFi0aJEQouqPRXK5XO+PDkIIkZ+fLwCIyZMnW3o3iagW+BskM6jVaoPLGwKQzi8uLCxEeno6vL29pZtj6cTExACAdM6/7t/o6Oga48g869atw7fffoslS5YYbEtPT5fO579dTEwMKioqkJGRIcWZkzsyTQiB7du3o3fv3gZ36F60aBFyc3Ph6OiI9PR0REVFoV69enoxMTExuHr1Kq5cuQLA/NxRzZ544glcuXJF7zcTQgj8/PPPaNGiBQAwJ1YWFBSEW7du4datW9i7d6/RGEu/V/Fzpmbm5OT69eto3749GjZsqNeuVqshk8lQWFgIwPSxDgsLg4eHh7T91KlT0Gg0BnEKhQLBwcGPfE6I7A0LJDOcPXsWy5YtM2hft24dACA4OBj5+fnw9/eHTCbTi2ncuDEAoKCgAACQn58PNzc3uLu768V5eXnByclJiqO7Ky4uxqRJkzBmzBh06dLFYHt+fr50/G9nLCfm5I5MKykpwc2bNxEUFASg6kt4SUmJXkxlZSUKCwvNzok5cVSz8ePHQ6lUYujQoTh58iQyMzORlJSEjIwMvPHGG8yJDTg4OMDFxQUuLi5wcnIyGmPp9yp+ztTMnJwsX74cZ86cMWjfvHkzVCoVgoODAVQdawAG+XNwcICfn59eTozF6doe9ZwQ2RsWSPdBq9XirbfewsqVKxESEoLo6Gjk5+fDy8vLIFbXdvubpLE4mUwGLy8vvkneg3feeQdarRYffPCB0e21zcmdcWTa1atXAQANGjTA2LFj4ePjAy8vL/j7+2PmzJlQqVT4448/oNVqmRMbio2NxYwZM7Bp0yaEh4cjNDQUS5YsQVJSEkaOHMmc2AlLv1fxc8Y6Nm3ahDFjxsDNzQ2vvPIKgD8LH09PT4P424+1Ls5U/pgTIvsif9ATeNgcP34cSUlJOHnyJJo0aYLNmzfDwcEBMpnM6OVTtVotgKpleQAm43SxujiqWXp6OpYtW4YVK1YYnJaiU9uc3BlHpimVSgBASkoKnnjiCbz//vvw8PDAzp078d577+Hq1atISUkBAObEhhYvXoxp06ahd+/eGDRoEICqyxF/9tlnCAwMRGJiIgDm5EGz9HsVP2csKy8vD3//+9/x1VdfwcXFBRs2bECTJk0AwGA173a3H2tdnKn8MSdE9oUFkpnUajVSUlIwZ84caLVavPTSS1iwYIH01yCFQmH0L0DFxcUAAH9/fynuzlOPgKo3zeLiYimOTNNoNBg7diy6dOmCkSNHmowzdayN5cSc3JFpuiL1scceQ3p6OlxcXAAAw4cPh1wux5IlSzBlyhQ4ODiYnRNz4si0nJwcTJgwAS+88ILefXJGjhyJ+Ph4vP3224iPj2dO7ICl36v4OWM5GzduxNixY1FSUoJOnTph5cqVePLJJ6XtCoUCQNVpxo0aNdLrW1xcjGbNmhnE3Yk5IbI/PMXODBqNBgMGDMDs2bPRsmVL/Pe//8WaNWv0lsr9/Pzw+++/S3/J09GdeqR78/Pz88PNmzdRWlqqF6dUKqHRaPgmaYZVq1YhMzMT06ZNQ15eHnJycpCTkwOg6maLOTk5KCkpgZ+fH/Ly8gz6G8uJObkj03THaODAgVJxpDNgwAAAwJkzZ9CoUSOzc2JOHJmWnp4OjUaDYcOG6bXLZDIMGzYMlZWVOHnyJHNiByz9XsXPGcuYM2cOhgwZAo1Ggy+++ALff/+9XnEEVB1rAAb502q1KCgo0MuJsTigKn/MCZF9YYFkhtmzZ2PXrl2IjY3FsWPH0K1bN4OYzp07o7i42ODu87o7oYeHh0txt7ebiiPTcnNzAQB9+vRBkyZNpAcA6dSHWbNmoXPnzsjMzERRUZFe/7S0NDg6OiIkJASA+bkj0zw8PBAQEICysjKDbeXl5QAAb29vdO7cGYcOHTK4w3xaWhoUCgWaNm0KAGbnjkzz9vYGAL0r2OmoVCoAVb99YE4ePEu/V/FzpvYOHjyIqVOnonnz5khPT8df//pXODgYfmUydaxPnTqFa9euScc6LCwMcrncIC4/Px9nz55lTojsjc0vLP6Q0Wg0IiAgQCgUClFeXm4yTncDv+HDh0s38CstLRVBQUEiLCxMilOpVMLHx0dERUXp3cAvIiJCKBSKR/4Gfub48ccfxY4dOwweAETPnj3Fjh07xLlz56SbL7777rtS399++034+vqK559/XmozN3dUs2nTpglvb2+Rk5MjtVVUVIhu3boJd3d3UVZWJt2UdMWKFVLM6dOnhbOzs5g0aZLUZm7uyDSlUilcXV313muEEEKtVouoqCjh6uoqlEolc2JD3377bY03irXUexU/Z8xnKieDBw8WAERGRkaN/XU3im3Tpo3ejWITEhKEo6OjwY1iGzVqpHej2LffflsAEFlZWZbbKSKqNf4G6S6uXLmCvLw8dOnSBcuXLzca88wzz6Bt27YYO3YsFi1ahGvXriE0NBRbt25FdnY2duzYIcU6OTnh7bffxuTJk9GjRw9ER0dj//79OHLkCFatWmVwDxkyFBQUJF1O+k5NmzZF3759AVSdb9+nTx/Mnj0b2dnZaNq0KTZs2ACNRoPp06dLffz8/MzKHdVs3LhxWLp0KTp27IjExEQ0bNgQmzZtQlZWFlauXIkGDRqgb9++CA0NRVJSEo4fPw53d3esXbsWfn5+mDhxovRcHTt2NCt3ZJqXlxc++ugjjBs3DmFhYXj++echhMDmzZtx9uxZLF26FF5eXsyJHTD32Jr7XsXPmdo7evQofH19cfjwYRw+fNhge5s2bdCjRw/IZDJMnz4dL774IiIjI9G/f38cP34cu3fvRkpKCnx9faU+77zzDiIiIhAVFYXBgwfj0qVL2LBhA0aNGiVdNpyI7MSDrtDs3bFjxwzuuH3nY9WqVUKIqr/QzZo1S/zlL38RHh4eokePHmLfvn0Gz6nVasXixYulv6xHRESI9evX23jP6h4AYvTo0Xpt169fF5MnTxbt27cX3t7eom/fvuLUqVMGfc3NHdXswoULIiEhQTRr1kx4eXmJqKgosX//fr2YgoICMXbsWNGqVSvh5+cnBg0aJC5fvmzwXObmjmq2b98+ER0dLXx9fYWvr6+Ijo42eG0zJ7ZharVCCMu/V/FzxjymcuLq6lrj535iYqJe/MaNG8UzzzwjPDw8RHh4uFiwYIG0yne7AwcOiD59+ghvb28RHBwspk+fLjQajTV3kYjug0wIE9cCJSIiIiIiesTwIg1ERERERETVWCARERERERFVY4FERERERERUjQUSERERERFRNRZIRERERERE1VggERERERERVWOBREREREREVI0FEhERERERUTUWSERERERERNVYIBEREREREVVjgURERERkQT/++CNkMhnkcjkKCwtrjC0tLYWzszNkMhkyMzPvq9/FixdrjB05ciRkMhlmzJhxr7tC9EhigURERERkQa1bt8bTTz+NyspKpKam1hi7fft2VFRUICwsDCEhIffVr02bNpacvkkHDx6ETCbDyJEjbTIe0YPCAomIiIjIwl566SUAwPr162uM0xVCQ4cOrVU/IrIcFkhEREREFjZ48GDI5XIcOXIE2dnZRmNKS0uxd+9eyGQyDB48uFb9iMhyWCARERERWVijRo3w3HPPAQA2bNhgNGbHjh2oqKhAZGQkHn/88Vr1u1/ff/89Bg0ahBYtWqBBgwbo2LEjPvroI9y8eVMvrnv37oiOjgYA/Otf/zLrVLvu3bvDxcUFWq0WixcvRvv27eHq6or27dvjlVdeQUFBQa3mTmQtLJCIiIiIrEB3utxXX31ldLup0+Tut9+9mj9/PiIjI7Fp0ybI5XK0bdsWFy5cwJtvvokePXqgpKREin366acRHh4OAAgICECvXr0QHBxs1jivv/46kpKSkJ+fjw4dOuDy5ctYvnw5OnbsiKysrFrtA5E1sEAiIiIisoJ+/frBw8MDWVlZOHfunN62a9euYe/evZDL5XjhhRcs0u9enDlzBm+99RYUCgWOHDmCH3/8ESdOnMCVK1fQs2dPHD16FNOnT5fi586di7lz5wIAevbsiT179uDNN9+86zgqlQqLFy/G+++/j4KCAhw7dgyFhYWIj49HXl4e/va3v933PhBZCwskIiIiIitwcXFBfHw8AMOLLuzYsQMqlQrPPvssfHx8LNLvXiQnJ0Or1WLFihXo2rWr1K5QKLBx40b4+vri888/x61bt+57DJ2ePXvi3XffhUwmAwDUr18fa9asQUBAANLS0nDs2DEAf16OvKbHwYMHaz0forthgURERERkJbefLieEkNrvdprc/fYz1/Hjx+Hu7o5evXoZbPPy8kJ0dDTUajVOnTpVq3EAYPTo0QZtLi4u0j5mZGQAAN58803s3r1beigUCvTo0UOvrUOHDrWeD9HdyB/0BIiIiIjqqm7duiEwMBC//PILjh07hs6dO6OsrAx79uyBm5sb+vfvb9F+5rh+/Try8vIAAHJ5zV8Fi4qK7nscnVatWhltb9myJQDg559/BgAEBwfr/a7J1dUVjz32GJ599tlaz4HoXrBAIiIiIrISBwcHDB8+HLNnz8b69evRuXNn6TS5oUOHon79+hbpt3v3bpw7dw5du3bVO2UOgN4KFACo1WoAgJubGyIiImqcv7u7+73usgFTRVi9evUAABUVFbUeg8iSeIodERERkRXpTiXbuHEjNBqNdJpcQkKCxfpt3LgRkydPxr///W+DbUqlEsCfBYmXlxd8fHzg6OiI3bt3Y8+ePSYfMTEx97nXf7p8+bLR9p9++gmA6RUmogeFBRIRERGRFbVu3Rrh4eEoKCjA9u3bsXv3bnh7eyM2NtZi/Zo0aQIAuHTpkl67EAIXL14EAAQFBUntoaGhKC0tlS6QcDutVosxY8YgPj4eKpXqnvf3TuvWrTNoKy8vx9q1awGAvysiu8MCiYiIiMjKdKtBr732GlQqFeLj4+Hk5GSxfl26dAEAbNmyBUeOHJHav/jiC1y6dAnOzs5SDFB1FTug6spxZ8+eldrVajWmTZuGL774AgDg7OxsMNbt90cyxzfffIOFCxdKp/rduHEDiYmJyMnJQbdu3e56mh+RrcnEnSemEhEREZFFFRYWIiAgABqNBgBw6NAhREZGWqyfEALPP/88tm7dCgcHBzz55JNQqVTSaWzz5s0zuG/R5MmTMW/ePDg4OKBNmzbw9fXFhQsXUFhYiFatWuHw4cNQKBRSfGZmJkJDQ+Hs7IxevXrhmWeewRtvvGFy7t27d8ehQ4fQv39/bN++HY0bN0ZgYCCysrJw8+ZN+Pn5Yc+ePQgNDTXav1mzZujevTtWr1591+NEZElcQSIiIiKyskaNGklXY3v88cfRrVs3i/aTyWRITU3FJ598grCwMFy5cgVlZWWIiYnB3r17jd7U9cMPP8TOnTvx3HPPoaysDD/88AMCAgIwc+ZMnDhxQq84AqpOhZs0aRJcXV1x4MABXLlyxax9+OSTT7Bs2TK0bNkS586dQ9OmTTFq1CicPn3aZHFE9CBxBYmIiIiILE63gvTLL7+gWbNm99yfK0j0oHAFiYiIiIiIqBoLJCIiIiIiomq8USwRERER2Z3s7OwHPQV6RHEFiYiIiIiIqBov0kBERERERFSNK0hERERERETVWCARERERERFVY4FERERERERUjQUSERERERFRNRZIRERERERE1VggERERERERVWOBREREREREVI0FEhERERERUTUWSERERERERNVYIBEREREREVVjgURERERERFTt/wFgUCPb5Q/9UQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_data(data):\n",
+    "    # Extract the pt, R2, and match information\n",
+    "    pt = data[:, 0]\n",
+    "    R2 = data[:, 1]\n",
+    "    match = data[:, 2].astype(bool)\n",
+    "\n",
+    "    # Define bins\n",
+    "    pt_bins = np.linspace(np.min(pt), np.max(pt), 50)\n",
+    "    R2_bins = np.concatenate((np.array([0.55]), np.arange(0.65, np.max(R2) + 0.1, 0.1)))\n",
+    "\n",
+    "    # Create 2D histograms\n",
+    "    plt.figure(figsize=(10, 7))\n",
+    "\n",
+    "    # Unsuccessful matches\n",
+    "    hist_unsuccessful, _, _ = np.histogram2d(pt[~match], R2[~match], bins=[pt_bins, R2_bins])\n",
+    "\n",
+    "    # Normalize the histogram\n",
+    "    hist_unsuccessful = hist_unsuccessful / np.maximum(\n",
+    "        1, hist_unsuccessful.sum(axis=1, keepdims=True)\n",
+    "    )\n",
+    "\n",
+    "    # Plot the normalized data\n",
+    "    plt.imshow(\n",
+    "        hist_unsuccessful.T,\n",
+    "        origin=\"lower\",\n",
+    "        aspect=\"auto\",\n",
+    "        extent=[pt_bins[0], pt_bins[-1], R2_bins[0], R2_bins[-1]],\n",
+    "        cmap=\"Reds\",\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    plt.colorbar(label=\"Normalized Count\")\n",
+    "    plt.xlabel(\"VVJet $p_T$\")\n",
+    "    plt.ylabel(\"R2\")\n",
+    "    plt.title(\"Unsuccessful Matches\")\n",
+    "    plt.show()\n",
+    "\n",
+    "    # Successful matches\n",
+    "    plt.figure(figsize=(10, 7))\n",
+    "    hist_successful, _, _ = np.histogram2d(pt[match], R2[match], bins=[pt_bins, R2_bins])\n",
+    "\n",
+    "    # Normalize the histogram\n",
+    "    hist_successful = hist_successful / np.maximum(1, hist_successful.sum(axis=1, keepdims=True))\n",
+    "\n",
+    "    # Plot the normalized data\n",
+    "    plt.imshow(\n",
+    "        hist_successful.T,\n",
+    "        origin=\"lower\",\n",
+    "        aspect=\"auto\",\n",
+    "        extent=[pt_bins[0], pt_bins[-1], R2_bins[0], R2_bins[-1]],\n",
+    "        cmap=\"Blues\",\n",
+    "        alpha=0.5,\n",
+    "    )\n",
+    "    plt.colorbar(label=\"Normalized Count\")\n",
+    "    plt.xlabel(\"VVJet $p_T$\")\n",
+    "    plt.ylabel(\"R2\")\n",
+    "    plt.title(\"Successful Matches\")\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Use the function\n",
+    "plot_data(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "416d8fb4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def read_data(filename):\n",
+    "    data = []\n",
+    "    with open(filename, \"r\") as file:\n",
+    "        for line in file:\n",
+    "            # Remove brackets and newline, and split by comma\n",
+    "            items = line.replace(\"[\", \"\").replace(\"]\", \"\").replace(\"\\n\", \"\").split(\",\")\n",
+    "            # Convert strings to floats and append to data\n",
+    "            data.append([float(item) for item in items])\n",
+    "    return np.array(data)\n",
+    "\n",
+    "\n",
+    "# Use the function\n",
+    "data = read_data(\"output.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61e07348",
+   "metadata": {},
+   "source": [
+    "Next we plot some generator information for the vbf jets. we plot how their R1 and R2s are actually distributed at the start. note that R1 is distance to Hbb and R2 is distance to HVV. These are reconstructed Higgs jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "2b97fe49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Tp47q169fpF1AQIBq1apla1cSwzCUnZ3t0HNIkp+fn/z8/By+HgAAOCYnJ0c5OTkOX1+ezTsoKJWB2WxWamqqu8MAAACVoLQPh0JDQ5WRkeHiiC4rKCjQM888o6VLlyoyMlJ33XWXxo0bp8DAwGJtAwICJBUtKBUeu5LJZFJAQMA1C0qZmZny9/d3OPYZM2Zo5syZDl8PAAAck5CQoPj4eJf0RUEJAADAw+zdu1exsbH68ssv1axZM7377rvy8vKSyWSy+8lhQUGBpMvT5iSV2K6wbWG7kjRt2lSHDx92OH5GJwEA4B5TpkxRXFycw9eHhYUpMzOzTG0pKAEAAHiI3NxcxcfHKyEhQQUFBXr00Ue1cOFC22ijoKAgu6OLsrKyJEnBwcG2dmfPni3WzjAMZWVl2dqVxGQyFZsuBwAAPF9Fp51fPa2+NBSUAAAAPEBeXp7uv/9+JSUlqU2bNnrrrbd05513FmljNpuVkpKigoICeXl52Y6fOHFC0v8KSmazWRcuXNC5c+eKTF07c+aM8vLyrllQAgAAuBavazcBAABAZZszZ46SkpLUt29fff7558WKSZIUHR2trKwspaSkFDm+fft2SbLtEhcdHV3keEntAAAAHEVBCQAAwM3y8/P1xhtvKCgoSO+//74aNGhgt93QoUPl7e2tF1980bZGUnZ2tpYsWaL27dsrIiJCktS/f381bNhQCxcuVF5enqTL0+leeeUVBQUF6Z577nHJcwEAgOqLKW8AAABudvz4cWVmZqpLly5688037ba5++67FRYWptGjR8tisSg7O1tRUVHauHGj0tLStGnTJlvbWrVq6ZlnntHTTz+t3r1766677tLHH3+szz77TMuWLZOvr6+rHg0AAFRTFJTKwGq1Kjw83O650rYaBgAAns9ischisdg9Z7VaXRLDqVOnJEl79uzRnj177LZZtmyZwsLCtGDBAjVp0kQbN27Uzp071alTJ82bN099+vQp0n7ixImqW7euVq1apRdffFGRkZFatWqVhg4dWunPAwAAqj+TUdKeslBoaKgyMjIUEhKi9PT0Sulj4OqB12yzaeima7YBAADO54pcwJNU9vOuHri6TO2GbqLoBQCAO5QnF2ANJQAAAAAAAJQLBSUAAAAAAACUCwUlAAAAAAAAlAsFJQAAAAAAAJQLBSUAAAAAAACUCwUlAAAAAAAAlIuPuwOoCqxWq8LDw+2ei42NVWxsrIsjAgAAzmKxWGSxWOyes1qtLo4GAACgaqCgVAZms1mpqanuDgMAAFSC0j4cCg0NVUZGhosjAgAA8HxMeQMAAAAAAEC5MEIJAAAARTDdHwCA6stZ0/0pKAEAAKAIpvsDAFB9OWu6P1PeAAAAAAAAUC4UlAAAAAAAAFAuFJQAAAAAAABQLqyhVAYsTAkAQPXlrIUpAQAAahIKSmXAwpQAAFRfzlqYEgAAoCZhyhsAAAAAAADKpcoUlAzD0A033KC///3vxc6dP39ekydPVmRkpBo2bKj77rtP27dvL9YuLy9PCQkJ6ty5sxo0aKC+fftq/fr1rggfAAAAAACg2qgyBaUPPvhAaWlpxY4bhqHBgwcrMTFRbdu21ZgxY5Samqp+/fppz549RdpOmDBBzz77rBo3bqwJEybIarVq8ODB2rBhg4ueAgAAAAAAoOrz6ILShQsXtG7dOo0bN05/+MMf7LbZv3+/kpKSNH36dK1atUqzZ8/Wzp071aBBAyUmJtraWa1Wvf7664qJidGmTZsUHx+vXbt2qU2bNpozZ46rHgkAAAAAAKDK8+hFuY8fP64hQ4aU2mblypXy8vLS+PHjbcdCQkI0ZMgQvfnmm8rKylJAQIBWr16t/Px8xcXFyWQySZLq1aunJ554Qk8//bRSU1NL3MkNAAAAQNW2euDqa7YZummoCyIBgOrBo0cotWnTRr/99pt+++03bdmyxW6b5ORk29pJV+rVq5cuXbqk/fv329oFBgYqIiKiWLvC8wAAAAAAALg2jx6h5OXlpdq1a0uSatWqZbeN1Wq1O7KoSZMmkqSTJ0/a2gUHB9tGJ5XUzh7DMJSdnV3+B/j//Pz85Ofn5/D1AADAMTk5OcrJyXH4esMwnBgNAABA9eHRBaWysFqtuuOOO4odDwgIkFS0oBQYGHjNdvZkZmbK39/f4RhnzJihmTNnOnw9AABwTEJCguLj490dBgAAQLVT5QtKJpPJ7qeHBQUFkqTc3NxytbOnadOmOnz4sMMxMjoJAAD3mDJliuLi4hy+PiwsTJmZmU6MCAAAoHqo8gWloKAgnT17ttjxrKwsSVJwcLCtnb1RSFe3s8dkMql+/fpOiBYAALhSRaedXz1VHoDnKcti2wAA56vyBSWz2Wz3k8MTJ05I+l+hyGw2KyUlRQUFBfLy8iqxHVBTDFw98JptNg3d5PJ7OYsnxgQAAAAA1YVH7/JWFtHR0UpJSdHp06eLHN++fbt8fX0VGRlpa5eVlaWUlJRi7SSpU6dOrgkYAAAAAACgiqvyBaWYmBgZhqGFCxfajqWnp2vdunUaOHCgGjVqJEkaOnSovL299eKLL9rWUsrOztaSJUvUvn17RUREuCV+AAAAT1O4i669L4vF4u7wAABABVgslhL/nbdarWW+T5Wf8tauXTsNGDBAc+bMUVpampo3b641a9YoLy9P06ZNs7Uzm80aPXq0LBaLsrOzFRUVpY0bNyotLU2bNjHtBdVHWaZ6eaqyxs5UNQCoXGazWampqe4OAwAAVILY2FjFxsbaPRcaGqqMjIwy3afKF5RMJpPWrl2r+Ph4ffTRR0pKSlLXrl0VHx+vqKioIm0XLFigJk2aaOPGjdq5c6c6deqkefPmqU+fPu4JHqhBnFnoqspFMwAA4LmcucD30E1DnXYvAPBEVaag1LNnT9tUtavVrVtXiYmJSkxMLPUevr6+mjp1qqZOnVquvguHfdtTWmUPAAB4PovFUuI0rvIM+wYAAKhJqkxByZ0Y9g3UbOwYB1Rvzhr2DQAAUJNQUKoC+I9ZuAtTywAAAAAA9lBQAqoQCjzO5ep1nSj8AkDZlGUdG9anAQDAvSgoAQAAAPBIzlwk2xNRPAVQlVFQAgAAAAAnc3UxjOIUAFejoAQAAAAAHqq6j9ICUHVRUAIAF3Hmmk2sxwQAACpDWQtYjHYCQEGpDKxWq8LDw+2eK22rYQAA4PksFossFovdc1ar1cXRAAAAVA0UlMrAbDYrNTXV3WEAAIBKUNqHQ6GhocrIyHBxRAAAAJ7Py90BAAAAAAAAoGphhBIAAAAAoFycuascO9QBVRMFJQCogpy5wHdZsAg4AABVHzvGAXAmprwBAAAAAACgXBihBFQyV48kAQAA8HSMlKkZeM9A9UZBCQBwTWUtjDI1DqgerFarwsPD7Z4rbVc8QKKIAPcp688e6zGhprNYLLJYLHbPWa3WMt+HKW9ulJOTo283fKv83Hx3h4IyyMnJ0cyZM5WTk+PuUFBG+bn5/I5VIfyOVT28s+rLbDYrNTXV7pejxaScnBxt+HaDcvNznRwtKorfZc+Um5/L74yH4nfGM/Feyi42NrbEf+fNZnOZ72MyDMOoxDirtNDQUGVkZMjHx0c33XST3TYV+ZQuOztb/v7+6rekn3zr+FYkVEYFuEDh+zp37pzq169f5uuY8uY+uRdyteXxLU75HYPzlPT3laO/Y3Cf6vLOSvuU7siRI8rLy1NISIjS09NdHJnrFeY+lfG8hT8vS/otUR3fOhW+HyMMnMfZv8uMUHKOC7kX9PiWx532O1PVleV33lUjlKrLv3/VDe/FOcqTCzDlrQwKP6UDrkaxCACqvtI+HCpMqgAAAFAUBSUAAAAAgEdj5BvgeSgoAQA8DouAA4Bn4j/qUR2U5eeYabXAtbEoNwAAAAAAAMqFEUpACa4eIZF74fIOG4PXD2aBZ8BDlGUkE6OYgOqJEQaXOWPE0IXcC06IBABQ01BQQrXCf1wCAAA4Zv3g9ewmBgAoMwpK1QTrjZQdO7MB7lXS7yCjAAEAAICqg4JSGVitVoWHh9s9V9pWw54mbWua5KKR3xaLxWXfF1f25UppW9PUsm9L+qpCquv3sbq+s+r691R1/TtRqrxns1gsslgsRY5lZWUpICBAVqvV6f2h8m1N26q+LftWej+O/kyWZ5qaq57FFVz1LNWtH1dwxbNUtfdS2u9peaeJOjpF11X/pruin+qWn1Sn71lF+qGgVAZms1mpqanuDqPC0j5Oc1lflfHDX9Kohh0JO7Q5cLNT+/IEaR+7sGBQTftyter6fayu7+zKv6cqe+Sivb+nKmvEaHVL2K5UWc9m78Oh8PBwpaamKjQ0VBkZGU7vE5Xr47SPPbqgVB6uehZXcNWzVLd+XMEVz1Id30tlTxOtCgUFT+rDlarT94yCUjmkpaVpxowZ2rFjhySpR48emjVrllq2bOnWuGoypqABAFB5yH0AwPPZG8WU/VN2keM1YaMBVC01qqB05swZdevWTb/++qtiYmIkSStWrNCOHTt04MABBQYGujlCAICzlVS0/in7JwraqPbIfQCg+ijrdFkKT3CVGlVQWr58udLT07Vt2zb16tVLknTvvfeqX79+evvttzVhwgQ3R+gZnLlTGv+xBgCA+9T03MeZ//FVltEDAOAJyvv3Uml/l1GcQmlqVEFp5cqVioqKsiVUktS3b1+1bdtW77zzTrVPqiTnFXiudR8++QcA17j679qS/v5ll8+aidwHAABUlhpTULp48aJSUlL0l7/8pdi5Xr166ZVXXlFeXp58fGrMtwQA4OGcWZh31r0oTFUd5D5lxygjALDPWX8/lnVEJyOiqhaTYRiGu4NwhR9//FEtW7ZUYmKinn766SLnEhIS9Oyzz+rnn39WcHCw7XitWrWUm5srLy+vIsfLy2Qy2T1uGIYyMzPlF+BXYhtnyjmbI78GfpXeT3XtyzAM5WTl8L6qUF+8s6rVF++ravTV8LqGtv9f+O9Y06ZNHXpnv/z2S7n6s1qtMpvN5e7HkVTn5MmTCgoK0okTJ1RQUCBfX19dunSp3PdxJ0/LfQp/XgL8Air9d/xszlk18GtQqX24qh9X9GEYhrJysir93VSn9+KKflz1XqTq87Psqn74nam8Pq5reJ3DfVyZl1w8c9Hh+5QlJkdzkvKoSB/lyX0Kc55C5cl9asxHUlarVZIUEBBQ7FzhsZMnTxZJnvLz8yVJBQUFyszMrLTYcrJyKu3eV3PmL1ZN7Yv3VbX6knhnVa0v3pdn95WhjGLHKvPfyKv7y8go3n9lufK5CnOCqsRTc5+snKxKue/Vzlw8U236cdWzuOLdVKf34qp+qtPvTHV6LxK/M5XShxP+mXf6v18lxOSKnMRVeY+971lZcp8aU1AqrBzbq9QVFBRIknJzc4scr127ti5evChvb281bty4wn0DAADXq8hg7FOnTik/P1+1a9d2YkSuQe4DAEDN5Krcp8YUlAqHcJ09e7bYuaysy5Xlq4d2nz9/vtLjAgAAqAzkPgAAoDJ5uTsAVymce2hvKNeJEyckqci8QQAAgKqM3AcAAFSmGrMotyRFRUXJMAylpKQUOd62bVv5+fnpq6++clNkAAAAzkfuAwAAKkuNGaEkSTExMTp48KD+9a9/2Y5t2bJFqampGjVqlBsjAwAAcD5yHwAAUFlq1AilM2fOKDIyUv/97381YsQI5efna+XKlbrhhhu0c+dO+fv7uztEAAAApyH3AQAAlaVGFZQk6dixY5o+fbp27twpk8mku+66S/Pnz1fDhg3dHRoAAIDTkfsAAIDKUOMKSgAAAAAAAKiYGrWGkqdIS0vTiBEj1KJFC7Vo0UIxMTFKS0tzd1goxQ8//KBBgwapRYsWql+/vrp27ap169aJeqzn++2339S6dWs9/vjj7g4FpcjJydG0adPUsmVL1a1bV127dtWuXbvcHRZKcPjwYT3wwANq0qSJGjZsqL59+yo5OdndYcGDkft4JvKbqoFcxnOQr3guchP38HF3ADXNmTNn1K1bN/3666+KiYmRJK1YsUI7duzQgQMHFBgY6OYIcbXMzExFRUVJkv74xz8qMDBQmzZt0pAhQ/Sf//xH06dPd2+AKFVCQoKOHj2qnj17ujsUlKCgoEAPP/ywPvjgAz300EOKiIjQ3//+d/Xs2VP79u1TZGSku0PEFY4cOaKOHTvK399fjz32mGrVqqV33nlHXbt21c6dO9WtWzd3hwgPQ+7jmchvqg5yGc9AvuK5yE3cyIBLzZ8/35BkbNu2zXZsy5YthiTjpZdecmNkKMnYsWMNScbevXttx/Ly8ozf//73ho+Pj/HDDz+4MTqU5j//+Y9Rq1YtQ5Lxxz/+0d3hoARbt241JBnPP/+87djJkyeNBg0aGAMGDHBjZLBn+PDhxnXXXWf8+OOPtmNnz541goODjTvvvNONkcFTkft4JvKbqoFcxnOQr3guchP3Ycqbi61cuVJRUVHq1auX7Vjfvn3Vtm1bvfPOO26MDCXZvXu3OnTooE6dOtmOeXt7Ky4uTnl5edq9e7cbo0NJDMPQmDFjFBYW5u5QcA1Lly5VgwYNNHnyZNuxxo0ba9iwYdq/f79yc3PdGB2ulpKSottuu03Nmze3HfP391e3bt108OBBN0YGT0Xu45nIbzwfuYxnIV/xXOQm7kNByYUuXryolJQU3XXXXcXO9erVS1988YXy8vLcEBlKk5ubq/bt2xc7XviuTp065eqQUAbvvPOOPvnkEy1atMjdoaAUhmHo/fffV//+/eXr61vknMViUUZGRrHjcK9WrVrp+PHjunTpku2YYRg6evSobrzxRjdGBk9E7uO5yG88H7mM5yBf8WzkJu5DQcmFrFarJKlJkybFzhUeO336tEtjwrUdOnRIb7zxRrHjhZ+q3nbbba4OCdeQlZWluLg4Pf744+rSpYu7w0Epzp49qwsXLqhNmzaSLv/jf/bsWfcGhVKNHTtWZ86c0bBhw/Tll18qJSVFsbGx2r9/v8aPH+/u8OBhyH08F/mNZyOX8SzkK56N3MR9KCi5UGFSFRAQUOxc4bGTJ0+6NCaUX0FBgf7v//5PS5cuVWRkpN1PXeFeU6ZMUUFBgf72t7+5OxRcw4kTJyRJ119/vUaPHq2GDRsqICBAwcHBmjVrlnJyctwcIa7Wt29fzZw5U//4xz/UqVMnRUVFadGiRYqNjdXIkSPdHR48DLlP1UF+41nIZTwL+YpnIzdxH3Z5cyGTySRJdrdiLSgokCTm3nq4vXv3KjY2Vl9++aWaNWumd999V15e1GU9SXJyst544w0tWbJEDRs2dHc4uIYzZ85IkuLj49WqVSs9//zz8vf314cffqgZM2boxIkTeu2119wcJa702muvaerUqerfv78efvhhSdL69ev16quvqkWLFpo0aZKbI4QnIfepGshvPAu5jOchX/Fs5CZu5K7VwGuitLQ0Q5KRmJhY7Nzs2bMNSUZ6erobIsO1XLp0yZg6darh5eVlSDIeffRR48yZM+4OC1fJzc01IiMjja5duxr5+fm242JnFI91+PBhQ5Jxyy23GL/99luRc8OHDzckGT/99JObosPVfvrpJ8PHx8d4+OGHixwvKCgwHnroIcPb25v3hSLIfTwb+Y3nIZfxTOQrnovcxL346MGFzGazJCkzM7PYucJhlEFBQS6NCdeWl5en+++/X3PmzFHr1q3173//WytXrrQ7fB/utWzZMqWkpGjq1KnKzMxUenq60tPTJUnnz59Xeno68909THBwsCTpgQceUO3atYucu//++yVJBw4ccHFUKElycrLy8vL0yCOPFDluMpn0yCOPKD8/X59//rmbooMnIvfxXOQ3nolcxjORr3guchP3oqDkQrVr11ZkZKS2b99e7Nz27dvVrl07dgfwQHPmzFFSUpL69u2rzz//XHfeeae7Q0IJMjIyJEkDBgxQs2bNbF+StGbNGjVr1kyzZ892Z4i4ir+/v5o2bapff/212LmLFy9KkgIDA10dFkpQ+C6u3EWlUOH6EfzHKK5E7uO5yG88E7mMZyJf8VzkJu5lMgw7k9pRaV588UVNnDhRH3/8sXr37i1J2rJli373u9/p5Zdf1rhx49wcIa6Un5+v5s2bKy8vT8ePH5efn5+7Q0IpvvvuO3333XfFjg8cOFB9+vTR+PHj1apVK4WHh7shOpTkueee06JFi3Tw4EGFhIRIurymSq9evXTw4EFlZGTo+uuvd3OUkC7vOhQSEqLbb79d//rXv+Tjc3kpxry8PPXu3Vt79+5VRkYGiRuKIPfxPOQ3notcxnORr3gmchP3oqDkYmfOnFFkZKT++9//asSIEcrPz9fKlSt1ww03aOfOnfL393d3iLjCsWPH1KpVK3Xp0kXDhg2z2+buu+9WWFiYiyNDeZhMJv3xj3/UkiVL3B0K7MjIyFBkZKS8vLw0YsQI1atXT//4xz908OBBLV26VKNGjXJ3iLjC4sWLNWbMGN1222168MEHZRiG3n33XR06dEiLFy/Wn//8Z3eHCA9D7uN5yG+qHnIZ9yNf8VzkJu7DLm8uFhgYqE8//VTTp0/Xu+++K5PJpAceeEDz588nofJAp06dkiTt2bNHe/bssdtm2bJlJFxABYSEhGjXrl2aNWuWNmzYoHPnzikiIqLIaAZ4jtGjR+vGG29UQkKCLBaLJOm2227T1q1b1adPHzdHB09E7uN5yG+A8iNf8VzkJu7DCCUAAAAAAACUC4tyAwAAAAAAoFwoKAEAAAAAAKBcKCgBAAAAAACgXCgoAQAAAAAAoFwoKAEAAAAAAKBcKCgBAAAAAACgXCgoAQAAAAAAoFwoKAEAAAAAAKBcKCgBAAAAAACgXCgoAQBQw5lMJrtftWrV0g033KDHH39cBw8edGp/LVu2dNr9yqpnz54lPqvJZFJISIi6d++ud955RwUFBS6PDwAAuAa5j3NyH5NhGEYlxA0AAKoIk8kkSerevbuuu+46SZJhGPrpp5/07bffqqCgQF5eXpo7d67i4uKc0l+LFi2UlpZmO9azZ0/t3LlTx44dq7SEq7CPW2+9VSEhIUXOXbx4UWlpafrxxx8lScOHD9fbb79dKXEAAAD3IvdxTu7j47RIAQBAlbZixYpiCc2FCxf017/+VXPmzNHEiRPVpEkTDR061D0BOsnEiRM1cuTIYscNw9D777+vBx98UH//+981YsQI9e7d2/UBAgAAlyD3qVjuw5Q3AABQojp16mj27NlasGCBJGnChAm6ePGim6OqHCaTSb///e/14IMPSpJ2797t5ogAAICrkfuUHQUlAABwTaNHj1ZwcLBOnTqljRs3FjufmpqqRx55RGFhYapTp47Cw8M1Y8YMnTlzptT77tixQyaTSTt37pQk3XDDDTKZTEWGhH/++ecaNGiQWrVqpdq1aysoKEidO3fWq6++qtzcXGc+pi0GSTpx4oTT7w0AAKoGcp9ro6AEAACuqXbt2rbh3vv37y9ybvXq1erYsaNWrVqlixcvKjIyUsePH9esWbPUpUsX/fTTTyXeNzAwUP369VNAQICky2sZ9OvXz7aewa5du3TnnXdqw4YNys/PV3R0tIKDg7V3716NGzdO48aNc/qz/vDDD5KkNm3aOP3eAACgaiD3uTYKSgAAoEwKP706evSo7diJEyf0pz/9Sd7e3nr//fd17Ngx7dmzRz///LOGDx+u7777TmPHji3xnhEREdq8ebMiIiIkXV7LYPPmzTKbzZKkOXPmKC8vT/Pnz1daWpp27NihgwcP6ssvv1Tt2rW1bNky5eTkOOX5DMPQBx98oI0bNyo4OFiPPvqoU+4LAACqJnKf0rEoNwAAKJPmzZtLKppUvfDCC/rvf/8ri8WigQMH2o7Xq1dPb731lvbt26cPPvhAR48e1Y033ljuPoOCgnTfffdpwoQJth1ZJKlDhw5q166dLYErz+4o8+fP15o1a4ocy8nJ0fHjx/XDDz+oY8eOWrlypRo2bFjueAEAQPVB7lM6CkoAAKBcrkxu9u7dK0kaNGhQsXa1atXSvffeq8OHD2vPnj0OJVUrVqwodswwDO3fv19Hjhwp9/0k6dChQzp06FCJ56+77jrVqlXLoXsDAIDqh9zHPgpKAACgTI4fPy5JRZKj7777TtLlT9NKc/r0aYf7/eGHH7Rnzx59/fXXtiHfp06dcvh+y5YtK7Z1bn5+vtLS0jR9+nStWrVKDz30kA4cOOBwHwAAoOoj9ykdBSUAAFAmx44dk1Q0qcrNzZXJZFLfvn1LvbZwXYDyio+P16xZs1RQUKDg4GDdfffdmjp1qjp27KhJkyYpOTnZoftezdvbWzfeeKPeeustffzxx0pJSdHx48dtQ90BAEDNQ+5TOgpKAADgmi5evKhVq1ZJktq3b2873rp1a+3bt09r1qxRgwYNnNrnZ599ppkzZ6p169ZatWqVOnToIC+v/+0n4u3t7dT+pMs7utx666365JNPZLVaKSgBAFBDkftcG7u8AQCAa3rttddktVptC0UWioqKkiRt3rzZ7nVTpkzR/fffL6vVWu4+P/vsM0nSo48+qk6dOhVJqM6fP19pU9Lq168vSTpz5kyl3B8AAHg+cp9ro6AEAABKdOHCBU2dOlUTJ06UJC1cuFC1a9e2nX/mmWfk6+urp556Srt27bIdLygo0Guvvaa//e1vysjIKPOw77Nnz9r+f9OmTSVJn376qS5evGg7npaWpvvvv1/nz5+X5PzCz3XXXSdJFVqrAAAAVE3kPmXHlDcAACBJGjFihC2hMAxD6enp+s9//qOCggJ5eXlp7ty5+sMf/lDkmtatW2vevHmKi4tTt27ddNNNN6lp06b6/vvvlZGRocaNG+udd965Zt8BAQGSpGHDhunWW2/VK6+8ov79+ys0NFTbtm2Tv7+/oqKidOHCBR0+fFg333yzHnzwQb377ru65557NHv2bD3xxBNO+T6EhYVJkr799lun3A8AAHgmcp/LHM19KCgBAABJlz8Nu5Kvr69CQ0PVq1cvTZgwwTbE+2rjx49X586d9cILL+jgwYPau3evbrjhBg0ZMkRPP/20goODr9n39OnTdezYMR0+fFi//PKLcnNzZTabtWPHDk2bNk2ffvqpUlNTFRkZqeeee06TJ0/WhQsX9Ntvv+mrr76Sr6+vM74FkqRbb71VkjR//nw98sgjuuWWW5x2bwAA4DnIfS5zNPcxGYZhOC0KAAAAAAAAVHusoQQAAAAAAIByoaAEAAAAAACAcqGgBAAAAAAAgHKhoAQAAAAAAIByoaAEAAAAAACAcqGgBAAAAAAAgHKhoAQAAAAAAIByoaAEAAAAAACAcqGgBAAAAAAAgHKhoAQAAAAAAIByoaAEAAAAAACAcqGgBAAAAAAAgHKhoAQAAAAAAIBy+X+SWlzzTnYjRwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def process_and_plot_refactored(filename):\n",
+    "    # Load the data from the file\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        lines = f.readlines()\n",
+    "\n",
+    "    # Extract the delta R values from the lines\n",
+    "    dr_values = [eval(line.strip()) for line in lines]\n",
+    "\n",
+    "    # Create a dictionary to store the data and labels\n",
+    "    data_dict = {\n",
+    "        \"drj1bb\": {\"values\": [val[0] for val in dr_values], \"color\": \"blue\", \"label\": \"drj1bb\"},\n",
+    "        \"drj2bb\": {\"values\": [val[1] for val in dr_values], \"color\": \"red\", \"label\": \"drj2bb\"},\n",
+    "        \"drj1VV\": {\"values\": [val[2] for val in dr_values], \"color\": \"green\", \"label\": \"drj1VV\"},\n",
+    "        \"drj2VV\": {\"values\": [val[3] for val in dr_values], \"color\": \"purple\", \"label\": \"drj2VV\"},\n",
+    "    }\n",
+    "\n",
+    "    # Plot individual histograms\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "    for i, key in enumerate(data_dict.keys()):\n",
+    "        plt.subplot(2, 2, i + 1)\n",
+    "        plt.hist(data_dict[key][\"values\"], bins=50, color=data_dict[key][\"color\"], alpha=0.7)\n",
+    "        plt.title(data_dict[key][\"label\"] + \" values\")\n",
+    "        plt.xlabel(\"Delta R\")\n",
+    "        if i % 2 == 0:\n",
+    "            plt.ylabel(\"Number of Events\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "    # Combined histograms\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "    # bb combined\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.hist(\n",
+    "        [data_dict[\"drj1bb\"][\"values\"], data_dict[\"drj2bb\"][\"values\"]],\n",
+    "        bins=50,\n",
+    "        color=[data_dict[\"drj1bb\"][\"color\"], data_dict[\"drj2bb\"][\"color\"]],\n",
+    "        label=[data_dict[\"drj1bb\"][\"label\"], data_dict[\"drj2bb\"][\"label\"]],\n",
+    "        alpha=0.7,\n",
+    "    )\n",
+    "    plt.title(\"Combined drj1bb and drj2bb values\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Number of Events\")\n",
+    "    plt.legend()\n",
+    "\n",
+    "    # VV combined\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.hist(\n",
+    "        [data_dict[\"drj1VV\"][\"values\"], data_dict[\"drj2VV\"][\"values\"]],\n",
+    "        bins=50,\n",
+    "        color=[data_dict[\"drj1VV\"][\"color\"], data_dict[\"drj2VV\"][\"color\"]],\n",
+    "        label=[data_dict[\"drj1VV\"][\"label\"], data_dict[\"drj2VV\"][\"label\"]],\n",
+    "        alpha=0.7,\n",
+    "    )\n",
+    "    plt.title(\"Combined drj1VV and drj2VV values\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.legend()\n",
+    "\n",
+    "    # All variables combined\n",
+    "    plt.subplot(2, 1, 2)\n",
+    "    plt.hist(\n",
+    "        [data[\"values\"] for data in data_dict.values()],\n",
+    "        bins=50,\n",
+    "        color=[data[\"color\"] for data in data_dict.values()],\n",
+    "        label=[data[\"label\"] for data in data_dict.values()],\n",
+    "        alpha=0.7,\n",
+    "    )\n",
+    "    plt.title(\"All variables combined\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Number of Events\")\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# The function call remains the same:\n",
+    "process_and_plot_refactored(\"output_deltaRtruthinfo.txt\")\n",
+    "process_and_plot_refactored(\"output_deltaRtruthinfo_bbtagging.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "340a8882",
+   "metadata": {},
+   "source": [
+    "Plots of generator higgs particle and generator vbf quarks delta R and pt. These are produced from events with absolutely no selections other than pt > 0 maybe some other very basic cuts on Hbb jets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "ff2fb70e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+/Tn3Ub169WLX//zzz3I4HFq8eLF+//vfS5ImTpyoQ4cOBeIUAABAhCGHF0YO944cXrxQ5PCDBw/qxhtv1J///GcdO3ZMlmXprrvu0ldffeV5DQOIHnRgAyGWf/maN4cPH1ZOTo6kvNEFvmy7f/9+z/9727Yo+ZcN/utf/5Jt25Kkt956S1LRoy0uuOAC/etf/9LBgwf1+uuv609/+pMaNWok27b18ccfq2fPnnrxxReN23B2O95//32dPHmyQDsGDBigmJgYr9sV95xIvz4vvjwnRckPzOcKXt5CenJysqS8SwzPnDljNJKoQ4cOhfZT1GWvJVGjRg1J8nqJ5NnO9RwXxbZtZWdne11+2223ady4cTpw4IC6dOmiRx99VMuXL9d3332no0ePaurUqefcv8lz8fLLL6tPnz565plnFBMTo6NHj+rhhx8uyekAAIAIRw4vvh3kcHJ4uObwzMxMtW/fXsuXL5cktWvXTp999pleeukln2/fAiAy0IENhNjZl7D91jfffOP5f2+XqJlsW6lSpXOOTDjbLbfcIknas2ePvvjiC+3atUvp6emKjY1Vv379it02ISFBAwYM0Jw5c7Rr1y6tXbtWbdu2lST97W9/8wRxE9dcc43Kly+vU6dO6YMPPtDx48e1atUqSYUvnzzb3r17debMmSLX5z8vgbjkL/8Svj179hR7bnv27Cm0rGnTppIkt9ut3bt3+92WQMgf1fP999/L6XQWWff999+XaP9ZWVk6evRooeUbNmzQP//5T0nSsmXL9NFHH2n8+PG67rrr1KRJE8XExBjfP7I4V155pQYPHixJ+t3vfqdhw4ZJkl566SVt3brV7/0DAIDIQg73jhxe+sjh5k6dOqUbb7zR87N75JFH9Mknn+jSSy/1u50Awhcd2ECIzZ07t8h1zz//vCSpbt26nr/Km2xr27Zn29atW/s0OqBx48a65JJLJOWN/si/bPHqq69WzZo1C9QeOHBA7du3V/v27T0TqeSzLEtXXXWVpk2bJinvvnE//fSTcTsqV66sa6+91tOOlStX6vTp07r44ovVsmXLIrdzuVxasGCB13VHjhzR4sWLJeU9L/66+OKLPfv997//7bVm8+bNXu/zV6dOHVWqVEmSCtxf8LfmzJmj1q1beya9Cab8LwLHjx/3PE+/dfToUS1btqzY/WRlZXldvnLlSq/LN23aJCnv0tmiJnP5z3/+U+wxTfx2tNCkSZOUmJgo27Y1YsQIn77YAQCAyEcO944cnoccnifccvhrr73madPLL7+siRMnFnvfcADRgQ5sIMQ++eQTvfbaa4WWr1q1SosWLZIkjRkzxmv4XbRokT766KNCy19++WVt3LjRs62v8kd/vPPOOwVmPf+tpKQkff755/rPf/6j+fPne91XfqBOTEz0Gv5N2rF8+XItWbJEUt7lk+f6IvDYY49p3759BZY5nU6NHDlSx48fV4UKFfTAAw/41BZvWrdurU6dOkmSxo8fX2hUw5kzZzRu3Div2zocDk8Ynjx5stfRIfv379f48eOVnp5e7JeFQOnYsaPny8CECRMKPYe2bevhhx/WsWPHvG6ffznmBx98UGikxrFjx/TYY4953a5KlSqS8r6AnDp1qtD6r776StOnT/f82+VyGZ5R8RITEz1t+vjjj/Xmm28GZL8AACAykMPP3Q5yODk83HL4K6+8Ikm66aabdOeddwakPQDCHx3YQAiVL19elSpV0h//+EeNHDlSa9as0dq1azVmzBj17t1bbrdb5513nu66665C2yYkJMi2bfXq1UuPPPKINmzYoPfee09Dhw7V3XffLUm67LLLSjSBRX5g3bx5szZt2qTKlSvrhhtuKFQXGxurq666SpI0ffp0PfTQQ/rkk0+0Y8cOrVu3TqNHj9aoUaMk5QUMX+8Td9111yk+Pl4HDx70BPgBAwYUu02tWrX0ww8/qH379po9e7Y2b96sRYsW6eqrr9arr74qSbrvvvs8Ic8flmXp8ccflyRt2bJFV1xxhebNm6ctW7Zo0aJF6t69u1avXu2Zcf23IwMmTpyoatWqKTs7W61bt9ZTTz2lDRs2aMuWLUpNTVXHjh2VkZGhBg0aeH6mwRQTE6PJkydLyrs8sV27dvrHP/6hTZs2admyZbrllls0c+ZMlStXzuv2l19+uSRpx44duvXWW7V9+3ZlZmZq5cqV6tixo77//nvPc+Ftu+PHj+vGG2/U6tWr9eWXX2rVqlUaOXKk2rVrV+CefdOnTy9wb0l/3H333WrRooUkadSoUZ77PAIAgOhGDi8eOZwcHo45/MSJE/r0008lSZ07d9Z33313zoe3jnkAEcgGYKxLly62JLtLly5G9R9++KEtyZZkf/jhh57lu3fvtiXZycnJ9tKlS+24uDhP3dmPGjVq2Fu2bPG6z8svv9x+9tlnvW4nyW7UqJG9e/fuEp9rixYtPPsaNGhQkXV79uyxExISimxHfltPnDhRonZcf/31nv107ty5yLqJEyfakuxRo0bZffv2LbIt11xzjX3q1KlC2+f/bCdOnOhzGx9//PEij/fAAw/Yzz33nC3JHjp0aKFt169fb1erVq3I7VNSUuzt27eXqL0NGjSwJdmvvPKK8bm43W77/vvvL7I9DRs29JxvgwYNCmx76tQpu2XLll63i4mJsefNm2f379/fa5uGDx9e5DGTkpLsjz76yK5Zs6Zn2R133OHZ1tvv2NnyXxtF/d6uXbvWs48JEyYYP1e/9corr9iS7GrVqpV4HwAAwDtyeGHkcHJ4vrKSw/N/f315FNU2AJGFEdhAiN1888369NNPdfPNN6tWrVqKj49X48aN9X//93/asWOH5z543gwfPlwffPCBevbsqcTERJUvX14XXXSRJkyYoPT0dL9m+M6ffVwqfrKWBg0a6LvvvtPYsWPVtm1bJSUlqVy5cmrSpIl69uypxYsX65NPPlHFihWD2o58DodDixYtUmpqqi655BJVqlRJVapUUceOHfXyyy9rxYoVKl++fInaUpS//e1vWrNmja6++mpVr15dFSpUULt27bR48WI999xzntnEvY026dSpk7755huNGjVKF110kSpUqKDExER16NBBjz76qL799lvPyITSYFmWnnvuOS1fvlxXX321EhMTFRcXp4YNG+rBBx/Uli1bVLt2ba/bli9fXhs2bNC4cePUokULVahQQTVr1tR1112ndevW6Y477ijyuDNmzNDrr7+ujh07qmbNmqpYsaJatmypRx55RN988426dOmit956S02aNFH9+vV1xRVXBOycr7rqKs/rbNq0afrhhx8Ctm8AABC+yOGBaUc+crh/yOHnzuEHDx4M2LEBRBbLtpm1CgCCadCgQVqwYIFmzZqle++9N9TN8du8efM0ZMgQNWjQwOs9AwEAAIBwQA4HgOjACGwAKKGjR49q8ODBGjx4cJGjBXJycvThhx9KypvdGwAAAIB/yOEAULbQgQ0AJVS1alWtXbtWr776apGzv8+fP1/79u1TuXLl1LVr19JtIAAAABCFyOEAULbQgQ0AJWRZlu68805J0qRJk/T000/r8OHDcrvd2r17tx577DHdc889kqQ///nPqly5ciibCwAAAEQFcjgAlC3cAxsA/OByuXTjjTfq3Xff9SyLiYmRy+Xy/Ltdu3ZavXq1qlWrFoomBhz33gMAAECokcP3hLo5AFBqGIENAH6IiYnR8uXL9dZbb6lHjx5q0KCBLMtS9erVdcUVV2j69On66KOPoiY0AwAAAOGAHA4AZQcjsAOgUqVKOn36tGJiYpSUlBTq5gAAgFJ08OBBuVwulS9fXidOnCj147dt21YZGRkB3WdKSoo2b94c0H0CwUAOBwCg7CKHlx2xoW5ANDh9+rTcbrfcbrf2798f6uYAAIAQOH36dEiOm5GRQf5AmUUOBwAA5PDoRwd2AMTExMjtdoe6GQAAIIRiYmJCenyHw1Lt2tX92sePP2bL7ebiPEQOcjgAACCHR78SdWDPnz9fdevW1e9///ti69auXaujR4/qD3/4Q4kaFymSkpLO+ouLt6fUWcRyb4JV60u9LcklKUaSFYS2BKvdvtb6ep6co0m9ZXm/tb5t58iy4n+zrLgvnMF6vYbLz8aX+rLwO1kWzlHifcfffftaX1q/k05JCvntC2rXrq7de5/xax+N6j+o/fuzAtMgBAU5vKBz5/BA8vU9JdKOV9rHLMlnfyBE3s+xqHz9W79ma36OHM9Xofg5Rvt7XCiOVxZ/juTwsqJEkzgOHjxY06dPP2ddamqq7rrrrpIcAgAAAD6ybadfD4Q/cjgAAED4IYcHl/GfSebPn1/g3/v27Su07GzZ2dlav369zpw5U/LWAQAAIKCen71Wqc+v9bouM/NoKbcGJsjhAAAAKMuMO7AHDx4sy8q7BMGyLH355ZcaMmRIsdvYtq1rr73WvxYCAADAgC3bdp2zauiwLho6rIvXdU0a/lX792cHuF2h9Z///Ec7duxQy5Ytdemll4a6OSVCDgcAAAhnZjn8XPuINoHM4cYd2BMmTJBlWbJtW48++qiaNm2q22+/vdhtEhISdMcdd/jVQCkvgDdu3FiTJ0/WwIEDC6w7fvy4JkyYoPfee08//PCDzj//fN1yyy0aM2aM4uML3m93yZIleumll7Rp0yZdcMEFuummmzRq1CjFxhZ8GtauXatnnnlGn3zyic477zxdffXVmjhxoipWrOj3uQAAAASDLcnt5+WHkRybN27cqKeeekrdunXT/fffL0maPHmyHnnkEU/N4MGD9dJLL8nhKNFd9EKGHE4OBwAA4YscHvwcbtyBffZB84PzxIkTS3RQX61YsUJ79uwptNztdqtbt2767LPPNHDgQA0aNEifffaZJk6cqHXr1un999/3PDFLlixRv3791Lp1a40YMUKbN2/W2LFjtX//fs2cOdOzz40bN6pXr15q2LChhg0bpl27dmnatGn66quvtHz5cs/oFwAAAISHTz75RF26dJFt2/rd734nSfrf//6nyZMnq1KlSrr11lv10Ucfad68eerRo4duvfXWELfYN+RwcjgAAEA4Kq0cXqKpQt1u97mL/HTy5EmtWLFCH3/8sebOneu1ZunSpfrss8/05JNP6qGHHvIsnz59uv7yl79o4cKFGjhwoGzb1uTJk3XRRRfp448/VuXKlWXbtgYOHKgXXnhBjzzyiGrWrClJeuKJJ5SYmKj169erdu3akqTGjRvr8ccf1/bt29WqVasSnI0vf10IVm1J6oO172C2m3P0j//t/nX288K1Ra8LRFuCtd9w+bn7KlzazTn6h3P0rz6cfidLgx2ACWAic+zH1KlTZdu2nnrqKf3pT3+SJP3zn/+U0+nUY489puHDh+vAgQNq1KiRXnrppYjrwD4bOTyUSvs9IhTvSZH+Pmgi8n6OvmXowBwz/EXezzG8jxcKZeE9jp9j9BzzXMjhwc7hPv3Uv/jiCz333HN64IEHNGPGDG3fvr1EBzWxd+9e9e/fXzNnztTJkye91mzcuFGSdM899xRYfv/99ys+Pl7r1q2TJKWnp+uLL77QPffco8qVK0vKu3/g//3f/8npdOrNN9+UJB06dEjvvfeeBgwY4AnNkjRixAhJ0uuvv17Cs6EDu+S1wd63LzhH/+rD5fVaFs7R132H03PiC86x5LXB3rcvwqnd4RiGfVNWZz9PT09Xs2bN9H//93+qWrWqpLxbUViW5bnVRp06dXTppZfqv//9byibWmLk8JLm8EAqCx0fkf8+eG78HKNDtP8c+RlGxzH5OUbPMc+NHB7cHG48Anv8+PF64okn5Ha7Zdu2LMuSZVkaM2aMHnvssRI3oCjNmjXTqVOnJEnr169Xr169CtUcP35cv/vd71SlSpUCy3Nzc2VZlg4dOiRJSktLkyRdddVVBeouueQSVatWTWlpabr33nu1ZcsWOZ3OQnVJSUlq0aKFZz8AAAAIH0eOHFHTpk09/3a73dq4caOaN2+uGjVqeJZXrlxZhw8fDkUT/UIOJ4cDAACEo9LK4UYd2P/+9781ZcoUSdLVV1+tiy++WF999ZX+/e9/a+rUqbrkkkt08803l7gR3jgcDpUvX16SCk0Ck++ll17yuvztt9/WmTNn1KJFC0lSZmamJBUYzZF/jOTkZB08eLDYuvxlP/zwg0HL/R3yz739AAAoXb5+dtu/+W+YsCXb7efojTA7JVMNGzbU119/LZfLpZiYGH3wwQc6fPiwBgwYUKBu165dSk5ODlErS4YcTg4HACB6RWj4/C3DHP5i6ka9+OJGr+sOZv4c6FaVitLK4Ubj7mfMmCHLspSamqqVK1fqySef1MqVKzV79mzZtq3nnnuuxA0ItKVLl+quu+5SxYoV9ec//1nSr4G4evXqheoTEhIKBeeEhIRi64rn8uMR/HsaAgCA33KrZJ/X+duFC1uynf49IvRLRLdu3XTw4EHdf//9+vjjjzVhwgRZlqUbb7xRknTq1Ck9/vjj2r17t2dymUhBDieHAwAQvXzN4b99hAuzHP7ne9pp85YHvT6SkiqH+iRKpLRyuFEH9jfffKM6dep4gmi+e+65R3Xq1AnqPfhMHThwQAMGDFCfPn3kcDi0aNEi1atXT5KKnbHc7XYrNze3QJ1tF/7ydnZd8WL8eITnfXwAAIhuDpXs8zp/O4TamDFjlJSUpBdeeEFdu3bVpk2b1KlTJ8/tKK688kqNHz9elmVp1KhRIW6tb8jh5HAAAKKXrzn8tw+EWmnlcKOk9r///U/nn39+oeWWZalZs2Y6evRoiRsQCIsXL9bFF1+sN954Q5dffrm2bNmi66+/3rM+KSlJkpSdnV1o26ysLKWkpPhUVzzLzwcAAChdJf28Dr/P7rI6eUzdunW1ZcsW/eUvf9E111yjv/zlL3rnnXc86ytWrKh27drp3XffVZcuXULYUt+Rw8nhAABEr+j57CaHBzeHG90D27Ztz33wfqtcuXIlPnggTJ06VWPHjlXlypX18ssva/DgwXI4CvbL599j5cCBA6pVq5Znudvt1sGDB9W+fftCdb+VkZFhGJwBAABCwZbcJqNUz7GPCFWnTh09+eSTXtd9/PHHpdyawCGHk8MBAEC4I4cHO4dH9LVyH330kcaOHatGjRopLS1Nd955Z6HQLMkTjNeuXVtg+ZYtW3Ts2DFddtllkvJmQ4+NjS1Ul5mZqS+//NJTBwAAUJbZtq1GjRrptddeK7TuxIkTGj16tFq1aqUaNWrohhtuKJStJMnpdGrq1Km6/PLLVb16dfXs2VNLliwpUXu6deumMWPGnLNuwoQJuu2220p0DBREDgcAAEBp5fCI7sBOTU2VlDfb+cUXX1xkXatWrdSiRQu9+OKLOn78uKS8L14zZsxQXFycZ2bMpKQkXXPNNXrjjTf0448/erZ/5plnJElDhgwJ0pmEo/C6FCN4ysJ5co7RgXOMHmXhPDnHUCmtSxdXrFihPXv2eDm+rX79+mnatGm6+OKLNWzYMO3YsUO9evXSp59+WqB2xIgRGjt2rGrVqqURI0YoMzNT/fr101tvveXzeX/00UdG94LesmVLgUsaUXLkcARGeL6Xwlf8HKMDP8fowM8xVMrqLURKK4cb3UJEyps1cu/evV6XS3n35/M26Yok1a9fv4TNK96nn36qmjVrasOGDdqwYUOh9RdeeKG6d+8uy7I0fvx49evXT507d9YNN9ygTZs26b333tOkSZNUs2ZNzzZ/+9vf1KlTJ3Xp0kX9+/fXd999p0WLFmnIkCFq0aJFUM4j/FgqGzfDLwvnyTlGB84xepSF8+QcQ8eW3P6G36IvXTx58qRWrFihjz/+WHPnzvVas3XrVq1cuVITJkzQpEmTJEnDhg1T69atNW3aNP3zn/+UlDeq9oUXXtCgQYM0b948WZalhx56SG3bttWUKVPUp0+fc7a0W7duBf69adOmQsvOlp2dre3btxfIfZGCHF6WcnhZEq7vpfANP8fowM8xOvBzDJ3g5vBwE4ocbtyBvX79ejVq1KjI9Q0bNvS63LIsOZ3B+UvCoUOHdOrUKT3wwANe199xxx3q3r27JKlv375avHixXnzxRT3zzDNq1qyZnn322ULbdujQQatWrdKMGTM0e/Zs1a1bV+PHj9fEiRODcg4AAACRYO/everfv3+xNfPnz5fD4dDw4cM9y+rWrav+/fvrpZdeUlZWlhISEvTGG2/I5XJp5MiRsqy8kUJVqlTR3XffrVGjRmnHjh1q3rx5scf66KOPPP9vWZaOHDlSYFlR/vrXv56zJtyQw8nhAAAA4SIUOdy4A7uoUR3B2u5sXbt29bqfkydP+rSffv36qV+/fues+/3vf6/f//73Pu0bAAAgpGz5P/KjmNjWrFkzz4jf9evXq1evXoVq0tLSPPe+Plu3bt00a9Ysbd26Vd26dVNaWpoSExPVsmXLQnX5+zlXB/bu3bvzmmzbaty4sbp06aJ58+YVu0316tVVrVq1YmvCETkcAAAgjAU5h4ebUORwow5st9td4gMAAACgNNg6c/qMzuS4SrwHdzEdng6HQ+XLl5ckxcfHe63JzMz02vFcu3ZtSdLBgwc9dSkpKZ7R10XVFadBgwae/7/jjjvUsmXLAsuiBTkcAAAg3NmS3/exjpwe7FDkcOMR2AAAAAhv05/ZrCee3BSy42dmZuqKK64otDwhIUFSwQ7sxMTEc9aZeuWVV3xtKgAAAAA/lVYOpwMbAAAgSvxleGvdP7Tkk921veINZWT4dmuIs1mW5fV2E/mjiHNzc32q88WuXbv0xhtv6Pvvvz9nG19++WWf9w8AAAAUxfJ7Ekcz+bftmDx5sgYOHFhg3YkTJ/Too4/q3//+t/bt26crrrhCDz74YKEJFp1Op5588kktW7ZM33zzjdq1a6e7775bffv2LVGbSiOH04ENAAAQJcrF2SoX5yjx9o7f3NLDV0lJScrOzi60PCsrS5KUkpLiqfM2yvq3daa2bNmiq666SidOnDjnfZ/pwAYAAEDAlVIH9ooVK7Rnz55Cy23bVr9+/bRy5Urddtttuv7667Vo0SL16tVL69evV4cOHTy1I0aM0OzZs9W7d29dffXVWrZsmfr166clS5aoT58+PrWntHI4HdgAAAAIiOTkZB04cKDQ8oyMDEm/dkwnJycrPT1dbrdbDoejyDpTEyZM0PHjx9W+fXs9+OCDSk5OLukpAAAAAGHl5MmTWrFihT7++GPNnTvXa83WrVu1cuVKTZgwQZMmTZIkDRs2TK1bt9a0adP0z3/+U1LerfxeeOEFDRo0SPPmzZNlWXrooYfUtm1bTZkyxecO7NLK4XRgAwAARAU7ACM//Js8pn379nruued0+PBh1axZ07N87dq1iouLU6tWrTx1ixcvVnp6utq0aVOgTpIuu+wyn4772WefKSEhQatWrVKVKlX8OgcAAADAN8HN4Xv37lX//v2L3Xr+/PlyOBwaPny4Z1ndunXVv39/vfTSS8rKylJCQoLeeOMNuVwujRw50jOhepUqVXT33Xdr1KhR2rFjh9dJ2YtSWjm85NeYogjOIh7MIA8AQORyq+jP+DBhS5bt9Ovh7+TngwYNkm3bevbZZz3L9u3bpzfffFPXX3+9p1P7tttuU0xMjKZPn+651PDYsWOaM2eOLrnkErVs2dKn454+fVrNmzen87rMI4cDABB9yOHNmjXTqVOndOrUKa1atcprTVpamlq1aqUaNWoUWN6tWzfl5ORo69atnrrExMRCeTv/PtlpaWk+nXpp5XBGYAccTykAANHHoaL/7h9G4TnE2rRpo969e2vKlCnas2eP6tevr0WLFsnpdGr8+PGeuuTkZA0dOlSzZs3SsWPH1Lp1ay1btkx79uzR8uXLfT5u27ZttWPHjkCeCiISORwAgOgTGTn8zBmXzuS4Sry9u5j7RzscDpUvX16SFB8f77UmMzPT68jp2rVrS5Jn/pnMzEylpKR4Rl8XVWeqtHJ4QFKe2+3W3r17VaFCBe45CAAAEBK25C55aPbsww+WZWnx4sWaNGmS3nvvPa1cuVIdO3bUpEmT1Lp16wK1M2bMUO3atbVs2TKtW7dOl112mZ566in16NHD5+NOnjxZ3bp109SpU/W3v/3Nr3OINORwAACAULP19D+2a+qML0PWgszMTF1xxRWFlickJEgq2IGdmJh4zjpTpZXD/erAfuONN/TEE0/o66+/ltOZ91ePSpUqqUOHDnrkkUcKzHAJAACA4LJKafbzrl27FjnLeKVKlTRt2jRNmzat2H3ExcVp3LhxGjdunN/tqVevnl544QUNHz5ca9euVd++fdWoUSOVK1fOa33nzp39PmaokcMBAADCx0PDLtADfzq/xNtf+vv39GPm6RJvb1mW13zudufdSi03N9enOlOllcNL3IF9880365133pFt26pYsaIuvPBCud1u7dq1S++//74++OADjR49Wo8//nhJDwEAAACcU8OGDT1hfM2aNZ7JIIvicvk7Uj20yOEAAADhpVy5GJUrF1Pi7X97Sw9fJSUlKTs7u9DyrKwsSVJKSoqnztso69/WmSqtHF6iDuzZs2dr2bJlSkxM1NNPP62BAwcqJibvh+R0OjVv3jz99a9/1d///ne1a9dON910U4kaBwAAAB/4fQuRyDRo0CC/Q3+kIIcDAACEoRDn8OTkZB04cKDQ8oyMDEm/dkwnJycrPT1dbrdbDoejyDpTpZXDS9SBvWTJElmWpaVLl6pLly4Fdxgbq7vuuksNGzZUz549NWfOHIIzAABA0NmS37cQ8e8e2KEyb968UDeh1JDDAQAAwk3oc3j79u313HPP6fDhw6pZs6Zn+dq1axUXF6dWrVp56hYvXqz09HS1adOmQJ0kXXbZZT4dt7RyeFHTeBZr69atatSoUaHQfLbu3burcePG2rRpU4kbBwAAAOBX5HAAAAD81qBBg2Tbtp599lnPsn379unNN9/U9ddf7+nUvu222xQTE6Pp06d77oV97NgxzZkzR5dccolatmwZkvafS4lGYMfGxqpatWrnrKtataqOHDlSkkMAAADAF7Zk+XvpYmQOwNb69et9qo/kSRzJ4QAAAGEmDHJ4mzZt1Lt3b02ZMkV79uxR/fr1tWjRIjmdTo0fP95Tl5ycrKFDh2rWrFk6duyYWrdurWXLlmnPnj1avny5z8ctrRxeog7s7t27a+nSpdq9e7caNWrktWb37t364osvdM0115SoYQAAAPCFHYB770VmD3bXrl2N7r1n27Ysy4roSRzJ4QAAAOEm9DncsiwtXrxYkyZN0nvvvaeVK1eqY8eOmjRpklq3bl2gdsaMGapdu7aWLVumdevW6bLLLtNTTz2lHj16+Hzc0srhJerAnjZtmj766CP17t1br776aqH7o2zZskV//OMfFR8frylTppSoYQAAAICJoiaPycnJ0Z49e/T5558rNzdX//d//6d69eqFoIWBQw4HAAAou7p27eq59cdvVapUSdOmTdO0adOK3UdcXJzGjRuncePG+d2e0srhRh3Yd955Z6Flv/vd77R27Vq1b99ezZs3V9OmTWVZlr777jt99dVXkqT+/fvrgw8+UIsWLUrcQAAAAJjx+9LFCHWuyWN+/PFH3XXXXXrjjTf02WeflU6jAoQcDgAAEP7I4d4FKodbdlHd9mdxOEo012PeASL8Mk0T5513nvbv3//Lv0o0qB0AAESsvBnH69atq3379pX60fNzSJ3kcvpu7ZV+7atptw06kHkmZOcSTMePH1fTpk3Vs2dPzZ8/P9TNMUYOLx45HACAsowcHgkCkcONUt6HH35Yop0DAAAgvKQu/J9efMN7KD54OKeUW1N6KleurPbt2+vf//53qJviE3I4AAAAIlkgcrhRB3aXLl1KfAAAAACUAts2unRx2K11NOzWOl7XNfn9Rh04GL2d2EeOHNGJEydC3QyfkMMBAADCnGEOP9c+opm/Odyv6+x+/vlnzZkzR1999ZW+++47VahQQeeff77at2+vW2+91a9LHgEAAOCjMnrvPROvvvqqNmzYoN/97nehbkpAkMMBAADCCDm8SIHI4SXuwF68eLEeeOAB/fTTTwVmv1y1apVmzZqlKVOmaM6cOerQoUOJGxeZnEUsd/zyAAAAkcf9ywPhqHHjxsWuP3LkiH7++WdZlqVhw4aVUquChxxeFHI4AADRhxwezkorh5eoA3vbtm264447lJOToy5duuiee+7RBRdcILfbrW+++UazZ8/Wxo0bdeONN2rLli2qV69eiRsYeZg8BgCA6FNcB1hRnWaly5L/s59bgWlKqduzZ885axo2bKg///nPEd+BTQ4vDjkcAIDoQw4PZ6WVw0uU8h577DHl5OTor3/9q5544okC6y699FINGDBAI0eO1DPPPKNHHnlEL7/8cokbCAAAABN2AC5djMx777ndZWdUDjkcAAAg3JDDg61EHdiff/65qlWrpqlTpxZZ8+STT2ru3Ln69NNPS9w4AAAAAL8ihwMAAESm1DczlPpmptd1mT/llnJrIkuJOrAzMzPVunVrWVbRA9xjYmLUvHlzbdu2raRtAwAAgA+sMjQSuSjZ2dn65ptvtGvXLjmdTjVr1kwXXnihqlevHuqmBQQ5HAAAIPyY5PBhfZI0rE+S13WNe2/T/oOR3YkdzBxeog7siy++WDt27JDL5VJMTIzXGpfLpZ07d+r888/3q4EAAAAwYMv/Sxcj88pFSVJWVpYef/xxzZw5Uzk5OQXWxcXF6YEHHtC4ceMiviObHA4AABBmyOFBz+Elmo579OjROnr0qIYPH15kzYgRI3T06FHdf//9JW4cAAAAcC5Op1O9evXS9OnTZdu2rrnmGt1///0aPny4rr32WknS9OnTdfXVV8vl8vf+hKFFDgcAAEC4KK0cbjQCe/369QX+XatWLQ0YMECpqalav369Bg8erKZNm8qyLH333Xd69dVX9eWXX+rOO+/UzTffXOLGAQAAwAd+Tx4TmZ588klt3rxZnTt31sKFC1WnTp0C6/ft26fbbrtNGzdu1NNPP62//vWvIWqp78jhAAAAEYAcHtQcbtm2fc5B6g6Hw+t99vI3/e26s3fpcDjkdDpL1LhIcd5552n//v2//KtEd2UBAAARKy/n1K1bV/v27Sv1o+fnkLq1YrX7nYv82lejG3dq/yFnyM6lpLp27ar//Oc/2rdvn2rUqOG1JjMzU/Xr11f79u21bt26Um5hyZHDi0cOBwCgLCOHh1pp5XCjlDdo0KBiJ4oBAAAAQmX79u1q0aJFkaFZkpKTk9WyZUt98cUXpdgy/5HDAQAAEK5KK4cbdWDPmzevxAcAAABAKSmjly5WrlxZ2dnZ56w7duyYqlSpEvwGBRA5HAAAIAKQw4vlbw4v0SSOABCOLMth9ACAqOV2+/eIUC1bttSuXbu0cePGIms2btyob7/9Vi1btizFlgGA/8i4ABAByOFF1gQihxuNwJ4/f36JDyDlXfoIAAAABMM999yjlStX6g9/+IOmTZumAQMGKC4uTlLezOgLFy7UX//6V1mWpXvuuSfErfUNORwAAADhqrRyuF+TOJpyuaJ7GD2TxwDhwXTkiW1H7l83AYSjMJk8pmasdr/V2K99NerzvfYfjrzJYyTpL3/5i2bMmCHLsuRwOFSvXj1JeTOfu1wu2batBx98UNOnTw9xS31DDi8eORxlARkXAIpCDg8HpZHDjVJe586diwzO69atU0JCApdjAgAAhJQty+97751zXEPYevrpp3XDDTdo8uTJ2r59u/bs2SNJqlmzplq2bKnx48erS5cuoW1kCZDDAQAAwh05PNg53KgD+6OPPipyncPh0OWXX66VK1f61ZDo4SxiuUPcchwAgEjl/uUR+WYvO6rUd456XZeZFdmjdbt06eIJx8eOHZMkVa1aNZRN8hs53BfkcAAAok/05PBoFuwcznV2AcdTCgBA9CmuA6yoTrMQMJgA5t4bqujeG7zPAN7w1r3afzhyOrHdbrc++OADLV68WE888YRq1arlWVe1alUlJSXpggsu0LXXXquRI0eqXLlyIWwtgo8cDgBA9ImeHB5NSjuHMxQBAAAgWpSh2c+PHDmiXr166ZprrtG8efOUm5tbqCYnJ0effPKJHn74YbVr105ff/11CFoKAACAqEcOLyDQOZwObAAAAEQUl8ulrl27as2aNapTp44efvhh1ahRo1Dd119/rblz5+r888/XF198oZtvvjnqJzUEAAAAgiVUOZwObKCMsyyH0SMS2Lbb6AEAUauMjPxYsGCBvvzyS1155ZXavn27Jk2a5PWyxJSUFA0ePFjbtm3TNddco2+++UYvvvhiCFoMAACAqEYOLyDQOTwyeqUAAABQPNuW3C7/HnZkzH6+cOFCWZal1NRUJSQknLO+fPnymjVrlhwOh/71r3+VQgsBAABQZpDDixSoHM5MJwAAAIgou3btUuPGjdW8eXPjbRo2bKhGjRpp165dQWwZAAAA4N3s5SeUuuKE13WZ2ZExCjtUOZwObAAAgChhRdDlh/7Yv3+/Lr/8cp+3S0lJ0WeffRaEFgEAAKAsM8nh9/WuoPt6V/C6rsEdB7X/p/DP8qHK4UYd2DExMUWusyxLq1atKrLGsiw5nc6StQ4AAADmykgHds2aNfXdd9/5vN0333yjqlWrBqFFwUMOBwAAiADk8GL5m8ON7oFt23aJH+4y8gMEAABA6bj44ouVkZGhH374wXib77//XocOHdKFF14YxJYFHjkcAAAA4SJUOdyoA3v37t1+PQLBtm01atRIr732WqF1J06c0OjRo9WqVSvVqFFDN9xwg9auXVuozul0aurUqbr88stVvXp19ezZU0uWLPF6rNTUVHXp0kXVqlVT586dlZqaKjtCbqgOAADKqDIy+3mfPn1k27buuusuuVyuc9a73W79+c9/lmVZuuGGG0qhhYFDDieHAwCACEAO9ypQOdyoA7tBgwZ+PQJhxYoV2rNnT6Hltm2rX79+mjZtmi6++GINGzZMO3bsUK9evfTpp58WqB0xYoTGjh2rWrVqacSIEcrMzFS/fv301ltvFah76qmnNGzYMNm2rZEjR8q2bQ0bNkxPP/10QM4FCCe27TZ6AADCnG37H5wjpJNw8ODBuuCCC7R27Vrdfvvt2rt3b5G1Bw8e1JAhQ7R27VolJSXpnnvuKcWW+o8cTg4HyOsAEObI4V4FModbdhgPZzh58qRWrFihjz/+WHPnztXJkye1YMECDRw40FPz+eef69JLL9WECRM0adIkSXk3FG/durWuvPJK/fOf/5QkZWZmqm7durr99ts1b948WZaln3/+WW3btlXFihW1detWSVJOTo5q166tVq1aafXq1YqNjVVubq66d++uHTt26MCBA4qLiyvQzvPOO0/79+//5V/MiwkAQNmSd4/hunXrat++faV+9PwcUjfR0g9zEvzaV4O7srT/iB2yc/HFzp071aVLF/3000+KjY1V37591bJlSzVu3Fjly5fXrl27tHPnTi1cuFAnTpxQfHy8Vq9erU6dOoW66RGBHA4AAMIfOTwUQpHDwzrl7d27V/379y+2Zv78+XI4HBo+fLhnWd26ddW/f3+99NJLysrKUkJCgt544w25XC6NHDlSlmVJkqpUqaK7775bo0aN0o4dO9S8eXOtXLlSR44c0YgRIxQbm/f0xMXF6YEHHlDfvn31/vvv69prrw3eSQMAAJSUO2zHJQTcRRddpM2bN+vuu+/W+++/r4ULF+qNN94oUJM/TuPCCy/U/Pnz1bZt21A0NSKRwwEAAHxADi9QE+gcHtYd2M2aNdOpU6ckSevXr1evXr0K1aSlpXnuuXe2bt26adasWdq6dau6deumtLQ0JSYmqmXLloXq8vfTvHlzpaWlSZKuuuqqIusIzgAAICxF0P3zAqF+/fpatWqVtm7dqpUrV+q///2vvv32Wx0/flzNmjXTBRdcoI4dO+qaa66Rw2F05zz8ghwOAADgA3J4UHN4WHdgOxwOlS9fXpIUHx/vtSYzM1PNmzcvtLx27dqS8u63kl+XkpLiGfVRXF3FihVVtWrVAnUJCQmKj4/31BXN37+4WOcuAQAAAVR2RktEqzZt2qhNmzahbkZUIYcDAIDgI4dHutLK4WHdgW0iMzNTV1xxRaHlCQl59545OxAnJiYa1eUvO5tlWUpISDAIzueegbNolqQYP7YHAAC+cytqwnMZG/mB0CKHAwAA/5DDYSbiO7Aty5K3eSjdv7xwcnNzA1KXX5tfVzSCLwAAkcXfS9r86TQLsDJ07z2EHjkcAAD4hxwOMxHfgZ2UlKTs7OxCy7OysiRJKSkpnjpvoza81Xnbn23bysrK8tQVjUsPAQCILGXrs3v26hw9/773jsDMbII3zJHDAQCAf/jshpmI78BOTk7WgQMHCi3PyMiQ9GsgTk5OVnp6utxud4Gbh3urO3nypI4ePapq1ap56o4cOSKn02kQnIGyzbLM/4Jq21xiAwABY0syeF+9t0es7u3hPQI2uP+U9mfRiQ0z5HAAwWT6vYLvFABCzjCHn3MfKFLET8fevn17paen6/DhwwWWr127VnFxcWrVqpWnLisrS+np6YXqJOmyyy7z1J29vKg6AACA8GLnXbroz4PkDB+QwwEAACRyePBFfAf2oEGDZNu2nn32Wc+yffv26c0339T111+vmjVrSpJuu+02xcTEaPr06Z576x07dkxz5szRJZdcopYtW0qSrr32WtWoUUPPPvusnE6npLz78v3jH/9QUlKSrrnmmlI+QwAAACD8kMMBAABQGiL+FiJt2rRR7969NWXKFO3Zs0f169fXokWL5HQ6NX78eE9dcnKyhg4dqlmzZunYsWNq3bq1li1bpj179mj58uWeuvj4eI0ZM0ajRo1S9+7dddVVV+n999/XJ598oldeeUVxcXGhOE0AAIBzY/IYlCJyOAAAwC/I4UEV8SOwLcvS4sWL9dBDD2nbtm1KTU1V8+bNtWbNGrVu3bpA7YwZM/TYY4/pwIEDevbZZ5WUlKQVK1aoZ8+eBer+8pe/aPbs2XK5XJo+fbocDocWLlyowYMHl96JAQAA+MrvSxcBc+RwAACAX5DDg8qy86/jQ4mdd9552r9//y//ivhB7YBfmMQRQNmTd6uDunXrat++faV+9PwcUjdB+mF6Ob/21WDkGe3PCt25AL4ihwPRi0kcAZxbZOXw2Wtcen6ty+u6bzNsOd3k8KKQ8gAAAKIE3+EBAACA0meSw4ddFaNhV8V4XdfwobyBJPCODmwAAIBoYMv/yw+5Lg8AAADwDTk86OjABhBQXMIHAAAAwF98rwAA5KMDGwAAIFrwXR8AAAAofeTwoKIDGwAAIFoQnAEAAIDSRw4PKrNpfQEAAFDm7dy5U3/4wx9Uu3Zt1ahRQz179lRaWlqBmsOHD+vee+/VBRdcoJSUFPXt21fp6ekhajEAAACASMcI7IBzFrHcIf5eAABApHIrIoZVBHHyl2+//VZt27ZVtWrVdOeddyo+Pl6vv/66OnbsqHXr1qlTp07KyclRjx49tHPnTg0cOFCJiYlasGCBOnfurM8//1xNmjQJXgMBcjgAAFGIHA46sIOApxQAgOhTXAdYUZ1mpc92W0Hb96OPPirbtpWWlqb69etLkh588EFdeOGFGjt2rD7++GOtWLFC27Zt09y5czVkyBBJ0sCBA9WuXTvNnDlTM2bMCFr7AHI4AADRiBwOhiIAAADAQHp6ulq0aOHpvJakatWqqVOnTtq+fbskaf78+UpMTNSgQYM8NS1btlT37t21aNEiOZ3h8yUDAAAAQGSgAxuIIJblMH4AAMoYW79eYVnSRzGXPjZu3Fh79+5VTk7Or4e0be3atctza5C0tDR16dJFMTExBbbt1q2bMjIytHfv3kCcKQCUOnI4AKBIQc7h4Do7AACAqHEmRzrjxyBndzHB+d5779V7772nAQMGaMyYMYqLi9MLL7ygrVu3au7cuXK5XDp06JBq165daNv8ZQcPHlTjxo1L3kAAAAAgHHELkaCiAxsAACBK/P19px5b5QrKvnv27KlHHnlEY8eO1dKlSz3L77//fg0ePFgHDx6U2+1WQkJCoW3zlx08eDAobQMAAACi2c6dOzV27FilpaUpJydHl156qR599FG1b9/eU3P48GFNmDBBa9as0dGjR9WpUyc9/PDDatWqVQhbHhh0YAMAAESJv/4+TiO6xJV4+5ZPnNaPx7yvmz17tsaNG6drr71Wffr0kSQtWbJEM2fOVIMGDXTHHXdIyrutyG+53Xkzx+fm5pa4bQAAAEC4CuYkjt9++63atm2ratWq6c4771R8fLxef/11dezYUevWrVOnTp2Uk5OjHj16aOfOnRo4cKASExO1YMECde7cWZ9//rnnln+Rig5sAACAqGCpnMOhcvEl34PDsuTtBnz79u3TiBEjdMstt2jJkiWe5YMHD1bfvn01ZswY9e3bVw6HQ9nZ2YW2z8rKkiSlpKSUvHEAAABAWLICcAsR7zlckh599FHZtq20tDTPhOoPPvigLrzwQo0dO1Yff/yxVqxYoW3btmnu3LkaMmSIJGngwIFq166dZs6cqRkzZvjZvtBihgkAAAAUKy0tTU6nU7fffnuB5ZZl6fbbb5fL5dLmzZtVq1YtHThwoND2GRkZkujABgAAAHyVnp6uFi1aeDqvJalatWrq1KmTtm/fLkmaP3++EhMTNWjQIE9Ny5Yt1b17dy1atEhOpx8T5YQBOrABAACihW359yhCYmKiJCknJ6fQujNnzkjKu891+/bttW7dukIBee3atUpKSioQugEAAICoEaQcLkmNGzfW3r17C2Rx27a1a9cuz61B0tLS1KVLF8XExBTYtlu3bsrIyNDevXsDf86liA5sAACAaGDn3XvPn0cRVy2qTZs2qlChgmbPnl2gc9rpdCo1NVUVKlRQmzZtNGjQIGVlZenVV1/11KSnp2vNmjUaOHCg4uJKfn9uAAAAICzZ0ukc6ejJkj/cReRwSbr33nt15MgRDRgwQJs3b1Z6erruu+8+bd26VcOHD5fL5dKhQ4dUu3btQtvmL4v0ydS5BzYQQWzbbVxrWWZ/n/JlnwCAsikhIUHTp0/XsGHDdMkll+jmm2+Wbdt6++239eWXXyo1NVUJCQm67rrr1Lp1a913333atGmTqlatqgULFig5OVkPPvhgqE8DAEosEjKzaf6XIuN8ACCSTPswR1M+CM6E5T179tQjjzyisWPHaunSpZ7l999/vwYPHqyDBw/K7XYrISGh0Lb5y+jABgAAQHhwB+/iuqFDh6pJkyaaOnWqZs2aJUlq0aKFVq9erR49ekiS4uPjtXr1ak2YMEFr1qzRsWPH1KlTJ02bNk316tULWtsAAACAUPprl/IacUX5Em/favpx/fiz92HYs2fP1rhx43TttdeqT58+kqQlS5Zo5syZatCgge644w5JebcV+S23O+8Plrm5welcLy10YAMAAEQLv2c/L16PHj08ndVFqVWrlp5//vmgtgMAAAAIJ+UcDpWLL/n2jiJi/L59+zRixAjdcsstWrJkiWf54MGD1bdvX40ZM0Z9+/aVw+FQdnZ2oe2zsrIkRf5k6twDGwAAAAAAAADCTFpampxOp26//fYCyy3L0u233y6Xy6XNmzerVq1aOnDgQKHtMzIyJEV+BzYjsAEAAKKALck+xwzmJvsAAAAAYC6YOTwxMVGSlJOTU2jdmTNnJOXd57p9+/Zat26dnE6nYmN/7e5du3atkpKSVL9+fb/aF2qMwA44ZxEPJskAACByuVX0Z3wYcTv8ewARjRwOAED0Kds5vE2bNqpQoYJmz54tp/PXc3Y6nUpNTVWFChXUpk0bDRo0SFlZWXr11Vc9Nenp6VqzZo0GDhyouLi4oJ5+sDECO+B4SgEAiD4OFf13/zAJz7Yl2997YNuWGIeNyEUOBwAg+pTtHJ6QkKDp06dr2LBhuuSSS3TzzTf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+      "text/plain": [
+       "<Figure size 1500x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def plot_2d_histograms_from_file(filename):\n",
+    "    # Load the data from the file\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        lines = f.readlines()\n",
+    "\n",
+    "    # Extract the values from the lines\n",
+    "    values = [eval(line.strip()) for line in lines]\n",
+    "\n",
+    "    # Extract pt and delta R values\n",
+    "    pt_Hbb = [val[0] for val in values]\n",
+    "    pt_HVV = [val[1] for val in values]\n",
+    "    delta_R_11 = [val[2] for val in values]\n",
+    "    delta_R_12 = [val[3] for val in values]\n",
+    "    delta_R_21 = [val[4] for val in values]\n",
+    "    delta_R_22 = [val[5] for val in values]\n",
+    "\n",
+    "    # Plotting\n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "    # Hbb with vbf gen quark 1\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.hist2d(delta_R_11, pt_Hbb, bins=50, cmap=\"inferno\")\n",
+    "    plt.colorbar(label=\"Counts\")\n",
+    "    plt.title(\"Hbb vs vbf gen quark 1\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Hbb Pt\")\n",
+    "\n",
+    "    # Hbb with vbf gen quark 2\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.hist2d(delta_R_12, pt_Hbb, bins=50, cmap=\"inferno\")\n",
+    "    plt.colorbar(label=\"Counts\")\n",
+    "    plt.title(\"Hbb vs vbf gen quark 2\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Hbb Pt\")\n",
+    "\n",
+    "    # HVV with vbf gen quark 1\n",
+    "    plt.subplot(2, 2, 3)\n",
+    "    plt.hist2d(delta_R_21, pt_HVV, bins=50, cmap=\"inferno\")\n",
+    "    plt.colorbar(label=\"Counts\")\n",
+    "    plt.title(\"HVV vs vbf gen quark 1\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"HVV Pt\")\n",
+    "\n",
+    "    # HVV with vbf gen quark 2\n",
+    "    plt.subplot(2, 2, 4)\n",
+    "    plt.hist2d(delta_R_22, pt_HVV, bins=50, cmap=\"inferno\")\n",
+    "    plt.colorbar(label=\"Counts\")\n",
+    "    plt.title(\"HVV vs vbf gen quark 2\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"HVV Pt\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# To visualize the histograms, you can call:\n",
+    "# plot_2d_histograms_from_file_updated('output_file.txt')\n",
+    "\n",
+    "# To visualize the histograms, you can call:\n",
+    "# plot_2d_histograms_from_file('output_file.txt')\n",
+    "\n",
+    "plot_2d_histograms_from_file(\"outputR2vsPtgenHH_posptreq.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe4f0145",
+   "metadata": {},
+   "source": [
+    "Plots above but integrated. shows the same information basically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4351302f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_1d_histograms_from_file(filename):\n",
+    "    # Load the data from the file\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        lines = f.readlines()\n",
+    "\n",
+    "    # Extract the values from the lines\n",
+    "    values = [eval(line.strip()) for line in lines]\n",
+    "\n",
+    "    # Extract delta R values\n",
+    "    delta_R_11 = [val[2] for val in values]\n",
+    "    delta_R_12 = [val[3] for val in values]\n",
+    "    delta_R_21 = [val[4] for val in values]\n",
+    "    delta_R_22 = [val[5] for val in values]\n",
+    "\n",
+    "    # Plotting\n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "    # Hbb with vbf gen quark 1\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.hist(delta_R_11, bins=50, color=\"red\", alpha=0.7)\n",
+    "    plt.title(\"Hbb vs vbf gen quark 1\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Counts\")\n",
+    "\n",
+    "    # Hbb with vbf gen quark 2\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.hist(delta_R_12, bins=50, color=\"blue\", alpha=0.7)\n",
+    "    plt.title(\"Hbb vs vbf gen quark 2\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Counts\")\n",
+    "\n",
+    "    # HVV with vbf gen quark 1\n",
+    "    plt.subplot(2, 2, 3)\n",
+    "    plt.hist(delta_R_21, bins=50, color=\"green\", alpha=0.7)\n",
+    "    plt.title(\"HVV vs vbf gen quark 1\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Counts\")\n",
+    "\n",
+    "    # HVV with vbf gen quark 2\n",
+    "    plt.subplot(2, 2, 4)\n",
+    "    plt.hist(delta_R_22, bins=50, color=\"purple\", alpha=0.7)\n",
+    "    plt.title(\"HVV vs vbf gen quark 2\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Counts\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Call the function to visualize the histograms\n",
+    "plot_1d_histograms_from_file(\"outputR2vsPtgenHH_posptreq.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fd23abe",
+   "metadata": {},
+   "source": [
+    "Same data as above but now looking at the tagging scores."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "db879a0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6dCnq1q1rUTuKFCmCrl27mm3Hpk2boNVqUbJkSXTp0sVkHR988AH8/f2zbFOr1ZgxYwZKliwJAFi4cKFF7cnO+fPnDW2cPHmy0fyIHh4e+Pjjj00eK4TAN998AwCYMWNGluedXs2aNTF+/HgAmX9HYWIePQ8PD0ybNs1o/rf27dvDw8MDQNbXWHb+/PNPbNmyBUDmHISVKlXKsl+tVmPatGlwd3e3qD5r1KhRA3379kVkZKTRcx74770vu7lHn3vuObRq1SrH+xo3bhwWLlwIlUqF7777Du3atbO94UREVGAwhzOH6zGHM4czh/8nP+XwAwcOoFWrVoiPj4dKpcInn3xi8pyIChN2YFOu+Pzzz7Fs2bIswURPq9Xi999/N9ouhMDp06cBAK+88orJelUqFfr06WO0/datW4ZVec0dW7x4cXTr1s3ic3ich4cHOnToYHKfqaCgD83BwcGoX7++yeN69epl0wrV+gXWpkyZgp07d0Kn0xn2KYqCiIgI9OvXD1WrVjVs/+OPPwz3+WSI1/vqq69w9OhRw0I5Fy5cMOyLiIgweYz+/p4s/7iwsDCTAeXs2bMAMheQe/DgAS5dumTypg+qhw8fNlm/KT169AAAbNu2zWjxGX1wfvnll7OEAP3jumDBAqxfv97ouBdeeAH9+vUz+/fMrh2///47EhISTLajR48eJh8flUpl9nEvXbo0XnzxRQCwarE+c/TPV09PT3Ts2NFkmRYtWhiFeABISkpCYmIiACAgIMDs31H/fIyLizO5gnbr1q1Rrlw5o+0qlcrk9uzoF1AqVqyYyfcLAChXrpzN7wfZGTduHJYtW4b27dsb7RNCWLQIkaljnzRjxgx88sknADIXssmNcyEiovyJOZw5XI85nDmcOTxTfsnhCQkJGDRoEFq1aoWrV68CAObPn4/w8HBp90GUX1n/qU1kBY1GgzNnzuDChQu4cOECTp8+jaioKJO/esbHxxtWdH489D3J1L6YmBjD/5saoZDdsZaoVq2aVb946tuT3f25ubnBz88PV65csaotn3zyCTp16oS///4bHTp0QEBAAJ577jmEhoaidevW8PT0tKk9lSpVyvILuX5V5NKlS5usU09fZ2xsLHQ6ndEv976+viaPu3TpkuG/pkbzPCm7X8qf9MILL+Ctt95CSkoKfv31V8MH/p9//ok///wTQGZwftyHH36Ibdu24fbt2+jVqxd8fX3x/PPPIzQ0FOHh4fDx8bH4/vXat2+PYsWKITU1FVu3bsWAAQMAACkpKfjll18AmP+iV7FiRcOIB1P0Qf/x576t9HVUqVLF7ArYiqKgatWqhiClp/87AsDTTz9t0f3duXMHFSpUyLKtWrVq1jQ5W/rzqVatWrZfTi153tlKCIFz587h/PnzuHDhAs6ePYuoqCiTXxqeZO41o3fixAns27cPKpUKOp0Of//9NxYsWICRI0fKaj4RERUAzOHM4czhzOFPYg7PXl7lcCEElixZgtGjR+PevXsAMjv6ly5dis6dO9tVN1FBwRHYlCvu3r2L0aNHw8fHBw0bNsSLL76ICRMm4Pvvv4cQAg0aNDA65vEPZFOjKfRMfajojy1SpEi2lyfm9IFkTnbB0RR9e7I7D1vb06FDBxw+fBhhYWFQFAV///03Pv/8c7zwwgvw9vZGx44dcfz4cZPtseb+bty4AQA5BkZ9nenp6bh165bR/icvg3uyTZb6559/LC7r7++Ppk2bAsi8RFBPP9qiWbNmCAgIyHJMYGAgoqOj0bVrV7i4uCA+Ph4LFy7ESy+9hPLlyyM0NBS7du2yqs1FixY1BI7H27FlyxZkZGTAz88PoaGhJo8tX758tnXrH/ekpCSr2mSKPc9Xa/+OgOm/pbWvsezo25TTY/hkeJfh0aNH+OSTT1CxYkU89dRT6N69O95//32sWrUK9+7dQ0hISI51mHvN6P3zzz9QqVRYu3YtnnnmGQDAxIkTTb7+iIio8GEOZw7XYw5nDn8Sc3j28iKHJyYmolu3bnjttddw7949KIqCQYMG4Y8//mDnNdFj2IFN0j169Ajt27fHzJkzkZycjE6dOuHTTz/Fzp07ceXKFSQlJWHEiBFGxz3+oZ3dB4CpoKA/9tGjR4ZfLC091hLWzjelb09OH2S2tqdJkybYu3cvbt68iSVLluDll19G+fLlodFosGPHDjRt2hTbt283ao81oyf0oeLJS+6epL9sTa1Ww8vLy2i/ucdOH8hbtWoFkbmgbLa3R48eWdx24L/LBn/66SfDXG/ff/89APOjLWrVqoWffvoJiYmJWLVqFQYOHIiqVatCCIFff/0V7dq1w1dffWVTO3bt2mUY2aRvx0svvQS1Wm3yOP3jas7169cBZD/SyVL2PF/1f0eVSoVHjx5Z9Lds1qyZUT0y53QrU6YMAOQ4yiKnx9gcIQRSUlJMbu/Tpw/Gjh2LGzduICwsDB999BE2b96MS5cu4e7du5g6dWqO9VvyWHz99dfo0aMHPv/8c6jVaty9exfjxo2z5XSIiKgAYQ5nDn8cczhzOHO4c+fwhIQEhISEYPPmzQCApk2b4ujRo1i8eLHV07cQFXTswCbp1q1bh8OHD6NIkSL4/fffsXXrVrzzzjto164d/P39oVKpjOY1AzJ/rXdzcwMAXL582Wz9+kU+Hvf4JUim9luyTyZ9e7I7D61Wi2vXrtl1Pz4+Phg4cCC+/fZbXLt2DZs2bUL16tWh0+myfIhWr14dQPbnf+XKFWzZsgV79uwBAMPIiJSUFJMhQU9/iWOVKlVMLgBjjr5NMuaOM6V79+4AMs/5zJkziImJQXR0NFxcXNCrV69sj/X09MRLL72EJUuWICYmBlFRUWjcuDEA4P333ze5+Ik5HTt2hLu7Ox4+fIhffvkF9+/fx86dOwEYXz75uKtXr2b7ZeH8+fMA5Fzyp3++xsbGZntupp4/+r+jTqfL9vnuSPrn7t9//w2NRmO2nP65a63k5GTcvXvXaPtvv/1mWORo48aN2Lt3L8aPH48uXbqgWrVqUKvVJt/7rNWyZUv069cPAPDUU09h6NChAIDFixfj5MmTdtdPRET5F3M4c7glmMOZw3MLc7jlHj58iG7duhn+dh9++CEOHDhgcv0CImIHNuWCI0eOAMich8vcXFymFgJRq9WoXbs2AGDVqlUmjxNCYO3atUbbK1SoYLhk0dyxDx8+zHL5WG6qV68eAODkyZOGhVuetHHjRqSnp1tV7/HjxxESEoKQkBCjX63VajWee+45w6rq+mAF/DdP2/fff282SHzyySfo2rUrZs+eDeC/QAT8t3r6k4QQhn01a9a06lz0bYqPjze61PJxQ4YMQXBwMGbMmGFV/QEBAWjYsCGAzNEf+ssWO3TogLJly2Ype+PGDcPjql9IRU9RFLRp0wbTp08HkDl65vbt2xa3o3jx4oYV3H/66Sds27YNaWlpqFevHgIDA80ep9VqsXLlSpP77ty5Y3gdBAcHW9wWc/TP1zt37mDHjh0myxw7dszkPH8VKlRAsWLFAABbt241ex9LlixBcHCwYdGb3KT/InD//n2T7xdA5uXVGzduzLae5ORkk9u3bdtmcrv+vc/f39/sYi7WLIJkzpOjhSZNmgQvLy8IITBy5EirvtgREVHBwhzOHG4J5nDm8NzCHG55Dv/2228Nbfr6668xceJEmxaXJSos2IFN0pUoUQJA5mVDj6/Mrbdnzx5DINBqtVn2jRo1CkBmwPv111+Njl2+fLlh1ezHqVQqw8IJCxcuNCwQ8rhPP/1UyjxllujSpYvh1+d3333XKCCnpKTgo48+srpeb29vHD58GIcPHzYbCPTB7/Egqw8rly9fxpw5c4yOuXbtGtatWwcgc7VyIHOeNf3/f/rppyYXufnqq69w6tSpLPdhqUaNGhkCzogRIwyX9T1u9+7dWLRoEaKjo9G8eXOr6gf+G/2xadOmLKueP8nb2xsnTpzA4cOHzX5J0D+uXl5ehkvjrG3H5s2bsX79egCZl0/mdInaxx9/jLi4uCzbNBoNIiMjcf/+fRQtWhTDhw+3qi2mBAcHo1WrVgCA8ePHG41qePToEcaOHWvyWJVKZfjbT5482eTokOvXr2P8+PGIjo7O9suCLM2bNzd8GZgwYYLRYyiEwLhx48xe5qy/HPOXX34xGqlx7949fPzxxyaP07/33blzBw8fPjTa/8cff2DWrFmGfz/5/mcrLy8vQ5t+/fVXw2uZiIgKH+Zw5nBLMIczh+cW5nDLc/jSpUsBAM8//7xhkVEiyoYgssDly5cFAAFALF26NNuymzZtMpR98cUXxd69e8WZM2fEli1bxMCBA4WLi4tQFMVQ5ttvvxWJiYlCCCEyMjJErVq1BADh5uYmJk2aJPbt2yd2794tIiMjBQDDfgDin3/+MdxvSkqK8PLyEgBEqVKlxMyZM8XBgwfFtm3bRL9+/bIcW6ZMmSxtXrp0qQAgKleunGX7xIkTBQARFhZm9nz79u0rAIi+fftm2f7tt98a2tmyZUuxatUqcezYMbFs2TJRt27dLO15++23c/wbCCGETqfL8vhMnjxZHDp0SJw9e1bs2rVLvPbaa4b7/Oijj7Ic99xzzxn2DRw4UGzZskWcPHlSLF++XFSrVk0AEP7+/iIlJcVw3KFDh4RKpRIARLly5cScOXPEoUOHxNatW8Xrr79uqK9Ro0YiPT09S1vDwsIEADFx4kSz5/PTTz8Z6qhVq5ZYunSpOHbsmDhw4IAYP3688PDwEABE586dhU6ns+gxetxff/1lqB+AKF68uEhNTTVZtl27doZyb7/9tvjtt9/EH3/8Ifbu3Sveeecd4eLiIgCIAQMGWN2Ou3fvCjc3NwHA8Ny/cuWKybL651y5cuUEAOHn5yfmz58vjh49KlavXi2eeeYZQztHjx5tdLwlj7spv/76q6HeevXqGf4Wq1evFi1btszyfO3evXuWY69evSpKlSpleO199tln4tdffxXHjh0TX375pfD39ze8vvSvdWvaW7lyZYveex73448/Gs6nfPnyYu7cueLw4cNiw4YN4n//+58AIIoUKWLydT9z5kzDsS+88IKIjo4W8fHxYuvWraJevXrC1dXV8Fg83qZTp04Zjmvbtq3YuXOnOHPmjNixY4d46623hIeHR5b3vunTp4u4uDjD8frte/bsMXlO2b0fZWRkiPr16wsAolKlSmaf55bQvx+WKlXK5jqIiEge5vAws+fLHM4cnhPmcOZwZ8zh9+/fN9znrFmzxMWLF3O8PXjwwJKHn6jAYgc2WcSa4PxkSHvyVq1aNbF3715DkHjyQzM6OlqULVvW5LG1atUyhCF3d3ejMPXLL7+IokWLmjy2efPm4sCBAwKAqF27dpbjciM4a7VaMWTIELOPw2effWb4MjBt2rRsH9PHHTlyxPCBb+72/PPPC41Gk+W45ORk0bx5c7PHlClTRvz6669G97dixQpDaDR1q127trh69arRcZYGuDlz5mQJE0/eQkJCxL179yx+fJ6kDxMAREREhNlysbGxwtPTM9vH9emnn7a5Y7Br166GekJDQ82W0z/nxowZI3r27Gm2LR07dhQPHz40Ot7W4CyEEJ988onZ+xs+fLiYO3euACCGDBlidOz+/fsN4dnUzdfXV5w+fdqm9toSnHU6nXjzzTfNtqdKlSqG833ydf/w4UMRGBho8ji1Wi2WLVsmevfubbJNI0aMMHuf3t7eYu/evVne3x5/37AnOAshRFRUlKGOCRMmWPxYPYkd2EREzoU5PMzs+TKHM4dbgjmcOdzZcvjj7+uW3sy1jaiw4BQiJJ2iKPj+++/xxRdfoHHjxihVqhSKFy+Opk2bYtasWThz5gzCwsLw7bffonz58qhVq5ZhjjQACAwMxNmzZ/Hmm28aFpTx9/fHmDFjcPToUcPlXj4+PkaXfj3zzDM4c+YM+vbtC19fX7i5uaFGjRqYMmUK9uzZg/v37xuOzW0qlQoLFizA+vXrERoaihIlSqBEiRJo06YNdu3ahdGjRxtWZ7amPU2aNMGlS5fw5ptvIjg4GGXKlEHRokVRq1YtPPfcc9ixYwd+/PFHo/m5Spcujb1792LBggVo3ry54e/SpEkTvPXWWzh//jxatmxpdH+vvvoqTp8+jYEDB6Jy5cpwc3NDyZIl0aRJE3z22Wc4ceIEKlWqZPPjNGLECJw8eRJ9+/ZFpUqV4ObmhooVK+KZZ57BypUrceDAAcMlYbbQrz4OZL9YS+XKlXHp0iV88MEHaNy4Mby9vVGkSBFUq1YN7dq1w9q1a3HgwAF4eHjkajv0VCoV1qxZg4ULF6Jhw4YoVqwYSpQogebNm+Prr7/Gli1b4O7ublNbzHn//fexe/dudOjQAaVLl0bRokXRtGlTrF27FnPnzs32+dqqVSucP38eY8aMQZ06dVC0aFF4eXmhWbNm+Oijj3Dx4kXUr19fanuzoygK5s6di82bN6NDhw7w8vKCq6srqlSpglGjRuH48eMoX768yWPd3d3x22+/YezYsahfvz6KFi2KsmXLokuXLti3bx/69u1r9n5nz56NVatWoXnz5ihbtiw8PDwQGBiIDz/8EOfPn0dYWBi+//57VKtWDf7+/mjRooW0c27Tpo3heTZ9+nSTlxsTEVHBxhyeiTncMszhpjGH24c5POcc/uQ8+kSUM0UIrvZE+UtUVBSeeeYZNGnSxLBYg6W++eYbDBw4ED179nSKeWJDQ0Px66+/YuvWrYYFRoicVUREBFauXIn58+dj2LBhed0cuy1btgz9+/dH5cqVTc4ZSERERFkxhxPlDeZwIirsOAKbnMrMmTPRr18/syuYA8DPP/8MIHOF4ce9//776NevH3bu3Gn1sbLFxMSgX79+6NevH1JTU02WuX37tmHV79xuD1F27t69a3i+mhstkJ6ejj179gDg85WIiKggYg4ncjzmcCIiy7jkdQOIHnf37l0sX74chw4dQs+ePeHm5pZlf1JSEr788ksAQMeOHbPsu379OlauXIlr166hXbt2Rpc1/vXXX4aVp588Vrby5ctj9erVSE9PxzPPPINXX33VqMzs2bPx4MEDVKxY0bBSM1FeKFmyJKKionDt2jVUq1YN48ePNyqzYsUKxMXFoUiRImjdurXjG0lERES5ijmcyPGYw4mILMMR2ORU+vTpA3d3d5w/fx7du3fH2bNnodVqkZycjG3btqFx48a4d+8efH190bt37yzH9uvXD4qiICoqCgMHDkRMTAx0Oh0SExOxZs0atGjRAjqdDkFBQbn+we/h4YE+ffoAAIYOHYqlS5fi3r170Gq1uHDhAkaOHIkpU6YAAN566y2jkE/kSIqiYMCAAQCASZMmYebMmUhKSoJOp8Ply5fx8ccf4/XXXwcAvPbaayhevHheNpeIiIhyAXM4keMxhxMRWYZzYJPT+fbbb9G/f39oNBoAmYto6HQ6w/6iRYtiy5YtCA8PNzp22rRpeP/99w3/VqvV0Gq1hn+XLVsWUVFRDlnEIjU1FWFhYYbLE021p2vXrvj++++NRrgQOZpWq0W3bt2wdetWw7Ynn69NmzbFzz//jFKlSuVFE6Xj3HtERERZMYcTOR5zeGxeN4eI8gGOwCan88orr+DcuXPo168f6tevD3d3d3h4eKBGjRp4/fXXcfr0aZOhGQDee+89HD9+HL169ULt2rXh4uKC4sWL46mnnsLbb7+NM2fOOGwF5mLFiuHQoUNYvHgxwsLC4OfnB0VRUKZMGTzzzDP45ptv8OOPPzI0k1NQq9XYvHkzvv/+e7Rt2xaVK1eGoigoXbo0WrRogVmzZmHv3r0FJjQTERGRMeZwIsdjDiciyhlHYBMRERERERERERGRU+Iijv8qVqwY0tLSoFar4e3tndfNISIiIieVmJgIrVYLd3d3pKam5kkbGjdujPj4+Fyp29fXF8eOHcuVuolMYQ4nIiIiSzCHF17swP5XWloadDoddDodrl+/ntfNISIiIieXlpaWZ/cdHx/PvEIFBnM4ERERWaMw5vAbN25kWZdCz8XFBb6+vgAy14D46KOPsGPHDsTFxaFFixYYNWqU0fRfGo0Gn332GTZu3Ijz58+jadOmGDx4MHr27OmQc7EFO7D/pVarTT4RSDZrVvkWJsqbO148Vj7n+1CgQEALBWoLW2JcVlHMH6sT6VApls2pp4IaGpEGF8XdovIa8RAuStGsdQjz09ln4AFc4WFR3YBAOh7CDUVzLvqvJ8trFa3ZslqRDrWFj4uADjqRAZXiCkufN08+7kKYf00LaKBY8RZob3mBnN5fdLB8WQJrypoqL+u9ToHp12l2nizPWawo/1KrLfsMyU0qlYLy5UtLqevmzRTodHxNkuMxhzuCNZ/V5liSPbSAhfnaHirlv7b8lxetp4Ztx2WIh3D9N4+rpDy2QE4ze1qS0bPL4dbJPCetSIPawu8oTxKwrS1P/j0dMeOppTnf1nPKrkbbX5v8vKbCrbDl8IyMDPj7+2dZ3FWvXr16OHv2LIQQ6NWrF7Zt24Y+ffqga9euWLNmDdq3b4/9+/ejWbNmhmNGjhyJBQsWoHPnzujQoQM2btyIXr16Yf369ejRo4eUc5LN7jmwL1++jN9//x1JSUnw8fFB7969cevWLZQrV05WGx2iYsWKj/2C8uSHl8bEtuxooFgRhgQyjMsr5j/IhEiHYkEnYOafNgOZIdLSD0bjc1UU82HV0rZkltVArSppYTsAre6eUXlFMf13EEIHre4O1CqvbNur5+ZSEg/Tr6GoWyWL2mKqbHEXH7Plbz84hTIewRbV7YMquPBgK2p6dM6xrFZk4NyD71HXowfUjwU7T10Js8ccS1uHxu69LGpLOjSITvsRQe4vWFQegFH5OPUVs2XjU3+Hb7FmZvc/Lk13D0kPjqKsRxOozPzdn/Tk4/5Ae9ts2dRHsShWpIpF9coon6E1f3mTEDqka27CzaV8js9fre4RNNpkuKg9LW7Lk+W1uvvZlhfiERSlSI71qhR3k69T8/UK6MRdqJRSUP59j9OJ7H85t7QttpR/sqwQmhyOsOazQOC/L/GWvP9a/znjHOWtPc/cbEtu1/34uWYGRz8/P8TFxVlxf/Loc4ufnycuX/1cSp1V/Ufh+vXkPD0vsk7hyOGyWfs+YTvl3/dFk1nf4kps+4L+5GecuQxtjSIuZXK4Tx3SMq7B3bVSDt8f7P+xorjbfzk8+cFZeHo8ZVM9/qKuTcede7gJdYt2AwCUtnigSPYeioxs959K+wHB7t2zLXNNHSulLe4oDp3IwN+p2xBQrJNNPxCkCNtGJz7593yUTYa2VE4dz5bm/LSMJPvb8tjz35oc/aScMrTp+xYAHgEo8m8Wd8wPhxb1GUj6oUIYOvYd917/n6z3qUj4cUtk+0OFLTlchrx4bB+/38zvbIUth1+8eBE1a9bE6NGjERYWlmVfiRIlEBYWhhMnTqBRo0aYMGECJk2aBAC4fv06goOD0bJlS2zYsAEAkJCQAD8/P7z88stYtmwZFEXBP//8g8aNG8PDwwMnT56Uck6y2fysu379OoYNG4YtW7YYtrVv3x69e/fGa6+9hps3b2LVqlWoVq2alIYSERERkbGcf3yhgoY5nIiIiCjvOSqHX7hwAQDQo0cPPP300ybLrFixAiqVCiNGjDBs8/PzQ+/evbF48WIkJyfD09MTq1evhlarRWRkpGFgWYkSJTB48GCMGTMG586dQ926tv3Am5usuQbdIDk5Gc888ww2b96M2rVrY/To0Vku7alduzaOHDmCli1bIjExUVpjiYiIiIiscfjwYSxduhTHjx/P66ZIwRxOREREVLjoO7Br1aoFjUaDu3fvGpU5dOgQgoKCUKZM1iumwsPDkZ6ebhhZfejQIXh5eSEwMNConH6/LDJzuE0d2HPmzMGFCxfw6quvIjo6Gp9++mmW/VOnTsW8efOQkJCAGTNm2N1IIiIiIjJFQAitlFt+n0/z4MGDeOGFFzBv3jzDtsmTJ6N58+YYNGgQmjZtioEDB+b7uZaZw4mIiIicwX85PC0tDXfv3rf5llM+vXjxIjw8PDBs2DAULVoUpUuXhr+/P2bMmGGYFzshIQHly5c3Ola/TT+wISEhAb6+vobR1+bKWcMROdymKUTWr1+P4sWLY8GCBXBxMV3F0KFDMXXqVGzfvh3Tp0+3uYFEREREZJoAoJN06WJ+7r4+cOAAwsLCIITAU09lzp167do1TJ48GcWKFcOLL76IvXv3YtmyZWjbti1efPHFPG6x7ZjDiYiIiPLe4zn802lb8cnH23Ltvi5cuIAHDx7g8uXLmDNnDtzc3LBmzRqMGTMGV69exdy5c5GQkIAWLVoYHevpmbke1uMd2F5eXjmWs5SjcrhNHdixsbEIDg5GsWLFzJZRFAXVq1fH0aNHbWoYEREREZElpk6dCiEEZsyYgYEDBwIANmzYAI1Gg48//hgjRozAjRs3ULVqVSxevDhfd2AzhxMRERE5lzHvtseIUc/YfHyD+h/h5k3jaUH0QkND0axZM4wdOxbu7u4AgAEDBqBr166YN28ehg8fDkVRskwrp6cf9ZyRkblYsKXlLOWoHG5TB3blypURHx+fY7kbN26gQoUKttyFk7F2ppXcLm/bauSWyb22PL4Sem6Ut5aL2vIVn60pCwBFXX2tKl/GtYZV5a1RwaWeVeV9XGrnWvnirhWtqtta1jzururSVtWd2+WtoVLcc7V8br6urWft+5015a2t26ZZt3Kpbmcrn5t1W1Pemc7TUYTExWPy7xjs6Oho1KxZE2+99ZZhW1RUFBRFwcsvvwwAqFChAho1amSYQzC/Knw5XLa8eN3nxX3m5vcF5+Lu6u3w+yznUsvh9+nrUsfh95kX8uLvmZu5PTu5n6OdRV68HxWW9/q8kFfn6YyP73853M1NgZubq801KTmc3oQJE4y2qVQqvPnmm9i2bRuOHj0Kb29vpKSkGJVLTk4GAPj6ZvaXeHt7mxxl/WQ5Szkqh9v0DGjQoAFiY2OxbZv54fEHDhzAxYsXjSYFz5+se5gUK9+grS6vOE8HtjVtUVn5AW1teWu5qkvlSlkA8LC6A7umVeWtYW0Htq+LdavNWlO+uFslq+q2ljWPu5tLaavqzu3y1lCpiuZqeUWx/LfN3H6dWtMWa8tbW3f+7dR1RPncrJsd2JSzO3fuZAnbOp0OBw8eRN26dbMsZFO8eHEkJSXlRROlKXw5XDbHv+6tzfpS7tPqz7j8q2hedGC7WjfgQwZrM3p+lRd/z9zM7dnJ7RztLHK3/8IcdmDnHnZgO5PKlSsDAG7dugUfHx/cuHHDqIx+4IM+K/v4+ODmzZtG81E/Wc5SjsrhNj0Dxo0bB3d3d/Tp0weLFi3C7du3Dfvu3buHdevWoWfPnnBxccG7775rc+OIiIiIKHtCaKTc8rMqVargr7/+Mixi88svvyApKQnPPJP1Us6YmBj4+PjkRROlYQ4nIiIicg6OyOGXL19Gly5d8MMPPxjti42NBQDUqVMHISEhiI6ONuokjoqKgqurK4KCggAAISEhSE5ORnR0tFE5AGjSpIlVj4GjcrhNHdh16tTBkiVLIITAsGHD4O3tDUVR8PPPP8PT0xN9+vTB7du3MXPmTKtPnIiIiIgsJACh00i55eMZRBAeHo7ExES8+eab+PXXXzFhwgQoioJu3boBAB4+fIhPPvkEly9fNiwuk18xhxMRERE5AQfl8MqVK+PUqVMYO3Ys7t+/b9iekZGBGTNmwNvbGyEhIYiIiIAQAnPmzDGUiYuLw7p169C1a1eULVsWANCnTx+o1WrMmjXLMBf2vXv3sGTJEjRs2NDqK/gclcNtvrasT58+CA8Px7Rp03D06FFcuHABGRkZqF69OoKDg/Hee++hWrVqNjeMiIiIiMgS7733Hr7//nssWrQIX331FYQQCA0NRZs2bQAALVu2xKlTp6AoCsaMGZPHrbUfczgRERFR4aBSqfD555+jZ8+eaNSoEV566SVotVps2rQJZ8+exerVq1GyZEk0aNAAnTt3xpQpUxAbGwt/f3+sWbMGGo0G48ePN9Tn4+ODIUOGYP78+bh37x6Cg4OxceNGxMbGYvPmzVa3z1E53K7J0Xx8fDB79mx7qiAiIiIimwmAizjCz88Px48fx+zZs/Hnn3+iTp06GDdunGG/h4cHmjZtiokTJyIsLCwPWyoPczgRERFRXnJcDu/Rowc2b96MadOm4fPPP4erqysaNmyIRYsWISQkBACgKArWrl2LSZMmYfv27di2bRuaN2+OSZMmITg4OEt9s2fPRvny5bFx40bs27cPTZo0wYwZM9C2bVurW+6oHK4I/XhxKzRs2BBBQUFYunSpzXfsbCpWrIjr16//+6+CsuiJAKBF5qq/is21KDkth2ox++sxtyCNEAJCpEJRikFRbD9Xa6gUdyn1lCzib3FZIbRIfhgNz6JBWRbDENBlc5TlHmnv2V2HRvtQQkuADO09u/+msv5GMmQ3r5UQAjpxFyqlVI7nqhNpEloj5/lircyPmwwAro+dp5z3F+ebv1fO+6/zKyznCWQ918z53fz8/BAXF5cnrdHnlgoVSuL8xfek1FmrxjTcuHEvT8+LcsYcnn+oVPbnEKF7JKElkuSQUUx/zhtTq4rb3RS1lQtUm+OqLmZ3HUXVnhJaAjzS3c+5UA7SNfZneT0hdHikuY4iLn42fR/U6OR8J5CTfWXlzXS761CktSXD+mMgAGgAuECBkuNr2pGEkPP9RJGQR0W++UG/MOXwx2V+72MOL3xsSogJCQnYsWOH7LYQERERkZWs+QHnq0WHsfirIyb3JSba33mSV8LDw9G0aVNMmzYt23ITJkzAxYsXsXr1age1TD7mcCIiIiLn4HwDqRzPUTncpg7s999/HyNGjMAPP/yA7t2723THRERERGQvAegsH4X12uCGeG1wQ5P7atf+HDdu/COrYQ61d+9euLvnPNr1+PHj2LNnjwNalHuYw4mIiIicgXU5PMe68ilH5XCbOrDffPNNuLq6YuDAgTh27Bg6duwIX19fsw3297d8igSSrTBdSuKa1w1wAAXuLr4oPH/XwvA3zaSgSF43wUFkTUmUHxSW12lhOU+gcJ2r8wsPD8/y7yNHjhhte1xKSgpOnz5tWIE9v2IOJ+dWmD7nCzoFalVJ8LOvoOFrtGDh65PyRl7kcJs6sD08PAAAaWlpmD59OqZPn262rKIo0Gg4pD5vKMicD6ngUxQFilLwOwAVRQUPtwp53QyHKCx/U0B/rnLmcnRmmfNhFpy5TbNXWN5/C8t5As58roX10sW9e/ca/l9RFNy5cyfLNnPeeeed3GuUAzCHk7MqXJ/zBZ+iKHBVl8rrZpBEihNnGbIF/57OgDnccTncpoTRtGlThy2UR0RERET0pMuXLwPIXDguICAAYWFhWLZsWbbHlC5dGqVK5e8OGeZwIiIiIspLeZHDberAtqRXnYiIiIhymwB0skZ+5K+59ypXrmz4/759+yIwMDDLtoKKOZyIiIjIGTCHA47L4bzGyyRzT0AVOGcUERFRYaD79+bkBOQF5/yVm7NYunRpXjeBpGEOJyIiKtyYw/MTR+Vwuzuwd+zYgejoaMTExECj0aBmzZoIDAxEp06dZLQvj7Bfn4iIqHDLrrOscM515+xiYmKwevVq/P3339mWUxQFX3/9tYNalbuYw4mIiKjgYQ7PbxyRw21OiMeOHcOwYcNw/PhxAJnznjzeoEaNGmHhwoVo2LChrXdBRERERNkSgLTFY/Lv0I/jx4+jTZs2SE1NzZJJTSkIHdjM4URERER5zbocvvjr01j89WmT+xJvpcpqlMM5Kofb1IGdlJSErl27IiEhAU899RQiIiJQrVo1qFQqXLp0CcuXL8exY8fQtWtXnD59GmXKlLGpcURERETkPG7cuAGdzviSThcXF/j6+gIAUlNT8dFHH2HHjh2Ii4tDixYtMGrUKISHh2c5RqPR4LPPPsPGjRtx/vx5NG3aFIMHD0bPnj2tbteECRNw//59hISEYNSoUfDx8bHtBPMB5nAiIiKi/GfwwEAMHhhocl+d+t/gxs382YntqBxuUwf2xIkTkZCQgBEjRmD27NlGK6FHRkbizTffxJdffokPP/wQX3zxhZTGEhEREVFWirTFY7KXkZEBf39/aLVao3316tXD2bNnIYRAr169sG3bNvTp0wddu3bFmjVr0L59e+zfvx/NmjUzHDNy5EgsWLAAnTt3RocOHbBx40b06tUL69evR48ePaxq29GjR+Hp6YmdO3eiRIkSdp+rM2MOJyIiInIOjsrhzsxROdymDuwjR46gVKlSmDlzplFoBjKHhM+ZMwerVq3C77//bncjiYiIiMgMBwXn2NhYaLVajB49GmFhYVn26cPqyZMnsW3bNkyYMAGTJk0CAAwdOhTBwcGYPn06NmzYAABISEjAokWLEBERgWXLlkFRFIwePRqNGzfGlClTrO7ATktLQ1BQUIHvvAaYw4mIiIicBjuwHZbDberA/uuvvxAUFAS1Wm2+YhcX1KtXD6dPm57fhYiIiIjyjwsXLgAAevTogaefftpkmRUrVkClUmHEiBGGbX5+fujduzcWL16M5ORkeHp6YvXq1dBqtYiMjDR0wpYoUQKDBw/GmDFjcO7cOdStW9fitjVu3Bjnzp2z4+zyD+ZwIiIiInIWjsrhNnVgV6pUCTExMTmWu3z5Mvz9/W25i3xNgfFoGGsJZ1pIKYdJ2C2mGM+ZaS2heyShIYCiKmJ3HTqRLqElQJo2xe46VIrN67Fmka65Z3cdLqqiEloCKLavMSudnL+1/c9/AFDMrsZsOSGML/+3iYmRf3lFUex/XGS91znV+zcVAkLiyI/sn7v6DuxatWpBo9EgNTUVpUqVylLm0KFDCAoKMpp3OTw8HPPnz8fJkycRHh6OQ4cOwcvLC4GBgUbl9PVY04E9efJkhIeHY+rUqXj//fctPi4/Kug53N4c7UzvwTpdmt11yPheAQCK4iqhEgmftZCTq1TC/iwPAI80d+2uQyXjsQWQlnHH7jq0uocSWgK4qIvZXYeimP+RzSrO85KGIuM7l5DznQASHl8Z3ysAQIgMKfXI4EyfAVQYOC6HOzNH5XCb3oGDg4Oxdu1aLFy4EEOGDDFZ5quvvkJ8fDzatGljVwOJiIiIyAwBKP+ufv7okRaP0m3/gUqnyz44X7x4ER4eHhg2bBjWr18PjUaDSpUqYcSIEXjrrbegVquRkJBgsuO5fPnyAIDExEQAmVOI+Pr6Gk2B8WQ5S1WqVAmLFi3CiBEjEBUVhZ49e6Jq1aooUsR0J1doaKhV9TsT5nAiIiIiJ/BYDpdRV37lqBxuUwf2hAkTsHHjRgwfPhyHDx82rH4OZI72WL58OVauXImiRYti/PjxNjWMiIiIiCw3c95ZTJ2Ve1NGXLhwAQ8ePMDly5cxZ84cuLm5Yc2aNRgzZgyuXr2KuXPnIiEhAS1atDA61tPTE0DWDmwvL68cy1mqSpUqUBQFQgjs3r0bUVFR2ZY3tRBlfsEcTkRERETOwlE53KYO7Nq1a2Pp0qUYNmwYli9fjhUrVmTZL4RA6dKl8eWXX6J27do2NYyIiIiIciIAXWYIfHtYHbw5qJbNNTVqswXxCeYvPw8NDUWzZs0wduxYuLu7AwAGDBiArl27Yt68eRg+fLghvD5Jp8u8ZDojI/MyY0vLWSoiIsLkgoYFEXM4ERERkTP4L4dLqSufclQOt3kSp969e6N9+/aYN28eTp8+jUuXLgEAqlevjsDAQLzxxhuGUTRERERElLuKFFGjSBHb58RU5ZA7J0yYYHyMSoU333wT27Ztw9GjR+Ht7Y2UlBSjcsnJyQAAX19fAIC3t7fJUdZPlrPUsmXLrCqf3zGHExEREZEzcFQOt2sVgtKlS2PcuHGy2kJEREREVlKkLR5jm8qVKwMAbt26BR8fH9y4ccOoTHx8PID/OqZ9fHwQHR0NnU4HlUplthyZxxxORERElLfyOocXJnZ1YK9btw4///wzxo0bhypVqgAADh8+jI8++gjdunXD4MGDC83lnERERER5Qtqli+ZdvnwZw4cPR//+/dG9e/cs+2JjYwEAderUQUhICObOnYukpCSULVvWUCYqKgqurq4ICgoCAISEhGDt2rWIjo5GgwYNspQDgCZNmljVvv3791tVPj8v4qjHHE5ERESUxxyQw52do3K4TR3Y6enpeOGFF7B9+3YAQGRkpGFfWloatm/fjh07duDHH3/E1q1boVbbfjkrEREREeWtypUr49SpUxg7dizat2+P4sWLA8icq3rGjBnw9vZGSEgIypYtizlz5mDOnDmYPHkyACAuLg7r1q1D165dDZ3affr0wejRozFr1iysWLECiqLg3r17WLJkCRo2bIjAwECr2te6dWuLOmuFEFAUJV8v4sgcTkRERETOwlE53KYO7AULFmDbtm2oV68epk2bhpo1axr2hYWF4fjx4xg1ahR27dqFBQsWYPjw4TY1joiIiIiyIwBply6aXzxGpVLh888/R8+ePdGoUSO89NJL0Gq12LRpE86ePYvVq1ejZMmSaNCgATp37owpU6YgNjYW/v7+WLNmDTQaDcaPH2+oz8fHB0OGDMH8+fNx7949BAcHY+PGjYiNjcXmzZutbrm5xWPS09MRGxuLEydOICMjA2+99RYqVapkdf3OhDmciIiIyBk4Joc7O0flcEWYWgI+Bw0bNsT58+cRExNjdo7ClJQUVKlSBbVq1cLhw4dtbqCjVKxYEdevX//3X3bNrAIF9l+uKZzoySvjfDIrklCP9U9XkxRVEQm1qHIuYgF317I5F8qBSrHvOauXlnHH7jpcVEUltATI0P5jdx2KpMdFJ9Jl1CKhDgDC/nqEyJDQEMh5TUt6HUl5fCW9vzjT+zfllsyg6ufnh7i4uDxpgT63VPB1x4UD7aXUWbPFTtyIT8v2vLZs2YJp06bhjz/+gKurKxo2bIgPP/wQISEhhjKpqamYNGkStm/fjhs3bqB58+aYNGkSGjZsmKWujIwMTJ8+HRs3bsTFixfRpEkTvPPOO2jbtq2U83nczZs3MWjQIJw6dQpHjx5FhQoVpN+HoxT0HK7A1a66Ctp7sKwcrij2Pa6Zlcj6zLafi6qElHqEhPxQVEKWB+TkcK3uoYSWAC7qYnbXoRNyOnW0ulQJtThT3pTznUDGc1eR9LjI+G5R0N67KTcV3hyeX8nK4TZ1YJcsWRJPPfUUDh48mG25Vq1a4cyZMyZXo3c27MA2jx3Y5rAD2xR2YJutRUIdYAe2WezAJkdyouDs446Lvz0rpc4aLX/BjYSCGZwB4P79+6hevTratWuHFStW5HVzbFbQczg7sLNiB7Zp7MA2jR3Y5jhT3mQHtsk6Cth7N+Um5vD8SEYOt6l3x9vbG7du3cqxXHx8PDw9PW25CyIiIiLKkZC4eEzB/vJYvHhxhISEYMeOHXndFLswhxMRERE5A+ZwS8nI4Tb95NawYUP8/fff+Pnnn82W+eWXXxATE4Pg4GBb20ZEREREJM2dO3eQmipjNF/eYQ4nIiIiyn8WrbqCRp32m7wlJsm46tu52ZvDberAfv/99+Hq6ooePXrg448/RmxsLLRaLYQQuH79OmbMmIGePXtCpVLh7bfftrlxRERERJQ9RaeVcivoli9fjt9++w3VqlXL66bYhTmciIiIyDlYk7WH9KmIE1tamLx5l3XL61PJVTJyuE1TiDRo0ACLFi3CG2+8gYkTJ2LixIlQFAVqtRoaTeZ8NGq1GtOnT0fLli1tblzeMTdflwqW9PkXuPmbFLWceoSEL8ey2iKBWtJczzqd/XOGafFIQkvk0EiZp07O/NU63QMJLQEgaS5tMkHG+4IkBe69m+ykg7S56ynXBQQEZLv/zp07+Oeff6AoCoYOHeqgVuWOgp7DBczlIhUUOCYHypp3WsrnipT1JgBh9vuNFXRyPidVKvvnV5Y117OMPK+TtbaIBK5qOXODayWs/6LR3pXQEkBRnKlTx/45o6W8FgEpGVpAUg53orWuqKBgDs9PHJXDbe6V6du3L9q1a4dPP/0UR48exaVLl5CWloYaNWogMDAQ7733HmrWrGlzw/IWO6uIiIgKt+x+tJb05VOWQjB6OiexsbE5lqlSpQpee+21fN+BDRTsHG7vIo5ERESU3zGH5yeOyuF29dSWL18en3/+uT1VEBEREZGthJA3/Uc+Hv2k0xW+UTrM4URERER5iDkcgONyuP3XwDzmzp07OHnyJNLS0mRWS0RERERE2WAOJyIiIqKCyqoR2NeuXcNPP/2Eu3fv4oMPPjBsT09Px7Bhw7B06dLMSl1c0KtXLyxevBju7u5yW0xERERE/7Fi5MfCNTewaO0Nk/sSbxeM1c9TUlJw/vx5xMTEQKPRoGbNmqhduzZKly6d102zC3M4ERERkZPhFCJZ5GYOt7gDe+vWrXj11Vdx9+5dNGrUKEtwfvvtt/HNN98AAEqVKoW7d+/iu+++w40bN7B79267G0lERERE9hvyYgUMebGCyX3V2h7GjcT824mdnJyMTz75BPPmzUN6etbzcHV1xfDhwzF27Nh82ZHNHE5EREREzsoROdyiDuzExET06tULDx8+xOuvv47u3btn2bdo0SKoVCp8//33eP7553Hjxg288MIL2Lt3L6KiohAeHm5zA4mIiIjINAWQNveeIqWWvKHRaNC+fXscP34crq6u6NixI6pVqwaVSoVLly5h165dmDVrFn799VccOHAAarU6r5tsMeZwIiIiIufDHJ7JUTncog7sWbNm4eHDh5g+fTpGjx6dZd+PP/4IjUaDiIgIPP/88wCAChUqYMmSJQgMDMRXX33F4ExERESUK4TESxfz7+Ixn332GY4dO4bQ0FB89913qFAh6yjzuLg49OnTBwcPHsTMmTPxzjvv5FFLrcccTkREROSMmMMBx+VwixZx/O2331C8eHG8+eabRvuioqKgKApeeeWVLNufeuopVKlSBRcuXLCpYURERERElti5cyeKFCmCH374wSg0A0DFihXx/fffw8XFBVu3bs2DFtqOOZyIiIiInJWjcrhFI7D//vtvVKtWzWghGCEE9u7dC3d3d4SGhhod5+fnh3PnztncOCIiIiLKnqLT5XUT8tzp06dRv359lClTxmwZHx8fBAYG4syZMw5smf2Yw4mIiIicE3O443K4RSOw7927hxIlShht/+OPP5CUlIRmzZrBzc3NaL9Go0FGRobNjSMiIiIiyknx4sWRkpKSYzlzmdaZMYcTERERkbNyVA63qAM7ICAAMTExRtt/+uknAEDr1q1NHnf58mVUqVLF5sYRERERUTYEMufek3HLv1PvITAwEDExMTh48KDZMgcPHsTFixcRGBjowJbZjzmciIiIyAkxhwNwXA63qAO7du3aiI+PzzJXiVarxaJFi6AoisngfPDgQSQmJqJ69eo2N46IiIiIciArOOdjr7/+OoQQ+N///ofly5dnGXms0WiwYsUKvPDCC1AUBa+//noettR6zOFEREREToo53GE53KIO7JEjR0IIgVdeeQXLly/H2bNn8f777+PatWvw9fVF8+bNs5S/ffs2Ro4cCUVRMHjwYJsbR0RERESUk65du+Ktt97CrVu3MGDAAHh4eCAgIAABAQHw8PBA//79kZiYiJEjR6JLly553VyrMIcTERERkbNyVA63aBHHFi1aYODAgfj6668xYMCALPs++OADqFSZ/eAXL17E7NmzsWnTJty8eRNPP/00OnbsaHPjyFlImpReUeyvwrLfXBxCCDm/krm6FLe7jnTtPQktAVSKq9116ISc+TaF0EipRwYh0iTU4jzPXRmvRQAQwv73BgWS2pKfr7kisouAIuG1qK8rP5s5cyaee+45TJ48GadPn0ZsbCwAoGzZsggMDMT48eMRFhaWt420AXN4/iXlM0440etSUn7QSchVimLR11gL6nGefCYkfOdSqezP8gCg0Ty0uw5ZfyPI+E7gRH9nWWRkX1k5XAZ+J6D8yboc/uUPt/HlD7dN7ku4k7/XLXFEDrf4U2Xx4sVo2LAhVq1ahejoaPj4+GD48OF44403DGXOnTuHhQsXAgBCQ0Oxdu1auxpHRERERGSpsLAwQzi+dy/zx92SJUvmZZOkYA4nIiIiyt+Gdi+Dod3LmNxXtdufuH7LeQbx2SK3c7hVP4sOHToUQ4cONbu/bt26WL16NRo1asQ594iIiIgcIZ/Pm2cPnU6HX375BWvXrsW0adNQrlw5w76SJUvC29sbtWrVQqdOnRAZGYkiRYrkYWvtwxxORERE5GSYwx2WwyVd15OpRo0aqFGjhswqiYiIiCg7OllTiOQvd+7cQe/evREVFQUAmDx5slGZ9PR0HDhwAAcPHsSaNWuwdu1a1K5d29FNdQjmcCIiIiIHYw4H4JgcXvAmgyIiIiKiAk2r1aJ169bYvXs3KlSogHHjxqFMGeNLMv/66y988803qFGjBs6cOYMXXngBWm3hHSlDRERERGSPvMrh7MAmIiIiyq8EMkd+yLjlo3WPVq5cibNnz6Jly5Y4ffo0Jk2aZPKyRF9fX/Tr1w+nTp1Cx44dcf78eXz11Vd50GIiIiIiKlCYwx2aw9mBTURERFRIfLkhBYF9Y03eEpLzz8Ix3333HRRFwcKFC+Hp6ZljeXd3d8yfPx8qlQo//fSTA1pIRERERFTw5FUOlzoHdsFh7gucCuzzJyIiKgx0/96cnYBixeIxw7qVwLBuJUzuq9IrFteT8sf0GjExMQgICEDdunUtPqZKlSqoWrUqYmJicrFlZC+BDDN7VFCgdmhbiIiIKC8UzByeU135RV7lcPbGmuRi5saHi4iIqHBQwXwecDKyLl3MR65fv44KFSpYfZyvry+uXbuWCy0iWRS4mrmx85qIiKhwYA53ZnmVw9kjS0RERET5StmyZXHp0iWrjzt//jxKliyZCy0iIiIiIir48iqHswObiIiIKD8rhCM/6tWrh/j4eFy5csXiY/7++2/cunULtWvXzsWWEREREVGhwRxuERk53Kbx9+Hh4RaVK1asGHx9feHj44M6deqgZ8+ecHNzs+UuiYiIiIgAAD169MCuXbswaNAg7NixA2p19tNL6HQ6vPbaa1AUBc8995yDWpk7mMOJiIiIKK/kVQ63qQN77969AABFUSCE6YnGn9ynKAoiIyMxduxYjBgxwpa7JSIiIqIn5bNRGzL069cPs2bNQlRUFF5++WVMnz4d/v7+JssmJiZizJgxiIqKgo+PD15//XUHt1Yu5nAiIiIiJ8Ec7rAcblMHtk6nw6hRozB37ly0bt0ar7zyCqpWrQq1Wo3Y2FisWbMGO3bswNKlS9G5c2fExcVhw4YNmDdvHt566y34+fmhe/fuNjeaiIiIiAAIAcha/dxMZ6gzcnV1xY8//oiwsDCsX78eGzZsQM+ePREYGIiAgAC4u7sjJiYGf/75J7777jukpqaiSJEiWLduHYoXL57XzbcLczgRERGRE2AOd2gOV4S5oRvZmD9/PkaMGIFPPvkE7777rskyc+bMwZgxY7B161a0bdsWAHD06FG0bNkSzz77LLZu3Wpzo3NDxYoVcf369X//5YQrm9pIgWJ3HQJyXkgy2iKLoiqS100wcFN72l2HgJxf/TK0/9hdh0qRdXmyjHOSM82/Vnff7joUxYneV4Sc54tOpNtdhzO9L8h6r6PCQAMA8PPzQ1xcXJ60QJ9b/MqoELvSV0qdVV6Nx/Xbujw9L2tdvXoVgwcPxq5duwBkjjR+nD7m1q5dGytWrEDjxo0d3kbZmMPzD2f6jIOS/eW9FlXhRBlPVt50VZewuw61pO8VaRlJdtchI5tlsj9DK5JyuPj3M9c+ktoi0iTU4kTLkAlJHW9OhHm+MGAOdxaOzuE2dWDXrVsXd+7cQXx8fLbl/Pz8ULFiRRw+fNiwrUOHDoiJicHFixetb20uYnA2jx3YuYsd2OawAzvXsAPbJAZespxzBecry72l1Fm5b2K+C856J0+exLZt23DhwgVcvHgR9+/fR82aNVGrVi00b94cHTt2hErlRJ0GdmAOzz+c6TOOHdimsQPbHHZgm8IObOfHPF8YMIc7G0flcJsS4pUrVxAUFJRjOX9/f5w+fTrLtipVqmD//v223C0RERERPakQzr33pAYNGqBBgwZ53QyHYA4nIiIichLM4Q7L4TZ1YNerVw9nzpzBgwcP4OHhYbLMgwcPcPbsWVSrVi3L9kuXLqFixYq23C0RERERUaHGHE5ERESU/yzY+gBfbntocl9CCjvCc2LTGO5nnnkGqampePXVV3H/vvGl9ampqYiIiMCDBw8QHh5u2L569WpERUWhefPmtreYiIiIiP6j08m5Ub7AHE5ERETkJKzI2sM6uuPMF54mbz6lnWh6ISdl0wjsiRMnYteuXdiwYQP27NmDbt26oWrVqlAUBZcvX8ZPP/2EO3fuoE6dOpgyZQoA4Nlnn8WePXtQrFgxTJgwQepJEBEREREVBszhRERERFTY2NSB7e7ujh07duCjjz7CokWLsHz58iz7FUVBv379MGXKFBQrVgwAcOvWLTRv3hzz589HQECA/S0nIiIiKuyEkDd62vp1vSkPMIcTEREROQHmcIeyeZnvsmXLYu7cuRg3bhyio6Nx8eJFpKeno2bNmqhfvz4qVaqUpfyRI0dQpIicFZqJiIiI6F86ywPvgu1p+HLnI5P7Eu4yOOcXzOFERERETsCKHE72sbkDW8/b2xtt27ZF27Ztsy1na2iOjY3FxIkTsXfvXgBAWFgYPvroI1SpUiXb4+7cuYMJEyZgx44diI+PR506dTBixAi88sorUBTFprYQERER5WfDOrpjWEd3k/sqv5aC63cYwvMT5nAiIiIiKgxs7sDOyMjA4cOH8ffff+dYNiIiwqb7uHPnDlq1aoV//vnHUMfy5cuxd+9enDp1Cl5eXiaPS0tLQ1hYGC5evIj+/fvD398fu3btQkREBK5du4YPPvjApvYQEREROR0uwFjoMIcTEREROQHmcIexqQP75s2b6NSpE06fPp1tOSEEFEWxOTgvW7YMcXFx2L17t2EV9S5duqB9+/ZYuXIlRo4cafK4VatW4ezZs1i/fj169OgBAHjvvffQpUsXfPjhh3jrrbdQtGhRm9pERERERJRXmMOJiIiIqLCxqQN7woQJiI6ORunSpfHiiy/Cx8dHdrsAACtWrEBwcLAhNANAu3btUK9ePaxatcpscI6OjgYAdO7c2bBNURR07twZ27Ztw19//YUGDRrkSpuJiIiIHIojPwoV5nAiIiIiJ8Ec7jA2dWDv2rULbm5uOHToEGrWrCm7TQAyLz+Mjo7GW2+9ZbQvPDwcX3zxBTQaDVxcjE9Bv7r6pUuXUL9+fcP2S5cuQVGUHOftIyIiIso3uHhMocIcTkREROQkmMMdRmXLQYmJiWjQoEGuhWYASEhIAACUL1/eaJ9+W1JSkslju3fvjkqVKuHll1/Grl278Ndff2HevHmYP38++vfvD09PzxzuXdh5IyIiIufFz3nKv5jD+fokIiLKv/g5L8PDhw9RvXp1DBo0KMv21NRUvPvuuwgKCkKZMmXw3HPPISoqyuh4jUaDqVOn4umnn0bp0qXRrl07rF+/3lHNt4lNI7Br1KiB27dvy25LFvrgbCrk6rclJibC19fXaH+lSpWwevVqtGzZEu3atTNsDwoKwqJFiyy4d61tjQYAKFBsXxvTQEh6YcqoR4Gk1eKlrDpv028uxoTzXOahFel21+GmLiGhJYBOaOyvQ/dIQksARZHwOpJwPgCgSHneyXnuCt1DKfU4C1nvdUTW0aHABGABeZ9pBeQhKeiYw7OjAmTlVich63NSEfY8rpl04oGElgAqxc3uOnSQk/E0ujS761AprhJaAigS6nFRikhoCaATGVLqkUEn4W8kJ8sDioTnrpD0XUnKd2tFbX8dkPediwoL5nCzdVlh6tSpiImJQevWrf+rQgj06tUL27ZtQ58+fdC1a1esWbMG7du3x/79+9GsWTND2ZEjR2LBggXo3LkzOnTogI0bN6JXr15Z1jBxNja9k48aNQqXLl3Czp07ZbfHQPn3DVkI47+i7t85ZjIyTH+wHj58GJ06dUKdOnUwd+5crFq1CkOGDMHZs2fRo0cPaDQ5vcGq7bhJ6mAlIiKiXKKCfZ/1RHmn4OdwFztuzOFERETOjTncXufPn8enn35qtP3kyZPYtm0bJkyYgO+++w4ff/wx9u3bh9KlS2P69OmGcgkJCVi0aBEiIiKwefNmTJo0Cb/99htq1qyJKVOmOPJUrGLTEMf+/fsjPj4effr0wdtvv42ePXuiatWqcHWV8+szAHh7ewMAUlJSjPYlJycDgMlRH0II9O/fH15eXjhy5AiKFy8OAHjppZdQr149DB8+HD/88AN69+6dzb0XrJEbRERE9LiC9DkvJM69V0BGwxRwBT2H23vlH6/sISIicmbM4WbrsqSUEBg6dCjq1KljWDhbb8WKFVCpVBgxYoRhm5+fH3r37o3FixcjOTkZnp6eWL16NbRaLSIjIw2DFkqUKIHBgwdjzJgxOHfuHOrWrSvpvOSxaZhCQEAAFi9ejLS0NEyYMAF16tSBu7s71Gq10c3U4i6W0K+ofuPGDaN98fHxAP4L14+7ffs2/vzzT3Tr1s0QmvVefvllAMBvv/1mU5uIiIiInI5OyLlRvsAcTkREROQkHJzDV61ahT179uDLL7802nfo0CHD3NePCw8PR3p6Ok6ePGko5+XlhcDAQKNy+v3OyKZUq9PpoCiKIdzmBnd3dwQFBZmcbDwqKgoNGjQwOdKkePHicHFxQXq68bzCjx5lzjeV8+IxRERERETOhzmciIiIqPBJTk5GZGQkBg0alGU+a72EhASTI6f1C3AnJiYayvn6+hpGX5sr52xs6sCOjY2V3AzTIiIi8Pbbb+OXX37Bs88+CwDYuXMnzp07h7lz55o8xt3dHU2aNMH69esxfvz4LKunz5s3DwDQsmXL3G88ERERkSNw9HShwhxORERE5CT+zeGPMgQe2bGeqSVx/v3334dOp8O0adNM7k9ISECLFi2Mtj++ALe+nJeXV47lnI1t1xU6SL9+/TB79mz07NkTffv2hVarxYoVKxAUFISIiAgAwPXr17FhwwbUrl3bEK4XLFiAkJAQQzkvLy/8+uuv2LFjB1588UW0bds2L0+LiIiIiMipMYcTERERWWbaVi0mb9LmWv2HDh3CV199hSVLlhhNEaKnKIpFC3BbWs7ZWNSBvWLFCvj5+eGZZ54BAOzfv9+qOwkNDbW+ZQC8vLywf/9+TJgwAT/++CMURcH//vc/zJw5E6VKlQIAXLx4EcOHD0ffvn0NwTk4OBinT5/GuHHjsH79ety+fRu1a9fGvHnzMGTIEKNh8kRERET5ldDldQsoNzGHExERETknfQ5/t6Mao9qqba7nqXHpuJliep9Go8GQIUPQrFkz9OvXz2wd3t7eFi3A7e3tbXKUdXYLdTsDizqw+/Xrhw4dOhiCc+vWra0Kn1qt7b9CVK1aFStXrjS7v3Xr1iZ/OahZsybWrVtn8/0SEREROT0BeVOIcCYSp8QcTkREROSEHsvhRdSZN1upstm3dOlSREdHY+vWrUYLbKempiIuLg7FixeHj49Ptgtw6zumfXx8EB0dDZ1OB5VKZbacs7GoA9vf3z/LQjEREREcPUFERESUzyzYo8XCPaY7NBPuObgxZBHmcCIiIqLC6/r16wCAzp07G+1bs2YN1qxZg7fffhshISGYO3cukpKSULZsWUOZqKgouLq6IigoCAAQEhKCtWvXIjo6Gg0aNMhSDgCaNGmSm6djM4s6sJ9cLGbZsmW50BQiIiIispoVU4gMC1NjWJjp4SFV3k3H9RQ5TSJ5mMOJiIiInJQDpvJ76aWX0LhxY6PtXbt2Rdu2bTFixAgEBAQgLS0Nc+bMwZw5czB58mQAQFxcHNatW4euXbsaOrX79OmD0aNHY9asWVixYgUURcG9e/ewZMkSNGzYEIGBgbl/UjZw6kUciYiIiIiIiIiIiAqjmjVrombNmib3+fv7o0uXLgAAIQQ6d+6MKVOmIDY2Fv7+/lizZg00Gg3Gjx9vOMbHxwdDhgzB/Pnzce/ePQQHB2Pjxo2IjY3F5s2bHXJOtrCpA9vSxWOKFSsGX19feHt7w9XV1Za7IiIiIqLscBHHQoU5nIiIiMhJOFEOVxQFa9euxaRJk7B9+3Zs27YNzZs3x6RJkxAcHJyl7OzZs1G+fHls3LgR+/btQ5MmTTBjxgy0bds2bxpvAZs6sK1dPAYAAgIC8MYbb2DAgAEoWbKkLXdLRERERE/i4ouFCnM4ERERkZPIwxxuaiHtYsWKYfr06Zg+fXq2x7q6umLs2LEYO3ZsbjVPOps6sCdMmIBLly7hu+++AwBUrFgRVatWhVqtRmxsLGJjY6EoCoYOHQogc86VPXv24O2338bcuXPx+++/Z1mMhoiIiIiIcsYcTkRERESFjU0d2BEREWjatClq1aqFRYsWITQ0NMv+w4cP47XXXsPx48cRFRWFokWL4u7du4iMjMTSpUvxySefYM6cOVJOwBkJCT/BKCiAq8ub+HXI6iqgkdAQAIr907+rFHcJDQHUipvddagUOZcGC5Fhdx0u6mISWgJkaP+xvxIh6/misr8K2F8HIOcHXhnvUbLIeq9zpnMicjShK4CZgcwq6Dnc3vdzZ8rQUr4TSMggAKTkcEVCfs6syHlylUrWOTkJnYQsn1lPmt11qCR8x5FG2uvI/rkCFFnf22R8LxZa++sogPj9hKzBHO44Nr2TjxkzBvfu3cP27duNQjMAPP3009iyZQtOnDiB0aNHAwBKlSqFhQsXonr16jh06JB9rSYiIiIiKoSYw4mIiIiosLGpA3vPnj2oX78+qlSpYrZMpUqVEBgYiO+//96wzdXVFSEhIbh48aItd0tEREREjxPIXDxGxo0DhfIF5nAiIiIiJ8Ac7lA2XTPl7u6Of/7J+fL+lJQUaLVZL0tRqVRQqSRdwkNERERU2PHSxUKFOZyIiIjISTCHO4xNCfbpp59GTEwMfv75Z7Nlfv75Z8TExKBJkyaGbampqdi5cydq1Khhy90SERERkZN4+PAhqlevjkGDBmXZnpqainfffRdBQUEoU6YMnnvuOURFRRkdr9FoMHXqVDz99NMoXbo02rVrh/Xr1zuq+fkWczgRERERFTY2dWC/9dZbUKlU6NmzJz755BNcuXIFOp0OOp0OV69exdSpU9GrVy8oioKRI0cCAH788UeEhYUhMTERr732mtSTICIiIiqshE6RcrPW1KlTERMTk7UtQqBXr16YPn066tWrh6FDh+LcuXNo3749fv/99yxlR44ciQ8++ADlypXDyJEjkZCQgF69emWZ9oKMMYcTEREROYe8yuGFkSKEbUtSf/XVVxg5ciTS09MBAGq1GgAMlyqq1WpMmzYNkZGRAID69evjjz/+wKuvvoqlS5c63eWLFStWxPXr1//9V96vRu1MK6g7E1kr+cpYRV2luEtoCeCqLiGhjuISWgI8zEiwuw61qqiElgAZ2pwvj86RkLA6NyBl5XJF0vuKTpdqdx3OtCI2V/mm/CnzvcXPzw9xcXF50gJ9bvErBfw9Uc77bsCkh7h+17LzOn/+PAIDA5Geno6BAwdiyZIlAIATJ06gUaNGmDBhAiZNmgQAuH79OoKDg9GyZUts2LABAJCQkAA/Pz+8/PLLWLZsGRRFwT///IPGjRvDw8MDJ0+elHJOBRVzuHnOlKFlfDYpEjIIAMC2r3xZKWr764CcHC4rV7moi9lfh6Tsmy4h+wqhzbmQBXQize46VIqbhJYAWt0Du+uQ8ZwDAAidnHokEJDwPUfG+wIKXg7n9xNnxxxeWNn8Tv7aa6+hS5cumD59Oo4ePYqLFy8iPT0dNWvWRHBwMN555x1Ur149S/m6devimWeekdLw3GXuw0AFGwetExERUb6iX1HF2SkS595TYMkKMkIIDB06FHXq1EF0dHSWfStWrIBKpcKIESMM2/z8/NC7d28sXrwYycnJ8PT0xOrVq6HVahEZGQlFyWx/iRIlMHjwYIwZMwbnzp1D3bp1JZ1XwcMcTkRERAVXwczhXx7IwMIDpnNOgoTxewWdXT9FVqhQAZ9//rlFZYcPH27PXTlY3o/AJiIioryUXWeZpKs7ZBGOHXG6atUq7NmzBwcPHkTz5s2z7Dt06JBh7uvHhYeHY/78+Th58iTCw8Nx6NAheHl5ITAw0Kicvh52YGePOZyIiIgKpoKZw4c2d8PQ5qavTgn4+AGu3+Wo/ewwIRIREREVAI80Ao/syPQ6CzJzcnIyIiMjMWjQIDRr1sxof0JCgsmO5/LlywMAEhMTDeV8fX0No6/NlSMiIiIiIrKpAzsgIMCicsWKFYOvry98fHxQt25dDBw4ED4+PrbcJRERERE9ScCw8Munu9Mx5ZeMXL27999/HzqdDtOmTTO5PyEhAS1atDDa7unpCSBrB7aXl1eO5cgYczgRERGRE3gsh8uoi7JnUwe2TqeDEALXrl0zW0ZRFFSqVAmXL1/GgweZCy989NFHeOONNzBz5kzbWktEREREJr3Txg0jW9m+aFbQzAe4ec98ej506BC++uorLFmyxGiKED1FUWBqfXCdLnMew4yMDKvKkTHmcCIiIiIqbGxaCeXSpUto1KgRXFxc0K9fP/zyyy+IiYlBbGws9u7diyFDhsDV1RWDBw/G/fv3cefOHXz99dcoX748Pv/8cyxevFj2eRAREREVTjoVoFOhiEqNkm6237ILhRqNBkOGDEGzZs3Qr18/s+W8vb2RkpJitD05ORkA4Ovra1U5MsYcTkREROQk/s3hdt8oRzaNwJ46dSo2bdqE5cuX45VXXsmyz9/fH6GhoWjTpg1efPFFBAQEoE+fPujfvz9atWqFhg0bYt26dRg8eLCUEyAiIiIq1GRdupiNpUuXIjo6Glu3bsWNGzey7EtNTUVcXByKFy8OHx8fo/0AEB8fD+C/jmkfHx9ER0dDp9NBpVKZLUfGmMOJiIiInIQDcjhlsqkDe+nSpahYsaJRaH5cz5498e6772LKlCno06cPAKB69epo3bo1Tp8+bVtrCxEhaQIcRZHwS46JS3xtokh4YUtqi2LbxQdZCOgktATQ6FLtrkMnZK3Ea//jkqH9R0I7AJVi+2XwenL+QoAQaXbXoSiS1sxV1PZXIaEZAAChlVWT3RR5Z2U3We/fRM7k+vXrAIDOnTsb7VuzZg3WrFmDt99+GyEhIZg7dy6SkpJQtmxZQ5moqCi4uroiKCgIABASEoK1a9ciOjoaDRo0yFIOAJo0aZKbp5OvMYdnT8Z7sKzPlIL22SQvP9if0BSVnNFqWt1Du+sQEs4HANzUJeyu45HmjoSWAC4q+9si47F1NlK+/zlRfibTmOWJnJNNn/yJiYkWjYwpV64cLl++nGVbhQoVcOvWLVvuloiIiIgeIwAIoci5ZXM/L730EjZv3mx0A4C2bdti8+bNGDBgACIiIiCEwJw5cwzHxsXFYd26dejatauhU7tPnz5Qq9WYNWuWYS7se/fuYcmSJWjYsCECAwNz6yHL95jDiYiIiPKeo3I4ZbJpSGBwcDCOHz+O27dvm13EJykpCadPn8ZTTz2VZXt0dDQqV65sy90SERER0ZMcMG9ezZo1UbNmTZP7/P390aVLFwCAEAKdO3fGlClTEBsbC39/f6xZswYajQbjx483HOPj44MhQ4Zg/vz5uHfvHoKDg7Fx40bExsYaOsbJNOZwIiIiIifB+asdxqZHumvXrnj06BGef/55XLlyxWj/1atX8cILLyA9Pd1wqemjR48wbdo0HDlyBM8++6x9rSYiIiIip6MoCtauXYvRo0fj1KlTWLhwIerWrYvdu3cjODg4S9nZs2fj448/xo0bNzBnzhx4e3tjy5YtaNeuXd40Pp9gDiciIiKiwkYRwvpJhXU6HTp27Ihdu3bBxcUFLVq0QNWqVaEoCi5fvozffvsNGo0GzZs3x969e+Hi4oKGDRsiOjoaPj4+OHnyJHx8fHLjfGxWsWJFw/yONg5Md0oFbQ5sWXPMyZhfGZLmNFZJqEelFJHQEkAnMiTUkS6hJZLmwJbUFhlzYKsUDwktAXQS2iIN5/AzifPmFQaZ6w74+fkhLi4uT1qgzy0VSqhw6W1PKXVWn5mMG//o8vS8KGfM4bnPmeaulrKGDORkaFnrechYi0alcpfQEjlk5XBXdTG765A1B7aMc5I1B7aM7CvruStkrDtUAPMzsy85FnN4YWVTelCpVNi0aRNmzJiBUqVKYd++fVi2bBmWLl2KvXv3wt3dHR9++CF27twJF5fMD4vSpUvjpZdewtGjR50uNBMRERER5QfM4URERERU2Nj8U6S7uzsiIyMxfPhwxMbG4uLFi0hPT0fNmjVRrVo1FCmS9Vdb/aryRERERCSRzolGi5JDMIcTEREROQHmcIex+1oaV1dX1KhRAzVq1DDa9/XXX8PV1RURERH23g0RERERmSAEg3NhxRxORERElHeYwx3Hrg7sjIwM3Lx50+S+O3fuYOzYsVCpVAzOREREREQSMYcTERERUWFhUwe2RqPBiBEjsHTpUqSnm18kTQiBTp062dw4IiIiIsqBTsKCzZRvMIcTEREROQnmcIexqQN70aJFWLhwIdzd3RESEoKLFy8iKSkJISEhUKvVOHXqFFJTU9G9e3csWrRIdpuJiIiIiAol5nAiIiKi/GfRsQf46vgDk/sSU3UObk3+Y1MH9sqVK6EoCg4cOIAGDRrgxIkTaNy4MWbOnIlmzZohNTUVzz//PP766y8UK1ZMdpuJiIiI6F+Ci8cUKszhRERERM7Bmhz+WsNieK2h6WxWc14ibvzDTuzs2DTW/fLly6hfvz4aNGgAAGjYsCHKlSuHY8eOAQCKFSuGxYsX46+//sL06dPltZaIiIiIshBCsfi26NgDNPoqyeSNIz/yB+ZwIiIiIudgTQ7P7kY5s6kD+969e/Dy8sqyrXHjxjh69Kjh31WqVEGdOnWwa9cu+1pIRERERFK81qgYjg0uZ/LmXYxz+OUHzOFEREREVNjYNIVIhQoVcPny5SzbAgMDsWLFiizbPDw8EB0dbXvr8ozGzHYVbOzzzzNC2D+aSoHz/BqkKHIefwEZj4scKqWI3XV4uJaV0BLgfvpNKfXIIWMkoKzRhPY/73QiTUI7ACnnJIT9dQCAIuFVIKktApLOichAB3nvIblIQN7iMXwZ5QvM4blP1meKlAwt6XNSUWz62pcrhNm/seMpiquEOuQ8L9O1/9hdh07IemztPydZj4sCNwm15IPPc2vJyOGSKBLeppjlKSvmcDJm0yPdsGFDXL16FXPmzIH4N1TVr18f8fHxOHHiBADg0aNHOHXqFEqVKiWvtQ7jYuaWvzqviYiIyFYqmM8DRHmHOZyIiIgKNuZwMmZTEhwzZgzUajUiIyPRvXt3AECzZs2gKApeeuklfPzxx+jatSvS09MRGhoqtcFEREREpKdA6OTc5F1bRLmJOZyIiIjIGTCHO5JNHdhNmzZFVFQU2rVrh5IlSwIAqlativHjx+PChQuYOHEifvnlF1SsWBGTJ0+W2mAiIiIi+g8XjylcmMOJiIiInANzuOPYPP6+ZcuW2L59e5ZtEydORNu2bXHgwAFUqFABnTp1gqenp92NJCIiIiKiTMzhRERERFSYWNSBvX//fnh5eeGpp57KsWzz5s3RvHlzuxtGRERERBaQtXgMOSXmcCIiIiInxRzuMBY90q1bt8a7776b220hIiIiIqLHMIcTERERUWFn8RQi+lXOiYiIiMh5ZC78QgUZczgRERGR82EOdxyb58AmIiIiojwmIG/hF/aREhERERFZhjncoThZCxERERERERERERE5JY7AJiIiIsrPuHgMEREREZHjMYc7jMUd2Pv27UNAQIDVd6AoCmJiYqw+joiIiIiImMOJiIiIqHCzuAP74cOHiI2NtfoOFIUTmhMRERHlBgF5i8dw6j3nxRxORERE5FyYwx3L4g7spk2b4tNPP83NthARERGRVRSrFo9ZcjYFX5+9a3LfrQdaWY0iyZjDiYiIiJwNc7gjWdyB7eXlhbCwsNxsCxVwQujsrkNR5MwvpCgFa/r3++k387oJ/xEaOdU40RqzihO1RcD+1xFkjcgT9v9OLCT91qzA/nOS1RYiZzboqdIY9FRpk/ueWnEZN1PlvIeTXMzhjiXjMwWQ87kiK/tC2P/FWFEVkdAQQEjJinIeFyHhcdFKqCOT/RlPpbhJaAegE+n2VyLpOwFkvQachaKWU4+M552k7wTM0ESWYQ63T8HqxSMiIiIqZGRdukhERERERJZjDnecAvZzJhEREREREREREREVFByBTURERJSPCcHxCEREREREjsYc7jgWdWBPnDgR1atXz+22EBEREZE1BABZly5yCkunxBxORERE5ISYwx3K4g5sIiIiIiJyLOZwIiIiIirsOIUIERERUT4mBBePISIiIiJyNOZwx2EHtkkaM9tV4LqXREREhYHu3xsRORZzOBERUeHGHE7G2IFtEh8WIiKiwi27zjJzHWx5Q8iae4/IKTCHExERFW7M4WSMCZGIiIgoH+Pq50REREREjscc7jh8pImIiIiIiIiIiIjIKXEENhEREVG+pUi8dJGXQBIRERERWYY53JE4ApuIiIiIiIiIiIiInBJHYBMRERHlY0JwxAYRERERkaMxhzsOO7ALOKWgXYYghKSKdPZXoTjTBQwSzgeAjIsyVCoPCe0AhLB/dWFF0lucDmlSanEesp67WrtrkPYepUioR9r7C5HjCMgLznwFEGUSfDWYJES6lHoUxd3uOnRCRjYDVIqb3XW4qktIaAkghP1ZMV17V0JLAEjI4fLIyK1ycrgioS0CzvTYyuFMfQ58/yZHYg53LHZgExERERUSy87fwrILSSb3JT3McHBriIiIiIgKB+Zw+7ADm4iIiCi/ErBq8Zi+NbzRt4a3yX2NN5xFPMMzEREREVHOmMMdih3YRERERPmYEM40pRURERERUeHAHO44fKSJiIiIiIiIiIiIyClxBDYRERFRPmbNpYtERERERCQHc7jjcAQ2ERERERERERERETkljsAmIiIiyseE4MgPIiIiIiJHYw53HKcfgR0bG4u+ffuicuXKqFy5MiIiIhAbG5vjcUIIrFixAsHBwShatCjq1auHr776CkKI3G80ERERkYMIoUi5ET2JOZyIiIjIPOZwx3HqDuw7d+6gVatW2LRpE7p164Zu3bph06ZNCA0NxZ07d7I9dsaMGejbty+KFi2K999/H76+vnj99dfx5ZdfOqj1RERERET5E3M4ERERETkLp55CZNmyZYiLi8Pu3bsRHh4OAOjSpQvat2+PlStXYuTIkSaPu3v3LiZMmIB27dph69atcHFxgU6nQ1hYGD744AMMHjwYrq6ujjwVIiIiolzBxWMoNzCHExEREWWPOdxxnHoEtv7SQ31oBoB27dqhXr16WLVqldnj1qxZg7S0NEydOhUuLpl99CqVCsOGDQMAnDp1KlfbTURERESUnzGHExEREZGzcNoO7LS0NERHR6NNmzZG+8LDw3H06FFoNBqTx27atAkVKlRAgwYNsmzv06cPUlJS0KRJk1xpMxEREZFDSZp3TwgF4Px79C/mcCIiIqIcODiHb9++HS1btoSnpyd8fX3RsWNHHD16NEuZpKQkDBs2DLVq1YKvry969uyJ6Ohoo7pSU1Px7rvvIigoCGXKlMFzzz2HqKgoaQ9NbnDaDuyEhAQAQPny5Y326bclJSWZPPb69euoWbMmFCXzCXD37l3odDor7l3YeSMiIiLnVbA+54VQSbkR6TGHExERUe4oWJ/zjsrhGzduRKdOnZCcnIyRI0diwIABOHXqFFq0aIHTp08DANLT09G2bVt88803aNWqFSIiIvDbb78hNDQUMTExj7VZoFevXpg+fTrq1auHoUOH4ty5c2jfvj1+//33XHus7OW0c2Drg7Onp6fRPv22xMRE+Pr6Gu2Pj49HjRo18MUXX2DmzJm4cuUKihcvjm7dumHmzJnw8fHJ4d61drRcAaC243i5hIQXuAI5I7IUxf6nmyLpNxcBa75ImaaWcD6AnLYoivPMJanVPZRSjxBpdtehVhWX0BJAkdGxI+n5AmH/80WIDAkNkUPGe1RmRc4XZsiZ6eCMAZjIWTCHOxFZn29KAXtcJH0nUClF7K5DSMhmAKAV6XbX4aouIaElQIb2rt11CGH6Kg1rKTLqkfa9Tc45kTFp3wkoH2AOt8X48eNRsWJFHDt2DEWLFgUAvPbaa6hduzamTp2K1atXY8uWLTh16hS++eYb9O/fHwDwyiuvoGnTppg3bx5mz54NADh58iS2bduGCRMmYNKkSQCAoUOHIjg4GNOnT8eGDRvy5iRz4LQd2PpRG8JEaNOP4sjIMN0Jc+fOHfz888/Yv38/Ro0ahYCAAJw4cQJz587FmTNncPToUbi5uWVz7wUt4BEREdF/7O34sKeDTS4BQCdp6g9+lSA95nAiIiLKHczh5uoyJz09HefOncPrr79u6LwGgCpVqqB+/fr4448/AGSuX+Ll5YWIiAhDmcDAQDz77LNYs2YNPvvsM7i4uGDFihVQqVQYMWKEoZyfnx969+6NxYsXIzk52eQghrzmtB3Y3t7eAICUlBSjfcnJyQBgctQHAJQpUwbJyck4evQoatWqBQB48cUXUatWLQwaNAjr16/Hyy+/nM29cw5IIiKigouf80TZYQ4nIiKi3MHPeWtpNBosWrTIaH0RIQRSUlJQv359AMChQ4cQFhYGtTrrYIDw8HBs3boVV69eRUBAAA4dOmSY+/rJcvPnz8fJkyezLOLtLJy2A1t/eeGNGzeM9sXHxwP4L1w/ydfXFwEBAYbQrPf8889j0KBBOHnyZA7BmYiIiCh/EDrLvwh8e/kGVl2+aXJf0iP7L1+ngoE5nIiIiChn+hyertUh3ao1P56oJ5sh2B4eHhg0aJDh3zExMbh58yaWLVuGS5cuYerUqdBqtbh161a265ckJiYiICAACQkJqFu3brblnJHTdmC7u7sjKCjI5CqYUVFRaNCgAVxdTc/7W69ePcMk5o9LS8ucU9fLy0tuY4mIiIjyiLDi0sWXq/jh5Sp+JveF7jqMhDR2YhNzOBEREZEl9Dl84cU4zLtw1SH3GRQUhNTUVABAZGQkunfvjlu3bkGn0+W4fgmQudZJixYtciznbJy2AxsAIiIi8Pbbb+OXX37Bs88+CwDYuXMnzp07h7lz55o9rn///mjbti127NiBDh06GLbPmzcPANCqVavcbTgRERERUT7GHE5ERERkmderV0L/ANODRCzRcc9xJFp4NeT69etx8+ZNbNiwAbNmzYKbmxsiIyMBWLZ+iaIoNq1zktecugO7X79+mD17Nnr27Im+fftCq9VixYoVCAoKMkxKfv36dWzYsAG1a9c2hOvw8HA0b94cXbt2Rd++fVGtWjUcOHAAW7duRd++fRmciYiIqMCwZgQ2kaWYw4mIiIiyp8/hrio1XFW2L0StWBHnO3bsCCBz0EDnzp3x2WefYdSoUVCpVBatX+Lt7W3TOid5zd7lP3OVl5cX9u/fjy5duuDHH3/ETz/9hP/973/YvXs3SpUqBQC4ePEihg8fjm+//dZwnEqlwtatW/Hmm2/i6NGjmDJlCm7evIkvvvgCX3/9dV6dDhERERFRvsAcTkRERJT3Ll68iIULF+L69etZtiuKgs6dO0Or1SI2NhblypXLdv0Sfce0j4+PReWcjVOPwAaAqlWrYuXKlWb3t27d2uTQ99KlS2P27Nm52TQiIiKiPMcR2JRbmMOJiIiIzHNEDr927RqGDh2KUqVKoU+fPln23b17FwDg5+eHkJAQ7Nu3DxqNBi4u/3X3RkVFwdvbG/7+/gCAkJAQzJ07F0lJSShbtmyWcq6urggKCsr1c7KFU4/AJiIiIqLsKNAJlZQbwI5wIiIiIiLLOCaH16lTB2q1GitWrDDMUw0AqampWLZsGapWrQo/Pz9EREQgOTkZy5cvN5SJjo7G7t278corrxgW4I6IiIAQAnPmzDGUi4uLw7p169C1a9csndrOxOlHYBMREREREREREREVNuXLl8ewYcPwxRdfIDQ0FB06dEBaWhpWr16NK1euYOfOnVAUBV26dEFwcDDeeOMNHDlyBCVLlsTKlSvh4+ODUaNGGepr0KABOnfujClTpiA2Nhb+/v5Ys2YNNBoNxo8fn3cnmgN2YBMRERHlVwIQOkkjp41ngiAiIiIiIlMcmMNnzZqFKlWqYNmyZZg6dSq8vLzQsGFDbNiwAYGBgQAANzc3/Pzzz5gwYQJ2796Ne/fuoVWrVpg+fToqVapkqEtRFKxduxaTJk3C9u3bsW3bNjRv3hyTJk1CcHCwnPPJBZxChIiIiIgssn37drRs2RKenp7w9fVFx44dcfTo0SxlkpKSMGzYMNSqVQu+vr7o2bMnoqOjjepKTU3Fu+++i6CgIJQpUwbPPfccoqKiHHUqRERERET5gouLCyIjI3H69Gmkpqbi2rVr2LRpk6HzWq9cuXL48ssvceHCBcTHx2P9+vWoWrWqUX3FihXD9OnTcebMGdy+fRubN29Gw4YNHXU6NmEHNhEREVE+JZC5eIyUWw73tXHjRnTq1AnJyckYOXIkBgwYgFOnTqFFixY4ffo0ACA9PR1t27bFN998g1atWiEiIgK//fYbQkNDERMT81+7hUCvXr0wffp01KtXD0OHDsW5c+fQvn17/P7777n3gBERERERSeDIHE6cQqTAUyQsyCQkvZQUobW/EpWcp6wi4amv06VJaAmgkvIzkpzfolSKq911CJEuoSWAorjZXYdOaCS0BBDQ5Vwox0oktUXY3xZFkfM6EhLOScZ7FCDvfYooP3LE6ucAMH78eFSsWBHHjh1D0aJFAQCvvfYaateujalTp2L16tXYsmULTp06hW+++Qb9+/cHALzyyito2rQp5s2bh9mzZwMATp48iW3btmHChAmYNGkSAGDo0KEIDg7G9OnTsWHDBoecE1GhISOHK5I+s4X9GVqleEhoiRwaXaqkmuzP81oZf2cAMtqiUtwltAPQiQf2VyIph8v4fiLruxIUtf11SHu+2I/fCSi/clQOJ47AJiIiIqIcpKen49y5c+jatauh8xoAqlSpgvr16+OPP/4AAKxYsQJeXl6IiIgwlAkMDMSzzz5rWBxGX06lUmHEiBGGcn5+fujduze2bduG5ORkB50ZERERERE5O3ZgExEREeVjOqFIuWVHo9Fg0aJFGDhwYJbtQgikpKSgevXqAIBDhw4hLCwManXWkWHh4eGIj4/H1atXDeX0c18/WS49PR0nT56092EhIiIiIspVjsjhlIlTiBAREREVAOk6HdJ1tk8zlN1Ftx4eHhg0aJDh3zExMbh58yaWLVuGS5cuYerUqdBqtbh16xbKly9vdLx+W2JiIgICApCQkIC6detmW46IiIiIiAhgBzYRERFRvqafe+/rvy9j0eW/HXKfQUFBSE3NnPc1MjIS3bt3x61bt6DT6eDp6WlUXr9N3zGdkJCAFi1a5FiOiIiIiMhZcQ5sx2EHNhEREVE+pg/O/atUw8v+VW2up/vvv+FW+iOLyq5fvx43b97Ehg0bMGvWLLi5uSEyMvLf9hiP5db9OzI8IyMDAKAoikXliIiIiIicFTuwHYcd2EREREQFgJtKBTeV7cubKFbk744dOwIA+vfvj86dO+Ozzz7DqFGjoFKpkJKSYlRevyijr68vAMDb29uickREREREROzANkljZrsKXPeSiIioMND9e3N+jlj45eLFi9i9eze6du0KPz8/w3ZFUdC5c2ds374dsbGxKFeuHG7cuGF0fHx8PID/OqZ9fHwsKkeFEXM4ERFR4VYwc/i6uCtYH3fV5L7b6emymlRgsQPbJD4sREREhVt2nWXmOtgKrmvXrmHo0KEoVaoU+vTpk2Xf3bt3AQB+fn4ICQnBvn37oNFo4OLyX56KioqCt7c3/P39AQAhISGYO3cukpKSULZs2SzlXF1dERQU5ICzIufEHE5ERFS4Fcwc3qtiZfSqWNnkvk4H9iDxkWVT+RVWHMZARERElI8JoUi5ZadOnTpQq9VYsWKFYZ5qAEhNTcWyZctQtWpV+Pn5ISIiAsnJyVi+fLmhTHR0NHbv3o1XXnkFrq6uAICIiAgIITBnzhxDubi4OKxbtw5du3bN0qlNREREROSMHJHDKROHOBARERHlVzJDbzb1lC9fHsOGDcMXX3yB0NBQdOjQAWlpaVi9ejWuXLmCnTt3QlEUdOnSBcHBwXjjjTdw5MgRlCxZEitXroSPjw9GjRplqK9Bgwbo3LkzpkyZgtjYWPj7+2PNmjXQaDQYP368nPMhIiIiIsotDsrhlIkjsImIiIgoR7NmzcLMmTNx7949TJ06FcuXL8dTTz2FEydO4JlnngEAuLm54eeff0b//v2xZ88erFy5Eq1atcLevXtRqVIlQ12KomDt2rUYPXo0Tp06hYULF6Ju3brYvXs3goOD8+gMiYiIiIjIGXEENhEREVE+JSBvEUeRw34XFxdERkYiMjIy23LlypXDl19+meP9FStWDNOnT8f06dOtaCURERERUd5zZA4njsAmIiIiIiIiIiIiIifFEdgFnChgv+MI4TwrziqSfv9RFFe76xBCK6ElgFb30O46FMVNQksAyPhbK7J+o9PlXCQHiqS3W0XCD7zCmVZulnFCACCc571Ogf3nVNDeuyl3ceEXov/wPTgfkPGZLeltTycy7K5DrSoqoSWAVvdISj0yqBT7c6tW90BCS2R9t5DznUCINBmV2F8HACj2fz/hex2R/ZjDHYcd2ERERET5GIMzEREREZHjMYc7DqcQISIiIiIiIiIiIiKnxBHYRERERPmYrMVjiIiIiIjIcszhjsMObCIiIqJ8jJcuEhERERE5HnO443AKESIiIiIiIiIiIiJyShyBTURERJSPWXPp4oabf2ND/GWT++6kP5LVJCIiIiKiAo9TiDgOO7CJiIiICon/lQ/A/8oHmNzX49gO3EpPc3CLiIiIiIiIsscObCIiIqJ8SgAQkDPyQ0iphYiIiIio4GMOdyx2YBMRERHlY1w8hoiIiIjI8azJ4RviY7Ap/m+T+5I5lV+O2IFNRERERERERERElEv+51sN//OtZnJfz+PbkMSp/LLFDmwiIiKifEuRuHgMR3ITEREREVmGOdyRVHndACIiIiIiIiIiIiIiUzgCm4iIiCi/EhLnwObqMURERERElmEOdyh2YJukMbNdBQ5at52Q8IpUoJPQEsCZ/o5CZNhdh06Ye85aR5HwuKgUNwktAbQi3e46VLL+zjLOSch57gqz709W1CGpLTI4U1tkkfFeR85A9+/N+cm7dJHIGdiXw/kebIbiRO8TEtoiJL0/u6iK2F2HrBwug073QEo9ioTsK+N7BQAInYSFzRS1/XUAkPIdUtZLUWjtrkKR1Bg53/Odpy3kDJjDyRg7sE3iw0JERFS4ZddZ5jydFUQFD3M4ERFR4cYcTsaYEImIiIjyMWmXLhIRERERkcWYwx3HeeZRICIiIiIiIiIiIiJ6DEdgExEREeVTAoBO2ryRRERERERkCeZwx2IHNhEREVE+xksXiYiIiIgcjznccTiFCBERERERERERERE5JY7AJiIiIsrHdBz5QURERETkcMzhjsMObCIiIqJCYkviBWy9ddHkvpSMNAe3hoiIiIiIKGfswCYiIiLKx6yZe69zuVroXK6WyX39zmzA7YyHsppFRERERFSgcQ5sx2EHNhEREVE+psvrBhARERERFULM4Y7DRRyJiIiIiIiIiIiIyClxBDYRERFRPsZLF4mIiIiIHM+aHL7t1nlsS7pgch/XoskZO7CJiIiIiIiIiIiIckmncrXQycxaNAPP/si1aHLADmwiIiKifEpAgU7SCGwBjuQmIiIiIrIEc7hjsQObcqQ41QtJ1rTt9k+1LyRN168ornbX4UyT2Wt196TUo0h4e9KJAngZjhB2V6Eokp4xEtoiYH8dRIUdAy8R5UQI+3OrtPwgg9BIqSZDe9fuOlSKm4SWyKFIaous7zlSKOq8boGBjMdX6AreCEsZ/QXO9J1AVv+HM50T5R7mcMdxohRCRERERERERERERPQfjsAmIiIiysdkXbpIRERERESWYw53HI7AJiIiIiIiIiIiIiKnxBHYRERERPmYjlMsEhERERE5HHO447ADm4iIiCgf4+IxRERERESOxxzuOJxChIiIiIiIiIiIiIicEkdgm6Qxs10F9vkTEREVBrp/b05OSFw8hpdAklNgDiciIircmMPJGDuwTeLDQkREVLhl11lmroONiOzHHE5ERFS4MYeTMSZEIiIionxKABBWjNj4+faf2HXnL5P7UjQP5TSKiIiIiKiAszaH51QXZY8d2ERERET5mM6KxWOeLVMXz5apa3LfiL/WIlnzQFaziIiIiIgKNGtyONmHE8kRERERERERERERkVPiCGwiIiKifEzIWjyGiIiIiIgsxhzuOByBTUREREREREREREROiSOwCzhFwnw8QtJ08opi/+8liqTfXITQ2l2HoioioSVy2qIT6RJaAigS3hLUquISWiLnnBRpCyro5FQkgYzXowK1hJYAgitAEzkFHUd+EEklIz87GynnJGulKkVODpFBpbjJqEVCHZKyr7Txac6TfeWdk/2EjO9csp7/Er5Dkmmy+j+ocLAmh/9y5xyi7vxpct9dLqaeI3ZgExEREeVj/JpFREREROR41uTwZ7zq4hkv04upR15YzcXUc+A8P2cSERERERERERERET2GI7CJiIiI8jFOIUJERERE5HjM4Y7DDmwiIiKifEpA3kylnIqEiIiIiMgyzOGOxSlEiIiIiIiIiIiIiMgpcQQ2ERERUb6lQEi7dJGXQBIRERERWYY53JGcegR2bGws+vbti8qVK6Ny5cqIiIhAbGys1fW88sorqF69uvwGEhEREREVQMzhREREROQsnHYE9p07d9CqVSv8888/iIiIAAAsX74ce/fuxalTp+Dl5WVRPbt378aqVatQrVq13GwuERERUZ7g4jEkG3M4ERERUc6Ywx3HaTuwly1bhri4OOzevRvh4eEAgC5duqB9+/ZYuXIlRo4cmWMdjx49wrBhw3K7qURERER5hou+kGzM4UREREQ5Yw53HKedQmTFihUIDg42hGYAaNeuHerVq4dVq1ZZVMenn36Ka9eucdQHEREREYC9yX9g0uV1Jm/3NA/yunnkJJjDiYiIiJzL33//jZ49e6Jy5cooWbIkmjdvjnXr1kGI/7rRNRoNpk6diqeffhqlS5dGu3btsH79eqO6hBBYuHAhwsLCUKpUKYSGhmLhwoVZ6nI2TtmBnZaWhujoaLRp08ZoX3h4OI4ePQqNRpNtHRcvXsQnn3yCiRMnomLFirnVVCIiIqI8pROKxbfQ0k9hfJXeJm8lXTzy+lTICTCHExEREVnGmhye3S0nN27cQHBwMHbu3IkXXngBY8aMgUajQe/evTF58mRDuZEjR+KDDz5AuXLlMHLkSCQkJKBXr174/vvvs9Q3Y8YMDB06FEIIREZGQgiBoUOHYubMmdIfI1mcsgM7ISEBAFC+fHmjffptSUlJZo8XQuCNN95AQEAA3nrrrdxpJBERERFRAcMcTkRERORcpkyZgn/++Qe7d+/G7NmzMX78ePz+++/o1q0bJk+ejMuXLyMhIQGLFi1CREQENm/ejEmTJuG3335DzZo1MWXKFENd6enpmDZtGtq0aYOoqChMnDgRUVFRCA0NxaeffoqMjIw8PFPznLoD29PT02iffltiYqLZ49euXYtdu3bhyy+/hJubmw0tEHbeiIiIyHkVrM95naQbEcAcTkRERLmpYH3OOyqHHzx4EI0aNUKTJk0M29RqNSIjI6HRaHDw4EGsXr0aWq0WkZGRUJTMUd0lSpTA4MGDcerUKZw7dw4AsG3bNty5cwcjR46Ei0vm0oiurq4YPnw4kpKSsGvXLnsfllzhlIs46h9oU3Ov6HSZf1pzvwjcvXsXb731Fl599VWEhYXZ2AKtjccBgAJAbcfxkikSVkSVNAeOEBK+HityvmIrqiL2VyLjfAAIZH8ZriVUii1fEE3WZHcNQth/PpkV2f/4CiHnl0NFcZVSjxSKhN8dhT3vcf9RYP/7i3DCEEKFgQ7OGIBtIQAISaufF4xHhOzFHC7vs0nG56SULA9AzrglSTlcRlsU5/kaW8SltJR6HmbE212HTqRLaAmgUtztrkOHNAktARQJ33OEkNMWGd9nFRlZ3skwz5vG70rmMIebqys7GRkZWTqv9fTTut26dQuHDh2Cl5cXAgMDs5TRr2ly6NAh1K1bF4cOHQIAo+niHi/XqVMnW04jVznPJ/9jvL29AQApKSlG+5KTkwEAvr6+Jo8dO3YsHj58iM8++8yOFjhRBzQRERFJZu+XRzk/AhE5I+ZwIiIiyj0FM4dn6LTQ2DFQLKfFE8+ePWtyu35x7fr162PTpk3w9fU1DEbQ008Bp7+CLiEhAR4eHihZsmSWcp6ennBzc8v2Sru85JQd2D4+PgAyJyl/Unx85i/T+nD9uPPnz2PBggUYN24cMjIyEBcXBwB49OgRNBoN4uLi4OrqaqjfPFkjHYiIiMj5FKzPeU7/QTIxhxMREVHuKVif8/oc/nPyCey8c9xx96vT4b333sM333yDoKAgtGnTBsOHD4eXl5dR2SengEtISDA5VZyiKPD09GQHtjXc3d0RFBSEqKgoo31RUVFo0KABXF2NL+m/efMmhBCYPHlyllU49SpVqoRGjRrh2LFjudJuIiIiIqL8jDmciIiIyDptPRuiTekgm4//5Moa3NM+sKjskSNH8MYbb+DYsWOoVKkSfvzxR/y/vTuPc6q6/z/+vpnFEdmpLKIgSikgMKwqbhVE/LrVDbQugK3aiv6QfhHrgmBR+WJdsLgVF4SC4I6oFBcURVEQURiKIFBhyqYsnWGbhUlyz+8PnIFhMjPJ5Exyk7yePubxwHtPTj7JTJL3Pbn3HJ/PJ8dxwpoCrrJ2pW29uoijJwewJWnw4MG6/fbb9dFHH6lfv36SpA8++ECrVq3SE088EfI2nTt31rvvvlth+913362dO3fq+eefV4MGDWq1bgAAgJgx9ubeS5LpCGEBORwAAKAah+TwNCddaVGszxDOnOl+v19jx47V+PHj5bquBg0apIkTJ5adTd20adOQZ08fPgVc06ZNQ04VZ4xRfn5+pVPFxZtnB7Cvv/56Pf744xo4cKCGDBmiYDCoadOmKTs7W4MHD5YkbdmyRW+99Zbat2+vfv36qUmTJrrooosq9PXoo4+qqKgo5D4AAIBE5jLwDMvI4QAAANWLVQ4PBAK69NJLNXfuXLVr106TJ0/WGWecUa5Ns2bNlJOTI9d15fMdnGu8dAq40oHpZs2aqbCwULt37y53ckFeXp4CgYBnB7A9uwRu48aN9dlnn+miiy7SrFmz9M477+iyyy7Txx9/XPYEr1u3TsOGDdNLL70U52oBAACS3/r16zVw4EC1bt1a9evX12mnnabXXnut3GWIgUBA48eP1ymnnKKGDRuqf//+ev311yv0ZYzRpEmT9Otf/1oNGjTQWWedpUmTJlW7iA1qHzkcAADAO8aNG6e5c+eqf//++uqrryoMXkvSqaeeqvz8fOXk5JTbXjotXK9evcraHbq9snZe4xiOEiRJxx57rLZs2fLz/3n2xPSIOU7031EY453loZwoLsmw3o+l58VxMi30Yeu7KBt/LwELdUjGlFjow87cTY5Tca7PeDGy8Px66G3fMGcBEtKB12HLli3LFqqLtdLc0iDtKI0+frCVPh/InabdwYJKH9fWrVvVvn17SdINN9ygxo0b691339XXX3+tsWPHasyYMZKkW2+9Vc8884wuvPBC9ejRQ7Nnz9aKFSv0+uuva8CAAWX9PfLII/rzn/+sM888U+ecc44++ugjLVy4UI888ohGjhxp5TEhMSRtDrexUJVja7ErG1nRUva18Tu2dEzgs9DPEekVF8yqiSL/T1H3YSuH+5ysqPtwTbGFSuwcKxlLtdg4LrZ23Eae9zwbnwE8t6GkXg4PBoNq1aqVAoGANm7cqCOOOCJkH9u2bVPLli119dVXa9q0aXIcR3v27FGvXr1Ut25dffPNgUUmS0pKdMwxx6hTp0766KOPlJ6eLr/fr3POOUdr1qwpW3jba5InIQIAAKDWjBs3Tnv37tWSJUvKzsy45557dMUVV+iBBx7QoEGDVKdOHT377LMaPHiwpk6dKsdxNHLkSPXs2VPjxo0rG8AuKSnRQw89pD59+ujDDz9Uenq67rnnHvXr109//etfNXz4cE8GZwAAACCWNm7cqK1bt6p37956/vnnQ7Y555xz1KFDB9188816+umntWfPHnXt2lWzZ89Wbm5uuXVKMjMzddddd+mOO+5Qv3791KdPH82bN09ffPGFpkyZ4tkMzgA2AABAAnNtLeJYjS+//FI9evQod1lhWlqaRowYobfffltffvmlduzYoWAwqBEjRsj5+czRevXq6aabbtIdd9yhVatWqWPHjpo7d67y8vI0fPhwpacfiKMZGRkaNmyYBg4cqHnz5umCCy6IyeMCAAAAaiIWOXzHjh2SpEWLFmnRokUh20yZMkUdOnTQ448/rhYtWmj27NlasGCBevXqpUcffVTnnntuufa33367jjrqKM2cOVMTJkxQdna2Zs6cqauvvrrWH09NMYANAACQoIxsXdSvai9S9fv9IefECwQOXMq5Y8cOLV68WI0bN1aXLl3Ktenbt68kafHixerYsaMWL14sSerTp0+l7RjABgAAgFfFKoeffPL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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_2d_histograms_with_score(filename):\n",
+    "    # Load the data from the file\n",
+    "    with open(filename, \"r\") as f:\n",
+    "        lines = f.readlines()\n",
+    "\n",
+    "    # Extract the values from the lines\n",
+    "    values = [eval(line.strip()) for line in lines]\n",
+    "\n",
+    "    # Extract pt, delta R values and tagging score\n",
+    "    pt_HVV = [val[1] for val in values]\n",
+    "    delta_R_11 = [val[2] for val in values]\n",
+    "    delta_R_12 = [val[3] for val in values]\n",
+    "    delta_R_21 = [val[4] for val in values]\n",
+    "    delta_R_22 = [val[5] for val in values]\n",
+    "    tagging_score = [val[6] for val in values]\n",
+    "\n",
+    "    # Plotting\n",
+    "    plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "    # Tagging score with vbf gen quark 1\n",
+    "    plt.subplot(2, 2, 1)\n",
+    "    plt.hist2d(delta_R_11, tagging_score, bins=30, cmap=\"inferno\")\n",
+    "    plt.colorbar(label=\"Counts\")\n",
+    "    plt.title(\"Tagging score vs vbf gen quark 1\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Tagging Score\")\n",
+    "\n",
+    "    # Tagging score with vbf gen quark 2\n",
+    "    plt.subplot(2, 2, 2)\n",
+    "    plt.hist2d(delta_R_12, tagging_score, bins=30, cmap=\"inferno\")\n",
+    "    plt.colorbar(label=\"Counts\")\n",
+    "    plt.title(\"Tagging score vs vbf gen quark 2\")\n",
+    "    plt.xlabel(\"Delta R\")\n",
+    "    plt.ylabel(\"Tagging Score\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# To visualize the histograms, you can call:\n",
+    "# plot_2d_histograms_with_score('output_file.txt')\n",
+    "plot_2d_histograms_with_score(\"outputR2vsPtgenHH_posptreq.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2196dd8",
+   "metadata": {},
+   "source": [
+    "Using bbVVskimmer output to graph deltaR vs respective higgs tagging. We want to see what is causing weird shape in delta R distributions when I do no vbf-fatjet exclusion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "da885753",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Hbb jets: 8681\n",
+      "Number of HVV jets: 8681\n",
+      "Total number of events: 8681\n",
+      "Average for Hbb: 0.9658018059341085\n",
+      "Average for HVV: 0.08863406942842983\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_parquet(\"0-30.parquet\")\n",
+    "\n",
+    "# Create a mask for Hbb\n",
+    "Hbb_mask = df[(\"ak8FatJetParticleNetMD_Txbb\", 0)] > df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "\n",
+    "# Extract Hbb and HVV values based on the mask\n",
+    "Hbb_Txbb_values_0 = df[Hbb_mask][(\"ak8FatJetParticleNetMD_Txbb\", 0)]\n",
+    "Hbb_Txbb_values_1 = df[~Hbb_mask][(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "HVV_Txbb_values_0 = df[~Hbb_mask][(\"ak8FatJetParticleNetMD_Txbb\", 0)]\n",
+    "HVV_Txbb_values_1 = df[Hbb_mask][(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "\n",
+    "# Concatenate to get complete values for Hbb and HVV\n",
+    "Hbb_Txbb_values = pd.concat([Hbb_Txbb_values_0, Hbb_Txbb_values_1])\n",
+    "HVV_Txbb_values = pd.concat([HVV_Txbb_values_0, HVV_Txbb_values_1])\n",
+    "\n",
+    "\n",
+    "# Compute delta R for each combination\n",
+    "def delta_r(eta1, phi1, eta2, phi2):\n",
+    "    deta = eta1 - eta2\n",
+    "    dphi = np.pi - np.abs(np.abs(phi1 - phi2) - np.pi)\n",
+    "    return np.sqrt(deta**2 + dphi**2)\n",
+    "\n",
+    "\n",
+    "# Compute all possible delta R combinations\n",
+    "delta_R_jet0_gen1 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 0)], df[(\"ak8FatJetPhi\", 0)], df[(\"vbfetaGen\", 0)], df[(\"vbfphiGen\", 0)]\n",
+    ")\n",
+    "delta_R_jet0_gen2 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 0)], df[(\"ak8FatJetPhi\", 0)], df[(\"vbfetaGen\", 1)], df[(\"vbfphiGen\", 1)]\n",
+    ")\n",
+    "delta_R_jet1_gen1 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 1)], df[(\"ak8FatJetPhi\", 1)], df[(\"vbfetaGen\", 0)], df[(\"vbfphiGen\", 0)]\n",
+    ")\n",
+    "delta_R_jet1_gen2 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 1)], df[(\"ak8FatJetPhi\", 1)], df[(\"vbfetaGen\", 1)], df[(\"vbfphiGen\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Assign delta R values to Hbb and HVV based on the Hbb mask\n",
+    "df[\"delta_R_Hbb_gen1\"] = np.where(Hbb_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_Hbb_gen2\"] = np.where(Hbb_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "df[\"delta_R_HVV_gen1\"] = np.where(~Hbb_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_HVV_gen2\"] = np.where(~Hbb_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "\n",
+    "\n",
+    "# Plot histograms\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.hist(df[\"delta_R_Hbb_gen1\"], bins=50, color=\"red\", alpha=0.7)\n",
+    "plt.title(\"Hbb vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.hist(df[\"delta_R_Hbb_gen2\"], bins=50, color=\"blue\", alpha=0.7)\n",
+    "plt.title(\"Hbb vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.hist(df[\"delta_R_HVV_gen1\"], bins=50, color=\"green\", alpha=0.7)\n",
+    "plt.title(\"HVV vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.hist(df[\"delta_R_HVV_gen2\"], bins=50, color=\"purple\", alpha=0.7)\n",
+    "plt.title(\"HVV vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Debugging/Printing statements\n",
+    "print(\"Number of Hbb jets:\", len(Hbb_Txbb_values))\n",
+    "print(\"Number of HVV jets:\", len(HVV_Txbb_values))\n",
+    "print(\"Total number of events:\", len(df))\n",
+    "print(\"Average for Hbb:\", Hbb_Txbb_values.mean())\n",
+    "print(\"Average for HVV:\", HVV_Txbb_values.mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c0b215d",
+   "metadata": {},
+   "source": [
+    "Same thing but applying tagging selections: (Hbb_Txbb_values >= 0.95) & (HVV_Th4q_values >= 0.6) note that this second tagging score is old. new one with parT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "037ea93f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.99365234 0.92480469 0.97509766 ... 0.99511719 0.85839844 0.99511719]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_parquet(\"0-30.parquet\")\n",
+    "\n",
+    "\n",
+    "# Create a mask for Hbb\n",
+    "Hbb_mask = df[(\"ak8FatJetParticleNetMD_Txbb\", 0)] > df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "\n",
+    "\n",
+    "# Compute delta R for each combination\n",
+    "def delta_r(eta1, phi1, eta2, phi2):\n",
+    "    deta = eta1 - eta2\n",
+    "    dphi = np.pi - np.abs(np.abs(phi1 - phi2) - np.pi)\n",
+    "    return np.sqrt(deta**2 + dphi**2)\n",
+    "\n",
+    "\n",
+    "# Compute all possible delta R combinations\n",
+    "delta_R_jet0_gen1 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 0)], df[(\"ak8FatJetPhi\", 0)], df[(\"vbfetaGen\", 0)], df[(\"vbfphiGen\", 0)]\n",
+    ")\n",
+    "delta_R_jet0_gen2 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 0)], df[(\"ak8FatJetPhi\", 0)], df[(\"vbfetaGen\", 1)], df[(\"vbfphiGen\", 1)]\n",
+    ")\n",
+    "delta_R_jet1_gen1 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 1)], df[(\"ak8FatJetPhi\", 1)], df[(\"vbfetaGen\", 0)], df[(\"vbfphiGen\", 0)]\n",
+    ")\n",
+    "delta_R_jet1_gen2 = delta_r(\n",
+    "    df[(\"ak8FatJetEta\", 1)], df[(\"ak8FatJetPhi\", 1)], df[(\"vbfetaGen\", 1)], df[(\"vbfphiGen\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Assign delta R values to Hbb and HVV based on the Hbb mask\n",
+    "df[\"delta_R_Hbb_gen1\"] = np.where(Hbb_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_Hbb_gen2\"] = np.where(Hbb_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "df[\"delta_R_HVV_gen1\"] = np.where(~Hbb_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_HVV_gen2\"] = np.where(~Hbb_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "\n",
+    "\n",
+    "# Correctly extract the Txbb values for Hbb\n",
+    "Hbb_Txbb_values = np.where(\n",
+    "    Hbb_mask, df[(\"ak8FatJetParticleNetMD_Txbb\", 0)], df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Correctly extract the Th4q values for HVV\n",
+    "# HVV_Th4q_values = np.where(~Hbb_mask, df[('ak8FatJetParTMD_THWWvsT', 0)], df[('ak8FatJetParTMD_THWWvsT', 1)])\n",
+    "HVV_Th4q_values = np.where(\n",
+    "    ~Hbb_mask, df[(\"ak8FatJetParticleNet_Th4q\", 0)], df[(\"ak8FatJetParticleNet_Th4q\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Apply selection criteria\n",
+    "selection_mask = (Hbb_Txbb_values >= 0.95) & (HVV_Th4q_values >= 0.6)\n",
+    "# selection_mask = (Hbb_Txbb_values>= 0.8)\n",
+    "df = df[selection_mask]\n",
+    "Hbb_Txbb_values = Hbb_Txbb_values[selection_mask]\n",
+    "HVV_Th4q_values = HVV_Th4q_values[selection_mask]\n",
+    "\n",
+    "# Extract Delta R values for the events satisfying the mask conditions\n",
+    "delta_R_Hbb_gen1_values = df[\"delta_R_Hbb_gen1\"]\n",
+    "delta_R_Hbb_gen2_values = df[\"delta_R_Hbb_gen2\"]\n",
+    "delta_R_HVV_gen1_values = df[\"delta_R_HVV_gen1\"]\n",
+    "delta_R_HVV_gen2_values = df[\"delta_R_HVV_gen2\"]\n",
+    "\n",
+    "\n",
+    "# Plot histograms 1d histograms\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.hist(df[\"delta_R_Hbb_gen1\"], bins=50, color=\"red\", alpha=0.7)\n",
+    "plt.title(\"Hbb vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.hist(df[\"delta_R_Hbb_gen2\"], bins=50, color=\"blue\", alpha=0.7)\n",
+    "plt.title(\"Hbb vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.hist(df[\"delta_R_HVV_gen1\"], bins=50, color=\"green\", alpha=0.7)\n",
+    "plt.title(\"HVV vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.hist(df[\"delta_R_HVV_gen2\"], bins=50, color=\"purple\", alpha=0.7)\n",
+    "plt.title(\"HVV vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Plotting heatmaps\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Hbb with vbf gen quark 1\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.hist2d(delta_R_Hbb_gen1_values, Hbb_Txbb_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"Hbb vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for Hbb\")\n",
+    "\n",
+    "# Hbb with vbf gen quark 2\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.hist2d(delta_R_Hbb_gen2_values, Hbb_Txbb_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"Hbb vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for Hbb\")\n",
+    "\n",
+    "# HVV with vbf gen quark 1\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.hist2d(delta_R_HVV_gen1_values, HVV_Th4q_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"HVV vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for HVV\")\n",
+    "\n",
+    "# HVV with vbf gen quark 2\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.hist2d(delta_R_HVV_gen2_values, HVV_Th4q_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"HVV vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for HVV\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(HVV_Th4q_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7a6ef09",
+   "metadata": {},
+   "source": [
+    "Now we can plot the gen information for the fully filtered events and see if the shapes have changed at all. score cuts also applied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d25a790e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np  # turn this into R1 R2 graphs\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = pd.read_parquet(\"0-30.parquet\")\n",
+    "\n",
+    "# Create a mask for Hbb\n",
+    "Hbb_gen_mask = df[(\"GenHiggsChildren\", 0)] == 5\n",
+    "\n",
+    "# Create a mask for Hbb\n",
+    "Hbb_mask = df[(\"ak8FatJetParticleNetMD_Txbb\", 0)] > df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "\n",
+    "\n",
+    "# Compute delta R for each combination\n",
+    "def delta_r(eta1, phi1, eta2, phi2):\n",
+    "    deta = eta1 - eta2\n",
+    "    dphi = np.pi - np.abs(np.abs(phi1 - phi2) - np.pi)\n",
+    "    return np.sqrt(deta**2 + dphi**2)\n",
+    "\n",
+    "\n",
+    "# Compute all possible delta R combinations\n",
+    "delta_R_jet0_gen1 = delta_r(\n",
+    "    df[(\"GenHiggsEta\", 0)], df[(\"GenHiggsPhi\", 0)], df[(\"vbfetaGen\", 0)], df[(\"vbfphiGen\", 0)]\n",
+    ")\n",
+    "delta_R_jet0_gen2 = delta_r(\n",
+    "    df[(\"GenHiggsEta\", 0)], df[(\"GenHiggsPhi\", 0)], df[(\"vbfetaGen\", 1)], df[(\"vbfphiGen\", 1)]\n",
+    ")\n",
+    "delta_R_jet1_gen1 = delta_r(\n",
+    "    df[(\"GenHiggsEta\", 1)], df[(\"GenHiggsPhi\", 1)], df[(\"vbfetaGen\", 0)], df[(\"vbfphiGen\", 0)]\n",
+    ")\n",
+    "delta_R_jet1_gen2 = delta_r(\n",
+    "    df[(\"GenHiggsEta\", 1)], df[(\"GenHiggsPhi\", 1)], df[(\"vbfetaGen\", 1)], df[(\"vbfphiGen\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Assign delta R values to Hbb and HVV based on the Hbb mask\n",
+    "df[\"delta_R_Hbb_gen1\"] = np.where(Hbb_gen_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_Hbb_gen2\"] = np.where(Hbb_gen_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "df[\"delta_R_HVV_gen1\"] = np.where(~Hbb_gen_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_HVV_gen2\"] = np.where(~Hbb_gen_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "\n",
+    "# Assign delta R values to Hbb and HVV based on the Hbb mask\n",
+    "df[\"delta_R_Hbb_gen1\"] = np.where(Hbb_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_Hbb_gen2\"] = np.where(Hbb_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "df[\"delta_R_HVV_gen1\"] = np.where(~Hbb_mask, delta_R_jet0_gen1, delta_R_jet1_gen1)\n",
+    "df[\"delta_R_HVV_gen2\"] = np.where(~Hbb_mask, delta_R_jet0_gen2, delta_R_jet1_gen2)\n",
+    "\n",
+    "\n",
+    "# Correctly extract the Txbb values for Hbb\n",
+    "Hbb_Txbb_values = np.where(\n",
+    "    Hbb_mask, df[(\"ak8FatJetParticleNetMD_Txbb\", 0)], df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Correctly extract the Th4q values for HVV\n",
+    "# HVV_Th4q_values = np.where(~Hbb_mask, df[('ak8FatJetParTMD_THWWvsT', 0)], df[('ak8FatJetParTMD_THWWvsT', 1)])\n",
+    "HVV_Th4q_values = np.where(\n",
+    "    ~Hbb_mask, df[(\"ak8FatJetParticleNet_Th4q\", 0)], df[(\"ak8FatJetParticleNet_Th4q\", 1)]\n",
+    ")\n",
+    "\n",
+    "# Apply selection criteria\n",
+    "if False:\n",
+    "    selection_mask = (Hbb_Txbb_values >= 0.95) & (HVV_Th4q_values >= 0.6)\n",
+    "    df = df[selection_mask]\n",
+    "    Hbb_Txbb_values = Hbb_Txbb_values[selection_mask]\n",
+    "    HVV_Th4q_values = HVV_Th4q_values[selection_mask]\n",
+    "\n",
+    "selection_mask = Hbb_Txbb_values >= 0.8\n",
+    "df = df[selection_mask]\n",
+    "Hbb_Txbb_values = Hbb_Txbb_values[selection_mask]\n",
+    "HVV_Th4q_values = HVV_Th4q_values[selection_mask]\n",
+    "print(HVV_Th4q_values)\n",
+    "\n",
+    "# Extract Delta R values for the events satisfying the mask conditions\n",
+    "delta_R_Hbb_gen1_values = df[\"delta_R_Hbb_gen1\"]\n",
+    "delta_R_Hbb_gen2_values = df[\"delta_R_Hbb_gen2\"]\n",
+    "delta_R_HVV_gen1_values = df[\"delta_R_HVV_gen1\"]\n",
+    "delta_R_HVV_gen2_values = df[\"delta_R_HVV_gen2\"]\n",
+    "\n",
+    "\n",
+    "# Plot histograms 1d histograms\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.hist(df[\"delta_R_Hbb_gen1\"], bins=50, color=\"red\", alpha=0.7)\n",
+    "plt.title(\"Hbb vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.hist(df[\"delta_R_Hbb_gen2\"], bins=50, color=\"blue\", alpha=0.7)\n",
+    "plt.title(\"Hbb vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.hist(df[\"delta_R_HVV_gen1\"], bins=50, color=\"green\", alpha=0.7)\n",
+    "plt.title(\"HVV vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.hist(df[\"delta_R_HVV_gen2\"], bins=50, color=\"purple\", alpha=0.7)\n",
+    "plt.title(\"HVV vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"Counts\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Plotting heatmaps\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# Hbb with vbf gen quark 1\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.hist2d(delta_R_Hbb_gen1_values, Hbb_Txbb_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"gen Hbb vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for Hbb\")\n",
+    "\n",
+    "# Hbb with vbf gen quark 2\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.hist2d(delta_R_Hbb_gen2_values, Hbb_Txbb_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"gen Hbb vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for Hbb\")\n",
+    "\n",
+    "# HVV with vbf gen quark 1\n",
+    "plt.subplot(2, 2, 3)\n",
+    "plt.hist2d(delta_R_HVV_gen1_values, HVV_Th4q_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"gen HVV vs vbf gen quark 1\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for HVV\")\n",
+    "\n",
+    "# HVV with vbf gen quark 2\n",
+    "plt.subplot(2, 2, 4)\n",
+    "plt.hist2d(delta_R_HVV_gen2_values, HVV_Th4q_values, bins=50, cmap=\"inferno\")\n",
+    "plt.colorbar(label=\"Counts\")\n",
+    "plt.title(\"gen HVV vs vbf gen quark 2\")\n",
+    "plt.xlabel(\"Delta R\")\n",
+    "plt.ylabel(\"ParticleNet Score for HVV\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "c11d693c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsEta</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsPhi</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsMass</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsPt</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">GenHiggsChildren</th>\n",
+       "      <th>...</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">lp_sf_num_sjpt_gt350</th>\n",
+       "      <th>delta_R_Hbb_gen1</th>\n",
+       "      <th>delta_R_Hbb_gen2</th>\n",
+       "      <th>delta_R_HVV_gen1</th>\n",
+       "      <th>delta_R_HVV_gen2</th>\n",
+       "      <th>delta_R_genHbb_gen1</th>\n",
+       "      <th>delta_R_genHbb_gen2</th>\n",
+       "      <th>delta_R_genHVV_gen1</th>\n",
+       "      <th>delta_R_genHVV_gen2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>...</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.095215</td>\n",
+       "      <td>2.210938</td>\n",
+       "      <td>0.742188</td>\n",
+       "      <td>-2.179688</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>292.0</td>\n",
+       "      <td>346.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.226641</td>\n",
+       "      <td>4.610678</td>\n",
+       "      <td>3.328089</td>\n",
+       "      <td>2.836331</td>\n",
+       "      <td>3.633838</td>\n",
+       "      <td>0.053952</td>\n",
+       "      <td>0.073345</td>\n",
+       "      <td>3.679762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.511719</td>\n",
+       "      <td>1.617188</td>\n",
+       "      <td>2.367188</td>\n",
+       "      <td>-0.904297</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>379.0</td>\n",
+       "      <td>376.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.749346</td>\n",
+       "      <td>3.191710</td>\n",
+       "      <td>4.251545</td>\n",
+       "      <td>4.288952</td>\n",
+       "      <td>0.011721</td>\n",
+       "      <td>3.001844</td>\n",
+       "      <td>3.008566</td>\n",
+       "      <td>0.005694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-1.097656</td>\n",
+       "      <td>-1.027344</td>\n",
+       "      <td>1.402344</td>\n",
+       "      <td>-2.023438</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>329.0</td>\n",
+       "      <td>536.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.266729</td>\n",
+       "      <td>4.193001</td>\n",
+       "      <td>2.661271</td>\n",
+       "      <td>3.845229</td>\n",
+       "      <td>0.285356</td>\n",
+       "      <td>2.708938</td>\n",
+       "      <td>2.851065</td>\n",
+       "      <td>0.008670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.664062</td>\n",
+       "      <td>1.675781</td>\n",
+       "      <td>-2.031250</td>\n",
+       "      <td>1.203125</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>548.0</td>\n",
+       "      <td>558.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.359773</td>\n",
+       "      <td>2.183986</td>\n",
+       "      <td>2.656073</td>\n",
+       "      <td>2.373098</td>\n",
+       "      <td>3.031049</td>\n",
+       "      <td>0.019363</td>\n",
+       "      <td>0.020775</td>\n",
+       "      <td>3.068399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>-0.377930</td>\n",
+       "      <td>-1.132812</td>\n",
+       "      <td>-1.550781</td>\n",
+       "      <td>1.664062</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>481.0</td>\n",
+       "      <td>596.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.248156</td>\n",
+       "      <td>2.861382</td>\n",
+       "      <td>2.159209</td>\n",
+       "      <td>3.394970</td>\n",
+       "      <td>3.158955</td>\n",
+       "      <td>0.001777</td>\n",
+       "      <td>0.006890</td>\n",
+       "      <td>3.160418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8672</th>\n",
+       "      <td>-0.249023</td>\n",
+       "      <td>-0.664062</td>\n",
+       "      <td>1.796875</td>\n",
+       "      <td>-0.478516</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>604.0</td>\n",
+       "      <td>572.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.229837</td>\n",
+       "      <td>2.072327</td>\n",
+       "      <td>3.078448</td>\n",
+       "      <td>0.442528</td>\n",
+       "      <td>0.009298</td>\n",
+       "      <td>2.308937</td>\n",
+       "      <td>2.314405</td>\n",
+       "      <td>0.014353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8675</th>\n",
+       "      <td>-1.449219</td>\n",
+       "      <td>1.675781</td>\n",
+       "      <td>0.636719</td>\n",
+       "      <td>-2.929688</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>459.0</td>\n",
+       "      <td>456.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.917393</td>\n",
+       "      <td>5.296150</td>\n",
+       "      <td>5.534533</td>\n",
+       "      <td>3.772136</td>\n",
+       "      <td>0.018889</td>\n",
+       "      <td>4.124520</td>\n",
+       "      <td>4.169367</td>\n",
+       "      <td>0.028613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8676</th>\n",
+       "      <td>-0.409180</td>\n",
+       "      <td>-0.169434</td>\n",
+       "      <td>-2.554688</td>\n",
+       "      <td>0.560547</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>748.0</td>\n",
+       "      <td>720.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.980082</td>\n",
+       "      <td>5.363349</td>\n",
+       "      <td>3.685854</td>\n",
+       "      <td>4.643807</td>\n",
+       "      <td>0.005439</td>\n",
+       "      <td>3.128053</td>\n",
+       "      <td>3.127700</td>\n",
+       "      <td>0.003323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8678</th>\n",
+       "      <td>0.227051</td>\n",
+       "      <td>-0.146484</td>\n",
+       "      <td>-1.398438</td>\n",
+       "      <td>2.070312</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>550.0</td>\n",
+       "      <td>730.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.864394</td>\n",
+       "      <td>2.962032</td>\n",
+       "      <td>2.334788</td>\n",
+       "      <td>0.481585</td>\n",
+       "      <td>2.835470</td>\n",
+       "      <td>0.003937</td>\n",
+       "      <td>0.054342</td>\n",
+       "      <td>2.893363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8679</th>\n",
+       "      <td>0.259766</td>\n",
+       "      <td>1.683594</td>\n",
+       "      <td>2.054688</td>\n",
+       "      <td>-1.171875</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>395.0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.548935</td>\n",
+       "      <td>4.683492</td>\n",
+       "      <td>3.156441</td>\n",
+       "      <td>3.575680</td>\n",
+       "      <td>3.362814</td>\n",
+       "      <td>0.023449</td>\n",
+       "      <td>0.017545</td>\n",
+       "      <td>3.384651</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3852 rows × 396 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     GenHiggsEta           GenHiggsPhi           GenHiggsMass         \\\n",
+       "               0         1           0         1            0      1   \n",
+       "0       0.095215  2.210938    0.742188 -2.179688        125.0  125.0   \n",
+       "4       1.511719  1.617188    2.367188 -0.904297        125.0  125.0   \n",
+       "7      -1.097656 -1.027344    1.402344 -2.023438        125.0  125.0   \n",
+       "8       1.664062  1.675781   -2.031250  1.203125        125.0  125.0   \n",
+       "13     -0.377930 -1.132812   -1.550781  1.664062        125.0  125.0   \n",
+       "...          ...       ...         ...       ...          ...    ...   \n",
+       "8672   -0.249023 -0.664062    1.796875 -0.478516        125.0  125.0   \n",
+       "8675   -1.449219  1.675781    0.636719 -2.929688        125.0  125.0   \n",
+       "8676   -0.409180 -0.169434   -2.554688  0.560547        125.0  125.0   \n",
+       "8678    0.227051 -0.146484   -1.398438  2.070312        125.0  125.0   \n",
+       "8679    0.259766  1.683594    2.054688 -1.171875        125.0  125.0   \n",
+       "\n",
+       "     GenHiggsPt        GenHiggsChildren      ... lp_sf_num_sjpt_gt350       \\\n",
+       "              0      1                0   1  ...                    0    1   \n",
+       "0         292.0  346.0               24   5  ...                  0.0  0.0   \n",
+       "4         379.0  376.0                5  24  ...                  0.0  0.0   \n",
+       "7         329.0  536.0                5  24  ...                  0.0  0.0   \n",
+       "8         548.0  558.0               24   5  ...                  1.0  0.0   \n",
+       "13        481.0  596.0                5  24  ...                  0.0  1.0   \n",
+       "...         ...    ...              ...  ..  ...                  ...  ...   \n",
+       "8672      604.0  572.0               24   5  ...                  0.0  1.0   \n",
+       "8675      459.0  456.0               24   5  ...                  0.0  0.0   \n",
+       "8676      748.0  720.0                5  24  ...                  1.0  1.0   \n",
+       "8678      550.0  730.0               24   5  ...                  1.0  0.0   \n",
+       "8679      333.0  395.0               24   5  ...                  0.0  0.0   \n",
+       "\n",
+       "     delta_R_Hbb_gen1 delta_R_Hbb_gen2 delta_R_HVV_gen1 delta_R_HVV_gen2  \\\n",
+       "                                                                           \n",
+       "0            3.226641         4.610678         3.328089         2.836331   \n",
+       "4            4.749346         3.191710         4.251545         4.288952   \n",
+       "7            0.266729         4.193001         2.661271         3.845229   \n",
+       "8            3.359773         2.183986         2.656073         2.373098   \n",
+       "13           1.248156         2.861382         2.159209         3.394970   \n",
+       "...               ...              ...              ...              ...   \n",
+       "8672         3.229837         2.072327         3.078448         0.442528   \n",
+       "8675         2.917393         5.296150         5.534533         3.772136   \n",
+       "8676         4.980082         5.363349         3.685854         4.643807   \n",
+       "8678         2.864394         2.962032         2.334788         0.481585   \n",
+       "8679         3.548935         4.683492         3.156441         3.575680   \n",
+       "\n",
+       "     delta_R_genHbb_gen1 delta_R_genHbb_gen2 delta_R_genHVV_gen1  \\\n",
+       "                                                                   \n",
+       "0               3.633838            0.053952            0.073345   \n",
+       "4               0.011721            3.001844            3.008566   \n",
+       "7               0.285356            2.708938            2.851065   \n",
+       "8               3.031049            0.019363            0.020775   \n",
+       "13              3.158955            0.001777            0.006890   \n",
+       "...                  ...                 ...                 ...   \n",
+       "8672            0.009298            2.308937            2.314405   \n",
+       "8675            0.018889            4.124520            4.169367   \n",
+       "8676            0.005439            3.128053            3.127700   \n",
+       "8678            2.835470            0.003937            0.054342   \n",
+       "8679            3.362814            0.023449            0.017545   \n",
+       "\n",
+       "     delta_R_genHVV_gen2  \n",
+       "                          \n",
+       "0               3.679762  \n",
+       "4               0.005694  \n",
+       "7               0.008670  \n",
+       "8               3.068399  \n",
+       "13              3.160418  \n",
+       "...                  ...  \n",
+       "8672            0.014353  \n",
+       "8675            0.028613  \n",
+       "8676            0.003323  \n",
+       "8678            2.893363  \n",
+       "8679            3.384651  \n",
+       "\n",
+       "[3852 rows x 396 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18af128a",
+   "metadata": {},
+   "source": [
+    "After running bbvv skimmer on 14 things, I want to analyze the best R1 R2 combo on the final selection (ak8 and vbf selections applied)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "86124b88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('GenHiggsEta', 0), ('GenHiggsEta', 1), ('GenHiggsPhi', 0), ('GenHiggsPhi', 1), ('GenHiggsMass', 0), ('GenHiggsMass', 1), ('GenHiggsPt', 0), ('GenHiggsPt', 1), ('GenHiggsChildren', 0), ('GenHiggsChildren', 1), ('GenbbEta', 0), ('GenbbEta', 1), ('GenbbPhi', 0), ('GenbbPhi', 1), ('GenbbMass', 0), ('GenbbMass', 1), ('GenbbPt', 0), ('GenbbPt', 1), ('GenVVEta', 0), ('GenVVEta', 1), ('GenVVPhi', 0), ('GenVVPhi', 1), ('GenVVMass', 0), ('GenVVMass', 1), ('GenVVPt', 0), ('GenVVPt', 1), ('Gen4qEta', 0), ('Gen4qEta', 1), ('Gen4qEta', 2), ('Gen4qEta', 3), ('Gen4qPhi', 0), ('Gen4qPhi', 1), ('Gen4qPhi', 2), ('Gen4qPhi', 3), ('Gen4qMass', 0), ('Gen4qMass', 1), ('Gen4qMass', 2), ('Gen4qMass', 3), ('Gen4qPt', 0), ('Gen4qPt', 1), ('Gen4qPt', 2), ('Gen4qPt', 3), ('ak8FatJetHbb', 0), ('ak8FatJetHbb', 1), ('ak8FatJetHVV', 0), ('ak8FatJetHVV', 1), ('ak8FatJetHVVNumProngs', 0), ('vbfptGen', 0), ('vbfptGen', 1), ('vbfetaGen', 0), ('vbfetaGen', 1), ('vbfphiGen', 0), ('vbfphiGen', 1), ('vbfMGen', 0), ('vbfMGen', 1), ('vbfR10.3R20.3', 0), ('vbfR10.3R20.4', 0), ('vbfR10.3R20.5', 0), ('vbfR10.3R20.6', 0), ('vbfR10.3R20.7', 0), ('vbfR10.3R20.8', 0), ('vbfR10.3R20.9', 0), ('vbfR10.3R21.0', 0), ('vbfR10.3R21.1', 0), ('vbfR10.3R21.2', 0), ('vbfR10.3R21.3', 0), ('vbfR10.3R21.4', 0), ('vbfR10.3R21.5', 0), ('vbfR10.3R21.6', 0), ('vbfR10.3R21.7', 0), ('vbfR10.3R21.8', 0), ('vbfR10.3R21.9', 0), ('vbfR10.4R20.3', 0), ('vbfR10.4R20.4', 0), ('vbfR10.4R20.5', 0), ('vbfR10.4R20.6', 0), ('vbfR10.4R20.7', 0), ('vbfR10.4R20.8', 0), ('vbfR10.4R20.9', 0), ('vbfR10.4R21.0', 0), ('vbfR10.4R21.1', 0), ('vbfR10.4R21.2', 0), ('vbfR10.4R21.3', 0), ('vbfR10.4R21.4', 0), ('vbfR10.4R21.5', 0), ('vbfR10.4R21.6', 0), ('vbfR10.4R21.7', 0), ('vbfR10.4R21.8', 0), ('vbfR10.4R21.9', 0), ('vbfR10.5R20.3', 0), ('vbfR10.5R20.4', 0), ('vbfR10.5R20.5', 0), ('vbfR10.5R20.6', 0), ('vbfR10.5R20.7', 0), ('vbfR10.5R20.8', 0), ('vbfR10.5R20.9', 0), ('vbfR10.5R21.0', 0), ('vbfR10.5R21.1', 0), ('vbfR10.5R21.2', 0), ('vbfR10.5R21.3', 0), ('vbfR10.5R21.4', 0), ('vbfR10.5R21.5', 0), ('vbfR10.5R21.6', 0), ('vbfR10.5R21.7', 0), ('vbfR10.5R21.8', 0), ('vbfR10.5R21.9', 0), ('vbfR10.6R20.3', 0), ('vbfR10.6R20.4', 0), ('vbfR10.6R20.5', 0), ('vbfR10.6R20.6', 0), ('vbfR10.6R20.7', 0), ('vbfR10.6R20.8', 0), ('vbfR10.6R20.9', 0), ('vbfR10.6R21.0', 0), ('vbfR10.6R21.1', 0), ('vbfR10.6R21.2', 0), ('vbfR10.6R21.3', 0), ('vbfR10.6R21.4', 0), ('vbfR10.6R21.5', 0), ('vbfR10.6R21.6', 0), ('vbfR10.6R21.7', 0), ('vbfR10.6R21.8', 0), ('vbfR10.6R21.9', 0), ('vbfR10.7R20.3', 0), ('vbfR10.7R20.4', 0), ('vbfR10.7R20.5', 0), ('vbfR10.7R20.6', 0), ('vbfR10.7R20.7', 0), ('vbfR10.7R20.8', 0), ('vbfR10.7R20.9', 0), ('vbfR10.7R21.0', 0), ('vbfR10.7R21.1', 0), ('vbfR10.7R21.2', 0), ('vbfR10.7R21.3', 0), ('vbfR10.7R21.4', 0), ('vbfR10.7R21.5', 0), ('vbfR10.7R21.6', 0), ('vbfR10.7R21.7', 0), ('vbfR10.7R21.8', 0), ('vbfR10.7R21.9', 0), ('vbfR10.8R20.3', 0), ('vbfR10.8R20.4', 0), ('vbfR10.8R20.5', 0), ('vbfR10.8R20.6', 0), ('vbfR10.8R20.7', 0), ('vbfR10.8R20.8', 0), ('vbfR10.8R20.9', 0), ('vbfR10.8R21.0', 0), ('vbfR10.8R21.1', 0), ('vbfR10.8R21.2', 0), ('vbfR10.8R21.3', 0), ('vbfR10.8R21.4', 0), ('vbfR10.8R21.5', 0), ('vbfR10.8R21.6', 0), ('vbfR10.8R21.7', 0), ('vbfR10.8R21.8', 0), ('vbfR10.8R21.9', 0), ('vbfR10.9R20.3', 0), ('vbfR10.9R20.4', 0), ('vbfR10.9R20.5', 0), ('vbfR10.9R20.6', 0), ('vbfR10.9R20.7', 0), ('vbfR10.9R20.8', 0), ('vbfR10.9R20.9', 0), ('vbfR10.9R21.0', 0), ('vbfR10.9R21.1', 0), ('vbfR10.9R21.2', 0), ('vbfR10.9R21.3', 0), ('vbfR10.9R21.4', 0), ('vbfR10.9R21.5', 0), ('vbfR10.9R21.6', 0), ('vbfR10.9R21.7', 0), ('vbfR10.9R21.8', 0), ('vbfR10.9R21.9', 0), ('vbfR11.0R20.3', 0), ('vbfR11.0R20.4', 0), ('vbfR11.0R20.5', 0), ('vbfR11.0R20.6', 0), ('vbfR11.0R20.7', 0), ('vbfR11.0R20.8', 0), ('vbfR11.0R20.9', 0), ('vbfR11.0R21.0', 0), ('vbfR11.0R21.1', 0), ('vbfR11.0R21.2', 0), ('vbfR11.0R21.3', 0), ('vbfR11.0R21.4', 0), ('vbfR11.0R21.5', 0), ('vbfR11.0R21.6', 0), ('vbfR11.0R21.7', 0), ('vbfR11.0R21.8', 0), ('vbfR11.0R21.9', 0), ('vbfR11.1R20.3', 0), ('vbfR11.1R20.4', 0), ('vbfR11.1R20.5', 0), ('vbfR11.1R20.6', 0), ('vbfR11.1R20.7', 0), ('vbfR11.1R20.8', 0), ('vbfR11.1R20.9', 0), ('vbfR11.1R21.0', 0), ('vbfR11.1R21.1', 0), ('vbfR11.1R21.2', 0), ('vbfR11.1R21.3', 0), ('vbfR11.1R21.4', 0), ('vbfR11.1R21.5', 0), ('vbfR11.1R21.6', 0), ('vbfR11.1R21.7', 0), ('vbfR11.1R21.8', 0), ('vbfR11.1R21.9', 0), ('vbfR11.2R20.3', 0), ('vbfR11.2R20.4', 0), ('vbfR11.2R20.5', 0), ('vbfR11.2R20.6', 0), ('vbfR11.2R20.7', 0), ('vbfR11.2R20.8', 0), ('vbfR11.2R20.9', 0), ('vbfR11.2R21.0', 0), ('vbfR11.2R21.1', 0), ('vbfR11.2R21.2', 0), ('vbfR11.2R21.3', 0), ('vbfR11.2R21.4', 0), ('vbfR11.2R21.5', 0), ('vbfR11.2R21.6', 0), ('vbfR11.2R21.7', 0), ('vbfR11.2R21.8', 0), ('vbfR11.2R21.9', 0), ('vbfR11.3R20.3', 0), ('vbfR11.3R20.4', 0), ('vbfR11.3R20.5', 0), ('vbfR11.3R20.6', 0), ('vbfR11.3R20.7', 0), ('vbfR11.3R20.8', 0), ('vbfR11.3R20.9', 0), ('vbfR11.3R21.0', 0), ('vbfR11.3R21.1', 0), ('vbfR11.3R21.2', 0), ('vbfR11.3R21.3', 0), ('vbfR11.3R21.4', 0), ('vbfR11.3R21.5', 0), ('vbfR11.3R21.6', 0), ('vbfR11.3R21.7', 0), ('vbfR11.3R21.8', 0), ('vbfR11.3R21.9', 0), ('vbfR11.4R20.3', 0), ('vbfR11.4R20.4', 0), ('vbfR11.4R20.5', 0), ('vbfR11.4R20.6', 0), ('vbfR11.4R20.7', 0), ('vbfR11.4R20.8', 0), ('vbfR11.4R20.9', 0), ('vbfR11.4R21.0', 0), ('vbfR11.4R21.1', 0), ('vbfR11.4R21.2', 0), ('vbfR11.4R21.3', 0), ('vbfR11.4R21.4', 0), ('vbfR11.4R21.5', 0), ('vbfR11.4R21.6', 0), ('vbfR11.4R21.7', 0), ('vbfR11.4R21.8', 0), ('vbfR11.4R21.9', 0), ('vbfR11.5R20.3', 0), ('vbfR11.5R20.4', 0), ('vbfR11.5R20.5', 0), ('vbfR11.5R20.6', 0), ('vbfR11.5R20.7', 0), ('vbfR11.5R20.8', 0), ('vbfR11.5R20.9', 0), ('vbfR11.5R21.0', 0), ('vbfR11.5R21.1', 0), ('vbfR11.5R21.2', 0), ('vbfR11.5R21.3', 0), ('vbfR11.5R21.4', 0), ('vbfR11.5R21.5', 0), ('vbfR11.5R21.6', 0), ('vbfR11.5R21.7', 0), ('vbfR11.5R21.8', 0), ('vbfR11.5R21.9', 0), ('vbfR11.6R20.3', 0), ('vbfR11.6R20.4', 0), ('vbfR11.6R20.5', 0), ('vbfR11.6R20.6', 0), ('vbfR11.6R20.7', 0), ('vbfR11.6R20.8', 0), ('vbfR11.6R20.9', 0), ('vbfR11.6R21.0', 0), ('vbfR11.6R21.1', 0), ('vbfR11.6R21.2', 0), ('vbfR11.6R21.3', 0), ('vbfR11.6R21.4', 0), ('vbfR11.6R21.5', 0), ('vbfR11.6R21.6', 0), ('vbfR11.6R21.7', 0), ('vbfR11.6R21.8', 0), ('vbfR11.6R21.9', 0), ('vbfR11.7R20.3', 0), ('vbfR11.7R20.4', 0), ('vbfR11.7R20.5', 0), ('vbfR11.7R20.6', 0), ('vbfR11.7R20.7', 0), ('vbfR11.7R20.8', 0), ('vbfR11.7R20.9', 0), ('vbfR11.7R21.0', 0), ('vbfR11.7R21.1', 0), ('vbfR11.7R21.2', 0), ('vbfR11.7R21.3', 0), ('vbfR11.7R21.4', 0), ('vbfR11.7R21.5', 0), ('vbfR11.7R21.6', 0), ('vbfR11.7R21.7', 0), ('vbfR11.7R21.8', 0), ('vbfR11.7R21.9', 0), ('vbfR11.8R20.3', 0), ('vbfR11.8R20.4', 0), ('vbfR11.8R20.5', 0), ('vbfR11.8R20.6', 0), ('vbfR11.8R20.7', 0), ('vbfR11.8R20.8', 0), ('vbfR11.8R20.9', 0), ('vbfR11.8R21.0', 0), ('vbfR11.8R21.1', 0), ('vbfR11.8R21.2', 0), ('vbfR11.8R21.3', 0), ('vbfR11.8R21.4', 0), ('vbfR11.8R21.5', 0), ('vbfR11.8R21.6', 0), 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('ak8FatJetParticleNetMass', 0), ('ak8FatJetParticleNetMass', 1), ('ak8FatJetPt_JES_up', 0), ('ak8FatJetPt_JES_up', 1), ('ak8FatJetPt_JES_down', 0), ('ak8FatJetPt_JES_down', 1), ('ak8FatJetPt_JER_up', 0), ('ak8FatJetPt_JER_up', 1), ('ak8FatJetPt_JER_down', 0), ('ak8FatJetPt_JER_down', 1), ('ak8FatJetMsd_JMS_down', 0), ('ak8FatJetMsd_JMS_down', 1), ('ak8FatJetMsd_JMS_up', 0), ('ak8FatJetMsd_JMS_up', 1), ('ak8FatJetMsd_JMR_down', 0), ('ak8FatJetMsd_JMR_down', 1), ('ak8FatJetMsd_JMR_up', 0), ('ak8FatJetMsd_JMR_up', 1), ('ak8FatJetParticleNetMass_JMS_down', 0), ('ak8FatJetParticleNetMass_JMS_down', 1), ('ak8FatJetParticleNetMass_JMS_up', 0), ('ak8FatJetParticleNetMass_JMS_up', 1), ('ak8FatJetParticleNetMass_JMR_down', 0), ('ak8FatJetParticleNetMass_JMR_down', 1), ('ak8FatJetParticleNetMass_JMR_up', 0), ('ak8FatJetParticleNetMass_JMR_up', 1), ('ak8FatJetParticleNetMD_Txbb', 0), ('ak8FatJetParticleNetMD_Txbb', 1), ('DijetPt', 0), ('DijetMass', 0), ('DijetEta', 0), ('bbFatJetPtOverDijetPt', 0), ('VVFatJetPtOverDijetPt', 0), ('VVFatJetPtOverbbFatJetPt', 0), ('DijetPt_JES_up', 0), ('DijetMass_JES_up', 0), ('DijetEta_JES_up', 0), ('bbFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverbbFatJetPt_JES_up', 0), ('DijetPt_JES_down', 0), ('DijetMass_JES_down', 0), ('DijetEta_JES_down', 0), ('bbFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverbbFatJetPt_JES_down', 0), ('DijetPt_JER_up', 0), ('DijetMass_JER_up', 0), ('DijetEta_JER_up', 0), ('bbFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverbbFatJetPt_JER_up', 0), ('DijetPt_JER_down', 0), ('DijetMass_JER_down', 0), ('DijetEta_JER_down', 0), ('bbFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverbbFatJetPt_JER_down', 0), ('DijetPt_JMS_down', 0), ('DijetMass_JMS_down', 0), ('DijetEta_JMS_down', 0), ('bbFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverbbFatJetPt_JMS_down', 0), ('DijetPt_JMS_up', 0), ('DijetMass_JMS_up', 0), ('DijetEta_JMS_up', 0), ('bbFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverbbFatJetPt_JMS_up', 0), ('DijetPt_JMR_down', 0), ('DijetMass_JMR_down', 0), ('DijetEta_JMR_down', 0), ('bbFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverbbFatJetPt_JMR_down', 0), ('DijetPt_JMR_up', 0), ('DijetMass_JMR_up', 0), ('DijetEta_JMR_up', 0), ('bbFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverbbFatJetPt_JMR_up', 0), ('MET_pt', 0), ('nGoodMuons', 0), ('nGoodElectrons', 0), ('nGoodJets', 0), ('weight', 0), ('weight_noxsec', 0), ('lp_sf_lnN', 0), ('lp_sf_lnN', 1), ('lp_sf_lnN', 2), ('lp_sf_lnN', 3), ('lp_sf_lnN', 4), ('lp_sf_lnN', 5), ('lp_sf_lnN', 6), ('lp_sf_lnN', 7), ('lp_sf_lnN', 8), ('lp_sf_lnN', 9), ('lp_sf_lnN', 10), ('lp_sf_lnN', 11), ('lp_sf_lnN', 12), ('lp_sf_lnN', 13), ('lp_sf_lnN', 14), ('lp_sf_lnN', 15), ('lp_sf_lnN', 16), ('lp_sf_lnN', 17), ('lp_sf_lnN', 18), ('lp_sf_lnN', 19), ('lp_sf_lnN', 20), ('lp_sf_lnN', 21), ('lp_sf_lnN', 22), ('lp_sf_lnN', 23), ('lp_sf_lnN', 24), ('lp_sf_lnN', 25), ('lp_sf_lnN', 26), ('lp_sf_lnN', 27), ('lp_sf_lnN', 28), ('lp_sf_lnN', 29), ('lp_sf_lnN', 30), ('lp_sf_lnN', 31), ('lp_sf_lnN', 32), ('lp_sf_lnN', 33), ('lp_sf_lnN', 34), ('lp_sf_lnN', 35), ('lp_sf_lnN', 36), ('lp_sf_lnN', 37), ('lp_sf_lnN', 38), ('lp_sf_lnN', 39), ('lp_sf_lnN', 40), ('lp_sf_lnN', 41), ('lp_sf_lnN', 42), ('lp_sf_lnN', 43), ('lp_sf_lnN', 44), ('lp_sf_lnN', 45), ('lp_sf_lnN', 46), ('lp_sf_lnN', 47), ('lp_sf_lnN', 48), ('lp_sf_lnN', 49), ('lp_sf_lnN', 50), ('lp_sf_lnN', 51), ('lp_sf_lnN', 52), ('lp_sf_lnN', 53), ('lp_sf_lnN', 54), ('lp_sf_lnN', 55), ('lp_sf_lnN', 56), ('lp_sf_lnN', 57), ('lp_sf_lnN', 58), ('lp_sf_lnN', 59), ('lp_sf_lnN', 60), ('lp_sf_lnN', 61), ('lp_sf_lnN', 62), ('lp_sf_lnN', 63), ('lp_sf_lnN', 64), ('lp_sf_lnN', 65), ('lp_sf_lnN', 66), ('lp_sf_lnN', 67), ('lp_sf_lnN', 68), ('lp_sf_lnN', 69), ('lp_sf_lnN', 70), ('lp_sf_lnN', 71), ('lp_sf_lnN', 72), ('lp_sf_lnN', 73), ('lp_sf_lnN', 74), ('lp_sf_lnN', 75), ('lp_sf_lnN', 76), ('lp_sf_lnN', 77), ('lp_sf_lnN', 78), ('lp_sf_lnN', 79), ('lp_sf_lnN', 80), ('lp_sf_lnN', 81), ('lp_sf_lnN', 82), ('lp_sf_lnN', 83), ('lp_sf_lnN', 84), ('lp_sf_lnN', 85), ('lp_sf_lnN', 86), ('lp_sf_lnN', 87), ('lp_sf_lnN', 88), ('lp_sf_lnN', 89), ('lp_sf_lnN', 90), ('lp_sf_lnN', 91), ('lp_sf_lnN', 92), ('lp_sf_lnN', 93), ('lp_sf_lnN', 94), ('lp_sf_lnN', 95), ('lp_sf_lnN', 96), ('lp_sf_lnN', 97), ('lp_sf_lnN', 98), ('lp_sf_lnN', 99), ('lp_sf_lnN', 100), ('lp_sf_lnN', 101), ('lp_sf_lnN', 102), ('lp_sf_lnN', 103), ('lp_sf_lnN', 104), ('lp_sf_lnN', 105), ('lp_sf_lnN', 106), ('lp_sf_lnN', 107), ('lp_sf_lnN', 108), ('lp_sf_lnN', 109), ('lp_sf_lnN', 110), ('lp_sf_lnN', 111), ('lp_sf_lnN', 112), ('lp_sf_lnN', 113), ('lp_sf_lnN', 114), ('lp_sf_lnN', 115), ('lp_sf_lnN', 116), ('lp_sf_lnN', 117), ('lp_sf_lnN', 118), ('lp_sf_lnN', 119), ('lp_sf_lnN', 120), ('lp_sf_lnN', 121), ('lp_sf_lnN', 122), ('lp_sf_lnN', 123), ('lp_sf_lnN', 124), ('lp_sf_lnN', 125), ('lp_sf_lnN', 126), ('lp_sf_lnN', 127), ('lp_sf_lnN', 128), ('lp_sf_lnN', 129), ('lp_sf_lnN', 130), ('lp_sf_lnN', 131), ('lp_sf_lnN', 132), ('lp_sf_lnN', 133), ('lp_sf_lnN', 134), ('lp_sf_lnN', 135), ('lp_sf_lnN', 136), ('lp_sf_lnN', 137), ('lp_sf_lnN', 138), ('lp_sf_lnN', 139), ('lp_sf_lnN', 140), ('lp_sf_lnN', 141), ('lp_sf_lnN', 142), ('lp_sf_lnN', 143), ('lp_sf_lnN', 144), ('lp_sf_lnN', 145), ('lp_sf_lnN', 146), ('lp_sf_lnN', 147), ('lp_sf_lnN', 148), ('lp_sf_lnN', 149), ('lp_sf_lnN', 150), ('lp_sf_lnN', 151), ('lp_sf_lnN', 152), ('lp_sf_lnN', 153), ('lp_sf_lnN', 154), ('lp_sf_lnN', 155), ('lp_sf_lnN', 156), ('lp_sf_lnN', 157), ('lp_sf_lnN', 158), ('lp_sf_lnN', 159), ('lp_sf_lnN', 160), ('lp_sf_lnN', 161), ('lp_sf_lnN', 162), ('lp_sf_lnN', 163), ('lp_sf_lnN', 164), ('lp_sf_lnN', 165), ('lp_sf_lnN', 166), ('lp_sf_lnN', 167), ('lp_sf_lnN', 168), ('lp_sf_lnN', 169), ('lp_sf_lnN', 170), ('lp_sf_lnN', 171), ('lp_sf_lnN', 172), ('lp_sf_lnN', 173), ('lp_sf_lnN', 174), ('lp_sf_lnN', 175), ('lp_sf_lnN', 176), ('lp_sf_lnN', 177), ('lp_sf_lnN', 178), ('lp_sf_lnN', 179), ('lp_sf_lnN', 180), ('lp_sf_lnN', 181), ('lp_sf_lnN', 182), ('lp_sf_lnN', 183), ('lp_sf_lnN', 184), ('lp_sf_lnN', 185), ('lp_sf_lnN', 186), ('lp_sf_lnN', 187), ('lp_sf_lnN', 188), ('lp_sf_lnN', 189), ('lp_sf_lnN', 190), ('lp_sf_lnN', 191), ('lp_sf_lnN', 192), ('lp_sf_lnN', 193), ('lp_sf_lnN', 194), ('lp_sf_lnN', 195), ('lp_sf_lnN', 196), ('lp_sf_lnN', 197), ('lp_sf_lnN', 198), ('lp_sf_lnN', 199), ('lp_sf_lnN', 200), ('lp_sf_lnN', 201), ('lp_sf_sys_down', 0), ('lp_sf_sys_down', 1), ('lp_sf_sys_up', 0), ('lp_sf_sys_up', 1), ('lp_sf_double_matched_event', 0), ('lp_sf_double_matched_event', 1), ('lp_sf_unmatched_quarks', 0), ('lp_sf_unmatched_quarks', 1), ('lp_sf_num_sjpt_gt350', 0), ('lp_sf_num_sjpt_gt350', 1)]\n",
+      "4047\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    1337.435423\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_parquet(\"0-14R1R2study.parquet\")\n",
+    "# df = pd.read_parquet('0--1.parquet')\n",
+    "# df = pd.read_pickle('outfiles/0-2.pkl')\n",
+    "# print(df)\n",
+    "print(df.columns.values.tolist())\n",
+    "df[(\"vbfR10.3R20.6\", 0)]\n",
+    "\n",
+    "Hbb_mask = df[(\"ak8FatJetParticleNetMD_Txbb\", 0)] > df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "Hbb_Txbb_values = np.where(\n",
+    "    Hbb_mask, df[(\"ak8FatJetParticleNetMD_Txbb\", 0)], df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    ")\n",
+    "HVV_Th4q_values = np.where(\n",
+    "    ~Hbb_mask, df[(\"ak8FatJetParticleNet_Th4q\", 0)], df[(\"ak8FatJetParticleNet_Th4q\", 1)]\n",
+    ")\n",
+    "m = (\n",
+    "    (Hbb_Txbb_values >= 0.95)\n",
+    "    & (HVV_Th4q_values >= 0.6)\n",
+    "    & (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "    & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "    & (df[(\"nGoodJets\", 0)] == 0)\n",
+    ")\n",
+    "# m = m & (np.abs(df[('vbfeta', 1)]) > 1.5) & (np.abs(df[('vbfeta', 0)]) > 1.5)\n",
+    "print(len(df))\n",
+    "df = df[m]\n",
+    "len(df)\n",
+    "np.sum(df[\"weight_noxsec\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "2dee42c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import re\n",
+    "\n",
+    "\n",
+    "def count_matched_jets(df):\n",
+    "    results = []\n",
+    "    for column in df.columns:\n",
+    "        # Extract columns with R1...R2 format\n",
+    "        # print(column, 'R1' in str(column) and 'R2' in str(column))\n",
+    "        if \"vbfR\" in str(column):\n",
+    "            l = len(df[column])\n",
+    "\n",
+    "            # Use regex to extract R1 and R2 values from column name\n",
+    "            match = re.search(r\"R1([\\d\\.]+)R2([\\d\\.]+)\", str(column))\n",
+    "            if match:\n",
+    "                R1 = float(match.group(1))\n",
+    "                R2 = float(match.group(2))\n",
+    "\n",
+    "                if R2 < 0.6 or R1 < 0.6:\n",
+    "                    continue\n",
+    "\n",
+    "                zero_count = np.sum(df[column] == 0)\n",
+    "                one_count = np.sum(df[column] == 1)\n",
+    "                two_count = np.sum(df[column] == 2)\n",
+    "                # print(zero_count,one_count,two_count)\n",
+    "                results.append(\n",
+    "                    [(one_count + two_count), R1, R2]\n",
+    "                )  # adjust this to see just two count and stuff\n",
+    "                # print([(two_count), R1, R2])\n",
+    "    return np.array(results)\n",
+    "\n",
+    "\n",
+    "def plot_heatmap(data, title=\"Num preselected events with 1 or 2 gen quark matched vbf jets\"):\n",
+    "    # Define grid\n",
+    "    R1_vals = sorted(list(set(data[:, 1])))\n",
+    "    R2_vals = sorted(list(set(data[:, 2])))\n",
+    "\n",
+    "    heatmap_data = np.zeros((len(R1_vals), len(R2_vals)))\n",
+    "\n",
+    "    for row in data:\n",
+    "        r1_idx = R1_vals.index(row[1])\n",
+    "        r2_idx = R2_vals.index(row[2])\n",
+    "        heatmap_data[r2_idx, r1_idx] = row[0]\n",
+    "\n",
+    "    # Plotting the heatmap\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.imshow(\n",
+    "        heatmap_data,\n",
+    "        aspect=\"auto\",\n",
+    "        origin=\"lower\",\n",
+    "        cmap=\"viridis\",\n",
+    "        extent=[R2_vals[0], R2_vals[-1], R1_vals[0], R1_vals[-1]],\n",
+    "    )\n",
+    "    plt.colorbar(label=\"# counted from total events passing selection criteria\")\n",
+    "    plt.ylabel(\"R2 (HVV jet seperation requirement)\")\n",
+    "    plt.xlabel(\"R1 (Hbb jet seperation requirement)\")\n",
+    "    plt.title(title)\n",
+    "    plt.xticks(R2_vals)\n",
+    "    plt.yticks(R1_vals)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "results = count_matched_jets(df)\n",
+    "plot_heatmap(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.22891566 0.5        0.5       ]\n",
+      " [0.24096386 0.5        0.6       ]\n",
+      " [0.24096386 0.5        0.7       ]\n",
+      " ...\n",
+      " [0.14606742 1.9        1.7       ]\n",
+      " [0.14606742 1.9        1.8       ]\n",
+      " [0.14606742 1.9        1.9       ]]\n"
+     ]
+    }
+   ],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/HHbbVV/VBF_binder/VBFjetsVisualizePrototype.ipynb b/src/HHbbVV/VBF_binder/VBFjetsVisualizePrototype.ipynb
new file mode 100644
index 00000000..f0e9a57f
--- /dev/null
+++ b/src/HHbbVV/VBF_binder/VBFjetsVisualizePrototype.ipynb
@@ -0,0 +1,1839 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7b82bb87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "import numpy as np\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 12})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1128f659",
+   "metadata": {},
+   "source": [
+    "Here we evaluate teh effects of different sorting methods for selecting the two vbf jets. we visualize the distributions for a few signal samples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "42e7add7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('vbfptGen', 0), ('vbfptGen', 1), ('vbfetaGen', 0), ('vbfetaGen', 1), ('vbfphiGen', 0), ('vbfphiGen', 1), ('vbfMGen', 0), ('vbfMGen', 1), ('vbfNumMatchedGen', 0), ('vbfpt', 0), ('vbfpt', 1), ('vbfeta', 0), ('vbfeta', 1), ('vbfphi', 0), ('vbfphi', 1), ('vbfM', 0), ('vbfM', 1), ('nGoodVBFJets', 0), ('vbf_dR_HH', 0), ('vbf_dR_j0_HVV', 0), ('vbf_dR_j1_HVV', 0), ('vbf_dR_j0_Hbb', 0), ('vbf_dR_j1_Hbb', 0), ('vbf_dR_jj', 0), ('vbf_Mass_jj', 0), ('vbf_dEta_jj', 0), ('vbf_cos_j1', 0), ('vbf_cos_j2', 0), ('vbf_prod_centrality', 0), ('ak8FatJetEta', 0), ('ak8FatJetEta', 1), ('ak8FatJetPhi', 0), ('ak8FatJetPhi', 1), ('ak8FatJetMass', 0), ('ak8FatJetMass', 1), ('ak8FatJetPt', 0), ('ak8FatJetPt', 1), ('ak8FatJetMsd', 0), ('ak8FatJetMsd', 1), ('ak8FatJetParticleNetMD_QCD', 0), ('ak8FatJetParticleNetMD_QCD', 1), ('ak8FatJetParticleNetMD_Xbb', 0), ('ak8FatJetParticleNetMD_Xbb', 1), ('ak8FatJetParticleNet_Th4q', 0), ('ak8FatJetParticleNet_Th4q', 1), ('ak8FatJetParticleNetMass', 0), ('ak8FatJetParticleNetMass', 1), ('ak8FatJetPt_JES_up', 0), ('ak8FatJetPt_JES_up', 1), ('ak8FatJetPt_JES_down', 0), ('ak8FatJetPt_JES_down', 1), ('ak8FatJetPt_JER_up', 0), ('ak8FatJetPt_JER_up', 1), ('ak8FatJetPt_JER_down', 0), ('ak8FatJetPt_JER_down', 1), ('ak8FatJetMsd_JMS_down', 0), ('ak8FatJetMsd_JMS_down', 1), ('ak8FatJetMsd_JMS_up', 0), ('ak8FatJetMsd_JMS_up', 1), ('ak8FatJetMsd_JMR_down', 0), ('ak8FatJetMsd_JMR_down', 1), ('ak8FatJetMsd_JMR_up', 0), ('ak8FatJetMsd_JMR_up', 1), ('ak8FatJetParticleNetMass_JMS_down', 0), ('ak8FatJetParticleNetMass_JMS_down', 1), ('ak8FatJetParticleNetMass_JMS_up', 0), ('ak8FatJetParticleNetMass_JMS_up', 1), ('ak8FatJetParticleNetMass_JMR_down', 0), ('ak8FatJetParticleNetMass_JMR_down', 1), ('ak8FatJetParticleNetMass_JMR_up', 0), ('ak8FatJetParticleNetMass_JMR_up', 1), ('ak8FatJetParticleNetMD_Txbb', 0), ('ak8FatJetParticleNetMD_Txbb', 1), ('DijetPt', 0), ('DijetMass', 0), ('DijetEta', 0), ('bbFatJetPtOverDijetPt', 0), ('VVFatJetPtOverDijetPt', 0), ('VVFatJetPtOverbbFatJetPt', 0), ('DijetPt_JES_up', 0), ('DijetMass_JES_up', 0), ('DijetEta_JES_up', 0), ('bbFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverbbFatJetPt_JES_up', 0), ('DijetPt_JES_down', 0), ('DijetMass_JES_down', 0), ('DijetEta_JES_down', 0), ('bbFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverbbFatJetPt_JES_down', 0), ('DijetPt_JER_up', 0), ('DijetMass_JER_up', 0), ('DijetEta_JER_up', 0), ('bbFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverbbFatJetPt_JER_up', 0), ('DijetPt_JER_down', 0), ('DijetMass_JER_down', 0), ('DijetEta_JER_down', 0), ('bbFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverbbFatJetPt_JER_down', 0), ('DijetPt_JMS_down', 0), ('DijetMass_JMS_down', 0), ('DijetEta_JMS_down', 0), ('bbFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverbbFatJetPt_JMS_down', 0), ('DijetPt_JMS_up', 0), ('DijetMass_JMS_up', 0), ('DijetEta_JMS_up', 0), ('bbFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverbbFatJetPt_JMS_up', 0), ('DijetPt_JMR_down', 0), ('DijetMass_JMR_down', 0), ('DijetEta_JMR_down', 0), ('bbFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverbbFatJetPt_JMR_down', 0), ('DijetPt_JMR_up', 0), ('DijetMass_JMR_up', 0), ('DijetEta_JMR_up', 0), ('bbFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverbbFatJetPt_JMR_up', 0), ('MET_pt', 0), ('nGoodMuons', 0), ('nGoodElectrons', 0), ('nGoodJets', 0), ('weight', 0), ('weight_noxsec', 0)]\n",
+      "69\n",
+      "50\n",
+      "294 64\n",
+      "69\n",
+      "tjtjt 50\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_parquet(\"0--1.parquet\")\n",
+    "# df = pd.read_pickle('outfiles/0-2.pkl')\n",
+    "# print(df)\n",
+    "print(df.columns.values.tolist())\n",
+    "\n",
+    "# df[('ak8FatJetParticleNet_Th4q', 0)]\n",
+    "# df[('ak8FatJetParticleNetMD_Txbb', 0)]\n",
+    "# df[('ak8FatJetParTMD_THWWvsT', 0)]\n",
+    "# df[('GenHiggsChildren', 1)]\n",
+    "\n",
+    "# print(df_sorted_pt[('vbf_cos1_j1', 0)])\n",
+    "# print(df_sorted_pt[('vbfeta', 0)])\n",
+    "print(\n",
+    "    len(\n",
+    "        df[\n",
+    "            (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodVBFJets\", 0)] >= 2)\n",
+    "            & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "        ]\n",
+    "    )\n",
+    ")\n",
+    "print(\n",
+    "    len(\n",
+    "        df[\n",
+    "            (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "            & (df[(\"vbfNumMatchedGen\", 0)] == 2)\n",
+    "            & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "        ]\n",
+    "    )\n",
+    ")\n",
+    "print(len(df), np.sum(df[(\"vbfNumMatchedGen\", 0)] == 2))\n",
+    "\n",
+    "\n",
+    "print(\n",
+    "    len(\n",
+    "        df[\n",
+    "            (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodVBFJets\", 0)] >= 2)\n",
+    "            & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "            & (np.abs(df[(\"vbfeta\", 0)]) >= 1.5)\n",
+    "            & (np.abs(df[(\"vbfeta\", 1)]) >= 1.5)\n",
+    "        ]\n",
+    "    )\n",
+    ")\n",
+    "print(\n",
+    "    \"tjtjt\",\n",
+    "    len(\n",
+    "        df[\n",
+    "            (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "            & (df[(\"vbfNumMatchedGen\", 0)] == 2)\n",
+    "            & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "            & (np.abs(df[(\"vbfeta\", 0)]) >= 1.5)\n",
+    "            & (np.abs(df[(\"vbfeta\", 1)]) >= 1.5)\n",
+    "        ]\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# before: 69/294 50/294\n",
+    "# None: 74/294 55/294\n",
+    "# On events: 64/294 49/294\n",
+    "# overall test pretty inconclusie but also shows that thingy is pretty insignificant. probably best for filtering initial events since it doesn't initially show much difference\n",
+    "\n",
+    "# ('vbfptGen', 0), ('vbfptGen', 1), ('vbfetaGen', 0), ('vbfetaGen', 1), ('vbfphiGen', 0), ('vbfphiGen', 1), ('vbfMGen', 0), ('vbfMGen', 1)\n",
+    "# ('ak8FatJetEta', 0), ('ak8FatJetEta', 1), ('ak8FatJetPhi', 0), ('ak8FatJetPhi', 1), ('ak8FatJetMass', 0), ('ak8FatJetMass', 1), ('ak8FatJetPt', 0), ('ak8FatJetPt', 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5c8862d",
+   "metadata": {},
+   "source": [
+    "bbSkimmer modified gives us the two VBF jets which we will be able to access and manipulate. it also gives us the results of cuts on the events (there needs to be atleast 2 VBF jets). We can apply these filters and graph the kinematic variables. We can also record the before and after applying these cuts. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3cbe320d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(294, 132)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Apply lepton veto selections ($N_\\mu =0$ and $N_e=0$)\n",
+    "print(np.shape(df))\n",
+    "df_em = df[(df[(\"nGoodMuons\", 0)] == 0) & (df[(\"nGoodElectrons\", 0)] == 0)]\n",
+    "\n",
+    "# 2 vbf jets\n",
+    "# df_vbf = df[ (df[('nGoodVBFJetsUnsorted', 0)] >= 2)]\n",
+    "\n",
+    "# lepton veto and 2 vbf jets\n",
+    "# df_unsorted = df[(df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJetsUnsorted', 0)] >= 2)& (df[('nGoodJets', 0)] == 0)]\n",
+    "df_sorted_pt = df[\n",
+    "    (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "    & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "    & (df[(\"nGoodVBFJets\", 0)] >= 2)\n",
+    "    & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "]\n",
+    "# df_sorted_M = df[(df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJetsSortedM', 0)] >= 2)& (df[('nGoodJets', 0)] == 0)]\n",
+    "# df_sorted_eta = df[(df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJetsSortedeta', 0)] >= 2)& (df[('nGoodJets', 0)] == 0)]\n",
+    "\n",
+    "\n",
+    "# generate all variables that are needed for the ak8 jet selections in VBF HH4b paper.\n",
+    "# df[('DijetDeltaPhi', 0)] = np.abs(df[('ak8FatJetPhi', 0)] - df[('ak8FatJetPhi', 1)])\n",
+    "# df[('DijetDeltaEta', 0)] = np.abs(df[('ak8FatJetEta', 0)] - df[('ak8FatJetEta', 1)])\n",
+    "# print(np.shape(df)[0],np.shape(df_unsorted)[0],np.shape(df_sorted_pt)[0],np.shape(df_sorted_M)[0],np.shape(df_sorted_eta)[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ebb9216",
+   "metadata": {},
+   "source": [
+    "Graphing the variables for our fatjets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "70c38944",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "('DijetDeltaPhi', 0)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/core/indexes/base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3653\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m   3654\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'DijetDeltaPhi'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/_libs/index.pyx:824\u001b[0m, in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/core/indexes/base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3654\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3655\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m   3656\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   3657\u001b[0m     \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3658\u001b[0m     \u001b[39m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3659\u001b[0m     \u001b[39m#  the TypeError.\u001b[39;00m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'DijetDeltaPhi'",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[8], line 22\u001b[0m\n\u001b[1;32m     20\u001b[0m         ax\u001b[39m.\u001b[39mhist(column, bins\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39marange(\u001b[39mmin\u001b[39m(column), \u001b[39mmax\u001b[39m(column) \u001b[39m+\u001b[39m binwidth, binwidth), alpha\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, label\u001b[39m=\u001b[39mlabel)\n\u001b[1;32m     21\u001b[0m \u001b[39melse\u001b[39;00m:  \u001b[39m# Variable has only one entry (0)\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m     column \u001b[39m=\u001b[39m df[(var, \u001b[39m0\u001b[39;49m)]\n\u001b[1;32m     23\u001b[0m     binwidth \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m((\u001b[39mmax\u001b[39m(column) \u001b[39m-\u001b[39m \u001b[39mmin\u001b[39m(column))\u001b[39m/\u001b[39mbinnums) \u001b[39m+\u001b[39m \u001b[39m0.1\u001b[39m\n\u001b[1;32m     24\u001b[0m     ax\u001b[39m.\u001b[39mhist(column, bins\u001b[39m=\u001b[39mnp\u001b[39m.\u001b[39marange(\u001b[39mmin\u001b[39m(column), \u001b[39mmax\u001b[39m(column) \u001b[39m+\u001b[39m binwidth, binwidth), alpha\u001b[39m=\u001b[39m\u001b[39m0.5\u001b[39m, label\u001b[39m=\u001b[39mvar)\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/core/frame.py:3760\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3758\u001b[0m \u001b[39mif\u001b[39;00m is_single_key:\n\u001b[1;32m   3759\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m-> 3760\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_getitem_multilevel(key)\n\u001b[1;32m   3761\u001b[0m     indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mget_loc(key)\n\u001b[1;32m   3762\u001b[0m     \u001b[39mif\u001b[39;00m is_integer(indexer):\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/core/frame.py:3815\u001b[0m, in \u001b[0;36mDataFrame._getitem_multilevel\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3813\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_getitem_multilevel\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[1;32m   3814\u001b[0m     \u001b[39m# self.columns is a MultiIndex\u001b[39;00m\n\u001b[0;32m-> 3815\u001b[0m     loc \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   3816\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(loc, (\u001b[39mslice\u001b[39m, np\u001b[39m.\u001b[39mndarray)):\n\u001b[1;32m   3817\u001b[0m         new_columns \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns[loc]\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/core/indexes/multi.py:2825\u001b[0m, in \u001b[0;36mMultiIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2821\u001b[0m \u001b[39mif\u001b[39;00m keylen \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnlevels \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mis_unique:\n\u001b[1;32m   2822\u001b[0m     \u001b[39m# TODO: what if we have an IntervalIndex level?\u001b[39;00m\n\u001b[1;32m   2823\u001b[0m     \u001b[39m#  i.e. do we need _index_as_unique on that level?\u001b[39;00m\n\u001b[1;32m   2824\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 2825\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m   2826\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   2827\u001b[0m         \u001b[39m# e.g. test_partial_slicing_with_multiindex partial string slicing\u001b[39;00m\n\u001b[1;32m   2828\u001b[0m         loc, _ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_loc_level(key, \u001b[39mlist\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnlevels)))\n",
+      "File \u001b[0;32m~/mambaforge/envs/cofenv/lib/python3.9/site-packages/pandas/_libs/index.pyx:827\u001b[0m, in \u001b[0;36mpandas._libs.index.BaseMultiIndexCodesEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: ('DijetDeltaPhi', 0)"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x2000 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variables = [\n",
+    "    \"ak8FatJetMass\",\n",
+    "    \"ak8FatJetPt\",\n",
+    "    \"ak8FatJetEta\",\n",
+    "    \"DijetPt\",\n",
+    "    \"DijetMass\",\n",
+    "    \"DijetDeltaPhi\",\n",
+    "    \"DijetDeltaEta\",\n",
+    "    \"nGoodJets\",\n",
+    "    \"vbfpt\",\n",
+    "    \"vbfM\",\n",
+    "]\n",
+    "\n",
+    "# Determine grid size:\n",
+    "grid_size = (np.ceil(len(variables) / 2).astype(int), 2)\n",
+    "\n",
+    "binnums = 20\n",
+    "\n",
+    "fig, axs = plt.subplots(*grid_size, figsize=(10, 4 * grid_size[0]))\n",
+    "axs = axs.flatten()  # Flatten to 1D for easier iteration\n",
+    "\n",
+    "# Loop over variables and their corresponding subplots\n",
+    "for ax, var in zip(axs, variables):\n",
+    "    # Check if variable has two entries (0 and 1)\n",
+    "    if (var, 1) in df.columns:\n",
+    "        for i, label in zip([0, 1], [\"H-bb\", \"H-VV\"]):\n",
+    "            column = df[(var, i)]\n",
+    "            if i == 0:\n",
+    "                binwidth = int((max(column) - min(column)) / binnums) + 0.1\n",
+    "            ax.hist(\n",
+    "                column,\n",
+    "                bins=np.arange(min(column), max(column) + binwidth, binwidth),\n",
+    "                alpha=0.5,\n",
+    "                label=label,\n",
+    "            )\n",
+    "    else:  # Variable has only one entry (0)\n",
+    "        column = df[(var, 0)]\n",
+    "        binwidth = int((max(column) - min(column)) / binnums) + 0.1\n",
+    "        ax.hist(\n",
+    "            column,\n",
+    "            bins=np.arange(min(column), max(column) + binwidth, binwidth),\n",
+    "            alpha=0.5,\n",
+    "            label=var,\n",
+    "        )\n",
+    "\n",
+    "    ax.set_xlabel(\"Value\")\n",
+    "    ax.set_ylabel(\"Count\")\n",
+    "    ax.set_title(f\"Distribution of {var}\")\n",
+    "    ax.legend()\n",
+    "    ax.grid(True)\n",
+    "\n",
+    "# Remove unused subplots\n",
+    "if len(variables) < len(axs):\n",
+    "    for ax in axs[len(variables) :]:\n",
+    "        fig.delaxes(ax)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ad9f98de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# example plotting masses of fatjets.\n",
+    "ak8FatJetMass_bb = df[(\"vbfM\", 0)]  # Mass of H-bb\n",
+    "ak8FatJetMass_vv = df[(\"vbfM\", 1)]  # Mass of H-VV\n",
+    "\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "# plot histogram for H-bb\n",
+    "plt.hist(\n",
+    "    ak8FatJetMass_bb, bins=range(min(data), max(data) + binwidth, binwidth), alpha=0.5, label=\"H-bb\"\n",
+    ")\n",
+    "\n",
+    "# plot histogram for H-VV\n",
+    "plt.hist(ak8FatJetMass_vv, bins=50, alpha=0.5, label=\"H-VV\")\n",
+    "\n",
+    "plt.xlabel(\"Jet Mass\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.title(\"Jet Mass Distribution\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e258d315",
+   "metadata": {},
+   "source": [
+    "Graphing the vbf jet variables with diffenent sorts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "456481ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_and_plot(df, sort_type, custom_variables=None):\n",
+    "    # Variables for each sorting type\n",
+    "    variables_dict = {\n",
+    "        \"Sortedpt\": [\"vbfptSortedpt\", \"vbfetaSortedpt\", \"vbfphiSortedpt\", \"vbfMSortedpt\"],\n",
+    "        \"SortedM\": [\"vbfptSortedM\", \"vbfetaSortedM\", \"vbfphiSortedM\", \"vbfMSortedM\"],\n",
+    "        \"Sortedeta\": [\"vbfptSortedeta\", \"vbfetaSortedeta\", \"vbfphiSortedeta\", \"vbfMSortedeta\"],\n",
+    "        \"Unsorted\": [\"nGoodVBFJetsUnsorted\"],\n",
+    "    }\n",
+    "\n",
+    "    # Retrieve the corresponding variables\n",
+    "    variables = variables_dict.get(sort_type)\n",
+    "\n",
+    "    # Add custom variables if provided\n",
+    "    if custom_variables:\n",
+    "        variables += custom_variables\n",
+    "\n",
+    "    # Apply mask to dataframe based on the sort_type\n",
+    "    df = df[\n",
+    "        (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "        & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "        & (df[(f\"nGoodVBFJets{sort_type}\", 0)] >= 2)\n",
+    "        & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "    ]\n",
+    "\n",
+    "    # Determine grid size:\n",
+    "    grid_size = (np.ceil(len(variables) / 2).astype(int), 2)\n",
+    "\n",
+    "    fig, axs = plt.subplots(*grid_size, figsize=(10, 4 * grid_size[0]))\n",
+    "    axs = axs.flatten()  # Flatten to 1D for easier iteration\n",
+    "\n",
+    "    # Loop over variables and their corresponding subplots\n",
+    "    for ax, var in zip(axs, variables):\n",
+    "        # Determine bins based on variable type\n",
+    "        if \"pt\" in var:\n",
+    "            bins = np.linspace(0, 1500, 51)\n",
+    "        elif \"mass\" in var:\n",
+    "            bins = np.linspace(0, 3000, 51)\n",
+    "        elif \"phi\" in var:\n",
+    "            bins = np.linspace(0, 5, 51)\n",
+    "        else:\n",
+    "            bins = 50  # default binning if variable type is not recognized\n",
+    "        if \"vbfpt\" in var:\n",
+    "            bins = np.linspace(0, 500, 51)\n",
+    "        if \"vbfeta\" in var:\n",
+    "            bins = np.linspace(-5, 5, 51)\n",
+    "        if \"vbfM\" in var:\n",
+    "            bins = np.linspace(0, 100, 51)\n",
+    "        if \"vbfphi\" in var:\n",
+    "            bins = np.linspace(-1, 5, 51)\n",
+    "\n",
+    "        # Check if variable has two entries (0 and 1)\n",
+    "        if (var, 1) in df.columns:\n",
+    "            for i in [0, 1]:\n",
+    "                column = df[(var, i)]\n",
+    "                ax.hist(column, bins=bins, alpha=0.5, label=f\"Jet {i+1}\")\n",
+    "        else:  # Variable has only one entry (0)\n",
+    "            column = df[(var, 0)]\n",
+    "            ax.hist(column, bins=bins, alpha=0.5, label=var)\n",
+    "\n",
+    "        ax.set_xlabel(\"Value\")\n",
+    "        ax.set_ylabel(\"Count\")\n",
+    "        ax.set_title(f\"Distribution of {var}\")\n",
+    "        ax.legend()\n",
+    "        ax.grid(True)\n",
+    "\n",
+    "    # Remove unused subplots\n",
+    "    if len(variables) < len(axs):\n",
+    "        for ax in axs[len(variables) :]:\n",
+    "            fig.delaxes(ax)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fcb59771",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x2000 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variables = [\"ak8FatJetMass\", \"ak8FatJetPt\", \"DijetPt\", \"DijetMass\"]\n",
+    "# df = pd.read_parquet('0--1.parquet')\n",
+    "process_and_plot(df, \"Sortedpt\", variables)\n",
+    "#'Unsorted': ['vbfptUnsorted','vbfetaUnsorted','vbfphiUnsorted','vbfMUnsorted','nGoodVBFJetsUnsorted']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f414bd00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_and_plot_all(df, num_bin=50, custom_variables=None):\n",
+    "    # Variables for each sorting type\n",
+    "    variables_dict = {\n",
+    "        \"Unsorted\": [\n",
+    "            \"vbfptUnsorted\",\n",
+    "            \"vbfetaUnsorted\",\n",
+    "            \"vbfphiUnsorted\",\n",
+    "            \"vbfMUnsorted\",\n",
+    "            \"nGoodVBFJetsUnsorted\",\n",
+    "        ],\n",
+    "        \"Sortedpt\": [\n",
+    "            \"vbfptSortedpt\",\n",
+    "            \"vbfetaSortedpt\",\n",
+    "            \"vbfphiSortedpt\",\n",
+    "            \"vbfMSortedpt\",\n",
+    "            \"nGoodVBFJetsSortedpt\",\n",
+    "        ],\n",
+    "        \"SortedM\": [\n",
+    "            \"vbfptSortedM\",\n",
+    "            \"vbfetaSortedM\",\n",
+    "            \"vbfphiSortedM\",\n",
+    "            \"vbfMSortedM\",\n",
+    "            \"nGoodVBFJetsSortedM\",\n",
+    "        ],\n",
+    "        \"Sortedeta\": [\n",
+    "            \"vbfptSortedeta\",\n",
+    "            \"vbfetaSortedeta\",\n",
+    "            \"vbfphiSortedeta\",\n",
+    "            \"vbfMSortedeta\",\n",
+    "            \"nGoodVBFJetsSortedeta\",\n",
+    "        ],\n",
+    "    }\n",
+    "\n",
+    "    # Add custom variables if provided\n",
+    "    if custom_variables:\n",
+    "        for sort_type in variables_dict.keys():\n",
+    "            variables_dict[sort_type] += custom_variables\n",
+    "\n",
+    "    # Determine grid size\n",
+    "    grid_size = (max(len(vars) for vars in variables_dict.values()), len(variables_dict))\n",
+    "\n",
+    "    fig, axs = plt.subplots(*grid_size, figsize=(10 * grid_size[1], 4 * grid_size[0]))\n",
+    "\n",
+    "    # Loop over sorting types and their corresponding subplot columns\n",
+    "    for sort_type, col_axs in zip(variables_dict.keys(), axs.T):\n",
+    "        # Apply mask to dataframe based on the sort_type\n",
+    "        df_filtered = df[\n",
+    "            (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "            & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "            & (df[(f\"nGoodVBFJets{sort_type}\", 0)] >= 2)\n",
+    "            & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "        ]\n",
+    "\n",
+    "        # Loop over variables and their corresponding subplots\n",
+    "        for ax, var in zip(col_axs, variables_dict[sort_type]):\n",
+    "            # Determine bins based on variable type\n",
+    "            if \"pt\" in var:\n",
+    "                bins = np.linspace(0, 1500, num_bin + 1)\n",
+    "            elif \"mass\" in var:\n",
+    "                bins = np.linspace(0, 3000, num_bin + 1)\n",
+    "            elif \"phi\" in var:\n",
+    "                bins = np.linspace(0, 5, num_bin + 1)\n",
+    "            else:\n",
+    "                bins = num_bin  # default binning if variable type is not recognized\n",
+    "\n",
+    "            if \"vbfpt\" in var:\n",
+    "                bins = np.linspace(0, 500, num_bin + 1)\n",
+    "            if \"vbfeta\" in var:\n",
+    "                bins = np.linspace(-5, 5, num_bin + 1)\n",
+    "            if \"vbfM\" in var:\n",
+    "                bins = np.linspace(0, 100, num_bin + 1)\n",
+    "            if \"vbfphi\" in var:\n",
+    "                bins = np.linspace(-1, 5, num_bin + 1)\n",
+    "\n",
+    "            weights = df_filtered[(\"weight\", 0)]\n",
+    "\n",
+    "            # Check if variable has two entries (0 and 1)\n",
+    "            if (var, 1) in df_filtered.columns:\n",
+    "                for i in [0, 1]:\n",
+    "                    column = df_filtered[(var, i)]\n",
+    "                    ax.hist(\n",
+    "                        column,\n",
+    "                        bins=bins,\n",
+    "                        alpha=0.5,\n",
+    "                        label=f\"Jet {i+1}\",\n",
+    "                        density=True,\n",
+    "                        weights=weights,\n",
+    "                    )\n",
+    "            else:  # Variable has only one entry (0)\n",
+    "                column = df_filtered[(var, 0)]\n",
+    "                ax.hist(column, bins=bins, alpha=0.5, label=var, density=True, weights=weights)\n",
+    "\n",
+    "            ax.set_xlabel(\"Value\")\n",
+    "            ax.set_ylabel(\"Count\")\n",
+    "            ax.set_title(f\"Distribution of {var} ({sort_type})\")\n",
+    "            ax.legend()\n",
+    "            ax.grid(True)\n",
+    "\n",
+    "        # Remove unused subplots\n",
+    "        for ax in col_axs[len(variables_dict[sort_type]) :]:\n",
+    "            fig.delaxes(ax)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ab181e36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4000x3600 with 36 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "variables = [\"ak8FatJetMass\", \"ak8FatJetPt\", \"DijetPt\", \"DijetMass\"]\n",
+    "df = pd.read_parquet(\"0-10.parquet\")\n",
+    "process_and_plot_all(df, num_bin=50, custom_variables=variables)\n",
+    "#'Unsorted': ['vbfptUnsorted','vbfetaUnsorted','vbfphiUnsorted','vbfMUnsorted','nGoodVBFJetsUnsorted']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "128988e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n",
+      "/tmp/ipykernel_3638209/971286838.py:25: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]  # Apply the mask\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sorting Method/Mask</th>\n",
+       "      <th>Weighted Number of Jets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Original</td>\n",
+       "      <td>38888.742185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Unsorted - At least 2 VBF Jets</td>\n",
+       "      <td>18855.039593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Sortedpt - At least 2 VBF Jets</td>\n",
+       "      <td>8862.773588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SortedM - At least 2 VBF Jets</td>\n",
+       "      <td>10531.169935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sortedeta - At least 2 VBF Jets</td>\n",
+       "      <td>10731.495320</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Sorting Method/Mask  Weighted Number of Jets\n",
+       "0                         Original             38888.742185\n",
+       "1   Unsorted - At least 2 VBF Jets             18855.039593\n",
+       "2   Sortedpt - At least 2 VBF Jets              8862.773588\n",
+       "3    SortedM - At least 2 VBF Jets             10531.169935\n",
+       "4  Sortedeta - At least 2 VBF Jets             10731.495320"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2901\n"
+     ]
+    }
+   ],
+   "source": [
+    "def compare_filter_effects(df):\n",
+    "    # Variables for each sorting type\n",
+    "    sort_types = [\"Unsorted\", \"Sortedpt\", \"SortedM\", \"Sortedeta\"]\n",
+    "\n",
+    "    # Different masks to apply\n",
+    "    masks = [\n",
+    "        (\"No Muons\", df[(\"nGoodMuons\", 0)] == 0),\n",
+    "        (\"No Electrons\", df[(\"nGoodElectrons\", 0)] == 0),\n",
+    "        (\"tt BG mask\", df[(\"nGoodJets\", 0)] == 0),\n",
+    "    ]\n",
+    "\n",
+    "    # Create an empty dataframe to store the results\n",
+    "    results = pd.DataFrame(columns=[\"Sorting Method/Mask\", \"Weighted Number of Jets\"])\n",
+    "\n",
+    "    # Include original DataFrame first\n",
+    "    weighted_jet_count = df[(\"weight\", 0)].sum()\n",
+    "    original_row = pd.DataFrame(\n",
+    "        {\"Sorting Method/Mask\": [\"Original\"], \"Weighted Number of Jets\": [weighted_jet_count]}\n",
+    "    )\n",
+    "    results = pd.concat([results, original_row], ignore_index=True)\n",
+    "\n",
+    "    # Loop over sort types and masks\n",
+    "    for sort_type in sort_types:\n",
+    "        df_temp = df.copy()  # Work with a copy of the original dataframe\n",
+    "\n",
+    "        for mask_name, mask in masks:\n",
+    "            df_temp = df_temp[mask]  # Apply the mask\n",
+    "\n",
+    "        # 'At least 2 VBF Jets' mask is a function, apply it separately\n",
+    "        df_temp = df_temp[df_temp[(f\"nGoodVBFJets{sort_type}\", 0)] >= 2]\n",
+    "        mask_name = \"At least 2 VBF Jets\"\n",
+    "\n",
+    "        # Store the number of remaining jets along with the corresponding sorting method and mask\n",
+    "        # Instead of counting rows, sum the weights\n",
+    "        weighted_jet_count = df_temp[(\"weight\", 0)].sum()\n",
+    "        new_row = pd.DataFrame(\n",
+    "            {\n",
+    "                \"Sorting Method/Mask\": [f\"{sort_type} - {mask_name}\"],\n",
+    "                \"Weighted Number of Jets\": [weighted_jet_count],\n",
+    "            }\n",
+    "        )\n",
+    "        results = pd.concat([results, new_row], ignore_index=True)\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "# Now you can use this function and display the result as a pandas DataFrame:\n",
+    "results = compare_filter_effects(df)\n",
+    "display(results)\n",
+    "\n",
+    "print(np.shape(df)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "b908ffd9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n",
+      "/tmp/ipykernel_2795089/2696378952.py:31: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  df_temp = df_temp[mask]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sorting Method/Mask</th>\n",
+       "      <th>Number of Jets</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Unsorted, At least 2 VBF Jets</td>\n",
+       "      <td>1989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Unsorted - No Muons, At least 2 VBF Jets</td>\n",
+       "      <td>1939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Unsorted - No Electrons, At least 2 VBF Jets</td>\n",
+       "      <td>1983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Unsorted - tt BG mask, At least 2 VBF Jets</td>\n",
+       "      <td>1452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Unsorted - No Muons, No Electrons, At least 2 ...</td>\n",
+       "      <td>1933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Unsorted - No Muons, tt BG mask, At least 2 VB...</td>\n",
+       "      <td>1415</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Unsorted - No Electrons, tt BG mask, At least ...</td>\n",
+       "      <td>1448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Unsorted - No Muons, No Electrons, tt BG mask,...</td>\n",
+       "      <td>1411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Sortedpt, At least 2 VBF Jets</td>\n",
+       "      <td>916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Sortedpt - No Muons, At least 2 VBF Jets</td>\n",
+       "      <td>888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Sortedpt - No Electrons, At least 2 VBF Jets</td>\n",
+       "      <td>913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Sortedpt - tt BG mask, At least 2 VBF Jets</td>\n",
+       "      <td>688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Sortedpt - No Muons, No Electrons, At least 2 ...</td>\n",
+       "      <td>885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Sortedpt - No Muons, tt BG mask, At least 2 VB...</td>\n",
+       "      <td>668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Sortedpt - No Electrons, tt BG mask, At least ...</td>\n",
+       "      <td>686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Sortedpt - No Muons, No Electrons, tt BG mask,...</td>\n",
+       "      <td>666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>SortedM, At least 2 VBF Jets</td>\n",
+       "      <td>1086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>SortedM - No Muons, At least 2 VBF Jets</td>\n",
+       "      <td>1055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>SortedM - No Electrons, At least 2 VBF Jets</td>\n",
+       "      <td>1083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>SortedM - tt BG mask, At least 2 VBF Jets</td>\n",
+       "      <td>814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>SortedM - No Muons, No Electrons, At least 2 V...</td>\n",
+       "      <td>1052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>SortedM - No Muons, tt BG mask, At least 2 VBF...</td>\n",
+       "      <td>792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>SortedM - No Electrons, tt BG mask, At least 2...</td>\n",
+       "      <td>812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>SortedM - No Muons, No Electrons, tt BG mask, ...</td>\n",
+       "      <td>790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Sortedeta, At least 2 VBF Jets</td>\n",
+       "      <td>1104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Sortedeta - No Muons, At least 2 VBF Jets</td>\n",
+       "      <td>1073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Sortedeta - No Electrons, At least 2 VBF Jets</td>\n",
+       "      <td>1101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Sortedeta - tt BG mask, At least 2 VBF Jets</td>\n",
+       "      <td>829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Sortedeta - No Muons, No Electrons, At least 2...</td>\n",
+       "      <td>1070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Sortedeta - No Muons, tt BG mask, At least 2 V...</td>\n",
+       "      <td>807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Sortedeta - No Electrons, tt BG mask, At least...</td>\n",
+       "      <td>827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Sortedeta - No Muons, No Electrons, tt BG mask...</td>\n",
+       "      <td>805</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Sorting Method/Mask Number of Jets\n",
+       "0                       Unsorted, At least 2 VBF Jets           1989\n",
+       "1            Unsorted - No Muons, At least 2 VBF Jets           1939\n",
+       "2        Unsorted - No Electrons, At least 2 VBF Jets           1983\n",
+       "3          Unsorted - tt BG mask, At least 2 VBF Jets           1452\n",
+       "4   Unsorted - No Muons, No Electrons, At least 2 ...           1933\n",
+       "5   Unsorted - No Muons, tt BG mask, At least 2 VB...           1415\n",
+       "6   Unsorted - No Electrons, tt BG mask, At least ...           1448\n",
+       "7   Unsorted - No Muons, No Electrons, tt BG mask,...           1411\n",
+       "8                       Sortedpt, At least 2 VBF Jets            916\n",
+       "9            Sortedpt - No Muons, At least 2 VBF Jets            888\n",
+       "10       Sortedpt - No Electrons, At least 2 VBF Jets            913\n",
+       "11         Sortedpt - tt BG mask, At least 2 VBF Jets            688\n",
+       "12  Sortedpt - No Muons, No Electrons, At least 2 ...            885\n",
+       "13  Sortedpt - No Muons, tt BG mask, At least 2 VB...            668\n",
+       "14  Sortedpt - No Electrons, tt BG mask, At least ...            686\n",
+       "15  Sortedpt - No Muons, No Electrons, tt BG mask,...            666\n",
+       "16                       SortedM, At least 2 VBF Jets           1086\n",
+       "17            SortedM - No Muons, At least 2 VBF Jets           1055\n",
+       "18        SortedM - No Electrons, At least 2 VBF Jets           1083\n",
+       "19          SortedM - tt BG mask, At least 2 VBF Jets            814\n",
+       "20  SortedM - No Muons, No Electrons, At least 2 V...           1052\n",
+       "21  SortedM - No Muons, tt BG mask, At least 2 VBF...            792\n",
+       "22  SortedM - No Electrons, tt BG mask, At least 2...            812\n",
+       "23  SortedM - No Muons, No Electrons, tt BG mask, ...            790\n",
+       "24                     Sortedeta, At least 2 VBF Jets           1104\n",
+       "25          Sortedeta - No Muons, At least 2 VBF Jets           1073\n",
+       "26      Sortedeta - No Electrons, At least 2 VBF Jets           1101\n",
+       "27        Sortedeta - tt BG mask, At least 2 VBF Jets            829\n",
+       "28  Sortedeta - No Muons, No Electrons, At least 2...           1070\n",
+       "29  Sortedeta - No Muons, tt BG mask, At least 2 V...            807\n",
+       "30  Sortedeta - No Electrons, tt BG mask, At least...            827\n",
+       "31  Sortedeta - No Muons, No Electrons, tt BG mask...            805"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import itertools\n",
+    "\n",
+    "\n",
+    "def compare_filter_effects(df):\n",
+    "    # Variables for each sorting type\n",
+    "    sort_types = [\"Unsorted\", \"Sortedpt\", \"SortedM\", \"Sortedeta\"]\n",
+    "\n",
+    "    # Different masks to apply\n",
+    "    masks = [\n",
+    "        (\"No Muons\", df[(\"nGoodMuons\", 0)] == 0),\n",
+    "        (\"No Electrons\", df[(\"nGoodElectrons\", 0)] == 0),\n",
+    "        (\"tt BG mask\", df[(\"nGoodJets\", 0)] == 0),\n",
+    "    ]\n",
+    "\n",
+    "    # Create an empty dataframe to store the results\n",
+    "    results = pd.DataFrame(columns=[\"Sorting Method/Mask\", \"Number of Jets\"])\n",
+    "\n",
+    "    # Loop over sort types\n",
+    "    for sort_type in sort_types:\n",
+    "        # Loop over all possible lengths of mask combinations\n",
+    "        for r in range(len(masks) + 1):\n",
+    "            # Loop over all possible combinations of masks of length r\n",
+    "            for mask_combination in itertools.combinations(masks, r):\n",
+    "                df_temp = df.copy()  # Work with a copy of the original dataframe\n",
+    "\n",
+    "                # Generate the mask combination name (sort_type + mask names)\n",
+    "                mask_combination_names = [mask_name for mask_name, _ in mask_combination]\n",
+    "                mask_name = (\n",
+    "                    f\"{sort_type} - \" + \", \".join(mask_combination_names)\n",
+    "                    if mask_combination_names\n",
+    "                    else sort_type\n",
+    "                )\n",
+    "\n",
+    "                # Apply each mask in the combination\n",
+    "                for _, mask in mask_combination:\n",
+    "                    df_temp = df_temp[mask]\n",
+    "\n",
+    "                # 'At least 2 VBF Jets' mask is a function, apply it separately\n",
+    "                df_temp = df_temp[df_temp[(f\"nGoodVBFJets{sort_type}\", 0)] >= 2]\n",
+    "                mask_name += \", At least 2 VBF Jets\"\n",
+    "\n",
+    "                # Store the number of remaining jets along with the corresponding sorting method and mask\n",
+    "                new_row = pd.DataFrame(\n",
+    "                    {\"Sorting Method/Mask\": [mask_name], \"Number of Jets\": [np.shape(df_temp)[0]]}\n",
+    "                )\n",
+    "                results = pd.concat([results, new_row], ignore_index=True)\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "# Now you can use this function and display the result as a pandas DataFrame:\n",
+    "results = compare_filter_effects(df)\n",
+    "display(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f6c82a97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       13.097274\n",
+       "1       13.859143\n",
+       "2       13.187113\n",
+       "3       12.276107\n",
+       "4       12.276107\n",
+       "          ...    \n",
+       "2896    13.266966\n",
+       "2897    13.430860\n",
+       "2898    11.011676\n",
+       "2899    13.450849\n",
+       "2900    13.092185\n",
+       "Name: (weight, 0), Length: 2901, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(\"weight\", 0)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d922a04",
+   "metadata": {},
+   "source": [
+    "Plotting potential bdt variables with qcd sample and signal sample to see if there are good differences we can use (HWW tagger scores not included since no inference was used)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "c1dcdd50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('GenHiggsEta', 0), ('GenHiggsEta', 1), ('GenHiggsPhi', 0), ('GenHiggsPhi', 1), ('GenHiggsMass', 0), ('GenHiggsMass', 1), ('GenHiggsPt', 0), ('GenHiggsPt', 1), ('GenHiggsChildren', 0), ('GenHiggsChildren', 1), ('GenbbEta', 0), ('GenbbEta', 1), ('GenbbPhi', 0), ('GenbbPhi', 1), ('GenbbMass', 0), ('GenbbMass', 1), ('GenbbPt', 0), ('GenbbPt', 1), ('GenVVEta', 0), ('GenVVEta', 1), ('GenVVPhi', 0), ('GenVVPhi', 1), ('GenVVMass', 0), ('GenVVMass', 1), ('GenVVPt', 0), ('GenVVPt', 1), ('Gen4qEta', 0), ('Gen4qEta', 1), ('Gen4qEta', 2), ('Gen4qEta', 3), ('Gen4qPhi', 0), ('Gen4qPhi', 1), ('Gen4qPhi', 2), ('Gen4qPhi', 3), ('Gen4qMass', 0), ('Gen4qMass', 1), ('Gen4qMass', 2), ('Gen4qMass', 3), ('Gen4qPt', 0), ('Gen4qPt', 1), ('Gen4qPt', 2), ('Gen4qPt', 3), ('ak8FatJetHbb', 0), ('ak8FatJetHbb', 1), ('ak8FatJetHVV', 0), ('ak8FatJetHVV', 1), ('ak8FatJetHVVNumProngs', 0), ('vbfptGen', 0), ('vbfptGen', 1), ('vbfetaGen', 0), ('vbfetaGen', 1), ('vbfphiGen', 0), ('vbfphiGen', 1), ('vbfMGen', 0), ('vbfMGen', 1), ('vbfpt', 0), ('vbfpt', 1), ('vbfeta', 0), ('vbfeta', 1), ('vbfphi', 0), ('vbfphi', 1), ('vbfM', 0), ('vbfM', 1), ('nGoodVBFJets', 0), ('vbf_dR_HH', 0), ('vbf_dR_j0_HVV', 0), ('vbf_dR_j1_HVV', 0), ('vbf_dR_j0_Hbb', 0), ('vbf_dR_j1_Hbb', 0), ('vbf_dR_jj', 0), ('vbf_Mass_jj', 0), ('vbf_dEta_jj', 0), ('vbf_cos1_j1', 0), ('vbf_cos1_j2', 0), ('vbf_prod_centrality', 0), ('ak8FatJetEta', 0), ('ak8FatJetEta', 1), ('ak8FatJetPhi', 0), ('ak8FatJetPhi', 1), ('ak8FatJetMass', 0), ('ak8FatJetMass', 1), ('ak8FatJetPt', 0), ('ak8FatJetPt', 1), ('ak8FatJetMsd', 0), ('ak8FatJetMsd', 1), ('ak8FatJetParticleNetMD_QCD', 0), ('ak8FatJetParticleNetMD_QCD', 1), ('ak8FatJetParticleNetMD_Xbb', 0), ('ak8FatJetParticleNetMD_Xbb', 1), ('ak8FatJetParticleNet_Th4q', 0), ('ak8FatJetParticleNet_Th4q', 1), ('ak8FatJetParticleNetMass', 0), ('ak8FatJetParticleNetMass', 1), ('ak8FatJetPt_JES_up', 0), ('ak8FatJetPt_JES_up', 1), ('ak8FatJetPt_JES_down', 0), ('ak8FatJetPt_JES_down', 1), ('ak8FatJetPt_JER_up', 0), ('ak8FatJetPt_JER_up', 1), ('ak8FatJetPt_JER_down', 0), ('ak8FatJetPt_JER_down', 1), ('ak8FatJetMsd_JMS_down', 0), ('ak8FatJetMsd_JMS_down', 1), ('ak8FatJetMsd_JMS_up', 0), ('ak8FatJetMsd_JMS_up', 1), ('ak8FatJetMsd_JMR_down', 0), ('ak8FatJetMsd_JMR_down', 1), ('ak8FatJetMsd_JMR_up', 0), ('ak8FatJetMsd_JMR_up', 1), ('ak8FatJetParticleNetMass_JMS_down', 0), ('ak8FatJetParticleNetMass_JMS_down', 1), ('ak8FatJetParticleNetMass_JMS_up', 0), ('ak8FatJetParticleNetMass_JMS_up', 1), ('ak8FatJetParticleNetMass_JMR_down', 0), ('ak8FatJetParticleNetMass_JMR_down', 1), ('ak8FatJetParticleNetMass_JMR_up', 0), ('ak8FatJetParticleNetMass_JMR_up', 1), ('ak8FatJetParticleNetMD_Txbb', 0), ('ak8FatJetParticleNetMD_Txbb', 1), ('DijetPt', 0), ('DijetMass', 0), ('DijetEta', 0), ('bbFatJetPtOverDijetPt', 0), ('VVFatJetPtOverDijetPt', 0), ('VVFatJetPtOverbbFatJetPt', 0), ('DijetPt_JES_up', 0), ('DijetMass_JES_up', 0), ('DijetEta_JES_up', 0), ('bbFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverbbFatJetPt_JES_up', 0), ('DijetPt_JES_down', 0), ('DijetMass_JES_down', 0), ('DijetEta_JES_down', 0), ('bbFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverbbFatJetPt_JES_down', 0), ('DijetPt_JER_up', 0), ('DijetMass_JER_up', 0), ('DijetEta_JER_up', 0), ('bbFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverbbFatJetPt_JER_up', 0), ('DijetPt_JER_down', 0), ('DijetMass_JER_down', 0), ('DijetEta_JER_down', 0), ('bbFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverbbFatJetPt_JER_down', 0), ('DijetPt_JMS_down', 0), ('DijetMass_JMS_down', 0), ('DijetEta_JMS_down', 0), ('bbFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverbbFatJetPt_JMS_down', 0), ('DijetPt_JMS_up', 0), ('DijetMass_JMS_up', 0), ('DijetEta_JMS_up', 0), ('bbFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverbbFatJetPt_JMS_up', 0), ('DijetPt_JMR_down', 0), ('DijetMass_JMR_down', 0), ('DijetEta_JMR_down', 0), ('bbFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverbbFatJetPt_JMR_down', 0), ('DijetPt_JMR_up', 0), ('DijetMass_JMR_up', 0), ('DijetEta_JMR_up', 0), ('bbFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverbbFatJetPt_JMR_up', 0), ('MET_pt', 0), ('nGoodMuons', 0), ('nGoodElectrons', 0), ('nGoodJets', 0), ('weight', 0), ('weight_noxsec', 0), ('lp_sf_lnN', 0), ('lp_sf_lnN', 1), ('lp_sf_lnN', 2), ('lp_sf_lnN', 3), ('lp_sf_lnN', 4), ('lp_sf_lnN', 5), ('lp_sf_lnN', 6), ('lp_sf_lnN', 7), ('lp_sf_lnN', 8), ('lp_sf_lnN', 9), ('lp_sf_lnN', 10), ('lp_sf_lnN', 11), ('lp_sf_lnN', 12), ('lp_sf_lnN', 13), ('lp_sf_lnN', 14), ('lp_sf_lnN', 15), ('lp_sf_lnN', 16), ('lp_sf_lnN', 17), ('lp_sf_lnN', 18), ('lp_sf_lnN', 19), ('lp_sf_lnN', 20), ('lp_sf_lnN', 21), ('lp_sf_lnN', 22), ('lp_sf_lnN', 23), ('lp_sf_lnN', 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('lp_sf_lnN', 77), ('lp_sf_lnN', 78), ('lp_sf_lnN', 79), ('lp_sf_lnN', 80), ('lp_sf_lnN', 81), ('lp_sf_lnN', 82), ('lp_sf_lnN', 83), ('lp_sf_lnN', 84), ('lp_sf_lnN', 85), ('lp_sf_lnN', 86), ('lp_sf_lnN', 87), ('lp_sf_lnN', 88), ('lp_sf_lnN', 89), ('lp_sf_lnN', 90), ('lp_sf_lnN', 91), ('lp_sf_lnN', 92), ('lp_sf_lnN', 93), ('lp_sf_lnN', 94), ('lp_sf_lnN', 95), ('lp_sf_lnN', 96), ('lp_sf_lnN', 97), ('lp_sf_lnN', 98), ('lp_sf_lnN', 99), ('lp_sf_lnN', 100), ('lp_sf_lnN', 101), ('lp_sf_lnN', 102), ('lp_sf_lnN', 103), ('lp_sf_lnN', 104), ('lp_sf_lnN', 105), ('lp_sf_lnN', 106), ('lp_sf_lnN', 107), ('lp_sf_lnN', 108), ('lp_sf_lnN', 109), ('lp_sf_lnN', 110), ('lp_sf_lnN', 111), ('lp_sf_lnN', 112), ('lp_sf_lnN', 113), ('lp_sf_lnN', 114), ('lp_sf_lnN', 115), ('lp_sf_lnN', 116), ('lp_sf_lnN', 117), ('lp_sf_lnN', 118), ('lp_sf_lnN', 119), ('lp_sf_lnN', 120), ('lp_sf_lnN', 121), ('lp_sf_lnN', 122), ('lp_sf_lnN', 123), ('lp_sf_lnN', 124), ('lp_sf_lnN', 125), ('lp_sf_lnN', 126), ('lp_sf_lnN', 127), ('lp_sf_lnN', 128), ('lp_sf_lnN', 129), ('lp_sf_lnN', 130), ('lp_sf_lnN', 131), ('lp_sf_lnN', 132), ('lp_sf_lnN', 133), ('lp_sf_lnN', 134), ('lp_sf_lnN', 135), ('lp_sf_lnN', 136), ('lp_sf_lnN', 137), ('lp_sf_lnN', 138), ('lp_sf_lnN', 139), ('lp_sf_lnN', 140), ('lp_sf_lnN', 141), ('lp_sf_lnN', 142), ('lp_sf_lnN', 143), ('lp_sf_lnN', 144), ('lp_sf_lnN', 145), ('lp_sf_lnN', 146), ('lp_sf_lnN', 147), ('lp_sf_lnN', 148), ('lp_sf_lnN', 149), ('lp_sf_lnN', 150), ('lp_sf_lnN', 151), ('lp_sf_lnN', 152), ('lp_sf_lnN', 153), ('lp_sf_lnN', 154), ('lp_sf_lnN', 155), ('lp_sf_lnN', 156), ('lp_sf_lnN', 157), ('lp_sf_lnN', 158), ('lp_sf_lnN', 159), ('lp_sf_lnN', 160), ('lp_sf_lnN', 161), ('lp_sf_lnN', 162), ('lp_sf_lnN', 163), ('lp_sf_lnN', 164), ('lp_sf_lnN', 165), ('lp_sf_lnN', 166), ('lp_sf_lnN', 167), ('lp_sf_lnN', 168), ('lp_sf_lnN', 169), ('lp_sf_lnN', 170), ('lp_sf_lnN', 171), ('lp_sf_lnN', 172), ('lp_sf_lnN', 173), ('lp_sf_lnN', 174), ('lp_sf_lnN', 175), ('lp_sf_lnN', 176), ('lp_sf_lnN', 177), ('lp_sf_lnN', 178), ('lp_sf_lnN', 179), ('lp_sf_lnN', 180), ('lp_sf_lnN', 181), ('lp_sf_lnN', 182), ('lp_sf_lnN', 183), ('lp_sf_lnN', 184), ('lp_sf_lnN', 185), ('lp_sf_lnN', 186), ('lp_sf_lnN', 187), ('lp_sf_lnN', 188), ('lp_sf_lnN', 189), ('lp_sf_lnN', 190), ('lp_sf_lnN', 191), ('lp_sf_lnN', 192), ('lp_sf_lnN', 193), ('lp_sf_lnN', 194), ('lp_sf_lnN', 195), ('lp_sf_lnN', 196), ('lp_sf_lnN', 197), ('lp_sf_lnN', 198), ('lp_sf_lnN', 199), ('lp_sf_lnN', 200), ('lp_sf_lnN', 201), ('lp_sf_sys_down', 0), ('lp_sf_sys_down', 1), ('lp_sf_sys_up', 0), ('lp_sf_sys_up', 1), ('lp_sf_double_matched_event', 0), ('lp_sf_double_matched_event', 1), ('lp_sf_unmatched_quarks', 0), ('lp_sf_unmatched_quarks', 1), ('lp_sf_num_sjpt_gt350', 0), ('lp_sf_num_sjpt_gt350', 1)]\n",
+      "     vbfptGen          vbfetaGen           vbfphiGen           vbfMGen       \\\n",
+      "            0        1         0         1         0         1       0    1   \n",
+      "0     112.500  690.000 -0.243164 -1.121094  2.031250 -1.535156     0.0  0.0   \n",
+      "1     540.000  266.000 -1.281250  0.609375 -2.007812  1.265625     0.0  0.0   \n",
+      "2     302.000  176.000  0.718750  0.572266  1.167969  0.796875     0.0  0.0   \n",
+      "3     310.000   56.125  1.039062  0.804688  2.960938  2.445312     0.0  0.0   \n",
+      "4     702.000   35.375  0.495117  2.531250 -1.984375 -0.746094     0.0  0.0   \n",
+      "...       ...      ...       ...       ...       ...       ...     ...  ...   \n",
+      "1005  315.000  510.000 -1.609375 -0.048950 -0.204102  2.734375     0.0  0.0   \n",
+      "1006   99.250  451.000  1.042969  0.503906 -2.171875 -2.351562     0.0  0.0   \n",
+      "1007   84.000  456.000  0.632812  1.660156  2.062500 -0.945312     0.0  0.0   \n",
+      "1008   56.125  574.000 -0.591797 -0.607422 -0.400391  2.273438     0.0  0.0   \n",
+      "1009  144.000  552.000  1.261719  0.953125  2.960938 -2.617188     0.0  0.0   \n",
+      "\n",
+      "             vbfpt            ... DijetEta_JMR_up  \\\n",
+      "                 0         1  ...               0   \n",
+      "0    -99999.000000 -99999.00  ...       -3.110984   \n",
+      "1    -99999.000000 -99999.00  ...       -3.605068   \n",
+      "2        76.937500     40.75  ...       -2.755229   \n",
+      "3    -99999.000000 -99999.00  ...        4.253825   \n",
+      "4    -99999.000000 -99999.00  ...       -0.817231   \n",
+      "...            ...       ...  ...             ...   \n",
+      "1005 -99999.000000 -99999.00  ...       -3.588631   \n",
+      "1006 -99999.000000 -99999.00  ...        3.254567   \n",
+      "1007     85.125000 -99999.00  ...        3.580411   \n",
+      "1008     44.343750 -99999.00  ...       -2.439667   \n",
+      "1009     28.640625 -99999.00  ...        3.023809   \n",
+      "\n",
+      "     bbFatJetPtOverDijetPt_JMR_up VVFatJetPtOverDijetPt_JMR_up  \\\n",
+      "                                0                            0   \n",
+      "0                        4.148137                     5.146283   \n",
+      "1                       15.793514                    16.552235   \n",
+      "2                        3.993605                     4.474588   \n",
+      "3                       20.664750                    19.674842   \n",
+      "4                       11.425006                    10.437048   \n",
+      "...                           ...                          ...   \n",
+      "1005                     8.456517                     7.689871   \n",
+      "1006                    10.489191                    11.133178   \n",
+      "1007                     7.208780                     6.511015   \n",
+      "1008                     6.515188                     6.279775   \n",
+      "1009                     5.226464                     6.223443   \n",
+      "\n",
+      "     VVFatJetPtOverbbFatJetPt_JMR_up      MET_pt nGoodMuons nGoodElectrons  \\\n",
+      "                                   0           0          0              0   \n",
+      "0                           1.240625  123.025307          0              0   \n",
+      "1                           1.048040   50.121899          0              0   \n",
+      "2                           1.120438   42.022152          0              0   \n",
+      "3                           0.952097   41.514629          0              0   \n",
+      "4                           0.913527   83.237740          0              0   \n",
+      "...                              ...         ...        ...            ...   \n",
+      "1005                        0.909343   52.337540          0              0   \n",
+      "1006                        1.061395   24.972651          0              0   \n",
+      "1007                        0.903206   26.944172          0              0   \n",
+      "1008                        0.963867   21.412642          0              0   \n",
+      "1009                        1.190756   59.015011          0              0   \n",
+      "\n",
+      "     nGoodJets        weight weight_noxsec  \n",
+      "             0             0             0  \n",
+      "0            0  6.639493e+07      0.992600  \n",
+      "1            0  6.627739e+07      0.990842  \n",
+      "2            0  6.700750e+07      1.001758  \n",
+      "3            0  5.353847e+07      0.800396  \n",
+      "4            0  7.004119e+07      1.047111  \n",
+      "...        ...           ...           ...  \n",
+      "1005         1  5.900371e+07      0.882101  \n",
+      "1006         0  7.236398e+07      1.081837  \n",
+      "1007         0  7.813245e+07      1.168075  \n",
+      "1008         0  7.423587e+07      1.109821  \n",
+      "1009         1  6.464294e+07      0.966408  \n",
+      "\n",
+      "[1010 rows x 132 columns]\n",
+      "[('vbfptGen', 0), ('vbfptGen', 1), ('vbfetaGen', 0), ('vbfetaGen', 1), ('vbfphiGen', 0), ('vbfphiGen', 1), ('vbfMGen', 0), ('vbfMGen', 1), ('vbfpt', 0), ('vbfpt', 1), ('vbfeta', 0), ('vbfeta', 1), ('vbfphi', 0), ('vbfphi', 1), ('vbfM', 0), ('vbfM', 1), ('nGoodVBFJets', 0), ('vbf_dR_HH', 0), ('vbf_dR_j0_HVV', 0), ('vbf_dR_j1_HVV', 0), ('vbf_dR_j0_Hbb', 0), ('vbf_dR_j1_Hbb', 0), ('vbf_dR_jj', 0), ('vbf_Mass_jj', 0), ('vbf_dEta_jj', 0), ('vbf_cos1_j1', 0), ('vbf_cos1_j2', 0), ('vbf_prod_centrality', 0), ('ak8FatJetEta', 0), ('ak8FatJetEta', 1), ('ak8FatJetPhi', 0), ('ak8FatJetPhi', 1), ('ak8FatJetMass', 0), ('ak8FatJetMass', 1), ('ak8FatJetPt', 0), ('ak8FatJetPt', 1), ('ak8FatJetMsd', 0), ('ak8FatJetMsd', 1), ('ak8FatJetParticleNetMD_QCD', 0), ('ak8FatJetParticleNetMD_QCD', 1), ('ak8FatJetParticleNetMD_Xbb', 0), ('ak8FatJetParticleNetMD_Xbb', 1), ('ak8FatJetParticleNet_Th4q', 0), ('ak8FatJetParticleNet_Th4q', 1), ('ak8FatJetParticleNetMass', 0), ('ak8FatJetParticleNetMass', 1), ('ak8FatJetPt_JES_up', 0), ('ak8FatJetPt_JES_up', 1), ('ak8FatJetPt_JES_down', 0), ('ak8FatJetPt_JES_down', 1), ('ak8FatJetPt_JER_up', 0), ('ak8FatJetPt_JER_up', 1), ('ak8FatJetPt_JER_down', 0), ('ak8FatJetPt_JER_down', 1), ('ak8FatJetMsd_JMS_down', 0), ('ak8FatJetMsd_JMS_down', 1), ('ak8FatJetMsd_JMS_up', 0), ('ak8FatJetMsd_JMS_up', 1), ('ak8FatJetMsd_JMR_down', 0), ('ak8FatJetMsd_JMR_down', 1), ('ak8FatJetMsd_JMR_up', 0), ('ak8FatJetMsd_JMR_up', 1), ('ak8FatJetParticleNetMass_JMS_down', 0), ('ak8FatJetParticleNetMass_JMS_down', 1), ('ak8FatJetParticleNetMass_JMS_up', 0), ('ak8FatJetParticleNetMass_JMS_up', 1), ('ak8FatJetParticleNetMass_JMR_down', 0), ('ak8FatJetParticleNetMass_JMR_down', 1), ('ak8FatJetParticleNetMass_JMR_up', 0), ('ak8FatJetParticleNetMass_JMR_up', 1), ('ak8FatJetParticleNetMD_Txbb', 0), ('ak8FatJetParticleNetMD_Txbb', 1), ('DijetPt', 0), ('DijetMass', 0), ('DijetEta', 0), ('bbFatJetPtOverDijetPt', 0), ('VVFatJetPtOverDijetPt', 0), ('VVFatJetPtOverbbFatJetPt', 0), ('DijetPt_JES_up', 0), ('DijetMass_JES_up', 0), ('DijetEta_JES_up', 0), ('bbFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverDijetPt_JES_up', 0), ('VVFatJetPtOverbbFatJetPt_JES_up', 0), ('DijetPt_JES_down', 0), ('DijetMass_JES_down', 0), ('DijetEta_JES_down', 0), ('bbFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverDijetPt_JES_down', 0), ('VVFatJetPtOverbbFatJetPt_JES_down', 0), ('DijetPt_JER_up', 0), ('DijetMass_JER_up', 0), ('DijetEta_JER_up', 0), ('bbFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverDijetPt_JER_up', 0), ('VVFatJetPtOverbbFatJetPt_JER_up', 0), ('DijetPt_JER_down', 0), ('DijetMass_JER_down', 0), ('DijetEta_JER_down', 0), ('bbFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverDijetPt_JER_down', 0), ('VVFatJetPtOverbbFatJetPt_JER_down', 0), ('DijetPt_JMS_down', 0), ('DijetMass_JMS_down', 0), ('DijetEta_JMS_down', 0), ('bbFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverDijetPt_JMS_down', 0), ('VVFatJetPtOverbbFatJetPt_JMS_down', 0), ('DijetPt_JMS_up', 0), ('DijetMass_JMS_up', 0), ('DijetEta_JMS_up', 0), ('bbFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverDijetPt_JMS_up', 0), ('VVFatJetPtOverbbFatJetPt_JMS_up', 0), ('DijetPt_JMR_down', 0), ('DijetMass_JMR_down', 0), ('DijetEta_JMR_down', 0), ('bbFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverDijetPt_JMR_down', 0), ('VVFatJetPtOverbbFatJetPt_JMR_down', 0), ('DijetPt_JMR_up', 0), ('DijetMass_JMR_up', 0), ('DijetEta_JMR_up', 0), ('bbFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverDijetPt_JMR_up', 0), ('VVFatJetPtOverbbFatJetPt_JMR_up', 0), ('MET_pt', 0), ('nGoodMuons', 0), ('nGoodElectrons', 0), ('nGoodJets', 0), ('weight', 0), ('weight_noxsec', 0)]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[('GenHiggsEta', 0),\n",
+       " ('GenHiggsEta', 1),\n",
+       " ('GenHiggsPhi', 0),\n",
+       " ('GenHiggsPhi', 1),\n",
+       " ('GenHiggsMass', 0),\n",
+       " ('GenHiggsMass', 1),\n",
+       " ('GenHiggsPt', 0),\n",
+       " ('GenHiggsPt', 1),\n",
+       " ('GenHiggsChildren', 0),\n",
+       " ('GenHiggsChildren', 1),\n",
+       " ('GenbbEta', 0),\n",
+       " ('GenbbEta', 1),\n",
+       " ('GenbbPhi', 0),\n",
+       " ('GenbbPhi', 1),\n",
+       " ('GenbbMass', 0),\n",
+       " ('GenbbMass', 1),\n",
+       " ('GenbbPt', 0),\n",
+       " ('GenbbPt', 1),\n",
+       " ('GenVVEta', 0),\n",
+       " ('GenVVEta', 1),\n",
+       " ('GenVVPhi', 0),\n",
+       " ('GenVVPhi', 1),\n",
+       " ('GenVVMass', 0),\n",
+       " ('GenVVMass', 1),\n",
+       " ('GenVVPt', 0),\n",
+       " ('GenVVPt', 1),\n",
+       " ('Gen4qEta', 0),\n",
+       " ('Gen4qEta', 1),\n",
+       " ('Gen4qEta', 2),\n",
+       " ('Gen4qEta', 3),\n",
+       " ('Gen4qPhi', 0),\n",
+       " ('Gen4qPhi', 1),\n",
+       " ('Gen4qPhi', 2),\n",
+       " ('Gen4qPhi', 3),\n",
+       " ('Gen4qMass', 0),\n",
+       " ('Gen4qMass', 1),\n",
+       " ('Gen4qMass', 2),\n",
+       " ('Gen4qMass', 3),\n",
+       " ('Gen4qPt', 0),\n",
+       " ('Gen4qPt', 1),\n",
+       " ('Gen4qPt', 2),\n",
+       " ('Gen4qPt', 3),\n",
+       " ('ak8FatJetHbb', 0),\n",
+       " ('ak8FatJetHbb', 1),\n",
+       " ('ak8FatJetHVV', 0),\n",
+       " ('ak8FatJetHVV', 1),\n",
+       " ('ak8FatJetHVVNumProngs', 0),\n",
+       " ('vbfptGen', 0),\n",
+       " ('vbfptGen', 1),\n",
+       " ('vbfetaGen', 0),\n",
+       " ('vbfetaGen', 1),\n",
+       " ('vbfphiGen', 0),\n",
+       " ('vbfphiGen', 1),\n",
+       " ('vbfMGen', 0),\n",
+       " ('vbfMGen', 1),\n",
+       " ('vbfpt', 0),\n",
+       " ('vbfpt', 1),\n",
+       " ('vbfeta', 0),\n",
+       " ('vbfeta', 1),\n",
+       " ('vbfphi', 0),\n",
+       " ('vbfphi', 1),\n",
+       " ('vbfM', 0),\n",
+       " ('vbfM', 1),\n",
+       " ('nGoodVBFJets', 0),\n",
+       " ('vbf_dR_HH', 0),\n",
+       " ('vbf_dR_j0_HVV', 0),\n",
+       " ('vbf_dR_j1_HVV', 0),\n",
+       " ('vbf_dR_j0_Hbb', 0),\n",
+       " ('vbf_dR_j1_Hbb', 0),\n",
+       " ('vbf_dR_jj', 0),\n",
+       " ('vbf_Mass_jj', 0),\n",
+       " ('vbf_dEta_jj', 0),\n",
+       " ('vbf_cos1_j1', 0),\n",
+       " ('vbf_cos1_j2', 0),\n",
+       " ('vbf_prod_centrality', 0),\n",
+       " ('ak8FatJetEta', 0),\n",
+       " ('ak8FatJetEta', 1),\n",
+       " ('ak8FatJetPhi', 0),\n",
+       " ('ak8FatJetPhi', 1),\n",
+       " ('ak8FatJetMass', 0),\n",
+       " ('ak8FatJetMass', 1),\n",
+       " ('ak8FatJetPt', 0),\n",
+       " ('ak8FatJetPt', 1),\n",
+       " ('ak8FatJetMsd', 0),\n",
+       " ('ak8FatJetMsd', 1),\n",
+       " ('ak8FatJetParticleNetMD_QCD', 0),\n",
+       " ('ak8FatJetParticleNetMD_QCD', 1),\n",
+       " ('ak8FatJetParticleNetMD_Xbb', 0),\n",
+       " ('ak8FatJetParticleNetMD_Xbb', 1),\n",
+       " ('ak8FatJetParticleNet_Th4q', 0),\n",
+       " ('ak8FatJetParticleNet_Th4q', 1),\n",
+       " ('ak8FatJetParticleNetMass', 0),\n",
+       " ('ak8FatJetParticleNetMass', 1),\n",
+       " ('ak8FatJetPt_JES_up', 0),\n",
+       " ('ak8FatJetPt_JES_up', 1),\n",
+       " ('ak8FatJetPt_JES_down', 0),\n",
+       " ('ak8FatJetPt_JES_down', 1),\n",
+       " ('ak8FatJetPt_JER_up', 0),\n",
+       " ('ak8FatJetPt_JER_up', 1),\n",
+       " ('ak8FatJetPt_JER_down', 0),\n",
+       " ('ak8FatJetPt_JER_down', 1),\n",
+       " ('ak8FatJetMsd_JMS_down', 0),\n",
+       " ('ak8FatJetMsd_JMS_down', 1),\n",
+       " ('ak8FatJetMsd_JMS_up', 0),\n",
+       " ('ak8FatJetMsd_JMS_up', 1),\n",
+       " ('ak8FatJetMsd_JMR_down', 0),\n",
+       " ('ak8FatJetMsd_JMR_down', 1),\n",
+       " ('ak8FatJetMsd_JMR_up', 0),\n",
+       " ('ak8FatJetMsd_JMR_up', 1),\n",
+       " ('ak8FatJetParticleNetMass_JMS_down', 0),\n",
+       " ('ak8FatJetParticleNetMass_JMS_down', 1),\n",
+       " ('ak8FatJetParticleNetMass_JMS_up', 0),\n",
+       " ('ak8FatJetParticleNetMass_JMS_up', 1),\n",
+       " ('ak8FatJetParticleNetMass_JMR_down', 0),\n",
+       " ('ak8FatJetParticleNetMass_JMR_down', 1),\n",
+       " ('ak8FatJetParticleNetMass_JMR_up', 0),\n",
+       " ('ak8FatJetParticleNetMass_JMR_up', 1),\n",
+       " ('ak8FatJetParticleNetMD_Txbb', 0),\n",
+       " ('ak8FatJetParticleNetMD_Txbb', 1),\n",
+       " ('DijetPt', 0),\n",
+       " ('DijetMass', 0),\n",
+       " ('DijetEta', 0),\n",
+       " ('bbFatJetPtOverDijetPt', 0),\n",
+       " ('VVFatJetPtOverDijetPt', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt', 0),\n",
+       " ('DijetPt_JES_up', 0),\n",
+       " ('DijetMass_JES_up', 0),\n",
+       " ('DijetEta_JES_up', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JES_up', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JES_up', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JES_up', 0),\n",
+       " ('DijetPt_JES_down', 0),\n",
+       " ('DijetMass_JES_down', 0),\n",
+       " ('DijetEta_JES_down', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JES_down', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JES_down', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JES_down', 0),\n",
+       " ('DijetPt_JER_up', 0),\n",
+       " ('DijetMass_JER_up', 0),\n",
+       " ('DijetEta_JER_up', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JER_up', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JER_up', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JER_up', 0),\n",
+       " ('DijetPt_JER_down', 0),\n",
+       " ('DijetMass_JER_down', 0),\n",
+       " ('DijetEta_JER_down', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JER_down', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JER_down', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JER_down', 0),\n",
+       " ('DijetPt_JMS_down', 0),\n",
+       " ('DijetMass_JMS_down', 0),\n",
+       " ('DijetEta_JMS_down', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JMS_down', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JMS_down', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JMS_down', 0),\n",
+       " ('DijetPt_JMS_up', 0),\n",
+       " ('DijetMass_JMS_up', 0),\n",
+       " ('DijetEta_JMS_up', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JMS_up', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JMS_up', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JMS_up', 0),\n",
+       " ('DijetPt_JMR_down', 0),\n",
+       " ('DijetMass_JMR_down', 0),\n",
+       " ('DijetEta_JMR_down', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JMR_down', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JMR_down', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JMR_down', 0),\n",
+       " ('DijetPt_JMR_up', 0),\n",
+       " ('DijetMass_JMR_up', 0),\n",
+       " ('DijetEta_JMR_up', 0),\n",
+       " ('bbFatJetPtOverDijetPt_JMR_up', 0),\n",
+       " ('VVFatJetPtOverDijetPt_JMR_up', 0),\n",
+       " ('VVFatJetPtOverbbFatJetPt_JMR_up', 0),\n",
+       " ('MET_pt', 0),\n",
+       " ('nGoodMuons', 0),\n",
+       " ('nGoodElectrons', 0),\n",
+       " ('nGoodJets', 0),\n",
+       " ('weight', 0),\n",
+       " ('weight_noxsec', 0),\n",
+       " ('lp_sf_lnN', 0),\n",
+       " ('lp_sf_lnN', 1),\n",
+       " ('lp_sf_lnN', 2),\n",
+       " ('lp_sf_lnN', 3),\n",
+       " ('lp_sf_lnN', 4),\n",
+       " ('lp_sf_lnN', 5),\n",
+       " ('lp_sf_lnN', 6),\n",
+       " ('lp_sf_lnN', 7),\n",
+       " ('lp_sf_lnN', 8),\n",
+       " ('lp_sf_lnN', 9),\n",
+       " ('lp_sf_lnN', 10),\n",
+       " ('lp_sf_lnN', 11),\n",
+       " ('lp_sf_lnN', 12),\n",
+       " ('lp_sf_lnN', 13),\n",
+       " ('lp_sf_lnN', 14),\n",
+       " ('lp_sf_lnN', 15),\n",
+       " ('lp_sf_lnN', 16),\n",
+       " ('lp_sf_lnN', 17),\n",
+       " ('lp_sf_lnN', 18),\n",
+       " ('lp_sf_lnN', 19),\n",
+       " ('lp_sf_lnN', 20),\n",
+       " ('lp_sf_lnN', 21),\n",
+       " ('lp_sf_lnN', 22),\n",
+       " ('lp_sf_lnN', 23),\n",
+       " ('lp_sf_lnN', 24),\n",
+       " ('lp_sf_lnN', 25),\n",
+       " ('lp_sf_lnN', 26),\n",
+       " ('lp_sf_lnN', 27),\n",
+       " ('lp_sf_lnN', 28),\n",
+       " ('lp_sf_lnN', 29),\n",
+       " ('lp_sf_lnN', 30),\n",
+       " ('lp_sf_lnN', 31),\n",
+       " ('lp_sf_lnN', 32),\n",
+       " ('lp_sf_lnN', 33),\n",
+       " ('lp_sf_lnN', 34),\n",
+       " ('lp_sf_lnN', 35),\n",
+       " ('lp_sf_lnN', 36),\n",
+       " ('lp_sf_lnN', 37),\n",
+       " ('lp_sf_lnN', 38),\n",
+       " ('lp_sf_lnN', 39),\n",
+       " ('lp_sf_lnN', 40),\n",
+       " ('lp_sf_lnN', 41),\n",
+       " ('lp_sf_lnN', 42),\n",
+       " ('lp_sf_lnN', 43),\n",
+       " ('lp_sf_lnN', 44),\n",
+       " ('lp_sf_lnN', 45),\n",
+       " ('lp_sf_lnN', 46),\n",
+       " ('lp_sf_lnN', 47),\n",
+       " ('lp_sf_lnN', 48),\n",
+       " ('lp_sf_lnN', 49),\n",
+       " ('lp_sf_lnN', 50),\n",
+       " ('lp_sf_lnN', 51),\n",
+       " ('lp_sf_lnN', 52),\n",
+       " ('lp_sf_lnN', 53),\n",
+       " ('lp_sf_lnN', 54),\n",
+       " ('lp_sf_lnN', 55),\n",
+       " ('lp_sf_lnN', 56),\n",
+       " ('lp_sf_lnN', 57),\n",
+       " ('lp_sf_lnN', 58),\n",
+       " ('lp_sf_lnN', 59),\n",
+       " ('lp_sf_lnN', 60),\n",
+       " ('lp_sf_lnN', 61),\n",
+       " ('lp_sf_lnN', 62),\n",
+       " ('lp_sf_lnN', 63),\n",
+       " ('lp_sf_lnN', 64),\n",
+       " ('lp_sf_lnN', 65),\n",
+       " ('lp_sf_lnN', 66),\n",
+       " ('lp_sf_lnN', 67),\n",
+       " ('lp_sf_lnN', 68),\n",
+       " ('lp_sf_lnN', 69),\n",
+       " ('lp_sf_lnN', 70),\n",
+       " ('lp_sf_lnN', 71),\n",
+       " ('lp_sf_lnN', 72),\n",
+       " ('lp_sf_lnN', 73),\n",
+       " ('lp_sf_lnN', 74),\n",
+       " ('lp_sf_lnN', 75),\n",
+       " ('lp_sf_lnN', 76),\n",
+       " ('lp_sf_lnN', 77),\n",
+       " ('lp_sf_lnN', 78),\n",
+       " ('lp_sf_lnN', 79),\n",
+       " ('lp_sf_lnN', 80),\n",
+       " ('lp_sf_lnN', 81),\n",
+       " ('lp_sf_lnN', 82),\n",
+       " ('lp_sf_lnN', 83),\n",
+       " ('lp_sf_lnN', 84),\n",
+       " ('lp_sf_lnN', 85),\n",
+       " ('lp_sf_lnN', 86),\n",
+       " ('lp_sf_lnN', 87),\n",
+       " ('lp_sf_lnN', 88),\n",
+       " ('lp_sf_lnN', 89),\n",
+       " ('lp_sf_lnN', 90),\n",
+       " ('lp_sf_lnN', 91),\n",
+       " ('lp_sf_lnN', 92),\n",
+       " ('lp_sf_lnN', 93),\n",
+       " ('lp_sf_lnN', 94),\n",
+       " ('lp_sf_lnN', 95),\n",
+       " ('lp_sf_lnN', 96),\n",
+       " ('lp_sf_lnN', 97),\n",
+       " ('lp_sf_lnN', 98),\n",
+       " ('lp_sf_lnN', 99),\n",
+       " ('lp_sf_lnN', 100),\n",
+       " ('lp_sf_lnN', 101),\n",
+       " ('lp_sf_lnN', 102),\n",
+       " ('lp_sf_lnN', 103),\n",
+       " ('lp_sf_lnN', 104),\n",
+       " ('lp_sf_lnN', 105),\n",
+       " ('lp_sf_lnN', 106),\n",
+       " ('lp_sf_lnN', 107),\n",
+       " ('lp_sf_lnN', 108),\n",
+       " ('lp_sf_lnN', 109),\n",
+       " ('lp_sf_lnN', 110),\n",
+       " ('lp_sf_lnN', 111),\n",
+       " ('lp_sf_lnN', 112),\n",
+       " ('lp_sf_lnN', 113),\n",
+       " ('lp_sf_lnN', 114),\n",
+       " ('lp_sf_lnN', 115),\n",
+       " ('lp_sf_lnN', 116),\n",
+       " ('lp_sf_lnN', 117),\n",
+       " ('lp_sf_lnN', 118),\n",
+       " ('lp_sf_lnN', 119),\n",
+       " ('lp_sf_lnN', 120),\n",
+       " ('lp_sf_lnN', 121),\n",
+       " ('lp_sf_lnN', 122),\n",
+       " ('lp_sf_lnN', 123),\n",
+       " ('lp_sf_lnN', 124),\n",
+       " ('lp_sf_lnN', 125),\n",
+       " ('lp_sf_lnN', 126),\n",
+       " ('lp_sf_lnN', 127),\n",
+       " ('lp_sf_lnN', 128),\n",
+       " ('lp_sf_lnN', 129),\n",
+       " ('lp_sf_lnN', 130),\n",
+       " ('lp_sf_lnN', 131),\n",
+       " ('lp_sf_lnN', 132),\n",
+       " ('lp_sf_lnN', 133),\n",
+       " ('lp_sf_lnN', 134),\n",
+       " ('lp_sf_lnN', 135),\n",
+       " ('lp_sf_lnN', 136),\n",
+       " ('lp_sf_lnN', 137),\n",
+       " ('lp_sf_lnN', 138),\n",
+       " ('lp_sf_lnN', 139),\n",
+       " ('lp_sf_lnN', 140),\n",
+       " ('lp_sf_lnN', 141),\n",
+       " ('lp_sf_lnN', 142),\n",
+       " ('lp_sf_lnN', 143),\n",
+       " ('lp_sf_lnN', 144),\n",
+       " ('lp_sf_lnN', 145),\n",
+       " ('lp_sf_lnN', 146),\n",
+       " ('lp_sf_lnN', 147),\n",
+       " ('lp_sf_lnN', 148),\n",
+       " ('lp_sf_lnN', 149),\n",
+       " ('lp_sf_lnN', 150),\n",
+       " ('lp_sf_lnN', 151),\n",
+       " ('lp_sf_lnN', 152),\n",
+       " ('lp_sf_lnN', 153),\n",
+       " ('lp_sf_lnN', 154),\n",
+       " ('lp_sf_lnN', 155),\n",
+       " ('lp_sf_lnN', 156),\n",
+       " ('lp_sf_lnN', 157),\n",
+       " ('lp_sf_lnN', 158),\n",
+       " ('lp_sf_lnN', 159),\n",
+       " ('lp_sf_lnN', 160),\n",
+       " ('lp_sf_lnN', 161),\n",
+       " ('lp_sf_lnN', 162),\n",
+       " ('lp_sf_lnN', 163),\n",
+       " ('lp_sf_lnN', 164),\n",
+       " ('lp_sf_lnN', 165),\n",
+       " ('lp_sf_lnN', 166),\n",
+       " ('lp_sf_lnN', 167),\n",
+       " ('lp_sf_lnN', 168),\n",
+       " ('lp_sf_lnN', 169),\n",
+       " ('lp_sf_lnN', 170),\n",
+       " ('lp_sf_lnN', 171),\n",
+       " ('lp_sf_lnN', 172),\n",
+       " ('lp_sf_lnN', 173),\n",
+       " ('lp_sf_lnN', 174),\n",
+       " ('lp_sf_lnN', 175),\n",
+       " ('lp_sf_lnN', 176),\n",
+       " ('lp_sf_lnN', 177),\n",
+       " ('lp_sf_lnN', 178),\n",
+       " ('lp_sf_lnN', 179),\n",
+       " ('lp_sf_lnN', 180),\n",
+       " ('lp_sf_lnN', 181),\n",
+       " ('lp_sf_lnN', 182),\n",
+       " ('lp_sf_lnN', 183),\n",
+       " ('lp_sf_lnN', 184),\n",
+       " ('lp_sf_lnN', 185),\n",
+       " ('lp_sf_lnN', 186),\n",
+       " ('lp_sf_lnN', 187),\n",
+       " ('lp_sf_lnN', 188),\n",
+       " ('lp_sf_lnN', 189),\n",
+       " ('lp_sf_lnN', 190),\n",
+       " ('lp_sf_lnN', 191),\n",
+       " ('lp_sf_lnN', 192),\n",
+       " ('lp_sf_lnN', 193),\n",
+       " ('lp_sf_lnN', 194),\n",
+       " ('lp_sf_lnN', 195),\n",
+       " ('lp_sf_lnN', 196),\n",
+       " ('lp_sf_lnN', 197),\n",
+       " ('lp_sf_lnN', 198),\n",
+       " ('lp_sf_lnN', 199),\n",
+       " ('lp_sf_lnN', 200),\n",
+       " ('lp_sf_lnN', 201),\n",
+       " ('lp_sf_sys_down', 0),\n",
+       " ('lp_sf_sys_down', 1),\n",
+       " ('lp_sf_sys_up', 0),\n",
+       " ('lp_sf_sys_up', 1),\n",
+       " ('lp_sf_double_matched_event', 0),\n",
+       " ('lp_sf_double_matched_event', 1),\n",
+       " ('lp_sf_unmatched_quarks', 0),\n",
+       " ('lp_sf_unmatched_quarks', 1),\n",
+       " ('lp_sf_num_sjpt_gt350', 0),\n",
+       " ('lp_sf_num_sjpt_gt350', 1)]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def apply_selection(df):\n",
+    "    print(df.columns.values.tolist())\n",
+    "\n",
+    "    Hbb_mask = df[(\"ak8FatJetParticleNetMD_Txbb\", 0)] > df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "    Hbb_Txbb_values = np.where(\n",
+    "        Hbb_mask, df[(\"ak8FatJetParticleNetMD_Txbb\", 0)], df[(\"ak8FatJetParticleNetMD_Txbb\", 1)]\n",
+    "    )\n",
+    "    HVV_Th4q_values = np.where(\n",
+    "        ~Hbb_mask, df[(\"ak8FatJetParticleNet_Th4q\", 0)], df[(\"ak8FatJetParticleNet_Th4q\", 1)]\n",
+    "    )\n",
+    "    m = (Hbb_Txbb_values >= -0.95) & (HVV_Th4q_values >= -0.6)\n",
+    "    m = (\n",
+    "        m\n",
+    "        & (df[(\"nGoodMuons\", 0)] == 0)\n",
+    "        & (df[(\"nGoodElectrons\", 0)] == 0)\n",
+    "        & (df[(\"nGoodJets\", 0)] == 0)\n",
+    "        & (df[(\"nGoodVBFJets\", 0)] >= 2)\n",
+    "    )\n",
+    "\n",
+    "    return df[m]\n",
+    "\n",
+    "\n",
+    "df1 = pd.read_parquet(\"0-5signal.parquet\")\n",
+    "df1 = apply_selection(df1)\n",
+    "\n",
+    "df2 = pd.read_parquet(\"0-5.parquet\")\n",
+    "print(df2)\n",
+    "df2 = apply_selection(df2)\n",
+    "df1.columns.values.tolist()\n",
+    "# df2[('vbf_dR_j0_Hbb', 0)]\n",
+    "# df2[('vbf_dEta_jj', 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "6d091f36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variables = [\n",
+    "    (\"ak8FatJetHbb\", 0),\n",
+    "    (\"ak8FatJetHbb\", 1),\n",
+    "    (\"ak8FatJetHVV\", 0),\n",
+    "    (\"ak8FatJetHVV\", 1),\n",
+    "    (\"ak8FatJetHVVNumProngs\", 0),\n",
+    "    (\"vbfptGen\", 0),\n",
+    "    (\"vbfptGen\", 1),\n",
+    "    (\"vbfetaGen\", 0),\n",
+    "    (\"vbfetaGen\", 1),\n",
+    "    (\"vbfphiGen\", 0),\n",
+    "    (\"vbfphiGen\", 1),\n",
+    "    (\"vbfMGen\", 0),\n",
+    "    (\"vbfMGen\", 1),\n",
+    "    (\"vbfpt\", 0),\n",
+    "    (\"vbfpt\", 1),\n",
+    "    (\"vbfeta\", 0),\n",
+    "    (\"vbfeta\", 1),\n",
+    "    (\"vbfphi\", 0),\n",
+    "    (\"vbfphi\", 1),\n",
+    "    (\"vbfM\", 0),\n",
+    "    (\"vbfM\", 1),\n",
+    "    (\"nGoodVBFJets\", 0),\n",
+    "    (\"ak8FatJetEta\", 0),\n",
+    "    (\"ak8FatJetEta\", 1),\n",
+    "    (\"ak8FatJetPhi\", 0),\n",
+    "    (\"ak8FatJetPhi\", 1),\n",
+    "    (\"ak8FatJetMass\", 0),\n",
+    "    (\"ak8FatJetMass\", 1),\n",
+    "    (\"ak8FatJetPt\", 0),\n",
+    "    (\"ak8FatJetPt\", 1),\n",
+    "    (\"ak8FatJetMsd\", 0),\n",
+    "    (\"ak8FatJetMsd\", 1),\n",
+    "]\n",
+    "\"\"\"not sure how to access 'Regressed mass of H → VV jet' maybe also in inf3erence. not sure if ('MET_pt', 0) is MET that we want. Also ('ak8FatJetPt', 0),\n",
+    "             ('ak8FatJetPt', 1) is just shuffling the pts instead of actually being for Hbb and HVV. same for eta.\n",
+    "             not sure what  ('ak8FatJetHbb', 0), is\n",
+    "             \"\"\"\n",
+    "\n",
+    "\n",
+    "labels = [\n",
+    "    \"Mass of dijet system\",\n",
+    "    \"H → VV jet pT over dijet system pT\",\n",
+    "    \"pT of dijet system \",\n",
+    "    \"pT of H → bb jet\",\n",
+    "    \"pT of H → VV jet\",\n",
+    "    \"H → VV jet pT over H → bb jet pT\",\n",
+    "    \"Missing transverse energy in event\",\n",
+    "    \"H → bb jet pT over dijet system pT\",\n",
+    "    \"Eta of H → bb jet\",\n",
+    "    \"Eta of H → VV jet\",\n",
+    "    \"Eta of dijet system\",\n",
+    "    \"Leading VBF-jet transverse momentum\",\n",
+    "    \"Subleading VBF-jet transverse momentum\",\n",
+    "    \"∆R distance between VBF-jet pair\" \"VBF-jet pair mass\",\n",
+    "    \"psuedo-rapidity separation of VBF-jet pair\",\n",
+    "    \"∆R distance between the two Higgs bosons\",\n",
+    "    \"∆R distance between the HVV and Leading VBF-jet\",\n",
+    "    \"∆R distance between the HVV and Subleading VBF-jet\",\n",
+    "    \"∆R distance between the Hbb and Leading VBF-jet\",\n",
+    "    \"∆R distance between the Hbb and Subleading VBF-jet\",\n",
+    "    \"Leading VBF-jet cos(θ) in the HH+2j center of mass frame\",\n",
+    "    \"Subleading VBF-jet cos(θ) in the HH+2j center of mass frame\",\n",
+    "    \" H1-centrality * H2-centrality\",\n",
+    "]\n",
+    "variables = [\n",
+    "    (\"DijetMass\", 0),\n",
+    "    (\"VVFatJetPtOverDijetPt\", 0),\n",
+    "    (\"DijetPt\", 0),\n",
+    "    (\"ak8FatJetPt\", 0),\n",
+    "    (\"ak8FatJetPt\", 1),\n",
+    "    (\"VVFatJetPtOverbbFatJetPt\", 0),\n",
+    "    (\"MET_pt\", 0),\n",
+    "    (\"bbFatJetPtOverDijetPt\", 0),\n",
+    "    (\"ak8FatJetEta\", 0),\n",
+    "    (\"ak8FatJetEta\", 1),\n",
+    "    (\"DijetEta\", 0),\n",
+    "    (\"vbfpt\", 0),\n",
+    "    (\"vbfpt\", 1),\n",
+    "    (\"vbf_dR_jj\", 0),\n",
+    "    (\"vbf_Mass_jj\", 0),\n",
+    "    (\"vbf_dEta_jj\", 0),\n",
+    "    (\"vbf_dR_HH\", 0),\n",
+    "    (\"vbf_dR_j0_HVV\", 0),\n",
+    "    (\"vbf_dR_j1_HVV\", 0),\n",
+    "    (\"vbf_dR_j0_Hbb\", 0),\n",
+    "    (\"vbf_dR_j1_Hbb\", 0),\n",
+    "    (\"vbf_cos1_j1\", 0),\n",
+    "    (\"vbf_cos1_j2\", 0),\n",
+    "    (\"vbf_prod_centrality\", 0),\n",
+    "]\n",
+    "\n",
+    "\n",
+    "len(labels)\n",
+    "df2\n",
+    "len(variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "b2dfdc8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_931921/1911420880.py:43: UserWarning: Glyph 8710 (\\N{INCREMENT}) missing from current font.\n",
+      "  plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 24 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# fig, axes = plt.subplots(len(variables), 1, figsize=(10, 5 * len(variables)))\n",
+    "\n",
+    "# for ax, var, label in zip(axes, variables, labels):\n",
+    "#     print(var)\n",
+    "#     # Select the appropriate column data and index\n",
+    "#     data1 = df1[var]\n",
+    "#     data2 = df2[var]\n",
+    "\n",
+    "#     # Plot histograms\n",
+    "#     ax.hist(data1, bins=30, alpha=0.5, label='Signal (df1)')\n",
+    "#     ax.hist(data2, bins=30, alpha=0.5, label='Background (df2)')\n",
+    "#     ax.set_title(label)\n",
+    "#     ax.legend()\n",
+    "\n",
+    "# # Adjust the layout\n",
+    "# plt.tight_layout()\n",
+    "# plt.show()\n",
+    "\n",
+    "n_rows_grid = 6\n",
+    "n_cols_grid = 4\n",
+    "# Plot density histograms in the grid format\n",
+    "fig, axes = plt.subplots(n_rows_grid, n_cols_grid, figsize=(20, 15))\n",
+    "\n",
+    "# Flatten the axes to iterate over them\n",
+    "flat_axes = axes.flatten()\n",
+    "\n",
+    "for ax, var, label in zip(flat_axes, variables, labels):\n",
+    "    # Select the appropriate column data and index\n",
+    "    data1 = df1[var]\n",
+    "    data2 = df2[var]\n",
+    "\n",
+    "    # Plot density histograms\n",
+    "    ax.hist(data1, bins=30, alpha=0.5, label=\"Signal (df1)\", density=True)\n",
+    "    ax.hist(data2, bins=30, alpha=0.5, label=\"Background (df2)\", density=True)\n",
+    "    ax.set_title(label)\n",
+    "    ax.legend()\n",
+    "\n",
+    "# Remove unused subplots\n",
+    "for ax in flat_axes[len(variables) :]:\n",
+    "    ax.axis(\"off\")\n",
+    "\n",
+    "# Adjust the layout\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/HHbbVV/combine/README.md b/src/HHbbVV/combine/README.md
deleted file mode 100644
index 9787fef3..00000000
--- a/src/HHbbVV/combine/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Combine Scripts
-
-- [Combine Scripts](#combine-scripts)
-  - [CMSSW + Combine Quickstart](#cmssw--combine-quickstart)
-  - [Run basic fits and diagnostics](#run-basic-fits-and-diagnostics)
-
-
-## CMSSW + Combine Quickstart
-```bash
-cmsrel CMSSW_11_2_0
-cd CMSSW_11_2_0/src
-cmsenv
-git clone -b py3 https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
-cd HiggsAnalysis/CombinedLimit
-scramv1 b clean; scramv1 b
-
-cd $CMSSW_BASE/src/
-git clone -b 113x https://github.com/cms-analysis/CombineHarvester.git CombineHarvester
-scram b
-```
-
-## Run basic fits and diagnostics
-
-```bash
-/uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh "./"
-```
-
-## Get data and toy test statistics for simple GoF
-
-```bash
-/uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/gof.sh "./"
-```
\ No newline at end of file
diff --git a/src/HHbbVV/combine/binder/BiasTest.ipynb b/src/HHbbVV/combine/binder/BiasTest.ipynb
new file mode 100644
index 00000000..ebed2432
--- /dev/null
+++ b/src/HHbbVV/combine/binder/BiasTest.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "import uproot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "import os\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../../\"\n",
+    "\n",
+    "plot_dir = f\"{MAIN_DIR}/plots/BiasTest/23Sep11\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cards_dir = \"23Jul17ResClipTFScale1\"\n",
+    "samples = [\n",
+    "    \"NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190\",\n",
+    "    \"NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125\",\n",
+    "    \"NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250\",\n",
+    "]\n",
+    "\n",
+    "biases = [0.0, 0.15, 0.3, 1.0]\n",
+    "r_dict = {}\n",
+    "\n",
+    "for sample in samples:\n",
+    "    r_dict[sample] = {}\n",
+    "    for bias in biases:\n",
+    "        file = uproot.concatenate(\n",
+    "            f\"/uscms/home/rkansal/hhcombine/cards/biastests/{cards_dir}/{sample}/higgsCombinebias{bias}.FitDiagnostics.mH125.*2.root\"\n",
+    "        )\n",
+    "\n",
+    "        r = np.array(file.limit)[::4]\n",
+    "        neg_lim = np.array(file.limit)[1::4]\n",
+    "        pos_lim = np.array(file.limit)[2::4]\n",
+    "        r_negerr = r - neg_lim\n",
+    "        r_poserr = pos_lim - r\n",
+    "        reldiff = r - bias\n",
+    "        reldiff[reldiff < 0] = (reldiff / r_poserr)[reldiff < 0]\n",
+    "        reldiff[reldiff > 0] = (reldiff / r_negerr)[reldiff > 0]\n",
+    "\n",
+    "        r_dict[sample][bias] = {\n",
+    "            \"r\": r,\n",
+    "            \"reldiff\": reldiff,\n",
+    "            \"neg_lim\": neg_lim,\n",
+    "            \"pos_lim\": pos_lim,\n",
+    "        }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r_bounds = [-1, 20]\n",
+    "# r_bounds = [-5, 40]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sample in samples:\n",
+    "    print(sample)\n",
+    "    for i, bias in enumerate(biases):\n",
+    "        num_toys = len(r_dict[sample][bias][\"r\"])\n",
+    "\n",
+    "        print(\n",
+    "            f\"For r = {bias}, # of successful fits: = {num_toys}, {np.sum(r_dict[sample][bias]['neg_lim'] == r_bounds[0]) / num_toys * 100:.0f}% of these with r- = -5, {np.sum(r_dict[sample][bias]['pos_lim'] == r_bounds[1]) / num_toys * 100 :.0f}% with r+ = 40\"\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sample in samples:\n",
+    "    print(sample)\n",
+    "    for i, bias in enumerate(biases):\n",
+    "        num_toys = len(r_dict[sample][bias][\"r\"])\n",
+    "        pfail = np.sum(\n",
+    "            (\n",
+    "                (r_dict[sample][bias][\"reldiff\"] < 0)\n",
+    "                * (r_dict[sample][bias][\"pos_lim\"] == r_bounds[1])\n",
+    "            )\n",
+    "            + (\n",
+    "                (r_dict[sample][bias][\"reldiff\"] > 0)\n",
+    "                * (r_dict[sample][bias][\"neg_lim\"] == r_bounds[0])\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "        print(\n",
+    "            f\"For r = {bias}, # of successful fits: = {num_toys}, {pfail / num_toys * 100:.0f}% of these with r-lim at boundary\"\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats\n",
+    "\n",
+    "xrange = 2.5\n",
+    "bins = 21\n",
+    "x = np.linspace(-xrange, xrange, 101)\n",
+    "\n",
+    "for sample in samples:\n",
+    "    fig, ax = plt.subplots(len(biases), 1, figsize=(12, 40))\n",
+    "\n",
+    "    for i, bias in enumerate(biases):\n",
+    "        # fit_fail = r_dict[sample][bias][\"pos_lim\"] == 40\n",
+    "        # fit_fail = r_dict[sample][bias]['pos_lim'] == 2000000\n",
+    "\n",
+    "        fit_fail = (\n",
+    "            (r_dict[sample][bias][\"reldiff\"] < 0) * (r_dict[sample][bias][\"pos_lim\"] == r_bounds[1])\n",
+    "        ) + (\n",
+    "            (r_dict[sample][bias][\"reldiff\"] > 0) * (r_dict[sample][bias][\"neg_lim\"] == r_bounds[0])\n",
+    "        )\n",
+    "\n",
+    "        r = r_dict[sample][bias][\"r\"][~fit_fail]\n",
+    "        reldiff = r_dict[sample][bias][\"reldiff\"][~fit_fail]\n",
+    "        reldiff = reldiff[(reldiff > -xrange) * (reldiff < xrange)]\n",
+    "\n",
+    "        mu, sigma = np.mean(reldiff), np.std(reldiff)\n",
+    "        ax[i].hist(reldiff, np.linspace(-xrange, xrange, bins + 1), histtype=\"step\")\n",
+    "        ax[i].plot(\n",
+    "            x,\n",
+    "            # scale by bin width\n",
+    "            stats.norm.pdf(x, loc=mu, scale=sigma) * len(r) * (2 * xrange / bins),\n",
+    "            label=rf\"$\\mu = {mu:.2f}, \\sigma = {sigma:.2f}$\",\n",
+    "        )\n",
+    "        ax[i].set_xlabel(rf\"$\\frac{{\\hat{{r}} - {bias}}}{{\\Delta \\hat r}}$\")\n",
+    "        ax[i].set_ylabel(\"Number of toys\")\n",
+    "        ax[i].set_title(f\"r = {bias}\")\n",
+    "        ax[i].legend()\n",
+    "\n",
+    "        hep.cms.label(\n",
+    "            \"Work in Progress\",\n",
+    "            ax=ax[i],\n",
+    "            data=True,\n",
+    "            lumi=138,\n",
+    "            year=None,\n",
+    "        )\n",
+    "\n",
+    "    plt.savefig(f\"{plot_dir}/{sample}.pdf\", bbox_inches=\"tight\")\n",
+    "    plt.show()\n",
+    "\n",
+    "    # break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/combine/binder/FTest.ipynb b/src/HHbbVV/combine/binder/FTest.ipynb
new file mode 100644
index 00000000..a061d022
--- /dev/null
+++ b/src/HHbbVV/combine/binder/FTest.ipynb
@@ -0,0 +1,337 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "import uproot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "import os\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../../\"\n",
+    "\n",
+    "plot_dir = f\"{MAIN_DIR}/plots/FTests/23Jul6WP06\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def p_value(data_ts: float, toy_ts: List[float]):\n",
+    "    return np.mean(toy_ts >= data_ts)\n",
+    "\n",
+    "\n",
+    "def F_statistic(\n",
+    "    ts_low: List[float],\n",
+    "    ts_high: list[float],\n",
+    "    ord_low: int,\n",
+    "    ord_high: int,\n",
+    "    num_bins: int = 10 * 14,\n",
+    "    dim: int = 2,\n",
+    "):\n",
+    "    numerator = (ts_low - ts_high) / (ord_high - ord_low)\n",
+    "    denominator = ts_high / (num_bins - (ord_high + dim))\n",
+    "\n",
+    "    return numerator / denominator"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nonresonant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "local_cards_dir = \"/uscms/home/rkansal/hhcombine/cards/f_tests/23May14/\"\n",
+    "test_orders = [0]\n",
+    "test_statistics = {}\n",
+    "\n",
+    "for o1 in test_orders:\n",
+    "    tdict = {\"toys\": {}, \"data\": {}, \"ftoys\": {}, \"fdata\": {}}\n",
+    "    tlabel = f\"{o1}\"\n",
+    "\n",
+    "    for nTF in [o1, o1 + 1]:\n",
+    "        # for nTF1, nTF2 in [(o1, o2)]:\n",
+    "        tflabel = f\"{nTF}\"\n",
+    "\n",
+    "        # test statistics for toys generated by (o1, o2) order model\n",
+    "        file = uproot.concatenate(\n",
+    "            f\"{local_cards_dir}/nTF_{nTF}/higgsCombineToys{tlabel}.GoodnessOfFit.mH125.444.root\"\n",
+    "        )\n",
+    "        tdict[\"toys\"][tflabel] = np.array(file[\"limit\"])\n",
+    "\n",
+    "        # data test statistic\n",
+    "        file = uproot.concatenate(\n",
+    "            f\"{local_cards_dir}/nTF_{nTF}/higgsCombineData.GoodnessOfFit.mH125.root\"\n",
+    "        )\n",
+    "        tdict[\"data\"][tflabel] = file[\"limit\"][0]\n",
+    "\n",
+    "        if not nTF == o1:\n",
+    "            tdict[\"ftoys\"][tflabel] = F_statistic(\n",
+    "                tdict[\"toys\"][tlabel], tdict[\"toys\"][tflabel], o1, nTF\n",
+    "            )\n",
+    "            tdict[\"fdata\"][tflabel] = F_statistic(\n",
+    "                tdict[\"data\"][tlabel], tdict[\"data\"][tflabel], o1, nTF\n",
+    "            )\n",
+    "\n",
+    "    test_statistics[tlabel] = tdict"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resonant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eos_cards_dir = \"/eos/uscms/store/user/rkansal/bbVV/cards/f_tests/23Jul6WP06/\"\n",
+    "local_cards_dir = \"/uscms/home/rkansal/hhcombine/cards/f_tests/23Jul6WP06/\"\n",
+    "# test_orders = [(0, 0), (1, 0), (0, 1), (1, 1), (2, 1), (1, 2), (2, 0), (0, 2)]\n",
+    "test_orders = [(1, 2)]\n",
+    "# test_orders = [(3, 1), (2, 2)]\n",
+    "test_statistics = {}\n",
+    "\n",
+    "for o1, o2 in test_orders:\n",
+    "    tdict = {\"toys\": {}, \"data\": {}, \"ftoys\": {}, \"fdata\": {}}\n",
+    "    tlabel = f\"{o1}{o2}\"\n",
+    "\n",
+    "    for nTF1, nTF2 in [(o1, o2), (o1 + 1, o2), (o1, o2 + 1)]:\n",
+    "        # for nTF1, nTF2 in [(o1, o2)]:\n",
+    "        tflabel = f\"{nTF1}{nTF2}\"\n",
+    "\n",
+    "        # test statistics for toys generated by (o1, o2) order model\n",
+    "        file = uproot.concatenate(\n",
+    "            f\"{eos_cards_dir}/nTF1_{nTF1}_nTF2_{nTF2}/higgsCombineToys{tlabel}Seed*44.GoodnessOfFit.mH125.*44.root\"\n",
+    "        )\n",
+    "        tdict[\"toys\"][tflabel] = np.array(file[\"limit\"])\n",
+    "\n",
+    "        # data test statistic\n",
+    "        file = uproot.concatenate(\n",
+    "            f\"{local_cards_dir}/nTF1_{nTF1}_nTF2_{nTF2}/higgsCombineData.GoodnessOfFit.mH125.root\"\n",
+    "        )\n",
+    "        tdict[\"data\"][tflabel] = file[\"limit\"][0]\n",
+    "\n",
+    "        if not (nTF1 == o1 and nTF2 == o2):\n",
+    "            tdict[\"ftoys\"][tflabel] = F_statistic(\n",
+    "                tdict[\"toys\"][f\"{o1}{o2}\"], tdict[\"toys\"][tflabel], o1 + o2, nTF1 + nTF2\n",
+    "            )\n",
+    "            tdict[\"fdata\"][tflabel] = F_statistic(\n",
+    "                tdict[\"data\"][f\"{o1}{o2}\"], tdict[\"data\"][tflabel], o1 + o2, nTF1 + nTF2\n",
+    "            )\n",
+    "\n",
+    "    test_statistics[tlabel] = tdict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats\n",
+    "\n",
+    "\n",
+    "def plot_tests(\n",
+    "    data_ts: float,\n",
+    "    toy_ts: np.ndarray,\n",
+    "    name: str,\n",
+    "    title: str = None,\n",
+    "    bins: int = 15,\n",
+    "    fit: str = None,\n",
+    "    fdof2: int = None,\n",
+    "):\n",
+    "    plot_max = max(np.max(toy_ts), data_ts)\n",
+    "    # plot_max = max(np.max(toy_ts), data_ts) if fit != \"chi2\" else 200\n",
+    "    # plot_min = min(np.min(toy_ts), data_ts, 0)\n",
+    "    plot_min = 0\n",
+    "    pval = p_value(data_ts, toy_ts)\n",
+    "\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    h = plt.hist(\n",
+    "        toy_ts,\n",
+    "        np.linspace(plot_min, plot_max, bins + 1),\n",
+    "        color=\"#8C8C8C\",\n",
+    "        histtype=\"step\",\n",
+    "        label=f\"{len(toy_ts)} Toys\",\n",
+    "    )\n",
+    "    plt.axvline(data_ts, color=\"#FF502E\", linestyle=\":\", label=rf\"Data ($p$-value = {pval:.2f})\")\n",
+    "\n",
+    "    if fit is not None:\n",
+    "        x = np.linspace(plot_min + 0.01, plot_max, 100)\n",
+    "\n",
+    "        if fit == \"chi2\":\n",
+    "            res = stats.fit(stats.chi2, toy_ts, [(0, 200)])\n",
+    "            pdf = stats.chi2.pdf(x, res.params.df)\n",
+    "            label = rf\"$\\chi^2_{{DoF = {res.params.df:.2f}}}$ Fit\"\n",
+    "        elif fit == \"f\":\n",
+    "            pdf = stats.f.pdf(x, 1, fdof2)\n",
+    "            label = rf\"$F-dist_{{DoF = (1, {fdof2})}}$\"\n",
+    "        else:\n",
+    "            raise ValueError(\"Invalid fit\")\n",
+    "\n",
+    "        plt.plot(\n",
+    "            x,\n",
+    "            pdf * (np.max(h[0]) / np.max(pdf)),\n",
+    "            color=\"#1f78b4\",\n",
+    "            linestyle=\"--\",\n",
+    "            # alpha=0.6,\n",
+    "            label=label,\n",
+    "        )\n",
+    "\n",
+    "    hep.cms.label(\n",
+    "        \"Work in Progress\",\n",
+    "        data=True,\n",
+    "        lumi=138,\n",
+    "        year=None,\n",
+    "    )\n",
+    "\n",
+    "    _ = plt.legend()\n",
+    "    plt.title(title)\n",
+    "    plt.ylabel(\"Number of Toys\")\n",
+    "    plt.xlabel(\"Test Statistics\")\n",
+    "\n",
+    "    plt.savefig(f\"{plot_dir}/{name}.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nonresonant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o1 = 0  # order being tested\n",
+    "tlabel = f\"{o1}\"\n",
+    "\n",
+    "data_ts, toy_ts = test_statistics[tlabel][\"data\"][tlabel], test_statistics[tlabel][\"toys\"][tlabel]\n",
+    "plot_tests(data_ts, toy_ts, \"gof\" + tlabel, fit=\"chi2\", bins=20)\n",
+    "\n",
+    "ord1 = 1\n",
+    "tflabel = f\"{ord1}\"\n",
+    "data_ts, toy_ts = pval = (\n",
+    "    test_statistics[tlabel][\"fdata\"][tflabel],\n",
+    "    test_statistics[tlabel][\"ftoys\"][tflabel],\n",
+    ")\n",
+    "plot_tests(data_ts, toy_ts, f\"f{tlabel}_{tflabel}\", title=f\"{o1} vs. {ord1}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resonant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "o1, o2 = 2, 0  # order being tested\n",
+    "tlabel = f\"{o1}{o2}\"\n",
+    "\n",
+    "data_ts, toy_ts = test_statistics[tlabel][\"data\"][tlabel], test_statistics[tlabel][\"toys\"][tlabel]\n",
+    "plot_tests(data_ts, toy_ts, \"gof\" + tlabel, fit=\"chi2\", bins=20)\n",
+    "\n",
+    "for ord1, ord2 in [[o1 + 1, o2], [o1, o2 + 1]]:\n",
+    "    tflabel = f\"{ord1}{ord2}\"\n",
+    "    data_ts, toy_ts = pval = (\n",
+    "        test_statistics[tlabel][\"fdata\"][tflabel],\n",
+    "        test_statistics[tlabel][\"ftoys\"][tflabel],\n",
+    "    )\n",
+    "    plot_tests(data_ts, toy_ts, f\"f{tlabel}_{tflabel}\", title=f\"({o1}, {o2}) vs. ({ord1}, {ord2})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_ts, toy_ts = tdict[\"data\"][tlabel], tdict[\"toys\"][tlabel]\n",
+    "plot_tests(data_ts, toy_ts, \"gof\" + tlabel, fit=\"chi2\", bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/combine/binder/GoF.ipynb b/src/HHbbVV/combine/binder/GoF.ipynb
new file mode 100644
index 00000000..cb090c0f
--- /dev/null
+++ b/src/HHbbVV/combine/binder/GoF.ipynb
@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "import uproot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "import os\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../../\"\n",
+    "\n",
+    "plot_dir = f\"{MAIN_DIR}/plots/GoF/23May13\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cards_dir = \"23May13MP\"\n",
+    "\n",
+    "file = uproot.concatenate(\n",
+    "    f\"/uscms/home/rkansal/nobackup/CMSSW_11_2_0/src/cards/{cards_dir}/higgsCombineData.GoodnessOfFit.mH125.root\"\n",
+    ")\n",
+    "\n",
+    "data_ts = file[\"limit\"][0]  # data test statistic\n",
+    "\n",
+    "file = uproot.concatenate(\n",
+    "    f\"/uscms/home/rkansal/nobackup/CMSSW_11_2_0/src/cards/{cards_dir}/higgsCombineToys.GoodnessOfFit.mH125.*.root\"\n",
+    ")\n",
+    "\n",
+    "toy_ts = np.array(file[\"limit\"])  # toy test statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def p_value(data_ts: float, toy_ts: List[float]):\n",
+    "    return np.mean(toy_ts >= data_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pval = p_value(data_ts, toy_ts)\n",
+    "pval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_max = max(np.max(toy_ts), data_ts)\n",
+    "plot_min = min(np.min(toy_ts), data_ts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "_ = plt.hist(\n",
+    "    toy_ts, np.linspace(plot_min, plot_max, 10), color=\"dimgrey\", histtype=\"step\", label=\"Toys\"\n",
+    ")\n",
+    "plt.axvline(data_ts, color=\"red\", linestyle=\":\", label=rf\"Data ($p$-value = {pval:.2f})\")\n",
+    "_ = plt.legend()\n",
+    "plt.title(\"Goodness of Fit\")\n",
+    "plt.ylabel(\"Number of Toys\")\n",
+    "plt.xlabel(\"Test Statistics\")\n",
+    "plt.savefig(f\"{plot_dir}/gof.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import chi2\n",
+    "\n",
+    "chi2_df = 100\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "x = np.linspace(plot_min, plot_max, 101)\n",
+    "plt.plot(\n",
+    "    x,\n",
+    "    chi2.pdf(x, chi2_df) * 1.3,\n",
+    "    color=\"blue\",\n",
+    "    linestyle=\"--\",\n",
+    "    alpha=0.6,\n",
+    "    label=rf\"$\\chi^2_{{DoF = {chi2_df}}}$\",\n",
+    ")\n",
+    "_ = plt.hist(\n",
+    "    toy_ts,\n",
+    "    np.linspace(plot_min, plot_max, 10),\n",
+    "    density=True,\n",
+    "    color=\"dimgrey\",\n",
+    "    histtype=\"step\",\n",
+    "    label=\"Toys\",\n",
+    ")\n",
+    "plt.axvline(data_ts, color=\"red\", linestyle=\":\", label=rf\"Data ($p$-value = {pval:.2f})\")\n",
+    "_ = plt.legend()\n",
+    "plt.title(\"Goodness of Fit\")\n",
+    "plt.ylabel(\"Number of Toys (A.U.)\")\n",
+    "plt.xlabel(\"Test Statistics\")\n",
+    "plt.savefig(f\"{plot_dir}/gof_chi2.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bbVV",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5b9eab485576227e6cf1b964bb8855c46cbdf15c3e77cecdb2bb309145d3e8d8"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/combine/binder/InterpolateLimits.ipynb b/src/HHbbVV/combine/binder/InterpolateLimits.ipynb
new file mode 100644
index 00000000..7150adb9
--- /dev/null
+++ b/src/HHbbVV/combine/binder/InterpolateLimits.ipynb
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "from scipy import interpolate\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mxmy(sample):\n",
+    "    mY = int(sample.split(\"-\")[-1])\n",
+    "    mX = int(sample.split(\"NMSSM_XToYHTo2W2BTo4Q2B_MX-\")[1].split(\"_\")[0])\n",
+    "\n",
+    "    return (mX, mY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dir = \"../../../../plots/XHY/Limits/23Aug24\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cards_dir = \"/eos/uscms/store/user/rkansal/bbVV/cards/Apr11\"\n",
+    "samples = os.listdir(cards_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load / process limits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limits = {\" 2.5\": [], \"16.0\": [], \"50.0\": [], \"84.0\": [], \"97.5\": []}\n",
+    "\n",
+    "for sample in tqdm(samples):\n",
+    "    limits_path = f\"{cards_dir}/{sample}/AsymptoticLimits.txt\"\n",
+    "    if os.path.exists(limits_path):\n",
+    "        mx, my = mxmy(sample)\n",
+    "        with open(limits_path, \"r\") as f:\n",
+    "            lines = f.readlines()\n",
+    "\n",
+    "        nums = 0\n",
+    "        for i in np.arange(len(lines) - 1, -1, -1):\n",
+    "            line = lines[i][:-1]\n",
+    "            for key in limits:\n",
+    "                start_str = f\"Expected {key}%: r < \"\n",
+    "                if line.startswith(start_str):\n",
+    "                    limits[key].append([mx, my, float(line.split(start_str)[1])])\n",
+    "                    nums += 1\n",
+    "\n",
+    "            if nums == 5:\n",
+    "                break\n",
+    "\n",
+    "for key in limits:\n",
+    "    limits[key] = np.array(limits[key])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scatter2d(arr, title, name):\n",
+    "    fig, ax = plt.subplots(figsize=(14, 12))\n",
+    "    mappable = plt.scatter(\n",
+    "        arr[:, 0],\n",
+    "        arr[:, 1],\n",
+    "        s=150,\n",
+    "        c=arr[:, 2],\n",
+    "        cmap=\"turbo\",\n",
+    "        norm=matplotlib.colors.LogNorm(vmin=0.01, vmax=100),\n",
+    "    )\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(r\"$m_X$ (GeV)\")\n",
+    "    plt.ylabel(r\"$m_Y$ (GeV)\")\n",
+    "    plt.colorbar(mappable)\n",
+    "    plt.savefig(name, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def colormesh(xx, yy, lims, label, name):\n",
+    "    fig, ax = plt.subplots(figsize=(12, 8))\n",
+    "    _ = plt.pcolormesh(\n",
+    "        xx, yy, lims, norm=matplotlib.colors.LogNorm(vmin=0.05, vmax=1e4), cmap=\"turbo\"\n",
+    "    )\n",
+    "    # plt.title(title)\n",
+    "    plt.xlabel(r\"$m_X$ (GeV)\")\n",
+    "    plt.ylabel(r\"$m_Y$ (GeV)\")\n",
+    "    plt.colorbar(label=label)\n",
+    "    hep.cms.label(\"Work in Progress\", data=True, lumi=\"138\", ax=ax)\n",
+    "    plt.savefig(name, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mxs = np.logspace(np.log10(600), np.log10(3999), 100, base=10)\n",
+    "mys = np.logspace(np.log10(60), np.log10(250), 100, base=10)\n",
+    "\n",
+    "xx, yy = np.meshgrid(mxs, mys)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interpolated = {}\n",
+    "grids = {}\n",
+    "\n",
+    "for key, val in limits.items():\n",
+    "    interpolated[key] = interpolate.LinearNDInterpolator(val[:, :2], np.log(val[:, 2]))\n",
+    "    grids[key] = np.exp(interpolated[key](xx, yy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for key, grid in grids.items():\n",
+    "    label = (\n",
+    "        f\"{key}% expected exclusion limits (fb)\"\n",
+    "        if key != \"50.0\"\n",
+    "        else f\"Median expected exclusion limits (fb)\"\n",
+    "    )\n",
+    "    colormesh(xx, yy, grid, label, f\"{plot_dir}/mesh_{key}_turbo.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = \"50.0\"\n",
+    "val = limits[key]\n",
+    "scatter2d(val, f\"Expected {key}% Limit\", f\"{plot_dir}/scatter_{key}.pdf\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/combine/binder/PlotScan.ipynb b/src/HHbbVV/combine/binder/PlotScan.ipynb
new file mode 100644
index 00000000..98ed516c
--- /dev/null
+++ b/src/HHbbVV/combine/binder/PlotScan.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "from matplotlib.colors import LogNorm\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 16})\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "\n",
+    "from typing import List\n",
+    "import os\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "import itertools\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", message=\"invalid value encountered in log10*\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nonres_scan_cuts = [\"txbb\", \"bdt\"]\n",
+    "res_scan_cuts = [\"txbb\", \"thww\"]\n",
+    "scan_labels = {\"txbb\": r\"$T_{Xbb}$ WP\", \"thww\": r\"$T_{HWW}$ WP\", \"bdt\": \"BDT\"}\n",
+    "\n",
+    "scan_txbb_wps = [\"LP\", \"MP\", \"HP\"]\n",
+    "scan_thww_wps = [0.4, 0.6, 0.8, 0.9, 0.94, 0.96, 0.98]\n",
+    "scan_bdt_wps = [0.6, 0.9, 0.96, 0.99, 0.997, 0.998, 0.999]\n",
+    "\n",
+    "plot_dir = \"../../../../plots/Scans/23Aug30NonResIntLumi/\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")\n",
+    "\n",
+    "# cards_dir = \"/eos/uscms/store/user/rkansal/bbVV/cards/23May13NonresScan/\"\n",
+    "cards_dir = \"/uscms/home/rkansal/hhcombine/cards/23May14NonresScan/\"\n",
+    "# cards_dir = \"/uscms/home/rkansal/hhcombine/eoscards/23May1Scan/\"  # resonant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resonant = False\n",
+    "\n",
+    "if resonant:\n",
+    "    scan_wps = list(itertools.product(scan_txbb_wps, scan_thww_wps))\n",
+    "    scan1_wps, scan2_wps = scan_txbb_wps, scan_thww_wps\n",
+    "    scan_cuts = res_scan_cuts\n",
+    "\n",
+    "    res_mps = [\n",
+    "        (900, 80),\n",
+    "        (1200, 190),\n",
+    "        (2000, 125),\n",
+    "        (3000, 250),\n",
+    "        (4000, 150),\n",
+    "    ]\n",
+    "\n",
+    "    scan_samples = []\n",
+    "\n",
+    "    for mX, mY in res_mps:\n",
+    "        scan_samples.append(f\"NMSSM_XToYHTo2W2BTo4Q2B_MX-{mX}_MY-{mY}\")\n",
+    "else:\n",
+    "    scan_wps = list(itertools.product(scan_txbb_wps, scan_bdt_wps))\n",
+    "    scan1_wps, scan2_wps = scan_txbb_wps, scan_bdt_wps\n",
+    "    scan_cuts = nonres_scan_cuts\n",
+    "    scan_samples = [\"HHbbVV\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mxmy(sample):\n",
+    "    mY = int(sample.split(\"-\")[-1])\n",
+    "    mX = int(sample.split(\"NMSSM_XToYHTo2W2BTo4Q2B_MX-\")[1].split(\"_\")[0])\n",
+    "\n",
+    "    return (mX, mY)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "limits = {}\n",
+    "\n",
+    "# parse limits\n",
+    "for sample in tqdm(scan_samples):\n",
+    "    limits[sample] = {\" 2.5\": [], \"16.0\": [], \"50.0\": [], \"84.0\": [], \"97.5\": []}\n",
+    "\n",
+    "    for wps in scan_wps:\n",
+    "        cutstr = \"_\".join([f\"{cut}_{wp}\" for cut, wp in zip(scan_cuts, wps)])\n",
+    "        sample_dir = \"outs\" if not resonant else sample\n",
+    "        limits_path = f\"{cards_dir}/{cutstr}/{sample_dir}/AsymptoticLimits.txt\"\n",
+    "\n",
+    "        if os.path.exists(limits_path):\n",
+    "            with open(limits_path, \"r\") as f:\n",
+    "                lines = f.readlines()\n",
+    "\n",
+    "            nums = 0\n",
+    "            for i in np.arange(len(lines) - 1, -1, -1):\n",
+    "                line = lines[i][:-1]\n",
+    "                for key in limits[sample]:\n",
+    "                    start_str = f\"Expected {key}%: r < \"\n",
+    "                    if line.startswith(start_str):\n",
+    "                        limits[sample][key].append([*wps, float(line.split(start_str)[1])])\n",
+    "                        nums += 1\n",
+    "\n",
+    "                if nums == 5:\n",
+    "                    break\n",
+    "\n",
+    "            if nums != 5:\n",
+    "                print(f\"{cutstr}/{sample} Limits not found!\")\n",
+    "        else:\n",
+    "            print(f\"{cutstr}/{sample_dir} Limits don't exist!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_limits(lim, sample):\n",
+    "    # convert to image\n",
+    "    im = np.zeros((len(scan1_wps), len(scan2_wps)))\n",
+    "    for wp1, wp2, l in lim:\n",
+    "        im[scan1_wps.index(wp1), scan2_wps.index(float(wp2))] = l\n",
+    "\n",
+    "    im = im[::-1]\n",
+    "\n",
+    "    plt.figure(figsize=(len(scan2_wps) * 2 + 2, 6))\n",
+    "    plt.imshow(im, cmap=\"turbo\")\n",
+    "    if resonant:\n",
+    "        plt.colorbar(label=\"Expected exclusion limit (fb)\")\n",
+    "    else:\n",
+    "        plt.colorbar(label=r\"Expected exclusion limit $\\times$SM\")\n",
+    "\n",
+    "    for wp1, wp2, l in lim:\n",
+    "        plt.text(\n",
+    "            scan2_wps.index(float(wp2)),\n",
+    "            len(scan1_wps) - scan1_wps.index(wp1) - 1,\n",
+    "            f\"{l:.2f}\",\n",
+    "            fontsize=18,\n",
+    "            color=\"white\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"center\",\n",
+    "        )\n",
+    "\n",
+    "    plt.xlabel(rf\"{scan_labels[scan_cuts[1]]} WP\")\n",
+    "    plt.ylabel(rf\"{scan_labels[scan_cuts[0]]} WP\")\n",
+    "\n",
+    "    plt.yticks(np.arange(len(scan1_wps))[::-1], scan1_wps)\n",
+    "    plt.xticks(np.arange(len(scan2_wps)), scan2_wps)\n",
+    "\n",
+    "    if resonant:\n",
+    "        mx, my = mxmy(sample)\n",
+    "        plt.title(f\"$(M_X, M_Y) = ({mx}, {my})$ GeV\", y=1.12)\n",
+    "\n",
+    "    hep.cms.label(\"Work in Progress\", data=True, lumi=\"138\")\n",
+    "\n",
+    "    # plt.title(\"Signifance Scan\")\n",
+    "    plt.savefig(f\"{plot_dir}/{sample}_limscan.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sample, lim in limits.items():\n",
+    "    plot_limits(lim[\"50.0\"], sample)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Old significance scan:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def to_image(x_range: List[float], y_range: List[float], pixels: np.ndarray):\n",
+    "    \"\"\"\n",
+    "    ranges: [x_min, x_max (exclusive), # bins]\n",
+    "    \"\"\"\n",
+    "\n",
+    "    im = np.zeros((x_range[2], y_range[2])) - 1  # missing values will show up blank in the image\n",
+    "\n",
+    "    for pixel in pixels:\n",
+    "        x = (pixel[0] - x_range[0]) / (x_range[1] - x_range[0]) * x_range[2]\n",
+    "        y = x_range[2] - ((pixel[1] - y_range[0]) / (y_range[1] - y_range[0]) * y_range[2]) - 1\n",
+    "        if x >= x_range[2] or y < 0:\n",
+    "            continue\n",
+    "        im[int(y)][int(x)] = pixel[2] + 1e-12\n",
+    "\n",
+    "    return im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak8_signs = to_image(BDT_RANGE, BB_RANGE, signs.values[:, :3])\n",
+    "ak8_limits = to_image(\n",
+    "    BDT_RANGE, BB_RANGE, np.concatenate((signs.values[:, :2], signs.values[:, 3:4]), axis=1)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(16, 12))\n",
+    "plt.imshow(\n",
+    "    ak8_signs,\n",
+    "    extent=[BDT_RANGE[0], BDT_RANGE[1], BB_RANGE[0], BB_RANGE[1]],\n",
+    "    norm=LogNorm(vmin=0.0001, vmax=0.1),\n",
+    "    cmap=\"jet\",\n",
+    ")\n",
+    "plt.colorbar(label=\"Significance\")\n",
+    "plt.xlabel(\"BDT Cut\")\n",
+    "plt.ylabel(\"Txbb Cut\")\n",
+    "plt.title(\"Signifance Scan\")\n",
+    "plt.savefig(f\"{plot_dir}/sigscan.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(16, 12))\n",
+    "plt.imshow(\n",
+    "    ak8_limits,\n",
+    "    extent=[BDT_RANGE[0], BDT_RANGE[1], BB_RANGE[0], BB_RANGE[1]],\n",
+    "    norm=LogNorm(vmin=10, vmax=1000),\n",
+    "    cmap=\"jet\",\n",
+    ")\n",
+    "plt.colorbar(label=\"Limit\")\n",
+    "plt.xlabel(\"BDT Cut\")\n",
+    "plt.ylabel(\"Txbb Cut\")\n",
+    "plt.title(\"Median xSM Expected Limits\")\n",
+    "plt.savefig(f\"{plot_dir}/limits.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('bbVV')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5b9eab485576227e6cf1b964bb8855c46cbdf15c3e77cecdb2bb309145d3e8d8"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/combine/gof.sh b/src/HHbbVV/combine/gof.sh
deleted file mode 100755
index 437a0eb7..00000000
--- a/src/HHbbVV/combine/gof.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/bash
-
-dataset=data_obs
-cards_dir=$1
-ws=${cards_dir}/combined
-wsm=${ws}_withmasks
-wsm_snapshot=higgsCombineSnapshot.MultiDimFit.mH125
-
-outsdir=${cards_dir}/outs
-mkdir -p $outsdir
-
-
-echo "GoF on data"
-combine -M GoodnessOfFit -d ${wsm_snapshot}.root --setParameterRange r=-20,20 --algo saturated -m 125 \
---setParameters mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin5=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin6=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin7=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin8=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin9=0 \
---freezeParameters CMS_bbWW_boosted_ggf_qcdparam_msdbin5,CMS_bbWW_boosted_ggf_qcdparam_msdbin6,CMS_bbWW_boosted_ggf_qcdparam_msdbin7,CMS_bbWW_boosted_ggf_qcdparam_msdbin8,CMS_bbWW_boosted_ggf_qcdparam_msdbin9 \
--n passData -v 9 2>&1 | tee $outsdir/GoF_data.txt
-
-echo "GoF on toys"
-combine -M GoodnessOfFit -d ${wsm_snapshot}.root --setParameterRange r=-20,20 --algo saturated -m 125 \
---setParameters mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin5=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin6=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin7=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin8=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin9=0 \
---freezeParameters CMS_bbWW_boosted_ggf_qcdparam_msdbin5,CMS_bbWW_boosted_ggf_qcdparam_msdbin6,CMS_bbWW_boosted_ggf_qcdparam_msdbin7,CMS_bbWW_boosted_ggf_qcdparam_msdbin8,CMS_bbWW_boosted_ggf_qcdparam_msdbin9 \
--n passToys -v 9 -s 42 -t 100 --toysFrequentist 2>&1 | tee $outsdir/GoF_toys.txt
\ No newline at end of file
diff --git a/src/HHbbVV/combine/impacts.sh b/src/HHbbVV/combine/impacts.sh
index 118fb598..7a416885 100755
--- a/src/HHbbVV/combine/impacts.sh
+++ b/src/HHbbVV/combine/impacts.sh
@@ -25,11 +25,13 @@ combineTool.py -M Impacts -t -1 --snapshotName MultiDimFit --bypassFrequentistFi
 --freezeParameters CMS_bbWW_boosted_ggf_qcdparam_msdbin5,CMS_bbWW_boosted_ggf_qcdparam_msdbin6,CMS_bbWW_boosted_ggf_qcdparam_msdbin7,CMS_bbWW_boosted_ggf_qcdparam_msdbin8,CMS_bbWW_boosted_ggf_qcdparam_msdbin9 \
 --setParameterRanges r=-15,15 -v 9 2>&1 | tee $outsdir/Impacts.txt
 
+
 echo "Collect impacts"
 combineTool.py -M Impacts -d ${wsm_snapshot}.root -m 125 -n ".impacts" \
 --setParameters mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0 \
 --freezeParameters CMS_bbWW_boosted_ggf_qcdparam_msdbin5,CMS_bbWW_boosted_ggf_qcdparam_msdbin6,CMS_bbWW_boosted_ggf_qcdparam_msdbin7,CMS_bbWW_boosted_ggf_qcdparam_msdbin8,CMS_bbWW_boosted_ggf_qcdparam_msdbin9 \
 --setParameterRanges r=-20,20 -o impacts.json
 
+
 echo "Plot impacts"
 plotImpacts.py -i impacts.json -o impacts
diff --git a/src/HHbbVV/combine/mv_bias_outputs.sh b/src/HHbbVV/combine/mv_bias_outputs.sh
new file mode 100755
index 00000000..50c861ed
--- /dev/null
+++ b/src/HHbbVV/combine/mv_bias_outputs.sh
@@ -0,0 +1,10 @@
+seed_last_digit=$1
+TAG=$2
+
+for sample in NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190 NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125 NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250
+do
+  cd $sample
+  mkdir -p bias/$TAG
+  mv higgsCombinebias*.FitDiagnostics.mH125.*$seed_last_digit.root bias/$TAG/
+  cd -
+done
\ No newline at end of file
diff --git a/src/HHbbVV/combine/parse_scan.py b/src/HHbbVV/combine/parse_scan.py
deleted file mode 100644
index b95452f4..00000000
--- a/src/HHbbVV/combine/parse_scan.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-Scrapes through the scan folders to find the significances.
-
-Author: Raghav Kansal
-"""
-
-from os import scandir
-from os.path import exists
-import pandas as pd
-
-import argparse
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument("--scan-dir", help="scan directory", type=str)
-
-args = parser.parse_args()
-
-
-cut_dirs = [f.path for f in scandir(args.scan_dir) if f.is_dir()]
-
-sigs = []
-
-for cut_dir in cut_dirs:
-    sig_file = f"{cut_dir}/outs/Significance.txt"
-    lim_file = f"{cut_dir}/outs/AsymptoticLimits.txt"
-
-    if not exists(sig_file):
-        print(f"{cut_dir} Doesn't exist")
-        continue
-
-    split = cut_dir.split("_")
-    bdt_cut = split[-3]
-    bb_cut = split[-1]
-
-    with open(sig_file, "r") as f:
-        try:
-            sig = float(f.readlines()[-2].split(" ")[-1])
-        except:
-            print(f"{cut_dir} Error parsing significance")
-            continue
-
-    with open(lim_file, "r") as f:
-        try:
-            lim = float(f.readlines()[-5].split(" ")[-1])
-        except:
-            print(f"{cut_dir} Error parsing limits")
-            continue
-
-    sigs.append([bdt_cut, bb_cut, sig, lim])
-
-print(len(sigs))
-
-sig_table = pd.DataFrame(sigs, columns=["BDT Cut", "Txbb Cut", "Sign.", "Limit"])
-sig_table.sort_values(["BDT Cut", "Txbb Cut"], inplace=True)
-sig_table.to_csv(f"{args.scan_dir}/signs.csv", index=False)
diff --git a/src/HHbbVV/combine/plot_scan.ipynb b/src/HHbbVV/combine/plot_scan.ipynb
deleted file mode 100644
index f5c202bf..00000000
--- a/src/HHbbVV/combine/plot_scan.ipynb
+++ /dev/null
@@ -1,856 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mplhep as hep\n",
-    "import matplotlib.ticker as mticker\n",
-    "from matplotlib.colors import LogNorm\n",
-    "\n",
-    "plt.rcParams.update({\"font.size\": 16})\n",
-    "plt.style.use(hep.style.CMS)\n",
-    "hep.style.use(\"CMS\")\n",
-    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
-    "formatter.set_powerlimits((-3, 3))\n",
-    "\n",
-    "from typing import List\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "BDT_RANGE = [0.97, 1.00, 15]\n",
-    "BB_RANGE = [0.97, 1.00, 15]\n",
-    "plot_dir = \"../plots/Scans/Sep12/\"\n",
-    "os.system(f\"mkdir -p {plot_dir}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDT Cut</th>\n",
-       "      <th>Txbb Cut</th>\n",
-       "      <th>Sign.</th>\n",
-       "      <th>Limit</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.008546</td>\n",
-       "      <td>238.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.972</td>\n",
-       "      <td>0.008592</td>\n",
-       "      <td>237.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>0.008884</td>\n",
-       "      <td>230.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.976</td>\n",
-       "      <td>0.009034</td>\n",
-       "      <td>226.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.97</td>\n",
-       "      <td>0.978</td>\n",
-       "      <td>0.012257</td>\n",
-       "      <td>168.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.000210</td>\n",
-       "      <td>1212.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1090.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>244</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.000165</td>\n",
-       "      <td>973.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>245</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.000038</td>\n",
-       "      <td>860.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>246</th>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>767.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>247 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     BDT Cut  Txbb Cut     Sign.   Limit\n",
-       "0       0.97     0.970  0.008546   238.5\n",
-       "1       0.97     0.972  0.008592   237.0\n",
-       "2       0.97     0.974  0.008884   230.0\n",
-       "3       0.97     0.976  0.009034   226.0\n",
-       "4       0.97     0.978  0.012257   168.5\n",
-       "..       ...       ...       ...     ...\n",
-       "242     1.00     0.992  0.000210  1212.0\n",
-       "243     1.00     0.994  0.000000  1090.0\n",
-       "244     1.00     0.996  0.000165   973.0\n",
-       "245     1.00     0.998  0.000038   860.0\n",
-       "246     1.00     1.000  0.000129   767.0\n",
-       "\n",
-       "[247 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "signs = pd.read_csv(\"../cards/04_07_scan/signs.csv\")\n",
-    "signs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def to_image(x_range: List[float], y_range: List[float], pixels: np.ndarray):\n",
-    "    \"\"\"\n",
-    "    ranges: [x_min, x_max (exclusive), # bins]\n",
-    "    \"\"\"\n",
-    "\n",
-    "    im = np.zeros((x_range[2], y_range[2])) - 1  # missing values will show up blank in the image\n",
-    "\n",
-    "    for pixel in pixels:\n",
-    "        x = (pixel[0] - x_range[0]) / (x_range[1] - x_range[0]) * x_range[2]\n",
-    "        y = x_range[2] - ((pixel[1] - y_range[0]) / (y_range[1] - y_range[0]) * y_range[2]) - 1\n",
-    "        if x >= x_range[2] or y < 0:\n",
-    "            continue\n",
-    "        im[int(y)][int(x)] = pixel[2] + 1e-12\n",
-    "\n",
-    "    return im"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ak8_signs = to_image(BDT_RANGE, BB_RANGE, signs.values[:, :3])\n",
-    "ak8_limits = to_image(\n",
-    "    BDT_RANGE, BB_RANGE, np.concatenate((signs.values[:, :2], signs.values[:, 3:4]), axis=1)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1600x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(16, 12))\n",
-    "plt.imshow(\n",
-    "    ak8_signs,\n",
-    "    extent=[BDT_RANGE[0], BDT_RANGE[1], BB_RANGE[0], BB_RANGE[1]],\n",
-    "    norm=LogNorm(vmin=0.0001, vmax=0.1),\n",
-    "    cmap=\"jet\",\n",
-    ")\n",
-    "plt.colorbar(label=\"Significance\")\n",
-    "plt.xlabel(\"BDT Cut\")\n",
-    "plt.ylabel(\"Txbb Cut\")\n",
-    "plt.title(\"Signifance Scan\")\n",
-    "plt.savefig(f\"{plot_dir}/sigscan.pdf\", bbox_inches=\"tight\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1600x1200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(16, 12))\n",
-    "plt.imshow(\n",
-    "    ak8_limits,\n",
-    "    extent=[BDT_RANGE[0], BDT_RANGE[1], BB_RANGE[0], BB_RANGE[1]],\n",
-    "    norm=LogNorm(vmin=10, vmax=1000),\n",
-    "    cmap=\"jet\",\n",
-    ")\n",
-    "plt.colorbar(label=\"Limit\")\n",
-    "plt.xlabel(\"BDT Cut\")\n",
-    "plt.ylabel(\"Txbb Cut\")\n",
-    "plt.title(\"Median xSM Expected Limits\")\n",
-    "plt.savefig(f\"{plot_dir}/limits.pdf\", bbox_inches=\"tight\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDT Cut</th>\n",
-       "      <th>Txbb Cut</th>\n",
-       "      <th>Sign.</th>\n",
-       "      <th>Limit</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.011663</td>\n",
-       "      <td>226.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>205</th>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.010600</td>\n",
-       "      <td>333.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>206</th>\n",
-       "      <td>0.994</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>767.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>207</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.009412</td>\n",
-       "      <td>214.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>208</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.972</td>\n",
-       "      <td>0.007920</td>\n",
-       "      <td>224.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>0.008152</td>\n",
-       "      <td>328.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.976</td>\n",
-       "      <td>0.008398</td>\n",
-       "      <td>330.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>211</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.978</td>\n",
-       "      <td>0.008542</td>\n",
-       "      <td>321.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>212</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.980</td>\n",
-       "      <td>0.013334</td>\n",
-       "      <td>186.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>213</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.982</td>\n",
-       "      <td>0.013688</td>\n",
-       "      <td>189.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>214</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>0.014264</td>\n",
-       "      <td>192.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>215</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>0.013929</td>\n",
-       "      <td>198.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>216</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.988</td>\n",
-       "      <td>0.015281</td>\n",
-       "      <td>196.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.029580</td>\n",
-       "      <td>168.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.026186</td>\n",
-       "      <td>177.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>219</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.024192</td>\n",
-       "      <td>220.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>220</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.091438</td>\n",
-       "      <td>223.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>221</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>767.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>222</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>0.000118</td>\n",
-       "      <td>1710.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>223</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1583.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>224</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.988</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1459.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1332.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>226</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.000210</td>\n",
-       "      <td>1212.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>227</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1090.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>228</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.000165</td>\n",
-       "      <td>973.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>229</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.000038</td>\n",
-       "      <td>860.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>230</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>767.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     BDT Cut  Txbb Cut     Sign.   Limit\n",
-       "204    0.994     0.994  0.011663   226.5\n",
-       "205    0.994     0.996  0.010600   333.0\n",
-       "206    0.994     1.000  0.000129   767.0\n",
-       "207    0.996     0.970  0.009412   214.0\n",
-       "208    0.996     0.972  0.007920   224.0\n",
-       "209    0.996     0.974  0.008152   328.0\n",
-       "210    0.996     0.976  0.008398   330.5\n",
-       "211    0.996     0.978  0.008542   321.5\n",
-       "212    0.996     0.980  0.013334   186.0\n",
-       "213    0.996     0.982  0.013688   189.5\n",
-       "214    0.996     0.984  0.014264   192.0\n",
-       "215    0.996     0.986  0.013929   198.0\n",
-       "216    0.996     0.988  0.015281   196.0\n",
-       "217    0.996     0.990  0.029580   168.5\n",
-       "218    0.996     0.992  0.026186   177.5\n",
-       "219    0.996     0.994  0.024192   220.5\n",
-       "220    0.996     0.996  0.091438   223.0\n",
-       "221    0.996     1.000  0.000129   767.0\n",
-       "222    0.998     0.984  0.000118  1710.0\n",
-       "223    0.998     0.986  0.000000  1583.0\n",
-       "224    0.998     0.988  0.000000  1459.0\n",
-       "225    0.998     0.990  0.000000  1332.0\n",
-       "226    0.998     0.992  0.000210  1212.0\n",
-       "227    0.998     0.994  0.000000  1090.0\n",
-       "228    0.998     0.996  0.000165   973.0\n",
-       "229    0.998     0.998  0.000038   860.0\n",
-       "230    0.998     1.000  0.000129   767.0"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "signs[-40:-13]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDT Cut</th>\n",
-       "      <th>Txbb Cut</th>\n",
-       "      <th>Sign.</th>\n",
-       "      <th>Limit</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>207</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.009420</td>\n",
-       "      <td>213.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>208</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.972</td>\n",
-       "      <td>0.007955</td>\n",
-       "      <td>224.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>209</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>0.008166</td>\n",
-       "      <td>327.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>210</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.976</td>\n",
-       "      <td>0.008425</td>\n",
-       "      <td>329.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>211</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.978</td>\n",
-       "      <td>0.008765</td>\n",
-       "      <td>320.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>212</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.980</td>\n",
-       "      <td>0.013348</td>\n",
-       "      <td>185.75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>213</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.982</td>\n",
-       "      <td>0.013725</td>\n",
-       "      <td>189.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>214</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>0.014293</td>\n",
-       "      <td>191.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>215</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>0.013939</td>\n",
-       "      <td>197.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>216</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.988</td>\n",
-       "      <td>0.015317</td>\n",
-       "      <td>195.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>217</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.029550</td>\n",
-       "      <td>167.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>218</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.028353</td>\n",
-       "      <td>176.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>219</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.024255</td>\n",
-       "      <td>218.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>220</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.091374</td>\n",
-       "      <td>219.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>221</th>\n",
-       "      <td>0.996</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>767.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>222</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.984</td>\n",
-       "      <td>0.000118</td>\n",
-       "      <td>1710.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>223</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.986</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1583.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>224</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.988</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1459.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>225</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.990</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1332.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>226</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.992</td>\n",
-       "      <td>0.000210</td>\n",
-       "      <td>1212.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>227</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.994</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1090.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>228</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.996</td>\n",
-       "      <td>0.000165</td>\n",
-       "      <td>973.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>229</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.998</td>\n",
-       "      <td>0.000038</td>\n",
-       "      <td>860.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>230</th>\n",
-       "      <td>0.998</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>767.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>231</th>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.970</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2651.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>232</th>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.972</td>\n",
-       "      <td>0.000017</td>\n",
-       "      <td>2504.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>233</th>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.974</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2366.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     BDT Cut  Txbb Cut     Sign.    Limit\n",
-       "207    0.996     0.970  0.009420   213.75\n",
-       "208    0.996     0.972  0.007955   224.00\n",
-       "209    0.996     0.974  0.008166   327.00\n",
-       "210    0.996     0.976  0.008425   329.50\n",
-       "211    0.996     0.978  0.008765   320.00\n",
-       "212    0.996     0.980  0.013348   185.75\n",
-       "213    0.996     0.982  0.013725   189.00\n",
-       "214    0.996     0.984  0.014293   191.50\n",
-       "215    0.996     0.986  0.013939   197.50\n",
-       "216    0.996     0.988  0.015317   195.00\n",
-       "217    0.996     0.990  0.029550   167.50\n",
-       "218    0.996     0.992  0.028353   176.50\n",
-       "219    0.996     0.994  0.024255   218.00\n",
-       "220    0.996     0.996  0.091374   219.00\n",
-       "221    0.996     1.000  0.000129   767.00\n",
-       "222    0.998     0.984  0.000118  1710.00\n",
-       "223    0.998     0.986  0.000000  1583.00\n",
-       "224    0.998     0.988  0.000000  1459.00\n",
-       "225    0.998     0.990  0.000000  1332.00\n",
-       "226    0.998     0.992  0.000210  1212.00\n",
-       "227    0.998     0.994  0.000000  1090.00\n",
-       "228    0.998     0.996  0.000165   973.00\n",
-       "229    0.998     0.998  0.000038   860.00\n",
-       "230    0.998     1.000  0.000129   767.00\n",
-       "231    1.000     0.970  0.000000  2651.00\n",
-       "232    1.000     0.972  0.000017  2504.00\n",
-       "233    1.000     0.974  0.000000  2366.00"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "signs[-40:-13]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('bbVV')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "5b9eab485576227e6cf1b964bb8855c46cbdf15c3e77cecdb2bb309145d3e8d8"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/HHbbVV/combine/run_blinded.sh b/src/HHbbVV/combine/run_blinded.sh
index 994d11a0..195916c3 100755
--- a/src/HHbbVV/combine/run_blinded.sh
+++ b/src/HHbbVV/combine/run_blinded.sh
@@ -1,36 +1,370 @@
 #!/bin/bash
 
+####################################################################################################
+# Script for fits
+# 
+# 1) Combines cards and makes a workspace (--workspace / -w)
+# 2) Background-only fit (--bfit / -b)
+# 3) Expected asymptotic limits (--limits / -l)
+# 4) Expected significance (--significance / -s)
+# 5) Fit diagnostics (--dfit / -d)
+# 6) GoF on data (--gofdata / -g)
+# 7) GoF on toys (--goftoys / -t),
+# 8) Impacts: initial fit (--impactsi / -i), per-nuisance fits (--impactsf $nuisance), collect (--impactsc $nuisances)
+# 9) Bias test: run a bias test on toys (using post-fit nuisances) with expected signal strength 
+#    given by --bias X.
+# 
+# Specify resonant with --resonant / -r, otherwise does nonresonant
+# Specify seed with --seed (default 42) and number of toys with --numtoys (default 100)
+#
+# Usage ./run_blinded.sh [-wblsdgt] [--numtoys 100] [--seed 42] 
+#
+# Author: Raghav Kansal
+####################################################################################################
+
+
+####################################################################################################
+# Read options
+####################################################################################################
+
+workspace=0
+bfit=0
+limits=0
+significance=0
+dfit=0
+resonant=0
+gofdata=0
+goftoys=0
+impactsi=0
+impactsf=0
+impactsc=0
+seed=42
+numtoys=100
+bias=-1
+
+options=$(getopt -o "wblsdrgti" --long "workspace,bfit,limits,significance,dfit,resonant,gofdata,goftoys,impactsi,impactsf:,impactsc:,bias:,seed:,numtoys:" -- "$@")
+eval set -- "$options"
+
+while true; do
+    case "$1" in
+        -w|--workspace)
+            workspace=1
+            ;;
+        -b|--bfit)
+            bfit=1
+            ;;
+        -l|--limits)
+            limits=1
+            ;;
+        -s|--significance)
+            significance=1
+            ;;
+        -d|--dfit)
+            dfit=1
+            ;;
+        -r|--resonant)
+            resonant=1
+            ;;
+        -g|--gofdata)
+            gofdata=1
+            ;;
+        -t|--goftoys)
+            goftoys=1
+            ;;
+        -i|--impactsi)
+            impactsi=1
+            ;;
+        --impactsf)
+            shift
+            impactsf=$1
+            ;;
+        --impactsc)
+            shift
+            impactsc=$1
+            ;;
+        --seed)
+            shift
+            seed=$1
+            ;;
+        --numtoys)
+            shift
+            numtoys=$1
+            ;;
+        --bias)
+            shift
+            bias=$1
+            ;;
+        --)
+            shift
+            break;;
+        \?)
+            echo "Invalid option: -$OPTARG" >&2
+            exit 1
+            ;;
+        :)
+            echo "Option -$OPTARG requires an argument." >&2
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+echo "Arguments: resonant=$resonant workspace=$workspace bfit=$bfit limits=$limits \
+significance=$significance dfit=$dfit gofdata=$gofdata goftoys=$goftoys \
+seed=$seed numtoys=$numtoys"
+
+
+####################################################################################################
+# Set up fit arguments
+# 
+# We use channel masking to "mask" the blinded and "unblinded" regions in the same workspace. 
+# (mask = 1 means the channel is masked off) 
+####################################################################################################
+
 dataset=data_obs
-cards_dir=$1
+cards_dir="./"
 ws=${cards_dir}/combined
 wsm=${ws}_withmasks
 wsm_snapshot=higgsCombineSnapshot.MultiDimFit.mH125
+CMS_PARAMS_LABEL="CMS_bbWW_hadronic"
 
 outsdir=${cards_dir}/outs
 mkdir -p $outsdir
 
+if [ $resonant = 0 ]; then
+    if [ -f "mXbin0pass.txt" ]; then
+        echo -e "\nWARNING: This is doing nonresonant fits - did you mean to pass -r|--resonant?\n"
+    fi
+
+    # nonresonant args
+    ccargs="fail=${cards_dir}/fail.txt failBlinded=${cards_dir}/failBlinded.txt pass=${cards_dir}/pass.txt passBlinded=${cards_dir}/passBlinded.txt"
+    maskunblindedargs="mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0"
+    maskblindedargs="mask_pass=0,mask_fail=0,mask_passBlinded=1,mask_failBlinded=1"
+
+    # freeze qcd params in blinded bins
+    setparamsblinded=""
+    freezeparamsblinded=""
+    for bin in {5..9}
+    do
+        setparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin}=0,"
+        freezeparamsblinded+="${CMS_PARAMS_LABEL}_tf_dataResidual_Bin${bin},"
+    done
+
+    # remove last comma
+    setparamsblinded=${setparamsblinded%,}
+    freezeparamsblinded="${freezeparamsblinded},var{.*lp_sf.*},CMS_XHYbbWW_boosted_PNetHbbScaleFactors_correlated"
+
+
+    # floating parameters using var{} floats a bunch of parameters which shouldn't be floated,
+    # so countering this inside --freezeParameters which takes priority.
+    # Although, practically even if those are set to "float", I didn't see them ever being fitted,
+    # so this is just to be extra safe.
+    unblindedparams="--freezeParameters var{.*_In},var{.*__norm},var{n_exp_.*} --setParameters $maskblindedargs"
+
+    excludeimpactparams='rgx{.*tf_dataResidual_Bin.*}'
+else
+    # resonant args
+    ccargs=""
+    maskunblindedargs=""
+    maskblindedargs=""
+
+    for bin in {0..9}
+    do
+        for channel in fail failBlinded pass passBlinded;
+        do
+            ccargs+="mXbin${bin}${channel}=${cards_dir}/mXbin${bin}${channel}.txt "
+        done
+
+        for channel in fail pass;
+        do
+            maskunblindedargs+="mask_mXbin${bin}${channel}=1,mask_mXbin${bin}${channel}Blinded=0,"
+            maskblindedargs+="mask_mXbin${bin}${channel}=0,mask_mXbin${bin}${channel}Blinded=1,"
+        done
+    done
+
+    # remove last comma
+    maskunblindedargs=${maskunblindedargs%,}
+    maskblindedargs=${maskblindedargs%,}
+
+    setparamsblinded="var{.*pass_.*mcstat.*}=0,var{.*fail_.*mcstat.*}=0"
+    freezeparamsblinded='var{.*pass_.*mcstat.*},var{.*fail_.*mcstat.*},var{.*xhy_mx.*},CMS_XHYbbWW_boosted_PNetHbbScaleFactors_correlated'
+    # freezing them here meant they weren't included in the impacts, so unfreezing now
+    # freezeparamsblinded="rgx{pass_.*mcstat.*},rgx{fail_.*mcstat.*}"
+
+    setparamsunblinded="rgx{.*passBlinded_.*mcstat.*}=0,rgx{.*failBlinded_.*mcstat.*}=0"
+    freezeparamsunblinded="rgx{.*passBlinded_.*mcstat.*},rgx{.*failBlinded_.*mcstat.*}"
+
+    # floating parameters using var{} floats a bunch of parameters which shouldn't be floated,
+    # so countering this inside --freezeParameters which takes priority.
+    # Although, practically even if those are set to "float", I didn't see them ever being fitted,
+    # so this is just to be extra safe.
+    unblindedparams="--freezeParameters ${freezeparamsunblinded},var{.*_In},var{.*__norm},var{n_exp_.*} --setParameters ${maskblindedargs},${setparamsunblinded}"
+
+    # excludeimpactparams='rgx{.*qcdparam_mXbin.*},rgx{passBlinded_.*mcstat.*},rgx{failBlinded_.*mcstat.*}'
+    excludeimpactparams='rgx{.*qcdparam_mXbin.*},rgx{.*mcstat.*}'
+fi
+
+echo "mask args:"
+echo $maskblindedargs
+
+echo "set params:"
+echo $setparamsblinded
+
+echo "freeze params:"
+echo $freezeparamsblinded
+
+echo "unblinded params:"
+echo $unblindedparams
+
+####################################################################################################
+# Combine cards, text2workspace, fit, limits, significances, fitdiagnositcs, GoFs
+####################################################################################################
+
+# need to run this for large # of nuisances 
+# https://cms-talk.web.cern.ch/t/segmentation-fault-in-combine/20735
+ulimit -s unlimited
+
+if [ $workspace = 1 ]; then
+    echo "Combining cards"
+    combineCards.py $ccargs > $ws.txt
+
+    echo "Running text2workspace"
+    # text2workspace.py -D $dataset $ws.txt --channel-masks -o $wsm.root 2>&1 | tee $outsdir/text2workspace.txt
+    # new version got rid of -D arg??
+    text2workspace.py $ws.txt --channel-masks -o $wsm.root 2>&1 | tee $outsdir/text2workspace.txt
+else
+    if [ ! -f "$wsm.root" ]; then
+        echo "Workspace doesn't exist! Use the -w|--workspace option to make workspace first"
+        exit 1
+    fi
+fi
+
+
+if [ $bfit = 1 ]; then
+    echo "Blinded background-only fit"
+    combine -D $dataset -M MultiDimFit --saveWorkspace -m 125 -d ${wsm}.root -v 9 \
+    --cminDefaultMinimizerStrategy 1 \
+    --setParameters ${maskunblindedargs},${setparamsblinded},r=0  \
+    --freezeParameters r,${freezeparamsblinded} \
+    -n Snapshot 2>&1 | tee $outsdir/MultiDimFit.txt
+else
+    if [ ! -f "higgsCombineSnapshot.MultiDimFit.mH125.root" ]; then
+        echo "Background-only fit snapshot doesn't exist! Use the -b|--bfit option to run fit first"
+        exit 1
+    fi
+fi
+
+
+if [ $limits = 1 ]; then
+    echo "Expected limits"
+    combine -M AsymptoticLimits -m 125 -n "" -d ${wsm_snapshot}.root --snapshotName MultiDimFit -v 9 \
+    --saveWorkspace --saveToys --bypassFrequentistFit \
+    ${unblindedparams},r=0 -s $seed \
+    --floatParameters ${freezeparamsblinded},r --toysFrequentist --run blind 2>&1 | tee $outsdir/AsymptoticLimits.txt
+fi
+
+
+if [ $significance = 1 ]; then
+    echo "Expected significance"
+    combine -M Significance -d ${wsm_snapshot}.root -n "" --significance -m 125 --snapshotName MultiDimFit -v 9 \
+    -t -1 --expectSignal=1 --saveWorkspace --saveToys --bypassFrequentistFit \
+    ${unblindedparams},r=1 \
+    --floatParameters ${freezeparamsblinded},r --toysFrequentist 2>&1 | tee $outsdir/Significance.txt
+fi
+
+
+if [ $dfit = 1 ]; then
+    echo "Fit Diagnostics"
+    combine -M FitDiagnostics -m 125 -d ${wsm}.root \
+    --setParameters ${maskunblindedargs},${setparamsblinded} \
+    --freezeParameters ${freezeparamsblinded} \
+    --cminDefaultMinimizerStrategy 1 \
+    -n Blinded --ignoreCovWarning -v 9 2>&1 | tee $outsdir/FitDiagnostics.txt
+    # --saveShapes --saveNormalizations --saveWithUncertainties --saveOverallShapes \
+
+    echo "Fit Shapes"
+    PostFitShapesFromWorkspace --dataset $dataset -w ${wsm}.root --output FitShapes.root \
+    -m 125 -f fitDiagnosticsBlinded.root:fit_b --postfit --print 2>&1 | tee $outsdir/FitShapes.txt
+fi
+
+
+if [ $gofdata = 1 ]; then
+    echo "GoF on data"
+    combine -M GoodnessOfFit -d ${wsm_snapshot}.root --algo saturated -m 125 \
+    --snapshotName MultiDimFit --bypassFrequentistFit \
+    --setParameters ${maskunblindedargs},${setparams},r=0 \
+    --freezeParameters ${freezeparams},r \
+    -n Data -v 9 2>&1 | tee $outsdir/GoF_data.txt
+fi
+
+
+if [ $goftoys = 1 ]; then
+    echo "GoF on toys"
+    combine -M GoodnessOfFit -d ${wsm_snapshot}.root --algo saturated -m 125 \
+    --snapshotName MultiDimFit --bypassFrequentistFit \
+    --setParameters ${maskunblindedargs},${setparams},r=0 \
+    --freezeParameters ${freezeparams},r --saveToys \
+    -n Toys -v 9 -s $seed -t $numtoys --toysFrequentist 2>&1 | tee $outsdir/GoF_toys.txt
+fi
+
+
+if [ $impactsi = 1 ]; then
+    echo "Initial fit for impacts"
+    # from https://github.com/cms-analysis/CombineHarvester/blob/f0e0c53298521921abf59c175b5c5616026d203b/CombineTools/python/combine/Impacts.py#L113
+    # combine -M MultiDimFit -m 125 -n "_initialFit_impacts" -d ${wsm_snapshot}.root --snapshotName MultiDimFit \
+    #  --algo singles --redefineSignalPOIs r --floatOtherPOIs 1 --saveInactivePOI 1 -P r --setParameterRanges r=-0.5,20 \
+    # --toysFrequentist --expectSignal 1 --bypassFrequentistFit -t -1 \
+    # ${unblindedparams} --floatParameters ${freezeparamsblinded} \
+    # --robustFit 1 --cminDefaultMinimizerStrategy=1 -v 9 2>&1 | tee $outsdir/Impacts_init.txt
+
+    combineTool.py -M Impacts --snapshotName MultiDimFit -m 125 -n "impacts" \
+    -t -1 --bypassFrequentistFit --toysFrequentist --expectSignal 1 \
+    -d ${wsm_snapshot}.root --doInitialFit --robustFit 1 \
+    ${unblindedparams} --floatParameters ${freezeparamsblinded} \
+     --cminDefaultMinimizerStrategy=1 -v 1 2>&1 | tee $outsdir/Impacts_init.txt
+fi
+
 
-echo "combine cards"
-combineCards.py fail=${cards_dir}/fail.txt failBlinded=${cards_dir}/failBlinded.txt pass=${cards_dir}/pass.txt passBlinded=${cards_dir}/passBlinded.txt > $ws.txt
+if [ $impactsf != 0 ]; then
+    echo "Submitting jobs for impact scans"
+    # Impacts module cannot access parameters which were frozen in MultiDimFit, so running impacts
+    # for each parameter directly using its internal command 
+    # (also need to do this for submitting to condor anywhere other than lxplus)
+    combine -M MultiDimFit -n _paramFit_impacts_$impactsf --algo impact --redefineSignalPOIs r -P $impactsf \
+    --floatOtherPOIs 1 --saveInactivePOI 1 --snapshotName MultiDimFit -d ${wsm_snapshot}.root \
+    -t -1 --bypassFrequentistFit --toysFrequentist --expectSignal 1 --robustFit 1 \
+    ${unblindedparams} --floatParameters ${freezeparamsblinded} \
+    --setParameterRanges r=-0.5,20 --cminDefaultMinimizerStrategy=1 -v 1 -m 125 | tee $outsdir/Impacts_$impactsf.txt
 
-echo "text2workspace"
-text2workspace.py -D $dataset $ws.txt --channel-masks -o $wsm.root 2>&1 | tee $outsdir/text2workspace.txt
+    # Old Impacts command:
+    # combineTool.py -M Impacts -t -1 --snapshotName MultiDimFit --bypassFrequentistFit --toysFrequentist --expectSignal 1 \
+    # -m 125 -n "impacts" -d ${wsm_snapshot}.root --doFits --robustFit 1 \
+    # --setParameters ${maskblindedargs} --floatParameters ${freezeparamsblinded} \
+    # --exclude ${excludeimpactparams} \
+    # --job-mode condor --dry-run \
+    # --setParameterRanges r=-0.5,20 --cminDefaultMinimizerStrategy=1 -v 9 2>&1 | tee $outsdir/Impacts_fits.txt
+fi
 
-echo "blinded bkg-only fit snapshot"
-combine -D $dataset -M MultiDimFit --saveWorkspace -m 125 -d ${wsm}.root --verbose 9 --cminDefaultMinimizerStrategy 1 \
---setParameters mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin5=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin6=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin7=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin8=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin9=0,r=0 \
---freezeParameters CMS_bbWW_boosted_ggf_qcdparam_msdbin5,CMS_bbWW_boosted_ggf_qcdparam_msdbin6,CMS_bbWW_boosted_ggf_qcdparam_msdbin7,CMS_bbWW_boosted_ggf_qcdparam_msdbin8,CMS_bbWW_boosted_ggf_qcdparam_msdbin9,r \
--n Snapshot 2>&1 | tee $outsdir/MultiDimFit.txt
 
-echo "asymptotic limit"
-combine -M AsymptoticLimits -m 125 -n pass -d ${wsm_snapshot}.root --snapshotName MultiDimFit --saveWorkspace --saveToys --bypassFrequentistFit --setParameters mask_pass=0,mask_fail=0,mask_passBlinded=1,mask_failBlinded=1 --floatParameters r --toysFrequentist --run blind 2>&1 | tee $outsdir/AsymptoticLimits.txt
+if [ $impactsc != 0 ]; then
+    echo "Collecting impacts"
+    combineTool.py -M Impacts --snapshotName MultiDimFit \
+    -m 125 -n "impacts" -d ${wsm_snapshot}.root \
+    --setParameters ${maskblindedargs} --floatParameters ${freezeparamsblinded} \
+    -t -1 --named $impactsc \
+    --setParameterRanges r=-0.5,20 -v 1 -o impacts.json 2>&1 | tee $outsdir/Impacts_collect.txt
 
-echo "expected significance"
-combine -M Significance -d ${wsm_snapshot}.root --significance -m 125 -n pass --snapshotName MultiDimFit -t -1 --expectSignal=1 --saveWorkspace --saveToys --bypassFrequentistFit --setParameters mask_pass=0,mask_fail=0,mask_passBlinded=1,mask_failBlinded=1,r=1 --floatParameters r --toysFrequentist 2>&1 | tee $outsdir/Significance.txt
+    plotImpacts.py -i impacts.json -o impacts
+fi
 
-echo "fitdiagnostics"
-combine -M FitDiagnostics -m 125 -d ${wsm}.root \
---setParameters mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin5=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin6=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin7=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin8=0,CMS_bbWW_boosted_ggf_qcdparam_msdbin9=0 \
---freezeParameters CMS_bbWW_boosted_ggf_qcdparam_msdbin5,CMS_bbWW_boosted_ggf_qcdparam_msdbin6,CMS_bbWW_boosted_ggf_qcdparam_msdbin7,CMS_bbWW_boosted_ggf_qcdparam_msdbin8,CMS_bbWW_boosted_ggf_qcdparam_msdbin9 \
---saveShapes --saveNormalizations --saveWithUncertainties --saveOverallShapes -n Blinded --ignoreCovWarning -v 9 2>&1 | tee $outsdir/FitDiagnostics.txt
 
+if [ $bias != -1 ]; then
+    echo "Bias test with bias $bias"
+    # setting verbose > 0 here can lead to crazy large output files (~10-100GB!) because of getting
+    # stuck in negative yield areas
+    combine -M FitDiagnostics --trackParameters r --trackErrors r --justFit \
+    -m 125 -n "bias${bias}" -d ${wsm_snapshot}.root --rMin "-5" --rMax 40 \
+    --snapshotName MultiDimFit --bypassFrequentistFit --toysFrequentist --expectSignal $bias \
+    ${unblindedparams},r=$bias --floatParameters ${freezeparamsblinded} \
+    --robustFit=1 -t $numtoys -s $seed 2>&1 | tee $outsdir/bias${bias}seed${seed}.txt
+fi
\ No newline at end of file
diff --git a/src/HHbbVV/combine/run_ftest_nonres.sh b/src/HHbbVV/combine/run_ftest_nonres.sh
new file mode 100755
index 00000000..16498857
--- /dev/null
+++ b/src/HHbbVV/combine/run_ftest_nonres.sh
@@ -0,0 +1,195 @@
+#!/bin/bash
+
+####################################################################################################
+# 1) Makes datacards and workspaces for different orders of polynomials
+# 2) Runs background-only fit (Higgs mass window blinded) for lowest order polynomial and GoF test (saturated model) on data
+# 3) Runs fit diagnostics and saves shapes (-d|--dfit)
+# 4) Generates toys and gets test statistics for each (-t|--goftoys)
+# 5) Fits +1 order models to all 100 toys and gets test statistics (-f|--ffits)
+#
+# Author: Raghav Kansal
+####################################################################################################
+
+goftoys=0
+ffits=0
+dfit=0
+seed=42
+numtoys=100
+order=0
+
+options=$(getopt -o "tfdlo:" --long "cardstag:,templatestag:,goftoys,ffits,dfit,limits,order:,numtoys:,seed:" -- "$@")
+eval set -- "$options"
+
+while true; do
+    case "$1" in
+        -t|--goftoys)
+            goftoys=1
+            ;;
+        -f|--ffits)
+            ffits=1
+            ;;
+        -d|--dfit)
+            dfit=1
+            ;;
+        -l|--limits)
+            limits=1
+            ;;
+        --cardstag)
+            shift
+            cards_tag=$1
+            ;;
+        --templatestag)
+            shift
+            templates_tag=$1
+            ;;
+        --o,order)
+            shift
+            order=$1
+            ;;
+        --seed)
+            shift
+            seed=$1
+            ;;
+        --numtoys)
+            shift
+            numtoys=$1
+            ;;
+        --)
+            shift
+            break;;
+        \?)
+            echo "Invalid option: -$OPTARG" >&2
+            exit 1
+            ;;
+        :)
+            echo "Option -$OPTARG requires an argument." >&2
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+echo "Arguments: cardstag=$cards_tag templatestag=$templates_tag sigsample=$sig_sample dfit=$dfit \
+goftoys=$goftoys ffits=$ffits seed=$seed numtoys=$numtoys"
+
+
+####################################################################################################
+# Set up fit args
+####################################################################################################
+
+templates_dir="/eos/uscms/store/user/rkansal/bbVV/templates/${templates_tag}"
+cards_dir="cards/f_tests/${cards_tag}/"
+mkdir -p ${cards_dir}
+echo "Saving datacards to ${cards_dir}"
+
+# these are for inside the different cards directories
+dataset=data_obs
+ws="./combined"
+wsm=${ws}_withmasks
+wsm_snapshot=higgsCombineSnapshot.MultiDimFit.mH125
+
+outsdir="./outs"
+
+# nonresonant args
+ccargs="fail=${cards_dir}/fail.txt failBlinded=${cards_dir}/failBlinded.txt pass=${cards_dir}/pass.txt passBlinded=${cards_dir}/passBlinded.txt"
+maskunblindedargs="mask_pass=1,mask_fail=1,mask_passBlinded=0,mask_failBlinded=0"
+maskblindedargs="mask_pass=0,mask_fail=0,mask_passBlinded=1,mask_failBlinded=1"
+
+# freeze qcd params in blinded bins
+setparamsblinded=""
+freezeparamsblinded=""
+for bin in {5..9}
+do
+    setparamsblinded+="CMS_bbWW_boosted_ggf_qcdparam_msdbin${bin}=0,"
+    freezeparamsblinded+="CMS_bbWW_boosted_ggf_qcdparam_msdbin${bin},"
+done
+
+# remove last comma
+setparamsblinded=${setparamsblinded%,}
+freezeparamsblinded=${freezeparamsblinded%,}
+
+
+####################################################################################################
+# Making cards and workspaces for each order polynomial
+####################################################################################################
+
+for ord1 in {0..3}
+do
+    model_name="nTF_${ord1}"
+    
+    # create datacards if they don't already exist
+    if [ ! -f "${cards_dir}/${model_name}/pass.txt" ]; then
+        echo "Making Datacard for $model_name"
+        python3 -u postprocessing/CreateDatacard.py --templates-dir ${templates_dir} \
+        --model-name ${model_name} --nTF ${ord2} ${ord1} --cards-dir ${cards_dir}
+    fi
+
+    cd ${cards_dir}/${model_name}/
+    echo ${cards_dir}/${model_name}/
+
+    # make workspace, background-only fit, GoF on data if they don't already exist
+    if [ ! -f "./higgsCombineData.GoodnessOfFit.mH125.root" ]; then
+        echo "Making workspace, doing b-only fit and gof on data"
+        /uscms/home/rkansal/hhcombine/combine_scripts/run_blinded.sh -wbg
+    fi
+
+    if [ $dfit = 1 ]; then
+        /uscms/home/rkansal/hhcombine/combine_scripts/run_blinded.sh -d
+    fi
+
+    if [ $limits = 1 ]; then
+        /uscms/home/rkansal/hhcombine/combine_scripts/run_blinded.sh -l
+    fi
+
+    cd -
+done
+
+
+####################################################################################################
+# Generate toys for (0, 0) order
+####################################################################################################
+
+model_name="nTF_$order"
+toys_name=$order
+cd ${cards_dir}/${model_name}/
+toys_file="$(pwd)/higgsCombineToys${toys_name}.GenerateOnly.mH125.$seed.root"
+cd -
+
+if [ $goftoys = 1 ]; then
+    cd ${cards_dir}/${model_name}/
+    
+    ulimit -s unlimited
+
+    echo "Toys for $order order fit"
+    combine -M GenerateOnly -m 125 -d ${wsm_snapshot}.root \
+    --snapshotName MultiDimFit --bypassFrequentistFit \
+    --setParameters ${maskunblindedargs},${setparams},r=0 \
+    --freezeParameters ${freezeparams},r \
+    -n "Toys${toys_name}" -t $numtoys --saveToys -s $seed -v 9 2>&1 | tee $outsdir/gentoys.txt
+
+    cd -
+fi
+
+
+####################################################################################################
+# GoFs on generated toys for next order polynomials
+####################################################################################################
+
+if [ $ffits = 1 ]; then
+    for ord1 in 0 1
+    do
+        model_name="nTF_${ord1}"
+        echo "Fits for $model_name"
+
+        cd ${cards_dir}/${model_name}/
+
+        ulimit -s unlimited
+
+        combine -M GoodnessOfFit -d ${wsm_snapshot}.root --algo saturated -m 125 \
+        --setParameters ${maskunblindedargs},${setparams},r=0 \
+        --freezeParameters ${freezeparams},r \
+        -n Toys${toys_name} -v 9 -s $seed -t $numtoys --toysFile ${toys_file} 2>&1 | tee $outsdir/GoF_toys${toys_name}.txt
+
+        cd -
+    done
+fi
\ No newline at end of file
diff --git a/src/HHbbVV/combine/run_ftest_res.sh b/src/HHbbVV/combine/run_ftest_res.sh
new file mode 100755
index 00000000..38e450ba
--- /dev/null
+++ b/src/HHbbVV/combine/run_ftest_res.sh
@@ -0,0 +1,190 @@
+#!/bin/bash
+
+####################################################################################################
+# 1) Makes datacards and workspaces for different orders of polynomials
+# 2) Runs background-only fit in validation region for lowest order polynomial and GoF test (saturated model) on data
+# 3) Runs fit diagnostics and saves shapes (-d|--dfit)
+# 4) Generates toys and gets test statistics for each (-t|--goftoys)
+# 5) Fits +1 order models to all 100 toys and gets test statistics (-f|--ffits)
+#
+# Author: Raghav Kansal
+####################################################################################################
+
+goftoys=0
+ffits=0
+dfit=0
+seed=42
+numtoys=100
+
+options=$(getopt -o "tfd" --long "cardstag:,templatestag:,sigsample:,goftoys,ffits,dfit,numtoys:,seed:" -- "$@")
+eval set -- "$options"
+
+while true; do
+    case "$1" in
+        -t|--goftoys)
+            goftoys=1
+            ;;
+        -f|--ffits)
+            ffitss=1
+            ;;
+        -d|--dfit)
+            dfit=1
+            ;;
+        --cardstag)
+            shift
+            cards_tag=$1
+            ;;
+        --templatestag)
+            shift
+            templates_tag=$1
+            ;;
+        --sigsample)
+            shift
+            sig_sample=$1
+            ;;
+        --seed)
+            shift
+            seed=$1
+            ;;
+        --numtoys)
+            shift
+            numtoys=$1
+            ;;
+        --)
+            shift
+            break;;
+        \?)
+            echo "Invalid option: -$OPTARG" >&2
+            exit 1
+            ;;
+        :)
+            echo "Option -$OPTARG requires an argument." >&2
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+echo "Arguments: cardstag=$cards_tag templatestag=$templates_tag sigsample=$sig_sample dfit=$dfit \
+goftoys=$goftoys ffits=$ffits seed=$seed numtoys=$numtoys"
+
+
+####################################################################################################
+# Set up fit args
+####################################################################################################
+
+templates_dir="/eos/uscms/store/user/rkansal/bbVV/templates/${templates_tag}"
+cards_dir="cards/f_tests/${cards_tag}/"
+mkdir -p ${cards_dir}
+echo "Saving datacards to ${cards_dir}"
+
+# these are for inside the different cards directories
+dataset=data_obs
+ws="./combined"
+wsm=${ws}_withmasks
+wsm_snapshot=higgsCombineSnapshot.MultiDimFit.mH125
+
+outsdir="./outs"
+
+# mask args
+maskunblindedargs=""
+maskblindedargs=""
+for bin in {0..9}
+do
+    for channel in fail pass;
+    do
+        maskunblindedargs+="mask_mXbin${bin}${channel}=1,mask_mXbin${bin}${channel}Blinded=0,"
+        maskblindedargs+="mask_mXbin${bin}${channel}=0,mask_mXbin${bin}${channel}Blinded=1,"
+    done
+done
+
+# remove last comma
+maskunblindedargs=${maskunblindedargs%,}
+maskblindedargs=${maskblindedargs%,}
+
+setparams="rgx{pass_.*mcstat.*}=0,rgx{fail_.*mcstat.*}=0"
+freezeparams="rgx{pass_.*mcstat.*},rgx{fail_.*mcstat.*},rgx{.*xhy_mx.*}"
+
+
+####################################################################################################
+# Making cards and workspaces for each order polynomial
+####################################################################################################
+
+for ord1 in {0..3}
+do
+    for ord2 in {0..3}
+    do
+        model_name="nTF1_${ord1}_nTF2_${ord2}"
+        if [ ! -f "${cards_dir}/${model_name}/higgsCombineData.GoodnessOfFit.mH125.root" ]; then
+            echo "Making Datacard for $model_name"
+            python3 -u postprocessing/CreateDatacard.py --templates-dir ${templates_dir} --sig-separate \
+            --resonant --model-name ${model_name} --sig-sample ${sig_sample} \
+            --nTF ${ord2} ${ord1} --cards-dir ${cards_dir}
+
+            cd ${cards_dir}/${model_name}/
+
+            /uscms/home/rkansal/hhcombine/combine_scripts/run_blinded.sh -wbgr
+
+            if [ $dfit = 1 ]; then
+                /uscms/home/rkansal/hhcombine/combine_scripts/run_blinded.sh -dr
+            fi
+
+            cd -
+        fi
+    done
+done
+
+
+####################################################################################################
+# Generate toys for (0, 0) order
+####################################################################################################
+
+if [ $goftoys = 1 ]; then
+    model_name="nTF1_0_nTF2_0"
+    toys_name="00"
+    cd ${cards_dir}/${model_name}/
+    toys_file="$(pwd)/higgsCombineToys${toys_name}.GenerateOnly.mH125.42.root"
+
+    ulimit -s unlimited
+
+    echo "Toys for (0, 0) order fit"
+    combine -M GenerateOnly -m 125 -d ${wsm_snapshot}.root \
+    --snapshotName MultiDimFit --bypassFrequentistFit \
+    --setParameters ${maskunblindedargs},${setparams},r=0 \
+    --freezeParameters ${freezeparams},r \
+    -n "Toys${toys_name}" -t $numtoys --saveToys -s $seed -v 9 2>&1 | tee $outsdir/gentoys.txt
+
+    cd -
+fi
+
+
+####################################################################################################
+# GoFs on generated toys for next order polynomials
+####################################################################################################
+
+if [ $ffit = 1 ]; then
+    for ord1 in {0..1}
+    do
+        for ord2 in {0..1}
+        do
+            if [ $ord1 -gt 0 ] && [ $ord2 -gt 0 ]
+            then
+                break
+            fi
+
+            model_name="nTF1_${ord1}_nTF2_${ord2}"
+            echo "Fits for $model_name"
+
+            cd ${cards_dir}/${model_name}/
+
+            ulimit -s unlimited
+
+            combine -M GoodnessOfFit -d ${wsm_snapshot}.root --algo saturated -m 125 \
+            --setParameters ${maskunblindedargs},${setparams},r=0 \
+            --freezeParameters ${freezeparams},r \
+            -n Toys${toys_name} -v 9 -s $seed -t $numtoys --toysFile ${toys_file} 2>&1 | tee $outsdir/GoF_toys${toys_name}.txt
+
+            cd -
+        done
+    done
+fi
\ No newline at end of file
diff --git a/src/HHbbVV/combine/scan.sh b/src/HHbbVV/combine/scan.sh
index 9cbbeac0..a2331181 100755
--- a/src/HHbbVV/combine/scan.sh
+++ b/src/HHbbVV/combine/scan.sh
@@ -1,13 +1,20 @@
 #!/bin/bash
 
-cutstrs=($(ls -d */))
+for txbb_wp in "LP" "MP" "HP"
+do 
+  # for bdt_wp in 0.6 0.9 0.96 0.99 0.995 0.997 0.998 0.9985
+  for bdt_wp in 0.9988
+  do 
+    cutstr=txbb_${txbb_wp}_bdt_${bdt_wp}
+    echo $cutstr
 
-for cutstr in ${cutstrs[@]}
-do
-  echo $cutstr
-  cd $cutstr
+    python3 -u postprocessing/CreateDatacard.py --templates-dir templates/23May13NonresScan/$cutstr \
+    --model-name 23May14NonresScan/$cutstr --no-do-jshifts --nTF 0
 
-  /uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh "./"
-  
-  cd ..
-done
+    cd cards/23May14NonresScan/$cutstr
+
+    /uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh -wbl
+
+    cd -
+  done
+done
\ No newline at end of file
diff --git a/src/HHbbVV/combine/setup_bias.sh b/src/HHbbVV/combine/setup_bias.sh
new file mode 100755
index 00000000..c976eb7f
--- /dev/null
+++ b/src/HHbbVV/combine/setup_bias.sh
@@ -0,0 +1,67 @@
+#!/bin/bash
+
+####################################################################################################
+# Sets up signal injection bias tests
+# TODO: add nonresonant option
+#
+# Author: Raghav Kansal
+####################################################################################################
+
+resonant=0
+scale=1  # scale templates by this much
+
+options=$(getopt -o "r" --long "resonant,cardstag:,templatestag:,scale:" -- "$@")
+eval set -- "$options"
+
+while true; do
+    case "$1" in
+        --cardstag)
+            shift
+            cards_tag=$1
+            ;;
+        --templatestag)
+            shift
+            templates_tag=$1
+            ;;
+        --scale)
+            shift
+            scale=$1
+            ;;
+        -r|--resonant)
+            resonant=1
+            ;;
+        --)
+            shift
+            break;;
+        \?)
+            echo "Invalid option: -$OPTARG" >&2
+            exit 1
+            ;;
+        :)
+            echo "Option -$OPTARG requires an argument." >&2
+            exit 1
+            ;;
+    esac
+    shift
+done
+
+cards_tag="${cards_tag}Scale${scale}"
+templates_dir="/eos/uscms/store/user/rkansal/bbVV/templates/${templates_tag}"
+cards_dir="cards/biastests/${cards_tag}/"
+mkdir -p ${cards_dir}
+echo "Saving datacards to ${cards_dir}"
+
+for sample in NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190 NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125 NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250
+do
+    if [ ! -f "${cards_dir}/${sample}/mXbin9pass.txt" ]; then
+        echo "Making Datacard for $sample"
+        python3 -u postprocessing/CreateDatacard.py --templates-dir ${templates_dir} --sig-separate \
+        --resonant --model-name $sample --sig-sample $sample --cards-dir ${cards_dir} --scale-templates $scale
+    fi
+
+    cd ${cards_dir}/${sample}/
+
+    run_blinded.sh -rwb
+
+    cd -
+done
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_bias.py b/src/HHbbVV/combine/submit/submit_bias.py
new file mode 100644
index 00000000..58abdf1a
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_bias.py
@@ -0,0 +1,76 @@
+#!/usr/bin/python
+
+"""
+Splits toy generation into separate condor jobs and fits lowest order + 1 models for F-tests.
+
+Author(s): Raghav Kansal
+"""
+
+import argparse
+import os
+from math import ceil
+from string import Template
+import json
+
+import sys
+
+from utils import add_bool_arg, write_template, setup, parse_common_args
+
+
+def main(args):
+    t2_local_prefix, t2_prefix, proxy, username, submitdir = setup(args)
+
+    prefix = f"bias_{args.bias}_seed_{args.seed}"
+    local_dir = f"condor/bias/{args.tag}/{prefix}"
+
+    # make local directory
+    logdir = local_dir + "/logs"
+    os.system(f"mkdir -p {logdir}")
+
+    jdl_templ = f"{submitdir}/submit_bias.templ.jdl"
+    sh_templ = f"{submitdir}/submit_bias.templ.sh"
+
+    # submit jobs
+    if args.submit:
+        print("Submitting jobs")
+
+    for j in range(args.num_jobs):
+        localcondor = f"{local_dir}/{prefix}_{j}.jdl"
+        seed = args.seed + j * args.toys_per_job
+        jdl_args = {
+            "dir": local_dir,
+            "prefix": prefix,
+            "jobid": j,
+            "proxy": proxy,
+            "bias": args.bias,
+            "seed": seed,
+        }
+        write_template(jdl_templ, localcondor, jdl_args)
+
+        localsh = f"{local_dir}/{prefix}_{j}.sh"
+        sh_args = {
+            "seed": seed,
+            "num_toys": args.toys_per_job,
+            "resonant": "--resonant" if args.resonant else "",
+            "bias": args.bias,
+        }
+        write_template(sh_templ, localsh, sh_args)
+        os.system(f"chmod u+x {localsh}")
+
+        if os.path.exists(f"{localcondor}.log"):
+            os.system(f"rm {localcondor}.log")
+
+        if args.submit:
+            os.system("condor_submit %s" % localcondor)
+        else:
+            print("To submit ", localcondor)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parse_common_args(parser)
+    parser.add_argument(
+        "--bias", help="expected signal strength to test", type=float, required=True
+    )
+    args = parser.parse_args()
+    main(args)
diff --git a/src/HHbbVV/combine/submit/submit_bias.templ.jdl b/src/HHbbVV/combine/submit/submit_bias.templ.jdl
new file mode 100644
index 00000000..0e0c76c4
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_bias.templ.jdl
@@ -0,0 +1,16 @@
+#!/usr/bin/env condor_submit
+
+executable              = $dir/${prefix}_$jobid.sh
+should_transfer_files   = YES
+transfer_input_files    = combined_withmasks.root,higgsCombineSnapshot.MultiDimFit.mH125.root,/uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh
+transfer_output_files   = higgsCombinebias${bias}.FitDiagnostics.mH125.$seed.root
+when_to_transfer_output = ON_EXIT_OR_EVICT
+request_memory          = 3000
+use_x509userproxy       = true
+x509userproxy           = /uscms/home/rkansal/x509up_u57474
+
+output                  = $dir/logs/${prefix}_$jobid.out
+error                   = $dir/logs/${prefix}_$jobid.err
+log                     = $dir/logs/${prefix}_$jobid.log
+
+Queue 1
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_bias.templ.sh b/src/HHbbVV/combine/submit/submit_bias.templ.sh
new file mode 100644
index 00000000..472d7819
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_bias.templ.sh
@@ -0,0 +1,32 @@
+#!/bin/bash 
+
+####################################################################################################
+# Script for running bias test
+# 
+# Author: Raghav Kansal
+####################################################################################################
+
+echo "Starting job on " `date` #Date/time of start of job                                                                       
+echo "Running on: `uname -a`" #Condor job is running on this node                                                               
+echo "System software: `cat /etc/redhat-release`" #Operating System on that node                                             
+
+####################################################################################################
+# Get my tarred CMSSW with combine already compiled
+####################################################################################################
+
+source /cvmfs/cms.cern.ch/cmsset_default.sh 
+xrdcp -s root://cmseos.fnal.gov//store/user/rkansal/CMSSW_11_2_0.tgz .
+
+echo "extracting tar"
+tar -xf CMSSW_11_2_0.tgz
+rm CMSSW_11_2_0.tgz
+cd CMSSW_11_2_0/src/
+scramv1 b ProjectRename # this handles linking the already compiled code - do NOT recompile
+eval `scramv1 runtime -sh` # cmsenv is an alias not on the workers
+echo $CMSSW_BASE "is the CMSSW we have on the local worker node"
+cd ../..
+
+ls -lh
+chmod u+x run_blinded.sh
+./run_blinded.sh $resonant --bias $bias --seed $seed --numtoys $num_toys 
+ls -lh
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_cards.py b/src/HHbbVV/combine/submit/submit_cards.py
new file mode 100644
index 00000000..e730ecd0
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_cards.py
@@ -0,0 +1,302 @@
+#!/usr/bin/python
+
+"""
+Submits jobs for making the datacards and running fits per sample.
+
+Author(s): Raghav Kansal
+"""
+
+import argparse
+import os
+from math import ceil
+from string import Template
+import json
+import itertools
+import sys
+
+from utils import add_bool_arg, write_template, setup, parse_common_args
+
+
+def add_bool_arg(parser, name, help, default=False, no_name=None):
+    """Add a boolean command line argument for argparse"""
+    varname = "_".join(name.split("-"))  # change hyphens to underscores
+    group = parser.add_mutually_exclusive_group(required=False)
+    group.add_argument("--" + name, dest=varname, action="store_true", help=help)
+    if no_name is None:
+        no_name = "no-" + name
+        no_help = "don't " + help
+    else:
+        no_help = help
+    group.add_argument("--" + no_name, dest=varname, action="store_false", help=no_help)
+    parser.set_defaults(**{varname: default})
+
+
+def write_template(templ_file: str, out_file: str, templ_args: dict):
+    """Write to ``out_file`` based on template from ``templ_file`` using ``templ_args``"""
+
+    with open(templ_file, "r") as f:
+        templ = Template(f.read())
+
+    with open(out_file, "w") as f:
+        f.write(
+            templ.safe_substitute(
+                templ_args,
+            )
+        )
+
+
+res_mps = [
+    (1000, 100),
+    (1000, 125),
+    (1000, 150),
+    (1000, 190),
+    (1000, 250),
+    (1000, 60),
+    (1000, 80),
+    (1200, 100),
+    (1200, 150),
+    (1200, 190),
+    (1200, 250),
+    (1200, 60),
+    (1200, 80),
+    (1400, 100),
+    (1400, 125),
+    (1400, 150),
+    (1400, 190),
+    (1400, 250),
+    (1400, 60),
+    (1400, 80),
+    (1600, 100),
+    (1600, 125),
+    (1600, 150),
+    (1600, 190),
+    (1600, 250),
+    (1600, 60),
+    (1600, 80),
+    (1800, 100),
+    (1800, 125),
+    (1800, 150),
+    (1800, 190),
+    (1800, 250),
+    (1800, 60),
+    (1800, 80),
+    (2000, 100),
+    (2000, 125),
+    (2000, 150),
+    (2000, 190),
+    (2000, 250),
+    (2000, 60),
+    (2000, 80),
+    (2200, 100),
+    (2200, 125),
+    (2200, 150),
+    (2200, 190),
+    (2200, 250),
+    (2200, 60),
+    (2200, 80),
+    (2400, 100),
+    (2400, 125),
+    (2400, 150),
+    (2400, 190),
+    (2400, 250),
+    (2400, 60),
+    (2400, 80),
+    (2600, 100),
+    (2600, 125),
+    (2600, 150),
+    (2600, 190),
+    (2600, 250),
+    (2600, 80),
+    (2800, 100),
+    (2800, 125),
+    (2800, 150),
+    (2800, 190),
+    (2800, 250),
+    (2800, 60),
+    (2800, 80),
+    (3000, 100),
+    (3000, 125),
+    (3000, 150),
+    (3000, 190),
+    (3000, 250),
+    (3000, 60),
+    (3000, 80),
+    (3500, 100),
+    (3500, 125),
+    (3500, 150),
+    (3500, 190),
+    (3500, 250),
+    (3500, 60),
+    (3500, 80),
+    (4000, 100),
+    (4000, 125),
+    (4000, 150),
+    (4000, 190),
+    (4000, 250),
+    (4000, 60),
+    (4000, 80),
+    (600, 100),
+    (600, 125),
+    (600, 150),
+    (600, 250),
+    (600, 60),
+    (600, 80),
+    (700, 100),
+    (700, 125),
+    (700, 150),
+    (700, 250),
+    (700, 60),
+    (700, 80),
+    (800, 100),
+    (800, 125),
+    (800, 150),
+    (800, 250),
+    (800, 60),
+    (800, 80),
+    (900, 100),
+    (900, 125),
+    (900, 150),
+    (900, 250),
+    (900, 60),
+    (900, 80),
+]
+
+full_samples = []
+
+for mX, mY in res_mps:
+    full_samples.append(f"NMSSM_XToYHTo2W2BTo4Q2B_MX-{mX}_MY-{mY}")
+
+
+res_mps = [
+    (900, 80),
+    (1200, 190),
+    (2000, 125),
+    (3000, 250),
+    (4000, 150),
+]
+
+scan_samples = []
+
+for mX, mY in res_mps:
+    scan_samples.append(f"NMSSM_XToYHTo2W2BTo4Q2B_MX-{mX}_MY-{mY}")
+
+scan_txbb_wps = ["LP", "MP", "HP"]
+scan_thww_wps = [0.4, 0.6, 0.8, 0.9, 0.94, 0.96, 0.98]
+
+nonres_scan_cuts = ["txbb", "bdt"]
+res_scan_cuts = ["txbb", "thww"]
+
+
+def main(args):
+    global scan_txbb_wps, scan_thww_wps
+
+    t2_local_prefix, t2_prefix, proxy, username, submitdir = setup(args)
+
+    local_dir = f"condor/cards/{args.tag}"
+
+    templates_dir = f"/store/user/{username}/bbVV/templates/{args.templates_dir}/"
+    cards_dir = f"/store/user/{username}/bbVV/cards/{args.tag}/"
+
+    # make local directory
+    logdir = local_dir + "/logs"
+    os.system(f"mkdir -p {logdir}")
+
+    # and eos directory
+    os.system(f"mkdir -p {t2_local_prefix}/{cards_dir}")
+    os.system(f"mkdir -p {t2_local_prefix}/{templates_dir}")
+
+    jdl_templ = f"{submitdir}/submit_cards.templ.jdl"
+    sh_templ = f"{submitdir}/submit_cards.templ.sh"
+
+    samples = scan_samples if args.scan else full_samples
+
+    if args.test:
+        samples = samples[:2]
+        scan_txbb_wps = scan_txbb_wps[-1:]
+        scan_thww_wps = scan_thww_wps[-2:]
+
+    for sample in samples:
+        if args.scan:
+            for txbb_wp in scan_txbb_wps:
+                for thww_wp in scan_thww_wps:
+                    os.system(
+                        f"mkdir -p {t2_local_prefix}/{cards_dir}/"
+                        f"txbb_{txbb_wp}_thww_{thww_wp}/{sample}"
+                    )
+        else:
+            os.system(f"mkdir -p {t2_local_prefix}/{cards_dir}/{sample}")
+
+    scan_wps = list(itertools.product(scan_txbb_wps, scan_thww_wps))
+    scan_cuts = res_scan_cuts if args.resonant else nonres_scan_cuts
+
+    # split along WPs for scan or along # of samples for regular jobs
+    njobs = len(scan_wps) if args.scan else ceil(len(samples) / args.files_per_job)
+
+    datacard_args = "--no-do-jshifts" if args.scan else ""
+
+    # submit jobs
+    nsubmit = 0
+
+    if args.submit:
+        print("Submitting samples")
+
+    for j in range(njobs):
+        if args.scan:
+            run_samples = samples
+
+            cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, scan_wps[j])])
+            run_templates_dir = templates_dir + cutstr
+            run_cards_dir = cards_dir + cutstr
+        else:
+            run_samples = samples[j * args.files_per_job : (j + 1) * args.files_per_job]
+            run_templates_dir = templates_dir
+            run_cards_dir = cards_dir
+
+        prefix = "cards" f"{'Scan' if args.scan else ''}"
+        localcondor = f"{local_dir}/{prefix}_{j}.jdl"
+        jdl_args = {"dir": local_dir, "prefix": prefix, "jobid": j, "proxy": proxy}
+        write_template(jdl_templ, localcondor, jdl_args)
+
+        localsh = f"{local_dir}/{prefix}_{j}.sh"
+        sh_args = {
+            "samples": " ".join(run_samples),
+            "in_templates_dir": run_templates_dir,
+            "in_cards_dir": run_cards_dir,
+            "datacard_args": datacard_args,
+        }
+        write_template(sh_templ, localsh, sh_args)
+        os.system(f"chmod u+x {localsh}")
+
+        if os.path.exists(f"{localcondor}.log"):
+            os.system(f"rm {localcondor}.log")
+
+        print("To submit ", localcondor)
+        if args.submit:
+            os.system("condor_submit %s" % localcondor)
+
+        nsubmit = nsubmit + 1
+
+    print(f"Total {nsubmit} jobs")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--tag", default="Test", help="process tag", type=str)
+    parser.add_argument("--templates-dir", help="EOS templates dir", type=str)
+    parser.add_argument(
+        "--site",
+        default="lpc",
+        help="computing cluster we're running this on",
+        type=str,
+        choices=["lpc", "ucsd"],
+    )
+    parser.add_argument("--files-per-job", default=5, help="# samples per condor job", type=int)
+
+    add_bool_arg(parser, "submit", default=False, help="submit files as well as create them")
+    add_bool_arg(parser, "resonant", default=True, help="Resonant or nonresonant")
+    add_bool_arg(parser, "scan", default=False, help="Scan working points")
+    add_bool_arg(parser, "test", default=False, help="run on only 2 samples")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/src/HHbbVV/combine/submit/submit_cards.templ.jdl b/src/HHbbVV/combine/submit/submit_cards.templ.jdl
new file mode 100644
index 00000000..87dddf03
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_cards.templ.jdl
@@ -0,0 +1,15 @@
+#!/usr/bin/env condor_submit
+
+executable              = $dir/${prefix}_$jobid.sh
+should_transfer_files   = YES
+transfer_input_files    = src/HHbbVV/postprocessing,src/HHbbVV/combine
+when_to_transfer_output = ON_EXIT_OR_EVICT
+request_memory          = 3000
+use_x509userproxy       = true
+x509userproxy           = /uscms/home/rkansal/x509up_u57474
+
+output                  = $dir/logs/${prefix}_$jobid.out
+error                   = $dir/logs/${prefix}_$jobid.err
+log                     = $dir/logs/${prefix}_$jobid.log
+
+Queue 1
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_cards.templ.sh b/src/HHbbVV/combine/submit/submit_cards.templ.sh
new file mode 100644
index 00000000..caf92201
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_cards.templ.sh
@@ -0,0 +1,92 @@
+#!/bin/bash                                                                                                                   
+
+####################################################################################################
+# Template for condor job script to 
+# 1) Make datacards for specified samples
+# 2) Do background-only fit in validation region and get asymptotic limits
+# 
+# Author: Raghav Kansal
+####################################################################################################
+
+echo "Starting job on " `date` # Date/time of start of job                                                                       
+echo "Running on: `uname -a`" # Condor job is running on this node                                                               
+echo "System software: `cat /etc/redhat-release`" # Operating System on that node                                             
+
+####################################################################################################
+# Get my tarred CMSSW with combine already compiled
+####################################################################################################
+
+source /cvmfs/cms.cern.ch/cmsset_default.sh 
+xrdcp -s root://cmseos.fnal.gov//store/user/rkansal/CMSSW_11_2_0.tgz .
+
+echo "extracting tar"
+tar -xf CMSSW_11_2_0.tgz
+rm CMSSW_11_2_0.tgz
+cd CMSSW_11_2_0/src/
+scramv1 b ProjectRename # this handles linking the already compiled code - do NOT recompile
+eval `scramv1 runtime -sh` # cmsenv is an alias not on the workers
+echo $CMSSW_BASE "is the CMSSW we have on the local worker node"
+cd ../..
+
+echo "testing combine"
+combine
+
+####################################################################################################
+# Install Python Packages
+# Need to install with --user, after changing the user directory using the PYTHONUSERBASE arg
+# (Can't install with normal --user since condor job doesn't have write access to my user dir)
+# See https://stackoverflow.com/a/29103053/3759946
+####################################################################################################
+
+mkdir local_python
+export PYTHONUSERBASE=$(pwd)/local_python
+
+echo "Installing hist"
+pip3 install --user hist
+git clone -b square_coef https://github.com/rkansal47/rhalphalib.git
+cd rhalphalib
+echo "Installing rhalphalib"
+pip3 install --user .
+cd ..
+
+export PYTHONPATH=$(pwd)/local_python/lib/python3.8/site-packages/:$PYTHONPATH
+
+echo "testing installed libraries"
+echo "import hist; import rhalphalib; print('Import successfully!')" > lib_test.py
+python3 lib_test.py
+
+ls -lh .
+
+####################################################################################################
+# Get templates from EOS directory and run fit script
+####################################################################################################
+
+mkdir templates
+mkdir cards
+
+templates_dir=${in_templates_dir}
+cards_dir=${in_cards_dir}
+
+# get backgrounds templates
+xrdcp -r root://cmseos.fnal.gov/${templates_dir}/backgrounds templates/
+
+for sample in $samples
+do
+    echo -e "\n\n$sample"
+
+    # get sample templates
+    xrdcp -r root://cmseos.fnal.gov/${templates_dir}/$sample templates/
+
+    python3 postprocessing/CreateDatacard.py --templates-dir templates --sig-separate --resonant \
+    --model-name $sample --sig-sample $sample ${datacard_args}
+
+    cd cards/$sample
+    ../../combine/run_blinded.sh --workspace --bfit --limits --resonant
+    cd ../..
+
+    # transfer output cards
+    xrdcp -r cards/$sample root://cmseos.fnal.gov/${cards_dir}/$sample/
+    rm -rf templates/$sample
+done
+
+rm -rf templates/backgrounds
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_ftest.py b/src/HHbbVV/combine/submit/submit_ftest.py
new file mode 100644
index 00000000..f7784c4f
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_ftest.py
@@ -0,0 +1,123 @@
+#!/usr/bin/python
+
+"""
+Splits toy generation into separate condor jobs and fits lowest order + 1 models for F-tests.
+
+Author(s): Raghav Kansal
+"""
+
+import argparse
+import os
+from math import ceil
+from string import Template
+import json
+import sys
+
+from utils import add_bool_arg, write_template, setup, parse_common_args
+
+
+def add_bool_arg(parser, name, help, default=False, no_name=None):
+    """Add a boolean command line argument for argparse"""
+    varname = "_".join(name.split("-"))  # change hyphens to underscores
+    group = parser.add_mutually_exclusive_group(required=False)
+    group.add_argument("--" + name, dest=varname, action="store_true", help=help)
+    if no_name is None:
+        no_name = "no-" + name
+        no_help = "don't " + help
+    else:
+        no_help = help
+    group.add_argument("--" + no_name, dest=varname, action="store_false", help=no_help)
+    parser.set_defaults(**{varname: default})
+
+
+def write_template(templ_file: str, out_file: str, templ_args: dict):
+    """Write to ``out_file`` based on template from ``templ_file`` using ``templ_args``"""
+
+    with open(templ_file, "r") as f:
+        templ = Template(f.read())
+
+    with open(out_file, "w") as f:
+        f.write(
+            templ.safe_substitute(
+                templ_args,
+            )
+        )
+
+
+def main(args):
+    t2_local_prefix, t2_prefix, proxy, username, submitdir = setup(args)
+
+    username = os.environ["USER"]
+    local_dir = f"condor/f_tests/{args.tag}_{args.low1}{args.low2}"
+
+    # make local directory
+    logdir = local_dir + "/logs"
+    os.system(f"mkdir -p {logdir}")
+
+    # and eos directories
+    for i, j in [(0, 0), (0, 1), (1, 0)]:
+        os.system(
+            f"mkdir -p {t2_local_prefix}//store/user/rkansal/bbVV/cards/f_tests/{args.cards_tag}/"
+            f"nTF1_{args.low1 + i}_nTF2_{args.low2 + j}/outs/"
+        )
+
+    jdl_templ = f"{submitdir}/submit_ftest.templ.jdl"
+    sh_templ = f"{submitdir}/submit_ftest.templ.sh"
+
+    # submit jobs
+    if args.submit:
+        print("Submitting jobs")
+
+    for j in range(args.num_jobs):
+        prefix = f"ftests_{args.low1}{args.low2}"
+        localcondor = f"{local_dir}/{prefix}_{j}.jdl"
+        jdl_args = {
+            "dir": local_dir,
+            "prefix": prefix,
+            "tag": args.cards_tag,
+            "jobid": j,
+            "proxy": proxy,
+        }
+        write_template(jdl_templ, localcondor, jdl_args)
+
+        localsh = f"{local_dir}/{prefix}_{j}.sh"
+        sh_args = {
+            "in_low1": args.low1,
+            "in_low2": args.low2,
+            "in_tag": args.cards_tag,
+            "in_seed": args.seed + j * args.toys_per_job,
+            "in_num_toys": args.toys_per_job,
+        }
+        write_template(sh_templ, localsh, sh_args)
+        os.system(f"chmod u+x {localsh}")
+
+        if os.path.exists(f"{localcondor}.log"):
+            os.system(f"rm {localcondor}.log")
+
+        if args.submit:
+            os.system("condor_submit %s" % localcondor)
+        else:
+            print("To submit ", localcondor)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--tag", default="Test", help="condor tag", type=str)
+    parser.add_argument("--cards-tag", default="Apr26", help="f tests dir tag", type=str)
+    parser.add_argument(
+        "--site",
+        default="lpc",
+        help="computing cluster we're running this on",
+        type=str,
+        choices=["lpc", "ucsd"],
+    )
+    parser.add_argument("--low1", default=0, help="low order poly in dim 1", type=int)
+    parser.add_argument("--low2", default=0, help="low order poly in dim 2", type=int)
+    parser.add_argument("--toys-per-job", default=100, help="# toys per condor job", type=int)
+    parser.add_argument("--num-jobs", default=10, help="# condor jobs", type=int)
+    parser.add_argument("--seed", default=444, help="# condor jobs", type=int)
+    add_bool_arg(parser, "submit", default=False, help="submit files as well as create them")
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/src/HHbbVV/combine/submit/submit_ftest.templ.jdl b/src/HHbbVV/combine/submit/submit_ftest.templ.jdl
new file mode 100644
index 00000000..8744f1f3
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_ftest.templ.jdl
@@ -0,0 +1,15 @@
+#!/usr/bin/env condor_submit
+
+executable              = $dir/${prefix}_$jobid.sh
+should_transfer_files   = YES
+transfer_input_files    = /uscms/home/rkansal/hhcombine/cards/f_tests/$tag
+when_to_transfer_output = ON_EXIT_OR_EVICT
+request_memory          = 3000
+use_x509userproxy       = true
+x509userproxy           = /uscms/home/rkansal/x509up_u57474
+
+output                  = $dir/logs/${prefix}_$jobid.out
+error                   = $dir/logs/${prefix}_$jobid.err
+log                     = $dir/logs/${prefix}_$jobid.log
+
+Queue 1
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_ftest.templ.sh b/src/HHbbVV/combine/submit/submit_ftest.templ.sh
new file mode 100644
index 00000000..6f648097
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_ftest.templ.sh
@@ -0,0 +1,140 @@
+#!/bin/bash 
+
+####################################################################################################
+# Script for generating toys and doing GoFs for F-tests
+# Needs workspaces for all orders + b-only fit snapshot of lowest order model transferred as inputs.
+# 
+# 1) Generates toys using b-only fit
+# 2) GoF on toys for lowest and lowest + 1 orders
+# 3) Transfers toys and GoF test files to EOS directory
+# 
+# Author: Raghav Kansal
+####################################################################################################
+
+echo "Starting job on " `date` #Date/time of start of job                                                                       
+echo "Running on: `uname -a`" #Condor job is running on this node                                                               
+echo "System software: `cat /etc/redhat-release`" #Operating System on that node                                             
+
+####################################################################################################
+# Get my tarred CMSSW with combine already compiled
+####################################################################################################
+
+source /cvmfs/cms.cern.ch/cmsset_default.sh 
+xrdcp -s root://cmseos.fnal.gov//store/user/rkansal/CMSSW_11_2_0.tgz .
+
+echo "extracting tar"
+tar -xf CMSSW_11_2_0.tgz
+rm CMSSW_11_2_0.tgz
+cd CMSSW_11_2_0/src/
+scramv1 b ProjectRename # this handles linking the already compiled code - do NOT recompile
+eval `scramv1 runtime -sh` # cmsenv is an alias not on the workers
+echo $CMSSW_BASE "is the CMSSW we have on the local worker node"
+cd ../..
+
+# inputs
+
+tag=${in_tag}
+low1=${in_low1}
+low2=${in_low2}
+seed=${in_seed}
+num_toys=${in_num_toys}
+
+cards_dir=$tag
+
+####################################################################################################
+# Fit args
+####################################################################################################
+
+dataset=data_obs
+ws="./combined"
+wsm=${ws}_withmasks
+wsm_snapshot=higgsCombineSnapshot.MultiDimFit.mH125
+
+outsdir="outs"
+
+# one channel per bin
+ccargs=""
+# mask args
+maskunblindedargs=""
+maskblindedargs=""
+for bin in {0..9}
+do
+    for channel in fail failBlinded pass passBlinded;
+    do
+        ccargs+="mXbin${bin}${channel}=mXbin${bin}${channel}.txt "
+    done
+
+    for channel in fail pass;
+    do
+        maskunblindedargs+="mask_mXbin${bin}${channel}=1,mask_mXbin${bin}${channel}Blinded=0,"
+        maskblindedargs+="mask_mXbin${bin}${channel}=0,mask_mXbin${bin}${channel}Blinded=1,"
+    done
+done
+
+# remove last comma
+maskunblindedargs=${maskunblindedargs%,}
+maskblindedargs=${maskblindedargs%,}
+
+setparams="rgx{pass_.*mcstat.*}=0,rgx{fail_.*mcstat.*}=0"
+freezeparams="rgx{pass_.*mcstat.*},rgx{fail_.*mcstat.*},rgx{.*xhy_mx.*}"
+
+####################################################################################################
+# Generate toys for lowest order polynomial
+####################################################################################################
+
+model_name="nTF1_${low1}_nTF2_${low2}"
+toys_name="${low1}${low2}"
+cd ${cards_dir}/${model_name}/
+mkdir -p $outsdir
+
+ulimit -s unlimited
+
+echo "Toys for ($low1, $low2) order fit"
+combine -M GenerateOnly -m 125 -d ${wsm_snapshot}.root \
+--snapshotName MultiDimFit --bypassFrequentistFit \
+--setParameters ${maskunblindedargs},${setparams},r=0 \
+--freezeParameters ${freezeparams},r \
+-n "Toys${toys_name}" -t $num_toys --saveToys -s $seed -v 9 2>&1 | tee $outsdir/gentoys$seed.txt
+
+toys_file="$(pwd)/higgsCombineToys${toys_name}.GenerateOnly.mH125.$seed.root"
+xrdcp $toys_file root://cmseos.fnal.gov//store/user/rkansal/bbVV/cards/f_tests/$tag/$model_name/
+xrdcp $outsdir/gentoys$seed.txt root://cmseos.fnal.gov//store/user/rkansal/bbVV/cards/f_tests/$tag/$model_name/$outsdir/
+
+cd -
+
+####################################################################################################
+# Run GoF for each order on generated toys
+####################################################################################################
+
+for (( ord1=$low1; ord1<=$((low1 + 1)); ord1++ ))
+do
+    for (( ord2=$low2; ord2<=$((low2 + 1)); ord2++ ))
+    do
+        if [ $ord1 -gt $low1 ] && [ $ord2 -gt $low2 ]
+        then
+            break
+        fi
+
+        model_name="nTF1_${ord1}_nTF2_${ord2}"
+        echo "GoF for $model_name"
+
+        cd ${cards_dir}/${model_name}/
+        mkdir -p $outsdir
+
+        ulimit -s unlimited
+
+        combine -M GoodnessOfFit -d ${wsm_snapshot}.root --algo saturated -m 125 \
+        --setParameters ${maskunblindedargs},${setparams},r=0 \
+        --freezeParameters ${freezeparams},r \
+        -n Toys${toys_name}Seed$seed -v 9 -s $seed -t $num_toys \
+        --toysFile ${toys_file} 2>&1 | tee $outsdir/GoF_toys${toys_name}$seed.txt
+
+        xrdcp "higgsCombineToys${toys_name}Seed$seed.GoodnessOfFit.mH125.$seed.root" root://cmseos.fnal.gov//store/user/rkansal/bbVV/cards/f_tests/$tag/$model_name/
+        xrdcp $outsdir/GoF_toys${toys_name}$seed.txt root://cmseos.fnal.gov//store/user/rkansal/bbVV/cards/f_tests/$tag/$model_name/$outsdir/
+
+        rm "higgsCombineToys${toys_name}Seed$seed.GoodnessOfFit.mH125.$seed.root" 
+        rm "$outsdir/GoF_toys${toys_name}$seed.txt"
+
+        cd -
+    done
+done
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_impacts.py b/src/HHbbVV/combine/submit/submit_impacts.py
new file mode 100644
index 00000000..884841d4
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_impacts.py
@@ -0,0 +1,126 @@
+#!/usr/bin/python
+
+"""
+Splits toy generation into separate condor jobs and fits lowest order + 1 models for F-tests.
+
+Author(s): Raghav Kansal
+"""
+
+import argparse
+import os
+from math import ceil
+from string import Template
+import json
+
+import sys
+
+from utils import add_bool_arg, write_template, setup, parse_common_args
+
+import ROOT
+
+
+def _tolist(argset):
+    return [x.GetName() for x in argset]
+
+
+def getParameters():
+    """Get nuisance parameters from workspace"""
+    f = ROOT.TFile.Open("combined_withmasks.root", "READ")
+    w = f.Get("w")
+    ps = _tolist(w.allVars())
+    pois = _tolist(w.set("ModelConfig_POI"))
+    obs = _tolist(w.genobj("ModelConfig").GetObservables())
+
+    ret_ps = []
+    for p in ps:
+        if not (
+            "mcstat" in p
+            or "qcdparam" in p
+            or p.endswith("_In")
+            or p.endswith("__norm")
+            or p.startswith("n_exp_")
+            or p.startswith("mask_")
+            or p in pois
+            or p in obs
+        ):
+            ret_ps.append(p)
+
+    return ret_ps
+
+
+def main(args):
+    t2_local_prefix, t2_prefix, proxy, username, submitdir = setup(args)
+
+    prefix = "impacts"
+    local_dir = f"condor/impacts/{args.tag}/"
+
+    # make local directory
+    logdir = local_dir + "/logs"
+    os.system(f"mkdir -p {logdir}")
+
+    jdl_templ = f"{submitdir}/submit_impacts.templ.jdl"
+    sh_templ = f"{submitdir}/submit_impacts.templ.sh"
+
+    ps = getParameters()
+    print(f"Running impacts on {len(ps)} parameters:")
+    print(*ps, sep="\n")
+
+    res_str = "-r" if args.resonant else ""
+    commands = [f"run_blinded.sh {res_str} -i"] + [
+        f"run_blinded.sh {res_str} --impactsf {p}" for p in ps
+    ]
+    impactfiles = ["higgsCombine_initialFit_impacts.MultiDimFit.mH125.root"] + [
+        f"higgsCombine_paramFit_impacts_{p}.MultiDimFit.mH125.root" for p in ps
+    ]
+
+    collect_command = f"run_blinded.sh {res_str} --impactsc {','.join(ps)}"
+
+    if args.local:
+        print("Running locally")
+        for command in commands:
+            os.system(command)
+        os.system(collect_command)
+        return
+
+    collect_sh = f"{local_dir}/collect.sh"
+    with open(collect_sh, "w") as f:
+        f.write(collect_command)
+
+    os.system(f"chmod u+x {collect_sh}")
+    print(f"To collect impacts afterwards, run: {collect_sh}")
+
+    # submit jobs
+    if args.submit:
+        print("Submitting jobs")
+
+    for p, command, impactfile in zip(["init"] + ps, commands, impactfiles):
+        localcondor = f"{local_dir}/{prefix}_{p}.jdl"
+        jdl_args = {
+            "dir": local_dir,
+            "prefix": prefix,
+            "jobid": p,
+            "proxy": proxy,
+            "impactfile": impactfile,
+        }
+        write_template(jdl_templ, localcondor, jdl_args)
+
+        localsh = f"{local_dir}/{prefix}_{p}.sh"
+        sh_args = {"command": "./" + command}
+        write_template(sh_templ, localsh, sh_args)
+        os.system(f"chmod u+x {localsh}")
+
+        if os.path.exists(f"{localcondor}.log"):
+            os.system(f"rm {localcondor}.log")
+
+        if args.submit:
+            os.system("condor_submit %s" % localcondor)
+        else:
+            print("To submit ", localcondor)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parse_common_args(parser)
+    add_bool_arg(parser, "local", help="run locally", default=False)
+    args = parser.parse_args()
+    main(args)
diff --git a/src/HHbbVV/combine/submit/submit_impacts.templ.jdl b/src/HHbbVV/combine/submit/submit_impacts.templ.jdl
new file mode 100644
index 00000000..379ef748
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_impacts.templ.jdl
@@ -0,0 +1,16 @@
+#!/usr/bin/env condor_submit
+
+executable              = $dir/${prefix}_$jobid.sh
+should_transfer_files   = YES
+transfer_input_files    = combined_withmasks.root,higgsCombineSnapshot.MultiDimFit.mH125.root,/uscms/home/rkansal/nobackup/HHbbVV/src/HHbbVV/combine/run_blinded.sh
+transfer_output_files   = $impactfile
+when_to_transfer_output = ON_EXIT_OR_EVICT
+request_memory          = 3000
+use_x509userproxy       = true
+x509userproxy           = /uscms/home/rkansal/x509up_u57474
+
+output                  = $dir/logs/${prefix}_$jobid.out
+error                   = $dir/logs/${prefix}_$jobid.err
+log                     = $dir/logs/${prefix}_$jobid.log
+
+Queue 1
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/submit_impacts.templ.sh b/src/HHbbVV/combine/submit/submit_impacts.templ.sh
new file mode 100644
index 00000000..62ead56e
--- /dev/null
+++ b/src/HHbbVV/combine/submit/submit_impacts.templ.sh
@@ -0,0 +1,32 @@
+#!/bin/bash 
+
+####################################################################################################
+# Script for running bias test
+# 
+# Author: Raghav Kansal
+####################################################################################################
+
+echo "Starting job on " `date` #Date/time of start of job                                                                       
+echo "Running on: `uname -a`" #Condor job is running on this node                                                               
+echo "System software: `cat /etc/redhat-release`" #Operating System on that node                                             
+
+####################################################################################################
+# Get my tarred CMSSW with combine already compiled
+####################################################################################################
+
+source /cvmfs/cms.cern.ch/cmsset_default.sh 
+xrdcp -s root://cmseos.fnal.gov//store/user/rkansal/CMSSW_11_2_0.tgz .
+
+echo "extracting tar"
+tar -xf CMSSW_11_2_0.tgz
+rm CMSSW_11_2_0.tgz
+cd CMSSW_11_2_0/src/
+scramv1 b ProjectRename # this handles linking the already compiled code - do NOT recompile
+eval `scramv1 runtime -sh` # cmsenv is an alias not on the workers
+echo $CMSSW_BASE "is the CMSSW we have on the local worker node"
+cd ../..
+
+ls -lh
+
+ulimit -s unlimited
+$command
\ No newline at end of file
diff --git a/src/HHbbVV/combine/submit/utils.py b/src/HHbbVV/combine/submit/utils.py
new file mode 100644
index 00000000..398d2037
--- /dev/null
+++ b/src/HHbbVV/combine/submit/utils.py
@@ -0,0 +1,69 @@
+import os
+from string import Template
+from pathlib import Path
+
+
+def add_bool_arg(parser, name, help, default=False, no_name=None):
+    """Add a boolean command line argument for argparse"""
+    varname = "_".join(name.split("-"))  # change hyphens to underscores
+    group = parser.add_mutually_exclusive_group(required=False)
+    group.add_argument("--" + name, dest=varname, action="store_true", help=help)
+    if no_name is None:
+        no_name = "no-" + name
+        no_help = "don't " + help
+    else:
+        no_help = help
+    group.add_argument("--" + no_name, dest=varname, action="store_false", help=no_help)
+    parser.set_defaults(**{varname: default})
+
+
+def write_template(templ_file: str, out_file: str, templ_args: dict):
+    """Write to ``out_file`` based on template from ``templ_file`` using ``templ_args``"""
+
+    with open(templ_file, "r") as f:
+        templ = Template(f.read())
+
+    with open(out_file, "w") as f:
+        f.write(
+            templ.safe_substitute(
+                templ_args,
+            )
+        )
+
+
+def setup(args):
+    if args.site == "lpc":
+        t2_local_prefix = "/eos/uscms/"
+        t2_prefix = "root://cmseos.fnal.gov"
+
+        try:
+            proxy = os.environ["X509_USER_PROXY"]
+        except:
+            print("No valid proxy. Exiting.")
+            exit(1)
+    elif args.site == "ucsd":
+        t2_local_prefix = "/ceph/cms/"
+        t2_prefix = "root://redirector.t2.ucsd.edu:1095"
+        proxy = "/home/users/rkansal/x509up_u31735"
+
+    username = os.environ["USER"]
+    submitdir = Path(__file__).resolve().parent
+
+    return t2_local_prefix, t2_prefix, proxy, username, submitdir
+
+
+def parse_common_args(parser):
+    parser.add_argument("--tag", default="Test", help="condor tag", type=str)
+    parser.add_argument("--cards-tag", default="Apr26", help="cards dir tag", type=str)
+    parser.add_argument(
+        "--site",
+        default="lpc",
+        help="computing cluster we're running this on",
+        type=str,
+        choices=["lpc", "ucsd"],
+    )
+    parser.add_argument("--toys-per-job", default=100, help="# toys per condor job", type=int)
+    parser.add_argument("--num-jobs", default=10, help="# condor jobs", type=int)
+    parser.add_argument("--seed", default=444, help="# condor jobs", type=int)
+    add_bool_arg(parser, "submit", default=False, help="submit files as well as create them")
+    add_bool_arg(parser, "resonant", default=True, help="resonant or nonresonant")
diff --git a/src/HHbbVV/combine/submit_bias_res_loop.sh b/src/HHbbVV/combine/submit_bias_res_loop.sh
new file mode 100755
index 00000000..dae72e4e
--- /dev/null
+++ b/src/HHbbVV/combine/submit_bias_res_loop.sh
@@ -0,0 +1,22 @@
+seed=$1
+TAG=$2
+
+for sample in NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190 NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125 NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250
+do
+  cd $sample
+  for bias in 0.0 0.15 0.3 1.0
+  do
+    python3 /uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_bias.py --seed $seed --num-jobs 100 --toys-per-job 10 --bias $bias --submit --tag $TAG
+  done
+  cd -
+done
+
+# need to submit extra jobs for these because of high fit failures
+sample=NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250
+cd $sample
+bias=0.0
+python3 /uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_bias.py --seed $((seed + 1000)) --num-jobs 100 --toys-per-job 10 --bias $bias --submit --tag $TAG
+
+bias=0.15
+python3 /uscms_data/d1/rkansal/HHbbVV/src/HHbbVV/combine/submit/submit_bias.py --seed $((seed + 1000)) --num-jobs 50 --toys-per-job 10 --bias $bias --submit --tag $TAG
+cd -
\ No newline at end of file
diff --git a/src/HHbbVV/corrections/TriggerSFs.ipynb b/src/HHbbVV/corrections/TriggerSFs.ipynb
index dd9c8137..561a45e0 100644
--- a/src/HHbbVV/corrections/TriggerSFs.ipynb
+++ b/src/HHbbVV/corrections/TriggerSFs.ipynb
@@ -2,12 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import awkward as ak\n",
     "import numpy as np\n",
+    "import hist\n",
     "from hist import Hist\n",
     "import matplotlib.pyplot as plt\n",
     "import mplhep as hep\n",
@@ -15,6 +16,7 @@
     "import math\n",
     "import os\n",
     "from hist.intervals import clopper_pearson_interval\n",
+    "import utils\n",
     "\n",
     "plt.rcParams.update({\"font.size\": 16})\n",
     "plt.style.use(hep.style.CMS)"
@@ -22,17 +24,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_dir = \"../../../plots/TriggerSFs/Feb26\"\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dir = \"../../../plots/TriggerSFs/23Jul22\"\n",
     "_ = os.system(f\"mkdir -p {plot_dir}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,41 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'den': Hist(\n",
-       "   Variable([0, 0.9, 0.95, 0.98, 1], name='jet1txbb', label='$T_{Xbb}$ Score'),\n",
-       "   Variable([0, 0.9, 0.95, 0.98, 1], name='jet1th4q', label='$T_{H4q}$ Score'),\n",
-       "   Variable([250, 275, 300, 325, 350, 375, 400, 450, 500, 600, 800, 1000], name='jet1pt', label='$p_T$ (GeV)'),\n",
-       "   Regular(15, 0, 300, name='jet1msd', label='$m_{SD}$ (GeV)'),\n",
-       "   storage=Double()) # Sum: 1114034.0 (3911097.0 with flow),\n",
-       " 'num': Hist(\n",
-       "   Variable([0, 0.9, 0.95, 0.98, 1], name='jet1txbb', label='$T_{Xbb}$ Score'),\n",
-       "   Variable([0, 0.9, 0.95, 0.98, 1], name='jet1th4q', label='$T_{H4q}$ Score'),\n",
-       "   Variable([250, 275, 300, 325, 350, 375, 400, 450, 500, 600, 800, 1000], name='jet1pt', label='$p_T$ (GeV)'),\n",
-       "   Regular(15, 0, 300, name='jet1msd', label='$m_{SD}$ (GeV)'),\n",
-       "   storage=Double()) # Sum: 152849.0 (156559.0 with flow),\n",
-       " 'cutflow': OrderedDict([('all', 742490867),\n",
-       "              ('muon', 314211610),\n",
-       "              ('ak8_jet', 4064792)])}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined[\"2017\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,303 +100,130 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 23,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "for year in years:\n",
-    "    w, jet1txbbbins, jet1ptbins, jet1msdbins = effs_txbb[year].to_numpy()\n",
-    "\n",
-    "    for k in range(len(jet1txbbbins) - 1):\n",
-    "        fig, ax = plt.subplots(figsize=(18, 14))\n",
-    "        mesh = ax.pcolormesh(jet1msdbins, jet1ptbins, w[k], cmap=\"turbo\", vmin=0, vmax=1)\n",
-    "        for i in range(len(jet1ptbins) - 1):\n",
-    "            for j in range(len(jet1msdbins) - 1):\n",
-    "                if not math.isnan(w[k][i, j]):\n",
-    "                    ax.text(\n",
-    "                        (jet1msdbins[j] + jet1msdbins[j + 1]) / 2,\n",
-    "                        (jet1ptbins[i] + jet1ptbins[i + 1]) / 2,\n",
-    "                        w[k][i, j].round(2),\n",
-    "                        color=\"black\" if 0.1 < w[k][i, j] < 0.9 else \"white\",\n",
-    "                        ha=\"center\",\n",
-    "                        va=\"center\",\n",
-    "                        fontsize=12,\n",
-    "                    )\n",
-    "\n",
-    "        ax.set_title(\n",
-    "            f\"{year}: {jet1txbbbins[k]:.2f} < $T_{{Xbb}}$ Score < {jet1txbbbins[k + 1]:.2f}\"\n",
-    "        )\n",
-    "        ax.set_xlabel(\"Jet 1 MassSD (GeV)\")\n",
-    "        ax.set_ylabel(\"Jet 1 $p_T$ (GeV)\")\n",
-    "        plt.colorbar(mesh, ax=ax)\n",
-    "        plt.savefig(\n",
-    "            f\"{plot_dir}/{year}_txbb_{jet1txbbbins[k]:.2f}_{jet1txbbbins[k + 1]:.2f}_effs.pdf\",\n",
-    "            bbox_inches=\"tight\",\n",
-    "        )\n",
-    "        plt.close()"
+    "## Plotting"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "for year in years:\n",
-    "    w, jet1th4qbins, jet1ptbins, jet1msdbins = effs_th4q[year].to_numpy()\n",
+    "def mesh2d(\n",
+    "    ax,\n",
+    "    xbins,\n",
+    "    ybins,\n",
+    "    vals,\n",
+    "    vmax,\n",
+    "    title,\n",
+    "    year,\n",
+    "    xlabel=r\"Jet 1 MassSD (GeV)\",\n",
+    "    ylabel=r\"Jet 1 $p_T$ (GeV)\",\n",
+    "    fontsize=28,\n",
+    "):\n",
+    "    mesh = ax.pcolormesh(xbins, ybins, vals, cmap=\"turbo\", vmin=0, vmax=vmax)\n",
+    "    for i in range(len(ybins) - 1):\n",
+    "        for j in range(len(xbins) - 1):\n",
+    "            if not math.isnan(vals[i, j]):\n",
+    "                ax.text(\n",
+    "                    (xbins[j] + xbins[j + 1]) / 2,\n",
+    "                    (ybins[i] + ybins[i + 1]) / 2,\n",
+    "                    vals[i, j].round(2),\n",
+    "                    color=\"black\" if 0.1 * vmax < vals[i, j] < 0.9 * vmax else \"white\",\n",
+    "                    ha=\"center\",\n",
+    "                    va=\"center\",\n",
+    "                    fontsize=fontsize,\n",
+    "                )\n",
     "\n",
-    "    for k in range(len(jet1th4qbins) - 1):\n",
-    "        fig, ax = plt.subplots(figsize=(18, 14))\n",
-    "        mesh = ax.pcolormesh(jet1msdbins, jet1ptbins, w[k], cmap=\"turbo\", vmin=0, vmax=1)\n",
-    "        for i in range(len(jet1ptbins) - 1):\n",
-    "            for j in range(len(jet1msdbins) - 1):\n",
-    "                if not math.isnan(w[k][i, j]):\n",
-    "                    ax.text(\n",
-    "                        (jet1msdbins[j] + jet1msdbins[j + 1]) / 2,\n",
-    "                        (jet1ptbins[i] + jet1ptbins[i + 1]) / 2,\n",
-    "                        w[k][i, j].round(2),\n",
-    "                        color=\"black\" if 0.1 < w[k][i, j] < 0.9 else \"white\",\n",
-    "                        ha=\"center\",\n",
-    "                        va=\"center\",\n",
-    "                        fontsize=12,\n",
-    "                    )\n",
+    "    ax.set_title(title, x=0.35, y=1.005)\n",
+    "    ax.set_xlabel(xlabel)\n",
+    "    ax.set_ylabel(ylabel)\n",
+    "    hep.cms.label(ax=ax, data=True, year=year, lumi=utils.LUMI[year] / 1e3)\n",
     "\n",
-    "        ax.set_title(\n",
-    "            f\"{year}: {jet1th4qbins[k]:.2f} < $T_{{H4q}}$ Score < {jet1th4qbins[k + 1]:.2f}\"\n",
-    "        )\n",
-    "        ax.set_xlabel(\"Jet 1 MassSD (GeV)\")\n",
-    "        ax.set_ylabel(\"Jet 1 $p_T$ (GeV)\")\n",
-    "        plt.colorbar(mesh, ax=ax)\n",
-    "        plt.savefig(\n",
-    "            f\"{plot_dir}/{year}_th4q_{jet1th4qbins[k]:.2f}_{jet1th4qbins[k + 1]:.2f}_effs.pdf\",\n",
-    "            bbox_inches=\"tight\",\n",
-    "        )\n",
-    "        plt.close()"
+    "    return mesh\n",
+    "\n",
+    "\n",
+    "def plot_all_wrapper(tbins, xbins, ybins, w, vmax, year, name, bins=\"txbb\"):\n",
+    "    plt.rcParams.update({\"font.size\": 36})\n",
+    "    fig, axs = plt.subplots(2, 2, figsize=(18 * 2, 17 * 2), constrained_layout=True)\n",
+    "\n",
+    "    for k in range(4):\n",
+    "        row = k // 2\n",
+    "        col = k % 2\n",
+    "        ax = axs[row][col]\n",
+    "\n",
+    "        match bins:\n",
+    "            case \"txbb\":\n",
+    "                title = rf\"{tbins[k]:.2f} < $T_{{Xbb}}$ < {tbins[k + 1]:.2f}\"\n",
+    "            case \"pt\":\n",
+    "                title = rf\"{tbins[k]:.0f} < Jet 2 $p_T$ < {tbins[k + 1]:.0f} GeV\"\n",
+    "\n",
+    "        mesh = mesh2d(ax, xbins, ybins, w[k], vmax, title, year, fontsize=28)\n",
+    "\n",
+    "    for i in range(2):\n",
+    "        fig.colorbar(mesh, ax=axs[i].ravel().tolist(), pad=0.01)\n",
+    "\n",
+    "    plt.savefig(f\"{plot_dir}/{name}.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot all tagger bins in the same figure"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Plot in same figure"
+    "Txbb bins"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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tw9/nCa52dCAOvg7EL774omW9Bw0a5HMbycnJpkmTJpZ2Fi9e7P9gcdUKtuPrnXfeaWm7du3a5u+///ZYfvny5aZSpUqWOu+//36ay/juu+8s5f/66y+/xQ8A+E9w9RoBkOucOHHCREREOL702Ww2s2HDhnTrnT9/3tSoUcPyhfHVV191W3b06NGWcrfeemu6nbMeeughr5JZKRITE82WLVvMlClTzPDhw03btm1dEmKZSTx26NDBUT8kJMSrH5T333+/Zbm+XqVut9tNlSpVXL6U79692/Jcnjx5zOnTp31eJ38kHo0x5q677rK006hRI7fljhw54nKlZUZ+JM2fP9/SRvny5U1ycnKGYs+srNi3fTFr1iyX5O6lS5fSrOOcyO7Vq5fHshs3brSULVOmjDl58mSa7f/www+WOk2bNs3QugXCuXPnsmW5zsekYJIbOxB3797d8npMnz49Q+0sXbrU0s7dd9/t50gRLAJx7Aim42tsbKzJmzevz9+dDh06ZBkJKywszOOJ7Li4OJMvXz7Libhdu3al2f6yZcssIySUK1cu3c84AAAAALhakMf3Dnl8z8jjB0ce/3//+5+l3aioqHTv4uSuXu3atT2uQ6DPE2S17Mjj04E4+DoQX3vttZb19uYz0J3x48db2nnhhRf8HCmCRSCOHcF0fP3nn38s7YaHh5stW7akW2/Dhg2Wz/kiRYqkOULwCy+8YFnO+fPn/RI/AMAqRACQjVasWKHExETH9C233KJGjRqlWy9//vz6/PPPLc/NmDHDbdnvv//eMv3WW2/JZrOl2f6IESMUFhbmmJ44caKMMR7Lz5kzR3Xr1lWvXr00fPhwLVq0KJ018N66des0f/58x/S9996rG2+8Md16Tz75pGX6999/92m5f/75p/bs2eOYvuaaa3TNNdeoSpUqat68ueP5S5cuadq0aT617U/t27e3TP/zzz+y2+0u5UqXLq1bbrnF8twPP/zg8/KmTp1qme7du7dCQrLn4zQr9u3MxDNixAhFRESkWWfo0KEqVqyYY/qXX37RuXPn3JadMGGCZfrZZ59VVFRUmu337t1b9erVc0yvWbNGO3fuTLNOVti6dau6du2a3WEgCBw8eNAyXbNmzQy106JFC8uxyLldXB0+/fRT/d///Z/f2w2m4+usWbOUkJDgmO7Tp4/LZ7070dHReuuttxzTSUlJ+uWXX9yW/fnnn3Xx4kXH9IMPPqiqVaum2X6rVq3UuXNnx/ShQ4e0ZMmSdOMCAAAAgKsBefz0kcdPG3n8/2RnHn/y5MmW6XfffVclS5ZMt17Xrl3Vo0cPx/S2bdu0efNmt2UDfZ4gKy1fvlz9+vXL7jAQBPyVx7/hhhvSbBc5n91u14svvqiff/7Z720H0/F1ypQplulnn31WderUSbdeo0aNLN99Tp8+bfn+5GzHjh2O/ytUqKD8+fP7HiwAIF1h6RcBgMBZuXKlZdqbhFqKli1bKjw8XJcvX5YkbdmyRcnJyQoNDXWUOXLkiNauXeuYvvbaa71KbJYpU0adO3fWzJkzJUkxMTH6+++/varrb85JvWeffdareg0aNND777+v8+fPS5Ly5cvn03KdOxP179/f8X/v3r0tr90PP/ygAQMG+NS+v6TuvCRd+WF2+vRpFS1a1KVs37599euvvzqmf/zxR7355pteJw6TkpI0ffp0y3N9+vTJQNSZF2z7dmJioiW5XbJkSXXp0iXdennz5tW9996rjz/+WJJ0+fJlLViwQHfccYdL2VmzZjn+DwsLU9++fdNt32az6YEHHtBTTz3leG7OnDmqUaNGunUDZdGiRerevbuKFCmSbTHkRHa7XStXrtSuXbt05MgR5c+fX6VLl1bz5s1Vrly5TLdvjNGGDRu0bds2xcTEKCIiQqVKlVKTJk1UvXp1P6yBe2fPnrVMHz161Kski7OwsDANGjTIccwvU6aMT/WPHDmilStX6siRIzp9+rSKFCmiihUrqlmzZipevLjP8aTYvXu31q1bp6NHj+rcuXMqVqyYSpcurZYtW2aq3dSMMfrzzz+1ceNGnTp1Sm3btlWzZs3SrbNlyxZt3rxZsbGxSkpKUuXKlVWlShXVqlXL58/MQEtOTtYzzzyjjz76SMOGDfN7+8F0fHVOFrr7PPCkb9++evzxxx3T06ZNs0ynSL2+0pUOxN544IEHNHv2bMf0nDlzdPPNN3sdHwAAAADkVOTx00ceP23k8a/I7n07M3mX/v37W/bzadOmqX79+pYyWXGeIKtMmjRJ/fv3t3TER/ouX76sZcuWad++fYqLi1PhwoVVpkwZtWrVSiVKlMh0+4E+T+CJuzx+pUqVfG6nTJkylk7pzu+h9Ozdu1dr165VbGyszp8/r6ioKFWrVk1NmzZV4cKFfY5H+i9XvmnTJsXFxenixYsqUaKEoqOj1bJlSxUqVChD7TpLTk7W4sWLtXXrVsXHx6tbt27pngux2+1av369du7cqdjYWIWGhlry+OHh4X6JzV8uXryofv36aerUqRo7dqxf2w6242tmP08++OADx/S0adM8DrqUetCSjHbcBwB4IfsGPwYAY+69917LbScmTZrkU/1rrrnGUv/IkSOW+c63gnn++ee9bvvTTz+11H3rrbc8ll2/fr3p16+f20fDhg0zdeuzmjVrOupWq1bNp7oZlZCQYKKioiy3AT969KhjfmxsrAkJCbHcsu7QoUM+LcNftz5bvXq1y+3lnPeDFBcuXDCFChWylF22bJnXy3K+7VmdOnXSvNWY822mMrOezrJq3/bW0qVLLW3eddddXtedPXu2pe7AgQNdyuzfv99S5vrrr/e6/c2bN1vq3nzzzV7X9bfvvvvOhIWFZettx5z3SV9NnDjRUv+mm27y6vZ/R44cMcWKFXPUK1q0qDl48KCljLtbkyUnJ5sPP/zQlCtXziX2lMe1115rZs6cme6t/9q0aeNy2ze73W6+//57l9tppn7UrFnTjB07NiC3Obzlllssy3r22Wf9vgxP7Ha7mTNnjmnWrJnHdZeu3Fbxjz/+8LrdS5cumdGjR5vq1at7bNNms5lmzZp59boZY8zYsWMddfPnz+94fv369S63jxs2bJjHds6fP29ef/11y77m/IiKijLPP/+82b9/v9frHEjnz5833bp182r9MiLYjq/O++P27dt9ql+pUiVH3Xz58pmkpCTL/EuXLpk8efI4ypQqVcqrfdAYY86cOWP5/lOjRg2fYgMAAACAnIo8fvrI46eNPH725/EvXrxoaa906dI+1d+3b5+l/i233OJSJtDnCbKC3W43b7zxhkueOis575MZOZeQeh0kmX79+nlVb9OmTSYiIsJRr2rVqubs2bOWMu7aTUhIMC+//LIpXry4x3xwmzZtvMozB/o8QUakPsZLMmPGjPH7MjxJTk42EyZMMPXr1/e47qGhoaZXr17mn3/+8brdc+fOmREjRqS5TcPCwkz79u29Pj8wbNgwR926des6np83b57L+YK0jrMnTpwwTz/9tClVqpTH2MqWLWvefPNNc+zYMa/XOZCOHj1qmjdv7vfPkRTBdnwtXbq05f0dHx/vdd3ExEQTHh7uqF+zZk235ZKSkiy5/McffzzTcQMA3Muee7UAwP93+vRpy3TFihV9qn/ixAnH/yEhIS63296yZYtl2pcrhVu2bGmZ3rp1q8ey1157rcaNG+f2cfvtt3u9TGcxMTGWW3O0bds2w235Ys6cOTp16pRjumvXrpYrg0uVKmW55ZgxRpMmTcqS2Jxt2rTJMm2z2TyOapkvXz717NnT8pwvtz9zvu3ZPffck+6txgIlq/btrIinRYsW6caTmfZr165tGe3XH+vrK2OMXn31VQ0YMEBJSUmSFNCr4QOpT58+uuuuuxzTixcv1ujRo9OsY4zRQw89ZDlmf/PNN+lug/j4eN16660aOnSoDh065LHchg0b1K1bN9155526ePGil2tyZTSSfv36qW/fvparmJ3t2LFD999/v9q0aaOTJ0963b43nEc3/uCDD/TLL7/4dRnuxMfHq3v37urcubNWrVqVZtnffvtNrVu31ksvvZTurRJ37typ+vXr6/HHH9e///7rsZwxRqtWrVK3bt3UsWNHnTlzxud1+P3339W6dWtt2LDBq/IrV65UjRo19Oqrr2r//v0ey506dUrvvvuuqlWr5nJLsKx25MgRtWnTxjHajOT/Y0ewHV+PHz9umfZ1fVOPgHzx4kWX13r37t26dOmSY7p58+Zef5YXKlTIMirIrl27LG0BAAAAwNWKPH7ayOOnjzz+FdmZx0/9PpR8z7lUqFBBefLkSTOeQJ8nCLTExEQNGDBAr7zyiuO5nJrHf/755y370Pjx4y05RncSExPVt29fJSYmSrpyp7Iff/xRBQsWTLNebGysWrRooTfffNMlt5fCGKOlS5eqdevWeuyxx5ScnOz1ugT6PIE3nPP4zz33nP7880+/LsOdY8eOqXXr1rrvvvtcjqOpJScna8qUKWrSpInGjBmTbrurVq1S9erVNWzYsDS3aVJSkhYsWKDWrVurX79+jn3DF+PHj1fHjh3TPF+Q2syZM1W1alV98MEHiouL81guJiZGL7/8sqpUqaJ58+b5HJc/7dixQ82aNbOM+l+2bFm/LiPYjq+p3+vFihVTZGSk13XDw8NVuXJlx7SnPPvBgwctzzMCMQAEDh2IAWSrp556SpMmTXI8nG9jlZbDhw/r4MGDjunKlStbkheSLEk7SSpfvrzX7TsnBdLqXBYozl/gK1SokCXLTeu2Zyl69+5tmZ44cWIgQ/Jo0aJFlum6desqLCzMY3nn27JPnTrVcfu8tLi77ZnzNshKwbZvZyaeqKgo5c+fP814MtN+SEiIoqOjHdOHDh1SfHy81/UzKyEhQffcc4/eeOMNx3Pt2rXT7NmzsywGf7LZbPr8888t+9ELL7yg7du3e6wzduxYy/oOHDgw3dsZ2e123XfffZbbIOXJk0c1a9ZU7dq13SYjpk+frs6dOzs6aafn2WeftRzvwsLCVLVqVdWrV8/tbbmWL1+u1q1bu9yuLDPuvvtuy7Tdblf37t115513avny5bLb7X5bVor4+Hh16NBBM2bMsDyfcvuvunXrur1d5ttvv63XX3/dY7ubN29WixYtXN7DISEhqlixoho2bOg2aZbSEdiXTsSbN29Wjx49LO/lwoULq3r16ipTpoxL+d9++01t27ZVTEyMy7zy5curSpUqLs9fvnxZ/fr108iRI72Oy582b96sZs2aaf369ZKuvPc+/vhjPfjgg35dTrAdX53fe3v37vWpvnOH4W3btlmmM7O+kvUz1G63a8+ePT7VBwAAAICciDx+2sjjp488/hXZuW9nNucSFxdn6ch18OBBnT9/3lIm0OcJAun06dPq2LGjxo0b53junnvu0bfffpulcfhLWFiYvv/+exUoUMDx3MCBA3Xs2DGPdd544w399ddfjumRI0eqadOmaS4nISFBd9xxh2WQh8jISNWpU0c1atRwOd5L0pgxY3T//fenO1iFlDXnCbzhfByJj49Xq1at9NBDD+mvv/7yal18deTIEbVq1UorVqywPB8REaHq1aurVq1aioiIsMy7fPmyHnvsMY0fP95ju4sXL1bbtm0VGxtreT7l/EjDhg0tF6Ok+P7779W5c2evjsUpFixYoAEDBlg6jBcrVkw1a9ZUsWLFXMp/99136t69u8uFSzabTVWqVLHkolOcO3dOnTp1yrb36tKlS9W8eXNHnjhv3ryaOnWqbr75Zr8uJ9iOr6k/U44fP65z5855XTc5Odny3TA5OVm7du1yKee8zjVr1tTFixf1ySefqEePHmrQoIEiIyNVtGhR1apVS3379tUPP/zAoB8AkBHZNvYxAGTSY489ZrndRq9evVzK1K1b11Jm7969Xrd/+fJlS90iRYpkKM7Ut2uRj7c++/jjjy11v/nmG2PMldvV/Pbbb+aBBx4wderUMVFRUSZPnjymfPnypn379ua9994zMTExGYr3+PHjltuGlCxZ0iQmJrqUO336tOW2IZLM5s2bvV6OP259NnfuXJdb1jz33HNp1klOTrbc3lySmTVrVrrLcr7tWYsWLdKtE8hbnwXDvp1a586dLW0uXrzYp/rly5e31D99+rRl/uDBgzO1HW+44QZL/b///tun+hl1/Phx06pVK8uy77//frfvqazivE9m1MKFCy3tXHfddeby5csu5fbt22cKFixouRXR+fPn3baZ+tZkqW+vWKxYMfP111+bc+fOOcpeunTJzJgxw9SuXdtlnUaOHOm2/TZt2rhtPzIy0rz33nvm+PHjjrJJSUlm4cKFlltOpTwGDBiQ4e3mTo8ePTzehqtMmTLmkUceMdOmTTMnTpzwy/IefvhhyzIiIyPNu+++a7nVV3JyslmzZo256aabLGVDQ0PNpk2bXNq8ePGiqVevnqVsgQIFzLvvvmvi4uIsZf/9918zePBgY7PZLOX79u3rMeaxY8c6ykVERJhq1aoZSaZQoULm1VdfNdu3b/d4a7r9+/ebIkWKWJZVu3ZtM3v2bMux5vTp02bOnDmmTp06Lq/DnDlzfN3MmTJv3jzLrTojIyPNzJkzA7KsYDu+On+eTJ061eu658+fd9mvnNfn/ffft8wfNmyYT/Hdd999lvozZszwqT4AAAAA5Dbk8cnjk8cPjjy+3W43+fPnt7SZOh+YHnevo/P6BPo8QaDs3bvXJc/8yiuveMw3ZkU8qWOpWLFihttKnVeVZO644w6367VmzRoTGhrqKNemTRuTlJTkts3U7aXOs1esWNFMmTLFJCQkOMqeP3/efP/99yY6Otpl//n+++/dth/o8wQZkZycbJo0aeKyjJRHlSpVzFNPPWV+/fVXS3wZZbfbzS233GJZRrFixcyXX35pzpw54yh3+fJls2DBAnPNNddYyhYqVMjExsa6tHv06FFTunRpS9kSJUqYL774wvJ+s9vt5p9//jF33323y7q+9tprHuNO/VlaqlQpU6xYMcf/o0aNMnv37vX4vlq3bp3lc02Sad68uVmyZInlnNKxY8fMTz/9ZMqVK2cpGxoaav7555+MbO4MmzBhgiXm4sWLmz///DMgywq246vz59vatWu9rrtt2zaX/crd+nzyySeWMi+//LIpW7asx/dhyiM6OtqMHz8+U+sHALkNHYgB5EgzZ860/JD19MXU+Qdp6h9V3nDucJScnOxzrJlJPD7++OOWurNnzzY7d+60dILz9MibN6958cUXfe6o+Nlnn1naGTp0qMeyzp3eXnjhBa+Xk5nEo91uNz/88IMpXry4pY2IiAhz4MCBdOu/+uqrlnq9e/dOt87AgQMtdT799FOv4w2EYNi3U2vZsqWlPV9/pDsnN5wTj/fcc49lvq8d6W6//fZM/bDOiJ07d5rq1atblvv6669nW9IxhfOxIjOefvrpNBNyycnJlg6o4eHhZv369R7bS50YTHlUqFDB7N+/32OdixcvuiROwsPD3Z58cXfsLFKkSJodHpOTk13e/5LMunXrvNhC3jl69KhXx3WbzWauu+468+KLL5qFCxeaixcv+rysZcuWWdosXLhwmq+J3W43d955p6VO//79Xcq9/fbbljIlSpRI92TU9OnTTVhYmKXesmXL3JZ1TnSn7Bs7duxId52d948nnnjCXLp0yWP5hIQEM2TIEEudKlWqmAsXLqS7LH/4+uuvLd9xSpUq5VPyzVfBdnx99913Le11797d67qffvqpy34yevRoS5mXX37ZMv/jjz/2Kb4nn3wyw99fAAAAACC3IY9PHp88fvbv26l17NjR0t4nn3ziVT13OUJJLgMNBPo8QSCsXr3alCxZ0rHMsLAw89133wV8uWnxZwdiu91uunfvbmnPuePuhQsXTK1atSw587Teo+6OZY0aNTKnTp3yWOfkyZMuHXCLFy/uNscd6PMEGbV7926XQTTcPcLDw03r1q3NyJEjzZ9//pmhAWUmTpxoabNcuXJpvh8SExNdBkMZPny4SznnwUWqV69uDh06lGYso0ePttQJDQ31GIvzZ6kk07BhQ3PkyJE0l5GcnOyybd977700j3lnz541vXr1stRp2bJlpo+T3rDb7Wb48OGWZdeoUcPs2rUrYMsMtuProEGDvP4e4uyZZ55x2U/cXSTkfBGar4+BAwd6vBACAGBFB2IAOc7kyZNdrpjv1KmT27KpkyuhoaE+d9yrWrWqZTmeRs1MS2YSj3379rXUHTdunONqTW8fN9xwg09JKecfmBs3bvRY9ueff3b5Ae/tDzPnxOPQoUPN3LlzPT7mzJljvvrqK/PMM8+Y6667zu26vv76614te+fOnZZ6kZGRaV4RfPnyZUuSMzQ01Bw9etSrZQVKMOzbqTn/8Dx48KBP9du1a2ep79zx0LmDmqdOhp488MADlvqzZ8/2qb6vli1bZnmvRkREmIkTJwZ0md5yft9kxsWLF039+vUdbYWFhVk6o3700UeWZY0aNSrN9pwTgzabzauOkxcuXHCMSJvyGDFihEs5dydtpk+fnm77ycnJbkeS9qfExETz7LPPuowCktYjb968pkOHDubTTz/1+mrxO+64w9KGp1EeUjt9+rRlfy5UqJBltOnExESXkyG//fabV/E4nwhyNwqRMe47EHvTUXXz5s0un4nuRsp2lpSU5LK/fPHFF16tU0YlJyebF1980bLMOnXqmH379gV0ucF2fN2+fbvLKMK///57uvX+/fdfExkZ6bKfvP3225Zyzh2AJ0yY4FN8r7/+uqV+dp+IBAAAAIBgRR6fPD55/ODYt1P76quvLO0VLlzY5e5h7kyaNMnta7ly5UpLuUCfJ/C3adOmmXz58lnynr689wPFnx2IjbkyYnmZMmUsr3vqDsLO+bL07gjmvB/kz58/zc69KWJjY01UVJSlrrtRQgN9niAzzp07Z+6//34TERHh9fG9YMGCpmvXrmbcuHFeDVJht9tNo0aNLG0sXbo03Xr79++3XLRTp04dy/wTJ05Y9vewsLA0PztSc74rm6dR5J0/SyMiIsz27dvTbf/XX3+11OvTp49Xx8vz58+bGjVqWOp6e24ioy5duuSyPW644QbLHS4DIdiOr/PmzbO0FxYW5lWby5cvd8n/SzKTJk1yKXvzzTe7fU+1a9fOfPjhh2bKlCnm559/Nh999JG54447XAaskXy7aAoAcjM6EAPIMeLi4lxGyUv54ezp6sjUtw0pUaKEz8ts2rSpZVneJFKcZSbx6HxVcOqOMeHh4aZHjx5m+PDhZvLkyebTTz81Dz/8sMttvSSZ2267zauE4L///mup17hx4zTLX7hwwRQsWNDnH7HGuCYeM/u47777fLqqtFmzZpb6aXUgcr7tWceOHb1eTqAEw76dmvNIu/Hx8T7Vd75KePXq1Zb5HTp0sMzfsmWLT+0/99xzlvqTJ0/2qb4vfvzxR0vyKCoqyuv3RVZwfu9k1saNGy3rW7duXXPx4kWzfft2kzdvXsfz7du3T/c96pwY9GZUkRTTp0+31K1Xr55LGecOoc2bN/c6ab9hwwZL3UKFCgXkSvZz586Zb7/91rRt29anJGRkZKR56KGH0hwt4NixY5ZbvtWsWdPr9Xc+ZqdO+i1YsMAyr23btl6vb3x8vClUqJAlyeTu+OHcgTi9z6cUzsnvbdu2eR2b82fiXXfd5XVdX128eNHlONi2bds0R+3wl2A8vjrfGi8qKsr88MMPbvdXu91upk2b5vY2iJLMG2+8YSn/0EMPWebPmTPHp9icR3h69913M7WuAAAAAHC1IY9PHj+tB3l83/g7j5+QkGAqVKhgabNu3boeO2deunTJvPXWW5Y8b+rH8uXLLeUDfZ7AX+x2u/nggw8sndgqVKgQ8A7L3vJ3B2JjjPn9998tbabk65csWWJ5/oEHHki3Lef94MUXX/Q6jg8//NBSt0uXLi5lAn2ewB+OHz9uPvroI9O8eXOXkfbTekRFRZmhQ4emmfd1Phdx8803ex1X6nMgNpvN8h785ptvLO0OGDDA63YPHDhgeb9ER0e7Lef8WdqjRw+v2k89yEVERITXA6YY43rsf/75572u66sTJ064nGfq06ePSUhICNgyUwTb8dVut7t8RpcvX95jB+7k5GTz1VdfuVxEkPJwNwiT87GgQoUKZsmSJR5j2rNnj8tgQO4+qwAArkIEAEEuPj5eb775pqpVq6YffvjBMq9kyZKaP3++oqOj3da12+2O/202m8/LTl1fki5duuRzG5lx/vx5y/SFCxckSTfeeKM2btyon3/+WcOGDVOvXr00ePBgffHFF9qyZYuGDBliWd///e9/+vHHH9Nd3sSJEy3T/fv3T7N8vnz51KNHD8tzzq9RoBUoUECjRo3S+PHjFRLi/cda3759LdNpbZ8pU6ZYpu+55x7fggyAYNu3ndvzNab04gl0+/5gjNFbb72lPn36KDExUZJUuXJlrVy5Uq1bt/b78oJF/fr19c477zimt2zZohdffFH9+vVTQkKCJKlYsWI+v0clqV+/fl6X7dq1q0qWLGmJ4+zZs+m27+2+1KhRIzVu3NgxffbsWW3dutXr+LxVoEABDRgwQAsXLtSpU6c0b948Pf/882rcuHGasV64cEFff/21mjRpolWrVrkts2LFCst74e677/Z6/YcMGaK3337b8YiIiLC0m9q9997rVZuSFBkZqTvuuMMxnZSUpPXr16dbr02bNl61v2TJEsf/FSpUUM2aNb2OrVq1aipfvrxjeunSpTLGeF3fW8eOHVO7du0snzX9+/fX3LlzVaRIEb8vz1kwHl9HjRql0qVLO6ZPnTqle+65R23atNELL7ygCRMmaPz48Xr22Wd1ww03qEePHjp8+LAkKX/+/Ja2ChYsmGa8wbC+AAAAAHA1II//H/L4rsjjB8e+nSdPHn3zzTeW12DLli26/vrrdeedd+r111/X1KlT9dlnn+nRRx9VvXr19NJLLznyvJGRkZb2cmLeJSkpSYMHD9bTTz/tyPU1btxYq1atUt26df2+vGBx880364knnnBML1iwQO+9957l+FG9enV99NFHPrftSx6/f//+CgsLc0yvWrUq3ZxroM8TZESxYsU0ZMgQ/fnnnzpx4oRmzpypJ554It196NSpU/rwww/VrFkz7dy5022ZP/74wzLdu3dvr+MaPny4I4f/1ltvOd67Uuby+OXLl7ec5zp8+LAOHjyYbj1v8vh2u11Lly51TDdv3lyFCxf2OrZWrVpZ9qnUbfnTnj171KJFC0v7L7/8siZOnKg8efIEZJmpBdvx1Waz6auvvrLk4w8ePKhbb71VnTp10muvvaaffvpJ33zzjZ588klde+21GjhwoE6dOiUp/Tz+xYsXdeDAAcd0pUqVtGzZsjT3qcqVK2vBggVq3ry55fnXXnstw+sJALlFWPpFACB7JCcna/z48Xr11VcVExPjMr9Zs2YaP368qlev7rGN/PnzO34Ynjp1SsYYn75Qnzx50qW9rJScnOzyXIsWLbRgwQKFhoa6rRMZGamPPvpIxhh98sknjudHjBihe+65x+P6G2Msicfw8HCvfpT26dNH48aNc0xPmTJFn3zyScB+LIWFhalEiRKqXbu2OnbsqP79+6t48eI+t3PXXXdpyJAhunz5siRp3rx5Onr0qCWxIF1JJv3yyy+O6cjISHXr1i1zK+EHwbZvO9c/deqU8uXL57d43LXvi0C/lxMTE/XII49o7NixjueaNWummTNnuuxTV6MhQ4Zozpw5WrhwoSS5JBm/++47lS1b1ud2r7nmGq/LhoWFqWHDhpo/f76kK8e0TZs2qWXLln5pX5KuvfZaS+fWf/75R/Xq1fOpDV9ERkaqQ4cO6tChg6Qr+/3ixYu1cOFCzZkzR/v373epExsbqzZt2mjx4sVq0aKFZd7KlSst03Xq1PE6lkaNGqlRo0Zu561bt84y7Wui3bn8unXrdMMNN6RZx9MJx9TOnz+vf/75xzFdqVIl/f777z7FVrp0aUciNDY2VmfOnPFrp94dO3aoU6dO2rNnj+O5kSNH6pVXXsnQSaWMCMbja3R0tGbNmqVOnTrp2LFjjueXLVumZcuWeazXp08fFSpUSF988YXjOefEYzCuLwAAAADkZOTxyeO7Qx7/imDctzt06KCvv/5aDz/8sJKSkiRd6Vg2bdo0TZs2zWO9MWPG6LPPPtOWLVscz3mTd/HneYLMOnfunO666y7NnTvX8VzXrl01adKkXJHjeeedd7RgwQLHwBgvvviiY15YWJh+/PFHFShQwKc28+XLpxo1anhdPioqSpUrV9a///4rSTp+/LhiYmLSzPcG+jxBZhUuXFi33XabbrvtNklX8siLFi3SwoULNXv2bB09etSlzo4dO9S0aVOtX79eVatWtczLTB7/xhtv1I033uh2nj/y+Kk7z65bt84yAIc73uTxd+zYYcnRlixZUr/99ptPsZUtW9bR2XTbtm0+1fXGypUr1a1bN0euOiwsTF9++aUGDBjg92V5EozH1/r162vKlCnq1auX4uPjHc/PnTvXcpx19vzzz2vz5s2aM2eO4znnz5OIiAjL98qCBQt6FXOePHk0btw41apVy3FxwqJFi3TkyBGVKVPG63UDgNyGEYgBBKWtW7eqVatWeuCBB1ySjnny5NF7772n5cuXp/ujNPUXycuXL1u+vHrD+cu0rz+cM8t5eXnz5tV3333nMemY2ogRI1SiRAnH9K5du7Rp0yaP5VeuXKndu3c7pi9fvqzixYvLZrOl+bj55pst7Zw+fTrNHwWejB07VsaYdB+XL19WTEyMFi5cqGeeeSZDSUdJKlq0qLp27eqYTk5O1tSpU13KLVmyRMePH3dMd+vWLcv3A3eCbd92/tHm3L6v8aSXePR3+5lx+vRpderUydJ5uEePHlq0aFGu6DwsSSEhIRo3bpzbjpWDBg1yJM58ERkZ6fP2q1atmmU6vf2kSpUqAW3f36KiotS9e3eNGTNGe/fu1Z9//qlHH31U4eHhlnKJiYl6/PHHXU5eOX+eVqxY0S9xpe7gKV25ytsXlSpVsky7S6g68ybxGBcXZxm94o8//lDHjh19eqxdu9bS5rlz57xbKS/88ccfat68uaPzcHh4uCZMmKBXX301yzoPS8F7fG3SpInWr1+vjh07pls2T548evPNN/X9999bPrPdxROs6wsAAAAAORF5fPfLI49PHj9FsO7bKXdA86ZjYtmyZTVjxgw9+uijiouLs8zLSXmXQ4cO6YYbbrDs948//rh++eWXXNF5WLrS2XfixIku+WRJevPNN9WkSROf26xcubLPuUxf8uxZcZ7A30qXLq0+ffro22+/1eHDhzV//ny3o/2eOXNGzzzzjMvzWZHHj4yMtHz2eCNQeXzn9Z06darPefzUI9WeO3fOr3cSnDp1qm666SbH9itUqJDmzp2bpZ2HpeA9vnbq1EmrVq1Ss2bN0i1buHBhff3113r77bfT/TwJDQ1V6dKlHQ9fjtM1atTQrbfeanku5aICAIB7dCAGEFQSExP1+uuv65prrnF7C/a77rpLW7Zs0bPPPutV8s35y6YvX6aTk5N15swZx3RERITllu1ZwfnL8E033eT17deLFCliuS28JC1evNhj+QkTJvgeoAfOt1ALVs63P3N32zbn25716dMnoDF5K9j27czEI7mOAOmcCA10+5nx/PPPO0belaRnnnlGU6ZM8enK36tBuXLl9PHHH1ueK1KkiEaNGpWh9qpUqRLQxGOBAgV8PnGR3YnH1Gw2m5o3b64xY8bo77//VtOmTS3zN2zYoMmTJ1uec463QoUKfokl9fsrf/78Pm9X58SjNyPCepPcDMTr48/b3XXo0MGxrkWKFPGYSA60YD6+li9fXr/++qtWr16tp59+Wtdee61KlCihsLAwlSxZUtddd51Gjhyp7du366WXXlJoaKhiY2MtbTh3aA/m9QUAAACAnII8vhV5/MAij3+Fv/ft1q1ba+PGjZo+fbruv/9+1axZU4ULF1Z4eLjKlSun9u3b6/PPP9e2bdvUrVs3Xb582dJJu0CBAipWrJilzWDOuzzwwAOOu5XZbDb93//9nz755BOvjlFXk0aNGumVV16xPFerVi23HVm94esgHZJvefZAnycItLCwMLVv314TJkzQihUrXC6omTFjhv7880/Lc6njzZMnj88dfT1J/f7KSMfvnJLHt9vtunDhgl/aOnz4sHr16qVLly5JupKvXrFihdq3b++X9n0RzMfXevXq6c8//9T8+fP16KOPql69eipatKjCwsJUpkwZtWzZUqNGjdKOHTv04IMPymazueTxnfevzGrbtq1lOvXFVwAAV2HZHQAApNixY4d69uzp9ur6G2+8Ue+9956uu+46n9qsXLmydu7c6Zg+efKk1x2mzpw5Y7lC0V9XePoiKirKMt2wYUOf6jsnKQ8fPuy23KVLl1w6mmXG7Nmzdfr0ab/e5j0QOnbsqKJFizp+ZK1cuVJ79+51dDZKSkrS9OnTHeWLFi3qMlJDdgm2fdu5g1ZmrnzNkyePyxXl/mxf8u/7OSVxIElVq1bVK6+8opCQ3HmN1oIFCyzTp0+f1k8//ZShK7HDwnz/mpo3b17LdFqduMPCwnxOkPnSvje2bt2qxMRERzz16tXLUDt16tTRr7/+qmuvvdZypf3y5cstJ0tS76vSlVEGgoHzrTJTtklavHmPpdza0p/82YE49Xo++OCDatOmjd/a9kUwH19TNG3a1KWTvCfOIxfUqlXLMp0T1hcAAAAAghl5fFfk8QOLPP4Vgdi3Q0NDdccdd7h0YnfH+S5kderUccmvBvo8QWakzo22aNFCgwYN8lvbOUlycrIWLVpkeW779u1atGhRhjpFZkUeP5Dte+Pvv/92/B8ZGZnuqPqetGjRwpHHT51nXr58uVq0aOGYTr2v5suXL2jON+W0PL4/RhZ3ju3555/P8HmczArm46t05cKM9u3be3UcMcZYRrAuWbJkhu9U4InzxQ3O5w0AAFbB8W0DQK63fv16tWrVyiXpWLFiRc2aNUuLFi3yOekouXYacb4NSlqOHDlima5du7bPy88s5x+hVatW9al+9erVLdOnT592W+7XX3+1XHnYsGFDzZ0716dH2bJlHfUvXbqkadOm+RRrdoiIiFDv3r0tz/3444+O/xcvXqwTJ044pnv16pXlo1d4Emz7dmbiOX/+vM6dO+eYrlmzpstV/5lp3xhjWedChQqpTJkyXtdPT4MGDRz/7969Wy1btrR04swtJk2a5HYElCFDhmjfvn0+t7dnzx6fbzOVegQM6crJAk9Onz7t1RXyGW3fG71791ajRo3UqFEjS4IwI4oVK6aXX37Z8tzWrVst087x7t+/P1PLTJH6JFN8fLzLdkrPnj17LNPOI5dklPP6Dh061KtbbKb1aN68uV9ik6T69es7/h81apSeeuopJScn+619bwXz8dVXdrvdEk/FihVdEsWZWV8pOL4fAgAAAEB2IY/vHnn8wCKPf0V25yCcO7a7iyfQ5wkyI3Uef8WKFbrllluydWTa7PLee+9p6dKlLs/ff//9Ho89aXHO7XrDlzx7oM8TeKNNmzaOPP6dd96ZqbaqVq2qwYMHW55LK49/+vRpvw1qkTqPv3fvXp+3a1bl8T/55JNM5/H9laMuUqSIypcv75h+7LHH9MEHH/i87fwhmI+vvjp+/LilA3ogPt+cR1h27gAPALCiAzGAbBcTE6POnTu7/KB77LHHtHnzZnXp0sXnUSJTOH+ZXr16tdd116xZY5nOjuRMnTp1LNO+dsJLffWe5PnHnHOnv379+unWW2/16dGzZ09LG+5uIxaM3N3+LOWH39SpUy3zguW2Z1Lw7duZiWft2rXpxpOZ9vft22c5vtSuXTvDxxR3hg4dqs8++8xxJfWWLVt0/fXXa/369X5bRrDbt2+fHnnkEcd06lsNnT9/Xv3795fdbvepzbNnz1oS/95IuQVdinLlyqVZ3tdbFvnafnpKlSrl+P/cuXM+d2h21qRJE8u08xXVzp8Be/fuzdTyUjjfhszXdp0Tj/660ty5HedRUrLb8uXLdcsttzimP/roI915551+u72at4L5+Oqrv/76S+fPn3dMt2zZ0qVMjRo1LDH6sr4JCQmW40CpUqVcRpkCAAAAgKsVeXzPyOMHHnn87O9A/Mcff1im3eVdAn2eIDM++ugjvfTSS47ppUuXqkWLFhnqAJtTrV27Vq+99ppjOnUe/9ChQ3riiSd8bnP37t0+d6ZMnV8LCQlR6dKlPZbNivME6Umdxz948KDP5zqcBUMe/8KFCz7ny3NjHr9IkSJavXq1rr32WsdzzzzzjAYPHqykpKQsjSWYj6++8ubzJLOcz7f5e8RlALja0IEYQLYbMGCA5cdReHi4fvrpJ40ePdrl6jBf3XTTTZbpJUuWeF3X+curc1tZoWHDhpakq7vbwqVl48aNluno6GiXMidPntScOXMc0zabzSWJ6A3nOkuWLNGhQ4d8bierXXfddZZbxG3btk3//POPy23PKlSoEJAfMBkVbPt2ixYtFB4e7pheunSp10kjb+KpUaOG5YrhNWvWKD4+3m/tZ9agQYM0a9YsxzErNjZWrVu31uzZs/2+rGCTlJSke++913EVfGRkpObPn697773XUWbp0qX66KOPfG7b+RiWlsTERMtrHRUVle6txHxp3xijhQsXWp67/vrrva7vjvNoNL///num2nN+zzlfZZ86ySXJcvvE9Bw+fFj33nuv4/Hzzz875jVu3NhSdtu2bV63K7mOsODcXkYVL17ccmvHdevW+aVdfylUqJBmz56tgQMHOp6bMWOGbrzxxiy9nVawHV+PHz+uL774wvFwvqViWlJ/n5Hk9vabkZGRlvfugQMHvD5RtHr1astt67LjuyEAAAAAZBfy+J6Rxw888vj+2bc3btxoybts377d67rO+99tt93mUibQ5wkyIyQkRG+++aa+/fZbhYWFSZJ27NihZs2a+dQRL6c6f/68+vTp4+j0WLp0aa1du1Y33HCDo8yECRN8HpU8Pj7epw6ux44ds3TwbdCgQbqfIYE+T5Ce1Hn806dPu3Tu91Ug8/h///23JY+ferTpYM3j16tXzzJqfLDl8cuUKaM//vhDXbt2dTz3+eef6/bbb7cMZhFowXZ83bt3r+XzxLmTclrSy+Pb7XY1aNBAlSpVUqVKldSkSROfO2w7HzecO2ADAJwYAMhGS5YsMZIsj9mzZ/t1GTVq1LC0v2vXrnTrnDt3zhQoUMBRp2DBgiYhISFDyx82bJhl+fPnz/epfqtWrRx1o6KizPHjx72ql5SUZOrXr29Z9pYtW1zKff7555YyrVu39im+FMnJySY6OtrS1nvvveexfL9+/Sxlx44dm6Hl+sObb75pieWZZ54x8+bNszz3/PPPZ1t8nmT3vu3s5ptvtsSzaNGidOskJyebKlWqWOrFxMS4LTtw4EBLue+++86ruG666SZLvVWrVvm0Xr7466+/TNmyZR3LCgkJMZ9++mnAlpcRzsfczBoxYoSlvTFjxhhjjDlx4oQpU6aM4/k8efKYzZs3p9lWxYoVLW317dvX6zgmTZpkqdutWzeXMm3atMnw8W7lypWWug0bNvS6riezZs2ytHndddeZ5OTkDLc3evRoS3uPPfaYZf727dst85s0aZLhtseNG+eY53y87NChg9ftxsfHm8KFC1veM2fPnnUpN3bsWMsyFi9e7FX79957b7qfg54cPXrU3HDDDaZly5amZcuWZtiwYV7X9YXdbjfvvvuuJc5KlSqZrVu3BmR57gTT8fXs2bMmJCTE0WbFihWN3W5Pt15CQoLl+JsnTx63+5Ixxrz11luWuF977TWvYrv//vst9X766Sef1g0AAAAAciry+Okjjx945PEzn8f/9ddfM5R/3bhxo6Vey5YtPZYN9HkCf5g/f74pVKiQY1l58+Y106ZNC9jyfLV3717LtqhYsWKm23TOa6Ucw3ft2mUiIyMdzxcvXtzExsam2Zbz54G3uTVjjHn77bctdYcMGeJSJtDnCXzlnBu/8847M9Xe008/bWlv1KhRlvm//fZbhpfn3PaSJUsc87766ivLvIceesjrdg8ePGjJ2ZYqVcptztb5s3Tv3r1etZ/6MzRfvnzm2LFjXse2efNmRw6/ZcuW5vPPP/e6ri+SkpLM448/blm/Ro0amcOHDwdkee4E0/F169atGfpeEhcXZ/LmzeuoV65cObf7Urdu3Szt/+9///M6NrvdburVq+eoGxoaak6dOuV1fQDIjehADCBb3XfffZYvf4888ojfl/HKK69YlvHggw+mW8e5I48vP06dZTbx+H//93+W+g888IBX9T755BNLverVq7v9At6iRQtLuZTOfxkxZMgQS1sNGjTwWDaYEo/79u2zxBIdHW0eeOABy3MbN27Mtvg8ye5929k333xjabtDhw7pdvqaPHmy1z8wFyxYYClbp04dc+nSpTTbX7VqlUuiLTMdNL1x8OBB07BhQ8tyn3766YAv11vOyb3MWLFihQkNDXW01b59e8tr7txBtlGjRmm+Zs6JwZCQELN+/fp04zh79qypUKGCpe6sWbNcyjl3IJZkZs6cmW77ly9fNtddd52l3ujRo9Otl54LFy6YYsWKWdodOXJkhto6f/68qVq1qsfkoDFXkiaNGjWylJkxY0a6bScnJ7t8Vvz777+O+YmJiZbO4r581r388suWenfccYfbchntQDx37lxLvdtvv92rzqjGGDN06FBL3a+//tqrehk1ZcoUS+KsSJEiXq9nZgXb8bVZs2aWtufNm5duHeeTD0888YTHsjt27DA2m81RtmTJkubkyZNptr93716TL18+R538+fObM2fO+LxuAAAAAJATkcdPH3n8wCOPn3mnT582efLkcbRbsGDBdDu7Jycnm44dO1rimT59usfygT5P4C+bN2+25JRtNpv5v//7P69zh4Hk7w7EztvXeR/89NNPLfO7du2a5nZwzrEXLFjQHDx4MN04Dh48aAoWLJjuezbQ5wl8dejQIRMeHm5pd/z48RlqKyYmxkRFRVnacu5km5CQYEqXLm1Z/3Xr1qXbdkJCgqlUqZKjXt68eS0DLBw7dsySfw4PD0930JcU99xzjyXmp556ym25jHYgdr5I5sknn/SqnjHGdO/ePVOf3776+OOPLbnl8uXLZ9lnTzAdX5OTky3nhWw2m9mxY0e69R5++GFLPB9++KHbchMmTLCUa9GihUlMTPQqNucLCTp37uzTugFAbkQHYgDZxm63m6JFi1q+wO3Zs8fvyzl+/LjLD9K0rlJbt26dJYESGhpqtm/fnuHlZzbxeObMGVOiRAlLG5999lmaPwgWL17sss7ff/+9S7ldu3a5/AiPi4vzeR1TrFixwiVxsGnTJrdlgynxaIwxN954oyWe1J0i69Wrl62xeZLd+7azS5cuWZITktIcfXfPnj0u+/aCBQs8lrfb7aZly5aW8s8884zH98KxY8dMrVq1LOW//fbbTK+nN86ePeuSVO3evbu5cOFCliw/Lc7v0Yw6ffq05fUuVKiQOXDggEu5/v37W5b3yiuveGzTOTEoyVSuXDnN5GN8fLxp3769pU6NGjVMUlKSS1l3HYiLFi2aZnInKSnJ5SRZ0aJF/dZx8LPPPnOJ6emnnzYXL170uo2jR4+6bIMWLVq47czpnHQpXry4x+N0infeecdS5/rrr3cp8/rrr1vKlCpVymzbti3NdqdMmWI51koyf/zxh9uyGe1AbLfbTZ06dSx1X331Va+SamFhYY46xYoVM+fOnfNqmZmxcuVKy3ExPDzcTJgwIeDLDbbj69dff21pu1y5cubo0aMey8+dO9eyL0VGRqY7Uopz0vuuu+5ye9ww5spxpnXr1i77EQAAAADkBuTxvUMeP2uQx88855xIx44dPV4UbrfbXTpAN2nSJM39OtDnCfzpyJEjpkmTJpZlP/bYYx5zRFnFnx2I9+/fb4oUKWJpyzm3nZyc7HKnsW+++cZjm87Hj5T94vTp0x7rHDt2zDRo0MBSp3379m7LBvo8QUY899xzLsee999/36f29+7da6655hpLO71793Zb1jnfXrVq1XQ7aT/yyCMu+U5nzhdd1KxZM93RaD/88EOXzyBP3wMy2oE4Pj7eZbAVbwb0cI6tTp06WTKYz8yZMy0jdxcqVMirQTAyK9iOr84DxFxzzTVpngd1zvtHR0d7PBd26dIlyx0HpSsXP6R3bmf58uUun7lr167N1HoCQG5AB2IA2ebUqVOWL2/Fixc3c+fOzfDDeaTF1JxvbRUWFmbeeecdy4/k8+fPm88++8zkz5/fUjazoylkNvFojPsOZp06dbLc3jw5Odns3LnTPPXUU5YrHyWZNm3auP0RO3z4cEs5X247705ycrIpV66cpU1PtwwLtsTjd9995zbpIcm8/fbbGWrTOckTiPUM9L7tHP+wYcPSLD9x4kSXOs8884yl49elS5fMjz/+aEqWLGkpd+utt6Ybzx9//OGyf/fr18/s27fPUSY5OdnMmTPHVKtWzVKubt265vLly+kuw18uX75sBg0aZInh+uuvz1Ry3x+cX5+Mck40e7ri/tSpU5bbIoaEhJhVq1a5LZs6MZg6SV6iRAnz7bffmvPnzzvKXrp0yUyfPt1Ur17dEofNZjPLli1z237qDsSp28+fP7/54IMPLCOQJiUlmfnz57uMPCzJ/Pjjjxnebs6SkpJMu3btXJZRoUIF8+abb5qNGze6HL/tdrs5c+aMWblypenfv79lXSSZAgUKeBw5ICkpybRt29ZSvmDBgub99993uS3YgQMHzMCBAy23JpPc3xYrPj7e1K5d21KuUKFCZtSoUS7t7ty50zzyyCNu38ueZLQDsTFXEkbOHZVvvvlms3DhQpdk+fbt281jjz3mEttXX33l9fIya/fu3aZmzZqW5Y8cOTLgo58E+vjq3IG/TZs2HssmJCS43N6zUqVKZsaMGZblxMTEmBdffNFlH/VmhPB///3XMuJGyner1LeJtdvtZtmyZS4nkkqVKpXmCREAAAAAuJqQx/ceefzAI4/vnnP8aeXxd+zYYSIiIizlW7dubf766y9L/unvv/92Gdkzb968Zs2aNenGE+jzBP50/vx5c/vtt1ti6Nq1a5YMJuCJvzoQJyUluVwU7y63a8yVjoap970CBQp47CSaur3UuenKlSubn3/+2XJns/Pnz5tx48ZZRtSVZPLly2e5y11qgT5PkBFnz5419evXd9mva9eubT7++GOzY8cOl/yt3W43J0+eNAsWLDA9evRwyVGXLl3a4yAI7vLtJUuWNF9//bXluGG3283WrVvNnXfeaSkbHh7u9sKD2NhYl/dcqVKlzDfffOPS7l9//WV69uzp0/Elox2IjTHmp59+clnW3XffbVauXGni4+MtZdetW+dyjkqS+e2337xeXmatW7fOsl+HhYVlySBGgT6+OnfgT+u8zfHjx106fjdo0MAsWbLE0pF7165dZuDAgS7v0fTuEPrjjz+6rGuLFi3M4sWLLecJLl68aP7++2/z1FNPuYwW7sto1gCQm9GBOIMOHTpkVqxYYVasWGEOHTrkc/2UBMHixYvN2rVr071lrjsXL140mzZtMosWLTIbN24MilENAV9s377dY6InI4+0fkAnJSWZLl26uNSJiIgwNWvWNLVq1XLpRCLJNGvWLNPvLX8kHu12u+nbt6/b9S5Tpoxp0KCBKVCggNv5VatWdTtqn91ud+kAlNYVxd568sknLW2WL1/e7dWewZZ4PHPmjOXW5KkfqTtP+SIrEo+B3rd9SQykGDx4sEu90NBQU7VqVVO3bl2XBGjKfurcydCT999/36W+zWYzFStWNPXr1zeFCxd2mV+sWDGza9cur9r3J7vdbkaNGmU5GVC5cmW/jhjhK+dtkxHOCYpu3bql2blx7ty5lvI1atRwSfgYY01MvP7666Zy5cqWennz5jW1a9c2derUcbsfSTLvvfeexzhSd2B84oknXDoHh4eHm+rVq3vcjySZRx991O8dOc+fP+92dOSUR2RkpKlSpYpp1KiRqV69uuXKdudHwYIFzYoVK9Jc3qFDh9yO4hASEmKqVKliGjZs6DKyUMrjmWee8djuP//847ZeSrvXXHONyxXjKY9GjRpZbqfmLDMdiI0x5qOPPvK4zcqXL2/q1avncuu4lIc3V7X728mTJ11G1Onfv78l+R4IgTy++tKB2JgrozG7+/yKiIgwdevWNRUqVHDpOCzJDBo0yOvXy/n2bSmP6Oho06BBA5fkp3TlpMXKlSu9ah8Acppz586Zv/76yyxatMhs2bLF61tDAkCwIocP+Ad5fO+Rxw888vjuOddLL4//8ccfu92GhQoVMg0aNHDpdJby+Omnn7xe50CfJ/CnpKQk89RTT1liufbaa9MdmTVQ/NWB+I033rC0M2TIkDTLf/HFF5byrVu3dntRQ+oyn3zyiUteNX/+/KZu3bqmZs2aLoNfSFfyxWkN0hHo8wQZFRcX53K3u9SPggULmmrVqplGjRqZKlWquH1PpzzKli2b7nmijRs3us23pz6PUahQIbftpzXAwvz5893GltJuw4YNXUatTXnceuutaQ4kkZkOxMYYl/dhysNms5kqVaqY2rVru4wum/J44403fFqWP+zfv9/Uq1fPEscrr7wS8PMJgTy++tKB2Bhjpk+f7nJBlHTlvFb9+vU9nhPy5j1qt9vNgAED3NYPCwsz1apVM1WrVnV7nkCS6dKlS5YOLAUAWSUQefxc34H4008/9eqHVIpff/3VZQQqSaZx48Zmzpw56dZPTk42H374oalQoYLLB3rnzp296lB0+vRpM3jwYJcvhAULFjSDBg1iNCzkGEuXLvX4wykjj/R+QJ89e9blKuK0HjfddJNfRgr1R+LRGGMSExNdbi2T3qN9+/bm+PHjbtv7888/XX4YnjhxIjOr6rZdSW5HlQi2xKMxxvTp08cl9latWmW4vaxIPBoT2H3bua43n5eXL182Dz/8sNfxNGzY0OOV5u7Y7XYzfPhwjz8InR+VKlXyalSEQJo+fbolsR0VFWWWLl2aLbE4bx9f7dmzx/IdpHjx4h6vkk/twQcftCz3sccecymTOjHx+eefm61bt7qMHODpERERke6oo6k7MD7//PMmJibG7QjD7h42m8288MILAUv8JCYmmrfeesttQtXbR+fOnb1OyMXExJjGjRt73XbK+qd3+69t27a5nNRK79GpU6c0Ow8bk/kOxMYYM2HCBI8nmNw9QkNDzSuvvJIltzxz59KlSy4nHdu2bWtOnToVsGUG8vjqawdiY4z5/fffLbdYTO/1Gj58uM8Jwa+++irNZH7qR8ooWwCQ3XzNZaUnJibG3HPPPS7Hw+LFi5uXXnrJ4+0kASCrkMMHshd5fN+Qxw888viunOt685n5zjvveJ0DKlq0qJk2bZpP6xvo8wSB8Omnn1q2SYUKFTze6S2Q/NGBeNWqVZYRbz0N6pGa3W437du3tyx71KhRLuVSz587d65Zvny5ywjDnh4FCxY0P//8c5pxBPo8QWacP3/ePPXUU16/d9w9+vXr53VH+W3btpmqVat63XZERIT55JNP0m13+fLlHi8U8PQYMGBAugNcZLYDsd1uN++8847LaM1pPfLly2fGjBnj03L86fTp0+bmm2+2xNSnTx+TkJAQsGUG8vjqawdiY4z5/vvvvc6x58uXz3z++eden2tLSkoyQ4YM8Wlftdls5tlnn3V7AQQAZKWclMfP9R2ImzVr5vWLNWrUqHQ/jN5//32P9ZOSktL9YZgvX740OxTFxsam+yWxSpUqXnXkAbLblClTMvzjyt3Dmx/QdrvdjBs3zjRo0MBjO9WrVzeffPKJ3zoM+SvxmGLevHmmTZs2bq/mS3k0bdrUTJ8+Pc0v34MGDbLU6dy5c6biSpGcnGzKly9vafuhhx5yKReMicfffvvNZVt+9tlnGW4vqxKPxgRu33Zuw5cvN7NnzzbNmzf3GE90dLQZMWJEhn9E//nnn6ZDhw4eEzXFihUzTz75pOW2S9lp9erVloRMpUqVsiUO5+3ki8uXL5sWLVpY6k+dOtWrumfOnHE5+Tpv3jxLGefEoDFXrqB78sknPXYijIiIMD169DA7d+5MNwbnDsTGXDmpM2LECI8JztDQUNOhQwezdu1an7ZVRsXExJi33347zZEMUj9KlSplHnnkEbN69WqfOzcnJSWZb7/91uVWaKkfNpvNdOnSJd1RjVNLSEgwH330UZrfWW02m2nevLmZNWuWV3H7owOxMcYcPnzYDBo0KM1OqREREaZ3797m77//ztAy/CmlQ2/q+IYPHx7w5Qbi+JqRDsTGXBnF4f7773e59ZjzMWDVqlUZXFtjtm7dau644w6PyyhQoIAZMGCAOXLkSIaXAQD+5EsuKz3bt283xYsXT/P7RtOmTS23iAWArEYOH8he5PEzhjx+4JDHd+Xchre/FVasWGHatWvnMZaiRYuaRx99NEMj+acI9HkCf5s9e7Zl9M4bb7wxy2PIbAfis2fPWr6LhISEeJ07279/v2WE14iICLNp0yZLmdSxpVxsf/ToUdO/f3+PAzgUKFDA3H///V6N6hzo8wT+sGvXLvPSSy+5jI6c1mffM888Y7Zs2eLzsi5evGhGjRrl9q6CKY/w8HDTp08fnzq8nz171gwbNsxER0d7bDcsLMx06NDBLF++3Ks2M9uBOMWOHTtMnz59PI4wnbJPDRw40OzZsydDy/CnxMRE89BDD1niGzduXMCXG4jja0Y6EBtjzObNm0337t09fu/Jnz+/6devn9m2bVuG1nXZsmWma9euaXYuj4yMNHfffXe2XPgBAO7kpDy+zRhjlEuNHTtWAwYMkCQNGzZMw4cP91h20aJFat++vYwxKl68uD7++GN17NhRkvT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i007/skJhXVxnEVRYl8xwkUXaOVmcUXf4Vao7dJS2/N+9l97wy6ymHitmXz8FtmynlPWrik1LWbdStTq5rtX6N7bvH9kuOsBm7t0u3+h68ijcPwpSkrSyhb9WtvDX1luGl1/jy5mHn59qtWqnBBfHSsLaFQot4VgJPHOsuLj/l7bHfqx4nnOsRF95laKHjNLWx933WDmTR6KL+6EJa1cotHPpebi8H7p7h/wK8zB5eGjn84/pyMxPi83nFVxL2UcPXdoGlLPsPJt2xOVpQAvnTvAmk0mDWgZq/Z/ZJSzp2uPz4hXka9azZ/UfCQv0UPYHbZT9QRv98M9G5dHsy6qmfo6WhDyA6oQ6vjujAzEuSUKW/bFpdQKLP16qTpCnErOsstnK1tFmZ3yePlifppdGhMvH03nXzM636YHvEzShY5D6NvYrYQ2VL8Vi/2+YZ/EPj3BPKdUi2UroeDS6llmeJumJ4zbtzDG0L9fQ8ydsOl0gXRtiX19Q4ZOxM108rS6jsNNKkuWSN6PCGYYhS16eLHl5shYUlGkZk9ks//BwZZwqPrR/xqmTkqSACPfpEFpWNTULn8LOu3kJxR9Blnf6lLxDw12O8lmQnipJ8qpVu9g0r2D7ez4RdSRJqwe30+7nH3Gax+ThoejR18mal6esP/dfwhaUL99wex6uHsmWE39KvmGu88hPS5UkebvIw6fwPb9Iex7BTWJVkJmpPu98oltOZGni0TRdu2W/YsffUj4bUU7IwtmZPLLji+eRdarkPPLKkId/YR7nCmnZWu3+8nete+ox2fLzL67hFcSvlP0ju5Q8zuwfPi7y8D4nD/86dZV2YP9Z010/Lquy+Ze6b5yUX0n7RmqqJMnHxXadyScgyp7FzhlfyFZQoKtmfK06Xbspon0HXfnf/ykopp7++N9H5bQl5YNzsPOrqeccAAAAAErGdYKzmprHmWtqVw9/DPc0lXpNPTbEfk396BGrtmfbtCfH0DPHrIovkK4PM1/wfO6qpu4bvmGl12p9Sqjjl1ar9S6s4/sW1p+CGttrtVe8/YnGH8vSjYfTdNWm/Wp8o3vVas/c08h1kUVuKVkUlJLFmfscvmfVaT38/NTpubd1+JvpStzwy6U3/DKrqcfKmQFr8l3e8zoprxDX+4el8J6XZ3DxWu2597wkyZafV/hyr7r92Uq7/1eWY+XMdp+t6HPjnGOlw/Nv68g305XkxseK93k+O0q8H1pKHmd/dhhWqw5P+0gpWzdKkvwbNlFo997q9OanCmjYRHHzZ5fPhpSThEz7SUedWi76ltTyVGKmpex9S+Jy9cGqJL1ybbR8vBznEiaTSb5eZvl6meXt4n6Bu6upn6MlIQ8AuDzc/6ocbu10YS+K2n7Fd6UQP7MsNik1t2zDsT27PFk96vtqeHP/YtNeXpWi5BybXh4RfmkNrmAphZ1KglwcWcEeJlnl6GRyrhZ+Jj1Yx6x1WYZu/9OqiYesWpRm6M4Is3oG2lcY7SUFmKXVmc4nznk2Q6sz7O+luejYUh34hYbK7OGh3MKOT2fLSUmRJAVGuu/o1OWpOmThHW5v35niyNkK0lJk9vJy2Uk459hhFWSkK2roGKf3zT4+ihp2lX3dIWEuf6bZx0ed/vuVQrr20vHZU2XJSL+0jShHfoV5nOn0eba8VHsergqMmUcPKz89XQ1GOOfh4eOj+iPtefiG2vMIbhorr8BA+dWJ1rpH7tWvD9yj3MQE9f3vVLX6i/t8O5ssnPlH2PPILyEPDy8v+dSuXWxaxhF7Ho1c5NHonDzO1f2JZxS/ab2OLvvh0hpfAXzDS8/D7OXlspNwRin7R8PCPHxCw+Tp7y/v4GDlp6epx/Ova8KBeN1yJEU37z2hjg89IZNn8aJWZfEv/JzPTU0rNi23cN/wdbFvpB05rLz0dMWOKp5F7Gh7Fn6F+8bpP37XykcfVONhwzVx9Xrdun6L2k66Veteek6Hly0t5y26NJyDVYzqcM4BAAAAoHxxneCsOuSRbLFf1wZ5FO9kE+whWSRllHDN29LfpIdjPLQ2w9Ck/VaN32fR9ymG7o4yq3ew+YLnq06qw77hc6Y26aKOn19KrTbrqL2OHzO8eB2/3ojCWlxhHT+oSWGtNipamx67Vxseukd5SQnqNWWqmt/lPrVan1LqkvlppWRReE+j7pXFs4i5svg9jVb3Py7vkFBt+89j5dd4N1cdjhWvsDP3vIrXai2F+4eni46guccPy5KRrojB5+wf3j4KH2rfP7xqu67ju6tSj5XUku//ZZdyrEQPL36stLjvcXnXDtWO59z7WPEp3DcKLvB+6Jk86gxzkYeLz44zBi79Tf3mr1HDm27Xvvde0allCy99I8rR6XR7B+La/h7FpoUEeMhilVJzylZof2ZBvK5o4q/hbQPPP3M1Vx0+R8sTeQDAhaueV+VwC2e+HFZgPf+3xH4/kad5OzL19JBQmc75lt3+xHy9vjpFTw8JVXSw+3TcuVBn8rCUEMdP6Ta9eMKmLv4mPRFt1r/rmtUr0KSPE2yal2Lv8eJpMum2cLM2ZRl67aRV+3INbcky9MBRm04VfuEquPj5drVnMts/ysxeLoZIqGGqRRYm+zaYPItvg2G16sB7Lyq87xC1ee5dBbVqr9AefdXti4Xyi2kgScpPTS62XFifweq77A9Fj7xWKb/9ql3PPlix21COSvudGlartr35ouoOGKIer7yrkLbtFdWrr4Z8vVCB9ex55KXY8zi1drW2vvSMll49VPu++ER7p36sxSP6KvH3zer85HPy8HPf0d3PIAtnRXmUcKxsfv1F1R84RH1ee1dhbdsruldfjfpmoYLq2/PITXFxrLTroKZXX6eNLz5ToW2vCOfbP35/40XFDBiinq++q9C27VWnV18Nm+28f5wp8rf+y2TVv3KUfn/tea38y0Sd+Pkndf338+r+n1cv2/ZcivNlseHVF9Vw0BANeuMdRbRrr3q9++qaed8r+Jx9o/nV12ro+x/q6M8rtfTvf9UPd9+pQ0uXqPe/n1X7O/96+TboEnEOVjGqxTkHAAAAgHLFdYKz6pDHme/ilnRNvSzVpuePWdU1wKSn6nnomfoe6h1k0genbJqTZL3g+WqK6rBvnK/+tOPNFxXdf4i6vvSuardpr8iefTVw1kIFFNbi8gvrT6d/Xa1trzyjn64ZqgPTPtGBLz7Wj6P6KumPzerwRNWo1Z7JwlRCFrvfflFR/Yao04vvqlbr9gq/oq/6frlQ/vWc72kENo5Vi8mPaOdrz7gcrbQmqg7His5zrBye8qJC+wxR82feUWCr9qrdva86fPa9fOva948CF/e8qqrz3dPY886Liuw3RB1ecBwrvWcslH+McxaBjWPVfPIj2lXFj5Xz5bGvMI/2z7+r4NbtFXZFX/WcXvr90E1/u0lbHrhTp5YtVPP7HlPzfzxRsRtRjmyFJx1l6ltyNEdzt6Trmasii/UtgbNq8TlajsgDAFyrur0x4RYiA+09JVJzig/plpJjldkkhQecvzfFB+tS1SjEU4ObOo8+bBiG/rEgQbFhXprcs3a5tLkihRRuqqsR7jJshsySaruII9dm6KUTNnXwl6Y0NMtceKI7upahR4/b9NYpmwYFmVTb06QJYSalWU2akWTom8Lh9tr7SZPCTJqWZCismh7VOcnJslmtLkdT9AsJkSRlnqq6F4kXojpkkZ94WpJcfuPaq3aIDKtV+cmJLpf98+M35R0SpqZ/e0SNbrePQJC8aa0O/vc1xU5+zOmxSGYfH7V84hU1vvN+2SwWHfrwDe199V9u93innMI8XI2c6l07RDarVXlJrvPYMeVN+YSGqd39j6j1X+15xG9Yq+3vvqb2/3xMOfH2PLa/U7zjo2G1at8Xn6jXWx+qdrOWStq2tZy26OKRhbPsBHserkav8CnMI7eEPP54/035hoap0z8fUfu77XmcXL9WW99+TZ0ffEzZLgpr7f56r9IP/6njq34qv40oR7mJF5/H9in2PNr/4xG1ObN/rF+rbe+8pg4PPKac06dkzcuTJBWkp2n+4B4qSLePVH7wm5kybFa1ved+/fHGiyX+jMsp+7Q9C9/axR9v51uYRU6i63b+9u5b8g0NU/cHH1Hnv9mziFu3VhvffE09Hn5MWfGn5Onnp6Hvf6gT69Zq9sihUuGjSndMn6qxX83VwFff1P7v5ionKamCtvDCcA5WMarDOQeAmsFkq2Hf4gAAoBJxneCsOuQRWvh47wwXHXbSrfaRiGq7uObNsRl67rhVHQJM+ripR9E19dgQkx48bNVrcTYNrmWWr1llmi+kCj5mvDTVYd/IO1ObdFHHP1+tdvd/7bXa1vc9ohaFT31L2LBWu957TW3+Ya/FSdKu91zXag9M+0Q93vhQwc1aKsUNarV5pdQlvWrZs8gvIYu9H9rvabS89xE1u9OeReLGtdoz5TW1uu+xog6QnV58V5l/HtD+T9+vmI1wU9XhWClIOnPPq3it1qtW6fe8jn76lrxCwtTw7kdU/1b7/pH621od+eg1NfrbY8pPcO9tP1dpx4p34f2/vBKy2F94rLSY/IhizzpW9k15TS3OOlY6vPCusg4f0MHP3P9YySvcN7xc3Q+tVXoeBz6y59Fs8iNqUphH0sa12j/lNTW/7zHlubjHc3ql/cmBR7/6XD1nLVGLf/xLh/73rixZmeW0RZcmsnCwuNTs4l8eSsku7FsSeP5C+/srk9QozEuDWzL6sFQ9PkfLE3kA7os6vvtiBGJckojCzsEnMizFpsVnWBUZ6CEPc+lFn8w8m2Zvy9DNHYNlPmfe5Qey9eP+bD3aP0TxmRYdTyvQ8TT7MG+Z+TYdTytQeq77fDs9pPB8NrGgeKEtyWKf7uHiW3CH8qR0mzQ02NFxRZJMJpOurGVSniHtzrWv09Nk0v1RHvqphYc+bOihb2M99EkjDxmSfE1SYDU9qg2bTdmJiQqOji42LSiqjiQpMz7+cjerUlSHLPKSEiRJvlF1i03ziYiyFxhsLnqBSTIsFu154TEtbR2iddf214qeTbRuXF+ZTCZZsrOKHhFl8vRUt2mL1PjO+5X6+0atubKTdj//iNt1Hpak3ER7Hn51iufhFxml3ITTMkrJ47enH9OXDUO0eFR/fdOhiRYPt+dRkJWlfBePzDpbVtwxSZJvhHs8qoUsnJ3JI8BFHv5RUcopJQ+bxaJ1Tz2m/9UP0bfD+2t6uyb6dlhf6Uweac55eAYEKPaaG7Rv9pdFnUXdTU5hHv6u9o+o8+8fG59+TNMahmjhyP76qkMTfT/cOQ9rbq7y09J0bNmSos7DZxxZvEAms1mhbdqX/4ZdhOwz+0a0q32jjrLPs2+sfvL/9F50qL4aOkCftGqqWYP7FR0reWlpCmvVWn6hodozZ3ax/WH317Pk5eenqE5dyn/DLhLnYBWjOpxzAEBly5o5WwljrnP5siW6xxdxAAC4EFwnOKsOeZy5pj5dUHxaksVQaEnX1LmG0q3S8NqmYtfUI0LM9mvqbKPM81U31WHfyE0qvVabl1h6LW7rs49pdpMQLRvTX991bqIfR9lrcZasLBWcp1abfaZWG+4etdq8UrLwLUMW2557TN82C9GKsf21qFsTrRjTVybZ72kUpKcpqt8QRQ8arj3vvSLfiCj5RcfILzpGkr1u6xcdI8/AoIrbwEpUHY6V/ML9wyey+P7hHVFH+Uml3/M68PL/6ef2odp84wCt7dtUm6/vJ5lMsmZnyZJR+rHibs53/y/3PPf/djz3mBY0D9HPV/fXku5N9PNVhZ8bhcdKZL8hqjNouPa6OFY83PBYObNv+Jby2VFaHjuff0yLWoRozbj++rF7E60Z65yHT0SUGt1ytwIaxxZbPv6nJfLw8ysasdgdRBR2Dj6R6qJvSbpFkcGe5+9bkmvV7E1pmnBF7WJ9S2qq6vA5Wp7IA8DFqOl1/Go4ThYuJ39vs9rV8dbKgzlO7xuGoZWHstW1nu951zFne4Yy8w3d3LH4yfyJdHvn4Nu+Kf4HfNqWDE3bkqEXrgzTYwNCL3ILypev2aRYH+m3c4pdhmHotyxDrX1dn8SeeeR18VNlx6PBannYl92YaVOop0mxviZ1DnDMtynLUL8gU7V+TMexjRvVZMCAYu83GThQp7Zvk6Vw5MiaoKpnYcvNUfrubQrrPbDYtPDeg5T2x6YSlw3rM1j5ifHK2LNDyRvWOJbrM1jxP84v+v9mDzyt8N6DdOL72frjgdtky80t340oR9acHCXv3KbovsXziO47SIlbS84juv9g5SbEK2XXDsX/usbp/aNL7Hn4RdVR73c+1v4vp+rI9/Oclg+o31CSlLZvT3lsyiUjC2eWnBwl7dimmP7F84jpN0int5ScR70Bg5V9Ol7Ju3bo5Fl51BswWH8unl9s/tirr5d3UJD2ff1l+TS+ApzZP+r2K55H3b6DlFDK/lG3/2DlFO4fp87KI2bAYB05K4+UPTvlFVj8W+sePvZzmvN1RL9cLDk5Sti+TQ1c7BsNBgzUqc2/lbhsg4H2fSNx5w4dX+vIosGgwTqwcIEkKTclRZJk9ix+ueDh7V04j/s8Po9zsIpT1c85AKCyBdx8gwJuvsHltPhBI2SLP32ZWwQAwKXjOsFZVc/Dz2xSM19pU2bxa+qNGYba+Jd0TW1/3+Ki7++Z7/cGe5Z9vuqoqu8b1pwcpezcpjp9B+qPc6ZF9R2kpFJqcXX62Wu1qbt36PQ6R/2pTv/BOv6DvRbnG1VHV7z1sQ7OnKpjC13XatP3u0et1pqTo9Rd2xTZp3gtLrLPICX/XnIWkf0GKy8hXmm7dyhx/Rqn9+MKszjTAbLHlGnFlm88/jY1Hn+btj3/uPa898qlbopbqurHii03Rxl7timkV/H9I6TnQKVvK7lWG9Lbfs8ra+8OpW507B+hvQcrYdmCCmlvRbLm5Cht1zZFlHCspJR2rPS1f26k7znnWOk7WCfOOVa6vV/8WGl0421qdONt2v7849r3vnscK0V59B6o3edMC+9deh4Rfe2fHel7dijpnM+Ok4V5ePj5q+Mr/9X2px7QwU/ecVreKyhYkpQbf6J8NqYc+PuY1S7GVyv3OI+IbBiGVuzJVLdGfuddxzeb05SZZ9OEHrUrqJVVU1X/HC1v5AHgQtX0On41HCcLl9vETsHadDxXaw87OhHP2Z6pE+lW3do5+LzLL9idpeggD7WI8Co2bVBTP313S3SxlySNaOGv726J1rVt3evRFCNrmbUzR/rjrA4sy9MNJVik0bVdF9rqeklhntKCFJvybI7lLIahb1NsCjBLTXzs732aaNOjx6zKPWu+1Rk27cktef3VxdYZ0xUUHa32N95Y9F6Dnj1Vv0cPbZlW/EKxOqsOWcTNma7aHbsrpGuvoveiR18v3zoxOv7NFyUu1+yfT6rL/+bJ7Ou4iIwcOka12nfR8dn25UweHqp/053KPvqntk6+2a07D59x8KvpiujSXZE9HHk0uvp6BdSN0YGZJefR8ZEnNWj6PHn4OfKoP2KMwjt2KVouJ/6UajVrqc7/ek6e/v5F83n4+anVXZOVvHObMo8frYCtujhk4WzvrOmK6tJddc7Ko+m46xVYN0Z7vyw5jy6PPqkRM+fJ86w8Go0Yo8hOXVwu12jUVco6eUKp+/eW7waUs/2z7PtH1Fl5NC7cP/aXsn90evRJDZnhvH80KNw/zl5u38wv1GDEVQpu4vjGvsnDQy1v+4vyUlOUsmdnOW/Rxds5c7qiu3ZX3SscWbS45noF1Y3RzhklZ9Hz//6lsV/Nddo3mo4aozqdumhH4XJph/9U5qmTanfbHfLw8Smaz+ThofZ33KW8tDQl7txRAVt18TgHqxjV4ZwDAAAAQPniOsFZdchjdIhZO7INbc1yjIL4Y5r9mvqqENe3EmO8pXBP6dvk4tfU85JsCjRLsb6mMs9XHVWHfePP2dMV1rm7Iro76k8Nxl4v/+gYHZpVcv2p7cNPqt8XzrW4mOFjFNahiw4WLpcbf0rBsS3V4Ynn5HFOrbb5nZOVsnObstyoVntk9nSFduqusG6OLOpdZc/i8FclZ9H6gSfV63PnLOpeOUahHboULRe/5ietmTim2EuSTixbpDUTx+j493MqaMsqX3U4Vk7Nm67gDt1Vq4tj/4gcZb/ndXJOyftH4/v+pfYfznW65xU+ZIyC23XRybklL+fOjnxT/FiJuep6+UXH6MjXJW9TywefVM9zjpXoYWMU0qFL0XKn1/yktZPGFHtJ0snli7R20hjFLXSvY+XYN9MV0qm7Qs/Ko+4Yex5HZ5ecR4sHnlT3z5zzqDNsjGq371K0XM6JYyrISFe9a26WycPxWHgPP3/Vv/4Wpe3apoK01PLfqEswqWdtbTyco7UHsore++a3NJ1ItejWXiHnXX7B7xmKruWpFnV8zjtvTVIdPkfLE3kAwIWppt/pxeV0a5dgvf1Liq6dcVKTOgcpO9/Q9C3p6tfYT6Na2odn2306XysOZKtbfV91r+8YldhiNfTzoRwNaurnctS2+rW9VL928Y7FktQ4xEujW7lX52HJ3oFkZrL0yDGrRtUyKceQFqca6uwv9Qmyb+OfeYY2ZRlq42dSGz/7Y7sejzbr4WM2TTxk1ZBgk0ySVmUYOpAnPVXXLN/CR3DcFm7WP47a9JfDVg0ONutYvqEf0gz1CjSpe0D1KbJFtGyppoMG6fimTTq+yf7tyz0LF+rU9m0a99HHati7tyx5+eo0caIS9uzRli+q5kV0WVTXLI7NnqrGf3lAXT79VnHfTJOHf4BirpukpHWrFL98oSQpMLalwvoMVurvG5VW+C3cg++/rO4zlqjXt2t0cuEcBTSOVd1xE3R6xWIlrlkuSfKNaSDfyDpK+nWlGt7yN5c//9QP3yrvlPt863b/zKlq8/cHNHjGtzowa5o8AwLU9MZJOvnLKh1bas+jVvOWqtt/sBI2b1Ri4ciz2956WcPmLNGoJWv05/w5Cm4cq6Y3TNDxZYt1YtXyovVvevJhDZ75ncas2KRDc7+Sh7e3Go+7Qf4x9bX06iGS4T6PCSQLZ3u+nKoO9z6gEbO+1d6Z0+TpH6AWN01S3JpVOvyDPY+Q5i0VM2CwTm/eqNOb7XlsefNljZm3ROOWrtHB7+aoVpNYNb9xgo78uFjHVi53+hkmDw/F9Bmg4z//dLk374LtmzlVbSc/oCFffqv9s6bJyz9AsePt+8fRwjxqn7V/JBTuH3+8+bKGz12iMT+s0Z/fzVFwk1jF3jBBx35crLiz9o9Dc2ep9V1/09iVG7VvxufKS01Ro9FXK7xDZ626+xZZ3egLCTtnfKGu9z+gq7+ep51fTpNXQIDa3DxJx1av0sHF9ixCW7RUw4GDdPK3TTr1mz2LDa+/ouvmL9ZNP63W3nlzFNI0Vq3GT9ChpUt0ZEVhFoahZff9TeO++U63rN+iffO+kWGzqdlV4xTRrr2W/PUOWdwoC4lzsPJQXc85AFR/JsPj/DMBAICLwnWCs+qax1WhZs1IsOnBP60aE2ooxyZ9n2JTlwCT+gXbr3kP5RramGlTG3+T2vmbZTaZ9GQ9D/3zsFU37rNoWG2zTJJWpNm0P1d6tr5H0TV1WeeryqrrvnFo1lS1/NsD6jftWx362l6bbHLDJMWvXaW4H+31p+BmLVWn32AlbdlYNCrxzrdf1qDZSzRs0RodnT9HgY1j1fj6CYpbtlinfnbU4rY8/bD6T/9OI5Zv0uF5X8nDy1sNxt2ggLr19dM17lWr/fPrqWp+zwPqPfVbHZltv6fR6IZJOr12lU4us2cR1KylovoOVvLWjUouzGLPuy+r31dLNGjBGh373p5Fw2sn6OTyxYpfbc8i58Rx5Zw47vLnZh09rJPLFl2ejbwMquuxcnLOF2pw5wNq/9E8nZw3TR5+AYq+ZpJS1q9S4gr7/uHftKVCew9S+h+blF74JM7DH7yiTl8sVpdvVuv04jnybxirOldPUOLKJUr+ZXlpP9JtHfl6qprd/YB6Fh4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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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ioqJMrVq1bB+uX3jhhWTLJg7fJJlbb7011aNt7733Xludp556KsXy165dM7t27TLfffedeemll1wGCGVF8Lhz506TN29ea7mffPJJpiw3qe3bt9vWv2zZsiYiIiLFOl9//bWtTsuWLX3SlxMnTtieE09fj2PHjtnOvBAUFJRiOD5lyhTbMv7++2+f9D8rREZGZslyCR79K3h8/PHHbX1655130tTOoUOHbO20atXKxz2Fv8iIfcfJkydNvnz5bD8M7t+/P8U6q1evtgWSFSpUcDsA1xtXr141ZcqUsW3Pn3/+ear1IiMjTaVKlWz11q1bl2zZ33//3VauRo0aJioqKsX2Y2NjzQ033GCr9+WXX6ZpHQEAAAAASECOn3HI8V1lRo4/Y8YMW/tt27ZNdTBwZGSkqV69uq3eqlWr0rSOvkaO74ocnxwfqcuofYfT6TQHDx40CxcuNG+99Zbp37+/7XNE4ltGDyC+cuWK7czcDofDrFmzJsU6e/fudfktwt1BSk6n0zRr1sy2Tt98802K7Z85c8b2+wJn+gaAnClAAACPrFmzRteuXbOmu3TpoiZNmqRaLzQ0VB9//LHtsQULFiRb9ssvv7RNv/baa3I4HCm2P378eAUFBVnTM2fOlDHGbfmFCxeqXr16uvPOO/XSSy9p+fLlqaxBxrpy5YoGDRqkK1euSJL69++ve++9N0v68tVXX9mmx4wZo6JFi6ZYZ+DAgapfv741vWHDBu3duzfdffnpp5+s50SSBg0apJtuuinVeuXLl9drr71mTcfGxur77793Wz4sLMy6HxgYqOrVq6exx1nr+PHjateuXVZ3A37g6NGjtunatWunqZ3KlSurUqVKbttFzjBv3jyNGTPG5+3OnTtXly9ftqbvueeeVPevbdu2Vbdu3azpY8eOaeXKlenuy6pVq3TixAlruk2bNho+fHiq9QoUKKAPP/zQ9th3333ndhmJDRw4UKGhoSm2HxgYqGHDhqXYDgAAAAAA3iLHzxjk+MnLjBw/aV4yfPhwBQYGpth+gQIF1L9//xTbyQrk+EhAjg9vZFSOL0kXLlxQ1apV1a1bN40ZM0bffvut7XNEZlq2bJnCw8Ot6R49euiGG25IsU7NmjU1cuRIa/rSpUtu3092796tTZs2WdMNGzbUnXfemWL7xYsX15NPPmlNG2P09ddfp1gHAJD9BKVeBAAgSWvXrrVNd+jQweO6bdq0UXBwsGJiYiRJu3btUlxcnC3k+ffff/XXX39Z002bNvUo2Cxbtqy6deumH374QZIUHh6urVu3elTXHzzzzDPavn27JKl69er69NNPUw1bM8pPP/1k3Q8KCtLdd9+dah2Hw6GRI0fqsccesx5buHChatWqla6+LF261Dbdp08fj+vefffdevjhh63pefPm2aYTSxySVqtWTXny5PGyp1lv27Zt6tatm44fP57VXclWjDHavHmzdu/erfDwcOXJk0elS5dW8+bNVbNmTZ8sY/fu3dq6dauOHz8uh8OhMmXKqH79+mrYsGGG/Z9fvHjRNn3q1Kk0tzV8+HAdOnRIkhQcHOxV3YiICP3xxx8KDw/X2bNnVbBgQVWoUEGtWrVSuXLl0tyn48ePa926dTp58qTOnTunYsWKqVSpUmrVqpXKly+f5naT2rx5szZv3qyTJ0+qefPm6tKlS6p1Dhw4oE2bNunkyZO6dOmSqlSpoqpVq+q6665TwYIFfdY3XzDG6J133tGYMWM82td7K/H7iRQ/gNgTI0eO1M8//2xNL1y4ULfccku6+pL0/aR3794KCPDsONJu3bqpePHiOnv2rKT495NJkya5/P8mDjUlqVGjRh6137hx4xTbAQAAAADAW+T4GYMcP3mZkePnlNyFHD9tyPFTR45Pjp9TpOd3hQ8++MCaXrhwoe677z6P2vfkf3zo0KF6+umnFRcXZ7Wf+CAYAED2xwBiAPBQ0qNWK1eu7HHdkJAQ1atXT1u3bpUkOZ1OnT59WmXKlLHKJA2abr75Zo/bv/nmm63gUZJ++eUXt8FjpUqVNHTo0GTnbd26Vdu2bfN4uen166+/6t1335Uk5cmTR999950KFSqUYp0qVaro8OHD1vTQoUM1ffr0dPflyJEj2rNnjzXdrFkzFS9e3KO6SV+rX375xRZEprU/iTVo0MDjuoUKFVKVKlWswGTDhg0uQXeCxAOI69Spk7bOZqFffvlF/fr1U1RUVFZ3Jc1WrVqlDh06WGccqVOnjjZv3qx8+fKlWO/SpUtq3Lix9u3bJyn+f2j9+vUuwXBSxhjNnDlTr7zyituzbNSuXVtjx47V3Xff7fFAw8R++eUXPffcc9q8eXOy8ytWrKhHH31UDz/8sM8HrVepUsU2vWjRIpeznHrqpZde8rrO2rVr9eKLL2rFihVWmJJUmzZt9NRTT6lnz54etel0OjV79my99dZbKe6jGzRooCeeeEJDhgxJ9SwkK1euVMeOHa3p06dPq0SJEjp48KBGjhypFStWWPOGDh3qNniMiYnRp59+qg8//FC7d+9Otkz+/Pk1dOhQPfzww7ruuutS7FdmiI2N1cMPP6wpU6ZkSPvXrl2zPX+lS5e2neEmJZ06dVJAQICcTqek+P+l9ErP+4nD4VCDBg2sMyEfPnxYJ0+etH1+keTyfxwdHe1R+4nP0JNcOwAAAAAAeIsc3/fI8VPuT2IZkePnhNyFHJ8cPylyfHL81GR0jp8gT548bt9vT5w4oV9//TVDl59Y4mUFBAR4fBBUo0aNVLJkSZ0+fVpS/GeV2NhY25UPkrYvef4ZpkSJEmrSpIk2btwoSdq+fbvCw8PTNcgeAOBfvP8kCQC51Pnz523T3gSPkqyz90nxH/qTXlJr165dtunWrVt73HabNm1s03///bfbsk2bNtX06dOTvfXu3dvjZabX6dOnbWHAq6++qqZNm2ba8pNKz/N/3XXXqUiRItZ0Ss+/p86cOWObrlChglf1E5854fLly7awNkFMTIz++ecfazqtl4jKKlOmTFH37t2t0NGXR21npnbt2unpp5+2pvfs2aNnn3021XpPP/20FTpK0uuvv55q6BgbG6uhQ4fq7rvvTvESfWFhYRo+fLjat2+viIiI1Ffi/xlj9Oyzz+q2225zGzpK8T/kPPnkk2rcuLEVkPtK0rMuzJkzR5MnT07xkpC+EBsbqwcffFA33HCDfvvtN7ehoxR/Kc1evXpp6NCh1hlt3Dl58qTatGmjIUOGpPrD0I4dOzRs2DBdf/31aTqjyObNm9WqVStb6JiSsLAwNWzYUA899JDb0FGKD8k//vhj1a1bV6+//nqGvxYpiYyMVI8ePWyho7f719QcOHBAV69etaZbt27t8Zk6ChUqZPuhaf/+/ba20sKX7ydS8u9xSdtM6f8/scSXS0tL3wAAAAAASIoc37fI8VOWGTl+ds9dyPHJ8ZNDjk+On5LMyPET5M+f3+377dixYzNkmcmJjo7WwYMHremGDRt6fEZoh8OhG264wZp2936S+D20aNGiXv0unPQzTErbEgAg+2EAMQB46LHHHtPs2bOtm6dnE5TiL1WT+MwHVatWVUhIiK1MWFiYbbpixYoet5/0S1NKgYK/eOaZZ3TixAlJUo0aNfTII49kaX/S8/wHBATYQq9jx455fBYAd5KewSHxl0ZPJP1imNwXuX/++ccWjtSuXVuxsbGaPn26BgwYoKZNm6pgwYIqXLiwatasqX79+umzzz5zubRUZnM6nRozZoxGjRpl9b9Ro0Zav359lvYrPcaPH28728i7775rnfEzOcuWLbNdjuiWW27Ro48+mupyxowZo6+++sqaDgoKUvXq1VW/fv1kzxryxx9/qF27dh6/5u+//74mTpxoTTscDlWuXFkNGzZUsWLFXMrv3r1bbdq0cTlTR3r06dPH5WwIjzzyiDp16qRff/011aAvLeLi4tSvXz99/PHHLvMqVaqkBg0aJBv0fPnll7r//vvdthseHq7WrVtr3bp1LvMqVKigxo0bJ7uv2rRpk1q1auXV8xoeHq5u3brZLhUXGhqq6tWrJ/tD2+bNm9WmTRvbGV8SlClTRrVq1Up20Owzzzyj++67L8VgNqMcO3ZMbdu2tZ3V96mnntLLL7/s0+Wk5/1Esr+nO51O24EeaZEZ7ye9e/e2vd5ffPFFqss5d+6c3nnnHdtj3lzmEwAAAACA5JDj+xY5fsoyI3fp27evbfqdd97RuXPnUmz34MGD+uKLL6xph8OhXr16edW39CLHJ8dPCTk+Ob47mZXj+5vEBxpI6ftdQXL9jHH+/HnbdlOhQgWPT3ziSfsAgOyNAcQA4KFOnTppwIAB1s3To/6k+COKE2vWrJlLmf3799umk/uS7k7io+Yl///QvmPHDk2bNs2afuONN7L8suXpef6TK5+0PW+VLVvWNp3cl3t3oqOjXbaBhMvWJJa0zM6dO1W3bl0NHz5c3377rbZs2aKoqChdvHhR+/fv19y5c3XfffepatWqevvtt+V0Or1YI9+4dOmS7rzzTk2aNMl67NZbb9Xq1auz7ZkLpPhLJM2cOVN58+a1Hhs2bFiygd+FCxc0fPhwa7pEiRKaMWNGqpcoO3r0qHWpwfz58+vNN9/UiRMntH//fu3YsUMRERFatmyZy1k7du3a5fGl/B5//HFJUmBgoMaOHatjx47p0KFD2rZtm86cOaP169erW7dutjrh4eEaMWKEz7anatWq6eGHH3Z5fOXKlbr11ltVpkwZDRs2TLNmzbJ+/Eivt956SwsWLLCmg4KC9PTTT+vo0aM6fPiwtm/frgsXLmj37t26/fbbbXWnTZtmC8ISOJ1ODRs2zPajQ3BwsJ5++mkdPnxYR48e1ZYtW3TkyBEdO3ZML7zwgu0HraNHj2ro0KEeP6+DBw/WiRMnFBISokceeURbt27VhQsXtH//fo0fP95W9sKFC7rjjjtsZ+SpUKGCZs2apdOnT+vff/9VWFiYIiMj9fvvv9uOfJekzz//XJ988olH/fKVLVu26Prrr9f27dslxW+jU6ZM0RtvvJGmy/ulxNfvJ+l9T0/P+4kk67KtCZJ7P6lRo4btbESRkZG6/fbb3Z7J59ixY+rfv7/tR9l27drZLscHAAAAAEBakOP7Djl+6jIjx+/QoYMtMzl69KjuvPNOW66S2O7du9W3b19FRkZaj911112qUaOGx31LL3J8cvzUkOPHI8e3y8wc399k9O8K/va7BQDAzxgAQIb64YcfTGBgoJFk3f766y+XcuXLl7eVuXDhglfLKVKkiK1+XFyc130dN26crY2lS5d63YYnunTpYi2jTZs2xul0ZshyvDF48GDbuv/www9e1e/du7et/ooVK9LVnzfeeMPW3u233+5x3Q8++MBWV5KZPHmyS7lJkya5lPPm1qNHDxMdHZ2u9fTGiRMnTMuWLW19uP/++01MTEym9SE5lStXtvXp4MGDaW5r8uTJtrZGjBjhUmbo0KG2Mj/99JPb9tq3b+/yuhUpUsRs3brVbZ24uDhz3333udTbuHFjqn2RZPLkyWOWLFnitn2n02leeeUVl3rz5s1L5dnxXFRUlOnXr59H23GDBg3MY489ZhYuXGguXrzo9bIOHDhggoODrfaCg4PN4sWLU6zz5JNP2vrQoUMHlzKzZ8+2lcmfP3+q+5U1a9aYAgUK2Op99dVXyZZdsWKFy3NRuHBhs3bt2lTXefTo0bZ6/fr1M5GRkW7Lx8XFmbfeestlOzxx4kSqy/KFn3/+2YSGhlrLLlCggFm0aFGGLe+5556zret7773nVf3//Oc/tvrTpk1LV3++/fZbW3vNmzc3sbGxHtX9+eefXbaTJ554ItmyV65cMZ07d7aVDQoKMoMHDzaTJk0y8+bNM++//7657777TP78+W3l6tWrZ86ePZuu9QQAAAAAID3I8V2R46cuM3J8Y4yJiIgwDRo0cMkL7733XvPee++ZefPmmbffftsMGTLEBAUF2cp16tTJXL58OV3r6Q1y/Hjk+KkjxyfHTyyzc3xPJH3+n3vuuQxb1meffWZb1mOPPeZV/XfffddWf9y4cbb5S5cutc3v06ePV+0vWLDAVn/o0KFe1QcA+DcGEANABvr2229NSEiI7QN1165dky2bODgMDAz0OoyrXr26bTlRUVFe9zczgsdffvnFtgxPvuRmhqTB4erVq72qP3LkSFv9n3/+OV392bNnj3E4HLY2f/3111Tr7du3z2VgliQzceJEl7LJhUuSzPXXX28mTpxoZs+ebebPn28++ugjM3jw4GTbHTBgQKYEx3///bepUqWKbdlvvPGGX4TWvgwenU6nLZiXZH788UdrftIv6A899FCK7SUXPM6fPz/VfsTFxZm2bdva6g0fPtylXHLB4zvvvOPRuiYN+zt27OhRPU85nU7z5ptvmuLFi3sUQErxgx3btm1rXn/9dY9Dsccee8zWxssvv5xqnWvXrpm6detadRwOh8vyrr/+elu7U6ZM8ag/U6dOtdVr2bJlsuWSCx6nT5+eavunT582+fLls+rUrFkzxdAxsbvuusu2vLFjx3pULz0++OADExAQYC2zXLlyZsuWLRm6zKQDgN2Fv+5MmDDBVv+DDz5IV3/OnTvnsv/+5JNPUq139uxZU6ZMGZft5P7773db5+rVq2bMmDEe/88l7FvSEvoDAAAAAOAr5PiuyPE9kxk5foKLFy+au+++2+PMxeFwmKefftpcvXo1XevoDXL8eOT4niPHJ8c3JmtyfE9k5gDipAOAJ0yY4FX9r776ylb/ySeftM1Pul8aOXKkV+2vXr3aVv+OO+7wqj4AwL/l7PP8A0AWOXXqlIYMGaL+/fvr6tWr1uOVK1fWp59+mmyd6Oho636xYsXkcDi8Wmbx4sXdtucv4uLi9OSTT1rT/fv3V6tWrbKwR/+T9Pny9tItvn7+a9eurf79+9seGzBggGbNmiVjjEt5Y4zmz5+vDh066NKlSy7z4+LiXB4LCwuzTRcrVkzz5s3TunXrNHbsWA0YMEB9+vTRqFGjNHPmTP3zzz/q06ePrc4333yjWbNmpWUVPbZ8+XK1bt1ahw4dkiSFhITou+++01NPPeX1/4m/czgcmjZtmm17uvfee3XmzBmdPn1a9913n/V4vXr19Oabb3rVfuvWrdW7d+9UywUEBOj999+3PTZv3rxUL6NVsWLFZC87lpxJkyYpODjYml6xYoXOnDnjUV1POBwOjRkzRsePH9e3336r7t27KzQ0NMU6sbGx+uOPPzR27FhVrFhRAwcO1O7du92Wj4uL0/Tp063pwoUL2/Zx7gQHB6tfv37WtDFGW7dutaYPHDig9evXW9M1atTQvffem2q7Uvwl82rWrGlNb9iwwaNLMZYuXVqDBw9OtdzXX3+ty5cvW9Pvv/++ChQo4FHfJk+erPz581vTK1as8KheWsTFxenxxx/XQw89ZG23jRo10vr169W4ceMMW67kf+8nRYoU0SOPPGJ77D//+Y/efffdZN8bJOn3339X27Ztk708oLs6UvxlHIcPH67rrrvOo75VqFBBDz74oFeXkwUAAAAAwFfI8ZNHju+5zMjxExQsWFCjRo1SmTJlPOpb/fr1NWLECOXJk8ej8ulFjk+Onxbk+PHI8TM/x/c3Gf3+5m/vnwAA/8IAYgDwoejoaL366quqUaOGvv76a9u8UqVKaenSpSpfvnyydRN/oU9LmJI0EEgcePqL6dOna+fOnZLiv3i/9tprWdyj/0n6/Hn7GmTE8z9p0iRbGHju3DkNHjxY7du319ixY/XVV19pxowZGjNmjG688Ub17dtXx48flySXgCW5wVl79+617hctWlTLli3T7bff7rY/pUuX1ty5c3XHHXfYHn/ppZdSDDbTY/r06erSpYsuXLggSSpRooRWrFhhC21ymrJly9p+oDh58qRGjRqlUaNG6dSpU5Liw9fZs2crX758XrU9dOhQj7ftJk2aqFmzZtb0xYsX9ffff6dYZ/DgwQoKCvKo/TJlyqhnz562x9atW+dRXW+EhITozjvv1E8//aSIiAitWrVK48aNU9u2bVPsa0xMjL755htdf/31+vnnn5Mts3PnTp07d86a7t27t8evyd13362JEydat9KlS1vz1qxZYys7aNAgBQR49rE9ICBAgwYNsj22du3aVOu1adPGo9du5cqV1v2QkBC1b9/eo35J8cFs4m1q48aNioqK8ri+p6Kjo9W3b1/997//tR679dZbtXr1alWoUMHny0vKH99Pnn32WdWrV8+avnz5sh577DG1aNFCTzzxhL744gvNmjVLzz33nLp06aIOHTpYobsn7ydS/P/MmDFj1LBhwxQD+8SOHTumli1b6p577kn2RzMAAAAAADICOX7KyPG9k9E5viRFRUVp2LBhat26dbIHfCdnx44dql+/vp555hnFxsamce08Q45Pjp9e5Pjk+FLm5vj+JqPf3/zx/RMA4D88+2QIAEhRXFycZsyYoRdeeEHh4eEu81u1aqUZM2bYjiZNKjQ0VBcvXpQUHzAZY7z68B4REeHSnj+JiYnRuHHjrOk777xT1apVy8Ie2SV9vhKHCZ7IiOe/fPny+umnn9S1a1edPn3aenz16tVavXq123qDBg1SoUKFNGXKFOux5ILHzZs3W/fz58+vQoUKpdqngIAAffrpp/r1118VGRkpSdq/f7/++usvn56FwhijcePGacKECdZjNWvW1KJFi1SjRg2fLcdf3X777RoxYoS++OILSdLcuXNt89966y01aNDA63a9PWK7adOm2rRpkzW9bds21a9f36ftz5s3z9Z+9+7dvWrDG3ny5NGNN96oG2+8US+99JKioqK0atUqLVu2TIsXL0520GNkZKR69uypb7/91iXwThro1a1b1+O+VKtWTWPHjk123saNG23TiQd+eiJp+Y0bN+quu+5KsY67H8WS+vPPP637VapU0e+//+5V3woXLmzdj4uL0/79+316JoETJ06oR48etufwvvvu04cffuhxKJ5e/vh+UrBgQf3444+65ZZbdODAAevxLVu2aMuWLW7rdezYUe3bt9dLL71kayupmJgYDR48WHPmzLE9PmTIEPXt21e1atVSlSpVdPz4cYWFhWnx4sX69NNPFRsbK2OMpk6dqgMHDujnn3/2u88vAAAAAICcgxw/deT43svoHD8yMlJdu3bVH3/8YT0WHBys+++/X7feeqtq1aql8uXL69ChQ9q7d6/mzp1rDYyPiYnR66+/rsOHD+vLL7/0eT5Gjk+OnxHI8d0jx8+5Mvr9zR/fPwEA/iN3vvsCgA/9/fffGjlyZLJH3IaEhGjChAl6/PHHFRgYmGI7iYPHmJgYRUdHe3w5Gcn1g7s3dTPDggULrKPqJemee+7Jwt64SvpFJ+nzmZqk5X11OfbmzZtr06ZNuv/++7V48eIUy4aEhOjFF1/U008/rQEDBqTaH08vdZZU0aJFNWTIEH388cfWY0uWLPHZAOKrV69qxIgRmjVrlvXYjTfeqO+//97lEjk52bvvvquVK1fqn3/+sT3etWtXPfTQQ2lq09uwP2nIm9r/RUa372sFChRQ165d1bVrV02aNEk7duzQrFmz9MEHH9guv2SM0WOPPaauXbva9hVJf2iqXLmyT/qV+IcGSapatapX9atUqWKbTjjjRUo8CR5jY2NtbYWFhem2227zqm9JJRyI4Au7du1S165ddeTIEeuxN954Q2PGjMnUyyT66/tJtWrV9Ndff+nhhx92exnNBAEBAXr00Uf1yiuv2H40dNefsWPH2gYPV6tWTXPnzlWTJk1s5WrWrKmaNWuqe/fueuyxxzRw4EArJF65cqWGDx+u7777Lj2rCQAAAABAssjxPUOOnzYZmeMPGzbMNni4RYsWmjVrlku2WrduXdWtW1e9e/fW448/rjvuuEMHDx6UJM2ePVsVKlTQm2++mdZVdEGOH48cP+OR4/8POX7OldHvb/76/gkA8A+eXUMBAODi2rVrmjBhgho3bpxs6Ni/f3/t2rVLY8aMSTV0lFw/aHvzwT0uLs66NJQUf3Runjx5PK6fGT788EPrfo0aNby6XE5mSM/zL7keqenL4LdixYpatGiR1q9fryeeeEJNmzZVyZIlFRQUpFKlSqlFixZ6+eWXtWfPHj377LMKDAx0uYyZt8FFajp16mSbTnxGy/R65513bKHjwIEDtXTp0lwVOkrx2+S0adNsjwUEBOiTTz5JU4hSoEABlShRwqs6OT14TMzhcKhhw4Z6/fXXtXv3bpdA7fjx43rvvfdsjyXtb6VKlXzSl6T7k/QGj54cSV6yZEmv++ULCT+4+cLtt99uhY4hISH69ttv9dRTT2V66OjP7ydFixbVzJkztXPnTj333HNq1aqVypQpo6CgIBUvXlwNGzbU008/re3bt+udd95R/vz5U30/OXbsmO09Pk+ePMkOHk6qRo0amjdvnooWLWo9NmfOHG3dujX9KwoAAAAAwP8jx/cOOX7aZUSOv3HjRs2fP9+aLlasmObNm5fq2X2bNm2qOXPm2LavyZMn699///XBmsYjx49Hjp+5yPHJ8XOqjH5/8+f3TwBA1uMMxACQBmFhYerXr5927NjhMq9Dhw5688031aJFC6/arFq1qvbu3WtNR0REePwl9sKFC7YzCfrq6Flf2bVrl+3yOPfcc4/ffRFM+sU+vUdeZsRr0LJlS7Vs2dKjsidPnrRN16lTx6d9SRowJV1eely9etW6X6RIEb355psKCQnxWfvZyW+//Wabdjqd+vTTT/Xyyy973VZQUJDX/3d58+a1TefLly/VZWRk+6nZv3+/oqKirOmGDRsqIMD74+UqVqyo+fPnq23btrZLvyU+04dk31YlKX/+/F4vKyMk/X+5du1aqnU8eZ5iYmLS3Cd3fBk8Jn49evXqpTvuuMNnbXsjO7yf1K1bV6+88opeeeWVVMsm3b8nvcTf119/bXvuR40alerg4QSVKlXSCy+8oMcff9x67PPPP9cHH3zgUX0AAAAAAFJCju8dcnzf8GWOP3XqVNv0Cy+8oIoVK3rUdrNmzfTAAw/o/ffflyRduXJFX3/9tZ588kmP6qeGHP9/yPG9Q44fjxw/a3N8f5PR72/Z4f0TAJB1OAMxAHhp06ZNatu2rUvoWLlyZf30009avny516Gj5BoMJb2kTkqSHjV+3XXXeb38jJT4rAVBQUEaOnRoFvYmeel5/o0xttegUKFCKlu2rM/65i2n02nrT+XKlV0uTZNeSY8s9WUwWL9+fSsgO3/+vFq1aqVt27b5rP3s4o8//tCrr77q8vhrr72mDRs2eN3e+fPnvT7y/MyZM7bpYsWKpVg+6WXafN1+ap544gk1adLEup0/fz7NbeXNm1evvfaa7bG///7bNp20v4cPH07z8hIrUqSIbTrhUoOeSvo6+OqsH4nPFCvFnynAGJOu28CBA33SNyk+aE7w3XffacCAAbpy5YrP2vdUet5PJP97T0982dKgoCCXM44k/tFUklq3bu1V+0nLh4WFedlDAAAAAABckeN7jxw/c3mS4/tz7kKOH48c33vk+PHI8bM2x/c3tWvXtk37+neF0qVLq3Dhwrb2Ex/UlN72AQDZGwOIAcAL4eHh6tatm8uX5Yceekg7d+5U9+7d03xEftLga/369R7XTRpC+NOH9osXL+qrr76ypnv06KEyZcpkYY+Sl57n/9ChQ7Zt4rrrrsvSMzNs2bLFdvR2mzZtfL6MpAFWqVKlfNb2HXfcofnz51tHsR8/flw33nijfv31V58tw9+dP39eQ4YMkdPplCSVL1/euoRiXFyc7rrrLl26dMnrdg8cOOBV+aSBb4UKFbK0/dSULl3aNp3eILB58+a26aRnBEka6HkbELqT9DJk6Q0evb3knTv58uWz/Yhx+vRpn7TrK3PnzrX9sDVnzhx17tzZ5T07o9WqVcv2HuDN+8mVK1ds/xelS5d2CXwz0+nTp7V7925rumXLlgoODraVOXbsmG066RmKU5O0/NGjR73sJQAAAAAAduT43iPHz3ye5Pj+nLuQ45PjpxU5fjxy/HhZleP7m6JFi9r+N7Zt2+bVwOrEnzEcDofLgGSHw2F7Dz19+rQOHTqUpvYl//oMAwBIPwYQA4AXRowYYfviGRwcrG+++UaTJ092OSOrtzp27GibXrlypcd1V61alWJbWemrr76yhWAjR47Mwt64V6tWLdvZBjZs2KDo6GiP6mbE83/mzBlNmTLFui1fvtzjugsXLrRN9+nTx6XMrbfeqipVqqhKlSqqWbOm10d4b9++3TadNLhNr969e+v333+3vixHRkaqW7du+uyzz3y6HH9kjNGoUaOs0MzhcOjbb7/VM888Y5XZu3evxo4d63XbSV+31PqxbNky22PXX3+9z9qX5NK+t2fQSKp69eq26fSG1UmPvk56RpKmTZvappOeESQlV65c0V133aUhQ4ZoyJAhmjJlijWvWbNmtrKJB3B6IukZFpK2lx6Jw9itW7cqNjbWZ22nV548eTRt2jTbpQH//PNPtW7dWvv27cu0fuTPn9/2v3LkyBGPz+qxfv162yXmfPF+cvXqVdv7yQ8//OBx3cWLF9v+D5J7P0l6Bo/0BuXpPYMJAAAAAADk+N4jx0+bjM7x/T13Iccnx08Lcvx45PhZm+P7o8TvS9euXdPatWs9qnf48GEdOXLEmr7++uuVP3/+FNuXPP8Mc+nSJf3111/WdNmyZVWrVi2P6gIAsgcGEAOAh37//XeXL7Hff/+9+vfv75P2a9eubfuwvWrVKo+OAo6KitLcuXOt6YIFC6pDhw4+6ZMvzJo1y7qfJ08ede7cOQt7415AQIB69OhhTV+5ckXfffedR3VnzJhhm+7du3e6+xMSEqLRo0dr1KhRGjVqlEaMGOHRpWSuXr2qTz75xNZOly5dXMrVqVNHhw8f1uHDh7V//37b2SU8kXQAWnLLSK8WLVpo3bp11lkS4uLidN999+nZZ5+1jujPib766it988031vSTTz6pNm3a6IUXXlCDBg2sxydPnqzffvvNq7aTbqspWb9+ve3MAo0aNUr1TKgzZ85UXFycR+0fPXpUP//8szVdrFgx1a9f3+P+Jadbt2626Q8//FCXL19Oc3tJw5l69erZplu1amWdUUKS5s+f73EQ99tvv2nmzJn6+uuv9fXXX9t+1Ep6tpFZs2Z5fCkpY4xmzpxpe8yXZyG/8cYbrfuRkZFasmSJx3WvXr2q2267TW3btlXbtm11//33+6xfCRwOh1544QXNnDlTefLkkSTt379frVq10h9//OHz5bnTs2dP27Sn/3sZ8X6SJ08ejRs3zno/GThwoO0HQXeMMXr//fdT7U/SwH/jxo1e9S9p+apVq3pVHwAAAACAxMjx04YcP20yOsfPDrkLOX48cnzPkeOT4/tLju9vkv6uMH36dI/qefr+ltb2586da/sf7dWrlwICGGoGADmKAQB45K677jKSrNsDDzzg82U8//zztmXcc889qdZ54403bHXuvvvuNC9/3LhxtraWLl2a5raMMebMmTMmICDAau/GG29MV3sZ7bfffrOtf926dc3Vq1dTrLNu3TpbncqVK5u4uDif9KdVq1a2tpcsWZJqnYkTJ9rqPPLII8mWW716ta1c9erVzfnz5z3q159//mmrW79+feN0Or1aN2+cO3fOdO7c2bbMAQMGmMuXL2fYMr1RuXJlW98OHjyY5rb2799vChQoYLVVr14923pu3rzZBAUFWfMrVKhgzp0757a99u3b2/omyfzwww+p9iMmJsa0aNHCVm/y5Mku5YYOHerS/nvvvZdq+06n09x+++22ek888USq9Txpt169erZ2R44cmabtMzY21rRp08bW1vTp013KJV2Pd99916P2Bw4caKv322+/2dajefPmtvmfffaZR+1+9tlntnpNmjRJdv1XrFhhKzdt2jSP2v/7779t9Vq0aGGuXbvmUd3333/fVve5557zqF5a/f7776Zo0aLW8kJCQsw333yToctMEBYWZhwOh7XsUqVKmYiIiBTrHDx40OTLl8+qExoaai5cuOCT/gwYMMD23H/66aep1pk9e7atTs+ePZMtl/R9sESJEub48eMe9Ss2NtY0a9bMVj+zXiMAAAAAQM5Eju89cvz0ycgc/5tvvrGVq1WrlomMjPSoX5GRkaZChQq2+uvWrfNq3bxBjk+O7ylyfHL8xLIyx09N0uc/o5+Lc+fOmdDQUNtzsX///lTrlC1b1qrjcDjMvn37ki0bGxtrKlWqZFunP/74I8X2r127Zho2bGirs3z58jSvIwDAPzGAGAA84HQ6TbFixWwfjv/55x+fL+fMmTOmYMGCtuX8+OOPbstv3LjRhISEWGUDAwPNnj170rx8XwePs2bNytQvVunldDpdgoYnn3zSbWhx+vRpU6dOHVv5qVOn+qw/SQOEChUqmFOnTrktv3jxYhMYGGiVz58/vzlx4kSyZZMLNm699VYTExOTYp/+/vtvU758eVu9uXPnpms9PXHt2jUzYsQI23Lbtm1rzpw5k+HLTo2vgsdr166Z66+/3monKCjIbNy40aXcSy+9ZFvekCFD3LaZXPBYrFgxs337drd1YmNjXX5oKVasWLIDGZMLHkNCQsyyZcvctu90Os1zzz1nqxMcHGwOHDiQyjPkmUWLFrn0afDgwR4PkDfGmIsXL5ohQ4bY2qhWrZqJjo52KZt0MH6+fPlSDU+SBv/lypUzsbGxtjJfffWVrUxoaKhZvXp1iu2uWrXKFi5JMl9++WWyZdMaPBpjzG233WarO3z48FTDx5UrV9re34KDg83hw4c9XmZa7dmzx1SvXt3W3zfeeCNDD3pIMHjwYNty+/fv7/I6J4iOjjbt2rWzlX/hhRd81pelS5fa2i5YsKAJCwtzW37Tpk2218vhcJht27YlW9bpdLoMAm7Xrl2q/3OXL182I0eOtNUrX768iYqKSte6AgAAAAByL3L8tCHHT5+MzPGjo6Nd8vj+/funOij3/PnzpmvXrrZ6TZs29dmgaXfI8f+HHD9l5Pjk+IllZY6fksweQGyMcfm/u/HGG82lS5eSLRsTE+Ny8pC77rorxfaTvmfVrl3b7T7a6XSaMWPGuOzTs/p1AQD4HgOIAcAD586ds304LlGihFm8eHGabytXrnS7rFdffdW2rKCgIPP666/bvvRHRUWZjz76yOULZnrPpuDr4DFpeOHJkfepSRryDB06NN1tJrZq1SrbWSMTlnHo0CGrTFxcnFm4cKGpUaOGrVy9evVSHYCbNAhq376927JXrlwxtWrVspWvUqWKWbBggW054eHh5plnnrGdJUJK/kjzxFavXu2yrvXq1TMLFiywnbHh2rVr5u+//zbjx4+3HVUvyfTp0yfDQ8cETqfT5f+jZs2abo+kzSy+Ch6ThgLjx49Ptty1a9dM48aNbWXdDeJOvL0l/pEiNDTUvP3227YzosbGxpqlS5e6nLFAkpk1a1ay7ScOHhO3HxQUZJ5//nnz77//WmWdTqdZu3at6dKli0v7r732WpqeM3eGDRvmsowSJUqYZ555xmzYsCHZM5JERUWZrVu3mkceecTlB6DAwEDbmQVSW16ePHnMs88+a44ePWord/r0afP000/bnit3oV9cXJzp2LGjS7vPPfecS7tHjx41zz77rMmTJ4+tfMeOHd0OWE1P8Lh3716XfUGLFi3Mjz/+6BI0HT161Dz33HMmb968tvLPPvusx8tLr1OnTpkbbrjBtvz77rvP4zMupNW+fftc1rtr165m165dVhmn02lWr17tckBH6dKlUw3Lkwb/lStXdlvW6XSaDh06uPxPTJ061fb/EBERYd566y2Xfqd2ZpE///zT9sOXJFOyZEnz0UcfmZMnT9oCxTNnzpgZM2a4nOlAkvn2229TeVYBAAAAAHCPHD9tyPFd+VOO/91337lkKFWqVDGzZs2y5btOp9P8+++/5r333jPFixe3lQ8ICDB//vlnKs+qb5DjxyPHTx05Pjl+YlmV46fEFwOIvcnxjYnP6EuWLGmrc/3115u1a9faym3dutXl/zRfvnypDvK/du2aue6662z1atWqZZYsWWLL8fft2+dykpSAgACzZs0ar58DAID/YwBxGh07dsysWbPGrFmzxhw7dszr+nFxcWbv3r1mxYoV5q+//kr1ksbJuXz5stmxY4dZvny52b59u9sjjwCk3549e1y+xKbnltKXg9jYWNO9e3eXOnny5DG1a9c2derUcfkCJ8m0atUq3fsBXwaPcXFxti84gYGBHl9aKyUZHTwaY8xbb73l8vw6HA5TuXJl06BBA1O4cGGX+cWLF0/1MjLGeBc8GmPM2rVrk3298+TJY+rVq2cqVarkEjhKMqNGjfLoCNCXX3452W00ICDAVKlSxdSpU8d2qa2kQcPFixc9fVp9Zvbs2baApUSJEln6hdUXwePvv/9uex2bNWuWYiiybds2ExwcbNv+Eod8CRJvb4888ohLqBgcHGxq1qzpdruWZB588EG321Li4KNfv36mV69eLttRtWrVTKNGjVxC64Rbz549Uw3svZXcUdeJbyEhIaZKlSqmUaNG5rrrrnO77gnP0fz581NcXmRkpGnSpEmy9StVqmQaN25sSpUqlez8fv36uR2Ef/ToUZftK3G7TZo0SXYQZsL7TEqfUdMTPBpjzNy5c93uG0qXLm0aNmzoEnIl3Dw527mvXb582fTv39/Wjy5duiR7Rg5f+vbbb5N9DsqXL28aNmyY7P9FSEiISxiYHG+Dx3379pkSJUq4LC8oKMjUqVPHVKtWLdnXtHv37m4D7MRmzJjh9v+oSJEipkGDBrZL0SW9vfzyy54+rQCADBIZGWm2bNlili9fbnbt2pWlP9IBgC+Q4QO5Dzm+98jxk+dvOf6ECRPcbqclS5Y0DRo0cBlMmfg2ffp0T59WnyHHJ8dPDTk+OX5SWZXju5MVA4iNiT8BVOJ9R8KtVKlSplGjRqZMmTLJvv+l9j+QICwszBQpUsSljaJFi5qGDRuaChUqJLtNvPfee16vPwDA9zIix8/1A4g/+OADI8mMGzfOo/KLFi1yOUNYwheEhQsXplo/Li7OvPPOOy4fEgMDA023bt08umTR+fPnzejRo02hQoVsbRQsWNCMGjXKq0t7APDM77//7vZLaVpuqX05uHjxoundu7fH7XXs2NGcPHky3evpy+Bx/fr1trZatmyZ7v4ZkznBo9PpNC+99FKygV5ytypVqpgNGzZ41La3waMxxvz666/JfpFL7hYYGGheeuklj7/UO51O88Ybb7gNENzd7rrrriz90Wv16tW2ICskJMR89913WdKX9AaPERERpmLFirZ1SXyGUndeeeUV23K7d+/uEhAm3t6efvppEx4enuyZCZK7ORwOM3bs2BQD7MTBR//+/U1kZKTLZbFSug0dOjTDBqY4nU7z2WefpRgqpnZr3bp1ipeJS+zChQvm1ltv9ar9YcOGmStXrqTYrjevWeL9bXJBdGLpDR6NMWbJkiUpDghN7nbPPfcke+aIzBAXF2eeeeYZW38aNmzociYIX/v000+T/QEpuVvCmZE8kZbgcdOmTW7Dv+Ruo0aNSvaSf+4sXLjQbcju7lawYEEzbdo0LnsGAF7wNstKTXh4uBk8eLDL+1WJEiXMs88+m+rlkQEgo5HhA/AUOb73yPGT5485/rRp01zOJprarXTp0ubnn3/2aBkZgRyfHD815Pjk+EllVY6fnKwaQGyMMT/88IPHr1v+/Pm9PlDkzz//tO3TUroFBQWZiRMnkuEDgJeyU46f6wcQt2rVyuMXa9KkSam+eb711ltu68fGxqYaJOTLl8/8/vvvbts4ceKEqV69eoptVKtWzZw4cSItTwcAN5K7RFR6bp58OXA6nWb69OmmYcOGbtupWbOmef/9990e9eotXwaPSdt68sknfdLHzAgeE/z555/m5ptvdhtAFi9e3PznP//x6sjXtASPxhhz+PBhM3z48GSPOJXiz2TQt29fs27dujSt644dO8zAgQNdLsmUdBldu3bNtEudpWbfvn2mZs2atj5mhfQEj06n09x55522+pMmTfKobkxMjMsPop999pmtTNLg0Zj4yxONHz8+2SOUpfjw+uabbzZ//fVXqn1IGjwmrNMHH3xgqlat6nZbat26tU8uheiJiIgIM3nyZNO8eXOPfkwoWrSoGTJkiFm2bJnX+1an02m+//77ZH+oTnxr166dRz9aJ4iNjTUzZswwDRo0SLHdhg0bmi+//NKjs8X6Ing0Jv75HTt2rCldurTbfgUEBJju3bun+Bk3M33++ee2gyaGDRuW4cv8+++/TZ8+fdzuwwsUKGBGjBiRamCcWFqDxzNnzpj//Oc/Jn/+/G5fry5duphffvklTesaERFhJk6caKpUqZLi9lqmTBnz7LPPmvDw8DQtBwByM2+yrNTs2bMn2TPUJ761bNnSREVFpb/jAJBGZPgAPEWOn/62yPHj+WuOf/z4cfPMM8+kmMVJMlWrVjWvv/56ms6e72vk+OT4niDHTx45fubm+Ell5QBiY+KvqHL33Xeb0NBQt+8pd9xxh9m7d6/X/TImfrt4+OGH3Q5UDggIMLfeeqvHB98AAOyyU47vMMYY5VLTpk3TiBEjJEnjxo3TSy+95Lbs8uXLddNNN8kYoxIlSui9997TbbfdJklatGiRHn30UZ09e1aS9Ntvv6lz584ubbz00ksaP368JKlp06Z6++231aJFC507d05ffPGFxo8fL6fTqWLFimnPnj0qWbKkrb4xRp06ddLKlSslSQMGDNDzzz+vatWq6cCBA3rllVf07bffSpLatWunlStXyuFwpOs5AuAfdu3apR07dig8PFxxcXEqV66crrvuOjVp0oT/80wQHh6u9evXKzw8XBcvXlSZMmVUuXJltW3bVnny5MnUvly8eFGrV6/WP//8o4sXL6p06dKqUKGCWrZsqWLFiqW7/ejoaG3ZskW7du3ShQsXFBsbq5IlS6pixYpq27atChQo4IO18J2zZ8+qd+/e+uOPPyTFv1fCM7Gxsfrjjz908OBBnThxQgULFlS5cuV04403unwGSQtjjNavX6+9e/cqPDxc+fLlU9myZdW6dWtVrFjRB2vgvaioKG3dulUHDhzQ+fPnFRkZqZCQEBUpUkQlSpRQo0aNVLVqVZ/sV8PDw/Xnn3/qxIkTOn/+vAoXLqwKFSqoTZs2KlWqVJrbPXbsmNatW6eTJ0/q/PnzKlKkiEqXLq1WrVqpQoUK6e53WjmdTm3dulV///23Tp06pStXrqhIkSKqXr26rr/+ehUpUiTL+pacpUuX6o477tDFixc1dOhQTZ8+PVOWGxERoT/++EPh4eGKiIiw9q/t2rVT/vz5M6UPCS5fvqw///xTe/bs0fnz51WiRAlVqFBBTZs2VdmyZdPdvjFGhw4d0oYNG3Tq1ClduHBBBQsWVIkSJdSsWTPVrl2bzzAAkAbeZFmpuXr1qho2bKi9e/dKkh5++GE9+uijKleunHbs2KFnn31Wy5YtkyTdfffdmjFjRrr7DwDeIsMHkJ2Q42et3JTjG2O0Z88ebd68WWfOnFFkZKQKFy6sUqVKqWXLlqpSpYpfbXPk+GlHjk+O70vk+NlTVFSUVq9erWPHjun06dMqVqyYKlSooLZt2/rkNbt69ar++OMPHTlyRCdOnFChQoVUrlw5tW7dWmXKlEn/CgBALpTdcvxcN4D4woUL2r59u6ZNm6aZM2cqJiZGUsovljFGrVq10oYNGxQUFKQNGzaoSZMmtjKbN2/W9ddfr9jYWLVs2VLr1q2zfWg9deqUqlWrpujoaFWtWlXbtm1TwYIFbW1MnjxZjzzyiCTpqaee0htvvGGbv2jRInXr1k2SdMcdd+i7776zLcMYo379+mnevHlW+YSAFACAnOrq1asaMWKEZs2aRfAIwGM7d+5Ut27d1LFjx1wbPAIAsoe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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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SJKYRwaNvBY979uyx9al48eImPDzc43Y+/fRTWzsZcak63Fg+//xzp39iHTx4MMmyp06dMrfccout/AsvvOC1vixcuNApEPz8889NTExMkuXPnj1revXqZatTt25dEx0dnew6WrRoYSvfrl07c/HixSTLxl9yM3H4uHfvXq+8XgAAAABA9kaOnzJy/KyV40dHR5uKFSvayj/wwAPm2rVrSZaPiYkxr732mq18gQIFzPnz59P6UrM0cnxyfMBd4eHh5qOPPjI5c+a07V8ZMYB40aJFtnUWLlzYbN68OcmyERERTpN13HPPPSm2v337duNwOKzyOXLkMEuXLk2ybFRUlHnooYds7Tdq1CjZ7wsAgKyLAcQA4KazZ8/aZmp1OBzJfmFPKCIiwlSrVs325Xr48OFJlk38Q/fOO+90+SX80UcftdV58cUXUyx//fp1s3PnTvPTTz+ZkSNHOgVVaQkeR40aZavfu3dvl3VCQkJMoUKFbPWOHDni0Xq95eTJkyZ37ty2IHf//v0p1lm9erXth1aZMmWSDe48ce3aNRMcHGzbLt98843LepcuXTLlypWz1Vu3bl2y5ZcsWWIr+8svv6S575nl+vXrJjIyMsPXS/DoW8HjJ598YuvTU089lap2rly5Yvs8CA4O9nJP4SsuXbrk9TavXr1qm53H4XCYNWvWpFhn7969Tscgb81CnHhw8muvveayTnR0tGnYsKGt3o8//phk2cOHDzuFmqGhoS7Xcd999zn9sxUAAAAAgLQgx3eNHD9r5fi///67rVyVKlVMREREiu1HR0ebZs2a2ep99913qXqN3kaO74wcnxwfrqVHjh8vJCTELF261IwbN87079/faWbr+Ft6DyCOjY01DRo0cCuTj3fmzBnbccjhcKQ4q33iE5HGjBmTYvtXr151muk4K/8vGQCQND8BANyyZs0aXb9+3Vpu3769br75Zpf1goKC9MUXX9gemzdvXpJlv/vuO9vy22+/LYfDkWL7o0aNUkBAgLU8bdo0GWOSLb9o0SLVrl1b9957r0aOHKkVK1a4eAXu++OPP2zLjzzyiMs6pUqVUocOHWyPrV692mt98sSsWbMUGRlpLT/yyCOqXLlyinWaN2+ujh07WsvHjx/XqlWr0tyXP/74Q2FhYdbybbfdpoceeshlvbx58+rzzz+3PfbTTz8lW37Pnj225Ro1anjYU98QHh6ujh072rYZsqdjx47ZlqtXr56qdnLnzq1GjRpZy2FhYbZjAG4Mf/75px588EGvt7t8+XKFhoZay507d1azZs1SrFO1alU9/PDD1vKVK1c0d+7cNPdl//792rx5s7VcqVIlDRs2zGU9f39/TZgwQf7+/tZjyR1PEh+3O3bsqJIlS7pcx4ABA2zLib9HAAAAAADgKXJ818jxs1aOn/j96t27t4KCglJs39/fX/3790+xncxAjo945PjwRHrl+PFq1aqlO+64Q08//bQmT56sS5cupdu6UrJr1y79/fff1nK9evV07733plinSJEiev75561lY4y+//77JMuGh4dr/vz51nLRokX19NNPp9h+zpw5NXLkSNtjU6dOTbEOACDrCXBdBAAgSWvXrrUtt2rVyu26t912mwIDAxUVFSVJ2rlzp2JiYmyDck6cOKGNGzday7fccotbwWbJkiXVsWNH6wt/aGiotmzZ4lZdb0s4WEqS6tev71a9m266ST/88EOy7WSUBQsW2JbdCU4l6eGHH9bChQut5UWLFumOO+5IU1+WLVtmW+7WrZv8/Nw776djx44qUqSIzp49K0maPXu2xo4dm2SIvXfvXut+zpw5Va5cuTT0OnMcOXJEHTt21M6dOzO7K1nOrl27tGXLFoWEhMjhcCg4OFh16tRRvXr1XP7Twx2HDx/Wxo0bFRISomvXrqlkyZKqXr26GjVq5Pb+7KmLFy/alk+dOpXqtu655x5VrFjRWo6MjFSOHDncqhsREaE///xTx44d0+nTp5UnTx4FBwfr1ltvVYUKFVK9fc+cOaM1a9YoLCxMZ86cUYECBVSsWDE1aNBAVapUSVWbSdm9e7fWrVunkJAQVatWTffcc4/LOiEhIVq/fr3CwsIUHh6ucuXKqWLFiqpRo4YKFy7stb55y/Tp09W/f381bdrU622n5Xjy2WefWcuLFi3SY489lqa+JD6edOjQQbly5XKr7k033aSbbrrJCi0XL16sK1euKE+ePLZyaTn+p9QOAAAAAACeIsd3jRw/TlbJ8W+U3IUcP/XI8VNGjk+Of6NI6vjmzj744IMP6qWXXlJMTIykuOPb22+/7VRuyZIlio6OtpYfeOAB5cyZ02X7Xbp0UdGiRXXmzBlbOwlPjAIAZG18ogOAmxKfDVu+fHm36+bMmVO1a9fWli1bJEmxsbE6ffq0goODrTKJg6Z27dq53X67du1sZwwuWbIk2eCxXLlyyZ6luWXLFm3dutXt9SaW+Af55cuXVaRIEZf1rl69mmI7CbVq1Uq///67tdyyZUuvzBRw/fp1rVy50louUaKE6tSp41bdNm3ayM/PT7GxsZLitn9aHT161LZct25dt+s6HA7VrVvX2i5HjhzRyZMnbftbvIQDiKtVq2YLw7OCTZs2qXPnzll6xoK9e/fq5ptv1pUrVyRJxYoV044dO1S8ePEU68XExKh169a2mT4WLVqku+66y+U6lyxZomHDhtlmJU2obNmyGjp0qIYMGeJ20JbQxo0b9dxzzyU7C0nx4sX16KOP6qWXXlK+fPk8bj8lFSpUsC0vWbJEw4cPT9W+PXjwYI/r/Pvvv3rttdf0yy+/6Nq1a0mWqVu3rp577jk98MADboU/xhgtXrxYb731ltauXZvs7DSVK1fW4MGDNXDgQJehz+HDh22h6saNG9WwYUOdOXNGTzzxhGbPnm0917Jly2SDx9jYWM2YMUNjx45Ndn8KDAzUvffeq6FDh9pmg8gsxhi9/fbbeu2119JtHb/++qt138/Pz+1/VtavX1/FihXT6dOnJcV9N0hrEJeW40l8+fgBxJGRkdq2bZuaNGliK5PU8d8dnhz/AQAAAABwBzm+a+T4WSvHvxFyF3J8cvzEyPHJ8V3JiBw/Xp8+fay/7YQiIiJs2zi9Jfy/guT+d4yiRYvq5ptv1qZNmyRJ27ZtU2hoqEqVKuWV9gMDA9WqVSvNmjVLknT+/Hlt3LgxWwzqBoDsIn1OGQOAG9CFCxdsy54Ej5Kss8iluMFEhQoVsj2f+KxrT75033bbbbblf//9N9myt9xyiyZPnpzkrVu3bm6vMyllypSxLSf3IzSxhJdjSaqdjHDgwAFbSNC0aVO3zyzOnz+/LRjcv39/soGDu+LP4ozn6TapVq2abTm5fSLhAOLUXiIqs8yfP18tW7a0QscCBQoob968mdwrz1WrVk0ffvihtXz69Gk9/vjjKV7CUJLGjRtnC/aeeuopl6GjMUavvvqqOnTokOLf57Fjx/T888/rpptu0uHDh917If9v/PjxatasWYqXMDx16pTeeust1ahRQ//8849H7btStWpV2/L69ev1yiuv2M6qTg/GGI0ePVr16tXT3LlzU/wM2L59u/r3768777zT5aWwIiIi1LVrV3Xs2FF//fVXivvFgQMH9Mwzz6hWrVopHgeSc+jQITVr1sztQCwsLEzNmzdXnz59UtyfoqKi9P3336tx48YaPHhwur8XKbl+/boGDBhgCx29fcy5fPmyDh06ZC3Xq1fP7YDd4XCoWbNm1nJkZKSOHDmSpv5kxPEkKx//AQAAAAA3FnJ817Ly7/jsmONn5fdLIscnx08aOT45fkoyIsdPaPz48Ukeb8eOHZtu60xKwu8YhQoV8uj/tom/Y+zatSvF9iU5TRTiSfup2XcBAL6LAcQA4KZnnnlG06dPt27untUuxV0OJuHMBxUrVnQ6q3TPnj225bJly7rdfuIfTQkHhWaknj172pZHjx6t69evp1hn/fr1tsuG5c2bV7fffnu69C8ladn+kv09iI2N1cGDB9PUn/z589uWEw5Gc0fiAWdJ/VCMjIy0zZBQvXp1GWM0d+5c9evXT7feeqsKFSqkvHnzqlKlSurUqZM++eSTNF1Kyls+/vhjde/e3TojuHz58lqzZo2KFi2ayT1Lnccee0ydOnWylufNm6epU6cmW/7ff//VsGHDrOW6devq3XffdbmeTz75RO+884617HA4VL58edWrVy/JS1Pt2rVLt912m9NMGsmZO3euBg0aZAuWypQpo/r166tEiRJO5UNDQ9WqVSuvho+3336704wp77//vho2bKg5c+YoMjLSa+tK6Omnn9brr79uXSIqXqlSpVSvXj2nfzZJ0tKlS9W9e3enOvEiIiLUqlUrp8tWSVJwcLDq16+vihUrOs3KcPDgQd12220ebdeIiAh17txZ+/btsx7LlSuXKlas6BTmSnEzHzRt2tTpsqCSVLhwYdWsWTPJWXM///xzdevWLcmz+dPbhQsX1KFDB02ePNl6rG/fvpo4caJX15NwG0ppO55IaT+mZ8TxpF27drZ//CxcuFDr169Psd3r169r9OjRtse6d+/uUd8AAAAAAEiMHN81cvw4WSXH79atm22Q9LfffutyPefPn7cNdpUyJ3chxyfHTw45Pjl+cjIqx/c1Fy5csP3/tUyZMm6fIBNfPqHE3zGMMbZjaL58+VSwYEGvtQ8AyNoYQAwAbmrTpo3uu+8+6+bJ5XrGjBljW27QoIFTmf3799uWkwoBkpP4C35mfWnv3bu3atSoYS1v3rxZ/fv317lz55Isv27dOt133322H93PPvusChQokO59TSwt2z+p8ml9D0qWLGlb3r17t0f14y+zF+/06dNOZfbv3287C/rEiRNq1KiRevTooe+//14bNmzQhQsXrNk0Fy1apKFDh6pixYp65ZVX0jw7Q2rExMRoyJAhevrpp62+N2jQQOvWrVPt2rUzvD/e4nA49M0336hYsWLWY0OGDHG65KIUdxb4Aw88YG3/XLlyafr06cqVK5fL9Tz77LOSJH9/f7388ss6fvy4Dh8+rK1bt+rMmTNav369OnbsaKsTGhqqAQMGWJf2S8nzzz9v3X/00Ue1b98+HTt2TFu2bFFYWJh27NjhdOnFixcvql+/fk6X1EutAgUKaOTIkU6Pb926VT179lSxYsV07733auLEiR7PypCcH3/8UZ988ontsUcffVR79+5VSEiItm7dqrNnz+ro0aN65JFH5Of331fw5cuXa8KECUm2+8wzz9hmCnE4HHr00Ue1a9cunThxQlu2bNHBgwcVFhamDz/80PYPiwsXLiR72a2kDBkyRDt37pTD4dCDDz6otWvXKjw8XAcPHnTqX1RUlO677z7b9itUqJDGjx+v0NBQnTlzRv/++68iIiK0YcMGde7c2VZ/0aJFSb5H6enw4cNq1qyZVqxYYT322muvaerUqS4vE+ep7Hg8KVCggF544QVrOSYmRr169UoymJakc+fO6aGHHrLNeFGtWjX17t3bo74BAAAAAJAYOb5r5Pj/yQq5S5UqVdS/f39r+dKlS+rRo0eysz8eP35cvXr1smXLLVq0UOvWrT3qW1qQ45Pju0KOH4cc3y4jc3xfk97Ht7Nnzyo8PDzd2gcAZHEGAJCu5s+fb/z9/Y0k67Zx40ancqVLl7aVCQ8P92g9BQsWtNWPiYnxuK8jRoywtbFs2TKP2zh8+LDTaylcuLAZPHiwGT9+vJk5c6YZM2aM6d69u62MJNOvX79U9dsbhg0bZuvLxx9/7FH9p59+2lZ/0qRJaerPjBkzbO01bNjQREdHu1V34cKFTtv2ueeecyo3a9Ysp3Ke3Jo0aWJOnTqVptfpiUuXLplOnTrZ+tClSxcTERGRYX1ISsuWLW19WrlyZarb+vnnn21ttW3b1ulvIvHf6WeffZZsew8++KDT+5YjRw6zdOnSZOvExsaaN99806ne7Nmzncom7kv8bfLkySm+zilTphiHw2Gr88EHH7jYOu6LiooyTz31lFv7cZUqVcwTTzxhZs2aZc6ePevxusLDw02hQoVsbX7zzTcp1vnss89s5StWrGhiY2NtZdauXWsr4+fnZ6ZPn55iu3v27DElS5a01Rs9enSSZQ8dOuS0LQIDA83PP//s8jW///77tnr/+9//zOnTp5MtHxsba3744Qfj5+dn1QkICDA7duxwuS5vWL9+vSlevLht3d9++226rW/ChAm27fPMM894VH/cuHG2+iNGjEhTf9avX29rr0yZMm5/bv7zzz8mICDAVr9nz55Jlo2JiTEPPPCA037VrVs3884775iZM2eaL774wgwZMsQULlzYVqZUqVLm0KFDaXqdAAAAAACkBTk+OX5qZESOb4wxV69eNW3btrWVDQgIMH379jVjx441s2fPNp988ol57LHHTJ48eWzlateunarcM7XI8eOQ47tGjk+On1BG5/juSLz9+/btm27rWrZsmW1d3bt396j+vHnzbPUffPBB2/P79u2zPX/zzTd71P6WLVts9Vu2bOlRfQCAb2MAMQCkoxkzZpicOXPavlDfddddSZZNGBz6+/s7/Qh1pXLlyrb1pCaM8UbwaIwxYWFh5s4773TrR398EPLhhx9mWuhojHNwOHXqVI/qjx492u0wyB3nz593Cvq++uorl/XOnj1rgoODnbbx448/7lT27bffTvL9qFOnjhk5cqSZNm2amTdvnvn666/Nww8/7DTgKz5wuHbtWppeqztCQkLMzTffbFv3U0895XYYm568GTwaY8zjjz9ua+/TTz+1ntu4caPtHxmdOnVK8bMiqeDxww8/dKsfffv2tdVr3bq1U5mkgsennnrKrfYTh/2VKlXy+mfAlClTnP4RktLN4XCYhg0bmuHDh7s9mPHjjz+2tTFgwACXdWJjY027du1s9TZs2GAr06tXL9vzL7/8slv9+e2332z1SpUqZa5fv+5ULqngceTIkS7bv3btmm2bFi1a1ISEhLjVt+HDh9vWd99997lVLy1mz55tcufOba0zf/78qT62uSvxAODkwt/kTJ061Vb/+eefT1N/YmJiTJkyZWxtvvLKKy7rXb161dStW9dpP2nfvn2K6xo7dqwJDAx0+++uY8eOJiwsLE2vEQAAAACAtCDHJ8dPrYzI8eNdu3bNvPDCC26/X5LMQw89ZC5evJim1+gJcvw45PieIccnx8+MHN8dGTmAOPEA4Icfftij+qtXr7bVv/vuu23PJx4A3LZtW4/aP3bsmK1+w4YNPaoPAPBt/113AQDgNadOnVK/fv3Uq1cv6/JEklS+fHl9/fXXSda5fPmydb9w4cJyOBwerbNIkSLJtpfRSpQoocGDBytPnjxulb/tttvUq1cv2+WAMlri7eXppVu8vf0LFiyop556yvbY008/rXHjxtkuFZfQ77//rubNmyssLMzpuaTq7Nmzx7acK1cuffHFF9q2bZtGjBihvn37qmvXrnr00Uf1zTff6NChQxo4cKCtzurVq/X+++97+vI8sm3bNt166636559/JMVd/mncuHH6+OOP5e/vn67rzgwffPCBqlatai2/+OKL2rt3r65evaoHHnjAei+Dg4P17bffevRZUbZsWQ0ZMsStsmPHjlVgYKC1vHLlSp05cybFOrlz59aoUaPcav+1115T8eLFreWDBw9a77G3PPDAAzpy5IgWLVqke++91+kykYkZY7Rp0yaNHj1alSpVUqdOnbRu3boU60ycONG673A43Hr9DodDffr0sT22efNm6/6VK1c0Z84cazlfvnx67bXXXLYrSW3bttXtt99uLYeGhur33393WS8wMFCDBw92WW7x4sUKCQmxlkePHq1SpUq51bfhw4erQoUK1vLKlSutSxh6mzFGH374oe6++25FRkZKksqVK6e//vrLtn3Sg68dT/z8/Jz2n/fee0/Dhg2zfUdJaOvWrWrVqpW2b9/u9Fxyx6D4dfXq1UtNmjRxq2/58uXTU089pRIlSrhVHgAAAAAAbyLHJ8fPCjl+vBw5cuihhx5SzZo13epbmTJl9OSTTypfvnxulU8rcnxy/NQixyfHz4wc39ek9/HN146fAADfwgBiAPCiy5cv66233lKVKlX0/fff254rXry4li1bptKlSydZNzY21rrvaeiYuL6kZAcFpbewsDB17txZnTp10pUrV9yqs3LlSlWvXl0fffRRuv0IdSXx9vP0PUiP7f/qq6+qdu3a1nJkZKSeeeYZNWrUSM8995y+/fZb/fDDDxo2bJjat2+vVq1aadeuXZKkoKAgW1tJhYR79+617ufMmVPz58/XE088kexrz58/v8aPH6/nnnvO9vjYsWMVHh6e6teZkiVLlqh58+Y6fvy4pLhga+7cuRo6dGi6rM8XBAUFadq0aVaoGhkZqQceeECvvPKK9f5K0pQpU1SsWDGP2u7bt68CAgLcKhscHKwuXbrYHnMVwnXt2tVluBcvV65cuv/++22PrV271q26nvD399ddd92lGTNm6MyZM9q4caPeeecd3X777cqVK1ey9YwxWrRokVq0aKFvvvkmyTLnz5/Xtm3brOUWLVqoTJkybvWrc+fOeuedd6xbjRo1rOc2bdqkqKgoa7l79+5Of9Mp6du3r23Zne1at25dpwAoKatWrbItt2/f3u1+BQYG6n//+5+1fPLkSdvnkLdER0dr0KBBeu6556xjSoMGDbRu3TrbZ2p68cXjycMPP6w2bdpYyzExMXr77bdVt25dPfXUU/rqq680Y8YMjRw5Ut27d7e2l+Te8USK+5t5//33Vb16da1evdqtfl26dEnt27dXjx49dPr06VS+OgAAAAAAPEOOT44fLyvk+JIUFRWlF154QfXq1bNlxCk5fvy4GjdurEceecTt9zi1yPHJ8dOKHJ8cX8rYHN/XpPfxzRePnwAA3+HeN08AQIpiYmI0ZcoUDR8+XKGhoU7PN2nSRFOmTLGdjZxYUFCQLl68KCnux6wxxqMv7+fOnXNqL6OFhYWpdevW2r17t60fQ4cOVcuWLVWtWjUVLVpUBw4c0J49ezR58mQtXrxYkhQREaFnn31WYWFhGjNmTKrC17RIvL3Onz/vUf302P758uXTzz//rDvuuEMHDhywHv/nn39SPMO7devWatmypUaOHGlrK7H58+dbZ8HnzJlThQoVcqtf77zzjmbPnq3Dhw9Lki5cuKDFixfrvvvuc6u+u7766isNGjTI6mOJEiW0YMECNWrUyKvr8UWNGzfWiBEj9Prrr0uS1q9fr/Xr11vPP/vss7rjjjs8bvemm27yqPwtt9yi2bNnW8tbt25Vp06dvNp+Qlu3bvWovqf8/f3VsGFDNWzYUC+//LKuXr2qtWvXavny5VqyZIn+/vtvpzpRUVF69NFHdfHiRT377LO25xK+J5JUq1Ytt/tSpEgRvfzyy0k+t2nTJtuyp2FZ4vKJ20tKcv8US+yvv/6y7hcsWFB79uxxms08JTly5LAt79q1S9WrV3e7viuXLl1Sr169rGOLFBfyTp8+PcOOi754PAkICNBPP/2kO++807Y/7Nu3T/v27Uu2Xp06dTRw4EANGjTIeiyp44kxRs8995w++ugj2+MdO3bUgw8+qOrVq6tSpUo6c+aM9uzZo99//10ff/yx9c+ruXPnau/evVqxYoVtRhMAAAAAALyJHD8OOX7y7aVGeuf4UVFR6tu3r2bOnGl7vF+/furZs6eqVaumChUqKCQkRHv27NHixYv19ddfKzo6WsYYTZw4UQcOHNDChQvTZX8jxyfH9zZy/OSR49+40vv45ovHTwCA72AAMQCk0b///quHH344yTN6c+bMqdGjR+vZZ591eYmmhMFjVFSULl++rLx587rdj8Rf3D2p6w2xsbHq1KmTLXTs0KGDvv32WwUHB9vK1q9fX/Xr19e9996rFStW6O6777Z+qLz33nsqX768nnzyyQztf+IfOom3pyuJy3vrsmCVKlXSxo0bNWTIEP3www8pzuzg5+enoUOH6s0339SIESNc9sfTs97jBQYGauDAgXrppZesx5YuXeq1AcSxsbF6+eWX9f7771uP1apVS4sWLbJdNulG98orr+iXX35x+mypX7++3n777VS1WalSJY/KV6lSxbbs6u8ivdv3tly5cql169Zq3bq13nzzTe3fv18zZszQuHHjnC7zNnz4cPXq1csW0CX+R1P58uW90q/EM7FWrFjRo/qJ/05OnTrlso67wWPC13zhwgV16NDBo74ldunSpTTVT+j48ePq1KmTLcAeMmSIPvroowy9TKKvHk+KFCmi1atX66WXXtIXX3xhmx0jKffff7/GjRun6dOnu+zPZ599Zhs8XLhwYc2aNUutW7e2lcubN68qVKig9u3ba8iQIRowYICWLFkiSdq5c6c6d+6sdevWZfg/HwEAAAAANz5y/Djk+Fkvx3/55Zdtg4crVaqkWbNm6eabb7aVq1q1qqpWrapOnTrpmWeeUe/eva0BiatWrdJDDz2kn376KS0v04YcPw45fvojx/8POf6NK72Pb756/AQA+Aa/zO4AAGRV169f1+jRo3XTTTclGTr26tVLO3fu1AsvvODWD57EX7Q9+eIeExOj8PBwazlHjhxOZ4emt7lz59rO+q1cubJ+/PFHp9AxsTZt2mjKlCm2x958801FRkamSz+Tk5btLzmfqenN4LdQoUKaNm2aduzYoWHDhqlJkyYKDg5WQECAihQponr16umll17Stm3b9OGHHypPnjwKCwuzteFpcOFKmzZtbMsJZ1ZIqxkzZthCx9atW2vNmjXZKnSU4mYMnTZtmtOlyr788kvlzJkzVW0SPKasSpUqGjZsmPbs2aMHH3zQ9tyVK1c0atQo22OJ+1uuXDmv9CPx54mnf79FixZVnjx5km0vKe6eUODt9yj+H27e8PDDD1uho8Ph0EcffaRPPvkkw0NHXz6e5MqVSx9//LH279+vN998Uy1btlSpUqUUGBioggULqmbNmho8eLDWrVun7777ToULF3Z5PLl8+bLefPNN22Pfffed0+DhxEqWLKkff/zR9rmxYcMG/fzzz2l8lQAAAAAA/Icc344cP2vl+MePH9fnn39uLefIkSPJwcOJValSRbNnz7ZddXDmzJnasmVL2l/o/yPHj0OOn/HI8V0jx8960vv45svHTwBA5mMAMQCkwp49e9SwYUO9/vrrTjP4tWrVShs2bNCPP/6oypUru91m4h+WnnxxDw8Pt53R7q2zZz3xzTff2JbHjh2r/Pnzu1W3c+fO6tKli7V84sQJLVq0yKv9cyUt2z+p8unxHtSqVUtvvvmm1q5dqxMnTigqKkpnzpzR1q1bNWbMGNvljk6ePOlU15sSB0yJ15cW165ds+4HBAToww8/VMGCBb3Wflby+++/Kzo62vbY+PHjU91e4hDTlVy5ctmWc+fOnantu3L06FFt2bLFul29ejVV7RQuXFjffvutOnfubHv8zz//tC0n3Fcl2cK+zORwOGzh9PXr113W8fNz72eBq1lrPeXN4DHh+9GsWTMNHDjQa217IiscT8qVK6dhw4Zp1apVCgkJ0fXr13X+/Hn9+++/+vTTT3XrrbdaZV0dTxYuXGibHaNr167q2LGjW/0oUKCAxo4da3tswoQJnr4cAAAAAACSRI7vjBw/a+X433//vS3zGjhwoMvBw/HKlSun4cOH2x5L/P6nBTn+f8jxPUOOH4ccP3NzfF+T3se3EiVK2P6WffH4CQDIPAwgBgAP/f3332revLm2b99ue7x8+fJasGCBVqxYoUaNGnncbo0aNWzLiS+pk5ITJ07YlmvWrOnx+tNq7969tuUmTZp4VL9p06a25T179qS5T55Iy/aXfOM9SCgkJMS6HxAQ4HSGeFolPrM0tWfSJ6VatWpWe9HR0WrVqpVWrFjhtfazin379umpp55yenzq1KmaPXt2qto8ePCgR+UTX/6rcOHCmdq+Kx988IFuvvlm6/bvv/+mui0/Pz+ngY379u2zhXiJ+3vkyJFUry+hxEH7oUOHPKp/4cIF29ngRYoU8Ua3JNlf8y233CJjTJpur7zyitf6Vq9ePev+mjVr1L59+0yZDaN69eq25RvpeCI59yfx8T/x8dyVxN8XMvr4DwAAAAC4MZHjJ40cP/Pfg4Rc5fhpzV3S8/0ix49Dju85cvw45PiZm+P7mhIlSqhAgQLWcmhoqO2kI1dcHd/8/Pxs/7sIDw/X5cuXvdY+ACBrYwAxAHggNDRUHTt2dPqxPHjwYO3YsUOdOnWSw+FIVduJg6/169e7XXfDhg225cz40n78+HHrfuHChV1e8iyxxGfWHzt2zCv9cle1atVs750n2//q1avW5XakuB95CS8NltFOnz6tXbt2WcuNGzdWYGCgV9eR+FI1xYsX91rbzZo104oVK1S0aFFJcT9i27dv73SJvBvZ9evX1adPH+vHe6FChWyXF3riiSdSNevzgQMHPCqfcL+WpDJlymRq+66UKFHCtpzWILBq1aq2GViio6NTDPQ8DQiTk/gyZJ62mzgAjv9b8oaEbZ0+fdpr7XrDuHHj9Oqrr1rLv//+u5o1a+ZxIJ5WhQoVsu2LW7du9WgWjYTHdIfD4TQgOSNFR0drzZo11nLp0qWdLvGX8PgveT7jfcmSJW3HzIw+/gMAAAAAbjzk+Mkjx89aOX5ac5f0fL/I8cnxU4scPw45fpzMyvF9jcPhsH3HOH36tA4fPux2fXe+YyT+DpO4TlrbBwBkXQwgBgAPDBgwwPZjPzAwUD/++KM+/fRTpxlZPdW6dWvb8qpVq9yu+8cff6TYVkZIeDbruXPndOHCBY/qJ/5hmNYzmD2VJ08e2yXbjx496vaP1fXr19suB+SN7X/t2jV9+eWX1m3+/Plu1128eLHtrNTu3bs7lXnkkUdUoUIF6+bpzAPbtm2zLSf+0ZlWzZo109q1a1W1alVJcYFP//79NXLkSI/OuM2qRowYoU2bNlnL48eP10cffWQtnzlzRo8++qjH2yLx++bK8uXLbcuuZrhI7/ZdSXy5yV9//TVN7TkcDsXGxlrL/v7+tuDtlltusZVPPCNISowxevLJJ9WvXz/169dPo0ePtp5r0KCBrWzCfyS4I/GMDYnbS4uGDRta948dO5aqADy9+Pn56a233tLEiROty/Dt2bNHTZo08eifSd6Q8Dhw/fp1rV271q16R44c0dGjR63lW2+91SuX1Pv222+t48m0adPcrvfXX38pPDzcWu7WrZvTZfISH689DcrPnz9vC/Qz+vgPAAAAALjxkOMnjxw/a+X4ac1d0vv9Iscnx08Ncvw45PiZn+P7mtR+x7hy5Yo2btxoLZcsWVLVqlXzWvvGGNt3mMDAQDVr1sytugCArIEBxADgpt9//93pR+zcuXPVq1cvr7RfvXp125f5P/74w62zgCMiIjRr1ixrOV++fGrVqpVX+uSJxD/4N2/e7FH9hCGLJFWsWDHNffJUly5dbMvunimfuFy3bt3S3JccOXJoxIgRGjhwoAYOHKjevXsrIiLCZT1jjD755BOX/alTp46OHDli3b7++muP+pc4CG3fvr1H9d1RpUoVrV27Vv/73/+sx0aNGqUHH3zQdvmpG83KlSv17rvvWsv33HOPevXqpQEDBujOO++0Hl+wYIEmTZrkUdvTpk1TTEyMW2WPHTumhQsXWsuFCxdWnTp1Uqwzd+5cXbx40a32L1++rO+++85a9vf312233eZW3eTccccd8vf3t5anTZumsLCwVLe3Y8cO299dzZo1be1XrlzZNkvLb7/95valtnbu3KkvvvhC33//vb7//ntbmN+4cWMrOJOkOXPm6MqVK273e+rUqbbltG7XhBL+PUry6DJ8xhj17dtXzZs3V/PmzdW1a9d0+UfCgAEDtHjxYmvWidOnT6tVq1aaM2eO19eVnMTHk8mTJ7tVLz2OJ5L01VdfWceT+++/3+1/rLlzPEl8/E98PHfl77//ti1nxvEfAAAAAHDjIMdPGTn+f7JCjp/W3CUj3i9y/Djk+O4jx49Djp/5Ob6vSe3/FWbNmqXIyEhruWvXrk4TgUhSp06dbMvfffedbfB9clavXm2bDblt27a2Wb8BAFkfA4gBwE0TJ060LT/xxBPq2LGjV9dx77332pbHjBnjss748eNtP4y7d++unDlzerVf7ujcubNteeTIkYqOjnarbkhIiObNm2ctOxwOpx8xGaFnz562y599+eWXtlkRk3L48GH9+OOP1nJQUJA6dOiQ5r44HA61adPGWo6MjNT06dNd1psxY4ZtMFaXLl1UpUoVp3KJZ5GcNGmS25cvO3jwoO3voVixYra+elORIkW0bNky9enTx3ps6tSpat++vcv3Jis6d+6c7r//fiuMKVGihMaPHy+HwyGHw6EJEyaoQIECVvmhQ4d6NOvEoUOH9Pnnn7ssZ4zR008/bQspH3roIVvolpSIiAiNHDnSrb68/vrrtvewW7duab5EV6FChWyD2S9fvqz+/funOqh+++23bct33323bdnhcNj2zevXr9tmIEjJDz/8YFtu3ry5dT9Pnjy2GUcuXrzo1Jfk/Pbbb1q6dKm1XKJECa/+fXbr1k25c+e2lt99913bDLUpWbhwoX744QetWbNGa9asUb58+VJ9uVBXbr/9dv31118qV66cpLhLVN59990aN25chsx+0qFDBwUFBVnLM2bMcPnPxAsXLujLL7+0lh0Oh3r27OmV/iTeBxJ/p0nKX3/9ZQtr69evn+S+1LFjR9v7+NNPP7k9i4kxxukzKXFICgAAAACAJ8jxU0aOHyer5PiJ368vvvhCoaGhbvUtJibGljXFryc9kOOT43uCHJ8cP15m5/i+pnHjxta2kOJOUlqzZk2KdaKiovTBBx/YHkv8PSVe6dKlbfvw4cOHXR6zjDFO33OSax8AkIUZAIBLsbGxpnDhwkaSdTt48KDX13PmzBmTL18+23p+/vnnZMtv2rTJ5MyZ0yrr7+9vdu/ener1jxgxwrbuZcuWuV03JCTEBAUF2eo/99xzJjo6OsV6J06cMA0aNLDV6969e6pfQ1r17dvX1pdevXol+xouX75sWrRoYSs/fPhwr/Vl2bJltrbz5ctn9uzZk2z5v//+27b/OBwOs3Xr1mTL33333bb2b7rpJnPp0qUU+xQSEmLq1q1rqzd27NhUv0Z3xcbGmuHDh9vWW7NmTXPo0KF0X7crLVu2tPVr5cqVqWonNjbW9OjRw+Xf/+TJk21lWrRokew++uCDD9rKSjI5c+Y0y5cvT7Efw4YNs9UJDAw0Bw4ccCqb+DMjfr+bOnVqiq/1q6++cqq3evVqF1vIPdu2bTN+fn62ttu1a2dCQ0PdbuPq1avmxRdftLVRsGBBc+LECaeyhw4dMv7+/rbX//3336fY/urVq211cuXKZc6dO+dUJuH6/f39zcyZM1Nsd+fOnaZEiRK2em+88UaSZQ8dOmQrN2LEiJQ3SgIDBw601b3zzjtdfnbs2LHDlCxZ0lZv3bp1bq8ztU6cOGEaNmxoW+/gwYNdHpu8IfHf0f/+9z9z5cqVJMtGRUWZ++67z1b+/vvv91pf9uzZ47Q//fHHH8mWP3jwoClTpoytzsKFC5Mtn/izq0aNGkn+vSQUHR1tXn/9dVu9oKAgc/z48VS/TgAAAABA9kaO7xo5ftbK8WNjY522e4sWLcyFCxdS7FNkZKR5+OGHbfVKly5tIiIi0vRaXSHH/w85fsrI8cnxE8rMHD8libd/3759032dEyZMsK2zevXq5syZM0mWjY2NNS+88IKtfPPmzU1sbGyy7Sc+ZhUtWjTF70qfffaZrXylSpXMtWvX0vw6AQC+hQHEAOCG8+fPO32ZXrx4capvq1atSnZdb731lm1dAQEBZsyYMSY8PNwqExERYcaPH+8U9D3xxBNpep1pCR6NMea9995zChPq1q1rFi1aZC5evGiVi4mJMUePHjUjR450eg158uRJ8YdK4pCnZcuWqX25Sdq3b5/JlSuXbR133XWX2blzp1UmNjbWrF692unHbIkSJVwGd4mDoPLlyydbNjY21rRq1cpp35s4caLtx9m5c+fM+++/79Tv5557zuVrzZ07t61OuXLlzOTJk22D3KKjo82+ffvMuHHjTPHixW3lb731VnP16lUXW9V7Jk+ebAICAqz1Fy9e3GzYsCHD1p8UbwWPiUOBhx56KMlysbGxplOnTrayyQ3iTri/JfwnRUBAgHnttddsQVpsbKxZu3atad++vdPf8dtvv51k+wk/MxK2L8kMHDjQKazcvn27U7gvyTz22GOp2mbJGTlypNM68uXLZwYPHmxWr16d5CDOyMhIs2vXLvPaa6+ZYsWKOdWfPHmy2+tzOBzmiSeeMPv27bOVu3TpkhkzZowpUKCAW6Ff//79ndodOHCg2bt3r63c6dOnzfvvv+/0j6saNWqYy5cvJ9l2WoLHM2fOmNKlSzuFWD/88IMJCwuzhVOnT5827777rilUqJCtfJ8+fdxeX1pFRESYbt262dbfuXNnl2FpWp07d85pX7r11lvN2rVrbeW2bNni9HeXO3fuJMP+hJIK/lOSeH8KCgoy77//vm0fuXz5spkwYYLTP1t79uyZYuh48OBBp+N53rx5zVtvvWWOHz9uq3vhwgUzb948U7t2baf+v/feeym+BgAAAAAAUkKO7x5y/KyV4//111+2QYySTLFixcz48ePNyZMnbbnLmTNnzJQpU0y5cuWc3uMZM2a42KreQ45Pju8Ocnxy/IQyK8dPiTcGEHua41+/ft3UrFnTVr5atWpm6dKltvds3759Tn+nfn5+Zs2aNSm2Hxsba9q1a2erFxwcbH766SfbgO2QkBDz9NNPO/V9+vTpHm8DAIDvYwBxKh0/ftysWbPGrFmzJlWzZMXExJi9e/ealStXmo0bNzqdreaOyMhIs337drNixQqzbdu2ZGc0A5B2u3fvdvqCnJZbSmFTdHS0U6ggyeTIkcNUr17d1KhRwylgkmSaNGmS5s+BtAaPsbGxTme1J7yVKlXK1K5d22nQasLXuHTp0hTXkd7BozHGzJgxI8n+lS5d2tSrV88UKVLE6bmcOXM6DQpLiifBozFxPwCLFi3qtL6AgABTo0YNU6lSJVsQF3/r1KmTW2fmJj4LPuGtTJkypnbt2k6BUvytUqVKJiQkxN3N6jUrVqwwBQsWtPqRO3duM2/evAzvRzxvBI+7d+82efLksdooW7ZsiiF2SEiILcjJmTOn2bFjh1O5hPvbPffcY7p27eoUJlSqVMnUr18/yf1akunSpYuJiopKsh8JPzNuvfVWM2TIEKf6ZcuWNTfddJMJDg5Osv1bb73V9o8Jb0jqrOvEfz/lypUz9erVM7Vr13YaLJn49sknn6S4vqioKNOhQ4dkP/duuukmp6Au/ta8efNkP7svXrxobr755hTbrVSpktNMDZJM4cKFU5yBPC3BozHG/PnnnyZv3rxJ9q1QoUKmXr16Jjg42DgcDqfnb775Zq+/565ER0ebZ555xtaPW265xaMZLVJj9erVJjAw0GkbFC9e3NSvXz/JvwuHw2HmzJnjsm1Pg8fTp0+bKlWqJLm+ypUrm2rVqiX5ed+gQYNkA+yEli1bZnLkyJHkPpE3b15Tt25dp5NQEt4GDBiQ4iBlAED6u3Tpkvnnn3/MihUrzM6dO83169czu0sAkCZk+ED2Q47vHnL8rJfjT5kyJdn3q2DBgqZu3bpOA/8S3pKb3TQ9keOT47tCjk+On1hm5fjJyYwBxMbEXVEw4edn4vcs8dUD428ff/yxW306efKkqVChglP9fPnymbp165oKFSokub8+88wzHr9+AID3pUeOn+0HEMdPue/ul61ffvnF6UxVKe4f64sWLXJZPyYmxnz44YdOZ376+/ubjh07unXJogsXLphBgwaZ/PnzOx3QBw4c6PKsWQCe+/3331P8UerpzVXYdPHiRaezLFO6tW7d2pw8eTLNrzOtwaMxcT/uxowZk+SAqZRuVapUcSu4y4jg0Rhjvv766yQD3qRu8TNZuMPT4NGYuEuaJfdjMKnbwIED3RrsFW/q1KlOM0i4urVv3z7ZS+ZkhH///df249bhcJhx48ZlSl/SGjxeu3bN6TJ0v/32m8t606ZNcwpzEl82KOH+1qtXL3Pp0qVkA7Kkbg8++GCKXzgTB49RUVFOZ9u72o8SzsribXPnznW63JYnt5o1a5o//vjDrXVdu3bNo9cuyXTo0MHlP6DDw8M9es/iP09dfadMa/BojDGbN282ZcuW9ahvXbp0ydTvqp999pkt+CpXrlySob03zZ8/P8V/4CS85cmTJ8VZMhJKTfB48ODBJGf+Te7Ws2dPc/bsWbdf67p160zlypU92icCAwPNmDFjMv1ydACQlXiaZbkSGhpq+vbt6/T7p2jRoubVV181kZGRXlkPAKQWGT4Ad5Hju48cP2W+mOMvWrQoxZOzk7rly5fPTJo0KdNO2ibHJ8d3Bzl+8sjx4/qTETl+UjJrALExcbPPu/u+BQQEmHfeecejz/pdu3Z59L+CIUOGkOEDgIeyUo6f7QcQN2nSxO03a+zYsS4PnO+//36y9aOjo10GCblz5za///57sm2EhYW5/Kd8pUqVTFhYWGo2B4Bk/PTTT6n+4ZrUzZ2wKTY21kyePNnUq1cv2XaqVq1qPvnkExMTE+OV1+mN4DHe3r17zaBBg5I8QzLhrXbt2mb8+PFuB2UZFTwaExdude/ePdkQNW/evGbAgAG2y0e5kprg0Zi4Sw09/fTTtrPbE978/PxM+/btzZIlS1L1Wg8fPmwee+wxp0snJV5HixYtzC+//OITs0SGhYWZW2+91dbHw4cPZ3g/0ho8vvjii7b6gwcPdqtebGys0/eKYcOG2cokDh7j63322WemYsWKyb7XTZs2dTmLiDHOwWO8GTNmmDp16iTbfr169cz06dMzZD+KiIgwkydPNi1atEhylo+k/q67du1q5s+fn6qz9VatWmVatWqV5NnZ8bebb77ZTJs2ze3XHxsba37++WfTtGnTJGcCiL9VqVLFfPzxx+bq1asu2/RG8GiMMVeuXDHvvvtukmerJ7y1atXKzJ8/3yc+OxYuXGg7aaJVq1bpvs7jx4+bBx54INmTNXLkyGHuvvtup8vapSS1wePly5fNiBEjUpyx47bbbjM//PBDqt6vy5cvm88//9xl+FiwYEEzePBgp0sEAgBc8yTLcmX37t1JzpSW8Na4cWMTERGR9o4DQCqR4QNwFzm+58jxk+arOf65c+fMO++84zKLCw4ONq+++mqmzdqZEDk+Ob47yPGTRo6fsTl+Ypk5gNiYuM/8IUOGJDtBiZ+fn7nzzjvNhg0bPO6XMXH7xWuvvZbiAP7mzZubX3/9NVXtA0B2l5VyfIcxxiibmjRpkgYMGCBJGjFihEaOHJls2RUrVuj222+XMUZFixbVxx9/rA4dOkiSfvnlFw0dOlRnz56VJP32229q27atUxsjR47UqFGjJEm33HKLPvjgAzVq1Ejnz5/Xt99+q1GjRik2NlaFCxfW7t27VaxYMVt9Y4zatGmjVatWSZLuu+8+vfbaa6pUqZIOHDigN998UzNmzJAktWjRQqtWrZLD4UjTNgLgG3bu3Knt27crNDRUMTExKlWqlGrWrKmbb77Z5//OY2JitG3bNm3fvl1nzpzRlStXVKhQIZUsWVJNmzZVyZIlM7uLLp07d05//vmnQkNDde7cORUrVkxly5ZVixYtlCdPngztS2RkpP766y/t3r1bFy5cUNGiRVWmTBndcsstXtmW165d09atW7V9+3adP39e165dU9GiRVWqVCk1b95chQoV8sKr8J7IyEjdf//9mj17tiTp0KFDqlChQuZ2Koswxmj9+vXau3evQkNDlTt3buvvsmzZsl5pf9u2bdqxY4dCQ0Pl7++vkiVLqkGDBqpWrZoXXoHnrl69qm3btmnPnj26cOGCLl68qMD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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# Efficiencies\n",
     "for year in years:\n",
     "    w, jet1txbbbins, jet1ptbins, jet1msdbins = effs_txbb[year].to_numpy()\n",
-    "\n",
-    "    fig, axs = plt.subplots(2, 2, figsize=(18 * 2, 14 * 2))\n",
-    "\n",
-    "    for k in range(len(jet1txbbbins) - 1):\n",
-    "        row = k // 2\n",
-    "        col = k % 2\n",
-    "        ax = axs[row][col]\n",
-    "\n",
-    "        mesh = ax.pcolormesh(jet1msdbins, jet1ptbins, w[k], cmap=\"turbo\", vmin=0, vmax=1)\n",
-    "        for i in range(len(jet1ptbins) - 1):\n",
-    "            for j in range(len(jet1msdbins) - 1):\n",
-    "                if not math.isnan(w[k][i, j]):\n",
-    "                    ax.text(\n",
-    "                        (jet1msdbins[j] + jet1msdbins[j + 1]) / 2,\n",
-    "                        (jet1ptbins[i] + jet1ptbins[i + 1]) / 2,\n",
-    "                        w[k][i, j].round(2),\n",
-    "                        color=\"black\" if 0.1 < w[k][i, j] < 0.9 else \"white\",\n",
-    "                        ha=\"center\",\n",
-    "                        va=\"center\",\n",
-    "                        fontsize=13,\n",
-    "                    )\n",
-    "\n",
-    "        ax.set_title(f\"{year}: {jet1txbbbins[k]:.2f} < Txbb Score < {jet1txbbbins[k + 1]:.2f}\")\n",
-    "        ax.set_xlabel(\"Jet 1 MassSD (GeV)\")\n",
-    "        ax.set_ylabel(\"Jet 1 $p_T$ (GeV)\")\n",
-    "        plt.colorbar(mesh, ax=ax)\n",
-    "\n",
-    "    plt.savefig(\n",
-    "        f\"{plot_dir}/{year}_txbb_effs.pdf\",\n",
-    "        bbox_inches=\"tight\",\n",
-    "    )\n",
-    "    # plt.close()"
+    "    plot_all_wrapper(jet1txbbbins, jet1msdbins, jet1ptbins, w, 1, year, f\"{year}_txbb_effs\")\n",
+    "    # break\n",
+    "    plt.close()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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KhtLViM+5SJzbyDkwth+Evtn9fZO3MVV7P0Vs+pnQhB5Sj5A4TJ37GRJHznZ73uHmdNrQWP8b4hJPhVTa+74nPvkMGDurYeg4OOCYxrpfUV/zM7Jz/zB4ifiJK7eNiEucIc4t6XQYO2tg0JcOOKax7jfU165H9ijvLtU+nJxOO5rb9yAmciykkkN6SVHjYbI0odNU63OMydyIMG2GqCejUOgQoolHp6l+ELPyjdNhQ0P1OsSnniX6PSemnw+jvgKGtgM+xxgNVVCqIoOm9wTwWDmc02lHU+tOxEZPEPddYybDZG5Ap7Ha5xilMgIx0RNEsWqVa2Elp8M6GOkMiNNpQ0PNesSnnine79POhdFQCUN7sc8xRkN117ESAkEIkmPFYUX9gZVIGD1PfP4w5nIYW4qhb3BfJdjbmMjUaW4/DQe+cZ2Pnvq3wU/OB06bFe0bv0LEjEtEveuo066ApeYATGU7PcZJVSEIHX82Ei5/EJrsCR4fY67eD13e9J7JwwCgCI+FJi0P5grPPfFAw16tGOvRi7UIRuzjBzJOIKZBER0bAaVKgYNFFaLtpftdtxOSY/0SE4iiYsJdeeyvEm0vPeB6U5OQFDOgmI8Wr8SSt1bi17UFfh754GA9xKJ7cqsUbS894MrV47HRj5glb63Ah28ux68/bfP30P2O+4YY6yHGevRiLcRYj142hxkCnNAowkXbNcoIAIDVbhhQTGJ4PhLDxyBcE3grAXlisxkgCA6EaOJF27tvWyxtPsfkpM9DRsoFosfYHWYYOquhVAbe5SMBwGpuhuC0QRueLdquDXd9Kc/U6f6BlrcxRkMlpFIltq25DWs+mYa1S0/F9p/u7lmZONA4WpoAmxXKzJGi7cqMEQAAe737BxYAII9LQsSVtyLiyluh9LD6MAAkPPkGwmdfDUEQ4GhthqVoJ1re/j/I45OhzvPcuB1uTVYBVgEYqRW3A0aEuG7XWPr+oGF9iwMfVjvwcp4Sag/dhL+PVOKvWeKJxQa7gN0GJxJUwdFg0cXHQ65SoWGPeMJIw15XUz081fMqNlFZWQhPScGd27bhCaMJfy0rx8wXX4RSpxv0MRMRERF5y9LZAMFhhS46V7RdF+M63zV3uJ/T+xIDAE2la1C98yOMm/WWaOJLoLAYGyE4rdBFi98n6KJct8169/cJ3sbom4ugCU3F3rUPY+074/HD6znY+L/ZaK/fMRip+IXV1OTKLfKw3CJd75vMevcv5XobY2gpgiY0BXvXP4af35+CNW+PxqYvL0V7g+cJMcPNammD4LRDG5om2q7VuW6bzY0DirGYW7Bv5yvIzv0DtLrAWyXzcFZLGwTB7jbW7ttmUx/18DLGYmnFvt2vInvUddDqAnN17kPZ7F29JPVhvSR1HADAYm33OSY7bTYyks8TPcbusKDTWANVAPafrJYWCE4bdGHiBaC0YZkAALPRfdKztzEmQzUkMiW2/XwPfvziDKxddg4KfrkfRoPnHk4g4LEiZrXpIQgOaDUJou3dty0W98UvvI3JG/kHJMVPhyAIsNoM0BsqUVb1DVTKSITq3BcYGW5Wc/d+nynarg3LANDHseJljKmzBhKpAtvW34sfvzwHa5dfgIIND8BocP+7HSh6zi1jDju3jPbifLQfMQDQdHANqnctwbiL3w7I81EAsLc3QLBboUnNE21Xp7hWFLY1es5NGZOM+Dn3IH7OPdCk5Xl8TOotLyHp6idE2xwmPYwHC6CITvLD6Acfe7VirEcv1oLIvwLzryQFvehY12W/O9rFE1faW12LskfFRvglJhBFd41T31ceMeGHh/gUEyxYD7HouKPt5x7q4UNMMOC+IcZ6iLEevVgLMdajl81hAgDIZSrRdrlU1XW/2S8xwcJqc/0+5XKNaLtc7rrMoacJ1d7G6LTJh8UZsOfAfyDAicS4aX4Yvf9ZTM0AAIVK/AGTQuXa363mFp9jTPoqmI11iEs7E6mjroTFWI/S3e9g83d/wMkXfwGFMtS/yQyQvaUBACANFR/r0nDXOZajxf0DnP5yGjpQdrHrG+0STQiSX/0SEoVywM87GBq7FmIJP6wbENm1wEWT1fME4jabgPv2WXF3hhxjQ6X4ocnh9pgxoeJZxU1WATfussAhAFcnBUf7QZfg+lDK1Nom2m5sdh0fuvj4w0MgkUoRmZmJsORkrHnySTTu3YvkyZNx6l/+gpicEXh/1kWDPm4iIiIib1g7XefGCnWEaLtC7Vq1z2Js8EuM02HDvjUPIfPEu6AODcwvpVqNrvcBClWEaLtC43qfYDE2+Rxj7axH5a73ERY3DqNPfQqQSFFe8BY2fTYX069ei5Bw8STTQGDpyU38vkmhduVmNbnXw9sYS2cjKgs/QFjsOOTOeByQSFGx8x1sXjYfJ1/xA0LCAmsSrbVrgppCIZ5E0P1e1+rhC8rexgiCgH07X4YuPBspGRehrcV91e9A0z32PnPzMGHW2xhBELBv1yvQhWUjJf1CtLW4r/wdaKw2V69ILleLtsvlIQBck4V9jdGFJLnF7T34AQTBiYSYE/0wev+ymF3vk+WH9YEUXZOdPfaevIwxdVbDYqxHXPJpSBsxH2ZTA0r3vo8tP96Caed/HHC9J4DHyuFstg4Avft5N7nCdbu7LzuQGIfDhN+3uq5yIJUqMT7/7oCcJGrp2q/lisP2+yP1ab2MMRmqYTE1uI6VnEthNjWidO9/sWXtHzHt3A8D81jp69xS03Vu2elhsr0PMa7z0QeReeLdUIcF5vkoANhaXZPBZboI0XZ5qCs3W5v7uba3QrLGi/+ttgYcfOZyCA47Ys6/0efnHUrs1YqxHr1YCyL/CrwzKDomCPD8obNE4lp1Si533/V8iQlIfSzYJZG6PkyXKzwsg+9LTLBgPUT6unTMEY8NH2KCAvcNMdZDjPXoxVqIsR5H1X1OJcA5qDEBp6/FTbv+jgqC+2TH/sYIggM1Db+hovp7OAU7RmVdBV1IgDYf+1zstSs3p939Li9j0kdfjayxNyEyflLPIyLiJ+L3ry9D9YHPkZG/0KchDxpJH4k5u/JyeKhFP0k1WiS98hnsTfXoWPZf1Nx5KZJe/QKq7NEDfm5/6+vX3H302/p4zXxkvw2JKgn+mHb0c0+7U8AHNQ68WGqD1Qm8nKdAfqiHJYsDUV/n3l1/M2QKhdt9Urkcn13/B9Tu2IGmoiIAwP7vVkFfV4u5b7yJlClTULV58+CNmYiIiMhrns8GBcF1NujxfYIPMZUF/4HdakDGlNt8G+aQ6Cuv7vc/Np9jbJYOqHUJmDz3E8i6vrAak3Ya1r1/Esq2vY68M54Z4NgHw5F/z04f9o3uGLu1AyptPCbN+hCyrgmUMamnYP2SU1Fe8JZrknVA6eNdk3CE99NextRUrEJ7615MPfU1SCRB8h7paL2T/vQXDq9H5Xdob92HqTMWBU89+nKk/lM/YwTBgdrGjaioXQOnYMfIzMvcJhcHAknfv2gAgFNw3ze8jUkbeSUy8/6AyNjeqztFxk7A799djeqDy5GRe7XP4x40PFYOc5TXRY/HSv9iZDIVxuXdCau1A7X1v2DHnlcwPv9uaAPseOlzv+/+e+mhFt7GpI24HJmjr0Nk7Pieh0TGnIDfVy9EdekKZIxa4PvAB83Rzi37cw7Wd0zl9ndd56NTb/dtmENE0kfvuic3h6d69I/gsKNp1WLUfvI0BJsFGX96FyGZJwz4eYcEe7VirEcv1oLIr4LlDJOCTHOD69vW4RHib7WFR7puN9Y3+yUmEDU3tgEAwiLE3xYNi3CtZNdU735JFl9iggXrIdZXbuFdtxvr3b9l6ktMMOC+IcZ6iLEevVgLMdajl1Lm+hDS7rCIttud1q77Q/wSEyyUXasx2O1G0fbu20qF+6Ue+xNjNDVgx95XcbBiOcJDszAx/17ERgVuk02ljgEA2Czi1U26b6tCYn2OiUmeIZo8DAC68CyEhKVB33rAD6P3L3mUa9zOjjbR9u7b8mj3b6L3l0Quh2b8SQg9ezYSn/8QkCvQ8eV/B/y8gyFW6WpKtx3We27rmjkcr3RvWn/b6MB3TQ78faQSFifQaRdgc7p6dJ12AWZHb7OuxOjEJdssePyADSdGSPHdVBUuigueL7zp6+oAACFRkaLtIVGuVT86atwvAemwWrFr6dKepmO3vStWAAASxo4djKHSISROWVD+EBERDTWVLg4AYDOL3wd331bpEgYc43TaUbZpEVInXA+ZInDfY3a/v+kzL637+wRvY5SaaESnndYzeRgA5KpQRCROhr5pj58y8K+e3A5bXdfedVutjfM5RqmJQkzqKT2ThwHX6qMR8ZOgb97rh9H7l1Llei9gs4pXurR1rXypUkf7FGM2NeDA3sXIHn095Aot7HYTnA5XP8bhsMBuN/k3ET9RKrtys4lX1u3NLcqnGLOpEQf2vYvs3IW99XB218MauPXo6SWJx2d3eNN/OnqM0dyIHUVv4mDV1wjTZWDC6LsQGznOfwn4kbLrWLBZO0Tbu2+rNDE+x8QkThNNHgYAbVgGQkJTYWgr9sPo/Y/Hilj3fm07rO/afVupdL8qYH9jJBIZIsJyEBczEWNyb4VUIkNN3S/+ScCPlF2/++4Vlrv17Pee/q54GROTeJJo8jDQdazoUmBoLxn44AeBquv8wGbqx/loP2OcTjvKNgb++SgAyCNc5492vTg3h6Hr6gZRiQN6fnP1fux/8CxUvX0fdHnTkfviRkROv2RAzzmU2KsVYz16sRbBabj78ezj940TiGlQtDS1w2a1ITs3XbS9+3Z9jfvlrnyJCUQtze2wWe3IHiW+7Fb37foa94nQvsQEC9ZDrGc/HyW+TF12ruv2EY+NfsQEA+4bYqyHGOvRi7UQYz16yWVqSCCFydYm2m6yuhpLSrnWLzHBQiHXQSKRodNUL9reaXI1EVTKCJ9jTOYm7Nz3OkzmZuSPuAF5IxZCo3b/ECSQKNVRkEjlMLSJG8XdH7KoQ9w/DPcmxm7rRGPVOlg8XL5WIpFBrgy8fUgWGQPIFbCWiptCltJ9AAB5vG+rkjgMHai49iwY1n0r2i5VayCPT4bT0NFH5PCKVgIKCVDUKV5xvKjTNQk4Se0+gXifwQmLE5i5xYK89WbkrTfj1Qo7TE4gb70Zfyx0fXBVbnLism0WlJkEvDdOicVjVUjXBFfbobOhAXarFfH5Y0Tbu2+3V1W5xUSkp2PUhTMhPezKIE6HazUYc0dg7gtERER0/FGGxEIiVUDfJJ60aei6rQ51v8JKf2MaDqyExVCP5DFX+XPofqcMiXHl1SJ+n2Bodt32XAvvYlTaeDjtZrd4wWmHXKlz2x4IlJpoSKQKGFr2i7bru3PTub9v8jZGFRIPh93iFu902iBTBN57SKUqHBKJHJ36ctH27tseJ0V6EWMy1sHpsKBo1ytY992lWPfdpdix+TEAwPbf/4oNPwTgiqo4Qm6GCgC9X0bub0xPPXa/inWrr8C61Vdgx5YnAQDbNz6EDT8uHIRsBk4h10IikcHo1kty3VZ5mBTpbYzJ0oxd+9+C2dKMvOzrkJd9DTQq94mFgUKpioREKkdn+0HRdkPXbbXGQ+/Jixi7rRONNb/AYvLQs5VIIQ/A1w2Ax8rhFApd135fK9puNLpuq5SRPsXY7SZs2fEMmlp2iB4jkymhUkXC7gi8CdVKdaTr99xeKtpu6LrtuU979Bi7zYjGmg2wmD0dKzLIA7TXr9T2cW7Z2HVuGZYy4JiG/SthMdQheWwgrsAsJg+PhUSugLlC/CUzU3khAEAZ414Pb1lqD+LAw+fCUnsQWX/7AlkPfgpVYtaAxjvU2KsVYz16sRZE/hVcn+RR0LDb7Pjp299wzsWnQCbvnY1//tzTUVlag6Jd7t948yUmENltDqz9bjPOvmiaKI/zZk9HZVkdigrL/BITLFgPMbvNjrWrNuGci6eLc5tzCirLalG0u9QvMcGA+4YY6yHGevRiLcRYj15SiQyR2jQ0G8rgFHonAjYbSqGShyJE6b6qhS8xwUIqlSM6YgwaWwrgdPZe9q2xZTvUqmiPl3r0Nqa06hs4BTsm5N+DqIjcwU/GD6QyBeJSz0R9+fdwHnIJt7qyVdDoUhEa5Z6HNzFSqRK7f/0bSna8IYrtbC9DZ3spwmMCb1UciUIJ7annw7BmBQR7b16G1csgT06HcsSYI0T3TaoNhaOxFsbffhRttzfVw1Z+AMqc0QMa92BRSiU4L1aGrxscsDl7Vw5eXu9AukaCfJ37BOLLEmX4bIJS9DM/QQaVFPhsghIPZrsuB/ZMiQ1WAVg5RYUzooPzW9kOmw17li3D2MvmixqJ4668As3Fxajdvt0tRhsTg2uXL8eYS8Srd+RdfDGcDgeqt2wZ9HETEREReUMqUyJ+xEzU7fsSzkMuh1y753NoIjIRFu9+Pt/fmKqC9xCVcRrUoQNbLW2wSWVKxGefj7r9K8R5FX0JTXgGwmLd3yd4GxOXdR4ay9bAeshKxZbOerTWbER4/AS35w0EUpkScZnnoq74a1FudcUroAlLR2hMvs8xcZnnoKn8R9jMbT2PsXQ2oK12M8Ljxw9aTr6SShWITZiG+tr1cDrtPdvratZCE5KI0LBsn2JCw7Ixcdpzop8RebcAAHLH3oXxU58a/OR8IJUqEBt/Iurrfjkst3XQhCQgNMx9IpI3MaFhWZh44jOinxGjbwIA5I65HeOnPD7ouflCKpUjOjwPja274BQO6SW17oBaFQWtxv21z9uYsupVcAoOjM+9A1HhowY/mQGSyhSISz4N9ZU/iH/PFd9Do0tGaORIn2KkUiUKNz6Jg4WLRbGdHeUwdpQjPNq3Ps5g47EiJpXKERM5Do3N20R914bmrVCrYqDTun9Rx5sYmUwNi7UdLW3iiaQWazuMpnpoPfSAh5tUqkBc8qmor1oj/j1XroZGm4zQiBE+xUhlChRu/jsOFr4riu3UV8CoL0d4tPvf7kAglSkRP/Ii1O394rBzy/8d+Xy0HzFVBf9BVMbpAX8+CgBShRLhJ16M1g2fiXrXreuXQpmQBU2m71dBrPngEQg2K3Jf2IDwSef5Y7hDjr1aMdajF2tB5F/Bcx1RCjpL3lyGt754Fs8tfgjfr1iH8VPzceGlZ+LJP70EQRAQFqFD/oRRqCytQVVZrVcxweKjt1fijU8fxbOv34Mfvv4NJ0zJxYXzTsFT97/pyj1ci7zxOagqq0NVeb1XMcGM9RBbsngF3vzfU/jnm/dj9VcbMH7KaFx4yel48s//7j02xo9AZVktqsrqvIoJVtw3xFgPMdajF2shxnr0SgjLw966VShuWIsobQYM5gY0dx5EZszJkEgksDssMFiaoFaEQt11CbijxQSz5IRTsXPf69hb8l/ERo1Hh74UDc3bMCJjPiQSKWx2I/SdFdCoYnpWED5ajCA40dK2BxFhI2A018NoFq8Wo1KEQxsSmI3I9NFXY8v3N2LnuvsRn34e2hq3o7Z0JfJOesxVD0s72pt3IyQ0FSGhaV7FSGRSZOT/AcXbF0EilSEqYSosxnqU7XkfYdF5SMy4YJiz9izisptQc9d81D1yC3RnzoJ552YYvv8CsQ88D4lUCkdHKyx7C6BIzoAiJdOr55RIJAibtxBtH7wCiVIFzfiT4GhvRfvSxZCFRyFs9jWDnJXvbkyR4/ICC/5YaMVFcTJsbnPiy3oHnstVQCqRoM0moKDDiQyNBBkhUiSrpUhWi5/jl1YnZBJgSoRrorBDEPBDkxPTI6Uo7hRQ3OkQPT5BJUGuLji+w7zh5Zdw45ofcdWnS7Hzf0uRMX06Jiy4Gl/cdCMEQYAmMhIpU6aiuaQYLSUlqN66FftXrcLs115HRHoGmg/sR9KEiZh+zz3Y8NJLaC0rG+6UiIiIiHqkT7kNmz+ehYLlC5GQOxdtVb+jds9S5J+/CBKJFFZTK9prtyIkMgvayCyvYrrZLR1oqfwVOac8NFzp9Uv6hFuw+fNLUPDNTUgYeTHaajajtuhz5J/1gqsW5la01xUgJCID2ohMr2IAIGXMAlTseBebPpuLjAk3A4KA0m2vQ64MR8aEW4Yz5SNKP+FGbFl+OXZ8/0ckZM9Ca91m1O7/EvmnP+d6D2luQ3tDAULCMxASnuFVDAAkj74CFbvex6Zl85F+wo2AIKCs4E3IVWHIOOHGYcy4b6mZc7D99wewe9s/EJd4KtpbClFf/RNyx93tqoVVj462Imi0SQjRJnkVI1doERElnswldE0m1YVlIczDBLJAkZo5G9s3Pozd2/+JuMQZaG/di/qatcgdc2dvPdoPQBOSiBBtolcxrnrkif6d3npkIiw8cOuRHD8DO/cvxr6DHyEmciw6DOVobClATtq8nv6TobMKalU0NOpor2IEwYmW9n2ICM2GydwIk7lR9G8qlWHQahKGI90jSht5Jbb+dBt2/voQElLPQlvTTtSVr8LoyQ/19p5a9iBEl4KQ0FSvYiQyKTJyr0HxrtcgkcgQFT8FZmM9youWIDQyFwlp5w5z1n3jsSKWnHg6dux5BXsPvIvY6Alo1x9EQ9MWjMy6sutY6YTeUAGNOgYadaxXMQCQlHAKKqtXQyqRIzwsBza7AdW1P0Mh1yIpfvpwptyntBGXYevPd2Hnb48gIfVM135f8T1GT3qga9/oQHvLXoTokhGiS/EqRiKRImPU1Sje/YarTxs3CWZjA8r3f4zQyFFISDtnmLPuW/rU27F5yUwUfHktEkbPc51bFi5F/gWv9H0+epSYbnZLB1oqNiDn1IeHK71+i7v4Thz423kofW4BImZcgs69v6H150+QdvtrkEilsOtbYDywBarEbKgS3b/I5IngcKB980qEjjsD5qp9MFftE92viE6CJj0wv5BxOPZqxViPXqwFkf8Ex6d3FJQKtxfhrqsehS5ciwf/eQfGTByFJ+55ESs+WQ0AGDE6E6989CQuvOQMr2OCRWFBCe6+7lmEhmnx16dvQP74bDx53xv4aulaAEDO6DQs+u8DuGDuDK9jghnrIVa4/QDuuuYphIZr8cAztyB/wgg8ee+/8dWnrtXrckanY9GHj+CCead5HROsuG+IsR5irEcv1kKM9eilU8diVPw5cDitKGv6HQZLIzJjTkZsqKtxbLS2Yn/9D2gyHPQ6JpiF6dIwZuSNsDvMKC77HPrOCozIuAwJsVMBAJ3GWhTufwcNzdu8jrHZjRAEB1rb96Fw/ztuP1V1Pw9Lrt4IjxmLCWe+CptVj72bnkZ7027kTXscyTlzAAD61gPY/uMdqD34jdcxAJCRtxCjJv8FLXWbsHvDw6gs+hTx6edi0tlvQiINzFVn1fkTkfj8B3Dq29H4/IMw79mO2AdeQNjMKwAA1pK9qL3vGui//6Jfzxu18B5E3fRXmLasR8Pf70bbR69BPXYKkt9cAVmo+2VLA8WEcCneH6dEhw14uMiGgg4n/i9XgcsTXd8x3mtw4rqdVnxZ7zjKM/VqswE2AVjb4oo9/Gdxpf3oTxIgqjZtwvszZ0IdEYHZr76GlKlT8fmNN2Dre+8BABLGjsPClSsx/qreyyB+suAqbH3vPzjx1ltx6XvvY+QF5+Ob+/6MVQ/8dZiyOL5IBFlQ/hAREQ2HiKTJmDT/f7Cb27Hn+3vRXrsVYy54BSnjrgYAGBoLse2zy1BbuNTrmG5NZWshOG2ISj15SHPyVUTCREyavQR2Swf2/PgA2uu2YczZ/0JK/pUAAEPTXmxbsQC1+z73OgYAVCGxOPGyr6GNyMSB3/6J4o3PIzz+BEy74lvIVaFDnqe3IuInYOLM9125rXsY7fUFyD/jOSSPvgwAoG/ei20rF6Jm/5dexwCuekyd9yVCwjNQvPH/ULL5XwiLG4eTLl0BuTIw6xEemYsTpj4Ju60TRbv/jY62IuSOuwdJqa6JiwZ9KXZsfgx11T95HRPMwiNG4YTJj3Xl9jo62vYjd+ydSEo9GwBg0Jdhx5YnUFez1uuYYBaqTUV+zkLYHSaUVCyHvrMSI9IvQULMZABAp6kOhSXvoaFlu9cx9u7+U8d+FJa85/ZTXf/LsOR6NOHR+Rh/6ouw2/TYu/U5tLcUIm/Kw0jOmgUA0LeXoGD9vagt/87rGABIz70aIyfci5aGLdi98TFUFX+O+NSzMen0VwK29wTwWDlcWGgGxubeCrvdhAOlS6E3lGNk1pVIiDsJANDZWYPd+95AfeMWr2MAID35PGSkzkRrexGKij9EZc0ahIVmYvyYeyGXhwx5nt4Ij87H+BnPu/b7bc+jvWUP8iY/gOTMmQAAfVsJCn65D7Xl33sdAwDpo67CyPF3o6VhK3ZvfBJVJV8gPuVMTDr1JUgkgXusRCRNxqTLPoPd0o49392L9potGHPhv5FygmtBBkPDbmxbeilqd3/qdUy3ptKfus5HA3MyuSfakVOQ/egyODrbUfnG3ejcvxlpd7yO6LOvAwCYynej5Km5aPn5E6+f025ogWC3oWPb9yh5aq7bT8PyRYOVjt+xVyvGevRiLYLPcPfj2cfvm0QI5iXXyGcpKSmorq6GQqrGuLgzh3s4AUGqDBvuIVCAEhyW4R5CQJHIVMM9BCKioMbXUTFVTHB8y30oqEcF/wcD/nTwltjhHkJA+fHR4J/k7y9vrrcO9xACxit2B/QAkpOTUVVVNdzDGVLdfQ1pfBwS1nx/9IAAVHfWuXDWNxyXvz8iIvJO9987lS4Rp99WONzDCQxm83CPIKAINp4bd7NX8BgRcQbPlzgHm7We+8ah1DlnHP1BxxGnvmG4hxAw7K1lwz2EgKJMmTjcQwgospyxwz2EgNFwomK4hxBQLpjPeTZER8M+Pvv4gY4rEBMRERERERERERERERERERERERERER1HOIGYiIiIiIiIiIiIiIiIiIiIiIiIiIjoOCIf7gEQERERERERERH5RADgDNLvxwvDPQAiIiIiIiIiIiIiokHGPn5AC9LfDBEREREREREREREREREREREREREREfmCE4iJiIiIiIiIiIiIiIiIiIiIiIiIiIiOI/LhHgAREREREREREZFvJJA4ZcM9CB9JhnsARERERERERERERESDjH38QMYViImIiIiIiIiIiIiIiIiIiIiIiIiIiI4jnEBMRERERERERERERERERERERERERER0HOEEYiIiIiIiIiIiIiIiIiIiIiIiIiIiouOIfLgHQERERERERERE5AsJAIlDNtzD8IlkuAdARERERERERERERDTI2McPbFyBmIiIiIiIiIiIiIiIiIiIiIiIiIiI6DjCCcRERERERERERERERERERERERERERETHEU4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOo7Ih3sAREREREREREREvpI6+f14IiIiIiIiIiIiIqJAxT5+4OIE4uOczWlBYeM6j/fFhqQhTpsxtAMiIiIiIiIioh5bnE5sdQoe7+sc4rFQ/+mXfgj9Z0s83udobhri0RARUbCyGhvxyzsnebwvbcKNSJt44xCPiIiIiIiIiIi6sY8f3I73Pj4nEB/nFFIV8mNPHe5hEBEREREREZEHk6VSTO7ji/mv2B3QD+1wqJ9CL7saoZdd7fG+6pmnwNFQP8QjIiKiYKQMicWMG34f7mEQERERERERkQfs4we3472Pz7WhiYiIiIiIiIiIiIiIiIiIiIiIiIiIjiNcgZiIiIiIiIiIiIKTIIHEKRvuUfhGkAz3CIiIiIiIiIiIiIiIBhf7+AGNKxATEREREREREREREREREREREREREREdRziBmIiIiIiIiIiIiIiIiIiIiIiIiIiI6DjCCcRERERERERERERERERERERERERERETHEflwD4CIiIiIiIiIiMhXEie/H09EREREREREREREFKjYxw9c/M0QEREREREREREREREREREREREREREdRziBmIiIiIiIiIiIiIiIiIiIiIiIiIiI6DgiH+4BEBERERERERER+UrqlA33EIiIiIiIiIiIiIiIqA/s4wcurkBMRERERERERERERERERERERERERER0HOEEYiIiIiIiIiIiIiIiIiIiIiIiIiIiouOIfLgHQESBSSJTDfcQAgZrISY4LMM9hIDC/aMXa0FHwv2jl1ybONxDCCjq0ecP9xACRtGdPE4OdfW09OEeQkB5c7gHQERERETDRxAgtLUO9ygCQvuZqcM9hICiaVcM9xAChio0bLiHEFCExrrhHkLAkMVmDfcQAoqgbxruIQQUqZqvHd0UcaOHewgBRRoRP9xDCCjC4/8e7iEEjAt0jw33EIiIiPyKE4iJiIiIiIiIiChoSZy8wBYRERERERERERERUaBiHz9w8TdDRERERERERERERERERERERERERER0HOEEYiIiIiIiIiIiIiIiIiIiIiIiIiIiouMIJxATEREREREREREREREREREREREREREdR+TDPQAiIiIiIiIiIiJfSAQJpA7ZcA/DJxJBMtxDICIiIiIiIiIiIiIaVOzjBzauQExERERERERERERERERERERERERERHQc4QRiIiIiIiIiIiIiIiIiIiIiIiIiIiKi4wgnEBMRERERERERERERERERERERERERER1H5MM9ACIiIiIiIiIiIl9Jnfx+PBERERERERERERFRoGIfP3DxN0NERERERERERERERERERERERERERHQc4QRiIiIiIiIiIiIiIiIiIiIiIiIiIiKi4wgnEBMRERERERERERERERERERERERERER1H5MM9ABpeNqcFhY3rPN4XG5KGOG3G0A6IiIiIiIiIiHpscTqx1Sl4vK9ziMcSqCROfj+eiIiObVZjIzYsPc/jfan5VyMt/5ohHhERERERERERdWMf/+jYxw9cnEB8nFNIVciPPXW4h0FEREREREREHkyWSjG5j77aK3YH9EM7HCIiIhoGypBYTL/su+EeBhERERERERF5wD4+BTNO7SYiIiIiIiIiIiIiIiIiIiIiIiIiIjqOcAIxERERERERERERERERERERERERERHRcUQ+3AMgIiIiIiIiIiLyldQhG+4hEBERERERERERERFRH9jHD1xcgZiIiIiIiIiIiIiIiIiIiIiIiIiIiOg4wgnERERERERERERERERERERERERERERExxH5cA+AiIiIiIiIiIjIJ4IEUmeQfj9ekAz3CIiIiIiIiIiIiIiIBhf7+AEtSH8zRERERERERERERERERERERERERERE5AtOICYiIiIiIiIiIiIiIiIiIiIiIiIiIjqOcAIxERERERERERERERERERERERERERHRcUQ+3AMgIiIiIiIKdHab0eN2mVwNiYTfyzxeSKRSKDQat+2CIMBm9LyPHMtYj16sxfCRAJA6gvN1WDLcAyAiIiIiGmZ2W6fH7TK55rjst7D/REREvmBvUoz1EGM9erEWw4d9/MDGCcQ0qCZOG4Ob/7wAuWOzUVFag8/eW4kVn6z2e0wgmnDiaNz0p0uQm5+JyrI6fPbBany1dK3fYsIjdPh2y+u4YMptaG/V+z8BP+sw1aG6bQeMlmaoFGGIDxuF2NARfouxOcwoqFiK8WmXQSFTD0YKfsV69OKxIsZ9Q6zDVIOqlq0wWpqgVoQjLjwPcWG5fouxOczYXvoBJmReE/D1YC3EOozVqGraiE5LI9SKCMRHjkVceN6AYzqM1ahs+h1GSzOkEhlCNUlIi50GtTJiELMZmHb9QVTUroHBWAONOgYJMSciIWbygGPaDaUor1kNo6keEokcYbp0ZCSdB406ejDTGbCWus04uON16Fv2QhOWhtSRVyB5xNwBxdhtnfjp45M8xp5yyWqotQl+zcGfzFs3oX3xK7DuK4QiLQO6S6+C7uJLvYo1rvsR7a++gMRPVx7539i+GQ23XI349z6DKm+sP4Y9ZDJOORVnPfookiZMQHPxAWx84w1sfe+9Ph8/8vwLcO3y5W7bm4uL8a/RR35NDgasRy/WgoiIiI5lLTUbUbL1ZeibChESnoHUvKuRnDt/wDFOpw3Fm15AY+VamA01CIsZg5En/hXhcScMYjYD07FnHao+ewrG0u1QJ+Yg7pxbEXfGQq9iW7d+jcqP/4Zxzxf0+Ribvhnbb03FhNcroAiL8c+gh1Db/p9R9tXj0FdsgyZuBJJPvw2J06/3KrZpx1c4uOxBTH1s9yCPcnC0VGxA8YZ/Ql+/AyGR2UidcD1Sxl09oBi71XCEaAnkSq2fRu9/LXWbUbL9Veib9yAkLB2puVcgeeQlA4qx2zrx44dTPMaeetmPAd1vYf+pV2tTAQ7ufR8dbQcQoktGSuZsJGdc6NeY1qad2Lr+Hkw9/XWERY7ydwp+xXqItTbtxMGiD6BvP4AQbTJSMmchKe18v8a0Nu/Ctg33YcqpryAsYqS/U/CbltpNKNn2Su+51OirkDzqyH1ab2KcThuKN7+Ixqqfe8+/ptwX0OdffWlZV4qSv/8IfUENQnKikXrziUheOKnPxwtOJ6rf34aK136HsaQZqoRQxF2Ui+y/nQl5WOB/znUk7E2KsR5irEcv1oLIPziBmAZN/oRRWLTkSWxaX4BnH3wN4yaPxqMv3gMAfU4I9iUmEOWPz8bL/30Am3/ZhX8+8i7GTRqJR/7vFgDoc5Jjf2JkMilu/vN8yBXBcQgbzI0oqv8B4epEpMecBIO5AaVNvwJAn5Mc+xMjCE5UtxZAgDC4ifgJ69GLx4oY9w0xg7kB+2q+RbgmGRmxM6A316O0YR0A9DkJtj8xguBEVcuWoKgHayGmN9Vhb9VyhIekIjP+dOhNtThYtwYA+pxE7E2M0dKMPZVfIkyThMz40+Bw2lDbWoBd5UtxQuYCKOWB9yGOvrMShcXvITw0B9lps6E3lKO44gsA6HMSsTcxnaY67Nr/NsJ1GchKnQWn04rq+g3YUfQaJubdDaUibGgS7Kf2xp3YvuaPiEo8CbknPoy2xgLs+e1RAOjzQxxvYkz6KgBA/vR/QKNLFsUr1VGDlc6AWXbvQOPdN0A15WRE/fVxWHZuQ8tTDwHAUScROw16dLzz6lH/DafBgObH/woIwfH6caiUqVOxcOVKlKxZgxV33oG0adMwb/HbANBngykqOwumtjZ8MGe2aLvdbB7s4Q461qMXa0H+1PT1W2heudjjfba2hiEeDREREdDWUIBt3/4BUcnTMXrGk2ir34bCdQ8AQJ+TiL2JEQQBBd/fBkPLPmSMuxFSmRJV+z7F1m+uw7RLv4FGlzQ0CfaD4cAm7HvmIoSPPRMZNyyCfv/vKH3zZgA46iRiu7Ed1V/844iPERx2VP3vCQgOu7+GPKQ6Sjdi56LzEZl7NkZe9RraS35F0X9vAICjTiK2m9pR/s1TQzHMQdFWswVbP5uP6PTTMPqc59FWswmFq+4CgD4nEXsTs+altD7/TXVYKk67dYefM/GPtsYd2Pb9zYhKmobR0x5BW0MBCjc8AgB9TiL2JsakrwQAjDnlWWhCg6ffwv5Tr/aWPdi+4a+IipuE3PF3o725EHu3/x8A9DkBtr8xdlsnCrc+AwRB75r1EGtv3YuC3x9CVOwE5I67E20te7C34F8A0OeE4P7G2G2d2LP9OQR6PdoadmDbqhsQlXQyRk9/3HUutd7Vp+1rErE3MYIgoGD1HTC0FiFj7PVd519LsXXV9Zg276uAPP/qS9umSmy7+H1EnZmN0S/PQtvvlSi89UsA6HMScdU7W7D3zhVI/sMkZD1wOowlzSj713q0b67ClB9uhEQWnCtdsjcpxnqIsR69WAvyp+O9jx8cM6ooKC24ZQ7KS6px/w1Pw2F3YNUXa6HVaXDd7Zf2ORnYl5hAdNWNM1FRUoO/3PIiHHYHvlu2AVqtGtfeOqvPSZHexjz35r3In5CD2PjIoUnGD+o69kCjCENO/BmQSqSI0WXBIdhQ274bMbocSCTuC757G3Og/kcYLE2wOUxDnZbPWI9ePFbEuG+I1bbthEYRgRGJ57pyCx0Bp9OG2tYdiA0d5bEe3sbsr/0eBnMDbI7guBQJayFW27odGmUkRiZfCKlEhpiwUXA4rahp3orYsNGe6+FFTE3LNihkIchNuRhSqes0OUqXjYLS/6KutQBpsdOHOtWjqq7/BRp1LEZnL4BUIkNc1HjYnVZU169DfPQkj7XwJqa6/hcoFTrk5yyEVKoAAESH52FL4QuoafgVGclHXiViuJTv+S9CwjJwwukvQipVIDHrIjhsRpQVvouknDke6+FNjLHrA624tDMhVwTeRPK+6Je8C3laJmL/79+QyBXQXnAxnMZOdLy/GNpZl3ish628FK3PPQHL7h0QjJ1QZB15FfzWfz0NZ3vrYKUwqKbffQ+a9hdhyfxL4bTbsePjj6EKDcUp993fd3MpKwstJcUo37BhaAc7BFiPXqwF+VPMRTcj5qKbPd6397pc2JprhnhERER0vCvf+Q60EVkYf+5rrvdAI2bDbjOgdMebSBp1qef3TV7EtNb+juaq9Zhx+ZqeiYCx6Wdjx+rb0Va3BZqci4c61aOqXfkSNEkjMeLe/0EqVyBmxlVwmvSoXfF/iD39Oo+1MNXsR9l/7obhwEY4zQZoUjx/kXn/C5fCULwZttbawU5j0FSu/hc08aOQ/8cvIJUpEH/iAjgselR8908knPwHj/Ux1u/HgY9vR8fB3+GwGBCSlD8MIx+48s2vQRuVg/Fz/gupTIGk/PlwWA0o3bgIyWMXeD5OvIiZetU3bnFOhwU7v7oJiaPnDUVqPinf/T604RkYf+bLrteA7Fmw2zpRuusdJI2Y57keXsQYO7r6LelnBVW/hf2nXhXF/0NIaCrGnfgkpFI5ElPPgd1uRPmBT5CUfoHHWvQ3pmjnv2GzdgxVSgPCeohVlHyBEF0Kxk55DFKpHAkpZ8FhN6H8wFIkpp7nuR79jNm/+w3YrIF/9dHyXe9CG56J8ef82/UakHOx6zVx52IkjfTcp/UmprV2I5qrf8GM+d/3nn+lnYUda+5EW91WaHKCZwJx+aIN0I6MwfhPr4JUIUPileNh11tQ+sJ6JF030WONSv9vHeJm5yH/9d4vb4RNSMK2We+jeU0xYs4N3BWpj4S9STHWQ4z16MVakD8d73384PzKDQU8uUKO08+fhtUr1sFhd/Rs/+bzn5Cek4KR+Vl+iQlEcoUMp507Gau//l2Ux7df/oL07CSMyEsfUEzBliJ89PZKrP7qt8FNxE+cggOtnRWI0mZCKul9yYnRZsFs64DR6j7hpD8xOnU8EsLzEKXNGNQ8/IX16MVjRYz7hphTcKDVUIYoXZYot+jQETDb2mG0Ng8oJlSdgMSIsYjSBf7fFtZCzJXbQUSHjoBUIuvZHhOWC7OtDUZLk88xZmsrQkMSeyYPA4BCroFGGQ2jpWUQs/KN02lHc/sexESOFeUVFzUeJksTOk3uH1J6G2MyNyJMm9EzeRgAFAodQjTx6DTVD2JWvnM6bGio/BEJGeeLxp2YdRGMHWUwtBb5HGPSV0KpjoJcoYXgdEAIgtV2BZsVxp9/QMg5F0Ii781Ne8Fs2CtKYdu/12OcRK2B+qRTEH7jHVCOHnPEf8P40/cwfv81Iu64369jHwoyhQJ5s2dj59L/wWnvXQ2sYMlHiB01CokneL7EX3RWNppLDgIApDKZx8cEI9ajF2sRnCROaVD+EBERDTWnw4qGsh8QnzVT/B5oxBwY20uhb3Z/n+BtTNW+pYhNOwOa0GQIggDB6YAqJBZTZy9FYgBOHnbarWjdsgJR0+ZDesh7pugZV8FcewDGcs8rwUpVIYgYdw5SLvkbtFkT+3z+0FHTkXjh3Yg66chXfwlUTrsVTTuWIW7y5ZDKeusTf+LVMNXvh6Gqj/ooQxCZdx7SL3oMurS+LzkeyJwOK+qLv0FC7lxR7ol582FsLYa+YbfPMZEpJ7n9NBR/C23UCOSc8vDgJ+cDp8OKhoo1iM+8QPwakD0Lxo4y6Fv2+Rxj1FdAqY4Oqn4L+0+9nE4bGmo3ID75DFE/NTH1HBgNlTC0lww4pqFmPeqrfsSIfM+TOQIJ6yHmdNrQWPcr4pNPE+WWkHImjJ1VMHQcHHBMQ+0vqK/+CTmjbxi8RPzA6bCiofwHxGddKH4NyJntOpdq6eP8y4uYqqL/ITb1dPfzr1mfIDFn1uAn5ydOqx0NK/Yi/tKxkCp6+2qJV54A44Em6HfWucU4jFaYK9oQfVa2aHvUqZmARAJDYXCuFsnepBjrIcZ69GItgtNw9+PZx+/b8ZElDbno2AgoVQocLKoQbS/d77qdkBzrl5hAFBUT7spjf5Voe+mBagBAQlLMgGI+WrwSS95aiV/XFvh55IPD5jBDgBMaRbhou0YZAQCw2g0DikkMz0di+BiEa8SXcwpUrEcvHiti3DfEbHYTBDgRohKvIH3EevQjJjFyHBIjT0BESKp/Bz4IWAsxm90IQXBCoxJftk+jdN222N1XG/A2JjP+DKTFnCx6jMNpRaelAUp54K36YbMbIAgOhKjjRdtD1HEAAIu13eeY7LTZyEg+T/QYu8OCTmMNVMowv+XgT1ZzMwSnDdoIccNQG+6aHG/qdJ9Q7W2M0VAJqVSJbatvwZqPpmDtJydj+4939qwME4gczc2Azea2grAiMwcAYK/zvAqWPD4BYdfcgLBrbjji6sOOpka0PP03RNx5PxSZ2X0+LlDp4uMhV6nQsKdQtL1h7x4AQHiq59fEqKwshKek4M5t2/CE0YS/lpVj5osvQqnTDfqYBxPr0Yu1ICIiomOZ1dQEwWmFLlJ8rq+LcL1PMBvcV9TxNsbQsh+a0BTs3fA4fv7wJKz5zxhsWn4Z2ht3DkYqA2Zrq4dgtyLksBWENSmjAQDWJs/v91TRKUicdS8SZ90LTUrfq+smXvQnJM66FxHjA/MKPkdj7XDVR3vYCsIhia56WVoqPIVBHZmCtHPvQ9q590GbfOQvpQYqS2cDBIcVuuhc0XZdzCgAgLmjyi8xANBUugbVOz/CuFlviSbLBRKrydU76T7mu+m6eilmT/0WL2NM+kpIZQps/e4m/PDBRPy05ERs/+H2gO63sP/Uy2pudf2ewzJE27Vdt80eFiHoT4zF3IK9259HzphbEBLqvuBMoGE9xKwWV27a0AzRdm3X2M0m98md/YmxmFuwb8dLyMm7EdrQNP8O3s96XhMPP5eK7D6XOsLr6FFiDK37oQlNxt5fn8TPH03HmvdPwKavrkB7467BSGXQWOsNEKwO6PLiRNt1o123zZVtbjESmRRTf74F8fPE5xutv5QBggBVUuhgDXdQsTcpxnqIsR69WAsi/+IEYhoU0bGuCUsd7eJJTe2trgk6UbERfokJRNFd49T3lUdM+OEhPsUEC5vDBACQy1Si7XKpqut+s19iggXr0YvHihj3DTGbwwgAkEnFuSmkatf9dpNfYoIBayFms7tykx+em0wtut+XGK06FmpluChub+VyQBAQHzHWTxn4j9Xmei2Uy9Wi7XJ5CADXZGFfY3QhSVAfMuHaajOgsPhdCIITCTEn+ikD/7KYXCtJKw6b4KxQRQBwNV19jTHpK2E21kEbkYUTTn8JIyb9CYaWfdi86tqAvWSgo7kRACANE+cmDY8AADhb3Ffr9pYgCGj++0NQ5uZDN/9qn59nOOkSEgAAptY20XZjs+t3rouPPzwEEqkUkZmZSBg7Ftv++wE+uPhibFq8GJOvvwFXfvzJoI95MLEevVgLIiIiOpZZjF3vgVTiHppC7erNW03u7xO8jbEYG1C550Pom/Yg9+RHMeaMfwEQsHnFFTB2BN7kN1u7a1KWTCv+8rUiNLrr/uBctc5frB2ulf7kIYfVRxvddX9gXp3IH6ydrt+9Qh0h2q5Qd30R3ehh0psPMU6HDfvWPITME++COjRwF30YzH6LUV8Jc6er3zL+zEUYMfnP0LfsxaaVC2CzBGa/hf2nXpauK7bJFeJJet15Ws3uV1X0NkYQBOzZ9hxCI0YiNWuOX8c9WFgPse6xyxXiCVk9uVnc6+FtjCAI2FvwL4SG5yAlM/CucnA4i8nVp1Wo+noN8HD+5WWMxdiIyr0fQd+8B7nT/oYxpz8PCMDmr68K2C8feGKpd30uoYjQiLYrol2fW1jr3T/rkKrkiDgxFcqY3sVf2rdUYdf1n0GdEo64i/PcYoIBe5NirIcY69GLtSDyr8D8OisFPQGeL6sjkUgAAHK5+67nS0xA6uOKQhKpa76+XOFhGXxfYoJc9+9bgHNQY4LFcVkPHiteOS73DQCAxOPWI+fmS0wwYC1EPKfWm5vgIbd+xgiCE/Vtu1DZtBGC4EBO4rnQqoPjSggAAKE7L8eAYwTBgdrGjaioXQOnYMfIzMugC0ny21D9q69ftLPrP3YPd3oXk553LbLG3YLI+Mk9D4mIm4zfv5qH6v2fIWPM9T6PetBIjpKb3VM9vGP48lNYdm5H4sdf9/wNDjp9XAa0Ox+ZQuF2n1Qux2fX/wG1O3agqch1edH9362Cvq4Wc994EylTpqBq8+bBG/NgYj16sRZERER0LOvrbULX+wSnp/dNXsbYrXqotPGYNPO/kHV9aTUmZQbWf3wGyne+jdEznhjY2P2uj96Js+s9k8M2lIMJQEd5T3lM16ePfcOH/sKRYioL/gO71YCMKbf5Nsyh0kd/4civG97FpOdfh6wT/oiohN5+S2TCZPy2bA6q9v8PmWNvGMjIBwn7T90kR8nLKbjXwtuY6rKv0N5SiJPOehcSSXD0nliPwxytN9mP147DY6rLV6K9dQ9OPP3NIKmHD6+jXsbYrXqoQuIx6YL3es+/kmdg/adnonznOxg9/fGBDX2o9PV3w+nq0zntR/5My9ZqQsnff0TlGxuhSgnDhC+ugUzj3sMLCuxNirEeYqxHL9aCyK+C4YyKglBzg+sbgOER4m9Mhke6bjfWu38D15eYQNTc2AYACIsQfzsyLML17bemevdvVPoSEyyUMtc3Be0Oi2i73Wntuj/ELzHBgvXoxWNFjPuGmELeR25dtxUyrV9iggFrIdY9dvfcXCtuK+Ue6tGPGJO1FbsrPkNZwzqEhSRhXMZViA4TXyYsUCi7VqawH7aitL1rBWqlImxAMUZzI3YUvYmDVV8jTJeBCaPvQmzkOP8l4GcqTdcqUZZ20fbuFVpUIe6TwL2NiUk+RfThDQDoIrIQEpYOfet+P4ze/2TRrrE7O8S5Odtdt2WxcW4x3rDX1aDtxWcQeef9kIaGwmnshGB2HUuC2QynsXMAox46+jrXalohUeLVtEKiXKtDddS4X7rZYbVi19KlPY2lbntXrAAAJIwNvJXKvcV69GItgpPUIQnKHyIioqGm0rjeJ9gsbaLt9q7baq37+wRvY5TqKMQkz+iZvAIAcmUoIuInQt+81w+j9y9FhGtFKntni2i73eC6rYhMHPIxBRJluGtFr8PrY+u6rYwI1C8XD5xK59qnbYetFtp9W6VLGHCM02lH2aZFSJ1wPWSKwO7hqjQxANx7J/au22qP/RbvYmJTThVNHgYAXUQ2QsLSYWgRv78KFOw/9VJ2rbB9+OrINqvripEqdYxPMWZjPfbveh0j8m+BXKGD3W6Cs6uv63CY3fqagYL1EFOqXH0Ve1cu3Wy27tyifYoxGxtwoPBN5OTd2FMPR4DXo/sY7/s10cP5l5cxSnUkYlKmH3b+pXOdf7Xs81MGg08V7/rc19Z62OcWLa7PLdSJoW4x3RpXFeHXiYtQ+dYmpN05DSdvvgOh+e4rjwYL9ibFWA8x1qMXaxGchrsfzz5+3ziBmAZFS1M7bFYbsnPTRdu7b9fXuF+Kw5eYQNTS3A6b1Y7sUami7d2362vcJ0L7EhMs5DI1JJDCZGsTbTdZXY0zTxO9fIkJFqxHLx4rYtw3xBQyjSs3q/hDiu7bKo+TRPsfEwxYCzGFvDs38fFutLhuKxXujSRvY8zWduyp+BwWaztGJc/CqOSLoFaGuz1foFDItZBIZDCaxJcN7ey6rfIwdm9jTJZm7Nr/FsyWZuRlX4e87GugUbk3dQOJUh0NiVQOQ1uxaLuh7QAAQK11/4DPmxi7rRONVT/3XG7yUBKJ1O2SeoFCFhUFyBWwlRwQbbeVuD5wksf79mG4vboSgtmEln88gqrTJqDqtAlovOcmAEDDLQtQff70gQ18iHQ2NMButSI+f4xoe/ft9qoqt5iI9HSMunAmpIddGcXpcK3cbe4IvMuJeov16MVaEBER0bFMqYmGRKqAoUU8EU3fdVutdX+f4G2MShsHR9cXdQ/lFGyQKQOvD6EIj4NEpoCpslC0vfu2KjrVU9hxQxnqqk9nzW7R9u7b6shjtz7KkFhIpArom8QT3w1dt9WhyQOOaTiwEhZDPZLHXOXPoQ8KpTqqq3ci7i/oe3onHl43vIix2zrRWLkWFmOjW7xEIoNMGZj9FvafeilVEZBI5OjUl4m2GzpKAQBqjftkam9ijJ21cDrM2FvwAtZ+dSHWfnUhCn57AACwdf09WP/NPP8n4wesh1h3bga33Fy3VUeox5FiTMZaOB0W7NvxEn7+ZjZ+/mY2dmz8GwBg24b78Mt3l/s7lQFzvSYqYGg57DWx64sBal1fr6NHj1GFxMFh93D+5bRBpgi886++KOO0kChkMBSKP7fQ72kAAKhTPH9O07BiD7bP+xDKWC1O3nYnRj17AeRhao+PDRbsTYqxHmKsRy/Wgsi/OIGYBoXdZsdP3/6Gcy4+BTK5rGf7+XNPR2VpDYp2lfglJhDZbQ6s/W4zzr5omiiP82ZPR2VZHYoKy/wSEyykEhkitWloNpTBecgl4psNpVDJQxGijPJLTLBgPXrxWBHjviEmlcgQqctAs+GgKLcmQzFUijCEqNy/re9LTDBgLcSkEhkiQ7PQ3HEATsHRs71Zvx8qRTi0KvfGo7cxFY0b4BQcGJtxOSJ1GYOey0BJpXJEh+ehsXWXKK/G1h1Qq6Kg1bg3Hr2NKateBafgwPjcOxAVPmrwk/EDqUyBuNSzUF+2Ck5n76VU60q/gSY0FaFRo32KkUqV2P3LgygpeFUU29leis72UoTHnjB4SQ2ARKFEyOlnw7j6Gwj23tw6v/sK8pQ0KEbl+fS8ylF5iFv8kegn8j5Xkz7q4b8j7pV3/TL+weaw2bBn2TKMvWy+qFk07sor0FxcjNrt291itDExuHb5coy55BLR9ryLL4bT4UD1li2DPu7Bwnr0Yi2IiIjoWCaVKRGXcQ7qDq4Uvwcq/gqasDSExuT7HBOXfjaaKtbCZm7reYzF2IC22i0Ijx0/aDn5SipXInLKbDT/9j84D3nP1LThE6jisxGSMX74BhcApHIlYsbPRcOWT+F09NanYdNHUMdmQ5c6YRhHN7ikMiXiR8xE3b4vRbnX7vkcmohMhMW7X52pvzFVBe8hKuM0qEMDf6VrqUyJuLSzUVd6WO/k4EpX7yTaU7/l6DFSqQK71j2Aku3/FsW6+i0HERGArxsA+0+HkkoViEuagfqqn+B02nu211WtgUabhNAI96u6eRMTFpGDyacsEv2MHHcnAGD0hPswYfr/DX5yPmA9xKRSBWITp6Oh+mdRbvXVP0ETkoTQ8ByfYkLDczBp+r9EPyPH3AYAyD3hTxg/7ZnBT66fpDIl4tLPRt3Bb8SvASVd51LR7n1ab2Pi0s9GU+XP7udfdVsD8nWjL1KlHHGzR6Pus11w2no/t6j7ZAc0WVEIHe/+99Jpc2DPXSsQMTUVU9feDO2I4P3c61DsTYqxHmKsRy/Wgsi/5Ed/CJFvlry5DG998SyeW/wQvl+xDuOn5uPCS8/Ek396CYIgICxCh/wJo1BZWoOqslqvYoLFR2+vxBufPopnX78HP3z9G06YkosL552Cp+5/05V7uBZ543NQVVaHqvJ6r2KCWUJYHvbWrUJxw1pEaTNgMDegufMgMmNOhkQigd1hgcHSBLUiFOquS6cfLSaYsR69eKyIcd8QS4wYhz3VX+FA3WpE67KhN9WhWV+MzLhTu+phhsHcCLUirGeV2KPFBCvWQiwxcgL2VH6BAzXfIjp0BPSmWjR1FCEr4axD6lEPtSIcamWEVzGC4ESroRTh2lSYrK09K3l3U8q1ATnxOjl+BnbuX4x9Bz9CTORYdBjK0dhSgJy0eZBIpLDZjTB0VkGtioam67JwR4sRBCda2vchIjQbJnMjTGbxSjBKZRi0GvfVVAJBet612PLdH7Bz7b2IzzgfbQ3bUXvwa+RNe9JVD0s72pt2ISQ0DSFhaV7FSGRSZIy5AcXbXoJEKkNUwkmwGOtQVvgfhEXnIzHzwmHOum+hC65H/S0L0PTXuxByzoWwFGyF8dsViHrkH5BIpXC0t8FauBPy1HQoUtOP/oQApLpQqMeLL6eJrm9nK0aOhioveC7xtOHll3Djmh9x1adLsfN/S5ExfTomLLgaX9x0IwRBgCYyEilTpqK5pBgtJSWo3roV+1etwuzXXkdEegaaD+xH0oSJmH7PPdjw0ktoLSsb7pQGhPXoxVoQERHRsSx93A3Y8tWV2LH6DiRkz0Rr3RbUFi9D/qnPut43mdvQ3rgDIWHpCAnP8CoGAJJzr0BF4X+x6asrkD72egACyna8BbkqDBnjrh++hI8gceY92PPkWTjw4uWInjYf+qJf0fzLR8i85S1IpFLYDS0wFG+GOiEb6gT3iU7HutRz7sX2509D4ZuXIm7y5Wgv/gX1Gz/EqGvfgUQqha2zBR2lm6CJy0FI3LFVn/Qpt2Hzx7NQsHwhEnLnoq3qd9TuWYr88xdBIpHCampFe+1WhERmQRuZ5VVMN7ulAy2VvyLnlIeGK71+Sx9zHbZ8ex12/HgPEjIvQGvDNtSWfIX86U919Vva0N64CyFhaQgJS/cqRiJTInPsjTiw9V+QSOWISjwJ5s46lO1+F2Ex+UjMmjnMWfeN/adeaTnzsXX9Pdi58TEkpJyBtuZdqKtcjdET7nfVwtqB9tZ9CNEmI0SX7FWMXKFDRIy4v9S9EEJoeA7CIgN3sQPWQywtex62bbgPu7Y8hfik09DWsht1VWswevy9PfXoaCuCJiSptx5HiZErtIiIFq82KfTUIxthESOHPE9vpI+9HltWLsCOH+5CQtaFaK3fitriFcg/5R+HnH/t7Dr/SvcqBgCSR12GisIPsOnrq5A+9g+AIKBs59uQK0ORMTYwz7/6kn7XdGw55x3suPJjJFw6Fq2/lqP24x3If2Ou67yjxYj2LVUIyY5GSHY02jdWwlpnQNLVE9D6S5nb8+nyE6BODhv6RPyAvUkx1kOM9ejFWhD5D1cgpkFTuL0Id131KHThWjz4zzswZuIoPHHPi1jxyWoAwIjRmXjloydx4SVneB0TLAoLSnD3dc8iNEyLvz59A/LHZ+PJ+97AV0vXAgByRqdh0X8fwAVzZ3gdE8x06liMij8HDqcVZU2/w2BpRGbMyYgNdX3b1mhtxf76H9BkOOh1TDBjPXrxWBHjviGmU8chN/ECOBxWlDash8HSgKy40xAXlgsAMFpaUFT7LZr0B7yOCVashVioJgG5KRfD7rCgtP4nGEx1yEo4C3Hhrm/dd1qasK9qBRo7iryOsTssEOBEW2c59lWtcPupaXH/pmogCNWmIj9nIewOE0oqlkPfWYkR6ZcgIcY1wbPTVIfCkvfQcMj4jxZjtxshCA60duxHYcl7bj/V9b8MS67eCI8dhwlnvwGbTY+9G59Ce9NO5J38JJJHzAXgusTb9jV/RO3Br72OAYCM/OsxasoDaKndiN2/PIDKfZ8gPuN8TDpnMSRSmds4AoVqzAmIW/QOnPoOtDz7KCyFOxD1yDPQXXwpAMBWXITGu29E57fLh3mkw6Nq0ya8P3Mm1BERmP3qa0iZOhWf33gDtr73HgAgYew4LFy5EuOvWtAT88mCq7D1vf/gxFtvxaXvvY+RF5yPb+77M1Y98NdhysJ/WI9erEWQEQCpUxqUPwju7z8SEVGQiogbj4kX/Ad2awf2rH8E7Q0FyD/tn0jOnQ8A0Lfsw7Zvr0fNgeVexwCAKiQGU2d/jpCwdBRvfgElW15CWOw4nDR3GeTK0CHP0xu6EVOR+8DXcBjbUfrOHTAUb0LWrYsRd8ZCAICxfBeKnp2FpvUfDe9Ah0lY5okYd9cq2I1t2L/kVnSUbsSoa99F4nTXhCRD1U7seuUC1G/8cJhH6n8RSZMxaf7/YDe3Y8/396K9divGXPAKUsZdDQAwNBZi22eXobZwqdcx3ZrK1kJw2hCVevKQ5jQQEbEnYOI5b8Fu1WPPb0+gvXEn8mf8HckjXau36Vv2Y9vqW1BT8pXXMQCQMfYG5J74EJprf8eudX9B5b6PkJB5Piad925A91vYf+oVHpWH8Sc/B7vNgL0FL6K9dS/yJv4FyRmuCc/69oMo+PWvqK1c7XVMMGM9xMIjR2P8Sf+A3WbAvp2L0NG6D6PH/xlJaecDAAwdpSj4/WHUVa3xOiZYRcSdgInnveM6l9rwKNobdiD/1GeQPMrVp9W3FGHbdzeipni51zFA9/nXUoSEp6N4y4so2bYIYbFjcdKcLyBX6oY8z4GImJqKiSuuhb3NjD13rkD75irkvzkXyQsnAQD0u+qw7eL/ouajAgCApd4AACh7fj22Xfxft5/mNcXDlcqAsTcpxnqIsR69WIsgwz5+QJMIwb5cI/kkJSUF1dXVUEjVGBd35nAPJyBIlcH5DbTBIpGphnsIFKAEh2W4hxBQeKz0Yi3oSLh/9JJrA//ylENJM2bWcA8hYBTdyePkUFdPc7/sJxGJvWJ3QA8gOTkZVVVVwz2cIdXT14hKwrjFJcM9HJ/svCkbtpaa4/L3R0RE3un+e6fSJuC0BRuGezgBof3M1OEeQkDRtCuGewgBQ1XSevQHHUeExrrhHkLAELqunEQugr5puIdAAUpw2IZ7CAFFFpcx3EMILP9eevTHHCcm6x4b7iEQUZBhH599/EDHFYiJiIiIiIiIiIiIiIiIiIiIiIiIiIiOI/LhHgANL5vTgsLGdR7viw1JQ5w2Y2gHREREREREREQ9tjid2Or0fPGoziEeCxEREQ0Pq7ERG5ae5/G+1PyrkZZ/zRCPiIiIiIiIiIi6sY9PwYwTiI9zCqkK+bGnDvcwiIiIiIiIiMiDyVIpJvdx/ajuS58d3ySQOiXDPQgfBeu4iYhoqClDYjH9su+GexhERERERERE5AH7+EfDPn4g62PXJSIiIiIiIiIiIiIiIiIiIiIiIiIiomMRJxATEREREREREREREREREREREREREREdR+TDPQAiIiIiIiIiIiJfSR38fjwRERERERERERERUaBiHz9w8TdDRERERERERERERERERERERERERER0HOEEYiIiIiIiIiIiIiIiIiIiIiIiIiIiouMIJxATEREREREREREREREREREREREREREdR+TDPQAiIiIiIiIiIiJfSARA4pAM9zB8IhGGewRERERERERERERERIOLffzAxhWIiYiIiIiIiIiIiIiIiIiIiIiIiIiIjiOcQExERERERERERERERERERERERERERHQc4QRiIiIiIiIiIiIiIiIiIiIiIiIiIiKi44h8uAdARERERERERETkK6lTMtxDICIiIiIiIiIiIiKiPrCPH7i4AjEREREREREREREREREREREREREREdFxhBOIiYiIiIiIiIiIiIiIiIiIiIiIiIiIjiOcQExERERERERERERERERERERERERERHQckQ/3AI5n1dXVKC8vBwCkp6cjOTm5X/FOpxMlJSWorq6GTqdDdnY2IiMjB2OoREREREREREQBSeKQDPcQhkRraysOHjwIvV6PpKQk5OTkQCrl2gBERIONfXwiIiIiIiIiooFhHz9wBfbogojNZsOJJ54IiUSCxx9//IiP/fbbbzFlyhSkpKRg+vTpmD59OlJSUjB58mR88803R/23nE4nXnzxRWRmZmLkyJE444wzMGXKFMTGxuKiiy5CUVGRn7IiIiIiIiIiIqLhtH//fsyaNQuxsbGYPHkyzjjjDIwaNQoZGRl44YUX4HA4/PrvrVy5EhKJBAsXLvTr8xIRBQL28YmIiIiIiIiIyN+CuY/PCcR+8re//Q2bNm066uNeeOEFXHjhhdiyZYvbfVu3bsXMmTPx/PPP9xnvcDhwySWX4N5770VFRYXbfStXrsSECROwbt26/idBREREREREREQBY/369ZgwYQK+/vprtwZjZWUl7rvvPsybN8+vzccPP/zQb89FRBRo2McnIiIiIiIiIiJ/CvY+PicQ+8Hq1avx3HPPHfVxP/74I+6//34AQExMDJYsWYKWlha0tLTgww8/RHR0NADg/vvvx5o1azw+x1NPPYVly5YBACZOnIiffvoJBoMBlZWVeOKJJyCVSmEymTB37lw0Njb6J0EiIiIiIiIiIhpSTU1NmDt3LoxGI6RSKZ588klUVlbCYDDgxx9/xIQJEwAAK1aswJNPPumXf3P16tVYunSpX56LiCjQsI9PRERERERERET+dCz08TmBeIDq6+txzTXXHPVxgiDgwQcfhCAIkMvl+P7773HVVVchMjISkZGRWLBgAb7//nvI5XIAwEMPPQRBEETP0dDQ0LOqQWZmJtauXYvTTz8dWq0WKSkpePTRR/HSSy8BAFpaWo64AgIRERERERER0bFA6pQE5c/RPPfcc2hubgYALFq0CI888ghSUlKg1WpxxhlnYO3atcjIyADgWinT1wlonZ2d2Lx5M+655x7MnTsXTqfTp+chIgpk7OMTEREREREREQ2f4e7Hs4/fN04gHgCn04mFCxeivr4eiYmJR3zszp07ey6NNmfOnJ7Z5YeaOHEi5syZAwDYtGkTCgsLRfd//PHH6OzsBAD8+c9/RmhoqNtz3H777YiLiwMA/Oc//+GHPkREREREREREQcbhcODdd98FAMTFxeHWW291e0xYWBjuu+8+AK7m4aefftrvf2f+/PnQ6XSYOnUqXn755Z6+ExHRsYR9fCIiIiIiIiIi8rdjpY/PCcQD8OKLL2LVqlVQq9VYvHjxER/79ddf9/z/7Nmz+3zcofetXLmyz+e4+OKLPcZLpVLMmjULANDY2IjNmzcfcVxERERERERERBRYfv/9955VC2bNmgWZTObxcYf2hw7vI3nD19UOiIiCCfv4RERERERERETkb8dKH58TiH20detWPPjggwBcDcj8/PwjPr6oqKjn/y+88MI+H3fofdu2bfP4HGPHjkVqamqfzzFz5sw+n4OIiIiIiIiIiAKbt32k1NRUjBs3DoBvPaDvv/8eJpNJ9ENEdCxhH5+IiIiIiIiIiAbDsdLH5wRiH+j1elxxxRWw2WyYM2cObrnllqPG1NbWAgB0Oh2ioqL6fFxUVBS0Wi0A8U4mCALq6uoAAOnp6Uf8tw5tSh76HERERERERERExxQBkDiC8wdC32l195EA7/tADQ0NaGtr61f5lEol1Gq16IeI6FjBPj4RERERERERUQBgHz+g+/icQOyD22+/HcXFxUhOTsbbb78NiURy1JjupuGRmo7doqOjAYh3spaWFthsNq+eozv+8OcgIiIiIiIiIqLA191HAtgHIiLyFfv4REREREREREQ0WI6VPr58uAcQbD744AN88MEHkEgk+PDDD0W/3CPp/sV78/jo6GhUVFSgs7PTLd6b5zj0/kOfwzMBDqftqGPqi0QihVQi8zmeiIiIiIiI6HhlFwQ4BhB/hC++0xBw2ixw2i2+P4HQ929w8PpARETHh2Oujy84YbfqjzqmvkhlSkhlKp/jiYiIiIiIiI5X7OMHN/bxj44TiPvhwIEDuO222wAADz30EE4//XSvYzs6OgAAGo3mqI9VqVyNPJPJ5BbvzXN0xx/+HJ7YnBYUNKw+6pj6kqjNQVLoSJ/jA4mEDVQR1oPIOzxWerEWYlKlbriHEFBk6qOv3nS8UCaeMNxDCCgVL+0Y7iEEjKvjrhvuIRDREPvVKeCXIzSfyDtS59FXlBwM5d88i/KvnxiU5x6sPhAR0fHgWOzjW4wN+PG98UcdU1+yznoEOWc/6nN8IDHp+LfuUFaV7wvEHGtia/17CddgJwmNGO4hBA7HQKZ7HHsk2tDhHgIFKEmIdriHEFB2XRdYKwMOt02z/jHcQwgYrw33AIhoyLGP7x/s4wduH58TiL1ktVpx5ZVXwmAw4KSTTsJjjz3Wr/jY2FjU1NSgra3tqI/tfsyhO1ZsbKzb/UeLP/w5PFFIVciPOfWoY+qLRCL1OZaIiIiIiIjoeHayVIIT4XvT7E2HEwY/jof6J/38B5F69r0+x296bDSs7TUe7zu8D3To7cP1pw9ERHSsO1b7+KrQREy/d/dRx9QXqZxf+iYiIiIiIiLyBfv4wY19/KPjBGIvPfTQQ9i6dSvCwsLw0UcfQaFQ9Cs+MTERNTU1aGlpOepjux+j0/WuXJiYmOh2/9HiD38OzySQSfuXCxERERERERENnFwiGVBjZni+r0/dpAoVpIoBTMiS9P0bPLwPdKTGY//6QEREx7Zjto8vkUKuDjvqmIiIiIiIiIjIv9jHD27s4x8dJxB7oaamBi+88AIA4LLLLkNRURGKiopEj6mvr+/5/+LiYqxatQoAEBcXh4kTJyIhIQGAa2cQBAGSPnYuQRDQ2toKAEhNTe3ZrtPpoNVq0dnZedTGY3f84c9BRERERERERESBr7uPBBx9AtqhfaDk5ORBGxMRUaBjH5+IiIiIiIiIiIbKsdLH5wRiL1it1p7/f/vtt/H2228f8fFLlizBkiVLAACzZ8/GsmXLemacW61WFBUVITc312NsUVERbDYbACAvL090X2JiIoqLi7Fz5044nU5IpVKPz7Fz586e/z/8OYiIiIiIiIiIjhUSABLHcI/CN0daeeLQlQt27NiBadOmeXyc0+nErl27AABpaWkIDQ315xCJiIIK+/hERERERERERIGHffzA7uN77lyR302ePLnn/7/66qs+H3fofSeffLLH56irq8PWrVt9eg4iIiIiIiIiIgps3vaRtm7dirq6OgDsARER+QP7+ERERERERERE5I1jpY/PCcReyMjIgCAIR/wpLS3tefxjjz3Ws33ZsmUAgFmzZvVc7uzzzz+HIAhu/44gCPjiiy8AABKJBBdffLHo/tmzZ/f8/+eff+5xrG1tbfjxxx8BAKNHj8aoUaN8T5yIiIiIiIiIiIbcqFGjeno6a9asEV3e7FDdfSQAmDt37pCMjYgoULGPT0REREREREREQ+VY6eNzAvEQSUpKwhVXXAEA2LhxI9566y23x7z55pv4/fffAQALFiwQLXMNuHag9PR0AMDLL7+MgoIC0f0OhwO33347zGYzAODPf/6zv9MgIiIiIqLjiEQqhVKrdftRhIQM99CGBeshxnr0Yi1oMNx7770AAIvFgjvvvBNOp1N0//bt2/HSSy8BADIzMzFnzpwhHiER0bGHfXwiIiIiCiZOo9Hjj3BYD4GObexNirEeYqxHL9aCBsOx0MeXD/cAjif/+Mc/8N1336GlpQW33norfvvtN1xwwQVwOp1YuXIllixZAgCIjo7G3//+d7d4lUqFl19+GXPmzIHZbMapp56KO++8E5MmTUJLSwvee+89bNiwAQBw0kkn4brrrhvS/DyZOG0Mbv7zAuSOzUZFaQ0+e28lVnyy2u8xgajDVIfqth0wWpqhUoQhPmwUYkNH+C3G5jCjoGIpxqddBoVMPRgp+FWHqQZVLVthtDRBrQhHXHge4sJy/RZjc5ixvfQDTMi8hvVAcNWDx4oY9w2xDmM1qpo2otPSCLUiAvGRYxEXnue3GJvDhG3F72Ji9vVQyDWDkYLftBsqUNmwHp2meqhVkUiImoj4qBMGHNPRWYmK+p9hNDdCIpEhLCQVaQmnQaOKGsx0BqytowQVNathMFZDo4pBYtw0JMROHXBMu/4gyqq/g9FYC4lUjnBdJjJSLoBGHTOY6QxIa9NOHCz6APr2AwjRJiMlcxaS0s73a0xr8y5s23Afppz6CsIiRvo7Bb8ybtiLpn9+AcuOMiiyExB5/dkIv/o0r2INq7ah8cmlyPz1Wffn/W0fmp75HJYdZZDq1Ag5fQxi/3YZ5ImR/k5hUGWccirOevRRJE2YgObiA9j4xhvY+t57fT5+5PkX4Nrly922NxcX41+jj/z3KRiwHmKsRy/WIrhIjtHPxP7whz/gnXfewaZNm7BkyRJUVlZi4cKFCAsLw6ZNm/Daa6/BbDZDIpHg5ZdfhlKpFMUvXLgQ77//PgDXKpuPP/74MGRBRBR8joc+fsvBdShZ8yT01dsREpOD1JNuRfLkPww4prX0FxT/8Dj01dshU4UiesRZyDn3KajDkgYznQEx7PoF9R89A1PJDqiSshF94Y2IOvcar2I7Nn6L2vcfx6jXNrrd17n7V9R9+BTMZYWQKFTQ5k1DwnWPQZWU7e8U/Eq/ez3qPv0HjAcLoErMRuz5NyH6bO/20fbN36Dmg0cxetEWt/sMezagdsmTMFUUQqJQQpd7MpKueQKqxMCtR0vZepSsfRr62gKERGUjdcrNSJ545Fp4E9NavgHFPz0FfW0BZEodorPPRM6Zjwf0cQIALVW/oXjjC9A37kJIRCZSx16HlPwrBxzjdNhw4Ldn0VT2I0z6KoTFnYCR0x9GRMKEwUxnwFqqf0PJ5hehb9yNkPAMpI65Fsl5Vww4xumwoXjjc2gs/xFmfTXC4sZh5LQHER4fuPVoqfkdJZtfgr6p0JVX/jVIHn3ZgGOcDhuKNz2PxvKfYDZUIyx2LEae9ADC48cPYjYDx3qItVRuQPFv/wd9/U6ERGYh9YQ/IGXsggHF2K2GvoMlEsgVWn8N368sW7ZA//rrsO3dC3laGrSXX44QL1cgNK9di46XX0bcl1+Ktjs7O1E3bZrHmPjvv4csIWHA4x4sv7U48K+DNuzWO5GpkeLaVDmuSPZ++tIrpTa8UmrD/jPFk/42tjrwXLEN+wxOqKTAlAgZHhihQGZIcK2tyN6kGOshxnr0Yi2CC/v4gdvH5wTiIZSRkYHly5dj3rx5aGxsxPvvv9+zA3SLi4vDsmXLelYoONzs2bPxyiuv4E9/+hP0ej3+8Y9/uD1m0qRJWL58OeTy4f315k8YhUVLnsSm9QV49sHXMG7yaDz64j0A0OeEYF9iApHB3Iii+h8Qrk5EesxJMJgbUNr0KwD0OcmxPzGC4ER1awEEuF9CLxAZzA3YV/MtwjXJyIidAb25HqUN6wCgz0mO/YkRBCeqWrawHl2CqR48VsS4b4jpTXXYW7Uc4SGpyIw/HXpTLQ7WrQGAPicE9ydGEJyoatoIAYF/pqo3VmNv2acI12UgK+lc6I3VKKn+BgD6nETsTUynuRG7Dy5BmDYVmYnnwCHYUNu0GbtK3sf4ETdBqdANTYL91GGoQOH+txERNgI56XPRYSjDgbL/AUCfk4i9iek01mHnvjcQHpqF7PQ5cDisqK5fj4I9izBpzH1QKsOGJsF+aG/di4LfH0JU7ATkjrsTbS17sLfgXwDQ54Tg/sbYbZ3Ys/05IAheO0xbilE1/zmEnDYG8c8vhGnTAdTdtRgAjjqJ2NFhRPPzyzzeZ95Riso5z0B7+hjEv3gDnO2daHn5a1Rd9n9I++EJSFUKf6cyKFKmTsXClStRsmYNVtx5B9KmTcO8xW8DQJ8NlajsLJja2vDBnNmi7fau1dKCGeshxnr0Yi0oUCgUCixfvhwXXnghtm/fjnXr1mHdunVuj1m0aBFmzZo1TKMkIjr2HOt9/LaKjdj2n5mIyjkLo2e/graK31D4+c0A0OckYm9iOqq3Ycs75yI652zkzXsdNlMrSn9+Htv+cxFOuv03SOWqoUmwH4xFm1H62DzoTjgdyX98AcZ9G1G16HYAOOokYkdnO+o/+afH+8xle1Dy0IXQ5k9H0i3/B6fFiKZlr6L43jMw8rVNUEQF5mSezv2bUfLUHISOOwOpN7+Izn0bUfHqbQBw1EnEjs521C11/zIuAJjKC3HgkfOhy5uBlBufh9NiRMOKf6PoL6dh9MuboYhK9Bg3nNqqNmHbh7MRlXUGRs98CW2Vv6NwxR8BoM9JxN7EdNRsx5b3L0R09pnIm/Vv13Gy4V/YtmQuTrppXUAeJwDQVrcNW5cvQHTqDIw+/Rm01W5G4RrX6ul9TSL2JkYQBBSsvAH65r3ImHgrpDIVqnYvwdZlV+LkBT9AE5oyNAn2U1vddmz76hpEpZ6C0ac+jba6LSj86X4A6HMSsTcxgiCgYNXNMDTtRcaEWyCVKVG15yNsXXE1pl3xPTShyUOTYD+01W/Htq+vQ1TKDIw+5Sm01W1F4dq/AECfk2a9iREEAQXf3QpD8z5kjL+pqxafYOvX12DaZasCshYA63G4ttqt2PrFFYhOOxWjz34ObdWbUPj9PQDQ5yRib2LWvJLZ57+pDkvFaTdt82se/mDduRPNf/wjVCeeiPCHH4a1oABtjz0GAEedROzU66H3cFUMAHBUVQEAIp5+GrJk8X4gjQrcRWK2tTtw9XYLTomS4ulcJba2OXHfHisAeDWJeFeHEy+U2KA8bE7wPoMT87dYcGKkFE/lKmF0AG9X2HDRRjN+PFmDeJVkMNLxO/YmxVgPMdajF2tBgeJY6ONzAvEQmzFjBnbt2oVFixZh2bJlKC8vh0QiQXp6OubMmYO7774bsbGxR3yOO+64A6eccgoWLVqENWvWoLa2FjqdDrm5ubjqqqtw8803Q6EY/kkMC26Zg/KSatx/w9Nw2B1Y9cVaaHUaXHf7pX1OBvYlJhDVdeyBRhGGnPgzIJVIEaPLck3Kat+NGF0OJBL3k1NvYw7U/wiDpQk2h2mo0/JZbdtOaBQRGJF4riu30BFwOm2obd2B2NBRHuvhbcz+2u9hMDfA5jAOdVo+Yz168VgR474hVtu6HRplJEYmXwipRIaYsFFwOK2oad6K2LDRnuvhZUxR9UoYTPWwOTqHOi2f1DRthloVhVHp8yCVyBAbOQYOpxXVjb8jLnKcx1p4E1PTuBEKuRZ5GZdDKnWdFkaHjcK2ojdQ27wZ6QlnDHWqXqmuWweNOhajc66DVCpDXPREOBwWVNWtRXzMFI/18Camqm4tlIpQjBl5A6RS17lUdOQYbNn1T1TX/4LM1AuHOtWjqij5AiG6FIyd8hikUjkSUs6Cw25C+YGlSEw9z2Mt+huzf/cbsFn1Q5XSgLS+9i2UOYlI/u/dkCjkCJs/HU6DGc2LvkLYglM91sNaXIv6+9+HaWsxBIMZylz3D6fal6yDPDYcyR/+CRKF61hR5iajcubfYd5SjJDpowc9N3+Yfvc9aNpfhCXzL4XTbseOjz+GKjQUp9x3f9/NlKwstJQUo7xrhbRjCeshxnr0Yi0okCQkJOD333/HW2+9hY8++ghFRUUwGAxISkrC2Wefjbvuugtjx44d7mESER1zjuU+fvkvL0MbOxLjr/4fpDIFEidcBbtFj9Kfn0fSpIUe3zd5E1O95T0odfEYf83nkMpceeni8rH5rTPQVrERUVmnDnWqR9W47FWokkcg4+ElkMgViDzjcjiMBjR8/iIiz7naYy0s1QdQ/fp9MBZthtNkgCrN/f1g45eLII+IQ+YTn0OqdF0BLHzaLOy7aTyaVryOxIVPDHpuvmhY8QrUSSOQ9dePIZErEHXaFXCY9aj/8kVEnXWtx3qYqw+gavGf0Fm0GU6zAepU93o0LH8Z8vA4ZD/yZW89TpyFPbeNQ8PXryH52qcGPbf+Kv/tFWijR2D85Z+49vlxV8BuMaB0w7+QNMFzLbyJqd7+Xyh1cRh/xdJDjpM8bP7POWir2oSojFOGOlWvlG9/E9rILIyf+Q6kMgWScufBYetE6dZXkZx3hed6eBHTUvUrmip+xinXboAmzNWPics8FwXf3ITWmk3QjArMCcTlO96CNiIb489/y/W7HjUXdlsnSre/jqTRl3uuhxcxrdW/obniZ8xYsK6nHrEZ52DHd7eirXYTNKHerVQ6lMp3vA1tRBbGn/eGK6+Rc7ryegNJufP7qMXRY1prfkNz5TrMuOqnnonksRlnY8d3t6GtdkvATphlPcTKt7wObWQ2xl/8nut1YPSlrteBza8gecxVnuvhRczUy79yi3M6rNi58hYkjpozBJn1n+GDDyDPyEDUiy9ColAgZOZMCJ2d0L/7LjRz5nishb2sDG3/+AdsO3dCMBohz3Zftd9eWQkAUJ95JqTawFx52ZPF5XZkh0iw+AQVFFIJ5iUCBoeA18psuDxJ5rEe3UwOAXfttkAthdvyQG+W2RCrlOCDCSqoZa7nuCBOhhkbTHinwoaHRijdnzAAsTcpxnqIsR69WAsKJMHexw+udfoDWEZGBgRBgCAIR11KOj4+Hk8//TQKCwthMBig1+uxe/du/P3vfz9q07HbCSecgHfeeQdlZWWwWCxobm7Ghg0bcPvttwfE5GG5Qo7Tz5+G1SvWwWF39Gz/5vOfkJ6TgpH5WX6JCUROwYHWzgpEaTMhlfQeYjHaLJhtHTBaWwcUo1PHIyE8D1HajEHNw1+cggOthjJE6bJEuUWHjoDZ1g6jtXlAMaHqBCRGjEWULoj2D9YDAI+Vw3HfEHPldhDRoSMglch6tseE5cJsa4PR0jSgmFBNIhKjxiMqNGdwE/EDp9OBlo79iAkfLcorNiIfZmsLjOYGn2NMlmaEaVN7Jg8DgEIeghB1DIzmxkHMyndOpx3NbbsRGzUeUmlvbnHRE2EyN6LTVONzjMnciDBdZs/kYQBQKnTQauJhNNUNYla+cTptaKz7FfHJp4l+hwkpZ8LYWQVDx8EBxzTU/oL66p+QM/qGwUvETwSrHYZvtiJ07kk9k3wBIGz+dNiK62DZXeExTqJRQnvmWMTcPxeq8Z5XrxDMVkhDNaLnlUWGdt1n82MWg0emUCBv9mzsXPo/OO32nu0FSz5C7KhRSDzB82rm0VnZaC5x7RdSmczjY4IR6yHGevRiLSgQKZVK3HHHHfj111/R3NwMi8WC0tJSLF68+IhNx/fee8/rXtWhumPeO8Ll/oiIgtXx3sd32q1o2LMc8eMu65m8CACJ4xfA2LQf+todPsc47WbIVWGixyhConruCzROmxUdv32NiFPmQSLvHXPkGZfDWl0Mc+kuj3ESVQhCJ56F+CsfgCZngsfHWKr2Q5t/cs9kWQCQh8dAnT4a5vI9/k3ET5w2K9o3foWIGZeI6hF12hWw1ByAqWynxzipKgSh489GwuUPQpPtuR7m6v3Q5U0X1UMRHgtNWh7MFYFXD6fdioZ9XyF+zKXifX7cFTA2H4C+zr0W3sZ4PE40gXucAK5JefUl3yFhxGxxbqPmwdh2EPqmQp9jqvd8hNjMs6EJS3G9NjsdUGnjcOL85UgaNW/wk/OB02FFw8HvET9ilji3kXO7cnPfp72NqdrzMWIzznKrx9R5XyBxZOBNHnY6rGgoXY34nIsOy2sOjO0HoW/uoxZexFTt/RSx6WdCE3pILULiMHXuZ0gcOdvteQMB6yHmeh34Fgmj5ohzG30pjK0l0Dfu9jkmMuUkt5+GklXQRuUgZ8ZDg59cPwk2G8w//gjNeedBcsj5n+aii+AoL4e9qMhjnESthvrkkxF6661Q5Hm+KqejshLSqChItVoIDgcEIfCvImh1CviuwYGLE+RQSHsnCs9LlOOgUcAew5FzeOaADQKAa1Ld10osNgqYGintmTwMANFKCUZppSgyBP7VSAH2Jg/HeoixHr1YCwpEwdzH5wRiGhTRsRFQqhQ4WCSeqFG633U7Idm9wepLTCCyOcwQ4IRGES7arlFGAACsdsOAYhLD85EYPgbhmsD8NunhbHYTBDgRoooUbT9iPfoRkxg5DomRJyAiJNW/Ax8krEcvHiti3DfEbHYjBMEJjUp8iSWN0nXbYndfDbU/MUlRE5EUNRERQTDB3GbvhCA4EKIW/x3UqGMAABZbh88xWcnnIT3hNNFjHA4LOk31UCpC/ZaDP9lsBldumnjR9u7bFkubzzE56fOQkXKB6DF2hxmGzmoolWF+ysB/rJZWCE4btKEZou3aUNcldM0m98nl/YmxmFuwb8dLyMm7EdrQNP8OfhDYG9ohWO1Q5Ypf95Wjklz3V7l/8QAAFMnRiLpzJqLunAnVaM8r24TOmwZrSR2a/u9L2GtbYdlXjYYHP4AiLRaaE0f6N5FBoouPh1ylQsMe8Qd7DXtdH0SEp3r++xCVlYXwlBTcuW0bnjCa8Neycsx88UUodbpBH/NgYj3EWI9erEUQEgCJQwjKHwT+53pERHSMsRrqITis0MWJJ6Ho4l2rxprbKn2OSTjhchib9qNkzdMwd9TAUL8H+76+F+rIDESknzwY6QyIva0Bgt0KVbp4xVxVWi4AwNpY5TFOGZOM2Hl3IXbeXVCne74aTfJtLyLhusdE2xxGPUwlO6CITvLD6P3P3u6qhyZV/HtWp7hytB2hHvFz7kH8nHugSfM8uSn1lpeQdLV41WWHSQ/jwYKArIe1s8G1z8eKf7+6WNe+YW73cJx4GZMwZj6MzQdQ8vMzruOkYS/2rbof6oh0RKROG4x0BsxibITgtEIXLe5/6KJct836ap9j9M1F0ISmYu/ah7H2nfH44fUcbPzfbLTXu3+ZIVBYjU2u3KL6yM3gXg9vYwwtRdCEpmDvukfw838mYc1bo7Dpi3lobwjMelhNXXlFHpZX5AgAgFnvvtCDtzE9tVj/GH5+fwrWvD0am768FO0Nnr/MEAhYDzFLZ6PrdTE6V7RdFz0KAGDu8PDa4UMMADSV/Yjq3R9h3Mw3RYtmBApnczNgs0Fx2ArCiizXgj+O2lqPcbKEBOgWLoRu4ULIczwvhmOvqoJEqUTzrbeidupU1E2fjuY774S9yvPf7UDQZBVgFYCRWvEqwyO6bleb+p7ou67ZgQ+q7Fg0RgW1h5lOz+Qq8UCO+Et6BruAXXonElTBMTWKvUkx1kOM9ejFWgQh9vEDWuCdQdExITrWNZmto1084a291TV5Kyo2wi8xgcjmMAEA5DKVaLtcquq63/1b5L7EBAubwwgAkEnFuSmkrtUGbHaTX2KCBevRi8eKGPcNMZvdlZv88NxkatH9A40JBt0TweUytWi7XKYB4Jos7GuMTpNwWFwn9pV9BkFwIiFqoh9G739Wm+u8QC7XiLbL5a7Lc3mabO9tjE6bfFicAXsO/AcCnEiMC7wPcaxm16rrcoX4Ta2ia7Kz1eK+kru3MYIgYG/BvxAanoOUzIvR1ux55aVAYm9oAwBII8SXapNFhXbd7z7Z3lva08cg9vEr0Pi3JWh+5nMAgEQpR8avz0KqUx8lOjDoElzHu6m1TbTd2OxarV4XH394CCRSKSIzMxGWnIw1Tz6Jxr17kTx5Mk79y18QkzMC78+6aNDHPVhYDzHWoxdrQURERMcyi951dR2FRvxldEVINADXZGFfY6JzzsLIC55B0cr7UfLD4wAAiUyJ6ffsgFwVeB/G2ltd45ZrI0Tb5aFRXfe7fynXW5ps8WpX9rZGlD11BQSHHdEXBuYVfmxd9ZDpIkTbu+tha/O9HiFZ48X/VlsDDj5zOQSHHTHn3+jz8w4Wi+Eo+3yney28jYnOPhMjz3kaRd/9FSU/PQWg6zi5bWtAHicAYDW6rlKmUEWItnfnajG6f2Hb2xhrZz0qd72PsLhxGH3qU4BEivKCt7Dps7mYfvVahIQH3hfaLUbX71KhEi9yolBHAHBNFvY1xmJsQOXuDxAWNw65pzwBSKSo2PE2Nn9xCU6+6keEhAVWPSw9v+fD83L9nq0mT7XwLsbS2YjKwg8QFjsOuTMed9Vi5zvYvGw+Tr7iB4SEBd5CKayHmLV7v1d7zq0794HGOB027Pvpb8iceifUoYH3pRQAcDS5fo+SMPEiJdKICNf9ze5XIPWWvbISjro6qM86C9qrroKjvh6GxYvRdO21iFu2DNKwwFsYpcHimoUVrhBPII7sut1o9RzXahNwb6EVf8pSYFyYFKs9XERzTJh4knCTVcD1BRY4BOBaDysWByL2JsVYDzHWoxdrQeRfwfFXkoKO0Mf0e4nEdeInl7vver7EBJPu/AR4f3kMX2ICj8Tj1iPn5ktMsGA9jobHithxu294Tq03N8FDbr7EBAFJX4l15eUUHAOOEQQn6pq3obJhPZxOB0akXgytxv2NVUDoM7Xu37N7PfobIwgO1DT8horq7+EU7BiVdRV0IQG4mrmkr53e2fUfu/t9XsZUl69Ee+senHj6m5BIguOb+ZK+cnN25Wb3UA8v6ZdtRNM/PkPEzedCe+ZYODtMaH1jFaqveQkpS++HIjna5+ceMn1cvk4idf1+ZR4unyyVy/HZ9X9A7Y4daOq6lN7+71ZBX1eLuW+8iZQpU1C1efPgjXkwsR5irEcv1oKIiIiOZX28b+rumTgdNp9j6nZ9hgPfP4a0abcjeuS5sJvbUb5hEQo+vBQT//A11OGer/gybI72/thTLfpJcNjR/M3bqF/yDzhtVqTd9zY0WeMG/LyDoa/31IKf69G0ajFqP3kags2CjD+9i5BMz5cWHl4+HCdextQVfoEDPz6BtKl/RHTO2bCbO1D++79R8OkVmLjgy8A7TgD0nVtXX83Zn3qIY2yWDqh1CZg89xPIur74H5N2Gta9fxLKtr2OvDOeGeDYB0Gfx0pX39VTPbyMsVs6oNImYNLFSw6px6lY/8F0lG9/E6NPe3qgo/ezo+z3nnqTXsbYrR1QaeMxadaHkMldX96PST0F65ecivKCt1wTzgMO6yHW1+dX3X3ofrx2HCGmcsd7sFsNyJj0R9+GORT6eg3o6ltjAH1r3TXXQHLzzVBNntyzTTlpEhovuQSdn32G0Ouv9/m5B8uRP70C7H305v6214oktQS3ZRx9zojdKeC/VXa8UGKD1Qm8MkaJ/NDg+JyDvcnDsB5irEcv1oLIr4LkryQFm+YG1+p14RHiy5+HR7puN9a7f5POl5hApOxa3dHusIi2253WrvtD/BITLBTyPnLruq2Qaf0SEyxYj148VsS4b4h1j909N9cq00q5h3r4EBMMFIruvMQrStvt3XmFDijGZGnGrpL/orR2NcK0qRg/4gbERHi+BGcgUCq6VpM9bEXp7ttKhfs36vsTYzQ1YMfeV3GwYjnCQ7MwMf9exEYF4odZgFLlWnnBbtWLttu6VlxWqd0ntXoTYzY24EDhm8jJuxFyhQ52uwmOruPK4TDDHqCrm8viXCtTOFrFq3J335YnRLrFeEOw2VF3zzsIu3wG4p+9FrpzJyDs0pOR8sUDcDS0o+Xlrwc28CGir3OthhQSJa5DSJRrNamOGvfLKDqsVuxaurSnkdJt74oVAICEsWMHY6hDgvUQYz16sRZERER0LFOFulZpsplaRNvtRtdtdZj7yn3exDgdNuz54lYkTbwauRe/hNjcC5E4/kpMvuE7WAz1KF37nN9zGSh5pOuL03aD+Oo9dr3rtiIqcUDPb646gOL7z0HNm3+Bdsx0jPz3r4g4Zd6AnnMwySO66qEX18Nh8FM9qvdj/4Nnoert+6DLm47cFzcicvolA3rOwaLSuWrhts+bXLVQh7rXwpsYp8OGPStuR9IJVyH3whcQO/ICJI67HJOvXek6Tn55we+5+IMqJBYAYDOL943u2yqt+yIE3sYoNdGITjutZ7IsAMhVoYhInAx90x4/ZeBfqpA4AIDN3Pb/7N13dBtV2gbwR71Zttx7t+OW3iAdCD2QQOiE3peFpSxtWcrCfrvLArvAwtJ7XQIESAgEQoAQQknvjhM77r3Jtqxevj9kW0xkJ4os2RJ5fufkcGZGr7jv69GM5urOHcF6e9+yctB6+BYjV8UgLn2usB5yLXRJU9DTXhqA1gfWwN/Zohest/ctKzUJfse4azFnYLAs0FeLxNCsBcB6HEyhGXy/t5n0fduTcLAjjXE67aja+AzSJ1wBiSx0fx+UxLr76Z3dwifkufqWxQne+4avlHPmCAYPA4AsJwfSzEzY9+/3+32DKV7hHkKstwkH//UvJyq8hxh/3mzHqlYH/l4oh8UJ9NpdsDnd4wd77S6YHJ73quh14qyNFjxQZsOx0RKsnqHEmUnhM1Ed+yaFWA8h1sODtSAKLA4gpqDoaOuCzWpDbmGmYH3/cnOD92Na/IkJRVKJEiKIYbLpBetNVnfHyGAD2PyJCRcyicqdm1XYcda/rBh0EOCRx4QL1sODnxUh7htCMml/bsKbR4wW93L/gNDhxoQDmUQDkUgMo1n4PCajxb2skA9SCx9jzJZO7DrwNszWThRlnY/CzHOhVPg3yHKkyKQREIkk6DUJH6vaa3JfKCrkOr9jTOY27Nj7HEzmdpTkX43i/CugUsYFPokAkSt0EImkMPRUCdYbut3LClW8XzEmYyOcDgv2bn8Saz9fhLWfL8L2X+4DAGxZfwd++PKCQKcSENL4KEAmgbW0TrDe0rfs7yzBji4jnN1GqKbkCtZLItWQF6TCVu3/Y1xHUm9LC+xWKxJLxgrW9y931dV5xegyM1Fw+gKID3oSiNPhnrXbfFCndzhhPYRYDw/WIjyJnOH5j4iIaKTJNQkQSWQwNO8WrO/pW1bqvGc/9SXGbtLDbu5CVPp0wWukykhEJBTB1FkVwCwCQxoVD5FUBnO1cJBi/7Is3v8nEVkaD+DAPafC2ngAWX/5EFn3vQdFcs6w2htsA/WoEdbDVO3+O8vj/J8Z19J4APv/fDIsjQeQc98y5Pzp/ZCuh1yTAJFYBkOLsBY9LX37/CCzBPsSYzfrYbd0ISp1muA1UmUkIuILYdJXBTCLwJGr4yASy9DTIRxoYWh3Lyu13p8VX2MUmkQ4+yY8+DWX0w6pPCIg7Q80uSrW/bc+KLf+XJUR3jdi+Bqj0CTC4fCuh9Nph0QWen39nrz2Cdb3tPtSi0PHKNSJcNgtXvFOpy0kawGwHgcbOA60CQc4G/oGPCsjD3Hs8DGmpfxzWHqbkTr24kA2PeDEsbGAVOo1oNfWtyxJ8h5M7Qtnby/Ma9fC0TbIGAqxGCJNaO4bcXIRZCKgzCAcQLy3bzlF6T2EqdTghMUJnPqLGQXfmlDwrQnPVNlhcgIF35pwww7356PK6MQ5m8yoMjnx5iQFXpmoQJY6vIZEsW9SiPUQYj08WIvwNNr98ezHH1p4nS0pbNhtdnz7xU84aeEcSKSSgfWnnn0caisbULazIiAxoUgskiBak4F2QxWcLs+RpN1QCYVUC7U8JiAx4UIskiA6IgvthgOC3NoM5VDIIqFWeA/M8icmXLAeHvysCHHfEBKLJIjW5qC9ez+cLsfA+vaefVDIoqBReA+M9CcmHIjFEsREFqCtq1SQV5t+N5RyHTRK784lX2Oqmr6F0+nA+LyrEK3N9XqfUCQWSxGrG4vWjm1wOj25tXZshVIRiwi1d0esrzGVdZ/D6bJjUsmtiNEVBj+ZYRKLZYhPnoWW+rWCR+A1138LlToF2qg8v2K0UXmYMuvfgn9jxt4IACiccBsmzgjBx0cCEMml0C6Yiu6Pf4bL5smt56MfIctOgGJ85iGihyaJ1UISFwnjj3sF6x2dBlj21EJRlD6sdo8Uh82GPZ98gnHnnyfoHBl/0YVoLy9H49atXjGauDhc9umnGHuOcFao4oUL4XQ4UL9pU9DbHSyshxDr4cFaEBER0W+ZWCpHQvFZaNqxFE6H5zHgTdvegyomF9rkSX7FyDRxkGni0Vm5ThBrM3agp2kXIhJLgpeUn8QyOSJnnImudcvgsnvy0q/9APLkbKhy/H8aUdNrD8JpsyL/qXWInHpyIJobdGKZHFHHLETn+g8F9ehctxTypByosv2vR8Nb98Nls6LwX+sRNeWUQDQ3qMRSORKKFqJp10fCfX7nUqiic6BNmuhXjEwdB5k6Hp3VPwhibcYO9DTvRkRC6H1OAEAskSMx91Q07VsuyK2x7GOoorIQGT/W75iEnFPQWrUG1l/NVGzpbUZnwy+ISvQ+HoUCsUSOhJxT0FS+Qvi33vcJVJGZ0A5RD19iErJPRlvVGsHMzZbeZugbNoRkPcQSORKyT0ZT+WfCvMqXu/OK896nfY1JyD4JbdXfCGaitfS2QN+4EVGJE4OW03CwHkJiiRyJeaejqexT4XGgdBlUuixEJnjP8nikMXXb30RM5lwoI/wbgDtSRDIZlPPnw/Tll3DZPHmZvvgCkvR0yAr9+y1CJJej89570fPss4L19spK2CsrIZ8Qok9WFItwaoIEy5vtsDk9g4g/abIjUyXCWK33DMQXpkixbKpC8O+CFAkUYmDZVAXuzZcDAP6+3warE1h1jBInxEm83iccsG9SiPUQYj08WAuiwAqfufop7Lzzwid4cdkjePSle/HV8u8xcXoJTj/3BDx825NwuVyI1EWgZFIBaisbUFfV6FNMuEiKLEZp0yqUt3yHGE0WDOYWtPceQHbcTIhEItgdFhgsbVDKtFD2PTb9cDHhLFk3HnvqV2B/02rERuSix9SE9p5yZCfM7auHGQZzK5SySCjlUT7FhDPWw4OfFSHuG0LJ0ZOwp3YZ9jd8gVhtPnpMjWjrLkNO0vxf1aMZSlkUlH0zyB4uJlylxE3DrgPvoKz6Y8TpitDdW4tW/W7kpp4OkUgEm90Eg6kBSnk0VIoYn2JcLic6e/YjSpMFk6UNJovwLnW5TAuN0v9HZwVTatJc7Nj7HEor3kR8zER091SipX0L8rPOg0gkhs1uRE9vDVSKuIEZhA8X43I50aHfA11kPozmZhjNwtmKFbIoaNTDe1xnMGTkLsaW9Xdg56a/IjFlHvQdu9BUtwZFE29318LajW59GVTqFKgjUn2Kkco00MUKf+xw9Q1E10blIlI3ZsTz9FX0jaeh9sy/oeGK/0B79rEw/VyG7qXrkfSfayESi+HoNMC8uQKynETIc3zrWBaJRIi79xw03/4aRCIRNPPHw6HvRefLqwGRCLrrwuPHYABY/9STuGbNN7j4/aXY8cFSZM2ahUlLLsGya6+By+WCKlDAq5kAAQAASURBVDoaadOmo72iHB0VFajfvBn7Vq3Comefgy4zC+379yFl0mTMuvVWrH/ySXRWVY12SsPCegixHh6sBREREf2WZc6+BZtemo/t71yApPHnobNqPRq3vYuSc16ESCyGzdiBrtqNUMfmQh2X51MMAOSd9BeUfvJ7iEQixI45BXZTJ2p+ehYiiJAx86bRTHlI8Wf9HhX3nI7qf1yKqDmLYdzzE/Tfvo+0P/wXIrEY9p4OmPZthjw5B4oU3268djkc6N7wOSImHAdLbRkstcIZR6WxKVBlheZA0YSFN2P/faeg8tEl0M0+B72lP6Fz7f+Q8ftnB+ph3L8JiuRcKJJ9r0fXxpXQjj8e5rq9MNcJb86VxaZAlek94HK0Zc64GZteOwXbl16MpJJz0VnzIxp3vIeShc95Pif1m6COyYU6NtenGADIO+F+lH72B/fnJO9k9+dkwwsQiUTIOOZ3o5nyIWVOuh4bPzoH2z6/FkljFkLfsBGNZR+hZP6/IBKJYTV3oqtpG9S6LGh02T7FAEDa2CWo2f4qNnx4NrImXQe4XKjc8hyk8ihkTbp+NFM+pMwJ12HTJ+dh+6rrkZR/JjobN6Jx3zKUHP+Yuz/O3Imu5m1QR2VB3V+Pw8QAQGrxxajZ8To2LDsXmROvBeBC1ZbnIFVEImvitaOY8dAyJ1yDTZ9egO1f/Q5JuWeis2kjGvd9jJLjHu2rhR5dLX21iMryKQYAUosuRM3ON7Dhk/OQOeEawOVC1bYX3LWYcM0oZnxorIdQ5tTfYeP7i7BtxVVIKjgL+vpf0Fj6AUpOftJ97DB1oqtpC9S6bGiic3yK6We39KCj7kfkzbpntNI7IhGXXoq2q65C5x//COUpp8C6dStMn30G3UMPQSQWw9nVBevOnZBmZECakeHTe4pkMkRcfTV6nnwSkEigOOYYOJqaYHj9dciKi6E6/fTgJjUM12ZKcd4mC67fYcWZiRJs1DuxrNGBx4vlEItE6LS5sK3LiSy1CNlqMVJVYqSqhO+xrsMBiQiYHu0eKOxwubC61YHZMWLs73Vhf69D8PokhQhF2vCYX5F9k0KshxDr4cFaEAWOyBVOozIpYNLS0lBfXw+ZWInxCScE7f8zZcY4XHfnEhSMzUVtZQOWvroCK97/emDbC8sewYuPv4MX//WuTzHBJBnkMd/D0W1qQr1+G3otHVDKtEiMLES8Nn9g296mL5Gim4C06Ik+xRystacclW3rMSnjAsgkyoC2HQBEEkVA36/b2IC6js3otbRBKY9CUlQJ4iMLBraVNnyG1OjJSIud6lPMwVq7y3CgZS0mZ18WlHoEWjjXw+XwfkzScPCzIhTO+0agawEAXcY61LX9AqOlFUqZDonR45EQVTywrbT2Y6TGTkd63DE+xRyspasUB5q+xpTcayCTqgZ9jb/EAX7MXpehGrUt69BraoZSHoOk2ClIjBk/sG135btIS5iNjMQ5PsXY7EZsLH1qyP9fvG4c8tPPCFj7JcrAzhCu765AdcNX6O2th0oZh+SEWUiKnzawbWfZ88hIOQmZqSf7FGO1GfDLtoeG/P8lxE5BQc6FAWm7PDmwd/53tm3HgbI30dNVAbUmBWnZi5CSccrAti0/3onsMZcgp/Ayn2KG+n9s+fFOTJv7TMAHENe93hLQ9zP+sAdtjyyDZUcVZLlJiL7mJEQtmTewrXbh3xF719mIu+ccr9jG378A89ZKZP/4iGC9y+WCYflGdDyzEtayeoi1KqiOGYO4+87zeSCyL85JuDxg7zWU7LnzMP/BB5E8cSLay/fj52efxZY33hjYds2aNVjz8MP45q8PAwAUkZGY/+CDKDl7MdSxsWjbV4aNL72EDS++GPS2jgTWQ4j18AiXWjxtd6AHQGpqKuoGeSzbb1l/v4YiKhVz/lI72s3xy7q/pMPSVX9U/v2IiMg3A+e7yFTM+1NVwN6348BaVHz9ELobtkEdm4eMGTcideoVA9s2vXQicubfj7wTH/ApBnBfNzXvWobqdf+GoaUUUoUWusxZyD/54YGByIHQNM4UsPcCAMOOdWh+9+8wVeyAIiUXsWdch5iTLhnYduDeBUi46B4kLbnXK7b2iRtg3L8VBc/+MrDO3tWGPUtyhvz/Rc+/GOm3PR+w9ktsgZ1dr2fX92h6728wVm6HIjkX8adfj9j5lw1sK7//NCRdcC+SL/yzV2z1f66DsXwLiv7jmbnL1tWKXVdkDfn/izl+CTL/EJjvzPHbAzt5QEfl96j47v/Q3bgd6thcZEy/AamTLhvYtumNU5Ez717kHX+fTzFA3+dkzyeo/vFJGFr3QqqIgC5jJvJP+MvAQORAEXXoA/p+HXU/ovznx9HdugsaXTYyJlyJ1OILB7ZtXHYucqffjrxj7/Appp+xqwZl6x6EvmkLRCIxYtJmomD2g1BoAjexgcvhOPyLjlBH/U+o2PAvdLfuhlqXjYxxlyO16IKBbZs+OR85025D3vTbfYrpZ+yuQdkPD6GraStEIhGiU2eiYNb9Aa0HnIGtR0f9T6jY+AS62/ZAHZWFjHGXIbXw/IFtm5ZfhJyptyBv2m0+xfQzdteibP3D6GreCpFIjOjUGSiY+Wco1KE56UW/cK6HSK0J2Hv166hdj/If/4nulp3Q6HKQMelqpI69eGDbxqVnIXfGncibeZdPMf2a9q3A9hVX4ZiLvoAuZSqCYefljQF9P8vGjeh59lnY9u6FNCMDmosugvqsswa2tV99NSJuuAGRN97oFdt5332w7d6NhI8/Fqx3uVzofe89GP/3P9gbGyFJSoJy3jxob7wRYrU6oO3f8MdjA/p+P3Y48K8KG3b3OJGtFuGKdBkuSJUObDt/swW35Ujxx1z5oPH/qrDihWo79p3gzrPd6sKEtUN/TzwvWYInxgbmd8xnvw3sb+WDCZe+yZHCegixHh7hUgv247MfP9RxAPFRaqQGEIeTQA8gDnfBGAhIvw2BHkAc7vhZ8WAthAI9gDjcBXoAcTgL9ADicBfoAcThbCQGEBPRbws7Ht0dj3Pvrxnt5vjl+79m/OY7HomIaHiCNYA4nAV6AHG4C/QA4nAW6AHE4S7QA4jDWTAGEIe1AA8gpt+OYAwgDmeBHkAc7gI9gDicjcQAYiL6bWE/PvvxQ114zNFPREREREREREREREREREREREREREREAcEBxEREREREREREREREREREREREREREREcR6Wg3gIiIiIiIiIiIyG9O12i3gIiIiIiIiIiIiIiIhsJ+/JDFGYiJiIiIiIiIiIiIiIiIiIiIiIiIiIiOIhxATEREREREREREREREREREREREREREdBSRjnYDaHTZnBbsbv1+0G3x6gwkaLJGtkFERERERERENGCT04nNQzzaq3eE20JERESjw2poxvonxg+6Lf3YG5Ax48YRbhERERERERER9WM/PoUzDiA+ysnECpTEzx3tZhARERERERHRIKaKxZg6xPOjnrY70DOyzQk9LkDkGLxjNuSFabOJiGjkySMSMeu2HaPdDCIiIiIiIiIaBPvxD4P9+CFtiF2XiIiIiIiIiIiIiIiIiIiIiIiIiIiIfos4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOgowgHERERERERERERERERERERERERERERERxHpaDeAiIiIiIiIiIjIb07XaLeAiIiIiIiIiIiIiIiGwn78kMUBxEc9F1wOy2g3IiSwDkS+4WeFyFcRo92AkCKWsx79JOlFo92EkPLVOYtGuwkh44XRbgAREREREYUmhwOorBntVoSEJGf6aDchtIhFo92CkCEy9ox2E0KKo6l6tJsQMkQS/hz+ayKlZrSbEFq4fwxwxaSMdhNCytgx7K39tWe/5e/DREREv1Xi0W4AERERERERERERERERERERERERERERjRwOICYiIiIiIiIiIiIiIiIiIiIiIiIiIjqK8JkcREREREREREQUvhzO0W4BERERERERERERERENhf34IYszEBMRERERERERERERERERERERERERER1FOICYiIiIiIiIiIiIiIiIiIiIiIiIiIjoKMIBxEREREREREREREREREREREREREREREcR6Wg3gIiIiIiIiIiIyC8uF+B0jnYrhlSz4XnUbnhx0G1WQ/MIt4aIiIiIiIiIiIiIaISxHz+kcQAxERERERERDUokFkOmUnmtd7lcsBmNo9Ci0cV6CLEeHqwFDSVj+g3ImH7DoNvW/isPlp6GEW4REREREVHosNsGv16SSJUQiY6+B+myHkREvmN/nAdrIcR6CLEeHqwFDeVo78fnAGIKqskzxuP6Oy9D4fh81ByowwevrcDy91YFPCYUTTqmCNfedg4KS7JRW9WED99ajRVLvwtYTJQuAl9seg6nTbsRXZ09gU8gwLpNTajXb4fR0g6FLBKJkQWI1+YHLMbmMGNbzVJMzDgfMokyGCkEFOvhwc+KEPcNoW5TA+o6NsNoaYNSFoWEqGIkRBYGLMbmMGNr5VuYlH1pyNejy1CD2pZ16DU1Q6mIRlLMZCTGTBh2THdvLWqa18JoboVIJEGkOh0ZSfOgUsQEM51h03ftQ1XNShh6a6BSJiAleR6SE2cOK8blcqG59RfUN6yB0dQCmVQDna4Q2RkLoVDogpyR/zrqf0LFxifQ07oL6qgspI+9DKnFFw47xumwofyXR9Fa/Q3MPfWITBiPMTP+hKjEScFMZ9h+7nTgiSo7dvc4kaUW4dJUKS5I9v2y55kqG/5bY0fpXGEHwga9A48fsGNvrxNyMTAtSoy7c2TIUofXjzdZc+Zi/gMPIGXSJLSX78cvzz+Pza+/PuTrx5x6Gi779FOv9e3l5fh30aGPx+GA9RBiPTxYCyIiIvot62j4BRWbn0JP2273NWHxJUgtPG/YMU6nDeUb/oXW2u9gNjQgMm4sxhxzN6ISDt1/MZo6Kr9HxZq/oqdhK9SxeUg/5gakTr1i2DGdVT+g/OuH0NOwFRKFFrF585F30sNQRqYEL5kA6DjwPSrWPIye+q1Qx+Uh/dgbkDr1ymHHtO79HJXfPYKepl1QaJMRl38S8k97BBJZ6PbHddSsR/n6f6KneTvU0blIn3QV0sZfMqwYu9VwiGgRpHJNgFofeJ0tW3Bg9yvo1pdBHZGOtNzFSM05c1gxdlsvvvv4xEFjZ5/xKZTqhIDmEEgdLZtxYOdL6OncC5U2A+l55yI1d+GwYuy2Xnz74XGDxs5Z9BmU6sRAphAwHU0bUbH1v+hp3wN1ZCbSCy9E6phzhh3jdNpQvvkptNZ97zmnTL0dUfHjg5nOsHU0bkDFlqc958uii5FacO6wY5xOG8o3PoHWurWeeky7I6TPsQDPs4fT8X0lKv7vG/Rsa4A6Lxbp1x2D1CumDPl6l9OJ+je2oObZn2GsaIciSYuEMwqRe98JkEaG7jnVV+yP82AthFgPIdbDg7UgCgwOIKagGTu5EE//7x/Y8P0WPHL3Uxg/rQQPPnUHAAw5INifmFBUMjEXT715Dzb+sBP/vP9VjJ8yBvc/dj0ADDnI8UhiJBIxrvvjeZDKwuMjbDC3oqz5a0Qpk5EZdywM5hZUtv0IAEMOcjySGJfLifrObXDBFdxEAoT18OBnRYj7hpDB3IK9DV8gSpWKrPjZ6DE3o7LlewAYckDwkcS4XE7UdWwKi3r0GOtRWvU+oiKykJNyMnqM9aio/xwAhhxE7EtMr7kVuw68g0hNOrKTT4LDZUNj20bsrHgDE/OvhVwWMTIJHqHunkrs3PMMdFGFyM+5CN09FdhX/hYADDmI2JeY1rZNKNv/BpISZiIj7VRYrd2oqVuF3t46TBp/N8RiycgkeAT0TVuxZcWliEmfg6K5f4O+aRN2f3snAAw5iNiXGJfLhW2rroOhrRRZk66HWCJH3Z53sXn5JZhx4VdQaVNHJsEjtLXLict2WDE7Woy/jpFhc7cTd+21AYBPg4h39jjxRJUd8oPGBJcZnLhgqxXTdWI8lC+DyQm8XGvHws0WrJ6uRKJCFIx0Ai5t+nRcsXIlKtaswfKbb0LGjBlY/NLLADBkh0pMbg5Mej3eOmuRYL3dbA52c4OO9RBiPTxYizDkCN1HnxEREYUafcs2bPniSsSkzkLR7Iehb96C3d/fAwBDDiL2JcblcmHbVzfC0LEXWeOvcV9H7n0fmz+/HDPO/RyqiNAb0KOv3YAtr5+BmNwTULTwP9DX/IzdH18HAEMObvIlprt+Kza9egpic09E8VnPwmbSo3Ld49jyxpk49nc/QixVjER6R0xf8wu2vLYAMXnzUbToaehrfsLuj/pzG3wQsS8xjdv+h53vX4rkiRchY+ZNMHfVo+r7x2FoKcWUq1dBJAq9a2p9wyZs/vA8xGbOQ9FJj0PfsAG7V/0BAIYcROxLzJonM4b8fyoj0zHvhu0BziQwutp3Y+u62xCTMA2Fk+9EV9sOlG76OwAMOYjYlxhTr3sWr5LpD0ClER4j5CF8M39X2y5s/e4WxCROQ+HUu6Fv24E9G/4KAEMOIvYlxmSoBwCUHPuQ1zFTrogOVjrDom/dji1fXYeYlBkomnE/9C3bsHv9/QAw5CBiX2JcLhe2fXMLDB1lyBp7pfucsu9DbP7qWsxY9HFInlMAQN+yHVtWXY2YlJkomvUX9/ly3b0AMOQgYl9iXC4Xtq2+CYbOMmSNu6rvHLsUm1ddhRmLV4RuPXiePST9hlpsWfgGYk7IRdFTZ0L/cy123/AxAAw5iLjulU0ovXk5Uq+cgpx7joOxoh1V/16Hro11mPb1NRBJwmuyi19jf5wHayHEegixHh6sRRhiP37ICo8RVRSWltxwLqoranHnlX+B3e7AFx99A3WEGpfffMGQg4H9iQlFF1+zADUVDbjr+ifgsDvw5SfrodEocdkNZw45KNLXmEdfuB0lk/IQnxianQWDaereA5UsEnmJx0MsEiMuIsc9SK1rF+Ii8gbtIPQ1Zn/zNzBY2mBzmEY6Lb+xHh78rAhx3xBq1O+ASqZDfvLJ7ty0+XA6bWjs3I54bcGg9fA1Zl/jVzCYW2BzhMejSBraNkKpiEFB5mKIRRLER4+Fw2lFfevPSIgeP2gtfIlpaP0FMqkGxVkXQCx2fy2MjSzAlrLn0di+EZlJx490qj6pq18DlSoRJYXXQyyWIDFhOhwOC2rrvkRSwoxB6+FLTH3jd9BFFaAg/9KBOIUiBrv3Po9eYz20EUP/yDNaqre/CI0uFxNPfRFiiQzJBWfDbutF5dbnkFJ0waC18CWms/4ntNesxewl30MVmQYAiM86Cdu/vAH6xg1Qac8e6VR98nKdHblqEV4YK4dMLMLZSUCv3Yrnq+04P0lyyB8lzQ4Xbt1jhVIMHHz5+mKtHXFy4I3xcigl7vc4JU6CeT+b8VqdHffkyoKYVeDMuuVWtO0rwzvnnQun3Y7t770HhVaLOXfcOXRnSk4OOirKUb1+/cg2dgSwHkKshwdrQURERL9l1TtegUaXg4knPwuxWIbk/EWw2wyo3P4CUgrOHfw60oeYzsaf0V63DrMvWDNw02l85onYvvr30Ddtgirv0DNzjobq9U9CEzcGE5d84L4+nngx7JYeVK57DClTLh+8Fj7E1G9+HfKIREy85EOIJe7rxYjEYmx86QToa39BTPbckU7VJ9U/PAVN/BhMvKQvt0l9ua19HClTrhi8Hj7E7F91L5ImXIhxF7w5EBebdwJ++s9UdNX8DF3mjJFM0yfVG5+FJiYPE896E2KJDCkl58FhNaDyl/8gddySwWvhQ8z0iz/3inM6LNix4lokFy0eidT8UrPvPai1mRg/6xGIxVIkZ54Cu92I6rK3kZJ9xqD18CWmf8BsfOpcSGWhO/vywarL3oFam4EJcx5z55Z1Ghw2I6pK30BKzpmD7x8+xBgNdQCAhLR5YVOP6l1vQBOVhYknPOU+P+Se6e5n3PkKUvIXD14LH2I6mzagvf4HzD7nC6gi+s4p6cdj+7e3Qd+8JWQHzFbvfBWaqGxMPOkZd255C9257XgJKWPOGbwePsR0Nv7irsd5nokd4jPmY/uam6Fv2gxVXojWg+fZQ6r+z3poxsRh4vsXQyyTIPmiibD3WFD5r3VIuXzyoPWpfOx7JCwqRslznv75yEkp2HLmG2hfU464k8eMZAoBxf44D9ZCiPUQYj08WAuiwAnfW5AopEllUhx32kys/mQt7HbHwPovPvwaWXnpGDM2NyAxoUgqk2DeyVOx+rOf4fh1Hh//gMzcFOQXZw4rZtumMrz78kqsXvFTcBMJEKfLgc7eGsRosiEWeQ45cZocmG3dMFo7hxUToUxEUlQxYjRZQc0jUFgPD35WhLhvCDldDnQaqhATkSPILVabD7OtC0Zr+7BitMokJOvGISYiJ7iJBIDT6UBH9z7ERRVBLPLMgBuvK4HZ2gGjucXvGJOlHZGa9IHBwwAgk6qhVsbBaG4NYlb+czrtaOvYjoS4KYIZgRPip8NkbkFvb53fMQq5DvFxwjv7lcpYAIDDYQlGOsPidFjRcuArJOafOdBRCgDJY86GUX8APW17/I6p2/Me4rPmQxWZBpfLBZfTAYUmAdMXL0PymNAcPGx1uvBVqwNnJEggE3s6V89OkuCAyYU9hkPPNv7IARtcAC5J9b7HssLoxHSdZGDwMADEykUYoxGjrDc87paVyGQoXrQIO5Z+AKfdPrB+2zvvIr6gAMkTBp/NPDYnF+0VBwAAYknozcLtL9ZDiPXwYC2IiIjot8zpsKKl6msk5iyAWPyra8L8s2DsqkRPe6nfMXV7lyI+43iotKme60h1PKYvWorkEBw87LRb0bJnORLHnye8Pp54MYxt+9HT6D0TrK8xTrsZUoVW8BqZOmZgWyhy5/YpEseff1BuS2Bs23eIehw6xmbsgLmrFrH5JwpitckToNRlomXP8uAl5Senw4rm8s+RVHi2MK/i82DsLEdPyy6/Y6LTjvX611L+BTQx+cib8+fgJ+cHp8OGlvrvkZg+X9CHmJx5Kow9NTDo9/sdYzTUQa6IhlSmgcvpgMsV+k+KczpsaKlbi6SMk4S5ZZ3Wl9s+v2NMhnrIFTFhUw+nw4qWmjVIzD5NeH7IPRPG7ir0dOz1O6Zu34eITzsOqoiDzikL3kZy7hnBT84PTocVLdVfIzHndGFueYvc58uOIc6xPsTUlX2A+PTjvM+xZ/4PyXmDzwI+2niePTSn1Y6W5aVIPHccxDJPP1LyRRNg3N+Gnh1NXjEOoxXmGj1i5wvHTMTMzQZEIhh2e/9WFC7YH+fBWgixHkKshwdrQRRYHEBMQREbHw25Qo6KsirB+gNl1QCApNSEgMSEopi4KMgVMhzYJxzAVLnffSd1UkrcsGLefWkl3nlxJX78bluAWx4cNocZLjihkkUJ1qvkOgCA1W4YVkxyVAmSo8YiShWaj1I/GOvhwc+KEPcNIZvdBBecUB/0aLZD1uMIYpKjxyM5egJ06vTANjwIbPZeuFwOqJXxgvUqpXt/t9i6/Y7JST0FmUnzBK9xOCzoNTVDLtMGLIdAstq64XLZoVYlC9Zr1O5ls6XD75jiwmuRkjQHLpcLVlsPegzVqKpeDoUiBpHa7GCkMyxWYxtcTisiYoSzCvQvm/tmcfEnxtBRBpU2DaXf34+1r03BmhcLsGHZYnS1hOajNAGgzeqC1QWM0QgvcfLV7uUGy9A/uqzrcODtegeeKpZDOcgV0v+NkePuHOHAYoPdhV0GJ5IUofeo1cFEJCZCqlCgZc9uwfqWUveg8aj0wY+HMTk5iEpLw81btuAhowl3V1VjwRNPQB4REfQ2BxPrIcR6eLAWRERE9FtmNfVdE0bnC9ZH6PIAAGZDg98xho597uvI9X/B2rePxZrXxmLDp+ejq3VHMFIZNquhGS6HFREJxYL1EQlFAABzV63fMUnjz4exbT8qvvkbzN0NMLTswd7P/ghldBZ0GTODkc6wDZlbYl9u+iOox69iJIpIiKUKmDoqBa+xm7th7W2Bpdt7nxttlt4Wd16xhYL1EXEFAABzt/fN6/7EAEBb5RrU73gX4898UTCwNJRYLR1wOW2IiBROxKCJdPeVmY3NfseYDPUQSeTYsvZWfLPseHz3yUnY9sOdMA7SpxUqrOZ2uJw2aKIOyi3KnZup13vQn68xRkMdxBIZtnx7M9Z8MAfffXQ8tn5/+8DMxKHGanLn1X8+6Behcw9uNPc2+h1j6CyHSpuK0p//D2vfn4s1b0/BhpWXoKvNewB/qBjI7eDzZXT/+fIQ9ThMjKFzn7sePz6Mte/Owpo3JmDDigvR1bozGKkEBM+zh2ZtNsBldSCiWDj+IaLIvWyu1XvFiCRiTF97PRIXjxWs7/yhCnC5oEgJzd90fMH+OA/WQoj1EGI9PFgLosAKzatRCnuxCe67/Lr1PYL1XZ3uAUux8dEBiQlFsfE6AEBPl3BwW1enO6+YuKiDQ/yKCRc2hwkAIJUoBOulYkXfdu87Qf2JCReshwc/K0LcN4RsDiMAQCIW5iYTK93b7aaAxISD/oHPUolSsF4qUQFwDxb2NyZClXRQXC/2Vn0Il8uJpJjJAWh94Fmt7u8FUqlasF4qdT/Wz2brGXaMw2HCTxvuAgCIxQpMGvfHkPwRx2J0zyggUwiPfTKlDoB7sLC/MRZjC2p3vYXIhPEonPMQIBKjZvvL2LjsHMy8+BuoIzMCmUpAtFrd/4066E8V3TcRRZt18AHEepsLd+y14pYsKcZpxfi6zeH1mrFa4ajiNqsL1+y0wOECLkkJvX1jMBFJ7s+7qVMvWG9sd8/OHpGY6BUjEosRnZ2NyNRUrHn4YbSWliJ16lTMvesuxOXl440zQ3PGF1+wHkKshwdrEZ5EzvCYDZ6IiGi0Wfqu+byvCd1971bTYNeRvsVYjC2o3fM2IuPGoXDmA+7ryJ2vYuPyCzHzvC+hjgytm7gtBvcARplK+LuDTO1+EpHV4D2Ln68xsXnzMebUf6Ds8ztRseYhAIBIIsesW7ZBqgjNH6YtPe4BjEPn5j1I1JcYsUSKpAkXoPqHp6BJKEJMznGwdNdh3xf3wmkzDVrn0Wbt7es76esr6SdTun/D6u9bGW6M02HD3jX3IvuYP0CpDd1JHyxm97WQVC4cmCaTRwIArGbvm/l9jTH11sNibEZC6jxk5J8Hs6kFlaVvYNM312PGqe9BJg+9wXD9ufXn0k8mdx8jD1WPw8WYeupgNjYjIe14pI+5ABZTCyp3v4qNq6/BzAUfhFw9LH3Hf6+8FDoA7sGx/sZYTK2o3fseImPHovCYe93nlN1vYuPnl2Lm2Sug1qYFMpWAsJjcT/STKYbKbZBzrI8xFmMrakvfRWTcWBTOuK/vHPs6Nn52MWae+znU2tA6xwI8zx6Opdn9G45MpxKsl8W6f8OwNntPoiNWSKE7Rvi37tpUh51XfQhlWhQSFhZ7xYQL9sd5sBZCrIcQ6+HBWoQn9uOHrvD4tZvCzlCP1RGJ3YMvpDLvXc+fmJA0xOR2njwGmQbfn5gw5+pL2gXfTxD+xISLo7Ie/Kz45KjcNwAAg8/oeejc/IkJfaIh8ur/QDhd3oMdjzTG5XKiqX0LalvWwel0ID99ITQq7wurUDAS9ZBIFJgw9nZYrXo0NK3Dtl3/xsRxdyBCE2I/5oiG2Of7vlM5nTa/Y+yWbig0SZiy8B1IpO5OzLiMuVj31ixUb30BRfP+NtzWB9xQf+X+T75tiHPI/ftsSFaI8LuMw3/XtDtdeKvBgScqbbA6gaeKZSjRhslDXQ7zXVsik3ltE0ul+PCqK9G4fTvaysoAAPu+XIWepkac/fwLSJs2DXUbNwavzcHEegixHh6sBREREf2WDXHh5HK5r5ycTrv3Rh9j7NYeKDSJmLLgTUik7hua49JmY917x6N6x8somv3Q8NoecENdH/fl5RjkmtrHmKadH2L/6geRMeP3iM07CXZLF6rXP41t75yHyZevgDIq9Aa+Dd1fcIh6+BhTeMYTcDls2Pm/SwbiMmffBmtv68Aj50PLofNyDfY58SOmdttrsFsNyJp2o3/NHCGH71fzzs3XmIwxFyG7+EpEx08aeEV0/CT8/OUlqD/wKbIKL/G73cFz6Nxcg9TD15jMwouRM/ZqRCd4JnbQxU/Cz19chPryj5FVfJmfbQ6Swx0DBj2n+BZjt3ZDoU7ElFNe9pxTUmZh3Ycno3rXayiacf9wWx8EftTDxxi7tcddj9Ne99QjdTbWvX8Cqne8gqJZfxle04OC59lDGuqz4Ow7TtoP/XuWrdOEiv/7BrXP/wJFWiQmLbsUEpV3n1XYYH+cB2shxHoIsR4erAVRQIXJr90Ubtpb3HfLRkUL75iM0rnvjm1t8r7L0p+YUNTeqgcAROqEdzhG6tyzHLY1dwYkJlzI+2a7tDssgvV2p7VvuzogMeGC9fDgZ0WI+4aQTDpEbn3LMokmIDHhQCZzt9vuEM6gbLe7Z5iWS71nnjiSGJOlHTsr3kRl42pEatIxMf9qxOmKApdAgMn7Zqew242C9f2zKivk3jORH2mMSCSBLiofCfHTMK74JohFUjQ0rQ1MAgGkULsfZ2Yz6wXr7X3LSo33IHBfY+SqGMSlzx0YPAy4Z43RJU1BT3tpAFofePFyd6er/qC+Z33fyOFEuXen7BetDnzZ5sD/jZHD4gR67S7YnO5+h167C2aHpwOiwujEOVss+Mt+G47RifHldAXOSAiTG9wA9DS5Z4ZSxwhn8FDH9D0FpMH7sbEOqxU7ly4d6EjpV7p8OQAgady4YDR1RLAeQqyHB2tBREREv2UKVTwAwGbRC9bb+5aVmgQczNcYuTIGcamzBwY2AX3XkYmTQ/I6UqF1X//ajMLZQu19y8rIZL9inA4b9nzyO6RMugSFZzyB+MLTkTzhIky9ahUsPc2oXPtYwHMJBIXWPYOXzTRUbil+x0iVkRh3wZs4/oEWHHvzBpzwYAcKTv8nbMYOKLTedR5tioj+vhNhX3P/siIiadgxTqcdVRv+g/RJV0EiC+0+XLnSPfOnre8JX/36lxWqOL9j4pJnCAYPA4AmMgtqbToM+vIAtD7wFKohcrN09W2P9zsmLmWWYPAwAEREZUOtzUCPfn8AWh9Y/X/H/jz62fuWlerBauFbjFwZg7iUmQedUyKgS5iEng7htXeoUKj7z5dD5TbIOdbHGLkyGnFps7zrkTgZPR17A5RBYPE8e2iKRPfvnLbOg37D6XD/hqFMHnrG8dZVZfhx8n9Q++IGZNw8AzM33gRtSWhOCOMr9sd5sBZCrIcQ6+HBWhAFFgcQU1B0tOlhs9qQW5glWN+/3FTfGpCYUNTR3gWb1Y7cAuEjRPqXmxu8H9njT0y4kEqUEEEMk00vWG+yujvO5FLvAX3+xIQL1sODnxUh7htCMonKnZtV2FHUv6wYJDd/YsKBTKKBSCSG0Sw8Dxot7mXFII+u8zXGbOnErgNvw2ztRFHW+SjMPBdKRbTX+4USmUwLkUiCXqPwwq9/WTFI+32JsdtN2Lj1YbS1bxO8RiKRD2wPNXJVLERiGQwHdZr3d6IrI7x/3PM1RqFJhMNh9op3Ou2QyELzsxQrB2QioKxXODtDWa97EHCK0nsA8V6DExYnsGCTBcXrzCheZ8Z/a+wwOYHidWb8brf7hoxqkxPnb7GgyuTC6+PleGmcApmq8LqU6m1pgd1qRWLJWMH6/uWuujqvGF1mJgpOXwCxVDhQ2ulwz9pt7u72igkXrIcQ6+HBWhAREdFvmeeacJ9gfU/fslLjPZjH1xiFJmHw60iXDRJ56F1HyjUJEElkMDTvFqzv6VtWRnk/Dt6XGLtZD7u5C1Hp0wWvkSojEZFQBFNnVQCzCJzD5qbzns3R15iu2g3obS2DTBWNyJRJkCoiYNbXwtJdj4ikkmCkMyxydTxEYhl62oQD3w19y0qt9xOqjjSmZf9KWAzNSB17cSCbHhRyRTREYil6uw4I1hv6lpWDPMXMlxi7rRetDT/AYhqkT18khjRE+5/kihiIxFIYuioE6/uXlerB6nH4GLutF63162AxeU+mJArResiVfXkdNLi5f7DzoOcUH2MU6gQ4DpooBHA/PS1UB927c5PB0HFQbp1958uIoepx+BiFOgEO+2B9tbaQ7avlefbQ5AkaiGQSGHY3C9b37GkBACjTvCdKAYCW5XuwdfHbkMdrMHPLzSh45DRII5WDvjacsD/Og7UQYj2EWA8P1oIosMLrV28KG3abHd9+vh4nn3UcpFLJwPpTzzkBtZX1KNvpfeewPzGhyG5z4LsvN+LEM2ZA8qs8Tlk0C7VVTSjbXRWQmHAhFkkQrclAu6EKTpdnQE+7oRIKqRZqufcjyvyJCReshwc/K0LcN4TEIgmiI7LQbjggyK3NUA6FLBJqhffMFv7EhAOxWIKYyAK0dZXC6XIMrG/T74ZSroNG6T3ria8xVU3fwul0YHzeVYjW5gY/mQAQi6WIi5mI1rbNcDo9ubW0boRSGY8IjXfHoy8xEokSFose7Z27BLEWaxeMpiZEaELvsWdiiRwJOaegqXyF4JFvTfs+gSoyE9r4sX7HJGSfjLaqNYKZcyy9zdA3bEBU4iSv9w0FcrEIp8RL8FmLAzanZ+bgT5sdyFSJUBLhPYD4/GQJPpwkF/w7L0kChRj4cJIcf8p1P+LoHxU2WF3AymkKHB8r8XqfcOCw2bDnk08w7vzzBJ0j4y+6EO3l5WjcutUrRhMXh8s+/RRjzzlHsL544UI4HQ7Ub9oU9HYHC+shxHp4sBZhyuEMz39EREQjTCyRIyHrJDQdWAmn81fXhOUroIrMgDbOezCnrzEJmSeireY7wRNvLMYW6Bs3ISp+YtBy8pdYKkdC8SI07fxAeH28439QxeRCmzzRrxiZOg4yTTw6K9cJYm3GDvQ070JEYnHQchoOd25noWnHUmFu297ry827L8DXmKp1T2DTK6fCabcOvKb25+chUWiROO68IGblH7FEjsT8BWja+7Egr8Y9H0Gly0Zk4vhhx9Rtex0xWfOgDMEZmA8mlsiQkDoPzbVfw+m0D6xvqvkKqohUaKPH+BUjFsux+5eHcWD3S4LY3u5qGLurERXr3a8VCsQSGRLSjkNz9WphbtVfQhWRBm10gV8xYrEcu356EBU7XxTE9nZXobe7ClFx3vvdaBNL5EjIOBFNlauE54cDK6HSpkMb6/2UO19jEtJPQFvdWsHs9xZjK/TNmxEVH3q1APpyyzwRTQc+F+ZW0Xe+jPU+/vsak5B5Itpq13qfY5s2Iyp+QvCSGgaeZw9NLJciYVERmj7cCafN85tF0/+2Q5UTA+1E7/OD0+bAnj8sh256OqZ/dx00+eH5m9dg2B/nwVoIsR5CrIcHaxGmRrs/nv34QwqfZ+5S2Hnn+Q/x4if/xj9feRCrP/0OE48ZiwXnnYSHbnkcLpcLkTotSiYVoraqHnWVDT7FhIt3X16J599/AI88dyu+/uwnTJhWiNMXz8Ff73zBnXuUBsUT81BX1YS66mafYsJZUmQxSptWobzlO8RosmAwt6C99wCy42ZCJBLB7rDAYGmDUqaFUhbpU0w4Yz08+FkR4r4hlKwbjz31K7C/aTViI3LRY2pCe085shPm9tXDDIO5FUpZJJTyKJ9iwlVK3DTsOvAOyqo/RpyuCN29tWjV70Zu6ukQiUSw2U0wmBqglEdDpYjxKcblcqKzZz+iNFkwWdpgsghnt5DLtNAovR+rFgrSUudj285/Y0/Zi4iPm4Lu7gq0tG7AmLxLIRKJYbP1osdQBZUyHipVgk8xAJCaPA81dV9CLJZBF5kPm82AuoY1kEkjkJI0ZzRTHlLmhOuw6ZPzsH3V9UjKPxOdjRvRuG8ZSo5/zF0Lcye6mrdBHZUFtS7bpxgASC2+GDU7XseGZecic+K1AFyo2vIcpIpIZE28dhQzPrRr0qS4YJsFv9ttxRkJEmzUO/FxswOPFsogFomgt7mwrduJLJUIWWoxUpVipB40QcMPnU5IRMA0nXugsMPlwtdtTsyKFqO814XyXofg9UkKEQojwuO+zPVPPYlr1nyDi99fih0fLEXWrFmYtOQSLLv2GrhcLqiio5E2bTraK8rRUVGB+s2bsW/VKix69jnoMrPQvn8fUiZNxqxbb8X6J59EZ1XVaKc0LKyHEOvhwVoQERHRb1nm+KuxacVF2L76JiTlLkBn0yY0ln+CkrmP9F1H6tHVuh3qyEyoo7J8igGA1MILUbP7TWxYcSEyx10FwIWq7S+6ryPHXzV6CR9C5qxbsenl+dj+7gVIGn8eOqt+ROO2d1Fy9osQicWwGTvQVbcR6thcqGPzfIoBgLwT/4LST38PkUiE2PyTYTfpUfPzsxBBhIwZN41myoeUOfsWbHppPra/05/bendu5/yqHrV99YjL8ykGADJm3oTmXcuwc+kVSBx7NvTVP6Lmx2dQsOBxSBURo5nykDKn3YiN752JbZ9egaTCs6Gv+xmNe5ai5NT/QCQSw2rqRFfjZqijc6CJzvEppp/d0o2O2h+RN+fe0UrviGWMuQibv70RO368F0np86Fv24Gm6lUomnqv+7hh6UJXxx6oI9Kg1qb7FCOSiJFVeCnKdz4LkUiCmMRpMBubUV32DrTRhUjKOHmUsx5aZsESbFpzPXb8cDcSM06CvnUbGqu+QPH0+z31aN8NtTZ9oB6HixFJxMgqvhzl25+BSOyuh8XYjKrStxEZU4TkzFNGOevBZY69HJu+uBzbv7kVSdmnobNlCxorVqBk1l/7aqFHV+tOqCMzoI7M9CkGAFILzkVN6TvY8PmlyCy5AoALVTtfgVQeiaySK0Yt38PJHHcVNq1cgu1f/wFJOaejs3kzGsuXo2TO3391jt3Rd47N9CkGAFILzkfN7rew4bOLkTnuSsDlQtWOlyGVa5E1LjTPsQDPs4eT+YdZ2HTSK9h+0XtIOnccOn+sRuN721Hy/Nnu+nQY0bWpDurcWKhzY9H1Sy2sTQakXDIJnT9Ueb1fREkSlKmRI59IgLA/zoO1EGI9hFgPD9aCKHBErnAfbUV+SUtLQ319PQARlBLVoK+JV6UhXuU9i9+RmDJzAq6/6zIUjMtD7YF6vP/Kp1jxvy8Htr34yb/wwmNv4sXH3vQpJpgkqviAvt/kY4tw3W3nYUxJJmqrmvDBG1/hsw/WDmx7/v0H8NITH+KlJz/yKeZgC86diwf/9TucNPE6dHX2BLTtACCSKAL6ft2mJtTrt6HX0gGlTIvEyELEa/MHtu1t+hIpuglIi57oU8zBWnvKUdm2HpMyLoBMEvqPagnnergGeWzUcPCzIhTO+0agawEA3cYG1HVsRq+lDUp5FJKiShAfWTCwrbThM6RGT0Za7FSfYg7W2l2GAy1rMTn7soDXQ6KKDej7dRmqUduyDr2mZijlMUiKnYLEmPED23ZXvou0hNnISJzjU4zNbsTG0qeG/P/F68YhP/2MgLVfFpkRsPcCAH3XPlTVfAZDby1UynikJh+HpMSZA9u273oCmekLkJVxhk8xAOB02lHX8DWaWn6GxdIBuSwSuqgxyMpYCIVCF7C2KyYGrq4A0FH/Eyo2/Avdrbuh1mUjY9zlSC26YGDbpk/OR86025A3/XafYvoZu2tQ9sND6GraCpFIhOjUmSiYdT8UmsAOLH9pj/dMNcPxU6cDT1TasdvgHih8eZoU5ydLB7ZduM2KW7OkuC1bNmj8E5U2vFhrR+lc93fkdqsLk9d7PyKw37lJEvyrSB6Qtr+wznr4Fw1T9tx5mP/gg0ieOBHt5fvx87PPYssbbwxsu2bNGqx5+GF889eHAQCKyEjMf/BBlJy9GOrYWLTtK8PGl17ChhdfPNT/JmywHkKsh0co1WKT04nNzsG7bjoBOAGkpqaibpDHsv2W9fdrKLQpOO73e0a7OX757r/FsPQ0HJV/PyIi8k3/+U4kkkAdlT3oa9JLLkFGyaVH9L4dDT+jYvNT6G7bA3VUJjJKLkNqwbkD2zZ9tgQ5k/+AvKm3+BTTz9hdi7Kf/g9dLdsgEokRnXIsCo69Fwp1APveM4f3m8XBOg6sRcWah9HduA3q2DxkHHsjUqdcPrBt0ysnIeeE+5A3/wGfYgDA5XKhefcyVP/wBAwtpZDKtdBlzUT+SQ8PDJAKGHFgb5jvOLAWFV8/hO6Gvtxm3IjUqVcMbNv00onImX8/8k58wKeYfi17VqB89QMwdVRCmzweqdOuFtQsEEQB7vvtqPkB5T88gu6WHdBE5yJj8jVIHbdkYNvG/y1E7sy7kDf7Hp9i+jWVLcf2T6/AMZd8CV3KtIC2+dccVWUBfb+Oli04sPsl9HTug1qbjvS8c5GSfcbAti3f/R7ZxVcjd+w1PsUA7s9KbfmHqCv/EGZjE5SqRMSlzEbu2GshkQ7+26U/RJLAz6fV0bwZFTtf8OQ25jyk5iwc2Lb5mxuQM/Za5I67zqcYoK8e+5eidt8H7nqo3fXIG39DYOuh1ATsvQCgo3EDKrY+g+6OUqgjM5FRdDFS8xcPbNu06grkTLwReZNu8immn7GnDmUbHkFX63aIIEZ08nQUTLsrsOcUAAjw/tHR8AsqtvwH3e17oI7MQkbJJUgdc87Atk2fX4qcSTchb8offIrpZ+ypRdnPf0dXy3b3OTb5GBQcc09g65GdFbj36hPW59k/Phq49xpCx9oDqPjrN+je3gh1XiwybjgGqZdPGdi26ZRXkfPn45F3/3w0fbQLO5b8b8j3KnlxMVIvmxy0tk6NeDBo790vlPrjRhtrIcR6CLEeHqFUC/bjD479+OGBA4iPUv0fUJlYgXGxs0e7OSEh0AOIw10wBgLSb0OgBxCHO35WPFgLoUAPIA53gR5AHM4CPYA43AV6AHE4G4kBxET02/K03YEesOORHY9ERPRbNXC+0yRh3pL1o92c0BDgAcRhL8ADiMNZoAcQh7tADyAOZ8EYQBzOAj2AOOxx//AIwgDisDYCA4jDyUgMICYi+q1iPz778UMdvxETEREREREREVF4crkAp3O0W+Ef3tNPRERERERERERERL917McPaeLRbgARERERERERERERERERERERERERERGNHA4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOopwADEREREREREREREREREREREREREREdFRRDraDSAiIiIiIiIiIvKbwzHaLSAiIiIiIiIiIiIioqGwHz9kcQZiIiIiIiIiIiIiIiIiIiIiIiIiIiKiowgHEBMRERERERERERERERERERERERERER1FOICYiIiIiIiIiIiIiIiIiIiIiIiIiIjoKCId7QYQERERERERERH5zekc7RYQEREREREREREREdFQ2I8fsjgDMRERERERERERERERERERERERERER0VGEA4iJiIiIiIiIiIiIiIiIiIiIiIiIiIiOItLRbgBRqHA5LKPdBKKwwM8KkW9cDutoNyGkSCJTRrsJIWPfJfbRbkJIeWEOPytERDQMLgAOx2i3wj+u0W4AERGFDacDzo6G0W5FSJDExI52EyhEuTraRrsJIcVl7hntJoQMfu0WEpkNo92EkCLJGTvaTQgZ5XNbRrsJIWX/px+PdhNCyn2j3QAiIgpv7McPaZyBmIiIiIiIiIiIiIiIiIiIiIiIiIiI6CjCAcRERERERERERERERERERERERERERERHEQ4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOopIR7sBRERERERERERE/nHB5XCMdiP85BrtBhARERERERERERERBRn78UMZZyAmIiIiIiIiIiIiIiIiIiIiIiIiIiI6inAAMRERERERERERERERERERERERERER0VGEA4iJiIiIiIiIiIiIiIiIiIiIiIiIiIiOItLRbgAREREREVEoEYnFkKlUXutdLhdsRuMotGj0sBZCrIcQ6+HBWowyp2O0W0BEREQA7PbBv/dIJEqIRJzPhoiIiCjQDObB+0TUcjHEYtEIt2ZksT/Og7UQYj2EWA8P1mKUsR8/ZHEAMQXV5Bnjcf2dl6FwfD5qDtThg9dWYPl7qwIeE4omH1uMa2+/AIVjc1BT2YgP31yFFe9/E7CYKF0EVm19FadOvhpdnT3BSCGgJh1ThGtvOweFJdmorWrCh2+txoql3wUsJkoXgS82PYfTpt3IeiC86sHPihD3DaFuUxPq9dthtLRDIYtEYmQB4rX5AYuxOczYVrMUEzPOh0yiDEYKAdPVW4u61p/Qa26GUh6NpOgJSIgeN+yY7t461LSuh9HcBrFYAq0qFRmJs6GSRwcznWHrbN+FyvL30NN9AGp1MlIzTkdK+okBjdF37MaWX/6MqTMfQ2TUofe70WTe8jP0rzwFS9luyNKzoF18CbRnnu9TrHHd1+h8/jGkvvPlof8f2zag6cYLkfzKJ1AUjQ9Es0dU1py5mP/AA0iZNAnt5fvxy/PPY/Prrw/5+jGnnobLPv3Ua317eTn+XVQYxJYGH2shxHoIsR4erAURERH9lnW2bsWBPa+hu3Mf1No0pOWcjdTsBcOKsduM+O7TUwaNnX36R1CqEwKaQ6B01PyA8h8eQU/zDqijc5A+6WqkTbh0WDF2q+EQ0SJI5ZoAtT7wWA+PjsYNqNjyNHradkMdlYX0oouRWnDusGOcThvKNz6B1rq1MBsaEBk3FmOm3YGohAnBTGfYOtt24EDZW+jp2g+1JhVp2WciJePUgMZ0tu/ElvV3YNrcpxGpGxPoFAKK9fDobNuGA6VvoFu/H+qIVKRlL0Jq1ukBjels24HN627F9OOeQ2R0QaBTCKiO2vUo/+kxzzFxwpVIG7dkWDGHPI6KRJDKQvM4CgCmrT+h47V/w7pvF2Rp2Yg8+zJELrjQp9je9avR/sIjyHhzjWC9y+WC4cuPoF/6Emy1ByCO1EE9ZQ5irrsL0rikYKQREGv39OChDxqwpdKI/GQlfndyPK46Pm7I1xstTvzt40Z8+HMnatusSI+TY8nsGNy1MAlKufvGrB6TA1FXbBs0vubZcUiLlQcjlaBhf5wHayHEegixHh6sBVFgcAAxBc3YyYV4+n//wIbvt+CRu5/C+GklePCpOwBgyAHB/sSEopJJ+Xjq7fuxcd0O/PPPL2H8lAI88K+bAGDIQY5HEiORiHH9HRdBKguPj3DJxFw89eY92PjDTvzz/lcxfsoY3P/Y9QAw5CDHI4mRSMS47o/nsR59wqke/KwIcd8QMphbUdb8NaKUyciMOxYGcwsq234EgCEHBB9JjMvlRH3nNrjgCm4iAdBjbERpzTLoNBnITj4RPcZ6VDR+BQBDDiL2JcZobsPu6qWIVKchO/kEOJ02NLZvxs4D72Ji7uWQyyJGJsEj1KUvw/ZNDyE6dgIKiq9Hl34v9u56GgCGHBB8pDF2mxF7djwJhPj+Ydm9Fc23XQHltFmIvfNhWHZsRvvf7waAww4idhq6oX/t6cP+P5y9PWh7+I+AK7RrMZS06dNxxcqVqFizBstvvgkZM2Zg8UsvA8CQnQgxuTkw6fV466xFgvV2sznYzQ0q1kKI9RBiPTxYCyIiIvot62rfja3r7kBM4lQUTr4dXe27ULr5EQAYchCxLzGm3gYAQMm0P0OlSRHEyxW6IGUzPPqGTdi89FzEZs5D0cmPQ1+/Abu/uBkAhhw060vMmn+nDfn/VEamY96NOwOcSWCwHh76lu3YsupqxKTMRNGsv0DfvAW7190LAEMOIvYlxuVyYdvqm2DoLEPWuKsglshRt3cpNq+6CjMWr4AqImXQ9x5tXZ2l2PbzvYiJn4TC8TdD37EHpdv+DQBDDoA90hi7rRd7tj6KUO+LA1iPX+vq2IOt6+9GTMIUFE68BV3tu1G69TEAGHJA8JHG2G292L35Hwj1WgCAvnEzNi+7ELEZc1F04qPuY+JXtwLAkIOIfYlZ83T2kP9PZWQ65l27JaB5BIp59xY03nEJVFPnIO72v8G8azNaH3H/7n+4QcQOQzc633hy0G2GNZ+i5W+3QrvgQkRfejMc7S3ofOsZWMr3IO3FFRBJZYFOZdh+2d+L0/6+HyeOi8R/r87Aj/t6cc3z1QAw5CDi296oxdvr2nHXwiSMy1Bhc6URf1vWhNZuO56+KgMAcKDZAgB44/dZyE5QCOLjI8Pjt8B+7I/zYC2EWA8h1sODtSAKnPD61kBhZckN56K6ohZ3XvkX2O0OfPHRN1BHqHH5zRcMORjYn5hQtOTahaipaMCd1z4Kh92BVR9/D3WECpffePaQgyJ9jXn05bsxdmI+4pNiRiqdYbv4mgWoqWjAXdc/AYfdgS8/WQ+NRonLbjhzyEGRvsY8+sLtKJmUh/jE0J4p89dYDw9+VoS4bwg1de+BShaJvMTjIRaJEReRA4fLhsauXYiLyINI5P3oJV9j9jd/A4OlDTaHaaTT8ktjxyao5NEYk74QYpEE8VFFcDhtqG/fiHjd2EFr4UtMQ/smyKRqFGUshljs/loYo83D1vJX0NixBZmJc0c6VZ/UVn4KtSYV4ybfA7FYiqTU4+Cwm1BT+RGS0+YPWo8jjdlf+jJs1tCfpbvrvVcgzcxBwiPPQySVIeKUs+A09qLr7ecRccZ5g9bCVnMA7Y8/AMvubXAZeyHLOfQMJh1P/BWOLn2QMgi+WbfcirZ9ZXjnvHPhtNux/b33oNBqMeeOO4fuQMjJQUdFOarXrx/ZxgYZayHEegixHh6sBREREf2W1exfCrU2A+Nn/A1isRTJGSfDbjOiuuwdpGSdPuh1pC8xpt56AEB8ylxIZeqRTssv1Rv+C01MHiYufhtiiQwpJefDYelB5S9PIXX8JYPWwpeY6Uu+8IpzOqzYsfxqJBefMxKp+YX18Kje+So0UdmYeNIzEItlSM5bCLutF5U7XkLKmHMGr4UPMZ2Nv6C9/gfMPu8rqLSpAID4jPnYvuZm6Js2Q5UXmgOIayqWQR2RhnHTHnT3q6XNh8NuQvX+pUhOP2Xw48YRxuzb9XxY9MUBrMev1ZR/ALU2HeOPedh9fkg/CXa7EdX7/4eUzNMGr8URxpTteAY2a/dIpTQs1ZuegyY6FxMXvu4+JhadC4etF5Ubn0bq2IsHP3b4EDP9ghVecU6HFTtWXo/kgrNGIDP/6Je+BFlGLpL+9hJEUhm0Jy+G02iA/p1noT39gkHrYa05gLYn/gzz7i1wmXohy/aecbrro9egmjwLCfc8PrBOmpiKpnuvhrViLxQFh35y42h4YmUzClKU+OiPuZBJRVgyJxYGkwOPftqEK4+L9aqFyerEy9+04S/npeD+c5IBAIuPiYbR4sRr37bhqSvSIRaLUNE3gPisaTpoVZIRzyuQ2B/nwVoIsR5CrIcHa0EUOOLRbgD9NkllUhx32kys/mQt7HbHwPovPvwaWXnpGDM2NyAxoUgqk2LeKdOxevl6OH6dx7K1yMxNxZjirGHFbN9QindfWoHVy8PjhCaVSTDv5KlY/dnPwtw+/gGZuSnIL84cVsy2TWV49+WVWL3ip+AmEiCshwc/K0LcN4ScLgc6e2sQo8mGWOT5uhKnyYHZ1g2jtXNYMRHKRCRFFSNGkxXUPALB6XKgo6cCcVEFEIs8HUDxUUUwWzthtLT6HWOydiBSnToweBgAZFI11Io4GC3tQczKf06nDa3NvyAhebag3Ykpx8HY2wBDT+WwY1qbfkJz4zrkFlwevEQCwGWzwvj9amjmnyGYVSHi1LNgr6mEdX/poHEipQqqY+ZCd9UfIC88dGdq73dfonf1ckT//u6Atn2kSGQyFC9ahB1LP4DTbh9Yv+2ddxFfUIDkCYM/HjQ2JxftFQcAAGJJeHe89mMthFgPIdbDg7UIUw5HeP4jIiIaYU6nDS0N65CYfoLg+jg542QYDbUwdJX7HWM01EOuiIZUpobL5YArxJ9i43RY0bx/JZKKFkMs8VxTJ4+9AMaOcvS0eM+K62tMdPoMr38t+z+HJiYfeXPvC35yfmA9PJwOK1qqv0ZizukQi3+VV94iGLsq0dPh3d/ia0xd2QeITz8OKm0qXC4XXE4HFOp4TD/zf0jOOzP4yfnB6bShtelHJKbOExwDktJOgLG3DobuA8OOaWn8Ac313yKv6OrgJRIgrIeH02lDS+N6JKYeLzw/pJ/Ud36oGHZMS8M6NNd9g/yS64KXSIA4HVY0V3yBpIKzhMfEonNh7KxAT+suv2Oi0471+tdSsQqamDzkzb43+Mn5wWWzonfdl4g4YaGg71p78mLYag/AWr5n0DixUgn19HmIufI2KArGD/oaaXwyNCcIj5nS5HQAgNNsDFAGgWO1O/HJRj3OnxENmdQzUHjJnFjsa7Rge7X3RDdt3XacUKLFSeO0gvXZCQpY7S7Yne7vWRXNFiRESaFVSeBwukL++9dQ2B/nwVoIsR5CrIcHaxGmRrs/nv34Q+IMxEc5m9OKPR2DDyaLV6UhXpXu1/vGxkdDrpCjoqxKsP5AmftRHEmpCdi3q2LYMaEoNi4KcoUMB/bVCtZX7q8DACSlxmPfniq/Y955cTkA4IzzjsdJC2cFIYPAihnIrU6wvnK/e0aKpJQ47N9T7XfMuy+tBAAsOHcuTjpzRlByCCTWw4OfFSHuG0I2hxkuOKGSRQnWq+Q6AIDVboBGEeN3THJUCQCgtaccHb1VgU8ggGz2XrhcDqgUsYL1/csWWzc0ygS/YnKS5kMiET7WyuGwwmBuQXxUcaBTCQirRQ+Xyw5NhPA7Sv+y2dQKbWSO3zEWSyf27vovcgsuhyZi6EdshgJHRxtgs0Keky9YL8t2Lzua64Ex3n9HaUIyopa4O92tB/bBunfwR4Xa21vR/sifEH3TPZBl5QW49SMjIjERUoUCLXt2C9a3lLo7qKPS09G4fbtXXExODowdHbh5yxYklIyFobERuz5ehtX33w+rwTAibQ801kKI9RBiPTxCsRabnE5sdg7+A1DvsN6ZRkLN7rdQu/vtQbdZjd43ghEREQ3Gau7AT19eMui2tNzFSM9b7PP7uJw2REQKH4WuicwCAJiNzdDq8v2KMfU2QCSWYcu629HZug1isRzR8RMxZsIfoI4IvVlVLb0tcDmsiIgrFKyPiHXPdGjurkNk4vhhxwBA24E1qN/5DmZf84tgwFwoYT08rKZ29z4fLfwsRES7+0bMhkZExhb7FWPo3IfYlBko/fFhNFeugt3ajci4sSg49s+Iig+9GTMBwGrphMtpg0abJViv0bonrzCbWqCNyvU7xmLuwN7tTyKv+BpotBnBSSKAWA8Pq7mz7/yQJVg/cH4wNUOry/M7xmLuQOnWx5E39nqotd4TrIQaS2+r+5gYO9QxsR6RCeOGHQMAbVXfoH7Xu5h95Y8heRwFftV3nS18+p08y32ctDfXQ5Ff4hUnTUiB7qIbAADWA2Uwl+3wek3Sw88DAFwuF5z6Dthb6tHx2hOQJqZCWTwp0KkMW7PeDqvdhZJ0lWB9cZoSAFDTZsXELOHTG9Lj5Fh9v7t2NrsLbT127K414dkvW3D2dB3kUvckOhXNFiikIpz6t/34bk8PlDIR5hVr8cTl6chJFP72E8pCsT9utLAWQqyHEOvhEYq1YD9+eDva+/FD8xsljRiZWI7imMAPJItNcA/Q6tYLH6/T1el+xExsfHRAYkJRbIK7nd1dwpNLV6c7r5j4qIDEhIvYeB0AoGeo3OIGqYcfMeGC9fDgZ0WI+4aQzeG+41p60OBWqVjRt90ckJhwYLO775iXipWC9VKJUrDdnxiNKtErbm/tJ4DLiaSYwe/MHG1Wix4AIJNFCNbL5O478a3WLr9jXC4X9u58GhGRuUjLPB36DuFFZ6hxtLsvVsRa4WddHOk+Vjo62vx+b5fLhfa/3w15QQm051wG87YN/jd0FEUkJQEATJ16wXpju3uG7YjExINDIBKLEZ2djcjUVKx5+GG0lpYidepUzL3rLsTl5eONM88IeruDgbUQYj2EWA+PUKzFVLEYU4d4ftTTdgdC/6G2R7eMkkuRUXLpoNvWvjMLlt6mEW4RERGFI7kyBjNOGfyHrCNhMXcAAKQy4Wx2MoX7utLat92fGJOhHhZTCxJS5yEj71yYTa2oLH0Tm777HWac/PbANXiosPa2AABkSp1gvUzl/p3C0uv9A6E/MU6HDXvX/AnZx9wCZWTqcJsdNKyHh8XkbrdMESlYL1PoAABWk3d/i68xFmMrakvfRWTcWBTOuA8QiVGz83Vs/OxizDz3c6i1/k3qE0xWs/upblKvfjV3rlaL95PifI1xuVwo3fZvaKPykJa9EPr2wW9yDyWsh4fFMsT5oT8vs3ctfI1xuVzYs+VRaHVjkJ5zFjrbvAeRhhqrsf+YKOyrlSndfbWWQQae+BPjdNiw99v7kD39Zii1oXeDTj97hzs3r77rqP6+6+EPxHEaulG10P07hkilRup/P4ZIJh/2+wZak94GAIjWCGe6jNW6h+o0d9kOGb9yaxcWP+6eZK0kTYlnr/EMqD/QbEFtuw1nT4/GzacloK7dir9/3IjZD+zFnn+XQKcJj+FAodgfN1pYCyHWQ4j18AjFWrAfP7wd7f344fGNgcLOUI/HEIndR0upzHvX8ycmFA2Zh8j9SBKp9AhyP0RM2BjiSSmev+sgjwXwJyZcsB4D+Fk5CPcNn7j6knbBGdSYcOJyHXleB8e4XE40dW5DbctPcLnsyEs93WtW45AhGmJ93/HB5bR7b/MxpqH2S3R17sX02f+BSDTEFV4oEQ2RWN/f12UfpBY+MnzyHiw7NyPl7VUDx5SwdJjvlxKZzGubWCrFh1ddicbt29FWVgYA2PflKvQ0NeLs519A2rRpqNu4MXhtDhbWQoj1EGI9PFgLIiIi+g0TDXWB3Hcd6XR5P5rT15iM/AuQXXQ5ouMnDrwkOm4Cfl59BeorlyOrYIn/DQ+KwfPq7zNxOQcbzHPkMbVbX4XdakDW9N/718wRw3p4HDov52B9Tz7G2K09UKgTMeW01yGRum/0j0udjXXvn4DqHa+gaNZfhtf0YDhc/9OgfXG+xdRXr0RX5x4cc9wL4dEXB7Aev3L484N3LXyNqa9aga6O3Th2/qthUQu3IY4D8OM4eoiY2u2vu4+jU37nXzNHylD7fd/MjC6H/33X/cQqDVKe/hD2tmZ0f/ImGm4+Fyn/XQZFbtGw3zuQhixFXy1s9iF+7OszpzACa+4fg32NZvzz0ybMun8vNvy9CGqFGLctSMR9i5Mxt9gzKH9uUQTG37kHL37dhrsWJQUsj6Bif5wHayHEegixHh6sBVFAhcs3bgoz7S3uO0ijooV3W0fp3F9eW5u879D2JyYUtbfqAQCROuGdxFF9y63N3rM4+BMTLobKLVKnAQC0NXvfgexPTLhgPTz4WRHiviEkl7gf5WR3WATr7U5r33Z1QGLCgUzq/nvancIZlO19My7L+7b7G2OydGBX5XuoavoWkZpUTMi9HHFRBYFLIMDkcvcMBTabcOZtm81936ZCGeNXjNnUiv17X0Vu4RWQyjSw201w9u07DocVdrspsIkEgCQ2HgDg7NYL1vcvS+K87671hb2pHh3/+T9E//4eiCMi4TT2wmVx70suswlOY/g8aKenyX03qDpG+CQLdUzfky8aGrxiHFYrdi5dOtB50K90+XIAQNK40Hyk6OGwFkKshxDr4cFahCGXC3A6wvPfEB3dREREwSLvu2a22boF621W97JCGet3TFzysYLBw4D7kfTqiDQYuiqG3/gAU2jcN07bTML+M1vf7JeKCO8BN0ca43TaUfXLf5A+6SpIZKHdL8V6eCjU7v4Wm0X4lCt737JS7X3Tva8xcmU04tJmDQweBgCpPAK6xMno6dgboAwCS65wXxvZrcI50zz9aoMcN3yIMRtbsH/3C8grvgZSWQTsdhMcff26Doc5JPviANbj1wbOD9aDzw/9ecX5FWM2NmPfzueQX3L9QC2cIV4L4FfHRLNesN5m0vdtP8Rx1McYp9OOqo3PIH3CFSF9HAUAacyh+66lsf71Xf+aSCqFauKx0J64CMmPvw1IZej++M1hv2+gJencg9g6DMIbtfqXU2IOPWtyrFaK48dqcf1J8Vh6Ww721JmxYrMeAHDapCjB4GEAKEpTYUyyEjtrQvOzMhj2x3mwFkKshxDr4cFahCH244c0DiCmoOho08NmtSG3MEuwvn+5qd77sST+xISijrYudx4FGYL1uYXu5eYG74HQ/sSEi472LtisduQWCB+91b/c3NAekJhwwXp48LMixH1DSCpRQgQxTDa9YL3J6v4BYrBBs/7EhAOZVA0RxDCahfu30ez++8plkX7HmK167K56H2abHoXpi1GYfhaUcl0QsggcuSIKIpEUvT3VgvW9hhoAQ3RM+xBjMjbB6bCgbNd/8f3qC/H96guxfdPDAICtv9yL9d9cEYRshkcSHQtIZbAe2CdY378sTUz2631tDbVwmU1of+Re1Mwfi5r5Y9Fy+5UAgKYbL0TtGdOH1/AR1NvSArvVisSSsYL1/ctddXVeMbrMTBScvgDig2a1dzrcHbrm7m6vmHDAWgixHkKshwdrQURERL9lcmW0+/q4q1Kw3tC3rFR7D+bxJcZuM6K1YT0s5kH6okQSSEOwT0auiYdILENPW6lgvaHVvayMTBt2TMu+lbAYmpA6LtRmX/bGenjIlTEQiWUwdOwXrO/pdPe3KCO8+1t8jVGoE+Cwm73inU4bJLLQ+5wAgFyhg0gkhaGnSrDe0O1eVqji/YoxGRvhdFiwd/uTWPv5Iqz9fBG2/3IfAGDL+jvww5cXBDqVgGA9PPrz6vXKq+/8cIhaHCrG2NsIp8OM0m3/wncrTsd3K07Htp/uAQBsXncr1n2+OPDJBIBcHTf4MbG9/5iYOuyYlvLPYeltRurYiwPZ9KCQRMe5+64rhYO0LJXumyWkiSl+va/D0I2ay+bD8P0XgvVipQrSxFQ4DaHXB5MQJYVMIsKuWuGA3v7l9FjvWTK/3tGNcX/cjbp2q2B9QYr7BhR9rwM9Jgc+26xHk957pmqJGNCqwucppeyP82AthFgPIdbDg7UgCiwOIKagsNvs+Pbz9Tj5rOMglXq+nJ56zgmoraxH2c7ygMSEIrvNju9WbcBJC2dB8qs8TjlrDmqrGlG2qzIgMeHCbnPguy834sQzZghzWzQLtVVNKNtdFZCYcMF6ePCzIsR9Q0gskiBak4F2QxWcfY8vA4B2QyUUUi3Ucu9ZZv2JCQdikQQxkXlo7y4TPEq0rbsUCpkOGqX3rCe+xlQ3fw+ny4Hx2ZciWpsd/GQCQCyWIT7xGDQ3/SB4ZGRTw/dQqZOgjczxK0YbmYPJx/xD8C+/6FoAQOHY32PitL8EPbcjJZLJoZ53Mnq//gwuu6eTsPerTyFNzYR8TIlf76sYU4yk55cK/sXc/iAAIPZP/0Dik6E3i8NQHDYb9nzyCcadf56gQ2D8RReivbwcjVu3esVo4uJw2aefYuw55wjWFy9cCKfDgfpNm4Le7mBgLYRYDyHWw4O1ICIiot8ysViGhNS5aK5bI7w+rl0NlSYVWl2+XzFiiQy7N/4fDux+VRDb21MDY081omL9uz4NJrFEjsQxZ6CpdBmcDs81deOeD6DSZSMycfywY+q2vYaYrOOg1Pp3g+9IYj08xBI5EjJPRNOBz+F0evJqqlgBVWQGtLHFfsckZJ6Ittq1gtlGLcYW6Js2Iyp+QvCSGgaxWIb45FloqV8rOAY0138LlToF2qg8v2K0UXmYMuvfgn9jxt4IACiccBsmzvhH8JPzA+vhIRbLkJAyG8113wrPD3VroNKkDH1OOUxMpC4PU+f8R/BvzPibAQBFk+7ApFmPBT85P4glciTmnY6msk+Fx8TSZVDpshCZ4D2r4ZHG1G1/EzGZc6EcZFb4UCOSyaGZeyoMa5YL+q4Nqz9x913njz1E9NDEGi0crY0w/vSNYL29rRm26v2Q5xUNq93BIJeKcfZ0HZb+1Amb3TOD4XvrO5CbqMCkLO/ZpOMipdhdZ8baPcKZy7/Z5V6ekKmCQibCZf+twoNLhbNsljWYUVpvxowxoXljymDYH+fBWgixHkKshwdrQRRY0sO/hMg/7zz/IV785N/45ysPYvWn32HiMWOx4LyT8NAtj8PlciFSp0XJpELUVtWjrrLBp5hw8c5Ly/HCB3/FP1+4E6tXrMfEaUU4/Zzj8PAfn+nLPQIlE/NRW9WIuqomn2LC2bsvr8Tz7z+AR567FV9/9hMmTCvE6Yvn4K93vuCuR5QGxRPzUFfVhLrqZp9iwhnr4cHPihD3DaGkyGKUNq1Cect3iNFkwWBuQXvvAWTHzYRIJILdYYHB0galTAtl34y6h4sJV8kxU7G7+n3sq12B2KgC9Bjr0dZVitzkkyESiWBzmGAwNUEp10Elj/YpxuVyorOnAlERmTBa22G0CmcJkksjoFF6zxIRCtKzF2HrL3/Grq3/RELybHR1lqK54TsUjr0ZIpEYNmsPurv2Q6VOhlqT7FOMVKaBLkb4A5Crb/B1RGQ2IqO8O7xDQdRFV6PxxgvRcu+N0Mw/A5Ydm9C76hPE3vtPiMRiOLr0sOzZDllaJmTpWT69pzgiEsoJ0wTrXH1338rzi6Eo8v7RMJStf+pJXLPmG1z8/lLs+GApsmbNwqQll2DZtdfA5XJBFR2NtGnT0V5Rjo6KCtRv3ox9q1Zh0bPPQZeZhfb9+5AyaTJm3Xor1j/5JDqrqkY7Jb+xFkKshxDr4cFaEBER0W9ZRv752Lz2D9jx0/1ISj8B+rYdaKr5CkVT7um7pu5GV0cp1BGpUEek+RQjEomRVXAJync9D5FYgpiEKTAbW1C97z1oowuQlHHSKGc9uMzpv8fGdxZg28eXIaloMfR1P6Nx91KUnPY0RCIxrKZOdDVuhjo6B5roHJ9i+tkt3eioWY+8uX8erfSOGOvhkTnuKmxauQTbv/4DknJOR2fzZjSWL0fJnL+7PydmPbpad0AdmQl1VKZPMQCQWnA+ana/hQ2fXYzMcVcCLheqdrwMqVyLrHFXjWbKh5SRuxhb1t+BnZv+isSUedB37EJT3RoUTbx94LjRrS+DSp0CdUSqTzFSmQa6WOEAwv6+OG1ULiJ1Y0Y8T1+xHh4Zeedh87pbseOXB5GUdjz07TvRVLsaRZPu9JxTOvdCrUn11OIwMVJZBHRxwoGzzoFa5CEyumDE8/RV5tTfYeP7i7BtxVVIKjgL+vpf0Fj6AUpOftJzHG3aArUu23McPUxMP7ulBx11PyJv1j2jld4R051/LRr+cB6a7r8eESecCfOOjTB8tQzx9zzu7rvu7oSldBtkqVmQpfk2wYlIJELk4iugf+tpiOQKqCYeC0dXJ7qWvgRJVAwiF10a5Kz8c9uCRBz3UBnOe6IC58+IwQ97DXh7XQdeviETYrEIHQY7NpT3Ii9JgbwkJSZkqjCrQIObXq1FTZsVBSlKlNab8fiKZpw5JQrH5GsgEolw96Ik/OndekglIswfq0VtuxWPr2jG1BwNLp4dXpPqsD/Og7UQYj2EWA8P1oIocDgDMQXNri17cfOFf4I2SoM/PXYLxk4uxF/+8BiWv7cKAJBfnINn3v8HTj/3RJ9jwsXurfvxh0v/Cm2UBvf843qUTMrHw7c/gxXvu++GzCvKxH/evh+nLZ7nc0w4272tArdc/gi0kRrc/berUTIxFw/f8TxWLP0OAJBXlIH/vHkPTjt7ts8x4Yz18OBnRYj7hlCEMh4FiSfB4bSiqu1nGCytyI6biXiteyCn0dqJfc1fo81wwOeYcKVVJ6Mo4xzYnRZUNn4Ng6kRuSmnICHa3ZFqNLdhb80ytOlLfY6xO8xwwQm9oRJ7a5Z5/Wts3zwqufoiSleACVMfhN3Wi7Jdz6Fbvw+F425GSrr7O4WhpwrbNz2EpobvfI4JV4qSSUh84g04e7rR/uh9sOzehtg/PwrtmecDAKwVe9Fy+xUwfPnJ6DZ0FNVt2IA3FiyAUqfDov8+i7Tp0/HRNVdj8+uvAwCSxo3HFStXYuLFnkeo/m/Jxdj8+ms45oYbcO7rb2DMaafi8zv+iFX33D1KWQQGayHEegixHh6sRRhy2MPzHxER0SiIii3BxNmPw27rQemWx9HVsQfFU+9BavYCAECPvgLbfrgDjdVf+RwDAJkFF2PMxFvQ0bIZu355GHUVy5CYdgKmzH0SIlFoPkJblzIVU87/EHZLF/Z8eTu6GjZh7OnPIG2Ce/CRoWUXtiw9F4273vc5pl9b5bdwOW2ISZ81ojkNB+vhoUuYgMmnvAK7tRt71j+ArpbtKJn7D6QWnAsA6Okow5Yvr0FD+ac+xwCAQh2H6YuWQh2VifJNT6Biy38QGT8Ox561DFJ5xIjn6auo6CJMPPbvsNsM2LvjP+ju3IuiiX9ESsapAABDdyW2/fxnNNWt8TkmnLEeHlExxZg481HYbQaUbnsCXZ2lKJ58F1KzTgcA9HQdwLYf70Zj7WqfY8KZLnkKppzzvvuY+PUd6GrcjLGnPIW0ce7+A0PbHmxZdiEaSz/0OaZfW/V37uNo2swRzWk4lCWTkfz4W3D2dKH18T/BvGcr4u/5FyIXXAgAsFaUovGOS9Hz1bIjet+YK25FzLV3w7RpHVr+7xbo330WynHTkPrCcki0UcFIZdiOydfgiz/lQ9/rwO9ersaG8l68ckMmrjo+DgCwo9qE0/9RjrfXdQBwD5T+6I+5OOcYHV74ug2XPF2J99Z34PYzEvG/W3MGJsy5a2EinroiHd/s6sYlT1fi2a9acf6MaHx9fz4k4vCaVIf9cR6shRDrIcR6eLAWYWi0++PZjz8kkSvcpyckv6SlpaG+vh4ysQLjYmcfPuAoIJZHjnYTQopIohjtJlCIcjkso92EkMLPigdrISRRxY12E0KKKnPuaDchZBx4ILxm8A22i+d4P9qRiIh887TdgR4AqampqKurG+3mjKj+fg2FOhHzLvh2tJvjl7XvHw+Lsfmo/PsREZFvBs53qnjMWXBkg2t+qyR53o+AJwIAV1vLaDchpDhaqka7CRSiRBLZaDchpEhyxh7+RUeJ8rP4+9+v7W9YNNpNCCn3Ldk22k0gIgpb7MdnP36o4wzERERERERERERERERERERERERERERERxHpaDeAiIiIiIiIiIjIPy64HI7RboSf+FAwIiIiIiIiIiIiIvqtYz9+KOMMxEREREREREREREREREREREREREREREcRDiAmIiIiIiIiIiIiIiIiIiIiIiIiIiI6inAAMRERERERERERERERERERERERERER0VFEOtoNICIiIiIiIiIi8pvTPtotICIiIiIiIiIiIiKiobAfP2RxBmIiIiIiIiIiIiIiIiIiIiIiIiIiIqKjCAcQExERERERERERERERERERERERERERHUU4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOgoIh3tBhAREREREREREfnFBcBhH+1W+Mc12g0gIiIiIiIiIiIiIgoy9uOHNM5ATEREREREREREREREREREREREREREdBThDMRHO5cLTod5tFsREkQOxWg3IaS4HJbRbkLIEEm4b/wa9w0i34gk8tFuQkiRpo8d7SaEjLX3Fo92E0LKC6PdACIiIiIiohDncthhb6sc7WaEBJFMOdpNoBBlb68e7SaEFGdv62g3IXRIZKPdgtDisI12C0KKaNbc0W5CyNjfMH20mxBSap/fM9pNICIiIhoRnIGYiIiIiIiIiIiIiIiIiIiIiIiIiIjoKMIZiImIiIiIiIiIKEy54HLYR7sRfnKNdgOIiIiIiIiIiIiIiIKM/fihjDMQExERERERERERERERERERERERERERHUU4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOgowgHERERERERERERERERERERERERERERERxHpaDeARpfNZUWpfsug2+KUyYhXJo9wi4iIiIiIiIio3yanE5udrkG39Y5wW0KW0z7aLSAiIgoqm60LGzbcM+i21NQTkZp64gi3iIiIiIiIiIj6sR/fB+zHD1kcQHyUk4nkKNJNHu1mEBERERGFDJFYDJlK5bXe5XLBZjSOQotGD2shxHoIsR4ewazFVLEYU4d4ftTTdgd6hvXuREREFA5ksihMn/7IaDeDjoDdNvh3QIlUCZHo6Hs4KOtBRESBZDA7Bl2vloshFotGuDU0Wtg36cFaCLEeQqyHB/vxiQbHAcQUVFNmTsT1d12FwgljUHugDktf+Rifvrsy4DHhYvKMsbjuj0tQOC4XNZUN+PD1lVj+v9UBjwlFwc49KlqLL7e/jVMmXoquju5gpBBQk48txrW3X4DCsTmoqWzEh2+uwor3vwlYTJQuAqu2vopTJ1+Nrs7Q/irCfUOI+4bQpGOKcO1t56CwJBu1VU348K3VWLH0u4DFROki8MWm53DatBtDvh5dhhrUtqxDr6kZSkU0kmImIzFmwrBjuntrUdO8FkZzK0QiCSLV6chImgeVIiaY6QxbR8MvqNj8FHradkMdlYX04kuQWnjesGOcThvKN/wLrbXfwWxoQGTcWIw55m5EJRy61qPp504HnqiyY3ePE1lqES5NleKCZN+/5j9TZcN/a+wonSu8YN6gd+DxA3bs7XVCLgamRYlxd44MWerw+2Eva85czH/gAaRMmoT28v345fnnsfn114d8/ZhTT8Nln37qtb69vBz/LioMYkuDj7UQYj2EWA8P1oKIiIh+y/T6vaiq+hg9PVVQqRKRmjofycnzhhXjcrnQ3LwedXWrYDQ2QSaLQHR0CbKzz4VCER3slPzW0bIZB3a+hJ7OvVBpM5Cedy5ScxcOK8Zu68W3Hx43aOycRZ9BqU4MZAoBxXp4dHbsRlXFUvT0VEKtTkJq+ilITp0f0Bh95x5s3fggphzzD0RG5QU6hYDSd+1DVc1KGHproFImICV5HpITZw4rxuVyobn1F9Q3rIHR1AKZVAOdrhDZGQuhUOiCnNHw6PVlqKpeDoOhBipVAlJSjkdy0uxhxbhcLjS3/IT6utUwmprd9YguQnbW4pCuB/cNoY6KtTjw1cPoqd8CVVw+0mfegNTpVw07pnXPSlR+8wgMjTuhiExGbMHJyF/wT0hkymCmM2xr9/TgoQ8asKXSiPxkJX53cjyuOj5uyNcbLU787eNGfPhzJ2rbrEiPk2PJ7BjctTAJSrm7X7rH5EDUFdsGja95dhzSYuXBSGXY2I9/eOyP82AthFgPIdbDg7UgCgwOIKagGTulGM8s/Rd+WbsJ/7jzX5gwfRz+8vSfAGDIAcH+xISLkkkF+M87D2PDum145E/PYvzUIjzwxK0AMOTAR39iQlGwc5dIxLj+zksglYXHIa1kUj6eevt+bFy3A//880sYP6UAD/zrJgAYctDnkcRIJGJcf8dFYVEP7htC3DeESibm4qk378HGH3bin/e/ivFTxuD+x64HgCEHBB9JjEQixnV/PC8s6tFjrEdp1fuIishCTsrJ6DHWo6L+cwAYchCxLzG95lbsOvAOIjXpyE4+CQ6XDY1tG7Gz4g1MzL8WclnEyCR4hPQt27DliysRkzoLRbMfhr55C3Z/736U61CDiH2Jcblc2PbVjTB07EXW+GsglshRt/d9bP78csw493OoIlJGJsEjsLXLict2WDE7Woy/jpFhc7cTd+21AYBPnY87e5x4osoO+UF9iWUGJy7YasV0nRgP5ctgcgIv19qxcLMFq6crkagIn1kc0qZPxxUrV6JizRosv/kmZMyYgcUvvQwAQ3YixOTmwKTX462zFgnW283mYDc3qFgLIdZDiPXwYC2IiIjot6y7uwI7djyG6OgSjBlzObq69qOs7BUAGHIQsS8xLS0/Y+/eF5GUNA8ZGQthtepRU7MCBkMNJk9+EGJx6PW/dLXtwtbvbkFM4jQUTr0b+rYd2LPhrwAw5KBZX2JMhnoAQMmxD3n1JchDeDA16+HRpd+HHVv+huiYcRhTdA269WXYu/s5ABhyQPCRxtjtRpTufAbA4I85DiXdPZXYuecZ6KIKkZ9zEbp7KrCv/C0AGHKgqC8xrW2bULb/DSQlzERG2qmwWrtRU7cKvb11mDT+bojFkpFJ8Ah1dx/Azl1PQqcrQn7eEnR3V2DfvtcBYMhBxL7EtLZuQFnZq0hKmo2MjAWwWrtQU/u5ux4T7w3J4yj3DaGu6l+w9aXTEZM/H4WLn4G+6ifsWXotAAw5iNiXmMat72HXO5cgafLFyJh9EyxdDaj69lH0Npdi8nVfQiQKzb7aX/b34rS/78eJ4yLx36sz8OO+XlzzfDUADDmI+LY3avH2unbctTAJ4zJU2FxpxN+WNaG1246nr8oAABxotgAA3vh9FrITFIL4+MjQ+5wA7Mf3BfvjPFgLIdZDiPXwYC2IAic0v0HRb8Ilv7sA1eU1uOPye2G3O/DFh6uhiVDjiluWDDkY2J+YcLHk+rNQXVGPO6/+Gxx2B1Yt+w6aCBUu//25Qw6U9CcmFAUz98de/TPGTipAfFLsSKUzbEuuXYiaigbcee2j7tw+/h7qCBUuv/HsIQeJ+hrz6Mt3Y+zEfMQnhfbMof24bwhx3xC6+JoFqKlowF3XPwGH3YEvP1kPjUaJy244c8gBxL7GPPrC7SiZlIf4xND8keJgDW0boVTEoCBzMcQiCeKjx8LhtKK+9WckRI8ftIPQl5iG1l8gk2pQnHXBQAd0bGQBtpQ9j8b2jchMOn6kU/VJ9Y5XoNHlYOLJz0IsliE5fxHsNgMqt7+AlIJzB62HLzGdjT+jvW4dZl+wBiptKgAgPvNEbF/9e+ibNkGVd+gZd0bDy3V25KpFeGGsHDKxCGcnAb12K56vtuP8JMkhO4/NDhdu3WOFUgw4D9r2Yq0dcXLgjfFyKCXu9zglToJ5P5vxWp0d9+TKgphVYM265Va07SvDO+edC6fdju3vvQeFVos5d9w5dAdCTg46KspRvX79yDY2yFgLIdZDiPXwYC3CjMsFOOyj3Qr/uEJ/sAgREf321NZ+AZUqGSUlf4BYLEVi4kw4HGbU1KxEUtLcQa8jfYmpr18Nna4YhYVXD8QplbHYtesp9PbWQavNGsEsfVNd9g7U2gxMmPMYxGIpkrNOg8NmRFXpG0jJOXPw/gUfYoyGOgBAQto8SGWakU7Lb6yHR131Z1BrUjB24p0Qi6VISp4Lu92E6spPkZRywqC1ONKY/Xtfg80W2k9E61dXvwYqVSJKCq+HWCxBYsJ0OBwW1NZ9iaSEGYPXw4eY+sbvoIsqQEH+pQNxCkUMdu99Hr3GemgjMkYyTZ/V1X8FlSoJJcU39h0Tj4XDYUZt7RdISpw1RD0OH1Pf8A10ukIUjLliIE6hiMHuPf9Fb289tNrMEczSN9w3hKq/fwLqhAJMuOIjiCUyJE9eAoelB1XfPoaUaVcOfhz1IaZ85Z+QNOlCjLv4rYG4mPwT8PO/J6Or+mfosmaMZJo+e2JlMwpSlPjoj7mQSUVYMicWBpMDj37ahCuPi/Wqh8nqxMvftOEv56Xg/nOSAQCLj4mG0eLEa9+24akr0iEWi1DRN4D4rGk6aFWhOZj8YOzHPzz2x3mwFkKshxDr4cFahBn244e08HtuAYUFqUyK40+fg68+WQO73TGwfuXSL5GVl4ExY70fxeRPTLiQyqQ47tQZWL38ezh+ldvnH32LzLw0jCnJCUhMKAp27tt+2YN3XvgEX336fXATCRCpTIp5p0zH6uXrBbl9sWwtMnNTMaY4a1gx2zeU4t2XVmD18tD/wsN9Q4j7hpBUJsG8k6di9Wc/C3P7+Adk5qYgv9i7s/RIYrZtKsO7L6/E6hU/BTeRAHA6Hejo3oe4qCKIRZ7OsHhdCczWDhjNLX7HmCztiNSkC2avkEnVUCvjYDS3BjEr/zkdVrRUfY3EnAUQiz2dX8n5Z8HYVYme9lK/Y+r2LkV8xvFQaVPhcrngcjqgUMdj+qKlSA7BwcNWpwtftTpwRoIEMrGng/HsJAkOmFzYYzj0xcwjB2xwAbgk1fuewgqjE9N1koFORwCIlYswRiNGWe/B3ZShSyKToXjRIuxY+gGcds9F6bZ33kV8QQGSJww+g3dsTi7aKw4AAMSS8OiEPhzWQoj1EGI9PFgLIiIi+i1zOu1oa9uChIRjBH0BiYkzYTI1wWCo8TtGoYhBQsJ0QaxSGQ8AcDgswUhnWJwOG1rq1iIp4yRBXslZp8HYUwODfp/fMSZDPeSKGEhlGricDrjC4MdG1sPD6bShtWUDEhJnCvJKSp4Lk7EBhp6qYce0Nv+Clqb1yB1zSbDSCBin0462ju1IiJsimPU1IX46TOYW9PbW+R2jkOsQHzdFEKtUuicECcXjBtB/TNyGhPhpgr91QsKxMJma0dtb63eMQh6N+Lhpglil0j1Lq8MZevXgviHktFvRsutTJE04H2LJr/qgJy+BsXUfDA3b/YqxGTtg1tcidsxJglhtygQoozPRusv7MeyhwGp34pONepw/IxoyqaePecmcWOxrtGB7tckrpq3bjhNKtDhpnFawPjtBAavdBbvTff6oaLYgIUoKrUoCh9MV8ucV9uMfHvvjPFgLIdZDiPXwYC2IAosDiCkoYuNjIFfIUbG3UrD+wL4qAEByWmJAYsJFbLwOcoUMB8qEHbCV+9zLSanxAYkJRcHO/Z0XPsbbzy/Dj99sDnTTgyI2Lsqd2z5hJ1LlfnenyKD1OIKYd15cjrdf+BQ/frsl0E0POO4bQtw3hGIGchN2Mlbudz/yMCnF+/FWRxLz7ksr8c6LK/Hjd9sC3PLAs9l74XI5oFYK9wFVX+exxdbtd0xO6inITBI+ntThsKDX1Ay5TNhJFyqspja4nFZEROcL1kfo3DcamQ0NfscYOvZBpU1D6fq/YO3bx2LNa2Ox4dPz0dW6IxipDFub1QWrCxijEX6lz1e7lxssQ3c8rutw4O16B54qlkM5yBXB/42R4+4cYYekwe7CLoMTSWH02LOIxERIFQq07NktWN9SugcAEJWePmhcTE4OotLScPOWLXjIaMLdVdVY8MQTkEdEBL3NwcJaCLEeQqyHB2tBREREv2VWaxdcLjs0mlTBerXavWyxtPsdU1JyE1JSToDL5YLV2o2enkpUVn4EhSIWkZG5wUhnWKzmdricNmiihJMYaKKyAQCm3ia/Y4yGOoglMmz59mas+WAOvvvoeGz9/vaBmXhDEevhMbDPRwi/+6v7li3mtmHFWCydKNvzPHLHXAL1QZ+rUGS1dcPlskOtShas16jdy2ZLh98xxYXXIiVpjvu4YetBj6EaVdXLoVDEIFKbHYx0hs1q7ctNkyJYr9Ecoh4+xhQX34CUlHl9x9Ee9PRUoarqk756hN6kQtw3hKyGZrgcVmiSigXrNYnuZZPe+yYdX2IkikiIpQoY24W/n9vN3bAaWmDp9u4PDwXNejusdhdK0lWC9cVpSgBATZvVKyY9To7V94/BsWMiYLO70Nhpw9c7uvHsly04e7oOcqm7I7ui2QKFVIRT/7Yfmku3IvrKbVj0aDkONIfm4HL24x8e++M8WAsh1kOI9fBgLYgCy/s2JaIAiE2MAQB064WPX+rq6AIAxMTHBCQmXMTGRwMAursMgvVdne5cY+J1AYkJRUdz7oOJTThcblEBiQkH3DeEuG8Ixfb9LXuGyi1ukHr4ERMOrHZ3PlKJUrBeKnF3vNnsvX7HRKiSDorrxd6qD+FyOZEUMzkArQ88i9H9g4tMIfx7ypTuz4PV5P0jjq8xFmMLave8jci4cSic+QAgEqNm56vYuPxCzDzvS6gjB7/YHC2tfX2sUQd9o4/um6yizTp4x6Pe5sIde624JUuKcVoxvm5zeL1mrFbYG9lmdeGanRY4XMAlKeFzCRGR5N7HTZ16wXpju/sH7ohE7xvURGIxorOzEZmaijUPP4zW0lKkTp2KuXfdhbi8fLxx5hlBb3cwsBZCrIcQ6+HBWoQnl8M22k0gIiIKC1aru69dKtUI1stkEYLtw4mx24348cebAABisQKTJt0nmHEzVFjM7u93MnmkYL1M7u47sJq9B775GmPqqYPZ2IyEtOORPuYCWEwtqNz9KjauvgYzF3wAmTz0btpmPTysFj0AQCobap/X+x3jcrmwd/dziIjMRmr6qdB37glcw4PEanVPRCCVqgXr+48JNlvPsGMcDhN+2nAXgL7jxrg/huRxAwCstv5j4sG5uf/WNqv3ZA9HGuNwmPDTz7cB6KvHxLtDsh7cN4Qs3e6bJmSqaMF6mcY9c7K1p9mvGLFEiqSJF6Jm3ZOISCxCdN7xsHTVYf/KP8FpM8Fi8H7fUNCkd1+nR2uEszvGat1/v+auQ1/Hr9zahcWPVwAAStKUePYazxMmDzRbUNtuw9nTo3HzaQmoa7fi7x83YvYDe7Hn3yXQaUJrH2E//uGxP86DtRBiPYRYDw/WIjyxHz90hc+3BgovQzwqRCR2f4mVygbZ9fyJCRMuDJGbyH3nn1TqnZs/MaHoaM59MEM9RueQ9fAjJhxw3xDivnGQIW649pwTBnmkiD8xYUCEoe4SdyfsdHl3Gh1pjMvlRFP7FtS2rIPT6UB++kJoVCE68/8Qqblc7sdxOZ12740+xtitPVBoEjFlwZuQSN2Dr+PSZmPde8ejesfLKJr90PDaHmBD/ZX7H0xmG+Izcf8+G5IVIvwu4/DHCLvThbcaHHii0garE3iqWIYS7SBTHYSqw3y/lMhkXtvEUik+vOpKNG7fjrayMgDAvi9XoaepEWc//wLSpk1D3caNwWtzsLAWQqyHEOvhwVoQERHRUcn9Hcg1SB/DkcZIJEpMnHgvLBY9GhrWYNu2v2PSpPsQERFaN+UOfVXdn9cg/Qs+xmQWXoycsVcjOsFzc7YufhJ+/uIi1Jd/jKziy/xsczCxHofVd63gch7B5+SgmIa61ejWl2HazH9DJAqP/pWR6JuUSBSYMPZ2WK16NDStw7Zd/8bEcXcgIgRnaB6xeoy/y12Pxu+wbftjmDjxbkRo0vxtdlBw3ziIaIjcnO7eWpdjsH5r32IKznoSTqcNO99ZMhCXOfd2WA2tkKlDcwKuoVNz/61t9qFn3QWAOYURWHP/GOxrNOOfnzZh1v17seHvRVArxLhtQSLuW5yMucWeG1DmFkVg/J178OLXbbhrUdIh3nnksR/fB+yP82AthFgPIdbDg7UgCqgw+tZA4aStxX1neVS08M7zKJ17ubXJe3ZAf2LCRXtLJwAgSie8kz4q2r3c2uz9aDh/YkLR0Zz7YNpb9QCASJ3wEQhRfcutzd4zOfgTEw64bwhx3xAaKrdInXsmgrbmzoDEhANZ38wldodJsN5uNwMA5FLvWVqOJMZkacfOijdR2bgakZp0TMy/GnG6osAlEGAKVTwAwNY3s0s/e9+yUpPgd4xcGYO41NkDg4cBQCrXQpc4GT3tpQFofWDFy91dj/qDbtbU9/U4Jsq9uya/aHXgyzYH/m+MHBYn0Gt3weZ0X2f32l0wOzwX3BVGJ87ZYsFf9ttwjE6ML6crcEZCeN2Y0NPknsVDHSOcxUMd0/fkiwbvR/w5rFbsXLp0oPOgX+ny5QCApHHjgtHUoGMthFgPIdbDg7UgIiKi3zK5XAcAsNuFT2+y2Qx926MPDjniGLFYAp2uEImJx2L8+DsgFkvR0LAmEM0PKIXKPbvjwTN/2ixdfdvj/Y6JS5klGCwLABFR2VBrM9Cj3x+A1gce6+EhV7j36f59vJ+t7zMgV3oP1vMlxmxqRXnZG8gdcwmkUjXsdhOcDve0lE6HFXa7sB8vVMj7Zpi2242C9f1POFPIvZ/6dqQxIpEEuqh8JMRPw7jimyAWSdHQtDYwCQTYQG424VPhbLb+3HTDjhGJJNDpxiAhYTrGjb0FYrEEDQ3fBqL5AcV9Q0ihdQ9atRmFv8vYTO5lRVSy3zFSZSTGXfwWjnu4DcfctgnH/58eY858FDZjOxSRKYFNJECSdO6BWx0G4aDw/uWUGPkh42O1Uhw/VovrT4rH0ttysKfOjBWb9QCA0yZFCQYPA0BRmgpjkpXYWRN6x1L24x8e++M8WAsh1kOI9fBgLYgCiwOIKSg6Wjths9qQW5QjWJ/Xt9xc3xKQmHDR0dblzq0wU7C+f7m5wXtwtD8xoehozn0wA7kVZAjW5xa6lw9ZjyOICQfcN4S4bwh1tHfBZrUjt0A4O03/cnOD92Bxf2LCgUyigUgkhtHcKlhvtLiXFYM85tHXGLOlE7sOvA2ztRNFWeejMPNcKBXePxaGErkqFiKxDIaOfYL1PX3LSo13R6yvMQpNAhwOs1e802WDRK7xWj/aYuWATASU9ToF68t63Z2HKUrvjse9BicsTmDBJguK15lRvM6M/9bYYXICxevM+N1u9w9X1SYnzt9iQZXJhdfHy/HSOAUyVeF36dDb0gK71YrEkrGC9f3LXXV1XjG6zEwUnL4A4oNmcXc63J3b5m7vR1GGA9ZCiPUQYj08WAsiIiL6LZPLIyESSdDbWy9Y39vr/o6jHGxgpA8xdrsRGzb8Ca2tmwSvkUgUUChivQaKhQK5IgYisRSGrgrB+v5lpdr7yUy+xNhtvWitXweLybtfTiQSQyoLvf4FgPX4Nfc+L0WvoVawvrenBgCgVMb6FWMyNcPptKBszwtY982lWPfNpdix9e8AgK2bHsCPa68NRjrDJpNp3ccAo3AQRv+yYpC+RF9i7HYTNm59GG3t2wSvkUjkA9tDkUzWd0w0HnRM7FsevB6Hj7Hbjdi46QG0tW0RvMZ9HI0JyXpw3xCSRyRAJJHB0LRbsN7QuAsAoNR5z8Tva0xXzQb0tpRBpo5GZOokSBURMOtrYemqR0RSSTDSGbaEKClkEhF21Qr/Xv3L6bHeM0N+veP/2bvv+Ciq9X/gn63pvTcSSEgg1NAEQUXEa6Er2PDarw17veoVFO9PvTZA+aI0OxZQpEgRpAiiSG8RAglJSO+9bpnfH0t2MySbbDa72dnk83698mJ35jyz5zw5TLJPzs5UYtCzycguaRRtTwg3XPCjvEaHqjodfj5cjvxLV+MCUMgBLzfp3YmSdfz2sR5nwlyIMR9izIcJc0FkW8732wM5Ba1Gi52b9uAf0ydAqTT9on79zIm4cD4bZ06ctUmMs9BqtNi15U9cO/UKKJqPbcZ4ZKXnIuVkmk1ipKgnj701Wo0Wu7cewLVTx4rGdt30K5CVkYeUU+k2iXEGnBtinBtiWo0Ou385iImTx4jHNm0ssjLykZKcYZMYZyCXK+DvnYDiitOiW7gVlyfDVe0LD9eWt+OyNCYjfxf0eh0Gx90HP69Y+w/GBuQKNYJjrkX++U3Q601FwvzUjXDz7gWvwJYFU0tjgqMnovjCbmjqy41tGmoLUZ53CD5BQ+02Jmup5TJcF6TAz4U6aPSmKw6sL9Ah2k2GAZ4tC4+3hCnwQ5Ja9DUrVAEXOfBDkhovxRoKt2+ladAoAJtGuuDqAOkVXS2l02jw97p1GHTLLFFBYPDtt6EkNRV5R4+2iPEIDMRd69dj4M03i7YnTp0KvU6HnEOHWsQ4A+ZCjPkQYz5MmAtnJEDQaZzyq+k2vURERF1FLlciMHA4Cgv/gl5vupV6YeGfcHUNhqdntFUxCoUbGhrKUFp6XBTb0FCO2tpceHi0XDTlaHKFCsGR41GQuV00rvzMX+DmGQkvvwSrYuRyNU79OQ9pJ5eJYmsqM1BTmQGfwMH2G1QnMB8mcrkKQcGjUJi/TzSugvzf4eYWCk+v3lbFeHn1RtLIN0RfffvdBwBISHwYQ4b/x/6Ds4JcrkSg/1AUFR+GXm+qMxYWHYSraxA8W/n/bUmMQuGKhoZylJSdEsU2NFagti4fnh6R9htUJxjOiUkoKjp4yTnxL8PYPHtZFdN0Hi0pPSmKNZxHpZkPzg0xuVKN4IHTUXB8NfS6ZjXoo9/CLSAWXuFJVsdk7n4fh5f+A3qtaWFt1h8fQ+HihZAht9hxVNZTK+WYMcoXq/8sg0Zreu/77b5SxIa4ICnGvUVMoLcSydn1+O3vKtH2nacMz4dEu8FFJcNd/5eBeavFi9BTcutxOqceY+Il+MEU1vHbxXqcCXMhxnyIMR8mzIUzYh1fypzr3gXkVL7++Hus2PAR3v38v/jlp51IGj0Yk2+5Hq89/hYEQYC3rxcGDktEVno2stJzLIpxZquWrsOytW/jneUvY9uGPRg6agBunDkB859eeDEfnhiQlICs9FxkZ+RZFOMsevLYW7Nq+QYsXfMG/rf0eWzfuA9DR/bHjTePx/xnF5vyMbQvsjLykJ2Rb1GMs+LcEOPcEPtmxSZ88v1cvP3xU/j15z8xZGQ/3HjTFXjj+aWGfPh4IHFoHLIz8pGdWWBRjLMKDxyJU+dXISXzJwT69kdlTRaKypMRG3EjZDIZNNo6VNflwlXtBzcXf4tiBEGPsqpz8PGIQV1DMeoaxFeCUau84OEa7Ijhtit68P04tPF2HN/+GEJjJ6Es/xDyUtdhwJVvQyaTQ1Nfjoqi43D3joa7T4xFMQAQ0e82XEj+Egc23oboQfcBEJBxfBmULt6IGXyf4wbchgcilbj1WAMeSW7E5GAFDpbr8VOBDu/0U0Euk6FcI+BYpR4xbjLEuMsR4SpHhKv4GL+X6aGQASN9DQVGnSDg12I9xvrJkVojILVGfJu5UBcZ+nk6z+cQ9y1aiAd27MQd36/GiTWrETN2LJJm34m1/3oAgiDAzc8PkSNHoSQtFaVpacg5fBhnt27FtCUfwzc6BiXnziI8aRjGPvUU9i1ciLKMDEcPyWrMhRjzIcZ8mDAXRERE1J1FRd2Ao0ffRHLyRwgOvgwVFWdRUPAHEhLuN7yn1lSjsvI83NxC4H7xqrPtxQBARMREXLiwEXK5Cj4+/aDRVCM7eytUKk+Eh09w5JDNik6YjUM7HsKJ319ESK9rUV50DHkZW5A46lVDLhoqUFGSDHevKLh7RVkUI1PIEZN4N1KPL4ZMroB/yEg01BYg4/TX8Pbvj7Do6xw8avOYD5PI6Mk4dmgeko+/h+DQsSgvO4OCvD3oN+CRi/9PqlBZkQo391C4u4dZFKNUecDXr7/odYSLH/z39IqBt09cl4/TUpER1+DYyQ/wd8oyBAUOR2VlGgqLDiA+7p8X81GDquoMuLkGwc0t2KIYAIgIuwoXsn+BXK6Cr3dfw3kjdwdUSk+Eh17hyCG3KTLiHzh2/B38ffoTBAWNRGXFORQW7kd8/D3G82hVVQbc3ILg5hZiUQwARIRPwIWszYZ8+MQb8pGz/eJ5dLwDR2we54ZY9JVP49DHV+PEl7MQMuQWlKfvQ96RVUi8ZTlkcjk0taWouHAA7oFxcA+MsygGAHpd8QQKTq7FqW/vQvCgm1Ce8Qeyfv8I8VM/gNLF05FDbtPTk0Iw/vUUzFqQhlvG+OP3M9X4em8pVjwcDblchtJqLQ6k1iAu1AVxoa4YEu2GsQkeeOzTLFwobkRCuCtO59TjvY0FmDLcB5f19YBMJsOL00Lx0jc5UCpkuGagF7JKGvHexgKM6OOBO8a1vJuCFLCO3z7W40yYCzHmQ4z5MGEuiGxHJjjzahqyWmRkJHJycqCSqTHAb6TdXmf42CQ88uJ9SBgcjwtp2fh+5Y/Y8M1m474VGz7CJ//7FEvf+dSiGHtSqH3s/hrDxwzCg8/PRsLAWGSl52L1pxux8ftfjfuWrn0by95bhWXvf2NRjDOx99gn3zIRry16GtcMuB0Vpba9tYBM4WLT4wHAsDED8NCztyF+QG9kpedhzedbsHH1TuO+pWvewLIPvsfyD763KOZSk2ddjXkLHsfEQXejoqyq1TbWEnQNNj2eM88NwPbzw5nnhl3+r4zujwefnoX4AdHIysjHmi+24ec1vxn3ffL9XCxf8AOWL/zRophLTZp5Jea9/wiuHfqgzfOh8ml5NZLOqKjORFbhXtTUFcBV7Y/QgOEI8R9s3Jec/g0ig8ehV8gVFsVotLU4eHqR2dcL8h2EvlGTbdZ/zzEP2OxYAFCaux9phxehsvhvuPtEo9eAuxCRMNO479DPs9Fn2BOIG/GkRTFNaiuzkPLnf1FReAwymRx+4aORMPpluLgH2azvy88NbL9RB/xZpsOCdC2Sqw0FxrsjlbglTGncd9uxRjwVo8TTvVveFg4AFqRrsCxLi9NXugEAShoFDNtXb/b1ZoYq8H5/tc36v3RvY/uNOqn3lVfhmnnzEDZ0KEpSz2H/kiU48sUXxn0P7NiBHfPnY+cb8wEALt7euGbePAyYcRPcAwJQfDYFB5cvx4Fly9p6GafAXIgxH2LMh4mz5OIjrQ5VACIiIpDdym3ZurOmuoaLawDGXvOlo7tjVnbGz8jO/LnVfXU1uRAEXY/8/hERkWWaft6p1X64/HLz7+E7qqzsNDIy1qK6OhNubqGIiJiIsLArjfuOH38L0dHT0bv3TRbFAIBer0VW1hbk5/+OhoZiqNU+8PXtj969Z7Z6G3trqcL6t9+oA0oLDiPt5FJUlZ2Fu1cUouJnIaLPVOO+wzsfRp+B/0LsoActigEAQRCQdW41ss6uQX1tPlzdQxAYPg5xgx+GQulm0/7bmjPnQ1uSabNjAUBZ6Smkp61GdWU63NzDENnreoRFTDDuO3boNcT0mYXecbdaFGPuNY4deg3DL3vb5guI9TVFNj1eecVZZFz4GdU1WXBzDUJE2HiEhlxu3Hf81AJER01CTK/JFsUAhvNGdu6vyC/cj4aGUqhV3vD1iUdMr6lwcfG1XecVrdfEOqO8/AwyMjeguvoC3NyCERE+AaGh44z7jp94D9G9piAmZppFMcDFfGRvQ37BH2hoKIFa5QNf3wTExMywbT6aXenWFpx6bgBQz3iw/UYdUJq6G2nbXkdVzlG4B/ZF1NhHETHqXuO+w59cgz7XzkXsdfMsimlSmLwBaVvnoq7kPDzDhyDysvsRPvIem/b9l6hRNj0eAOxOrsJra3JxNKMWfUNdMee6INx7daBx34T5ZzF3ZhhemxUOACis0ODlb3Pw68kqFFZo0CfEBbde7o/npoTATW1YDCsIAhZvLcKSbYXILGpEVKAak4f5YP4t4fBwtd1VeLM++dtmxwJYx7eEs9TjugJzIcZ8iDEfJs6SC9bxWceXOi4g7qG6agGxM+mKBcTknOyxKNKZ2XoBsbPj/DBhLsRsvYDY2dl6AbEzs/UCYmfXFYVHIqLuioVH6Rce27Jvx11oqC/pkd8/IiKyjL0WEDszWy8gpu7D1guInZ2tFxA7NTssIHZqNl5A7OxsvYDYmdljAbEzs/UCYmfHOj4RkfVYx2cdX+qUju4AERERERERERGRVQQAeq2je2EdfqSfiIiIiIiIiIiIiLo71vEljQuIrVRdXY3U1FTU1taib9++CAwMhEwmszg+JycHmZmGT0NHR0cjIiKiQ6+v1+uRlpaGnJwceHp6IjY2Fn5+trsNGBERERERERERERGRM2Mdn4iIiIiIiIiIyDy5ozvgTOrr6zF//nxER0fDy8sLSUlJGDt2LIKDg+Hv749XXnkFlZWVbR5jy5YtGDlyJCIjIzF27FiMHTsWkZGRGDFiBDZv3txuH/R6PRYsWIDevXsjPj4eV199NUaOHImgoCBMnjwZKSkpthouEREREREREREREZFTYR2fiIiIiIiIiIjIMlxAbKGamhpcddVVmDdvHi5cuNBif3l5Od58803ExcXh5MmTrR7j/fffx4033ohDhw612Hf48GFMmjQJ7733ntk+6HQ63HzzzXjmmWda9EGn02HTpk1ISkrCnj17Ojg6IiIiIiIiIiIiIiLnxjo+ERERERERERGR5biA2ELPP/88Dhw4AABISkrC1q1bkZubi9LSUuzZswfXXnstAKCoqAgzZ86ERqMRxe/cuRPPP/88ACAwMBCrVq1CaWkpSktL8fXXXyMgIMD4Ojt27Gi1D2+88QbWrVsHABg2bBh27dqF6upqZGVl4fXXX4dcLkddXR1mzJiBoqIie6SBiIiIiIiIiEhCBAg6jVN+AYKjk0dE1O2wjk9EREREREREJDWs40sZFxBboKKiAitXrgQAxMbGYv/+/bjuuusQFhYGPz8/XHHFFdi6dSuuvPJKAMDZs2exd+9eY7wgCHjppZcgCAKUSiW2bduGO+64A35+fvDz88Ps2bOxbds2KJVKAMDLL78MQRBPvsLCQuNVDXr37o3du3dj/Pjx8PDwQGRkJObOnYuFCxcCAEpLS9u8AgIRERERERERERERUXfCOj4REREREREREVHHcAGxBU6fPo3GxkYAwHPPPQe1Wt2ijVwux9NPP218fvz4cePjEydOGK96MH36dCQlJbWIHzZsGKZPnw4AOHDgAJKTk0X7v/32W9TU1AAAnn32WXh5ebU4xpw5cxAcHAwA+Oyzz6DX6zsyTCIiIiIiIiIiIiIip8Q6PhERERERERERUcdwAbEFzp07Z3w8YMAAs+369u3baszPP/9sfDxt2jSz8c33bdq0SbSv+TGmTp3aarxcLseUKVMAGG7BdvDgQbOvRURERERERERERETUXbCOT0RERERERERE1DFKR3fAGcTFxeGtt94CACQmJpptl5OTY3wcGhpqfJySkmJ8fOONN5qNb77vyJEjon1Nxxg0aBCioqLMHmPSpEnG27QdOXIEl112mdm2REREREREREROT6dxdA+IiEgCWMcnIiIiIiIiIpIo1vEliwuILTBmzBiMGTOmzTYajQZvv/228fmMGTOMj/Py8gAAnp6e8Pf3N3sMf39/eHh4oKamRlSsFAQB+fn5AIDo6Og2+9G8KNn8GERERERERERERERE3RXr+ERERERERERERB3DBcRWys3Nxe+//47S0lKcPXsWq1evNl654I033sCgQYOMbZuKhm0VHZsEBASgpqbGWKwEgNLSUmg0GouOERAQYHzc/BjmCBCgE7TttjNHBjnkMrnV8UREREREREQ9lVYQoOtEvGCznhAREXVP3aWODwjQaussaNc6uVwJuVxldTwRERERERFRT8U6PnV3XEBspT179uD2228XbfP398eqVatw/fXXi7Y3FQCbFwXNCQgIwIULF1BTU9Mi3pJjNN/f/BjmaAUNTpb91W47c0JcoxDm3svqeCkRdA2O7gJJFOcGEVlD6RHafqMeJGTJh47ugmQsdW10dBeIiEgi/tAL+F1g+ZCIiMheuksdv7GxHL///lC77cyJCrkKvUKvtjpeSvT1ZY7ugqQoPIId3QXJ0JSlO7oLkqLXVDm6C5Ihk7s4uguSonDzc3QXJCXvgS8c3QXJ+E/gMUd3gYiISJJYx6fujguIbai0tBRPPfUUFixYgBtuuMG4vbKyEgDg5ubW7jFcXAxvYuvqTFcTaIq35BhN8ZcewxylTIX+vsPabWeODLz6MBEREREREZE1LpfLcBlkVscv1elRbcP+OCNBECDoNI7uhlUEFp2JiBzCGev4KqUXhvWb0247c+Qy/imIiIiIiIiIyBqs43ce6/jSxtWfVrrtttsgCAIqKipw7NgxzJ07F15eXkhJScGUKVOwbt06Y9ugoCAAQHl5ebvHbWrTvMDYFG/JMZrvt6TQKYMMCpnS6i+5jFOIiIiIiIiIyBpKmQwunfiyvmRJRETUM3SbOr4MUCpcrf6Sy7mAmIiIiIiIiMgarONTd8fVn53k7e2NIUOG4PXXX8fmzZshl8uh0+nwwgsvQKfTAQDCwsIAGK5s0J6mNp6ensZtTfGWHKP5/ubHICIiIiIiIiIiIiLqiVjHJyIiIiIiIiIiaokfO7ehcePG4brrrsOWLVtw7tw5ZGdnIzo6GqGhoQAMRUFBECCTtf7ZAkEQUFZWBgCIiooybvf09ISHhwdqamraLTw2xV96DCIiIiIiIiKi7kjQNzq6C0RE5ERYxyciIiIiIiIi6lqs40sXr0BsgX//+9+488478eqrr7bbtn///sbHubm5AExXHmhsbERKSorZ2JSUFGg0GgBAYmKiaF/TMU6cOAG9Xm/2GCdOnDA+vvQYRERERERERERERETdEev4REREREREREREHcMFxBY4ceIEVq1ahRUrVrTbNicnx/jY19cXADBixAjjto0bN5qNbb7v8ssvF+1rOkZ+fj4OHz5s1TGIiIiIiIiIiIiIiLoj1vGJiIiIiIiIiIg6hguILRAfHw/AUPQ7c+aM2XbV1dXYu3cvAEPRMS4uDgAwZcoU4+3OfvzxRwiC0CJWEASsXbsWACCTyTB16lTR/mnTphkf//jjj62+fnl5OXbu3AnAcAWFhIQEi8ZHREREREREREREROTMWMcnIiIiIiIiIiLqGC4gtsBll11mfPzII4+gurq6RZuGhgY89thjxtud3XXXXVCpVACA8PBw3HbbbQCAv/76C8uWLWsRv3TpUuzfvx8AMHv2bOOtzprMmDED0dHRAIBFixbh2LFjov06nQ5z5sxBfX09AODZZ5+1ZqhERERE1APJ5HKoPTxafKnc3R3dNYdgPkyYCzHmQ4z5MGEuHEkAdBrn/ELLhWlERGQ91vGJiIiIuhfWW8SYDzHmw4S5EGM+xJgPE+bCkVjHlzKlozvgDGbOnIn/9//+H5KTk7F7927069cP999/P/r27QuFQoGzZ8/iq6++QlpaGgAgLi4O8+bNEx3jzTffxC+//ILS0lI8/PDD+PPPP3HDDTdAr9dj06ZNWLVqFQAgICAA//3vf1v0wcXFBYsWLcL06dNRX1+PK6+8Eo8//jiGDx+O0tJSfP7559i3bx8AYPTo0bj77rvtnBXLDL98KB564T70GxKPrPPZWL3yJ6z/ZpPNY5zFsDGD8dDzd6Hf4L64cD4baz7biA3fbrV5jLNgPkyYCzHmQ2zYmIF48NnZ6DcoFhfSc/HD55uw4bvtNo+RomGjE/GvZ25Fv4F9cCE9Dz98uRUbv99psxgfX09sPfoprh92PyrKquwxBJspr0xFZvYWVNdkw801COEh4xAaPLpTMYIgoLD4ILLzdqOuvghKpTv8fOIREzUZLmofew+pUwp+y8bx+X+h9GghvPr6IuHhwYi7d4DZ9oJeQOrnyUhZfByVqeVwD/NA5OQ+GDzvMqi9XVq0byipw5qIFZiZ/QBcA93sORS7iLniSlwzdy7Ck5JQknoOf33yCQ5//rnZ9vHX34C71q9vsb0kNRUf9O9nx552DebDhLkQYz7EmA8T5oKIiMj5sI5vuYrqDFzI342aujy4uvgjLGAkQgKGdTqmojoTF/J3ora+ADKZEt4evRAddg3cXALsOZxOYb1FrKzsb2Sc/xFVVelwdw9FROS1CAu/ulMxgiCgIH8vsi5sRm1tPlQqD/j5D0Kf2Fvg4uJv7yFZraLqPC7k7UB1bS7cXAMRGngZQgNHdDqmojodmbnbUVt38f+JZzRiwq+Dm6t0/58AQEX1BWQV7kVNXQFcXfwQ6j8MIf5DOh1TWZOFCwW/oba+CDKZAt7uUegVehXcJDw3AJ5Hm+N5VKzm9xQUvr0edScyoe4TjID7J8Dvn1dYFFu55RgK5v+Ivn++0fK4f5xF4ZvrUHciE3JPV3iOT0TIqzdBFeZn6yHYFestYsyHGPNhwlyIMR9izIcJc0FkG1xAbAGVSoUNGzZg0qRJOHPmDHJycjB//vxW244ZMwbfffcd/P3Fb2xjYmKwfv163HTTTSgqKsIXX3yBL774QtQmODgY69atM16h4FLTpk3DRx99hKeffhpVVVV48803W7QZPnw41q9fD6XS8d/agcMTsXj1+/jrt0N46/n3MWTUILz20UsAYHZBsDUxzmLgsH746Lu3cGDPEbz94iIMHjkA8xY9BwBmFzlaE+MsmA8T5kKM+RAbkJSAD1fNx4G9x/D2S0sweER/zF3wFACYXRBsTYwUDUjqi0Vfv4qDe0/gf68sx+DhCZj7/mMAYHZBcEdiFAo5HnrudihVjv+Z2Z7KqgycOv0JfH3iEdd7Fiqr0nH2/LcAYLYYa0lMUckRpKStQmjQaPSKuBaNmipk5WxHTU0uhg58BnK5omsG2EFFf+Vjx6R1CL2mF0YtvhpFf+bhz3/9CgBmFxGfW34Sf83Zhbj7B2DQy6NQmVqOv987jOKD+fjHrpmQK0w35tBr9Tj22n4IWn2XjMfWIkeNwj2bNiFtxw5sePwx9BozBjctXwEAZt80+8f2QV15Ob6aPk20XXvxqljOjPkwYS7EmA8x5sOEuSAiInJOrONbpqomG3+f/xo+Xn3QJ3ISqmqykJq9AQDMLn6zJKamrgCn0j6Ht0c0ekfcCL2+EblF+3Hi3HIkJcyBWuXVNQPsANZbxCoqUnHi2Nvw8x+E+H73obL8LM6cNlyJ29wiYktiCgv+wOm/P0ZY+NWIjpmOxsZyZGasR3V1JoaPeANyufRqc1U1WUhO/Rw+XnGI7TUNVdWZSL2wFgDMLiK2JKamLh8nz66Aj2cM+kRNgV7fiJyCfTiesgTDEp+EWuXdNQPsoKraHJzO+N7Q7/B/oKo2B2k5mwHA7CJiS2Jq6otw6vwqeHtEoXfYtdAJGuQVH8TJtC8wtO+/oFZ5ds0AO4jnUROeR8VqD6UhY+YH8LwqEeHv3YnaA6nIefwzAGh3EbGuohZF725sdV/dsUxkTHsXHlcPQMTCu6Err0HRwi3InLkAfXa+CrmLyuZjsQfWW8SYDzHmw4S5EGM+xJgPE+aCyHak965covr06YPDhw/jyy+/xC+//ILz588jPT0dLi4uSEhIQHx8PKZNm4bJkydDoWj9Tcu4ceNw8uRJfPjhh1i3bh0yMzMhk8kQHR2N6dOn48knn0RQUFCb/XjsscdwxRVX4MMPP8SOHTuQl5cHT09P9OvXD3fccQcefPBB4y3XHO3OR25FZuoFPHf3y9Bqddjyw3Z4eLrjnidnm10MbE2Ms5j98ExkpmXh+XtfM4ztx51w93TH3Y/fanaBozUxzoL5MGEuxJgPsdkPTUdmWg6ev///QafVYeva3fDwdMPdc2aaXQxsTYwUzf7XVFxIy8Xz/3rHMI6f9sDd0w13PzrD7AJiS2PeWfEiBg7ti6BQaV/JoklO3m64uQUjMf5+yOUKhASOgE7XgKzcHQgJugwymcyqmNz8PfD17ov42NuNcS5qP/x9dgVq6nLh5RHVlcO02OmFR+Cd4IfxP0yCXKVAnzv6QVvViOR3DyH2nsRW83HqnUOImh6LMUsnGrcFDAvGjhvXIW/7BURcHwMA2H3zzyg+kI+6vJquGo7NjX3yKRSfTcGqWTOh12px/Ntv4eLlhSuee978G+Y+fVCalorMi1fC6k6YDxPmQoz5EGM+TJgLIiIi58U6fvtyiv6Em0sA+sXcCrlMgWC/wdDpG5BdtA/B/kmt1xgsiMkt+gMqpQcG9JkNudwwtgCf/jh8+kPkFu1HTPi1XT3UdrHeIpZ9YRPc3cMxcNDTkMuVCA0dB62uHpmZGxEaNr7VfFgSk539C3z9BqBf/weNcS6ugTh14n3UVGfBy7t3Vw7TIjkFv8PNNQj9Y2cb5rz/UGj1jcgp2IOQgOGtzw0LYnIKfoda5YkBcfc0+3+SiEPJ7yO38A/ERFzf1UO1SG7xQbi6+CMh+ibIZQoE+Q2ETt+InKL9CPYb3Go+LInJLfoLKqUHEmNuNS4kD/BOwJGUT5BXchDRoW1f/dpReB414XlUrPj/tsElLhS9vp4DmUoJ31vGQF9Vj6JFm+F757hW89GQmo/c575G3aE06Ksb4NI/okWbslV7oQj2RvSqxyC7eEEUl34RSL/xbdQdPA+PcQl2H5stsN4ixnyIMR8mzIUY8yHGfJgwF0S2I2+/CTVxd3fHww8/jJ9++gnHjx9HZWUlioqK8Pvvv+PTTz/FtGnTzBYdm4SEhBhvo1ZdXY2qqiqcOnUK//3vf9stOjYZMmQIVq5ciYyMDDQ0NKCkpAT79u3DnDlzJLN4WKlS4uobr8C2dTug1eqM2zet/gUxcb0QPzDOJjHOQqlSYvwNl2P7ut9EY9vyw6+IiYtC/MBYm8Q4C+bDhLkQYz7ElColxl8/Bts37IGu2dg2/7gL0XGRiB/QxyYxUqRUKXHVdaOwfcM+0Ti2rP0N0bERiE+M6VTM8QOn8c3yjdi+QfpvDvR6LYrLTiAoIEl0ZYXgwBGoqy9ETW2O1TFqtS8CA5JEsa4Xbw+o1zXaYzidpmvUIWv9eUTPiodcZRpb7zv6ofJsOcqOF7eI0dZqUJNZhbCJvUTbQ66KAGRAeXKJcVvw2HD0fyoJ0bP62m8QdqRQqZA4bRpOrF4DvVZr3H5s1TcISkhA2JDWrwoT0CcWJWnnAQDydn6fdSbMhwlzIcZ8iDEfJsyFMxIg6Bqd8gsQHJ08IqJuiXV88/R6LUorzyDQdyDkMlMOgvwGo76hBDX1+VbH1DYUw9sj2rjoDQBUSg+4uwajtr7QjqOyDustYnq9FkVFhxAcMlp0ReDQ0HGoq81DdXWm1TEuLv4IvuRKpG6uhv9HOl2DPYbTKXq9FiUVfyPQb5Bozgf7D0VdQzFq6vKsjqmrL4K3R4z4/4nKE+5uIaipK7DjqKyn1+tQWnkWgT79xecA3wGobyxt9f+3pTF1DSXw9ogSzR+V0h3uroGorS+y46isx/OoCc+jYvpGLao2HYXPTaOMi3wBwOfWMWhMLUD9yaxW4+RuanhdMxDBL06D69DW724g1Gug8HITHVfh72F43QaNDUdhP6y3iDEfYsyHCXMhxnyIMR8mzIUzYh1fyriAmOwiIMgfahc10s6ki7afP5sBAAiLDLFJjLMICPIzjC0lQ7T9fIqhgBYaEWyTGGfBfJgwF2LMh1hAkC/ULiqcT7kg2p5+1vA8NKLlH6ysiZGigEAfwzjOiotq6eeyAZgZewdiVi3bgK+Xrscfu47Yuus216ipgiDo4OEWKtre9LyhoczqmMT4exEeMhaCIKBRU42q6ixkZG+Gi9oPXp6tFysdrb6gFvpGHXwHBIi2+yQaCsg1WVUtYmQKGa7//RZEzxQvCi7cmwsIgHuE6XaIic8Mw4BnhxuvSOxsPENCoHRxQeHfyaLthaf/BgD4RLV+dQ7/Pn3gExmJx48cweu1dXgxIxOTFiyA2lOat4q0FPNhwlyIMR9izIcJc0FERETdmUZbA0HQGRdvNnF3MTxvaKywOiY2YjJiwq4RtdHqGlBTlwe1yttmY7AV1lvEGhsrIAhaeHhEira7exiuhNlQ3/ID25bGDBz0FCIiJxry0ViJqsrzOH9+NVxcA+HtI70Lx2i01RAEHdxdxX+Tcnc11Jlb/39iWUxsr2mIibhO1Eara0BNbS5c1NL7fwKYzgHul5wD3FwDAQANmkqrY/pEXIfo0KtEbXS6BtTUFUCt8rLZGGyJ51ETnkfFtIWVEBq1cOkXLtrummB4rskuaS0Mqgh/BD5+PQIfvx6u/SNbbeNz8yg0pBag8J0N0OSVof5MDvL+/S1UvQLhfpn0zqOtYb1FjPkQYz5MmAsx5kOM+TBhLohsS9l+E6KOCwgxLOKpLBcv4qkoNbwJ9g9qect4a2KcRUCwmbGVGYokAUF+NolxFsyHCXMhxnyINfW9sqJatL2izDBW/yBfm8RIUUBwe+PwsUmMM9BcLKgrle6i7UqV4XmjpuWC2Y7G6HR12H/4FQCAXK7G0AFPiq78ISV1+TUAALWvi2i7S4AbAMMC40spXJQIGh0m2lZ8MB+/3/0L3KM8ETXNea9UfinPUEOxva6sXLS9tsRQoPYMafmBLJlcDr/eveEdEYEd8+ej6PRpRIwYgStfeAGBcX3xxZTJdu+3vTAfJsyFGPMhxnyYMBdERETUnTVqDTUTpcJNtL2pfqDR1lgd4+kuft/dqKnGmYzvIAh6hAaOsEHvbYv1FrHGxnIAgFIp/sO56uIizsZWFkV2NEarrcW+vQ8BABQKFyQNf02S+WjUXJzzSlfRdtOcr7Y6xtM9vEXc6fNfXfx/cpkNem97pnPAJWO7eE5o+7zRdoznJYtIG7U1OJPxgyEf/sNs0Hvb43nUhOdRMW2h4Zyn8PUQbVf4G86R2qKWi+0t5Tl+AEJfn4X8V75D4ZvrAAAytRJx+9+AwtO17WCJYL1FjPkQYz5MmAsx5kOM+TBhLohsS5q/YZPzE1q/fLdMbrjotVLVytSzJsZJCFaMzZoYZ8F8mDAXYsyHmGDmVggymQwAoFS2kg8rYqTI7Pe1rbFbEeMcZK1vvjheQdB1OkahcMHgxMfR2FiJvILfcfzvjzB0wJPwuOSPGlLQ9P28lKA3jE2vaS0fJg1l9Tgx/y+kLDkO9ygvXL1uCpRuzjo3WtHOOVHRym1y5UolfrjvXuQdP47ilBQAwNlftqIqPw8zPlmKyJEjkX3woP36bE/MhwlzIcZ8iDEfJswFERER9URt1hg6FiMIOuQVH0JWwS7oBR3io2+Cp1tYa0dwMNZbLKMH0MG5YSZGoXBF0rC5aGgoQ07Odhw9PB/DRrwGT89etuqsfdn6/0nRX7iQtwN6QYv43re0WFwsFTJz8/5iHVrfSj46GiMIeuSXHEFW4V7o9Tr0jZoKDzcnuyspz6MmPfQ8aqZsDVysWwvt1K3bUrHuIAr+31r4P3gNvK4ZCF1lHUo+3o4Ldy5GzJqnoYpwgotwsd4ixnyIMR8mzIUY8yHGfJgwF0Q2JXd0B6h7Ki4sBQD4+IlvqePja3helN/ydlfWxDiLErNjM3wSv7WxWRPjLJgPE+ZCjPkQKyk03K6raSxNfPwujq2g5S2vrImRopKicgCAt6/4CiY+F58XFZTaJMYZNN2eTqMVX1m36bla3fLKyh2NkckU8PWOQ3DgMAzs9zDkMgVy83+3zQBszDX04tUoyupF2xtLDc/dws3fYiZnczo2Dv4aZz85gf5PJGHykTvgNzDQfp11gKr8fACAu7/46uvu/hev1p6b2yJG19iIk6tXG98sNzm9YQMAIHTQIHt0tUswHybMhRjzIcZ8mDAXTkgABF2jU36Z+ewfERGR3agvXilWq6sTbW96rlZ5dSqmtr4YJ1I/RXruFnh7RGNo/CMI9B1ouwHYEOstYmq1L4CWV9fVaC7eCcql5V3eOhojlyvg69cfIaGXY8jQf0MuVyIne7stum9TTXNaq710zl/8Pqu8OxVTW1+E4ylLcT77Z3h7xiCp/xMI8htsuwHYmEpluJpqi3OA1lCLUytbnjc6ElPXUIKTaV8iPW87vD2iMLTv/Qj07W+7AdgYz6MmPI+KKYMNfdeVia9Crb34XBXqa9VxBY0WOU98Dr/bLkf4O7Phdd0Q+M4ajZh1z0FbUIGihZs71e+uwnqLGPMhxnyYMBdizIcY82HCXDgh1vEljQuIyS5Ki8qgadQgtn8f0fa4i88LcgptEuMsSovLDWPrFyPa3vQ8P6fIJjHOgvkwYS7EmA+x0uKKi2OLFm1vel6Q23JxtDUxUmQcR4L4CiSx/QzP2xx7B2KcgUrlCZlMgdq6PNH22lrDcxd1yz/gWBKj1dbh0PG3UFx6XNRGoVDDxcWvRUFbKlyD3SFXyVF+SrwYvum5R2TrC4gvrE/Drukb4RrshinH78Twd6+A2tvF7v3tajWFhdA2NiJkgPgPC03PK7KzW8T4Rkcj4cZJkF9ylW69znBVjPpK62+v52jMhwlzIcZ8iDEfJswFERERdWcqpQdkMjlq68W19qbnLqqWi70sjalrKMWptM9Q31CKxN6z0b/37XBzke4VEVlvEVOrfSCTKVBTnSXa3vTc1SXAqhitthYH9j+PokLxlbwUChe4uAZCqxUvtJMCw5xXoLauQLS95uJzl1YWRVoaU9dQgpNnl6G+oQSJsXcjMfafcGslt1KiUjSdA8R19toGw3MXdSsLiC2MqW8ow6nzX6O+sQz9Y25Bv+iZcG1lsbqU8DxqwvOomCLIGzKVAvWnxXWDhovPVZHWfS91FbXQV9bBbbj47+cKbze4JISjMdM5/u7BeosY8yHGfJgwF2LMhxjzYcJcENkWFxCTXWg1WuzctAf/mD4BSqXCuP36mRNx4Xw2zpw4a5MYZ6HVaLFr8z78Y/p48dhunoCs9ByknEy1SYyzYD5MmAsx5kNMq9Fi15Y/ce3UK6BoPrYZ45GVnouUk2k2iZEirUaL3VsP4NqpY0XjuG76FcjKyEPKqXSbxDgDuVyJQL/BKCo5Ar3edJuzwpLDcHUJhKdHhFUxCoUrGhorUFp+WhTb0FiB2roCSd4GDgAUagWipsciY81Z6Jvd9i39uxR4xfrAPym4RYxeo8OBOTsReFkort97C7zjpf2HiM7QaTT4e906DLpllugN8ODbb0NJairyjh5tEeMRGIi71q/HwJtvFm1PnDoVep0OOYcO2b3f9sJ8mDAXYsyHGPNhwlwQERFRdyaXK+Hv3R/F5aegb3ab+KLyk3BV+8HDLdTqmMy87dDrtRgS/xD8vPvafzCdxHqLmFyuRFDQSBQW/gm9XmvcXlCwD25uIfD0irEqRqFwQ0NDKUpKxL9HNzSUobYmB56e0Zce1uHkciUCfBJRVHZSPOfLjsPVxR8ebmFWx2TkbIVe0GFov8fg75Ng/8HYgFyugL93AoorTovGVlyeDFe1LzxcWztvWBaTkb8Ler0Og+Pug59XrP0HYwM8j5rwPComVyvhNXkYKtYehKAxnRPL1/wFde9guA7u1Ua0eYoALygCvVDzh/jv5NqyajT8nQPX/i3zLEWst4gxH2LMhwlzIcZ8iDEfJswFkW0p229CZJ2vP/4eKzZ8hHc//y9++WknkkYPxuRbrsdrj78FQRDg7euFgcMSkZWejaz0HItinNmqT37AsnUf4H8r52H7+t0YetlATJp1LV5/8j1jPgYk9UNWRg6y03MtinFmzIcJcyHGfIitWroOy9a+jXeWv4xtG/Zg6KgBuHHmBMx/euHFfHhiQFICstJzkZ2RZ1GMs1i1fAOWrnkD/1v6PLZv3IehI/vjxpvHY/6zi01jH9oXWRl5yM7ItyjGWUWEjcfxvz/C6XOfIiggCRVV51FYfAjxfW6HTCaHRluDquoLcHMNhJtrkEUxABAeegWycrZDLlPCxzsOGm01cvJ+g0rpgfCQsY4ccpv6PzUM2yb8gN9u2YyYW/qicF8u0ledwZjlEyGTy9BQWo/iA/nwivOFd5wvivbnoy6/Fn3+2R8Fe3JaHM9vUCDcI1q/crEz2rdoIR7YsRN3fL8aJ9asRszYsUiafSfW/usBCIIANz8/RI4chZK0VJSmpSHn8GGc3boV05Z8DN/oGJScO4vwpGEY+9RT2LdwIcoyMhw9pE5hPkyYCzHmQ4z5MGEuiIiIqDuLCBqDk2mfISVjNQJ9B6Cy5gKKyk4gLnLqxRpDLaprc+Dq4m+8Mmp7MYKgR2llCnw9+6Cuvgh1l1x1VK3yhodbiCOG2ybWW8Qie03CsSPzkXxyIYJDxqC8/AwK8n9Hv/4PGvKhqUZlZSrc3ELh7h5qUQwARET+A5kZ6yGXq+DrmwiNphJZWVugUnkhPGKiI4dsVkTIOJw4uxxnzn+DQL9BqKzORFHpMcT1usn0/6QmG64uAXBzDbAoRhD0KK04A1+v2Nb/n6i9W118KgXhgSNx6vwqpGT+hEDf/qisyUJReTJiI26ETCaDRluH6rpcuKr9jFfMbS9GEPQoqzoHH48Y1DUUo65BfBVVtcoLHq4tLxQgBTyPmvA8KhY45x9In/Q/XLhrCXxuGoXa/edQsfpPRHx0L2RyObRl1ag7nA51n2C49LHs+ymTyRDyygzkPv0lIAO8Jg6CrrwGJct2AjIg4CFpnkdbw3qLGPMhxnyYMBdizIcY82HCXBDZjkxw5tU0ZLXIyEjk5ORAJVNjgN9Iu73O8LFJeOTF+5AwOB4X0rLx/cofseGbzcZ9KzZ8hE/+9ymWvvOpRTH2JFe42v01hl8+BA+9cBcSBsUh63wOvl+5Hhu/+8W4b9m697H03S+x7N0vLYpxdsyHCXMh5kz5kClc7P4aw8cMwoPPz0bCwFhkpedi9acbsfH7X437lq59G8veW4Vl739jUYy92CMXw8YMwEPP3ob4Ab2RlZ6HNZ9vwcbVO437lq55A8s++B7LP/jeophLTZ51NeYteBwTB92NirIqm/bdPWq8TY9XXnEOmdlbUF2bDTfXIISHXIHQ4NHGfSdOL0aviOsRE3WDRTEAoNdrkZ23CwVFB9DQUAaV2gu+3n0REzWp1dsxdkb07+dterz83dk4/vp+lB0rgldfXyQ8Ohhx9www7ts+8UcMfvUyDJk3Gpk/nMOe28z/LnH5ymsRe3eiaFvaF3/jj/u3Y1b+g3ANdLNp36NcF9v0eK3pfeVVuGbePIQNHYqS1HPYv2QJjnzxhXHfAzt2YMf8+dj5xnwAgIu3N66ZNw8DZtwE94AAFJ9NwcHly3Fg2TK797UrMB8mzIUY8yHGfJg4Sy4+0upQBSAiIgLZrdyWrTtrqmuoVd4YNejfju6OVQ6cfBuNmsoe+f0jIiLLmH7eeWFk4rM2O25FdTou5O9GTV0eXF0CEBY4CiH+ScZ9p9K+QFTIVegVerVFMRptDQ4kv2v29YL9hqBvrxk26bvCzbZ3FXL2eovCw7YLLMvK/kb6+TWorsqEm3soIiP/gbDw8cZ9x468gZjeN6N3n5kWxQCGfGRd+Bn5eXtRX18EtdoXvn6J6BN7C1wuLja1BU2Zbe8+Vl51HhfyfkVNrWHOhwePQUjAcOO+U+dWICp0AqLDJ1oUo9FU46+Tb5p9vWD/YYiPmWl2f0fpNbatdVZUZyKrcC9q6grgqvZHaMBwhPgPNu5LTv8GkcHj0CvkCotiNNpaHDy9yOzrBfkOQt+oyTbpu0xu+7o1z6Mmzn4eLd3qZdPjVe89g8K31qH+5AWo+4Qg4F8T4HfnFcZ9GVPeQdCLUxHy0vQWsdmPrETdsQz0/fMN0XZBEFC54TCKP9qKhpRcyD1d4T66L0JevcnihciWmBr4L5sdyxxnqbd0FeZDjPkwYS7EmA8x5sPEWXLBOj7r+FLHBcQ9VFctIHYmXbGAmIi6n65YQOwsmAsxWy8gdna2XkDszLpiATEREfUMLDyy8EhERN2bvRYQOzNbL3xzdrZeQOzMbL2A2NnZegGxM7PHAmJnxvOomK0XEDuzrlhATERE1BOxjs86vtTJHd0BIiIiIiIiIiIiIiIiIiIiIiIiIiIi6jpcQExERERERERERERERERERERERERERNSDKB3dASIiIiIiIiIiIusIEHSNju6ElQRHd4CIiIiIiIiIiIiIyM5Yx5cyXoGYiIiIiIiIiIiIiIiIiIiIiIiIiIioB+EViHs4jdCI0+VHWt0X6BqGINewLu4RERERERERETU5pNfjsL71T7jXdHFfqOPyig8gr/hAq/saNfwOEhGRZRo1NThyZnGr+8ICRyEscFQX94iIiIiIiIiImrCO79x6eh2fC4h7OJVMjf6+wxzdDSIiIiIiIiJqxQi5HCPM3D/qI60OVV3bHekRAEHf4OhemBXqPwSh/kNa3Xfo9GI0anv8d5CIiCygVnlgWL/HHN0NIiIiIiIiImoF6/jtYB1f0sxMXSIiIiIiIiIiIiIiIiIiIiIiIiIiIuqOuICYiIiIiIiIiIiIiIiIiIiIiIiIiIioB+ECYiIiIiIiIiIiIiIiIiIiIiIiIiIioh5E6egOEBERERERERERWUeAoGt0dCesJDi6A0REREREREREREREdsY6vpTxCsREREREREREREREREREREREREREREQ9CK9ATHSRXlfv6C5Iilzh6uguEDkFQdfg6C5Ihlzt7eguSIrrwBsc3QVJiXLt7+guEBERERERETklQa+DpjLd0d2QBL2mytFdkBRtTb6juyAZ+sZqR3dBUli3NpEpXBzdBUnxuOyfju6CpGyaMdDRXZCMpY7uABERERE5BK9ATERERERERERERERERERERERERERE1IPwCsREREREREREROSkBAi6Rkd3wkqCoztARERERERERERERGRnrONLGa9ATERERERERERERERERERERERERERE1INwATEREREREREREREREREREREREREREVEPwgXEREREREREREREREREREREREREREREPYjS0R0gIiIiIiIiIiKyhiAAgq7B0d2wiiA4ugdERERERERERERERPbFOr608QrEREREREREREREREREREREREREREREPQivQExEREREPZJMLofKza3FdkEQoKmtdUCPHIe5EGM+TJgLMeZDjPkwYS6IiIiIiIiIiGyL9RYx5kOM+TBhLsSYDzHmw4S5IGodFxCTXQ2/fCgeeuE+9BsSj6zz2Vi98ies/2aTzWOcBfMhNmzMYDz0/F3oN7gvLpzPxprPNmLDt1ttHuMMmAsx5kOsJ+cj6bL++NfTN6PfgN7IysjHD19tx8bVu20W4+PriS2HPsYNIx9FRVmV7QdgQ6V5B5B25CNUFSfD3ScGUf3vQETCzE7H6PUapB5cgKLs31BfnQvvwIGIH/kcfIKH2HM4dhFzxZW4Zu5chCcloST1HP765BMc/vxzs+3jr78Bd61f32J7SWoqPujfz449tT/mQoz5MGEuxJgPMebDhLkgIiKi7qyyLh855cdR21ACF5U3QrwTEOTV12YxGl09jl1YjaG9boFK4WqPIdhMRU0Wsov+RE19AVzVfgj1G4Jgv0GdjqmsycaFon2orS+GXK6Al1sEeoWMg5vaz57D6bSK6gvIKtyLmroCuLr4IdR/GEL8264RWRJTWZOFCwW/oba+CDKZAt7uUegVehXcXPztOZxOqazNQXbxX6hpKIKryhchfoMQ7JNosxiNrg5HUj/FsNj7oFK2XMQgNZV1ucguPYzahmK4qnwQ7JOIYO+23+t0JEajq8fR9K+Q1Pufkj9vAJwfzZXm/oW0w4tMNejEOxHRb1anY/R6DVIPvI+irN2muvVlL0q+br2/TIcFGVokV+kR4y7DPyOUuDXM8iUZizM0+L8LWpy+Uvx9P1Cuw3vntThTo4daDoz0kePFPirEuDvXDadZbxFjPsSYDxPmQoz5EGM+TJgLItvgAmKym4HDE7F49fv467dDeOv59zFk1CC89tFLAGB2Aaw1Mc6C+RAbOKwfPvruLRzYcwRvv7gIg0cOwLxFzwGA2UWO1sQ4A+ZCjPkQ68n5GDA0Fou+/DcO/n4S/3v1UwweHo9X330IAMwuCO5IjEIhx4PPzoJSJf1fh8oLj+PI1vvhH345+o99DeUFR5C892UAMLuI2JIYQRBwbPtjqC5LQcyg+yBXqJF9ZjUOb70PY27aCDfP8K4ZoA1EjhqFezZtQtqOHdjw+GPoNWYMblq+AgDMvlH0j+2DuvJyfDV9mmi7tr7e3t21K+ZCjPkwYS7EmA8x5sOEuXA2AgRdg6M7YSXB0R0gIqIeqLq+CCkFv8LHNQzRgaNRXV+I9OI/AMDsguCOxAiCHjllxyA4wc+5qto8nL6wFr4evdA7bCKqanOQlrcNAMwuIrYkpra+GMmZq+HtHoneYROg12uQV3IYJ89/g6Gxd0Ot8uyaAXZQVW0OTmd8Dx/PGPQJ/4dhbDmbAcDsImJLYmrqi3Dq/Cp4e0Shd9i10Aka5BUfxMm0LzC0778kmY+qunyczl4PH/co9A4Zj6q6PJzP3wEAZhd8diRGEPTILv4LAvT2HYiNVNcX4kzuFvi4RSAmaByq6guQXrgHAMwuCO5IjCDokV16yCnOGwDnR3PlhcdwZMu98I8Yi/7j5htq0Hv+DQBmFxFbEiMIAo5texTVpWcQM/iBi3Xr73F4890YM3OzZOvWRyv0uOtEI8b5yfFGvAqHK/V44YwGACxaRHyySo8FGVqoL1kTnFKtx61HGzHKV47X+6pQpwdWZGkx9XADto9yRYiLzB7DsTnWW8SYDzHmw4S5EGM+xJgPE+bC2bCOL2XSXzFDTuvOR25FZuoFPHf3y9Bqddjyw3Z4eLrjnidnm138ak2Ms2A+xGY/PBOZaVl4/t7XDGP7cSfcPd1x9+O3ml3gaE2MM2AuxJgPsZ6cjzsemIQLabl44aEF0Gl1+GXdPnh4uOKuh6eYXUBsacw7S5/BgKQ4BIVI+6ovTTJPfgoPn94Yeu1iyOUqhMVNhVZTg/QTyxEefzNkspYFQktiyvL+QknO7xg3axvcvCIAAEG9rsHxHY+jPP8w3OKkWYhtzdgnn0Lx2RSsmjUTeq0Wx7/9Fi5eXrjiuefNv0ns0welaanI3LevaztrZ8yFGPNhwlyIMR9izIcJc0FERETdWX7l33BTeSMu5GrIZXIEevYxLOisOIVAz7hWawyWxpwr2InqhmJodHVdPSyr5JUegpvaD/FRUyGXKRDk0x86vQY5JQcR5Duw1VxYEpNbcggqpTv697oJcrnhz3D+XnE4mroSeaVHEB1yZVcP1SK5xQfh6uKPhOibDGPzGwidvhE5RfsR7De41XxYEpNb9BdUSg8kxtxqzEeAdwKOpHyCvJKDiA69uquH2q68sqOG73PEjZDLFAj0ToBO34jcksMI8u7f+tywMCYlZxOq6wqg0dV09bCslld+Am4qX/QN+4fhHODV17Awvuw4grwSWs+HhTFn87ahur4QGp3z3DKa88Mk88RKePj2wdB/LDHUoPtOg1ZTjfTjSxGeMLP1urUFMWV5+1GSvRfjbt1hqltHT8Tx7XNQnn8IbnFTu3qoFlmRrUWsuwxLB6qhksswIxSo0Tbik0wtbglVtJqPJvU6AU/93QhXOVosHV+WpUWgGvhisBquCsMxrgtU4Kr99fgsW4t/x6rsOCrbYb1FjPkQYz5MmAsx5kOM+TBhLohsx7nuaUFOQ6lS4uobr8C2dTug1eqM2zet/gUxcb0QPzDOJjHOgvkQU6qUGH/D5di+7jfR2Lb88Cti4qIQPzDWJjHOgLkQYz7EenI+lCoFrvrHCGz/eT90zcfx0++Ijg1H38ToTsUcO5SCb1ZswvaNf9p3IDag1zWiMPNXhPS5EXK5qRAYFjcNtRXpqCo9bXVMdsoaBEWNh5tXBARBgKDXwcU9CKOmfIewuCn2H5yNKFQqJE6bhhOr10Cv1Rq3H1v1DYISEhA2pPWr5AT0iUVJ2nkAgFyh6JK+2htzIcZ8mDAXYsyHGPNhwlwQERFRd6YXdCiruQB/j96Qy0x/Hgr06IN6TSVqG8s6FePpGoJQn0T4e8TYdRy2oBd0KK1KQ6BPAuQy0+9vQT79Ud9YhtqGIqtj6hpL4e0eYVwsCwAqpTvcXQJR21Bix1FZT6/XobTyLAJ9+ovH5jsA9Y2lqK0vtDqmrqEE3h5RLfPhGoja+pZ5djS9oENZ9XkEePUVjSvQux/qNeWobSjuVIyXWxjC/IfC38s5/sZjGFsG/D37iM4BAV59Ua+pQG1jyzndkRgv11CE+Q6Cv2cf+w7ERjg/TPS6RhRm/IqQPpPENei+0w016BIzdWsLYrLPrEZQr6tb1q2nrUaYRBcPN+oFbCvSYXKwAiq5aaHwjFAFztcJ+Lu67SvXvX1eAwHAnREtr/+WVqvHKF+FcfEwAASoZYj3kCOlRvpXqgZYb7kU8yHGfJgwF2LMhxjzYcJcENkWFxCTXQQE+UPtokbamXTR9vNnMwAAYZEhNolxFsyHWECQn2FsKRmi7edTMgEAoRHBNolxBsyFGPMh1pPz4R/oA7WLCufPZou2p5/LAQCEhgd2Kuab5Zuwatkm/LH7mI17bnuNdSUQ9Bp4+olvCerpZygi11fnWR1TXXYWbl4ROP3HfPz2zVjs+GIIDmy8DRVFJ+0xFLvxDAmB0sUFhX8ni7YXnv4bAOATFdVqnH+fPvCJjMTjR47g9do6vJiRiUkLFkDtKb1bZ1qKuRBjPkyYCzHmQ4z5MGEunJHh1mfO+NUTbn1GRETSotHVQ4Aebiof0XY3tS8AoFFb3amYMJ8BCPMZCB+3CNt23A402hoIgg5uLgGi7U3PGzSVVsf0Cb0GvYKvELXR6RpRXV8ItVKavx82jc3dNUi03c3VUE9rKx/txfSJuA7RoVeJ2uh0DaipK4Ba5WWzMdiKRlsLQdDDzcVftN1NbXjeoK3qVEy4/zCE+w+DrxMstAcAjbYOAvRwdxHfya3N80YHYsL8BiPMbwh83Vt/ryU1nB8mjXXFEPSNLWvQvk016FyrY6pLz8LNKxKn972G374ejR2fDcSB9begouiEPYZiE8WNAhoFIN5DvPyir7vheW6D+fd/e0t1+DpHh0WJari2snrjv/FqvNhHvLC4WivgVLUeoS7mr2osJay3iDEfYsyHCXMhxnyIMR8mzIUzYh1fylp+hI3IBgJCDG96K8vFb5QrSisAAP5B/jaJcRbMh1hAsJmxlRkKigFBfjaJcQbMhRjzIdaT8xEQ5AsAqKoQF6Arygzj8g/0uTTEqhhn0FBnuBKLysVbtF3l4gvAUHS1NqahtghZp7+Bd+BA9BvzH0Amx4WTn+Pgz3fg8pmb4e7lHIV7z9BQAEBdWbloe22J4WomniEtP3Qjk8vh17s3vCMisGP+fBSdPo2IESNw5QsvIDCuL76YMtnu/bYH5kKM+TBhLsSYDzHmw4S5ICIiou5Mo6sDACgVLqLtSrnLxf31NolxBhptLQBAKXcVbVcqXEX7rYnxcAtpEXcmax0g6BHq3/qVsBytaUFn01iaKBVuAAyLha2N8XQLvSSuBmcyfoAg6BHqP8wGvbct0/dZPOdVFs0Ny2OchUZn6Lvi0rHJm8ZWZ5MYZ8H5YdJQa6gxq1zEdXeVq+FvEK3WrS2MaagtRNbfX8M7cBD6XT73Yt36UxzccBsun/UL3L2lV7cuajT863PJ6gu/ixdaLm5sfeFJuUbAc2ca8WSMEoO85Pi1WNeizUAv8ari4kYBD5xsgE4A7gx3juUerLeIMR9izIcJcyHGfIgxHybMBZFtOcdvlOR8hNbfBMnkhjc4SlUrU8+aGGfBfIgIVozNmhhnwFyIMR9iPTofZj7EZRpHK7cUsSbGKbR+BQFBMNyaTK/XtrLXshhtYxVc3EMw/IbPoVAaCtaBEeOw9/sJyDyxEv3Hvta5rneVdua9QqVqsU+uVOKH++5F3vHjKE5JAQCc/WUrqvLzMOOTpYgcORLZBw/ar8/2wlyIMR8mzIUY8yHGfJgwF0RERNQDCReLKgIsvw26NTHOpKmG0pkYQdAjv+wYsgr/hCBoERdxIzxcpXl3MJmZWlJTwU0vtFzQ1tEYQdAjv+QIsgr3Qq/XoW/U1BaLrSXBzLCMc761uWFNjNMwU2ds8xxgTYyT4PwwMTeuturWFsZoG6vg4hGC4ZO+NNWtI8dh77dXI/PECvQf93rn+m4H5s6ITd9djZm/X7x6VoMwFxke6dX+3260egFf5eqwIF2DRj2wKFGFAZcsLpYs1lvEmA8x5sOEuRBjPsSYDxPmgsimnOQ3SnI2xYWlAAAfP/GVD318Dc+L8lt+6tSaGGfBfIiVmB2b4XZlTWPTC3rk1pyHXtBbHONsrBlXd80F4Hz5aD5H7cHZ8mFLJUXlAABvX/HtQrx9PQAAxQVlNolxBi7uhttAahoqRNu1F5+7urf8w1NTTENtCVIPfwi9rrHVGLWrHwIjxxqLsACgVHvCN2QYqkrP2Hgk9lOVnw8AcPcXX2Hb3f/iFblzW94uT9fYiJOrVxvfIDY5vWEDACB00CB7dNXunC0XWkHAHp0eWjNv9DvL2fJhT8yFmKX5aD5HmQ+x7poPZ8tF979xFhEREdmS+uKVYbW6BtF2rb7x4n53m8Q4A5XSUC/S6sVXUNZevOKy+uJ+a2PqGkpxKv1bZOTvgrdHBIbE3o1AnwTbDcDGVKqLY9OJrwyr1RrGqlZ6mY3JKzksWijYWkxdQwlOpn2J9Lzt8PaIwtC+9yPQt79tB2EjKkVTLi6Z87qmcbUyN6yIcRYqpZlzwMXnTWPvbIy96AUdsor/gl7fchG8NTg/TFzcmurW5aLt2ovPXT1aqVtbGKN29UdgxLhL6tZehrp1yWkb9N72gtSGJcTlGvH28osrh0PULZcYbynS4ZdiHebFqfB+ugalGj00esOaqBqtgHqd6V1/Wq0eNx9pwGvnNLjMV45fRrlgcrCTXDAGzldvsTdnywfr+F2HuRBjHV+M88PE2XLBOj5JHRcQ91BNV6gU7HSaKi0qg6ZRg9j+fUTb4y4+L8gptEmMs2A+xEqLyw1j6xcj2t70PD+nCMDFKxLUpkMQ9BbHOBtrxtVdcwE4Xz6az1F7cLZ82FJpSQU0jVrEJohvRdb0vCC3xCYxzkDt6g+ZXIXq0nOi7VVlZwEArp5hbcScwfmji40LiC+NcXEPhk7b8lajer0GCpXzFK5rCguhbWxEyICBou1Nzyuys1vE+EZHI+HGSZArxUVWvc7wx4T6yko79da+nC0XOgC/CwJs8yeclpwtH/bEXIhZmo/mc5T5EOuu+XDWXJi7C0WPIAgQdA1O+WXuShlERERNTD/jbfMzQ6lwhQxy1GnKRdvrGg0fum5tEZs1Mc5ApXSHDHLU1os/bF9bb6gfqVXeVsfUN5YjOeN71GvK0S/qJvSLmg5Xta8dRmE7KoUHZDI5auvFtcPaBsNzF3UrC4gVHpBBjpKK06KrDV8aU99QhlPnv0Z9Yxn6x9yCftEz4eri1+J4UqFSuhnmfKO4lljb0PR9biUXVsQ4C5WiaWylou1Nz11aXTDb8Rh70Qs65JQcaPUq2tbg/DBRuwVcrEGfFW2vuvjc1aOVurWFMS4ewdDpWqlbCxoo1NL8uROgBlQyIKVG/PeilBrDz/Bw15YLiM9U69GgB6YfacSSCzok/d6A/7ugRZ0eSNxbj0eSDXX9zDo9bjnSgIw6AZ8PVmP5IBdEuznXMg9nrbfYi7Plg3X8rsNciLGOL8b5YeKsuWAd3/E1edbxW+dcv1mS09BqtNi5aQ/+MX0ClErTLeOvnzkRF85n48yJszaJcRbMh5hWo8Wuzfvwj+njxWO7eQKy0nOQcjLVJjHOgLkQYz7EenI+tBoddv9yEBMnj4Gi2TiumzYWWRn5SEnOsEmMM5Ar1AiOnoj885uh15suX5CfthFu3r3gFZBoNqYgY5to+6UxwdETUZz1GzT15cY2DbWFKM8/DJ+gIfYZkB3oNBr8vW4dBt0yS/Smb/Dtt6EkNRV5R4+2iPEIDMRd69dj4M03i7YnTp0KvU6HnEOH7N5ve2AuxJgPE+ZCjPkQYz5MmAsiIiLqzuQyBfw8eqGkOkN0R62S6nS4KL3grva3SYwzkMsU8PeOQ0llimhhYXHlabiofOHh2vLKmZbGZBbsgV7QYXDvf8LPq7f9B2MDcrkC/t4JKL5kMXBxeTJc1b7wcA1tNcbPOw4A2ozJyN8FvV6HwXH3wc8r1s4j6Ty5TAE/rz4oqTwnGldJ1Vm4qHzg4RJkkxhnIZcp4OcZg5Jq8Z34iqtT4aLyhrtLoE1inAXnh4lcoUZwzLXIP79JXLdOvViDDhxgdUxw9EQUX9jdsm6ddwg+QUPtNqbOUMtluC5IgZ8LddDoTYtM1hfoEO0mwwDPlguIbwlT4IckNb4crAYAfDlYjVmhCrjIgR+S1Hgp1nD79bfSNGgUgE0jXXB1gKLFcZwB6y1izIcY82HCXIgxH2LMhwlzQWRbznNfC3I6X3/8PVZs+Ajvfv5f/PLTTiSNHozJt1yP1x5/C4IgwNvXCwOHJSIrPRtZ6TkWxTgz5kNs1Sc/YNm6D/C/lfOwff1uDL1sICbNuhavP/meMR/9h/ZF9ZFsoNyyGGdlSS4GJPVDVkYOstNzLYpxZsyHWE/OxzcrNuGT7+fi7Y+fwq8//4khI/vhxpuuwBvPLzWM3ccDiUPjkJ2Rj+zMAotinFX0oPtwaNNsHP/1CYT2uRFlBYeRl7oBA654EzKZHJr6clQUnYC7dzTcfaJNMT/PBgDkn9+CypJkUQwARCTcggvJX+HAz3cgetC9gCAg48QKKNVeiBl0n8PGa419ixbigR07ccf3q3FizWrEjB2LpNl3Yu2/HoAgCHDz80PkyFEoSUtFaVoacg4fxtmtWzFtycfwjY5BybmzCE8ahrFPPYV9CxeiLCPD0UOyGnMhxnyYMBdiluQjZuRIxKacBTLSmY8eND+YCyIiIurOQr0TcTp/K1ILd8PfIwbV9YUoqTmP3oGXQyaTQatrQHVDMVxVXnC9eEXd9mKcVZj/CCRnfo+zWRsR4JOAqtocFFecRmzYPyCTyaDR1aG6Lh+ual+4qf0sihEEPcqq0uDjGY3axhLUXnLFUbXSEx6u0lwsGB44EqfOr0JK5k8I9O2PyposFJUnIzbiRkM+tHWorsuFq9oPbi6GheOh/sNRWnkWqVk/I8hvQIsYQz7OwccjBnUNxahrEF+9Wa3yanWxtqOF+SXh76y1OJe7BQFefVFVl4fiyhT0Cb3m4v+TelTXF8BV5WO8unR7Mc4szHcw/s7ZiHP52xHgGYuqunyUVKWid/CVzfJRBFeVN1zVPhbFODPOD5Powffj0MbbcXz7YwiNnYSy/EPIS12HAVe+3axuffxi3TrGohgAiOh3Gy4kf4kDG29D9KD7AAjIOL4MShdvxAyWbt36gUglbj3WgEeSGzE5WIGD5Xr8VKDDO/1UkMtkKNcIOFapR4ybDDHuckS4yhHhClRpDX+7GOYjx5FKPRQyYKSvYaGwThDwa7EeY/3kSK0RkFojvgZsqIsM/Tyd45pxrLeIMR9izIcJcyHGOr4Y54cJc0FkOzLBmVfTkNUiIiKQm2tYaOYid2u1TaBrGIJcW95epiOGj03CIy/eh4TB8biQlo3vV/6IDd9sNu5bseEjfPK/T7H0nU8tinF2zpQPucLV7q8x/PIheOiFu5AwKA5Z53Pw/cr12PjdL8Z9y9a9j9deew3rFu+GQq5sN8aZWZKLpe9+iWXvfmlRjLNzlnzo9FocL/kNQwKuMs5Re3CWfCjcbP/Hj2Gj++PBp2chfkA0sjLyseaLbfh5zW/GfZ98PxfLF/yA5Qt/tCjmUpNmXol57z+Ca4c+iIqyKpv23e8f8216vNLcv5B25ENUlvwNd+8Y9BpwJyLibzbuO7T5n+iT9Bjihj9hjCm6sBtHtz0IhcqjRUyT2qospOx/ExWFxyGTyeEXdhkSLvs3XNxt+/0c8Xl/mx6vNb2vvArXzJuHsKFDUZJ6DvuXLMGRL74w7ntgxw7smD8fO98wfG9cvL1xzbx5GDDjJrgHBKD4bAoOLl+OA8uW2b2v9uYsuWgQBLyv0+NZhRwudvxjibPkoyswF2KW5OO1115D9X/fgItMxnz0oPkhpVwc0utxWN966aYUhhuah4eHIycnp9Ov5UwiIyORk5MDldwVQ8JucHR3rHI8bws0+npEREQgu5Xb6hEREZnq+DLjYt5LhXj3Q4h3vw4dt7IuHznlx1DTUApXlRdCvPshyKuvcd+Z/F8Q7jsEkX5DLYq5VFFVKtKL9yGp161Q2bjOrHCz7ZVLK2qykFX0B2rrC+Gq9kWofxKCfQca9/2duRqRgWMQFXy5RTEabS0Onf3Y7OsF+QxAXMT1Nuu/TKG22bEAoKI6E1mFe1FTVwBXtT9CA4YjxH+wcV9y+jeIDB6HXiFXAAC0ugYc+PsDeLlHoLa+qEWMRluLg6cXmX29IN9B6Bs12SZ91zdW2+Q4TSpqs5Fd/BdqG4rgqvJFiN9gBPskGvedzvoJEQGjEBV4mUUxlyqsOI3z+b9ieOwDUClb/ztdZwi6Bpser7I2F9mlh1HTUAxXtQ9CfQYgyDvBuO907s+I8BuGyIARFsVcqqgyBecLf8Ow3nfZ9Lyh1Tfi8PnPMSLuISht+P/FmeeH91VP2fR4pbn7kXZ4ESqL/4a7TzR6DbgLEQkzjfsO/TwbfYY9gbgRT1oU06S2Mgspf/4XFYXHDHXr8NFIGP2yzevWy88NbL9RB/xZpsOCdC2Sqw0Lhe+OVOKWMKVx323HGvFUjBJP91YZY6q0AgburcepK1yxIkuLZVlanL7S8H0vaRQwbF+92debGarA+/1tM7eX7m20yXHaIqV6ixQ4Sz5Yx+96zIUY6/hinB8mUsoF6/itYx3fOXABcQ/VVHhUylQY6DfK0d0hCeqKBcTt6arFmUTW4hwVs8cCYmdm6wXE1tA2VmPnl8Mw4a4jUKo9HdqXrlhATM6nqwqPRNbiHCWp+1CrQzVYeBwcPMHR3bHKicKd3b7wSEREndNUx1cp3JDU6xZHd0cSbL2A2NnZegFxRzUtIB6V+AyUCheH9sXWC4idna0XEDsrey0gdma2XkDs7Gy9gNgazRcQeykdV3/qigXE5JxYIyWp4xwlqWMdn3V8qeNqJyIiIiIiIiIiIokrKyvD+fPnUVVVhfDwcMTFxUEut92taqurq5GamoqysjKEhISgb9++UKlU7QcSEREREREREREREZFT1vFt1zsiIiIiIiIiIiKyqbNnz2LKlCkICgrCiBEjcPXVVyMhIQExMTF4//33odPpOnX8vLw83HnnnQgKCkJSUhImTJiAAQMGIDw8HK+88grq683fLpeIiIiIiIiIiIiIqKdz5jo+FxATERERERERERFJ0N69e5GUlISff/65RYExKysLzz33HG666Sari48pKSkYPHgwVq1a1aLAWFxcjDfffBNXXXUVampqrB4DEREREREREREREVF35ex1fC4gJocrqs9zdBcAsB+XKqrLcnQXJEMquWA/xKTSDymQSi4Kqs45ugsApNOPC39/7eguSMohvd7RXZBEHwD2Q6qkkA8p9AFgP6RKKvmQQj+k0AdAOv0gAYKuwSm/AKHNkRUXF2PGjBmora2FXC7H/PnzkZWVherqauzcuRNJSUkAgA0bNmD+/PkdzlxDQwOmTp2K4uJiAMDjjz+O1NRU1NbW4q+//sI111wDADhw4AAeffTRDh+fiIi6l4LKM47uAgAgv/Soo7sAQDr9yCs57OguSEZ+2QlHdwGAhPpRnuzoLgCQTj+kQCpz40LyV47uAgDp9OOLbK2juyAZUqlzsB9iUumHFEglF+yHmFT6IRVSyIcU+gCwH3Qp1vGlXMfnAmJyuGKJLJhlP8SK6rId3QXJkEou2A8xqfRDCqSSi0KJLNyVSj+y/l7l6C5IymF9279Y95Q+AOyHVEkhH1LoA8B+SJVU8iGFfkihD4B0+kHd1zvvvIOSkhIAwIcffohXX30VkZGR8PDwwNVXX43du3cjJiYGAPD++++jqKioQ8dfuXIlzp49CwB47rnn8OGHHyI2NhZubm4YNWoUtmzZglGjRgEAvvrqKyQnc/EHEVFPJp0FxMcc3QUAEuoHFxAbFZRLY3GmZPpRIY3f3aTSDymQytzISpbGhS+k0o8vc7iAuIlU6hzsh5hU+iEFUskF+yEmlX5IhRTyIYU+AOwH9RzdoY7PBcREREREREREREQSotPp8OmnnwIAgoOD8fDDD7do4+3tjeeeew4AUFNTg++//75Dr7F8+XIAgFKpxCuvvNJiv0qlwty5cwEAgiDgs88+69DxiYiIiIiIiIiIiIi6q+5Sx+cCYiIiIiIiIiIiIgnZv3+/8aoFU6ZMgUKhaLXd1KlTjY83bdpk8fFzcnJw7NgxAMBVV10FX1/fVttdc8018PDw6PDxiYiIiIiIiIiIiIi6s+5Sx1d2OIKIiIiIiIiIiEgKBAF6Xb2je2Edwfzt81JSUoyPb7zxRrPtoqKiMHjwYJw4cQJHjhyx+KUtPb6rqyuuueYabNiwAWfOnEFNTY2xEElERERERERERERE1C7W8SVdx+cViImIiIiIiIiIiCQkLy/P+Dg6OrrNtlFRUQCAwsJClJeX2+34AHDu3DmLjk9ERERERERERERE1J11lzo+FxATERERERERERFJSH5+vvGxv79/m20DAgKMj5sXFB15fCIiIiIiIiIiIiKi7qy71PFlgtDGdZap21Kr1dBoNAAAlUzt0L5ohEaH94H9aK0fGqjkju6HAI2+8WI/ZA7rhakPjsV+SLEfnKMt+qFwdXQ3oNHVS6IfWn0j1G6BDu6FgIbaQri4B8ORcxQANLUFcPTNrmsAh/cBYD+aEwBUA/CEo2eoNPIhhT4A7EdznKMtSaEfUugDII1+VMMwT1UqFRobGx3cm64VGRmJnJwcAI6tawiwvqymFQx1mYiICGRnZ4v2zZw5Ez/++CMAoKKiAt7e3maP88wzz2DBggUAgIMHD2LEiBHtvvZzzz2H999/HwBw/PhxDB482GzbDz/8EE8++SQAYM2aNZg5c2a7xyciItsQ1fEV7g7ti0ZXB5XCzaF9aOqHWuno38KARm2NRPpR69B+CBCg0VZDpfSEzMHvmhq11VArHfv/xNCPWsn0w9HnDQDQ6BzdD+FiHzwgc/Abe6nMDY2uHmr3IEd3A421RZLoh7Y2H0EO/vOKIAAFjUCIGg6dp4UNjq9zANKotwDsR3NSqZFKIRcA+3EpKfRDKnMUkEY+pNAHgP1ojnV81vEBadfxlRa3pG5Fp9MZH2sEx5+cpNAHgP24lEbf4OguADAsSnQ06eSC/WhOOv3gHG2i0dU5ugsApNOPhtoCR3cBANBQW+joLgAAqhzdAUijDwD7calqR3fgIinkQwp9ANiPS3GOikmhH1LoAyCdfjR/j98TSeU9tC1VVlYaH7u5tb1Yy8XFxfi4rs6y34PtfXwiIrINUR1fV+vAnkinD4BhoagUSKcfjv+tVCOZXNQ4ugsApNMPjY79kFIfAOnMjYaa/PYbdQGp9CNfGn9eQYEE3to6/ieKAfshJpV+SOGnvVRywX6ISaUfUpijgDTyIYU+AOzHpVjHl8AvOzbWXer4XEDcQ7m6uqK+vh4KhQLBwcGO7g4RERERERERdVBhYSF0Oh1cXR1/94WuFhoa6uguAAA6e2MvmUzW6liCgkxX4iovLxc9v1R5ebnxcXtFRHPHb4s1xyciIttgHZ+IiIiIiIjIubGO73is47eNC4h7qJoaaXzClYiIiIiIiIioow4dOuToLthVWFiY8XFpaWmbhcfS0lLjY09PT6uO3xZrjk9ERLbBOj4REREREREROSvW8SHa30RqdXx5h1oTERERERERERGRXTW/mkF7hcGysjLj44iICLsePyoqyqLjExERERERERERERF1Z92ljs8FxERERERERERERBLS/MoCx48fN9tOr9fj5MmTAIBevXrBy8vLpscHgBMnTgAAFAoF+vbta9HxiYiIiIiIiIiIiIi6s+5Sx+cCYiIiIiIiIiIiIgkZMWKE8fHGjRvNtjt8+DDy8/MBAJdffrnFxx80aBBUKlW7xy8oKMCBAwcAACNHjoRarbb4NYiIiIiIiIiIiIiIuqvuUsfnAmIiIiIiIiIiIiIJSUhIQEJCAgBgx44dotuPNbd27Vrj4xkzZlh8fG9vb0yYMAEA8Pfff+PMmTOttlu3bh0EQejw8YmIiIiIiIiIiIiIurPuUsfnAmIiIiIiIiIiIiKJeeaZZwAADQ0NePzxx6HX60X7jx49ioULFwIAevfujenTp1t1fAB45JFHUF9fL9qfmZmJuXPnAjAUKu+///4OjoCIiIiIiIiIiIiIqPvqDnV8mdC0/Jh6jLKyMpw/fx5VVVUIDw9HXFwc5HKuJSfnkZOTg8zMTABAdHQ0IiIiHNwj6mk6Owf1ej3S0tKQk5MDT09PxMbGws/Pzx5dJbIK5yjZG8+j1N1xjlJ7qqurkZqaitraWvTt2xeBgYGQyWQWx/M82jNoNBqMGzfOeOuxK6+8Evfccw+8vb1x4MABLFmyBNXV1ZDJZFi/fj2mTJkiir/nnnvwxRdfAADmzZuH1157TbRfEATMnDnTePWDIUOG4OGHH0ZISAhOnjyJxYsXo6ioCACwePFizJkzx84jJiKi5ljHJ2fHOj45Et8zUU/AeUr2xnMpdXeco9Qe1vHJEt2iji9Qj5GSkiJMnjxZUCgUAgDjV1RUlPDee+8JWq3W0V2kbmzixImiedfW12effdbqMTZv3iyMGDGiRfvhw4cLmzZt6toBUbewePFiAYAwb948i9p3dg7qdDrhgw8+EHr16iWKVygUwqRJk4QzZ850ckTU3Vg6R7VaraBWqy0+z6anp7d6HM5Rak9+fr7w2muvCZMnTxbi4+MFNzc3ITExUZg1a5awfPnydn+f5HmU7M3aOcrzKHWFuro64fXXX28xPwAIvr6+wssvvyxUVFS0eQyeR3uevLw8ISkpyez5SKVSCR9//HGrsXfffbexnbnfJ6uqqtp9v/7KK68Ier3ejqMkIqLmWMcnR2Idn6SGNXxyBqzjk9Swjk9Sxzo+SRnr+GQNZ6/jcwFxD7Fnzx7B3d29zYk0depUFh/Jblr74dqRwuN7773Xbty7777b9QMjpzZ69GiLi4+dnYNarVaYPn16m/Fubm7Cb7/9ZsMRkrOzdI6eP3/e4nOsuTfMnKPUns2bNws+Pj5tzpFhw4YJhw8fbjWe51Gyt87MUZ5Hyd6qq6uFUaNGtTu3goKChBMnTrR6DJ5He66Ghgbho48+EsaMGSP4+/sLarVaiImJER544AGz80UQLCs8CoKhIP3FF18IEyZMEIKCggSVSiVERkYKt99+u7B37147jIiIiMxhHZ8cjXV8khrW8MkZsI5PUsI6Pkkd6/gkZazjU2c4cx2fC4h7gKKiIiEgIEAAIMjlcmH+/PlCVlaWUF1dLezcuVO0An7u3LmO7i51Q3V1dYJMJhMACI8++qiwZcuWNr+ys7NF8Tt27DDGBwYGCqtWrRJKS0uF0tJS4euvvzbObwDCr7/+6qBRkrP59NNPLfohLAi2mYPz5s0TvenZtWuXUF1dLWRlZQmvv/66IJfLBQCCv7+/UFhYaIcRk7PpyBz95ZdfjG0/+eSTds+ztbW1LY7BOUptSU5OFtzc3IxzZMqUKcKiRYuE1atXC2+88YaQmJho3Ofj49Pik688j5K9dXaO8jxK9vbII48Y50dSUpKwdetWITc3VygtLRX27NkjXHvttcb98fHxQmNjoyie51EiIqLuj3V8cjTW8UlqWMMnZ8A6PkkJ6/gkdazjk9Sxjk89FRcQ9wDPP/+88eSyePHiFvsrKiqEmJgYAYDg4eHBEwzZ3KlTp4xzcPPmzR2K1ev1xk/4KJVK4ciRIy3aHD58WFAqlQIAYdSoUby1KplVXl4u7NmzR7j33nsFlUplUVHHFnOwoKBA8PDwEAAIvXv3FiorK1sc48MPPzT254UXXuj0WMk5WTNHBcF0izQAQnV1dYdfl3OU2nPLLbeIfp+89DzX0NAgPProo8Y21113nXEfz6PUFTozRwWB51Gyr/LycuOt9WJjY4WGhoYWbXQ6nXDllVca58iOHTuM+3geJSIi6hlYxydHYx2fpIA1fHIGrOOTVLGOT1LHOj5JGev41JNxAXE3p9VqjZ9gCA4ONntrs+Y/aD/66KMu7iV1dz/99JNxfqWmpnYo9tixY8bYmTNnmm03c+ZMY7uTJ092tsvUDY0cOdI4Ry79aquoY4s5uHDhwjb/ACQIhl82g4ODBcBwywudTmfVOMl5WTtHBUEQnnzySQGAEBkZadVrc45SW+rq6oyF8GHDhpn9A19DQ4MwcOBA41xquhIRz6Nkb52do4LA8yjZ159//mmcHx9//LHZds3fN33wwQfG7TyPEhERdX+s45MUsI5PjsYaPjkD1vFJqljHJ6ljHZ+kjnV86snkoG5t//79KCkpAQBMmTIFCoWi1XZTp041Pt60aVOX9I16jnPnzgEAVCoVoqOjOxT7888/Gx9PmzbNbLvm+ziHqTWFhYVWxdliDjY/RvPzbXNyuRxTpkwBABQVFeHgwYNW9Zecl7VzFDCdZ+Pj462K5xylthw/fhwajQYAMHPmTMhkslbbqdVqTJ482fj86NGjAHgeJfvr7BwFeB4l+2qaXwAwYMAAs+369u3bagzPo0RERN0f6/gkBazjk6Oxhk/OgHV8kirW8UnqWMcnqWMdn3oyLiDu5lJSUoyPb7zxRrPtoqKiMHjwYADAkSNH7N4v6lmafmjGxsZCqVRCp9MhIyMDu3fvxuHDh1FZWWk21tI53Hwf5zC1JiUlBXV1dcavM2fOWBzXxNo52HSMQYMGISoqyuwxJk2aZPYY1P1ZO0eBlm+Y6+vrcfr0aezatQsnT540viFv67UBzlFqXUFBgfFxe39ADAsLMz6uq6sDwPMo2V9n5yjA8yjZV1xcHN566y289dZbSExMNNsuJyfH+Dg0NNT4mOdRIiKi7o91fJIC1vHJ0VjDJ2fAOj5JFev4JHWs45PUsY5PPZnS0R0g+8rLyzM+bu+HcFRUFE6cOIHCwkKUl5fD19fXzr2jnuLs2bMADL/ovfXWW3jvvfdQWlpq3K9UKjFx4kTMmzcPo0ePFsU2zWFPT0/4+/ubfQ1/f394eHigpqZG9IOZqImLi0ubz83p7BwUBAH5+fkALDsPN+E87nmsnaMajQbnz583xjz00EP49NNPodVqjW28vLxw6623Yu7cuS3ebHCOUnsGDhyIzz77DABw1VVXtdm2+adcmwo4PI+SvXV2jvI8SvY2ZswYjBkzps02Go0Gb7/9tvH5jBkzjI95HiUiIur+WMcnKWAdnxyNNXxyBqzjk1Sxjk9Sxzo+SR3r+NSTcQFxN9d0cgHQ5gkKAAICAoyP8/LyWHgkm2n6JNiuXbuwa9euFvu1Wi22bt2Kbdu2YeHChXj88ceN+5rmcHvzFzDM4ZqaGlHBnaizOjsHS0tLjZ927Oh5mMgSGRkZ0Ol0AIBFixa12qaqqgorVqzA2rVrsW7dOlxxxRXGfZyj1J4+ffqgT58+7bY7ceIEvvvuOwBAZGQk+vfvD4DnUbK/zs5RnkfJEXJzc/H777+jtLQUZ8+exerVq41XLnjjjTcwaNAgY1ueR4mIiLo/1vFJCljHJ2fF90zkDFh/IntjHZ+kjnV8ckas41NPIXd0B8i+mp8omp9AWtN8f01Njd36RD1LTU0NcnNzjc+TkpKwfft2lJeXo7i4GLt27cKtt94KANDr9XjiiSewefNmY/umOdze/G3ehvOXbKmzc5DnYbK3pj/uNLnttttw9OhR1NTUICMjA+vWrUNSUhIAwxuP6dOnIzs729iec5Rs4fDhw7j++uuNn/R+6aWXoFarAfA8StLQ1hzleZQcYc+ePbj11lvxyCOPYMGCBcjJyYG/vz+2bNmC//znP6K2PI8SERF1f/x5TY7GOj45M75nImfA+hNJAev4JHWs45PUsI5PPQUXEHdzlZWVxsdubm5ttm1+i5W6ujq79Yl6lrS0NCgUCigUCkyZMgV79uzBxIkT4ePjg4CAAIwfPx7fffcdPvjgA2PMs88+C71eD8A0h9ubv4BpDnP+ki11dg7yPEz21vw8+/rrr+Obb77B0KFD4e7ujujoaEybNg1//fUXbr75ZgCGN81vvPGGMZ5zlDqjsrISL730EkaPHm18Y3vHHXfg4YcfFrUBeB4lx7BkjvI8SlJRWlqKp556Clu2bBFt53mUiIio++PPa3I01vHJmfE9EzkD1p/IkVjHJ6ljHZ+cCev41B1xAXE3FxQUZHxcXl7eZtvm+y05oRFZYvDgwdBqtdBqtdiwYQM8PT1bbffEE09g+PDhAIAzZ87g2LFjAExzuL3527wN5y/ZUmfnIM/DZG+PP/648Tw7d+5cyGSyFm1UKhUWLVoEDw8PAMDq1auNn97lHCVraDQaLFmyBHFxcXj77beN8+mBBx7A559/Drnc9DaD51FyhI7MUZ5HyRFuu+02CIKAiooKHDt2DHPnzoWXlxdSUlIwZcoUrFu3ztiW51EiIqLujz+vydFYxydnxvdM5AxYfyJHYB2fpI51fJI61vGpp+AC4m4uLCzM+Li0tLTNts33mysOEdmLQqHAjBkzjM8PHjwIwDSH25u/zdtw/pItdXYO8jxMUhEREYExY8YAMLypSEtLA8A5Sh137NgxXHbZZZgzZw6KiooAAL169cLmzZuxfPlyqFQqUXueR6mrdXSOWornUbIHb29vDBkyBK+//jo2b94MuVwOnU6HF154ATqdDgDPo0RERD0Bf16Ts2Adn6SI75moO2H9iWyFdXySOtbxyZmwjk/dHRcQd3OhoaHGx+2dYMrKyoyPIyIi7NYnInP69etnfFxSUgLANIdLS0shCILZWEEQjHM4KirKjr2knqazc9DT09P4KceOnIc5j8keWjvPco6SpXQ6HV599VWMHDkSR48eBQD4+/vj/fffR0pKCm644YZW43gepa5i7RztCJ5HyZ7GjRuH6667DgBw7tw5ZGdnA+B5lIiIqCdgHZ+cCev4JDV8z0TdDetP1Bms45PUsY5Pzo51fOqOuIC4m2v+CYXjx4+bbafX63Hy5EkAhk/1eHl52b1vRJdqun0EAPj4+AAwzeHGxkakpKSYjU1JSYFGowEAJCYm2rGX1NPYYg42HePEiRPQ6/Vmj3HixAnjY85jsofWzrMA5yi1TxAEzJkzB//973+h1Wohk8nwxBNPIC0tDc888wxcXV3NxvI8Sl2hM3O0I3geJWv8+9//xp133olXX3213bb9+/c3Ps7NzQXA8ygREVFPwDo+ORPW8Ulq+J6JuhvWn8harOOT1LGOT1LGOj71ZFxA3M2NGDHC+Hjjxo1m2x0+fBj5+fkAgMsvv9zu/aKeY/bs2Rg4cCCuueaaNn/AAcDp06eNj+Pj4wFYPoeb7+McJluyxRxsOkZ+fj4OHz5s1TGIWlNTU4OhQ4di4MCBeOGFF9pt33SeValUiI6ONm7nHKX2/Pe//8XSpUsBACEhIfj999+xaNEi+Pr6thvL8yh1BWvnKM+j1BVOnDiBVatWYcWKFe22zcnJMT5umr88jxIREXV/rOOTo7GOT86M75lI6lh/oq7COj5JHev4JGWs41NPxgXE3VxCQgISEhIAADt27BBdxry5tWvXGh/PmDGjS/pGPcPQoUORnJyMnTt34tdffzXbrq6uDl999RUAw6X5x40bBwCYMmUKZDIZAODHH39s9VL/giAY57BMJsPUqVNtPQzqwWwxB6dNm2Z8/OOPP7b6OuXl5di5cycAwyfWms7dRG3x8PBAREQEkpOTsWTJErM/5wEgOTkZ+/btAwBcddVV8PT0NO7jHKW2VFZW4n//+x8AwM/PD/v27evQm1GeR8neOjNHeR6lrtC0qCI/Px9nzpwx2666uhp79+4FYCg6xsXFAeB5lIiIqCdgHZ8cjXV8cmZ8z0RSx/oTdQXW8UnqWMcnqWMdn3o0gbq9pUuXCgAEAMLs2bMFnU4n2n/kyBHB1dVVACD07t1baGhocFBPqTu6cOGCIJfLBQBCRESEcOHChRZtamtrhfvvv984T19++WXR/ttvv92475NPPmkR//HHHxv333nnnXYbC3Uv6enpxnkzb968Ntt2dg7W19cL0dHRAgDB1dVVOHr0qGi/VqsV7rjjDuMxVqxY0ZmhUTdh6Rz95ptvjO3+8Y9/CPX19S3aZGdnCyNHjjS227Ztm2g/5yi1ZcmSJcbv/ZIlS6w6Bs+jZE+dnaM8j5K9NZ9j48ePF6qqqlq0qa+vF+6++25juyeeeEK0n+dRIiKi7o91fHIk1vFJiljDJ2fAOj5JBev4JHWs45PUsY5PPZlMEFpZ8k7dikajwbhx43DgwAEAwJVXXol77rkH3t7eOHDgAJYsWYLq6mrIZDKsX78eU6ZMcXCPqbtZuHAhnn76aQCGT4c99NBDGDp0KJRKJU6fPo1vv/0WqampAIDhw4dj7969cHNzM8ZnZGRg+PDhKC0tBQDcfffduOGGG6DX67Fp0yasWrUKABAQEIDDhw+LbkNBZE5GRgZ69+4NAJg3bx5ee+21Ntt2dg6uX78e06dPBwB4eXnh8ccfNx7z888/N34ScvTo0di7dy+USqUNR0vOyNI5qtfrcfPNN2PdunUAgF69euHBBx9EfHw8KisrcerUKaxcuRJVVVUAgEcffRT/93//1+I4nKNkzu23347vvvsOAPDFF18gODjYorhRo0bB398fAM+jZF+dnaM8j5K9aTQaJCUlITk5GQAQERGB+++/H3379oVCocDZs2fx1VdfIS0tDQAQFxeHv/76y3gOBXgeJSIi6glYxydHYx2fpIY1fHIGrOOTVLCOT1LHOj5JHev41KM5egUzdY28vDwhKSnJ+CmES79UKpXw8ccfO7qb1E3p9XrhueeeM17BwNzXddddJ5SUlLR6jL179wpBQUFmY4ODg4U//viji0dGzqwjVy8QBNvMwY8++khQKpVmjzF8+HChoKDARiMkZ9eROVpVVSVMmDChzXOsTCYTnn/+eUGr1Zo9Ducotebqq69uc26Z+9q1a5foODyPkr3YYo7yPEr2lpaWJvTr16/deTlmzBghMzOz1WPwPEpERNT9sY5PjsQ6PkkNa/jkDFjHJ6lgHZ+kjnV8cgas41NPxQXEPUhDQ4Pw0UcfCWPGjBH8/f0FtVotxMTECA888IBw4sQJR3ePeoAzZ84I999/vzB8+HAhMDBQUKvVQq9evYTbbrtN2Lx5s6DX69uMz8/PF15++WUhMTFR8PDwEDw9PYUBAwYIr7zyilBYWNhFo6DuoqPFR0GwzRw8duyYcN999wnR0dGCWq0W/P39hcsvv1xYvHix0NjY2IkRUXfT0Tmq1+uF7du3C9OnTxcGDBggeHp6Ch4eHkK/fv2ERx99VDh16pRFr8s5SpdKTEy0SeFREHgeJfuw1RzleZTsraamRvj444+F6dOnC4MHDxa8vLyEwMBAYezYscK9994rrFu3rs3CtiDwPEpERNQTsI5PjsY6PkkFa/jkDFjHJ6lgHZ+kjnV8chas41NPJBMEQQARERERERERERERERERERERERERERH1CHJHd4CIiIiIiIiIiIiIiIiIiIiIiIiIiIi6DhcQExERERERERERERERERERERERERER9SBcQExERERERERERERERERERERERERERNSDcAExERERERERERERERERERERERERERFRD8IFxERERERERERERERERERERERERERERD0IFxATERERERERERERERERERERERERERH1IFxATERERERERERERERERERERERERERE1INwATEREREREREREREREREREREREREREVEPwgXEREREREREREREREREREREREREREREPQgXEBMREREREREREREREREREREREREREfUgSkd3gIiIiIiIiKin0Ov1SElJwZEjR1BUVITq6mr4+voiKCgII0aMQJ8+fSCTyRzdTUmoqanBoUOHkJubi9zcXGi1WgQEBKB379647LLL4Onp6eguEhEREREREREREVE3wzq+5VjHJ3J+XEBMRERERETdQvNizWeffYZ77rnHcZ1xMpcWukaMGIGDBw9adayysjKEhIRAo9EYt1111VXYvXt3Z7ro9EpKSvDhhx9i6dKlKCgoMNsuIiICDz74IB577DH4+/u3ecyMjAz07t3botf38PBAeHg44uLiMH36dMyYMQNBQUEdGoM10tPTMWDAAHh7eyMtLQ0eHh5tthcEAZs2bcJnn32GLVu2oK6urtV2crkcEyZMwGOPPYapU6d2ebF2/Pjx+O233wAAMTExOH/+vFV9WLBgAZ555hnj87S0NPTp0weHDh3CyJEjMWjQIBw+fBgqlcpmfSciIiIiIiIiInIU1vGtxzq+/bGOzzp+a1jHp+5O7ugOEBERERERUfseeughyGQy3HnnnXZ/rUOHDiE9Pd2q2I0bN4qKjgTs3LkTAwcOxPz589ssOgJATk4O5s2bh4EDB+LXX3+1WR9qampw7tw5bNmyBQ899BBiYmLwzjvv2PV7JQgCHn/8cdTV1eGll15qt+iYlpaGyZMnY8qUKVi7dq3ZoiNguALEr7/+iunTp2PixInIyMiwce/bdscddxgfZ2Rk4NChQ1YdZ+3atcbHI0eORJ8+fQAYiv9Tp07FyZMn8dFHH3Wus0RERERERERERNQlWMd3Xqzjs45vDuv41N3xCsRERERERERWiomJQWZmJgDDJ7RjYmLs8jqNjY1Ys2aNXY5tzg8//IDnn3/eqjgy2bt3L2688UY0NDQYt0VFRWHChAmIj49HQEAALly4gPPnz2P37t3Iz88HAOTl5WHy5MnYtm0brrzySoteKzw8HG5ubi22a7VaFBYWigp5tbW1ePHFF7Flyxb8/PPP7RYFrbFu3Tps2rQJEREReOihh9pse+DAAVx33XUoLy83bpPL5Rg/fjxGjx6NyMhI1NXV4fTp0zh16hT2799vbLdz506MGTMGu3btQr9+/Ww+jtbcfPPNmDNnDrRaLQDg+++/x8iRIzt0jPz8fOzbt8/4/NZbbxXtf/3117FhwwbMmzcPt956KyIiIjrfcSIiIiIiIiIioh6GdfzW48iEdXzW8c1hHZ96Ai4gJiIiIiIikrhvvvkGZWVlXfqaa9as6XDhsbKyEtu2bbNTj5xPXV0dZs+ebSw6urq64u2338bDDz8MFxeXFu1ra2vxySefYP78+aioqEBDQwPuuusunDlzBq6uru2+3qpVqzB+/PhW9wmCgKysLKxbtw7vvPMOcnJyAAC7d+82FjhteXut6upqPPnkkwCA//znP232/6+//sLEiRNRXV1t3Hbvvffi//2//4ewsLBWY/bv348XX3wRe/bsAWAo4l177bU4ceIE/Pz8bDYOcwICAnDDDTdg48aNAAz/X959990O3f5s/fr1EATB+HzWrFmi/UOHDsXNN9+MH3/8EU899VSX//GBiIiIiIiIiIiILMc6vnNiHZ91/Lawjk89gdzRHSAiIiIiIiLzTpw4gccee6zLXs/HxwcAcPDgwQ7fTmrTpk3GIlvTcXqyzz77DFlZWcbny5cvx5NPPtlq0REA3N3d8cwzz+CXX36BQqEAAGRmZuLzzz/vdF9kMhl69eqFJ554AqdPn8bMmTON+3bv3o2FCxd2+jWaW7FiBbKysuDn54d7773XbLvq6mrcfvvtxqKjSqXC2rVr8emnn5otOgLA6NGjsXv3btEVEbKzs43Fzq5w++23Gx9fuHBBdDUFS/z000/Gx2PGjEGvXr1atGkq/v/www84efKklT0lIiIiIiIiIiIie2Id33mxjs86fltYx6eegAuIiYiIiIiIJEQQBFy4cAE///wzbrvtNgwbNgw1NTVd9vrTpk0zPu7oJ6V//PHHVo/TUzUvLE2YMAF33nmnRXGXXXYZ7r77buPz9evX27RfXl5e+Oabb3Dttdcat82bN894NYPO0uv1WLJkCQDDp/HNFVoB4MUXX0R6errx+XfffYcZM2ZY9DoymQyLFy/G9ddfb9z29ddfIy0tzcqed8zUqVPh7u5ufL569WqLY8vLy7Fjxw7j80tve9Zk1KhRiI2NBQD83//9n5U9JSIiIiIiIiIiIltiHb/7YB2fdXxzWMennoILiImIiIiIiCTi9OnT8PHxQXR0NKZMmYLvv/8eOp2uS/vQ/BPtHSk81tTUYPPmzQCA0NBQXH755Tbvm7M5deqU8fG4ceM6FNu8/fHjx23WpyYqlQoff/wx1Go1AMNt2j777DObHPvXX3/FuXPnAACzZ8822y43NxfLli0zPr/vvvtw0003dei1lEolFixYYHwuCAKWL1/ewR5bx8PDA9OnTzc+X7NmDfR6vUWxP//8M7RaLQBDAbX5/7vmZDIZ7rjjDgCGomp5eXmn+kxERERERERERESdwzp+98I6Puv45rCOTz2F0tEdICIiIiIicpSCggLs2bMHOTk50Gq16NOnDxISEpCYmAiZTNbl/WloaEBVVVWXv25z/fv3x4ABA5CcnGy8/VlMTEy7cVu3bkVdXR0A4KabbjLeustSGo0GJ0+eRFZWFrKzs1FVVYXw8HBEREQgMTGxzdtgtaagoAApKSlITU1FUVERoqOjERsbi8TERHh4eHTJcUpKSoyPvb29O9T/UaNGYciQIQAMxTVBEGw+J2NjY3Hbbbfhyy+/BACsXLkSL7/8MuTyzn3WuOmqBVFRUW0WXJctW2YsvqnVarz99ttWvV6/fv0wZswY/PnnnwCA7du3W3yszMxM7Nu3D3l5eVAoFIiNjUX//v0RFxdnUfwdd9yBb775BgCQk5ODP/74w6Ii89q1a42Px40bh4iICLNtZ8+ejTfeeAM1NTX44osvuvT2bkRERERERERERI7GOn5LrOPb7jis47OObw7r+NRTcAExERERERH1OCdPnsTzzz+Pbdu2QRCEFvuHDRuGZ555BnfccUeLYs/nn3+Oe++9t0VM7969jY937dqF8ePHd7hfoaGheOONN1ps37lzJ3bt2tXh41lr1qxZSE5OBgD88MMPeO6559qN+eGHH4yPZ86cafHtp2pra/H2229jyZIlokJdcwqFAjNmzMBzzz2Hyy67rM3jpaSkYP78+fj2229b/d4GBgbilVdewcMPPwxXV1e7Hic8PByZmZkAgN9++w3PPvtsm31vbsCAATh27JjF7a11++23GwuPGRkZSEtLQ9++fa0+XmFhITZu3AjAMA/aKmJ+/fXXxse33norgoKCrH7dZcuW4cyZMwAAuVzebqF27969eOGFF7B///5W91999dV49tlnMWnSpDZf99prr4W/vz9KS0sBGG5/1l7hsba2Flu3bjU+N3fbsyYJCQkYMmQIjh8/jpUrV7LwSEREREREREREPQLr+G1jHZ91fGuxjs86PpGIQERERERE1A0AMH599tlnZtt9+umngkKhELU393XnnXcK9fX1ovjPPvus3bhdu3bZdGzz5s0zHnv27Nk2PbYgiHN37tw5ITk52fh81KhR7cbX1dUJnp6eAgAhKChI0Gg0wvLly43HuOqqq1qNq66uFsaOHWvR9wKAoFAohLVr15rtx6pVqyz+3l5++eVCbW2tXY9z7bXXitq+++67gk6nazeflkpPT+/0vKuurhZkMpnxGKtWrepUn7777jvjsX788Uez7XJyckR9X79+fade11J6vV54/fXXLZ5zL774Yrvfs4ceesjYPjQ0VNBqtW22X7t2rbG9XC4X8vLy2u33nDlzjDEFBQUdGjMREREREREREZFUsI5vPdbx7XMc1vFZx28N6/jUk3TueuZEREREREROZM2aNbjvvvug0+kAAL6+vvjnP/+Jt99+GwsXLsS9996L8PBwY/uvv/4at912m+iT615eXoiNjUVsbKzo9l69evUybndzc+u6QdlBYmIiEhMTAQAHDhxARkZGm+23bduG6upqAMCMGTOgVFp2s5s333wT+/btMz4PDw/H008/jYULF2Lp0qWYO3cuxowZY9yv0+nw0EMPQa/XtzhWSkoK7r33XuP3VqVS4Y477sA777yDpUuX4j//+Q/i4+ON7f/44w+89957djsOADz88MOi588//zxGjx6NFStWoLi42JIU2Z2HhwdiY2ONz48ePdpsujmZAAEAAElEQVSp4zW/wkZbV5lo/n0HgMsvv7xTr2up999/H/PmzTM+DwsLw/3334/3338f7777LmbPng0/Pz/j/v/973/tXnHijjvuMD7Oz8/H77//3mb75rc9u+qqqxAaGtpuv0eNGmV8vHv37nbbExEREREREREROSvW8S3DOj7r+NZiHZ91fCIRR69gJiIiIiIisgW0c+WC/Px8wc/PT3QFgMLCwhbtKisrhaeeekp0PHOflo+Ojja2SU9Pt/GITLr6ygWXvua7777bZvxdd91lbLtt2zZBEIR2r1yg0+mE4OBgY5ubbrpJaGhoaPX4X3/9taiPf//9d4s2zT/ZHRoaahxHc1qtVrj55puN7ZKSkux2HEEwfEr+0qsXNH3J5XJhzJgxwrx584R9+/YJGo2m1WO0xRZXLhAEQbjmmmuMx7jnnnusOkaT+Ph4AYAQHh7eZru3337b+JphYWGdek1LnTp1SlCpVMbXffbZZ4WqqqoW7QoKCoTbb7/d2E4mkwkHDx40e1ydTidERkYa2z/yyCNm2zY0NAi+vr7Gtp988olFfT9z5owx5uGHH7YohoiIiIiIiIiISGpYx7ce6/i2P44gsI7fFtbxWcennoFXICYiIiIioh5hyZIlKCsrAwBMnDgRX3zxBYKCglq08/LywoIFC3D//fcbt7388std1k+pmDVrlvHxmjVrzLZrbGzE+vXrAQD+/v4YP368RcfPzMxEYWEhAECpVGLlypVQq9Wttp09e7bok9vHjh1r0ebPP/80Pn7xxRcRFxfXoo1CocD8+fONz5OTk6HVau1yHACQyWTYuHEj7r333hb79Ho9/vzzT7z++usYO3YsAgICMGPGDHz88cfIzMxs0d6evLy8jI+b/o9YIzc3F2fPngXQ9lULAKC0tNT4OCAgwOrX7Ih33nkHGo0GAHD//ffjvffeg6enZ4t2wcHB+PrrrzFx4kQAgCAIePXVV80eVy6X4/bbbzc+//HHH1udD4DhqgPl5eUADPPopptusqjvffv2hY+PDwDx1SGIiIiIiIiIiIi6E9bxO4Z1fNbxO4p1fAPW8YlMuICYiIiIiIi6PUEQsGLFCuPzjz/+WHTbstYsWrQILi4uAIAzZ84gNzfXrn2UmsTERPTr1w+A4fZn5ophO3fuREVFBQBg+vTpUKlUFh1fqVRizpw5mDNnDubPnw9fX9822zf1BQAaGhpa7G++ra1bScXHx+Orr77CV199hZUrV6KxsdEux2ni4uKCTz/9FEePHsWcOXPMFtkqKyuxbt06PProo+jduzcmT56Mbdu2mX19W/Lw8DA+brqFnTWSk5ONj1sr2DbXvPDo7e1t0fEfeOAByGQyi74+//xzUWxVVRW+++47AIbvycKFC9t8Lblcjk8++cT4fM+ePcaiZWuaFx4LCwvx22+/tdqu+W3PJkyY0OofP8z1pymnKSkpbfaFiIiIiIiIiIjIGbGO33Gs47OO31Gs4xuwjk9kwgXERERERETU7aWmphoLh7Gxse0WRQBDMWb48OHG5/v27bNb/6RIJpNZdPWCH374wfh45syZFh8/KioKixcvxuLFi/HSSy+12769T/PHx8cbHy9atMhsAU2pVOLOO+80frm7u9vlOJcaOnQoFi9ejMLCQhw6dAhvvfUWJkyYYCxuNycIAjZt2oTrrrsOzzzzDHQ6XZvH7qza2lrj4+ZXMeiogoIC4+P2rkbg6ura6uvby/79+43F4XHjxrV6xYJL9enTB+Hh4QAMfWztihlNhg4dioSEBOPz1atXt2ij1+uxbt064/Nbb73Vwt4bNM9pUVFRh2KJiIiIiIiIiIikjnX8jmMdn3X8jmIdn3V8okspHd0BIiIiIiIiezt8+LDxcW5urkWFRwDIz883Ps7Ly7N5v6Ru1qxZeOONNwAYCo/PPfecaL9WqzUWUnx8fHDNNdfY9PXr6+uRnJyMlStXmv0keJObb74ZP/30EwBDkalv376YM2cOJk2ahCFDhkAut+zzs7Y6jjlyuRzDhw/H8OHD8e9//xt1dXXYu3cvtm/fjh9//BHp6emi9gsWLEBFRQVWrlzZqddtS1VVlfFxe1eQaEvzwqO/v3+bbQMDA42PLb3dWlBQEGJjY9t8fXOF4ubngIMHD1p8DigpKTE+buscIJPJMHv2bMydOxeA4fZnixcvFl3J488//zTmSKlUYvr06Rb1oUnznBYUFBiLokRERERERERERN0B6/jWYR2fdfyOYB2fdXyiS3EBMdH/Z+++45sq+/+Pv9NBS8seLRtkb2QKoiAoKCKCoDIV3CLwdW9BHMitwu28HTcCKuIAEb1BQRRR2RuZIntaCi2jFEpHrt8f/Bp7mrRN0rRJm9fz8QiPnJzrXOeTcE5O8u6VcwAAAFDsZf2V7/nz57Vnzx6P+8gazgSL5s2bq1GjRtq5c6fj8me1a9d2zP/tt98cwUzfvn1VokQJr9Zjt9u1ceNGrVixQrt27XLc9u3bJ7vd7lYfQ4YM0U8//aRPPvlE0sXQeOzYsRo7dqzKly+vK664Ql26dNGNN95oOTtBQfXjrpIlS6pnz57q2bOnXnvtNa1atUrjxo3Tzz//7Ggzbdo03XvvvbrsssvyvT5XDh486Ljv7qW4XPHkzAV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3Sj46XpOfPlKWGqtWPLlObx/5hS5cO0OhicFd2U2Praiw68LVjZoUadQD/cxaVmnXnzdYJMmjwtk1VXY9sd0q/73qYDdW23XOikaNjTDqvr5m1dmlf++y6rRlDfpubKDiA3zvc4Wx2D9fy113J18bC/L46Ml8c+YfXcYcEa8jHlvtlWPlf/GkgpL6qe8t/5XRZFbMpPNkr6tS/qePKPbYi9z+sK3bvUnbX71R1Zt/k72+WkEpg9wee9NjZ6k6Z4ksZfleaWtnq33tbZl6pyni6X/KYDYpaPo02WtqVPPvNxR0xnS3Y2HdtkOVDzwiy6q1ctTWypTVtuDPttNZvBJw3DEyhvjumeh7K3nuWwVkJSjtzWtlMJsUcfZ42avqVfzUl4qYM8nteDTkFGj3n95U3dItslc3KGBg2x84ZW/Nl19cuNLfuk4Gs/PtLGBAsrad/LDqlmxVyCTfXEl0x6p/KySij4af+C8Z/cxK7He6rJYabVvxLyUNmOV2PDyJKdu9UHt2/axJ5/2goLAUSVJsxnFa9c01Ks9f6pM/EncseV4hUVkafvobMvqZlTR4lmyN1dr229NKHnq++7HwIGbseV+2ibPbGrT6s8uVOPCMruhah+xY/i+FRGVq+PRXnX0beJazb0ufVfLgc92PhwcxY8/+tE2c3dag1V9epcR+M9rs8xU7c/6r4LBUDTvyfhmNJiWmHi+rtVY7Nr+rpPST3I7HgcZsXP2sLI2VXdWlDtu56R0Fh6Vr2MSHnf1KP9HZr41vKqn3qe7HwoOYumrn50ps8tE+vRL13nZt+0TBockaOupOGY0mJSRPls1ap51bPlBiyvFux+NAYzave0kWi+8/N3LzvlVQUIIGD7pGRqNJ8fHjZLPVa9eur5QQP9HtWHgSk7f7e0VEDFD/fhe3xAUERCl73XOqqclTWFh6F/bSc3wH27eXXs1XZu8gvfRcP5nNRs2cEaOaGpteeHm3zjkrts341NXb9fb7RbrlhhTddJ3zu8VJJ0aprs6u9z8o1n1/zZDR2HMSa3ubeONNKtm0UW/NOkt2q1Wr3nlHAWFhOupPt7afXOrTR6VbcrRjwYKubWwXYDxa+dpYjDYaNbqdhTCesdpU5fW/CAAAfI05Ik4Dn1nmlWMVffqMApP6qs9t78hgMivqmNmy1Vep8OMnFDX1Qre/m+rzNiv35ZtVs3GJ7PXVCkwd2Pa4c5+SqVecMu/6WEZ/50qZvY6crnXXDFPR588r+cK2V5frbuXvvyxzWqYSHnxZBpNZYSecIXtttcrfel5hJ5/jdiwad25VyRN3qj57uRx1NTL3bpuDrvjwVQWNnKi4vzzass0Un6yCOy5V45YNCug/tFP71VHVL38sU2aKol7+qwxmk4LPmCxHdZ2qnv+vgs5xn2+xbs1V+Z3Py7J8oxw1dTL1b5sviP7okbZ/rNGismv/oaDTjumMrhw08taudix6RiHRfTV81tvO+Ymh58jaUK1tC55Q0hEXuB8PD2LyVr4h/9A4DT/7XRn9nIX1obEDteT1E1Wet1hR6Z5dxbKr7fjlKYXE9tPwOf919m3EebI2VGnbT48qadTF7sfDg5jNX9+hhCNma+g5b7TERWdN0cKnR6ti5yJFpI/vym56JHfH5woOSdKQ4bc6c66JR8tqrdOObXOVkDTFfW7yAGM2b3hVFkvP+OW7Y2HT8/4c53M6cdjspuf940oa4f4z1pOYvBVNr5XZ77e+VuIGacmrx6s8d7GiMnz0tbL6Fefc5gnPy2g0K7HvDFkt1dq26kUl9T/L/Xh4EFOWv0h7cudr0jnzWuY+Y9OP06rvrlV5wVIFZXl2NbquxNywq+VPrFNU/3BN/3Cy/MxGDTy/jxqrLVr6z7Ua/Icst+Ox9B9rlDUzTce/PKFlW/yoKH007X/a+V2+MqYla9lj2QqOD9TML4+TKdBPkpQ1M02v9v1IK55ar6Medn9CXnf7d65VmcEGvTjEX2ajQTMTpBpro/61w6qzE/z2eQJJvc2hm9Y1KtAo2ffa99Iuq2L8pdeH+SvQz3mME2P8dMyier2aa9VfMn3vpDbGYv98LXfdnXxtLMjjoyfreWuxwyfZrY0qW/qposbPkvF3Z89HTzpP9fmbVbvD/VnTxoBgRQw7Xiln/lUhfUa2e/yw/hOVePKNihp3ltfb7m2ORovq5/2kwJOObylqlaSg006SbftOWTdsdhtnCApUwKRxCr32MpkGu18a37YrV8boKBlDQuSw2eRw+P7ZtvZGq6q+WKFeZ4x1GY9e54xXY06h6tfschtnDPJX2NQhirtthgKHuy9SctRb5BcW5HJcvyhnUY+9weLFXniP3daoom3fKT7r1JYf+ZKU2O901VZsVdWedR2OyV3/nmLTpygoLEUOh0MOu00BwXEaO/MDnywStdsaVZjzpRIGzHTt16BZqi3LUVXR2g7HRKaMa/OvKOcrhUT1VdZRd3Z+5zrAbmtU4ZavldBvhmvfBp6l2rItqirO7nBMZPKRbf4VbfnGOR4Tb+/8znWA3W5RUf4CxSdPdlnpNDH1eNVW71J1xZaDjinaPV+Fud+r7+ArOq8jXmC3WVSU97PiU6e69it9mmqrdqq6vO3niqcxtdW58g+IlMkcIoe9h3yu2C0qLlyouMSjXfoWnzxZtTV5qq7cetAxxQW/qnD3T8ocsO/LDnY3u92qkpKViosd49KvuLhxqqsrVE1N289YT2MC/CMVGzPGJTYwMEaSZLM3dEZ3DhrfwfatsdGub78r0/RTomU2t/4MnHlajLZuq9e69bVtYsrKLJo4PlxHT+zlsj0tJUCNFrus1p43Ds38zGYNmjFDq9//r+zW1iucrHzrbcX276/EI9xf7jC6T6b2bHG+Zxj9/LqkrV2B8WjFWPREBslu7Jn/1HNPwgAA9Ex2S6MqfvtMEZPOdFkFN+qY2WrYvVl1290vNmIMCFbY8OOUcM7tCsoc4fY+9XmbFDpoYkvxsCSZe8UqKG2Q6ne2zXl2N4elUTXzv1HolNNcxiLshDNk2bVVjTnu22wMDFTw2GMU9YebFdDf/dUdTbGJCpniesUoU2KqJMle3/a3ly9wNFpU/81CBZ12lOtv6jOmyLY1T9Z1bfMtkmQIDFTgMaMUdvN5Mg/LcnufgLGD2/yr/3aRTFkpCvuz+9VruxN5a1d2W6OKNn6u+EFnuvZt6GzVlm5WVeGaDsfYrQ0y+Ye53MccFNWyzxfZrY0qWjdX8cPOdu3b8PNVW7JJVflt50M9ibHUlqq+Ypei+x7nEhuWeIQCI9JVtK7tIiHdzW63qLhoseLiJ7jkGRMSj1Zd7W5VV20/6Jjiwt9UVLBAmf3cr/ztS+zWRhVt+EzxQ85yfZyHzVbtns2qKmj7GetpjN1aL1NAeDuvFd9cPdJua1TR9v8pvs8pMhp/17e+p6u2Ypuq9qzvcEzuhvcVmzZZQWHJv5sPjdXYGe8r0QeLh5kbdmVrtGnLJzvV7+ze8vtdnnrg+X1UtqlSxavK2sRYaq2q3FGjtONcr2iRfEyCZJBK1jpjSjdUKPmo+JbiYUkKjg1U9JAI7cku75wOHaRGu0PfFtt0apyfzL9brGNmgp+21jm0rnrfOfiHt1rkkDQnue3akVtq7Rob4ddSMCtJ0f4G9QsxamPN3iW23Y+x2D9y160Yi56IPL4vo4AYXmEpL5TD2qjgvVYQDkpxrkTQWOK+SDQgOkWJ029R4vRbFJQyuN3jJ556sxKn36KI4dO81+hOYt9TKlksbVavM2U6L/Fsyy9wG+eXEK+QS+Yo5JI5MvXNdHsf665cyd+s0suuV+GIo1U0dorKrvmjrE2X1fZF1qJKORqtChjgermYwP7O25bcPW7jzMlRirl+mmKun6bAgSlu79PrzLFqyClU0T8/lSW/TPUb8pT/l3dkTotR8JHuk5XdrbGuRA57o0Ij+7lsD410Xo6lvmp3h2OqSzcqKCxF6+ffo59eH6N5/x6oxR+fpYoi76wy7m0NNUVy2BoVGu1arBUa41y1o74y1ysxklSybZ7yVr+tYdNfcklK+ZKG2uKmvrmuWhIa5Xzc66vcjEcHYiSpZPsPyst+R8NOesFnx6OxvkwOu0Wh4Rku20OabtfXFR5UTEN9qdaveFRZQ65UsI+upNqssaG0qV+unysh4c7PlfpaN2PhYUxddZ4Mfv5a/tNN+v6jyfrxk+O18pdbVVvtu58rjQ3lctitCglzveRjSKjzdn198UHFNNSXasPqZ5Q54A8KCU31dvO9qrGxUg6HVcEhrp+xISHOJFp9Q2mHYwYNukpJScfI4XCosbFKVVXbtX37JwoIiFJ4WNsVen0B38H2rWSPRY0Wh/r1DXLZ3i/LeTsvv+3EXFJigN55fZBGjgiTxWJXYVGj5i+o0OtvFWra8VHy3/s6YD1IaHy8TAEBKlrneoJO0Xpn8r1XqvvXf1SfPuqVkqLrly/XfbV1um37Dp3yxBPyDw3t9DZ3JsajFWMBAAAOZdaKIjmsjQpKdc3jBzbl8S3F7nNJ/jHJij/9JsWffpOC0txfRTD1yieVNOc+l222uirVbl0pc7TvXUrcVloiWRrl39s15+qf4cy5Wgvd/94zxSUp4tyrFHHuVfJ3s/qwJCXc/y/1mjFHDodDtrI9ati4WqX/fkSm+GQFDnJfgN3d7CXlUqNV5n6ueTJzX2fuxJbXNt8iSX5JMQq96kyFXnWmTP0zPPpb9T8uU+173ynymdtkMPnepD15a1eN1U19i3VdeTw01tnX+oq283+exiQMPku1pTna8vPDqq/KV3Xxem345s8KjEhXROq4zujOQWusLnT2Lc71vTA03tnX+nJ347H/GL+AcBlNAaor3eZyH2t9pRpritRQ2XbuqLs1NlbI4bC2yaEGN91uqC85qJiGhjJtXPcvZfabo+AQ31xF9fcaazrwWvEwJmHILNXu2awtP/1d9ZW7VV20Xhu+vrXpteJ7K1NLv5/b7OuyPTTCOX9bX72v+dB9x1SXbnLOhy64Vz+9OU7zXh2ixXPPVkWxb86HMjfsqrawXrZGu6IHuy5aETUoQpJUtbO6TYzBz6DZv56sfrMyXLbnzS+UHFJocrAkaerz4zTxIddF6xqrLCpaXqrQpGDvdcKLShodanRI/UJcc+19g523dze0XzQ7v9SmN/NsemqQvwLdpOr/1s9ft/Vx/X5RbXVobbVdCQG+VwzHWOwfuetWjAXgXb75axQ9jqXCWYjkFxLpst0cFt20v6jL29Rd7CXOglhjeJjLdmNEr6b9bYt5PGXbmSd7fqFMx01W8JxzZC8sUvWLr6r0vMsU88X7bf6mL7AWVUiS/CJcL/ftF+X8ALYWd/zy8KHHDlbCfbNUcOe7KnroE0mSwd+krEUPyC80cN/B3aSh1ploNQe4/igyBzpfO411bRMqnsY01BRrV/Z/FB47TAMm3SsZjNq5+hUt+WSWJsz+n4LDfasQrrHG+b5gDoxw2W4OdJ413VDb9n2jIzF2m0Ub5t2h3kfeoEAfvVSP9Lu+BUS4bDcHOR/n5ufBwcbYbRZt+Oku9R5znQLDfG/ypllDUxGjyez6vmb2D5fkLBbuaIzD4dC65f9UWEQ/pfY5XWUlvptIkaSGeufnism/vX61/VzxNKauJk8NtYWKSz5GaX1nqb6uSNvWv66l31+p8dPekdnf9z5XGhucj6PZ7PpDrrmtjQ3lHY5xOBzasPophfbKVErGqSovbbuijC9ptDg/Y00m18SXyeTsp6Wx7WfsgcbYbHVauOhmSZLRGKARw2/z2QktvoPtW1Gx8+oMvcJdH7+ISOcKFiUl+756w7wfy3X5NZskSf36Bumh+3t3Qiu7TmhCgiSprqzcZXvtHufzKDQ+vk2MwWhUZO/eCk9O1rz771fx+vVKHj1aR//5z4rJ6qvXp5/a6e3uLIxHK8YCAAAcyixlTXn80AiX7aYwZ27NUt7xPH5wn+Guf6u8SFv/fo4cNqtipl3W4eN2Fmups6/GMNecq7GXM69mK3VfMHsg7NWV2n6ac+UrQ1Cwkp/7WAaz/0EftzPYipy5E0Mv19yJMdL5e9dWXO6Vv+OwWFV570sKveYs+SXFeOWY3kbe2lVDjfN9wxwU4bK9efXT5r53JCa6z2T1m/o3bfzuL9ry098kSQY/f028aolM/r5ZxNFQ5TxBvTkH38wc7JwPbaxuu9iDJzFGP5MSjjhHO355SiFxAxXV51g1VOZq01d3yG6pU2O1782zNudUTWbX+b/mHGxjY3mHYxwOhzZkv6DQ8N5KTp2m8jLfW8l+bw3V+3mc3b1WPIyJzpyifsc/qI3f3KYtPzwgqem1cs0ymQJ89LVS65y7PLD5UM9iGmqLtGvdmwqPGaoBE+52zoeu+T8t+XS2Jsz6xufmQ5kbdlVTUCdJCogMcNkeFO28XVvYdlVtU4CfEsfFumwrWFKiry+Yr7DUEGWd7jzhKW5EtMt9aovq9OnpP8hhtWvY1e5P/OpuxY3O//baa9qlKW2vkkb3RbPlFof+tKFRN2aYNDTMqP+V2NrcZ0iYayVtSaNDl61pkM0hzUnyvXkexmL/yF23YiwA7+o574Twce7PynHYncv9O2z7Lkg4pLRzgpLD3vSF5nfL5x+okIvPU+jVl8h/TOuZc/6jR6hkxnmqff9jhV7me5f7MrR3wlbTeDgsbb/AearikyUqfPAjRV0xVWFTh8hWWac9L3ynnXOeVcZ/b5Y5OarDx+487bxWHM7Xit3u7vnhWYy1sVIBIfEaNf1N+ZmcBdQxqUdp/ltHa8fKlzTw6AcOsu3etu9+OTowFu5idq18VdbGamWMuaZjzewyXTQeq193jsfIqzrWzC5iaP/NVJJkd7Ttm6cxeds/U0VptsZN/T8ZDL6/ema7/ZLzffSAxmKvmLR+56r3oD8oMrZ19ZvI2BFa9M0c5W2dq4wBvnhpuPYe56bPlQN4rewds3vn16ooW6+xRz9/iDw32n7GHmiMn1+Ajhj2ZzU2lmt3/o9aueoRDR9+m0JD3F8doFvxHWyf2vtO1jw+Fuu+L/915Ogwvfefgdq6rV7Pv7RbM8/O1ucfDVFQkO+tFuURh/v+GozO176f2dxmn9Fk0geX/EH5q1apZONGSdKmb75WVUG+Zv7rRaWMGaPcJUs6r82difFoxVgAAIBDmKGdHwYtuSQv5PEdNqtKvn5Z+e8+KIelQRk3/5+Ce7u/fGy3au9HUnPe2tbx35DNjEEhSnrmA1lLClX5yRvaff1ZSnruIwVkDtx/cFdrN3Vy8L+pf6/mP1/KXl2rkCtmeuV4nYO8tav9zU+4e9/wLKZg3Ufa/OP9ShtzlaIzj5O1vlI7Fj+nle+fq5HnfazAcB8srN7P+6jd3fuohzEDTn1CDptFa96d0xKXPulmNdYUyxzsi/Nd7WjJuR7A/N9eMbtzv1Nl+UaNmfB4j8jTOnXgueFhTEH2R9r8/X1KG3u1orOaXiuLntXK92Zr5PkfK7BXD8rV7ms+1MMYa2OVcz70lDda50NTJmn+O5O1Y/W/NXDSfe4P1G2YG3axnzy13WLfZ3h9WYMW3rtKq57foLDUEM34dIpMQa5lT3arXate2KiF966UrcGuaW8erbjhvvk+2m4pRdN/Le2k7e/aZFFigEFXp+2/5Mtqd+g/u216YptFjXbpqUFmDQ7zvfdWxsID5K5bMRaAV/Wgd0L4MnOE8+wNa43rym7Waudtc2Ril7epuxhjnGe22StcV/BzNN02xnX8rPqAoye4FK5Izsty+2Wkybopp8PH7UymOOeZkbayGpft1qbb5oSIDh3XYbEq74bXFDl7gpL+eb7CTjxCEbPGKeOTP8laWKHiJ788qHZ3loBg59mRlr1WyLQ23Q4MietwjH9QlGJSj2r5gSg5Vx+NiB+lqj3rvdB67woIdbbbstdKss23A0ITDjrGbrdq++KnlTriEvmZffPSNM0Cmh5HS8PefStv2t/2LLkDjbHbrdq+9DmlDrvI58fDv2l1jr1XQ7U0VkmSAgLbvpd6ElNfW6hNa15Q38FXymQOldVaJ7utQZJks9XLaq3zbke8wD+waTX/Nv1y3g4IcjcWnsXEJI53KR6WpJDwDAWHpaq63Dc/V/wDnGfYNz+uzSyW5sc5ukMx9XVF2rz+ZWUOvEQmc0jTc8N5urPN1uCbz42mFaWtFtfPWEvT7QD/iIOOMRj8FBHRT3FxYzV0yI0yGv20e/cP3mi+1/EdbN/iYpzJkvIK14R08+34uH2vghUZadaEcb0059x4vfB0X23KqdN337ddDb6nqCpwrvASHOW6wktwlPOzpHJ320sH2hobteb991sSS83Wf/qpJClh6NDOaGqXYDxaMRY9k8Hu1yP/AQDQ1UzNefwq1+/ytuqmK/dEHVwevz5vkzbdPlW5//6TQgdN1IAnflPkxDMP6pidxRTlzLnaK8tdtjffNkW3zcUdKIPJpKDh4xR23AwlPvqmZDKr8uM3Dvq4ncEv1vl9117umjtxNN02xh980Y3DalP1Cx8o5MJTZAzyzSsISuSt9xYQ6nwtWOpc+2Ztuh0Y2vZ9w5MYu82idZ9fr6Rh52rAtEcV23eaEoeerdFzPlNDTaG2LXjM633xhoAw52NpqdtrPrTWeTswvO1V/zyNMQWGa+g5b2jy3UUad/1iTbmnVP1P/ocstaUKCPO9edaWnKul2mW7xeq83ZyvP9CY+rpi5Wx8XZn95shkCnbJ09ptjT6Zp5V+/7zf63Fuft67eQw9ibHbLFr36bVKOuI8DTj5McX2O0mJw87R6Au/UEN1obb94qOvlaAOzId6GOMfGKWY5Elu5kNH+uZ8KHPDLkISgiRJDaUNLtvrS52v85Ck9j8Xt32ZqzeGzNXqf23UyBsHas7K6YoZ4prDK91YofcmfaUfb1yslKPjdcGq6ep/doZ3O+FFsf7Ostnyvc4xKG+qlo33b1tW+1WxTd+U2PS3fv5qsEs1Vocsdmc9ZY3VoXpba2Hlllq7zlzeoHs3W3RkhFHfjA3QqXG+uc4kY7F/5K5bMRY9U3fn48njt48CYniFuVecDH5m1e3KdtnefDsg2vcuj9FZjFFRktkk6+YtLtubb/sldizxaK+pUf0P82UrbnsZE4PRKENIiJuo7ucXGy6D2U/163Ndtjc03TandCzxaKuolb2yTkGj+rj+vfAgBfRPUuOOtuPkC/yDomUwmlVduslle9Ue55eUwNC2ySVPYwKC42WzNrSJt9st8jP73vPDPzhWBqNZVSWuP2Crm267u2zbgcYUbXYmUJKHnOfNpncK/+CYpr5tcNle3XTb/XgcWExRzldqqClU8uBzvdn0TuEfECGDwaSaqu0u26srt0mSAoNiOxRTW5Mvu61e61c+ph8/O1k/fnayVi78iyRp2fybNP/LM7zfmYPkHxApg9GkmoqtLturm24HBrX9XPEkxmqpUfHuX9RQt6ftHzUY21xGzlf4B/Rqepx3uGxvvu22oNqDmLraAtltDdq45hn9/M1Z+vmbs7RqyT2SpBWLbtOC//neasxmc7gMBj/V1Oa5bG++HRAQ2aEYq7VWS5berZKS5S738fMLUEBAlM8m6fkOtm/R0WaZzQZt3OT6+G3cVCtJSkpsW0A8f0GFpp68Svn5rt8vMns7k7yVlR2/kkR3qykqkrWxUfGDh7hsb75dkZvbJiYiPV39Tz5FRtNeK1rYnONQX1nZJqanYDxaMRYAAOBQZuoVK4PJrPqdrpeCr9vhzOP7x3R8BcOG/K3afOcJasjfqj5//Uh9bn9PAYl99h/YTfwiYySTWY3bXCePG7Y582qm+LZ5Wk/Yqiu188Kpqv75K5ftxsAgmeKTZa/2ze+Gxphezt/UG11zJ5YN2yVJfkltc3EHqv7rhbIXlSr47OMP+lidiby1K/8QZ9+qi1zfN6qKnbfdrXzqSYy1vlzWhgr1Sh7jch9TQLhCYwaortz1uegr/EOc86HVha7zoVVNtwMj3I2HZzEVuxarpnijzEGRCk8aIVNAqOrLd6mhMk+hCYM7ozsHxd8/3Jlzrd7lsr2maqckKdDdQg8exNTVFcpub9DGdS9q/vcXaP73F2j1iockSSuW3q1ff7q8M7pz0PxD4tw/74uaHme3r5X9x7T7WgkMV2jsANWVb/diL7yn3bnNptuBIW0Lqj2NCQiJk81W3ybe7rDIz9/3crXMDbsKjguU0WxUydpyl+0la52F82Gp7tud88lOzT3tewXHBerCNafp6EfHKCDcNaddvqVS/z3ma5XnVOn0L6bqtI+nKCIzvFP64S3R/pLZIG2scV15eWONs/A1KbBt0eyGarsa7NIpSxs0aH69Bs2v13M7raqzS4Pm1+vqbGcx9o46u85e3qDtdQ69NsxfLw8NUHqQ75aIMRb7R+66FWMBeFfPe0eETzKa/BU5Zob2LPyv7NbWU4JKFryrgPhMBWcM777GdTGDv1mBU49V/Vf/k8PSusJb3effyC8tRaaB/Tt2XLO/Kv5yr6qffdllu3XbDlm3bpf/cN88G8bob1LYqSNV8dESl/Eo/+9v8u8dp8BhaR06rl90mPxiwlTzq+sPJ2tZtRrW5SlwoA9e2kqS0c9fcb1PUEHO5y6XKyrI+VRB4ekKi2mbBPI0Jq738SrZ8X3L6rOS1FBTpPL8JeoVP7zT+tRRRj9/xfc9RQUbPnbpV/66DxUU0Vvh8cMOOiZ35WuKyjjG7Zndvsbo56/4rJNUsGmua982fKSgXhkKj2v7Gj/QmNw1bygq7WgFulklw9cYjWbFJU1SYe4PLpdvKsidp6CQJIVF9O1QTHhElkYf9bTLv37DrpckDRzxJ42Y+Ejnd+4AGf3Miks+RoW7/ufar53fKig0WWGR/ToUYzT6K/u3+7U12/VzpaZyh2ord6hX9JC9D+sTjEazYhPGqzB/vmvfdv+ooOBEhYVndigmLDxTI8f/0+Vf30FXSpIGDL1Bw8f63qW+jEaTYmJGqLh4iUu/iop+U2BgrEJD237GehLj5xekhoYy7Sld4xLb0FCu2toChYb44CXxxHew/fH3N+rE4yP12Zd7ZPndZeA++WyP0tMCNGRQ28RsVKRJmzbXadFi1xWoFiyqkCQNGuDbqyTti81i0bpPPtHQs2e5JIuGnTtbe3JylL9iRZuYkJgYXTh3roac6bqC2qDTTpPdZlPe0qWd3u7Owni0YiwAAMChzGj2V68jT1PZgg/k+F0ev2z++/JP6KOg3kd0+Ni7/3OXHJZGDXhsgXqNOtEbze1UBrO/Qo6epup5n7qMRfV3n8iUnC7/vh3LixhDwmQrzlftwu9dtltLCmXZsVn+WQMPqt2dxeBvVuC08ar79GfX39Sf/Ci/9ESZh7TNtxyomre+VMCkEfJLaFtU6EvIW7sy+vkrbsB0Faz70HV+Yu37Corso7CEtu8bnsSYg2NkDo5R2Y4FLrGWulJVFa1TaOygzuvUQTCa/BU36HQVrH7ftW8r31FQVKbCEkd0OGb7/Ce09JVpslsbW+6za9G/5BcQpvihszqxVx1jNJoVGzdWRQULXPKMhQW/KCgoQaFhvTsUExbWWyPGPODyr++ASyRJ/QddpSNG/bXzO9cBRpO/4gaepoK1ez3v1zQ/74d3KMb5WolV2Y5fXGIttaWqKsxWaJzvFZdLTe8DGcerYOsXstt/P7f5mYLC09qfD/UgJi79OJXs/NF1PrS2SOX5S9Urdnin9amjmBt25efvp6yZadr0/nbZfpen3vDONvXKDFPciLaLj9ksds27ZpESxsXonAUnK7JfL7fHnv+X5bI12HT+slPV+yTfnMfYm7/RoBNj/fR5kU0We+tquXMLbUoPMmhwaNui2bMT/fTBCH+Xf7MS/BRglD4Y4a/bM51XI/z7FosaHdIXYwI0Odr3V9BkLPaP3HUrxgLwrp61Hjt8WuIpN2nd/VO1+YlzFD1+lqo2/qo9v7yt3le+JIPRKGt1qapzligwIVOBCVnd3dxOFXzxeSq98CqV3/QXBZ50nBqXrVL9Z18p/G9/lcFolL28QpY165zFLOmerc5s8Dcr5LILVf34czL4+cl/3BjZCgpV839vyjx4oAJP9d2kbMy1J2jbKf/QzgufV68zxqp20WZVvL9Qyc/8wfncKKtW3bJt8u8Tp4A+nq0OaDAYFH/nTO2++Q3JIIUdN1S28hrteel7ySBFX3lcJ/eq49KPuExL556jVd9erYTM6SorWKL8TR9r8LH/lMFglKW+XBVFKxXcK0PBvTI8ipGk5IGztXPN61r8ySylH3GZ5HBo+8oXZQoIV8YRl3Vjj9uXPuYaLXlnulbOvVgJA2aqPHeR8te9r8HTnpbBYFRjXZkq8pcpOLKPQiL7eBTTzNpQqdJdvyrrqDu6q3sHLH3kVVry35la+fmlSug/Q+V5i5W/4QMNPv4J53jUl6miYIWCI3orJKK3RzHNrA1VKs1dqKwJt3VX9w5YWtYsLZt/k1b/do8SUiarfM8aFez6TgNH3Op8rTRWqqJsg4JDkhUcmuxRjMkcqogY12I/u8N5VmFYryyFR3aswLCzpfU7V8t+uEarf71DCalTVV6yWgU7vtbA0Xc4x6KhQhWl6xQcmqLgsFSPYgx+RmUMuEA5a56XweCnqPgxqq8t1I6NbykscoAS0k7o5l63L7X36Vqx6C9au/whxSUerYrSbBXm/aABw25sem5UqbJ8o4JCkhQckuRRjMkcoogo10Sdo+m5ERreR+FuitZ9QUryCVq56p9at/5fio0do8qKzSoqWqR+/S52joWlWlVV2xUUFKugptWq9xcjSclJU7Rz15cyGs2K6NVPFku1cvO+k9kcqqSkY7uxx/vGd7B9u+IPiZp1/jpdef1mTT85WkuWVemjuSV65KE+MhoNKiu3auXqamWkB6p3eqAGDQzWmFFh+ut925SX36DM3kHavKVOL/57t46bEqkRw0O7u0sHZcFTT+qyed/rvPfe1+r/vq+MiRM14vw5+ujyy+RwOBQUGamUMWO1Z0uOSrdsUd6yZdr09dea8fwLikjP0J7Nm5Q0YqQm3nSTFjz5pMq2b+/uLh0UxqMVYwEAAA5lcaddr81/PVHb/nm+IiadqZr1C1X207tKu/Z5Z662qlS1m5cqIDFTAYmeFY06bDZVLPlCYcMmqz53g+pzXa+WZY5OUlC6752oHHH25dp9wywV3HWlQqdMV/3qJar+9iPF/uVRGYxG2SrL1LB+pczJGTKntC2Ec8dgMCj8jItV/p9nZPAPUNDwcbJVlKni/Zfl1ytK4TMu6ORedVzo5TNVMus2lV35kAKnH6XGJetU99EPinj0Judv6rIqNa7cKFNGkky9D2yFZntVrRoXrVXYn3y3/79H3tpV+rjrtfT1aVr1wRwlDDpTZbt+Vf6adzV4+vPO/FNdqSrylik4qo+CozI9ipGkrGPv0vovb5TBYFB05nGy1pdr55IXZTAYlDb26u7s8j6lT7pRS1+eqlVvnaOEYbNUtn2B8le+rcFnOudDLbWlqti1RMHRmQqOyfIoRpLSJlynwrUfac37Fyt+yEyV7/hVO399Vv1PeVSmAN/MwaSkn6qVS+9R9qpHFZcwUeVlG1SY/7MGDL66KTdZpcqKHAUFJyg4ONGjGJM5RBGRridbtORpwzIU3st355jTx1+vpa+eqFXvn6eEwWepbOevyl/9jgaf9kLrcyNvqYKjMhUcnelRjCRlTblL6z+/wflayTpB1roy7Vzc9Fo50odfK8Mu1dLPztWq765TQuYpKitYqvycTzT46Idb50OLVyk4PL11PnQ/MZKUPGC2dma/ocWfzVb60EskObR91UvO+dBhl3Rfh/eBuWFXI28epP8e+7U+n/Wj+p+dobxfirThza06/t8TZDAaVF/aoILFJYrIClNEVrjyFxWrtqBOgy7MVN7PhW2OFzM0QsEJQdr66S6lHZeo0vUVKl1f4XKf0ORgxQxte9VGX3BZiknnrGzQ1dmNOjXOT0vK7fq40KZ/DjDLaDCo3OLQykq7MoIMygg2KjnQqORA12P8UmaXn0EaE+EsjrU5HPpfiV0TI43KqXEop8b1aoIJAQYNCPW99SYZi/0jd92KsQC8x+BwOBz7vxsONSkpKcrLy5M5Klkjn9/mteNWZv+k3A/uV832lQpMyFLCidco9tiLWvatf+B4JZ/5V6XMurtN7JbnL1XN1mUa9ujKdo9f/OMb2vqvyzTypd0yh7e9RPnB2HFE+3+3IxoWL1P1My/Jun6T/NJTFXz+LAWfMb1lX9lFVyvk2ssUdt0VbWLLb79P1rXrFfPZuy7bHQ6Hat96X7VvfyDb7gL5JcQpYPJRCr3+ShmDg7za/pjEtV49XvX8DSr6+yeqX7NT/n3iFX35FEXOOapl3/bp/1Tsbacp/vbT28TmXv2K6lZuV9+Frqs/OhwOVX66TCXPfK2GjbtlDA1U8Li+ir/rDI8LkT2RdPsUrx2rWWneQm1Z8oQqS9YpuFeG0oZeqOQBZ7fsW/rpueoz+kZljbnZo5hmtZW7tHHB/aooXCGDwajI5PHqP+FOBQTHea3thjDvXuqldOcvyvnlYVUWrVZIZKbSRl6m5KHnt+xb8u5pypzwZ2VN+otHMc0KNn6qVXMv1pFzvlFEkuulnbzKYtn/fQ5A6a4Fyln4iCqL1ygkoo/Shl+i5MHntuxb8sEZyhz3J2WNv9WjmGYFmz/Xqs8v1ZGzv1BE4mivtrmZbfu6/d/pAJUWr9TW9a+qqiJHwaHJSu0zU0npJ7XsW/7Lzeo94CJlDrzYo5j2/sbyX27W2GP/5d0CYnPg/u9zAEqLlmtr9suqKtuk4LBUpWadpaTep7bsW/7jteo96FJlDrnMoxjJ+T66K+cD5eZ8oPraAgUGxSsmaZIyh1wuP5N3P1fsNaVePV7ZntXatuktVVVuUXBwkpIzpisp9fiWfSsW3a6MvuepT7/zPYpp72+sWHS7Rk980qsFxLaqfK8dS5LKyzdo+45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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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0aFGn6/MEu92uBQsWONTl7DZ66qmnlD9/fmu6VKlSni4vXb60/ffs2aPjx49b09WrV1eNGjWcWjZv3rzq3r27xo8fL0k6c+aMVq1aleLv4p9//nGYrlatmts1e8u1a9fUr18/zZ07V9OmTfN2OfCgS5cuOUxHRkaqZs2aLvcTFBSkQYMG6fLly5KkkiVLOr3suXPn9Oeff+rkyZOKiopSvnz5VKZMGTVt2jRD+6lLly5p9erVOnHihM6cOaPQ0FCFh4erdu3aql27tmw2m9t9J3b06FGtWrVKR44cUXh4uB577LF0lzHGaNeuXdq5c6ciIiIUFxenW265RRUrVlT16tWVJ08ej9TmKZm5D/C348LJkycdpuvVq+dU/3ny5FH16tX1999/S5JiY2MVFRWlYsWKObU8AAAAAACeRI6fNnJ88hpvIMfPucjxM44cP2fJ7OOULx0HAQDZBwOIAcBDjh075jBdvnx5p5cNCQlRrVq1tG3bNkk3Q7ozZ86oRIkSVptffvnFYZl27do53X+7du2sD/zGGC1btky9evVKsW2TJk3Ur1+/FOctWbLE4UuHKzZs2KBTp05Z0w888IDTX54rV66s119/Pd12v/32m9q0aePws5UrV7oUAqcmq7a/M44ePeowXadOHZeWT9p+3bp1Tg0grl69ukvr8bYzZ86oc+fOyf4pgJyhQoUK2rt3rzX9008/uf23PmHCBJfar127Vq+99ppWrlyp+Pj4FNu0aNFCL7zwgu6//36n+12zZo3eeOMNrVixQnFxcSm2KV26tB599FE999xzDv+QSU3i/ezcuXP14IMP6urVq3rmmWc0bdo0az3ly5dPM3i8cuWKPvjgA02ePFlHjhxJsU2hQoX0+OOP66mnnlK5cuXSrS2zZfY+wN+OC7ly5XKYvnLlitP9X79+Pc2+AAAAAADIKuT4aSPHJ6/JauT4ORs5Pjl+erJqH5BZx01XZfZxypeOgwCA7CPA2wUAQE5x4cIFh2lXgkdJioqKsp4HBASoUKFCDvN37drlMN2sWTOn+27RooXDdMJZ5Snp3bu3pk+fnuIjIwNIV6xY4TDdtm1bt/vyhqza/s44e/asw3SZMmVcWr5q1apO1ZN0AHHS5XzZ3r171bRpU4fAwRtXvEDmqVKlisP0+PHj9f3332fqOuPi4vTUU0+pefPm+vXXX1MNHSVp9erV6ty5s/r162ddlSY1sbGxevzxx9WiRQstXbo01dBRkk6cOKHRo0ercuXKWrVqlcuvISoqSm3bttWkSZPSXE9ia9euVdWqVTVy5MhUQ0dJOn/+vN59911VrlxZX3/9tcu1eVJW7AP87biQtM+tW7fKbren2/f58+d18OBBazpv3rxOheYAAAAAAGQGcvy0keP/D3lN5iPHz/nI8cnx05KV+4DMOm66KrOPU750HAQAZB8MIAYAD3n22Wf13XffWY/atWs7veyJEyccrnxwyy23KCQkxKFN4jN0Jals2bJO9580REo6MDQrJP2S4QtntbrCl7Z/0iDv0KFDLi2fNDjYvXt3iu0Sv+bSpUsrb9682r9/v5555hm1a9dOFSpUUEhIiEqVKqXbbrtNI0aM0Pr1612qJTP8/vvvatasmRWA5s6dW3PnzlX79u29XBk8qWfPng7TdrtdXbt21YMPPqg///zTqaDcFfHx8erevbs+//zzZPPKlSunOnXqKF++fMnmff3113riiSfS7PeBBx7QpEmTks0rUqSI6tatq8qVKye7AsiZM2fUrl07LVmyxOnXEBcXpx49ejj8nQYFBal8+fKp3jZuyZIlatu2bbLbIUo394MVK1ZM9vPY2Fj169dPo0ePdro2T8qqfYC/HReKFi2qVq1aWdMXLlzQBx98kG7fo0aNcph25cpFAAAAAAB4Gjl+2sjx/4e8JnOR4/sHcnxy/NT46z4gs49TvnQcBABkIwYA4HVDhgwxkqzHQw89lKxNWFiYNd9ms5m4uDin+z9w4IBD/7feeqtbdbZu3dqhn9jYWKeXrV+/vsOyMTExxhhjoqKizMSJE0379u1NhQoVTO7cuU2BAgVMtWrVzCOPPGJmz57t9HpWrlzpsA5JZuXKle681GR8Yfsn2Lhxo0N/tWrVcmn5F1980WH58uXLp9iuQYMGVpvatWubxx57zAQGBibbxkkf7dq1M7t3787Qa3TXN998Y4KDg61aihYtatasWeOVWhIk/bvx1HsSxnTr1i3V92HJkiXNk08+aebPn2+ioqIyvK6xY8c69B8UFGRefPFFc+zYMauN3W43u3fvNl27dk1Wz88//5xiv2+99Vaytl27djWbN282drvdanfp0iUzdepUU6JECYe2xYoVMxEREanWnbht7dq1reedO3c2y5cvN1evXk112SNHjpiCBQs69FGjRg2zePFic+HCBavdhQsXzI8//mhq1qyZ7LX8+OOPrmzmDMvKfYA/HheWL19uAgICrHbBwcHmq6++cnivJoiJiTHjxo1z6DdPnjxmx44d7rxEAAAAAAC8jhyfHN8V5DWpI8f3L+T45PhJ+do+ICPHTVdl9nHKl46DAIDsgwHEAOBlP/zwQ7JBmRs3bnRoExsb6zC/UKFCLq3jwoULTgVN6cnIF6h8+fI5fBE0xpgZM2aYokWLphocJDyqVKlilixZ4lbNnuAr2z9BTEyMyZ8/v9VfQECA2bp1q1PLXrp0yVSoUMGhniJFiiRrZ7fbTd68edP93aT2yJcvX5YGDna73bz++usONVStWtXs378/y2pIDcFj5omMjEy2fVN62Gw207hxY/PSSy+Z5cuXm2vXrrm0ngMHDjiEWcHBwakGiQmGDx/uUMMdd9yRrM3Ro0dNUFCQQ7v3338/zX4jIiKSBXyPPvpoqu1T2h4TJ0506nV37NjRYbl///vf5saNG6m2v379unn66acdlqlYsWKa4aanZPU+wB+PCwkmTZqU7D3VpEkTM3LkSDNz5kzz1VdfmZdeesnUqFHDoU1QUJBZvHhxhl4nAAAAAADeQo5Pju8q8prkyPH9Ezk+OX4CX90HZNUA4sw+TvnacRAAkH0wgBgAvGj27NkmJCTE4cP4vffem6xd0g/slSpVcmk9drvd4cttQvDnKne/QMXFxTksV7t2bfPOO++kGxYkDQ7Gjx/vVt0Z5SvbP7HevXs71NS8eXMTHx+f7nJPPPFEsm0bEhKSrN3JkydT/D0ULFjQPProo2bSpElm4cKFZsaMGebVV1819erVS9Y2T548ZvPmzRl+rem5ceOGefjhhx3Wffvtt5uzZ89m+rqdQfCYuWJiYszzzz/vcFZ1eo/cuXObdu3amQkTJjicgZ+aZ5991mH50aNHO1VX4oDQZrMlu8JA0quI9OzZ06nX/PfffzsEoblz5071/Z70tffv39+pdezcuTPZ35Qz+/y4uLhk73lng053eWMf4I/HhcR++eUXU7x4caf/5qpVq2Y2bdqU4dcIAAAAAIA3kOOT47uLvOZ/yPH9Gzk+Ob4v7wOyagBxZh+nfPE4CADIHhhADABecPr0adOnT59kXwjLly9vjh8/nqz9iRMnHNo1btzY5XWGh4dby+fJk8etut39AnXp0iWH5UJDQ5O97scee8x8+OGHZs6cOebNN9803bt3d/hinfCYO3euW7VnhK9s/8R27dplbDabQ12dO3c2J0+eTLH96dOnTb9+/VIMYYKCgpK1T+k2ct26dTNnzpxJsX+73W7mzp3rcEWFhBAyM2/1ExUVlex92bt3b3P9+vVMW6erMho8vvbaa9ayt9xyi7l8+bJTy02dOtVarlSpUg63/5o2bZo179ChQy7V48k+PCk6OtpMmTLFtG3b1uTKlcvpEDI0NNT861//SnHfa8zNIK1QoUJW+wIFCjh9Jv6oUaMc1pX4Cix2u92UKVPGIZg8fPiw06/3sccec+h70qRJKbZL+nqdvR3hM88847Dc7t27na5t3759Dsv26NHD6WVd5a19gD8eFxK7cuWKeeqpp5z+O3v//fcz9VgAAAAAAEBmIMcnx88o8pqbyPFTR45Pjm8MOb63ZdUA4sw+TvnicRAAkD0wgBgAstDly5fNW2+95XAbsIRHeHi4+eeff1Jc7tixYw5tmzRp4vK6E99iLCAgwK363f0CldrVbIODg83LL7+c6pf4HTt2mIYNGzosU6hQIafONPYkX9n+SSUOpBJvn4EDB5rx48ebefPmmffee8888sgjDsFJ0jO8U7qFzRdffOHQ5tFHH3XqyggbN25MFvp8/fXXHnm9SR04cMBUq1bNYV2vvPKKsdvtmbI+d2U0eIyJiTENGjSwln/66afTXeb48eOmQIEC1jK//PKLw/ycGDwmduXKFbN06VLz4osvmoYNGyYL6VN6lChRwqxduzZZX9u2bXNo169fP6frOHDggBk7dqz1SHyLwiNHjjj026pVK5deY9JB/gMHDkyxXeI2hQsXdrr/W2+91VquXLlyLv9dlS1b1mHbZsbfpTf3Af54XEjw008/Ofx+nX3UrVuXqxADAAAAALIFcnxyfPIazyHHTx05Pjl+AnJ878qqAcSZfZzy1eMgAMD3BQkAkOni4+P11VdfaeTIkTp58mSy+U2bNtVXX32lKlWqpLh8WFiYw/T58+ddWr8xxmGZpP1ltvj4+BR//sknn+iJJ55IdbnatWvrt99+U7Vq1aztdv78eX3yySd69dVXM6XWlPjq9h81apROnDihKVOmONQ2derUVJfJnz+/Zs6cqU6dOlk/y5cvX7J2vXv31v33329Nh4eHKyAgIN2aGjVqpBdeeEFvvfWW9bOZM2fq4YcfTndZV6xdu1adO3fWmTNnJElBQUH64osvNHDgQI+uxxcEBwfr66+/VoMGDRQTE6OPP/5Y3bt3V4sWLVJsb4zRE088oYsXL0qShg4dqvbt22dlyV4XGhqqdu3aqV27dpJu/l2sXLlSy5cv148//qgjR44kWyYiIkKtW7fWypUr1bx5c+vna9eudWhXs2ZNp+uoWLGiRowYkeK8TZs2OUzXqlXL6X5Tap+0v5SULl3aqb4vX76sv/76y5quUKGCfvnlF5fqK1GihI4dOybp5ra9ePGiChYs6FIfafH2PsAfjwuSNG/ePPXs2dPhuF6rVi09++yzqlWrlqpWrarY2Fj9888/2rp1q8aNG2e9D7Zv3642bdrol19+UbNmzTL6UgEAAAAA8DhyfHJ88hrP8naGl5XI8V1Hjp8ycvycK7OPU756HAQAZANeG7oMAH5i165dpmnTpimeKRsSEmLee+89ExcXl2YfN27ccFiuSJEiLtVw4cIFh+VLlSrl1mtx9wzM8+fPJ3vtbdu2dfrs0pkzZzosW79+fbfqd5evbP+U2O12M3HiRIczQlN7NGnSxOzYscPs3LnT4ee1atXyWD3GGBMREeHQf+7cuT16G6I5c+aYkJAQq//8+fObZcuWeax/T8volQsSvP/++1YfVatWTfWKH19//bXVrnr16ubKlSvJ2uT0KxekxW63mzVr1pinnnoqxdsrNmjQwGGfPHLkSIf5s2bN8kgdEydOdOj3vffec/l1JL6NZIkSJVJsl3gd99xzj1N979+/P939iauPo0ePuvT60uIL+wB/PC5s3rzZBAYGOrT75JNP0vwscPXqVTNixAiHZfLly2dOnTrlsdcLAAAAAIAnkOOT45PXeDav8YUMzxXk+L6FHD995Piek1VXIM7s45QvHwcBAL4t/UsJAgDcEhMTozfffFO33nqr1q1bl2x+jx49tGvXLj3//PMKDAxMs69cuXIpV65c1vT58+dlt9udriXpGYZ58+Z1ellPSOkMxSeeeEI2m82p5Xv27KncuXNb01u3btWFCxc8VV66fHn722w2PfHEEzp48KAmTpyozp07q0KFCgoNDVVoaKgqVqyoHj16aP78+VqzZo1q166tiIgIhz5uueUWj9UjScWLF3c4q/r69espXrHDHSdOnNBDDz2kGzduSJLKli2r1atX66677vJI/77s2WefVcuWLSVJ//zzj15//fVkbU6dOqWnn35a0s0zub/55huFhoZmZZk+z2azqVmzZvr000+1bds2NWnSxGH+li1bNHv2bGv63LlzDvPLlSvnkTqS7hdc/Tu02WyqUKGCQ3/GmDSXKVasmFN9J33NnnDp0iWP9OMr+wB/PC6MGjXK4Uo2L7zwgoYMGaKgoNRvapMnTx69/fbbevDBB62fRUdH69133/XAKwUAAAAAIOPI8f+HHJ+8xlN8JcPzBnJ8zyDHTx85fvaT2ccpXz4OAgB8GwOIASAT7N27V40aNdJrr72m2NhYh3l33HGHNmzYoFmzZqlSpUpO95n4S6ndbnfpS1zSL5Hly5d3ellPCA4OThY+1qtXz+nlAwICkt0WzlMDUp3l69s/X758euKJJ7Rw4UIdOnRIV65c0ZUrV3TgwAHNmjVLXbt2tQLu06dPOyzryq2cnFWxYkWH6aTrdFfSv6cXX3xRtWvX9kjfvi4wMFDTp0+3/pbGjRunjRs3WvONMRo0aJD1BX/UqFFq1KiRV2rNbH///be2bdumbdu2aefOnW73U7NmTf3000/JwsQ///zTep4QcCXwpSA3JCTEep70byMlAQHOffR3pi9XeSp49KV9gD8dFyIiIrR48WJrukiRInrrrbecqsNms2nixIkOP5s8eXK6QTkAAAAAAJmNHN8ROT55jafyGl/K8LIaOf7/kOPfRI7vX/uA9GT2ccrXj4MAAN/EAGIA8LDNmzerZcuW2rFjh8PPy5cvr0WLFmnFihVq3Lixy/1Wr17dYdqV4O3UqVMO0zVq1HB5/RlVtWpVh+mkA0zTkzR4zMorF0jZf/snduLECYfpzKgn6VmpiQOSjChYsKDKli1rTQ8ZMkTjx4/3m4FolSpV0vjx4yXd/OI/YMAAKxibNWuWfvjhB0lS06ZNNWLECKf7vXr1qt555x01aNBABQoUUGhoqKpWrarBgwfrn3/+ybI+nNWrVy/Vr19f9evXV/PmzTPUV5EiRfTKK684/Ozvv/+2nhcuXNhh3pEjRzK0vgQFCxZ0mD506JBLyxtjdPDgQWu6cOHCTl8NJj1JX/OwYcNkjMnQo1mzZh6pzZf2Af50XNi3b5/DdOPGjRUcHOx0/0WKFFG1atWs6cuXL2f5PxABAAAAAEiMHD9l5Pj/Q17jPl/K8LyBHP8mcnxy/AT+tg9IS2Yfp3LScRAAkHUYQAwAHnTy5El17NhRZ8+edfj5kCFDtHPnTt13331ufzFM+oF//fr1Ti+7YcMGh2lvfOBPena8q1/eIyMjHaaLFCmS4Zpckd23f2J//PGHw3SLFi08vo6kt7kJDw/3SL8FCxbU+vXr1aBBA+tnw4cP1+DBgxUXF+eRdfi6xx9/XPfcc48kadeuXRozZoxOnz6toUOHSrp5Zv0333yT5m3qEjt16pQaNWqkl156SVu3btWlS5d07do17du3T5999plq1aqlzz77LNP7cEXx4sWt59HR0cneb65KeoWHxFf3SLqvcTUgTE3S25C52u/58+d18eJFa7po0aIeqSulvs6cOeOxvjPKl/YB/nRcOH78uMO0O1euT7rMsWPHXO4DAAAAAABPIMdPHTn+/5DXuM+XMjxvIccnx5fI8f15H5CazD5O5aTjIAAg6zCAGAA8aODAgQ5fWIODgzVr1ix98sknya7I6qo2bdo4TP/2229OL5s0aLrjjjsyVIs7En9JlJTsyg5pMcYka1+yZEmP1OUsX9v+S5Ys0cSJE62Hs7eguXr1qlauXGlN16pVK9lVIZYsWaIKFSpYjzFjxrhUmzFG27dvt6YLFCjgEBRlVMmSJfXHH3+oU6dO1s8+//xzdenSRZcvX/bYenyVzWbT5MmTrTPfx44dq27duikqKkqS9MEHH6hy5cpO99enTx/t3r1bFStW1IgRIzRr1iyNHj3aOtM8Li5OgwcP1pw5czK1D1ckvW3kL7/8kqH+kp71nnj/knTf5cpVGK5fv66HH35Yffv2Vd++fR1uC9iwYUOHtrt373alZIerK6TUX0YULVrU4dZUmzZt8ljfnuAr+wB/Oi4kvZqFOwF84ittpNQnAAAAAABZhRw/deT4/0NekzG+kuF5Czk+Ob5Eju/P+4DUZPZxyteOgwCAbMIAADzit99+M5IcHosXL/ZY/9evXzd58+a1+g4LCzOXLl1Kd7lDhw4Zm81mLVe9enW3a2jdurXD64uNjXV62f379zss269fP6eX3bhxo8OyjRs3dqP6jPGF7Z/YCy+84LBNpk6d6tRyn3zyicNyr7zySrI2Z8+edai5ePHiJiYmxunakv6+unXr5vSyroiLizNDhw51WFf9+vXNiRMnMmV97kr6d7Ny5UqP9Dtz5sxk+5yOHTsau92e7rLTpk1zWK5du3bm/PnzDm3i4+PNa6+9ZrUpVqyYuXbtmkf7cNeiRYuS7RPi4+Pd7i/p38WQIUOsedHR0SYwMNCaV6JECaf3fUnrfP311615drvdlCpVyppns9nM0aNHna758ccfd+j7s88+S7Gdu/vdvn37Oiy7a9cup5eNjIw0t99+u2nRooVp0aKFGTVqlNPLusLb+wB/Oi7s27fPoU3ZsmWd2tckuHr1qsPfUXBwsLl8+bLTywMAAAAA4Cnk+Gkjxyev8TRvZ3jOIscnxyfHH+X0sq7IDvuAjBw3XZXZxylfOw4CALIHrkAMAB4yZcoUh+knn3xSHTt29Fj/ISEh6tKlizV95coVffzxx+ku99577zmclfvQQw95rCZXVKpUSfXq1bOmv/rqq2RnMqYkPj5eTz31lMPPEm+HrOJr279t27YO01OnTpXdbk9zmaioKI0dO9aazp07twYNGpSsXZEiRRz6P336tL788kun6jLGaOTIkQ4/69mzp1PLuiowMFAff/yxPvroI+uWglu3blXTpk1dujJGdtWrVy+1b9/ems6VK5cmT57s8u0Vw8LC9O2331pXQkgQEBCgN954Qx06dJB08/ZX8+bNy7Q+XHHnnXc63JJs48aNLl8pO8GVK1f04YcfOvzswQcftJ7nzZtXnTt3tqYjIiL06aefOtX3t99+6zDdsmVL67nNZlOfPn2saWOMXnrpJaf63bVrl6ZNm2ZNh4SEqGvXrk4t66zEtUnSK6+8kuwKD6l55513tGrVKq1evVqrV69WmTJlPFpbAm/vA/zpuFC5cmWHW58dO3ZMkydPdrq2L7/8UvHx8db0nXfeqbCwMKeXBwAAAADAU8jx00aOfxN5jed4O8PzNnJ8cvwE5Pj+uQ9IKrOPU752HAQAZBPeGbcMADmL3W43hQsXdjg78eDBgx5fz+7du01AQIC1jpCQELNx48ZU2yc9azZ//vwmKirK7fVn9AzMWbNmOSxfsWJFs2PHjlTbx8TEmGHDhjksU7BgwWRnR2cVb2//xOLi4kzZsmUd+h87dmyq7S9fvmzat2/v0H748OGptl+8eLFD26CgoHTPuI+PjzfPP/+8w3J16tTJ0Bnlzvrhhx9MaGiow7ZeunRppq/XGZl15YLDhw+b/PnzO/T9xRdfOLVs4qsODBs2LM2269evt9oOGDDAo31kxGeffZbsyg3PPfecS1dGiIyMNHfddZdDH82bN0/2nl21apVDmzx58pgVK1ak2XfS/V2pUqVMXFycQ5tDhw45XBVBkvnPf/6TZr8nT5401apVc1hm4MCBqbZP3M6VKxfY7XZTs2ZNh+VHjhyZ7pUxZs+ebYKCgqxlihQpYqKjo51er7u8tQ/wp+PCxx9/7NA2d+7cZs2aNenWtWjRIhMSEuKw7A8//ODWawQAAAAAICPI8Z1Djk9ek1nI8cnxyfFTRo7vXVl5BWJjMv845UvHQQBA9sAAYgDwgPPnzzt8sC5atKj5+eef3X789ttvqa7rX//6l8O68ubNa7788kuHL9vnzp0zb731VrIvtO+8806GXmdGv0DZ7XZz2223OfSRK1cu8+677zqEiVeuXDG///67adiwYbJgYfLkyan2v3LlymTtPRXyJMjM7e9q/dOnT0/W/rHHHjOnTp2y2sTHx5uff/7Z1K1b16Fd5cqVzblz51Lt2263Jwsqg4ODzdChQ83Ro0cdwoczZ86YH3/80bRq1SrZ73bdunVObFXP2LRpkylRooS1/qCgIDNlypQsW39qMiN4jI+PN3fccYfVZ8J7LSwszOzbty/d5ROHhnPmzEmzbVxcnMmVK5eRZG677TaP9pERcXFx5s4770z2N1CuXDkzZswYs3379mRBn91uNxcvXjRr1641/fv3TxaS582b1+zcuTPF9fXv3z/Z+/vll182x44dc2h35swZ8+KLLybre9q0aSn2+/rrryd7DT169DDbtm1z+Du7ePGimTRpkgkPD3doGx4ebiIiIlLdTu4Gj8YY8+effybbj7Vv394sX77cXLx40aHtnj17zJAhQxxucSXJfPnlly6tMyO8tQ/wl+NCTExMsmVsNpsZMGCA+fvvvx0+E1y7ds2sX7/e3Hfffcnq6dChg0u30wQAAAAAwFPI8Z1Djk9ek5nI8cnxyfGTI8f37j4go8dNd45rmf05wZufQwAA2Q8DiAHAA/bs2ZPsi0FGHuXLl091XVeuXDGNGjVKtkyePHlMzZo1TeXKlU1wcHCy+V27ds3wlWA9cQbmkSNHTOnSpZPVFxQUZKpUqWKqV6/ucFZk4sfTTz+dZt9ZETxm5vZ3tX673W66du2a4rYqWbKkqV27drIz26WbV3/Ys2dPuq/12LFjya6OkPDInz+/qVOnjilatGiK8wMDA82MGTNc3bwZduTIEVO7dm2HWl599VWvDlbLjOBx/PjxVn8dOnQwn3/+uTXdrFmzdP82E4eGmzdvTnd91atXN5JMlSpVPNpHRl2+fDnZ9k38CA0NNRUrVjT169c3VapUcTizPekjX758ZvXq1amuKzo62tSvXz/FZcuVK2duvfXWZKFgwqN79+6p/v3Hxsaae+65J8XlihUrZurVq2eqVKmS4n4ld+7cZtmyZWluo8TtXQ0ejTHmww8/THWblS1b1tSuXdsUKlQoxfmPPfZYlv/teWMf4E/HhaNHj5py5cql2H9wcLCpXr26KV++fLIAOuFx6623eu3qQwAAAAAAkOM7jxzfc/WT1yRHjk+On/hBjk+O7+19gDcGEGf25wRvfg4BAGQ/DCB20/Hjx83q1avN6tWrzfHjx11ePj4+3vzzzz9m5cqVZuPGjWmePZqaa9eumR07dpgVK1aY7du3m6tXr7rcBwDP+P3331P9YubOI63g0Zibt75p2bKl0/099NBD5sqVKxl+nZ66hcuuXbtMjRo1nK4/MDDQjB8/Pt0vjlkRPBqTedvfnfqvXbtmunXr5nQtderUSfN2c0kdOnQo2RUM0nuEh4ene1uozHThwoVkV0/u3bu3uX79ulfq8XTwuGPHDuus+Pz585tjx46Z+Ph406ZNG2sdY8aMSbOPxKHh1q1b011nwq22ateu7dE+PCEmJsa8/fbbya4U4MqjY8eO5tChQ+mu6+LFi6mGhKk9+vfvn+5778aNG2bAgAEu9Vu8ePE0g9IEiZdxJ3g0xphvvvnG5MmTx6V99quvvuq1kMkb+wB/Oi6cOHHCtGvXzuW/s759+zJ4GAAy6Ny5c2bTpk1m5cqVZu/evfxDBwDSQYYPIClyfNeQ43uufvKa5MjxyfFdfZDjp40c333eGEBsTOZ/TvDW5xAA8HfZMcf3+wHEEyZMMJLMqFGjnGr/008/pXimTsOGDc2PP/6Y7vLx8fHmP//5T7IzQQMDA03Hjh2dOpP0woULZvDgwcnORs2XL58ZNGiQuXDhglOvBYDnzJkzx+0vvCk90gsejbl525///Oc/plKlSqn2c+utt5qZM2d67IxNTwWPxhhz/fp18+abb5oKFSqkWn9YWJh55JFHnLqVkzFZFzwakznb39367Xa7+eqrr6xgJ6VH5cqVzdtvv+1waxpnxcTEmI8++sg68zyt9+2YMWNMdHS0y+vwtJiYmGS355k+fbpXavFk8Hjjxg1z6623Wn1NmjTJmrd//34rHAoKCjJbtmxJtZ/EoeG8efPSXGdsbKwJCgoykkybNm082ocnnTx50owdO9bUrFnTqf1s8eLFzZNPPmnWr1/v0j7Sbreb77//PsXPg4kfrVq1cuqzYWKrVq0y7dq1s7ZVSo/SpUub119/3Vy6dMmpPhMv627waMzNf0IMGjTIFCxYMNXacuXKZXr16mW2bdvm9no8xRv7AH86LsTHx5v//ve/5s4770z1akMJ74kuXbqY33//3eV1AAD+Z+/evea+++5LdkvJsmXLmnHjxiW73asrHnvsMae/pzqbnQGAp5HhA/AUcnzXkeN7rn7ymuTI8cnx03uQ47uGHN893hpAbEzmf07wxucQAPBX2TnHtxljjPxYs2bNtG7dOo0aNUqvv/56mm3Hjx+v4cOHp9nm/fffT7VNfHy8HnzwQS1cuDDV5fPkyaMlS5aoVatWKc4/ffq0WrRooQMHDqTaR8WKFbVmzRoVL148zVoB5AzGGG3atEn//POPTp48qcDAQJUuXVp169ZVjRo1vF1euowx2rZtm/bs2aOIiAhdv35d4eHhqlixopo3b66QkBBvl5gmX9r+xhj9/fff2rBhgyIjIxUcHKwyZcqocuXKql+/vmw2W4b7P3TokDZv3qyTJ08qOjpaefPmVXh4uOrXr6/q1atneB2eZIzRe++9pxEjRkiSpk2bpv79+3u3qAx6+eWXNXbsWElSu3bt9Msvvzhs848++kjPPPOMJKlmzZravHmzcufOnayf6dOna8CAAZKkF154Qe+++26q61y7dq2aN2+erK0n+sgsFy5c0ObNm3X06FFdvHhR0dHRCg0NVcGCBa33a+nSpTP8fj158qTWrFmjiIgIXbhwQQUKFFCZMmXUokULhYeHu93vpUuXtGrVKp04cUJnz55Vnjx5FB4errp166p27dpe/TuLi4vThg0btG/fPkVGRiouLk6FCxdW9erV1ahRI4WFhXmttqS8tQ/wp+OCdPP9unbtWh05ckTnzp1TQECAChcurCpVqqhJkybKkyePB14JAPivVatW6Z577tHVq1dTbXP//fdrwYIFCgwMdLn/1q1b648//nCqrTPZGQBkBjJ8ADmBL+UF7iDH92wt5DX/Q45Pjk+O73nk+NlPZh+nfOk4CAA5UbbP8d0e2pwDTJ061enR18uXLzc2m81IMkWLFjUzZ840586dM+fOnTMzZswwRYoUsfr69ddfU+xj1KhRVpsGDRqYlStXmsuXL5tjx46ZN954wzobtHDhwiYyMjLZ8na73dxxxx1WHz179jQ7d+40V69eNTt27DA9evRwOFuOM4QAALh5ZZGQkBAzbdo0b5eSIatXr7Y+K+TNm9ccPnw4WZu4uDjTvHlz6/PAs88+m2Jfia86kD9/fhMVFZXqehPf+i7xVSk80QeQFXLKPgAA4H/OnDlj5U0BAQFm9OjR5tixY+by5ctmxYoVpn79+tZnrNdee82tdZQsWdJIMt26dTM///xzmg9nrygHAJ5Ehg8AgH/IKRkeOT7gnpyyDwAA+J+ckOP73QDiCxcumD/++MMMGDDABAcHOxU+2u1206RJEyOlfiuRzZs3W7epaNKkSbLg7/Tp0yYsLMxIMrfcckuKt6v4+OOPrXpeeOGFZPN//PFHa/6DDz6YbB12u91069bNavPTTz85uVUAAMjZ1qxZYxYvXuztMtwWHR1tKlasaB3jP//881Tb7tmzx4SEhFhtV6xYkaxN4tBQkrnnnnvMxYsXHdrExcWZV155xWrTsGFDh88enugDyCrZfR8AAPBPzz//vPU5asKECcnmX7x40bqVdFhYWIoD2dISHR1t9f/ZZ595qmwAyDAyfAAA/FN2z/DI8YGMye77AACAf8oJOX5A0isS52RNmjRRwYIF1apVK02bNk2xsbFOLbd9+3Zt2LBBktSlSxfVr18/WZsGDRqoS5cukqQNGzZo165dDvO/++47XblyRZL03HPPKV++fMn6GDx4sHWbjGnTpslutzvMnzRpkvX89ddfT3bbC5vNpjfeeMOanjJlilOvDwCAnK5Zs2bq2LGjt8tw27Bhw3Tw4EFJUps2bfT444+n2rZatWoaPXq0Nd2vXz9dvHgxxbb58+dXyZIltWTJEjVs2FCvvPKKZs+erTFjxqhly5YaM2aMJCl37tyaMmVKirfc8kQfQGbL7vsAAID/iY+P19SpUyVJ4eHhevLJJ5O1yZ8/v4YPHy5JunLlimbPnu3SOvbv3289r1q1agaqBQDPIcMHAMB/ZfcMjxwfyJjsvg8AAPifnJLj+9UA4sjISLeWW7x4sfW8c+fOqbZLPO/HH39MtY/7778/xeUDAgLUqVMnSdKZM2e0ceNGa96NGze0dOlSSVKlSpVUs2bNFPuoWbOmKlWqJElaunSp0wErAADwTT/++KP1D8jQ0FBNnjxZAQFpf4QbNmyYGjduLEk6duyYhg4dmmK7fPnyadGiRSpevLj279+vt99+Wz179tSrr76qdevWSZIKFy6sn376SfXq1cu0PgAAAOBo3bp1ioqKkiR16tRJgYGBKbZLnDElzaLSs2/fPus5A4gB+AoyfAAAkB2R4wMAAPifnJLj+9UA4r179+ratWvWY8+ePU4vl+Dee+9NtV3ieVu2bEmxjzp16qhs2bKp9pH4jKrEfZw4cUJXr1611pPaWX82m83qIzo62mEUOgAAyF7Onj2rRx991Jp+5513VLFixXSXCwoK0tSpUxUcHCxJ+uabbzR//vwU2zZs2FA7duzQiBEjVL16dYWGhipv3ryqV6+eRo4cqX/++Udt2rRJc32e6AMAAAD/42wWVbZsWdWtW1dS8iwqPQnBY548eVS6dGnZ7XadOHFCf/zxhzZs2GAFnwCQlcjwAQBAdkOODwAA4J9ySo4flOEespGQkJA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8vs/iAGIiIiIiIiIiIqJW4H/NDPhfM8Pta2UXzIK9pOIcl4iIiIiIiIiIiIiIiOq19zy+2NsFICIiIiIiIiIiIiIiIiIiIiIiIiIionOHA4iJiIiIiIiIiIiIiIiIiIiIiIiIiIjaEam3C0BERERERERERNQ0IsDRVu+PF3m7AERERERERERERERErYx5fF/WVj8ZIiIiIiIiIiIiIiIiIiIiIiIiIiIiagIOICYiIiIiIiIiIiIiIiIiIiIiIiIiImpHpN4uABERERERERERUZM4ANgl3i5F0zi8XQAiIiIiIiIiIiIiolbGPL5P4wzERERERERERERERERERERERERERERE7QgHEBMREREREREREREREREREREREREREbUjHEBMRERERERERERERERERERERERERETUjki9XQAiouaw6w1u14v8lBCJ2989ElaL+/aQSJUQidpfexA1xmpt5FiRtL9jhecNIiLPicRiyPz8XNY7HA5YDO7Pp+cztkcDtoWX2SXeLgERERERkcfYr0FERNQ6anVWt+vlKgnEYtE5Ls25w9ykENtDiO3RgG3hZczj+ywOIKYWpcn8EwXfPwn98X1QxnRFzPgliB5znUexVXt/QN7ahzHg+f2NvseircTu2+IxeEUhZIERLVXsVmHY/w8qPn0JpqMHIYvvhNDpCxE8aZ5Hsbp/fkH5B8+g40d/CdY7HA7UbP4a1d+8C3NhDiSBoVANHI3IxQ9AGhHTGtVoMebUvdCt+ACWjCOQJCVCNe8KqGZN9Si2dus26F59GxE/fiFYb9fpUTZovNuYyD83QBIT1exyt5aq0t04duBdaKsOwy8wEYnd5iC+y+XNirFa9Pj9q1FuYy+csQlK/+gWrEHLqcrfjuztL0Bbuh+q0M5IHLAICX0XNCvGatadJloEqdy/hUrf8moy/kLhN0/BkLsPytguiLp4CaLGLfQotnrPjyhY8zD6vpzW6Hss2krsW5KIAW/nQxbk2+dRAKgu34djGR+jpjoLqsAEJHSagfiOk5sVY7UY8Mf6S93GjrrsWyhVvnnuqC7bi2PpH6JGfQSqgEQkdJ6J+E6nP4+eKcZq0eOP7ye4jR01Zb3PtgUAqDVZOJ6/ETp9PvyUUYiLHYPY6AuaFeNwOFBavhNFJ7bAYCyDTOqPkJAe6NhhGhSKkFauUdNVV6XjeM5aaLW5UKliEJ94KWLj3X8/NjVGXZ2Bfbsew6BhzyEouEtLV6FFVR37CzlbnoS2aB9UEV2QOHwJ4gef/veoJzHVuX8j+7fHoS3aB4kiEOFdx6PLJU9BGRTXmtVpcf/urMHyFYU4lK5HcrIS18yLxtzZjR/rRqMNb7xdhI0/V+FEsQlxsQrMnBaBJTfGQalo+52ayReOxvhHH0XcgAGozD6Kne+8gz2ffNLo+7tNnIRr1q93WV+ZnY1XU3q0YknPDbZHA7YFERERtSfVR/9E7k+PQ1uwF6rIrogfvRRxIxZ5FFtx8AfkbHgAwx461MqlbB36v4+g7Pn1MB7Ig7xTFMIXX4TQqy/0KLbm5zSUPvktuv77lOt2/8lC2bPrYDyQB3GAEgFjeyL6kZmQxYa2dBVa1PE/y/HH45ko3luNsK4BGLK0MwYuSm70/Xa7A2kfH8fON3JQla1DQKwS3afFYuzjPaEMkgFw5lv2f56PHcuPojJLB78wOTpNiMJFz/RCUJxr570vMafuhe6t92HJOOzs17hqFlRXTPMotvb3bdC9+hYifvhSsN6u16Ns8Di3MZG//wBJjG/m8d2pzvoTxzfWnTuinOeO2As8PHcc+AHH1j+AoY+0zXMH+0NPrz1/r1Tl/43sv5+HtvQAVKGdkDhgMRL6Xd2smLbc56XR5aOgbBv0xlIoFaGICRuI6LB+zY6p0Rcgv/RPGGrLIRJJEKRKRIeYMfBThLVmdZql6sQO5Ox6DdqKdKiCk5HY62rEp8xpdozdZkF26ssoz/sdtboiBEX2Qbfh9yM4un8r1qb5jvypxoYnjiN/rxZRXf0w7uZ4jFoU2+j77XYHtn9cgi0rClGWbURwrBz9p0Zg2uPJ8AtyDnuq1Vpxa/DfbuNfzB+OsARlq9SlNTA3KcT2EGJ7NGBbELUMDiBu58yaUuy7r4/b12Im3IzYi5d6vC1t9k5kvjQZwb3Go9O1b0J79F/kfHADAJzxotlq0KBw/TOnfY/DZkXBt4/DYXN/15gvMWbuQeF9V0E18EJE3/48jOm7UfLSnQBwxkHENl0NKj9/1e1r2q3fo+T52xB82TyEz7sd1qoyVH7xOkw56Uha+TNEUlmL16UlmPcfQtX1d0JxwWAEPXYPLPsOouYh5+d9pkHEdq0O+pUfu33NVngCABD8wmOQJAgvKMRhIc0veCvRVBzEvq23IixmGHoMfQDq8v3I2PEEADQ6iNiTGKOuEADQ64Kn4BcQL4iXK30zMa0+sRt7vpmN8KQxSLn4ZahPpCJ90/8AoNFBxJ7EbHmtQ6P/pjIoEWOWNJ6Y8ybd0VQcfm4KgvtchOTFb0CbtQO5794IAGccRGw1aFD03bOnfY/DZkXh10+0ifMoAGgq07Fv290Iix6MHgOXQVN5CJl7ngeARgcRexJj1DvPHb2GPAQ/f+HAP7mPDhJ11utOhEUNQY+B90BTcQCZu52fd2ODiD2JOdkWQx9tM20BADXaXBzMeBMhwT3QtdNVqNHmICv7cwBodBCxJzHlFbtx5OiniIm6AB0SJsJsrkF+4Sbo9YUY0Pc+iMW+d1ekRp2FA3ufQWhYH3RLuR416iM4nP42ADQ6IPhsY6xWAzIPvgnA0Wr1aCnq/J3Y+/FkhHUZj5TpK6DO/xfp3zrPo40NIvYkpqZoL3Z/eAnCu0xAz5lvw2KsRu6fL2Pvx1Mw/JZ/IZYqzk0Fm2lfmhZXL87EhRcE45nHO2L3Pi3uefAYADQ6iPiJZ/Lw7foKLL0xDj26qXAgXYc33i5CZbUFTz3a8VwWv8UlDB2KhRs3ImfLFmy47VZ0GDECM9//AAAaTTCFde4Eo1qNzy+fLlhvra1t7eK2OrZHA19ri912O/bY3Z+D9c3eOhEREbUF5ppS7Hi6p9vXEi68BQljbmnytjXHd2L/yokI7T4B3a9cCc2xf3B49WIAOONgL6tRg9xNroNn2wrD7hwcn/UqAsb0RNzLC2BIzUbRbc5c9JkGEds0BpS/9IPb14xpeTg+/SX4j+uF+NeuhU2tR/lrPyNv1nJ02voIxArfzOMX7qzCqol/o9OEKExeOQAF/1Riw+I9ANDoIOI97+Vi4837MPD6ZIx+uAeqsnXY/mIWilKrcd2fYyCWiHDoy0Ksu3Y3BixOxoUP9oCupBbbnj2Ckv0a3LBzHCQy37w51bz/EKpuvB2KEUMQ9Oh9sOw7gJqHnwaAMw4itmt10L/9odvXbAVFAIDg5x+HJEGYjxOH+WYe3x1N7k4ceMt57ug2dyVqjv2Dw6uc544zDSK2GjU4/nPbPXewP/T02vP3ivrEbuxZO8vZf3XJy1AXpSL959sAoNFBxJ7EbHk1odF/UxmUiDFLD7ZwTVqG1lCEzONfITggGZ3iLoHWUIScop8AoNFBxJ7E6GvLcejYagT5J6Jj7MWwOSwortiFgzmfon/XGyCXBZybCp4Fdek+7P3xWoQljELKhU9BXbIH6X/cCwCNDiL2JMbhcCDtlyXQVR5Gcv8bIJbIUZjxJfb8eDVGzNkEv8B4t9v2tmM7a/DapANImRCK+W91Q84/Gnxy/REAaHQQ8V/vncCqpUdx4eJYTHkoCWXZRmx6qQC5u2pw7x8DIJaIUH7MmYdb/GkPRHQUDhYOjJS3bqVakK/lJr2N7SHE9mjga23BPD61ZRxA3M7Jg6Mx4IWWuag4sek1KGO7o/vt30AslSFy5DzYarUo+vElRI1eCJHI9ZEQxuIsHPv0NmhzdsJeq4NffC+32z782hXQ5qTCoi5ukbK2tuqv34E8sTPin/wYIqkMQRNmwW7QoXLNmwiaeJXbtjAX5KD0jfthzNgDh1EPeXJ31+1+/yFUA0Yh5u7lJ9dJo+Nx4pGFMB3LhLJb31atV1MZPl4DaccOCFnxAkQyKfymTYRdb4D+/c/hd8UUt+1hzc1HzZMvwZKWDofBAGnXTi7vseU7B8wqJoyGOMB37649VV7mKqiCktBvzMsQi2WI7XgZbBY9jqd/grjO0922hycxBq2zPaISx0EqaxvtkbdrJfzDuqD/5Z9BLJEhrtds2Mw65O58A/F95rtvCw9ihs77ySXObjPhwA83IDZl5rmoWpMUb3wNfnHd0HXZ1xBLZYgYNQ92oxbFG15C5Nhr3Z9HT2Th+Me3Q3e07jya4L4zKeuVWdBl74Klum2cRwEg/+haqAI7oO+IZyAWSxHb4RJYLQbkHVmNuOTL3LaHJzFGvTNJHxk3GlKZ6lxXq0nys9ZAFZiEviOfd9Yr6VJYrQbkHVmFuI7uz6OexBh1dW0RP7rNnDcAoLBoC/z8otGrx00QiyWIjhoKm82EgsJfEBM1wm17eBJTVPwHQoK7o3vXhmSuQhGG9MPvQG8oQmBA4zcneEth3o9Q+cehd/97IBZLERM7GlarEXm56xETd5H7tjjLmKOHP4bFoj1XVWqWvL9fh39kN/Rf8DXEEhliB8yD1aRF7p8vI26Q+9+jnsQU7f4E8oBo9L/6W4glzo7egKhe2PXeOKjzdyKs0+hzXdUmee/jYnTu6If33uoGmUyMGdMjoNfb8Pb7J3DlrEiX9jHW2vHF2jIs+18C7rjV2VEx6dIwGI12rP2mHE88nNymH/s28vY7UJF1BKtnz4LdasX+NWugCAzEhXff03hyqVMnVOVkI2/79nNb2HOA7dHA19pisFiMwY2MqVhhtaFtnKGJiIioOeRB0Rj+cEarbLtg66tQRXVHnxu+g1giQ8yQ+bCZtMjf/AJih1/n9jrKUJqFI2tvQc3xHbCZdPCPdZ/X93UVb/0KRZcYdFh1C0QyKULmjIBdW4vy139CyIJRbutuyi7BibtXwbg7B3adCYoU14E51au3QRIVhKTVt0Ikc3bDKXrEI/ey52HcdQz+o1xz/77g31ePIrx7IK78bgQkMjH6zu8Ak9aK7S8cwYDrkty2x9/PH0GPGXGY9v6gk+tiB4Vi1aV/I2dzKbpOjMHOFdnoeFEkpn/Q8J7gDip8efm/KD2oQdxA3xw0a/jkC2e/xhsvOvs1pk6EXa+H/oPP4DdzaiP9GnmoeeolWPYfcvZrdHHXr+HMxykmjIHYv+3k405VuOVVqKK7o/dNdeeOofNhrdUi79cXEDOi8XNH1le3oCa3bZ872B96eu35eyUv9S1n/9XMVXX9V3NgM2mRu/N1xPdd4D436UHM0Pk/u8TZbWYc2LAYsT2vOBdVa5ITFbugVIShe9JMiEUSRIb2hs1uRlH5DkSF9nXbHp7EnCjfCZnUHz2Tr4RY7PyeDQ/qjr1H3kFx5S4kxbif5d2b8vZ/AP+QTuh/6TvOHHS3y2G16JG77x3E9Zjtft/wIKb6xL+oLPgLo+b9Dr9AZ/42MnkC9v+yFOri3T47gPjX5QWI6e6Hpd/2glQmxvD50ajV2fDzi/kYeV2M2/b4+YV8DJgRgWvfb/gdlTQoEMsnHkDG5ir0nhiOshwjAGDA5RFQBrbdoVC+lpv0NraHENujga+1BfP41Jb55m291ObYrWZU7VmPiGGzIf7PLLiRI+ehtiQLhnz3s32KFSqE9LkEiZc/Av+Og9y+BwACu41E3MQ7ED5sdouXvaU5LGbotm9C4LjpghmBgybMgqUwB6acdLdxIqUf/AePQ8Q1d0PRzf1dl7LIOASOFd4JI4txDmay1xpaqAYty2G2oPa3v6C8bMLJZCkA+E2bCNvxfFgPH3UbJ/JTQjFqOAJuXQxpL/ePCrDlF0EcHgpxgD8cNhscDt+fGdFus6Cs4HfEJF0Csbhh/4jtOBkGbR501VlNjjFqCyFXhkEq84fD7vvtYbeZUZr9E2J6zDg5GAsAYnvOhqE6G9oy18dTeRoTmjDc5a8s+2f4h3VFlwsfav3KNYHdakb17g0IGyE8j4aPmofa4qMw5J3mPNr3YiRc8TD8Ow1sdPuB3Uci9rLbETZ8VouXvTXY7RaUndiG6MSLTiaAACC2wyUw6Aqg02Q3OcagK4JcEQqpTAWHoy0cKxaUFf2F6MTxwnolTYRBmw+d2vU86mmMQVdY1xZt47wBAHa7FRVV+xEVMUgwI3BU5FAYa8ug1xc2OUYhD0FkhPD3iFIZDgCw2UytUZ1msdstKC9LRVT0BYLPOSZ2NIyGE9Bpjzc7prx0J8pKtqNzN/ezwvsSu9WMsoz1iO47R/gd0X8+DBVZ0Ba7nkc9jbFbayFVBAneI1OFnXytLTCb7fh1czWmTg6H7D+zO82YFoFjubXIyHT9LVldbcHIEUEYPTJYsL5DggJmix1Wq++fMxojkcnQc/p0HFj7NezWhhl90lZ/gcju3RHbz/3v8fBOnVGZ45y1WSzxvVnJm4rt0YBt0RaJALu4bf6h7d6EQURE5we71YzyA+sQNfBKwfVO9JAFMJRlQVfUSD5KrkJ4yqXoOOkxBCY2ntf3ZXazFdqN+xA8c6ggbx185QiYs0tRe7DAbZzYT47A8b0Rdd90KPsnuX2Po9YCSaCfYLuSMOdAUbvJ0oK1aDlWsx2H151A7ysTBDMC913QAZVZOpTs17jEmA1WaPIM6Hyx8Ik2yWMiABFQdqgGABCU4Idec4SzZ4Z0dLaHRW9r6aq0CIfZgtotf0I56eJT+jUmedavccv1jfdrFBRCHB4GsX/b6dc4VWPnjpihC2Asy4KusPFzR1jKpUi+7DEEdmij5w72h55Wu/5esZlRenQjYlJmCvOMva+EoSob2jLXCb08jQlNHOHyV3b0J2ef1+iHW79yTWC321BVk4WI4BSIRQ15ksiQXqg1V8FQW9bkGKOpEkH+iYL8tkyqgkoZAUNteSvWqmnsNjPKcjcjussU4efc7XIYNMegrXS9SczTmMLMrxCZdBH8AhPgcDjgsNugUEVh6IxvENttust2fYHVbEfaugoMmRMF6X9+cwyfH43SLCMK9utcYkwGGyrzTOg5QXjTUbcxIRCJgKJDzrk9y3OMCIySQRkohd3maJPfscxNCrE9hNgeDdgWbRHz+L6MA4ipRVg0pXBYzVAlCO8I9Yt3zoRpqnSfbFOEJSB+8l2In3wXVI3cbQsA8ZctQ/zkuxDad2LLFbqVWKvL4bCYoUgSziIgT+7mfL3UdWAT4BwcHHblUoRduRSKju4TS3GPvY+QqdfA4XDAqq5AbdZ+VHz0PKRRCfBLaXzgoDfZK6sAiwXSLsJHXNcv206UuI2TxETBf/F8+C+eD2k317v0AcCaXwTI5ahafDtK+41F2eAJqF5yN6x1jwDzRebaSjjsFvgHdxas9w921tGod72r3NMYg64AYrEce7csxZYvR+CPtaOx7/fbT85M7GtM+jI4bGYEhAv394AI57FTW+Na7qbEAEBF7hYUHfgCfae+J0go+BKLuv48KpxB2C8hBQBgrmjkPBqegNipyxA7dRn8Eho/j8ZOuROxU5chpL/vn0cBwFxbBYfdgoAg4bnDPygZAFBrKG1yjFF/AiKxDHu3LcPW7y/GH+snIW37/TDoTrR8RVqA2VRfL+G50L+unm7bwsMYo64IIokce/+8A1u/G4c/1l2MtL/vgUHnw+dRSw0cDitUfsLHWPmrnMu1pqomx/TscQPiYi6Ew+GA2aKFVpeH43kboFCEISiwo8t2vc1s1sDhsMI/IFGwXlW3bKqtaFaMyVSNIxnvoHO3BVD5++ZMBf9l1pU6vyOihOfRgGjnebRW7Xoe9TQmpt+VMFRkIWfLM6itOQFdaQYO/7gMytBkhCRd0BrVaXEVlRaYLQ506+onWN+ti3O5qNh1kHxcrAJrPu2JgQMCYbHYUVpmxrbtGny6uhQTLw6DXN52LycDoqMhVShQliG8ua8s05l8D05MdBeGsE6dEJyQgNv27sUTBiPuO56HycuXQx7ge49GPBtsjwZsCyIiImpPzFpnPurUmR79Y5zXSLVV+W7jlKEJ6DDhbnSYcDf843q3ejlbg7WsBg6zFYoecYL1yu7OZUthpds4WXwYIm6biIjbJkKZ4v6R8sFXDIUpuxRlL26ApbgatYeLUHz/Gsg6REA1rEvLVqSF6EtrYTPbEdkrSLA+smcgAECT73rTqVgiwuJ/x6LnbGE75G2rABxAULzzenPO2uEYfFMnOBwO6MtNOLGnGlsfTkdwBxXih4W1Uo2ap6FfQ5hbk3au69cobqxfIxr+ixbAf9ECSLt2dvsea0EhIJeh6vrbUDpgNMqGXoTqpXf5dL/Gqcw1deeOOOG5QxVb1ydYfZpzx8V3o8PFbffcwf7Q02vP3ysn+68iTum/Cvegz+ssYgCg4tgWFB1cjb7TPvDdPi+rHg6HDSplpGC9nzICAGCy1DQ5plP8pUiKGSN4j81mgt5YCrkssMXq0FLMxgo47GYEhHYTrA8I7QoAqNW69k15GqOrOgK/wARkbnsMf346BFs+SEHq97OgKTvQGlVpETWlZljNDsT1Es7CH9vT+cTQqnzXPLVYIsID/wzA4NnCfePoNjUcDiAkXgHAOYBYphBj+cT9WOr/F/4X+jdWTD+I8mPGVqpNy2NuUojtIcT2aMC2IGpZvvmLktocs8aZLJH6hwjWSwOcs/ZZNK6Dm85X1irn3Y/iwBDBekmQ8444a3Xz73y062uQM9N58S1SqtDhjR8gksmbvd3WYC93JlrFwcLEozjEuWyvdB3o5SlbQSHsxaWQXjwWqqvnwF5SBt07n6DqqhsR8dOXEAf53kWiyehsD5lC2B4yhXNmP3Ota3t4GmPUFqLWUIKoDhchsftVMBlKkXvoQ+z65TpcMO07yOS+1R5mvfNYkSlDBOtlSmfS2GRwvfu4KTF2mwWHtzyIjsP+B6WPPqoHaDhPSvyFd8/KAuvPo651O5+Z6vZr6SnJntMeKx7GGHVFMBnLEBU/Bh26zEKtsRy5mZ9h9x83Y8Qlq3zuWDHVOs8B0lPKJZM7zwnu28KzGKO+CCZDqbMtus5GrbEMuZmfYvfWmzBi4hqfawsAMJudSUKpVCVYL5XWzVxjcX0AzNnG2GxG/Jt6LwBALFZgQJ+7fDIRazapAQBSmTDRJpM5L3LNZnWTYxwOBw6nv42AoI6IT5wIdXXrPCq3JZm0zt+jMr9TzqMq53nUrHP9PeppTHiX8eg26Tkc2XgPcn57HAAgksgx8o79kCraRlKhrNw5y1VwkHBfDgl1zmBRUXH6WbC2/KHGDUudTz3o1tUPzz7pe4Pqz0ZATAwAwFitFqw3VDrPnwHR0S4xIrEYoR07Iig+HluefBLlmZmIHzwYo++9FxFduuLTqVNavdythe3RgG1BRERE7Ym5pu6aSHXKNZF/3TWR9vzN61vLnDPqSkKE18eSMOc1nrXcdWCTpwLG9kLME7NR8tCXKHt2HQBAJJeiy46nIAlQNnm7rUlX4ny6jl+osJ9BFe4ckKMvdR3MI1VIkDg8XLCuaFcVvluwC0GJfuhxuXBwdq3GgpeifgQAyPwlWPT3WEh99MZUe0Vdv8YpfQzikOC615vRr5Ff5OzXmDAOqgVXwl5aBt27H6Nq3vWI2LjWJ/s1TlV/7pA2du6oOX/PHewPPb32/L3SaP+VX13/ld61f7gpMc4+rwfQcdjtUAb5bp+X2eqcRVYqEX7vSSXOm0ssVn2TYwL8Yk6J0+Pw8W/gcNgRE+Z7E26ZDM7Psb6vqp5M6TxOzEbXiUA8jTHpy1GQ/jmCIvuix6jHAZEY+Qc+xK51s3HB3N+gCnI/gM6bNCVmAIAqVJinDgh35qlrSs0uMTKFGJ2HC9sid1cNPrg6E2GJCgy43DnIvOxYLaoKTBg4IwLjb0tAdaEJG5/Nw/Oj9uGpjCFQhchctu1rmJsUYnsIsT0asC2IWpbvjYSgNknU2HTddrvzPzbffCxXaxCJTt8WDpvV/etnQeznj8Tl62CtLIF6w6couPNydHh9AxSdep45+FxrpD0c9rpHhlia3h7+C69CwNJFkA8ZcHKdfEh/VExdAMNX6xBww9VN3naraXRme2d7OOxu2sPDmKSUBejU5waERjc83ikkeiB2/DgHRUe/RXKvhU0qcutpZN9w1B0r7tqiCTEFaR/DatYhecjSphXznGnsWKk/d7Sf8yhwmu+Vus/a7nB9vKGnMR26XomOKdciNLL/ybeERvTDjs0LUZS7Acnd5ze94K2g0XrVnQPsDtf93tOYDt2uQsee1yE0suE8Gho5ADt+WYCiY+uR3GNBk8vdWs5ct7PYNxqJkUgU6Nd7GcxmNU6UbEPaoVfRv8/dCGgDs/ACABz13w9n8RjQU2JOFG5GjfoIhlzwKkQi3+zMc9HYbw7HaX6PehhTcvAbHP31MXQYcQvCu10Ca60GedvfQNqqWRh43Y9QBrufdcqXNPYTtf43mcV6+se5DRsciK8+T8Gx3FqsfO8EZsxJx4/f9YafXxt9xFMjj68TiZ37u0TmmkwWS6X4ZtF1KN6/HxVHjgAAsn7ZBG1JMWa88y4ShgxB4a5drVfm1sT2aMC2ICIionbl9LmU8zkf1dg1EuqukRyWs7imPoVm3S6UPvMdwm4cj8DxvWGrMaLy7c3IX/Amkr++E7J4H5x19wx5fJvFftpwY7UZfzyegdS3jiE40Q9X/XABZKdcL8oDpFj452hoT9Ri99vH8MmYP7Ho77GI7hPcyFa96AzX0LA2p19jHgJuXgT5kIZBbvLBA1AxfR4Ma79HwPXXNHnb50zjSQbnf87ncwf7Q8+g/e4bZ+6/clf3s48p2PeRs89r6C1NK+Y5ci7y+A6HHSWVe1FQtg12uw1dE6fB3891wJj3nSEH3YT+0PoYq7kGCv9oDJq6ChKpc+B1ROKF2LZ6NPLS3kPK6KeaWfaWd4avENgsp89T66st2PD4cfy+sghhiUrctqE35HW/OS65MwFTH05Ct9EhJ9/fdXQwHu+7C3++V4xJ93ZoiSq0LuYmhdgeQmyPBmwLohbVRkYDkK+ThTjv7rDqqgXrrXrnXdjykDiXmPOVJCwKAGDTCtvCplUDAKThzb9wEUmkUPUbgaCLZiDh+S8gkspRvf6TZm+3NYgjnXcV29XCGRscGueyODrSJcZTijEXCAYPA85HiEmSE2E9kt3k7bYmRd1jdiwmjWB9/bJC5doensZExI8SDB4GgIDgTlAFdYC2+mgLlL5lKQKcx4qlVnis1C8rAmKaHWO3W3E89Q0kDlgEiUw486ivkYU4zw315816Vp1zWRYae87L5E3yulmlLac8xspSN5OsQhne5JiI2OGCwcMA4B+UDFVAAnSanOYXvoXJ68pdX496J+vlF9HkmIjYEYLBw0BdWwQmQqf2zfOovG4WZatV+OjM+tkHFHLXTqezjRGJJAgJ7oqoyCHo0/NWiEVSnCj5s2Uq0ILkCudsAxaLTrDeUjdTQ/0xcbYxtcZyZB/5FJ27LYBUqoLVaoTd5rzr324zw2r1zcd9KQKd3wEW4ynnUYNzWRnk+nvUkxi7zYKM75YgbuAC9Jj2GiJ7XIbY/ldh8OJfYNKVIvePF1u8Lq0hKsKZLFFrhAnp+uXoqNM/zSI0VIYLhgdjwVXRePuNrsjKNmLz1urTxvgybYlzRhxVmHBGHFWY87ipOeH66ECb2YyDa9eeTCzVy9ywAQAQ06dPaxT1nGB7NGBbtE0iu6RN/hEREXmbPKjumsggvCaqX1YEn795fWmUMxdgqxbOgGitW5bFhDRpuw6LFUX/+wShcy9A3IvzEXhpP4TMHo7kdXfDWqpB+Ws/NavcrSUgxjnTsLFKOOtf/XJgXOMzJ2f9VIy3em3GrrePYfgdXbBk/wRE93bNz0ikYiSPjkSfuYmY//NISORi7Frpe7k4ABBH1PVraBrp14hyzcd5SjH6AsHgYaC+X6MDrFm+mY871clzxym57Ppl+Xl87mB/6Om15+8VhX9d/5XxLPq8zjLGbrfi+M420udV9wQ8q02YS7ZanTPey6Wus62fTYzRVImDOZ8ht3gzgvwT0b/rYkSEpLRcBVpQfT+upe7pgPWsdcvKuv2gKTFyvzBEJF54cvAw4HwyZUj0IGgrM1ug9C0vKMaZh9ZXCfPU+irngPmQuMbz1Ad+qsSjvXfhj3dOYMLtCXgsbTDiezc8IbDPpHDB4GEAiEvxR3Q3FQoP6tAWMDcpxPYQYns0YFu0Td7OxzOP3zgOIKYWIQuKgkgig6HwkGB9/bIi3PdnZWsp0pAIQCqDOfewYL2pblkW1bTZC226GuQuGg3tNmGCUaxUQRoVD7tO00ikd4nDQgGZFNajwkSgNcu5LIlp2oBqu06P2t//hq280uU1kUQCUYC/myjvkyvDIBJLoVML26N+oJ5S5doensRYLXqUF/4Fk5vH3IhEEkjlvtceclUkRGIZtBXCC1hd3bIy0PVYOduYsqMbYdKVIr73vJYsequQBTvPo8aCdMH6+mVFuO89Zqg1yZWhEImk0GtyBet1dcvuj5Uzx1gtBpSf2A5Treu5AyIJpFIfPFYUoRCJpdBrjgnW6+qWlW7uqPckxmrRo/zE3zAZ3bWFGFKZ77UFAMhkgRCJJNAbhBd+9csKRWiTYqxWI3btexIVlWmC90gk8pOv+xq5PMi5z+sKBOv12nwAgNLdQHsPYozGUtjtJhzJeBfbtl6NbVuvxoF9zwIA9u1+FP/8eUNrVKfZ5P7O86iuVHge1dYtK0Ncf496EmM1qmGt1SA4cajgPVJlEAKiUmCsPt6CtWg94eEyyGQiHMkS7stHspwD6+NiXROz27ZrMP6y/SguFj6qtnNH52MDa2qaPiOXt+nLymA1mxHdq7dgff2yprDQJSYkKQndL5sMsVT4IB+7zdkOtTVNf8Sxt7E9GrAtiIiIqD2RBzqvifQnhHn9+mVF6Pmbj5JEBkEkk6A2U/j7zlS3LEto2izBNo0B9hoj/AZ1Ev57QX5QdI+DOc81f+sL/KOUEMtEKDsk/O1avxyc6H6gWua6IqyZ+g/8oxRYeuhiXPpyXyiDGmb7qtVY8Fbvzcj8vkgQJ1dJEdxBhVq1b85GKg4Lc9+vUbcsiW1iv4Zej9rft8FW7iaPLxZD5O+b+bhTNXruKHYuK8PO33MH+0NPrz1/r8j9G+m/Kq/rvwpyl5s8u5iyrI0w6UoQ38e3nqLojkziD5FIDENtuWC9weRcVsjdDCD2MKbWVI1Dx1ah1lyNlOQ56JE0C0o3/QK+Qu4XDpFYBl1VlmC9ttI5gE0Z4Dqw3tMYhSoaNqvJJd5ut0Dio308QVFySGQiFB0S3sRVvxya6P6mpX3ryrFi2kEERcnwxMEhmPNyF/gFNeTiarVW7P+xApoS1/YQS0TwC2wbD2dnblKI7SHE9mjAtiBqWRxATC1CLJUjbPDlqNj5NezWhoRPxb9fQhnVGf5JA04TfX4RyeQIHDUJNX+sh+M/baHd8h1kcclQdGnaXSti/0BYy4uh3/mbYL21shTm/KNQdO7VrHK3FpFcBuWEMaj96Tc4LA13Ehp//BWSDgmQ9uzWxO3Kobn3CehWvC9Ybz2WB2vOccj7++bdQWKJDFGJF6E071fY//PooZLjm+AXkIjAsB5NihGL5Tj0z8PI2f+OIFavOQ69JhfBEX1br1JNJJbIEd11MkoOfy94rFdxxrfwC+mIoGjXMp9tTGHaJwhLHgNloO/P3iuWyhE6ZDoq/z3lPLr9SyiiO0OV3N97hfMCsViGqPjRKC3cInh8U0nBZvj5xyMwpGuTYsQSGdJ3PY1j6R8JYvXafBi0eQgO971zqVgiQ1T8GJQW/CasV/6v8AuIR2Co63nUkxixWI70nU/iWLrwPKqvyYOhJg/B4b1P3axPEIuliAjrj/KKPbDbGwYvlpXvglIZiQB/1yS0JzESiRImkxqV1cLktsmsgcFYggB/30v+i8UyREYNRVnJdsHnXFryN/z8YhAQ2LFJMYGBHTFgyFOCv649FgEAuvdcgn6DHm79yjWBWCpHVM/LUXJgreA7oiRtDfzCOiMw1vX3qCcxMv8IyPwjUZ27TRBrMVRBW3IIAdG+d95wRy4X49KLQ/HDT5Ww/OfRs+t+qERSBwV693RNKIeFSpF11IgdqVrB+u07nDeu9ezh2zOdnI7NYkHGunXoM2e2IFnU96q5qMzORvG+fS4x/hERuGb9evS+4grB+p7TpsFus6Fo9+5WL3drYXs0YFsQERFReyKWyhHZbwZK934lvCba/QX8IjojMOH8zeuL5VIEThkIzXe7BHlr9dc7Ie8YBWXfpj3iWhIeCElEIPT/CAf8WKt1MGUUQZnStAlGWptULkbKjHgc+qoAtv9cMx78ogChnf0RMyDEJcZmsWPjzWlIGB6Oxf+MRUQ318FgiiApagqNOPpTiWC9ttiI8kwtovu5btcXiOQyKMePRe3Pp/Zr/OLs10jp3rTtyuTQ3P84dG+e0q+RmwfrMd/t1zhV/bmjbI/w3FG66wv4RXZGwPl87mB/6Gm16+8ViRzR3aagJPO7U/qvvj59n9dZxBSmfYyw5LFto89LLEFYUHdUaDJhdzTk5CvU6VDKQ+CvdJ2R2dOY4yW/w263oW+XRQgN7Nz6lWkmsUSOqI6XoCT7R+Fxkb0BfkFJCIxwzS97GhPV8WJU5G2FpVZ98j0mfRnUxbsQHN2/1erUHFK5GANmRGDX2jJY//ObY+eaMkR2VqLDgACXGKvFjlVLj6LT8CDcv30gYrq55qWlCjE+vOYw1j92XLC+5IgBxZl6dB4R1OJ1aQ3MTQqxPYTYHg3YFkQtq23cZkNtQtzEO3Do2YtwZMUcRAybg5qs7Sjfvhqdr38fIrEYFl0VdMdSoYzuAr/oLt4ubqsKnb0EBXfMwInHFyNw3HQYD6ai5rdvEHPPcojEYthqqlF7eB9k8R0hj3cd3OOOSCRCyOWLUPXF6xDJFVD1uwA2TRWqvnkXkuAwhEy7tpVr1XSq665C1YKlUP/vASgvmwDznv2o3bAJQc88BJFYDLtaA8uBDEiSEiBN8uzuY5FcBv8brobulZUQSSSQjxgMW3EZ9B+ugqx3DyinXtLKtWq6pJQF2P3r9Tjw1z2ITroU6vJ9KM7diJ7DH4NIJIbFpIGm8hBUgYlQBXbwKEYkESO513XI3vcGRGIJwmKGwmQoxfGMTxEU3hOxyZO8XGv3koYsxa41U5G2fiFiesyAunAHijPWotfENyASiWE2VkNTvAeq0E7wD+3kUUw9q6kGVQX/oMuFD3qremctdvIdyHhyPI4uvxLhI2ZDe+QfVP79BTre9B5EYjGsuirosndBGdMZypjz+zwKAB26zsGeP/+HA/8+gpjEi6CuOICS/F+RMuh+57FiroGmKhOqgHioAhI8ihGJxEjuvgDZh95xHitRg1BrKENe1hoEhnZHTIeLvVxr9zp0uwp7fl+KA/88iJjE8c565W1CyuAHG84bVRlQBSRAFZjoUYxIIkZyj6uRfXAlRCIJwqKHoNZQirwjqxEY2gMxHXz3PJoQPx5pB19FxpH3EBkxCDU1OSgrT0W3Llc728Oih1Z3HH7KSPj5RXkUAwDxsWOQX/gLxGIZQoK6wmLRofDEFsikAYiLudCbVW5UQtIUpO1+DOn7X0ZUzEioqw+jtPgv9Oh1c11baFGjyYafKgYqVaxHMVKZP0JChY97c9QlbAMCkxEU7Lvnn6RRt2P3++Oxf/WViOk7G9XHt6M47Qv0usJ5HrUYqqAp2AVVeGeoIrp4FAMAXS5+HJnrboFIJEJ4t0thNVYj/9+VEEGEDhfc6s0qn5Ubr4vF7PkZuOm2o5h6WTh27dHiu/UVeOnZThCLRahWW5F2QIfkJCU6JinRM0WFIYMC8fATuSgqNqFzRz8czTHi3Q9OYMJFoRjQ3zWZ25Zsf/01XL9lK+Z9tRYHvl6L5JEjMWD+Anx3w/VwOBzwCw1FwpChqMzJRlVODor27EHWpk2YvvJthCQlo/JoFuIGDMTIO+7A9tdeQ/Xx496uUrOwPRqwLYiIiKg9SbxoGfa9NgaHPpiFqIFXQnPsb5TuWoUe8z90Xkfpq1CTlwq/yC5QRfru9WBTRNxyCXInv4D8a1YieOZQGHYchWbtv4hfcZ0zF1etg3FPLuSdoqDo5NmMsyKRCNEPzcCJOz8DREDghD6wqfWofG8rIALCb5rQyrVquhHLuuLjMX9i7awd6HVlAvL/rsSBVfmY9uEgiMUiGKrMKEqtQliXAIR3CUDhjiroSmrR79oOyPvLdUbd6D7BCIr3w9BbO2Pbs4chVUqQNCYChgozdiw/ClWEHIOXeNY/4g2qhfNQdc0SqO+4H8pJdf0aP/yMoKcfbujXOJjhHFB8Nv0a118D3atvOfs1hg+BraQU+o9WQdYrBcopl7ZyrVpOwvhlSFs+Bunvz0LUoCuhzv4bpamr0GPBf84dx+vOHVHn17mD/aGn156/V5KG3oJdqycj7ftrEJMy09l/lb4WvSataLzP6wwx9aymGlTlb0eX0Q95q3pnLS5iCA4dW40jed8jIiQFNfoClKvT0Tn+MohEIlisRuiMJ6CUh8JPEeZRjMNhR7X2KIL9k2E0VcBoEn7/yGWB8FdGeaO6p5XU73rsXn8l9v96M2I6T0V1yS4UZ32PXmNfdObxa9XQlKVBFZwMVXCyRzEAEJ8yF/kHP0XqutlI6nc94HDgeNq7kCqCkNzvei/W+PQuuTMRL47dh3dmp2PInCgc/VuDHatKsfCD7hCLRdBVWXA8tQaRXfwQ3UWFYztqoCkxY8Q10Tj6l9ple/F9AhAar8Ck+zrg2weOQSIVocf4UFQXmPDLy/lIHhyIYfOa9vQAb2BuUojtIcT2aMC2IGo5nIGYWkxgl2Hoec9G2PRqHPt4KXQ5qeh8wweIHnMdAMBQcACZL01B+fbVXi5p6/NLGYSEF9bApqtB6fJ7Yczci5h7XkPwpHkAANOxDBTefxVqNn9zVtuNuOYuRCx+AIY9f6H4uVtR9eUK+PUegqQ3f4IkILg1qtIi5P16I/SD5bBrdah57AVYDqQj6NmHoZo1FQBgOZKN6hvuhHHDprParv8NVyPw4WUw/bsb6rsfh+GLb6C8bAJCP3kTIomkNarSIoIj+mDARW/BYtYiM/UZaCoOoeeIxxHf5XIAgLb6KPZtvRXFx37yOAYAknsuRPfB96KqJBWHtj+EgiNfITrpEgya8C5EYt9sj5C4wRg0+2tYazXI+HUZNMV70HvSCiT0XQAA0JWnY+83c1CcvtbjmHoVx/+Aw25BWOIF57ROzRHQdSh63P8jbAYNcj+8FbrsVHRa8j6ixi0EABjyDuLI81NRse0L7xb0HAkO74X+o16G1aJF5t6XoanKQM/B9yO+42QAgFadg7S/70Zx3q8exwBAUvd56Nb/dlSV7cGhnU+iMOc7RCdchEGjX4NI5JvHSnB4L/QfvdxZrz0vQlOVjp5DHkJ8J+d5VKvJQdq2ZSjO+8XjGABI6rEA3QYsQ1XZbhza+RgKs79FdOIEDBq7wmfPGwAQFNgRfXvdBqvViKM5a1CjzUX3LlcjNtp5vOsNRTiY8SZKy1M9jgGApMTJ6Jg0DdXqTBw++gkKin5FcFBnDOh7L6RS35xpNTikG/oOfAgWqwFHMt5DjeYoevRaitj48QAAnTYPB/Y+g9ITf3kc05aFdBiGgddthLVWjYx1t0BTkIpeV7yP+MHO36PakoPY+8kUnEj7wuMYAEgYegP6zvsSutIMpH97I3K2PA3/yBQMW7odymDfnD3KnQH9A/H5Rz1QU2PFg4/mIm2/Di8/1wlzZzuT6oeP6HHN4sP4fr3zsYAikQjvvdUNl00Mw6o1pbjtrqNY/0MFblgUi5Wvd4VIJPJmdZqtMDUVn06eDGVICKa/tRIJQ4fi2+sXY88nnwAAYvr0xcKNG9F/XsNjIb+cPw97PvkYw5YswaxPPkW3SRPx0913YdP993mpFi2H7dGAbdH2iBySNvlHRETkC4KTh6Hf0k2wGtU48tUS1BzfiR7zP0LcCOeTaHRFB7B/5SSUpK7ycklbnmpwZyR9sww2jQEnln0Gw+5jiH/zOoRe7byJuPZQIfJmLYf6q3/ParuhC8cg8dOlMB0+gaLbPkbZCxug6B6LTlsehizOdx+xnjAsDAs2jUKt2oIfl+xD0c4qTP9oEAYuSgYAlB7QYPWk7TiwKh8AoCupBQBsfyELqydtd/nL2VwKABjzaArGP9MLOZtL8f3Vu7D9hSNIHBmO63eMg1+I3Ct19YS8X2+Evv867DVa1Dxe16/xzCNQXTENAGDJykb1jbfD+MPPZ7Vd/+uvQeBDd8G0YxfU9z4KwxdfQzlpAkI/fsun+zVOFdxxGPreugkWgxpH1tSdO67+CLEXNJw7Drw1CaXn4bmD/aGn156/V0LiBmPQnG9gNWmQ8csyaE7sRu/L3kRCv6sBALqyQ9i7dhaKD33lcUy9itzf6/q8Rp7TOjVHoCoePZOvhM1ei2NFm6AzFKNz/GREh/UDABhqy5B5fC3K1ekex1httXA47FDrjiHz+FqXvxP/6RPwJSHRAzBw8qewmmqQ8ddD0JSmode4FxGfMgcAoK3MxN6NC3Ei63uPYwBAoYrE0JnfQxWcjOydLyFn16sIiuqL4bM2QCp3fTKAr+g0LAh3/NwXBrUVn9+chWOpNVj4YXeMWuScBKXwgA6vXXYQO1Y5f0vUlJgBAJteLMBrlx10+cvYXAUAmHhvIq56vQsyt1bjgwWZ+H1lEQbPicJdv/WHWNJ2ctnMTQqxPYTYHg3YFm2Pt/PxzOM3TuRwOBzeLgSdewkJCSgqKoI8NB6D38jzdnF8QmV8pbeL4FM08bneLoLP6POUwttF8CniuCRvF8GnqAe27dkXW1LgNzu9XQTfIuaDHv7LUpTm7SL4DLF/pLeL4FNk46Z7uwg+48NZvjnTtbe8nbLL20Ug8nkrrDZoAcTHx6OwsNDbxTmn6vMa4ugoxGz59cwBPqhk/CWwl5a1y8+PiIg8U/99pwiJx8in+V0BAGULPvZ2EXzKrOCN3i6Cz3jnMDv9/yvl9yHeLoLPMAXavF0En6LUtI9BEJ6SFWq8XQSfod34greL4FMCRt3k7SL4lDlvLfR2EXzGMfk2bxeBiNoY5vGZx/d1nIGYiIiIiIiIiIiIiIiIiIiIiIiIiIioHeEAYiIiIiIiIiIiIiIiIiIiIiIiIiIionaEz9YmIiIiIiIiIqK2yQHA3kbvj3d4uwBERERERERERERERK2MeXyf1kY/GSIiIiIiIiIiIiIiIiIiIiIiIiIiImoKDiAmIiIiIiIiIiIiIiIiIiIiIiIiIiJqR6TeLgAREREREREREVHTiCCyS7xdiCYSebsAREREREREREREREStjHl8X8YZiImIiIiIiIiIiIiIiIiIiIiIiIiIiNoRDiAmIiIiIiIiIiIiIiIiIiIiIiIiIiJqRziAmIiIiIiIiIiIiIiIiIiIiIiIiIiIqB2RersARERERERERERETSECILJJvF2MJhF5uwBERERERERERERERK2MeXzfxhmIiYiIiIiIiIiIiIiIiIiIiIiIiIiI2hEOICYiIiIiIiIiIiIiIiIiIiIiIiIiImpHOICYiIiIiIiIiIiIiIiIiIiIiIiIiIioHZF6uwBERERERERERERNJbbz/ngiIiIiIiIiIiIiIl/FPL7v4gDids6sKcW++/q4fS1mws2IvXjpOS4REREREREREdXbbbdjj93h9jX9OS4LnT3t2lXQfrPa7Wu2yopzXBoiImqrzDWl2PF0T7evJVx4CxLG3HKOS0RERERERERE9ZjHb9vaex6fA4jbOXlwNAa8cNDbxSAiIiIiIiIiNwaLxRjcyI35K6w2aM9tcegsBc5ZgMA5C9y+VjT5QtjKSs9xiYiIqC2SB0Vj+MMZ3i4GEREREREREbnBPH7b1t7z+JwbmoiIiIiIiIiIiIiIiIiIiIiIiIiIqB3hDMRERERERERERNQ2OUQQ2SXeLkXTOETeLgERERERERERERERUetiHt+ncQZiIiIiIiIiIiIiIiIiIiIiIiIiIiKidoQDiImIiIiIiIiIiIiIiIiIiIiIiIiIiNoRDiAmIiIiIiIiIiIiIiIiIiIiIiIiIiJqR6TeLgAREREREREREVFTiey8P56IiIiIiIiIiIiIyFcxj++7+MkQERERERERERERERERERERERERERG1IxxATERERERERERERERERERERERERERE1I5IvV0AIiIiIiIiIiKiphLbJd4uAhERERERERERERERNYJ5fN/FGYiJiIiIiIiIiIiIiIiIiIiIiIiIiIjaEQ4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiakek3i4AeZdDBNhkdm8XwyfIBm70dhF8ijJznLeL4DMsFyZ7uwg+Rb7nmLeL4FOC0+XeLoLvCAz3dgl8i0zh7RL4FEX4JG8XwXdERHi7BD7lySUjvF0EnyGak+HtIhARERERkQ+yymw4PqDA28XwCRYjr6n/6+MjL3q7CD5DFKD2dhHIRwWm67xdBJ9ijQ3ydhF8iiNQ5e0i+IzAaQ94uwg+xRYR4O0i+JRFX2u9XQSf8bC3C0BERNTCOICYiIiIiIiIiIjaLJGdD9giIiIiIiIiIiIiIvJVzOP7Ln4yRERERERERERERERERERERERERERE7QgHEBMREREREREREREREREREREREREREbUjHEBMRERERERERERERERERERERERERETUjki9XQAiIiIiIiIiIqKmEDlEENsk3i5Gk4gcIm8XgYiIiIiIiIiIiIioVTGP79s4AzEREREREREREREREREREREREREREVE7wgHERERERERERERERERERERERERERERE7QgHEBMREREREREREREREREREREREREREbUjUm8XgIiIiIiIiIiIqKnEdt4fT0RERERERERERETkq5jH9138ZIiIiIiIiIiIiIiIiIiIiIiIiIiIiNoRDiAmIiIiIiIiIiIiIiIiIiIiIiIiIiJqRziAmIiIiIiIiIiIiIiIiIiIiIiIiIiIqB2RersA5F0WdSn239XX7WvRl9yMmEtvPsclIiIiIiIiIqJ6u+127LE73L6mP8dl8VUiO++PJyKi85utqhwF8y9y+1rQzGsRfMW157hERERERERERFSPefwzYx7fd3EAcTsnC4lGv1cOeLsYREREREREROTGYLEYgxvJq62w2qA9t8UhIiIiL5CERSJx9VZvF4OIiIiIiIiI3GAen9oyDu0mIiIiIiIiIiIiIiIiIiIiIiIiIiJqRziAmIiIiIiIiIiIiIiIiIiIiIiIiIiIqB2RersARERERERERERETSW2SbxdBCIiIiIiIiIiIiIiagTz+L6LMxATERERERERERERERERERERERERERG1IxxATERERERERERERERERERERERERERE1I5IvV0AIiIiIiIiIiKiJnGIILa30fvjHSJvl4CIiIiIiIiIiIiIqHUxj+/T2ugnQ0RERERERERERERERERERERERERERE3BAcRERERERERERERERERERERERERERETtCAcQExERERERERERERERERERERERERERtSNSbxeAiIiIiIiIqC0QicWQ+fm5rHc4HLAYDF4okXexPRqwLbxHBEBsa5v3x4u8XQAiIiIi8gq73v01gshPCZG4bf62JSIi8gW6Wpvb9Sq5GGLx+ZuJYW5SiO0hxPZowLbwHubxfRsHEFOLqsn4C4XfPAVD7j4oY7sg6uIliBq30KPY6j0/omDNw+j7clqj77FoK7FvSSIGvJ0PWVBEyxS6lej/PoKy59fDeCAP8k5RCF98EUKvvtCj2Jqf01D65Lfo+u9Trtv9Jwtlz66D8UAexAFKBIztiehHZkIWG9rSVWhRtXt3QP3h6zAdSYcsMRmBMxcgcOocj2IN235D9TsvIX71L6f/N9JSUbJ0LmI/XAdFSt+WKPY5o876E8d/eBza/L3wi+qK+LFLETtykUexFft/wLF1D2DoY4dauZSto+rETuTseR3ainSogpOR2HMB4nvMbnaM3W5BduorKC/4A7W6EwiK6I1uw+5DcFS/VqxN81XlbUPOn89CW7IfqrDOSBx8A+L7X9PsmOr87cj+42loS/ZDIg9AeKeL0GXcY1AGxrZmdZqtqmQXcva9BW1lBlRBSUjsMRfx3a5odozdbkH2ntdRXvhXw/4xeBmCI3333MFjRajqxA7k7HqtoW69rkZ8yum/VzyJsdssyE59GeV5v6NWV4SgyD7oNvx+BEf3b8XaNA/PG0J7t2vxwfMncGS/AYmdFbhicRSmLmj8d6Pd7sCPqyuw9t0yFOaaEBEtw6hJIbjhvjj4B0lOvi/tHy3ee865XVWgGEPHBmHJw/GIjJWfi2o12b9VNrx6zIJDWjs6+olxTaIUc+M9vwxckWvBilwLsi5SCdbvrLbhxWwLDuvsUIiBISES3N9Vho6qtnXxn3zhaIx/9FHEDRiAyuyj2PnOO9jzySeNvr/bxEm4Zv16l/WV2dl4NaVHK5b03GB7NGBbEBER0fnMuG8Hqj9cDlPWIcgSkhE08xoETbnSo1j935tR9c4LSFz1m2C9w+GAbtO30Hz1ISwFxyAOCoHf4FEIu/EeSCNjWqMaLaL2n0OoeWUNzAdzIO0Yh4CFkxBw1cUexRp/TYX62c8Q+8ebgvV2vbHxIJEIYpWyOUVuVbV7d0L9/usw1+fxr1iAgGmnzz/VM2zbAvXKlxC3ZtPp/419u1B681WI+fg7n8/jm3btge6td2HJPAxpUgeo5s6GauZ0j2Jrf/8L2tfeROT6tYL1dr0epUNHu42J2vITJDHRzS73uVKd9SeOb3wc2oK9UEV1RfzopYi9wMN+jQM/4Nj6BzD0kTbar5H/N7L/fh7a0gNQhXZC4oDFSOh3dbNirGbdaaJFkMr9W6j0Las6+08c+/kJaAv3QhXZFQmjbkbc8NPvB57EVKRvxPHNz0F34iAUwbEI63EJukx7ERKZ755DAaAq9y/kbHkK2hP7oArvgsRhSxA/eGGzY6qP/43s356A9sQ+SBSBCO8yHl0ufhLKoLjWq0wLqMr7G9nbnnPmoUM7I3HQ9Ujof4ZjxYOY6vx/cPSvZxpy1x3HoevYR306d12d/SeO/fIEagr3QhXh3O/jh3lwrJwhpiJ9I3J/ew664oNQBMUivMcl6DLV94+VPzO0eOLrE9iba0DXWCVuviQSi8Y1nsc3mOx45vtifLOjGgUVZiRGyDF/VBjunRYDpdyZk9YabQhemOY2Pn9lHySE+3Yu/7+YmxRiewixPRqwLYhaBgcQU4vRHU3F4eemILjPRUhe/Aa0WTuQ++6NAHDGQcRWgwZF3z172vc4bFYUfv0EHDZrSxW51Rh25+D4rFcRMKYn4l5eAENqNopu+xgAzjiI2KYxoPylH9y+ZkzLw/HpL8F/XC/Ev3YtbGo9yl/7GXmzlqPT1kcgVshavC4twZS+D6V3LoRyyEiE3/MkTAf2oPLZ+wDgjIOI7boaqD9eccZ/w67XouLJuwCHo0XKfC7V5O7EgTcmIrTHBHSbtxKanH9w5LPFAHDGQcRWowZ5P7kONG8r1GVp2PvzdQiLH4mUUU9CXboX6X/dDwCNDoz0JMbhcCDt16XQVR1Gct/rIZbIUXj4K+z56VqMmPUT/AJ8M6GiLtqFvV/MQFjHcUiZtBzqwh1I/2EpADQ6GNCTmJriNOz+fArCO12EnpPfgKVWjdx/lmPvFzMwfPGfEEsV56aCZ0ldvh97f70RYXEjkDLiEajL0pC+/REAaHQQsScxDocDaVtvh67qCJJ7X+fcP7K+wZ5fb8CI6d/75P7BY0VIXboPe3+8FmEJo5By4VNQl+xB+h/3AkCjg4g9iXE4HEj7ZQl0lYeR3P8GZ3tkfIk9P16NEXM2wS8w/txU8CzwvCF0aLcOd87KwuAxQbjn5Q44mKrDM7cdB4BGBxGv+6QcL96Vj2nXROC6u2NReMyEVW+UIGOPHm9v7A6JRITD+/W49fIsDB0bhPtfS4JWbcXnr5fgztlH8dGWFMgVvjlodq/GhgX7TLgwTIxnesixR23H3RlmAPBoEPHBGjteybFAfkr1DuvsmL3bhGGhYjzVQw6DDfgg34IpO2ux9QI/RCvaxr24CUOHYuHGjcjZsgUbbrsVHUaMwMz3PwCARhNMYZ07wahW4/PLhR3G1tra1i5uq2N7NGBbUEuq+PE9VG583+1rFnXZOS4NERERUJu+DyXLrobfkFGIuOtpmA7tQcVz9wDAGQcR23U1UH/yhtvX9L9tQPnTyxA4ZS5CrrkFtspyqD9/EyXZmYh/fz1EUt/LXZv2HkHF/MehuLAfQp9bAvPuw6he5sxFn2kQsb1Gj5rlX7l9rahL4+0oSYhC3K4Pml7oVmQ6lIayO66DcshIhN37JEwH9qLyGWcu6UyDiO06LTQfvXna99S/r+KJu9tEHt984CCqbroNiuFDEfzI/TDvOwDNI08CwBkHEdu1Wujecf852wqKAADBzz0Jabww9yYO8+2JYv5Lk7sTB96aiNDuE9Bt7krUHPsHh1fV9WucYRCx1ajB8Z/bcL/Gid3Ys3YWwpPGIOWSl6EuSkX6z7cBQKODiD2J2fJqQqP/pjIoEWOWHmzhmjSf5vhOpL0zCWHdJqDH7Legzv0HmWuuB4BGBxF7ElOyZw3SP5uPmEHzkTj6Npg0RTj+24vQl2RiwNJfIRL5Zu5JXZCKvZ9MQVjni5Ay7Q2o83cg/XtnX3ljg4g9iakp2ofdH12K8M4T0PPylbAY1cjd9jL2fjoVw2/+x2dzteqiXdjz5UyEJ49FysRXoC7cifSNtwBAo4OIPYmpKU7DrtVTEd5xHHpe9jqsRjVy/30Ne768AiOu+90n20OTtxP73puEsK4T0GPWW9Dk/oPML537fWODiD2JKdm7Boc+Fx4reVvqjpWbffdY2XlUj0nPHsWEPkF4a3EH/JOlx/Xv5AFAo4OI7/y0AKu2VeLeaTHo08EPe3INeOa7EpTXWLFiUQcAwLFSEwDg01uS0TFKuB9EBrWdoVHMTQqxPYTYHg3YFtSS2nsev+18S5LPK974GvziuqHrsq8hlsoQMWoe7EYtije8hMix17r9gWo8kYXjH98O3dGdsNfq4JfQ0+22s16ZBV32Lliqi1u7Gi2i4q1foegSgw6rboFIJkXInBGwa2tR/vpPCFkwym1bmLJLcOLuVTDuzoFdZ4IixXWQUvXqbZBEBSFp9a0QyZyHr6JHPHIvex7GXcfgP6p7q9etKTRrPoQ0qROinn8HIqkMAZdeDrtBD82qdxAwZbbb9rDkH0Ply4/ClJ4Gh0EPWadup/03qpY/BZtG3Uo1aF0Fm1+FX3R39Lr5O4glMkQPmw+bSYv8X15AzAXXuW0fQ2kWjq65BTXHdsBm0kEV18sLJW++vAMfwj+kE/pfshJisQyxXafDatEhd/+7iOs+y23dPYmpLt6BysJtGHXllpMD/iKTJmD/5lugLtkNvy7TznVVPZK3YwX8w7ui/+wvIJbIENvnSlhNOuRuX464fle7bw8PYorSPoM8IAr953wJscTZWRMQmYJdn14KdVEqwpI8mx39XMs79Cn8g5PR/6LXnZ9156mwWvTIPfgh4rrOdN8eHsRUl6SisuhvjLriZ/gF1O0fieOw//c7oS7d65ODZnmsCOXt/8BZt0vfce733S53fs773kFcD/ffK57EVJ/4F5UFf2HUvN/hF+hM2EcmT8D+X5ZCXbzbJwcQ87whtGZlKTp0UeKFzztDKhNj4pxwGHR2fP56MabMD3fbHp+9VoIxU0Lw4OvJJ9f16K/C7VccRervNRgxIRg/rqpAWKQUL652bhcAOvXww5LJR3Botx4DRwaeqyqelffzrOisEuH9fgrIxCLMjAV0NgdWHrfgyjjJaRPIRpsD/ztkglIM2E957d3jFkTKRfh8gAJKiXMbk6IkGLXdiA/zLXiwa9uYyWHk7XegIusIVs+eBbvViv1r1kARGIgL776n8eRSp06oyslG3vbt57aw5wDbowHbglpSxJQbETHlRrevZV7bA5bKE+e4RERE1N5pvnwfsg6dEf3sexBJZQi8dAbsBh00q95G4OQ5bq8TzPnHUPnKw6hN3weHUQ9ZR9dcreabT6AcdAEiH3jx5DppTDxK778e5pzDUHTv06r1agrtu+sh7RyPiA8fgEgmhf8VY2HXGaF96zv4z53gPm+dU4TqB9+BeU8WHHojpN07uLwnat3zLuscZgsqb34Zqst983oaAGrWfAhph06IfGGlM48/cTocBh00n78L/6nu80+W/FxUvfQYTIfq8/hdT/tvVC1/GvY2ksfXf7oa0uQkhL7+EkQyGfymXAaHwQDdh5/Cb8Y0t+1hPZ4HzdMvwLL/IBwGA6RdOru+p6AQAKAcPxZif9+cUdYThVtehSq6O3rf5OzXiBk6H9ZaLfJ+fQExIxrv18j66hbU5Dr7Nfxj22i/Rupb8A/rgv4zV0EskSGu1xzYTFrk7nwd8X0XuM/HeRAzdP7PLnF2mxkHNixGbM/TP5XPW/L/WA5VVHf0Wfytcz8YPB82kw55W15E7DD3+4EnMdkb7kf0wKvQ65rPT8aFdhuP1BcHoOb4DgR3HHEuq+mxvO2vwT+iG/rP/9qZd+0/D1aTFrnbXkLcIPd95Z7EFO35BPKAaPRf8E1Drja6J3a9fxHUBTsR1tH9rObelrfzLfiHdUX/Waud+33vK2Ez65D773LE92vkWPEgpnD/51D4R2HA7DWC3HXq5xOhLtqFsKRR57qqZ5T/x3KoIruj7yLnfh87aD6sdft93NDTHCtniDn6g/NY6b2g4VgJ6zoeO18eAE3eDoQk++axsnxjKbrHKfHtXZ0hk4ow/8Jw6Iw2vLi+BNeNdc3jG812fLC1Ao/PjsMjVzhnmZ45LBQGkx0f/16B1xcmQiwWIaduAPHlQ0IQ6Cdx+XfbCuYmhdgeQmyPBmwLakntPY/vm1NlUZtjt5pRvXsDwkbMhvg/MwmEj5qH2uKjMOTtdxsnVqgQ0vdiJFzxMPw7DWx0+4HdRyL2stsRNnxWi5e9pdnNVmg37kPwzKEnB/kCQPCVI2DOLkXtwQK3cWI/OQLH90bUfdOh7J/k9j2OWgskgX6C7UrCnAkmu8nSgrVoOQ6LGYa/NsN//BTBLBMBEy+HNT8X5qOZbuNESj/4DRuNkEX/g7zH6ZPL+j9+gX7zBoTecl+Llv1csFvNqNi/DlGDrzx5kQsA0cMWwFiaBV1hI8eOXIXQnpciacpjCOgw6FwVt0XZbWaUHf8N0Z0mQyxuqHts18th0ORCW+m6b3gaU3h4LSI7jINfYDwcDgccdhsUqkgMnb4WsT46INJuM6PsyI+I7nmFYF+I7TMXhqqj0Ja6zi7gaYzdaoJUHih4j8wv7ORrvshuM6MsfwuiO04Sftadp8JQcxzaqsNNjinM+gaRCWPhF3DK/jF5FWI7T2n9yp0lHitCdpsZZbmbEd1linC/73Y5DJpj0FZmNDmmMPMrRCZdBL/AhP+0RxSGzvgGsd08ezzlucTzhpDFbMdfG9WYMCPs5CBfAJg4Jwz52SYcPeT6CNlagw0lBWYMHRskWD9gZCBEIuBYpjPGZHLAP0gi2G5wmPP3mLn21OG1vsFsd+CXMhumxUghEzckXGfGSnHM4ECG7vSzPT131AIHgKsTXe85zTY4MDRUfHLwMACEy0Xo7i/GEZ1vtsepJDIZek6fjgNrv4bd2vCEk7TVXyCye3fE9uvnNi68U2dU5hwDAIglbTcJfSq2RwO2Rdsksovb5B8REdG55rCYod/2q2uu9pKZsBQcg/mo6zU1AIiVfvAbNgahi+6AvJGBwNKoGARcJMyrSGMTAQB2o6GFatByHGYLjJt2QDVtlCDf7n/FWFhzimBJz3UbJ/JTQDl2IILumgtZ3y5u36MY1tPlz/hLKmRd4hF834JWqU9zOSxmGP78Df4TJgv2Df+6PL6lsTy+QgnlsAsRvPg2yHv0Pu2/YfjjFxg2/4DQW+9t0bK3BofZgtotf8Bv0sUQyRraw2/KJNiO58F6OMttnEiphGLkcAQsvQGyXilu32MrKIQ4PAxif384bDY42sBszKeyW80oP7AOUQOF/RoxQxfAWHb6fo2wlEuRfNljCGzD/RqlRzciJmWmMLfW+0oYqrKhLXOfj/MkJjRxhMtf2dGf4B/WFV1GP9z6lTtL9ftB9IA5wv1g8HwYyrKgK3LdDzyJseirYFIXIKyHcCb4wPh+UIYlofzgularU3PYrWaUZWxAdN/Zws+5/zwYKo5CW+y+PTyJsVtrIVWckqtVhZ18zRfZbWaUZv2ImJ6N7PeN5K49ibFbTZCc2h5+vtsedqsZZQdd9/vYQfNhKM+C7kQj+8YZYuqPlfDubo6VUN89VsxWO9btUmPOiFDIpA355fkXhiOr2IT9ea55/IoaKy7qFYiL+wgn8ugYpYDZ6oDV7vwuzSk1ISpYikA/CWx2R5v8jmVuUojtIcT2aMC2aJu8nY9nHr9x7aOW1Oos6lI4rGaoTplB2C/BmSAxV7gfNKsIT0Ds1GWInboMfgmN32kcO+VOxE5dhpD+E1uu0K3EWlYDh9kKRQ/hDJbK7s5lS2Gl2zhZfBgibpuIiNsmQpni/jFFwVcMhSm7FGUvboCluBq1h4tQfP8ayDpEQDXMfbLS22xVFYDFDPkpMw/IOjqXbaVFbuOkUbEInn8jguffeNrZh62V5ah8/gGE3no/ZMm+2QanY65xHjv+p8wgrIp1Hkumqny3ccrQBHS45G50uORu+MefPjHrq8zGCjjsZgSECveNgBDn51irc72Dx9MYXVUW/AITkLn9cfy5aji2fNwbqevnQFN+oDWq0iLMujI4bGYERAoTywGRPQAAtRrX86inMTG9ZsFQlY2cv55HrbYYuvJMHP7lXihDkhCSOLw1qtNsZmMlHHbLyc+2XkCIc9aOWr3rjPSexuiqs+EXGI/MHU/jz69GY8uqQUjduACaikOtUZVm47Ei1FA34XdDfV1rtadrj9PH6KqOONtj22P489Mh2PJBClK/nwVNmW+2B88bQlVlVljMDnTs4SdY37G7c7mkwOwSI5aI8MGvPTD+cuEjQtP+1cHhACJjncnZi2c6ByF/9NIJlBebkXvYiFcfyEdsBzn6DgtopRo1T4XZAbMD6OYvnK2ha91ykbHxgb5/VdrweaEVb/RWQOnmivG5HnLc30X4+GGd1YGDWjtiFG3jEjMgOhpShQJlGemC9WWZzgETwYmJbuPCOnVCcEICbtu7F08YjLjveB4mL18OeYBv7geeYns0YFsQERHR+exkrvaUGYTldbla62lytSHzbkLIvJsg7+T+KXjRT72NoMsXwOFwwFZdCdPhA6h+7yVIo+Oh7DWgZSvSAmzlasBsheyUGYSl3Zy/92xF5W7jpHERCLp5BoJungFZD9fZh90x/r4X+i9/Q9jKuyGS+mYndf2+ceoMwrKOzlyStcT9bEvS6FgEL7gBwQtugKxz43l8W2U5Kp99CCG33gdZsuusvL7GXlkJWCyQdhaWVdq5EwDAVlziNk4SE42A665BwHXXuJ19GHAOIIZMhsobbkHJoJEoHT4GVbfceXJm4rbgjP0a1afp17j4bnS4+G74x7XNfg2Tvi63FtFDsD4g3HlurK1x/RybEgMAFce2oOjgavSd9gHEYt97qLBZWwqHzQz/GOF+4B/j3A9q3ewHnsRIlEEQSxUwVh4TvMdaWwOztgwmjW8+rdasc9YtIErYVx4Q5czDus/VehYT03cODBVHkbP1GdTWnICuLAOHf7wLytBkhHS4oDWq02wn9/tT89B1x8Fpj5UzxMT2usKZu972Ql3u+jAOb74PfsG+mbuu3+8DmnCsnC7mtMeKznePlVK1FWarA70ShXn8nglKAEB+hWsePzFCjs2PdMPwbgGwWB0orrbgtwM1WPlLGWYMDYFc6sxJ55SaoJCKMPGZo/C/eh9Cr0vD9BezcazUNydFcYe5SSG2hxDbowHbgqhl+d7VBrVJFk0pAEDiLxx8IQsMr3u97JyXyVusZRoAgCRE+OgpSZjzC8daXtPkbQeM7YWYJ2aj5KEvUfbsOgCASC5Flx1PQRKgbPJ2W5Ot0ploFQcGC9aLg5z7iq2qosnbdjgcqHz2Psi790LgFdegNi216QX1EnONM9EoVZ1y7PiH171ees7LdK6YDM7PXqYQ7hsypbMtzEbXfcPTGJOhDAUZqxAU0Qc9LngUEImRf/Aj7NowFxfM/gWqIPc/GL3JpHd+1jK/EMH6+rumzXrX86inMeGdxqHb+KdxZPP9yPnzaQCASCLHyCW7IJX75o9hU91nKZMLZwWVKUIAOAcLNzXGZCxHweE1CArvjR7DHnTuH+mfYddPV+OCGT9AFej+Jg5v4bEiZDI4v1fOrj08izHpy1GQ/jmCIvuix6jHne1x4EPsWjcbF8z9zefag+cNocoy59MYgkKEnbFBdTMFV5W7Pq1BrhCj9xBhfTL26vH4TccQHS/H6MnOfWTo2CDc+kQ8Xn+oEO896+w0lclF+OLfXlAF+Gbnb5nJObtCsEw4gDi0brncNQ8LAKi2OLAs3Yw7O8nQN0iMzW76zHsHCQcJV5gdWJRmgs0BXONmxmJfFBATAwAwVqsF6w2Vzu+KgOholxiRWIzQjh0RFB+PLU8+ifLMTMQPHozR996LiC5d8elU35vF3lNsjwZsCyIiIjqfWasaydUG1+Vqq5ueq61n19Ugb4pzwLDIT4W4ld9CJJM3e7stzVauBgCIg4XXhOLQQMHrzeWwWKF+9AME3jIT0riIFtlma7BVOj/7RveN5ubxn77fmcefdTVM+3w/j2+rcP7+FwcL84ziEGf72CvdTxTjCWtBIewlpZBOuAj+C+bCVloG3bsfonLBIkT+8C3EQYFn3oiXted+jfrcmUwZIlhfn1sz6V0TKU2JsdssOLzlAXQcdjuUQfHNLXaraHQ/UNXtB1rX/cCTGLFEiuiBV6Hgj9fgH9MToV3GwaQpRPaG+2G3GN1u1xeYdPV510bqpnOTq/UwJrzLeHSb+ByO/HQPcrY8AaAuV3t7GqQK38zVmuvr1uh+79oensaEdxyHbhc9iSO/PYjsv54BUNceN+70ydy1SVu33zf2ObvZpz2JqT9W8v98Df7RPRHadRxM6kJk/+Dbx0qJ2pmnD/UX5tXDA5155VLN6Z+6vHGfBjNfzgEA9EpQYuX1DU92PlZqQkGlBTOGhuK2SVEorDTj2e+LMerRw8h4tRdC/H0/d83cpBDbQ4jt0YBtQdSyfP8bktoIkdu1DrtzZjOH7fQ/9M4nIvdNAdQ9OsNhsTV525p1u1D6zHcIu3E8Asf3hq3GiMq3NyN/wZtI/vpOyOLDmrztVtNYgzjq9o3/PE7gbOnWrYHp4B7ErdoEkbhtzHbn6gztcz4fO41Vva7udrubfcPDGKtZC4V/NAZN/gwSqXNwfUTCKGxbMw55Bz5Ayqgnmlf2VtHIefRk3dztC57FlGR8h6N/PIkOQ5YgvPMEWGtrkJf6FtLWXoWB8773zQRkI+eO0+8fnsVYzTVQqKIx6NIPGvaPuJHY9s0lyDv0MVJGPNLc0rcsHiunaMK+4WGM1VzjbI+pqxraI/FCbFs9Gnlp7yFl9FPNLHtL43njvxr/yeH8DWa1nP5xZTVqKz54/gS+/aAMUfFyvLymK5R+zt8XW9ZV4d1nTmD2jVEYflEQdDU2fPVOKe5fkIPlX3dFVLzvdYY39pO0vhWsjTy+7eFMM+KUIixNPvOlotXuwGeFVrySY4HZDqzoLUevwDbym6yR+tf/ppTIZC6viaVSfLPoOhTv34+KI0cAAFm/bIK2pBgz3nkXCUOGoHDXrtYrc2tiezRgWxAREdF5rLHrBNibn6utJ/bzR+xbX8NWUYqa7z/HiVvnIP7tbyHv3OPMwedQo21R93vQYW16Hv+/dJ9tgkNvROBNl7fI9lpNoxeRdU+vaU4e//s1MB3ci9jVP7WdPH5jecb6fp5mtIf/tfMRsOR6KAYPPLlOPmggKmZcCcPX3yFg8bVN3vY5c6Z+n/O5X+MMuTVHE/Jx7mIK9n0Eq1mH5KG3NK2Y50JT9gMPY7pd8RocNgvSP513Mq7DuGWw6MohVflgXyiAM+Zd3R4XnsWUHPwGRzc/hg4jbkF4l4thNWmQt30F0lbPxsBrf4Ay2LcmRXE6w37fhPaojynJ/B7Zfz6NDoNvQkSn8bCYapCXuhJp38zHoLnf+lzuWnSGvmB3+4anMd1nOI+VQ583HCtJY5fBrC+HzN83j5XGTgP2uu9Yi/X0efwLewRgyyPdkFVcixfWl2DkI4eR+mwKVAox7pwcjYdnxmJ0z4abcUanBKDvPRl477cK3Ds9psXq0WqYmxRiewixPRqwLYhaVBu5UidfJwtx3r1h1VcJ1lt1zmVZaOw5L5O3SKOcd6DbqvWC9da6ZVlMSJO267BYUfS/TxA69wLEvTgfgZf2Q8js4UhedzespRqUv/ZTs8rdWiThkQAAe41asL5+WRLheuePJ6wlRah642mE3nI/xAFBsBv0cJhqAQCOWiPsBv0ZtuAb5MHOC5VTjx1L3bI8JO6cl+lcUfg59w2LSS1Yb61bVvpHNTlGrgxDRPyokwMAAUAqD0RI9EBoKzNboPQtTxHgPBYsxmrBemvdsjLA9TzqSYzdZkHGj7chru9V6DHxZUR2nYjYPnMweMEPMOlLkbv9lRavS0tQ+DlnZLGYNIL11rplpSqyyTFyZRgi4i44Zf8IQEjUAGirjrRQDVoOjxUhhaoJ7eFhjNwvDBGJF7ppj0E+2R48bwiFRzmTATVqYSdeTbWz0zcy1jVZUO+fX9WYNyId331Ujitvjsaqbb3QuafzEWpWix3P3Z6HSVeG464XOmDkpSG4dHY4VnzfHZVlFnz2mm8+Ci5S4czEqk8ZOF2/HK1wzdT+VGrFpnIbnu0hh8kO6K0OWOzOPIze6oDR1rCtHL0dl+8y4dEjFgwPlWDzCCWmxrSd+1O1Jc5ZPFRhwlk8VGHOxHrNCdfH89rMZhxcu/ZkYqle5oYNAICYPn1ao6jnBNujAduibRLbRG3yj4iI6FyThDuvf+1aYe6kPlcrbWKu9r9EUin8+g9DwIRpiHnlM4hkMmi++6zZ221p4ijn7z27WitYb692Lkuimz/oxmG1QbvyOwRcMwlilaLZ22tNkrAz5PEjXfMtnrCWnED1688i5Nb7IA48NY9f67N5fEmEc4ZHu0Z4rDjqliVRrrlJTykvHCkYPAwAss4dIU3qAEvW0SZv91ySBzn7NSyN9WsEn8f9GnV5xFNza5Za57IiwHVw2tnG2O1WHN/5BhIHLIJEpmqZgreC+v3AajhlP6hbVrjZDzyNkSqD0OuazzH6uUoMvWcPxrygQdfpL8Gir4Qi2Df7nBWBdXnXU+pWX1dlkJtcrQcxdpsFGetuRtyABegxZTkie1yG2H5XYfCiTTBpS5H750stXpeW0Fge+uR+H+h57vq/MXabBekb/4e4Plch5dKXENl1IuJ6z8GQ+Rtg0pfi2D+vtnhdmuvkObMJx8qZYqTKIPRe8DnGPFOJoXftwdjnNOg6re5YcbPP+YKYEGeevkonvFmrfjku7PSTdYQHSjGudyBuujgSa+/shIzCWvywRw0AmDQgWDB4GABSEvzQLVaJg/nGFqpB62JuUojtIcT2aMC2aJu8nY9nHr9xHEBMLUIWHAWRRAZjQbpgff2yIty3HvndmiSRQRDJJKjNLBSsN9UtyxKalni0aQyw1xjhN6iT8N8L8oOiexzMec1/vFxrkISGA1IZzMeyBOvrl6XRTbt4sZwogKPWiMrnH0T++N7IH98bZcuuAwCULJ2LgilDm1fwc0Qe6Dx29CcOCdbXLytDz99jR+4XDpFYBl2VcN/Q1i0r/V33DU9jFP5RsNlqXeLtDgskcv8WKX9Lk/tHOutWliFYry13Lru7g9yTGGutGlaTBsHxQwTvkSqCEBDRA0Z1XktWo8XIlWEQiaXQqYVJc23dstv9w8MYhSoKNpvJJd5ut/hkQpbHilCjdat0XuwpA9wk2zyMUaiiYbM2tm/4XnvwvCEUGimFVCZCToYwEZiT6VyObmSW4D83VuPuq7IRGiHFF//0wv+eSoR/UMPj07QaG3Q1NvQaLNwH/IMk6NhdiRN55hauScuIkIsgEwFHdMIBxIfrluOUrpeCmTo7THZg4s5adP/diO6/G/HmcSuMdqD770YsOeA8Po4b7Lhidy2OG+34bIACH/ZXIFnVti4t9WVlsJrNiO7VW7C+fllTWOgSE5KUhO6XTYZYKhwobbc5k9u1NTWtVNrWx/ZowLYgIiKi81lDrlbYYVq/LI1qWq7WrqtBwYIJ0P+5SbBerPSDNDoOdp3v/R6SRAQDMiksh/MF6+uXpXERzf43jJt2wFZaBf+545u9rdYmCXPuG5ZT8viWHOeypIn7hrUoH45aI6qeewgF4/qiYFxflN25GABQuuQqFF42vHkFbyXisDBAKoXlaI5gvSXbuSyJadoMhna9HrV//AVbuZv+HIkYYn/fyz+502i/RnFdv0bYedyvUZdb01YIJxvQlTuXlUGN5+M8jSnL2giTrgTxfea3ZNFbnDzAuR/oioX7Qf2ywk3/lqcxmrxU6EuPQKYKRWDCAEgVAaitLoBJU4SA2N4u2/UFcv+6upUK+8q1dcvKYDft4UGMtVYNa60GwYnCfk+pMggBUSkwVh9vwVq0nJP7fbkwD12fl3Y3S7AnMdZajTN3HTdY8B5fzl3X7/f6klP2+7plZUjjx8qZYjR5qdCXOY+VoFOOFX8fPVaigqWQSUQ4VCDM49cvJ4a7TgTy24Ea9LkrHYWVwlx89zjnZDBqvQ1aow0/7lGjRO06o7NEDAT6SVzW+yLmJoXYHkJsjwZsC6KW1bZ6eclniaVyhA6Zjsp/v4bd2vCjrGL7l1BEd4Yqub/3CneOieVSBE4ZCM13u+CwNMyAp/56J+Qdo6Ds26FJ25WEB0ISEQj9P8IEnrVaB1NGEZQpvvU4lnoimRyqMZdA/9uPcPxn39D/uh7S+CTIu/Vq0nYV3Xoi5p21gr+wZY8BAMIfeA7Rr/nerBbuiKVyRPSfgbLdXwkeUVOW+gWUkZ0RkDjAi6VrXWKJHFHJF6Pk2EbY//OIrpLsH+AX1AGBEa77hqcxUUkTUJH/Byy16pPvMRnKoC7ejeDI/q1Wp+YQS+SI6jEVJRnfCvaFkkNr4RfaCYEx/ZoUI1NFQKaKQHXedkGsxVgFbVkGAiJ7tl6lmkEskSOqwwSU5G4SftbHNsIvMBGB4SlNjolKvAgVhX8KZqM1GcqhLt2D4Mi+rVepJuKxIiSWyBHV8RKUZP8o3O+zN8AvKKnx9vAgJqrjxajI2ypsD30Z1MW7EBzdv9Xq1FQ8bwjJ5GKMmRKC376vhtViP7n+128qkdBRgW59XW8QsFrsePGufPQa7I/3f+mBDl2ULu8JCZciNEKKtH+Es1Jpqq3IyTCiU4pfy1emBcjFIkyMkmBDqRUWe8Mg4nUlViT5idA70PWO2blxUnw3WCH4uzJOAoUY+G6wAg92dQ7CfvaoBWY7sGmYEhdFtI3E66lsFgsy1q1DnzmzBcmivlfNRWV2Nor37XOJ8Y+IwDXr16P3FVcI1vecNg12mw1Fu3e3erlbC9ujAduCiIiIzmcimRz+oy+FfssPglytbvO6ulxt0waaiPwDYSsvgeHfrYL11opSWI5nQ9HFNY/jbSK5DH6ThsOwYZsgj2/4/k9Ik2Mg69PpNNGe0X2+CcoL+0ESE97sbbU2kUwO1diLof9tozCP/8sPkCZ0gLx70/L48u69EP3uV4K/0LseBQCEPfgsot74pCWK3+JEchmUE8ahdtOvcFga2qN24yZIEhMgTenetO3K5FA/8Ch0b70rWG/NPQ7rseOQ9fe93KQ7Yqkckf1moGyPsF+jdNcX8IvsjICE87tfI7rbFJRkfieoe3HG1/AL6YigaNfP8GxjCtM+RljyWCjdzNDqS8RSOSL7zkDZ3rXC/WDPGvhFdEZgvOt+4GlM/tZXsG/lxbBbGwYLFv69EhJFIKIGzGnFWjWdWCpHVM/pKDn4tTDveuBL+IV1RmBs/ybFyFQRkPlHojp3myDWYqiCtvQQAqJ9M1crlsgR3X2q636f/jX8QjsiqJHc9ZliZKpwyFURqM4X5q7Nxipoy9IREOV77SGWyhHVZwZK9wn3+5IzHCuexOT9/gr2vn3KsbLdeaxE9/fNY0UuFWPG0BCs/bcaFmtD3nrN9ip0jlZgQLJrHj8iSIr0wlr8mSHM0W895Fzul+QHhUyEa946jsfWCmcdPXKiFplFtRjRrW3cpMPcpBDbQ4jt0YBtQdSy2s4zZsnnxU6+AxlPjsfR5VcifMRsaI/8g8q/v0DHm96DSCyGVVcFXfYuKGM6QxnTxdvFbVURt1yC3MkvIP+alQieORSGHUehWfsv4ldc52yLah2Me3Ih7xQFRSfPHgsnEokQ/dAMnLjzM0AEBE7oA5taj8r3tgIiIPymCa1cq6YLvmoxipfORdmDS+E/fgpMB3ZDv2kdwh98ASKxGDaNGqaM/ZAlJEGWmOzRNsUBQVD2E86M6Ki7M0jetScUKW0j0QYAiRcvw76XxyD93VmIGnwlNNl/o3TnKnS/5kOIxGJY9FWoyU2FX1QXqKLOr2Mnqe9i7P7hKuzffCtiOk9GdcluFGevQ6/Rz0MkEsNSq4amfD9UQUlQBSd7FAMA8T3mIj/9M6T+MBdJfRYBcOD4/vcgVQQhue8i71X4DJKG34bdn07E/m8WIKbnFagu+AfFB79Er6krne1hrIKmaA9UYZ2gCuvsUQwAdBn7CDJ/uh0ikQjhnSfAWqtG/q53IRKJ0GHozd6s8mkl9b4Wu3++Fvu33oGYjpNQXbYXxTk/oNfIp5ztYVJDU34QqqAOUAUleRQDAPHdZyE/czVSf7oaSb0WAnDg+MEPIZUHIbnXQq/V93R4rAgl9bseu9dfif2/3oyYzlNRXbILxVnfo9fYFxvaoywNquDkhvY4QwwAxKfMRf7BT5G6bjaS+l0POBw4nvausz36Xe/FGjeO5w2hq5ZGY+mUI3jg2mOYMCMU+3fosGltFR5akQyxWARNtRUZe/RI6KRAYiclDu3So7LUgsvmhmPfPzqX7XXu6YeoODlufDAeLyzLg0gkwvDxQdCqbfj6/TJABMy5qWmPcT0XbkiSYvZuE246YMbUaAl2qe34rtiGl3vKIRaJUG1xIE1jR7JKhI4qMeL9xIg/ZTz0tiobJCJgaKhzoLDN4cDmchtGhYlxVO/AUb3wUXMxChFSAtvGfarbX38N12/ZinlfrcWBr9cieeRIDJi/AN/dcD0cDgf8QkORMGQoKnOyUZWTg6I9e5C1aROmr3wbIUnJqDyahbgBAzHyjjuw/bXXUH38uLer1CxsjwZsCyIiIjqfBc+9AcW3zkHpQ0sQMH4qag/sgu6X7xHxwEvOXG2NGqaMNMgSkiFLSPZomyKRCEFXXAv1Z29CJFdCOWAY7OoqaL76EOKQMARevqB1K9VEgTdNR9nMB1Fxw/NQTbsQptQMGL79A6Gv3uZsi2otzPuyIO0YC1lH1ycenY5da4Dp33QE3+vbM4j+V9BVi1Fy81Uov/9W+F88GbX76/L4Dz1/Mo9vztgP6Vnl8QOh7C+cJRI254BtebcUn87j+187H5XX3ojqO++D38SLYd6bBuMPPyH4qUchEothV2tgPpgOaYdESJM8m3FXJJchYPFCaJevACQSyIcPhb2kBLqPP4esVwr8Jk9s5Vq1nITxy5C2fAzS35+FqEFXQp39N0pTV6HHgv/0axxPhV/kedivMfQW7Fo9GWnfX4OYlJlQF+5Acfpa9Jq0AiKRGGZjNTTFe6AK7QT/0E4exdSzmmpQlb8dXUY/5K3qnZUOY+/E3hVjcfCj2YgeMAfqY3+jZPcqpFz1QcN+kJ8Kv4guUEV28SgGABLH/A9l+79F+qprENVvJtTHtqPwrxXoOuNVSBUB3qzyaSWNvAO7PxiP/V9ciZi+s1F9/B8Up32BXjOcfeUWQxU0hbugCu8MVXgXj2IAoMuEx5G5/hZnrrbrJbAa1cjfsRIiiNBhxK3erPJpJQ27Bbs+vwxp316NmJ4zoS74F8WHvkKvyW/VHStV0JyoO1bqc9dniAGALmMeRsbPdwAiESI6T4DFqEb+nvcgEomQNHiJF2vcuA5j78SeN8fiwCezEdN/DtS5f6NkzyqkzG04VjT5qVCdcqycLgYAOoz+H3a/+S3SV1+DqL4zoc7djoJtK9Dtct8+Vu6cHI2xTxzB7OU5mDMiDH8f1mHVtip8sCQJYrEIVTorUrP16BKjQJcYJfol+WFkd3/c+lEB8ivM6B6nRGZRLV7+oRRTBwVjWFd/iEQi3Dc9Bg98UQSpRITxvQNRUGnGyz+UYnAnf8wb1bSnRHsDc5NCbA8htkcDtgVRy2kbPbvUJgR0HYoe9/8Im0GD3A9vhS47FZ2WvI+ocQsBAIa8gzjy/FRUbPvCuwU9B1SDOyPpm2WwaQw4sewzGHYfQ/yb1yH06gsBALWHCpE3aznUX/17VtsNXTgGiZ8uhenwCRTd9jHKXtgARfdYdNryMGRxoa1RlRah6DUA0cs/hV1bg8oXH4YpPQ3hD72IwKnOOx/NOYdRtmwhdL+s825BvSSo4zD0/d8mWA1qZK1egprcneh+zUeIHekcvKcrPICDKyahdOcqL5e05YVE9cfASR/Daq5BxrZHoClLQ68xLyC+x2wAgLbqMPb+vAgnjq73OAYAFKoIDJ3+LVRBScje9Qpydr+GoMi+GD5jHaTywHNeT0+FxA/BwHnfw1qrRsZPt0NTtBu9pr6N+P7XAAC0pYewd80MnDj4pccxAJAwcBH6XvE5dOWZSP/hFuT89Rz8I7pj2KLfoQw6u86Pcykksh8GXvwerGYtMv59ApryA+g16mnEd3PeFaitysLezTfhRM4PHscAgMIvAkOnrHHuH3tfQ87eFQiK6I3hU9dCKvfNhAqPFaGQ6AEYOPlTWE01yPjrIWhK09Br3IuIT3F+r2grM7F340KcyPre4xgAUKgiMXTm91AFJyN750vI2fUqgqL6YvisDT7bHjxvCPUeHIDl33SFTmPFC3flIX2PHg+/mYypC5yPns1ON+DO2UexaW0lAKCyzDl7w+evl+DO2Udd/lL/cD6u6PKFEXj2k044dtiIZ/53HB++eALJ3ZX48LcURMXJvVNZDwwMluDzAQpoLA48kGnGPo0Nr/SUY2688z7STK0dV+8z4bti6xm21EBtASwO4PdKZ+ypf+/luT4izlcVpqbi08mToQwJwfS3ViJh6FB8e/1i7PnkEwBATJ++WLhxI/rPa+jw/3L+POz55GMMW7IEsz75FN0mTcRPd9+FTfff56VatBy2RwO2RRvjAMR2cZv8g+PM1SMiImppyl4DEPPKZ7DralD+0oOozdiHyAdfRtCUKwEA5uxMlNx1DbS/fHdW2w297naE3ngPjLu2ofzJO6Be/Q4UfQcj/t11kAQGt0ZVmk0xsDsiv3gcDo0e1fethHlvFkKX/w8BV10MALBkHkfF/Cdg+PaPs9527Z9pgMUKxYimzdzrDYre/RH92sew62pQ+fwjMB9KQ/jDLyBgmjOXZMk+jLI7FkG/af0ZtnR+kPftg7B3V8Ch1ULz5HMwHzyE4Kcfg2rmdACAJesoqpfcBuOPP53Vdv0XX4ugB++BaWcq1Pc9DP2ar+E38WKEffgORJK285Sf4I7D0PfWTbAY1DiyZglqju9Ej6s/QuwFdf0aRQdw4K1JKE09D/s14gZj0JxvYDVpkPHLMmhO7Ebvy95EQr+rAQC6skPYu3YWig995XFMvYrc3+GwWxCWOPKc1qmpgpOHof+Sn2E1qnF47c2oyUtFylUfIm543X5w4gDS3rkMJbtXeRwDACGdRqLv4u9gKMlE5heLoS3Yg5R5H6HD2DvOdRXPSkjiUAy89kdYazXIWH8rNIWp6DXzfcQPXggA0JYcxN5Pp+JE2hcexwBAwpDr0feqNdCVZSD9+5uQs/Vp+Ef1wLCb/4Yy2DefUAs489CDrvrOud//fCc0J/ag95SVSOhff6ykY++XVwiPlTPEAEDCgOvQb+Zn0JVn4tCPtyJn2/MICO+OYQu3+mzuOjhpGPrf5NzvM7++GZq8VPSc+yHihzn3e23xAaS9dxmK96zyOAZwHiv9Fn0HfWkmMr5cjJqCPeh51UfoMOaOc13FszKsqz9+fqAr1Hobbv4gD6nZeny4JAmLxjnz+AfyjLjsuWys2lYFwHmz2rd3dcYVw0Lw7m8VWLAiF2u2V2HZlGh8eUcniETOp+3dOy0ary9MxNZDNViwIhcrfy3HnBGh+O2RrpCIXZ/I56uYmxRiewixPRqwLdoY5vF9msjhcLSDatKpEhISUFRUBFlYPAauzPV2cXyCfuL5l8RoDl3mOG8XwWckH0j2dhF8inzPMW8XwbdEezaLeLtQmO/tEvgWmcLbJfAtbagDpNVFRHi7BD7lyWVjvV0En5EwJ8PbRfApK383ebsIRD5vhdUGLYD4+HgUFhZ6uzjnVENeIw5938/xdnGa5MANnWGpOtEuPz8iIvJM/fedJDIGSetSvV0cn2DplObtIvgUcX7bGaDc2swBam8Xwaf0+HOgt4vgM+T5Gm8XwadYY4O8XQSfItF5fmP9+U5kqPV2EXyKLcI3J5zxll/jhpz5Te3Ew/PTvF0EImpjmMdnHt/XcQZiIiIiIiIiIiIiIiIiIiIiIiIiIiKidkTq7QKQd1nUpdh/V1+3r0VfcjNiLr35HJeIiIiIiIiIiOrtttuxx+7+4VH6c1wWIiIi8g5bVTkK5l/k9rWgmdci+Iprz3GJiIiIiIiIiKge8/jUlnEAcTsnC4lGv1cOeLsYREREREREROTGYLEYgxt5flT9o8/aNxHEdpG3C9FEbbXcRER0rknCIpG4equ3i0FEREREREREbjCPfybM4/uyRnZdIiIiIiIiIiIiIiIiIiIiIiIiIiIiOh9xADEREREREREREREREREREREREREREVE7IvV2AYiIiIiIiIiIiJpKbOP98UREREREREREREREvop5fN/FT4aIiIiIiIiIiIiIiIiIiIiIiIiIiKgd4QBiIiIiIiIiIiIiIiIiIiIiIiIiIiKidoQDiImIiIiIiIiIiIiIiIiIiIiIiIiIiNoRqbcLQERERERERERE1BQiByCyibxdjCYRObxdAiIiIiIiIiIiIiKi1sU8vm/jDMRERERERERERERERERERERERERERETtCAcQExERERERERERERERERERERERERERtSMcQExERERERERERERERERERERERERERNSOSL1dACIiIiIiIiIioqYS20XeLgIRERERERERERERETWCeXzfxRmIiYiIiIiIiIiIiIiIiIiIiIiIiIiI2hEOICYiIiIiIiIiIiIiIiIiIiIiIiIiImpHOICYiIiIiIiIiIiIiIiIiIiIiIiIiIioHZF6uwDtWVFREfLy8gAASUlJiI+PP6t4u92OnJwcFBUVISAgAJ07d0ZoaGhrFJWIiIiIiIiIyCeJbCJvF+GcqK6uxrFjx6DVahEXF4cuXbpALObcAERErY15fCIiIiIiIiKi5mEe33f5dunaEIvFgmHDhkEkEuHxxx8/7Xt//vlnDBkyBAkJCRg5ciRGjhyJhIQEDB48GD/99NMZ/y273Y7ly5ejY8eO6NatG8aNG4chQ4YgMjISU6ZMwZEjR1qoVkRERERERERE5E1ZWVmYOnUqIiMjMXjwYIwbNw7du3dHcnIyXnnlFdhsthb99zZu3AiRSISFCxe26HaJiHwB8/hERERERERERNTS2nIenwOIW8jDDz+M1NTUM77vlVdewWWXXYbdu3e7vLZnzx5MnjwZL7/8cqPxNpsNV1xxBZYtW4b8/HyX1zZu3IgBAwbgr7/+OvtKEBERERERERGRz9i2bRsGDBiAH3/80SXBWFBQgLvvvhszZ85s0eTjqlWrWmxbRES+hnl8IiIiIiIiIiJqSW09j88BxC1g8+bNePHFF8/4vq1bt+Kee+4BAERERGD16tWoqqpCVVUVVq1ahfDwcADAPffcgy1btrjdxlNPPYV169YBAAYOHIjff/8dOp0OBQUFeOKJJyAWi2E0GjFjxgyUl5e3TAWJiIiIiIiIiOicqqiowIwZM2AwGCAWi/Hkk0+ioKAAOp0OW7duxYABAwAAGzZswJNPPtki/+bmzZuxdu3aFtkWEZGvYR6fiIiIiIiIiIha0vmQx+cA4mYqLS3F1Vdffcb3ORwOPPDAA3A4HJBKpfj1118xb948hIaGIjQ0FPPnz8evv/4KqVQKAHjwwQfhcDgE2ygrKzs5q0HHjh3xxx9/YOzYsfD390dCQgIeffRRvPbaawCAqqqq086AQERERERERER0PhDbRW3y70xefPFFVFZWAgDeeOMNPPLII0hISIC/vz/GjRuHP/74A8nJyQCcM2U2dQCaXq/Hrl27cMcdd2DGjBmw2+1N2g4RkS9jHp+IiIiIiIiIyHu8nY9nHr9xHEDcDHa7HQsXLkRpaSliY2NP+94DBw6cfDTa5ZdffnJ0+X8NHDgQl19+OQAgNTUV6enpgtfXrFkDvV4PALjrrrsQGBjoso1bbrkFUVFRAICPP/6YnT5ERERERERERG2MzWbDRx99BACIiorCkiVLXN4TFBSEu+++G4AzefjVV1+d9b8ze/ZsBAQEYOjQoXj99ddP5p2IiM4nzOMTEREREREREVFLO1/y+BxA3AzLly/Hpk2boFQq8f7775/2vT/++OPJ/58+fXqj7/vvaxs3bmx0G9OmTXMbLxaLMXXqVABAeXk5du3addpyERERERERERGRb9mxY8fJWQumTp0KiUTi9n3/zQ+dmkfyRFNnOyAiakuYxyciIiIiIiIiopZ2vuTxOYC4ifbs2YMHHngAgDMB2atXr9O+/8iR/7N33+FNle0Dx79Jk6Z779IBLXujgrJEQESQKSCKA/dAHO/PvfceuHDyqq/iQFFAURy4kE3Zm5ZS2tI9k67M3x+h1ENSCCWhKd6f6+qlOefch+e5e5Im9/PkOXuO/P+YMWOaPe6f+zZu3Oj0HD179iQpKanZc4wdO7bZcwghhBBCCCGEEEII7+ZqHSkpKYlevXoBLasB/fzzz9TV1Sl+hBDidCJ1fCGEEEIIIYQQQgjhCadLHV8mELeAXq9n+vTpmEwmJk6cyI033njcmIKCAgCCgoKIiIho9riIiAgCAwMB5UVms9koLCwEICUl5Zj/1j+Lkv88hxBCCCGEEEIIIcRpxQYqS9v8wdZ8txrrSOB6Hai4uJjKysoTSp+vry9+fn6KHyGEOF1IHV8IIYQQQgghhBDCC0gd36vr+DKBuAVmzZpFZmYmiYmJfPDBB6hUquPGNBYNj1V0bBQZGQkoL7Ly8nJMJpNL52iMP/ocQgghhBBCCCGEEML7NdaRQOpAQgjRUlLHF0IIIYQQQgghhBCecrrU8TWt3YC25pNPPuGTTz5BpVLx6aefKn65x9L4i3fl+MjISA4ePEhNTY1DvCvn+Of+f57DKZsVc231cdvUHLVWh1qra3G8EEIIIYQQQgjxb2W22bCcRPwxvvguTgGrqQGruaHlJ7A1/xv0WB1ICCH+JU6/Or4Na43+uG1qjkrri8pX6vhCCCGEEEIIIcSJkjp+2yZ1/OOTCcQnYN++fdxyyy0APPDAAwwbNszl2Opq+yRdf3//4x6r09kLeXV1dQ7xrpyjMf7oczhjqigg45qo47apOTGX3kfcjAdaHO9NyvP6tXYTvEr8objWboLXyO2W39pN8CrpB6NbuwneparlX8I47cQmtHYLvIuP3Ojhn2xBx38P9K9x5/Ot3QKvsiRoU2s3QQghWs0qq42/j1F8Eq5RW4+/oqQn5PzwHDnfP+6Rc3uqDiSEEP8Gp2Md31JaxIFR3Y/bpuZEz7iX2Mvvb3G8Nzk/dmFrN8GrLMzr0tpN8BrhCdtbuwlexRDd8teM042/NqS1m+BVdFXyGfSfSrqZWrsJXqOg7/rWboJXGZf0dWs3was8pNvc2k0QQohWI3V895A6vvfW8WUCsYuMRiOXXnopBoOBs88+m0cfffSE4qOjozl06BCVlZXHPbbxmH9eWNHR0Q77jxd/9Dmc0UTE0fmdDcdtU3NUsvqwEEIIIYQQQgjRIgPVKgbQ8qLZuxYrBje2R5yYlNH3kzTyPy2OX/doV4xVh5zuO7oO9M/HRzuROpAQQpzuTuc6fsf31h23Tc2ROr4QQgghhBBCCNEyUsdv26SOf3wygdhFDzzwABkZGYSEhPDZZ5+h1WpPKD4+Pp5Dhw5RXl5+3GMbjwkKClLEH73/ePFHn8MZlUqNT4B881YIIYQQQgghhDjVNCrVSRVmWuf7+qKRWqtDfTITslTN/waPrgMdq/B4InUgIYQ43Z2udXxUanwCpY4vhBBCCCGEEEKcalLHb9ukjn98MoHYBYcOHeLll18GYNq0aezZs4c9e/YojikqKjry/5mZmSxbtgyAmJgY+vXrR1xcHGC/GGw2G6pmLi6bzUZFRQUASUlJR7YHBQURGBhITU3NcQuPjfFHn0MIIYQQQgghhBBCeL/GOhIcfwLaP+tAiYmJHmuTEEJ4O6njCyGEEEIIIYQQQohT5XSp48sEYhcYjcYj///BBx/wwQcfHPP4+fPnM3/+fAAmTJjAokWLjsw4NxqN7Nmzhy5dujiN3bNnDyaTCYBu3bop9sXHx5OZmcnWrVuxWq2o1Wqn59i6deuR/z/6HEIIIYQQQgghhBCnCxWgsrR2K1rmWCtP/HPlgi1btnDOOec4Pc5qtbJt2zYAkpOTCQ4OdmcThRCiTZE6vhBCCCGEEEIIIYT3kTq+d9fxnVeuhNudeeaZR/7/u+++a/a4f+4bOHCg03MUFhaSkZHRonMIIYQQQgghhBBCCO/mah0pIyODwsJCQGpAQgjhDlLHF0IIIYQQQgghhBCuOF3q+DKB2AWpqanYbLZj/mRnZx85/tFHHz2yfdGiRQCMGzfuyO3OFi5ciM1mc/h3bDYb33zzDQAqlYrx48cr9k+YMOHI/y9cuNBpWysrK/ntt98A6Nq1K507d255x4UQQgghhBBCCCHEKde5c+cjNZ3ly5crbm/2T411JIBJkyadkrYJIYS3kjq+EEIIIYQQQgghhDhVTpc6vkwgPkUSEhKYPn06AGvXruW9995zOObdd99lzZo1AMyYMUOxzDXYL6CUlBQAXnvtNTZv3qzYb7FYmDVrFvX19QD83//9n7u7IbyAtbbW6Y/Nam3tpgkhhBBCnBZUajW+gYEOP9qAgNZu2iknuVCSfChJPppILoQn/Oc//wGgoaGB2bNnYz3qc/+mTZuYM2cOAO3bt2fixImnuIVCCHH6kTq+EEIIIYQQbYPU45pILpQkH0qSjyaSC+EJp0MdX9PaDfg3eeaZZ/jpp58oLy/npptuYvXq1Vx44YVYrVaWLl3K/PnzAYiMjOSpp55yiNfpdLz22mtMnDiR+vp6hg4dyuzZsznjjDMoLy/no48+YuXKlQCcffbZXHXVVae0fwCGbX9T9Nmz1GVtQZeQRuSY64gYdYVLsdVrf6Tg48foPHetw76a7aso/PRJ6g/sQKXVEdjtHOKuehRdQpq7u+A2Des3oJ/7DqZdu9EkJxM4fRoBkye6FFv/x59Uz3mdmEXKFSqsNTUUDhjkNCb215/wiYs92WZ7jH7HCg59+RS12Zvxi08n+oIbiBrh2jVauX4p+fMfofuc5m/5Z9aXseXaVHp/kI0mJMpdzfaYuk2rKf/wFYx7t6Nt156QSVcSMna6S7E1K3+h7N3nSP7fcsV2m82G4aeFVC54H1PuftQhYQScMYSIG+5BExXniW64RXnO32SueBZ94RYCwtNIOuM62vU59uvG8WLMRsMxolVofAPd1Hr3K89bTebal9GXbCMgrD1JPa+iXfdLTzrGajGxb/VzlB74jTp9HiExvek06EHC4vp6sjsnrfzg32T+/Rz6oq0EhHcgqe+1tOt9nOvjODFt9frwxHMFoOLgKvb99TT6wi34+AYR2f48Og57BL/g+GbO6h3K9/9F1vIn0OdvIiAqnaSzbyLxzKtPOqYi+28yf30Mff4mfHTBRHYcQfqoJ/ELSfBkd9yu/K9ssp76Df3mQwSkR5J0wwASZ57R7PE2q5X8jzdycO4aarPK0MUFE3NRF9IeGo4mxO8UttwzUocMZcQjj5DQty9lmftY+847ZHz0UbPHdxp9IVcuXuywvSwzk1e6dvFgSz1PcqEk+VCSfDSRXLQtqtP0+7RXX3018+bNY926dcyfP5/c3FxmzpxJSEgI69atY+7cudTX16NSqXjttdfw9fVVxM+cOZOPP/4YsK+y+dhjj7VCL4QQou35N9Txa7b+TfH856jL3IpvYgcix15L+AUu1vHX/EjRR0/Q8Z3Vjufdvoqij5+i4cBOVFodAd3PJvbqR9EldHB3F9wm789CVj++heKNZYR1DKH3zZ3pcU3HZo+3WW3s+DCTTW/sojJTT2C8P2njkjj7sd7oQpr+FuetKGLVw5so3VaBj86HxMExDH6mH2HpIaeiWy1Wn7GOqvffwLh7B9rkVIKmXEbQ+Ckuxdb+9RtVb71M/JdLj/1vbFpP8Y2XE/vR1+i69XRHsz2iZuVuSp77lvotOfimxRJ+7QjCLx/qUqz+x00UP/EVaaufcTzvqj2UPPsN9VtyUAf7ETisOzEPTUEbH+7uLrhV9Y6/OPTVU9Tu34wuPp2YC24gevhMl2IrNywl77OH6fHKxmaPMevL2Hx9Cr3fO4C2DYzxHK1yz5/kfPc4hoMb8Y/pSMKwm4kbfI1LsWVbviP7m+UoEfQAAQAASURBVAc48/FtHm6lZ5Rn/cn+n59An78R/6iOJA28icT+x+67KzElO5eS/dtzGAq2oQuJJ7LzKDqOfR4frXfXKvXbV1D45TOHnytpRI++nsiRrv2tr1r/A4c+eYSur29w2GfYuZKC+U9Qd3AHKq0vQV0GknDF4+jivXesHMC4ZguGOR9j2pGJT2oiAVeMJ2DahS7F1v+6CsML84j6eZ7DPpvJjOGleTT8vg5LfhHanp0Iuu8GfPt4bw2m4M9DZDyxntKNpYR2DKXbTd3pfE3XZo+3WW3s+XA3O97cRnVmFQHxASSPS+WMR8/C9x/vOQpWHGLDI+uo2FaOWudD3KA4znrmbELTQ09Ft9xK6nFNJBdKkg8lyUcTyUXbInV8763jywTiUyg1NZXFixczefJkSkpK+Pjjj49cAI1iYmJYtGjRkRUKjjZhwgTeeOMN7rzzTvR6Pc8841h8OOOMM1i8eDEazan99dbuWU/2o5MJ6j2MxJtfpnb3WvJenwVw3EnElpoqir543um++gM7yXpgDIHdB5Fw44tYG2opXfQWmf85j05z16GN8L6Jkcat2yi7aRa6swcQ+tD9GDdvofKRxwCOO4nYqtejf8dxZQsAS24eAGHPPIVPu0TFPnWE9xaXavauY99T4wnpOZzk6+dQs2ctOXNvAjjuJGJLTRUFXz97zGNsFjOHvngSLGa3tdmT6ndspOCuy/E/cwhR/3ma+u0ZlDx3F8BxJxFbDNVUfDzH6T7D8sUUP30HwWOnE37FbCxlxVR88iYNmTtp9953qDRad3flpFXmryfji8lEpg6j6+iXqcxby46l9teN5iZGuhKz/MXmJ/r5hSZz7q3b3dwT96gs3EjG4hlEJg2m67BnqSxYz47l9lVomptE7EqMzWZj89Jr0ZftIrXfTah9dORtn0/GoksZOONX/IPbnZoOnqDKQxvIWDCFyJRz6TrqJSrz17Hjx9kAzU4idiVm+SvN99cvJIlzb/G+Qq2nnivVBZtZP38cke3Po9uY1zDXVZK9eg4ZX1zMOVf/jlqjOzUdPEGVB9ey8cOxRKSPoOuEN6g8uJodC28AaHYSsSsx1fkb2TBvFJHpI+k2+W1MdRVk//kSGz+8iLNnrfbafBytcl0uG8d/TMTwNLq+No7KNbnsuOlbgGYnEefN28Cu2UtIvPoMOtw3jNqsMg68soKq9Xmc9et1qHza7o1L2vXvz8ylS8lavpwls28l+ZxzmPz+BwDNFhEi0jpQV1nJJxMnKLabD68Q1lZJLpQkH0qSjyaSC+EttFotixcvZsyYMWzatIm//vqLv/76y+GY119/nXHjxrVSK4UQ4vRz2tfxd2/gwMNTCOpzLgmzXqJ21zry59hrJ8ebRGypqaLk8xed7qs/sJPsey8isMdA4m9+AWt9LWWL5rL/9uGkv7vGK+v4BWtL+ObCX0keGc/wt86mYFUxv1y3CqDZScRb39vLb7esoce1HRnwYC8qM/VseHE7hetLmfrHBah91JRur+Dr834icWgs570+AFONmU1zdvLZgKVcuX0CQfHeubpVw/YtlNx+LbqzBhJx72M0bN1I+ZMPABx3ErHVoKd63lvH/TesBgNlj90LNptb2uwptRuyODjlJQLP7U78S1dSuy6Tgtn2CWzHm0Rsqaql5CXHSQkAdVsOkDPxeYKG9SB+ztVYKmsoe20pB6e+TPvlj6LWeV8NH8Cwbx17nx5PSK/hpFz/GoY9azjwtn2M53iTiM01VRxyYYwn/8snsLWRMZ6jVe9fy/bXLiSs60jSL3uL6qxV7P34OoDjTiI211Zx8HvHL6O0FVU5a9n0/hgiOo6gy+Q3qTywmp0LrgdodhKxKzEFmz5n+/zLiet3GcmDb6Wh6hAHfn+BmqJd9LvhJ1Qq1anp4Amq2buerCcnEtzrPJJueJWa3Ws5+NYtAMedRGypqaJwwXNO99Xl7GDfw6MJ6jaYdte9hLWhluIlb7LnnnPp+tp6tBHeuRiIcdMuyq+6D93gfoQ8eRumjB1U3/MSwHEnEVurDdS88anTfTabjcqbHsW0ez+B109D5aul9osfqLjiHqKWvY9PovctuFW8togfx3xP4oh2DH5zCEWrC/nr+j8Amp1EvOu9nayc9Redr+1K3wfOoDqrii0vbqJkfTEX/T4BtY+a8u1lLB2+hLih8Qx8bTCmGjPbX9vC4rMXMmXbJQTEe+eCOc5IPa6J5EJJ8qEk+WgiuRDe4nSo48sE4lNs8ODBbNu2jddff51FixaRk5ODSqUiJSWFiRMncvvttxMdHX3Mc9x6660MGTKE119/neXLl1NQUEBQUBBdunThsssu44YbbkCrPfVFhpJFb6FL7Ejqg/NRabSEn3cJlloDxQtfJfz8y51+mGvI30f+23dRu2c91joDumTHN8gl376OJiyG9o8vRO1r/1Zp6Dnj2H19H0qXvE38zMc93rcTZfj4EzSpKUTMeRmVVkvARWOx1dSi/++H+E+a4DQX5gM5VD71DKat27DV1qJJd/zGqPnwBGK/EeehDmw7b/iLvnsdv4ROpN3zBSqNlsihl2Kp01O46GUih1/pNB/1h/Zx8P07qNm7Dmu9Ab+kbk7PnfX8NGr2rcdUUejpbrhN5YL30SanEff0+6g0WoJHTcZaa6By/lyCx1ziNB/Gg/spffVB6ndsxFZXg7Z9Z4djqhZ+iH+/QcTc99KRbZrYRAofuBZj1m50nb1vNYectW8RGNGRPlPmo/bRktDjEixGA9mrXyWxt/PXDVdi+l/5k0Oc1dzA1kXXEN/t4lPRtRbJ2fQugeEd6DN2nr1vXSZjMdWQnfEWid2mO8+HCzHleasoPfgnQ65ciX+IffJsTPtRbP7heioOrcO/s3dOIM5Z9xaBEen0mfypvW/dp2Fp0JO99jUSezVzfbgQ03/Gjw5xVouRrUuu9drrw1PPlbwtn6ALjKHv1M9R+9jfOwRFd2XdJ6OpzF9PRMrgU91Vl+T8/RqB0Z3oc/lXqH20xPe9DHODnuw/XyLhjJnO8+FCTP6Gj/ANiqXPFQub8hHTnfXvnUflwbVEdHBtlZnWlvP6SgI7RdHny8tQa32Iv7QPZn0D2S+vIOGqfk7zk/3iX8RM6Eb3tycd2RbSN4GN4z6mbHkmUaM6ncouuNWg2++gdO8e5k+dgtVsZsvnn6MLDmbIXXc3X0Do0IHyrExyDq8KdrqQXChJPpQkH00kF8KbxMXFsWbNGt577z0+++wz9uzZg8FgICEhgZEjR3LbbbfRs6f3fdYTQoi27nSu45d++xa6dukkP/wpKo2WsOHTsNbpKfnqNcJGNVPHz8vk0Ny7qNu9wV7HT3Gs45cufANNWAwpT359pI4fMvAi9l7bj7JF7xB3zWMe7tmJ2/jqTiI6hzBu4Xn4aNV0ndEBo8HEhhe20/3qdKe52PD8NtInJXP++wOPbIs9I4JvRv/KwV8KSB2dSMbLOwiI9WPSDyPR+PkAkD4pmQ87fsOm13Yx5Lnm7xDUmvTz/4smuT3RL76JSqMl8MLxWGtrqP74fQLHXew0H6acbCpeeJyG7Vuw1dag7dD86s0AFa88jbWqwlNdcJvyucvwTY8n6ZPZqLQaQqcNxGqoo+y1pYTNGOL8eZJZSOHd/6NuQxZWQz26LokOx1R++hea6FCS5t+GSmsfotV1SSRn7DPUbcgicJB3rnpWeHiMJ/2uL1FrtEQOaRrjiTrvqmbHeHI+uB3DPvsYj38zYzz7XphGTeZ6TBUFnu6Gx+T/8ir+sZ3pdvNC1BotsWfPwFJvIHfZC8QOutppfmoL95L52a3o96/B0mAgIKF7K7T85OX89SoBMZ3pPdNeT43vNwNLg54Dv79IwlnO++5KTObS+4nrO52el31yJC6i43DWvNKPqpw1hKWecyq76bLiJW/gl9CRDvd+jkqjJeLc6Vjq9RR9+yoRI5oZD83fR977d1KzZ/3h8VDHv7HFi19DExpD2sPfNo2VDxjHzlt6Ufz9XBKvfNLjfWuJ2g++RtMhibB3Hkel1eA/cSTWmjpq3vkC/6mjnY+X78+l+pHXMW3aha2mDk2nVIdjjKs30/DXBqJ//x8+7eyThXUjB1J5y+MYN2zH3wsnEG97dQuhncM4f+EFqLU+pM/ohFFvYsuLm+h0dRenudjywiZSJ7Zn6HvDjmyL6hfNjxd+T/4veSSNTmbry1vwj/Vn9NKxaPzsf1dSJ7Xny06fsf31bfR/9uxT1cWTJvW4JpILJcmHkuSjieRCeJO2Xsdvu0uJeZnU1FRsNhs2m+24S0nHxsby9NNPs2PHDgwGA3q9nu3bt/PUU08dt+jYqHfv3sybN48DBw7Q0NBAWVkZK1euZNasWa1SdLSajFSv/p6wIZMVq5yGn3cJxvxM6rOdr+ao0gUQ3G8EsZfeh396X6fHNOTtJbD7wCMfiAA0oVH4pXSlPmenezviBjaTifrffsd/9AWo/vG78L9oDJYDOZj37HEap/Lzw2/QQIJvvhFtN+eFFEtuLuqICNSBgdgsFmxe/i19sF8bleu+I3zQFMW1ETn0UhoO7aPuwFancWqdPyF9RhI/7QEC0vo1e/6grgOJGXcb4QO9c9Lf0WwmIzUrfiJo+HhFPoJHTcaUux9jpvNrWu3nR0D/c4m4+k50nXs5PUYTHU/gcOW3VTTxSQBY62vd1AP3sVqMFO39nrhuk49M1AOI73EJteWZ6IscXzdcjQlPOsfhp3jfDwRGdiJ92MOe71wLWC1GirJ+Iq7jBGXfOk+mtnI/+tIdLY7J3/kZ0e1H4h/Szv63ympBFxjDgKmLSeg82fOdawGrxUjRvqXEdW3md13czPXhQkyz10dER9KHPuT5zp0gTz5XrOYGfHTBimO0/hGH93nnNy2tZiPFOxcT22uasm99ZlBbuhd9wZYWx1jN9Wh0Icp8BHh3Po5mNZopXrKL2Ck9UWt9jmyPv7Q3tftK0W91/MKNpdZI/cFKIkcov7wUMbQ9qFQYdhR7vN2e4qPV0m3CBLYu+AqruWkVm83zPyO6c2fie/d2GhfZIY2yrP0AqH18nB7T1kgulCQfSpKPJpIL4Y18fX259dZbWbVqFWVlZTQ0NJCdnc37779/zKLjRx995HKt6p8aYz46xu3+hBCirZI6vhH9qqWEDlXW8UMb6/j7ndfx1Tp/gvuNIOaye/Hr2MfpMQ25+wjocY6yjh8WhV9qVxpydrm1H+5gMVrIWnSQTtPa46NtGirrOqMDFXurKdniOMnVVGumOqeG5JHKlR4Tz40DFZRut8eU764icUjskcnDAAHRfkT2CKNsR6VnOnSSbCYjtX/+SsD5YxTXRuCFEzAfzMa01/nvUOXnj9/ZQwi97lZ8u/Y45r9R+/vP1P78PWG33u3WtrubzWhGv3QjoZP6H5nkCxA6bSDGzEIath90Gqf29yVweA+i7pmAX59U5+duMOET4qc4ryYiyL6v3uS+TriR1WSkcv0SIgZNQX3UGE99wfHGeM4nYeqDBHRofownuOtAYi+6jfBzjr3Ktbeymo2Ubl5E9FnTFPmJOXsGdUV7qcl1rFUC+PgGEN59FMnjHiEoxTu/VHA8VrOR4u2Liet9VM213wxqS/ZiONRMnfY4Mabacuorc4nsdL4iNjihN37hKZRsd77Cd2uzmoxUrf2OsMEXK15HI86dfpzx0ACC+4wk7pL78U9zPlZen7+XoG6DFH9jtaHR+Cd3o/6g942VA9iMJup/WYnfRcMUr3n+E0di2Z+HeWeW0ziVvx+6oWcRdPuVaHo6X9Si7ssf0Q0/G592sfb3dRYLPjERRH79Gv4TRnikPyfDYrRwYHE2adPSFTX7jjM6UbW3ivItZQ4x5loThhw9iSOVCwDFn5sAKqjYUQ5A1Z4K4gbHH5k8DOAf7U9EjwjKt5d7qEfuJ/W4JpILJcmHkuSjieRCeKO2XMeXCcTCLcyVxdjMRoeVB3TJ9m9LG0vynMb5RiUSPfk2oiffhp+TVQsAEm95lbirHlVss9TqqcvagjYywQ2tdy9raRmYTGiPWkFYm2Z/bClwvlKuT1wsQVdfRdDVV6Hp6Lj6MNhXIFb5+lJ2w80UnHk2hecMoezW24+sTOyNzFVF2MxG/I/6xqxf0uFrozTXaZxvZDviJtxJ3IQ7m/1mOkDs+DuIm3AnIX0vcF+jPchSXgomI77tlR96fVPtKzOYi/KdxmliEgi79CbCLr0JXyerDwPEPfEOoRMux2azYakoo2HPVso/eBFNbCJ+3ZwXHVpTQ00xNouRoGjltREUZb826qsdr+uWxACUZv1K/pZP6TVxHmq1dy6+31Bbgs1qJChSeW0ERdgf1+sdrw1XY/Rle/APTmLXHw/yx7w+/Pp2Omu/mkBVkfPipTc48ruOUq66ERRpv/6PeX2cQAxA6f7l5G+bT6/xH3jl9eHJ50p894upLc8ka8Xz1OsLMJTsZvcv9+IfmkJYknd+M91oKLL3LUb5tyEo1t7X+krHvyuuxsT1voTa0r1kLX+a+upDGIp2svv7/+AXnkpYykCH83ojY5EBm9FCULcYxfagrvbH9bmVDjEqHzX9/7yR2MnKgb6Kvw+AzYYuIdhTzfW4oNhYNDodxTuVX8Io3mUvrIcmJTmNi+jQgdB27Zi9cSOP19Zx74Ecxr76Kr5BQR5vs6dILpQkH0qSjyaSizbIBiqLrU3+4P3fCRZCCHGaaarjK2snfsn22ompmTq+NjqRqCmziZoyu9k6fsKtLxM300kdP3MLmkjvu7V6bVE9FqOVyO6hiu0R3cIA0B80OMSofFRMXzWGTlNTFdvzVxSBDYISAwAYMfdsBj2jnDBp1Jso3lhOUEKA+zrhRpayw+MaR60grG2fDoC50PnqsJrYOEKuuJaQK6495urDltISyp9+iLDZd6Nt73z8w1uYi6uwGc0OKwjrOtsfm3IdJ3oBaBMjiJo9hqjZY9B1dX7Ht5DJZ9OQWUTJi4sxFVTQsDufwvvno02OImDAsVdvbi2mxjGedsq6mn87+2tBwzHGeOLH30n8+GOP8cSNu4P48XcS1neU+xp9Chmr7fkJPGoF4cB4e58byp1PONdFtCPpgrtIuuAuAhOOPfneWzXWXAPjlL/fwFj747pKx767EuOjC0Gt0VFblq04xlxfjdFQTEP1IXd2w23MVcWHx0OVffM7/Fxp7m+sb1QisRPvIHbiHfgnO3+uJN04h4TLlXfktdTpqd2/2SvHygGspRVgNDmsIKzpmAKA5ZDzRSt84qMJvGEagTdMc7r6MIB5TzY+7WKpfvQNSs6aSlHXsZRNuR3TVueLeLW2uqI6rEYr4d3CFdvDDj82HNQ7xKh81IxfOYkOU5V/MwtXFIANAhLsdyoe9NZQznpGOZZj1Bsp3VhCYGLbuZux1OOaSC6UJB9Kko8mkos2SOr4Xs37ZsmINslcUQSAJjBMsV0THHF4f8tXrvNPU34zxFxZwoEnp2OzmIkcc22Lz+spltJSAFQhIYrt6jB7IdJS5ry45Apzbi6WwkL8Rg4ncMalWIqKMbz3AaVXzCRmyTeoj/o3vYGp0n5t+Bx9bQRFHt7fdlc1bAlzub2/6mBlYVodav+QaCkvOel/w2qo5sB4+/NG5R9A4lvfotL6nvR53c1osF8bWr8wxfbG1U8bahyvjZbEWC0mdv9yH+3PuQO/EMfbxnkLY639d6/VhSm2a/3t10ZDbWmLY4w1ReRu+5iQmF50HfokqNTkbH6PdV9PYtDlfxAQmuzOrriF8fDvsvnfteNzpSUxVouJ3cvvp/2A2732+vDkcyWy/Xl0Gv4Ee359gMy/ngZA5ePLoBvWovH1zg9KDXr7F3Ear/NG2gD735XGvrckJjJ9BJ0ufJY9S+8m69fHgMP5uGMLGp135uNoDUX2AU5tmL9iuzbSPkBpLHIcAFXrNIQNUH6QrtqQx7ZrvsavXSgx45sf5PF2QXFxANRVVCq21x5+PxYU63g7O5VaTXj79oQkJrL8iSco2bWLxDPPZOg99xCV3pGPx13k8XZ7guRCSfKhJPloIrkQQgghxOmssTbpExSm2O4T0ljHb3lt0j/dsY6f8/hl2CxmIsZe0+LzekpNYR0AunCdYrt/pP1xbZHjnYg0Oh/iz1auPl24vpRlV6wgOCmQ9In2GltM30jFMbXFdSyZ+Ds2s5VeNztfHKK1Wcrsv/ujxxjUoWEAWMsda5OustlslD31AL5duhM09XIaNq1v8blOBXNxFQDqMOXEK5/DKwWbS6pbfO6gYd2JfXwaRQ9+Tskz3wCg8tWQtvoZ1EF+x4luHY1jPJqjXjcax3jMVY61uH8TU5W97qgJUNYdG/NjrD5989NQ3UzNNfBw3/VO6rQuxKh9NMT1mc7BFXMIiu1KePp5NFTlsW/p/VhNdTQ4qf96A9PhsfKj/8Y2jpWfzHhoQIc+yn+rspj9z16CzWImavR1LT6vJ1lL7KvfqkOVdXV1uP3vjLXUcaX/Ezl37SdL0PbqTPBjt6JSq6iZt5CyqXcQ9et/0SR51xeX6grtd4n1Peo9h1+k/XW/rqjOIcZH50Ps2XGKbSXri/n9yl8JTAoidWJ7AKL6Kt+X1BXX8vOkZVjNNrrdpPxigzeTelwTyYWS5ENJ8tFEciGEe8kEYuEeKpXz7Tar/T+Wk7/1ks1ipuyHDyia/wxWk5Hkuz7Av0Ovkz6v2zWTC5v18FcS/rF8/okKuuoKVDfdgO7MptsZ+Z7Rj5LJU6n5eiHB11zd4nN7TjP5cOO10aY091w5fH3YLC2/Phqp/QNJeONrzKVFVC/6H4dmTyHhrW/QpTlfHaT1tOTaOPGY3I3zMBsNpA64tWXNPGWa69vha8N6IvlQxpgaqvELiuPMSV/go7FPKoxKPpe/Pj6bAxvfptt5z55k2z3hOL/rE8pH8zG5m/5rvz76z2pZM08Jzz1XCnd9S+afT5F85o1EdRiBqaGanHVz2fz1DM6YvtA7J1U393f2cN+szvLhYkzhtq/Z9/OjJJ8zi8hOozDXV5Gz8nU2fzqFfld/j1+o89VjvMpx3odYzdZjhpsq6sh66jdy31mLrl0Ifb+5Ah//U39bXbexOf9KqEptvxGLj5NbBqs1Gr6+5moKtmyhdI991Yq9Py1DX1jApHfepd1ZZ5G33rsHO52SXChJPpQkH00kF0IIIYQ4jalOUR2//Pt5FH/6LFaTkaR73sc/zRvr+M43H/n8bDr25+f6igZWP7aFLXN3E5wUyIQlw9H4K4fcrGYrW97ew+rHNmNpsDL606HE9IlwS/Pd7njXxkmMaxi+/ZKGrZuI//z7I++rvdrxavgmS4tPXb1oHcVPLyTihvMJHN4Da3UdZe/8RO7lr5P81f+hTfS+66O5142mOuPJj2m0aafgddVrNftcOca14WJM54lzsFpNbJs/40hcytD/YDSUoA3wvucJuPJccc/f2NJl71PwxdPYTA2k3vlfAto7v0V7q2u2Tn347+tJ/F2xVhvwiY0i4tMXUPnZJ+X6DjmTkqGXU/veAkKevL3F5/aI473nOE7NvqGigYzH17Nz7nYCk4K4YPGFTt9z7HxnBxsfW4+lwcJ5n4wgsk+UW5p/Skg9ronkQknyoST5aCK5EMKt2sAnddEWaMLt394wG5TfFjTr7Y+1ESf3Tb/6vH1k3n0+h969h8Aeg+j05irChkw+qXN6ik+U/c24tapKsd12+LE6OtohxlV+QwYrJg8DaNM6oElJwbxnX4vP60naw9eG5ahrw2Kwf/PUN9y7vgXqaZoI++/fWl2p2N74WBPp+E2oE6XSaPDvczbBIycQ/9KnoNFS/e3/Tvq87qYLsvfVVKe8Nkz19se6YMdr40RjrFYzB1a/RlK/a/HReuftARvpAuzXRmNfGh3pW6DjteFqjK9/JJHJ5x6ZPAyg0QUTFn8m+tKdbuqBe+kCY4Bj/K6D4k46xmo1c2Dt6yT1vcarrw9PPVesFhM7lt5GQs9L6XrBi0R3HE1Cj2mcNWMJDTVF7F/1itv74g66YPvv0VRXrthurrU/9gtxvGWbKzFWi4md39xEQr/L6TJ+DtFdxhDf51LOvPYnGgxFZP/xgtv74gm6WPuKDqYK5aoF5nL7Kgd+8cHNxpYs28Oqfq+T+946kmefw8D1txLc/eT/LrUmfaF9VZOACOWqJgER9oGG6kOOtzy0GI1sW7DgSPGg0a4lSwCI69nTE031OMmFkuRDSfLRRHIhhBBCiNOZJtxeO7Hom6vjO9ZbTkRD3j72/98FFLxzLwE9B5I+dyWhQyed1Dk9JTDOXidrKG9QbK8vN9r3JzRfK8r+IY//9VjM1nf20O/2rly+eRxRPZTvH8v3VPHl4B/54/Z1tBsayxVbxtF5Wqp7O+FGPpGNdWvluEbjOIdPdEyLzmsuPETlq88SPvtu1MHBWGtrsNXbV3e21ddjra05iVZ7hibGfvdAa6WybZYK+12dNPFhLTqvzWTm0O0fEnbJIOKev5zgC/oQOvUcUr69F3NxFaVzvj+pdnuKNqyZ8b/DYzzaf9kYz9F8Qw7XHWuVdUdTzeExsDDHWuXp4kjN9ei+H67B6kKd1K1djNH4hdDzsk8Y9kQpA+7cwHlPVdJp3AuYasvQOan/egNN43NFf/R4qJvGyvP3svf+EeR9cBdB3QbR5dW1hA+6+KTO6UnqaHsdxVqpV2y3HX6sjol0iHH53BGh+A4548jkYQB1cCC+Z3THtGt/i8/rKf5x9vcUR7/naHwcEB/oENPo4A85fN3zC3a9s4Met/fi4k3TiOihzF3lngqWDPmW1bf/TdyQeC7ePI20aelu7oVnST2uieRCSfKhJPloIrkQwr1kArFwC01oNCqNlvoc5US0xsfa6JavYNhQsJ/9943GWLCf1Me+JvWhz9HFdzip9nqSOjICNBrM+zIV202Z9sc+cS0rwlpraqj/4y8spU5uFeajRhXU/IeL1qQJiUGl0VJ3UHltND7WRrWB1RzdyCc8CjRajNnKNyUN2bsB0MS2rPBhMVRz8MoRGP76UbFd7eePJjYRq6Hlt1XzFN/AaFRqLfoS5bVhKLY/drby6YnGFO/5ngZDIYm9L3dn0z3CNyDK3rdy5bVhKLM/9gt2kg8XY3SBsVjNjrddtFnNaHyDHLZ7gyO/69Jdiu2GEvtjvxDH144TjSneu9R+ffSc4c6mu52nnivm+irMDVWEJpypOEajCyEoqgt1lTnu7Ibb+AbGoPLRYijaodiuP/zYL8zZtXH8GHNdJeb6KkKT+iuO0fiFEBTTlbqKA27shef4xgSi0vpg2KG8lZ9+p/0WeX7tQp3GFS/ZyabJn+IbHcjAjbPp/NyFaEK889aZJ6KmuBiz0Uhs9x6K7Y2Pq/LyHGLCUlLoPGYsas1RqzhY7CsM1Vd7399UV0gulCQfSpKPJpKLtkllbZs/QgghxKnmE3a4jn9AWTtpOPxYG93yWm3DoWz23z0G46H9pDzxFSmPfIYuof1JtdeTAmL8UGvVlG6vVGwv3W6f6BWc5LzenrnoIIvH/0ZAjB9XbhvP0JfOQhfiqzimMquar85dRmWmnolLRzD+2+GEpYV4pB/u4hMRARotpizlQiWmrL0AaGJbNvHNnJ+Lrb6O8mceJu/cvuSd25eSO64HoPjGGeSPHnRyDfcATXQIaH2o36l871+/y/64pasEW6pqsVbX4n9mmmK7T4g/us4JmHKcjP14AU2Iva5Wd1BZV6vLtdcZfSP/XWM8R9Mezk9t/nbF9sbHuoik1mjWKeEbdLjmWqi8NgwF9r77hTn23dWYqoPrqCnegzYgnJDEvmh0QdRX5tJQlU9QXHdPdOekHRkrP3o8NMfeV9+TGA9tKNjPvgdH0VCwnw4PfUOH+7/06rFyAHVkGGg1mPceUGw378kGwCehZV9MAVDHRmFrMDpst5nMqAL9nUS0Lv8Yf9RaNRU7lBPnK7aXARCY5Hyc7sCibH6e8CN+Mf5cvPUSzn5xIL5Hveeozqri+2GLqc6sYvT3Yxj17YWEpDkfA/BmUo9rIrlQknwoST6aSC7aptaux0sdv3kygVi4hVrrS8g546ha8Q02c9MtWCr//Arf+Pb4d2j57VMKP3wUq8lIx9dWEHLmKHc016NUWi1+I4ZTt+xnbKamXNQt/RGfpCS0Xbu07Ly+vlQ88CD6t95WbDdnH8C8Pxvf3t55ixq11pew/uOpWPm14tooX/ElurgOBLTv03qNawUqrS+BQ0djWL5EkQ/DL4vQJKbg27HHMaKbpw4MxlJSQO3q3xTbzaVFmHL24Zve9aTa7QlqH19iO4+jcNc3WP9x66aCHV/hH96ekDjHa/pEY/I2fUhE+2H4OVmh1duofXyJTRtN4d4lyr7t+Rb/0FRCoh2vDVdjYjpcQMmB5Rj/sVJxQ00RFYfWEhrb14O9ajm1jy+xnS5y/F3v/Ar/sPaExDre+vJEY/I2f0hEqvdfH556rmgDIvENiKLi4EpFrLGuHH3xDoJiunmuUydBrfElpttECrcuUPStcPPn+EekERzveE27EqMNjEIbGE1F9gpFrKm2HH3hdoJivbMwfTS1r4aYCV0p/Hob1n/cUrPwiy34d4gguI/j9W41Wdh52xLC+ifR/48bCOzYhm5tdhwWk4mdixbRc9pURUGg16XTKcvMpGDTJoeYwKgorly8mB4XK1fw6DZ+PFaLhfwNGzzebk+QXChJPpQkH00kF0IIIYQ4nam1vgQPvIiqv46q4/9hr+P7pTnWW1xV9N9HsZkaSHvjT4LPOt8dzfUoH18f0icls3fBASymphHB3Z9nE5oWTExfx0miFpOV5besIe7sKC5ZOYbwTs4n6Ky4byOWBgszMi6i/YVtY3KlSutLwLCR1P7yg+LaqPnpOzTtktF2blmdyLdzN2Le/0zxE37XQwBEPPgUMW/81y3tdyeVr4aQi86g+tu12EzmI9urv16Dtn0Mfr1SWnRen8hgfKKCqV2lXAzCUmGgfmceuq4tX4jHk+xjPBMoX/U1VsUYzxfoYv99YzxHU2t8ieo7iZL1CxT5KV73OX7RaQQleWf93R3UGl9iekykaMtRNddNn+MfmUZwQjN1Whdicv54mYx3R2E1N00SzV31Nj66YGJ7T/Ngr1pOrfUldIDjeGjFigX4xnXAv33Lx3EPffIwNpORLi+vJPSMC9zRXI9T+WrxGzWY+u9/V7yW1i35DZ+UBDTdW75Crt/5A2n4bQ3WyqaJXpbiMkzrt6Ht433joT6+PqRObM/+BZmKmn3m55mEpIUQ1dexHm81Wfh71l/EnB3LhL8nEdYpzOm51923BkuDhUkbppJ0Ycv+PnkDqcc1kVwoST6UJB9NJBdCuJfm+IcI4ZroibPIum8MOc9eQeiQydTuXE3l71/S7ra3UKnVmPXl1O3NwDe+A7qEtOOfELBZLFSv+4Gg3sNoyN1DQ66ysKKJTMA/1fsm9ARddQWlM6+l4j934zd6FMaNm6j7filhTzyGSq3GWlWFcdt2NMlJaJKTXTqnSqsl6Npr0L/6Gvj4oBswAEthIYaPPkbbvRv+Yy/0bKdOQuy429jzyCiyXrqMiEEXY9i1mvK/PifllneOXBs1mRvQxaXhF+/atdGWhU27nkO3TaXw4RsJGj6O+q3rMfz8DdH3vYRKrcZSXUHDrs1oE1PRtnNtlQ6VSkXI5JlUfvIGKl8d/n3OxlJVQdWC9/EJjSBkwhUe7lXLpAyYxfpPxrB54RXEdZtMZe5qCrZ/Sfexb6FSqTHWlVN1KIOA8A4ERqS5FNPI3FBNec7fpJ/7UGt174Sl9L2R9QsvZvMP1xPXaTyVh9ZTsGch3Ue8bM9HfQVVhZsJCEslMKy9SzEA7XrM4OCW/7Lu60mk9r0BbDayN76NxjeU1L43tmaXjyml/yzWzx/L5m+vJK7rZCrz1lCwYwHdL3zj8PVRQVXB4esjvINLMY3MDdWUH1xJ+tAHW6t7J8RTz5X0cx9i5493gEpFVNpITHWVHMx4D5VKRcqZN7Vij48tZfDtbHh/BFvmX0Jcr6lUHFhJwebP6H7xe6jUaky15VTlricgMo2AqHSXYgDSz3+MXYtmoVKpiOx0Aea6Cg6unosKFckDb23NLp+QlNsGseH8eWy59HPipvSkYlUOBZ9vofs7k+z5Ka+lakMeAWmRBKRFUrU2F2OhgYTL+1Lx9wGH8wV1j8Mv0btXSjqWla/N4brlv3HZlwvY+tUCUgcNou+My/nm+uuw2Wz4h4fT7qz+lGVlUp6VRX5GBnuXLWPC3LcJS0mlbN9eEvr2Y9Add7ByzhwqDhxo7S61mORCSfKhJPloIrkQQgghxOksatIssu8Zy8GnryR06GRqd6yh6rcFJN7xxuFabQV1ezLwTeiALsG11Q1tFgv6tT8S2GcYDbl7acjdq9ivjYzHr7331fH73dmNr4Yt4/upf9B5Wir5fxez+9P9nP/BQFRqFfXlDRSuKyUsPZiw9BAK1pRQW1hHtyvTyP+ryOF8UT3DCIjzZ/+SXJJHxlO+q4ryXVWKY4ISA4jqGe4Q6w2CZ1xD0Y0zKL33NgLOH0PD5gxqf1xCxMPP2OvWVZUYd2xFk5SCNsm1SUrqoGD8+ijvfsXhFb60nbqi6+adtwmOuGU0ORc9S+5VbxI6aQC1a/ZStWAV8W9ca89FhYG6jP34dojFt0OsS+dUqVTEPHAxBf/5CFQqgkb0xFJZQ/n7v6JSQcSN3jvxPm7cbex+9HyyXr6UiIFT0O9eRdmKz0m9+d85xnO0xPPvZMuLw9j1zlSiz5pGVebfFK/5lE5XfWCvxdWUo89eh39MOv4xLZ806Y1Sht7JhrfPY+v/phLbexqV2Ssp2DifbtPeb6rTHlxHQFR6U532ODEAyUNuo2jbN2z//Epiek6m8sAqcv9+g07jX0Gj8867KgLEjJ/NvocuIPuFGYQNvpiaXaup+PMLkmfNPfJcqd23AV18GjoXnys2i4Wq9UsJ7nUe9Xm7qc/brdivjUzAP6VlixN5WsB1Uyi/5E4qb34cv3HDMK7fTv23vxLywl328fLKakybd+OTmogm1fUvUfhPH0Ptx99SPvUOAq6bAjYbNe9+iSokiMDrpniwRy3X887efH/eYn6d+jMdpqVRuLKQzPl7Gfr+sMPvOeopWVdMSHoooemhFK0poq6wlk5XdqbgrwKH80X0jMA/LoCc7w6QOKIdlbsqqNxVoTgmMDGQiJ6Rp6qLJ03qcU0kF0qSDyXJRxPJhRDuIysQC7cJ6HwW7R//BktNFflv3Untngza3T6XiFH2iYv12TvIfvRiKn7/0uVzWgwV2Mwm9Bm/kP3oxQ4/pd++4anunBTfXj2JfOctrHo9VU88jWnbdsKefIyAyRMBMO3ZS/lNs6j9bukJnTfompmE3H8vxrXrqLjvAWq++BL/0RcQ+cF7qHx8PNAT9wjs1J+ODy3BUlNJzru3UbNvPSmz3iVqxFUA1OVsJ/OpCZT/9Xkrt/TU8Ovej/iXPsGqr6Lkpfup37mJ6PteJmTsdACMWbsouOsK9D9/c0LnjZh5BxHX30vdhhUUP3U7lZ/Nxa/nWSS+uwSfYO+8XU1Y4lmccek3mBuq2PnjnVQdyqDHRXNp18f+umEo3sHGLy6mYPuXLsc0Ks3+HZvVRESK990Grzlhcf04Y8J8zA3V7PztPqoKN9Jj5Cu0634pAIbSXWxcMoOC3QtdjgHQBUQzYNr3BIa1Z9/q58lc+xKhsb05Z/qPaHTBp7yfrgpLOJMzpn1t/13/9B+qDm2gx5g3ade78frYzsYFU5TXx3FiGh25PpLaxvXhqedKu75X03vy/zCU7GL797eSteI5giI7M2Dmb/iFJJzyfroqLHkA/a5eirm+kp2LZlGVu47uF79P4plXA6Av3MbGjy7i0ObPXI4BaNf/enpd9gWGop3sWHgDWcufIjC6KwNuWYlfqHeuAuNMWP8k+i25EnNlPTtnL6FqfR7d351E4swzANBvK2Tj+P9x6LPNADQUGQA48NIKNo7/n8NP2fLM1uqKW+StW8fHY8fiFxbGhLfm0q5/fxZedy0ZH30EQFzPXsxcupQ+l804EvPFjMvI+OhDBtx0E1M++phOF47mh7v+j2X33dtKvXAPyYWS5ENJ8tFEciGEEEKI01lAlzNJeeprLIYqDr3xH2r3bCDxzjcJv6Cxjr+dnIenUPnbCdTx9fY6vmHDL+Q8PMXhp/SbNz3VnZMSPyCaST+eT0OlkeU3r6FwXQmj5g2kxzUdASjZWsG3Y35l16f7AagtrANgwwvb+XbMrw4/Ob8UUF9uxGqycuDHfKfHZLyyo9X6ezy6Hr2JeX0eVn015c89QsOOLUQ8/CxB4+2TsUyZeyi5/Tpqflzcyi31vIAz00j++v+wVtVS8H8fUZeRRcKb1xJ++VAA6nfkcnDqy1QuWHVC5w2bOYx2H91Kw+58Dt02j5IXFqHrnED7Xx9Fm+C46rW3COrYn04P2sd4Drw/m5p960m95V2ih88EoPbgNvY+M56yFZ8d+0SnqZAOA+h5x4+Y6yrZ9+nN6Pevo9PMecQNvgaAmrytbH9tDEVrPm3llrpfaMoA+l73A6a6KnYtvIWqg+voNu0DEvvb+64/tJVNH4ylIGO+yzEAYe0H0XvmQmqKd7NzwXVU52XQ/ZJ5pAy9/ZT38UQEdjqLtEcWYampIved26nZu57kW98mcmTTeGjWk5Mo//MLl89pNpRjM5uo3vgzWU9OcvgpXvy6p7pz0nz7diX84+ewVhuofnAOps27CHnhbgKm2RfFMu3aT8XM+6n79tcTOq9PdAQR376JT2oihhf/i+GVj9H26kLkkrdRBwd6oisnLWZALKN/GEtDVQN/3/IXJeuKGPrBeXS+xr5icvnWMpaNXUrmfPuXsOoOv+fY8sImlo1d6vCT/0seDeUNWE1WcpcddHrM1le2tFp/W0LqcU0kF0qSDyXJRxPJhRDuo7LZbLbWboQ49dq1a0d+fj7ayAS6frz7+AH/AqWp+1u7CV4lfk/n1m6C16iMKWvtJniV9GVtdxVKj9AbWrsF3iMgoLVb4F185Hta/2QL8m/tJniPO59v7RZ4lTODHm3tJgghRJv1htmCHkhMTCQvL6+1m3NKNdY1dKGJDH34YGs3p0X+ejKZhqr8f+XvTwghhGsa/95pIhPo8unO1m6OVzj/Au+ePHaqLcxoG3fXOhWC0le3dhO8SuCvl7R2E7yGf6VvazfBq+iqZGrAP5Wm1bd2E7xGQd81rd0ErzIu6evWboJXida919pNEEKINkvq+FLH93Yys0UIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiH8RmUAshBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcS/iKa1GyCEEEIIIYQQQgghRItZ5Ra8QgghhBBCCCGEEEIIIYTXkjq+15IViIUQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE+BeRCcRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQvyLaFq7AaJ1mSqL2XPzWU73RY69nqiLbjjFLRJCCCGEEEIIIUSjDVYrGc3c2qvmFLdFCCGEEK3DXFnMvhsGON0XMe46Isddf4pbJIQQQgghhBBCiEZSxxdtmUwg/pfThsXQ+e31rd0MIYQQQgghhBBCOHGmWs2Zzdw/6g2zBf2pbY73sYHK4rww6/XaaLOFEEKcepqwGDq+t7a1myGEEEIIIYQQQggnpI5/HFLH92rNXLpCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQojTkUwgFkIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDiX0QmEAshhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII8S+iae0GCCGEEEIIIYQQQgjRYlZba7dACCGEEEIIIYQQQgghhBDNkTq+15IJxP9yarOKmH1Brd0Mr1DS3tLaTfAqZQmFrd0Er9Hpp9jWboJXsVWWtXYTvItVXjuOKDW0dgu8iioiqrWb4FUM7VSt3QSv8V7F4tZuglf5rLUbIIQQQgghhBBeTgWoLXJDSYD1db1buwleJTIvsbWb4DUKwtJbuwleJaZc19pN8BqaWpmoIIQrQkMKWrsJXuWFzVtauwle5cXWboAQQgghPEYqTkIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC/IvIBGIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIf5FNK3dACGEEEIIIYQQQgghWsxibe0WCCGEEEIIIYQQQgghhBCiOVLH91qyArEQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEP8iMoFYCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQoh/EZlALIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEv4imtRsghBBCCCGEEEIIIUSL2GxgtbZ2K5p1cN075K57z+k+o6HoFLdGCCGEEEIIIYQQQgghhDjFpI7v1WQCsRBtjLWm1ul2lb8fKrUsKi6EEEIIcbJUajVaf3+H7TabDVOt8/dipyvJhZLkQ0ny0URyIZqT3P8mkvvf5HTfny+n06A/dIpbJIQQQgghhBBCCNH2ST2uieRCSfKhJPloIrkQzfm31/FlArFwq4rMP9n/0+NU520kIKoj7QbfTOKAa046pnTHUrJ/fRZDwTZ0IfFEdhlF+rgX8NH6ebI7J6VhfQaGt97FtGs3mpRkAqZPJWDyBJdi63//C/2cN4levECx3VpTQ1H/oU5jYpb/gE9c7Em321PqN66hct5rNOzZgTYpleDJlxM8bppLsbUrfqXinRdJnP/Tsf+NzesovGU68fMWoevayx3N9pjyAyvI+uNp9AWbCYhII+msG0jsd9VJx1TkrCTz9yfRF2zGxzeIyLThpA9/DL+QBA/25uSU568ma/2r6Eu2ExCaSlKPK0nsNv2kY6wWE5lrX6Ak5zfq9fmExPSi0zn3Exrb15PdOWnlh9aQtX4O+tId9r51v4LErsd+rrgSY7WYyFz3EiU5v1NvyCckuiedzr6P0Ng+HuzNySsvWEfWxjea+tb1MhI7TznpGKvVROb6VynJ+5N6wyFConrQ6ay7CI3p7cnunJTygyvJXPk8+qItBISnkdT3Gtr1uvykYsxGwzGiVWh8A93Uever2vUnud8+Qc2BTfjFdSRuxE3Ennu1S7HlG78jZ8FD9H1uS7PHmPRlbJidyJlv5KENjnJXsz1i/apq5r58iF3baklur2P6zBgmXxrd7PFWq41vvyhl/rwicrMbiIrVct6oMG65K5GgYJ8jx/35ayXvv17Avl21RMf6MnBYCP/3UBI6v7b3haXUIUMZ8cgjJPTtS1nmPta+8w4ZH33U7PGdRl/IlYsXO2wvy8zkla5dPNhSz5NcKEk+lCQfTSQXQgghhDidGbb9TdFnz1KXtQVdQhqRY64jYtQVLsVWr/2Rgo8fo/PctQ77aravovDTJ6k/sAOVVkdgt3OIu+pRdAlp7u6CR1WtyOTgMz9h2JyPf1oUcdcPIu6qAc0eb7NaKfpkHYfmrqB+fxm+ccFEjOlB8oMXoAnx3jEMZ/TbV1D45TPU7t+MLj6N6NHXEzny2HXrRlXrf+DQJ4/Q9fUNDvsMO1dSMP8J6g7uQKX1JajLQBKueBxdvPdeG8a1mzC8MQ/Tjr34pLYj4LLJBEy9yKXY+uV/Y3j5HaJ++NRhn81kxvDKuzT8uRrLoUK0PboQdPct+Pbu5u4ueFzl3j858N1j6A9uxD+mI4nDbiF+0LHHBBuVbvmO/Yvup/+j2z3cSs+Q8dAm5Vl/sv/nJ9Dnb8Q/qiNJA28isf+xc+FKTMnOpWT/9lxTLjqPouPY5706FyCvo0erXbmL0ue/oWHLAbRpcYRfM5LQy891KdawbCMlTyyg/arnHM+7ejelzy6kYcsB1EF+BAzrQfRD09DEh7u7C26zMqOK597NZcvuGtKS/bh2ahyXT2h+bL+2zsLL/81j8a9l5BcZSYz1ZdqF0dx2VSJ+OscafXmlic6jNrD7pzOJDNd6siseI/W4JpILJcmHkuSjieRCCPeQCcTCbapy1rLpvQuJ6DiSLlPeoip7Fbu+uA6g2Q/NrsQUbvyc7Z/MIO6MGSQNnU1DVT45y1+gpnAXfW/+GZVKdWo6eAKMW7dRfuNsdGf3J/Th+zBu2krVw08AHHcSsVWvx/DOB073WXLzAQh99gk0icoJoeoI7/1A1LBjE0V3zsTvrEFE3v0EDVszKHvmXoDjTiK2Gqqp/PCN4/4b1ho9pU/8n33Zey9XmbeOjZ9OIKLDeXQdO4fK3DXsWHIzQLOTiF2JqT60iQ0fjyEybTjdxr2Jqa6C7JWvsHH+JM6+/i/UGt2p6eAJqCzcxMbvriAiaQhdhz5NZeEGdvx+N0Czk4hdibHZbGxedgOG0l2k9r0RtY8veTs/I2PJ5Zwz/Wf8gxNPTQdPUGXRJjZ+fxUR7QbTdciTVBZmsOOPewCanUTsSozNZmPzTzdhKNtNap/rD+fjCzK+v4Jzpi3z3nwUb2HjsmuJSBhI10GPUVm0kR0rHgBodhKxKzE2m43Nv9yKoWIPqT2vsedj9wIyll3DOZO/wz/I+ybcVx7aQMbXU4lMOZeu579E5aF17Fh2G0Czk4hdiVk+J7nZf9MvJIlzb2p+gm1r0meuZdeLYwntPoIOV72Jft9qsj64HuC4k4jNtVXkLX76mMfYLGZyFz6GzWJ2W5s9ZetGAzfN2MvZQ0J46NkUNm8w8Mh/DgA0O4n4q09KePK+HC6+LIob70jgYHY9H84tZNumGj76tgs+Pip++LaMe27Zz9jJEcy4NobiAhPz3ipg/9563v+yk1e+/2pOu/79mbl0KVnLl7Nk9q0kn3MOk9+3v9dqrogQkdaBuspKPpmofN9mrq/3dHM9SnKhJPlQknw0kVy0QRbvvfWZEEII4W1q96wn+9HJBPUeRuLNL1O7ey15r88COO4kYktNFUVfPO90X/2BnWQ9MIbA7oNIuPFFrA21lC56i8z/nEenuevQRsS5vS+eoF+fw46J7xF6XifSXr0Y/doDZN7yJUCzk4gL/7uGrNu/JnbmAJLvPZ+6rFLy5vyOIeMgPX+ahcqnbXwRt2bverKenEhwr/NIuuFVanav5eBbtwAcd/KbpaaKwgWOE7wA6nJ2sO/h0QR1G0y7617C2lBL8ZI32XPPuXR9bT3aiHi39+VkGTfvoPza/6AbeBYhj/0fpk3bqX7gWYDjTiK26g3UzP3I6T6bzUblrAcw7ckk8NpLUfn6UrtgCRVX30nU9x/jk9A2nicA1dlr2fr6aMK7jKTTZXOpylrFnv9dC3DcScTmuipyfnjyVDTTI2Q8tElVzlo2vT+GiI4j6DL5TSoPrGbnAnudtrlJxK7EFGz6nO3zLyeu32UkD76VhqpDHPj9BWqKdtHvhp+8Mhcgr6NHq9uQSd7UFwg4twexL82kbt0+Cm97H+C4k4gt1bWUvbTI6b76LdnkTnyWwGE9iH31WqxVNZS/9j15014k+dfHUeu8b/Lshm16pszexbn9Q3npvg6s26pn9hNZAM1OIn7glQMsWFrC7TMT6ZYewOZdBl6al0dppYkX7umgONZstvHMO7mYLd4/Tt4cqcc1kVwoST6UJB9NJBdtkNTxvZZMIBZuc/CPVwmI7kyvaxai9tESf8YMzA0Gcpa/QEL/q51+mHMlZt939xHb71J6XP7JkbiIjiNY+1JfqnLWEJZ6zqnspktqPp6PJjWF8NdeRKXV4n/RGGy1tRjmfYz/pPFOc2E+kEPVU89j2rINW20tmnTHb4yac/MA8BsxDHWg964KebSqz+ehSelAzHPvoNJoCbpgItbaGqo+fYegi6Y6zYfp4H7KXnqEhh2bsdXWoO3Q6Zj/RvmrT2KpqvRQD9wrZ/UbBEZ2pM8lX9iv+17TMTcYyF75Cgl9r3SaD1di8jf9D9+gGPpMX4Dax/7hOCimG+s/PJ/KvHVEpA451V09rpwt7xEYlkaf0e/Z+9V5EmZTDdmb3iah6yXOc+FCTEX+asoO/sngGX/hH9IOgOjU89ny001UFqzDP3jSqe6qS3K2fEBgWAf6XPCOvW+dJh7u2zskdHH+XHElpuLQaspy/2LwZb/jH9yYj5Fs+ekWKgs2eO0E4pxt/yUwtD19zn8TtVpLfPp4e9+2vk9Cp4ud58OFmIqCtZTl/83gqU2TyaOTR7Bl+WwqCzPwT/e+CcQ56+cSGJFOn4n/Q+2jJaH7VCxGA9lrXyex5wznuXAhpv9lPzjEWS0NbP3ueuK7Tj4VXWuRQ8vm4Bffmc63f41aoyV60GVY6vXkf/8iMUNnOs1HXcFe9n88G33WWqz1BvwTuzs99+45F6PPWoepssDT3XCLj98tJDXNjznz0tFq1Vx0cSQ1Bgv/fauQSdOjnOZi3psFjLgwjMdfbn9kW7degdx46V5W/1nN4OGhvPJUHmMmRfD8W03vRwYMCWHKyB1syaihz5lBp6R/7jDo9jso3buH+VOnYDWb2fL55+iCgxly193NFxA6dKA8K5OclStPbWM9THKhJPlQknw0kVwIIYQQ4nRWsugtdIkdSX1wPiqNlvDzLsFSa6B44auEn3+508+RDfn7yH/7Lmr3rMdaZ0CX3NXxvN++jiYshvaPL0Tta18dMvSccey+vg+lS94mfubjHu+bO+S/8Sf+HWPo+vnVqLU+xEw/A7O+gfxXfyP2yv5O85P38nIix/ek41uXHNkW1DeJHRPepXL5HsJHOebLGxUveQO/hI50uPdzVBotEedOx1Kvp+jbV4kY4bxuXZ+/j7z376Rmz3qs9Qb8khz7Wrz4NTShMaQ9/G3TtTFgHDtv6UXx93NJvNL7JpLWfvgFmvbJhL31DCqtBv8JF2CtqaXm/U/xnzLW+RhP9kGqH3sZ05Yd2Grq0HRs73CMce1GGv5eS/QvX+KTaJ8srBsxmMrZD2HcsBX/8W1nAnHuL6/gH9uZ7jd/g9pHS+yAGVga9Bz86XniBjofE6wt2su+z2dRvX8NlgYDAQnO63PeTsZDm+T89SoBMZ3pPfNwv/rZr4MDv79IwlnOc+FKTObS+4nrO52el/0zF8NZ80o/r80FyOvo0Srm/ohvejyJ/7sdlVZDyNRBWA31lL3+HSEzhjrNhzGzgKK7P6YuIxOboR7fLu0cjqma/xea6FASP70TldY+3cW3SyK5Y5+ifkMmAYO87+/u3PmHSE/x45MXO6PVqpk2JhpDrYXXPs5nxvgYh1zU1Vv437dF3H9jEndfnwTAuOGR1NVb+XRxMc/d1R612h5z+f/tJmO7nsJS0ynvlztJPa6J5EJJ8qEk+WgiuRDCfdrG156F17OajRRvW0Rs32lHJi4CxJ8xg9qSvRgOOa5g6EqMqaachspcIjufr4gNTuyNX3gKJdsWeaxPLWUzmqhf/gf+F56PStvUL/+LLsRyIAfz7r1O41R+fugGnU3QLdej7e78g40lNw91ZATqwEBsFgu2NrDars1kpPavXwgccREqTVM+gkZPxHwwG+O+XU7jVH7++A8YStg1t+Hbpecx/42aP36i5pclhM+6161t9wSr2Ujx7u+I7TFFed33mk5t2T70hVtbHGM116PRhSiO0fpHHNnnbawWI8X7fya24zhlvzpNorZyP/rSnS2Oydv5OdGpI/APaYfNZsNmtaALjKH/5G+I7+Sdk4etFiPF2b8Qm37RUX2bSG3VfvRlzeTDhZi8XV8SnTIc/+B/5CMghv6Tvia+07FXRW8tVouR4pxfie0wBrX6H31Ln0BtVTb6csfXDldj8vZ8RXTSMPyDE/+Rj2j6j/uC+PRxnu/cCbJajBRl/kBcl0nK33O3qdRWZKIvdrzNn6sx4e3OdvgpzvyRwIiOpA950POdawGr2Uh5xmKiBkxF/Y+/K9GDLqO+cC+1B52vmqzWBRDWcxRJEx8msP0ZzZ4/uNMgEkbfQeSAqW5vu7uZjFZ+W1bJ6PERaLVNb+svujiSA1n17NlR5xBTV2vhUJ6Rc84NVWw/65xgVCrYt6eWqgozhYeMDDzqmC7dA0ho58tvyyo80yEP8NFq6TZhAlsXfIXV3LSi9Ob5nxHduTPxvXs7jYvskEZZ1n4A1D4+p6Stnia5UJJ8KEk+mkguhBBCCHE6s5qMVK/+nrAhkxW12vDzLsGYn0l99jancSpdAMH9RhB76X34p/d1ekxD3l4Cuw88MrEJQBMahV9KV+pzHOta3shqNFP23TaiLu6DWtv0ni5m+hnU7SuhZushhxhLrZGGgxWEDVcugBE6JA1UKmp2Fnq83e5gNRmpWvsdYYMvVlwbEedOp+HQPuoOONatwV5vCe4zkrhL7sc/zfm1UZ+/l6BugxTXhjY0Gv/kbtQf9L5rw2Y0Uf/LCvzGjjgyMQ3Af/woLNm5mHftcxqn8vNDN2QAQbdeg6ZHZ6fH1C34Dt2wgfgkxtnrkhYLPtGRRH7xNv7jR3mkP55gNRsp3bKImDMvUdQeYwdcTl3RXgx5zdTnfAMI73YBKRc9SlBy8/U5bybjoU2sZiPF2xcT1/uofvU7Ti6OE2OqLae+MpfITkflIuFwLrY73nbcG8jrqJLNaMbwQwbBk85WvJaGTB2EKbOQhu0Hncap/H0JHN6TqLsnoevj+EUMAFu9EXWwv+K8PuHBh/d53yRao8nK0j/KmXR+lKKOP+3CaDJz6tm+t9YhpqzSzNCzQhl2dphie0qCHyazTbHS8Nl9grllRgKTzo/0WB88TepxTSQXSpIPJclHE8mFEO4lE4iFWxj1RdgsRoLilN8WDozrBkB9heOHAFdifPxCUGt01JXtVxxjrq/GaCimocr7Vge0lpWByYQmTbmCsCbNfisRS4HzgqFPXCxBV19J0NVXOl19GOwTiNFqKbt+FoVnDKLo7HMpn3XnkZWJvZGlvBRMRnw7dFRs17a3P7YU5TuN08TEEzrjBkJn3HDM1YfNZSWUPXc/4bfehzY13X0N9xBjTbH9uo9WThIPiu4CQH1Vbotj4npMpbZsH1l/Pkt99SEMxbvYvexu/MJSCEvyvm9jG2tLsVmNBEUof7+Nj+sNjteGqzGG8j34B7dj118P8+eHZ7D8vc6s+2YyVcXOC5fewFh3uG/hR/Ut3P5cqdc7DlC4GnMkHyse5c+Pz2L5B11Z9+0UqoqdF6y8gbGuDJvVdKQvjYLC7c/zeoPj67+rMYaKvfgHJ7Jr1RP8+dkgln/cm3XfTaeqxPkgWWtraHwNiOyi2B4UZR+IqK92/BvQkhiA0uzl5G/9jF7j3kOt9s4bVZiqirCZjQS0U75/8E+0v39oKHN8HQXQRbQjcez/kTj2/whoZvVhgMQx/yFx7P8R3mu0+xrtIaUlJkxGG+md/RXb0zrZHxfkNzjE+PiomP99Vy64KFyxPWOtHpsNYuN8CQz2wVenIi9HGW/QWygvNVNc6H1F2OYExcai0eko3rlDsb14l72wHpqU5DQuokMHQtu1Y/bGjTxeW8e9B3IY++qr+Aa1nZWXjya5UJJ8KEk+mkguhBBCCHE6M1cWYzMb0aUo64y6ZHv9wFjivF7gG5VI9OTbiJ58G34pzhe/SLzlVeKuelSxzVKrpy5rC9pI77vbkzOmYj02o4WAbspVYAO62m8t3pDn+IValY+aXr/fRtQk5QB19cr9YLOhSwh1iPFG5ir7teGf1E2x3a+d/fdtOsa1ETvxDmIn3oF/cjenxyTdOIeEy5UrUFvq9NTu3+yV14a1rNw+xnPUCsKadPtjS0GR0zif+BgCr7uMwOsuQ9Oxg9NjzPuy8WkXT/UTr1AycDxFvUZSNv1mTNucL7TirYzV9vpc4FErCAfEH67PlTufGOgX3o7kUXeRPOouAhN7eLydniDjoU2MBnu/GvvRKDDW/riu0kkuXIjx0dlzUVuWrTjmSC6qHcdKvIG8jiqZi6uwGc3ouijvgOnb2d5ec16p0zhtYiQRs8cSMXssuq6Oqw8DBE8+B2NWIaUvfou5oIKG3fkU3/8J2uRo/Acc+462raG4zITRZKNLWoBie+cO9jp+bqFjHb9dnI5Fb3fnrJ7BmExWCkuM/LG2kg++KmDssAh8/zER+dYrEpl9ZSIjB4Y7nKetkHpcE8mFkuRDSfLRRHIhhHt558wQ0eY06O2TYjX+yjem2gD7N92MeseCiisxah8Nsf0u5eCfcwiM7UZ4x/NoqMwj87v7sJrqnJ63tVlKywBQh4YotqvD7IVCa1lZi89tzs3DWliEZuRwAi+fjqWoGMO78yi7/Bqiv1uIOiS45Q33EEtZCQDqYGWhVB1i/71byp1/QHSFzWaj7Jl78e3cneCLr6R+87qWN/QUaTDYr3ttc9d9TXGLYyLThtPp/KfZ89O9ZP1uv12RyseXQbdkoNF53xuehlp7u7U65bWh9QsD7JOFWxrTUFtM7vZPCInpRZchj4NKzcEtH7D+m4sZeNlvBIQku7MrbtFQa3+uOPbN/ns31jnLh2sxDTUl5O74hJDoXnQZ/Jg9H1vnsX7RVAZO/5WAEOdvoFtTQ11j35SvpVpdGNBMPlyMaagtIXfXZ4RE9aDLOQ/Z87HtI9Z/fxkDp/xAQLB35aPxOd54nTfS+tlXGG98XpxsjNViYvfyB2g/4Db8ghMd9nsLY9Xh9w+BYYrtmiD7a6KpyvveG3hKaYl9Im9IqPItfVi4/XFZidkhxlenpvcZyr8J2zYbuO/W/cQl+DJ8dDgajYoxEyP533uFdOjkR/9BIRQdMvLq03nU11spK2lDE4jj7IO+dRWViu21h9+PBcXGOsSo1GrC27cnJDGR5U88QcmuXSSeeSZD77mHqPSOfDzuIo+32xMkF0qSDyXJRxPJRdukslpbuwlCCCFEm2CusH9mdvhMHRxxeL9jvcBV/mnKCbTmyhIOPDkdm8VM5JhrW3zeU8lYpAdAE6ac4KOJsD82FesdYtQ6DSH9UxXb9BkH2XPtfHTtwogY1zYmSZoOXxs+QWGK7Y3Xhqmy5ddGQIc+yn+rspj9z16CzWImavR1LT6vp1hLygEcxlvU4YfHeEpbfmcma3EptfO/RduzC8EP34FKpabmoy8pu/QWon6cjybJOycCHs1Yfbg+F3DUmEXg4TGL6tO3PifjoU0aqpsZuwo8Ri5ciFH7aIjrM52DK+YQFNuV8PTzaKjKY9/S+7Ga6mgweF8uQF5Hj2YurgRAHRao2O4TEXx4f3WLzx04rAfRj02n5KH5lD27EACVr4bUVc+hDvI7TvSpV1xmBCAsRFnHjzhc1y8pO3a9/ee/K7j8rj0AdEnz5+X7nX9JpS2TelwTyYWS5ENJ8tFEctE2SR3fe8kEYuEWKlTOd9jsT36rxfGNr6sxnSfNwWYxsf2Tyw4HqkgZ9h+MNSVoAyNOruGeoHLeL5vVfisRm9lxMo+rAq+aQdBN16E7s9+Rbb5n9KN00iXUfvUNQdde1eJze0wz+Wj8PZ9MPgyLPqdhWwYJny5DpW4rC6o3c30c47niakzhjm/Y99vjJPe/mcj0kZjrq8lZ8yabv5xOvxnf4hfq/Ju6raa554rN/lyxWp3kwsUYc0M1usA4zhg/Hx+N/Ru8UclDWfHJIHI2vUvXc58+2dZ7wHF+z1ZnzxXXYszGanSBsZwx7lN8NPbiSVTSEFbMH0rO5vfoOvTJk2y7J3gyH3p0AbGcceFHTflIHMyKL4eTs3UeXQc9dnJNd7tj98vWglw4i8nd/CFmo4HUs25pWTNPkWbfP1iP9Tp6emouF9bDr4lms83p/kZVlWbmvpTPFx8VE5fgy5v/64ifv/3v6X1PJmMyWbnnZvuqJyoVXHVTHOVlJkLD29BHCJvzHDS+b/DRah32qTUavr7magq2bKF0j70wu/enZegLC5j0zru0O+ss8tav91ybPUVyoST5UJJ8NJFcCCGEEOJ0drxarRs+U9ssZsp++ICi+c9gNRlJvusD/Dv0OunznhLN5aextm869mCnuaKWnKd/ouC9lejahdHtq2vx8fd1dys9QtVs3dW910bpsvcp+OJpbKYGUu/8LwHtnd9auFU1+zQ5/Ps/iTENq96AT2w0ER/NQeWnA8B3cH9KRkyldt7nhDz2fy0+96nl+dcSbyXjof/Q7Gtm43Xg5LniYkzniXOwWk1smz/jSFzK0P9gNJSgDfDCXCCvo0drLh9Hftcn8VqqX7SW0me+JuyGUQQO74m1uo6Kd5aRf8Uc2i24G21iZIvP7QnN5eLw2wtM5mO/vzinbwhL3ulO5sE65nyUz+hrtrH8f70I8Pdxd1Nbj9TjmkgulCQfSpKPJpILIdyqrcy4E17ON8T+7Q5Tbblie+NjXajjt6ZdjdH4hdDj8k849+ky+v9fBsOeraLj+Bcx1ZShC4l3b0fcwCfK/qHEWlWl2G47/NgnJrrF5/YbMkgxeRhAm9YeTUoypr37WnxeT/KJtPfXWl2p2N742CfK8Zs/rjAX5lP++lOEz7oPdVAI1toabA31ANjq67DW1rS4zZ6kC7L311SnvO7NdfZVC/yCHa9pV2KsFhM7l8wiofdldBnzMtGdLiS+1yWceeVSGgxFZP/9stv7crJ0ATEAmOorFdvNhx/7BTpeG67G+PpHEJU09MjkYQCNbzBhcWegL/PO28HpAuzPFVNDpWK7+fBjv8CYFsfY8zHkyGRZOJyP2LaQD+VrqfnwY7+AY+Xj2DG+fuFEtRt0VD6CCIvth758t5t64D66oMbrXrm6SeNjXVDcScdYrWYOrHudpL7X4KNVrrLjbbRh9rabDcq+mWvsr5G+YW1jpRZ3iIqxf/itqlQWW6sq7I+jYx0/HDf6a3klE4dt58v/lXD59bEsXN6Djl2afvdBwT48/1YaK3f15aufu7F2Xz/ueiSJqgozMcc4r7fRF9pXNQmIUK5qEhBhH2ioPuR4y0OL0ci2BQuOFA8a7VqyBIC4nj090VSPk1woST6UJB9NJBdCCCGEOJ1pwu21M4fP1Hr7Y23EydXb6/P2kXn3+Rx69x4Cewyi05urCBsy+aTOeSr5xh5eFbGiVrG98bFvfIhDTKPyZTvZeOYLFH6wisRZQ+m75i4Cu3vf+EVzNGGHrw298tqwGNx0beTvZe/9I8j74C6Cug2iy6trCR908Umd01PUR8Z4lCtO26rsq2WqY6Jafu6IMHwH9T8yeRhAHRyIb98emHZntvi8p5pv6OH6XM1R43v/gvqcjIc20QU306+6xn45Ge9yMUbjF0LPyz5h2BOlDLhzA+c9VUmncS9gqi1DF+Kd15e8jir5xNhXbbdUKMdrGx9r4sIdYlxhM5kpvGMeIZcMJva5Kwka1ZeQKQNp9819WIqrKH/t+5NruAfERNrr6ZVVyjp+xeHH8dHH/rJRRJiWIWeFcvXFcXz0fGd2769j2YqWr4bvjaQe10RyoST5UJJ8NJFcCOFeMoFYuIVvUAwqHy01hdsV2w2HH/uFOd4O3tWYqpx11BTvQRsQTki7vmh0QdRX5NJQlU9gvPfd/ksdEQEaDaZ9WYrtpkz7Y584x4lerrDW1FD/x19YSkodd/qoUQcGOm73Aj7hkaDRYty/V7G98bEmtmUfmE2HcrHV11H23AMcHNGDgyN6UPyfqwEovGU6uRf1P7mGe4hvYAwqtRZD8U7Fdn3xDgCnqwS7EmOur8TcUEVo4lmKYzR+IQRFd6Gu8oAbe+Eevv6R9n6VK9+g6Q8/9gtyUmhzMUYXGIvFUu8Qb7Wa8dF653OlqW/K54q+zJV8HDtGFxCLxdzgEG+1mrw3H34Rh/um/HKEvsLeV78gx9cOV2N0ATFYzM6uD+/Mh29ANCq1Fn2pcrK34fBjv+DEk44p3mf/skFij8vc2XSP0IbY3z/U5infPzQ+1kV62WrrHhQZpUGjVbFvd51ie+bhx3EJzguPy3+s4NYr9xERpWHR7z24+9FkgoKVqxVs22QgO7OO0DANXXsGEhDoQ0F+A0UFJtK7+Ds9rzeqKS7GbDQS2135nrHxcVVenkNMWEoKnceMRa1RrrRstVgAqK9u+e31WpPkQknyoST5aCK5EEIIIcTpTBMajUqjpT5HWWdsfKyNdqwxuKqhYD/77xuNsWA/qY99TepDn6OLb1u32NZGB6HS+lC7s0CxvWaHfXBa1y7MaVzZd9vYOXUe2ugg+q2/m/bPjkcT4n23UD+WI9fGQeW1UZdjr0H7RrW83tJQsJ99D46ioWA/HR76hg73f+nV14Y6Mhy0Gsx79yu2Nz72iXdc2MDlc8dEYWtwrNPazBZUgd79pf5/8g0+PL53SFmfa3zsF+44Jni6kPHQJo39MhTuUGw3FBw/F8eLqTr4j1wkHs5FpT0XQXHdPdGdkyavo0qa6FDQ+mDcpayjNBx+3NJVgi1VtVira/E/I02x3SckAN/OiZhyilvWYA+KjtCi1ajYmaX8gtKuw48T43QOMX+sreScaZvIL1L+zUhPsdfmq/QtX8HZG0k9ronkQknyoST5aCK5EMK9ZAKxcAu1xpeYnpMo2rRAcXuewozP8Y9KIzixb4tjcn5/mY1vn4/VbDxyTN7KufjogontM82DvWoZla8Wv5HnUb/sZ2ympn7VL12GT1I7NF07t+y8Wl8q738Ew1vvKrabsw9g3n8AbR/vvA2cSutLwLmjqPn1e2zmpnzU/LwYTWIKvp1a9kFf16kbce8sUPxE/OdRACLvf5bYOf9zS/vdTa3xJabreAq3L1Re99sW4B/egeC4Pi2K0QZEoQ2IpiLnb0WsqbYcfdEOgmK8r6Ci9vElpsMFFGZ+p+zX3kX4h6QQHO1YEHM1Jqb9KEoPLFesvtpQU0TloXWExjq+HnkDtY8vMe1HUZj5vbJvmUvsfYty/B26GhPT/nxKc35TrNzcUFNMZcF6QmP7eKxPJ0Pt40tMykgK9/+A1fqPvmV9h39IMsGR3VocE5MyktLcP5X5qC2msjCD0Gjvu92X2seX2I5jKdz9reL3XLBzIf5h7QmJdXz9P9GYvM0fEZF6rtNV0L2NWuNLxJkTKV37FdZ//F0pXf0FfjFpBKZ453PcE7S+akZcGMayJeWY/nEb1aXflpOUqqNrT8eBJ5PJypP35dDrjCA+XdKV1DTnA5ofvV3IdZfswWRsOu8XHxUTGKRm9HjvvE2gMxaTiZ2LFtFz2lRFQaDXpdMpy8ykYNMmh5jAqCiuXLyYHhcrV/DoNn48VouF/A0bPN5uT5BcKEk+lCQfTSQXbZTF2jZ/hBBCiFNMrfUl5JxxVK34RlGrrfzzK3zj2+PfoeV1kcIPH8VqMtLxtRWEnDnKHc095dS+GiLH96Tk681YTZYj20sWbMSvQySBvR0nWFtNFjJv/5rg/in0+u02/Du2fHJpa1JrfQkdMJ6KlV8rro2KFQvwjeuAf/uWXxuHPnkYm8lIl5dXEnrGBe5orkepfLX4nT+U+h+WYzM1TdCq++4XfJIT0XTr1OJz+40YQsMfq7BWNk1OsBSXYtqwBW1vx3qnt1JrfInqM4niDV8qao/F6z7DLzqNoKTTtz4n46FN1BpfYnpMpGjLUf3a9Dn+kWkEJzSTCxdicv54mYx3RylykbvqbXsuentfLkBeR4+m8tUQPPZMqr9do3gt1S9chbZ9DLpeKS06r09kMD5RIdSuUt5N0lJhoGFnLrqu3vcFBl+tmovOi+Dbn0sVdfyvl5XSvp0fvTo7LmwTGaZld1YdKzOUk9lWrLffcbNHJ+9bDOdkSD2uieRCSfKhJPloIrloo1q7Hi91/GZpjn+IEK5JHnYnGW8OY+tHU4nrM43K7L8pzPiUrtM/QKVWY6opp+rgOgKi0gmITncpBiB56G1seHMhO+ZfSUyvyVRmryR3xRt0mvgKGl1Qa3a5WYFXzaDsqhuouPNe/Eefj3HjZuq++4HQJx9BpVZjrazCuG0HmuQkNCmufZBR+WoJunYm+lffAB8ffM/uj7WwEMOHn6Dt3hX/saM93KuWC730WgpumU7xA7cQOOIiGrZuoGbZIiIfeB6VWo2lqpKGnVvQtktBm5Tq0jnVQSH49Vautms7/M0g347d0HX1zgnVACnnzGbDhxewZcFlxHWfQsXBVRRs/Zzu49+2P1dqy6nK30BARBoBkWkuxQCkD3+YXd/fhkqlIjJ9FOa6Cg6uexeVSkXygJtbs8vNSul9AxsWTWXLshuJ6ziOioL1FOz9hu7nvYhKpcZUX0FV0WYCQlMJCGvvUgxAYrfLOLj1I9Z9M4WUPtcDNg5sfBuNLoTUPte3Yo+PLaX3dWxYfAlbfr6ZuLRxVBSup2Dvt3Qf9sLhfFRSVXw4H6GpLsUAJHadzsFtH7Nu0VRSel8HNhsHNr9rz0fv61qxx8eW0vMaNiydwZZfbyOuwxgqijIoyFxC9yHPNOWjZCsBISkEhKa4FAOQ2HkaB3d8wrrvLyOl59X2fGz9AI1vMKk9r2nNLjcr5axbWP/5ODYvnklcl0lU5q2hYOcCuo9+HZVKjbGugqqCDALCOxAY3sGlmEbmhmrKc1eRPuSB1ureCUsYfQfbnxnOnjemETVgGtV7V1Kycj5p171vfx01lGPYvw6/2HT8Y9Nbu7keddWNccycvJv/XJ/F6PERbFyn5/uFZTzxSipqtYqqCjPbNtWQ3F5Hcns/tmbUUFpsYsK0SDLWGBzO17GrP7Hxvsy4LpZfllZw3+z9nD82gk3r9MyfV8y9jycREOjjpCXea+Vrc7hu+W9c9uUCtn61gNRBg+g743K+uf46bDYb/uHhtDurP2VZmZRnZZGfkcHeZcuYMPdtwlJSKdu3l4S+/Rh0xx2snDOHigMHWrtLLSa5UJJ8KEk+mkguhBBCCHE6i544i6z7xpDz7BWEDplM7c7VVP7+Je1uewuVWo1ZX07d3gx84zugS0g7/gmx12Wr1/1AUO9hNOTuoSFXefcwTWQC/qnet8CBM4mzz2XrBW+xe8ZHRF3ch+rV2ZR8kUH63EvsNYfyGgwbDuKXFoV/WjT6dTmYivTEzjiL6r+zHM4X0CMeXULYqe9IC8SMn82+hy4g+4UZhA2+mJpdq6n48wuSZ809cm3U7tuALj4NXbzr10bV+qUE9zqP+rzd1OcpJ3xpIxPwT/G+lVUDrp5O+YxbqZz9IH5jRmDM2Er94p8Ieeb+w2M81Zi27sQnpR2aFNdXFfW/ZDy1n3xN+aU3E3DNdLDZqHn/M1TBQQRePd2DPXK/pPP/w6aXzmXHu1OIOfMSqjL/pmjtp3S+ct6RMcHq7HX4x6QTEHN61edkPLRJytA72fD2eWz931Rie0+jMnslBRvn023a+03jXY25iEp3KQYgechtFG37hu2fX0lMz8lUHlhF7t9v0Gm89+YC5HX0aOG3XEjuuKc5NPN1giedTd2aPVQvWEnc69fbx4crDNRnZKHtEItvB9fu4KtSqYh64GKK/vMhKpWKwBG9sFTWUPHBL6BSEXaDd36J6ZYZCVx0w3auuncPk86PYs3mahb8UMIbj6ShVquoqDKRscNAhyQ/OiT506NTAAN6B3P38/vJK2ygY6o/e7LreON/+YweGs6ZPbz3edBSUo9rIrlQknwoST6aSC6EcB+VzWaztXYjxKnXrl078vPzUak1BER3dH7M4FtIGjzrhM5bnvkH+5c9hj5vEwHRHUkaPIuEAVcf2bfxreG0v+AR0kY/5lJMo5LtS8j64WHqyvYTlNCbxHOuI6H/zBNq2/HsGOX4DZST0bBuA4a33sW0aw8+KUkEXnYJAZPGH9lXfvWNBN1yA8GzbnSIrXzgUUw7dhG9eIFiu81mo/azL6n5fAGWQ4X4xMXiN2wIQbfejDrAvbcT11W7d3XBuo1rqHz/VYx7d6JNSiV4ypUEXzT1yL6iWZcSeu3thF93h0NsyZN3Ydy9jcT5Px333yiadSnx/13s1gnEnX6Kddu5GpVn/0XWH09RXbCFgMg0kvvfRGLfK4/s2/DxaDqc+wDp5z3kUgzYr4+inYvIWTUHQ8luNLogwpIH0nH4Y0cmIrtFSZn7zgWU568ma93LVJfsICCsPck9ryKx6yVH9m1YNI0OZ91Jev//uBTTqLb6IHv+fpyqwk2oVCrCEwfSedDD6ALdvAqI1XL8Y05Aef5qsta/SnXpTgJCU0nueSWJXaYd2bdhyaV0OPN20s+606WYRrXVuexZ+QRVRZtQqdSEJ55D54EPogtwYz5MxuMfc4LKD60la+PrVJftJCAkleTul5PY6eIj+zb8cAUd+t5K+hm3uRTTqFafy541z1BVvMWej/gBdB5wH7qAaLe1XRUR5bZzAZQf/JvMv5+jungrgeFpJPe7jsSeM47sW//FeNIG3kP64PtcimlUuGcJWxbPZMDlPxGWoPxihjvpe4a49XxVu/4gd+Hj1ORsxi+uI3Hn30Ls0JlH9u14ZiTtJj1M8uRHHWL3vXsNhuwM+j63pdnzF//1MZnvX8tZcwvRBrv3d/neyHPcer51q6qZ++Ihdm2vIaW9H5deE8Ok6dFH9l1z8R5u/r8EZt2VyE/flfN/NzgOaDZ6ak57Jl5i7+/vP1Xw+vP55OU00Ll7AFNmRB/Z506fJW10+zmP1n7ouYx49FHi+/ShLHMfa+bOZePHHx/Zd93y5Sx/4gl+e/IJAHQhIYx49FG6T5pMQGQkpXv3sP7991n33nseb6unSS6UJB9Kko8m3pSLDVYrGVbnpZsKwAokJiaS5+S2bKezxrqGLjiBYbN2Hj/AC/3xVjca9If+lb8/IYQQrmn8e4ePptnJvJFjryfqohtO6LyGrSso+uwZ6rK2oktII/KiG4g4//Ij+/Y/MJaYS+8jbobjl41zX72J2n2b6Dx37ZFt5qpSds5o/lbq4SMuI+nOd06ojc0JOe8Dt5znWCr/yuTg08uo2ZKPX1o0CTcOJvbK/kf2bb9wLkkPjCLlwdGUfrOZ3Vc0fze8ju9MJ/aK/h5ra83PjuMLJ0O//S8KP3+a2uwt6OLTiB5zI5EjrjyyL/PhC4m75AHipz/oEJvz+g3UZm6k6+tNK3eZqkrYPjO12X8v4rwZpNzmns8PBT02u+U8jRrWbsTw2jzMO/fik5pEwOUXEzBl7JF9FZfPJnD2NQTfdq1DbOU9T2HevpuoHz512GfOPYT+mdcxbd4BahW+A/oRfP9sfKIj3dr+Lr8NdOv5nKnY8wcHvnsUQ+4m/GM6kjjsVuIHXX1k35ZXziPlokdpP+4xh9hdH81En7OB/o9u93g7NbXuHwpvy+OhKot781Ge+QdZPz+OPn8TAVEdSRp0C4n9m3KR8c4IOpz/CGkXPOpSTKPiHUvIWvbIkVy0G3AtCWfNdGvbAUrT6t16vrb8Olp77rduOY/inH/vpPS5b2jYegBtWhzh151P6Ixzj+zLHf8MkfdMIuq+ix1iC2a9S/2mbNqvek6x3WazYViynvI3l2Lck4862B//AZ2IemiqyxORXbEme67bzgXw94Yqnn03l217auiQ5Mf1l8QxY3zskX3jbtzBvTe0474bkwEoKTfy5JsH+X1tJaUVZlITdUweFcWtVyTg7+e4yMdnS4qZ9Xgmmb+eRWS41q1tB3hxwNrjH3SSvKke19okF0qSDyXJRxNvyoXU8Z2TOn7bIBOI/6WOPEFDExnyWG5rN8cruHsCcVvn7gnEbZknJhC3aW6eQNzmuXkCcZvmgQnEbZm7JxC3de6eQNyWuXsCcVt3KiYQCyHE6eoNswU9UniUwqMQQojTVePfO21kAl0/3n38gH+BUzGBuC1x9wTitszdE4jbulMxgbit8MQE4rbM3ROI2zp3TyBuyzwxgbgtc/cE4rbuVEwgFkKI05XU8aWO7+00rd0AIYQQQgghhBBCCCFaxGYDq7W1W9Ey8p1+IYQQQgghhBBCCCGEEKc7qeN7NXVrN0AIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCHHqyARiIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCH+RWQCsRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ/yKa1m6AEEIIIYQQQgghhBAtZrG0dguEEEIIIYQQQgghhBBCCNEcqeN7LVmBWAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIfxGZQCyEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxL+ITCAWQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJfRNPaDRBCCCGEEEIIIYQQosWs1tZugRBCCCGEEEIIIYQQQgghmiN1fK8lKxALIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPEvIhOIhRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYT4F9G0dgNEKzNb8ckubO1WeIXuP/dt7SZ4FatW1dpN8Bqq8oLWboJXsTXUtXYTvIqtpqq1m+A1bBZzazfBq6jbp7Z2E7xKWWJZazfBa3yWtLG1myCEEEKcPmyAxdLarWgZW2s3QAghRFuhsqoILg1s7WZ4hcyifq3dBO/Sd01rt8BrdP1heGs3wav4HpS69RENDa3dAq9iqzG0dhO8StFofWs3wWtcEvZLazfBq7w4YG1rN0EIIYQ4fUgd36vJCsRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQvyLyARiIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCH+RWQCsRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ/yKa1m6AEEIIIYQQQgghhBAtY8NmsbR2I1rI1toNEEIIIYQQQgghhBBCCCE8TOr43kxWIBZCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4l9EJhALIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPEvIhOIhRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYT4F9G0dgOEEEII4XlmU63T7T4aP1Sqf9/3iSQfQiip1Gq0/v4O2202G6Za58+X05nko4nkQknyoST5aCK5aGVWS2u3QAghhBBCCCGE8BhrM7UFlZ8fKrWMafxbSP1JSfLRRHKhJPlQknw0kVy0Mqnjey2ZQCzcqjxvNZlrX0Zfso2AsPYk9byKdt0vPakYs7Gm+WCVCo02wF3Nd6uKzD/Z/9PjVOdtJCCqI+0G30zigGtOOqZ0x1Kyf30WQ8E2dCHxRHYZRfq4F/DR+nmyOx5Rse9Psn94DH3uRgKiO5I49BYSzjl2jhqVbvuOrCX3M+DB7R5upWd44rkCYLWY2Lf6OUoP/EadPo+QmN50GvQgYXF9Pdmdk1JesI6sjW+gL91BQGgqSV0vI7HzlJOOsVpNZK5/lZK8P6k3HCIkqgedzrqL0JjenuzOSSsvzmD/tvfRV+zGPziZpPQpJKaNP6kYs6mG378e5jR2yITv8QuIdWcX3KqieCP7d8yjunIPAUFJtEubTGKHcScVYzbV8Me3I53GDr5oMX4BMW7tg7uUZ/9F1vIn0R/aREBkOkkDbiLxzJknHVNx4G8yf30c/aFN+OiCiUwfQfr5T+AXkuC5zrhB7ZZVlH78Ig37tqFN7ED4hJmEXniZS7GGVT9R8sHTtP/vX4rtNpuN6l++ouLrdzHmZeETHE5Av6FEX3s/mqg4T3TDY1KHDGXEI4+Q0LcvZZn7WPvOO2R89FGzx3cafSFXLl7ssL0sM5NXunbxYEtPDclHE8mFkuRDSfLRRHIhhBBCiH+Tqt1/cnDRE9TkbMQvtiPxw28idqhrddryTd+R8/WD9H16q4db6RnGtZsxvPYRpu378ElNJODyiQRMG+NSbP3yVRhefI+oZR857LOZzBhe/oCGP9ZgyS9C27MzQffeiG/vrm7ugXsZ12zBMOdjTDsy7fm4YjwB0y50Kbb+11UYXphH1M/zHPbZTGYML82j4fd1h/PRiaD7bsC3T9t6r1yx908OLD08phFjH9OIH+jimMbW79i/+H76P9xGxzQO/k3m38+hL9pKQHgHkvpeS7veV5xUjNloOEa0Co1voJta737leavIXP0i+uJtBIR1IKn3TNr1OHZt8ngxxx4PBY3WO/NRfmgtWRmvNY3XdLucxC5TTzrGajWRue5lSnL/aBrjGXCv14/xNKzfgH7uO5h27UaTnEzg9GkETJ7oUmz9H39SPed1YhYtVGy31tRQOGCQ05jYX3/CJ857x3jy/ixk9eNbKN5YRljHEHrf3Jke13Rs9nib1caODzPZ9MYuKjP1BMb7kzYuibMf640uxLfpvCuKWPXwJkq3VeCj8yFxcAyDn+lHWHrIqeiW20j9SUny0URyoST5UJJ8NJFcCOEeMoFYuE1l4UYyFs8gMmkwXYc9S2XBenYs/z+AZidGuhKz/J3mP0T4Bbfj3KvXubknJ68qZy2b3ruQiI4j6TLlLaqyV7Hri+sAmp1E7EpM4cbP2f7JDOLOmEHS0Nk0VOWTs/wFagp30ffmn1GpVKemg25QdWAtW+aOJrzzSDpfMpeq/avYPf9agONOIjbXVZG97MlT0UyP8NRzxWazsXnptejLdpHa7ybUPjryts8nY9GlDJzxK/7B7U5NB09AZfEWNi67loiEgXQd9BiVRRvZseIBgGYnEbsSY7PZ2PzLrRgq9pDa8xrUPr7k7V5AxrJrOGfyd/gHeefEyKrS7Wz643YiYs+iy5n3Ulm6lZ3r7Nd6c5OIXYmpM+QD0P3sxx367qsL91R3TlpV2Q42rbiTiJiz6NLvbqpKt7JrwzMAzU4idiWmruYQAN37P4J/4NH5CPNQb05OZe46Nn50ERFpw+k6/nUqD65hx7c3ADQ7idiVmOr8TWz47wVEpo2k28S5mOoqyV7xEhs/HsfZN69CrdGdiu6dsLpdGeTdeykB/YYQe/tz1O3YQOGLdwIcdxKxxVBN2SevON2n/+1bCp+bTeiYy4i87HbM5cWUffYaDVk7SJn7IyqN1u198YR2/fszc+lSspYvZ8nsW0k+5xwmv/8BQLMfmiPSOlBXWcknEycotpvr6z3dXI+TfDSRXChJPpQkH00kF0IIIYT4N9FnrWXny2MI7TaCDle8iT5zNZn/vR7guJOIzbVV5C55+lQ00yOMm3dSPvMedIPOIOSJOzBt3EH1fS8AHHcSsbXaQM0b/3O6z2azUXnzw5h27yfw+ktQ+Wqp/eJ7Kq68i6gf/otPondO9DJu2kX5VfehG9yPkCdvw5Sxg+p7XgI47iRiez4+dbrPZrNRedOjh/Mx7XA+fqDiinuIWva+1+bjaFXZa9n6ln1Mo9P0uVTvX8XuT+1jGsebRGyuq+LAj214TOPQBjIWTCEy5Vy6jnqJyvx17PhxNkCzk4hdiVn+SvNjFn4hSZx7yzY398Q9KgsyyPjmUiKTh9B1+HNUFmxgxy/22mRzk4hdiVn+Vodm/02/kCTOvXaDm3ty8iqLN7Pxx6uJSBxE18FP2Mdr/roPoNlJxK7E2Gw2Nv98C4by3aT2uu7wGM+XZPxwFedM+cFrx3iMW7dRdtMsdGcPIPSh+zFu3kLlI48BHHcSsVWvR//Oe073WXLzAAh75il82iUq9qkjvHeMp2BtCd9c+CvJI+MZ/tbZFKwq5pfrVgE0O4l463t7+e2WNfS4tiMDHuxFZaaeDS9up3B9KVP/uAC1j5rS7RV8fd5PJA6N5bzXB2CqMbNpzk4+G7CUK7dPICjeOxcfO5rUn5QkH00kF0qSDyXJRxPJhRDuIxOIhdvkbHqXwPAO9Bk7D7WPloQuk7GYasjOeIvEbtOdTm51Jab/lG8d4qwWI1uX3UJ8pwkO+7zBwT9eJSC6M72uWYjaR0v8GTMwNxjIWf4CCf2vdpoLV2L2fXcfsf0upcflnxyJi+g4grUv9aUqZw1hqeecym6elNzfXiEgpjM9r/8GtY+WuLNmYGnQc/CX54k/23mOaov2smfBLKoPrMHSYCAwvnsrtPzkeeq5Up63itKDfzLkypX4h9gLbzHtR7H5h+upOLQO/87eN4E4Z9t/CQxtT5/z30St1hKfPh6zqYbsre+T0Oli57lwIaaiYC1l+X8zeOrP+AfbiynRySPYsnw2lYUZ+Kd7Z3EpZ898AoKT6T3kRdRqDfGpF2Ix1XJg18ckdBjnPB8uxNQa7MWlmHbneu0qBc4c3Ps5AcEp9Br0nL1vKRdgNteSs+dTEtpf5Py11IWYxgnV0YlD20w+clbOITCqE31mfGX/G9HnMswNerJXvEjCGVc5vzZciMnP+AjfoFj6XP41ah/75Nig2G6sf384lblriWg/9FR31SUVX72Db1IaiU98iEqjJWTkFKy1Bso+f5OQ0Zc6zYcxN4ui1++jbmcGtroafFM7O57323kE9B1M3F2vHtmmiU3k0MMzadi/C79OvTzaL3cZdPsdlO7dw/ypU7CazWz5/HN0wcEMuevu5j8wd+hAeVYmOStXntrGngKSjyaSCyXJh5Lko4nkQgghhBD/Jvk/vYp/XGe6zF6IWqMlZuAMLPV68n54kZghzuu0dYV7yfrkVvRZa7HWGwhIbJt12tp5X6HpkETY20+i0mrwn3g+1ppaat79HP+pFzrtu3l/LtWPzsG0eSe2mjo0nVIdjjGu2UzDivVE//YpPon2OxrpRg6k8pZHMW7Yhr+XTpit/eBrez7eefxwPkZiramj5p0v8J86uvl8PPI6pk27ms/H6s00/LWB6N//h087e9/t+Xgc44btXpuPo+Utf4WA2M70uPHwmEb/GZjr9eT8/Dxx5zQ/prH3y1lUZ7fxMY11bxEYkU6fyZ/axye6T8PSoCd77Wsk9rrceW3ShZj+M350iLNajGxdci3x3S4+FV1rkZyN7xAYkUafcR/a+9Z1ChajgewNb5LY3Xlt0pWY/tOWOMRZLQ1s/eEmrx0Pzdk6j8CwDvQZNdc+XtNxAmaTgewt75LQeYrzXLgQU1GwhrK8FQy+ZHnTGE/KSLb8MovKwg34px/7ro2txfDxJ2hSU4iY8zIqrZaAi8Ziq6lF/98P8Z80wfnr6IEcKp96BtPWbdhqa9Gkpzkec3gCsd+I81AHto0xDYCNr+4konMI4xaeh49WTdcZHTAaTGx4YTvdr053mo8Nz28jfVIy578/8Mi22DMi+Gb0rxz8pYDU0YlkvLyDgFg/Jv0wEo2fDwDpk5L5sOM3bHptF0OeO+OU9fFkSP1JSfLRRHKhJPlQknw0kVwI4T7q1m6AOD1YLUaKsn4iruOEIxOQAOI7T6a2cj/60h0tjglPGODwU7z/ZwLD00k/517Pd+4EWc1GirctIrbvNGW/zphBbcleDIe2tCjGVFNOQ2UukZ3PV8QGJ/bGLzyFkm2LPNYnd7OajZRsXURMv0sU/Y0963Jqi/diyHfMEYDaN4DIrhfQ/sJHCU5qGx/+jubJ50r+zs+Ibj8S/5B22Gw2bFYLusAYBkxdTELnyZ7v3AmyWowU5/xKbIcxqNX/6Ff6BGqrstGX72pxTN6er4hOGoZ/cGJTLgKi6T/uC+LTna9c29qsFhPFeX8Sl3w+anXT93viUy+kVn8QQ+XeFsfUGfLx1UWg0QZis1qw2Wye79BJslpMFOf/RWzSCGXfUkYf7tu+FsfUGvLw1YW3mXxYzUaKdy4httdU5WtAn8uoLd2HvqCZvysuxFjN9Wh0wYpjtAERR/Z5I5vJiGHlMoLPm6BYEThk5BRMeVk0ZDm+jgKo/PwJPPM8oq68C10n57e500YnEDxMWYzXxiUDYK2vdVMPPMtHq6XbhAlsXfAVVrP5yPbN8z8junNn4ns773tkhzTKsvYDoPbxOSVtPRUkH00kF0qSDyXJRxPJRRtlsbTNHyGEEKKVWc1GyjcuJmrANNT/+IwdPXAG9YV7qTnYfJ02vMcFJE94hMDUtlmntRlN1P/yN35jh6PSNtWR/CeejyU7F/OuTKdxKn8duqFnEXTbVWh6dHJ6TN2XS9Gddw4+iXH22qTFgk90JJFfvYn/hJEe6c/JsudjJX4XDTsqHyOx7M/DvDPLaZzK38+ej9uvRNOzuXz8iG742fi0i23KR0wEkV+/hv+EER7pj7s1N6YR1/9y6or3Yshr/rkS0fUCUsc8SnBy23yuWC1GivYtJa7rZGWdsccl1JZnoi92XCXY1ZjwpHMcfor3/UBgREfShz7k+c61gH28ZhlxnY4ar+k6hdqKLPQlzY3xHD8mPHGAw09x1k/2fAy63/OdO0FWi5HiA78S22Gscrym40T7eE1ZM2M8LsTk7V5AdPJ5jmM8ExYQ76WTh20mE/W//Y7/6AtQaZv65n/RGCwHcjDv2eM0TuXnh9+ggf/P3n2HR1WlfwD/Tm/JpPdegCT0Kk2wN1TAFRv2Xtey6xbdFcWtvy26umvX1bVjWcCuCyJFOiShhISE9J5MypRMn98fk2S4TALDZCaZwPfzPDx675335pw3M3cy7zlzLsLvuQuygoIBH+OorYU4OhpijQYuR+iPaQCAw+pAxeoajL0qCxKZZ0pK/vJsdJR1o7WowyvGZrKju9qI9POSBPtTFiYCIqBtvztGd6gLKWcm9E8eBgB1nBIxEyLRfqAzOB0KMNafhJgPD+ZCiPkQYj48mItRaqTr8azjD4orEJ/mrKZWbH574YDH0ifdjPTJt/h0HoupFS6nFWExwgJRWLR726yvhzZuwpBjAKCtegPqD36A+df/IJggFiqs+ma4HFaEJQq/Sa5JdH/oM3fUIDxlyknHaBInQCxVoKf9iOAxdnM3rIYWWLoaA9yT4LHqm+GyW72+bd/fX10NwlOneMUpo1KRft7PAQCGxv3Q14Te7ZpOJJivFX17KWJS56Nkw+NoKv8cdks3tPGTkLdgJSISBv4DaSRZe9rhctoQFiW8TVFYVC4AwGxohDamwK8YQ0cZYpLnoOTHlWiu/Bp2aze0sRMwbvbjiIibGMRe+c9qdvdNEyG8TZkmIgsA0GNsQnjUOL9iTIY6iCUy7Pn+AehadkMikSMyfhrGTXsE6rDQW5kaAKwWnft3rT2mb1p338ymZoRHjfUrpsdQD5FEjj0/PISO1j0QS+SIipuKsVMegjpMePuvUGA19L5HxAtfD2Hx+QAAc1cttMlT/IpJnHQVGva+jYr1v0fKjFtgN3fi0Oc/gzIqE5HpcxGK7B2tcNmsUGQIXw/yTPfzwd5cB+R6//0gi0tG9NX3AgAsVYdgLi30ekzyilcBuG+R5+hqh72lHm1v/RXS+FSo8qcFuCfBEZaQAKlCgZaDwsGKlpKDAICItDQ0FnkPakVnZ8Ok0+GBPXsQP34CDI2N2P/fT/Hdb38Lq8EwLG0PBubDg7kQYj6EmA+PUMzFLqcTu50DDw4ah3RmGg41B95G7YGBb+dtNbUOc2uIiGi0snY1Y89j3p91ASDpnHuRdN69fp3X1uWu06pShPUDdbJ726KrQVjGFK84RXQqUi7+GQDAVLcfhqrdfv38keRs6wCsNq8Vc6W5GQAAR0MLZAXet1eXJMVDc8c1AABbWSXs+7wnhNnLKiGfOw3dT/4D5i83wNltgGziOGh/ez9kk/IC35kAGDQfY47Kx/hcrzhJUhw0d14FALCVVsK+z3sRBHtpJeTzpqJ7xfMwf/GDOx+TxkH7xL2QTfK+Q1Qosnb3jmkkC8c01Em9r5WOGoSnTfGKU0alIv1895iGsXE/9NWjcEzD2OKuM8YKn7thMe7fnbm7DtqESUOOAYC2I+tQv+9dzL99e0iO/wG94zUOa39f+njGa+qgjR9gjOckYwCgrep71B94H/Nv2hyS+bD2tLnHro4dr4nsG69pgDb22DEe32IMujLEpMxFyZYn0Xzkq94xnokYN/c3iIgLzbvEOdvaAZsNsmNWEJbluLcdjU2Q5Xm/B0gSExB2y00AAFt5OWwHDno9xl5bB5FcjvY774Fl5y6IFArIZ0xHxC8fhTQtNMd4TM1mOKxOxIyPEOyPLogEAOhrDIifEi04JpKIcM2PlyAyN1ywv35TM+ACwlLUAIBzX5gNeYRc8Bir3oaWPToU3OC9gnMoCsX600hiPjyYCyHmQ4j58AjFXLCOP7qd7nX80Pu0QcNKro7D/Bt+GPJ5+l4sMkWkYL9MFQUAsJjaAhLjdNhwaOMTyJp+H5ThyUNtdlBY9E0AAGlvP/rI1DEA3JNn/YkRS6RImHYtan54FpqEAkSNORuWzjqUf/YrOG09A543VFm73f2VqY/pr2bwHJ0qgvlasRqbUbvvLWjjJyF/wdOASIzqwlew4+OlmHf9Bqgj0gPZlSGz9PT1SyvY39dPa493LnyNsZhaUVvyHrSxE5A35zeASIyafW9i5+fXYe6VX0IdnhbIrgSExdwOAJDJj+mb3F1csZp1fsf06OtgNjUjPvVspI29GpaeFlQeeAM7v7sdcxd9BJlcWIwJBX19kx7Ttr6+Hi8fJ4rpMdbDYmpGfMpCpI9ZBnNPCypL3sKu9XdhzkXvh1w+LAb3NVE22HuEocXvmJjcczH2oj+i9MtHUbHuKQCASCLHvAcLIVWEBbAXgWPXudsuDo8U7Jdo3X21dwz9D3insRsVV7gHhERKNdKf+wwimfwEUaEhLNF9a9Sejk7BflO7+/URluB9W1CRWIyorCxoU1KwbuVKtJaUIGXGDCz4xS8QmzsGb112adDbHSzMhwdzIcR8CDEfHqGYixliMWYMcv+o5+0O6Id0dgq29PE3IH38DQMe++HdebAYm4a5RURENBrJIxIw7Q/7A35ea1dvXfqYOq00zF0/sHWdunVaZ6u7TiTWCutA4ih3Xc3Z5r0you/nbofpndWQTRyH8BU/hUgkgvHfH6P96p8i9tu3IE1LOvFJhll/PiKE9SBxlLuuNrR86GB6ey1kk8Yh/Mn7IRKLYHz9E7Qvewix/3sjJPNxrL4xjWNfK/1jGt2n7mvFanTX4mTKSMF+mco98c9i9K7F+RPjdNhwaN2vkXXGg1BqQ2+Rhz79fRt0vOY4+TiJGKfDhkM//BZZM+8P3fHQ3rEpmUI4QVSmdPdrwDEeH2MsphbUHnwH2tiJyJv7RO8YzxvYufYazF32DdTa0BvjcbS52y7SCsdrxJHuvjp66wr+sNfWwtHUBOV550Cz/Fo4mltgeOU1tN1wM+LXfgrxMT8zFBibegAAiiiFYL8qxr1tava+A6JUIUHS7DjBvqadbfj6hk0IT9Mgd4l7nDN+aozgMaaWHqxd8j1cdicm3TM6vpgSivWnkcR8eDAXQsyHEPPhEYq5YB1/dDvd6/icQEwBIhpwb98tVFxOW0Biavf/B3arEZlT7/SvmcNANEi/4HICcH/o9zdm3NJn4XLYsP/t63oDRcg46xFYja2QaaIHPkdIOn5/XQPk6NQRvNeKzdINZVgiZiz9ABKpCgAQm74QG9+ajao9L6Lg7D8Ose2BNli/ep/3TvsAR32LsVv1UKgTMP3iNyGRKgEAsSnzsenDc1Bd/Dry5z05tKYHxSCvC/T+nl2+5+PYmIy865A94TZExXtWUI2Mm4ptX12L+vL/IrPgRj/bHDyDXhd7++YcIB++xqSPvRZZBbcgKm5q/yOi4qZi2zfXo/7IGmTmXe93u4PjBM/7Aa+ZvsU07fsYh79bgfQ59yEm93zYLV2o3vI8Ct9dhmk3fQZlROitXiASDfJ7dva9hwz0Wjk5YpUGac+shr29CZ1r30Ltw0uQ/o+1UGQPfAu5kDLI7etEYvcnVslRt8/rI5ZK8fGtt6CxqAhtvbfSK/vma+ibGrH0pZeROnMm6nbuDF6bg4n58GAuhJgPIebDg7kgIiKi08lgn7FdgfuMHbIG7bq777D533dntwGShFhEv/03iJTuiVLyM2ei9axrYXr1Q2hXPuT3uYNmkOdCfz7sAcjHO/93VD5moHXB9TC9sgrapx/0+9zD5oSvldNxTKO37yc1pjF4TO3eN2C3GpA56z7/mjlsTtS3kx/XGCimtvgtdz6m3e1fM4fDYC+L443x+Bhjt+qh0CRg+qL/eMZ4Uudj0/tno7r4NeTPf2pobQ+GQa+jvXWGIVxHw266AaK774RixvT+ffLp09B6xTIYP/4E4bf6dkfhYTXoZbN3vMbmPG64ucOCrU8WoeiFQwhP02Dx2nMgVQmntjjtThS9WIqtTxbCYXHioncWeK1qHLJYfxJiPjyYCyHmQ4j58GAuiAJqkLnvRCdHoXZ/G9BmFn4LvW9bofH+dsfJxjiddlTtfgFpk26ERKYOTMODQK51f9PFZhKujtm3rYjw/qawrzFSpRYTrn8bC3/fjlk/242z/tiFMZf/BTZjOxTa0P+Wfh9/cnSqCOZrRa6KQUz6wv7JwwAgVYQjMmkG9G3etzwaaf39snQJ9tt7t5XqeL9j5MooxKbO6y8sAYBUHobIhGnQ6w4FqAeBpVD1ruxi7Rbs7+urQhXnd0xs8jzB5GEACIvIgjo8HfrOwwFofeDJlYP0rXdboYr1OyY2aY5g8jAAaLSZUIenwdBZHoDWB5Yi3P0aP/aaae/dVg5w/fclxumw4eDqe5A89XrkXfoM4vIuQdLkazHj1q9h0Tej8oe/BLwvgSCJdr/OHXrhNdGh7wQASGO8r6MnSySRQj15DrTnLEXqn96DSCpHx5o3h3ze4aBvcn/7UR0tXBFHHe0unHY3NHjFOKxW7Fu1qv/Dcp+StWsBAIkTJwajqcOC+fBgLoSYDyHmw4O5GIVcLsDpGJ3/Bil0ExERDRd5hLtOazceUz8wuLflkaOn5nyyxHHuv++cXcI6kqvTvS6UOCHGK8bnc0dHQn7mzP7JsgAgDtdAPm0CbCWhV3sCjspHp3BdrP58xA8lHxGQnzndOx/Tx8NWcsTv8w6n/jGNY14rfdvyU3lMQ+Ouxdl6BhmfCEsccozTaUfV9ueQNvXWkB7/A47qm+XYvnX2Hh9gjOckY5xOO6p2/Qtpk24K6Xz0jT/YLJ2C/fbebaVmgDEeH2PkymjEpsw/Zown3D3G014SgNYHniTWPf7g7BKOX7l6t8Vx3mM8vlKeOV8weRgAZDnZkGZkwF4ammM8mkT3WKVFZxHsN+us7uPJgz+3K7+sw38mrEHxS6WY9mA+ri+8DLEThHUaXWkXPpz/FTY8uAOpCxJwQ9FlGHdVZmA7EUSsPwkxHx7MhRDzIcR8eDAXoxDr+CGNE4gpIOTqWIjEMuh1wgutod29rQz3vt3Qyca0VHwNi7EFKflXB7LpAScPi4dIIoOxSXhLOUPvtjLS+7Y6vsZ0Ve+AsaUUMnUUtKlTIVWEwdxRC0tXPTRJE4LRnaCQh/f2t0HY375tRVTo3XooUIL5WlFoEuC0e9/yx+W0QyoP89o/0uTKaIjEMhh0wuKGvqMMAKAM8x6g8DVGoY6HY4BcOJ02SGSagLQ/0OSKaIjEUhi6KgT7+7aVau/Coy8xdpsRrfWbYBngdmEikRjSkM1HFERiKYxdwkEEQ++2UjVQPk4cY7cZ0dqwGZaeAW4XFqL5kGvc10xD8wHBfn3vtjJigPcVH2Ls5k7YzV2ISJsleIxUqUVYfD56OqoC2IvAkUbGAlIZrJXCLwNYerdl8f7d4tBh6EblrQug3/SlYL9YqYY0PgVOQ9cgkaHF2NICu9WKhPHCvwv6trvq6rxiIjMyMO6SRRBLj1nBweEAAJi7u71iRgvmw4O5EGI+hJgPD+aCiIiITicyrbt+YKoT1g9M9b112phTt04rjokCZFLYyyoF+/u2JUneE998Pnd8DFxmi9d+l90OUVhoTgYUx0T25qNKsN9e2puP5CHkIyEWLovVa7/LZodIoxogIvQMOqbR2DuGE33qvlbkmjj3+ESbcNKmodW9rdR638HsZGNayr6AxdCElInLA9n0oOgfr2kT1iYNvdvHHePxMaal/CtYjM1IGX9tIJsecHJVTO94TZlgv753W6kZYIzHxxiFJh4OxwBjPC4bJPLQq+EDgDgmGpBKYT8s/KKIrdy9LUn0nmzvC6fRCPOGjXC0eY/xQCKGKCw086GOV0IsE6Ntf6dgf9t+90T68LSB212+ugZrLl8PdbwSN+67HAv+OhMKrVzwmM6Kbny08Gt0luux5Itzcfl/z0FkjjYo/QgW1p+EmA8P5kKI+RBiPjyYC6LA4gRiCgixRI6EnIvQVLZWcCv1xtL/QhWRCW2c9+TWk42p2/82otPOhHKAbzOHErFUjviJS9G8d5WgX02734cqNgfhKVP9jqn+/m/Y8+L5cNo9xba6LS9AoghHwpSrgtirwBJL5YibvBTNez4U9nfXe+7+pnrn6FQRzNdKfPaFaK1aB+tRKxVbjM3oaNiOiITQy6lYIkd8xnloOvIlnEfdsqyp4jOotOkIjynwOyY+4zy01f7Q/w1+ALCYWtDZtBsRcZOD16khEEtkiE89C83V3wlu7dVU/Q1UYakIjxrnV4xYLMf+rStQse8VQayxuwrG7ipExE4KXqeGQCyRIT5lIZpr/yfsW823UIWlIDxqrF8xYrEcB7avxJEDrwpijd3VMHVXIyIm9L6MIZbKEV+wGE37PhJeM4s/gCo6B+FJU/yKkaljIdPEoaNykyDWZtJB37wfYQner8FQIJLJET7/YnRvWAOX3dM3/bpPIUvOhCLXv2+HijXhsLc2wrj9f4L99vZmWGsOQ5EzfkjtHi4Omw0HV6/GxKuWCT4AT7r2GrSXl6Nx716vGE1sLG5cswYTfvITwf6Cyy+H0+FA/a5dQW93sDAfHsyFEPMhxHx4MBdERER0OhFL5YievgRtO1bBedRn7Nat70MZnwNNeujVFANFJJdBecGZMH/+PVw2Tx2pZ+3/IMlIhnT8GL/PrTx/Hizfb4Oz0zMA7Whph21nMWST84fU7mBx52P+APlY35uPXL/PrTx/LizrB8rHPsimhGY+jtU3ptGyWzim0bzzPajichB2qo9pjL0UTSWfCscnDn4EVWQWtAne9eWTjakr/DeiM8+CMjz0Vz0XS+RIyL0YTWVrhH079Kl7vCbeuzZ5sjF1+/6D6PQFoT8eKpEjPvN8NB35QjheU947XhPrXU/1NSY+4zy01WzwHuNp3IWIuClB69NQiGQyKM89Bz1ffwuXzdO3ni++giQtDbL8PP/OK5ej47HHof/Xi4L99soq2I9UQj45NMe8JHIJcpemo2xVFRw2Z//+Q+9XIiInHPFTo71iHDYn1t27DYmzY3H1lksQNTZiwHNv+tUeOCwOLN99KbIu9v4Sw2jA+pMQ8+HBXAgxH0LMhwdzQRRY0hM/hMg3GVPvws5PfoLCL+9A4tjL0dmwE42ln2D8uX+DSCSG1dyBrqZCqCMzoYnM8immj92ih65+G3JnPzpS3Tsp6Wc9jN3/PAvFby5D4pSr0Fm5GU2730H+Na9BJBbDZtShq2YH1LG5UMfl+hQDAOkLfopd//wEB969EfGTrkBn5RbUbnoeY5f8HVJF6K0wezxp5zyCvc8uxP7XrkT8tKvRdWQzmne+g7zlr/fnqLt6B1RxnhydKoL1WkmdsBw1RW9gx8dLkTn1TsDlQuWeFyGVRyBz6l0j2eVBZUy8Fbu+WI6i//0UidmXoKN5NxrL12L8mX+ASCSGzdyJrtZiqLUZUEdk+BQDACnjrkLNgbex4/PrkDHxFsDlQlXxa5DKw5E58daR7PJxZYxbjl3r7kLx5l8iIf18dLYWorHqKxTM+q07H5YudLUfgDo8DerwNJ9iRBIxMgtuQnnRPyESSxCdMBMWUzOqSt6BNjofSRkXjnCvB5c+9lrs/v5eFP/4GBLTzkVnWzGaqr9G/ozHPPnQHYQ6LLU/HyeKEUnEyMy7AeX7XoBI5M6H2dSM6tJ3ER6Vh8T0C0a41wPLmPcQdr12LoreuxqJk5aho+pHNBa+h/FLX3FfM006dNXthDomB+qYXJ9iACD3vCdRsuY+iEQixIy5APaeTtRsewEiiJA+5/6R7PJxRS27G7UPLUXDk7ch/OzF6Nm3A93/+xiJjz4DkVgMR3cHzIf2QpaSBXlKlk/nFIlEiFxyK3Tv/QMiuQLqyXPh6NJB9/HLkEREI/Lym4Lcq8DZ8o9ncfu69bjuw1Uo/mgVMufNw9Tl1+PTO26Hy+WCKioKqTNnob2iHLqKCtTv3o2yr7/G4hdeRGRGJtoPlyF56jTMe+ghbHn2WXRUVY10l4aE+fBgLoSYDyHmw4O5ICIiotNJyoUPY98fz0bpv5YhdtZV6D68Ba1b30Xura+6aw4GHQxHdkCZkAtVwqlVp1Xftgy6ax5E530roFx0Nqy79sG8+jto//QLiMRiODu7YSsqgSQjBdJM3ycoqa6+FKa3/gvd1T+F+tZlAFwwvvwBRNowaG4L3YVA1LdfCd3VD6PznqegvOwsWHfuh/m//4P2/37uyUfhIUgyUyDN9P0OUKprLnHnY9lDUN9+JeBywfjyh+583H5lEHsUWKnnPoLCZxbiwKtXIn761egs34zmHe8g7/qjxjSqesc04k+t10rGrPuw891FKPzvjUjMvwKdddvQeGAVxl/8vHtMo6cDXY27oY7KhiYq26eYPnZLN3Q1W5C74PGR6t5Jy5h2N3Z+tBSFn9+GxHGL0Vm/A42HPsb48585aoxnL9SRWZ4xnhPE9LFb9NDVbUXu3F+OVPdOSsak27Drs2tR9N39SMxZhI6mXWgsX43xC/501BhPUe8YT6ZPMQCQkncNag78Bzs+uwYZE28F4EJV0SuQKrTInBS6YzxhN92AtptvQ8cjj0J50QWw7tmLns+/QOTKJ93X0a4uWPfthzQ9DdL0dJ/OKZLJEHbbrdA/8w9AIoHijDPgaGqC4c23IBtfANWii4PbqSGY9nABPjrra3y+bAPGXZWJ+s0tOPTOEZz/2lyIxCKYdRY07WhDZG44InO1aNzWClNTDwpuzEH9xmav88VOjIQ6UYUja2uRfl4SdCVd0JUI7xwYlqJG7MQor9hQxPqTEPPhwVwIMR9CzIcHc0EUOCKXy+Ua6UbQ8EtNTUV9fT0UmiScddvugJ1XV/cjyrf9Fd2t+6GJzEL65FuQUnBN/7Gdn16JnFmPIHf2z32K6dNU/gWKvrwDZyz7DJFJ0wPW3qM5sgL7TV5d+QYc+fpJ6Ov2Qh03Bmnz70PyGbf0H9vzr3OQdeETyLnoSZ9i+rTuX4uKL3+LnvYjCEuejJQ5tyN51s0BbTsAOGWigJ/zWB1lG1D55Yr+/qYsuB/Jc27pP7b3ubORefEKZC960iv24Ns3Q1+zC2c8vt/rWKDJyhoDfs5gvVZMXTUo3bQCnU17IBKJEZ06F+Pmr4BC4/8t5o7lMhkCdi4A0DVsR8We59DdfhBqbSbSx1+PlLE/6T+268sbkD31fuRO/6lPMX1M+lqUbvsDulqKIBKJEZV0Bsad8Sso1HEBbb/L2HXiB50EXfNuVOx7GfqOMqjD05A2dhlSsi/vP7Z7/d3InnAHcibe6VMMALhcLtQeXoXaso9gNjVBqU5AbPJ85E66GxJp4G4T6HLYT/ygk6Rr2YMjB1719C33SiRnXdp/bM+G+5BVcBtyJtzuUwzQm4/yj1FX/rE7Hyp3PnIm3BHQfIgnB/b9SnfkB1SsW4nuxkKoY3KRPvtepEy/qf/YrtfPR/Y5v0HuuU/4FAO4c9F84FNUb34GhpYSSOXhiMycizHnr+yfiBwo1XM6Tvygk2Aq3IK2N/8CS/k+yFKyEbXkVkRcfG3/sdpHrkDMjT9H7M3eXz5q/PNPYS4tRNYbGwX7XTYrdB+9iO5vVsHWXAdpdBxUU+Yh7tZfQxobuL8TfnJBcsDONZisBQtx7ooVSJoyBe3lh7HthRew5623+o/dvm4d1q1cifVPrwQAKLRanLtiBcYvvQLqmBi0lZVi56uvYscrrxzvx4wazIcHcyHEfAgxHx6jJRfP2x3QA0hJSUHdALdlO5X11zXUCVh49fcj3Ry//PDh2bCYmk/L3x8REfmm7/1OHpWCmc/UBO3ndJVsQM3qp2Cs3gtlwhgknXcvEs68pf/Y/j+fi7TFTyB96Qqv2MOv3gJD1W5M/X1x0Np3tIoFm078oJNg2bYXhmffhP3gYUgyU6G+YSnUyy7uP9Zx3cPQ/PQmhD90i1ds56N/hH1fKWK/ftPrmL22Efrf/RO2vQcBsRjy2VMR/vg9kMTFBLT9EDsCejrL1kIYnnkL9oPlkGSmQH3jEqivuqj/WMe1P4PmwRsR/rD3F607f/Zn2PeVIfbb172O2WsboV/5Amx7S9z5mDMF4Y/fDUm89wqU/sr/8pyAnWswHWUbUPn5Chjq9kIVNwapZ92PpKPGNAqfPRuZl6xA1qVPesWW/Odm6Kt3YdZvgz+mAQDymgDXras3oXzzH9HdvA+aqGykT78DKZOu7z+28/3LkDPvl8g989c+xfRpOrQGRatvwhk3fIfIlJkBbXM/iyXgp9TVbkH51r+gu3UfNJHZSJ9yK1LGX9t/bOfHVyBn9s+RO+dRn2L6NB3+HEWf34YzrvkCkUkzAt5uAHAZAz3Gsw0Vu/+B7raDUEdkIH38jUgZd2X/sV2fL0f2tJ8id8aDPsX0MXXXonTr79DVUuge40mejXGzHwv4GM/+n+oDej7Ljp3Qv/ASbCWHIM1Ih+baq6FeuqT/WPutdyDsnrugve8er9iOx38L24GDiF/9iWC/y+WC8b0PYPrgQ9gbGiFJTIDyrIUIv+9eiNWBG9O4uuCZgJ2rT+2GJmx9shCte3WIHBOOKfflYfwtY/qPfXzON5j9xGTMeXIKyj6qwhdX/zDouS54Yx6yFqXi5YQPB31MwU05uPDf8wPSdpX8nYCc53hGS/1puDAfHsyFEPMhxHx4jJZcsI7POn6o4wTi01SwJhCPZoGeQDzaDccE4tEiGBOIR7NATyAe7QI9gXg0C8YE4tEs0BOIR7tATyAezYZjAjEREZ0eWHhk4ZGIiE5twzWBeDQJ9ATiUS/AE4hHs+GYQDyaBHoC8agWhAnEo1mgJxCPdoGeQDyaBWMC8Wg2HBOIiYjo9MA6Puv4oU584ocQERERERERERERERERERERERERERHRqUI60g0gIiIiIiIiIiLyjwsux2hdeY83BSMiIiIiIiIiIiKiUx3r+KGMKxATERERERERERERERERERERERERERGdRjiBmIiIiIiIiIiIiIiIiIiIiIiIiIiI6DTCCcRERERERERERERERERERERERERERESnEelIN4CIiIiIiIiIiMhvTvtIt4CIiIiIiIiIiIiIiAbDOn7I4grEREREREREREREREREREREREREREREpxFOICYiIiIiIiIiIiIiIiIiIiIiIiIiIjqNcAIxERERERERERERERERERERERERERHRaUQ60g0gIiIiIiIiIiLyiwuAwz7SrfCPa6QbQEREREREREREREQUZKzjhzSuQExERERERERERERERERERERERERERHQa4QrEpzuHFbaKvSPdipAgdUwa6SaEFIlGM9JNCBnOzvaRbkJIcbQeGekmhBSX1TTSTQgZEm3iSDchpIhM5pFuQkj5yQXJI90EIiIiIiIiolFJZHVA82PVSDcjJBQYFo50E0KKtMs20k0IHdWsWx/Naeoe6SaEDJfZMNJNCCkideRINyGkTMj9fKSbEDIeqSgd6SaElBdHugFEREREw4QrEBMREREREREREREREREREREREREREZ1GuAIxERERERERERGNUi64HPaRboSfXCPdACIiIiIiIiIiIiKiIGMdP5RxBWIiIiIiIiIiIiIiIiIiIiIiIiIiIqLTCCcQExERERERERERERERERERERERERERnUY4gZiIiIiIiIiIiIiIiIiIiIiIiIiIiOg0Ih3pBtDIslo6se2Huwc8lppxKVIzLx3mFhERERERERFRn11OJ3Y7XQMeMw5zW0KW0z7SLSAiIgoqq6kVW1ZdOOCxtPHXI338DcPcIiIiIiIiIiLqwzq+D1jHD1mcQHyakysiMXvhSyPdDCIKELvNNOB+iVQJkYiLzhMRHU0kFkOmUnntd7lcsJkGvp6eqpgLIebDg7kQYj6EmA+PYOZihliMGYP8Kf+83QH9kM5OREREo4FcHYd5V30z0s0gIgo4jmkI2e2D5ENy+uWDzw0ib6zFCTEfHsyFEPMhxHx4sI5PNDBOIKaA6mjfh8qyd6HvroBak4yUjEVITrsgoDGduv3Ys/VXmDHvGWgjxwS6CwGjq9+Kip3PQN+6H+qITKRNuBEpBdcMOcbpsKF8+/+htXo9zPp6aOMnYeycXyMiYWowuzNkupotKN/yZ+ibi6COykHa1FuROun6IcXYrYbjRIsglWsC1PrA0zXtxJGiF6HXlUClTUfa2GuQMmbpkGLsNiO+f3/2gLFn/uQ7KDWJAe1DoHS070dl+fvQdx+BWp2ElPRLkJx2XkBjOnUHsGf745gx9y/QRoTudQMAOjoOourIJ9DrK6FWJyIl9XwkJZ89pBiXy4Xmpk2orfkSJlMTZDINoqInIjvnKigU0cHu0pB0tBXiSMlb6O48DHVYClKzFiMl85KAxnS0FWP3pocw66wXoY0aF+guBIyuejPKN/0R+qbea+L025E65fgrDPkS01HzIw5v/D30TUWQyMMQk3U2xpz1BJThScHsTlBknrkA5z7xBJKnTkV7+WFsf+kl7H7zzUEfP/aii3HjmjVe+9vLy/H3/LwgtjT4mAsh5sODuRBiPoSYDw/mgoiIiE5luobtqNj9D+jbDrjr0AXXIyVv2ZBjnE4bynf8Da21G2A2NEAbOwFjz/glIuInB7E3gddx+AdUfvkk9LV7oI4bg5QF9yJ5zq0+xbbt+wwVa3+NMx7fH+RWBoeuciMq1j0NfcNeqGNykXbG3UiZcfOQYzqqNqP8f09B37AXEkU4YnLPRe75K6HUJgevM0PE14mQrnkXjhS/DL3uEFThaUgbexVScpcMKcZuM+L7D+cPGHvm0q+h1CQEsAeB1dG6F0cO/hvdHWVQh6ciNXspUrIWDSnGbjNhw5qBV5uff8knUKrjA9qHQOF414npNlai4nfroS9sgDo3Bml3noGUm6cP+niX04n6t/ag5oVtMFW0Q5EYjvhL85Dzm3Mg1SqHseWBt3V7N555vg77DxiRmanEjdcl4Jplgz+3e3oceO7FenzxlQ4NjRYkJylwxeWxuPvOZCgVo38iOetPQsyHB3MhxHwIMR8ezAVRYHACMQVMV8chFO14AlGxUzBuwr3o6ijBoeJ/AMCgE4JPNsZuM+Fg4d8BDLzse6jobNqLPZ/dgOi0M5G/4PfobNqFA98/CgCDTiL2JcblcqHw6zthaCtB5tS7IJbIUXfwPexeez3mXPMtVOEpw9PBk9TZsAu7P16GmIyFyD//r+hs2IEDX/8UAAadROxLzLpn0wf9mUptGhbeXRTgngRGV2sx9q67B9FJs5F3xuPobC3Ewa1PAMCgRRVfYnr0dQCA8fP+AFWY8LkgV4bmJNGuzlIU7XoKUTGTMa7gLnR1HsKh/c8DwKATgk82xm4z4WDxswj16wYAdHWVo7jwT4iKnoixebeiu7MMh0peAYBBJxH7EtPS/CNKDr6IpOSzkZG5BFZrJ6qr1sBgqMb0GU9DLA7NPwe6dAexd8svER0/HXlTHkRX+wGU7P0LAAw6IfhkY+w2Iw7s/iNC/fnRWb8Tuz+4AjGZZyH/or+hs247DnxxHwAMOonYl5juxkLsfPcyxGSdjYJL/gF7Tycqtz6L3R/8BHNu+R5iqWJ4OhgAqbNm4eYvvkDFunVY+8D9SJ8zB1e8+hoADPpBMTonGz2dnXh7yWLBfrvZHOzmBhVzIcR8eDAXQsyHEPPhwVwQERHRqayzpRB7vroF0SnzkD9/JTqb9+DAxl8BwKCTI32JcblcKPz2Xhh0h5A56XZ37frQh9j95U2Yc+WXUIWF7kTRo3VVbUfRCxchatx5GHf1C+g68iMOvXsbAJxwErG9pwuVXz89HM0Mis7aHdjz5qWIzjkH+Zc/h86abTjw3zsBYNBJxL7EdNfvxa43LkRMznkoWPICbD2dqNz0V+x56zLMvufHkKw/8XUi1NW2D3vX34/oxDOQN+vX6GwtwsFtTwHAoJOIfYnpMfSOacx9eoAxjajgdCYAutoPYO+mnyM6YQbypj2Crvb9KNn9JwAYdBKxLzE9xgYAwPiZj0OlET4X5IrIIPVmaDjedWKdO2qx5/K3EH1ODvL/cRk6t9XiwN3/BYBBJxHXvb4LJQ+sRcot05H9q7NgqmhH1d83oWtnHWb+73aIJKNz4uzeQj1uuK0EZ86NwO+fzMKuvXo8+tgRABh0EvFTv6/GJ2vacO+dycgbq0bxAQOee7Ee7R02PP1E1nA2P+BYfxJiPjyYCyHmQ4j58GAuiAInNGcM0ahUW7ka6rAUTJz+OMRiKRJTzobD3oOaio+RlHo+RCLRkGMOH3wFNlv3cHXJb9VFr0ATmYMpF70CsUSGpHFLYbcZUbn3RSTnXz1gLnyJ6ajfivaaHzB/+UaotKkAgLjM81H0zd3obNwBVfjxv9E7Uqp3vgBNdC6mLPkPxBIZkscvg8NqQOX255AycfnA+fAhZtZ1X3rFOR0WFH92B5LyrxiOrvml+uB/oNZmYvJZz0AsliEp+1I4bCZUHXgDyblLBs6HDzEmfS0AID79HEhlobv68tFqK9dArUnBxGm/6r0GnOW+BlR+gqTUcwe5bpxczOGS12Czjo4bQtTVfAG1OhkTJj7s7lvifNgdZlRXf4bEpLMGzIcvMXV13yAyajzy8u/sj1MoY7G/+G8wGmoRrg3NIktN+UdQh6dh0hkrIRZLkZR2Pux2E6oPf4DkjIsHzMfJxpQW/xM26yh4X9n+L2iix2DKle+6r4kTrnZfE7c+g5TJ1w983fAhpq7obSg08Zi67H2IJTIAQFhcPna8fRE663ciOmPgFUBC0bwHH0JbWSneXXYlnHY7it5/H4rwcJz580cH/5CYnQ1dRTmqt2wZ3sYGGXMhxHx4MBdCzIcQ8+HBXIwyLhfgsI90K/zjCu0vsRER0ampuvh1aCKzMeWCF9x1xjGLYbcZUFn0MpLHXTlwjcGHmI7GbWiv24T5V6/rX+giLuM8FH13HzqbdkGVe/lwd9Uvtev/DnX8OEy841OIJTIkzlwOh0WPmu/+jKTZtwyYH1NzGUpX3Yfuqm1wWAzQJI0fgZYPXfWWZ6GJHYspyz9yj1FMuQ52ix6Vm/6C5Ok3Dfzc8CGmfvebkIclYMr1H3vqTwkF2PnqOeis3Y7orAXD3dUT4utEqLrkHai1GZi88K/uvmVdAofNiKoDbyI5Z/HA+fAhxtQ7STQ+7exRM6YBADWHV0Edno5Jc37vrkGnXwC7zYTq0neRnHnJwHVrH2J6jPUAgLjkBZDK1MPdLb9wvOvEqp/bAs3YWEz58DqIZRIkXTsFdr0FlX/bhOSbpg2Yo8q/bET84gKMf9Ez7qudmow9l72F9nXliL1g7HB2IWBe+XcjcrJUeOVfYyGTibF0cSyMRgdefLUBV18Z55WLHrMT761qwSM/TcVD97vHxS++MBo9PU6s+rgVT/0mE2Kxd/5GC9afhJgPD+ZCiPkQYj48mItRhnX8kDY6v55GIcfptKG1eSvikxYIVrJMSDkbJmM9DN1HhhzT2vQjmht+QE7eLcHrSAA4HVa0HPkWCWMu6y+GAUDS2KUwdR6Bvu2g3zF1B99HXOa5UGlT4XK54HI6oNDEY9YVnyJpbGhOHnY6rGgu/xKJeUuFfStYBlNHOfQt3rdy8zUmKnW217+W8q+giR6D3DMfD37n/OB02NBSux6JmRdBLD6qb9mXwtRdBUNHqd8xPfpayJXRkMo0cDkdcIX4m5j7GrAd8UnzhdeA5LNgMjbAoK8cckxr01Y0N25CzribgteRAHE67Wht3YX4hNmCviUmzkePqREGQ7XfMQpFNOLjhbf7UinjAAAOhyUY3Rkyp9OGlsYtSEg5W9C3pLTzYTLUwtBVMeSYloZNaK5bjzHj7zz2VCHF6bCiuexzJBZcIbwmTrgaJl059M37/I5x2i2QKMIFj5GponuPjZ5vWkpkMhQsXoziVR/Bafd88Ch89z3EjRuHpMkD3wIyJjsH7RXuvzfEEsmwtDXYmAsh5sODuRBiPoSYDw/mgoiIiE5lTocVLVX/Q0L2ImGdccwSmLoqoW8v8Tum7tAqxKWfDVV4iqd2rY7DrMWrkBSikyKP5bRb0Vq8GvHTrhbUShJmXg9TSxkM9QPf8U4sVyMm/0JkXbwC4WmD35I+lDntVrQcXIuEScuEtaQp18HUdhj6Ru+++xrjtJshPbb+pA7d+hNfJ0Lu8YnvkZhxgbBvWYtg0lfD0FHmd0yPvm5UjWkAvTXohk1ISDtHWINOv6C3Bl3ud4zJUA+5IgpSmRouV+jng+NdJ+a02tGytgQJV06EWOapFSRdOxmmw23QFzd5xThMVphrOhFzbo5gf/SCLEAkguFAS9DbHQxWqxPffteByxbFQCbzTFFZenksjlSacbDE5BXT0WHDvDlaLJgXIdifnqqA1eaE3T46nxcA60/HYj48mAsh5kOI+fBgLogCixOIKSCslk64nHZowtMF+zVh7m2zuXVIMRazDoeKn0dO3i3QhKUFuvkBZTW1weW0Iixa+O3Pvm2zod7vGIOuFKrwVJRs/C1++Pd0rHtlHHZ8egW6WgYuXIYCi7EFLocVYTF5gv1hseMAAObuuoDEAEBb5TrUF7+HSZe9IijChBKruR0upw2aSOEHf01ENgCgx9jod4zJUAuxWI49392Fde/NxIYP5mLv+gf6v6kdaqyWTrhcdq/XdN+2uWeQ64aPMRZLBw7t/xdyxt0ETVhqoJsfcFZrl7tvGmFb1Rr3ShQWc5vfMRMmPoSU1PPgcrlgtXZD330ER46sgkIZC21EbjC6M2RWcwdcThvCtJmC/ZrebXNP85BiLGYdSvb+FbkT7oI6PCOQTQ+4/mtiXL5gf1is+xp53OvoCWKSxv8EJl05Kjb9GWZ9Iwyth3Dou19CFZGByLTZXucNVWEJCZAqFGg5eECwv6XE/QWciLSB/3aIzs5GRGoqHtizB0+ZevDLqmoseuYZyMPCgt7mYGEuhJgPD+ZCiPkQYj48mAsiIiI6lVl7euvQUWME+8Mi3fUhs6HB7xiDrsxdu97yJH54ZzbW/XsCdqy5Cl2txcHoSlBY9c1w2a1eKwhrEgsAAGZdzYBxyqhUpJ/3c6Sf93NokicEvZ3BYDU0u2tJ8QWC/WHx7tqSucu7xuxrTOKkq2BqO4yK9b+HubsBhpaDOPT5z6CMykRk+txgdGdI+DoR6h+fiPBjTOMEMf1jGuvuxboP5mDDqgXY+/2D/SsThyKrWddbgxbe1a+/Bm0aqG7tW0yPsQEisQx7Nj2C9f89HxvWXIzCLb+CaYDnXCjgeNeJWZsNcFkdCCuIF+wPy3dvm2s7vWJEEjFm/XAXEq4Qvp90bK4CXC4oksOD1dygamu3wWpzYewYlWD/2Fz3dn2j92I3yUkKvP9WAaZNDYfN5kRzixWbtnThrXebcdH50ZDLR+9UF9afhJgPD+ZCiPkQYj48mAuiwArNGXY06lgtHQAAmUx4UZXJw3uPd/od43K5cKj4HwiLyEFq5qXo1HmvWBtKLCb3Nz9lCuG3IWXKSADuycL+xlhMLajd/za08ZOQd+ZTgEiMmqLXsPPTn2Dudeuh1gonY4cCq7G3b7196SNTulcY6Ov7UGOcDhsOrXsMWWf8FMreW3+FIkuP+3cpk2sF+2WKSACAtafd75gefS3MpibEZ5yHtPzlsJiaUFn8KnZ+fSPmLl7jFT/S+l7jg14DrF1+x7hcLhza9zzCtDlIzbgEnTrhH46hyGrtBABIpcf0TXacfJxkjN1uwpZNdwEAJBIFpk5/MmQn21ssOgCAVCYshvU9j63mDr9jXC4XDu75P4RHjkVa9hJ0tIVugR5wD8YAA1wTe1cKthgHuI76GBOTdTbGnrMSpf97DOUbfw8AEEnkmHfndkjlo+eDUlhiIgCgp6NTsN/U7r4+hiUkeMWIxGJEZWVBm5KCdStXorWkBCkzZmDBL36B2NwxeOuyS4Pe7mBgLoSYDw/mQoj5EGI+PJiL0cnlsI10E4iIiEYFS2+d2bsOHQXAPQnS3xiLqQW1B9+BNnYi8uY+4a5d73sDO9deg7nLvoFaG9oLgwCAtdu9EqRMHSXYL9PEuI/rvScGniosfbUk1TF9V/f23eBdf/I1Jib3XIy96I8o/fJRVKx7CkBv/enBQkgVoVd/4utEyNI7/iBTHDs+4e6r1azzO6ZHX+ce00g/B2njroXF1IzK/a9j5ze3YO7ln/bX/UOJxTxIDfp4+fAxpsdQD0tPC+JTFiI990qYe1pRWfIf7NpwD+Zc8E7I5YPjXSdmaTYAAGSRwkmzshg1APcE42OJFVJEniG8FnTtqsO+Wz+GMjUC8ZcXeMWMBi2t7s/tEVrhmFRklHsl6ra243+uX7ehE3fc6169fOwYFf6wMuu4jw91rD8JMR8ezIUQ8yHEfHgwF6MT6/ihKzRnDdEoJBp4d+8tZVxO+wAHfYtpqPkaXR0lmLXgBYhEo+CbhKKB+9V3ex2nc4ALoo8xdks3FJpETL/8XUik7g+bsekLsOnteaje+zLyF/5+qK0PgsH65nT/9ySeG8eLqS38N+xWAzJn3utfM4fNYM/7k8/HsTEZBTcie9JdiEqY0f+QyPgZ2PbZFagv+xiZE271u9VBMUi3jnvd8DGmofYbdHUcwqz5z42O68Zx9f6eXY4hx0gkSkyd9gQslg7U13+HvbtXYtqMJxEWFnpfPhCd4HnvdHk/P3yNqa/6DF26A5h97huj5PlxgmvigH9o+xbTVPJflP/wO6TPuAux2efCZulG9Y4XUPjxcky/5hMotaH7hQyBQW5hJxK7f78SmczrmFgqxce33oLGoiK0lbpvjVf2zdfQNzVi6UsvI3XmTNTt3Bm8NgcLcyHEfHgwF0LMhxDz4cFcEBER0als0JJ8b+3kJOpxx8bYrXooNAmYvug/kEiVAIDY1PnY9P7ZqC5+Dfnznxpa24fFCeqwp/Rg5/FrSU4/6k99MU37Psbh71Ygfc59iMk9H3ZLF6q3PI/Cd5dh2k2fQRkRYneP4+tEaLCaPPyo4x8Tk5F/PbIn3oGohOn9j4hMmIZtn1+F+sOfIHP8zX41OZhOXIP2ruP7GpM+5mpk5d+EqLgp/Q+Jip2Mbd/djPrKtcgct9z/hgcFx7tOaLDxX2fv+K/dedxwW0cPKn63HrUvbYciVYupn94Aicq7LjEaDJKK/lzY7APXY/qcMSMcH76djyOVZrzwSgOWXnUAn386ASrVKL39POtPQsyHB3MhxHwIMR8ezAVRQI2GWTM0CsgV7m9R26x6wX6bzb2tUMb4FWPuacHhkleRk38rpDIN7PYeOB1WAIDDYYHd3hPYjgSAQu2+7YzN3CnYb+/dVmq8v+nia4xcFY3YtAX9k4cBQCoPR2TidOjbSwLQ+sBThPX1TbhaaN+2IixxyDFOpx1VO55D2tRbIZGpA9PwIFGo3K8Fm0W4MqzN2u0+ro7zOyY25UxBMQUAwiKzodZmQN9RFoDWB5Zc3nsNsAm/Ye25BkT7FWPuacXhQ28gJ+9mz3XD2XfdsIbkdQMA5PJIAIDNfmzfjO7jiqhjQ046RiyWIDIqHwmJczF5yq8gFktRX/ddIJofcPLe33/f87xP33uGQhnrV4zZ1IyyfS9izPi7IJWF9b6vuG+N5XCYQ/L5oQhzvwfYega5JoYn+RXjdNhw4IufInnitci/8C+IG3MRkidchZnL18JibMaRH/8e8L4Ei77JvUKQOlr4nFdHu58T3Q3et/lzWK3Yt2pV/wfEPiVr1wIAEidODEZTg465EGI+PJgLIeZDiPnwYC6IiIjoVKZQueuItmPuGGjv3VZq4nEsX2PkymjEpszvnxQJ9NauE6aFbO36WHKtu+5sMwlXEO3bVkQkD3ubhosivLeWdEzf7b3bSu0A9ScfYpwOGw6uvgfJU69H3qXPIC7vEiRNvhYzbv0aFn0zKn/4S8D7MlR8nQj11WG9xid6twcc0/AxJjZlvmDyMACERWRDrU2HvuNwAFofeP01aNuxNeje8ZqBxkN9jIlNmi2YPAwAGm0m1GGpMHRVDL3xAcbxrhNTJLhXWbd1CMcd7DoTAECZNPiq0q1fl+LHac+h9pUdSH9gDubuvB/h473HmEeL+Fj3RK7OLuHE8r7thHj5ceOjomSYOzsC11+bgBefG4Oy8h58t977TpWjBetPQsyHB3MhxHwIMR8ezAVRYHECMQWEXBEBkUgKo75asL9vW6EaYKKXDzE9piY4HRaU7nseG7+5Ehu/uRJFO1cAAPZu+yW2/O/6YHRnSOSqGIjEMhh0wjcdfe+2Msy7yOhrjEKTAIfD7BXvdNohkWkC0v5Ak6vjIBLLoG8TFr8MvdvKcO/VLU82puXwF7AYmpEy4bpANj0o5MoYiMRSGDrLBfsNne5imFLjPaHalxi7zYjWuh/6b/90NJFIDKks9G4FN+g1wFADYJAJoj7E9F839v8LG7+7Bhu/uwZFu1YCAPZufwxb1t8chN4MnVweAZFIAqOhVrC/b1upGKDw6EOM3W7Cjm2PorVF+G05iUQBhTIWdrsxkN0IGLkisvd3XSXYb+iuBAAoVd7FR19iTMZGOB1mlBT+DRs+uwQbPrsEhVt/BQDYvekhbPryisB3Zojkmt5rYutBwX5Di3t7oFWCfYmxm7tgt3QhIllYiJUqtAiLzUNPp/B1FsqMLS2wW61IGD9BsL9vu6uuzismMiMD4y5ZBLFUeEMOp8O94oe5u9srZjRgLoSYDw/mQoj5EGI+PJgLIiIiOpV56tDCyVf63m2lxnuSqK8xCk38wLVrlw0SeWjWro8lD4+HSCKDsWG/YH/ftiIqbaCwU4Jc4+67ofmAYL++d1sZ4d13X2Ls5k7YzV2ISJsleIxUqUVYfD56OqoC2IvA4OtESK6M7h2fEE5g7RuvUKq9JzT6EuMe09g4yJiGBNKQzUeUuwbdVSnYb+jdHjgfJ46x20xobdgCi7nd+4eKJJBKQy8fHO86MXm8BiKZBIYDzYL9+oMtAABlasSAcS1rD2LvFe9AHqfB3D0PYNyfLoZUqxzwsaNFTIwMMpkIpWXCydSlZe7J1MlJ3hOIN23pwrmXFKGx0SLYn5PlXmiru/tk7twZWlh/EmI+PJgLIeZDiPnwYC6IAosTiCkgxGIZ4hLnoLlxk+D2TU0NG6BSJyFcm+NXTLg2B9Pm/J/g35iCuwAAeRN/iimzng5+506SWCJHfPaFaCr/THBbr6ay1VBpMxAeN8HvmPisC9BWtU6wMq/F2IzOhh2ISJgaxF75TyyRI2HMIjQd+q+gb40HP4EqMgvahElDjqkrfBPRmQuhHGAVzlAjlsgQn3Yumqu+htN51O+68kuowtMQHp3vV4xYLMf+zb9GReG/BLHGrkoYuyoRETc5eJ3yk1gsQ1zCGWhu2nzMNWAjVOpEhGuz/YoJ12Zj2hl/FPwbk38HACBvwn2YMvPJoPfNH2KxFHFxM9HSslXQt+bmLVCpEhAWnulXjESigsWiQ3v7XkGsxdIBk7EeYWEZQevTUIjFMsQnz0dz3ffC33XdOqg0yQiPHONXjDYyFzPOfE7wb+ykBwAA+VN/jqnzQm/VE7FEjoRxl6Gp5FPhNfHAR1BFZUGb6P369iVGpo6BXB2Ljpotglhrjw76lgMIiy8IXqcCzGGz4eDq1Zh41TLBh75J116D9vJyNO7d6xWjiY3FjWvWYMJPfiLYX3D55XA6HKjftSvo7Q4G5kKI+fBgLoSYDyHmw4O5GI1ccDlso/Jf3y2ciYiIhotYIkd85vloOvKFsM5Y/hlU2nSEx473OyY+4zy01WwQ3GXPYmpBZ+MuRByzomaoEkvliJu8FM17PhTW6Xe9B1VsDsJTQ7MGHwhiqRzxBYvRtO8jYd+LP4AqOgfhSVP8ipGpYyHTxKGjcpMg1mbSQd+8H2EJoVd/4utEyD0+cQ6aq78V9q3qa6jC0hAenedXjFgsx/4ff4OKopcEscauKveYRqz32FEoEItliE9ZgOa6dcIadO13UGlSBq9bnyBGLJHhwM7f4ciBNwSxRn0NTPpqRMR4P+9GGse7TkwslyJ+cT6aPt4Hp80z2bXpgyKosqMRPsV7XNNpc+DgT9ciclYaZm24E5ox3ovtjEZyuRgXnh+Fz75sh83m7N+/+rN2ZKQrMKHAe5J8dJQUZYd7sG2H8G7GW7a5V7AuyAvtu9IeD+tPQsyHB3MhxHwIMR8ezMVoxDp+KJOe+CFEvknLWoK9236F/Xv+gPikBejSHUBz/ffIm/QgRCIxbFY9ujtLodIkQ61J9ilGKtMgMlr4odjlcn/ACtNmQzvAB/FQkDH5TuxavQxFX9+FxDGXoaNxJxrLPsX4s//izoW5A13NhVBHZEIdmeVTDACkFFyHmuI3sePTK5Ex5Q4ALlTteRFShRaZU+4YwR4fX8bMe7Hz/ctQuOZmJOYtRWfdNjQeXIXxFz0HkUgMa08Huhp3Qx2VDU1Utk8xfeyWbuhqf0TumY+NVPdOWkbBjdj1zS0o3vAIEjIvQmfLXjQe+RwFc1a6nx+WLnS17YM6PB1qbbpPMSKJGJkTbkP5nmchEksQnTgbFlMTqg78G9qY8UjKumSEez2wtKzF2Lv9cezf+2fEJ81HV0cJmhs2IG/CA57rRtdhqNRJUPeu0nCiGPd1Q1h49lw3sqCNCM3rBgCkpi9C4Z6VOLDvWcQnzEFn5yE0N21GXv6d7nzYDOjuLodKlQi1OtGnGABISb0A1VVrIBbLEBlZAJutG7W1X0EmC0dyynkj2eXjSs9dht2bHkLx9hVITD0bne370FT7HfKnPtr7/OhGV8chqDUpUIel+BQjlYUhMlZ4+xFn7/MjPCIX2qhxw95PX2SccR92vn0JCj+5AYkFV6Czdisa93+I8Yv+1Xsd1aGrofc6Gp3jUwwA5C78DQ5+9RAgEiE25zzYejpRs/sViEQiZMy4ewR7fPK2/ONZ3L5uPa77cBWKP1qFzHnzMHX59fj0jtvhcrmgiopC6sxZaK8oh66iAvW7d6Ps66+x+IUXEZmRifbDZUieOg3zHnoIW559Fh1VVSPdJb8xF0LMhwdzIcR8CDEfHswFERERncoyJt2GXZ9di6Lv7kdiziJ0NO1CY/lqjF/wp97adSe6Woug1mZAHZHpUwwApORdg5oD/8GOz65BxsRbAbhQVfSKu3Y96daR6/BJSjvnEex9diH2v3Yl4qddja4jm9G88x3kLX8dIrEYNqMO3dU7oIrLhToud6SbG1AZ8x7CrtfORdF7VyNx0jJ0VP2IxsL3MH7pK+6+m3ToqtsJdUwO1DG5PsUAQO55T6JkzX0QiUSIGXMB7D2dqNn2AkQQIX3O/SPZ5UHxdSKUkX89dn17O4o3PoqEjAvR2boXjZVfoGD2Cs+YRvt+qMPToA5P9ylGJBEjc/wtKN/7XO+YxixYTM2oOvgWtDEFSMq8eIR7Pbj0MVdh9w8/RfHW3yIx7Rx0thWjqeZb5E//ladurSuBOiwF6rBUn2JEIjEyx12P8v0vufMRPx1mUwuqy95HeNQ4JKafP8K9HhjHu04s46fzsOv811F07ftIvHIiOn6sRuP7RRj/0lL3tVVnQteuOqhzYqDOiUHX9lpYmwxIvn4qOjZXeZ0vbHwilCna4e9IANx5SxKWLT+Iux44jMsuicHO3Xp8uqYNf/lDNsRiETo67SgsNiAzQ4msDCUK8tWYOT0cv3mqEvWNFuRkqXC4ogcvv9aA886JwtQpo2c16oGw/iTEfHgwF0LMhxDz4cFcEAWOyOVynfrTpMlLamoq6uvroVDGYN65/wnYeTvai1FZ9i703RVQq5ORknkZktPO7z+2d9uvkTnmOmSPXe5TzGA/Y++2X2PGvGcDOoFYmhnYbzPr6reiYsff0N16AOrILKRPvAkp+Vf3H9u1+ipkz3wYubMe8Smmj6m7BqWbn0JX016IRCJEpczFuHm/hUITH9D2izSBvR2SrmYzyjf/Cd0txdBE5SB92u1Imbi8/9jODy5HztxfIHf+r3yK6dNUuhZFa27GGdd/g8jkmQFtcx9nU33Az6lr2omKon9BrzsEdXg60vKuRUru0v5ju7+9FdmT7kHOlHt9igEAl8uF2kPvobb0A5iNjVCqExGbthC5k++DRBa4b+E6Wo8E7FwA0NG+D5WH34e++wjUmmSkZFyC5NTz+o/t3fEbZOZeg+wx1/oUM9jP2LvjN5gx968Bn0DsspoCer6OjoOoPPIRDPpqqNSJSE29AEnJZ/UfK9zzNDKzfoKs7Ct9igEAp9OO2prP0dS4CWZzK+TySERGFSA75yooFNEBa7tE631LsqHStRbiSMm/oe8qhzosBWnZS5GccXH/sT2bH0ZW3k3Iyb/Zp5jBfsaezQ9j1lkvBXQCsaRgesDOBQC66k0o3/gHdDcVQxOdg/QZdyJl8vX9x3a+swg5Z/4KuQse8ykGcF83mg+tQdW252BoK4VUHoaotDnIPeu3/RORA2X6n6MCer6BZC1YiHNXrEDSlCloLz+MbS+8gD1vvdV/7PZ167Bu5Uqsf3olAECh1eLcFSswfukVUMfEoK2sFDtffRU7Xnkl6G0NNuZCiPnwYC6EmA8h5sNjtOTiebsDegApKSmoG+C2bKeyYNU1Aq2u6nPUVX8+4LEeYwNcLsdp+fsjIiLf9L/faRKxcPmWEwf4SNewDRW7/4HutoNQR2QgffyNSBl3Zf+xXZ8vR/a0nyJ3xoM+xfQxddeidOvv0NVSCJFIjKjk2Rg3+zEo1HEBa7t9kvddywKto2wDKr9cAX3dXqjjxiBlwf1InnNL/7G9z52NzItXIHvRk16xB9++GfqaXTjj8f1BbycASLtsJ37QSdAd+QEV61aiu7EQ6phcpM++FynTb+o/tuv185F9zm+Qe+4TPsUAvfWnA5+ievMzMLSUQCoPR2TmXIw5f2X/ROSAqK4N3Lkwul8nAOAyBfYWzLqmXagofhH6jlL3+MS4q5GSs7j/2O7/3YHsiXchZ/LdPsUAvWMapR+gtmxV75hGAmJTFyB38r2QSFUBa7vLbAjYufroWvbiyMHXoe88DHVYKtJyr0By5qL+Y3s2/hRZ+bcgZ/ytPsUAvfmo+AR1Ff+F2dgEpToesUnzkDP+toDmQ6SODNi5gNE93gUAolfWBPR8A9H9cAQVT69Hd1Ej1LkxSL/7DKTcNL3/2K4L30D242cj97fnoumT/She/sGg5xr/yhVIuXFaUNr5eu3An10Daev2LvztH3U4cNCErEwlbro+AVdfGd9/7KrrS/DwAyl45KdpAIC2dhv+/LcabNrShbZ2GzLSlLhsUQzuuj0ZKmVwb7b9Yv7OoJ4fGD31p+HCfHgwF0LMhxDz4TFacsE6Puv4oY4TiE9To+UFOpwCPYF4tAv0BOLRLBgTiEezQE8gHu0CPYF4NAvGBOLRLNATiEe74ZhATEREdLph4XF01zW2rLsRFnP7afn7IyIi3wRrAvFoNhwTiEeTQE8gHtUCPIF4tAv0BOLRLBgTiEezQE8gHu2GYwLxaDEcE4hHk+GYQExERKcH1vFZxw910pFuABERERERERERkV9cAJz2kW6Ff/iVfiIiIiIiIiIiIiI61bGOH9I4gdhPBoMB5eXlMJlMGDNmDGJjYyESiXyOr6+vR3V1NQAgIyMDKSkpJ/XznU4nKioqUF9fj7CwMOTk5CAqiiv7EREREREREREREREBrOMTEREREREREREdj3ikGzCamM1mrFy5EhkZGQgPD8fUqVMxb948xMfHIzo6Go8//ji6u49/S6CvvvoKM2fORGpqKubNm4d58+YhNTUVM2bMwJdffnnCNjidTjzzzDPIysrC2LFjcfbZZ2PmzJmIi4vDpZdeitLS0kB1l4iIiIiIiIiIiIhoVGEdn4iIiIiIiIiIyDecQOwjo9GIhQsXYsWKFaipqfE63tnZiT/84Q/Izc3Fvn37BjzH3/72N1xyySXYtWuX17Hdu3dj0aJF+Otf/zpoGxwOB37yk5/gkUce8WqDw+HAF198galTp2Ljxo0n2TsiIiIiIiIiIiIiotGNdXwiIiIiIiIiIiLfcQKxjx599FHs2LEDADB16lR8/fXXaGhogE6nw8aNG3H++ecDAFpbW3HllVfCZrMJ4tevX49HH30UABAbG4t3330XOp0OOp0O77zzDmJiYvp/zrp16wZsw9NPP43Vq1cDAKZNm4bvv/8eBoMBtbW1eOqppyAWi9HT04OlS5eitbU1GGkgIiIiIiIiIgohLrgctlH5D3CNdPKIiE45rOMTEREREREREYUa1vFDGScQ+6Crqwuvv/46ACAnJwfbtm3DhRdeiKSkJERFReHMM8/E119/jQULFgAAysrKsGnTpv54l8uFX//613C5XJBKpfj2229x3XXXISoqClFRUVi+fDm+/fZbSKVSAMBjjz0Gl0v45Gtpaelf1SArKwsbNmzAWWedBY1Gg9TUVDzxxBN49tlnAQA6ne64KyAQEREREREREREREZ1KWMcnIiIiIiIiIiI6OZxA7IOSkhJYrVYAwM9//nPI5XKvx4jFYjz88MP920VFRf3/X1xc3L/qwZIlSzB16lSv+GnTpmHJkiUAgB07duDAgQOC4++//z6MRiMA4Gc/+xnCw8O9znHfffchPj4eAPDvf/8bTqfzZLpJRERERERERERERDQqsY5PRERERERERER0cjiB2AeHDx/u///x48cP+rgxY8YMGPP555/3///ixYsHjT/62BdffCE4dvQ5Lr/88gHjxWIxLrvsMgDuW7Dt3Llz0J9FRERERERERERERHSqYB2fiIiIiIiIiIjo5EhHugGjQW5uLv74xz8CAAoKCgZ9XH19ff//JyYm9v9/aWlp//9fcsklg8YffWzPnj2CY33nmDhxItLS0gY9x6JFi/pv07Znzx6cccYZgz6WiIiIiIiIiGjUc9hGugVERBQCWMcnIiIiIiIiIgpRrOOHLE4g9sGcOXMwZ86c4z7GZrPhT3/6U//20qVL+/+/sbERABAWFobo6OhBzxEdHQ2NRgOj0SgoVrpcLjQ1NQEAMjIyjtuOo4uSR5+DiIiIiIiIiIiIiOhUxTo+ERERERERERHRyeEEYj81NDRg8+bN0Ol0KCsrw6pVq/pXLnj66acxceLE/sf2FQ2PV3TsExMTA6PR2F+sBACdTgebzebTOWJiYvr//+hzDMblcsFuM53wcYMRi2UQS2R+xxMRERERERGdruwuFxxDiHcFrCVERESnplOljg+XE3ar/sSPG4RYIodYovA7noiIiIiIiOh0xTo+neo4gdhPGzduxLXXXivYFx0djXfffRcXXXSRYH9fAfDoouBgYmJiUFNTA6PR6BXvyzmOPn70OQZjteiw8dtlJ3zcYNISFiI98Wy/40OJJCp5pJsQUpytdSPdhJBhqy8c6SaEFKfVMNJNCCkiiXykmxAyRHL1SDchpMz/3S0j3YSQYv3zSLeAiIgo9PzodGGzi+VDIiKiYDlV6vgWUwvWvznlhI8bTFbBbciZcLvf8aFEVqkd6SaEFJfpxM+f04WjqWKkmxBS7J01I90EClGyxPEj3YSQMjNsxUg3IWS8ONINICIiClGs49OpjhOIA0in0+Ghhx7CM888g4svvrh/f3d3NwBApVKd8BwKhXsVgJ6eHq94X87RF3/sOQYjk4ZjWt59J3zcYMQiPoWIiIiIiIiI/DFXLMIZEPkd/7LDidP9630ulwsuh22km+EXF4vOREQjYjTW8eXKWMy9+IMTPm4wYjHvIkhERERERETkD9bxh451/NAmHukGjFbXXHMNXC4Xurq6UFhYiCeeeALh4eEoLS3FZZddhtWrV/c/Ni4uDgDQ2dl5wvP2PeboAmNfvC/nOPq4L4VOkQiQSpR+/xOLOYGYiIiIiIiIyB9SkQiKIfzzv2RJRER0ejh16vgiSGUav/+JeQctIiIiIiIiIr+wjk+nOk4gHiKtVovJkyfjqaeewpdffgmxWAyHw4Ff/OIXcDgcAICkpCQA7pUNTqTvMWFhYf37+uJ9OcfRx48+BxERERERERERERHR6Yh1fCIiIiIiIiIiIm9cPjaA5s+fjwsvvBBfffUVDh8+jLq6OmRkZCAxMRGAuyjocrkgEg383QKXy4WOjg4AQFpaWv/+sLAwaDQaGI3GExYe++KPPQcRERERERER0anI5bSOdBOIiGgUYR2fiIiIiIiIiGh4sY4furgCsQ9+9atf4frrr8dvf/vbEz42Pz+///8bGhoAeFYesFqtKC0tHTS2tLQUNpsNAFBQUCA41neO4uJiOJ3OQc9RXFzc///HnoOIiIiIiIiIiIiI6FTEOj4REREREREREdHJ4QRiHxQXF+Pdd9/Fa6+9dsLH1tfX9/9/ZGQkAGDGjBn9+z777LNBY48+NnfuXMGxvnM0NTVh9+7dfp2DiIiIiIiIiIiIiOhUxDo+ERERERERERHRyeEEYh+MHTsWgLvod+jQoUEfZzAYsGnTJgDuomNubi4A4LLLLuu/3dknn3wCl8vlFetyufDpp58CAEQiES6//HLB8cWLF/f//yeffDLgz+/s7MT69esBuFdQGDdunE/9IyIiIiIiIiIiIiIazVjHJyIiIiIiIiIiOjnSkW7AaHDGGWf0//8999yDzz77DGFhYYLHWCwW3H///f23O7vxxhshk8kAAMnJybjmmmvw/vvvY/v27XjllVdw1113CeJffvllbNu2DQCwfPny/lud9Vm6dCkyMjJQXV2Nf/zjH7jmmmswZcqU/uMOhwP33XcfzGYzAOBnP/tZYDpPIcduMw24XyJVQiQ6/b4TwHwQEZG/RGIxZCqV136XywWbaeD3l1MVcyHEfHgwF0LMhxDz4cFcjCQX4LCNdCP85D0xjYiI/Mc6/shjrZaIiALldH5PYY3Bg7kQYj6EmA8P5kKI+RBiPjyYi5HEOn4o4wRiH1x55ZX4/e9/jwMHDmDDhg3Iy8vDbbfdhjFjxkAikaCsrAxvv/02KioqAAC5ublYsWKF4Bx/+MMf8M0330Cn0+Huu+/G1q1bcfHFF8PpdOKLL77Au+++CwCIiYnB7373O682KBQK/OMf/8CSJUtgNpuxYMECPPDAA5g+fTp0Oh3efPNNbNmyBQAwe/Zs3HTTTUHOysC6DFWoadoAY08jlIpoJMXMRELMtCHHdBmqUdO0HiZzM0QiKbSadGQknQuVIiaY3RkSXfMuHCl+GXrdIajC05A29iqk5C4ZUozdZsT3H84fMPbMpV9DqUkIYA8Cq6NlD44ceB3dnaVQh6UhNecKpGRfNqQYu82IDf89b8DY+ZeugVIdH9A+BFJnVxmqar6AwVgDlTIeyUkLkZRw/NsVnijG5XKhuXU76hvWwdTTAplUg8jIPGSlXw6FIjLIPfJfZ3cFahq+g8FUD5UiFknxc5AYN2vIMV36I6iq/wYmUyNEYikiwrKQmXoxVMrYYHZnyDq7y1Fd9xUMxjqolHFITpiPxPjZQ4pxuVxoaduJusYN6DG3QipVIypiLDLTLoVCHhHsLg1JR/t+VJa/D333EajVSUhJvwTJaQO/7v2N6dQdwJ7tj2PG3L9AGzEm0F0ImKofWrHhyRI07ulA9JgwzLw3B9NuzRz08U6nC4X/rsL25yqgKzcgLEmJcZcn4awnC6DUugdHXS4Xit6uwbZnDqO9zABVtBzZ58XjnN+PhzbZ+8NTqMs8cwHOfeIJJE+divbyw9j+0kvY/eabgz5+7EUX48Y1a7z2t5eX4+/5eUFsafAxF0LMhwdzIcR8CDEfHswFERHR6MM6vu9Yq/XQ1W5B+da/QN9cDHVUNtIm34LUicuHFGO3GgYPFokglWkC1fyA0zVsQ8XOZ6FvOwB1RCbSxt+AlPyrhhzjdNhQvuOvaK3+HmZDPbRxEzF29q8QkTAliL0Zmo62YhwpfRv6rsNQa1KQmnUZktMvCmhMR/s+7Nnyc8xc8Dy0kWMD3YWA6uwsRVX1WhgMNVCp4pGcfDaSEgcer/I1xuVyobllK+rrvoOpp9k9phGVj6zMK0J6TANgPo7G95QTY43Bg7kQYj6EmA8P5kKI+RBiPjyYC6LA4ARiH8hkMqxduxaLFi3CoUOHUF9fj5UrVw742Dlz5uCDDz5AdHS0YH9mZibWrFmDK664Aq2trXjrrbfw1ltvCR4THx+P1atXIyMjY8BzL168GM8//zwefvhh6PV6/OEPf/B6zPTp07FmzRpIpcP/q9Ub63DwyDuICM9Gduoi6I21KK9bCwCDTiL2JcbY04z9FW9Cq8lAVsolcDqtaGjdhuLDr2LquPsgl4UPTwdPQlfbPuxdfz+iE89A3qxfo7O1CAe3PQUAg04i9iWmx1AHABg/92mowlIE8XJlVHA6EwBd7Qewd9PDiI6fibxpj6KrrRglu9zP38GKCL7E9BjdK4WMn/UEVJpkQbw8hIsp3fpK7Dv4T0RG5GFM9rXo1legrPxtABh0ErEvMa1tu1B6+C0kxs9FeupFsFq7UVP3NYzGOkyd9EuIxZLh6eBJ6DbU4EDZa4jUjkFuxlJ0G6pwuOojABh0ErEvMUZTE4oPvYSI8GzkZCyBw2FFffMmFB58DtMn/BxyuXZ4OniSuvVV2F/yEiIjxiI3axm69ZUoO/I+AAw6idiXmNb2PSiteBeJcbORnnI+rDY9auu/g9HYgCkTHgnJ5wYAdHWWomjXU4iKmYxxBXehq/MQDu1/HgAGnRB8sjF2mwkHi59FqH9rrG67Du9ctBnZ58Vj0QtTUftjO9bethsABp1EvPuVSnxxz15Muz0TC36TB125AVv+rwz1Ozpwyw8LIZaIsP+DOqy+aRem3paJMx/Lg6HJjE1/KEVTURfu2H42JLLRs5JD6qxZuPmLL1Cxbh3WPnA/0ufMwRWvvgYAg35QjM7JRk9nJ95esliw3967EtRoxVwIMR8ezIUQ8yHEfHgwF0RERKMT6/i+Ya3Wo7NxN3Z/eg1i0hcg/7z/Q2f9Dhz49iEAGHQSsS8x657PGvRnKrVpWHjHnoD2I1A6m/diz+c3ITp1PvLPfBqdTbtxYMMvAGDQScS+xLhcLhR+czcM7YeQOeUOiCVy1B38ALs/vwFzrvoaqvCUAc89kro6SlC47TFEx01F3qQH0Kk7iJLCvwPAoBOCTzbGbjPi4N7/Q6jXJQGgu/sI9u1/FpGR+RiTuxzd3RUoK3sTAAadNOtLTGvrDpSWvoHExPlIT18Eq7ULNbVfusc0pjwGsTg0h7GZDw++p5wYawwezIUQ8yHEfHgwF0LMhxDz4cFcEAVO6H3SCFHZ2dnYvXs3/vOf/+Cbb77BkSNHUFlZCYVCgXHjxmHs2LFYvHgxLr30UkgkA0/Emj9/Pvbt24fnnnsOq1evRnV1NUQiETIyMrBkyRI8+OCDiIuLO2477r//fpx55pl47rnnsG7dOjQ2NiIsLAx5eXm47rrrcOedd/bfcm241bduhUoRg7zMqyEWSRAfNQkOpwV1rVsQHz0VIpHIr5iG1h8hk2owPns5xGJ332Ii8rG75Dk0tG5DZvL5w93VE6oueQdqbQYmL/wrxGIZkrIugcNmRNWBN5Gcs3jAXPgSY9K7JxDHp50d0qsUHKum7H2owzMwad6fIBZLkZRxIex2E6pL30Fy1qUD5sOXmB5DPQAgLmXBqMpHXf06qFQJGJ93F8RiCRLiZ8HhsKC27hskxs8ZMB++xNQ3bkBkxDiMG3NDf5xCEY0Dh16C0VSP8LD04eymT+qbNkKljEN+7k0QiyWIj5kGh8OCuqYNSIidOfB1w4eYuqYNkMvCMWHsbZ7rRtQE7Nr3Z9Q3b0ZW2iXD3VWf1DdugEoVj4Kxt7l/z7Ez3L/nhnVIiDtj4Hz4ENPQtBGR2jEYm3Ntf5xCHoWDZa/B2NOAcE3acHbTZ7WVa6DWpGDitF9BLJYiMeUsOOw9qKn8BEmp5w6Yj5ONOVzyGmxW/XB1yW9b/34YMePCcfWncyCRiTFpeToseju2/LkUU2/JGDAXm/9Uirylybj81en9+5KmR+GdCzej4rtmjLkoEdufL0fWOXFY/JrnMRHpanywZCua93UheVrofjnlWPMefAhtZaV4d9mVcNrtKHr/fSjCw3Hmzx8d/ENidjZ0FeWo7l396VTBXAgxHx7MhRDzIcR8eDAXREREoxfr+CfGWq1H9a4XoYnKwZTL34RYIkNy/pVw2Iyo3Pk8UiZcN3Ad34eYWVd/5hXndFhR/MVdSBq3ZBh65p/qotegiczGlAtfglgiQ9LYJbDbjKjc+xKS85YNnA8fYjoatqK9diPmX/c9VOGpAIC4zPNQ9M296GzcFZITiGsqPoU6LBUTZ65w1xhTz4XD3oPqw6uQlHbhwK+Tk4wp2//SqKhLAkBd/bdQqRIxvuBeiMVSJCTMhsNhRm3tV0hMmDfImMaJY+ob1iMyMg/jxt7cH6dQROPAwX/BaKxHePjAX9QYacyHB99TTow1Bg/mQoj5EGI+PJgLIeZDiPnwYC6IAmf0LCsXAtRqNe6++27897//RVFREbq7u9Ha2orNmzfjjTfewOLFiwctOvZJSEjov42awWCAXq/H/v378bvf/e6ERcc+kydPxuuvv46qqipYLBa0t7djy5YtuO+++0as6Oh02qHrPoTYyAkQizw5iIuaBLOlHUZzk98xJksbtJqM/kmAACCTaqBWxsNkbglir/zjdNjQUvs9EjMuELQ5KWsRTPpqGDrK/I7p0ddBroyGVKaBy+mAyxX630x3Omxoqd+IhLRzBd+OTsq4CCZ9DQydh/2OMRnqIFdEja58OO1o0xUhPna6YNXX+LhZ6DG3wGis8ztGIY9EXOx0QaxSGQMAcDgswejOkDiddrR37kdc9BRhv2KmocfcCmNPg98xPeZWaMOyBK8nuSwMGlUCTD3e16NQ4HTa0dZRjLiYqcK+xc5w/55N9X7HyOWRiI2ZKohVKtwr7Dgd1mB0Z8icThtam7cjPmm+4DqQkHwWTMYGGPSVQ45pbdqK5sZNyBk3MrcL9ZXd6sSh1Q2YcHWqYEXgSdeno73MgKaiLq8Yq8mOrmoTcs4X3sotc2EsIAJa9ncDALSpKoy/KlXwmMgsd0HWZnQEuitBI5HJULB4MYpXfQSn3d6/v/Dd9xA3bhySJk8eMC4mOwftFUcAAOIT/A03WjAXQsyHB3MhxHwIMR8ezMVo5ILLYR2V/0bDanNERKMR6/iDY63Ww+mworniKySOWwKx5KiafP6VMHVUQN+63++YqNTZXv9aKr6GJjoXufMfC37n/OB0WNFS+R0Sci8V9m3sEpi6jkDfftDvmLqSDxGXcQ5U4alwuVxwOR1QqOMxa+nHSBq72Ou8I83ptKG16UckpCwUPOcTU8+ByVgHQ/eRIce0NG5Gc/33yM2/LXgdCRCn0462tkLEx80U9C0+fjZ6epphNNb6HaOQRyEudqYgVqmMBQA4nKE3pgEwH0fje8qJscbgwVwIMR9CzIcHcyHEfAgxHx7MxWjEOn4o4wRiCgib3QiXywGVUlg8VSvc2xar9+QmX2NyUi5FZtK5gsfYHRYYexohl2kD1odAsZrb4XLaoInIEezXRGQDAHqMjX7HmAy1EIvl2LPuXqz7YA42rFqAvd8/2L8ycSiyWnRwOW0I02YL9mu07tu4mU3Nfsf0GOohksix54eHsP7Ts7Fh9fko3PwoTAbviZahwmrrhstlh1qVJNivUbu3zRad3zEFeXcgOfFMuFwuWG166A3VqKpeC4UiGtrwwW+bN1JsNgNcLgfUqgTB/r5ti6XT75jcjCuQmXqx4DF2hxkGYz3k8tC7bgCA1aaHy+WARpUo2N+3bbF0+B1TMPYWJCfM631uGKA31KKq7kso5FEIDwu9VQsAwGrphMtlhyZMuDpy37a5p3VIMRZLBw7t/xdyxt0ETZhwAm2oMTab4bA6ETde+NyNKwgHAHTVmLxixBIRbtt6FgqWCftWvakNcAHaFBUA4KpVszHjrmy4XC4YWy1o2N2B9b85gIh0NVLOiPY6b6gKS0iAVKFAy8EDgv0tJe6Bqoi0gVfZjs7ORkRqKh7YswdPmXrwy6pqLHrmGcjDwoLe5mBhLoSYDw/mQoj5EGI+PJgLIiIiOpWxVuthMbbC5bAiLCZPsD8sZhwAwNzt3W5/YgCgrWo96ve/h0mLXhZMmAsl1p42uJxWhEWNFewPixoDADDrvRd78DXGoCuFKjwVJZtW4Ie3ZmLda/nY8d8r0dVSHIyuDJnV0uEerwnPFOzX9K7+au7xXtjmZGIsZh0OFT2L3ILboQkPvbsGHstq7R2f0CQL9ms0xxnT8DGmoOBuJCcvdNetrXro9VWoqlrdO6aR7XXeUMB8ePA95cRYY/BgLoSYDyHmw4O5EGI+hJgPD+aCKLBCs1JBo47VbgAASCUqwX6pVA3APVnY35gwtXDSpNVmwKGqD+ByOZEYOyMArQ8sS087AECmEE70kikiAABWs3fxwNeYHn0dzKYmxKefg7Rx18Jiakbl/tex85tbMPfyTyGThwe2MwFgMbv7Jj2mbbLeSZwD5sPHmB5jPSymZsSnLET6mGUw97SgsuQt7Fp/F+Zc9H5I5sNqda/62fc87yOV9q74afO+ZdnJxjgcPdi64xcAALFYgakTfxaShWlrb7ul0mOvAe5+9V0j/IkJ06QcE2fAwcP/hgtOJMXPCUDrA89mG+T3LHNvWwd4bpxsjMPRg227HwcAiMVyTBn/YEg+NwD3ZGAAkMmEf6z3va6tA3wxxdcYl8uFQ/ueR5g2B6kZl6BTJ/xgEWoMTWYAgCpKLtivjlEAAIzN3qtPSBUSpM2OEeyr36nDp9fvhDZNhbwlwmK1ucuGv8R/DgCQaSS4dfNZkMpHz/fMwhLdk+Z7OjoF+03t7veTsISEY0MgEosRlZUFbUoK1q1cidaSEqTMmIEFv/gFYnPH4K3LLg16u4OBuRBiPjyYCyHmQ4j58GAuiIiI6FTGWq2H1eSe0ClTRgj2y5RRAACLaYAvr/sR43TYcOj73yBr1gNQhid7HQ8VfW3vG5Po09c3a0+b3zEWYytqD7wNbdwk5M1/EhCJUVP8OnauXoa51/wPau3Ag/sjxWp2L8og9aox9j7nB1rowccYl8uFksK/IzwiF6lZl6OzfV9gGx8EVpu7puo9PuHuq613/GIoMQ5HD7ZuexhA75jGlF+Gbt2a+ejH95QTY43Bg7kQYj6EmA8P5kKI+RBiPjyYC6LACr1PG3Rq6b2ljMt1ErdBHyTG5XKgsW0Xapu/h9PlwNiMKxB2zIqsIUE02IHefjnt3od8jMnIvx7ZE+9AVML0/kdEJkzDts+vQv3hT5A5/ma/mhxMokE75+6b0+WdD19j0sdei6yCWxAVN7X/EVFxU7Htm+tRf2QNMvOu97vdwXLivnm/Vk42RiJRYPKER2C1dqKhaRMK9/8dUyb+3GtS7YgbtFvHuW6cZIzL5UBDy1bU1H8Lp8uOcdnXIUwdYnnoN0jnjnsdPbkYiUSBSQUPwGrtRmPzZhQdfB5Txj8IjToEBy9O9Ls+mWvpMTENtd+gq+MQZs1/DiLRKJgkKxq4Yy6nu18Om/O44T0dVmx48iB2/OsIItJUuPazuZCphLdkkYdJcfMPC6BvMGPXi0fw5sIfcOvms5AwMWKQs4aYQW5hJxK7f7+SAW4NK5ZK8fGtt6CxqAhtpaUAgLJvvoa+qRFLX3oZqTNnom7nzuC1OViYCyHmw4O5EGI+hJgPD+aCiIiITmGs1R5tkHoL3HUWl9MWkJjaojdhtxqQOf0e/5o5bAbpm8vdN+dAtTgfY+zWbig0CZh+2TuQSJUAgNi0M7Hp3QWoLnwF+QueHmLbA2yQWhxcfb/ngeqSvsXUV3+Bro6DOOOsl0dHXRLDOKYx6RfuMY3GDSgs+gumTPklwjShd+c45sOD7yk+YI3Bg7kQYj6EmA8P5kKI+RBiPjyYC6KAGh2fTinkyXu/JWt39Aj2923LZd7fBD2ZGJO5DcXlb6Cy4StoNRmYMvYexEZOCFwHAkihjAUA2CzC1TH7thXqOL9jYlPmCyYPA0BYRDbU2nToOw4HoPWBJ1e6V8A89hvUfdsKVazfMbFJcwTFAwDQaDOhDk+DobM8AK0PPHnvt6jtdpNgf9+K2wq592S9k40RiSSIjBiD+LiZmFhwP8QiKRqafghMBwKo7zV+bL/6tuUy7ZBiTD0tKCr5F47UrEFEeDamjX8EcdGTA9eBAOtru83r99zbt4GeGycZIxJJEKnNRXzsNEzIuxtikQQNTZsD04EAk8vdK5XYbMKVqPtW3FYoo/2KMfe04vChN5CTdzOkMg3s9h44nVYAgMNhhd0ufE8KBWGJ7pWGe3RWwf6+7fBk5aCxZV824l/jv8POF49g9kO5uLvoPCRM8H4uSaRiZC6Iw8Rr0rD8q3mQyMXY+UJFAHsRXPqmJgCAOjpKsF8d7X6edDd432bTYbVi36pV/R8Q+5SsXQsASJw4MRhNDTrmQoj58GAuhJgPIebDg7kYhVyAy2Edlf8wcJ2biIgoaFir9VBo4gEANnOnYL+tp7P3eOKQY5xOO6p2/hNpk2+GRCZceTTU9I1B2Hrv8NXH3rut7O27PzFyVTRi087snzwMuFcfjUyYDn17SQBaH1hyhfuzgN0qvLubp8YY41eM2dSCwwdeRm7B7ZDKwmC398DhcN9ZzOEwh2RdEjhqfMImvNOozdY3PhE55BiRSILIyLGIj5+FiRMehFgsQUPD94FofsAxHx58Tzkx1hg8mAsh5kOI+fBgLoSYDyHmw4O5GIVYxw9pnEBMASGTaiASiWEytwj2920rZN6TlXyN6bHosL/i3zBbdCjIWo78rGuhUnhPHAsVcmU0RGIpDJ3CiVd9H2iVau+l8n2JsduMaK3bCMsAtwoTiSSQyjWB6kJAyRVREImlMHYdEew39G4rVQPkw4cYu82I1obNsPS0e/9QkRhSWWjmQyYLh0gkgdEk/IOlb1uhiPIrxm7vwc69K9HWXih4jEQi7z8eamTSMHe/epoF+4097j/2Biq0+RrTY25D8aEX0WNux/gxt6FgzM1QKb2LVaFEJnP3zdTTKNhvMrm3FfKBnhsnjrHbe7Cr6I9o0xUJHtP/3HCE3nMDAOSKCIhEUhj11YL9RkMNAM8XL042psfUBKfDgtL9/8LG767Bxu+uQdGulQCAvdsfw5b1NwehN0OjiVdCLBOhZb+wqNq3HZE28ABUyep6vH/Zj9DEK3Dv/vNx4V8nQan1fNvS3GXDvyZ8h5L/1gvi5GopItLVMHcOtNJOaDK2tMButSJhvPDLRX3bXXV1XjGRGRkYd8kiiKXCG3I4He4VP8zd3rcbHA2YCyHmw4O5EGI+hJgPD+aCiIiITmWs1XrI1bEQiWXQtwknsBp6J7Qqtd53MTvZmJbyL2ExNiNlwnWBbHpQyFUxEIllMOjKBPv17e7BdWWY9x3MfI1RqBPgsFu84p1OGySh+NxQREIkksKgrxLsN3S7txUq70VifInpMTXC6bDgUNGz+OHLxfjhy8Uo2v4bAMCeLT/H5m+uDnRXAkIm0/aOTwhriH3bA49pnDjGbjdh564n0Na2R/AYiUQBhSI6JMc0AObjaHxPOTHWGDyYCyHmQ4j58GAuhJgPIebDg7kgCixOIKaAEIuliNbmo61zv+BWO62d+6CUR0Gj8v62vq8x1Y3fwem0Y/LYuxClHRP8zgyRWCJDfNo5aK7+Fs6jblnWVPU1VGFpCI/O8ytGLJZj/4+/QUXRS4JYY1cVjF2ViIidFLxODYFYIkN8ykI01/5PcJuzpppvoQpLQXjUWL9ixGI5DmxfiSMHXhXEGrurYequRkRMaK5QLRZLERs9Ba1tu+F0ep73La07oVTGIUyT5leMRKKExdKJ9o79gliLtQumnqaQu7UV4O5XTOQEtOoKBf1q1e2FUhGDMLV3UdrXmMq6L+F02TF1/EOIjvR+zYUisViK2KhJaG3fI/w9t++GUhGLMI33oIUvMRKJEhZrF3SdwoEN93OjGZoB8hwKxGIZ4hLOQHPTZuF1oGEjVOpEhGuz/YoJ12Zj2hl/FPwbk38HACBvwn2YMvPJoPftZEnlYuQvTcH+D2vhsDn79+97rxZRORokTo30inHYnPjinkKkzo7BbT+ehdix3ncCUGil6K7rweEvmwT79Y09aC3RI2Gy93lDlcNmw8HVqzHxqmWCD32Trr0G7eXlaNy71ytGExuLG9eswYSf/ESwv+Dyy+F0OFC/a1fQ2x0MzIUQ8+HBXAgxH0LMhwdzQURERKcy1mo9xBI5EnIvQVPpGjgdnpp8Y8mnUEVmQhvvvfrUycbUFf0H0RkLoAzzHh8JNWKJHPFZF6Cp/HNB35rK10KlzUB47Hi/Y+Kzzkdb9XrBys0WYws6G3ciImFK0PrkL7FYhrikeWip/0HwnG+u/x4qdTLCI3L9igmPyMX0eX8X/Bs74V4AQN7khzFlzh+D3zk/iMVSxMZORWvrTkHfWlq2u8cnwtL9ipFIVLBYOtCu2yeItVg6YTKF5pgGwHwcje8pJ8YagwdzIcR8CDEfHsyFEPMhxHx4MBdEgSU98UOIfJMSNwf7Kv6N0qpViI0cj25jDVo7ipGbejlEIjFsdhMMpnooFdFQKWJ8inG5nNB1lyIyLBs95lb0mFsFP1Mu00IzwDdYR1pG/vXY9e3tKN74KBIyLkRn6140Vn6Bgtkr3LmwdKGrfT/U4WlQh6f7FCOSiJE5/haU730OIrEE0YmzYDE1o+rgW9DGFCAp8+IR7vXg0sdei93f34viHx9DYtq56GwrRlP118if8ZgnH7qDUIelQh2e5lOMSCJGZt4NKN/3AkQiCaITZsJsakZ16bsIj8pDYvoFI9zrwaWmnIvCfX/HwdJXEBc7Hd3dFWhp3YGxuTe482EzQm+ogkoZB5Uq3qcYAEhJWoiaum8gFssQqR0Dm82AuoZ1kEnDkJx45kh2eVApiQtQfOhFlFT8B3HRU9Ctr0RL+x6MyVzWf93QG2ugUsT2ryB8ohiXywld50FEasfAZG6GySxcrVghi4BGnTQS3T2hlKSzUHTweZQcfgNxMVPRpT+ClrZdGJt9bW8+jNAbaqBSxkKljPMpBgCSE89Ebf13EIukiNDmwmY3oL7xB8ikGiQnzBvJLh9XWtZi7N3+OPbv/TPik+ajq6MEzQ0bkDfhAXc+rHp0dx2GSp0EtSbJpxipTIPI6ALBz3H1foklTJsFbURoflFlziNj8O+FP2DVldsw/upU1GxuR/E7Nbj89ekQi0Uw6ayo36FDdG4YYnLDULdNB0OTGZNvSkf1Ru+V6xMmRkCbosKs+3Ow6Q+HIFVKkLEwFqY2K7Y9cxjqWDlm3J01Aj3135Z/PIvb163HdR+uQvFHq5A5bx6mLr8en95xO1wuF1RRUUidOQvtFeXQVVSgfvdulH39NRa/8CIiMzLRfrgMyVOnYd5DD2HLs8+io6pqpLvkN+ZCiPnwYC6EmA8h5sODuSAiIqJTGWu1Hhkz7sHODxej8LNbkThuCTrrt6Ox5COMv+BZiERiWHs60NW0B+rILGiisn2K6WO36KGr+xG58341Ut07aRmTb8euNVej6Nt7kJhzGTqadqKx7L8Yf9b/uZ8b5k50tRRCHZEJdUSmTzEAkJJ/DWr2vYUdq5chY/LtgMuFqsKXIVVokTn59hHs8eDSc67Ani0/x75dTyMheSE6dfvRVLcO+VMe6a1LdqO7sxQqdTLUYSk+xUhlGkQeM/Gxry4ZHpEDbaT3ZMtQkZpyAQqL/g8HS15CXNxMdHcdRkvLNowde3PvmIYBen0VVKo4qHrH7E4UAwApyeegpvZL95hGxFj3mEb9d5DJwpCcfNYI9vj4mA8PvqecGGsMHsyFEPMhxHx4MBdCzIcQ8+HBXBAFjsjlcrlGuhE0/FJTU1FfXw+5LBwzC34WsPN2GSpR07QBxp5GKBUxSIqdhYToqf3H9le8hbSEhUhPPNunGJvdiB0H/jLoz4uPmowx6UsD0nb1xMUBOU8fXdMuVBS/CH1HKdTh6UgbdzVSchb3H9v9vzuQPfEu5Ey+26cYAHC5XKgt/QC1ZatgNjZCqU5AbOoC5E6+FxKpKqDtd1nNAT2frmUPjhx4FfqOMqjD05CWeyWSsy7tP7Znw33IKrgNORNu9ykG6M1H+ceoK/8YZlMTlKoExCbPR86EOwKaD1t9YcDO1aezqwxVNZ/DYKyFShmHlKSzkJgwt/9Y0f5nkJG2CJnpl/oUAwBOpx11Df9DU8s2WCw6yGVaREaMRWb65VAoIgPWdqfVELBzAUBndwWqG76F0VgPlTIWSfHzkBg3s//YvtKXkJ58PjJSLvApxmozYHvhU4P+vPiY6RiXfU3A2i+SyAN2LgDo7DqM6rqvYDDVQaWMQ3LCmUiMn91/rLjkn0hPuQiZaRf7FAP0Pjcav0dz6w5YLB2QycMRqR2DzLRFUMgjAtZ2aVRmwM7Vp6N9HyoPvw999xGoNclIybgEyann9R/bu+M3yMy9BtljrvUpZrCfsXfHbzBj7l8DOoF4wZf/Cdi5AKByQys2rDiIxr2diBkThln352DqLZn9x946eyMWrsjH2U8W4MBHdfjoqu2Dnmvxv6dj6s2ZsFud2Pq3MhS+VY2uahPCEpXIPDsO5/xuPLTJgX1fscpWB/R8A8lasBDnrliBpClT0F5+GNteeAF73nqr/9jt69Zh3cqVWP/0SgCAQqvFuStWYPzSK6COiUFbWSl2vvoqdrzyStDbGmzMhRDz4cFcCDEfQsyHx2jJxfN2B/QAUlJSUDfAbdlOZZ66hhazJo6eiTlH27HvT7Dauk/L3x8REfmm7/1OoYrDmZetDdh5R3OtVpLmvfrrUOhqt6D8xz+ju2UfNJHZSJ96G1ImXNd/bOeqJciZ8yhy5/7Cp5g+TWWfoeizW3HGtV8hMnlGQNt8NJfJGNDz6eq3omLnM+huOwh1RCbSJ96IlLyr+o/tWnstsmc8iNyZD/sU08fUXYvSLSvR1bwXIpEYUSlzMG7u41Co4wPWdkd9WcDOBQAdbUU4Uvof6LsqoNYkIzVrMZLTL+w/tufHR5E19npk593oU8xgP2PPj49i5oJ/BnwCsb2zJqDn6+w8hKrqtTAYaqBSxSMl+RwkJs7vP1ZU/FdkpF+GzMzFPsUAvXXrum/R1PwjLJZ2yGURiIwch8zMpQEd0wiG0ZwPWaL3iuJDMZrfUwBg1qfzT/ygIRotNYbhwFwIMR9CzIcHcyHEfAgxHx6jJRes47OOH+o4gfg0FawJxKNZoCcQj3aBnkA8mgVjAvFoFugJxKNdoCcQj2bBmEA8mgV6AvFoNxwTiImIiE43LDyy8EhERKe2YE0gHs0CPYF4tAv0BOLRLNATiEe7QE8gplNHoCcQj3bDMYGYiIiITm+s47OOH+rEJ34IERERERERERERERERERERERERERERnSo4gZiIiIiIiIiIiIiIiIiIiIiIiIiIiOg0Ih3pBhAREREREREREfnHBZfDOtKN8JNrpBtARERERERERERERBRkrOOHMq5ATEREREREREREREREREREREREREREdBrhCsSnOavNiD2H/jngsaTYWUiKnTXMLSIiIiIiIiKiPrucTux2DvwNd+Mwt4VOXmPbDjS27RjwmNXG3yAREfnGatZh69fXDngsNecnSBtz5TC3iIiIiIiIiIj6sI4/up3udXxOID7NyWUaTMu7f6SbQUREREREREQDmCEWY8Yg94963u6AfnibE3pcgMtpGelWDCoxejISoycPeGxXyT9htZ/2v0EiIvKBXBmNORe9P9LNICIiIiIiIqIBsI5/Aqzjh7RBnrpERERERERERERERERERERERERERER0KuIEYiIiIiIiIiIiIiIiIiIiIiIiIiIiotMIJxATERERERERERERERERERERERERERGdRqQj3QAiIiIiIiIiIiL/uOByWEe6EX5yjXQDiIiIiIiIiIiIiIiCjHX8UMYViImIiIiIiIiIiIiIiIiIiIiIiIiIiE4jXIH4dOdywmFuH+lWhATTvjUj3YSQ4nJaRroJIcNpNYx0E0KKSCIf6SaEFLEsfKSbEDJcdz4w0k0IKVbZFSPdBCIiIiIiIiI6BbhsZphK1o50M0KCrClnpJsQUsQyzUg3IWTYu+tGugkhxWHWjXQTQoZUkzjSTQgpSe99MNJNCC3KkW4AEREREdHI4grEREREREREREREREREREREREREREREpxGuQExERERERERERKOUCy6HdaQb4SfXSDeAiIiIiIiIiIiIiCjIWMcPZVyBmIiIiIiIiIiIiIiIiIiIiIiIiIiI6DTCCcRERERERERERERERERERERERERERESnEU4gJiIiIiIiIiIiIiIiIiIiIiIiIiIiOo1IR7oBRERERERERERE/nC5AJfDMtLN8IvLNdItICIiIiIiIiIiIiIKLtbxQxtXICYiIiIiIiIiIiIiIiIiIiIiIiIiIjqNcAViIiIiotOESCyGTKXy2u9yuWAzmUagRSOL+fBgLoSYDw/mQoj5EGI+PJgLIiIiIiIiIgoE1hg8mAsh5kOI+fBgLoSYDyHmw4O5IBoYJxBTQHWb6lHXth1GSyuUskgkRE1EfERBwGJsjh7sKX8D03JuhUzqfVEPJV2GKtQ0bYCxpxFKRTSSYmYiIWbakGO6DNWoaVoPk7kZIpEUWk06MpLOhUoRE8zuDFmXoQa1LZtg7GmGUhGFxOhpSIiePOSYbmMtapp/gMncCpFIAq06DemJC6FSRAezO0MWrNdKt6ketW3bYLK0QyySIFyVjPS4OVDKI4PYm6HpMtairnUrjOZmKOVRSIyajPioiUOO6TbWoaZ1C0zmNojFEoSrUpCeMB8qeVQwuzNkvHYcX2fpD6hZ8xQM1Xugih+DpHPuQeKZt/oU2174Gao+eQzTn94X5FYOn8wzF+DcJ55A8tSpaC8/jO0vvYTdb7456OPHXnQxblyzxmt/e3k5/p6fF8SWDg/mw4O5EGI+PJgLIeZDiPnwYC6IiIjoVMZarUdnZymqqtfCYKiBShWP5OSzkZQ4f0gxLpcLzS1bUV/3HUw9zZBJNYiMykdW5hVQKCKD3KOh6eg4iKojn0Cvr4RanYiU1PORlHz2kGJcLheamzahtuZLmExNkMk0iIqeiOycq6AI5edGdwVqGr6DwVQPlSIWSfFzkBg3a8gxXfojqKr/BiZTI0RiKSLCspCZejFUythgdmfIeN0Q6uwuR3XdVzAY66BSxiE5YT4S42cPKcblcqGlbSfqGjegx9wKqVSNqIixyEy7FAp5RLC75LfmH+pQtHI7dHtbED4mEuPunoTcW8YP+niX04XyNw+g9J9F6C7vhDpJg9RLszFpxRmQaxVej7e09+CjlNdwZd3tUMaG9tjwYFhj8GAuhJgPIebDg7kQYj6EmA8P5oIoMDiBmAJG39OEkro1iFCnISvhLOh7GnGkaR0ADDox8mRiXC4n6tq2wwVncDsSAHpjHQ4eeQcR4dnITl0EvbEW5XVrAWDQiYC+xBh7mrG/4k1oNRnISrkETqcVDa3bUHz4VUwddx/ksvDh6eBJ0pvqUVL1ISLCMpGdfAH0pnpU1H8JAIMWmHyJMZpbsf/Iu9Bq0pCVdD4cLhsa23ZiX8VbmDLmDshlYcPTwZMUrNeKydKOg7X/hVaVjKyEhXA4bWjsKMS+6lWYnLUccqlmeDp4EvSmRpTUfIpITTqyks5z/54bvwWAQScR+xJjMrfhQPUqaNWpyEo6B06nDY3tu7HvyHuYknNT6D43eO04ru6K7Tjw94sRWXAecq//F7rLf8Thf98OACecRGw3daHms98NRzOHTeqsWbj5iy9QsW4d1j5wP9LnzMEVr74GAIN+MIrOyUZPZyfeXrJYsN9uNge7uUHHfHgwF0LMhwdzIcR8CDEfHszFaOOCy2EZ6Ub4yTXSDSAiotMQa7Ue3d1HsG//s4iMzMeY3OXo7q5AWdmbADDoJGJfYlpbd6C09A0kJs5HevoiWK1dqKn9EkZjHaZOeQxicWgOzXV1laO48E+Iip6IsXm3oruzDIdKXgGAQScR+xLT0vwjSg6+iKTks5GRuQRWayeqq9bAYKjG9BlPh2Q+ug01OFD2GiK1Y5CbsRTdhiocrvoIAAadROxLjNHUhOJDLyEiPBs5GUvgcFhR37wJhQefw/QJP4dcrh2eDp4kXjeEuvVV2F/yEiIjxiI3axm69ZUoO/I+AAw6idiXmNb2PSiteBeJcbORnnI+rDY9auu/g9HYgCkTHoFYLBmeDp6E1u1NWLdoNRLPTcesf56N1q2N2HrH/wBg0EnEh1/dh+33fY/c28Zj4mOz0F3eiYN/3Y22nU244PsrIZaI+x/rtDtR+OQ2uOyhPzY8GNYYPJgLIeZDiPnwYC6EmA8h5sODuRhtWMcPZaH3qZxGrcaOvVDJozA25RKIRRLEasfB4bSioX034rT5EIlEfseU1n8BQ08zbA7jcHfLL/WtW6FSxCAv82qIRRLER02Cw2lBXesWxEdPHTAXvsQ0tP4ImVSD8dnLIRbLAAAxEfnYXfIcGlq3ITP5/OHuqk8a2nZCqYjGuIwrIBZJEBc1AQ6nFfWt2xAfNWnAfPgS09C6HTKpBgWZV/cXGWO047Cn9CU0tu9ERuLxV0YYKcF6rTTo9kAmUSMv9fL+fESH5aCw8j9o6ihEety84e7qCTXqdrn7lXa5+/cckQ+H04b69p2Ii5wwcC58iGlo3wWZVI389Cs8uQjPxd7y19Go24OMhAXD3VWf8NpxfPXfPgNV4jjk3/cJxFIZ4ucsh8NsQN2X/4eE+bcMmB9TUxkq3rkf+optcFgMUKcMvvLBaDPvwYfQVlaKd5ddCafdjqL334ciPBxn/vzRwT8UZWdDV1GO6i1bhrexw4D58GAuhJgPD+ZCiPkQYj48mAsiIiI6lbFW61FX/y1UqkSML7gXYrEUCQmz4XCYUVv7FRIT5g2YC19i6hvWIzIyD+PG3twfp1BE48DBf8ForEd4eMYw9tJ3dTVfQK1OxoSJD0MsliIxcT7sDjOqqz9DYtJZA+fDh5i6um8QGTUeefl39scplLHYX/w3GA21CNdmDWc3fVLftBEqZRzyc2+CWCxBfMw0OBwW1DVtQELszIHrtD7E1DVtgFwWjgljb/PUaaMmYNe+P6O+eTOy0i4Z7q76hNcNofrGDVCp4lEw9jaIxRIkxM6Aw2FBbcM6JMSdMfDzw4eYhqaNiNSOwdica/vjFPIoHCx7DcaeBoRr0oazmz4peXYPtOOicNbHiyCWSZAwDa9kAABdKUlEQVR9XR7seisO/GUXcm4uGDAX+/9vF9KW5GDOy+f174uZFo91l6xG43c1SLkoEwCw4Sefo21HE3oaR8fY8GBYY/BgLoSYDyHmw4O5EGI+hJgPD+aCKHDEJ34I0Yk5XQ50GI4gJnwMxCLPN2BjtXkw2zphsrQNKSZclYSk6CmIDs8NbkcCwOm0Q9d9CLGREwT9iouaBLOlHUZzk98xJksbtJqM/sISAMikGqiV8TCZW4LYK/85nQ7oussQG5Ev7FvkeJitugHb7WtMj6UdWk2aYIUCmVQNtTIWJnNrEHvlv2C+VszWDoSrk47JhwoqeQxMFl0Qe+Ufp8sBnb4CsRHjhL/niHyYrR0wWbx/h77G9Fh10KpTvJ8biliYLO1B7JX/eO04Pqfdiva9qxE36yqIpZ5+xM9Zjp7mMhhriwaMk8jViJpwAdIXP4GwjOnD1dygk8hkKFi8GMWrPoLTbu/fX/jue4gbNw5Jkwde+SMmOwftFUcAAGJJ6K1Y4S/mw4O5EGI+PJgLIeZDiPnwYC6IiIjoVMZarYfTaUdbWyHi42YK2hwfPxs9Pc0wGmv9jlHIoxAXO1MQq1TGAgAcztBcccnptKO1dRfiE2YL+paYOB89pkYYDNV+xygU0Yg/ZlVWlTIOAOAIwRWonE472jv3Iy56imDF1/iYaegxt8LY0+B3TI+5FdqwLEGdVi4Lg0aVAFOPd/03FPC6IeR02tHWUYy4mKnC33XsDPSYW2A01fsdI5dHIjZmqiBWqYh2n8NhDUZ3hsRhdaB2zRFkLBsLsczTr6zr8tBd1omOIu/xLrvJBmO1HknnpQv2JyxMAURA5wHP+E38vGTkPzQVGcvGBK8TQcYagwdzIcR8CDEfHsyFEPMhxHx4MBdEgcUJxBQQNrsJLpcTqt4Psn1Ucve2xa4fUkxy9DQkR09DpCYzwC0PPJvdCJfL0V8A66NWuLct1i6/Y3JSLkVm0rmCx9gdFhh7GiH///buOzyqKvH/+GcmmXQgBEJJ6L2JIEWwIUVdC82ySvmuov7WgmVXsa0rCOwu1rWxoKvIupZ1FREs4KIIggjSqxAg9EASIIT0Mpn7+yPmJkN6MsPcSd6v58nz3Dv3nDPnnpwcyCd37nVY89FWRecWds65hf4amObmp9W4TofYa9S2xRC3MgUFucrMTlKQo4HHzsGTvPmz0r75ULVpeolbmQJXnjJzkxUUGO6xc/AUc94HN3F7vWi/orlRWZ0OLYarTbPL3coUFOQpIydZQYHWfOwZa0fF8tOSZDjzFBbjfgfhsJgekqTc00fKrBcc1UqtfjNFrX4zRWGtenm9n+dLRPPmCgwOVvIvu9xeT979iySpUeuy70YR1aGDGrVqpQc3b9b0rGw9ceiwrn/lFQVFWPPnoqoYj2KMhTvGoxhj4Y7xcMd4FGMs/FHho8/88as+PPoMAGAtZLXF8vLSZBhOhYXHuL0eHt5SkpRTxg0ZqlqnR497FRMzRIZhKC8vXenph3To0CIFB0epYYMO3jidWsvLOyvDcCo8vJXb62HhsZKk3JzSFwJWtU6vC/6g2FYjfh2PNKWnHdCBA58oOKSpGjay3k1j8vMzCud8aHO314v2c3NTa1ynU9sb1a7VtW5lnAU5yshMUFCQNXNa1g13efnpMowChYe2cHu9aD8390yN6/ToMkkxzS8t/FnJz1B6xlEdOrZEwUGN1SDCencuz0nKkiuvQJE93f9e06hH4d+uMo+W/nuXLcCm3/z4W7W92f2i4OTVxyVDCost/h26xyMXqeej/cw7EvsjMoZijIU7xsMd41GMsXDHeLhjPIoxFv6IHN/KAisvAlQu35klSQq0B7u97ggIcTte2zr+IM+ZIUkKDAh1ez0wMExSYXBS0zoRYS3d6+VnaM+hj2UYLrVo2t8Dvfe84nMLcXu96FwrHo+K60ScE7bkOTO159CCwvGIusgDvfc8b/6shJ8TxuU7sxSX8LVkGGoeeYEHeu9Zxed17ve5KmNRcZ3wc4LafGeW9hxdJBkutYgq+9NmvsbaUbG8s4V34AgMb+z2emBEYTiZl5Z03vvkSxEtCte/7DOpbq9nnS68Q0NE8+bnVpHNblfj9u3VMDZWy2fM0MnduxXbv7+uePxxNe3UWe+NvMHr/fYWxqMYY+GO8SjGWLhjPNwxHsUYCwAAUJeR1RbLyy/84H1RjlYk8NebD+Tnlb4osrp1CgqytXbdHyVJdnuw+vZ5wu1Oq1aSl5cqqfhcijh+vYgzr4ybG1S3jtOZpTWr75EkBQQEq2+/Zy05Hnn5hRc9Bgaem7kW3qSj6GeiJnUifr24urhehn7ZN1+GXGrZbLAHeu95rBvu8n+9+LnUOuAo3C+aC7WpU1CQrXWbnpYk2e1B6tPzYUv+rGQnFn4fgyLd/3YV3KTw+5yTVPpvPAHBgYoe5P73ilMbEvXj7f9TWOsItR7d0Uu99Q0yhmKMhTvGwx3jUYyxcMd4uGM8ijEWgGdZ77cN+Cdb2S8bv16Fbxguz9TxZ0bReRXUuo5hFOjEqY06mrRCLqNAXdreqIjQlmW14HO28r7Rv36fXWWMR3XrGIZLiac362jyarlcBercelSpC0gt4zz8rBiGS0mpO3T01M8yjAJ1anl1qYuL/UFN1oCyxiLxzFYdTV4rw3CqU+x1Cg9p5qkunh/1dO0orbwfhMLvuVGQfx77YgFG2Z9ys9kLHy4R4HCUOmYPDNSCOyfpxLZtOhUXJ0na+79vlJ54QmPffEutBgzQsQ0bvNdnb2I8ijEW7hiPYoyFO8bDHeNRjLEAAAB1GFltsfMxFgEBwbqw9+PKy0vV8RMrtXXbi+rT5wlFnHPHXmv7NXurTjZZTp2AgBD1vWiqcnPPKCHhW23ZNEMX9X9WERFtPNVZzyj321xBTlvNOoZRoOPJa3UkYZlchlNdO4xXRFhsWS34HOvGucrLqSvK8atXJyAgWL17PKi8vDSdSPpR2355Q316PqzwsJiyWvEZm63s8zJcv36f8yteN3LP5Gj7jJ8VN2ebwlo30NBFIxUYWscuXSBjKMZYuGM83DEexRgLd4yHO8ajGGMBeJTd1x2Ab3nqJtuOgMJPUTsLct1edxbkSJKCfv2UdW3r+IOgXz9t7yzIdnu9aL+sxy5Vp05Wzilt3/+uDh5fqobhbdWny31qGtnLcyfgYQ5H0ff5nHNzFn2fS49HUZ28/AwdSVotl8tZbp3s3NPaEf9vHTzxrRqGt1afznepaWR3z5+Ih3j7ZyU774x2HlmgQ8mr1DAsRr3bjVeThu6PgrIKx6/9drpy3F43531ZY1GNOtm5Kdp58D86lLhCDcNjdWHH29W0UVfPnYAkl8upI4krzDlaG6wdFQtqVPgpQmem++Mji/aDIq0VnnpbemLhHZnDotzvyBwWVfh4uLTjxyVJTsPQqgKXnIahgrw87fjkE/MXoiK7v/hCktTiAuvdqbyqqjoeJdXV8fC3sSg5R73B38bDmxgLd6yj7pgfxfxtLOr+g7MAAEAhz/yrX5ustip1/CmrDQpqKEly5rvfPTX/1/3goMha17HZAhQZ2UXNmg3UBb0elt0eoOPHV3ii+x4X9Gvf88+5u27RuQUFNz63ilkn4dgyuVz5ldax2wMU2bi7mre4RBf2eVJ2e6ASjn3rqVPwmKJM1XnOE/KK9oMcDWtVJys7Wdt2/0MHjixWowYddFHPRxRt0afmSf6/brhcTh06utQjGb5U/L089wmKRftBQY1qXcdmC1Bkw05q1vQi9ep2r+y2AB1P/NEj/fekkBa/3kH5jPvfa/JSCvdDY8p/FHjCkoP6svcH2vvmdnV/qK9u2DxejXs19V5nfYT8qZi/5S3e5m/jQY5//jAW7lhH3TE/ivnbWJDjw+q4gLieMor+c+ehVcoRGCqb7MrOO+32elZu4X5ZF77VpI4/cASGy2azKysn2e31ov1gR+nwoKp1snNTtDN+vnJyU9Sj/QR1bz9OocFR3jgNj3EEFJ3bSbfXs3IL94ODypgbJeocS/7R/DT6uXVycs9o54EPlJN3Rt3b/Vbd2t6skDKCTCvx5s9KTt5Z/XLkM+XmnVXX2JHqGnuDQsoIq6zCERgmm+zKyjnl9npWTtF5lQ5iq1onJy9Vuw79Vzn5qerW+kZ1az1GIWWE/rXlMgp0NOkHuYzah4+sHRVzNGwmW4BDmcd2ur1etB8c1doX3fKZzORkOfPy1Lyn+0XgRftnjx2TJBVI+tEwVCApsm1bdb3uetkD3e/i4CooXGNz0ko/mtNfVHU8Sqqr4+FvY1FyjnqDv42HNzEW7lhH3TE/ivnrWBhe+gOOXzAMGQW5fvlV3p0yAAAoUvRvvCdvBFKbrLaiOv6W1TocDWWzBSgzK8Ht9aL94DL6XpU6TmeWNmycqlOnNruVCQgIVnBwlJxO9wsqrSIoqFHhuWUcdXu9aD8kuEm5dZKT17pdQFyyjtOZpfXrHtPJZPc7eQUEBCs4pKmcTveLsa3AERhROBbZSW6vZ2YXXqRQ1sXlVa2TnXNK2/fMVXbOafXsfJd6dL5DoSHWvmjS39cNl1GgIwnfeOwCYoej8HudlX3C7fWsrML94KCy1o7K6zid2dq4bZZOpWxzKxMQEFS4thRYb+0IaRYmu8Ou1J3uf7sq2g9vVfYFxEcWx2vFmC8V0ixUI7dNVL8XL1dQw2Cv99cXyJ+K+Wve4i3+Nh7k+OcPY+GOddQd86OYv44FOb7vM3ly/LJxATE8wm4LUOMGHXQ6bZ/bo4dOp+9VsKORwoOjPVLHH9jtgYpq2F2nUne6ndfJ1B0KCWqs8NAWNa5z+MS3crmcurDLPWps0bvKnstuD1BUw646dXa327mdSt2lkKBIhYeUNR6FdU6nuX/y59w6hxJXyOUqUO9Od6pxg47ePREP8ebPypGTa+QyCnRBu1vVOKKd18+ltuy2AEU17KTTaXHucyNtt4IdkQoPaVbjOoeTVsllFKh3+/9T4wbtvX8yHsDaUTF7YJCaXDRWJzd8Ipez+A8SJ3/+j0KadVREm74+7N35V5Cfr18WLdIFv73F7Zec3uNu0+n9+3Viy5ZSdcKbNtXvFi9Wr5tucnu9x6hRchUUKGHjRq/321sYj2KMhTvGoxhj4Y7xcMd4FGMsAABAXVabrLayOv6W1drtgWratK9OntzgdmFhcvLPCgmJVkREmxrVCQgIVW7uGZ1O2eFWNzc3VVlZiYoIb+W9k6oFuz1Q0dEDfr0YuPjckpLWKDS0uSIatCuzTpOmF0lSuXUKxyNFp0+7/z86N/eMsjITFBHR1jsnVAt2e6CaRPbSyZStcrlKZK4pWxQS3EQRYaWfhFbVOgePLZHLcKpvzz8oKrKb90/GA1g33NntgWrauLdOnt7s9r1OPr1JIcFNFREeW6M6AQEhys07q5TU3W51c/POKis7SeFlzDtfCwgKUOs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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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6evTo4Vo+duyYXnvttSLrPfnkk6bla665xm+3/SkOq+3/0o7Hav0tjl9//VXdunVzJazDw8P1+eefq3fv3mUaB0rXkCFDTMtOp1MDBw7Utddeq0WLFnn1h43icDgcuu666/Tee++5ratfv77atWunmJgYt3X//e9/dfvttxfa7jXXXKOJEye6rYuPj9c555yjpk2bul2x5fDhw+rVq5fmzZvndR9yc3N1/fXXa9myZa7XgoOD1aBBgwJvGzdv3jxdeumlbrevlPLGhcaNG7u9npOTo5tuuklPP/2017H5U1mNAVYaJ8/+g9zOnTuLVf/shPKmTZvcylipvwAAAAAA+Io8fuHI459GvqZ0kcevHMjjk8cvCHn80vlckMx9rlOnjqKjo7Vt2zbde++96tWrlxo2bKiwsDDVrl1b559/vh555BHT8QYAqIQMAEDAjR492pDkegwePNitTFRUlGu9zWYzcnNzvW5/+/btpvbPPfdcn+Ls2bOnqZ2cnJxi1f/pp58Mu93uqh8SEmJ8+OGHhtPpdCubnZ1tvPzyy6btRUREGOvWrSuw/QULFpjKSzIWLFhQ3G56ZIX9X5bxWK2/3po+fboREhLi2nb16tWN33//vcy278nZ7xt/HZMwjEGDBrm95/MftWrVMu644w5j1qxZRkpKSom39dxzz5naDw4ONh5++GFj7969rjJOp9PYtGmTMXDgQLd4vv/+e4/tPvPMM25lBw4caKxcudI0NqalpRlTpkwxatasaSpbo0YNIzk5ucC4zyzbtm1b1/P+/fsbP/30k5GRkVFg3d27dxuxsbGmNlq1amV8++23xrFjx1zljh07ZsydO9do3bq1W1/mzp1bnN1cYmU5BlhpnFyxYoWpvTZt2hSr/sMPP2yq36BBA7cyVuovAAAAAACBQB6fPH5xkK8pGHn8yoU8Pnn8s5HHL73PBcMwjI4dO5qOp5EjRxpBQUEFvg/zH7169TI2bdpUoj4CAMonJhADQIB9/fXXbl/aV6xYYSqTk5NjWl+tWrVibePYsWNe/aAoSkkTj4ZhGBMnTnT7QdKlSxdj7NixxowZM4wPP/zQ+Pe//220atXK7Uf+t99+61PcJWWV/V9W8Vitv95wOp3GuHHjTNtt3ry5sW3btlLfdlFIPJaeQ4cOue1fTw+bzWacd955xr///W/jp59+MjIzM4u1ne3bt5uSWSEhIQUmEvM98MADphguvvhitzJ79uwxgoODTeVeeumlQttNTk52S/DdeuutBZb3tD8mTJjgVb/79Oljqvd///d/xqlTpwosn5WVZdxzzz2mOo0bNy40uekvZT0GWG2czM7ONqpUqeJqz263G6tXr/aqblpamtGwYUNTPPHx8aYyVusvAAAAAABljTw+efziIl/jjjx+5UQenzx+PvL4pfu5YBh5+zg6OrrI91tBj5iYmDKfUA4ACDwmEANAAH322WdGWFiY6Yv5VVdd5Vbu7B8oTZo0KdZ2nE6n6cdt9erVfYrXH4lHwzCMH374wUhMTPT6x0qLFi2MP/74w6dt+YNV9n9ZxWO1/hbl1KlTxo033miK+aKLLjKOHDlSqtv1FonH0pWdnW08+OCDprPIi3qEh4cbvXr1Mt5++23TGfgFue+++0z1n376aa/iOjNBaLPZ3K4wcPbZ4kOGDPGqzxs3bjQlQsPDwws83s/u+4gRI7zaxvr1693eU96M+bm5uW7HvLeJTl8FYgyw4jg5bNgwU0wXXHCB4XA4iqx3++23ux0nYWFhpjJW7C8AAAAAAGWFPD55fF+RrzmNPH7lRh6fPD55/Dyl+blgGIaxf/9+j++n2NhY49ZbbzUmTpxozJ492/joo4+Mxx9/3Gjfvr1b2YiICGPlypUl7isAoPywCwBQ5g4dOqQbbrhB119/vU6dOuV6vUGDBvrggw/cyqenp5uW4+LiirU9m81mqnN2e2Xtwgsv1KBBg7wuP3LkSLVv374UIyqc1fZ/acdjtf4WJjU1Vb1799b06dNdrw0bNkzz589XfHx8qW23LD355JOy2Wyy2Wxq3Lix1/tz6tSprnp16tRRamqqa920adNc63bt2uVTXP5owx9CQkL04osvKjk5WZMnT9all16q0NDQQutkZWVp/vz5Gj16tGrXrq3bbrtNSUlJHss6HA5NmzbNtVy1alU98MADXsV13XXXuZYNw9CaNWtMyzNmzHAt22w2Pf/880W2K0mtWrXSTTfdZOrPV1995VXd+++/36tykyZNMi1/8MEHCg4OLrJeUFCQW90FCxZ4tU1fBGoMsOI4+dhjj8lms7mWf//9dw0cOFAHDhzwWP7QoUMaMWKE3n//fbd1DofDtGzF/gIAAAAAUNrI45PHLynyNXnI4xeMPH7ByOMXjTx+4aw4Tpbm54Ikbdmyxe21QYMG6a+//tKkSZM0cuRI9e/fX8OHD9d//vMfrV69Wp9//rmqVKniKp+Zmalhw4YpNzfXly4CAMohJhADQBlKT0/X+PHj1bRpU9MPTklKSEjQ/PnzVadOHbd6TqfTtHzmDwtvndnGmcnOsvb999+rZcuWevfdd72u8+CDD6pTp05auXJlKUZWMKvt/9KOx2r9LciOHTt0wQUX6Ndff3W99thjj+mjjz5SWFhYqWwzEB5//HF17NhRkrRz50499thjRdZJSkrSfffd51qeOnVqsRMj5U10dLRuueUW/fTTTzp69Kh+/PFHPfzww+rUqVOhx3BGRoYmTpyozp07a+nSpW7r169fr6NHj7qWBwwYoIiICK9i+uc//6nnnnvO9UhMTHSt27t3r/bt2+davuiii9SgQQOv2pWk4cOHm5aXLFlSZJ24uDi1bdvWq/Z/+eUX1/P69eurRYsWXsfWtGlT1atXz7X866+/yjAMr+t7K5BjgBXHydatW2vs2LGm177++mu1adNGt956q1599VXNmjVLL730km666Sa1bNlSH374oSQpKirKVC8mJqbAWCVr9BcAAAAAgNJCHp88vkS+xl/I4xeMPD55fE/I4/uPFcfJ0vxckKStW7ealm+99VbNnDlT1atX9xiPzWbTtddeq59++sk0qX/Lli365JNPfOojAKD8Kfr0IwBAiTkcDn344YcaO3as9u/f77a+a9eu+vDDD9WsWTOP9c/+QXDmj2BvGIZhqnN2e2Xliy++0JAhQ0xnRLZp00b33Xef2rRpo+bNmysnJ0dbt27V6tWr9fLLL2vv3r2SpLVr1+qSSy7RDz/8oG7dupVp3Fbb/6Udj9X668mSJUvUv39/HT58WJIUHBys999/X7fccovftxVoISEh+u9//6uOHTsqOztbb775pq677jp1797dY3nDMHT77bfr+PHjkqS7775bvXv3LsuQAy4yMlK9evVSr169JOUdwwsWLNBPP/2kuXPnavfu3W51kpOT1bNnTy1YsEAXXHCB6/WzE3qtW7f2Oo7GjRvrkUce8bjujz/+MC23adPG63Y9lT+7PU88/WHLk5MnT+rPP/90LTds2FA//PBDseKrWbOma/xOTk7W8ePHFRsbW6w2ChPoMcCq4+STTz6ppKQkTZ482RTblClTCqxTpUoVzZgxQ3379nW9dnbi0ar9BQAAAADAn8jj5yGP77k9X1XmfE2gc3hliTx+8ZHH94w8vv9YdZwsrc8FKe/Kzv369XMtJyQkyG4v+rqSnTt31kMPPaRnnnnG9dqMGTN04403FlkXAFABGACAUrVhwwaja9euhiS3R1hYmPHiiy8aubm5hbZx6tQpU734+PhixXDs2DFT/dq1a/vUl549e5raycnJ8bruypUrjaCgIFP9t956q9A2MjIyjEceecRUJyYmxjhw4IBP8fvKKvu/rOKxWn/PNnPmTCMsLMzVfpUqVYz58+f7dRv+dPb7ZsGCBT6189JLL7naaN68uZGRkeGx3H//+19XuZYtWxrp6eluZaZOneoqs3PnTp/i8UcbgeB0Oo3ff//duOuuu4yQkBC3cbljx46mMXns2LGm9Z9++qlf4pgwYYKp3RdffLHY/YiMjHTVr1mzpsdyZ27jiiuu8Krtbdu2efzMKsljz549xepfYawwBlh5nHQ6ncaECROM6tWrF/nv0qVLF2PdunXG+vXrTa+3adOm3PQXAAAAAAB/II+fhzw++Rp/sUIOrzjI41sLefyikccvmpXHydL4XCip5ORkU/vh4eFGVlaWX7cBALCmok81AQD4JDs7W//5z3907rnnerydzvXXX68NGzbowQcfVFBQUKFthYaGmm4bcvToUbfbrhTm7DMqo6Ojva7rL08++aTpigUPPfSQRo8ereDggi+GHxERoWeffVbXXnut67UTJ07ohRdeKNVYz2a1/V/a8Vitv2dKSkrS4MGDXbcJqlevnhYvXqzLL7/cb9uwqvvuu08XXnihpLxbEI0bN86tzIEDB3TPPfdIyjuTe/r06YqMjCzLMC3PZrOpW7dueuedd7RmzRp16dLFtH7VqlX67LPPXMupqamm9fXr1/dLHGe/Txo1alSs+jabTQ0bNjS1ZxRxe7EaNWp41fbZffaHtLQ0v7RjlTHAyuOkzWbT7bffrh07dmjChAnq37+/GjZsqMjISEVGRqpx48a6/vrrNWvWLP3+++9q27atkpOTTW2cfTxaub8AAAAAAJQEeXwz8vinka/xnVVyeIFAHt8/yOMXjTx+0aw8TpbG50JJJSYmmq6anZWV5fGODACAiocJxABQCrZs2aLOnTvriSeeUE5OjmndxRdfrOXLl+vTTz9VkyZNvG7zzB8BTqezWD/izv4R2aBBA6/r+kNycrK+/fZb13J8fLzpFiiFsdlsmjBhgum1SZMmFfkD29+stv9LOx6r9Tff2e+nhx9+WG3btvVb+1YWFBSkadOmuW6R9PLLL2vFihWu9YZh6M4773QlNJ588kl17tw5ILGWto0bN2rNmjVas2aN1q9f73M7rVu31nfffeeWTFy0aJHreX6CK5+VErlhYWGu52e/Nzzx5jZV3rZVXP5KPFppDLDqOJkvJiZGt99+u2bPnq2dO3cqPT1d6enp2r59uz799FMNHDjQ9YfPgwcPmup6usWf1fsLAAAAAEBxkcc3I49PvsZfrJTDK2vk8U8jj5+HPD55/ML4+3OhpBo3bmxaPnubAICKiQnEAOBnK1eu1IUXXqh169aZXm/QoIHmzJmjn3/+Weedd16x223ZsqVpuThn/B04cMC03KpVq2JvvyT++usv0/J5552nkJAQr+vHx8erRYsWruWTJ0+W+RmPVtv/pR2P1fqbLzY2VvXq1XMtjx49Wq+88kqZJ6IDpUmTJnrllVck5SU6br75Zldi7NNPP9XXX38tSerataseeeQRr9vNyMjQ888/r44dO6pq1aqKjIxU8+bNNWrUKG3durXM2vDW0KFD1aFDB3Xo0EEXXHBBidqKj4/XY489Znpt48aNrudxcXGmdbt37y7R9vLFxsaalnfu3Fms+oZhaMeOHa7luLg42Ww2f4Tm1ucxY8bIMIwSPbp16+aX2Kw0Blh1nPRFUlKSabk8fS4AAAAAAOAL8vjuyONbK39RnvM1VsrhBQJ5/Dzk8cnj5yOP7x/efC6U1NlXWT5zAjwAoOJiAjEA+NH+/fvVp08fHTlyxPT66NGjtX79el199dU+/zA8+wfOsmXLvK67fPly03JZ/8DZt2+fadmXMyLPrrN3794SxVRcVtv/pR2P1fqbLzY2VsuWLVPHjh1drz3wwAMaNWqUcnNz/bYdK7vtttt0xRVXSJI2bNig8ePH6+DBg7r77rsl5Z1ZP3369EJvK3imAwcOqHPnzvr3v/+t1atXKy0tTZmZmfrrr7/07rvvqk2bNnr33XdLvY3iSExMdD0/ceKE222kiuvsKzyceUZ1fHy8aV1xE4QFOfs2ZMVt9+jRozp+/LhruXr16n6Jy1Nbhw8f9lvbJWWlMcCq46QvfvvtN9Ny9+7d3cpUpP4CAAAAACo38viekce3Vv6iPOdrrJTDCxTy+OTxJfL4VhgDrDpO+sKbz4WSOvt9mpCQ4PdtAACshwnEAOBHt9xyi+kHa0hIiD799FO99dZbbmfsFdcll1xiWv7ll1+8rnv2D4qLL764RLEU19lnwfryw/3MM3Q9tVnarLb/Szseq/X3TLVq1dJvv/2mvn37ul577733NGDAAJ08edKv27Iim82mSZMmuc58f+655zRo0CClpKRIkl577TU1bdrU6/aGDx+uTZs2qXHjxnrkkUf06aef6umnn3adaZ6bm6tRo0Zp5syZpdpGcZx928gffvihRO2dfdZ7rVq1XM/PTHBJKtZVGLKysnTjjTfqhhtu0A033GC6jWOnTp1MZTdt2lSckE1XV/DUXklUr17ddCuuP/74w29t+4NVxgCrjZPz5s3ThAkTXA9vb8WWkZGhBQsWuJbbtGmjZs2auZWzWn8BAAAAAPAVeXzPyOOTr/Enq+TwAoU8Pnl8iTy+FcYAq42Tpfm5MG/ePDVs2ND1GD9+fLFiMwxDa9eudS1XrVrVdCIAAKACMwAAfvHLL78YkkyPb7/91m/tZ2VlGdHR0a62o6KijLS0tCLr7dy507DZbK56LVu29DmGnj17mvqXk5PjVb2//vrLVK9evXqG0+n0ersZGRlGUFCQq35ISIhx8uRJX7vhEyvs/7KMx2r99SQ3N9e4++67TcdWhw4djKSkpFLbpi/Oft8sWLDAL+3OmDHDbczp06ePV++tqVOnmur16tXLOHr0qKmMw+EwnnjiCVeZGjVqGJmZmX5tw1dz5swxbfu8884zHA6Hz+299dZbpvZGjx7tWnfixAnT+FOzZk2vx76z4xw3bpxrndPpNGrXru1aZ7PZjD179ngd82233WZq+9133/VY7swyN910k9ft33DDDaa6GzZs8LruoUOHjIsuusjo3r270b17d+PJJ5/0um5xBHoMsNo4+dBDD5n2xZQpU7yqd/bx/9hjj3ksZ7X+AgAAAADgC/L4BSOPT76mNAQ6h+ct8vjk8cnjP+l13eII9BhgtXGyND8Xjhw5Yoo5MTHRyM7O9jq2FStWmLYxaNAgr+sCAMo3rkAMAH4yefJk0/Idd9yhPn36+K39sLAwDRgwwLWcnp6uN998s8h6L774oums3MGDB/stJm81bdrUdIuYvXv3atKkSV7X/+CDD+RwOFzLl112maKiovwaY1Gstv9LOx6r9deToKAgvfnmm3rjjTdctxRcvXq1unbtqnXr1pXadq1i6NCh6t27t2s5NDRUkyZNKvbtFaOiovTxxx+7roSQz26366mnntKVV14pKe/2V1988UWptVEcl112memWZCtWrCj2mdT50tPT9frrr5teu/baa13Po6Oj1b9/f9dycnKy3nnnHa/a/vjjj03LF154oeu5zWbT8OHDXcuGYejf//63V+1u2LBBU6dOdS2HhYVp4MCBXtX11pmxSdJjjz3mdoWHgjz//PNauHChFi9erMWLF6tu3bp+jS1foMcAq42Tl156qWl5ypQpcjqdhdZJSUnRc88951oODw/XnXfe6bGs1foLAAAAAIAvyOMXjDw++ZrSEOgcXqCRxyePn488Pnl8qXQ/F+Lj403tHzx4UB988IFXcRmGobFjx5peGzJkiFd1AQAVQAAmLQNAheN0Oo24uDjTWXk7duzw+3Y2bdpk2O121zbCwsKMFStWFFj+7LNmq1SpYqSkpPi8fV+vXGAYhvHmm2+a6oaHhxu///57kfXmzJljhIWFmep+/fXXPvehJAK9/8s6Hqv1tzBff/21ERkZadr2jz/+WCbbLkppXblg165dRpUqVUxtv//++17VPfOqA2PGjCm07LJly1xlb775Zr+2URLvvvuu25Ub7r///mJdGeHQoUPG5ZdfbmrjggsucLsKwsKFC01lIiIijJ9//rnQtj/99FNTndq1axu5ubmmMjt37jRdFUGS8eqrrxba7v79+40WLVqY6txyyy0Flj+zXHGuXOB0Oo3WrVub6o8dO7bIK2N89tlnRnBwsKtOfHy8ceLECa+366tAjQFWGidzc3ONevXqmdp/7rnnCix/8uRJo3fv3qbyDzzwQKHbsFJ/AQAAAAAoLvL4RSOPT76mNJHHJ49PHt8z8vily0rjZGl/Lnz77bemssHBwUWOZw6Hw3jwwQdN9dq1a1eiK4YDAMoXJhADgB8cPXrU9KW6evXqxvfff+/z45dffilwW//6179M24qOjjY++OAD04/t1NRU45lnnnH7Qfv888+XqJ8lSTxmZ2cb55xzjqm+zWYzbr75ZmPjxo2mtjIzM41ly5YZV199tVti4corryzwh++CBQvcyvsryZOvNPe/L/GX9vEQyOOtuP744w+jZs2aph/FkydPLtMYPCmNxKPD4TAuvvhiV5v5+z4qKsr466+/iqx/ZtJw5syZhZbNzc01QkNDDUnG+eef79c2SiI3N9e47LLL3N4z9evXN8aPH2+sXbvWLdHndDqN48ePG0uWLDFGjBjh9keN6OhoY/369R63N2LECFPZ0NBQ49FHHzX27t1rKnf48GHj4Ycfdmt76tSpHtsdN26cWx+uv/56Y82aNaax7vjx48bEiRONhIQEU9mEhAQjOTm5wP3ka+LRMAxj0aJFbu/r3r17Gz/99JNx/PhxU9nNmzcbo0ePNt0eS5LxwQcfFGubJRGoMcBKnwvTpk1zKz9y5EjjwIEDrjIOh8P4/vvv3T6TmzZtaqSmpga0vwAAAAAAlCby+EUjj0++prSRxyePTx7fHXl88vj++lxwOp1uE45DQkKMu+++29izZ4/peD18+LAxd+5co0ePHm7vm6VLl3qxVwEAFQUTiH20b98+Y/HixcbixYuNffv2Fbu+w+Ewtm7daixYsMBYsWKFVz/+zpaZmWmsW7fO+Pnnn421a9caGRkZxW4DgH9s3rzZ7Yt+SR4NGjQocFvp6elG586d3epEREQYrVu3Npo2bWqEhIS4rR84cGCJzxQsSeLRMAxjz549Rv369T32OSQkxGjZsqXRoEEDtx+u+Y9zzz3XOHr0aIHtl0XisTT3vy/xl/bxEMjjzRe7d+822rZta4rl8ccfL/Js69JUGonHV155xdXelVdeabz33nuu5W7duhX53jwzabhy5coit9eyZUtDktGsWTO/tlFSJ0+edNu/Zz4iIyONxo0bGx06dDCaNWtmOrP97EdMTIyxePHiArd14sQJo0OHDh7r1q9f3zj33HPdkoL5j+uuu67A90NOTo5xxRVXeKxXo0YNo3379kazZs08vs/Cw8ON+fPnF7qPzixf3MSjYRjG66+/XuA+q1evntG2bVujWrVqHtePHDmyzN97gRgDrPS54HQ6jYEDB3r896hVq5bRtm1btyueSDJiY2ONzZs3B7y/AIDCpaamGn/88YexYMECY8uWLYyrAFAEcvgAzkYe3zvk8cnXlDby+OTxz3yQxyePXxZjQGX6XNi7d6/bVY7zH1WqVDHatWtnVK9e3eP6oKAg46OPPiru7gUAnKE85vEr/QTit99+25BkPPnkk16V/+677zx+sejUqZMxd+7cIus7HA7j1VdfdfvhHRQUZPTp08erD/xjx44Zo0aNcvvSEBMTY9x5553GsWPHvOoLAP/59ddfC/xh5sujsMSjYeTd+ubCCy/0ur3Bgwcb6enpJe5nSROPhmEYSUlJRq9evYq9T2644YZCk46GUTaJR8Movf3va/ylfTwE6njz1bFjx9zOrh02bJiRlZUVkHj8nXhct26d66z4KlWqGHv37jUcDodxySWXuLYxfvz4Qts4M2m4evXqIreZf6uttm3b+rUNf8jOzjaeffZZtysFFOfRp08fY+fOnUVu6/jx4wUmCQt6jBgxoshj79SpU8bNN99crHYTExMLTZTmO7OOL4lHwzCM6dOnGxEREV7HFhQUZDz++OMB+zEUiDHASp8LmZmZxqBBg7yOpV27dsa6dess0V8AgGdbtmwxrr76arcr4NSrV894+eWX3a7WVBwjR470ejz3NncGAP5GDh+Av5DH9x55fP/GT77GHXl88vjFfZDHLxx5/KJVps+FnTt3ul29uKhHQkKC8fPPP3u9DQCAWXnO41f6CcRdu3b1eue9/PLLRf4jvPTSSwXWz83NNQYMGFBo/YiICOPXX38tsI3k5GSjSZMmhbbRuHHjQm+BAcD/Zs6c6fMPXk+PohKPhpE3prz66quFjgnnnnuuMWPGDL+dsemPxKNh5P0h5ptvvjEuu+wyw263Fxh/aGioMWDAgELHxTOVVeLRMEpn/5ck/tI+HgJxvJVEdna22+2Ipk2bFpBY/Jl4PHXqlHHuuee62po4caJr3bZt21zJoeDgYGPVqlUFtnNm0vCLL74odJs5OTlGcHCwIcm45JJL/NqGP+3fv9947rnnjNatW3s1ziYmJhp33HGHsWzZsmIds06n0/jqq688/jH6zEePHj28+sP0mRYuXGj06tXLta88PerUqWOMGzfOSEtL86rNM+v6mng0jLw/Gt15551GbGxsoWP20KFDjTVr1vi8HX8JxBhgpc8Fp9NpfPjhh66Ev6dH06ZNjWeffdZ0i7ZA9xcA4O63334r9OpLkox+/fr5nHw8+zaVhT2YQAwgUMjhA/AX8vjFQx7fv/GTr3FHHp88flEP8vjFQx6/aJXpcyE7O9t44403XFcWL+z7zPjx440TJ04UexsAgDzlPY9vMwzDUCU1depU3XLLLZKkJ598UuPGjSuw7M8//6zLL79chmGoevXqeuONN3TllVdKkr777jvdc889SklJkST973//02WXXebWxrhx4/TUU09Jkjp27KhXXnlF5513no4ePaopU6boqaeektPpVFxcnDZv3qwaNWqY6huGoUsvvVS//PKLJGnIkCF6/PHH1bhxY23fvl3PPPOMPvvsM0lSjx499Msvv8hms5VoHwGwPsMw9Mcff2jr1q3av3+/goKCVKdOHZ1zzjlq1apVoMMrUlpampYsWaLdu3crNTVVdrtdcXFxatasmbp06aKIiIhAh1goq+3/0o7Hav0tjGEYevHFF/XII49IyvvcHzFiRGCDKqFHH31Uzz33nCSpV69e+uGHH0yf9W+88YbuvfdeSVLr1q21cuVKhYeHu7Uzbdo03XzzzZKkhx56SC+88EKB21yyZIkuuOACt7L+aKO0HDt2TCtXrtSePXt0/PhxnThxQpGRkYqNjVVCQoI6dOigOnXqlPh70v79+/X7778rOTlZx44dU9WqVVW3bl11795dCQkJPreblpamhQsXKikpSUeOHFFERIQSEhJ0zjnnqG3btgH9fpebm6vly5frr7/+0qFDh5Sbm6u4uDi1bNlSnTt3VlRUVMBiO1ugxgArjZOGYWjjxo1avny5Dh06pJCQENWtW1dNmzZVhw4d/HIsWam/AFDRHDlyRC1btlRKSorsdrvGjRunm2++WdWqVdPy5ct1//33a/Xq1ZKkJ554wpVzKo7atWvrwIEDGjRokEaOHFlo2aZNm6pp06Y+9QUAfEUOH0BFUd5/P5PH928s5GtOI49PHp88vv+Rx/duu1YZJ0v7c8EwDO3cuVMrV67U/v37deLECUVHR7veZy1btuQ3CQCUQIXI4/s0rbkcO3bsmPHbb78ZN998sxESEuLV7Gun02l06dKl0DMBV65c6TrLrEuXLm5nIx08eNCIiooyJBmNGjXyeLbZm2++6YrnoYcecls/d+5c1/prr73WbRtOp9N0m4PvvvvOy70CAEDFNXPmTCMsLMyYOnVqoEMpkcWLF7uu9BEdHW3s2rXLrUxubq5xwQUXuL4L3HfffR7bOvOqA1WqVDFSUlIK3O6Ztyo88yoi/mgDKAsVZQwAAFQ+Dz74oOs71Ntvv+22/vjx40bDhg0NSUZUVJRx6NChYrV/4sQJV/vvvvuuv8IGgBIjhw8AQOVTUXJ45PEB31SUMQAAUPlUhDy+vXjTjcu3Ll26KDY2Vj169NDUqVOVk5PjVb21a9dq+fLlkqQBAwaoQ4cObmU6duyoAQMGSJKWL1+uDRs2mNZ/8sknSk9PlyTdf//9iomJcWtj1KhRrrPcpk6dKqfTaVo/ceJE1/Nx48a5nQVks9lMs9QnT57sVf8AAKjIrrvuOi1YsMDtqkDlycmTJ3XjjTe6vhu89NJLatCggVu5oKAgTZkyRWFhYZKk1157TQsWLCi07bS0NA0fPlxpaWmm1x0Ohx5//HHNnz9fktSpUydddNFFpdYGUFoqwhgAAKh8HA6HpkyZIklKSEjQHXfc4VamSpUqeuCBByRJ6enpritaemvbtm2u582bNy9BtADgP+TwAQConCpCDo88PuC7ijAGAAAqn4qSx69UE4gPHTrkU71vv/3W9bx///4Fljtz3dy5cwtso1+/fh7r2+129e3bV5J0+PBhrVixwrXu1KlT+vHHHyVJTZo0UevWrT220bp1azVp0kSS9OOPP3qdYAUAoCLr1q2b+vTpE+gwfDZmzBjt2LFDknTJJZfotttuK7BsixYt9PTTT7uWb7rpJh0/ftxj2SpVqqhWrVqaN2+eOnXqpMcee0yfffaZxo8frwsvvFDjx4+XJIWHh2vy5Mkeb2HkjzaA0lbexwAAQOWzdOlSpaSkSJL69u2roKAgj+XOzDGdnYsqyl9//eV6zgRiAFZBDh8AgMqrvOfwyOMDJVPexwAAQOVTUfL4lWoC8ZYtW5SZmel6bN682et6+a666qoCy525btWqVR7baNeunerVq1dgG2d+ITqzjaSkJGVkZLi2U9CXdpvN5mrjxIkTplnoAACg/Jk7d67rCkaRkZGaNGmS7PbCv8KNGTNG5513niRp7969uvvuuz2Wi4mJ0Zw5c5SYmKht27bp2Wef1ZAhQ/T4449r6dKlkqS4uDh99913at++fam1AQAAADNvc1H16tXTOeecI8k9F1WU/MRjRESE6tSpI6fTqaSkJP32229avny5K/EJAGWJHD4AACiPyOMDAABUPhUlj1+pJhCHhYUpPDzc9ci/LUhRDhw4IEmKjo5WXFxcgeXi4uIUFRUlyXyAGIah5ORkSfJ4m5IznZmYPLON/BhK0gYAAChfjhw5oltvvdW1/Pzzz6tx48ZF1gsODtaUKVMUEhIiSZo+fbpmzZrlsWynTp20bt06PfLII2rZsqUiIyMVHR2t9u3ba+zYsdq6dasuueSSQrfnjzYAAABwmi95oEOHDunYsWNeb2Pr1q2SpPr16+v9999XgwYNVLduXfXs2VPnn3++atSooQsvvFDz5s0rfgcAwEfk8AEAQHlDHh8AAKByqih5/GCfa1Yi+YnDwhKP+eLj45Wenm46QFJTU123ISuqjfj4eNfzM9vIj6EkbQAAgPKlevXqpu8AxdG2bVtlZ2d7XDdixAiNGDHCtVyjRg0999xzeu6557xu3x9tAAAAwLOS5IFiY2O92kb+lQu2bNmiu+66y229YRhavHixrrzySj344IN64YUXuI0tAMsihw8AAAKFPD4AAEDlVFHy+Ewg9kJ+Au/Mf8iCxMfHa8+ePUpPT3er700bZ673dxtnioqKUlZWloKCglSjRo1C2ysMfzgCAAAAAMB3hmH4XPfw4cNyOBwKDw8v8Pd/RdW5c2ef/0DrTyX595Py8io1a9bUH3/8YXrd33kgT/ITj5LUsGFDvf7667rooosUFBSk9evX65NPPtE777wjSXrppZfUqFEj3XnnnV63DwBlqSLm8CXy+AAAAAAAWAF5fN+Qx5fH9VbL4zOB2AtpaWmSpIiIiCLL5t9SLTMz062+N22ceUs2f7dxpqysLDmdTjmdTu3fv7/Q9gAAAAAAgHVlZWUFOoQyl5ycrKSkpECHUWr8nQfy1H5qaqqCgoLUpUsXzZ49WwkJCa713bt3V/fu3XXZZZdp4MCBkqTHH39cN9xwg2JiYorTFQAoExUxhy+RxwcAAAAAoKIgj1/xVJQ8PhOIvVCjRg3t379fx44dK7JsfpkzD4ozrwxQVBtnrvd3G2cKCgqS0+mUJIXYQwttr3Alv3JBjjO7hDH4R47zlEJsFojDyLZIHDkW+Hcxzjg+AneVDOsco8RhvTjyj9EwBfYYPfV3DIGV4zylkKDwQIehHEeWdeII+L+LccbxEdirDVnhOLVCDMRxtvxj1ALvWQvsD8ZRq8aRqZCgoieilH4cWQoJigx0GMpxZAQ8DivE4L84SnbWe44jQ1Leb/zKyiYpOkDbLtm/npReSBtn54EKu+qkN3mgs1WpUkW5ublFlrvmmmt0zTXX6KuvvlJqaqrmz5/vSkQCgJVUxBy+dDqPb7NL0TVL8N3Y5vqPz9IPZikqMfDfz08k5ygornqgw5Aj9Yg14jh6SMEJVQMXgNNQbvIxBdeMlewBvtL1waOqXj3Q+WLpyJFsS8SRfNip0KqJgQ5D2ccPBjYOw1D2sf2WOEZzDx0P7Ps1P46DabJXL/z2ymXBeSTVInEcDfx4bhhyHDmooOqJUgDvGuBMOaLQGAuMGycOWiKOUycPBv7YkEW+c/x9jMbUDg/oMWqV76MZ+zNVI/Af9TqcLUvEcchhC/y/i2HoxP6sgB+jkjWOD8scG6ekqEAHobwcbEnj8FcemDx+YJDHL1qZTiA+fvy4Dh48qNTUVFWrVk2JiYmKjY0tyxB8UqtWLe3fv1+pqalFls0vEx19+rCvVauW2/qi6pdGG2fKT6iG2EPVLv6iQtsrbRtTl6h1XLeAxiBJG1J+V6vYjoEOQ5uOrbJEHJuPrw74v4vDmas/U35V62rdFGQP3PkOVjlGicN6ceQfo22q91CQPSRgcWw4/Jva1OgRsO274jiySO1qXxXoMLRu/3fWiCPp24D/uzicOVpzaH7Aj1HJGsepFWIgDrP8Y7Rt4uUBP0bXH/yf2iZeHuAYfrLG+GWVcdQCcTicOVq1b5ba1boq4MfougPz1L7B4IDGIEl/7p4Z8DisEINV4li18yPlODJKdEvz8i5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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# Errors\n",
     "for year in years:\n",
     "    _, jet1txbbbins, jet1ptbins, jet1msdbins = effs_txbb[year].to_numpy()\n",
     "    w = errors_txbb[year]\n",
-    "\n",
-    "    fig, axs = plt.subplots(2, 2, figsize=(18 * 2, 14 * 2))\n",
-    "\n",
-    "    for k in range(len(jet1txbbbins) - 1):\n",
-    "        row = k // 2\n",
-    "        col = k % 2\n",
-    "        ax = axs[row][col]\n",
-    "\n",
-    "        mesh = ax.pcolormesh(jet1msdbins, jet1ptbins, w[k], cmap=\"turbo\", vmin=0, vmax=0.5)\n",
-    "        for i in range(len(jet1ptbins) - 1):\n",
-    "            for j in range(len(jet1msdbins) - 1):\n",
-    "                if not math.isnan(w[k][i, j]):\n",
-    "                    ax.text(\n",
-    "                        (jet1msdbins[j] + jet1msdbins[j + 1]) / 2,\n",
-    "                        (jet1ptbins[i] + jet1ptbins[i + 1]) / 2,\n",
-    "                        w[k][i, j].round(2),\n",
-    "                        color=\"black\" if 0.01 < w[k][i, j] < 0.45 else \"white\",\n",
-    "                        ha=\"center\",\n",
-    "                        va=\"center\",\n",
-    "                        fontsize=12,\n",
-    "                    )\n",
-    "\n",
-    "        ax.set_title(\n",
-    "            rf\"{year}: {jet1txbbbins[k]:.2f} < $T_{{Xbb}}$ Score < {jet1txbbbins[k + 1]:.2f}\"\n",
-    "        )\n",
-    "        ax.set_xlabel(\"Jet 1 MassSD (GeV)\")\n",
-    "        ax.set_ylabel(\"Jet 1 $p_T$ (GeV)\")\n",
-    "        plt.colorbar(mesh, ax=ax)\n",
-    "\n",
-    "    plt.savefig(\n",
-    "        f\"{plot_dir}/{year}_txbb_errs.pdf\",\n",
-    "        bbox_inches=\"tight\",\n",
-    "    )\n",
-    "    # plt.close()"
+    "    plot_all_wrapper(jet1txbbbins, jet1msdbins, jet1ptbins, w, 0.5, year, f\"{year}_txbb_errs\")\n",
+    "    # break\n",
+    "    plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TH4q bins"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2p/VFRUU5fenLT0GCx1OnTpmHH37YZYzu3bt71P+6665z6vf+++/n2d5ut5tGjRo59dmxY4dXNfuTVbbtLJcuXTLlypVzjBcWFmY2b97sUd/k5GRTp04dp3oqV67s0s5ut5syZcrku93ndilbtmyhhg12u92MHz/eqYZGjRqZvXv3FloN7hA8Bt7x48ddnmd3F5vNZtq1a2eee+45s2LFCnPx4kWv1/XHH384BVqRkZFug8TsRo0a5VTH9ddf77T80KFDJiIiwqnNG2+8keeYCQkJLgHfvffem2cfd8/J1KlT833M3bt3d+rzj3/8w6SlpeXaPjU11Tz66KNOferVq5drsOlPhf0+YLV9gzGFs3/IMm3aNJdtqn379mbs2LHmk08+MTNnzjTPPfecadq0qVObiIgIs2TJkgI/VgAAAACAK3J8cnxy/OBv21nIaVyR44cucnxy/CxF5X0gkBOIA72/str+EABCFROIARQrc+fONVFRUU4fFP/+97+7tMv5YbJ+/fpercdutzt9+coK/rzl6wf8jIwMp34tWrQw//znP/P9Mpvzi+2kSZM8rnX58uVO/Xv16pVn++y/NpVkPvzwQ4/XlbPWpUuX5nlZtGiRefPNN819991nKleu7NK/TJky+R61IEvO8Kpz5855tt+yZYtT+1atWnn8OAPBKtt2dgMGDHCqqWPHjiYzMzPffg888IDLaxkVFeXS7ujRo2638QoVKph7773XTJs2zXz22Wdm1qxZ5vnnnzetWrVyaRsdHW02btxY4Mean7S0NHP33Xc7rfu6664zJ0+eDPi680PwWDguXbpknnrqKadfVOd3KVmypOnWrZt55513PP4jzuOPP+40xoQJEzyqLXtIaLPZnI4ykPNIIv369fOoll27djmFoCVLlsxzm8/5+IcOHZrvOnbs2OHy/8qTfWpGRobLtu9JyFkQwXgfsOK+wZjA7x+yW758uYmNjfX4/13jxo3NL7/84pfHCQAAAABwRo5Pjk+Ob41tOztymj+R44Mcnxy/KL0PBHICcaD3V1bcHwJAKGICMYBiITEx0QwcONDly0rt2rXNkSNHXNrHx8c7tWvXrp3X64yJiXH0j46O9qluXz/gJycnO/UrVaqUy+MePny4+de//mXmzZtnXnrpJXPXXXc5ffHLusyfP9+jdQ4aNMip3+LFi/Ns/8orrzi1v+GGGzxajzHuf7Xq66VkyZL51ppdUlKSS3h98ODBXNvnPO3Za6+95vG6AsEq23Z2O3fuNDabzamunj17mqNHj7ptn5iYaIYMGeL29YyIiHBp7+40cnfeeWeuR9aw2+1m/vz5TkdUyAohA/kl+9SpUy7/5wcMGGBSU1MDtk5v+Ct4HDdunGOMGTNmeNxvxowZjn7jxo3zad1FSUpKivnwww9N165dTYkSJTx+TytVqpS577773O7bsmRkZJiKFSs6+pQvX97jX+Nnf/0kOY5yY7fbTVxcnON+m81mDhw44PHjHT58uNO406ZNy7VtzsfsyWkJH3vsMac+v/76q8e1/f777059+/bt63FfbwXrfcCK+wZjAr9/yO78+fNuj2yU2+WNN94I+KnfAAAAACDUkOOT4+d2IccPflZDTnMZOX7eyPHJ8bPkfMzk+IWnMCcQB3p/ZcX9IQCEIiYQAyjSzp07ZyZOnOh0GrCsS0xMjPntt9/c9jt8+LBT2/bt23u97uynGAsLC/Opfl8/4Od2xNXIyEgzevToXL9gbt++3bRp08apT8WKFfP9JWxKSopTuBkbG2suXbqUZ599+/Y5rcdms5nDhw979Pj8FTq2aNHC5bR3nujTp49HYaLdbjcNGzZ0anvo0CGv1+dPVtm2c3rhhRdcXp+KFSuaYcOGmUmTJpn//e9/5vXXXzeDBw92Ckxy/rrb3alr3n//fac29957r0dHRtiwYYNL4PPf//7XL483pz/++MM0btzYaV1jxowxdrs9IOvzRVEOHtPS0kz79u0dz2tRc/78efP111+bZ555xrRp08YlqHd3qVatmvnxxx/djpfziCpDhgzxuJY//vjDvPrqq45L1qkKDx486DRmfkd1ySnnRP9hw4bl2jZ7u0qVKnk0/lVXXeXoc8UVV3j9f6tWrVpOz20g/m8G833AqvsGYwK7f8jy1VdfOb3Gnl6uvPJKjkIMAAAAAH5Ajk+On9eFHN86WU2o5zTk+PkjxyfHz5K9HTl+4SrMCcSB3l9ZdX8IAKEmQgBQBGVmZmrmzJkaO3asjh496rL8mmuu0cyZM9WwYUO3/UuXLu10Oykpyav1G2Oc+uQcL9AyMzPd3j958mQ98MADufZr0aKFvvvuOzVu3NjxvCUlJWny5Ml6/vnnc+23aNEiXbhwwXF70KBBioyMzLPGunXrqmPHjlq3bp2ky8/ZnDlz9NRTT+XZryBKly6tatWqqUOHDrrjjjt0++23KywszOtxBg8erHnz5jluf/LJJ3r66add2m3btk2///6743bnzp1Vq1Yt34r3E6tu2+PGjVN8fLw+/PBDp9qmT5+ea59y5crpk08+UY8ePRz3lS1b1qXdgAEDdNtttzlux8TEePS6t23bVk8//bQmTpzouO+TTz7R3XffnW9fb/z444/q2bOnTpw4IUmKiIjQ+++/r2HDhvl1PaHs+eef1/r164Ndhs9KlSqlbt26qVu3bpIu/99YtWqVVqxYoS+//FIHDx506ZOQkKAuXbpo1apV6tixo9OyH3/80el2s2bNPK6lXr16evbZZ13u/+WXX5xuN2/e3OMx3bXPOV5uatasmW+bc+fOaevWrY7bderU0fLly72qr1q1ajp8+LCky8/t2bNnVaFCBa/GyEuw3wesum+QArt/kKT//e9/6tevn9Nnp+bNm+vxxx9X8+bN1ahRI6Wnp+u3337T5s2b9eabbzq2hW3btumGG27Q8uXL1aFDh4I+VAAAAAAIOeT45PjukONbd9sO5Zwm2PldKCDHJ8cnxy96Ar2/sur+EABCTtCmLgOAj3bu3GmuueYat7/kjIqKMq+//rrJyMjIc4y0tDSnfpUrV/aqhjNnzjj1r1Gjhk+PxddfCCYlJbk89q5du3r868dPPvnEqW/r1q3zbN+tWzevfw3u7tKqVSuP6svZr7BdunTJ6XQnksyOHTtc2uU87dn7779f6LXmZJVt2x273W6mTp3q9EvQ3C7t27c327dvNzt27HC6v3nz5n6rxxhjEhISnMYvWbKkX09BNG/ePKdT6ZUrV8588803fhvfn4rqkQuWLl3q8kvw4sRut5t169aZhx9+2O3pK6+++mqXfd7YsWOd2nz66acFrmPq1KlOY77++uteP47sR8CpVq1arm2zr+eWW27Jd+y9e/f6ZR+V/eLPo9BY4X3AyvsGYwK3f9i4caMJDw93ajd58uQ8P29duHDBPPvss059ypYta44dO+bXxwwAAAAAxR05Pjl+oJHjF60c38o5jRXyO0+R41sTOT45fmEqzCMQB3p/ZeX9IQCEEu9/zgkAQXLp0iW99NJLuuqqq/TTTz+5LO/bt6927typp556SuHh4XmOVaJECZUoUcJxOykpSXa73eNacv76rUyZMh739Qd3v5574IEHZLPZPOrfr18/lSxZ0nF78+bNOnPmjNu2R48e1YoVK3yqM6etW7dqx44dfhkrkCIjIzVgwACn+z755BOn28YYp6MbREZGqnfv3oVSX16svG3bbDY98MAD2rdvn6ZOnaqePXuqTp06KlWqlEqVKqV69eqpb9++WrBggdatW6cWLVooISHBaYy6dev6rR5Jio2NdfpFdWpqqtujofgiPj5effr0UVpamiSpVq1a+uGHH3TTTTf5ZXxIx44d0+DBg4NdRkDZbDZ16NBBU6ZM0ZYtW9S+fXun5Zs2bdLcuXOd7jt9+rTT7SuuuKLAdeR8b/D2/6LNZlOdOnWcxjPG5NuvatWq+bbJ+Xj9ITk52S/jWOV9wMr7Bilw+4dx48Y5HdHm6aef1siRIxURkfuJeKKjo/XKK6847dNTUlL02muv+eGRAgAAAEDxR47/J3L8wCLH/1NRyPGtmtNYJb8rzsjxyfElcvyiKtD7KyvvDwEglDCBGECRsGfPHrVt21YvvPCC0tPTnZZdf/31Wr9+vT799FPVr1/f4zGzf2my2+1efcnI+SWndu3aHvf1h8jISJfwsVWrVh73DwsLczktXG6TJmfPnu3VB/X85AzwrOruu+92uj1nzhynL8k5T3v2t7/9TZUqVSq0+vJi9W27bNmyeuCBB/TZZ59p//79On/+vM6fP68//vhDn376qXr16uX440FiYqJTX29O4eSpevXqOd3OuU5f5XyveuaZZ9SiRQu/jI3L2/bgwYN14sQJVa9ePdjl5GnXrl3asmWLtmzZUqA/vjRr1kxfffWVS5C4du1ap9tZIVeWUqVK+bxOf4qKinJcz/n/IzeenL7S07G84a/g0UrvA1bfN0j+3T8kJCRoyZIljtuVK1fWxIkTParDZrNp6tSpTvd98MEHHoXlAAAAABDKyPGdkeMHHjn+ZVbP8a2c01gpvyuOyPH/RI5Pjl9UBXp/ZfX9IQCEAiYQA7C8jRs36tprr9X27dud7q9du7YWL16slStXql27dl6P26RJE6fb3hx19NixY063mzZt6vX6C6pRo0ZOt3NOgsxPzuAxtyMXfPzxx06333jjDS1dutTjy9NPP+3U/5NPPvFrkBkorVu3dgq5Dhw4oHXr1jluZz9qgSSXIx0EU1HftrOLj493uh2IenL+GjV7OFIQFSpUUK1atRy3R44cqUmTJjEBzU9ee+01ffvtt4qOjtYHH3zgVd+1a9fq7rvvVu3atRUVFaXY2FjdfPPNjventWvXymazeXw0mPz0799frVu3VuvWrdWxY8cCjVW5cmWNGTPG6b5du3Y53c75R5CDBw8WaJ3S5e05u/3793vV3xijffv2OW5XqlTJb89vzsf7xBNPyBhToEuHDh38UpuV3geK075Byn//kP2Pg5LUrl07RUZGejx+5cqV1bhxY8ftc+fO+e0I9QAAAABQHJHju0eOH1jk+JcFO6spyjmNlfK74ogc/0/k+OT4RVWg91fFaX8IAEUVE4gBWNrRo0fVvXt3nTx50un+kSNHaseOHbr11lt9/uKS88Pozz//7HHf9evXO90OxofRnL/g9vaL5fHjx51uV65c2aXNtm3btG3bNsftSpUq6dFHH9Utt9zi8WXUqFFOp6I7fPiwy69srchms2nIkCFO92UddcEYo/nz5zvuL1OmjHr06FGo9eWlqG/b2X3//fdOtzt16uT3deQ8vU1MTIxfxq1QoYJ+/vlnXX311Y77Ro0apREjRigjI8Mv6whVP/74o8aOHStJevvttz0+MnVmZqZGjBih6667TrNmzdKhQ4d06dIlHT9+XN98840GDRqkbt26KSUlxa/1xsbGOq6npKS4bHPeatu2rdPtnEf4yPl+7m1I6E7OU5B5O2ZSUpLOnj3ruF2lSpUC15TbWCdOnPDb2AVlpfeB4rRvkPLfPxw5csTpti9HsM/Z5/Dhw16PAQAAAAChgBw/d+T4gUWOf1mws5qinNNYKb8rbsjxyfGzI8cvugK9vypO+0MAKKqYQAzA0oYNG+b0hSoyMlKffvqpJk+e7HLUUG/dcMMNTre/++47j/vmDEOuv/76AtXii+xfYiS5HNkhL8YYl/buTh2U86gFvXv39upX4dLlL6s5n+tZs2Z5NUawDBw40CnYnjdvntLT011Oe3bHHXdY5tRCkvW27WXLlmnq1KmOi6ennrlw4YJWrVrluN28eXOXI24sW7ZMderUcVxefvllr2ozxjiF6+XLl3cKiQqqevXq+v77752C6ffee0+33367zp0757f1hJKkpCT1799fmZmZ6t27t4YPH+5x31GjRundd9+VdHl/cs899+j999/XBx98oHvuuUeRkZFauXKl7rvvPr/WnPO0nMuXLy/QeDl/9Z7z/Tvn/uG3337zeOzU1FTdfffdGjRokAYNGuQ4PWCbNm2c2v3666/elOxydIWc4xVElSpVnE5L9csvv/htbH+wyvuA1fYNUmD3DzmPaOFLAJ/9aBvuxgQAAAAAXEaOnzty/MAjx7d+jm/1nMYq+V1xQo5Pjp8TOX7RFej9ldX2hwAQkgwAWNR3331nJDldlixZ4rfxU1NTTZkyZRxjly5d2iQnJ+fbb//+/cZmszn6NWnSxOcaunTp4vT40tPTPe67d+9ep75DhgzxuO+GDRuc+rZr186lTUZGhqlRo4ZTu5UrV3q8juz+85//OI1ToUIFk5qammv7nK97MHXr1s1lGxw9erTTfUuXLg1qjTlZYdvO7umnn3Z6vqZPn+5Rv8mTJzv1GzNmjEubkydPOtUcGxtrLl265HFtOf8v3HnnnR739UZGRoZ55JFHnNbVunVrEx8fH5D1+SLn+9GqVat8GmfcuHGOMZ544gmzdOlSjy5PPPGEo9+4cePcjm23282dd95pJJkrrrjCJCUlGWMub7t5bSfGGLN+/XpHm+rVq5sff/zRpc33339vKlWq5Pf3n8WLF7u852ZmZvo8Xs7/GyNHjnRanpKSYsLDwx3Lq1Wr5vH+JWet48ePN8Zcfu6z7xNsNps5dOiQxzXff//9TuO+++67ubb1Zd82aNAgp347d+70uLbjx4+b6667znTq1Ml06tQp1+2voIL9PmC1fYMxgd0//P77705tatWqZex2u8e1Xbhwwen/UWRkpDl37pzH/QEAAAAgVJDj540cv3CQ4xccOU3w8ztPkOOT47urlRzff4ri+4A3n0t8Eej9ldX2hwAQiphADMCy7r77bqcPvw8++KDf15Hzi8rEiRPz7fPQQw859XnhhRd8Xn9BP+C3atXKqf/q1avz7ZORkWHatWvn1O/ll192aff11187talWrZrJyMjwqr4sJ06ccPoSLMksXLgw1/ZWCh7/+9//OtXSv39/07BhQ8ftqlWrBvyLmS+CvW1nt2zZMqdxr7322nxDl5MnTzqFHCVLljRHjhxx2/bGG290Gv+dd97xqC673W5uueUWp77z58/3+vF54+2333b6MlurVi2zbdu2gK7TU4EIHn295Bb8vPfee0aSCQ8PN2vXrnXc70nwOHDgQEebBQsW5Fr/1KlT/f7+c+HCBVO5cmWncSdMmODTWOfOnTP169d3Guu7775zaderVy+nNv/61788Gr9///5O/b799lvHsqeeespp2cCBAz0ac8eOHSYyMtLRLyoqyiQkJOTa3pfgcenSpU79br/9do//CJE99JZkpk2b5lE/XwXzfcBK+wZjAr9/aNKkidP4//nPfzyu7V//+pdT31tuucWrxwYAAAAAoYIcP3/k+IFHjl9w5DR/IscnxyfHJ8cvSu8DhbF/C/T+ykr7QwAIRUwgBmBJdrvd5dej+/bt8/t6fv31VxMWFub0ZWjDhg25ts/5i85y5cqZU6dO+bz+gn7A//TTT53616tXz2zfvj3X9pcuXXL5glWhQgXHr3+zyxn8PvLII94+PCc5jwCQ15FerRQ8pqSkmNKlSztqyRmg5vzFsFUEe9vOLiMjw9SqVctp/FdffTXX9ufOnTM333yzU/tRo0bl2n7JkiVObSMiIvINzTIzM11ClJYtWxbo1+Se+vzzz02pUqWcnuuvv/464OvNj9WDx23btpmSJUsaSeall15yWpZf8JiZmenYp7Rs2TLPQCo1NdXExMT4/f3n3XffdXmcTz75pLl48aLHYxw/ftzcdNNNTmN07NjR7Xa7Zs0ap3bR0dH5Hn0m5z6lRo0aTn9w2r9/v8t74FtvvZXnmEePHjWNGzd26jNs2LA8+/gSPNrtdtOsWTOnvmPHjs03fJw7d66JiIhw9KlcubJJSUnxaJ0FEaz3ASvtG4wJ/P7h3//+t1PbkiVLmnXr1uVb1+LFi01UVJRT388//9ynxwgAAAAAxRk5vmfI8QOPHL/gyGmckeOT45Pjk+MXlfeBwphAHOj9lZX2hwAQiphADMCSkpKSnD70ValSxePT57i7uPtVZ5b77rvPaV1lypQx//nPf5y+DJ4+fdpMnDjR5QvXP//5zwI9zoJ+wLfb7eYvf/mL0xglSpQwr732mlOYeP78ebN69WrTpk0bly++H3zwgcu4586dcwrbJJkffvihQI912rRpLnW6CzyNsVbwaIwxgwcPzjUgcXf6JE+sWrXKZSxfg57cBHLb9rb+jz76yKX98OHDzbFjxxxtMjMzzdKlS82VV17p1K5Bgwbm9OnTuY5tt9tdgsrIyEjzyCOPmEOHDjkFDydOnDBffvml6dy5s8v2+NNPP3nwrPrHL7/8YqpVq+ZYf0REhPnwww8Lbf3uBCJ4nDFjhsf9ZsyYkWvweP78edO0aVMjyVx//fUuR1HJL3jct2+fY/l9992Xby3Zj0ztLxkZGS5Hy5Yun8Lt5ZdfNtu2bXN5XHa73Zw9e9b8+OOPZujQoS5BeZkyZcyOHTtyXefQoUNdtvPRo0ebw4cPO7U7ceKEeeaZZ1zGd/f6jR8/3uUx9O3b12zZssXp/9rZs2fNtGnTnEJcSSYmJibPoxYY41vwaIwxa9eudXkvu/nmm82KFSvM2bNnndru3r3bjBw50ukIApJ3Rz4pqGC9D1hp32BMYPcPly5dculjs9nMPffcY3bt2uX0uevixYvm559/NrfeeqtLPX/729+8Oq0mAAAAAIQKcnzPkOMXDnL8gtdPTuOMHN8VOT45fpbs7cnxC1dBP5f4um8L9GexYH7WA4BQF/xvcwDgxu7du3MNeny51K5dO9d1nT9/3rRt29alT3R0tGnWrJlp0KCB0ylbsi69evUq8NFK/fELwYMHD5qaNWu61BcREWEaNmxomjRp4vSLveyXRx991O2YH3/8sVO7WrVqFfixujv9mbvQ0xjrBY/ffvut2+evXr16PgdVhRE8BnLb9rZ+u93uciqmrEv16tVNixYtTLly5VyWVahQwezevTvfx3r48GGXoyNkXcqVK2datmxpqlSp4nZ5eHi4mTVrlrdPb4EdPHjQtGjRwqmW559/PmiT1KwcPA4fPtxIl39V7u4UePkFj+vXr3csf+211/Kt5eGHHw7I+8+5c+dcnufsl1KlSpl69eqZ1q1bm4YNGzr9sj3npWzZsvn+QSglJcW0bt3abf8rrrjCXHXVVS7BYNblrrvucvs+kJ6e7hTMZr9UrVrVtGrVyjRs2NDte0vJkiXNN998k+/zlL2PN8GjMa6nM8y5L2vRooWpWLGi2+XDhw8v9P9/wXgfsNK+wZjA7x8OHTpkrrjiCrfjR0ZGmiZNmpjatWu7hNBZl6uuuirXP5QCAAAAQKgjx/ccOX7gkeMXvH5yGlfk+M7I8cnxs2TvQ45fuII1gTjQn8WC+VkPAEJd8L/NAYAbq1evzvWLgy+XvIJHYy6fmuXaa6/1eLw+ffqY8+fPF/hx+usUIzt37nT8qteTS3h4uJk0aVKuX2y8Oe2UN3KOe/3117ttl7PeYMvIyDBxcXEudT3//PM+j1kYwaMxgdu2fan/4sWL5s477/S4lpYtW+Z5Kr+c9u/f73IEg/wuMTEx+Z4OKpDOnDnj8v9iwIABJjU1tdBrsWrw+Ouvvzruf+SRR9wenSb7kTH69evnuP/nn382xlz+RXvWck+Cx8ceeyxg7z+XLl0yr7zyistRAry5dO/e3ezfv9+j9Z09ezbXoDC3y9ChQ/PcBtPS0sw999zj1ZixsbEeHwEnez9vg0djLv/xLDo62qt94vPPPx+0gCkY7wNW2jcYE/j9Q3x8vMspWD25DBo0iMnDAAAAAJAHcnzvkOMHFjm+f+onp3FFjv8ncnzPL+T4eSPH912wJhAbE/jPYsH6rAcAoS743+YAwI158+b5/IXM3SW/4NGYy+HSW2+9ZerXr5/rOFdddZX55JNP/PaLQn8Fj8YYk5qaal566SVTp06dXOsvXbq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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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T7cSfLSREPY+fqvUf+Mdj7dzX1anfACUGOOfoPGykJGnL9/7x2PzlJwrrFK+0g7zxGPiPC5T51ad1FywMm01V+Xn6+LhDg/aChRESoqSjpyjv07myGsQj98PXFdV7gKIHNY1HzOCRkqSin/3jse3bTxUSF6/4/Q6re2/7gl+U+cJM5X06t3V2oAUZISHqctxUbf3IPxaZ776umL4DFDekaSziho6UJOX95B+LnK8/UWineHU+wBuLqpyt2vrxO36fqcjcKEmyR0a15G60mLq+W4rDr+92YqpDGyp33Hfr5PDvi8SHKHDfLd+tj/I8urlvcPfdpPq2kv9Z07YS2Uxbifa1leL/868fhd9520qnRm1lSztpKwguv//+e92sBZMmTZLdHnimg8mTJ9f9/7PPPvtbv/O9997TM888I8Mw9Oqrr/6tbQEA0F65Kiu19uuv9f099yhr4YKdft4eEqLBU6Zo6dx3/PqiS15/Q0kDBihtRNPzyfaCWPjrqPEg3+KPXG096sau6cjHjj6TpmpNo+vDq3aQu669Pry50fXhDb7rw+m+tpL9269NfvocP1lFGav1613BeX04kI5aN5pDPOoRC3/Eox6xAILP3pLHZwAxWsQ2pyWnJfWP8q9S/SK9r7NrAl+EPinFrvdyPfo836NtTku/F3v0TKZbx3W2K6rBxen8Gks3ZTh1c58Q9Y1sOhtWMClyewf89g7zf79XmLfcuc3MwjWhk02flVj6vtRUkdvSogpLrxaaOjzWUKTNUJRNCjWkLKf/+uUeS0VuaVvzTxtuU5HJKbKHhalo1Qq/94tWr5QkxXTr3mSdyoJ8SVJcr95+73fq3VeSFJUWePau7uOO0eCzz9NX550py+P522VvDVEpKXKEhWnbypV+729b5X0dFyAeFb54dGoUj/g+3njEdPHGY+U7byuhX38ddPOtik5LU+eBg3T0I7NUsmmjtv72a4vvy99VF4tV/rEo9NWN2N2IRbSvbqx6523F9+uvA2/yxiJx4CAd9XDwxkIiHo1FJXvjUfgX4lGZ38yxo4//sWO1Lx7733SronzxOOKhWdq+aaOygjAe1A1/rXUcrSktlbu6WvG9evl9JjQmRlFJyYruEnwzR9JO/EX4zjmKG51zFPviEZ3eNB5VvroR29M/HnG+c45IXzwSBg1V2ebNOujBx3X2mmydn1OuyV/+rM6j9mnx/WgpoZ1TZAsLU8Va//pR+zq8a9N4OAu98Yjo7h+PiJ7eeIQ2mEE18/mZynzuURX++GWLlrs1hCd560Zphn/dKFvjjUVkgFjUbPPGIqpRLKJ9sYjwxeKPi0/TxleflSSFJnZWp+GjNeSme1SxdbOKFv3RsjvSQur7bv79qn6+11nVgW9MPCnVoXdz3fo8313Xd3t6s1vHJTXtu9242qlb+oaob1Twpxzq2soa/7ZSuc77OqxL0/rh8rWV8GbaSsPZhre8MFNbnn9URT8Ff1tBcMnIyKj7/4QJE5r9XLdu3TR8+HBJ0qJFi3b7923evFkXXnihJOmGG27QuHHjdntbAAC0Z6VZWfpl5qP6ZeajyluxYqefj/blKfJX+n82vy5P0a1VyrknEAt/HTUe5Fv8kautR93YNR312BFZm7tutB9Fvv2ICVA/mr0+XJu7Tg2co+9x1DEacvZ5+iKIrw8H0lHrRnOIRz1i4Y941CMWQPDZW/L4jhbZCjq8Aqf337hGNSreN+HUNmfgQbNX9HRoWbmpf65w1r3XJ9LQrMH1M1VZlqUbVjs1NNqmc7ra9UdJcM60W6vQN5A3ptEjfON8NxkUNTPQ94LOhlZXGbppa/3+9QyV7u7qvejuMAwdE2vozSJLvcJM7RNlKN8lPZFvqsZqfrttLTIlVZJUU1Ls937tLIaRySlN1slftEBFGat02KNP6IfLL1HxujVKHjFahz36RLPr2BwOHfLgTC2c+aAqsrNaejdaTLQvHtWN4lF7F3lUStN9y1m4QNtWr9Kxs2brixnTVbR2jVJGjtaxj832rpOcLEna9P13+v6WG3X0Q4/qsDvuliS5a2r03KhhclVUtNo+7a6oncUiwPecu3CBClev0jGzZuvLy7yxSB05WkfP8sYi0heLzT98px9vvVHjHnxUhzSIxQujgzMWEvFoLCrVF4/iRseOwuaPHbmLvPE4auZsfX3ZdBWv87aVcTP920rmD9/p51tv1BEPPqqDb6+Px0v7BGc8qBv+Wus4ank8WjH3Le17+VXatmqVNv/0g2K6puuI++5XSGRkwDi3NdqJv52dc0QEiEfB4gUqzlilgx9+Qj9feYm2r1ujziNG6+BH/M85IlPTNPjCf6pg8QL9euOVskxTwy+9SlO++Flz9xuiss2bWnHPdk9okjce7u3+8XAVe+MR2rlpPMqWLlDFulUacPdsrb5luio3rlHMkNEacPds3zrJrVzq1hGe7I2Fs1EsanyxCEtqGoviPxeodM0qjbz/CS26/hKVr1+jTsNHa9T9TwRcJyQ2TpNWeC9yuCvK9ePkQ2S5XC2+Ly2hwNc3i2sym7D39TZnk1UkSVf2cmhZmanpy+o/0DfS0GNDQuteW5alG1bVaGiMTeekO4K+7yY1aCulf72t9L9rtjJurW8r/e9q322lvbJkyN1O01uWmr9BuuFjx3r06LHD7XTr1k1Lly5Vfn6+SkpK1KlTp79UDrfbrTPPPFMlJSXad999dffdd/+l9QEA6MiifX3zquISv/crfX3z6AB5ir0VsfC3t8SDfIs/crX1qButY285dkQ1Uz+qdnB9OM+Xuz7i0Sf0Xe314ZGjdeROrg8f9sBMzX/0QZUH8fXhlrC31I2WQjzqEQt/xKMesUAwIY8f3Hn84J8OCO1Cc02l9nJxoEl3LcvSv9e79UeJqZt6O/TK8FDd1S9EZW5LV610yWV6V3oj26OFpaYeGhgqmxHcsw/vSG0I3M3EYna+qUWVlmYk2/RYd5uuS7Wp3JRuzzLltrwrXZNq06Exhv6VZWr8Go/O3ejRgHCpb5gUG3gW9LZnBR48bti8hx9bSNPHGntqavT56SfKVVamk77+WRduyNVxr87VwkcfkCRVFxc1WWfohdMVGh2jJbMfbcHCtzxrZ/FwNP2D6amp0XvTTpKzrExnf/eTrszM0YlvvK3fHn5QklRV5O18DzzxZB125z2aP2e23p46SR+ec5byl/6pk+a+p5iuXVtpj/6GndaNwLF4/9STVFNepjO//UmXb87RlNff1u++WNQOohtw4sk65I57tODJ2XrnhEn6+NyzlL/sT534dpDGQiIejeysrdibicdHp50kZ3mZTv/2J126KUeTXntb8x7xj0f/E07WwXfco0VPztb7J07Sp+eepYJlf2rKW+8pOhjjQd3w05rH0W+uvUprP/1YU199Q1dm5ui8X/9Q3pIlylv6p6qLmv7taWu0E387rRvNnHN8feaJcpWXacqXP+sf63J19MtztWSm/zlHaFwnVebl6LOpx2jDB+9o40fv6fOTJ8hVWaERl1/XSnv0NzUTD/niYQRoK6azRksvOUnuijLtM/cnHTI/R0PnvK1NT3vrh7tRwr+92J26YdbU6PfzT5S7okyHf/SzJi7P1f7PzVXGbG/dcJb4HxPcFeX6aeph+uOS01W8dKEO++BHxQ4c2sJ70rpq+27uAPGyLEv/XufSHyUe3dQnRK+ODNPd/UNU6pauXOGs67u9nuXWgu2mHh7UjvpuO2srAY6lprNGy6efJE9FmUa//ZMOnpejIU+8rc2+tuJqp20FwSU3N7fu/wkJCTv8bGJiYt3/GyYsd9Xdd9+tX3/9VdHR0XrjjTcUGhq685UAAIDXTvvmTfsbey1i4W8viQf5lkbI1dahbrSSveTYsbvXhz853Vs/pn3zsy7ZmKvjX52r+Y9460dNgOvDwy+crtCYGC0K8uvDLWJvqRsthXjUIxb+iEc9YgG0ur0lj98+h3Yj6CSFei8OlzSaZKvEN3I4JbTpxeOlZZae3eLWE4NDNCnFWxUPkzQsxtCJi5w6fptdI2MN3bPOpTv6hSjWIVW4LdU+UbfKtFThtvwelxsMEn2tqtS01HBodanviSmdA7S6VdXSa4WW7utq09Fx3j/WB0gaGG7owk0eHRVraVysoWi7obu72nVdqqUcp9QtTIq0Gfp6uzvgdoNBRZ73YBkW73+gDPe9rsjJDrhe8ZoMzT18f0WldVF4QoKKM1YrpnuPgOsYdrtGX3W9lj3/tNxVVS29Cy2qNh7hjePh+0NS3swficI1GXrpkAMU3aWLIuITVJixWnG+eJTnZMvmcGjCk89o+euv6ptrr6pbb93nn+rSlWt1wHU36uurr2iFPdp9uxuLojUZetUXi/D4BBVlrFZso1gcN+cZrXj9VX3XIBbrP/9Ul6xYq/2uu1HfBlksJOLRWF08EgIfO3YUj9cP3XE8jpnzjFa88aq+v+6quvU2fPGpLly+Vvtde6O+uya44kHd8Ndax1FJcpaV6ePz/qGvr75CcT16qmjdWrkqKnTZtNNU1szfq7ZEO/FX1cw5R+3ryma+w5K1GfrgyP0VmeaNR8ma1Yr2xaN2neptBdr6/TfyVFfXrecqK1PeH/9TwtDhLb4vLcFZ4I2Ho5N/PEJ8r2vyAtePyg0ZWjD1AIWldJGjU4Iq169WeFdvPGryg68d7IqafG8sQhvFovZ1dW7g/Spbl6Efxu+v8NQuCu2UoLJ1qxWZ3iPgOpbHo22//58kKefrjzV+wSb1Oe9SLb7x0hbdl5aQ7OubbW90Z2Nd3y0sUN/N1DOZbj0xNFSTa/tuiXYNi7XphAU1mpjs0cg4m+5Z69Id/UMV6zC8fTdfH6jao6Dsu0n1bSUkLnBbce6grSw84QCFpnRRSOO2ktc+2wr+OrPGLbPmbzwOyGxmALv8E4gNE4uBNFxe8RdnJvvxxx917733SpKefPJJ9e3b9y+tDwBAR1fmu1gYmRDv936kr69emt1xzg2Jhb+9JR7kW/yRq61H3Wgde8ux4+9cH37zsMDXh8sDXB8ec/X1Wvpc8F8fbgl7S91oKcSjHrHwRzzqEQvAizz+zgXpkEO0N4mhUoghZVSYOiapfircjApvI+oS3vRC8dZq77KRsf4TYY+IscmQtKXaVEKITVWmdFOGSzdl+I9OnrbYqUi7tOrQiBbem78nweFtWOurpcNi6t9f79vflJCmscj2XawfEuG/bHCEdwhytu8pwSuqLEXbpB5hhmJ9u53nspTvlvoEuNAfDKoK8uVxOpU4eKg2fvZx3fuJg70zspVt3dJkHVtIiNL2P1BFGatUkZNd14lM3Xd/SVLhqhV+n+89aaqiUtO06tX/ttZutJiKfG88koYM0dpP6+OR5ItHaTPxSD/gQBWuXqXy7GyV+07kuuznjUfBqhUK79RJ4Z06KWv+PL91nWVl2rZ6pTr17NVau7Tb6mIxeIjW/YVYdN3/QBVmNIrFWG8stq1coTBfLLKbi0WP4IuFRDwaq/TFo/PgIVrfIB6dh+z42BEoHmkB4pETIB6Fq1cqjrYS9HWjtY6jkpQ2Zl/VbN+uorVrVF2yRJIUk56u2PR0Faxc0WS7bY124q/2nCNh8FBt/rw+HgmDvPEozwocj5T9DlRJxipV5mTXXaRIGeONR/Fq7/dekZstR3h4wPVd5WUtvi8twVmYL9PpVHT/Idr2TX08ovt741GT0zQeRkiI4kYfqMr1q1STl103CDJulDceFWuCrx3siupt3ljEDhyqnK/qY1E7Q3BlduBYJO57oMrWrFJ1bnbdgOGEfbyxKM1YIUdMrA7/5BetfOB2ZX/xYd26nqoqVW7NVEhsXCvu1e5LDDW8fbdyS8ck1b+fUe7tk6SFNX1Q0ZYq77JRzfbdLCWEWt6+22qnblrtv/4pi2oUaZdWHx7ZovvSEmrbSlT/Idr2bX39iPK1leodtJWK9avkzMuW09dWYkf62sra9tlW8NdtePAnbbjvh1bZdmlpad3/IyJ2nPcICwur+3/VX7hoWlhYqLPOOkuWZenMM8/U2Wef/dcLCgBAB1eRny+306mUIUO16pNP6t5P8fXNt2/d2lZF2+OIhb+9JR7kW/yRq61H3Wgde8uxo7KgNnc9VBs+2/XcdZcA14drc9eNrw/39V0fXtEOrg+3hL2lbrQU4lGPWPgjHvWIBeBFHn/nml4ZBHZDqM3QsUl2fZrvqXt8rSR9lOdRjwhDQ6KbDm7tF+V9b9520+/9+dtNWZIGRNk0JMamd0eF+v3c2c87jf5/BoTo1RHB91jNEMPQ4bGGvi01/R7/+1WppfQQaUDT/q56+wb/Lq70v2vhz0pLlqQ+vmPIa4WmZmz2yNVgu+8WmYqySUfFBecAYtPl0oZPPlC/k6b5PVa+/7TTVbJ+nQr+XNx0Hbdbx738lg59cFbde4bdrqEXXKwtP32v0o0b/D4/9PyLteWHb1WR+9eneN/TTJdLGR99qMEn+8djyGmnq2j9OuUuCRyPE159U0c/PKvuPcNu1+gLL9KmH79XyYYNqty2TRX5+ep+8CF+64bHxytpyDAVrFjeavu0u0yXS2s+/lADG8Vi0Kmnq3j9OuU1E4spr76poxrFYuSFF2nzj9+rZOMGVfli0a25WKwMvlhIxKMx0+XS2o8/1MBGx46B03Ycj4mvvKkjH5pV955ht2vEBRcp88fvtd0Xj8r8fKUHiEfnIcO0jbbSLupGaxxHJWn/q67VGV987ff4tH0u+adqSku16t25rbdTu4l24s90ubTp0w/U5wT/ePQ95XRt37BO25o55zjqv2/pwAdm1b1n2O0adN7Fyvq5/pxj02cfq/uxxyssvv4u7ciUVKUdcIjyF8xrvNmgYLlcKvj6QyVPnCajQTxSppyuyk3rVLaiaTwst1vDnnhT/W+fVfeeYber6xkXqeh/36sqc0OTddoDy+VS1hcfqNsU/1h0O+F0lW9cp5JlgWOx3zNvacS9s+reM+x29T77YuX/8r0qNm+Qu6xUEWnpSh03wW/d8ORUxfQbpJIVS1ttn/6O2r7bJ/luv77bh3lu9YgwNDSmab+if5Q3dfBHyU76bvuE+f3c1d97PH1gYKheGxnWeLNBodm2MtnbVsqbaStDZr+pfrfNqnvPsNvV5YyLVPy/71XdTttKu2VJHsveJj89rj9Sh+Xdsds/oWkxze5WUlL9CP+SkpIdhqDh8p0lKevCZlk6//zzlZWVpV69eunJJ5/cpfUAAIA/j8ullR9+qGHTTvHriw4//TQVrlunnMVNzyf3VsTC394SD/It/sjV1qNutI696dix7uMP1P/kprnrkvXrlN9M/Zjwyls6vNH14eHnX6zMn7y564aGXXCxMtvJ9eGWsLfUjZZCPOoRC3/Eox6xQFAhjx/UeXxmIEaLuTDdoVOX1OifK5yamGzX/BJTH+R59ODAENkMQyUuS0tKTfWMMNQz0qZ+kYYmJ9v1rzUuba6yNCjapk2Vpp7d4taYOJsOibfJYTO0bye73+9xW97n4A6Jtml4bHCOgT8jwabpmz26aYupo+IM/Vlp6Yvtlv6VBNmS+AABAABJREFUZpPNMLTdY2lllaX0UEPdQg31CpWOiTX0YI6pLKelfuGGMp3S64WmRkRIY30DsE9LsOmHUo/uyDJ1RIyhpVWW3i6ydHWKTZG24BxALEmLZ8/UiV/+qPGvvaO1781V2oEHa+DpZ+vbf14gWZbC4uOVss9Ybd+wTts3rJcsS0uemKUD7rxPJevXqThjtQZMO11JI0bro6nH+W07JCZGXQ8+TL/fc3sb7d1fN+/xmTrrmx904pvvaOW7c9XtwIM07Iyz9OklF0qWpfD4eHUZM1bF69ep2BePebNn6fC771PR+rUqzMjQkFNPV8rI0Xpr0vi67f589x0a/8RTsixLG77+SuHx8Roz/VJZlqX5T85uwz1u3vzHZ+qMr3/Q1Dfe0ar35ir9gIM09Iyz9HmDWKT5YlHii8X8J2bpsLvuU/G6tSpck6HB07yxmDu5Phb/d/cdOu6JpyTL0oZvvlJ4p3iNnn6pZFlaGKSxkIhHYwtmz9RpX/2gyW+8o9XvzlX6gQdpyBln6cvp9fFIHTNWJQ3isfCJWTrkrvtUvH6tijIyNMjXVt5tEI9f7rlDx8z2xmOjr62MusQbj0VPBWc8qBv+Wus4On/O4xpwwoma/OIryvjwfaUfcKD2vewKfXPd1XL9xceI7Cm0E39Ln5ipSZ//qKNfeUfr35+r1AMOVv/TztaPM+rPOZJHj9X2jetU6ovHsidnaezt92n7+nUqWbNafU85XZ1HjNbnJ9afc6x++TkNveQyTf7iZy2dM1OGYWjElderZnuJls55tA33eMcyX5ip0W/9oGFPvqO8T+eq05iDlHbCWVp5g7d+OOLiFTtirKo2r1PVZm88Ml+YpT7X36fem9aqYn2GUiefrpgho7X4nPE7/4VBbO3TM3XYBz9q/+ff0daP5ipxv4PV45SzteBqb90I6RSvhFFjVb5xnSo2eWOx9plZGnrLfSrfsE5l61ar24mnq9Pw0frltPq6sf7FORp45c3y1FRr228/KTShs/pdfJWcRdu08ZWn23CPd+yi7g5NW1Sj6cucmpTi67vlevTQoNDAfbcoQ5NT7PpXhlObq0wNjrZpY6WlZzNd3r5bgrfvNrZR383jG588OMbQiFh7gJIEh60vzNTIt37QkDnvKP8zb1tJPeEsrbqx+bay5cVZ6n3dfaratFaVGzKU4msrf57bvtsK/hpbmEO2sN1PrRlG8/34tLS0uv8XFRX5JSIbKyoqqvt/dHT0Lv3uOXPm6OOPP5bdbtebb76p2NjYXVoPAICOLiI+Xun7jlXh+nUqWr9ekvTrY7N04Xff64y352rpO3PV86CDNOrMs/T+RRfKajAZyN6GWPjbm+NBvsUfudp61I2/b28+diyaPVOnfPWjJr7+jjLem6uuBxysQaefra8bXB+uzV3XXh9e/MQsHXTnfd7c/hrv9eHkkaP1/hT/68OhMTFKP/gw/a8dXR/+q/bmurE7iEc9YuGPeNQjFkBg5PF3LjhHX6JdGhVn08vDQ1Xqkm7NcGlJqamHBobo1DRvI1xVbuqcpU59kOcdAGwYhh4aGKLLejj0Wb5HV6506q0cj05Kdei/w0PlCOIBsTszNNLQY91tKjMtPZBjakWVpdu62DQ53tvk1lVbujLT1Be+GbwMw9BtXWw6L8mm70ot3Z5l6uMSU8fHGZrZ3S6H72A0ItLQg91s2lRj6d5sU6uqLN3exabTE4O7KectmKePTxivsE6ddMRjTyl1zFh9O/18rfI9Uqbz0OGa8uEXGnDaWXXrLJr1kH6/+zYNPO0sHfbw45Jh6IOJRynnt1/9tt39yKNlDw1V1i8/7dF9+juy58/TW5MnKLxTnMbPflJd9h2rTy++QEtf9sYjedhwnfbJ5xp6xpl16/z+6MP66c7bNPSMs3TMo49JhqE3xh+trf+rj8fi55/V+6dPU9KgwTr+6ed0yC23advq1Xr5kAPqHokVbHLmz9PcyRMU1ilOxz7ujcXnF1+gZa94Y5E0dLimffy5hpxeH4t5jz6sn++8TUPOOEtHPeKNxdsTjlZWg1j8+cKz+vCMaeo8eLDGP/WcDrr1NhVmrNYrhwZvLCTi0Vju/Hl6b8oEhcXF6ejHn1TqvmP15SUXaHmDeJz80eca3CAe82c+rF/uuk2DTz9LR/riMXfC0cpqcOxY+sKz+viMaUocNFjHPvWcDrjFG4/XDwveeFA3/LXWcXTrb//Te9NOUueBA3X8M88rdZ999MlF52v+E4/v8X3cVbQTf/kL5+nzk8YrNK6TDpn5lJLHjNWPl56vjNe88UgYMlwT3v9C/abVn3P8+dhDmn/vbep/6lk66KHHZRiGPp18lHJ/r49HVUG+PjzqAG1fv1Zjb7tXY265SwWL5uv9w/eVqyx4H5tYumSelpwzQY7YOA2890nFjhyrlddfoJx3vPGIHjh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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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B6vnCW+X26fnSu8pY+a82vT+pprpQrdJLJiI4ZGy5SnJGZTgrPjf+E21Rj2Dp4T1uDd3i1q273AqwSo829pxTNotFZ4VZ9EWGoV+z3Up3GlpXaOjBPW4VGVK6y/dmtvCP85wbzmzzueEoOTcOPCYeLGfFYuVtWqu2j72qiF6nyh4br+jTB6ndU2+W7BNfw632ffXleRYAAAAAagLvmcyoRxlqYVZf6hGUcOy59f5li5W5Ya16P/ea4vt4cusmAwepz4sl2eSB3DqplUJKcut5V1+gJeM9ufWgGX/I7qO5NVltmbSSgbwVzSYsVZ6nXhNjUc8Q6cHdbg3e5NYtOzw57fgmZTnt2eEWfZ5h6Jdst9JKctr7k0ty2kryX28jqz281ELP7+3QK0hGH/jMp7DiAcT3nRio/o3suvr3XJ3wVaaG/pqjID/p7VNCarbBPqi+PK+g+nFuAACOFwOIAV9lVPwG+MBlnPzs9tpsTZVZKvnyp7vkfaCjgjf6Wbme0WHnnxKtd+5po6F9o3XNeQma8nA7rd5WoO/+zCi3z9s/pii3wKU7L2hcbW33WfXk3Ki0HxZPP6y28v1wFxVp8XWj5MzL0Slfz9fZy/aq+5tTten1ZyVJjsx087H8/NTyP7fq9N/WKqxDFy277Uplr1lezR2pHsYRfq/WCn6vrqIizRk9So7cHA2eMV+XbdirAR9M1YqJnnoUZXjqsfr1lzRjyOla+OBd2jVrhtZ/+LZmXTBIgbFxajvmhhrq0fGp7Hvj7pIyOSrIkgzD0GOri/Tnfqce6RigL/oG65kugcp2Grp5cYEcbnONnW5D72wu1im/5mpNtktv9AxS50O+/e4TKjk3VHJuWCr5W1nwn1Fy5OXozO/na/iqver77lSte9VzbhSXnButRt+g2F6naNGd11Z+P3XEgVOiogkoDMPQpP2GlhVIN8da9GJTq+6KsyjXJT2+x5CzpO93xll0WqhF/91raNgWt67b4dYJARa18ZfCfXFm+yOdG3ZbuVXu4iKtuukCufJy1P2L33Tqwj3q9NoX2v7mc5IkR2b559gGp748zwIAAABATeA9kxn1KEMtzOpLPaqQW7uLijTvqlFy5uZo0PT5unjdXp3+/lStetmcTa55/SX9NPR0LX7oLiX/MkMbP3pbv15Ukltf7Zu5NVltmcrS0gMtr2icr2EYem2foaX50i1xFk1MtOqeBIvy3NL43WU57d3xnpz2kd2Ghmxy6z/b3GoXaFGbAB/NaSWy2iOo9DOfkn8r+8zn0aUFWpDi1PiuQfpqQKie6xmsbIehG/7MK/eZT71XX55XUP04NwAAx6n8K1UAPiFn715JUnB0lGl5cHS0JCl79+5ab1NVJER5XpCm55iHdB243Tim/GyfsZGeh6ahfaNNy/t0DFVYsJ9WbM7Thf1jSpc7XYYmTN2tG4YmKDjQBwf/VbP6cm4U7fP0wz/S/Hu2l9wuTKm4H3mb12vB0D4KaNRE/pHRyt20TkGJLcrtE9KqrU56+SNFdTtZe3/6VmufGKf87VtqoivVoiDVU4+AQ+oREOW5nb+n4npkbVyvH8/qo+DGTRQQFa3MDesU2sxTj/y9nn2Sfy1/SausDeuUvWmDojt1rrY+VKf4QE+clOkwv+nNLPbcbhRYPm5alunWpE3FertnkEYmeh57zpR0UqRVg+fna/gep85v6lm+Kcelsf8W6N8Mt85rZNNjnQOVFOKb36sqLDk3/KPM58aBv52CSv5Wcjat1+xBfRTUqIn8o6KVvXGdQkr+VgpSdiu4aTN1fXyClj1ylxxZmbIFh8gv0DNTs19QsGzBIXLm59VUt6ospuRhPueQQDG7ZGbiWFv5c2NdkTQlw9BjjS06q2Tq4pNDLGofaOjGnW6dmWvRgDApxM+i/za26K54Q3sdUqK/FGy1aESOS7E++PRSXPI4ao+o+HG0OGVPhfvlb1mvJSP7yj+hieyR0crfvE6BTT3nRlEl51NDUl+eZwGgIbG4ffCJGgCAeor3TGbUowy1MKsv9SjNrSvJJg9k0IfK3rheM87uo6DGTRQQGa2sjeVz692zK8mtN29QlI/m1mS1ZWJKRhkcyGUPOHA7roKcdm2hNDnd0BNNLDo73JPT9pEnp71+u1sDcyw6M9yT0z7WxKJ7EgztcUjNSnLaWZtcivXR0Q1ktYcXH+T5fWcccnXJjCJP0N8oqPznM0vTXXp1baHe7xeiUS09V58dKKlrtJ/OmZWjkS38Nby5711VsqbUl+cVVD/ODQB1BTm+7/LNkTIAlJeaKmdxsRI6nWhafuB21q5d3mjWMYuPtMtus2j1tnzT8gO3m8WVf2PXONqzrLDIPDrMMDyDhcOCzU8q3y1I1570Yl19bv25lM/h1Jdzo2h/qtzFxQprZ+5HWHvP7YLdO8vtY7HbFd33dPnHxqto727lrFslw+lUVPc+kqTcDaslScEtWqnvV78ppGUbLRw9WEuuG+XTg4clqXBfqlzFxYrsaK5HZAfP7bzk8vWw2u1q1O90BcbFK3/PbmWs8dQjrpenHplrV8sWGqrEc4coKL785WkMt0vFOTk10JvjFxtgkd0irc02Pw6szfGkj02Dy7+E2ZHv2bZHlPkxoluknywHrd+a59b5f+Rra66hKX2D9HGfYJ8dPCx5/lZcxcWKaG8+Nw7cLqjk3Ig75XQFxMWrYO9uZa31nBvRPTznRta61Qpp0Uq24BD1nPC2Rm3N0aitOeo/ZYYk6czvftOwVRUHmt4WZfN8A27LIUHjlmLPv/EVBMh7HJ5/Ox0y8Lx9oGfmg90lA9XXFBjaXmwo3M+itoEWBVstSnEY2ueUWgVUc0eqQXGa53E0pG0n0/KQtp5zo3BPxY+jkSefLntsvIpTditvvefcCO/qOTfyNq6u+Yb7uPryPAsA8A15n03VvmEXVvjj3p/m7eYBAHDMeM9kRj3KUAuz+lKP0ty6w7Hl1gkluXXBnt3KLMkmY3uW5NbrPLl103OGKLCi3NrlksNHc2uy2jIHctrNh+S0m4s8/yZUMOFlZTlth0Ny2tUFhrYXeXLadofktK19MKeVyGqPJC7AIrtVWptlHnG+NtNzO7Giz3xySz7zOWTUePcYmyyStue6yu1Tn9WX5xVUP84NADh+DT3H993RMkAD53I4tObbb9X54otktZW9Mepy2aVK27RJe5Yu9WLrjp6/3arh/aL15W9pcjjLBgJ+Pne/WjcJUNc2IeX2CQ3y05ndIvTZ7H0yDrrkxox/MlRQ5Fbv9qGm7d+dnqKB3SPUpILZjOuj+nJuGA6H9v70jRqff7EsB/WjyYjLlLdtk7JXle+H4XSq++ufq9NjE0uXWfz81PyKG7R/wZzSQcLtH/yfrP4B+mNQD+2bW34WA1/kdji048dvlDTSXI9WF16m7C2blLaifD3cTqfOeP9znfy/iaXLLH5+ajfmBu2ZP0c527bIXVSk/m9+rG4PPGbaN7xNW0W07aB9i/6qsT4dD3+rRYOb2PRdssN0GaqvdzrVMsSizhHlX8K0L5lZ9q8084zn/6S5ZEhqXzKjweOrC1XkMjR7QIjOqijF9DFuh0PJM79Rs+Hmc6P5qMuUs3WTMlZWfG70fftzdXtyYukyi5+fWl91g1L+mKO87VuUuXKp5gw7zfTz74O3S5IW3X295l98bo33rSrsFov6h1o0O6fsknaS9Eu2oaZ2qW0FAXJSydPDsgJzmL2iwHNJvVYBnsB6SoahO3e55TjouF9nGgq2SmeG+d6l8QyHQ/tmfav4oeZzI+H8y5S/bZNyV1f8ONrp1Sk64ZGJpcssfn5qcvn1yvhzjgp3+PaXLWpDfXmeBQD4hpDLL1bcD9Mq/LHGxhz5AAAA+BjeM5lRjzLUwqy+1MPtcGjn9G/UcoQ5f0oqya3TK8mt+7/7uXo9PbF0mcXPT22vvkF7fp+j3JLc+tQ3PlbX++tWbk1WW8Zusej0MIt+PSSnnZVtKLGSnPbAJA2H5rTL88057Wfphm7bac5pv8rw5LQDw30vp5XIao/E38+ioYn++np7sekzny+3FSsp1Kou0eVnJGwf6Vn2Z6r5M5+/9jllSOoQ0bBmMawvzyuofpwbAHD8GnqO76MX+QAanqCoKCX26q20zZuUvnmzJGnByxN13ew5uvyLqVrx5VS17NdP3a64Ul9ff51pYK2vu3NUYw0ct1qXPLFBF50eoz9X5eiz2fv19t2tZbValJ7t0KL1uWrdJFBtmnouyTTu4iYa9tBajXx0vS49M1Y7Uor0vym7NLRvlPp3CS89dnaeU/NXZmv81c281b0aV5/Pja3vvKQ+X85T97e+1J4fpiq616lKvGC0lt9zrWQYskdGKbJrb+Vt26T8bZslw9DWdyeq3f1PKW/rJuVuXqemIy5TeOfuWnjFIM9BrVYlnH2+9v/xq0JP6KDQEzqY7rNwb7Jy1q3yQm+PbNXrL+m8H+fpzI++1NZvpiq+z6lqc8lo/XGrpx7+kVGK69Fb2Vs2KWerpx6r35ioHo88pezNm5S1cZ1aXXiZYrp016wLPfVwOxxaOfFZ9Rz/P7mdTu35bbZCmjbTibeN0/6li7Xly8+83OvK3dzaX8P/yNc1Cws0oqld/6Q79eUuhyZ2C5TVYlFGsaF/M1xKCrGqVahVbcOsGtnUpvuWF2pbnludIvy0Jdet1zcVq3e0n86Is8llGPp5j1P9423akOPWhhzzDMeNAy3q6IOh04Y3X9KAb+bplPe+1M7vpiq296lqedFoLbqz7NyI7tZbuVs3Kbfkb2XDWxPV+cGnlLtlk7I3rVPzkZcpqkt3zb/Ec244crK1f+EC0/0cCDYzVy1TxvIltd7Po3VplEW37jL00G63BoZZtKJA+jnH0AMJFlktFmW7DK0plBLtUqK/RS39pbPCLJqQYii52K02ARbtchiakmGoS6DUK9hz3IuiLJq309DjewydEWZoZYH0Zaah2+M8s1z4ol3vvaSun89Vp0lfKnX6VEX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-      "text/plain": [
-       "<Figure size 3600x2800 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "for year in years:\n",
     "    w, jet1th4qbins, jet1ptbins, jet1msdbins = effs_th4q[year].to_numpy()\n",
@@ -460,20 +265,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 7200x1400 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(1, 4, figsize=(18 * 4, 14), gridspec_kw={\"wspace\": 0.05})\n",
     "\n",
@@ -509,20 +303,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 7200x1400 with 8 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots(1, 4, figsize=(18 * 4, 14), gridspec_kw={\"wspace\": 0.05})\n",
     "\n",
@@ -556,6 +339,209 @@
     "plt.savefig(f\"{plot_dir}/th4q_overlap.pdf\", bbox_inches=\"tight\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot per Txbb bin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for year in years:\n",
+    "    w, jet1txbbbins, jet1ptbins, jet1msdbins = effs_txbb[year].to_numpy()\n",
+    "\n",
+    "    for k in range(len(jet1txbbbins) - 1):\n",
+    "        fig, ax = plt.subplots(figsize=(18, 14))\n",
+    "        mesh = ax.pcolormesh(jet1msdbins, jet1ptbins, w[k], cmap=\"turbo\", vmin=0, vmax=1)\n",
+    "        for i in range(len(jet1ptbins) - 1):\n",
+    "            for j in range(len(jet1msdbins) - 1):\n",
+    "                if not math.isnan(w[k][i, j]):\n",
+    "                    ax.text(\n",
+    "                        (jet1msdbins[j] + jet1msdbins[j + 1]) / 2,\n",
+    "                        (jet1ptbins[i] + jet1ptbins[i + 1]) / 2,\n",
+    "                        w[k][i, j].round(2),\n",
+    "                        color=\"black\" if 0.1 < w[k][i, j] < 0.9 else \"white\",\n",
+    "                        ha=\"center\",\n",
+    "                        va=\"center\",\n",
+    "                        fontsize=12,\n",
+    "                    )\n",
+    "\n",
+    "        ax.set_title(\n",
+    "            f\"{year}: {jet1txbbbins[k]:.2f} < $T_{{Xbb}}$ Score < {jet1txbbbins[k + 1]:.2f}\"\n",
+    "        )\n",
+    "        ax.set_xlabel(\"Jet 1 MassSD (GeV)\")\n",
+    "        ax.set_ylabel(\"Jet 1 $p_T$ (GeV)\")\n",
+    "        plt.colorbar(mesh, ax=ax)\n",
+    "        plt.savefig(\n",
+    "            f\"{plot_dir}/{year}_txbb_{jet1txbbbins[k]:.2f}_{jet1txbbbins[k + 1]:.2f}_effs.pdf\",\n",
+    "            bbox_inches=\"tight\",\n",
+    "        )\n",
+    "        plt.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for year in years:\n",
+    "    w, jet1th4qbins, jet1ptbins, jet1msdbins = effs_th4q[year].to_numpy()\n",
+    "\n",
+    "    for k in range(len(jet1th4qbins) - 1):\n",
+    "        fig, ax = plt.subplots(figsize=(18, 14))\n",
+    "        mesh = ax.pcolormesh(jet1msdbins, jet1ptbins, w[k], cmap=\"turbo\", vmin=0, vmax=1)\n",
+    "        for i in range(len(jet1ptbins) - 1):\n",
+    "            for j in range(len(jet1msdbins) - 1):\n",
+    "                if not math.isnan(w[k][i, j]):\n",
+    "                    ax.text(\n",
+    "                        (jet1msdbins[j] + jet1msdbins[j + 1]) / 2,\n",
+    "                        (jet1ptbins[i] + jet1ptbins[i + 1]) / 2,\n",
+    "                        w[k][i, j].round(2),\n",
+    "                        color=\"black\" if 0.1 < w[k][i, j] < 0.9 else \"white\",\n",
+    "                        ha=\"center\",\n",
+    "                        va=\"center\",\n",
+    "                        fontsize=12,\n",
+    "                    )\n",
+    "\n",
+    "        ax.set_title(\n",
+    "            f\"{year}: {jet1th4qbins[k]:.2f} < $T_{{H4q}}$ Score < {jet1th4qbins[k + 1]:.2f}\"\n",
+    "        )\n",
+    "        ax.set_xlabel(\"Jet 1 MassSD (GeV)\")\n",
+    "        ax.set_ylabel(\"Jet 1 $p_T$ (GeV)\")\n",
+    "        plt.colorbar(mesh, ax=ax)\n",
+    "        plt.savefig(\n",
+    "            f\"{plot_dir}/{year}_th4q_{jet1th4qbins[k]:.2f}_{jet1th4qbins[k + 1]:.2f}_effs.pdf\",\n",
+    "            bbox_inches=\"tight\",\n",
+    "        )\n",
+    "        plt.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3D efficiencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined = {}\n",
+    "years = [\"2018\"]\n",
+    "\n",
+    "for year in years:\n",
+    "    with open(f\"trigEffs/{year}_4d_combined.pkl\", \"rb\") as filehandler:\n",
+    "        combined[year] = pickle.load(filehandler)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rebin_pt = [250, 300, 400, 600, 800, 1000]\n",
+    "c_rebinned = {\n",
+    "    key: utils.rebin_hist(combined[year][key][:, :, :, sum, :, sum], \"jet2pt\", rebin_pt)\n",
+    "    for key in [\"num\", \"den\"]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "effs = {year: c_rebinned[\"num\"] / c_rebinned[\"den\"] for year in years}\n",
+    "\n",
+    "errors = {}\n",
+    "intervals_pt = {}\n",
+    "\n",
+    "for year in years:\n",
+    "    intervals = clopper_pearson_interval(\n",
+    "        c_rebinned[\"num\"].view(flow=False),\n",
+    "        c_rebinned[\"den\"].view(flow=False),\n",
+    "    )\n",
+    "    errors[year] = (intervals[1] - intervals[0]) / 2\n",
+    "    intervals_pt[year] = intervals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w, jet1ptbins, jet1msdbins, jet2ptbins = effs[year][-1, :, :, 1:].to_numpy()\n",
+    "plot_all_wrapper(\n",
+    "    jet2ptbins, jet1msdbins, jet1ptbins, np.moveaxis(w, 2, 0), 1, year, f\"{year}_jet2pt\", bins=\"pt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(4):\n",
+    "    w, jet1txbbbins, jet1ptbins, jet1msdbins = effs[year][:, :, :, i + 1].to_numpy()\n",
+    "    plot_all_wrapper(\n",
+    "        jet1txbbbins, jet1msdbins, jet1ptbins, w, 1, year, f\"{year}_jet2pt_{i}\", bins=\"txbb\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(4):\n",
+    "    _, jet1txbbbins, jet1ptbins, jet1msdbins = effs[year][:, :, :, i + 1].to_numpy()\n",
+    "    w = errors[year][:, :, :, i + 1]\n",
+    "    plot_all_wrapper(\n",
+    "        jet1txbbbins, jet1msdbins, jet1ptbins, w, 1, year, f\"{year}_errs_jet2pt_{i}\", bins=\"txbb\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check statistical compatibility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comp = (intervals_txbb[year][0][..., None] <= intervals_pt[year][1][..., 1:]) & (\n",
+    "    intervals_txbb[year][1][..., None] >= intervals_pt[year][0][..., 1:]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.mean(comp)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -580,7 +566,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.11"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2016.coffea b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2016.coffea
new file mode 100644
index 00000000..a9f77368
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diff --git a/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2016APV.coffea b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2016APV.coffea
new file mode 100644
index 00000000..69384288
Binary files /dev/null and b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2016APV.coffea differ
diff --git a/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2017.coffea b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2017.coffea
new file mode 100644
index 00000000..83f48091
Binary files /dev/null and b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2017.coffea differ
diff --git a/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2018.coffea b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2018.coffea
new file mode 100644
index 00000000..2336e48c
Binary files /dev/null and b/src/HHbbVV/corrections/btag_effs/btageff_deepJet_M_2018.coffea differ
diff --git a/src/HHbbVV/corrections/corrections.ipynb b/src/HHbbVV/corrections/corrections.ipynb
new file mode 100644
index 00000000..cf5727b9
--- /dev/null
+++ b/src/HHbbVV/corrections/corrections.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/fh/cwyrvktn5bz76x4cpy_lbgnh0000gn/T/ipykernel_40544/1077085208.py:2: DeprecationWarning: Please use `interp1d` from the `scipy.interpolate` namespace, the `scipy.interpolate.interpolate` namespace is deprecated.\n",
+      "  jec_compiled = pickle.load(f)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"jec_compiled.pkl\", \"rb\") as f:\n",
+    "    jec_compiled = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'_jec': campaign   : Summer19UL16\n",
+       " data era   : V7\n",
+       " data type  : MC\n",
+       " jet type   : AK4PFchs\n",
+       " levels     : L1FastJet,L2Relative\n",
+       " signature  : (JetEta,Rho,JetPt,JetA),\n",
+       " '_junc': campaign   : Summer19UL16\n",
+       " data era   : V7\n",
+       " data type  : MC\n",
+       " jet type   : AK4PFchs\n",
+       " levels     : AbsoluteStat,AbsoluteScale,AbsoluteFlavMap,AbsoluteMPFBias,Fragmentation,SinglePionECAL,SinglePionHCAL,FlavorQCD,TimePtEta,RelativeJEREC1,RelativeJEREC2,RelativeJERHF,RelativePtBB,RelativePtEC1,RelativePtEC2,RelativePtHF,RelativeBal,RelativeSample,RelativeFSR,RelativeStatFSR,RelativeStatEC,RelativeStatHF,PileUpDataMC,PileUpPtRef,PileUpPtBB,PileUpPtEC1,PileUpPtEC2,PileUpPtHF,PileUpMuZero,PileUpEnvelope,SubTotalPileUp,SubTotalRelative,SubTotalPt,SubTotalScale,SubTotalAbsolute,SubTotalMC,Total,TotalNoFlavor,TotalNoTime,TotalNoFlavorNoTime,FlavorZJet,FlavorPhotonJet,FlavorPureGluon,FlavorPureQuark,FlavorPureCharm,FlavorPureBottom,TimeRunBCD,TimeRunEF,TimeRunGH,CorrelationGroupMPFInSitu,CorrelationGroupIntercalibration,CorrelationGroupbJES,CorrelationGroupFlavor,CorrelationGroupUncorrelated,jes\n",
+       " signature  : (JetEta,JetPt),\n",
+       " '_jer': campaign   : Summer20UL16\n",
+       " data era   : JRV3\n",
+       " data type  : MC\n",
+       " jet type   : AK4PFchs\n",
+       " levels     : PtResolution\n",
+       " signature  : (JetEta,Rho,JetPt),\n",
+       " '_jersf': campaign   : Summer20UL16\n",
+       " data era   : JRV3\n",
+       " data type  : MC\n",
+       " jet type   : AK4PFchs\n",
+       " levels     : SF\n",
+       " signature  : (JetEta)}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jec_compiled[\"jet_factory\"][\"2016postVFPmc\"].jec_stack.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jec_compiled[\"fatjet_factory\"][\"2016preVFPmc\"].jec_stack.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pog_correction_path = \"/cvmfs/cms.cern.ch/rsync/cms-nanoAOD/jsonpog-integration/\"\n",
+    "pog_jsons = {\n",
+    "    \"muon\": [\"MUO\", \"muon_Z.json.gz\"],\n",
+    "    \"electron\": [\"EGM\", \"electron.json.gz\"],\n",
+    "    \"pileup\": [\"LUM\", \"puWeights.json.gz\"],\n",
+    "    \"jec\": [\"JME\", \"fatJet_jerc.json.gz\"],\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def get_jec_key(year: str):\n",
+    "    thekey = f\"{year}mc\"\n",
+    "    if year == \"2016\":\n",
+    "        thekey = \"2016postVFPmc\"\n",
+    "    elif year == \"2016APV\":\n",
+    "        thekey = \"2016preVFPmc\"\n",
+    "    return thekey\n",
+    "\n",
+    "\n",
+    "def get_vfp_year(year: str) -> str:\n",
+    "    if year == \"2016\":\n",
+    "        year = \"2016postVFP\"\n",
+    "    elif year == \"2016APV\":\n",
+    "        year = \"2016preVFP\"\n",
+    "\n",
+    "    return year\n",
+    "\n",
+    "\n",
+    "def get_UL_year(year: str) -> str:\n",
+    "    return f\"{get_vfp_year(year)}_UL\"\n",
+    "\n",
+    "\n",
+    "def get_pog_json(obj: str, year: str) -> str:\n",
+    "    try:\n",
+    "        pog_json = pog_jsons[obj]\n",
+    "    except:\n",
+    "        print(f\"No json for {obj}\")\n",
+    "\n",
+    "    year = get_UL_year(year)\n",
+    "    return f\"{pog_correction_path}/POG/{pog_json[0]}/{year}/{pog_json[1]}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json, gzip\n",
+    "\n",
+    "with gzip.open(get_pog_json(\"jec\", \"2017\"), \"r\") as fin:\n",
+    "    jec = json.loads(fin.read().decode(\"utf-8\"))\n",
+    "\n",
+    "# for corr in jec[\"corrections\"]:\n",
+    "#     print(corr[\"name\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/corrections/ratio_feb20.root b/src/HHbbVV/corrections/ratio_feb20.root
new file mode 100644
index 00000000..145d2e47
Binary files /dev/null and b/src/HHbbVV/corrections/ratio_feb20.root differ
diff --git a/src/HHbbVV/corrections/trigEffs/2018_4d_combined.pkl b/src/HHbbVV/corrections/trigEffs/2018_4d_combined.pkl
new file mode 100644
index 00000000..eba8bc6e
Binary files /dev/null and b/src/HHbbVV/corrections/trigEffs/2018_4d_combined.pkl differ
diff --git a/src/HHbbVV/corrections/utils.py b/src/HHbbVV/corrections/utils.py
new file mode 100644
index 00000000..2a1a1585
--- /dev/null
+++ b/src/HHbbVV/corrections/utils.py
@@ -0,0 +1,66 @@
+import itertools
+import numpy as np
+import hist
+from hist import Hist
+
+
+LUMI = {  # in pb^-1
+    "2016": 16830.0,
+    "2016APV": 19500.0,
+    "2017": 41480.0,
+    "2018": 59830.0,
+}
+
+
+# from https://gist.github.com/kdlong/d697ee691c696724fc656186c25f8814
+def rebin_hist(h, axis_name, edges):
+    if type(edges) == int:
+        return h[{axis_name: hist.rebin(edges)}]
+
+    ax = h.axes[axis_name]
+    ax_idx = [a.name for a in h.axes].index(axis_name)
+    if not all([np.isclose(x, ax.edges).any() for x in edges]):
+        raise ValueError(
+            f"Cannot rebin histogram due to incompatible edges for axis '{ax.name}'\n"
+            f"Edges of histogram are {ax.edges}, requested rebinning to {edges}"
+        )
+
+    # If you rebin to a subset of initial range, keep the overflow and underflow
+    overflow = ax.traits.overflow or (
+        edges[-1] < ax.edges[-1] and not np.isclose(edges[-1], ax.edges[-1])
+    )
+    underflow = ax.traits.underflow or (
+        edges[0] > ax.edges[0] and not np.isclose(edges[0], ax.edges[0])
+    )
+    flow = overflow or underflow
+    new_ax = hist.axis.Variable(edges, name=ax.name, overflow=overflow, underflow=underflow)
+    axes = list(h.axes)
+    axes[ax_idx] = new_ax
+
+    hnew = hist.Hist(*axes, name=h.name, storage=h._storage_type())
+
+    # Offset from bin edge to avoid numeric issues
+    offset = 0.5 * np.min(ax.edges[1:] - ax.edges[:-1])
+    edges_eval = edges + offset
+    edge_idx = ax.index(edges_eval)
+    # Avoid going outside the range, reduceat will add the last index anyway
+    if edge_idx[-1] == ax.size + ax.traits.overflow:
+        edge_idx = edge_idx[:-1]
+
+    if underflow:
+        # Only if the original axis had an underflow should you offset
+        if ax.traits.underflow:
+            edge_idx += 1
+        edge_idx = np.insert(edge_idx, 0, 0)
+
+    # Take is used because reduceat sums i:len(array) for the last entry, in the case
+    # where the final bin isn't the same between the initial and rebinned histogram, you
+    # want to drop this value. Add tolerance of 1/2 min bin width to avoid numeric issues
+    hnew.values(flow=flow)[...] = np.add.reduceat(h.values(flow=flow), edge_idx, axis=ax_idx).take(
+        indices=range(new_ax.size + underflow + overflow), axis=ax_idx
+    )
+    if hnew._storage_type() == hist.storage.Weight():
+        hnew.variances(flow=flow)[...] = np.add.reduceat(
+            h.variances(flow=flow), edge_idx, axis=ax_idx
+        ).take(indices=range(new_ax.size + underflow + overflow), axis=ax_idx)
+    return hnew
diff --git a/src/HHbbVV/postprocessing/BDTPreProcessing.py b/src/HHbbVV/postprocessing/BDTPreProcessing.py
index ca1af517..02ebc7b1 100644
--- a/src/HHbbVV/postprocessing/BDTPreProcessing.py
+++ b/src/HHbbVV/postprocessing/BDTPreProcessing.py
@@ -7,7 +7,18 @@
 import warnings
 import pandas as pd
 from pandas.errors import SettingWithCopyWarning
-from hh_vars import samples, sig_key, data_key, jec_shifts, jmsr_shifts, jec_vars, jmsr_vars
+from hh_vars import (
+    samples,
+    res_samples,
+    nonres_samples,
+    nonres_sig_keys,
+    res_sig_keys,
+    data_key,
+    jec_shifts,
+    jmsr_shifts,
+    jec_vars,
+    jmsr_vars,
+)
 import os, sys
 
 # ignore these because they don't seem to apply
@@ -16,9 +27,6 @@
 from copy import deepcopy
 
 
-BDT_samples = list(samples.keys())
-
-
 BDT_data_vars = [
     "MET_pt",
     "DijetEta",
@@ -45,32 +53,28 @@ def main(args):
     # make plot, template dirs if needed
     _make_dirs(args)
 
-    # save cutflow as pandas table
-    cutflow = pd.DataFrame(index=list(samples.keys()))
-    systematics = {}
+    BDT_sample_order = nonres_sig_keys + ["QCD", "TT", "ST", "V+Jets", "Diboson", "Data"]
 
-    events_dict = utils.load_samples(
-        args.data_dir, samples, args.year, postprocessing.filters if args.filters else None
+    sig_keys, sig_samples, bg_keys, bg_samples = postprocessing._process_samples(
+        args, BDT_sample_order
     )
-    utils.add_to_cutflow(events_dict, "BDTPreselection", "weight", cutflow)
 
-    # print weighted sample yields
-    print("")
-    for sample in events_dict:
-        tot_weight = np.sum(events_dict[sample]["weight"].values)
-        print(f"Pre-selection {sample} yield: {tot_weight:.2f}")
+    # save cutflow as pandas table
+    all_samples = sig_keys + bg_keys
+    cutflow = pd.DataFrame(index=all_samples)
 
+    filters = postprocessing.new_filters
+    events_dict = postprocessing._load_samples(args, bg_samples, sig_samples, cutflow, filters)
     postprocessing.apply_weights(events_dict, args.year, cutflow)
     bb_masks = postprocessing.bb_VV_assignment(events_dict)
-    _ = postprocessing.postprocess_lpsfs(events_dict[sig_key], save_all=False)
-    cutflow.to_csv(f"{args.plot_dir}/{args.year}/cutflow.csv")
-    print("\nCutflow:\n", cutflow)
+    if args.control_plots:
+        cutflow.to_csv(f"{args.plot_dir}/{args.year}/cutflow.csv")
 
     control_plot_vars = postprocessing.control_plot_vars
     del control_plot_vars["BDTScore"]
 
     if args.control_plots:
-        print("\nMaking control plots")
+        # print("\nMaking control plots")
         postprocessing.control_plots(
             events_dict,
             bb_masks,
@@ -78,14 +82,13 @@ def main(args):
             f"{args.plot_dir}/{args.year}",
             args.year,
         )
-        print("Made and saved control plots")
+        # print("Made and saved control plots")
 
     if args.bdt_data:
-        print("\nSaving BDT Data")
-        save_bdt_data(
-            events_dict, bb_masks, f"{args.data_dir}/bdt_data/{args.year}_bdt_data.parquet"
-        )
-        print("Saved BDT Data")
+        # print("\nSaving BDT Data")
+        data_dir = args.data_dir if args.data_dir else args.signal_data_dir
+        save_bdt_data(events_dict, bb_masks, f"{data_dir}/bdt_data/{args.year}_bdt_data.parquet")
+        # print("Saved BDT Data")
 
 
 def _make_dirs(args):
@@ -93,7 +96,11 @@ def _make_dirs(args):
         os.system(f"mkdir -p {args.plot_dir}/{args.year}/")
 
     if args.bdt_data:
-        os.system(f"mkdir -p {args.data_dir}/bdt_data/")
+        if args.data_dir:
+            os.system(f"mkdir -p {args.data_dir}/bdt_data/")
+        else:
+            if args.signal_data_dir:
+                os.system(f"mkdir -p {args.signal_data_dir}/bdt_data/")
 
 
 def save_bdt_data(
@@ -116,20 +123,27 @@ def save_bdt_data(
                 jec_jmsr_vars.append(f"{var}_{jshift}")
 
     bdt_events_dict = []
-
-    for sample in BDT_samples:
-        save_vars = BDT_data_vars + jec_jmsr_vars if sample != "Data" else BDT_data_vars
+    bdt_sample_order = []
+    for key in events_dict.keys():
+        save_vars = BDT_data_vars + jec_jmsr_vars if key != "Data" else BDT_data_vars
         events = pd.DataFrame(
-            {var: utils.get_feat(events_dict[sample], var, bb_masks[sample]) for var in save_vars}
+            {var: utils.get_feat(events_dict[key], var, bb_masks[key]) for var in save_vars}
         )
-        events["Dataset"] = sample
+        events["Dataset"] = key
         bdt_events_dict.append(events)
+        bdt_sample_order.append(key)
 
     bdt_events = pd.concat(bdt_events_dict, axis=0)
-
     table = pa.Table.from_pandas(bdt_events)
     pq.write_table(table, out_file)
 
+    sample_order_dict = OrderedDict(
+        [(sample, len(bdt_events_dict[sample])) for sample in bdt_sample_order]
+    )
+
+    with open(out_file.replace("bdt_data.parquet", "sample_order.txt"), "w") as f:
+        f.write(str(sample_order_dict))
+
 
 if __name__ == "__main__":
     import argparse
@@ -139,18 +153,36 @@ def save_bdt_data(
     parser.add_argument(
         "--data-dir",
         dest="data_dir",
-        default="../../../../data/skimmer/Feb24/",
+        default=None,
         help="path to skimmed parquet",
         type=str,
     )
-
+    parser.add_argument(
+        "--signal-data-dir",
+        default=None,
+        help="path to skimmed signal parquets, if different from other data",
+        type=str,
+    )
     parser.add_argument(
         "--year",
         default="2017",
         choices=["2016", "2016APV", "2017", "2018"],
         type=str,
     )
-
+    parser.add_argument(
+        "--sig-samples",
+        help="specify signal samples. By default, will use the samples defined in `hh_vars`.",
+        nargs="*",
+        default=None,
+        type=str,
+    )
+    parser.add_argument(
+        "--bg-keys",
+        help="specify background samples",
+        nargs="*",
+        default=["QCD", "TT", "ST", "V+Jets", "Diboson"],
+        type=str,
+    )
     parser.add_argument(
         "--plot-dir",
         help="If making control or template plots, path to directory to save them in",
@@ -158,11 +190,13 @@ def save_bdt_data(
         type=str,
     )
 
-    utils.add_bool_arg(parser, "control-plots", "make control plots", default=False)
-    utils.add_bool_arg(parser, "bdt-data", "save bdt training data", default=False)
+    utils.add_bool_arg(parser, "data", "include data", default=True)
     utils.add_bool_arg(
-        parser, "filters", "use pre-selection filters when loading samples", default=True
+        parser, "read-sig-samples", "read signal samples from directory", default=False
     )
+    utils.add_bool_arg(parser, "control-plots", "make control plots", default=False)
+    utils.add_bool_arg(parser, "resonant", "for resonant or nonresonant", default=False)
+    utils.add_bool_arg(parser, "bdt-data", "save bdt training data", default=False)
 
     args = parser.parse_args()
     main(args)
diff --git a/src/HHbbVV/postprocessing/BDT_hyperparam_scan.numbers b/src/HHbbVV/postprocessing/BDT_hyperparam_scan.numbers
new file mode 100755
index 00000000..61d26385
Binary files /dev/null and b/src/HHbbVV/postprocessing/BDT_hyperparam_scan.numbers differ
diff --git a/src/HHbbVV/postprocessing/CreateDatacard.py b/src/HHbbVV/postprocessing/CreateDatacard.py
index f0de1dfa..58b3939a 100644
--- a/src/HHbbVV/postprocessing/CreateDatacard.py
+++ b/src/HHbbVV/postprocessing/CreateDatacard.py
@@ -9,14 +9,16 @@
 """
 
 
-import os
-from typing import Dict
+import os, sys
+from typing import Dict, List, Tuple, Union
+from dataclasses import dataclass, field
+
 import numpy as np
 import pickle, json
 import logging
 from collections import OrderedDict
 
-# import hist
+import hist
 from hist import Hist
 import rhalphalib as rl
 
@@ -27,186 +29,715 @@
 adjust_posdef_yields = False
 
 # from utils import add_bool_arg
-from hh_vars import LUMI, sig_key, qcd_key, data_key, years, jecs, jmsr
+from hh_vars import LUMI, res_sig_keys, qcd_key, data_key, years, jecs, jmsr, res_mps
+import utils
+
+import argparse
+
+
+@dataclass
+class Syst:
+    """For storing info about systematics"""
+
+    name: str = None
+    prior: str = None  # e.g. "lnN", "shape", etc.
+
+    # float if same value in all regions/samples, dictionary of values per region/sample if not
+    # if both, region should be the higher level of the dictionary
+    value: Union[float, Dict[str, float]] = None
+    value_down: Union[float, Dict[str, float]] = None  # if None assumes symmetric effect
+    # if the value is different for different regions or samples
+    diff_regions: bool = False
+    diff_samples: bool = False
+
+    samples: List[str] = None  # samples affected by it
+    # in case of uncorrelated unc., which years to split into
+    uncorr_years: List[str] = field(default_factory=lambda: years)
+    pass_only: bool = False  # is it applied only in the pass regions
+
+    def __post_init__(self):
+        if isinstance(self.value, dict) and not (self.diff_regions or self.diff_samples):
+            raise RuntimeError(
+                f"Value for systematic is a dictionary but neither ``diff_regions`` nor ``diff_samples`` is set."
+            )
+
+
+@dataclass
+class ShapeVar:
+    """For storing and calculating info about variables used in fit"""
+
+    name: str = None
+    bins: np.ndarray = None  # bin edges
+    order: int = None  # TF order
+
+    def __post_init__(self):
+        # use bin centers for polynomial fit
+        self.pts = self.bins[:-1] + 0.5 * np.diff(self.bins)
+        # scale to be between [0, 1]
+        self.scaled = (self.pts - self.bins[0]) / (self.bins[-1] - self.bins[0])
+
+
+def add_bool_arg(parser, name, help, default=False, no_name=None):
+    """Add a boolean command line argument for argparse"""
+    varname = "_".join(name.split("-"))  # change hyphens to underscores
+    group = parser.add_mutually_exclusive_group(required=False)
+    group.add_argument("--" + name, dest=varname, action="store_true", help=help)
+    if no_name is None:
+        no_name = "no-" + name
+        no_help = "don't " + help
+    else:
+        no_help = help
+    group.add_argument("--" + no_name, dest=varname, action="store_false", help=no_help)
+    parser.set_defaults(**{varname: default})
+
+
+def mxmy(sample):
+    mY = int(sample.split("-")[-1])
+    mX = int(sample.split("NMSSM_XToYHTo2W2BTo4Q2B_MX-")[1].split("_")[0])
+
+    return (mX, mY)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--templates-dir",
+    default="",
+    type=str,
+    help="input pickle file of dict of hist.Hist templates",
+)
+add_bool_arg(parser, "sig-separate", "separate templates for signals and bgs", default=False)
+add_bool_arg(parser, "do-jshifts", "Do JEC/JMC corrections.", default=True)
+
+add_bool_arg(parser, "only-sm", "Only add SM HH samples for (for debugging nonres)", default=False)
+add_bool_arg(
+    parser,
+    "combine-lasttwo",
+    "Check combining last two bins in nonres for HWW review",
+    default=False,
+)
 
+parser.add_argument("--cards-dir", default="cards", type=str, help="output card directory")
 
-# (name in card, name in templates)
+parser.add_argument("--mcstats-threshold", default=100, type=float, help="mcstats threshold n_eff")
+parser.add_argument("--epsilon", default=1e-3, type=float, help="epsilon to avoid numerical errs")
+parser.add_argument(
+    "--scale-templates", default=None, type=float, help="scale all templates for bias tests"
+)
+parser.add_argument(
+    "--min-qcd-val", default=1e-3, type=float, help="clip the pass QCD to above a minimum value"
+)
+
+parser.add_argument(
+    "--sig-sample", default=None, type=str, help="can specify a specific signal key"
+)
+
+parser.add_argument(
+    "--nTF",
+    default=None,
+    nargs="*",
+    type=int,
+    help="order of polynomial for TF in [dim 1, dim 2] = [mH(bb), -] for nonresonant or [mY, mX] for resonant."
+    "Default is 0 for nonresonant and (1, 2) for resonant.",
+)
+
+parser.add_argument("--model-name", default=None, type=str, help="output model name")
+parser.add_argument(
+    "--year",
+    help="year",
+    type=str,
+    default="all",
+    choices=["2016APV", "2016", "2017", "2018", "all"],
+)
+add_bool_arg(parser, "mcstats", "add mc stats nuisances", default=True)
+add_bool_arg(parser, "bblite", "use barlow-beeston-lite method", default=True)
+add_bool_arg(parser, "resonant", "for resonant or nonresonant", default=False)
+args = parser.parse_args()
+
+
+CMS_PARAMS_LABEL = "CMS_bbWW_hadronic" if not args.resonant else "CMS_XHYbbWW_boosted"
+
+if args.nTF is None:
+    args.nTF = [1, 2] if args.resonant else [0]
+
+# (name in templates, name in cards)
 mc_samples = OrderedDict(
     [
-        ("ggHH_kl_1_kt_1_hbbhww4q", "HHbbVV"),
-        ("diboson", "Diboson"),
-        ("ttbar", "TT"),
-        ("singletop", "ST"),
-        ("vjets", "V+Jets"),
+        ("TT", "ttbar"),
+        ("V+Jets", "vjets"),
+        # ("Diboson", "diboson"),
+        ("ST", "singletop"),
     ]
 )
 
-all_mc = list(mc_samples.values())
-lumi_run2 = np.sum(list(LUMI.values()))
-
+bg_keys = list(mc_samples.keys())
+nonres_sig_keys_ggf = [
+    "HHbbVV",
+    "ggHH_kl_2p45_kt_1_HHbbVV",
+    "ggHH_kl_5_kt_1_HHbbVV",
+    "ggHH_kl_0_kt_1_HHbbVV",
+]
+nonres_sig_keys_vbf = [
+    "qqHH_CV_1_C2V_1_kl_1_HHbbVV",
+    "qqHH_CV_1_C2V_0_kl_1_HHbbVV",
+    "qqHH_CV_1p5_C2V_1_kl_1_HHbbVV",
+    "qqHH_CV_1_C2V_1_kl_2_HHbbVV",
+    "qqHH_CV_1_C2V_2_kl_1_HHbbVV",
+    "qqHH_CV_1_C2V_2_kl_1_HHbbVV",
+    "qqHH_CV_1_C2V_1_kl_0_HHbbVV",
+    "qqHH_CV_0p5_C2V_1_kl_1_HHbbVV",
+]
+
+if args.only_sm:
+    nonres_sig_keys_ggf, nonres_sig_keys_vbf = ["HHbbVV"], []
+
+nonres_sig_keys = nonres_sig_keys_ggf + nonres_sig_keys_vbf
+sig_keys = []
+hist_names = {}  # names of hist files for the samples
+
+if args.resonant:
+    if args.sig_sample != "":
+        mX, mY = mxmy(args.sig_sample)
+        mc_samples[f"X[{mX}]->H(bb)Y[{mY}](VV)"] = f"xhy_mx{mX}_my{mY}"
+        hist_names[f"X[{mX}]->H(bb)Y[{mY}](VV)"] = f"NMSSM_XToYHTo2W2BTo4Q2B_MX-{mX}_MY-{mY}"
+        sig_keys.append(f"X[{mX}]->H(bb)Y[{mY}](VV)")
+    else:
+        print("--sig-sample needs to be specified for resonant cards")
+        sys.exit()
+else:
+    for key in nonres_sig_keys:
+        mc_samples[key] = key.replace("HHbbVV", "hbbhww")
+
+    mc_samples["HHbbVV"] = "ggHH_kl_1_kt_1_hbbhww"
+    sig_keys = nonres_sig_keys
+
+all_mc = list(mc_samples.keys())
+
+if args.year != "all":
+    years = [args.year]
+    full_lumi = LUMI[args.year]
+else:
+    full_lumi = np.sum(list(LUMI.values()))
+
+rate_params = {
+    sig_key: rl.IndependentParameter(f"{mc_samples[sig_key]}Rate", 1.0, 0, 1)
+    for sig_key in sig_keys
+}
 
-# https://gitlab.cern.ch/hh/naming-conventions#experimental-uncertainties
 # dictionary of nuisance params -> (modifier, samples affected by it, value)
 nuisance_params = {
-    "lumi_13TeV_2016": [
-        "lnN",
-        all_mc,
-        1.01 ** ((LUMI["2016"] + LUMI["2016APV"]) / lumi_run2),
-    ],
-    "lumi_13TeV_2017": [
-        "lnN",
-        all_mc,
-        1.02 ** (LUMI["2017"] / lumi_run2),
-    ],
-    "lumi_13TeV_2018": [
-        "lnN",
-        all_mc,
-        1.015 ** (LUMI["2018"] / lumi_run2),
-    ],
-    "lumi_13TeV_correlated": [
-        "lnN",
-        all_mc,
-        (
-            (1.006 ** ((LUMI["2016"] + LUMI["2016APV"]) / lumi_run2))
-            * (1.009 ** (LUMI["2017"] / lumi_run2))
-            * (1.02 ** (LUMI["2018"] / lumi_run2))
+    # https://gitlab.cern.ch/hh/naming-conventions#experimental-uncertainties
+    "lumi_13TeV_2016": Syst(
+        prior="lnN", samples=all_mc, value=1.01 ** ((LUMI["2016"] + LUMI["2016APV"]) / full_lumi)
+    ),
+    "lumi_13TeV_2017": Syst(prior="lnN", samples=all_mc, value=1.02 ** (LUMI["2017"] / full_lumi)),
+    "lumi_13TeV_2018": Syst(prior="lnN", samples=all_mc, value=1.015 ** (LUMI["2018"] / full_lumi)),
+    "lumi_13TeV_correlated": Syst(
+        prior="lnN",
+        samples=all_mc,
+        value=(
+            (1.006 ** ((LUMI["2016"] + LUMI["2016APV"]) / full_lumi))
+            * (1.009 ** (LUMI["2017"] / full_lumi))
+            * (1.02 ** (LUMI["2018"] / full_lumi))
         ),
-    ],
-    "lumi_13TeV_1718": [
-        "lnN",
-        all_mc,
-        ((1.002 ** (LUMI["2017"] / lumi_run2)) * (1.006 ** (LUMI["2018"] / lumi_run2))),
-    ],
-    # these values will be added in from the systematics JSON
-    "triggerEffSF_uncorrelated": ["lnN", all_mc, 0],
-    "lp_sf": ["lnN", [sig_key], 0],
+    ),
+    "lumi_13TeV_1718": Syst(
+        prior="lnN",
+        samples=all_mc,
+        value=((1.006 ** (LUMI["2017"] / full_lumi)) * (1.002 ** (LUMI["2018"] / full_lumi))),
+    ),
+    # https://gitlab.cern.ch/hh/naming-conventions#theory-uncertainties
+    "BR_hbb": Syst(
+        prior="lnN", samples=nonres_sig_keys + res_sig_keys, value=1.0124, value_down=0.9874
+    ),
+    "BR_hww": Syst(prior="lnN", samples=nonres_sig_keys, value=1.0153, value_down=0.9848),
+    "pdf_gg": Syst(prior="lnN", samples=["TT"], value=1.042),
+    "pdf_qqbar": Syst(prior="lnN", samples=["ST"], value=1.027),
+    "pdf_Higgs_ggHH": Syst(prior="lnN", samples=nonres_sig_keys_ggf, value=1.030),
+    "pdf_Higgs_qqHH": Syst(prior="lnN", samples=nonres_sig_keys_vbf, value=1.021),
+    # TODO: add these for single Higgs backgrounds
+    # "pdf_Higgs_gg": Syst(prior="lnN", samples=ggfh_keys, value=1.019),
+    "QCDscale_ttbar": Syst(
+        prior="lnN",
+        samples=["ST", "TT"],
+        value={"ST": 1.03, "TT": 1.024},
+        value_down={"ST": 0.978, "TT": 0.965},
+        diff_samples=True,
+    ),
+    "QCDscale_qqHH": Syst(
+        prior="lnN", samples=nonres_sig_keys_vbf, value=1.0003, value_down=0.9996
+    ),
+    # "QCDscale_ggH": Syst(
+    #     prior="lnN",
+    #     samples=ggfh_keys,
+    #     value=1.039,
+    # ),
+    # "alpha_s": for single Higgs backgrounds
+    # value will be added in from the systematics JSON
+    f"{CMS_PARAMS_LABEL}_triggerEffSF_uncorrelated": Syst(
+        prior="lnN", samples=all_mc, diff_regions=True
+    ),
 }
+
+for sig_key in sig_keys:
+    # values will be added in from the systematics JSON
+    nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{mc_samples[sig_key]}"] = Syst(
+        prior="lnN", samples=[sig_key]
+    )
+
+if args.year != "all":
+    # remove other years' keys
+    for key in [
+        "lumi_13TeV_2016",
+        "lumi_13TeV_2017",
+        "lumi_13TeV_2018",
+        "lumi_13TeV_correlated",
+        "lumi_13TeV_1718",
+    ]:
+        if key != f"lumi_13TeV_{args.year}":
+            del nuisance_params[key]
+
 nuisance_params_dict = {
-    param: rl.NuisanceParameter(param, unc) for param, (unc, _, _) in nuisance_params.items()
+    param: rl.NuisanceParameter(param, syst.prior) for param, syst in nuisance_params.items()
 }
 
-# syst_vals = {
-#     "lumi_13TeV_2016": 1.01 ** ((LUMI["2016"] + LUMI["2016APV"]) / lumi_run2),
-#     "lumi_13TeV_2017": 1.02 ** (LUMI["2017"] / lumi_run2),
-#     "lumi_13TeV_2018": 1.015 ** (LUMI["2018"] / lumi_run2),
-#     "lumi_13TeV_correlated": (
-#         (1.006 ** ((LUMI["2016"] + LUMI["2016APV"]) / lumi_run2))
-#         * (1.009 ** (LUMI["2017"] / lumi_run2))
-#         * (1.02 ** (LUMI["2018"] / lumi_run2))
-#     ),
-#     "lumi_13TeV_1718": (
-#         (1.002 ** (LUMI["2017"] / lumi_run2)) * (1.006 ** (LUMI["2018"] / lumi_run2))
-#     ),
-# }
-
-# dictionary of shape systematics -> (name in cards, samples affected by it)
+# dictionary of correlated shape systematics: name in templates -> name in cards, etc.
 corr_year_shape_systs = {
-    "FSRPartonShower": ("ps_fsr", [sig_key, "V+Jets"]),
-    "ISRPartonShower": ("ps_isr", [sig_key, "V+Jets"]),
-    "PDFalphaS": ("CMS_bbbb_boosted_ggf_ggHHPDFacc", [sig_key]),
-    "JES": ("CMS_scale_j", all_mc),  # TODO: separate into individual
-    "txbb": ("CMS_bbbb_boosted_ggf_PNetHbbScaleFactors_correlated", [sig_key]),
+    "FSRPartonShower": Syst(name="ps_fsr", prior="shape", samples=nonres_sig_keys_ggf + ["V+Jets"]),
+    "ISRPartonShower": Syst(name="ps_isr", prior="shape", samples=nonres_sig_keys_ggf + ["V+Jets"]),
+    # TODO: should we be applying QCDscale for "others" process?
+    # https://github.com/LPC-HH/HHLooper/blob/master/python/prepare_card_SR_final.py#L290
+    # "QCDscale": Syst(
+    #     name=f"{CMS_PARAMS_LABEL}_ggHHQCDacc", prior="shape", samples=nonres_sig_keys_ggf
+    # ),
+    # "PDFalphaS": Syst(
+    #     name=f"{CMS_PARAMS_LABEL}_ggHHPDFacc", prior="shape", samples=nonres_sig_keys_ggf
+    # ),
+    # TODO: separate into individual
+    "JES": Syst(name="CMS_scale_j", prior="shape", samples=all_mc),
+    "txbb": Syst(
+        name=f"{CMS_PARAMS_LABEL}_PNetHbbScaleFactors_correlated",
+        prior="shape",
+        samples=sig_keys,
+        pass_only=True,
+    ),
+    # "top_pt": Syst(name="CMS_top_pT_reweighting", prior="shape", samples=["TT"])  # TODO
 }
 
 uncorr_year_shape_systs = {
-    "pileup": ("CMS_pileup", all_mc),
-    "JER": ("CMS_res_j", all_mc),
-    "JMS": ("CMS_bbbb_boosted_ggf_jms", all_mc),
-    "JMR": ("CMS_bbbb_boosted_ggf_jmr", all_mc),
+    "pileup": Syst(name="CMS_pileup", prior="shape", samples=all_mc),
+    # TODO: add 2016APV template into this
+    # "L1EcalPrefiring": Syst(
+    #     name="CMS_l1_ecal_prefiring", prior="shape", samples=all_mc, uncorr_years=["2016", "2017"]
+    # ),
+    "JER": Syst(name="CMS_res_j", prior="shape", samples=all_mc),
+    "JMS": Syst(name=f"{CMS_PARAMS_LABEL}_jms", prior="shape", samples=all_mc),
+    "JMR": Syst(name=f"{CMS_PARAMS_LABEL}_jmr", prior="shape", samples=all_mc),
 }
 
-shape_systs = {**corr_year_shape_systs}
+if not args.do_jshifts:
+    del corr_year_shape_systs["JES"]
+    del uncorr_year_shape_systs["JER"]
+    del uncorr_year_shape_systs["JMS"]
+    del uncorr_year_shape_systs["JMR"]
+
 
-for skey, (sname, ssamples) in uncorr_year_shape_systs.items():
+shape_systs_dict = {}
+for skey, syst in corr_year_shape_systs.items():
+    shape_systs_dict[skey] = rl.NuisanceParameter(syst.name, "shape")
+for skey, syst in uncorr_year_shape_systs.items():
     for year in years:
-        shape_systs[f"{skey}_{year}"] = (f"{sname}_{year}", ssamples)
+        if year in syst.uncorr_years:
+            shape_systs_dict[f"{skey}_{year}"] = rl.NuisanceParameter(
+                f"{syst.name}_{year}", "shape"
+            )
 
-shape_systs_dict = {
-    skey: rl.NuisanceParameter(sname, "shape") for skey, (sname, _) in shape_systs.items()
-}
 
-# systematics which apply only in the pass region
-pass_only = ["txbb"]
+def main(args):
+    # (pass, fail) x (unblinded, blinded)
+    regions: List[str] = [
+        f"{pf}{blind_str}" for pf in [f"pass", "fail"] for blind_str in ["", "Blinded"]
+    ]
 
+    # templates per region per year, templates per region summed across years
+    templates_dict, templates_summed = get_templates(
+        args.templates_dir, years, args.sig_separate, args.scale_templates, args.combine_lasttwo
+    )
 
-CMS_PARAMS_LABEL = "CMS_bbWW_boosted_ggf"
-PARAMS_LABEL = "bbWW_boosted_ggf"
+    # TODO: check if / how to include signal trig eff uncs. (rn only using bg uncs.)
+    process_systematics(args.templates_dir, args.sig_separate)
 
+    # random template from which to extract shape vars
+    sample_templates: Hist = templates_summed[regions[0]]
+    # [mH(bb)] for nonresonant, [mY, mX] for resonant
+    shape_vars = [
+        ShapeVar(name=axis.name, bins=axis.edges, order=args.nTF[i])
+        for i, axis in enumerate(sample_templates.axes[1:])
+    ]
 
-def main(args):
-    # (pass, fail) x (unblinded, blinded)
-    regions = [f"{pf}{blind_str}" for pf in [f"pass", "fail"] for blind_str in ["", "Blinded"]]
+    # build actual fit model now
+    model = rl.Model("HHModel" if not args.resonant else "XHYModel")
+
+    # Fill templates per sample, incl. systematics
+    # TODO: blinding for resonant
+    fill_args = [
+        model,
+        regions,
+        templates_dict,
+        templates_summed,
+        mc_samples,
+        nuisance_params,
+        nuisance_params_dict,
+        corr_year_shape_systs,
+        uncorr_year_shape_systs,
+        shape_systs_dict,
+        args.bblite,
+    ]
 
-    templates_dict = {}
+    fit_args = [model, shape_vars, templates_summed, args.scale_templates, args.min_qcd_val]
 
-    for year in years:
-        with open(f"{args.templates_dir}/{year}_templates.pkl", "rb") as f:
-            templates_dict[year] = pickle.load(f)
+    if args.resonant:
+        # fill 1 channel per mX bin
+        for i in range(len(shape_vars[1].pts)):
+            logging.info(f"\n\nFilling templates for mXbin {i}")
+            fill_regions(*fill_args, mX_bin=i)
+
+        res_alphabet_fit(*fit_args)
+    else:
+        fill_regions(*fill_args)
+        nonres_alphabet_fit(*fit_args)
 
-    templates_all = sum_templates(templates_dict)
+    ##############################################
+    # Save model
+    ##############################################
 
-    with open(f"{args.templates_dir}/systematics.json", "r") as f:
-        systematics = json.load(f)
+    logging.info("rendering combine model")
 
-    process_systematics(systematics)  # LP SF and trig effs.
+    os.system(f"mkdir -p {args.cards_dir}")
 
-    # random template from which to extract common data
-    sample_templates = templates_all[regions[0]]
-    shape_var = sample_templates.axes[1].name
+    out_dir = (
+        os.path.join(str(args.cards_dir), args.model_name)
+        if args.model_name is not None
+        else args.cards_dir
+    )
+    model.renderCombine(out_dir)
 
-    # get bins, centers, and scale centers for polynomial evaluation
-    msd_bins = sample_templates.axes[1].edges
-    msd_pts = msd_bins[:-1] + 0.5 * np.diff(msd_bins)
-    msd_scaled = (msd_pts - msd_bins[0]) / (msd_bins[-1] - msd_bins[0])
+    with open(f"{out_dir}/model.pkl", "wb") as fout:
+        pickle.dump(model, fout, 2)  # use python 2 compatible protocol
 
-    msd_obs = rl.Observable(shape_var, msd_bins)
 
-    # QCD overall pass / fail efficiency
-    qcd_eff = (
-        templates_all[f"pass"][qcd_key, :].sum().value
-        / templates_all[f"fail"][qcd_key, :].sum().value
-    )
+def _rem_neg(template_dict: Dict):
+    for sample, template in template_dict.items():
+        template.values()[template.values() < 0] = 0
 
-    # transfer factor
-    tf_dataResidual = rl.BasisPoly(
-        f"{CMS_PARAMS_LABEL}_tf_dataResidual",
-        (args.nDataTF,),
-        [shape_var],
-        basis="Bernstein",
-        limits=(-20, 20),
-    )
-    tf_dataResidual_params = tf_dataResidual(msd_scaled)
-    tf_params_pass = qcd_eff * tf_dataResidual_params  # scale params initially by qcd eff
+    return template_dict
 
-    # qcd params
-    qcd_params = np.array(
-        [
-            rl.IndependentParameter(f"{CMS_PARAMS_LABEL}_qcdparam_msdbin{i}", 0)
-            for i in range(msd_obs.nbins)
+
+def _match_samples(template: Hist, match_template: Hist) -> Hist:
+    """Temporary solution for case where 2018 templates don't have L1 prefiring in their axis
+
+    Args:
+        template (Hist): template to which samples may need to be added
+        match_template (Hist): template from which to extract extra samples
+    """
+    samples = list(match_template.axes[0])
+
+    # if template already has all the samples, don't do anything
+    if list(template.axes[0]) == samples:
+        return template
+
+    # otherwise remake template with samples from ``match_template``
+    h = hist.Hist(*match_template.axes, storage="weight")
+
+    for sample in template.axes[0]:
+        sample_index = np.where(np.array(list(h.axes[0])) == sample)[0][0]
+        h.view()[sample_index] = template[sample, ...].view()
+
+    return h
+
+
+def sum_templates(template_dict: Dict):
+    """Sum templates across years"""
+
+    ttemplate = list(template_dict.values())[0]  # sample templates to extract values from
+    combined = {}
+
+    for region in ttemplate:
+        thists = []
+
+        for year in years:
+            # temporary solution for case where 2018 templates don't have L1 prefiring in their axis
+            thists.append(
+                _match_samples(template_dict[year][region], template_dict["2016"][region])
+            )
+
+        combined[region] = sum(thists)
+
+    return combined
+
+
+def combine_templates(
+    bg_templates: Dict[str, Hist], sig_templates: List[Dict[str, Hist]]
+) -> Dict[str, Hist]:
+    """
+    Combines BG and signal templates into a single Hist (per region).
+
+    Args:
+        bg_templates (Dict[str, Hist]): dictionary of region -> Hist
+        sig_templates (List[Dict[str, Hist]]): list of dictionaries of region -> Hist for each
+          signal samples
+    """
+    ctemplates = {}
+
+    for region, bg_template in bg_templates.items():
+        # combined sig + bg samples
+        csamples = list(bg_template.axes[0]) + [
+            s for sig_template in sig_templates for s in list(sig_template[region].axes[0])
         ]
-    )
 
-    # build actual fit model now
-    model = rl.Model("HHModel")
+        # new hist with all samples
+        ctemplate = Hist(
+            hist.axis.StrCategory(csamples, name="Sample"),
+            *bg_template.axes[1:],
+            storage="weight",
+        )
+
+        # add background hists
+        for sample in bg_template.axes[0]:
+            sample_key_index = np.where(np.array(list(ctemplate.axes[0])) == sample)[0][0]
+            ctemplate.view(flow=True)[sample_key_index, ...] = bg_template[sample, ...].view(
+                flow=True
+            )
+
+        # add signal hists
+        for st in sig_templates:
+            sig_template = st[region]
+            for sample in sig_template.axes[0]:
+                sample_key_index = np.where(np.array(list(ctemplate.axes[0])) == sample)[0][0]
+                ctemplate.view(flow=True)[sample_key_index, ...] = sig_template[sample, ...].view(
+                    flow=True
+                )
+
+        ctemplates[region] = ctemplate
+
+    return ctemplates
+
+
+def _combine_last_two_bins(templates_dict):
+    for year in years:
+        for region in templates_dict[year]:
+            templates_dict[year][region] = utils.rebin_hist(
+                templates_dict[year][region],
+                "bbFatJetParticleNetMass",
+                list(range(50, 240, 10)) + [250],
+            )
+
+
+def get_templates(
+    templates_dir: str,
+    years: List[str],
+    sig_separate: bool,
+    scale: float = None,
+    combine_lasttwo: bool = False,
+):
+    """Loads templates, combines bg and sig templates if separate, sums across all years"""
+    templates_dict: Dict[str, Dict[str, Hist]] = {}
+
+    if not sig_separate:
+        # signal and background templates in same hist, just need to load and sum across years
+        for year in years:
+            with open(f"{templates_dir}/{year}_templates.pkl", "rb") as f:
+                templates_dict[year] = _rem_neg(pickle.load(f))
+    else:
+        # signal and background in different hists - need to combine them into one hist
+        for year in years:
+            with open(f"{templates_dir}/backgrounds/{year}_templates.pkl", "rb") as f:
+                bg_templates = _rem_neg(pickle.load(f))
+
+            sig_templates = []
+
+            for sig_key in sig_keys:
+                with open(f"{templates_dir}/{hist_names[sig_key]}/{year}_templates.pkl", "rb") as f:
+                    sig_templates.append(_rem_neg(pickle.load(f)))
+
+            templates_dict[year] = combine_templates(bg_templates, sig_templates)
+
+    if scale is not None and scale != 1:
+        for year in templates_dict:
+            for key in templates_dict[year]:
+                templates_dict[year][key] = templates_dict[year][key] * scale
+
+    if combine_lasttwo:
+        _combine_last_two_bins(templates_dict)
+
+    templates_summed: Dict[str, Hist] = sum_templates(templates_dict)  # sum across years
+    return templates_dict, templates_summed
+
+
+def process_systematics_combined(systematics: Dict):
+    """Get total uncertainties from per-year systs in ``systematics``"""
+    global nuisance_params
+    for sig_key in sig_keys:
+        # already for all years
+        nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{mc_samples[sig_key]}"].value = (
+            1 + systematics[sig_key]["lp_sf_unc"]
+        )
+
+    tdict = {}
+    for region in systematics[years[0]]:
+        if len(years) > 1:
+            trig_totals, trig_total_errs = [], []
+            for year in years:
+                trig_totals.append(systematics[year][region]["trig_total"])
+                trig_total_errs.append(systematics[year][region]["trig_total_err"])
+
+            trig_total = np.sum(trig_totals)
+            trig_total_errs = np.linalg.norm(trig_total_errs)
+
+            tdict[region] = 1 + (trig_total_errs / trig_total)
+        else:
+            year = years[0]
+            tdict[region] = 1 + (
+                systematics[year][region]["trig_total_err"]
+                / systematics[year][region]["trig_total"]
+            )
+
+    nuisance_params[f"{CMS_PARAMS_LABEL}_triggerEffSF_uncorrelated"].value = tdict
+
+    print("Nuisance Parameters\n", nuisance_params)
+
+
+def process_systematics_separate(bg_systematics: Dict, sig_systs: Dict[str, Dict]):
+    """Get total uncertainties from per-year systs separated into bg and sig systs"""
+    global nuisance_params
+
+    for sig_key in sig_keys:
+        # already for all years
+        nuisance_params[f"{CMS_PARAMS_LABEL}_lp_sf_{mc_samples[sig_key]}"].value = (
+            1 + sig_systs[sig_key][sig_key]["lp_sf_unc"]
+        )
+
+    # use only bg trig uncs.
+    tdict = {}
+    for region in bg_systematics[years[0]]:
+        if len(years) > 1:
+            trig_totals, trig_total_errs = [], []
+            for year in years:
+                trig_totals.append(bg_systematics[year][region]["trig_total"])
+                trig_total_errs.append(bg_systematics[year][region]["trig_total_err"])
+
+            trig_total = np.sum(trig_totals)
+            trig_total_errs = np.linalg.norm(trig_total_errs)
+
+            tdict[region] = 1 + (trig_total_errs / trig_total)
+        else:
+            year = years[0]
+            tdict[region] = 1 + (
+                bg_systematics[year][region]["trig_total_err"]
+                / bg_systematics[year][region]["trig_total"]
+            )
+
+    nuisance_params[f"{CMS_PARAMS_LABEL}_triggerEffSF_uncorrelated"].value = tdict
+
+    print("Nuisance Parameters\n", nuisance_params)
+
+
+def process_systematics(templates_dir: str, sig_separate: bool):
+    """Processses systematics based on whether signal and background JSONs are combined or not"""
+    if not sig_separate:
+        with open(f"{templates_dir}/systematics.json", "r") as f:
+            systematics = json.load(f)
+
+        process_systematics_combined(systematics)  # LP SF and trig effs.
+    else:
+        with open(f"{templates_dir}/backgrounds/systematics.json", "r") as f:
+            bg_systematics = json.load(f)
+
+        sig_systs = {}
+        for sig_key in sig_keys:
+            with open(f"{templates_dir}/{hist_names[sig_key]}/systematics.json", "r") as f:
+                sig_systs[sig_key] = json.load(f)
+
+        process_systematics_separate(bg_systematics, sig_systs)  # LP SF and trig effs.
+
+
+def fill_regions(
+    model: rl.Model,
+    regions: List[str],
+    templates_dict: Dict,
+    templates_summed: Dict,
+    mc_samples: Dict[str, str],
+    nuisance_params: Dict[str, Syst],
+    nuisance_params_dict: Dict[str, rl.NuisanceParameter],
+    corr_year_shape_systs: Dict[str, Syst],
+    uncorr_year_shape_systs: Dict[str, Syst],
+    shape_systs_dict: Dict[str, rl.NuisanceParameter],
+    bblite: bool = True,
+    mX_bin: int = None,
+):
+    """Fill samples per region including given rate, shape and mcstats systematics.
+    Ties "blinded" and "nonblinded" mc stats parameters together.
+
+    Args:
+        model (rl.Model): rhalphalib model
+        regions (List[str]): list of regions to fill
+        templates_dict (Dict): dictionary of all templates
+        templates_summed (Dict): dictionary of templates summed across years
+        mc_samples (Dict[str, str]): dict of mc samples and their names in the given templates -> card names
+        nuisance_params (Dict[str, Tuple]): dict of nuisance parameter names and tuple of their
+          (modifier, samples affected by it, value)
+        nuisance_params_dict (Dict[str, rl.NuisanceParameter]): dict of nuisance parameter names
+          and NuisanceParameter object
+        corr_year_shape_systs (Dict[str, Tuple]): dict of shape systs (correlated across years)
+          and tuple of their (name in cards, samples affected by it)
+        uncorr_year_shape_systs (Dict[str, Tuple]): dict of shape systs (unccorrelated across years)
+          and tuple of their (name in cards, samples affected by it)
+        shape_systs_dict (Dict[str, rl.NuisanceParameter]): dict of shape syst names and
+          NuisanceParameter object
+        pass_only (List[str]): list of systematics which are only applied in the pass region(s)
+        bblite (bool): use Barlow-Beeston-lite method or not (single mcstats param across MC samples)
+        mX_bin (int): if doing 2D fit (for resonant), which mX bin to be filled
+    """
 
     for region in regions:
-        region_templates = templates_all[region]
-        pass_region = "pass" in region
+        if mX_bin is None:
+            region_templates = templates_summed[region]
+        else:
+            region_templates = templates_summed[region][:, :, mX_bin]
+
+        pass_region = region.startswith("pass")
         region_noblinded = region.split("Blinded")[0]
         blind_str = "Blinded" if region.endswith("Blinded") else ""
 
         logging.info("starting region: %s" % region)
-        ch = rl.Channel(region.replace("_", ""))  # can't have '_'s in name
+        binstr = "" if mX_bin is None else f"mXbin{mX_bin}"
+        ch = rl.Channel(binstr + region.replace("_", ""))  # can't have '_'s in name
         model.addChannel(ch)
 
-        for card_name, sample_name in mc_samples.items():
+        for sample_name, card_name in mc_samples.items():
+            # don't add signals in fail regions
+            # also skip resonant signals in pass blinded - they are ignored in the validation fits anyway
+            if sample_name in sig_keys:
+                if not pass_region or (mX_bin is not None and region == "passBlinded"):
+                    logging.info(f"\nSkipping {sample_name} in {region} region\n")
+                    continue
+
+            # single top only in fail regions
+            if sample_name == "ST" and pass_region:
+                logging.info(f"\nSkipping ST in {region} region\n")
+                continue
+
             logging.info("get templates for: %s" % sample_name)
 
             sample_template = region_templates[sample_name, :]
 
-            stype = rl.Sample.SIGNAL if "HHbbVV" in sample_name else rl.Sample.BACKGROUND
+            stype = rl.Sample.SIGNAL if sample_name in sig_keys else rl.Sample.BACKGROUND
             sample = rl.TemplateSample(ch.name + "_" + card_name, stype, sample_template)
 
+            # # rate params per signal to freeze them for individual limits
+            # if stype == rl.Sample.SIGNAL and len(sig_keys) > 1:
+            #     srate = rate_params[sample_name]
+            #     sample.setParamEffect(srate, 1 * srate)
+
             # nominal values, errors
             values_nominal = np.maximum(sample_template.values(), 0.0)
 
@@ -219,40 +750,65 @@ def main(args):
             logging.debug("nominal   : {nominal}".format(nominal=values_nominal))
             logging.debug("error     : {errors}".format(errors=errors_nominal))
 
-            if not args.bblite:
+            if not bblite and args.mcstats:
                 # set mc stat uncs
                 logging.info("setting autoMCStats for %s in %s" % (sample_name, region))
 
-                # same stats unc. for blinded and unblinded cards
-                stats_sample_name = region.split("Blinded")[0] + f"_{sample_name}"
-                sample.autoMCStats(sample_name=stats_sample_name)
+                # tie MC stats parameters together in blinded and "unblinded" region in nonresonant
+                region_name = region if args.resonant else region_noblinded
+                stats_sample_name = f"{CMS_PARAMS_LABEL}_{region_name}_{card_name}"
+                sample.autoMCStats(
+                    sample_name=stats_sample_name,
+                    # this function uses a different threshold convention from combine
+                    threshold=np.sqrt(1 / args.mcstats_threshold),
+                    epsilon=args.epsilon,
+                )
 
             # rate systematics
-            for skey, (_, ssamples, sval) in nuisance_params.items():
-                if sample_name not in ssamples:
+            for skey, syst in nuisance_params.items():
+                if sample_name not in syst.samples or (not pass_region and syst.pass_only):
                     continue
 
                 logging.info(f"Getting {skey} rate")
 
                 param = nuisance_params_dict[skey]
-                val = sval[region_noblinded] if isinstance(sval, dict) else sval
-                sample.setParamEffect(param, val)
 
-            # shape systematics
-            for skey, (sname, ssamples) in corr_year_shape_systs.items():
-                if sample_name not in ssamples or (not pass_region and skey in pass_only):
+                val, val_down = syst.value, syst.value_down
+                if syst.diff_regions:
+                    region_name = region if args.resonant else region_noblinded
+                    val = val[region_name]
+                    val_down = val_down[region_name] if val_down is not None else val_down
+                if syst.diff_samples:
+                    val = val[sample_name]
+                    val_down = val_down[sample_name] if val_down is not None else val_down
+
+                sample.setParamEffect(param, val, effect_down=val_down)
+
+            # correlated shape systematics
+            for skey, syst in corr_year_shape_systs.items():
+                if sample_name not in syst.samples or (not pass_region and syst.pass_only):
                     continue
 
                 logging.info(f"Getting {skey} shapes")
 
                 if skey in jecs or skey in jmsr:
                     # JEC/JMCs saved as different "region" in dict
-                    values_up = templates_all[f"{region_noblinded}_{skey}_up{blind_str}"][
-                        sample_name, :
-                    ].values()
-                    values_down = templates_all[f"{region_noblinded}_{skey}_down{blind_str}"][
-                        sample_name, :
-                    ].values()
+                    if mX_bin is None:
+                        up_hist = templates_summed[f"{region_noblinded}_{skey}_up{blind_str}"][
+                            sample_name, :
+                        ]
+                        down_hist = templates_summed[f"{region_noblinded}_{skey}_down{blind_str}"][
+                            sample_name, :
+                        ]
+                    else:
+                        # regions names are different from different blinding strats
+                        up_hist = templates_summed[f"{region}_{skey}_up"][sample_name, :, mX_bin]
+                        down_hist = templates_summed[f"{region}_{skey}_down"][
+                            sample_name, :, mX_bin
+                        ]
+
+                    values_up = up_hist.values()
+                    values_down = down_hist.values()
                 else:
                     # weight uncertainties saved as different "sample" in dict
                     values_up = region_templates[f"{sample_name}_{skey}_up", :].values()
@@ -267,15 +823,26 @@ def main(args):
                 )
                 sample.setParamEffect(shape_systs_dict[skey], effect_up, effect_down)
 
-            for skey, (sname, ssamples) in uncorr_year_shape_systs.items():
-                if sample_name not in ssamples or (not pass_region and skey in pass_only):
+            # uncorrelated shape systematics
+            for skey, syst in uncorr_year_shape_systs.items():
+                if sample_name not in syst.samples or (not pass_region and syst.pass_only):
                     continue
 
                 logging.info(f"Getting {skey} shapes")
 
                 for year in years:
+                    if year not in syst.uncorr_years:
+                        continue
+
                     values_up, values_down = get_year_updown(
-                        templates_dict, sample_name, region, region_noblinded, blind_str, year, skey
+                        templates_dict,
+                        sample_name,
+                        region,
+                        region_noblinded,
+                        blind_str,
+                        year,
+                        skey,
+                        mX_bin=mX_bin,
                     )
                     logger = logging.getLogger(
                         "validate_shapes_{}_{}_{}".format(region, sample_name, skey)
@@ -290,13 +857,55 @@ def main(args):
 
             ch.addSample(sample)
 
-        if args.bblite:
-            channel_name = region_noblinded
-            ch.autoMCStats(channel_name=channel_name, threshold=0.1)
+        if bblite and args.mcstats:
+            # tie MC stats parameters together in blinded and "unblinded" region in nonresonant
+            channel_name = region if args.resonant else region_noblinded
+            ch.autoMCStats(
+                channel_name=f"{CMS_PARAMS_LABEL}_{channel_name}",
+                threshold=args.mcstats_threshold,
+                epsilon=args.epsilon,
+            )
 
         # data observed
         ch.setObservation(region_templates[data_key, :])
 
+
+def nonres_alphabet_fit(
+    model: rl.Model,
+    shape_vars: List[ShapeVar],
+    templates_summed: Dict,
+    scale: float = None,
+    min_qcd_val: float = None,
+):
+    shape_var = shape_vars[0]
+    m_obs = rl.Observable(shape_var.name, shape_var.bins)
+
+    # QCD overall pass / fail efficiency
+    qcd_eff = (
+        templates_summed[f"pass"][qcd_key, :].sum().value
+        / templates_summed[f"fail"][qcd_key, :].sum().value
+    )
+
+    # transfer factor
+    tf_dataResidual = rl.BasisPoly(
+        f"{CMS_PARAMS_LABEL}_tf_dataResidual",
+        (shape_var.order,),
+        [shape_var.name],
+        basis="Bernstein",
+        limits=(-20, 20),
+        square_params=True,
+    )
+    tf_dataResidual_params = tf_dataResidual(shape_var.scaled)
+    tf_params_pass = qcd_eff * tf_dataResidual_params  # scale params initially by qcd eff
+
+    # qcd params
+    qcd_params = np.array(
+        [
+            rl.IndependentParameter(f"{CMS_PARAMS_LABEL}_tf_dataResidual_Bin{i}", 0)
+            for i in range(m_obs.nbins)
+        ]
+    )
+
     for blind_str in ["", "Blinded"]:
         # for blind_str in ["Blinded"]:
         passChName = f"pass{blind_str}".replace("_", "")
@@ -311,85 +920,141 @@ def main(args):
         # was integer, and numpy complained about subtracting float from it
         initial_qcd = failCh.getObservation().astype(float)
         for sample in failCh:
+            if args.resonant and sample.sampletype == rl.Sample.SIGNAL:
+                continue
             logging.debug("subtracting %s from qcd" % sample._name)
             initial_qcd -= sample.getExpectation(nominal=True)
 
         if np.any(initial_qcd < 0.0):
             raise ValueError("initial_qcd negative for some bins..", initial_qcd)
 
+        # idea here is that the error should be 1/sqrt(N), so parametrizing it as (1 + 1/sqrt(N))^qcdparams
+        # will result in qcdparams errors ~±1
+        # but because qcd is poorly modelled we're scaling sigma scale
+
         sigmascale = 10  # to scale the deviation from initial
+        if scale is not None:
+            sigmascale *= scale
+
         scaled_params = (
             initial_qcd * (1 + sigmascale / np.maximum(1.0, np.sqrt(initial_qcd))) ** qcd_params
         )
 
         # add samples
         fail_qcd = rl.ParametericSample(
-            f"{failChName}_{PARAMS_LABEL}_qcd_datadriven",
+            f"{failChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
             rl.Sample.BACKGROUND,
-            msd_obs,
+            m_obs,
             scaled_params,
         )
         failCh.addSample(fail_qcd)
 
         pass_qcd = rl.TransferFactorSample(
-            f"{passChName}_{PARAMS_LABEL}_qcd_datadriven",
+            f"{passChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
             rl.Sample.BACKGROUND,
             tf_params_pass,
             fail_qcd,
+            min_val=min_qcd_val,
         )
         passCh.addSample(pass_qcd)
 
-    os.system(f"mkdir -p {args.cards_dir}")
 
-    logging.info("rendering combine model")
+def res_alphabet_fit(
+    model: rl.Model,
+    shape_vars: List[ShapeVar],
+    templates_summed: Dict,
+    scale: float = None,
+    min_qcd_val: float = None,
+):
+    shape_var_mY, shape_var_mX = shape_vars
+    m_obs = rl.Observable(shape_var_mY.name, shape_var_mY.bins)
 
-    if args.model_name is not None:
-        out_dir = os.path.join(str(args.cards_dir), args.model_name)
-    else:
-        out_dir = args.cards_dir
-
-    model.renderCombine(out_dir)
-
-    with open(f"{out_dir}/model.pkl", "wb") as fout:
-        pickle.dump(model, fout, 2)  # use python 2 compatible protocol
-
-
-def sum_templates(template_dict: Dict):
-    ttemplate = list(template_dict.values())[0]  # sample templates to extract values from
-    combined = {}
-
-    for region in ttemplate:
-        thists = []
-
-        for year in years:
-            thists.append(template_dict[year][region])
-
-        combined[region] = sum(thists)
-
-    return combined
-
-
-def process_systematics(systematics: Dict):
-    """Get total uncertainties from per-year systs in ``systematics``"""
-    global nuisance_params
-    nuisance_params["lp_sf"][2] = 1 + systematics["lp_sf_unc"]  # already for all years
-
-    tdict = {}
-    for region in systematics["2017"]:
-        trig_totals, trig_total_errs = [], []
-        for year in years:
-            trig_totals.append(systematics[year][region]["trig_total"])
-            trig_total_errs.append(systematics[year][region]["trig_total_err"])
+    # QCD overall pass / fail efficiency
+    qcd_eff = (
+        templates_summed[f"pass"][qcd_key, ...].sum().value
+        / templates_summed[f"fail"][qcd_key, ...].sum().value
+    )
 
-        trig_total = np.sum(trig_totals)
-        trig_total_errs = np.linalg.norm(trig_total_errs)
+    # transfer factor
+    tf_dataResidual = rl.BasisPoly(
+        f"{CMS_PARAMS_LABEL}_tf_dataResidual",
+        (shape_var_mX.order, shape_var_mY.order),
+        [shape_var_mX.name, shape_var_mY.name],
+        basis="Bernstein",
+        limits=(-20, 20),
+        square_params=True,
+    )
 
-        tdict[region] = 1 + (trig_total_errs / trig_total)
+    # based on https://github.com/nsmith-/rhalphalib/blob/9472913ef0bab3eb47bc942c1da4e00d59fb5202/tests/test_rhalphalib.py#L38
+    mX_scaled_grid, mY_scaled_grid = np.meshgrid(
+        shape_var_mX.scaled, shape_var_mY.scaled, indexing="ij"
+    )
+    # numpy array of
+    tf_dataResidual_params = tf_dataResidual(mX_scaled_grid, mY_scaled_grid)
+    tf_params_pass = qcd_eff * tf_dataResidual_params  # scale params initially by qcd eff
 
-    nuisance_params["triggerEffSF_uncorrelated"][2] = tdict
+    for mX_bin in range(len(shape_var_mX.pts)):
+        # qcd params
+        qcd_params = np.array(
+            [
+                rl.IndependentParameter(f"{CMS_PARAMS_LABEL}_qcdparam_mXbin{mX_bin}_mYbin{i}", 0)
+                for i in range(m_obs.nbins)
+            ]
+        )
 
-    print("Nuisance Parameters")
-    print(nuisance_params)
+        for blind_str in ["", "Blinded"]:
+            # for blind_str in ["Blinded"]:
+            passChName = f"mXbin{mX_bin}pass{blind_str}".replace("_", "")
+            failChName = f"mXbin{mX_bin}fail{blind_str}".replace("_", "")
+            logging.info(
+                "setting transfer factor for pass region %s, fail region %s"
+                % (passChName, failChName)
+            )
+            failCh = model[failChName]
+            passCh = model[passChName]
+
+            # sideband fail
+            # was integer, and numpy complained about subtracting float from it
+            initial_qcd = failCh.getObservation().astype(float)
+            for sample in failCh:
+                logging.debug("subtracting %s from qcd" % sample._name)
+                initial_qcd -= sample.getExpectation(nominal=True)
+
+            if np.any(initial_qcd < 0.0):
+                # raise ValueError("initial_qcd negative for some bins..", initial_qcd)
+                logging.warning(f"initial_qcd negative for some bins... {initial_qcd}")
+                initial_qcd[initial_qcd < 0] = 0
+
+            # idea here is that the error should be 1/sqrt(N), so parametrizing it as (1 + 1/sqrt(N))^qcdparams
+            # will result in qcdparams errors ~±1
+            # but because qcd is poorly modelled we're scaling sigma scale
+
+            sigmascale = 10  # to scale the deviation from initial
+            if scale is not None:
+                sigmascale *= scale
+
+            scaled_params = (
+                initial_qcd * (1 + sigmascale / np.maximum(1.0, np.sqrt(initial_qcd))) ** qcd_params
+            )
+            # scaled_params = initial_qcd * (1.1 ** qcd_params)
+
+            # add samples
+            fail_qcd = rl.ParametericSample(
+                f"{failChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
+                rl.Sample.BACKGROUND,
+                m_obs,
+                scaled_params,
+            )
+            failCh.addSample(fail_qcd)
+
+            pass_qcd = rl.TransferFactorSample(
+                f"{passChName}_{CMS_PARAMS_LABEL}_qcd_datadriven",
+                rl.Sample.BACKGROUND,
+                tf_params_pass[mX_bin, :],
+                fail_qcd,
+                min_val=min_qcd_val,
+            )
+            passCh.addSample(pass_qcd)
 
 
 def _shape_checks(values_up, values_down, values_nominal, effect_up, effect_down, logger):
@@ -424,20 +1089,32 @@ def _shape_checks(values_up, values_down, values_nominal, effect_up, effect_down
     ):
         valid = False
         logger.warning("Up and Down vary norm in the same direction")
+        # print("Norm nominal", norm_nominal)
+        # print("Norm up", norm_up)
+        # print("Norm down", norm_down)
+        # print("values nominal", values_nominal)
+        # print("values up", values_up)
+        # print("values down", values_down)
 
-    if valid:
-        logger.info("Shapes are valid")
+    # if valid:
+    #     logger.info("Shapes are valid")
 
 
 def get_effect_updown(values_nominal, values_up, values_down, mask, logger):
     effect_up = np.ones_like(values_nominal)
     effect_down = np.ones_like(values_nominal)
 
-    mask_up = values_up >= 0
-    mask_down = values_down >= 0
+    mask_up = mask & (values_up >= 0)
+    mask_down = mask & (values_down >= 0)
 
-    effect_up[mask & mask_up] = values_up[mask & mask_up] / values_nominal[mask & mask_up]
-    effect_down[mask & mask_down] = values_down[mask & mask_down] / values_nominal[mask & mask_down]
+    effect_up[mask_up] = values_up[mask_up] / values_nominal[mask_up]
+    effect_down[mask_down] = values_down[mask_down] / values_nominal[mask_down]
+
+    zero_up = values_up == 0
+    zero_down = values_down == 0
+
+    effect_up[mask_up & zero_up] = values_nominal[mask_up & zero_up] * args.epsilon
+    effect_down[mask_down & zero_down] = values_nominal[mask_down & zero_down] * args.epsilon
 
     _shape_checks(values_up, values_down, values_nominal, effect_up, effect_down, logger)
 
@@ -448,63 +1125,40 @@ def get_effect_updown(values_nominal, values_up, values_down, mask, logger):
     return effect_up, effect_down
 
 
-def get_year_updown(templates_dict, sample, region, region_noblinded, blind_str, year, skey):
+def get_year_updown(
+    templates_dict, sample, region, region_noblinded, blind_str, year, skey, mX_bin=None
+):
+    """
+    Return templates with only the given year's shapes shifted up and down by the ``skey`` systematic.
+    Returns as [up templates, down templates]
+    """
     updown = []
 
     for shift in ["up", "down"]:
         sshift = f"{skey}_{shift}"
         # get nominal templates for each year
-        templates = {y: templates_dict[y][region][sample, :] for y in years}
+        templates = {y: templates_dict[y][region][sample, ...] for y in years}
         # replace template for this year with the shifted tempalte
         if skey in jecs or skey in jmsr:
             # JEC/JMCs saved as different "region" in dict
-            templates[year] = templates_dict[year][f"{region_noblinded}_{sshift}{blind_str}"][
-                sample, :
-            ]
+            reg_name = (
+                f"{region_noblinded}_{sshift}{blind_str}"
+                if mX_bin is None
+                else f"{region}_{sshift}"
+            )
+            templates[year] = templates_dict[year][reg_name][sample, :, ...]
         else:
             # weight uncertainties saved as different "sample" in dict
-            templates[year] = templates_dict[year][region][f"{sample}_{sshift}", :]
+            templates[year] = templates_dict[year][region][f"{sample}_{sshift}", ...]
+
+        if mX_bin is not None:
+            for year, template in templates.items():
+                templates[year] = template[:, mX_bin]
+
         # sum templates with year's template replaced with shifted
         updown.append(sum(list(templates.values())).values())
 
     return updown
 
 
-def add_bool_arg(parser, name, help, default=False, no_name=None):
-    """Add a boolean command line argument for argparse"""
-    varname = "_".join(name.split("-"))  # change hyphens to underscores
-    group = parser.add_mutually_exclusive_group(required=False)
-    group.add_argument("--" + name, dest=varname, action="store_true", help=help)
-    if no_name is None:
-        no_name = "no-" + name
-        no_help = "don't " + help
-    else:
-        no_help = help
-    group.add_argument("--" + no_name, dest=varname, action="store_false", help=no_help)
-    parser.set_defaults(**{varname: default})
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--templates-dir",
-        default="",
-        type=str,
-        help="input pickle file of dict of hist.Hist templates",
-    )
-    parser.add_argument("--cards-dir", default="cards", type=str, help="output card directory")
-    parser.add_argument("--cat", default="1", type=str, choices=["1"], help="category")
-    parser.add_argument(
-        "--nDataTF",
-        default=2,
-        type=int,
-        dest="nDataTF",
-        help="order of polynomial for TF from Data",
-    )
-    parser.add_argument("--model-name", default=None, type=str, help="output model name")
-    add_bool_arg(parser, "bblite", "use barlow-beeston-lite method", default=True)
-    args = parser.parse_args()
-
-    main(args)
+main(args)
diff --git a/src/HHbbVV/postprocessing/GoF.ipynb b/src/HHbbVV/postprocessing/GoF.ipynb
deleted file mode 100644
index 2b059bc2..00000000
--- a/src/HHbbVV/postprocessing/GoF.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import uproot\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mplhep as hep\n",
-    "import matplotlib.ticker as mticker\n",
-    "import os\n",
-    "\n",
-    "plt.style.use(hep.style.CMS)\n",
-    "hep.style.use(\"CMS\")\n",
-    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
-    "formatter.set_powerlimits((-3, 3))\n",
-    "plt.rcParams.update({\"font.size\": 20})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MAIN_DIR = \"../../../\"\n",
-    "\n",
-    "plot_dir = f\"{MAIN_DIR}/plots/GoF/Mar7\"\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cards_dir = \"Mar3\"\n",
-    "\n",
-    "file = uproot.concatenate(\n",
-    "    f\"/uscms/home/rkansal/nobackup/CMSSW_11_2_0/src/cards/{cards_dir}/higgsCombinepassData.GoodnessOfFit.mH125.root\"\n",
-    ")\n",
-    "\n",
-    "data_ts = file[\"limit\"][0]  # data test statistic\n",
-    "\n",
-    "file = uproot.concatenate(\n",
-    "    f\"/uscms/home/rkansal/nobackup/CMSSW_11_2_0/src/cards/{cards_dir}/higgsCombinepassToys.GoodnessOfFit.mH125.*.root\"\n",
-    ")\n",
-    "\n",
-    "toy_ts = np.array(file[\"limit\"])  # toy test statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pval = np.sum(toy_ts > data_ts) / len(toy_ts)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 8))\n",
-    "_ = plt.hist(toy_ts, np.linspace(0, 30, 16), color=\"dimgrey\", histtype=\"step\", label=\"Toys\")\n",
-    "plt.axvline(data_ts, color=\"red\", linestyle=\":\", label=rf\"Data ($p$-value = {pval:.2f})\")\n",
-    "_ = plt.legend()\n",
-    "plt.title(\"Goodness of Fit\")\n",
-    "plt.ylabel(\"Number of Toys\")\n",
-    "plt.xlabel(\"Test Statistics\")\n",
-    "plt.savefig(f\"{plot_dir}/gof.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from scipy.stats import chi2\n",
-    "\n",
-    "chi2_df = 13\n",
-    "\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "x = np.linspace(0, 30, 101)\n",
-    "plt.plot(\n",
-    "    x,\n",
-    "    chi2.pdf(x, chi2_df) * 1.3,\n",
-    "    color=\"blue\",\n",
-    "    linestyle=\"--\",\n",
-    "    alpha=0.6,\n",
-    "    label=rf\"$\\chi^2_{{DoF = {chi2_df}}}$\",\n",
-    ")\n",
-    "_ = plt.hist(\n",
-    "    toy_ts, np.linspace(0, 30, 16), density=True, color=\"dimgrey\", histtype=\"step\", label=\"Toys\"\n",
-    ")\n",
-    "plt.axvline(data_ts, color=\"red\", linestyle=\":\", label=rf\"Data ($p$-value = {pval:.2f})\")\n",
-    "_ = plt.legend()\n",
-    "plt.title(\"Goodness of Fit\")\n",
-    "plt.ylabel(\"Number of Toys (A.U.)\")\n",
-    "plt.xlabel(\"Test Statistics\")\n",
-    "plt.savefig(f\"{plot_dir}/gof_chi2.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "bbVV",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "5b9eab485576227e6cf1b964bb8855c46cbdf15c3e77cecdb2bb309145d3e8d8"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb b/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb
index 22dca9ea..cf23e4d6 100644
--- a/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb
+++ b/src/HHbbVV/postprocessing/InferenceAnalysis.ipynb
@@ -20,20 +20,25 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import mplhep as hep\n",
+    "import matplotlib\n",
     "\n",
-    "plt.rcParams.update({\"font.size\": 16})\n",
     "plt.style.use(hep.style.CMS)\n",
     "hep.style.use(\"CMS\")\n",
+    "plt.rcParams.update({\"font.size\": 24})\n",
     "\n",
     "import os\n",
     "from os import listdir\n",
     "from os.path import exists\n",
     "import pickle\n",
     "\n",
+    "from tqdm import tqdm\n",
+    "\n",
     "import utils\n",
-    "from hh_vars import samples, res_samples, sig_keys, res_sig_keys\n",
+    "from hh_vars import samples, nonres_samples, res_samples, nonres_sig_keys, res_sig_keys\n",
     "\n",
-    "MAIN_DIR = \"../../../\""
+    "MAIN_DIR = \"../../../\"\n",
+    "\n",
+    "samples = samples | nonres_samples"
    ]
   },
   {
@@ -52,14 +57,65 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_dir = f\"{MAIN_DIR}/plots/TaggerAnalysis/23Mar6\"\n",
+    "plot_dir = f\"{MAIN_DIR}/plots/TaggerAnalysis/23Aug28\"\n",
+    "# plot_dir = f\"{MAIN_DIR}/plots/BDT/23May12\"\n",
     "os.system(f\"mkdir -p {plot_dir}\")\n",
     "\n",
     "samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
-    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Mar2\"\n",
+    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Mar10_2\"\n",
     "year = \"2017\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_nearest(array, value):\n",
+    "    array = np.asarray(array)\n",
+    "    idx = (np.abs(array - value)).argmin()\n",
+    "    return idx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "\n",
+    "\n",
+    "res_samples = OrderedDict()\n",
+    "\n",
+    "res_mps = [\n",
+    "    # (1000, 125),\n",
+    "    # (1400, 125),\n",
+    "    (1400, 150),\n",
+    "    # (1800, 125),\n",
+    "    (1800, 150),\n",
+    "    (1800, 190),\n",
+    "    # (2200, 125),\n",
+    "    (2200, 150),\n",
+    "    (2200, 190),\n",
+    "    (2200, 250),\n",
+    "    # (3000, 125),\n",
+    "    # (3000, 150),\n",
+    "    # (3000, 190),\n",
+    "    # (3000, 250),\n",
+    "    # (3000, 350),\n",
+    "]\n",
+    "\n",
+    "for mX, mY in res_mps:\n",
+    "    res_samples[\n",
+    "        f\"X[{mX}]->H(bb)Y[{mY}](WW)\"\n",
+    "    ] = f\"NMSSM_XToYH_MX{mX}_MY{mY}_HTo2bYTo2W_hadronicDecay\"\n",
+    "\n",
+    "res_sig_keys = list(res_samples.keys())\n",
+    "nonres_sig_keys = [\"HHbbVV\"]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -71,12 +127,12 @@
     "    [\n",
     "        (\"('ak8FatJetParticleNetMass', '0')\", \">=\", 50),\n",
     "        (\"('ak8FatJetParticleNetMass', '1')\", \">=\", 50),\n",
-    "        (\"('nGoodElectrons', '0')\", \"==\", 0),\n",
+    "        # (\"('nGoodElectrons', '0')\", \"==\", 0),\n",
     "    ],\n",
     "]\n",
     "\n",
     "events_dict = utils.load_samples(\n",
-    "    samples_dir, {sig_key: samples[sig_key] for sig_key in sig_keys}, year, filters=filters\n",
+    "    samples_dir, {sig_key: samples[sig_key] for sig_key in nonres_sig_keys}, year, filters=filters\n",
     ")\n",
     "events_dict |= utils.load_samples(signal_samples_dir, res_samples, year, filters=filters)\n",
     "# events_dict[\"HHbbVV\"] = pd.read_parquet(\"../../../0-1.parquet\")\n",
@@ -95,6 +151,10 @@
     "    (\"ak8FatJetPt\", 2),\n",
     "    (\"ak8FatJetMsd\", 2),\n",
     "    (\"ak8FatJetParTMD_THWW4q\", 2),\n",
+    "    (\"ak8FatJetParTMD_probHWW4q\", 2),\n",
+    "    (\"ak8FatJetParTMD_probHWW3q\", 2),\n",
+    "    (\"ak8FatJetParTMD_probQCD\", 2),\n",
+    "    (\"ak8FatJetParTMD_probT\", 2),\n",
     "    (\"ak8FatJetParticleNet_Th4q\", 2),\n",
     "]"
    ]
@@ -149,8 +209,8 @@
     "        bg_column_labels.append(f\"('{key}', '{i}')\")\n",
     "\n",
     "\n",
-    "# bg_keys = [\"TT\", \"QCD\"]\n",
-    "bg_keys = [\"QCD\"]\n",
+    "bg_keys = [\"TT\", \"QCD\"]\n",
+    "# bg_keys = [\"QCD\"]\n",
     "\n",
     "for bg_key in bg_keys:\n",
     "    events_dict[bg_key] = {}\n",
@@ -158,6 +218,10 @@
     "        if bg_key not in sample:\n",
     "            continue\n",
     "\n",
+    "        # doesn't have probT for some reason\n",
+    "        if sample in [\"QCD_HT300to500\", \"QCD_HT200to300\"]:\n",
+    "            continue\n",
+    "\n",
     "        if \"HH\" in sample or \"GluGluH\" in sample:\n",
     "            continue\n",
     "\n",
@@ -203,6 +267,24 @@
     "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sample, events in events_dict.items():\n",
+    "    if \"ak8FatJetParTMD_THWWvsT\" not in events:\n",
+    "        events[\"ak8FatJetParTMD_THWWvsT\"] = (\n",
+    "            events[\"ak8FatJetParTMD_probHWW3q\"] + events[\"ak8FatJetParTMD_probHWW4q\"]\n",
+    "        ) / (\n",
+    "            events[\"ak8FatJetParTMD_probHWW3q\"]\n",
+    "            + events[\"ak8FatJetParTMD_probHWW4q\"]\n",
+    "            + events[\"ak8FatJetParTMD_probQCD\"]\n",
+    "            + events[\"ak8FatJetParTMD_probT\"]\n",
+    "        )"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -235,7 +317,7 @@
     "cutvars_dict = {\"Pt\": \"pt\", \"Msd\": \"msoftdrop\"}\n",
     "\n",
     "all_cuts = [\n",
-    "    {pt_key: [300, 2500], msd_key: [0, 300]},\n",
+    "    {pt_key: [300, 3000]},\n",
     "    # {pt_key: [400, 600], msd_key: [60, 150]},\n",
     "    # {pt_key: [300, 1500], msd_key: [110, 140]},\n",
     "]\n",
@@ -267,6 +349,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -286,10 +369,15 @@
     "        \"colour\": \"orange\",\n",
     "    },\n",
     "    \"thvv4q\": {\n",
-    "        \"title\": \"ParT MD THVV4q\",\n",
+    "        \"title\": \"ParT MD THVV\",\n",
     "        \"score_label\": \"ak8FatJetParTMD_THWW4q\",\n",
     "        \"colour\": \"green\",\n",
     "    },\n",
+    "    \"thvv4qt\": {\n",
+    "        \"title\": \"ParT MD THVV\",\n",
+    "        \"score_label\": \"ak8FatJetParTMD_THWWvsT\",\n",
+    "        \"colour\": \"green\",\n",
+    "    },\n",
     "}"
    ]
   },
@@ -299,7 +387,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "samples = {\"qcd\": \"QCD\", \"HHbbVV\": \"HHbbVV\"}\n",
+    "samples = {\"tt\": \"TT\", \"qcd\": \"QCD\", \"HHbbVV\": \"HHbbVV\"}\n",
     "\n",
     "plt.rcParams.update({\"font.size\": 16})\n",
     "plt.style.use(hep.style.CMS)\n",
@@ -308,8 +396,8 @@
     "for t, pvars in plot_vars.items():\n",
     "    for cutstr in cut_labels:\n",
     "        plt.figure(figsize=(16, 12))\n",
-    "        plt.suptitle(f\"HVV FatJet {pvars['title']} Scores\", y=0.95)\n",
-    "        plt.title(cut_labels[cutstr], fontsize=20)\n",
+    "        # plt.suptitle(f\"HVV FatJet {pvars['title']} Scores\", y=0.95)\n",
+    "        # plt.title(cut_labels[cutstr], fontsize=20)\n",
     "\n",
     "        for sample, colour, skip in [(\"HHbbVV\", \"red\", 1), (\"QCD\", \"deepskyblue\", 4)]:\n",
     "            _ = plt.hist(\n",
@@ -323,7 +411,7 @@
     "                weights=events_dict[sample][\"weight\"][cuts_dict[sample][cutstr]][::skip],\n",
     "            )\n",
     "\n",
-    "        plt.ylabel(\"Normalized # Jets\")\n",
+    "        plt.ylabel(\"# Jets (A.U.)\")\n",
     "        plt.xlabel(f\"{pvars['title']} Score\")\n",
     "        plt.legend()\n",
     "        plt.savefig(\n",
@@ -350,46 +438,51 @@
     "\n",
     "rocs = {}\n",
     "# sig_key = \"HHbbVV\"\n",
-    "bg_key = \"QCD\"\n",
-    "bg_skip = 1\n",
+    "bg_keys = [\"TT\", \"QCD\"]\n",
+    "bg_skip = 4\n",
     "\n",
     "\n",
     "for cutstr in cut_labels:\n",
     "    # print(cutstr)\n",
     "    rocs[cutstr] = {}\n",
-    "    for sig_key in sig_keys + res_sig_keys:\n",
+    "    for sig_key in tqdm(nonres_sig_keys + res_sig_keys):\n",
     "        rocs[cutstr][sig_key] = {}\n",
     "        sig_cut = cuts_dict[sig_key][cutstr]\n",
-    "        bg_cut = cuts_dict[bg_key][cutstr]\n",
+    "        bg_cuts = [cuts_dict[bg_key][cutstr] for bg_key in bg_keys]\n",
     "\n",
     "        y_true = np.concatenate(\n",
     "            [\n",
     "                np.ones(np.sum(sig_cut)),\n",
-    "                np.zeros(int(np.ceil(np.sum(bg_cut) / bg_skip))),\n",
+    "                np.zeros(int(np.ceil(np.sum(np.concatenate(bg_cuts)) / bg_skip))),\n",
     "            ]\n",
     "        )\n",
+    "        # print(y_true[np.sum(sig_cut):])\n",
     "\n",
     "        weights = np.concatenate(\n",
-    "            (\n",
-    "                events_dict[sig_key][\"weight\"][sig_cut],\n",
-    "                events_dict[bg_key][\"weight\"][bg_cut][::bg_skip],\n",
-    "            )\n",
+    "            [events_dict[sig_key][\"weight\"][sig_cut]]\n",
+    "            + [\n",
+    "                events_dict[bg_key][\"weight\"][bg_cut][::bg_skip]\n",
+    "                for bg_key, bg_cut in zip(bg_keys, bg_cuts)\n",
+    "            ],\n",
     "        )\n",
+    "        # print(weights[np.sum(sig_cut):])\n",
     "\n",
     "        for t, pvars in plot_vars.items():\n",
     "            score_label = pvars[\"score_label\"]\n",
     "            scores = np.concatenate(\n",
-    "                (\n",
-    "                    events_dict[sig_key][score_label][sig_cut],\n",
-    "                    events_dict[bg_key][score_label][bg_cut][::bg_skip],\n",
-    "                )\n",
+    "                [events_dict[sig_key][score_label][sig_cut]]\n",
+    "                + [\n",
+    "                    events_dict[bg_key][score_label][bg_cut][::bg_skip]\n",
+    "                    for bg_key, bg_cut in zip(bg_keys, bg_cuts)\n",
+    "                ],\n",
     "            )\n",
+    "            # print(scores[np.sum(sig_cut):])\n",
     "            fpr, tpr, thresholds = roc_curve(y_true, scores, sample_weight=weights)\n",
     "            rocs[cutstr][sig_key][t] = {\n",
     "                \"fpr\": fpr,\n",
     "                \"tpr\": tpr,\n",
     "                \"thresholds\": thresholds,\n",
-    "                \"auc\": auc(fpr, tpr),\n",
+    "                # \"auc\": auc(fpr, tpr),\n",
     "            }"
    ]
   },
@@ -407,9 +500,48 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xlim = [0, 0.6]\n",
+    "sig_split_points = [\n",
+    "    # [\n",
+    "    #     (1000, 125),\n",
+    "    #     (1400, 125),\n",
+    "    #     (1800, 125),\n",
+    "    #     (2200, 125),\n",
+    "    #     (3000, 125),\n",
+    "    # ],\n",
+    "    [\n",
+    "        (1400, 150),\n",
+    "        (1800, 150),\n",
+    "        (1800, 190),\n",
+    "        (2200, 150),\n",
+    "        (2200, 190),\n",
+    "        (2200, 250),\n",
+    "    ],\n",
+    "    # [\n",
+    "    #     (3000, 125),\n",
+    "    #     (3000, 150),\n",
+    "    #     (3000, 190),\n",
+    "    #     (3000, 250),\n",
+    "    #     (3000, 350),\n",
+    "    # ],\n",
+    "]\n",
+    "\n",
+    "sig_splits = [\n",
+    "    [\"HHbbVV\"] + [f\"X[{mX}]->H(bb)Y[{mY}](WW)\" for (mX, mY) in mps] for mps in sig_split_points\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlim = [0, 0.8]\n",
     "ylim = [1e-6, 1]\n",
     "\n",
+    "plot_thresholds = [0.98, 0.96, 0.94, 0.9, 0.8, 0.6, 0.4]\n",
+    "th_colours = [\"#9381FF\", \"#1f78b4\", \"#a6cee3\", \"#ff7f00\", \"#7CB518\", \"#EDB458\", \"#36213E\"]\n",
+    "\n",
     "plt.rcParams.update({\"font.size\": 24})\n",
     "\n",
     "sig_colours = [\n",
@@ -417,40 +549,81 @@
     "    \"#ffbaba\",\n",
     "    \"#ff7b7b\",\n",
     "    \"#ff5252\",\n",
-    "    \"#EDB458\",\n",
+    "    # \"#EDB458\",\n",
     "    \"#a70000\",\n",
+    "    \"#885053\",\n",
+    "    \"#3C0919\",\n",
     "]\n",
     "\n",
+    "plot_vars = {\n",
+    "    # \"th4q\": {\n",
+    "    #     \"title\": \"ParticleNet Non-MD Th4q\",\n",
+    "    #     \"score_label\": \"ak8FatJetParticleNet_Th4q\",\n",
+    "    #     \"colour\": \"orange\",\n",
+    "    # },\n",
+    "    # \"thvv4q\": {\n",
+    "    #     \"title\": \"ParT MD THWW\",\n",
+    "    #     \"score_label\": \"ak8FatJetParTMD_THWW4q\",\n",
+    "    #     \"colour\": \"green\",\n",
+    "    # },\n",
+    "    \"thvv4qt\": {\n",
+    "        \"title\": \"ParT MD THVV\",\n",
+    "        \"score_label\": \"ak8FatJetParTMD_THWWvsT\",\n",
+    "        \"colour\": \"green\",\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "\n",
     "for cutstr in cut_labels:\n",
     "    for t, pvars in plot_vars.items():\n",
-    "        plt.figure(figsize=(12, 12))\n",
-    "        for i, sig_key in enumerate(sig_keys + res_sig_keys):\n",
-    "            roc = rocs[cutstr][sig_key][t]\n",
-    "            c = sig_colours[i]\n",
-    "            plt.plot(\n",
-    "                roc[\"tpr\"],\n",
-    "                roc[\"fpr\"],\n",
-    "                label=f\"{sig_key} AUC: {roc['auc']:.2f}\",\n",
-    "                linewidth=2,\n",
-    "                color=c,\n",
-    "            )\n",
-    "            plt.vlines(\n",
-    "                x=roc[\"tpr\"][np.searchsorted(roc[\"fpr\"], 0.01)],\n",
-    "                ymin=0,\n",
-    "                ymax=0.01,\n",
-    "                colors=c,\n",
-    "                linestyles=\"dashed\",\n",
-    "            )\n",
-    "        plt.hlines(y=0.01, xmin=0, xmax=1, colors=\"lightgrey\", linestyles=\"dashed\")\n",
-    "        plt.yscale(\"log\")\n",
-    "        plt.xlabel(\"Signal Eff.\")\n",
-    "        plt.ylabel(\"BG Eff.\")\n",
-    "        plt.suptitle(f\"HVV FatJet {pvars['title']} ROC\", y=0.95)\n",
-    "        plt.title(cut_labels[cutstr], fontsize=20)\n",
-    "        plt.xlim(*xlim)\n",
-    "        plt.ylim(*ylim)\n",
-    "        plt.legend()\n",
-    "        plt.savefig(f\"{plot_dir}/roccurve_allsigs_{t}_{cutstr}.pdf\", bbox_inches=\"tight\")"
+    "        for j, plot_sig_keys in enumerate(sig_splits):\n",
+    "            split_str = \"allsigs\" if len(sig_splits) == 1 else f\"sigs{j}\"\n",
+    "            pths = {th: [[], []] for th in plot_thresholds}\n",
+    "            plt.figure(figsize=(12, 12))\n",
+    "            for i, sig_key in enumerate(plot_sig_keys):\n",
+    "                roc = rocs[cutstr][sig_key][t]\n",
+    "                c = sig_colours[i]\n",
+    "                plt.plot(\n",
+    "                    roc[\"tpr\"],\n",
+    "                    roc[\"fpr\"],\n",
+    "                    # label=f\"{sig_key} AUC: {roc['auc']:.2f}\",\n",
+    "                    label=f\"{sig_key}\",\n",
+    "                    linewidth=2,\n",
+    "                    color=c,\n",
+    "                )\n",
+    "                for th in plot_thresholds:\n",
+    "                    idx = find_nearest(roc[\"thresholds\"], th)\n",
+    "                    pths[th][0].append(roc[\"tpr\"][idx])\n",
+    "                    pths[th][1].append(roc[\"fpr\"][idx])\n",
+    "\n",
+    "                # plt.vlines(\n",
+    "                #     x=roc[\"tpr\"][np.searchsorted(roc[\"fpr\"], 0.01)],\n",
+    "                #     ymin=0,\n",
+    "                #     ymax=0.01,\n",
+    "                #     colors=c,\n",
+    "                #     linestyles=\"dashed\",\n",
+    "                # )\n",
+    "\n",
+    "            for k, th in enumerate(plot_thresholds):\n",
+    "                plt.scatter(\n",
+    "                    *pths[th],\n",
+    "                    marker=\"o\",\n",
+    "                    s=40,\n",
+    "                    label=f\"THVV > {th:.2f}\",\n",
+    "                    color=th_colours[k],\n",
+    "                    zorder=100,\n",
+    "                )\n",
+    "            hep.cms.label(data=False, rlabel=\"\")\n",
+    "            # plt.hlines(y=0.01, xmin=0, xmax=1, colors=\"lightgrey\", linestyles=\"dashed\")\n",
+    "            plt.yscale(\"log\")\n",
+    "            plt.xlabel(\"Signal efficiency\")\n",
+    "            plt.ylabel(\"Background efficiency\")\n",
+    "            # plt.suptitle(f\"HVV FatJet {pvars['title']} ROC\", y=0.95)\n",
+    "            # plt.title(cut_labels[cutstr], fontsize=20)\n",
+    "            plt.xlim(*xlim)\n",
+    "            plt.ylim(*ylim)\n",
+    "            plt.legend(loc=\"lower right\")\n",
+    "            plt.savefig(f\"{plot_dir}/roccurve_{split_str}_{t}_{cutstr}.pdf\", bbox_inches=\"tight\")"
    ]
   },
   {
@@ -499,6 +672,233 @@
     "    plt.savefig(f\"{plot_dir}/roccurve_{cutstr}.pdf\", bbox_inches=\"tight\")"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BDT Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inferences_dir = f\"{samples_dir}/inferences/\"\n",
+    "inferences_dir_binary = f\"{samples_dir}/inferences_binary/\"\n",
+    "rocs = {\"inferences\": {}, \"inferences_binary\": {}}\n",
+    "\n",
+    "for arr in [\"tpr\", \"fpr\", \"thresholds\"]:\n",
+    "    print(arr)\n",
+    "    rocs[\"inferences\"][arr] = np.loadtxt(f\"{inferences_dir}/{arr}.txt\")\n",
+    "    # rocs[\"inferences_binary\"][arr] = np.loadtxt(f\"{inferences_dir_binary}/{arr}.txt\")\n",
+    "\n",
+    "roc_labels = {\"inferences\": \"Multiclass\", \"inferences_binary\": \"Binary\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlim = [0, 1]\n",
+    "ylim = [1e-6, 1]\n",
+    "\n",
+    "roc_colours = [\n",
+    "    \"#23CE6B\",\n",
+    "    \"#a70000\",\n",
+    "]\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 24})\n",
+    "\n",
+    "pths = {th: [[], []] for th in plot_thresholds}\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "for i, inf in enumerate([\"inferences\", \"inferences_binary\"]):\n",
+    "    roc = rocs[inf]\n",
+    "    c = roc_colours[i]\n",
+    "\n",
+    "    plt.plot(\n",
+    "        roc[\"tpr\"],\n",
+    "        roc[\"fpr\"],\n",
+    "        label=roc_labels[inf],\n",
+    "        linewidth=2,\n",
+    "        color=c,\n",
+    "    )\n",
+    "\n",
+    "hep.cms.label(data=False, rlabel=\"\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Signal efficiency\")\n",
+    "plt.ylabel(\"Background efficiency\")\n",
+    "plt.xlim(*xlim)\n",
+    "plt.ylim(*ylim)\n",
+    "plt.legend(loc=\"upper left\")\n",
+    "plt.savefig(f\"{plot_dir}/roc_multi_binary_bdt.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xlim = [0, 1]\n",
+    "ylim = [1e-6, 1]\n",
+    "\n",
+    "plot_thresholds = [0.6, 0.9, 0.96, 0.99, 0.997, 0.998, 0.999]\n",
+    "th_colours = [\n",
+    "    \"#36213E\",\n",
+    "    \"#9381FF\",\n",
+    "    \"#1f78b4\",\n",
+    "    # \"#a6cee3\",\n",
+    "    # \"#32965D\",\n",
+    "    \"#7CB518\",\n",
+    "    \"#EDB458\",\n",
+    "    \"#ff7f00\",\n",
+    "    \"#a70000\",\n",
+    "]\n",
+    "\n",
+    "roc_colours = [\n",
+    "    \"#23CE6B\",\n",
+    "    \"#a70000\",\n",
+    "]\n",
+    "\n",
+    "plt.rcParams.update({\"font.size\": 24})\n",
+    "\n",
+    "pths = {th: [[], []] for th in plot_thresholds}\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "for i, inf in enumerate([\"inferences\"]):\n",
+    "    roc = rocs[inf]\n",
+    "    c = roc_colours[i]\n",
+    "\n",
+    "    plt.plot(\n",
+    "        roc[\"tpr\"],\n",
+    "        roc[\"fpr\"],\n",
+    "        # label=roc_labels[inf],\n",
+    "        linewidth=2,\n",
+    "        color=c,\n",
+    "    )\n",
+    "\n",
+    "    for th in plot_thresholds:\n",
+    "        idx = find_nearest(roc[\"thresholds\"], th)\n",
+    "        pths[th][0].append(roc[\"tpr\"][idx])\n",
+    "        pths[th][1].append(roc[\"fpr\"][idx])\n",
+    "\n",
+    "for k, th in enumerate(plot_thresholds):\n",
+    "    plt.scatter(\n",
+    "        *pths[th],\n",
+    "        marker=\"o\",\n",
+    "        s=40,\n",
+    "        label=f\"BDT Score > {th}\",\n",
+    "        color=th_colours[k],\n",
+    "        zorder=100,\n",
+    "    )\n",
+    "\n",
+    "hep.cms.label(data=False, rlabel=\"\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.xlabel(\"Signal efficiency\")\n",
+    "plt.ylabel(\"Background efficiency\")\n",
+    "plt.xlim(*xlim)\n",
+    "plt.ylim(*ylim)\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "# plt.savefig(f\"{plot_dir}/roc_bdt.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trimming features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpr_tpr_th = np.array(\n",
+    "    [\n",
+    "        6.841912584548129e-05,\n",
+    "        0.06309407980527611,\n",
+    "        0.997972309589386,\n",
+    "        5.5970691547507736e-05,\n",
+    "        0.06309112513484341,\n",
+    "        0.9981555342674255,\n",
+    "        6.062037416775686e-05,\n",
+    "        0.06309433205678786,\n",
+    "        0.9980131387710571,\n",
+    "        6.282697172598808e-05,\n",
+    "        0.06309181657972177,\n",
+    "        0.9981679916381836,\n",
+    "        7.333505621545634e-05,\n",
+    "        0.06308829354373516,\n",
+    "        0.9979708790779114,\n",
+    "        7.116201530873543e-05,\n",
+    "        0.06309218251750907,\n",
+    "        0.9979798197746277,\n",
+    "        7.2475727068077e-05,\n",
+    "        0.06310060756610338,\n",
+    "        0.9979470372200012,\n",
+    "        7.738615489911652e-05,\n",
+    "        0.06309448855598651,\n",
+    "        0.9979099631309509,\n",
+    "        8.047411596104979e-05,\n",
+    "        0.06309097845150874,\n",
+    "        0.9977815747261047,\n",
+    "        8.503279607366034e-05,\n",
+    "        0.06308554741097783,\n",
+    "        0.9978626370429993,\n",
+    "        8.495399767538146e-05,\n",
+    "        0.06309224241230124,\n",
+    "        0.9976242184638977,\n",
+    "        8.807786735948429e-05,\n",
+    "        0.06308749819238176,\n",
+    "        0.9974913597106934,\n",
+    "        0.00034950987699599445,\n",
+    "        0.06309245484995998,\n",
+    "        0.9928722381591797,\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "fpr = fpr_tpr_th[::3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.plot(fpr)\n",
+    "plt.xlabel(\"Features Removed\")\n",
+    "plt.ylabel(\"Background efficiency\")\n",
+    "plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0, 0))\n",
+    "plt.savefig(f\"{plot_dir}/feats_removed.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uls = np.array(\n",
+    "    [[0, 53.25], [1, 50.25], [3, 49.5], [4, 49.9375], [6, 50.25], [8, 52.75], [11, 58.8]]\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.plot(uls[:, 0], uls[:, 1])\n",
+    "plt.xlabel(\"Features Removed\")\n",
+    "plt.ylabel(\"Median expected upper limit xSM\")\n",
+    "# plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))\n",
+    "plt.savefig(f\"{plot_dir}/feats_removed_uls.pdf\", bbox_inches=\"tight\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/HHbbVV/postprocessing/PlotFits.ipynb b/src/HHbbVV/postprocessing/PlotFits.ipynb
index 5c719f50..8a8cc77b 100644
--- a/src/HHbbVV/postprocessing/PlotFits.ipynb
+++ b/src/HHbbVV/postprocessing/PlotFits.ipynb
@@ -6,13 +6,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from collections import OrderedDict\n",
+    "\n",
     "import uproot\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import hist\n",
     "from hist import Hist\n",
     "\n",
     "import plotting\n",
-    "from hh_vars import sig_key, data_key\n",
+    "from hh_vars import data_key\n",
+    "from postprocessing import nonres_shape_vars, get_nonres_selection_regions\n",
     "\n",
     "import os"
    ]
@@ -35,7 +40,7 @@
    "source": [
     "MAIN_DIR = \"../../../\"\n",
     "\n",
-    "plot_dir = \"../../../plots/PostFit/Mar3\"\n",
+    "plot_dir = \"../../../plots/PostFit/23Sep1Nonres\"\n",
     "_ = os.system(f\"mkdir -p {plot_dir}\")"
    ]
   },
@@ -45,8 +50,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "cards_dir = \"23May16RemFeats/rem_feats_3/\"\n",
     "file = uproot.open(\n",
-    "    \"/uscms/home/rkansal/nobackup/CMSSW_11_2_0/src/cards/Mar3/fitDiagnosticsBlinded.root\"\n",
+    "    f\"/uscms/home/rkansal/hhcombine/cards/{cards_dir}/FitShapes.root\"\n",
+    "    # f\"/uscms/home/rkansal/eos/bbVV/cards/{cards_dir}/FitShapes.root\"\n",
     ")"
    ]
   },
@@ -56,17 +63,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hist_label_map = {\n",
-    "    \"data\": data_key,\n",
-    "    \"bbWW_boosted_ggf_qcd_datadriven\": \"QCD\",\n",
-    "    \"vjets\": \"V+Jets\",\n",
-    "    \"singletop\": \"ST\",\n",
-    "    \"diboson\": \"Diboson\",\n",
-    "    \"ttbar\": \"TT\",\n",
-    "    \"ggHH_kl_1_kt_1_hbbhww4q\": \"HHbbVV\",\n",
-    "}\n",
+    "# (name in templates, name in cards)\n",
+    "hist_label_map_inverse = OrderedDict(\n",
+    "    [\n",
+    "        (\"QCD\", \"bbWW_boosted_ggf_qcd_datadriven\"),\n",
+    "        # (\"Diboson\", \"diboson\"),\n",
+    "        (\"TT\", \"ttbar\"),\n",
+    "        (\"ST\", \"singletop\"),\n",
+    "        (\"V+Jets\", \"vjets\"),\n",
+    "        # (\"X[3000]->H(bb)Y[190](VV)\", \"xhy_mx3000_my190\"),\n",
+    "        (\"HHbbVV\", \"ggHH_kl_1_kt_1_hbbhww4q\"),\n",
+    "        (data_key, \"data_obs\"),\n",
+    "    ]\n",
+    ")\n",
     "\n",
-    "hist_label_map_inverse = {val: key for key, val in hist_label_map.items()}\n",
+    "hist_label_map = {val: key for key, val in hist_label_map_inverse.items()}\n",
     "samples = list(hist_label_map.values())"
    ]
   },
@@ -76,15 +87,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# bb regressed mass is final shape var\n",
-    "shape_var = (\"bbFatJetParticleNetMass\", r\"$m^{bb}_{reg}$ (GeV)\")\n",
-    "shape_bins = [20, 50, 250]  # num bins, min, max\n",
-    "blind_window = [100, 150]\n",
-    "regions = {\"fail\": \"Fail\", \"pass\": \"Pass\"}\n",
     "shapes = {\n",
-    "    \"shapes_prefit\": \"Pre-Fit\",\n",
-    "    \"shapes_fit_s\": \"S+B Post-Fit\",\n",
-    "    \"shapes_fit_b\": \"B-only Post-Fit\",\n",
+    "    \"prefit\": \"Pre-Fit\",\n",
+    "    # \"shapes_fit_s\": \"S+B Post-Fit\",\n",
+    "    \"postfit\": \"B-only Post-Fit\",\n",
+    "}\n",
+    "\n",
+    "shape_vars = nonres_shape_vars\n",
+    "\n",
+    "selection_regions = {\n",
+    "    \"pass\": \"Pass\",\n",
+    "    \"fail\": \"Fail\",\n",
     "}"
    ]
   },
@@ -95,33 +108,33 @@
    "outputs": [],
    "source": [
     "hists = {}\n",
-    "data_errs = {}\n",
     "\n",
     "for shape in shapes:\n",
+    "    print(shape)\n",
     "    hists[shape] = {\n",
-    "        region: Hist.new.StrCat(samples, name=\"Sample\")\n",
-    "        .Reg(*shape_bins, name=shape_var[0], label=shape_var[1])\n",
-    "        .Double()\n",
-    "        for region in regions\n",
+    "        region: Hist(\n",
+    "            hist.axis.StrCategory(samples, name=\"Sample\"),\n",
+    "            *[shape_var.axis for shape_var in shape_vars],\n",
+    "            storage=\"double\",\n",
+    "        )\n",
+    "        for region in selection_regions\n",
     "    }\n",
     "\n",
-    "    data_errs[shape] = {}\n",
-    "\n",
-    "    for region in regions:\n",
+    "    for region in selection_regions:\n",
     "        h = hists[shape][region]\n",
+    "        templates = file[f\"{region}_{shape}\"]\n",
     "        for key, file_key in hist_label_map_inverse.items():\n",
     "            if key != data_key:\n",
+    "                if file_key not in templates:\n",
+    "                    print(f\"No {key} in {region}\")\n",
+    "                    continue\n",
+    "\n",
     "                data_key_index = np.where(np.array(list(h.axes[0])) == key)[0][0]\n",
-    "                h.view(flow=False)[data_key_index] = file[shape][region][file_key].values() * 10\n",
+    "                h.view(flow=False)[data_key_index, :] = templates[file_key].values()\n",
     "\n",
     "        data_key_index = np.where(np.array(list(h.axes[0])) == data_key)[0][0]\n",
-    "        h.view(flow=False)[data_key_index] = file[shape][region][\"data\"].values()[1] * 10\n",
-    "\n",
-    "        data_errs[shape][region] = np.stack(\n",
-    "            (\n",
-    "                file[shape][region][\"data\"].errors(which=\"low\")[1] * 10,\n",
-    "                file[shape][region][\"data\"].errors(which=\"high\")[1] * 10,\n",
-    "            )\n",
+    "        h.view(flow=False)[data_key_index, :] = np.nan_to_num(\n",
+    "            templates[hist_label_map_inverse[data_key]].values()\n",
     "        )"
    ]
   },
@@ -131,17 +144,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for shape, slabel in shapes.items():\n",
-    "    for region, rlabel in regions.items():\n",
-    "        plotting.ratioHistPlot(\n",
-    "            hists[shape][region],\n",
-    "            \"all\",\n",
-    "            [\"QCD\", \"ST\", \"V+Jets\", \"TT\", \"Diboson\"],\n",
-    "            data_err=data_errs[shape][region],\n",
-    "            title=f\"{rlabel} Region {slabel} Shapes\",\n",
-    "            name=f\"{plot_dir}/{region}_{shape}.pdf\",\n",
-    "            show=True,\n",
-    "        )"
+    "pass_ylim = 20\n",
+    "fail_ylim = 600000\n",
+    "for shape, shape_label in shapes.items():\n",
+    "    for region, region_label in selection_regions.items():\n",
+    "        pass_region = region.startswith(\"pass\")\n",
+    "        for i, shape_var in enumerate(shape_vars):\n",
+    "            plot_params = {\n",
+    "                \"hists\": hists[shape][region],\n",
+    "                \"sig_keys\": [f\"HHbbVV\"],\n",
+    "                \"bg_keys\": [\"QCD\", \"V+Jets\", \"TT\", \"ST\"],\n",
+    "                \"sig_scale_dict\": {\"HHbbVV\": 10},\n",
+    "                \"show\": True,\n",
+    "                \"year\": \"all\",\n",
+    "                \"ylim\": pass_ylim if pass_region else fail_ylim,\n",
+    "                \"title\": f\"{shape_label} {region_label} Region\",\n",
+    "                \"name\": f\"{plot_dir}/{shape}_{region}_{shape_var.var}.pdf\",\n",
+    "            }\n",
+    "\n",
+    "            plotting.ratioHistPlot(**plot_params)"
    ]
   },
   {
@@ -168,7 +189,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.15"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/postprocessing/PlotFitsRes.ipynb b/src/HHbbVV/postprocessing/PlotFitsRes.ipynb
new file mode 100644
index 00000000..66efac12
--- /dev/null
+++ b/src/HHbbVV/postprocessing/PlotFitsRes.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "\n",
+    "import uproot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import hist\n",
+    "from hist import Hist\n",
+    "\n",
+    "import plotting\n",
+    "from hh_vars import data_key\n",
+    "from postprocessing import res_shape_vars, get_res_selection_regions\n",
+    "\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../\"\n",
+    "\n",
+    "plot_dir = f\"{MAIN_DIR}/plots/PostFit/23Sep2\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cards_dir = \"f_tests/23May2/nTF1_2_nTF2_1\"\n",
+    "# cards_dir = \"biastests/23Jul11ResScaled100/NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250\"\n",
+    "file = uproot.open(\n",
+    "    f\"/uscms/home/rkansal/hhcombine/cards/{cards_dir}/FitShapes.root\"\n",
+    "    # f\"/uscms/home/rkansal/eos/bbVV/cards/{cards_dir}/FitShapes.root\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mx, my = 3000, 250\n",
+    "\n",
+    "# (name in templates, name in cards)\n",
+    "hist_label_map_inverse = OrderedDict(\n",
+    "    [\n",
+    "        (\"QCD\", \"XHYbbWW_boosted_qcd_datadriven\"),\n",
+    "        # (\"Diboson\", \"diboson\"),\n",
+    "        (\"TT\", \"ttbar\"),\n",
+    "        (\"ST\", \"singletop\"),\n",
+    "        (\"V+Jets\", \"vjets\"),\n",
+    "        # (\"X[3000]->H(bb)Y[190](VV)\", \"xhy_mx3000_my190\"),\n",
+    "        (f\"X[{mx}]->H(bb)Y[{my}](VV)\", f\"xhy_mx{mx}_my{my}\"),\n",
+    "        (data_key, \"data_obs\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "hist_label_map = {val: key for key, val in hist_label_map_inverse.items()}\n",
+    "samples = list(hist_label_map.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes = {\n",
+    "    \"prefit\": \"Pre-Fit\",\n",
+    "    # \"shapes_fit_s\": \"S+B Post-Fit\",\n",
+    "    \"postfit\": \"B-only Post-Fit\",\n",
+    "}\n",
+    "\n",
+    "shape_vars = res_shape_vars\n",
+    "\n",
+    "selection_regions = OrderedDict(\n",
+    "    [\n",
+    "        (\"fail\", \"Fail\"),\n",
+    "        (\"pass\", \"Pass\"),\n",
+    "        (\"failBlinded\", \"Validation Fail\"),\n",
+    "        (\"passBlinded\", \"Validation Pass\"),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hists = {}\n",
+    "\n",
+    "bins = list(shape_vars[0].axis)\n",
+    "binsize = np.array([b[1] - b[0] for b in bins])\n",
+    "\n",
+    "for shape in shapes:\n",
+    "    hists[shape] = {\n",
+    "        region: Hist(\n",
+    "            hist.axis.StrCategory(samples, name=\"Sample\"),\n",
+    "            *[shape_var.axis for shape_var in shape_vars],\n",
+    "            storage=\"double\",\n",
+    "        )\n",
+    "        for region in selection_regions\n",
+    "    }\n",
+    "\n",
+    "    for region in selection_regions:\n",
+    "        h = hists[shape][region]\n",
+    "\n",
+    "        for i in range(len(shape_vars[1].axis)):  # mX bins\n",
+    "            # templates = file[shape][f\"mXbin{i}{region}\"]\n",
+    "            templates = file[f\"mXbin{i}{region}_{shape}\"]\n",
+    "            for key, file_key in hist_label_map_inverse.items():\n",
+    "                if key != data_key:\n",
+    "                    if file_key not in templates:\n",
+    "                        # print(f\"No {key} in mXbin{i}{region}\")\n",
+    "                        continue\n",
+    "\n",
+    "                    data_key_index = np.where(np.array(list(h.axes[0])) == key)[0][0]\n",
+    "                    # h.view(flow=False)[data_key_index, :, i] = (\n",
+    "                    #     templates[file_key].values() * binsize\n",
+    "                    # )\n",
+    "                    h.view(flow=False)[data_key_index, :, i] = templates[file_key].values()\n",
+    "\n",
+    "            data_key_index = np.where(np.array(list(h.axes[0])) == data_key)[0][0]\n",
+    "            # h.view(flow=False)[data_key_index, :, i] = (\n",
+    "            #     templates[hist_label_map_inverse[data_key]].values()[1] * binsize\n",
+    "            # )\n",
+    "            h.view(flow=False)[data_key_index, :, i] = np.nan_to_num(\n",
+    "                templates[hist_label_map_inverse[data_key]].values()\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pass_ylims = [16, 3]\n",
+    "fail_ylims = [14000, 1700]\n",
+    "\n",
+    "for shape, shape_label in shapes.items():\n",
+    "    for i, shape_var in enumerate(shape_vars):\n",
+    "        # add \"invisible\" subplots between main plots to add spacing\n",
+    "        # https://stackoverflow.com/a/53643819/3759946\n",
+    "        fig, axs = plt.subplots(\n",
+    "            5,\n",
+    "            3,\n",
+    "            figsize=(25, 29),\n",
+    "            gridspec_kw=dict(\n",
+    "                height_ratios=[3, 1, 0.5, 3, 1], width_ratios=[1, 0.12, 1], hspace=0, wspace=0\n",
+    "            ),\n",
+    "        )\n",
+    "\n",
+    "        for ax in axs[2]:\n",
+    "            ax.set_visible(False)\n",
+    "\n",
+    "        for ax in axs[:, 1]:\n",
+    "            ax.set_visible(False)\n",
+    "\n",
+    "        for j, (region, region_label) in enumerate(selection_regions.items()):\n",
+    "            row = (j // 2) * 3\n",
+    "            col = (j % 2) * 2\n",
+    "            pass_region = region.startswith(\"pass\")\n",
+    "\n",
+    "            plot_params = {\n",
+    "                \"hists\": hists[shape][region].project(0, i + 1),\n",
+    "                \"sig_keys\": [f\"X[{mx}]->H(bb)Y[{my}](VV)\"],\n",
+    "                \"bg_keys\": [\"QCD\", \"V+Jets\", \"TT\", \"ST\"],\n",
+    "                \"sig_scale_dict\": {f\"X[{mx}]->H(bb)Y[{my}](VV)\": 10},\n",
+    "                # \"show\": False,\n",
+    "                \"year\": \"all\",\n",
+    "                \"ylim\": pass_ylims[i] if pass_region else fail_ylims[i],\n",
+    "                \"plot_data\": region != \"pass\",\n",
+    "                # \"title\": f\"{shape_label} {region_label} Region\",\n",
+    "                # \"name\": f\"{plot_dir}/{shape}_{region}_{shape_var.var}.pdf\",\n",
+    "                \"divide_bin_width\": True,\n",
+    "                \"axrax\": (axs[row, col], axs[row + 1, col]),\n",
+    "            }\n",
+    "\n",
+    "            plotting.ratioHistPlot(**plot_params)\n",
+    "            axs[row, col].set_title(region_label, x=0.55, y=1.002)\n",
+    "\n",
+    "        plt.savefig(f\"{plot_dir}/{shape}_{shape_var.var}.pdf\", bbox_inches=\"tight\")\n",
+    "        plt.show()\n",
+    "    #     break\n",
+    "    # break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for shape, shape_label in shapes.items():\n",
+    "    _ = os.system(f\"mkdir -p {plot_dir}/{shape}\")\n",
+    "    samples = (\n",
+    "        [\"Data\", \"TT\", \"V+Jets\", \"QCD\", \"X[3000]->H(bb)Y[190](VV)\"]\n",
+    "        if shape == \"shapes_prefit\"\n",
+    "        else [\"TT\", \"V+Jets\", \"QCD\"]\n",
+    "    )\n",
+    "\n",
+    "    plotting.hist2ds(\n",
+    "        hists[shape],\n",
+    "        f\"{plot_dir}/{shape}/\",\n",
+    "        regions=[\"pass\", \"fail\", \"passBlinded\", \"failBlinded\"],\n",
+    "        region_labels=selection_regions,\n",
+    "        samples=samples,\n",
+    "        # fail_zlim=5e3,\n",
+    "        # pass_zlim=1.0,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "bbVV",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "5b9eab485576227e6cf1b964bb8855c46cbdf15c3e77cecdb2bb309145d3e8d8"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/postprocessing/PostProcess.ipynb b/src/HHbbVV/postprocessing/PostProcess.ipynb
index 7a99d5e8..82e46657 100644
--- a/src/HHbbVV/postprocessing/PostProcess.ipynb
+++ b/src/HHbbVV/postprocessing/PostProcess.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,27 +10,22 @@
     "import plotting\n",
     "import postprocessing\n",
     "import corrections\n",
+    "from collections import OrderedDict\n",
     "\n",
     "from utils import CUT_MAX_VAL\n",
     "from hh_vars import (\n",
     "    years,\n",
-    "    sig_key,\n",
     "    data_key,\n",
     "    qcd_key,\n",
     "    bg_keys,\n",
     "    samples,\n",
+    "    nonres_sig_keys,\n",
+    "    nonres_samples,\n",
     "    txbb_wps,\n",
     "    jec_shifts,\n",
     "    jmsr_shifts,\n",
     ")\n",
-    "from postprocessing import (\n",
-    "    shape_var,\n",
-    "    shape_bins,\n",
-    "    blind_window,\n",
-    "    selection_regions,\n",
-    "    selection_regions_label,\n",
-    "    # selection_regions_year,\n",
-    ")\n",
+    "from postprocessing import nonres_shape_vars\n",
     "\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -59,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,21 +64,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "MAIN_DIR = \"../../../\"\n",
     "samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
-    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
-    "year = \"2017\"\n",
+    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Jun10\"\n",
+    "bdt_preds_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24/23_05_12_multiclass_rem_feats_3/inferences/\"\n",
+    "# samples_dir = \"/eos/uscms/store/user/rkansal/bbVV/skimmer/Feb24/\"\n",
+    "# signal_samples_dir = \"/eos/uscms/store/user/cmantill/bbVV/skimmer/Jun10/\"\n",
+    "# bdt_data_dir = \"/eos/uscms/store/user/cmantill/bbVV/skimmer/Jun10/bdt_data/\"\n",
+    "year = \"2018\"\n",
     "\n",
-    "plot_dir = \"../../../plots/PostProcessing/Feb28\"\n",
-    "templates_dir = \"templates/Feb28/\"\n",
+    "date = \"23Aug21\"\n",
+    "plot_dir = f\"../../../plots/PostProcessing/{date}/\"\n",
+    "templates_dir = f\"templates/{date}\"\n",
     "_ = os.system(f\"mkdir -p {plot_dir}\")\n",
     "_ = os.system(f\"mkdir -p {plot_dir}/cutflows/\")\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}/templates/\")\n",
-    "# _ = os.system(f\"mkdir -p {templates_dir}\")"
+    "_ = os.system(f\"mkdir -p {plot_dir}/ControlPlots/{year}/\")\n",
+    "# _ = os.system(f\"mkdir -p {plot_dir}/templates/\")\n",
+    "# _ = os.system(f\"mkdir -p {plot_dir}/templates/wshifts\")\n",
+    "# _ = os.system(f\"mkdir -p {plot_dir}/templates/jshifts\")\n",
+    "# _ = os.system(f\"mkdir -p {templates_dir}\")\n",
+    "\n",
+    "selection_regions = postprocessing.get_nonres_selection_regions(year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{bdt_preds_dir}/{year}/sample_order.txt\", \"r\") as f:\n",
+    "    BDT_sample_order = list(eval(f.read()).keys())\n",
+    "\n",
+    "for key in nonres_sig_keys.copy():\n",
+    "    if key not in BDT_sample_order:\n",
+    "        del nonres_samples[key]\n",
+    "        nonres_sig_keys.remove(key)\n",
+    "\n",
+    "for key in bg_keys.copy():\n",
+    "    if key not in BDT_sample_order:\n",
+    "        del samples[key]\n",
+    "        bg_keys.remove(key)"
    ]
   },
   {
@@ -96,67 +121,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded GluGluToHHTobbVV_node_cHHH1                       : 147046 entries\n",
-      "Loaded JetHT_Run2017D                                    : 125980 entries\n",
-      "Loaded JetHT_Run2017C                                    : 263891 entries\n",
-      "Loaded JetHT_Run2017B                                    : 92308 entries\n",
-      "Loaded JetHT_Run2017E                                    : 181075 entries\n",
-      "Loaded JetHT_Run2017F                                    : 445476 entries\n",
-      "Loaded ST_s-channel_4f_hadronicDecays                    : 19893 entries\n",
-      "Loaded ST_tW_top_5f_inclusiveDecays                      : 16222 entries\n",
-      "Loaded ST_tW_antitop_5f_inclusiveDecays                  : 11969 entries\n",
-      "Loaded ST_s-channel_4f_leptonDecays                      : 10241 entries\n",
-      "Loaded ST_t-channel_antitop_4f_InclusiveDecays           : 31411 entries\n",
-      "Loaded QCD_HT300to500                                    : 15 entries\n",
-      "Loaded QCD_HT200to300                                    : 0 entries\n",
-      "Loaded QCD_HT700to1000                                   : 148986 entries\n",
-      "Loaded QCD_HT1000to1500                                  : 95305 entries\n",
-      "Loaded QCD_HT2000toInf                                   : 51615 entries\n",
-      "Loaded QCD_HT1500to2000                                  : 93470 entries\n",
-      "Loaded QCD_HT500to700                                    : 16447 entries\n",
-      "Loaded TTToSemiLeptonic                                  : 386398 entries\n",
-      "Loaded TTToHadronic                                      : 795244 entries\n",
-      "Loaded WW                                                : 940 entries\n",
-      "Loaded ZZ                                                : 1406 entries\n",
-      "Loaded WZ                                                : 2261 entries\n",
-      "Loaded WJetsToQQ_HT-200to400                             : 0 entries\n",
-      "Loaded ZJetsToQQ_HT-200to400                             : 1 entries\n",
-      "Loaded ZJetsToQQ_HT-400to600                             : 1228 entries\n",
-      "Loaded WJetsToQQ_HT-800toInf                             : 153651 entries\n",
-      "Loaded ZJetsToQQ_HT-600to800                             : 72308 entries\n",
-      "Loaded WJetsToQQ_HT-600to800                             : 32805 entries\n",
-      "Loaded ZJetsToQQ_HT-800toInf                             : 321938 entries\n",
-      "Loaded WJetsToQQ_HT-400to600                             : 240 entries\n",
-      "\n",
-      "Pre-selection HHbbVV yield: 3.08\n",
-      "Pre-selection Data yield: 1108730.00\n",
-      "Pre-selection ST yield: 9979.99\n",
-      "Pre-selection QCD yield: 2297339.83\n",
-      "Pre-selection TT yield: 139361.71\n",
-      "Pre-selection Diboson yield: 946.71\n",
-      "Pre-selection V+Jets yield: 63475.58\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "filters = postprocessing.filters\n",
-    "systematics = {}\n",
+    "filters = postprocessing.new_filters\n",
+    "systematics = {year: {}}\n",
     "\n",
     "# save cutflow as pandas table\n",
-    "cutflow = pd.DataFrame(index=list(samples.keys()))\n",
+    "cutflow = pd.DataFrame(index=list(samples.keys()) + list(nonres_samples.keys()))\n",
     "\n",
     "# utils.remove_empty_parquets(samples_dir, year)\n",
-    "events_dict = utils.load_samples(signal_samples_dir, {sig_key: samples[sig_key]}, year, filters)\n",
-    "events_dict |= utils.load_samples(\n",
-    "    samples_dir, {k: samples[k] for k in samples.keys() - [sig_key]}, year, filters\n",
-    ")\n",
+    "events_dict = utils.load_samples(signal_samples_dir, nonres_samples, year, filters)\n",
+    "events_dict |= utils.load_samples(samples_dir, samples, year, filters)\n",
     "\n",
     "utils.add_to_cutflow(events_dict, \"BDTPreselection\", \"weight\", cutflow)\n",
     "\n",
@@ -177,106 +154,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "QCD_SCALE_FACTOR = 0.8141631998851222\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDTPreselection</th>\n",
-       "      <th>TriggerEffs</th>\n",
-       "      <th>QCD SF</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HHbbVV</th>\n",
-       "      <td>3.080614e+00</td>\n",
-       "      <td>1.777757e+00</td>\n",
-       "      <td>1.777757e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QCD</th>\n",
-       "      <td>2.297340e+06</td>\n",
-       "      <td>1.194347e+06</td>\n",
-       "      <td>9.723930e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TT</th>\n",
-       "      <td>1.393617e+05</td>\n",
-       "      <td>9.016284e+04</td>\n",
-       "      <td>9.016284e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ST</th>\n",
-       "      <td>9.979986e+03</td>\n",
-       "      <td>6.717597e+03</td>\n",
-       "      <td>6.717597e+03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V+Jets</th>\n",
-       "      <td>6.347558e+04</td>\n",
-       "      <td>3.886090e+04</td>\n",
-       "      <td>3.886090e+04</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Diboson</th>\n",
-       "      <td>9.467148e+02</td>\n",
-       "      <td>5.956462e+02</td>\n",
-       "      <td>5.956462e+02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Data</th>\n",
-       "      <td>1.108730e+06</td>\n",
-       "      <td>1.108730e+06</td>\n",
-       "      <td>1.108730e+06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         BDTPreselection   TriggerEffs        QCD SF\n",
-       "HHbbVV      3.080614e+00  1.777757e+00  1.777757e+00\n",
-       "QCD         2.297340e+06  1.194347e+06  9.723930e+05\n",
-       "TT          1.393617e+05  9.016284e+04  9.016284e+04\n",
-       "ST          9.979986e+03  6.717597e+03  6.717597e+03\n",
-       "V+Jets      6.347558e+04  3.886090e+04  3.886090e+04\n",
-       "Diboson     9.467148e+02  5.956462e+02  5.956462e+02\n",
-       "Data        1.108730e+06  1.108730e+06  1.108730e+06"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "postprocessing.apply_weights(events_dict, year, cutflow)\n",
     "bb_masks = postprocessing.bb_VV_assignment(events_dict)\n",
@@ -286,35 +166,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "postprocessing.load_bdt_preds(events_dict, year, bdt_preds_dir, jec_jmsr_shifts=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.800000011920929"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "np.min(utils.get_feat(events_dict[sig_key], \"bbFatJetParticleNetMD_Txbb\", bb_masks[sig_key]))"
+    "Control plots"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "postprocessing.load_bdt_preds(\n",
-    "    events_dict, year, f\"{samples_dir}/inferences/\", list(samples.keys()), jec_jmsr_shifts=True\n",
+    "# {var: (bins, label)}\n",
+    "control_plot_vars = {\n",
+    "    # \"MET_pt\": ([50, 0, 300], r\"$p^{miss}_T$ (GeV)\"),\n",
+    "    # \"DijetEta\": ([50, -8, 8], r\"$\\eta^{jj}$\"),\n",
+    "    # \"DijetPt\": ([50, 0, 750], r\"$p_T^{jj}$ (GeV)\"),\n",
+    "    # \"DijetMass\": (\n",
+    "    #     # list(range(800, 1400, 100)) + [1400, 1600, 2000, 3000, 4400],\n",
+    "    #     [40, 600, 4500],\n",
+    "    #     r\"$m^{jj}$ (GeV)\",\n",
+    "    # ),\n",
+    "    # \"bbFatJetEta\": ([50, -2.4, 2.4], r\"$\\eta^{bb}$\"),\n",
+    "    # \"bbFatJetPt\": ([50, 300, 1500], r\"$p^{bb}_T$ (GeV)\"),\n",
+    "    # \"bbFatJetParticleNetMass\": ([20, 50, 250], r\"$m^{bb}_{reg}$ (GeV)\"),\n",
+    "    # \"bbFatJetMsd\": ([50, 0, 300], r\"$m^{bb}_{msd}$ (GeV)\"),\n",
+    "    # \"bbFatJetParticleNetMD_Txbb\": ([50, 0.8, 1], r\"$T^{bb}_{Xbb}$\"),\n",
+    "    # \"VVFatJetEta\": ([50, -2.4, 2.4], r\"$\\eta^{VV}$\"),\n",
+    "    # \"VVFatJetPt\": ([50, 300, 1500], r\"$p^{VV}_T$ (GeV)\"),\n",
+    "    # \"VVFatJetParticleNetMass\": (\n",
+    "    #     # list(range(50, 110, 10)) + list(range(110, 200, 15)) + [200, 220, 250],\n",
+    "    #     [20, 50, 250],\n",
+    "    #     r\"$m^{VV}_{reg}$ (GeV)\",\n",
+    "    # ),\n",
+    "    # \"VVFatJetMsd\": ([40, 50, 250], r\"$m^{VV}_{msd}$ (GeV)\"),\n",
+    "    # \"VVFatJetParticleNet_Th4q\": ([50, 0, 1], r\"Prob($H \\to 4q$) vs Prob(QCD) (Non-MD)\"),\n",
+    "    # \"VVFatJetParTMD_THWW4q\": (\n",
+    "    #     [50, 0, 1],\n",
+    "    #     r\"Prob($H \\to VV \\to 4q$) vs Prob(QCD) (Mass-Decorrelated)\",\n",
+    "    # ),\n",
+    "    # \"VVFatJetParTMD_probT\": ([50, 0, 1], r\"Prob(Top) (Mass-Decorrelated)\"),\n",
+    "    # \"VVFatJetParTMD_THWWvsT\": (\n",
+    "    #     [50, 0, 1],\n",
+    "    #     r\"$T^{VV}_{HWW}$\",\n",
+    "    # ),\n",
+    "    # \"bbFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{bb}_T / p_T^{jj}$\"),\n",
+    "    # \"VVFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{VV}_T / p_T^{jj}$\"),\n",
+    "    # \"VVFatJetPtOverbbFatJetPt\": ([50, 0.4, 2.0], r\"$p^{VV}_T / p^{bb}_T$\"),\n",
+    "    # \"nGoodMuons\": ([3, 0, 3], r\"# of Muons\"),\n",
+    "    # \"nGoodElectrons\": ([3, 0, 3], r\"# of Electrons\"),\n",
+    "    # \"nGoodJets\": ([5, 0, 5], r\"# of AK4 B-Jets\"),\n",
+    "    \"BDTScore\": ([50, 0, 1], r\"BDT Score\"),\n",
+    "}\n",
+    "\n",
+    "hists = postprocessing.control_plots(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    [\"HHbbVV\", \"qqHH_CV_1_C2V_1_kl_1_HHbbVV\"],\n",
+    "    control_plot_vars,\n",
+    "    f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "    year,\n",
+    "    bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Diboson\"],\n",
+    "    # bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "    sig_scale_dict={\"HHbbVV\": 2e5, \"qqHH_CV_1_C2V_1_kl_1_HHbbVV\": 2e6},\n",
+    "    show=True,\n",
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cuts = [0.01, 0.1, 0.5, 0.9, 0.99]\n",
+    "# bdtvars = [\"\", \"TT\", \"VJets\"]\n",
+    "bdtvars = [\"\"]\n",
+    "sig_scales = [1e5, 5e4, 2e4, 1e4, 2e3]\n",
+    "\n",
+    "# for ttcut in [0.01, 0.1, 0.5, 0.9, 0.99]:\n",
+    "#     ttsel, _ = utils.make_selection({\"BDTScoreTT\": [ttcut, CUT_MAX_VAL]}, events_dict, bb_masks)\n",
+    "#     cutstr = f\"tt{ttcut}\"\n",
+    "\n",
+    "#     hists = postprocessing.control_plots(\n",
+    "#         events_dict,\n",
+    "#         bb_masks,\n",
+    "#         nonres_sig_keys,\n",
+    "#         control_plot_vars,\n",
+    "#         f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "#         year,\n",
+    "#         hists={},\n",
+    "#         bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Diboson\"],\n",
+    "#         selection=ttsel,\n",
+    "#         cutstr=cutstr,\n",
+    "#         show=True,\n",
+    "#     )\n",
+    "\n",
+    "for var in bdtvars:\n",
+    "    for i, cut in enumerate(cuts):\n",
+    "        sel, _ = utils.make_selection({f\"BDTScore{var}\": [cut, CUT_MAX_VAL]}, events_dict, bb_masks)\n",
+    "        cutstr = f\"bdt{var}{cut}\"\n",
+    "        sig_scale = sig_scales[i]\n",
+    "\n",
+    "        hists = postprocessing.control_plots(\n",
+    "            events_dict,\n",
+    "            bb_masks,\n",
+    "            nonres_sig_keys,\n",
+    "            control_plot_vars,\n",
+    "            f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "            year,\n",
+    "            hists={},\n",
+    "            bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Diboson\"],\n",
+    "            selection=sel,\n",
+    "            cutstr=cutstr,\n",
+    "            sig_scale_dict={\"HHbbVV\": sig_scale},\n",
+    "            combine_pdf=False,\n",
+    "            show=True,\n",
+    "        )"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -325,123 +305,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "BDT Scale Factor: 0.82 ± 0.15\n",
-      "{'syst_unc': 0.14195442334809535, 'stat_unc': 0.03553479891157332, 'sj_pt_unc': 0.004660662592473464, 'sj_matching_unc': 0.11200010721561059}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDTPreselection</th>\n",
-       "      <th>TriggerEffs</th>\n",
-       "      <th>QCD SF</th>\n",
-       "      <th>BDTScore &gt; 0.99</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>HHbbVV</th>\n",
-       "      <td>3.080614e+00</td>\n",
-       "      <td>1.777757e+00</td>\n",
-       "      <td>1.777757e+00</td>\n",
-       "      <td>0.338860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QCD</th>\n",
-       "      <td>2.297340e+06</td>\n",
-       "      <td>1.194347e+06</td>\n",
-       "      <td>9.723930e+05</td>\n",
-       "      <td>720.153977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TT</th>\n",
-       "      <td>1.393617e+05</td>\n",
-       "      <td>9.016284e+04</td>\n",
-       "      <td>9.016284e+04</td>\n",
-       "      <td>186.938381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ST</th>\n",
-       "      <td>9.979986e+03</td>\n",
-       "      <td>6.717597e+03</td>\n",
-       "      <td>6.717597e+03</td>\n",
-       "      <td>17.328825</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>V+Jets</th>\n",
-       "      <td>6.347558e+04</td>\n",
-       "      <td>3.886090e+04</td>\n",
-       "      <td>3.886090e+04</td>\n",
-       "      <td>74.629383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Diboson</th>\n",
-       "      <td>9.467148e+02</td>\n",
-       "      <td>5.956462e+02</td>\n",
-       "      <td>5.956462e+02</td>\n",
-       "      <td>0.410420</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Data</th>\n",
-       "      <td>1.108730e+06</td>\n",
-       "      <td>1.108730e+06</td>\n",
-       "      <td>1.108730e+06</td>\n",
-       "      <td>1415.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         BDTPreselection   TriggerEffs        QCD SF  BDTScore > 0.99\n",
-       "HHbbVV      3.080614e+00  1.777757e+00  1.777757e+00         0.338860\n",
-       "QCD         2.297340e+06  1.194347e+06  9.723930e+05       720.153977\n",
-       "TT          1.393617e+05  9.016284e+04  9.016284e+04       186.938381\n",
-       "ST          9.979986e+03  6.717597e+03  6.717597e+03        17.328825\n",
-       "V+Jets      6.347558e+04  3.886090e+04  3.886090e+04        74.629383\n",
-       "Diboson     9.467148e+02  5.956462e+02  5.956462e+02         0.410420\n",
-       "Data        1.108730e+06  1.108730e+06  1.108730e+06      1415.000000"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sel, cf = utils.make_selection(\n",
-    "    selection_regions[year][\"BDTOnly\"], events_dict, bb_masks, prev_cutflow=cutflow\n",
-    ")\n",
-    "lp_sf, unc, uncs = postprocessing.get_lpsf(events_dict[sig_key], sel[sig_key])\n",
-    "print(f\"BDT Scale Factor: {lp_sf:.2f} ± {unc:.2f}\")\n",
-    "print(uncs)\n",
-    "systematics[\"lp_sf_unc\"] = unc / lp_sf\n",
-    "cf"
+    "nonres_sig_keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "postprocessing.lpsfs(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    nonres_sig_keys,\n",
+    "    nonres_samples,\n",
+    "    cutflow,\n",
+    "    selection_regions[\"lpsf\"],\n",
+    "    systematics,\n",
+    "    all_years=False,\n",
+    ")"
    ]
   },
   {
@@ -454,126 +340,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pass\n",
-      "\n",
-      "Cutflow:\n",
-      "          BDTPreselection   TriggerEffs        QCD SF  BDTScore > 0.99  \\\n",
-      "HHbbVV      3.080614e+00  1.777757e+00  1.777757e+00         0.338860   \n",
-      "QCD         2.297340e+06  1.194347e+06  9.723930e+05       720.153977   \n",
-      "TT          1.393617e+05  9.016284e+04  9.016284e+04       186.938381   \n",
-      "ST          9.979986e+03  6.717597e+03  6.717597e+03        17.328825   \n",
-      "V+Jets      6.347558e+04  3.886090e+04  3.886090e+04        74.629383   \n",
-      "Diboson     9.467148e+02  5.956462e+02  5.956462e+02         0.410420   \n",
-      "Data        1.108730e+06  1.108730e+06  1.108730e+06      1415.000000   \n",
-      "\n",
-      "         bbFatJetParticleNetMD_Txbb > 0.987  \n",
-      "HHbbVV                             0.161608  \n",
-      "QCD                               29.151636  \n",
-      "TT                                14.172425  \n",
-      "ST                                 1.322972  \n",
-      "V+Jets                            15.473229  \n",
-      "Diboson                            0.000000  \n",
-      "Data                             140.000000  \n",
-      "Trigger SF Unc.: 0.02519890558032111\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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JwcWLF9GmTRsAwOzZs/Hu3btc6ZNbHB0dASjPQgJpqwAAzUv+NSlTpgwMDAwAKC99/phiYzJtKZZ3ZzSmXC6X6hXxFwQWFhb466+/EB0djWHDhuHZs2cYP348qlSpIm0m+LFDhw7B3d0dpqamcHV1ha+vr9qfT23bZWTdunUYNmwYZs+erVX7fv36Sf/WdNtEVuTGe/PJkydISUnRWK/4OU//c5Pd98DH8vP9TURUEDDJJyKiPHHhwgUAQJ8+fVCyZEmVeiGE0sZaCikpKWjYsCHq16+vtLu2Qu3atfHXX38BSEtCQ0NDs9UnrxgbGwMAoqKilMoVG4cdP34cHz58UNs3Pj4ejRs3hpubG44fPw4gbbfyihUrAoDG+5iFENiyZUuW4lTcM71lyxYkJCSobbN7925ER0cDAMqXL5+l8fNbly5d0K9fP2zfvh2tWrVCXFwcAgICUKRIEZW2586dQ5cuXeDg4IDRo0fD1NQU06ZNQ9euXZWWf2vbLjOKa3/lyhXEx8dn2l7xPQDSPsDIqey+N9NLTk5GUFCQ2rqYmBjp6Q2Kn10g+++Bz+n9TURUEDDJJyKiPKG4X//58+dq62fPni39MZ6amiqV6+npITIyEufPn8fKlSvVJk/Xr1+X/u3i4pKtPnlFsWz61atXSnH06tULOjo6ePHiBXx9fdXGOHv2bJw5cwaPHz9GvXr1pPKff/4ZALB161acOnVKpV9AQIC0c7m2FLufv3jxAtOnT1eJ5/Xr15g8eTKAtM31ateunaXxPwe///47zM3Nce/ePYwePRq1atVS2+7+/fv4888/ERAQAF9fX5w8eRJ9+vTB/v37cejQoSy3y4zisXWvX7/GxIkTM22f/gMcdY9QzKrsvjc/NnXqVJVbSORyOcaNG4e3b99CR0cHPj4+Ul123wOf0/ubiKhAEERERGoMHDhQABAODg7Z6j9ixAgBQOjo6Ag/Pz9x4cIFceXKFbFp0ybRsmVLAUDIZDIBQNjY2Ijjx4+LmJgYIYQQ3333nQAgAAhvb29x7NgxERISIs6cOSOmT58uihUrJgCIFi1aSMfLTh9tTJkyRRpXG6tXr5bav337Vqlu1KhRUp2Hh4fYunWruH79ujh06JAYMGCAVDd37lylfsnJyaJChQoCgDAwMBBTp04VJ06cEEeOHBEjR44UAKR6ANJ1FEKIx48fS+WPHz+WyuVyuejYsaNU17t3b7Fjxw5x8eJFsXz5cuHk5CTV7dixQykeTWOmd+zYsSxdN03SH2vNmjVZ6nvy5EnpZ6xZs2YiNTVVpQ0A4ejoqFIXGhoqAIiRI0dmqZ222rVrJ53XwIEDxbNnz1TaREdHizlz5ggdHR0BQHTr1k2pPrvfh5y8NxXvB0W9k5OTWLFihbh48aIIDAwUHh4e0vEGDBigEk923wN59f4mIiqMmOQTEZFaOU3y3759K8qUKSP9Yf7xq3379mLt2rVKZceOHRNCCBEVFSWcnZ019gUgypYtKyIjI6XjZaePNrKa5B84cEBqv27dOqW6xMRE0adPnwxj9PHxEXK5XGXc69evixIlSqjtU6FCBXH37l0BQBgaGir1zygRfPv2rahfv36G8cyZM0clloKQ5H/48EE4OzsLMzMzMWjQIAFALF68WKUdANGpUyeVcrlcLooVKybVadtOW69evRJVq1aVzk1XV1e4uLiIdu3aic6dO4tatWoJExMTqb569eri9evXSmNk9/uQk/em4v3QpEkT0b9/f41j1KpVS7x580Ylnuy+B/Lq/U1EVBhxuT4REeUJc3NzXLlyBSNHjkSVKlVgZGQES0tLtG/fHlu3bsW+ffvQv39/jBs3DmZmZmjUqBHs7OwApO1Sf+PGDcyZMwf169eHra0tDAwM4OjoCHd3d6xYsQIhISFKO89np09eqFu3rrRkf/DgwUq71xsYGGDDhg3YuXMnOnTogJIlS8LAwADly5dH9+7dcebMGfzxxx9qd0qvVq0abt26haFDh0qb8ZUpUwZjxozBxYsXpT5WVlZa77Rubm6OkydP4s8//0Tjxo1RvHhx6Ovrw87ODv369cPFixcxZsyYXLgqn964cePw6NEjzJs3D0uWLIGzszPGjRsnPas9PR0d9X8OCSGUHrWnbTttlChRAufPn8e4ceNgbm6O1NRUPHz4EAcPHsTu3btx+fJlxMbGokSJEpg8eTLOnTsHS0vLLB1Dk5y8NxV0dHSwdu1aBAQEoF69ejAxMYGxsTFq1qyJ+fPn48yZM2r3D8jue+BzeX8TERUEMiH4QFEiIqKC7ujRo2jZsiXq1Kkjbaz2pTp27BhatGiBZs2a4ejRo9DR0cHhw4fRunVrNGnSBMePH5cSdplMBmdnZzx48EApib9//z4qVKiACRMmYMaMGVq3y46EhAQ8ePAAoaGhePToEfT19eHs7AwnJye4uLhIT1f4HPj5+WHq1Klo1qyZtDEeERF9XvTyOwAiIiLK2Pz583Hz5k20bt1a6XFq6f3zzz8A0h659yWLiYnBoEGDUKRIEaxYsUJKyFu1aoWBAwciICAAS5YswfDhw6U+jx49wvr16zFgwAAAaZvNKZ633rNnzyy3yypDQ0O4urrC1dU122MQEREpMMknIiL6zEVHRyMgIAD//vsvevbsqTKz+/r1a/z5558AgPbt2+dHiJ+NX375BWFhYZg5c6bKY//mz5+P/fv3Y/z48fDw8JAeZVerVi188803OHbsGJydnXHw4EGcPXsW3333Hdzc3KT+2rYjIiLKT7wnn4iI6DPXp08fGBoa4t69e/D09MStW7eQmpqKd+/eYf/+/ahduzbev38Pa2tr6dF4X6LDhw/jr7/+gqurK0aPHq1Sb2lpiUWLFiE+Ph5ff/219Hi4Hj16YOPGjXjw4AHmzp2LhIQELFiwQHr2uoK27YiIiPIT78knIiIqANavX4+vv/4aKSkpANI2PpPL5VK9kZER9u7dixYtWuRXiPQF4D35RESfP87kExERFQD9+/fH7du34e3tDVdXVxgaGsLY2BjlypXD999/jxs3bjDBJyIiIs7kExERERERERUWnMknIiIiIiIiKiSY5BMREREREREVEkzyiYiIiIiIiAoJJvlEREREREREhQSTfCIiIiIiIqJCgkk+ERERERERUSHBJJ+IiIiIiIiokGCST0RERERERFRIMMknIiIiIiIiKiSY5BMREREREREVEkzyiYiIiIiIiAoJJvlEREREREREhQSTfCIiIiIiIqJCgkk+ERERERERUSHBJJ+IiIiIiIiokGCST0RERERERFRIMMknIiIiIiIiKiSY5BMREREREREVEkzyiYiIiIiIiAoJJvlEREREREREhQSTfCIiIiIiIqJCgkk+ERERERERUSHBJJ+IiIiIiIiokGCST0RERERERFRIMMknIiIiIiIiKiSY5BMREREREREVEkzyiYiIiIiIiAoJJvlEREREREREhQSTfCIiIiIiIqJCgkk+ERERERERUSHBJJ+IiIiIiIiokGCST0RERERERFRIMMknIiIiIiIiKiSY5BMREREREREVEkzyiYiIiIiIiAoJJvlERERERES5QAgBFxcX3L17N79DoS+YXn4HQEREREREVNAlJSVhwYIFCA0Nze9Q6AvHJJ+IiIiIiCgH/P39MWTIECQmJuZ3KERcrk9ERERERPnj9evXsLa2Rrt27TJs9+7dOxgYGGDz5s1ZPsbBgwdRtWpVGBoaYs6cOQAAR0dHODo6ZidktTp37oxr167h8OHDuTYmAKSmpmLRokVo2LAhihcvjtKlS6Nz5874999/1bYXQmDt2rWoUaMGjIyMUKpUKQwYMADh4eEZHicn1ze7tm3bBicnJyQlJWUrpri4OIwbNw41a9ZE0aJFUbZsWfTs2RN37tzR6vh+fn6QyWRav7y9vbN8jteuXYNMJoO1tTVSU1MzbNuvXz/IZDIsXLgQSUlJcHBwwPbt27N8TIAz+bnu6dOn2LJlCw4ePIiHDx8iMjISRYsWhYuLC1xcXFC3bl3069cPFhYWKn2PHz+O5s2bqx137dq1+Oqrr9TW/fnnn/jxxx/V1h07dgzu7u4q5f/99x/WrVuHa9euISwsDGFhYUhISICDgwMcHBxQrlw5eHp6okmTJpDJZNpfACIiIiIiLQgh8MMPPyAyMjLTtuvWrUNycnKWj5GSkoLevXvj/fv3qFatGmxtbbMTaqYsLCxgYWEBQ0PDXBtTCIFOnTrhwIEDMDIygqurK969e4c9e/Zg7969WLJkCXx8fJT6zJw5E5MmTYJMJkP16tXx5MkTrFu3DkePHsXly5dhZWWl9liZXV9t8wEhhFbtPnz4gOHDh8PPzw8GBgZZjikhIQF169ZFSEgIihcvjurVq+PFixfYunUrdu3ahSNHjqBJkyYZxuDi4oK2bdsqlT18+BChoaEoW7YsXFxclOpcXV21Orf0qlevjgoVKuDevXs4deqU2rwMSLvVY9++fQCAHj16wMDAABMmTMDPP/+Mtm3bwsTEJGsHFpQrEhISxJgxY0SRIkUEgAxfJiYmYt68eSI1NVVpjGPHjmns88MPP2g89oABAzT2O3bsmFLblJQUMX78eKGnp5dpnABE06ZNRWhoaF5cMiIiIiL6gm3cuFH6m7Nt27Zq20RHR4vly5cLY2NjAUBs2rQpS8cICwsTAES7du2Uyh0cHISDg0N2Q9fo8ePHAoC4c+dOjsfauXOnACBq164tIiIipPKjR48KMzMzUaRIEXHv3j2p/MWLF0JXV1eYmZmJW7duCSHS/vb/7rvvBADx008/qRxD2+vbtm1bja9WrVoJAMLa2lrrc5s8ebKwtbUViYmJ2Ypp7ty5AoDo0qWL+PDhgxBCCLlcLlauXCkAiMqVK2sdS3pTpkwRAMSUKVOy1T+jMX/88UeNbYKDgwUA0aBBA6ksISFBWFtbCz8/vywfk0l+Lnj37p1o1qyZVklz+lf//v1FSkqKNE5GSX716tU1Hr98+fJaJ/mDBw/Ocpy2trbiyZMnmV6HmzdvSn3WrFmjts2sWbMEAGFqaqpxnBEjRggAolmzZpkeMztatmwpAIjHjx9ne4yvvvpKABBdu3bNvcDSWbNmjcbvh56ennBychIDBgwQDx8+zJPjExEREeWl58+fC3Nzc+Hs7KwxyW/Tpo3K30FZTfIVSffAgQOVygtCkt+tWzcBQJw4cUKlbtGiRQKAmD59ulS2YMECAUD88ccfSm2TkpKEra2tKFmypEhOTpbKc+P6CiHEnDlzBAARHBysVfuEhARRqlQpMXLkSJU6bWNq0KCBACDCwsI01kVGRmb5XPIiyb9z544AIKysrJRyv/SGDBkiAIgFCxYolQ8fPlxYW1ur/TAkI7wnP4eEEBg0aBBOnDihVG5lZYXRo0dj3bp1WLduHSZNmgRra2ulNuvXr8eiRYu0Os7Nmzfx/v17lfI3b97g/v37Wo1x7NgxrFq1SqnMxcUFU6dOxcaNGxEQEIBff/0VlStXVmoTHh6O4cOHa3WMjHz48AHz588HALx//x4pKSkqbaKiorBy5UoAwPjx43N8zI+lpqbiwoULsLa2hoODQ7bHEUKgbdu2GDhwYC5G9z+K+6wqVaqEtm3bSq82bdqgcuXKCAsLw9q1a9GsWTO8ffs2T2IgIiIiygtCCAwePBgJCQn466+/NLZr2LAhvvnmG3zzzTeoUaNGlo/j7e0NJycnAEBAQABkMhn8/PyU2sTExGDo0KGwtraGqakp3N3dsXr1apVl55ru0x48eHCW49LWo0ePoKuri4YNG6rUKZZ937hxQyrbsmULgLT9AdLT19dHhw4d8OrVK6WcJafXFwBCQkIwadIkjBgxAm3atNGqz/bt2/Hy5Uv06dNHpU7bmB49egRLS0u1f89XqFABAPD48WMtzyJ7du3ahY4dO8LKygolS5ZEu3btcPToUZV2FStWhJubGyIjI3H69GmVerlcjl27dgFIW6qfXp8+ffDixQvs3Lkza8Fl6+MIkqRfZqR4eXh4iKioKJW2sbGxomnTpipL96Ojo4UQ6mfydXR0pH8fOnRIZcx9+/apbat4pZ/J/3hZv7u7u0hISFAZUy6Xi/Hjx6vEkdmnYZnN5CuW1SheL1++VGmj+CSwRo0aQi6XZ3i87Lh165YAILp165brY+cmNzc3jd9zIYS4evWqMDU1FQDE6tWrP3F0RERERNm3YsUKAUAsXrxYmvnWtFxfQTHDmpWZ5nnz5kl/e9va2oq2bduKdevWCSHSZvJtbW1Fo0aNpOXd5cqVk/5OzWhpdUZycyZ/6NChYtiwYWrrDh06JACIQYMGSWVWVlbCzs5Obfu///5bABArVqxQW5+d65ucnCxq1qwpKlasKOLj47Xu17VrV1GyZMlM/9bPKKZTp06JixcvqpTL5XLRuHFjAUCrlciajpnZTP6ECROkFbY1atRQWlm9bNkylfazZ88WAISPj49K3blz5wSgvFQ//flYWlqK7t27Z+k8OJOfA0II/Pbbb0pltra22Lp1K8zMzFTaGxsbIygoSGlDjtjYWGzbtk3jMapXry79++zZsyr16XfWTN9WnZs3byp9/eOPP6JIkSIq7WQyGWbMmKG0MYlcLsfVq1czHD8jcXFxmDt3LszMzFCvXj0AUJmBTk5OxuLFiwGkzeLnxYZ/58+fBwA0aNAg18fOLXFxcbh58yZkMhnq1Kmjto2bmxs8PDwApG2iSERERFQQPH78GCNHjkTz5s1VNo3LbaNGjUJAQAAAoHXr1jh48CD69+8v1YeHhyMkJASnTp1CSEgI7t+/j6NHj8LMzAzLli3DhQsX8jS+zCxZskT62zi9xMREKQdRbC6XmpqKV69ewdLSUu1YivKXL1/mWnwBAQG4cuUKZs6cqfWGg3K5HCdOnECdOnVy9Ld+48aNUbt2bQBpOVl0dDRCQkIwYsQInD59Gp07d0bp0qWzPX5Gzp8/j5kzZ6Jq1aq4c+cOrly5gnv37uHw4cMoXrw4hg8fjocPHyr16dWrF4C0Jwp8vMv+jh07lNqkJ5PJULt2bZw4cQJyuVzrGJnk58DNmzdx/fp1pbLRo0fDyMhIY59SpUqha9euSmWXLl3S2D798pzMknx1S3nS+/i5na9fv9bYViaTYc6cOZg2bZr0srOzy3D8jKxYsQIvX77E8OHD4ezsDEA1yQ8MDMSzZ89Qrlw5dO/eXanu6NGj6NGjB+zs7GBsbAw3NzdMnTpV7bNIK1WqhHLlyiEhIQHjx49HtWrVULVqVQCak/zly5dDX18fzs7OuH37dobncv78echkMvTt21cq27ZtG2QyGVauXIlbt25hyJAhqF27NkxMTFC1alUEBgZqeaWAy5cvIzU1FZUrV1b7YZGC4vpVqlQJQNov96JFi6J169aIioqCj48PKlSogA4dOkh9EhISsGjRItSrVw/FixdHiRIl0KJFCxw6dEjtMYQQCAoKQsuWLVGiRAkUK1YMnp6eePHiBcaOHQuZTKZ0u0hG114xXmBgINq3bw8rKysUK1YM9erVw5o1a9T+4nr79i0mTpyIunXronjx4rC0tETLli3VxhsXF4c5c+agYcOGKFmyJMzMzNCgQQNp6RoRERHlL7lcjq+//hoAsHr1aujo5H8q4uvri8aNG0tfN2/eHNOmTQMALFy4ML/C0ujDhw/o3LkzDh8+DBcXF2nJ+9u3byGXy2Fubq62nyLJ1+ZJBtpISEiAn58f6tatq5LbZOTGjRt49+6dlKDnhm3btqF48eKoWrUqfv/9d3Tu3Bnr1q3LtfE/9uuvvwIANmzYoLQDf8uWLTF79mwkJyer3CLt5OSE+vXr48WLFzhz5oxULoSQluJ7enqqPV6dOnXw5s0bhISEaB9klub9ScmSJUtUNkSLiYnJtF9sbKx49eqV9MpouX762wFMTU2VduRPTU2VlmwDEBs2bMhwuX7Xrl2V6szMzMTGjRtFUlJSrlwPTcv14+PjhY2NjTA1NRVv374VPj4+AoDYu3ev1EYul4saNWoIAGLVqlVK56hoD0DY2dmJOnXqSE8xaNq0qdI1iY6OFjKZTDRq1EjUqVNHyGQyUbZsWTF79mwhhBDVqlUTenp6Ii4uTgiRtsxo+PDhAoBo0qSJePXqVabnqfi+p98YY9y4cQKAGDBggLSraYMGDUTJkiWl2x2uXLmi1XVU3LIwePBgjW3u3bsnjIyMhJ6enrThiOL6d+zYUbi4uAg9PT1RqVIlERAQIIRIWz5WqVIlAUDIZDJRo0YN4eLiIl3blStXKh0jISFBeHp6CgBCX19f1K1bV1SpUkUAEG5ubqJFixbCzMxMuv6ZXfuYmBjRtm1b6b1SrVo14ebmJt1m8u233yod/+7du8LCwkIAEI6OjqJRo0bCzMxMABC6urrizJkzUtvIyEjh5OQkAAgbGxvRuHFj6doDEFu2bNHq2hMREVHeWbhwocqS8bxcrp9+fHUb7wEQr1+/VukTExMjgIw3vv7U5HK5CAoKEqVLl5Y2cbt7965U//LlS+lvY3WOHz8uAIghQ4aorc/q9Z0/f74AIA4fPpyl89i6davK3/uaaBvTyZMnRevWrUWVKlWEjo6O0NfXFzNnzsxSXB8fM6Pl+lZWVqJcuXJq68LDwwUA0aJFC5W633//XQAQQ4cOlcpCQkIEANGwYUONx1Pc3rJ9+3atz4NJfg58vFO9k5NTjsZTl+TfvXtX6bF8N2/elNor7i8HIAwMDKSdGzUl+X/88YdKPQBRvHhx0aFDBzFt2jSxd+9e8ezZs2zdD68pyV+8eLEAICZNmiSESHtkBgAp+RQi7VEgiiQ+/T4Bo0aNEgCEs7OzuHz5shTXw4cPhb29vQAg1q9fr/YaNmzYUOl+qJiYGKGjoyPq1KkjhBAiKipKSjq9vb3V7k+gzsCBAwUAcfLkSalM8egQHR0d8dtvv0k7l3748EHUqVNHABBz5szRanxFYq3pl9/p06el+34U11QI5R35O3XqJJ49eybVRUVFSUnwoEGDxJs3b4QQaf/DUPziKFasmIiNjZX6KPZwqFevnnj+/Lna47Rs2VIqz+jaC/G/HWLbtm2rtBPquXPnhKGhoQAgzp49K5UrPgDYsmWL9H1PSUmR4ho/frzUVvEB1sKFC6VdS+VyuZg0aZIAIPr06aPFlSciIqK8cufOHWFoaCjatWun9Hdmfib5GT3tydbWVhQtWjRLx8orkZGRolOnTkp/5328V1ZKSorQ0dERrq6uasfYsWOHACCmTp2qtj4r1zc5OVnY2NiIypUrZzlnWLp0qdYJa3a+57dv3xaOjo4CUP9UAm2PqSnJf//+vdp86uNXtWrVVPqGh4cLmUwmbGxspEmy6dOnS3/DaqL4YOTPP//U+jz0QNn25s0bpa/LlCmT68coUqQIateuLS3rOHv2rLT8Of1S/Vq1amV6L8yQIUOwefNmlV0do6KisG/fPuzbt08qs7KyQuvWrdGuXTt06dIFRYsWzVb8inuGihUrhhEjRgAALCwsACgv11fsuj9q1Chpn4ALFy5gwYIFKF26NM6cOaP0dIKyZctixIgRGDVqFM6cOYN+/foBAC5evAgAcHBwwL59+1C8eHGpz+XLlyGXy9GgQQOEhoaiY8eOuHfvHn777TeMGTNG6/uCLl68CB0dHWnHTyGEdMvF1KlT8csvv0htTUxM0LlzZ1y8eBF6etq93RS3FCxfvhxBQUFSeXJyMh48eICnT58CSNttc9KkSUpxAWlLerZu3QoDAwOpbuLEiXj8+DG+++47/PXXX9K5KnaFXbJkCW7evIlbt26hbt26uHTpEtauXYvatWvj8OHDSt//gQMHYs6cObhz547SngEZXfvz589jx44daNSoEXbt2qW0F0T9+vXh7e2Nv/76C6dPn0aDBg0QGRmJa9euoXz58ujZs6cUr66uLn788UeULl0arVu3BgCkpKTg0KFDMDIywtChQ6GrqyudW//+/SGEyNUlYURERJR1Bw8eREJCAl6+fIn27dtL5fHx8QDSbl9t164dTExMMtyvKjdldLuAXC7P8BbcT+XChQvo1KkTXr58CRcXFyxatEjpVkwFXV1dlCxZUiU/UVCUf/y0r+zYt28fIiIiMGLEiCzfV//u3TsAyHZukZlKlSrBz88P3t7e2LdvH5o2bZqr4yueDmZubo66detqbJd+bzMFGxsbuLu749ixYzhz5gyaNGkiLdX/eFf99ExNTQGo3uqcESb5OfDxI+1y402jTqNGjZSS/O+++w4AcO7cOalNZvfjA2lv/n/++Qdz5szB7NmzkZCQoLFtZGQk1q9fj/Xr16N06dJYtmwZOnbsmOXYV69ejefPn2PChAlScv9xkn/37l3s27cPFhYW+Pbbb6W+CxYsgBBC7eMHAUj3wCh+WQD/SzQnTZqklGQC/0uek5KSULduXbx9+xYVKlTIUoIfExODO3fuoHLlytIvp9DQUERFRcHS0hJjx45V6aN4fIc2jyYJDw/Hs2fPlM4lPXt7e3Tp0gX9+/dX+WWgaD9z5kylBP/t27f4+++/UbRoUcyYMUPlXGUyGVxcXHDz5k3pWk6YMAEAMHv2bJVfwjKZDHZ2dhqTfHXXfsqUKQCAOXPmqN3sUfG9VPzPVrHXwv379zF06FAMGTIEVatWhUwmQ7169aTNG4G0D1mSk5ORlJSEfv36YeTIkahTpw50dHRQoUIFTJ8+XeV4RERElD+uXLmitvzNmzcIDg7OcD+i3BYVFYV3796p3Mf+/v17vHjxAvXr1/9ksagTFhYmJfjdu3dHQEBAhsmxk5MT/v33Xzx9+lRl0zlF3pCTR0grrFixAjo6OtIkW1YornVMTEy2jx8SEoIuXbqgY8eOah9Hrjj3uLi4bB9DE3NzcxQvXhxWVlY4ePBglvt7eXnh2LFjCAoKgqOjIy5duoSGDRvC3t5eYx9FzqlpvwV18n+3iwKsWLFiSl9ntJFdTjRq1Ej6d/rN97Ky6Z6CkZERpkyZghcvXmDHjh348ccf4erqKs1+qvP06VN06dIlyzuMJiUlYdasWShatChGjhwplSt+QBVJ/oIFCwAAP/30k/SLSwiB4OBgmJiYwNvbW2NcQNpmhgqXLl2Crq4uunXrptJecb3++usv2Nvbo2LFirh3757ShyWZuXr1qsrMsGIWv1u3btDX11fpc+HCBchkMtSqVSvT8RUxtmjRAiLtdhql19OnT7Fz506VBD8pKQnXr19HiRIlpOemKpw6dQoJCQno06cPSpQoofa46a/lmzdvcOjQIVSuXBktWrRQ2z4sLAwAlJJ8Tdc+JiYGwcHBANJ+ltU9Y3b06NEA/vc/njJlysDPzw+6urpYtmwZqlWrBltbW/Tr1w979+5Venatvr4+/vjjDxgZGSEwMBD169dHyZIl0b17d2zcuFFlB1MiIiL69H7++We1f9soJkPatm0LIQSioqI+aVzr169XKVu9ejUA5PtKwGnTpkkbVwcFBWU6+927d28AwO7du5XKk5OTceDAAZQoUQLNmzfPUUwRERE4cOAAmjZtqna2OjNWVlYAlCfpssrW1hahoaE4evSo0t+ECpcvXwYAuLq6ZvsYGXF1dcXDhw+lv4fTu3TpEnr06IFNmzap7evp6Qk9PT1s27Ytw13101PkTIprpw0m+Tnw8SMqnjx5kifHSb8T/IMHD/Dq1StER0cr7QKf1UfCmZmZoWvXrli6dClu3LiBmJgYnD9/Hr///jt69OihsjxJLpcrJeraCAgIwNOnTzF06FCla5V+Jv/ly5dYu3YtTExMMHToUKnN69evERUVhQoVKijNSqenOH/FL+DXr1/j8ePHcHV1Vfv4EMVMvoeHB06fPo0hQ4YAAFauXKn1OSlmq9P/0leUqVsOFBMTg5CQEFSpUkXlQyF1FDFmtPxHnZs3byIpKQmNGzdWuS1Asfu9pl90KSkpuHfvHgwNDVG5cmXcvXsXQNpj+tStcPjw4QPCwsJgZWUlfeqY0bVXPLqxdOnSaNu2bYavKlWqSP2mTJmCR48eYdGiRfDw8EBsbCw2btyITp064eeff1Y6xrfffouwsDAsX74c3bt3h0wmw44dO9CvXz90795d7f8AiIiIiKZMmaI0iXbkyBFMmTIFenp60iREfkhMTERQUBBKlCiB3377TasnEfTt2xd6enqYOHGi9Pdcamoqhg0bhvDwcPTv31/j39XaCg4OhhAi28vgFas3c/IIaHNzc1SrVg03b97EvHnzlJ7QdPr0acyYMQNFixZFu3btsn2MjEyYMAEpKSkYMGAAwsPDpfLw8HAMGjQI27ZtQ/ny5dX2tbS0RJs2bRAeHo6ZM2cC0LyrvoJiMi79Tv6Z4XL9HPj4ufRPnjxBQkJCpvfGR0ZGIiIiQvrawsIiw/v5S5YsifLly0vJ2rlz52BsbCwlLk5OTrCxsVH7aZK2jIyMULduXdStWxc//fQT3rx5g4EDByrdp3/lyhXI5XKtfskkJydj5syZMDExwahRo5Tq0if5y5YtQ2JiInx8fJSSw3v37gHQvKQoOjoaGzduhIGBAdq0aQPgfzPq6p4t/+zZM4SHh6NBgwbYtWsX9PT00K9fP4wZMwaBgYFYtGiRVsvD0t/3rqA4bvol5OnrhBBaJ+3ZTfLVxaWQ2bXctGkTYmJi0KVLFxQpUgR37tzJsP2qVauQkpKi9HzTjK69Isn/6quvMGPGjKycFsqUKYPhw4dj+PDhiIuLw19//YVRo0Zh1apVWLRokdKHEKVKlcJ3332H7777DsnJyQgMDMSAAQOwe/duvHr1SmnFBxEREVHDhg0RERGBRo0aoUqVKkhOTsb9+/chk8kwf/78XFnanl5kZCQGDhwIIG0yLKOZ2fDwcMTExEBXVxddunTR2K5169bS39qlSpWCn58fJk2ahMqVK8PNzQ1hYWF49+4dSpcujfHjx+f4HBSrM9M/djArXF1dYW5unuEjxLWxdOlSuLu745dffsGiRYtQtmxZREZG4v79+9DV1cWaNWvyZL80IG3VyaBBg7B69WqULVsWVapUgZ6eHq5cuYLk5GT4+vpmuILXy8sL+/fvR2RkJBo1apThUn0g7e98CwsLpcdSZ4Yz+Tnw8SdYiYmJWLt2bab9xo0bhxo1akgvdfeSfOzjJftZvR//7t276Nixo9IrOjpaY3tLS0ssWbJEqSw+Ph4vX77M9FhA2tKnsLAw+Pj4qCwRVyT5z58/x7Jly6Cvr6+ySkCRvKn7lE8IgWnTpiEqKgoDBgyQfkFmlGgqkucWLVpIM90lSpRAp06dEBcXh40bN2p1XpcuXYKenh6qVasGIO3T0StXrqB48eIoV66cxuOq+wDgYykpKRmeQ2ZxaeqX0bV89+4dpk6dCgDSp9WK9upWpkRERGDu3LkAoPaWBXXHV2wWommly/Lly9G1a1ccO3YMQNomjPb29tJxFIyNjTF48GDIZDLY2tpCJpNhy5YtsLe3x08//aTUVl9fX7o9wcjIKEv3MBEREdGXwdzcHGfPnsVXX32FiIgIvHr1Cu3atcPu3bulDaNzU3x8PIKDgxEcHCxtOKjJq1evAKTtG6Doo+6lmExRmDBhAvz9/VGtWjXcvn0burq6GDBgAM6fP5/jCQ+5XI5Dhw5BR0cn2/sV6OjooFmzZrh48WKOVlo2btwY169fR/fu3WFoaChNePXq1QvXr1/HV199le2xMyOTybBq1Sr4+/ujYcOGePz4MUJDQ9GoUSPs3r0bfn5+GfZXTKwBQM+ePTNsq9jg293dXauJ1vQdKZvkcrlwdXVVelxCuXLlpMenqRMbGytsbGyU+vzxxx9CCPWP0Hv8+LEQQoiVK1dKZU2aNBEeHh7S10uXLhVC/O8RIelfikfohYaGqtTt27cvw/N78uSJSp+MHjOX/hF6AISxsbHK4z2EECIxMVGpnbrnwb9//156dGD6x/FFR0eLoUOHCgCidOnS0qPghBCic+fOAoC4evWqynijR48WAMTOnTuVyvfs2SOAtOe+Z/YIkDdv3khtFRTPtmzTpo3aPopHu6mL6WPXrl0TAIS1tXWWH0dSrVo1AUC8fftWpU7xs1OmTBnp50kul4uQkBBRqVIlgY+e13n16lWB/38s47lz56T2169fF2XLllX785PRtX/y5IkoUqSIMDAwEEePHpXKExMTxbx584SOjo6wsbGRHt/n6+srAIi6desqPdLv3bt3om/fvgKA+Omnn4QQQqxevVoAEI6OjuLVq1dS29jYWOl73rlz5yxdSyIiIqK8IpfLhaWlpXjy5El+h5JvNm3aJACICxcu5Hcon73z588LAGLLli1Z6sckP4fWrVunkgj3799fxMXFqbRNSEgQXl5eKu3/++8/IUTGSf7t27elMkNDQ2Fubi59rUisMkry5XK5cHBwUKorXbq0NP7HUlNTxbfffqvUvkaNGhlei4+T/NGjR2tsW7RoUQGkPVf+/v37atssWrRIGqtChQqiZs2aQkdHR/o6NDRUqb2NjY0wNDQUSUlJKmM1adJEAFB6drwQac/5tLa2FgDExYsXMzy/4OBgAUB8++23UllAQIAAICZPnqzSXi6XCxsbG2FkZJThBz8Ky5cvFwBEly5dMm2bXmxsrNDV1RUuLi5q65OSkkTt2rWln5169eqJ0qVLS9f2+++/l54tr6BIpnV1dUWtWrVEmTJlBADRtWtXYWlpKXR1dZU+UMjo2gshxMKFC6XjValSRTRp0kRYWloKAMLOzk7cunVLanv16lVhaGgoAAhzc3Ph7u4u6tevL4yMjAQAUb9+fen99ezZM2kcExMT0bRpU9GoUSNhamoqAIiyZcuKly9fZul6EhEREeUFuVwuLl26JExMTLT627CwSkxMFKVKlRIjR47M71A+ez///LOwtrYWiYmJWerHJD+H5HK5NIuZ/uXg4CC+//57sWrVKhEQECAmT56slFipm8XOKMlPTU0VFhYWKvVFixaVfklklOQLoZxoKV76+vqib9++YuHChSIwMFCsXbtWTJ06VVSpUkWl7cqVKzO8FumTfCMjI/HixQuNbRVJY69evTK8tjt27BCNGjUSZmZmwtTUVNStW1csWLBAxMfHK7V9/vy5ACAaNGigMk5ycrIwNjYWNjY2ao/zyy+/CADiu+++y/D8ZsyYIQCI5cuXS2XDhg0TAMTevXtV2itWQjRu3DjDcRUGDRokAIjp06dr1V7hzJkzAoDo06ePxjYxMTFi3LhxolKlSqJIkSLC2tpaeHp6iiNHjqhtn5iYKKZOnSoqVqwoihUrJtzd3cXSpUvFw4cPVc4po2uf3sGDB0WLFi2EpaWlMDc3F7Vq1RKzZ89Wu/rg33//Fd26dRN2dnbCwMBAlClTRjRv3lysXbtW5X+Kd+7cEQMGDBAODg7CwMBA2NraisaNG4vFixer/bCNiIiIKD88fvxYWFtbi3Xr1uV3KPlu8uTJwtbWNsvJ65ckMTFR2NjYCD8/vyz3lQnBbadzKioqCp06dcLp06ez1K9evXpKzwM9fvy4ymMtHj9+DEdHRwBAx44dlTbCA4CWLVvi8OHDANIea+bk5KRUf+zYMemRaikpKejevTv27NmTpTgBoGvXrti2bVvW7gWhQmfZsmXw8fHB9OnTMXHixPwOh4iIiIgKoJiYGFSqVAlTpkzBt99+m9/hfJZWrFiB6dOn486dOzAxMclSX2ZsuaB48eI4dOgQRowYodVjKWQyGX788UccOnRIqx3dFdJvvqegzaZ7CopnMk6YMEHrx2cYGBhg/PjxCAwMZIL/Bdi+fTuqVq2K+fPnq9QlJCRg/vz50NXVzfRRH0REREREmhQrVgy///47Zs6ciaSkpPwO57OTlJSEmTNnYtGiRVlO8AE+Qi/XGBoaYsGCBRg+fDg2bdqEgwcPIjQ0FC9fvoSuri7Mzc1RpUoVNGnSBAMGDMjWIznUJfkNGjTI0hj6+vqYMWMGhg4dis2bN+PixYt48uQJ/vvvP7x8+RJWVlYoU6YMypQpg9q1a8PLywu2trZZjpUKJmNjY4SEhCAgIAA//PADjI2NAQAfPnzAkCFD8OjRI0yePBkVK1bM50iJiIiIqCDz9PTkxJEGBgYGOXo8OpfrE5EkNjYW9erVQ0hICKytrdG0aVPExMTg0qVLePXqFbp3745NmzZpvRKEiIiIiIg+LSb5RKTkxYsXmD59Og4cOIDw8HCUKVMGlStXxuDBg9GhQ4f8Do+IiIiIiDLAJJ+IiIiIiIiokOBOakRERERERESFBDfeyyITExMkJCRAV1cXpUqVyu9wiIiIiIiIqJB7+fIlUlNTYWhoiNjY2Azbcrl+Funq6kIul+d3GERERERERPSF0dHRQWpqaoZtOJOfRYokX0dHBzY2Nrk6dmRkJKysrHJtPCEEwsPDYWtrC5lMlmvj5nacX/KYefU9AgrG+ReUMfleyv0x82Jcfp8+/zH5O69gjPmlv5fyalx+n768Mfk7r2CMWVDeSxEREZDL5dDV1c28saAssbOzEwCEnZ1dro9dqVKlXB0vOjpaABDR0dG5Om5ux/klj5lX3yMhCsb5F5Qx+V7K/THzYlx+nz7/Mfk7r2CM+aW/l/JqXH6fvrwx+TuvYIxZUN5LWclDufEeERERERERUSHBJJ+IiIiIiIiokMiVJP/GjRu4ffu29PXDhw/Rvn17ODg4oHPnzrh582ZuHIaIiIiIiIiIMpCjJP/Dhw/w8PBAjRo1sHnzZgBASkoKunXrhuDgYDx9+hR79+5F06ZN8fjx41wJmIiIiIiIiIjUy1GSv3DhQhw8eBAODg6oVasWAODYsWMICQlB+/bt8ezZM8ybNw/R0dFYtGhRbsRLRERERERERBrkKMkPDAxEsWLFcOnSJXTp0gUAsGfPHshkMkyYMAG2trYYMWIEypQpgyNHjuRKwIWZj49PfoeglbyI80seM68UlPMvKGPmhYJy7nl1Pfl9+jLHzCsF5fwLyph5oSCde0GKNbcVlHMvKGPmlYJy/gVlzLyQn3HKhBAiu52LFi2KmjVr4uTJk1JZrVq18OjRI7x9+1Z6zmDr1q1x/vx5vH//PucR5zN7e3s8f/4cdnZ2ePbsWX6Hk6H379/DzMwM0dHRMDU1ze9wSA1+jwoGfp8KBn6fPn/8HhUM/D4VDPw+ff74PSoYCsr3KSt5aI5m8osVK4a4uDjp64iICFy/fh2NGjWSEnwASEhIgJ6eXk4ORURERERERESZyFGSX6FCBVy7dk3aWf/vv/+GEAIdOnSQ2oSHh+PSpUuwt7fPWaRERERERERElKEcTa8PHToUJ0+eRKNGjVCzZk2cOnUKJiYm8PT0BAAsXrwYCxYsQFJSEnr06JErAX8uIiMjUblyZbV1Pj4+BeZeESIiIiIiIsp/S5cuxdKlS9XWRUZGaj1OjpJ8T09PTJo0CTNmzMCxY8dgYGCApUuXolSpUgCANWvW4MmTJ2jUqBGGDx+ek0N9dqysrKQVDEREREREREQ5kdFkseKefG3kKMmXyWT49ddfMWbMGNy7dw/Ozs6wsLCQ6seMGQNLS0u0atUKurq6OTkUEREREREREWUiR0n+yZMnYWFhgapVq6J27doq9X379gUA3Lp1C/Hx8ahTp05ODkdEREREREREGcjRxnvu7u4YO3Zspu2mT5+utBkfEREREREREeW+LM/knzx5UunrN2/eqJSlFxUVhfPnz+PDhw9Zj45ypEiRIpgyZQqKFCmS36GQBvweFQz8PhUM/D59/vg9Khj4fSoY+H36/PF7VDAUxu+TTAghstJBR0cHMpksSwcRQqBx48YZfhhQUCg2PLCzs8OzZ8/yOxwiIiIiIiIq5LKSh2Z5Jn/AgAFSkh8QEAA7Ozu0atUqwz7m5ub4+eefs3ooIiIiIiIiIsqCLCf5/v7+0r8DAgLg6uqKNWvW5GZMRERERET0Gfvj1097vKG+Oevv7++Pr7/+WqW8ePHiqFmzJoYPH47OnTtL5X5+fpg6dSrWrFkDb29v6eunT5/C3t4+Z8EQ5bEc7a7/+PFjGBkZ5VYsREREREREeaZfv36oX78+ACApKQm3bt1CcHAwunTpgrFjx2L27NkAAA8PD5QoUQL16tXLz3CJsiVHSb6DgwMAICUlBeHh4Zm2L1OmTE4OR0RERERElG2tWrWCt7e3UtmbN2/QrVs3/Pbbb+jatSvq16+PunXrom7duvkTJFEO5SjJT0xMxIgRI7BmzRokJSVl2FYmkyElJSUnhyMiIiIiIspVlpaW8Pf3h4uLC3799Vfs378/v0MiyhGdnHSeP38+/vrrLyQmJqJChQpo2rSpxleTJk1yK2YiIiIiIqJc4+zsjHbt2uHAgQOIiYnB+vXrIZPJcPz4caV2cXFx+O677+Dk5IRSpUqhU6dOuHfvnlIbIQQCAgLQqFEjmJqawsXFBV999RUiIiKU2n348AG+vr6oUKECjIyMUKZMGXz//fd49eqVUrvw8HB89dVXKFu2LMzMzNCoUSMEBAQg/UPSjh8/DplMhmPHjmHFihUoV64cjIyM4OrqisDAwNy9WPTZy9FM/rp16yCTybB582b07Nkzt2IiIiIiIiL6pKpUqYIDBw7g8ePHGtv06NEDADBw4ECEh4cjICAAtWvXxqVLl1ChQgUAwIQJEzB79mzUq1cPo0ePxosXL+Dv748DBw7g2rVr0sZ9AwcOxI4dO9CxY0f0798f169fx99//43//vsPBw8eBAA8e/YMbm5uiIuLg7e3N6ytrXHgwAF4e3vj7t27mDVrllJ8y5cvx5kzZ/DVV18hNTUVy5cvh5eXF8qWLYtatWrlxWWjz1COkvywsDBUrVqVCT4RERERERVoiuT70aNHGtvo6enh9OnTMDY2BgB4eXmhZcuW8PX1xZYtW/Dw4UPMmzcPnTp1wvbt26Gnl5ZuDRw4EPXr18fUqVOxcuVKxMTEYMeOHRg8eDBWrFghjT9ixAhs2bIFCQkJMDQ0hK+vL96+fYuzZ89KGwZOmDAB3bt3x/z58/Htt9/C2dlZ6n/o0CHcvn0bVlZWAIB69erB09MTR44cYZL/BcnRcn0rKyvpB5eIiIiIiKigkslkSv9VZ/To0VKCDwAtWrRA8+bNcejQIQBAcHAwUlJSMHHiRKU8qV69emjdujV2794NIO3DAplMhrNnz+K///6T2i1cuBDh4eEwNDQEAOzZswdt27aVEnxF33HjxiE5ORnBwcFK8Q0aNEhK8AFIiX1cXFzWLgYVaDlK8vv06YObN29m+GkXERERERHR5+7p06cAoDQz/rFq1aqplLm5ueHdu3d4+/YtHj58CCBt6f/HqlatipcvXyImJgZGRkaYM2cO7t69CycnJzRq1Ajjx4/H6dOnIZfLAQDR0dF4/fq12rEUZaGhoUrlilsGFHR1dTM6ZSqkcpTkT506FS1btkSHDh1w5syZ3IqJiIiIiIjokwoJCQEAODo6ZqmfYgO8IkWKZNhORyct9UpNTQUAjBo1Cg8ePMC8efNgamqKJUuWoEmTJmjRokWmM++K5P3jp5cpVgDQly1Ha+2HDBkCKysrnD59Gk2bNoWVlRWcnJzU/oDLZDIcOXIkJ4cjIiIiIiLKdaGhoTh48CA8PDxQrFgxje1u3bqFqlWrKpVduXIFtra2MDExgYuLCwDg9u3bqFu3rlK7kJAQlCxZEsWLF0d0dDQeP34MJycnjBw5EiNHjkRsbCymTZuG3377DRs3bsTgwYNRokQJ3L59WyUOxQcS5cuXz+mpUyGUo5l8f39/rF27FrGxsRBC4MWLFzh37hyOHz+u9kVERERERPQ5efPmDb7++msIITBlypQM286dO1dplv2ff/7ByZMn0atXLwBA27Ztoaenh5kzZ0oz9gBw/vx5BAcHw8PDAwDw4MED1KhRA4sWLZLamJiYoGPHjgD+N9vfsWNHHDx4EBcvXpTapaSkYMaMGdDT00Pr1q1zdvJUKOVoJj+jx0sUdpGRkahcubLaOh8fH/j4+HziiIiIiIiIKCOHDx/Ghw8fAADJycm4desWDh48iPDwcIwbN05l9j09AwMDpKSkoEGDBujevTueP38Of39/2NjYYNy4cQAAFxcXjBw5EnPmzEHTpk3Rvn17REREYM2aNbCwsMD06dMBpN2fX65cOUyfPh3Pnz+Hs7Mz7ty5g3379qFkyZJSsv/rr79i9+7dcHd3x6BBg1CqVCns378f//77L8aOHYty5crl8RWjT2np0qVYunSp2rrIyEitx8lRku/g4JCT7gWalZWV2qUzRERERESF3VDf/I4gezZs2IANGzZIX5uamqJWrVpYtmwZunTpkmFffX19HDt2DMOHD8fKlSshhECPHj0wf/58pR3tZ8+ejYoVK2LFihWYM2cOSpQoAU9PT/z222+wtbUFkHbv/P79+zFp0iTs27cPr1+/hpWVFdq3b49JkybBzs4OAFC6dGncuHEDv/zyi9SuSpUq8Pf3x4ABA/LgClF+ymiy2N7eHs+fP9dqHJlQ7BSRQ4mJibh8+TJev34NU1NTuLu7Izk5Gfr6+rkx/GdDcXHt7Ozw7Nmz/A6HiIiIiIiICrms5KE5uicfAN6/f4/hw4fDzMwMTZo0Qbdu3TBnzhwAQL9+/eDl5YXXr1/n9DBERERERERElIkcJfnx8fFo3749/vjjD5iamqJHjx5IvzDA1NQUgYGBaNKkCWJiYnIcLBERERERERFplqMk/6+//sK5c+fQunVrPHjwAFu2bFGqX7lyJcaOHYt79+5hyZIlOQqUiIiIiIiIiDKWoyR/3bp1MDQ0xPr162FmZqZSL5PJMG3aNJQsWRJbt27NyaGIiIiIiIiIKBM5SvIfPHgAV1dXlChRQmMbPT09VKpUCaGhoTk5FFGhFBERAT8/P0REROR3KJkqSLESEREREX2pcpTkW1lZ4f3795m2e/36NczNzXNyKKJCKSIiAlOnTi0QiXNBipWIiIiI6EuVoyTfzc0N9+/fx/nz5zW2CQkJwd27d1GtWrWcHIqIiIiIiIiIMpGjJH/kyJHQ0dFBjx49EBwcrLSzPgBcuHABPXv2hBACP/74Y44CJSIiIiIiIqKM5SjJb9iwIWbPno2IiAh4eHjAwsICMpkMp06dgoWFBRo0aIC7d+9i9OjRaNeuXW7FTERERERERERq5CjJB4BRo0bh6tWr6Ny5M0xMTCCEQGxsLHR1ddGiRQucOHECv/32W27ESkREREREREQZ0MuNQVxdXbFjxw4AQGxsLJKTk1G8ePHcGJqIiIiIiIiItJSjmfyZM2fi6dOnSmUmJiZM8ImIiIiIiIjyQY5m8idNmoTJkyejRYsW8Pb2Rrdu3WBsbJxbsRERERER0efIT/aJjycyb5OJ5ORkrFy5EuvXr8eDBw+QlJSEsmXLom3bthgzZgwsLCxw/PhxNG/eXKvx1qxZA29v7xzHRZTbcpTk//jjj9i2bRuOHDmCo0ePwsTEBD179oS3tzeaNGmSWzESERERERFlW2pqKtq2bYtjx46hSZMm8PHxga6uLq5fv445c+bA398fV69eRbly5bBkyRKlvr6+vgCAX3/9Vam8Xr16nyx+oqzIUZL/xx9/YPHixThx4gS2bNmC7du3Y82aNfD394eDgwMGDhyIr776Cs7OzrkVLxERERERUZasWrUKx44dw6RJk/Drr79CJvvfSoSTJ0+iRYsWGDNmDNatW4ehQ4cq9Z03bx4AqJQTfa5yvLu+jo4Omjdvjr/++gsREREIDg7G119/jejoaEydOhXlypVDs2bNsGbNmtyIl4iIiIiIKEtOnjwJAPj555+VEnwAaNq0KZo1a4bTp0/nR2hEuS7HSX56urq6aN26NVatWoUXL15g7969qFKlCk6dOoVvv/02Nw9FRERERESkldjYWABAWFiY2vr169fjwIEDnzAioryTK4/QSy81NRUnT57E9u3bsWPHDkRERABAoduQLzIyEpUrV1Zb5+PjAx8fn08cERERERERqePh4YFdu3ahefPmGDx4MAYNGoQqVapIs/o2NjawsbHJ5yjpS7d06VIsXbpUbV1kZKTW4+RKkp+YmIhDhw5h+/bt2LNnD96+fQshBIyMjODp6YnevXvDw8MjNw712bCyssLt27fzOwwiIiIiIsrEN998g//++w8LFizAwoULsXDhQlhbW6Nly5Zo06YNPD09YWJikt9h0hcuo8lie3t7PH/+XKtxcrRcf8uWLfDy8kLJkiXRpUsX+Pv7IyYmBp06dcKGDRsQGRmJwMBAeHp6wsjIKCeHIiIiIiIiyhZdXV3MmDEDL168QGBgIL777jsYGxtjw4YNGDhwIOzs7LBr1678DpMoV+RoJr9Pnz5pg+jpoW3btujduze6du0KMzOzXAmOiIiIiIgot5iZmaFnz57o2bMnAODGjRuYN28eNm7ciN69e+Pu3btwdHTM3yCJcihHM/nNmzfH8uXLERERgf3792PgwIFM8ImIiIiI6LMRGxuLwYMHIygoSKWuWrVqWLt2Lfz8/JCYmIj9+/fnQ4REuStHM/lHjhzJrTiIiIiIiIhynbGxMXbt2oXw8HBpBv9jig219fRyfV9yok8uSzP5J0+exK1bt7J8kAULFmDQoEFZ7kdERERERJQTMpkM7dq1w4EDB7Bp0yaV+tTUVPz999+QyWRo0qRJPkRIlLuy9FGVu7s72rdvj3379qnUOTs7w93dHatXr1apO3ToEP755x+1dURERERERHlp8eLF+Pfff9G3b1+sWLEC9evXh6WlJSIiIrBz5048evQIc+bMQaVKlfI7VKIcy/J6FCGE2vKwsDC8ePEixwEREREREdFnzk99TvC5Mjc3x/Xr1/Hnn38iKCgIq1evRkxMDBwcHFCzZk1s2LAB9evXz+8wiXIFbzohIiIiIqJCz9jYGKNGjcKoUaOy1C8sLCxvAiLKIznaXZ+IiIiIiIiIPh9M8omIiIiIiIgKCSb5RERERERERIUEk3wiIiIiIiKiQoJJPhEREREREVEhkeXd9ePj4/HkyZMs1cXHx2c9MiIiIiIiIiLKkiwn+SdPnoSTk5NKuUwm01hHRERERERERHkvy0m+ECJbB5LJZNnqR0RERERERETayVKSL5fL8yoOIiIiIiIiIsohbrxHREREREREVEgwySciIiIiIiIqJLJ8Tz6liYyMROXKldXW+fj4wMfH5xNHRERERERERAXV0qVLsXTpUrV1kZGRWo/DJD+brKyscPv27fwOg4iIiIjok5u83+aTHm+aR0S2+x4+fBitW7fG0KFDsWTJErVtXr16BWtrazRu3BgnTpzI9rEAwN3dHSdOnMj2huX05cpostje3h7Pnz/Xahwu1yciIiIiokLL3d0dpUqVwrZt2zRuJL5nzx7I5XL07NnzE0enbPHixfD398/XGKjgY5JPRERERESFlp6eHnr16oWIiAicOXNGbZsdO3ZAJpPB09PzE0enjEk+5QYm+UREREREVKh5eXkBAAIDA1XqPnz4gEOHDqFJkyawsfm0tyEQ5QUm+UREREREVKg1aNAAZcqUwbZt25CamqpUd/DgQSQmJqJXr155dvzU1FTMmTMHdevWhYmJCZycnDBq1ChERUUBAMLCwiCTyRAaGooTJ05AJpNJM/qvXr3CsGHD4OzsDENDQzg7O2Ps2LGIjY3Ns3ipYGOST0REREREhZqOjg569+6tdsm+Yql+9+7d8+TYQgh0794d48ePR7ly5TB58mQ0adIEv//+Oxo3bozY2FhYWFhgyZIlsLS0hIuLC5YsWYJ69epBLpfDw8MDK1euRO3atTFhwgRUrFgRc+bMwahRo/IkXir4mOQTEREREVGh16dPHwBAUFCQVJaUlIR9+/ZluFTf0dExR/fJ7969G7t370ZgYCA2bNiAcePGYe3atdi1axdCQkKwePFimJqaYujQoShevDjs7OwwdOhQVKpUCXfu3MGlS5fg6+uLwMBA+Pr6Yv/+/fD09MS///6b7ZiocGOST0REREREhZ6bmxsqVKiArVu3Skv2jx8/jujo6Dxdqr9lyxbY29ujXr16ePbsmfSqXr06nJyccOjQIY199fX1AQBHjhxRek761q1bce3atTyLmQo2vfwOgIiIiIiIKK/JZDJ4eXlh6tSpOHPmDJo2bap2qX5sbCzevXsnfZ2SkoK3b9/i2bNnAABdXd0sbdB3//59PHv2DKVLl1Zbb2RkpLFv+fLl8fPPP2PRokWwt7dHs2bN0KRJE3Tu3Bk1atTQOgb6snAmn4iIiIiIvgjpd9mXy+XYtWsXmjZtqpS0BwUFoXTp0tLr+fPnGDVqlPR1gwYNAADr169Hu3btcOPGDaVjfLyxX0pKCsqVK4c9e/aofS1ZsiTDmBcuXIhbt27Bz88PqampmDlzJmrWrIk+ffpALpfnxmWhQoYz+URERERE9EWoWLEi3NzcsG3bNvTp0wcRERGYOHGiUpuWLVtiz5490teDBg1C37590apVKwD/m3l/+vQpgoOD4ePjg2rVqkntX758CVNTU+nrcuXK4fjx4/Dw8ICOjvIc67///gsLCwuN8b569QrPnz9HhQoVMHHiREycOBFv377Fzz//jHXr1uGbb76R4iJS4Ew+ERERERF9Mfr06YMXL17gl19+gUwmg6enp1J96dKl0bFjR+llbGwMNzc36euWLVsCSPvAAACOHj0q9Q0NDcWDBw/g6uoqlXXq1AmvX7/GihUrlI5z+PBhNGjQACdOnFAqTz87f+LECdSoUQOBgYFSmYWFBdq0aQNAddUAEcCZfCIiIiIi+oL07t0bY8eOxdmzZ9GsWTNYW1tnaxwPDw9UrFgRv//+O6Kjo+Hg4ICAgAAIIeDr6yu169+/P1atWoUffvgBx48fR7Vq1XDt2jXs3bsXderUQb9+/aS2BgYGuHHjBpYvXw53d3c0bdoUlpaWGDp0KK5cuQJra2tcv34de/fuRbly5dCoUaMcXw8qfApMki+EgLOzM6ZNm4b+/ftL5RERERl+gpXZxhjx8fF48+aN2jozMzMUK1Ys+0ETERERERVC0zwi8juEbHNwcEDDhg1x9uxZ9OzZM9vjFClSBEeOHMHYsWPxzz//4P3796hatSqWLVsmzbQDgI6ODg4dOoTp06dLj9OztbXF0KFDMWHCBBgbG0ttf/nlF8yePRsjRoyAv78/evXqhX/++QeTJ0/Gli1b8O7dO9jZ2WHAgAGYOHEiihYtmqNrQYVTgUny9+7di7CwMJXyBg0a4L///tPYz8HBQW0/hS1btuDrr79WWzd37lyMHj06q6ESEREREdFn7MyZM1q3zSiXsLW1xbp16zIdo0iRIpg2bRqmTZuWYTtvb294e3srldWsWRP79u3TJlQiAJ95kh8XF4e9e/fi1KlTWL16tdo2f//9N+Lj41XKo6OjMWjQIHh4eGR4jAcPHkBPTw/bt2+HTCZTqqtUqVL2gyciIiIiIiL6xD7rJP/Jkyfo3bt3hm0UG1987JtvvoGTkxPmzZuXYf/79+/DxcUFnTp1ynacRERERERERJ+Dz3p3/fLlyyM+Ph7x8fEIDg7Wut++ffuwdu1abNiwQekeF3Xu378v7YwZExODpKSkHMVMRERERERElF8+65l8HR0dGBoaAkjbaVIbSUlJGDZsGAYNGoRatWpl2FYul+Phw4coVqwYXF1dcevWLejr66NmzZpYtGgR6tevr7GvEALv37/X/mQ+UqRIERQpUiTb/YmIiIiIiKhgSExMRGJiYrb7CyG0bvtZJ/nZsXLlSoSHh2Py5MmZtg0PD0dcXBzOnDmDIUOGYOzYsXj27BkWLlyIRo0a4dy5c6hbt67GvmZmZtmOc8qUKfDz88t2fyIiIiIiIioYZs2ahalTp36SYxWqJD82NhbTpk3Djz/+CHt7+0zbJyUlwcfHBy1atED37t2lci8vL1SqVAmjR4/GyZMn1fa1tbXFnTt3sh0rZ/GJiIiIiIi+DOPHj8fIkSOz3b9SpUoIDw/Xqm2hSvI3b96MyMhIfP/991q1d3Z2xh9//KFS7ujoiM6dO2PXrl1ITU2Frq6uShuZTAZTU9Mcx0xERERERESFW05v1/74SXAZ+aw33suqlStXon79+qhQoUKOx3JwcEBiYiI+fPiQC5ERERERERER5b1Ck+TfvHkT58+fx4ABA7Tus2rVKnTs2BFRUVEqdWFhYbCxscnRffdEREREREREn1KhSfJ37NgBAGjTpo3WfRwcHLBv3z7Mnz9fqfz27dvYtWsXunTpkqsxEhEREREREeWlQnNP/j///AMrKys4OztrbBMQEICYmBgMHToUANCyZUt06tQJ06dPR0hICOrVq4enT58iICAA1tbWmDFjxqcKn4iIiIiIiCjHCsVMfnR0NP799180atQoww0JpkyZgmHDhklf6+joYNOmTZgyZQpu3boFPz8/HD58GAMGDMD169dhYWHxKcInIiIiIiIiyhUFZibf3d0dQgi1dWZmZkhJScl0jLCwMJUyExMT+Pn58Zn1RERERESFkLe3NwICAnJ1zDVr1sDb2ztXxyTKLQUmySciIiIios+D4/h9n/R4YbM6ZLtv//79Ubt2benrBw8eYPHixWjfvj08PDyk8jdv3sDS0lKpr6+vLwDg119/VSqvV69etuMhymtM8omIiIiIqNBq1aoVWrVqJX19/PhxLF68GHXr1pX26tJk3rx5AJBpO6LPSaG4J5+IiIiIiIiImOQTERERERERFRpM8omIiIiIiIgKCSb5RERERERERIUEk3wiIiIiIiKiQoJJPhEREREREVEhwSSfiIiIiIiIqJBgkk9ERERERERUSDDJJyIiIiIiIiokmOQTERERERERFRJ6+R1AQRUZGYnKlSurrfPx8YGPj88njoiIiIiIiIgKqqVLl2Lp0qVq6yIjI7Ueh0l+NllZWeH27dv5HQYRERER0ScXNqtDfoeQbe7u7hBCaNU2LCwsb4MhSiejyWJ7e3s8f/5cq3G4XJ+IiIiIiIiokGCST0RERERERFRIMMknIiIiIiIiKiSY5BMREREREREVEkzyiYiIiIiIiAoJJvlEREREREREhQSTfCIiIiIiIqJCgkk+ERERERERUSHBJJ+IiIiIiIiokGCST0RERERERFRIMMknIiIiIiIiKiT08jsAIiL6sgghkBIXl99hZImesTFkMll+h0FERESUKSb5RET0SaXExWGzg1N+h5ElXv89hr6JSX6HQURERJQpJvlERERERJQlm64++6TH61PDPlfGSU5OxurVq7Fx40bcunULsbGxsLOzQ6tWrTBy5EhUqFBBbb/g4GAsW7YMFy5cwLt37+Do6Ii6devC19cXLi4uSm29vb0REBCgVCaTyVCqVCk0b94cEydORNWqVXPlfIjUYZJPRET5psedEOgZG+d3GGqlxMVha6Uq+R0GERHlkoiICHTs2BFXrlxB1apV0bt3b1hbW+PSpUvYsGEDVq9ejcWLF+OHH36Q+sjlcgwfPhx//PEHypYtiy5dusDR0REPHjzApk2bsHnzZhw6dAjNmjVTOZ6fnx8sLS0BADExMbhy5Qp2796Nbdu2YevWrejcufMnO3f6sjDJJyKifKNnbMxl8ERElOcSEhLg4eGBGzduYObMmRg7dix0dP63B/mDBw/g6emJH3/8EaVKlYKnpycA4O+//8Yff/yBXr16ISAgAIaGhlKfiRMnom7duujduzcePnyIokWLKh1z4MCBcHR0VCq7evUqOnbsiEGDBiEsLEylD1Fu4O76RERERERUqC1btgzXrl3DiBEjMH78eKUEHwDKlSuHf/75B8bGxhg6dCgSEhKQmJiIadOmwcHBAevXr1dK8AHA2dkZEydORGRkJPbv369VHDVq1MDMmTPx5s0bLFu2LNfOjyg9JvlERERERFSo+fv7o1ixYpgwYYLGNtbW1hgxYgRevHiBgwcPYtu2bXj69ClGjBgBfX19tX369euHJUuWwNraWutYvLy8ULx4cWzZsiXL50GkDS7Xz6bIyEhUrlxZbZ2Pjw98fHw+cURERERERPSx5ORk3L17F02bNoWFhUWGbdu0aYMZM2bg1q1biI+PBwA0atRIY/tSpUph6NChWYqnSJEicHFxQWhoaJb6UeG3dOlSLF26VG1dZGSk1uMwyc8mKysr3L59O7/DICIiIiKiDISGhiI5ORnlypXLtK1id/3bt28jNTUVAFTuq88N9vb2uHTpEqKiolC8ePFcH58Kpowmi+3t7fH8+XOtxmGST0REREREhVZCQgIAqNxTr46BgQEA4O3bt9IS/SJFiuR6TDKZTOm/RLmJ9+QTEREREVGhVaFCBchkMjx48CDTtvfu3QMA2NjYwMXFBQDw+PHjTPso+mnr6dOnsLCwgJmZWZb6EWmDST4RERERERVaRkZGcHZ2xqVLl6RZ/fRSUlIghAAAnDp1CgDQuHFjlC9fHgBw6dIljWNHR0ejSpUqWdqPKzExEQ8fPoSTk1NWToNIa0zyiYiIiIioUOvVqxciIyPx+++/q9T99NNP6N+/P/777z/MmDEDxYsXh6enJ3r27IlixYph9uzZSElJUTvuvn37kJqaiubNm2sdy6ZNmxAVFYXevXtn+3yIMsIkn4iIiIiICrUxY8bA0dERfn5++Pvvv6WZewDo3Lkz9uzZg8qVKyM6OhqzZ89G8eLFUaJECYwdOxYPHjzA119/jbi4OKUxnz9/jrFjx8LU1BQDBgzQKo6rV69i4sSJKFGiBH744YdcPUciBW68R0REREREhZq5uTkOHjwIDw8PDB48GMuXL0eDBg1gb2+Pu3fvQiaTIS4uDoaGhnB1dZX6jR49GpcvX8b69etx5swZtGzZEi4uLnj8+DE2bdqEuLg4rFu3DqVLl1Y5ZkBAACwtLQEAMTExuHr1Kvbt24fk5GRs3boVRYsW/WTnT18WJvlERERERJQlfWrY53cIWVahQgXcvHkTf/75J4KCghAQEIDExETY29vD09MTAwYMwNy5c9GsWTPs3r0b7du3R5EiRbBt2zZs2bIFf//9N7Zv3464uDg4OTmhQ4cO8PX1RcWKFdUez8/PT+nrkiVLolOnTpg4caLSBwlEuY1JPhERERERfRGMjY0xatQojBo1Sm1906ZNsWzZMjRt2lQqk8lk8PLygpeXl1bH8Pf3h7+/f26ES5QtTPKJiIiIiIgA6OjoYOjQofkdBlGOcOM9IiIiIiIiokKCST4RERV48rg4PLMrjWd2pSH/aPdjIiIioi8Jk3wiIiIiIiKiQoJJPhEREREREVEhwSSfiIiIiIiIqJBgkk9ERERERERUSDDJJyIiIiIiIiokmOQTERERERERFRJM8omIiIiIKE/xUadEnw6TfCIiIiIiIqJCQi+/AyioIiMjUblyZbV1Pj4+8PHx+cQRERERERERUUG1dOlSLF26VG1dZGSk1uNwJj+brKyscPv2bbUvJvhERERERJ8PPz8/yGQyHD9+XGMbR0dHODo65qiPTCbD4MGDM41H23aHDx+GTCbDsGHDNLZ59eoVdHV10axZsyy3/5Ti4+Ph4uKi1XkDwIEDB9C4cWOYm5vD2toa7du3x8WLF/M4yjRhYWEYOHAgHBwc4ODggAEDBiAsLExt20ePHqFnz55wcHCAqakpGjZsiMDAQAghsnxcHx8fjTmmlZWV1uMwySciIiIiIvoMubu7o1SpUti2bRvkcrnaNnv27IFcLkfPnj2z3P5TmjVrFkJDQ7Vqu3PnTnh4eODdu3cYPnw4Bg0ahGvXrqFRo0a4ceNGnsb59u1bNGnSBLt27UKXLl3QpUsX7Nq1C02bNsXbt2+V2oaHh8PNzQ3BwcHo3r07xowZg5SUFPTu3RvTpk3L0zgzwiSfiIiIiIjoM6Snp4devXohIiICZ86cUdtmx44dkMlk8PT0zHL7T+XevXv47bfftG4/efJk2Nvb49KlS/Dz88PMmTNx7tw56OjoYNasWXkYKeDv749nz55h+/btWLx4MRYvXoygoCA8ffoU69atU2o7Y8YMxMTE4MiRI1i4cCEmT56Mc+fOoUuXLpg2bRoeP36cp7FqwiSfiKgQEEIgOTa2QLxSuKsyERGR1ry8vAAAgYGBKnUfPnzAoUOH0KRJE9jY2GSrfV4TQuCHH35ApUqVtGqflJSE27dvo1OnTjAyMpLKHR0d4erqipCQkLwKFQCwdu1auLm5oUWLFlJZmzZtUKVKFWzYsEGp7dmzZ1GrVi3UqVNHKtPV1cXIkSORkpKCs2fP5mmsmnDjPSKiQiAlLg6bHZzyOwwiIiICsH//fsyePRvXrl2Ds7Mz+vbti9GjR0NHRyfL7Ro0aIAyZcpg27ZtWLRoEXR1daW6gwcPIjExEb169cp2+7y2YcMGHDt2DGfPnkXDhg0zbZ+SkoLly5ejRo0aSuVCCERFRcHV1VWp/Pnz5xg3bhzOnj2LyMhIVKlSBT/99BP69euX5VgTEhJw/fp1jBgxQqWuRYsWWLJkCVJSUqCnl5ZGJycnKyX46c8BSNv/ID8wySciIiIiIq0IISDi41XK5Zms0pLH/a9Pyus30DHW3F7H2FhtuczICDKZTMtI1Xv16hWePXumti598paTPqdPn8bGjRvRt29ftGzZErt27cLYsWPx8OFDrFixIsvtdHR00Lt3b8ydOxdnzpxB06ZNpTrF0vvu3btnu31GhBAq11xdmSbv3r3DyJEjMXjwYDRo0ECrPsbGxkqb84WGhiIiIgL+/v54+PCh0nL9Bw8eoH79+jAwMMBXX32FokWLYu/evejfvz9CQ0Ph6+ur1TEVFDvYq1vloCh7/fo1rK2tAQC3bt1SO45ixv/jDyQ+FSb5RESFTI87IdDT8AfS56agxElERGlEfDzCy1XI0RgvG2Q+m6uO7YN7kOXw/xuZzWA7ODjkuM+9e/ewadMmadn8+PHj4eHhgVWrVmH48OGoUqVKltoBQJ8+fTB37lwEBQVJSXtSUhL27dundul9VttrMmzYMJQtW1aa2U5NTcU333yDpk2bYtCgQZn2Hz9+PORyOWbPnq3V8dSpXr06YmNjAQAjR45U2kvgl19+QdGiRXHlyhVYWloCACZNmoS+ffvi119/hbe3N8qUKaM03vHjx9G8eXO1u98rknxzc3OVOkXZy5cvpST/Y3K5HOPGjcPq1atRvXp1NG/ePBtnnHNM8omIChk9Y2Pom5jkdxhERESfnZkzZ2qcXdWUtGa1T/ny5dG7d2/pawMDA/j6+uLIkSM4cuSIlLxr2w4A3NzcUKFCBWzdulVagn/8+HFER0er/RAiq+01qV+/PgYOHAgA+Omnn/DNN99g586dWj0y/N9//8WKFSuwatUqKQHPjqCgIERERGDHjh1YsGABDAwMMGvWLMTHx2PXrl34+eefER8fr7TawsvLC1u2bMHx48cxYMAArY+lWKGg7gMAxdMKkpOT1fa9cOECfHx8cOnSJZQuXRrbt29XuT3jU2GST0REREREWpEZGcH2wT2Vcm2W6ytm8EudOwsdYyONbTNarp9TDRo0gLu7u9o6Yw3HzWofV1dXleXsbm5uAICHDx9muR2Qlnx6eXlh6tSp0hL8jJbeZ7W9Jv379wcADBw4EJs3b8a9e/dw6NAhtfehp5eSkoIhQ4agQYMG8Pb21vp46rRv3x4A8PXXX6NDhw6YO3cuRo4cicjISAghsHDhQixcuFBtX8XM/KtXr5CYmCj9G4DShwKmpqYwNTVFqVKlAABRUVEqY7179w4AVGbxk5OTMXXqVMyaNQtyuRxfffUVfv/9d7WrAT4VJvlERERERKQVmUymdsm8psRcIf2HAHolLDNtX9goZoGNMvmgIqN2iqQ9MDAQjRs3lp7drmnpfVbba9K3b1+sWrUKJ06cwIgRIzJN8AFgzZo1uH79Ovbt24fw8HClutjYWDx79gxFixZF8eLFVfo+ePAAR44cQadOnWBnZyeVy2QydOjQAQcOHMCjR49QpEgRAMAPP/wADw8PtXGUL18eANCzZ0+cOHFCqa506dLSv6dMmQI/Pz9YWVkBgErMAPDixQsAkD4IANI+zOjatSv279+P8uXL4++//0bjxo01XpdPhUk+ERERERFRLgkJCVHZnO7q1asAABcXlyy3U6hYsSLc3Nywbds29OnTBxEREZg4caLGOLLaXh25XI7Bgwfj6tWrmDRpEmbOnAknJycMGzYsw37Pnz8HAHTo0EGlbvPmzdi8eTNGjRqFefPmqdQ/ffoUP/zwA8zMzNCnTx+luujoaACAnZ2d9AGBkZEROnbsqNTu7du3uH37tpSQz5o1C2/evAEA3Lx5ExMmTMCePXuk9ooPAwwNDVG9enUcPXpUJa6jR4+iRo0a0NfXl8pmzJiB/fv3o02bNtiyZYvaDy3yA5N8IiL6pDTtzJwT6WeIMlsyqi2lGNXcm0dERKTO3bt3ERQUJN37npiYiF9//RWGhoZKyai27dLr06cPxo4di19++QUymUxpE7rcaP+xIUOGYNu2bQgODkb9+vVRrlw5fP311zAwMMD333+vsV/fvn1Ru3ZtlfJOnTqhdevW+Omnn+Ds7Ky2b6VKlaCrq4u1a9eid+/e0n3tsbGx8Pf3h5OTE+zs7CCTydC8eXOsXr0ao0aNgq2tLYC0vzN++uknbNu2TZp9T7+zf9GiRQFA4zUeMGAARo0ahcOHD6NVq1YAgODgYNy+fRuLFy+W2qWmpmLFihUoVaoUdu/eLa0s+BwwySciok8qN3ZmzsiL6jUyb6SF1HSJvUhIAP7/jwIiIqKM1KpVC1999RUOHz4MOzs77Nq1C1evXsWvv/6qtFRe23bp9e7dG2PHjsXZs2fRrFkzjbu8Z7f9x9q2bYtBgwahfv36ANISYF1dXaVNAdUpX768NDv+sTJlyigl2Hfu3MGRI0dQt25d1K1bFzY2Nvjxxx+xZMkSNG3aFO3atUNCQgI2bdqE//77D8HBwdLqhwULFqBx48aoXr06vL29YWRkhH/++Qfnz5/HvHnzYGZmlqXzBQBvb28sXLgQPXv2xMCBA5Gamoq1a9eievXqSpv4PXnyBOHh4WjQoAFWrlypdqyWLVuiUqVKWY4hp5jkExERERER5RIvLy+MGjUKixcvxubNm1GpUiWsXr0aX3/9dbbapefg4ICGDRvi7Nmz6NmzZ6axZLX9x9TN/Pfr1y/L42Tk/PnzGDZsGKZMmYK6desCSEveHR0d4e/vj1mzZsHCwgI1a9bEjh07UK1aNamvm5sbrl69inHjxmHLli2IiopCpUqVsHnz5iw9RSA9CwsLnDx5Er6+vti+fTtkMhm6deuG+fPnK31ooNjA79y5czh37pzasdasWZMvSb5MqHs+AGlkb2+P58+fw87OTmlHRqLsuHLlCmrVqoXLly+jZs2a+R1OhgpSrF+i5NhYbHZwAgB4/ff4s36EnjwuTprJt7l+NcfPPFaMqZjBt75+NVc2dEp+/RqBNdOWGva+exsGJUrkeEwioi9V+t/9tg/ufXEb7xHlVFbyUM7kExFRvpEZG+f6H3o6uTRmbnz4QERERPSpMcknIiIiIqI8pWNsDPvnT/M7DKIvgk5+B0BEREREREREuYNJPhEREREREVEhweX62RQZGYnKlSurrfPx8YGPj88njoiIiIiIiIgKqqVLl2Lp0qVq6yIjI7Ueh0l+NllZWeH27dv5HQYREREREREVAhlNFit219cGl+sTEZFa8rg4PLMrjWd2pSGPi8vvcIiIiIhIC0zyiYiIiIiIiAoJJvlEREREREREhQSTfCIiIiIiIqJCgkk+ERERERHlqeTYWKwrUQrrSpRCcmxsfodDVKgxySciIiIiIiIqJJjkExERERFRoeXn5weZTIbjx49rbOPo6AhHR0fp67CwMMhkMnh7e2s97vHjxyGTybB+/fpMY5LJZBg8eHCGbd6/fw8jIyO4urpm2K569eowNjbGhw8f0L9/f8hkMly+fFlj+4ULF0Imk2HNmjUa2yjOxc/PT2Mbb29vyGQyhIWFZRgffXpM8omIiIiIiD4zpqam6NChA27duoW7d++qbfPo0SPcuHEDHh4eKFq0KLy8vAAAQUFBGsfdsWMH9PT00KVLlzyJm/Ifk3wiIiIiIqLPUJ8+fQBoTtp37NgBAOjVqxcAoE2bNjA3N0dQUBCEECrtX758iTNnzqB169awsLDIo6gpvzHJJyIiIiIi+gwpZugDAwPV1u/cuRNGRkbo0KEDAMDAwACenp549OgRrly5otJ+z549kMvl6NmzZ57GTfmLST4REREREVEuEUJg3rx5cHNzg5mZGZo0aYK9e/eqbbt//340bdoUpqamcHNzw5w5cyCXy6V6IyMjdOvWDbdu3cKdO3eU+kZGRuLMmTPo0KEDTExMpPKMZv8VS/W7du2aC2eqzM/PD4aGhvjw4QO+//57ODk5wcbGBp6enrxv/xPTy+8AiIiIiIioYBBCICUuTqVcXZmm+oTXrzNsr2dsrLFcJpNpGamqV69e4dmzZ+rjS0mBnp5qahQbG6uxz/v379WWz507Fy9evMDAgQOljfg6deqEdevWoX///lK706dPY+PGjejbty9atmyJXbt2YezYsXj48CFWrFghtfPy8sK6desQFBQEX19fqXz37t0QQqjMyjdr1gzW1tYIDAzErFmzpGsWExODw4cPo3Xr1jA3N9dwlXImNTUVbdu2RXR0NAYOHIgXL17A398fx48fx5UrV+Dg4JAnxyVlBSbJF0LA2dkZ06ZNU3pzAMCbN28QHx+vtp+NjQ10dXUzHDsoKAgrV67EhQsXUKFCBXTt2hVjxoxR+0YnIiIiIvpSpcTFYbODU47G2FmrTrb6ef33GPrpZqyzSnHfuibqEtCtW7di69atWTrOw4cPcfPmTZQtWxYAMGLECLi5uWHChAno2bMnihQpAgC4d+8eNm3aJG2WN378eHh4eGDVqlUYPnw4qlSpAgBo3bo1LC0tVZL8HTt2KC3VV9DV1UWvXr2wePFiXLlyBbVq1QIAHDhwAImJiXm6VD8lJQWxsbE4f/68tLrAy8sLzZs3x/Tp07Fy5co8Ozb9T4FZrr93716Nyzw8PT1RunRpta93795lOG5QUBB69eqFV69eYfjw4ShRogQmTJiAn3/+OfdPgoiIiIiI8sXMmTOxZ88eta+SJUuq7dOiRQuNfRTL4j/Wv39/KcEH0iYdfXx88PTpU9y7d08qL1++PHr37i19bWBgAF9fXwghcOTIEalcX18fPXr0UFqy//79exw5ckRlqb6CIrb09/Lv3Lkzz5bqpzd69GilmNzd3eHu7o7du3fn6XHpfz7rqeq4uDjs3bsXp06dwurVqzW2e/DgAdq2bYuhQ4eq1JmammrsJ4TAtGnTUKlSJZw6dQpFixaFEAL9+/fH8uXL4efnhxIlSuTKuRARUd7RMTaG/fOn+R0GEVGhp2dsDK//HquUa7NcXzGD3/XyRY1L8hXHyEq5tho0aAB3d3e1dcYaxi5dujQ6duyotu7SpUtqy6tVq6ZS5ubmBiBtll9R7+rqqnL7Qfp26Xl5eWH58uXSbP6BAweQlJSkcVa+Xr16cHR0RFBQEGbPno3k5GTs27cvT5fqK6g7/+rVq+P48eP48OEDihYtmqfHp888yX/y5InSp1vqfPjwAeHh4fjpp580vgE1uX79Om7evIlFixZJP2wymQwjRozAxo0bERgYiB9//DHb8RMRERERFSYymUztkvnMltEnx8ZK/zYsUSJHy+4LIsXj7IyMjDJsp9h07+N2TZo0gY2NDQIDA+Hr66txqb6CTCaDl5cXZs+ejcuXL+P169d4//59prcsKOjr6wNIW36vSXJyMoC0FQjpqbvlWXH7tKIP5a3Perl++fLlER8fj/j4eAQHB6tt8+DBAwBAxYoVIYTA27dv1T4TUp1///0XANC8eXOl8po1a8LMzEyqJyIiIiIi0satW7dUyhSPs3NxcZHKQkJCVPKWq1evqrQD0pLk3r17IyQkBNevX8f+/fs1LtVXUNzrHxQUhJ07d0JfXx9dunTR6hwqVaoEACo7+qd3584dmJubw8bGRqn87t27Km1v3LgBS0vLPF9FQGk+6yRfR0cHhoaGMDQ0VPmESEGR5AcGBqJ48eKwtLSEpaUlvv32W0RFRWU4fmRkJACo/GDq6OjAysoKL1++zPlJEBERERHRF2PdunV49OiR9PXz58+xbNky1KhRQ+le/bt37yo95i4xMRG//vorDA0N1a5QVtxn/+OPPyImJibTWflq1aqhYsWKCAoKwq5du7K0VN/CwgINGjTA7t27cfLkSbXnePXqVXTu3FnlloN58+YhMTFR+vrkyZM4fPiwxlUHlPs+6+X62rh//z6AtB+e8ePHo3Tp0jh+/DhWrVqFS5cu4eLFixp3yVck+cWLF1epMzc3zzDJF0JofGyGNooUKSLtrElERERERIVDlSpV0KBBA3h7ewMA1q5di+joaGzatAk6Ov+bY61Vqxa++uorHD58GHZ2dti1axeuXr2KX3/9VWUSEgDq1KkDZ2dnnD17FkZGRvDw8MgwDplMhj59+mDKlCkAkOVd9deuXYt69eqhefPm8PT0hJubG5KSknDu3Dn8888/qFixIn7//XeVfv/99x8aNmyILl26ICIiAmvWrEHx4sUxderULB2/sElMTFT68COrtF2tDnzmM/naKFu2LIYNG4azZ89i3Lhx6NevH1auXInffvsN165dw9q1azX2zeg5m3K5PMN7RsLDw2FmZpbt16xZs3J03kRERERE9PlZsmQJvvvuO+zduxcrVqxA1apVcfLkSbRq1UqpnZeXF/z9/XHz5k3Mnz8f+vr6WL16NSZPnqx2XMV99gAyXaqvoJj9z8pSfQUXFxc8ePAAo0aNQmhoKH777TcsXboUMTExmDt3Lq5evQozMzOVfsHBwahatSpWrFiB7du3w8PDA1euXIGjo2OWjl/YzJo1K0f5Y3h4uNbHKvAz+X369FH7+AofHx+MHTsW//77LwYNGqS2b6lSpQAAUVFRKo/NePfuXYY/iLa2thneo5IZzuITEREREeU9Pz8/+Pn5Zdjm40d1Ozo6Zjpz+vG47u7uUp969eph2rRpGvumH1vTo/jUmTFjBmbMmKF1+3LlymVpBvhjFhYWmDNnTpb6FC9eHAEBAdk+ZmE1fvx4jBw5Mtv9K1WqpHWiX+CTfE1MTExQsmRJvHr1SmMbKysrAGmz8umTfLlcjpcvX6J+/foa+8pksgwfz0dE9Eml+x+4iI+HPIOVStqSp3sckjyTRyNlhcjFsYiIiIgKgpzerp3RKvSPFegkXwiBvn37okKFCiqfzn348AGvXr2SdoZUR5HEHz16FNWrV5fKL1++jPfv36NOnTp5EjcRUW4TCQnSvyOquUE3F5L89F5Ur5Gr4xER0ZdF38QEX73mptZEn0KBvidfJpNBR0cHs2fPRkhIiFKdYllJp06dNPavXr06XF1dsWLFCnz48AFA2gcHCxcuhL6+Pvr27Zt3wRMRERERERHlsgI9kw8AU6dOxf79+9G4cWMMGjQIJUqUwOnTp7F//34MGzYMDRo0kNoGBAQgJiYGQ4cOBZD2IcHkyZPRq1cvNG3aFJ07d8aFCxdw4MABTJ06FSVKlMiv0yIiyjbrf89CPxd+f8nj4qQZfOvrV6FjbJzjMT8mMzLK9TGJiIjo09Nm7wP6NAp8ku/i4oJTp07B19cXGzduxIcPH+Dq6gp/f38MGDBAqe2UKVPw33//SUk+kPYoiS1btmDFihVYtGgRypcvj99//x3Dhg371KdCRJQrZMbGuZ6Q6+TBmERERESU+wpMkp9+t8qPVa1aFdu3b890jI93zVTo1asXevXqlZPwiIiIiIiIiPJdgb4nn4iIiIiIiIj+h0k+ERERERERUSHBJJ+IiIiIiIiokCgw9+QTEVEhIQSQGJ/fUWQuMR56OrK0f2vYE4aIiIjoc8Mkn4iIPq3EeKBPg8zb5TMDAH0aWQMAkpIS8jcYIqKCLiHuf7/7N50DDPnEFqK8wuX6RERERERERIUEZ/KJiCj/rDkKGBrldxRqJYU/h8GoHvkdBhER5ZCfnx+mTp2KY8eOwd3dXW0bR0dHAP975HZYWBicnJwwcOBA+Pv7azXu8ePH0bx5c6xbtw79+/fPMCaZTIZvvvkGq1at0tjm/fv3sLKygouLC27evKmxXfXq1fHgwQO8fPkSQ4YMwYYNG3Dp0iXUqlVLbfuFCxdi5MiRWL16Nb7++mu1bRTn8rFixYqhatWq+O677zBgwADo6HDO+HPEJJ+IiPKPodHnu2SziGF+R0BERF8wU1NTdOjQAdu2bcPdu3dRsWJFlTaPHj3CjRs34OnpiaJFi8LLywsbNmxAUFCQxiR/x44d0NPTQ5cuXTKNoX379vDw8AAApKam4u7duzh8+DC+/vpr/PPPP1i3bh10dXVzdqKU6/jRCxERERER0WeoT58+AICgoCC19Tt27AAA9OrVCwDQpk0bmJubIygoCELNprEvX77EmTNn0Lp1a1hYWGR6/Lp162Lo0KEYOnQohg8fjj///BPXr19Hjx49sGnTJmzdujW7p0Z5iEk+ERERERHRZ8jDwwNFixZFYGCg2vqdO3fCyMgIHTp0AAAYGBjA09MTjx49wpUrV1Ta79mzB3K5HD179sx2TMbGxvj7779RrFgxTJs2DXK5PNtjUd5gkk9ERERERJRLhBCYN28e3NzcYGZmhiZNmmDv3r1q2+7fvx9NmzaFqakp3NzcMGfOHKWk2cjICN26dcOtW7dw584dpb6RkZE4c+YMOnToABMTE6k8o9l/xVL9rl275ugcTU1N0b9/f4SEhCAkJEQqT0xMxMSJE1G9enUULVoU1atXx8SJE5GYmAgAiI+Ph5GRkcqtAq1bt4ZMJsPy5culstjYWBgYGKBjx44AAG9vbzg6OiIqKgre3t6wsLBA8eLF0blzZzx69ChH51PYMMknIiIiIiLtCJH2OLyPX1FvMn5Fv/3fGNFvM26rbvyEuLRj58CrV6/w7Nkzta+UlBS1fWJjYzX2ef/+vdo+c+fOxZw5c9C6dWt8//33CA0NRadOnbB+/XqldqdPn0aPHj1Qvnx5jBo1Cjo6Ohg7diyGDBmi1M7LywuAatK+e/duCCFUZuWbNWsGa2trBAYGKi3Zj4mJweHDh9G6dWuYm5trd9EyUKVKFQDA48ePAaTds9+iRQvMnDkT5cqVw4QJE1C+fHnMnDkTLVq0gFwuh5GREZo1a4aTJ08iNTUVAJCSkoJz584BAE6cOCGNf+7cOSQnJ6N9+/ZSWUpKCjp27Ihnz55h9OjRaN26Nfbs2YPu3bvn+HwKE268l02RkZGoXLmy2jofHx/4+Ph84oiIiIiIiPJYYvz/nnefXUM6ZK/fpnM52qxVcd+6Jg4ODiplW7duzfJ95w8fPsTNmzdRtmxZAMCIESPg5uaGCRMmoGfPnihSpAgA4N69e9i0aZOUxI8fPx4eHh5YtWoVhg8fLiXRrVu3hqWlJYKCguDr6ysdZ8eOHUpL9RV0dXXRq1cvLF68GFeuXJE24Dtw4AASExNztFQ/PXt7ewCQZtE3bNiAs2fPYs6cORgzZozUbu7cufjll1+wadMm9OvXD+3atUNwcDCuXbuGWrVq4dq1a4iNjUWVKlVw4sQJCCEgk8mkhD99kv/8+XO0aNECAQEBkMlkAIDOnTtjz549ePnyJUqVKpUr55Zfli5diqVLl6qti4yM1HoczuRnk5WVFW7fvq32xQSfiIiIiOjzMnPmTOzZs0ftq2TJkmr7tGjRQmMfxbL4j/Xv319K8AHAxsYGPj4+ePr0Ke7duyeVly9fHr1795a+NjAwgK+vL4QQOHLkiFSur6+PHj16KC3Zf//+PY4cOaKyVF9BEVv6e/l37tyZK0v1FRRJtuK/e/bsgampKX766SeldsOGDUOxYsWwe/duAEC7du0AAMeOHQMAnDp1CkWLFsWwYcMQHh4ufWhw4sQJlC9fHs7OzkrjjR07VjomAOlDjLi4uFw5r/zk4+OjMce0srLSehzO5BMRERERkXaKGKXNqH8sIT7jfonx/5vB/2tf2jiaGGqoy6iPFho0aAB3d3e1dcbG6lcIlC5dWron/GOXLl1SW16tWjWVMjc3NwBps/yKeldXV6Vk9eN26Xl5eWH58uXSbP6BAweQlJSkcVa+Xr16cHR0RFBQEGbPno3k5GTs27cv15bqA8DTp08BQErCHz58iHLlykkrFRQMDQ3h4uKC0NBQAECFChXg4OCAo0ePYvTo0Th16hQaNWqEVq1aAUhL7m1tbXH+/Hn88MMPKsctV66c0td8hJ8qJvlERERERKQdmUz9kvnMltEnpJtlNbPI0bL7gkhxb7yRUcYfVCg23fu4XZMmTWBjY4PAwED4+vpqXKqvIJPJ4OXlhdmzZ+Py5ct4/fo13r9/n+ktC1mh2HDPyckp07Y6OjrSvgcymQzt2rXDhg0bkJycjNOnT2P48OFwdnaGvb09Tpw4AScnJyQlJUmz/gq6urowMDDItXMorLhcn4iIiIiIKJfcunVLpUzxODsXFxepLCQkROVZ9levXlVpB6Qlt71790ZISAiuX7+O/fv3a1yqr5B+w76dO3dCX19fZVf77Po/9u49PIry7v/4Z3YDgYAgCIRAwKBBIRY5iIqCZ59aFfVpPWKNjYSKukYq6lMPVcBDUau2GPd5RBtA0KJV21qt1nqkgvATEPEQwKCkSBIWKCchIYfd+f2BiaFswpJMMnNP3q/r4mLZ2b3znf1kEr573zO7c+dOPfvsszrmmGPqrlOWmZmptWvX1l1Jv1ZlZaWKiop01FFH1d33ox/9SLt27dL8+fO1efNmnXLKKbIsS6eddpoWLFigBQsWqEOHDjrttNMcqbetockHAAAAAIfMmzdvn490Kykp0f/+7/9q+PDh+5yrv3r16n2umF9ZWal7771XHTp0iHuKQO159jfccIO+/fbbA87KH3vssRo0aJBefPFFvfLKK44t1S8vL9f48eP17bff6p577lEgsLelHDt2rHbs2KEnnnhin8c//vjj2rlzp84777y6+84880wlJSXp/vvvV/v27XXCCSdIkk4//XT961//0h/+8AedccYZB1z5gPhYrg8AAAAADjnmmGN00kknKScnR5I0d+5c7dixQ/Pnz69riKW9F4zLzs7W22+/rb59++qVV17RihUrdO+99yotLW2/cY8//ngdccQR+vDDD9WxY8d9muZ4LMvSuHHjNGXKFElq0lX1P/roo7qmPRqNas2aNXrrrbe0du1ajRs3TpdcckndY6+66io9+eSTuvXWW/XRRx9p2LBh+vjjj/XSSy/ppJNOUnZ2dt1ju3TpotGjR2vBggUaPXq0OnToIEl110woKipSXl7eQdeLvZjJBwDEFUhJUXrJN0ov+UaBBi5IBAAA9pWfn69rr71Wr732mp566in94Ac/0D//+c+6C8vVuuKKKzRnzhx99tlnevTRR9WuXTvNmjVLd999d9xxa8+zl3TApfq1amf/m7pU/4033lBeXp7y8vL0i1/8QnPnztVhhx2mWbNm6dlnn93nTYtgMKj33ntPt99+u1atWqX7779fa9as0Z133qn33ntvvwvk1Z5vf8opp9Tdd+SRR6pv376S9v3oPBwcy/7PE0HQqPT0dJWUlKhv377asGGD2+XAcLWfXbp8+XKNGDHC7XIaZVKtbVHVli16YdDec+IuX12o9j16uFxRI/aUf/8Zy838zOOWVFXyjdrfuHe5ZNUTr6l9334uVwQABjPkZz/gVQfThzKTDwAAAACAT9Dk46CUlZVp6tSpKisrc7sUAAAAmKJDivTnlXv/MIsPtCiafByUsrIyTZs2jSYfAAAAADyIJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImH3BJUVGR8vPzJUn5+fkqKipyuaKGmVQrAAAA0JbR5AMumD17tgYNGqR58+ZJkubNm6dBgwZpzpw57hYWh0m1AgAAAG0dTT7QyoqKijRhwgTFYjFFo1FJUjQaVSwWU25urtauXetyhd8zqVYAAAAANPlAq5s1a5Ysy4q7zbIsFRQUtHJFDTOpVgAA4G1lZWWaOnWqysrK3C4F8LUktwswVSQSUVZWVtxtoVBIoVColSuCKYqLi2Xbdtxttm2ruLi4dQtqhEm1AgAAbysrK9O0adN04YUXKi0tze1yAM8Jh8MKh8Nxt0UikYTHoclvotTUVBUWFrpdBgyUkZHR6Ox4RkZG6xbUCJNqbfNsW0mB77KqrJD2lLtbT2P2VLhdAQAAgOc0Nlmcnp6ukpKShMahyQda2fjx4/Xwww/H3WbbtnJzc1u5ooaZVGubV7VH40b33nv7xrHu1gIAgMfMmTNH11xzzX73H3rooRoxYoQmTZqkCy+80IXKAOdxTj7QygYOHKiCggIFAgEFg0FJUjAYVCAQUEFBgTIzM12u8Hsm1QoAAHAgP/3pT5Wfn6/8/Hw9+uij+vGPf6zVq1froosu0u23396kMR9//HE+dQiewkw+4IKcnByNGTNGDzzwgObMmaPs7GzdddddnmyaTaoVe1U9+pLa9+nrdhmJSe7odgUAgFZQVFSk/Px8SVJ+fr7uvPNODRw4sNXrOPvss5WTk7PPff/+97/14x//WA899JD++7//W6NGjTqoMR9//HGlp6fvNy7gFmbyAZdkZmYqLy9PkpSXl+fpptmkWiEpuYPUIcWMPw1c8wEA4B+zZ8/WoEGDNG/ePEnSvHnzNGjQIM/Mfh922GGaM2eOLMvSvffe63Y5QLPR5AMAAABoEUVFRZowYYJisZii0agkKRqNKhaLKTc3V2vXrnW5wr2OOOII/ehHP9Ibb7yhb7/9VpL0xRdf6JJLLlHfvn2VnJys/v3765prrtE333wjae+nEFmWpa+++koLFiyQZVl1b1zEYjE99thjGjJkiDp37qxDDz1Uxx9/vJ555hm3dhFtCE0+AAAAgBYxa9asRj+pp6CgoJUratgxxxwjSVq3bp22bt2qs88+W++++67GjRunKVOmaMSIEXrmmWd00UUXSZK6d++u/Px8HXbYYcrMzFR+fr5OPPFESdLDDz+sW265Rb1799bdd9+tq6++Wlu3blVOTo7++te/uraPaBs4Jx8AAABAiyguLpZt23G32bat4uLi1i2oEenp6ZKkr7/+WuvXr9fGjRv1yiuv7HPV/WuvvVZPP/20/v3vf+uwww7TjTfeqN/97nfq27evbrzxxrrHvfTSSzruuOP05ptvKhDYO696++23q2/fvnrrrbe4kj9aFE0+AAAAgBaRkZHR6Ex+RkZG6xbUiNo6LcvSmWeeqbKyMvXs2bNuu23b2r59uySpoqKi0bFef/11JScn1zX4khJ+LtBcNPkAAAAAWsT48eP18MMPx91m27Zyc3NbuaKG1Z5rf8QRRyglJUU1NTV644039Mknn2jFihVatGiRIpFIQmP16tVLK1eu1NKlS/XJJ59o2bJlWrZsWUuWD9ThnHwAAAAALWLgwIEqKChQIBBQMBiUJAWDQQUCARUUFHjqE3u++OILSXtXH3z66acaOnSofvKTn2jBggUaOXKk5s2bp1/84hcHHMe2bd1yyy0aNmyY7rvvPkWjUV1//fVauXJlC+8BsBcz+QAAAABaTE5OjsaMGaMHHnhAc+bMUXZ2tu666y5PNfhfffWV/v73v+u8887TIYccol//+tcqLy/Xhg0b1KtXr7rHvfnmmwcca82aNXrsscc0depU3XPPPXWnAezatavF6gfqYyYfAAAAQIvKzMxUXl6eJCkvL89TDf6///1vXXPNNbJtW1OmTJEkrVq1Sr17997nnPzNmzfrpZdekrT3I/Lqq//vVatWSZKOPfbYfa5H8OSTT8Z9LuA0ZvIBAAAAtAlvv/123Yx6dXW1Pv/8c/39739XaWmpbr/9dp1wwgmSpLPOOku//e1vdd555+mUU07R5s2b9dxzz6lHjx6SpDvvvFP333+/MjIy1L59e3366aeaOXOmTj/9dJ144onq1KmTQqGQ3n//faWmpmrBggVauXKlunXrpr///e+aO3eurr76atdeB/gbM/kAAAAA2oTnnntOeXl5ysvL0+TJk/XSSy/p6KOP1l/+8hdNnz697nH333+/brrpJn366ad68MEH9fHHH+uJJ57QsmXL9F//9V965513tHnzZknS//zP/6h37966+eabtXLlSvXp00evvvqq+vfvr9///veaPXu2+vfvr88++0z/+7//K0l65513XNl/tA3M5AMAcAA15RWydu92u4wDSkpJafCjqgCgLcvJyVFOTk7Cj09JSdGMGTM0Y8aM/bb94x//OODYZ5xxhpYsWbLfc6+44gpdccUVCdcBNAVNPgAAB/DK6FNUE7PdLuOArvjXOrXr1MntMgAAgItYrg8AAACgxaWlpWnKlClKS0tzuxTA15jJBwAgjmCHjnW3L16xTNah3V2spmE15eV6afAxbpcBAAeUlpamqVOnul0G4Hs0+QAAxFH/3PakjikKsAweAAAYgCa/iSKRiLKysuJuC4VCCoVCrVwRAAAAAMBU4XBY4XA47rZIJJLwODT5TZSamqrCwkK3ywAAAAAA+EBjk8Xp6ekqKSlJaBwuvAcAAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ARNPgAArSRWXq4NfftpQ99+ipWXu10OAADwIZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJB+ArZWVlmjp1qsrKytwuBQAAAGh1NPkAfKWsrEzTpk2jyQcAAECbRJMPAAAAAIBP0OQDAAAAAOATSW4XYKpIJKKsrKy420KhkEKhUCtXBAAAAAAwVTgcVjgcjrstEokkPA5NfhOlpqaqsLDQ7TIAAAAAAD7Q2GRxenq6SkpKEhqH5foAAAAAAPgETT4AAAAAAD5Bkw8AAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ARX1wcA4ADsinLFyjs2e5xYeXnc281hV1TU+4ftyJgAAMBcNPkAABzAxlEny445PObQ4Y6ME63X2Nt79kidOzsyLgAAMBPL9QEAAAAA8Alm8gEAiMPq+P3y/LSVn0gdnFmuXzuD33vlCgVSUpo9ZvWWLdKIkc0eBwAA+ANNPgAAcVjW97cDKR2lDs1vyOsLpKQ40uRbDowBAAD8g+X6AAAAAAD4BE0+AAAAAAA+YUSTb9u2BgwYoGeffXa/bbt27dLkyZM1ePBgpaSkaOjQobr33ntVVVV1wHGrq6u1YcOGuH+2bdvWErsCAAAAAECLMaLJf+2111RcXLzf/bFYTGeeeaZ++9vfauTIkbr77rt15JFHasqUKTr33HMVizX+eUeLFi1Sv3794v65++67W2hvAAAAAABoGZ698F55eblee+01ffDBB5o1a1bcx7z88staunSpfvOb3+jWW2+tu/+xxx7TLbfcoj/84Q+66qqrGvwaRUVFkqRnnnlG3bt332dbRkZG83cCAAAAAIBW5Nkmf/369br88ssbfcyHH34oSZo4ceI+999444264447tGDBgkab/C+//FIdO3bUVVddpUDAiEUNAAAAAAA0yLOd7VFHHaWKigpVVFTozTffjPuYXbt26Qc/+IEOOeSQfe6vrq6WZVnavHlzo1/jyy+/1NFHH61AIKDy8nKVl5c7Vj8AAAAAAK3Ns01+IBBQhw4d1KFDB7Vv3z7uY55++ml99tln+93/pz/9SZWVlRoyZEijX6OoqEjRaFQnn3yyOnXqpEMOOURDhgzR66+/7sg+AAAAAADQmjy7XL+pXn75ZU2YMEEpKSm69tprG3xcNBrV2rVrVV1drSuuuEITJ07Utm3b9MQTT+j888/XSy+9pIsvvrjB59u2rZ07dza5zuTkZCUnJzf5+QAAAAAAM1RWVqqysrLJz7dtO+HH+qbJLy0t1a233qr58+erQ4cOev7559WvX78GH79r1y5de+21Gjp0qH7+85/X3X/NNddo0KBBuvnmm/WTn/xElmU1+PW6du3a5HqnTJmiqVOnNvn5AAAAAAAzTJ8+XdOmTWuVr+WLJv+FF17Qddddp+3bt+vEE0/UrFmzlJWV1ehzunbtqieeeCLu/VdffbUefvhhlZWVqU+fPnGf36dPH61atarJNTOLDwAAAABtwx133KHJkyc3+fmDBw9WaWlpQo81vsmfPn267rzzTnXu3FkFBQXKyclp9pXyDz/8cEnS5s2bG2zyLctSly5dmvV1AAAAAAD+19zTtRtaYR6PZy+8l4j3339fd955pwYMGKAlS5Zo/PjxCTf4f/3rXzV27Fh9+eWX+20rLi6WZVk66qijnC4ZAAAAAIAWY/RM/pNPPilp79X0jznmmIN67qBBg/S3v/1N3bp109y5c+veGYlEIpo1a5bOOeccdezY0fGaAQAAAABoKUY3+YsXL1aPHj20cOFCLVy4cL/tgwYN0tlnny1J+vOf/6ySkhJdffXV6tKli4466ijdeOONeuKJJ1RWVqazzz5bmzdv1rx58xSLxZSfn9/auwMAAAAAQLMY3eRv3rxZFRUVysvLi7v9Zz/7WV2TP2PGDC1YsEBjx46tO5f+scceU2Zmpp566inde++96t27t370ox/pwQcfbPBcfAAAAAAAvMqIJv/000+P+7mA5eXlCY/x/vvv73dfu3btNGnSJE2aNKk55QEAAAAA4AlGX3gPAAAAAAB8jyYfAAAAAACfMGK5PgAAfhBISVF6yTdulwEAAHyMmXwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn+Aj9AAAOJA9FW5X0LDKCiUFrL23bdvdWgAAgOto8gEAOJBrznS7gga1lzRudG9JUlXVHneLAQAArmO5PgAAAAAAPsFMfhNFIhFlZWXF3RYKhRQKhVq5IgCAo5I7SvMXu13FAVWVlqj9LZe4XQYAAGimcDiscDgcd1skEkl4HJr8JkpNTVVhYaHbZQAAWoplSR1S3K7iwJI7uF0BAABwQGOTxenp6SopKUloHJbrw5fKyso0depUlZWVuV0KAAAAALQamnz4UllZmaZNm0aTDwAAAKBNockHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCeS3C7AVJFIRFlZWXG3hUIhhUKhVq4IAAAAAGCqcDiscDgcd1skEkl4HJr8JkpNTVVhYaHbZQAAAAAAfKCxyeL09HSVlJQkNA5NPnAQ5q/Y4Oh461bvfUfu76sjWmM5O/a44emOjgcAAADA+zgnHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAAAAAACfSHK7AFNFIhFlZWXF3RYKhRQKhVq5IgAAAACAqcLhsMLhcNxtkUgk4XFo8psoNTVVhYWFbpcBAAAAAPCBxiaL09PTVVJSktA4LNcHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAARisrK9PUqVNVVlbmdikAALiOJh8AABitrKxM06ZNo8kHAEA0+QAAAAAA+AZNPgAAAAAAPkGTDwAAAACAT9DkAwAAAADgEzT5AAAAAAD4BE0+AAAAAAA+QZMPAAAAAIBP0OQDAAAAAOATNPkAAAAAAPhEktsFmCoSiSgrKyvutlAopFAo1MoVAQAAAABMFQ6HFQ6H426LRCIJj0OT30SpqakqLCx0uwwAAAAAgA80Nlmcnp6ukpKShMZhuT4AAAAAAD5Bkw8AAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ARNPgAAAAAAPkGTDwAAAACAT9DkAy46tEcv/eTam3Voj15ul3JAaWlpmjJlitLS0twuBQAAAEADktwuAGjLuvVM1SXXTXa7jISkpaVp6tSpbpcBAAAAoBHM5AMAAAAA4BM0+QAAAAAA+ARNPgAAAAAAPkGTDwAAAACATxjT5Nu2rQEDBujZZ5/db9vu3bv1y1/+UkOHDtVhhx2mCy+8UO+++27C4z755JM67bTT1LVrV5166ql68sknZdu207sAAAAAAECLMqbJf+2111RcXLzf/bZt67LLLtPDDz+sY445Rtdff70KCwt1zjnnaPHixQcc95FHHtH1118v27Y1efJk2bat66+/Xo8++mgL7AUAAAAAAC3H001+eXm5/vjHPyovL09XXHFF3MesWLFCr7/+uu655x794Q9/0P33368FCxbo0EMP1cMPP9zo+FVVVXrwwQd1xhln6N1339WUKVP07rvv6tRTT9VDDz2k6urqltgtYxUVFSk/P1+SlJ+fr6KiIpcrAgAAAADU5+kmf/369br88sv1xBNPqLy8PO5j5s6dq0AgoJtuuqnuvr59++ryyy/X66+/rm3btjU4/uuvv66tW7dq0qRJSkpKkiS1a9dOeXl52rJli9566y1nd8hgs2fP1qBBgzRv3jxJ0rx58zRo0CDNmTPH3cIAAAAAAHU83eQfddRRqqioUEVFhd588824j1myZEndufj1nXnmmaqqqtKKFSsaHH/JkiWSpDPOOGO/59bf3tYVFRVpwoQJisViikajkqRoNKpYLKbc3FytXbvW5QoBAAAAAJKU5HYBjQkEAurQoYMkqX379nEfE4lElJWVtd/9aWlpkqRNmzY1OH4kElFKSoq6dOmyz/3dunVT+/btG32ubdvauXPnAfehIcnJyUpOTm7y81vTrFmzZFlW3G2WZamgoEDTp09v5aoAAAAAwAyVlZWqrKxs8vMP5sLwnm7yExGJRDR69Oj97u/WrZukAzf5tY+rz7IsdevWrdHnlpaWqmvXrk2oeK8pU6Zo6tSpTX5+ayouLm7wm8q27bgXRAQAAAAA7DV9+nRNmzatVb6W8U2+ZVlxG9BYLCZJjV48r6Hn1j6/sef26dNHq1atOshqv2fKLL4kZWRkNDqTn5GR0boFAQAAAIBB7rjjDk2ePLnJzx88eLBKS0sTeqzxTX6vXr20ffv2/e6vveBe7969D/q5tm1r27ZtjT7Xsqz9lvn71fjx4xv8pALbtpWbm9vKFQGtx7Zt1TRw4U8vqSmvUPyTmgAAAOC25p6u3dCkazzGN/mpqalx39HYuHGjpMab/NTUVJWXl2vHjh37LL3funWrampqGn1uWzJw4EAVFBQoNzdXlmUpGo0qGAzKtm0VFBQoMzPT7RKBFlNTXq7nDx/gdhkHlBSwNG40P7MAAADaOk9fXT8Ro0aN0sqVK7Vly5Z97n/33XfVrl07DR06tNHn1j72P58rSccff7zD1ZorJydHa9asUXZ2tiQpOztba9asUU5OjruFAQAAAADqGD+Tf/XVV2vGjBmaMWOG7rvvPknShg0b9Mc//lEXXHCBevTo0eBzzzvvPB122GGaMWOGLrjgAiUlJam6ulr5+fnq1auXzj333NbaDSNkZmYqLy9Pc+bMUV5eHjP4aHMuWfWFklJS3C4jLnv7VunGsZKkYIeOLlcDAAAAtxjf5A8fPlznn3++HnjgARUXF6t///56/vnnVVNTo7vvvnufxz7xxBM65JBD9LOf/UzS3o/lu/3223Xbbbfp7LPP1hlnnKG33npLixYt0uzZs9WuXTs3dgmARyWlpKhdp05ulxFXrHJP3e2DOWcLAAAA/mL8cn3LsvTCCy/o1ltv1SeffKInn3xSWVlZeueddzRs2LB9HpuXl6cpU6bsc98tt9yi//3f/1U0GtVjjz2mQCCgP/zhDyxDBwAAAAAYx5iZ/NNPP73Bj7vr1KmTHn744QavAF8r3vMty9L111+v66+/3pE6AQAAAABwi/Ez+QAAAAAAYC+afAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAFpZrLxcG/r204a+/RQrL3e7HAAAAPgITT4AAAAAAD5Bkw8AAIA6ZWVlmjp1qsrKytwuBQDQBDT5AAAAqFNWVqZp06bR5AOAoWjyAQAAAADwiSS3CzBVJBJRVlZW3G2hUEihUKiVKwIAAAAAmCocDiscDsfdFolEEh6HJr+JUlNTVVhY6HYZAAAAAAAfaGyyOD09XSUlJQmNw3J9AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCq+sDQGNs+/ubFRWKWVazh4yVl8e93Rx2hTPjAAAAwGw0+QDQCHvPnrrbZccOU9CBJr++jUOHOzKOFZD6juzuyFgAAAAwF8v1AQAAAADwCWbyASBBvZd8qHY9ejR7nFh5ed0Mfu+VKxRISWn2mNpTIeWeKUmyOnZs/ngAAAAwEk0+ACTISklxpiGvJ+DUmPXWZTl8RgEAAAAMwnJ9AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh+AbxQVFSk/P1+SlJ+fr6KiIpcrAgAAAFoXTT4AX5g9e7YGDRqkefPmSZLmzZunQYMGac6cOe4WBgAAALQimnwAxisqKtKECRMUi8UUjUYlSdFoVLFYTLm5uVq7dq3LFQIAAACtgyYfgPFmzZoly7LibrMsSwUFBa1cEQAAAOCOJLcLMFUkElFWVlbcbaFQSKFQqJUrAtqu4uJi2bYdd5tt2youLm7dggAAAICDFA6HFQ6H426LRCIJj0OT30SpqakqLCx0uwwAkjIyMhqdyc/IyGjdggAAAICD1NhkcXp6ukpKShIah+X6AIw3fvz4Rmfyc3NzW7mixgVSUpRe8o3SS75RICXF7XIAAADgIzT5AIw3cOBAFRQUKBAIKBgMSpKCwaACgYAKCgqUmZnpcoUAAABA66DJB+ALOTk5WrNmjbKzsyVJ2dnZWrNmjXJyctwtDAAAAGhFNPkAfCMzM1N5eXmSpLy8PGbwAQAA0ObQ5AMAAAAA4BM0+QAAAAAA+ARNPgAAAAAAPkGTDwAAAACAT9DkAwAAAADgE0luFwAAnmbbSgpYe29XVkh7yt2tpyF7KtyuAAAAAB5Akw8Ajanao3Gje++9feNYd2sBAAAADoDl+gAAAAAA+AQz+QCQoKpHX1L7Pn3dLuPAkju6XQEAAABcQpMPAIlK7iB1SHG7CgAAAKBBLNcHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ/gwntNFIlElJWVFXdbKBRSKBRq5YoAAAAAAKYKh8MKh8Nxt0UikYTHoclvotTUVBUWFrpdBgAAAADABxqbLE5PT1dJSUlC47BcHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8+E5RUZHy8/MlSfn5+SoqKnK5IgAAAABoHTT58JXZs2dr0KBBmjdvniRp3rx5GjRokObMmeNuYQAAAADQCmjy4RtFRUWaMGGCYrGYotGoJCkajSoWiyk3N1dr1651uUIAAAAAaFk0+fCNWbNmybKsuNssy1JBQUErVwQAAAAArSvJ7QJMFYlElJWVFXdbKBRSKBRq5YpQXFws27bjbrNtW8XFxa1bEAAAAAAkKBwOKxwOx90WiUQSHocmv4lSU1NVWFjodhmoJyMjo9GZ/IyMjNYtCAAAAAAS1NhkcXp6ukpKShIah+X68I3x48c3OpOfm5vbyhUBAAAAQOuiyYdvDBw4UAUFBQoEAgoGg5KkYDCoQCCggoICZWZmulwhAAAAALQsmnz4Sk5OjtasWaPs7GxJUnZ2ttasWaOcnBx3CwMAAACAVkCTD9/JzMxUXl6eJCkvL48ZfAAAAABtBk0+AAAAAAA+QZMPAAAAAIBP0OQDAAAAAOATNPkAAAAAAPgETT4AAAAAAD5Bkw8AAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ESS2wU0V01NjTZu3NjoY1JSUtS9e/e427Zv365du3bF3darVy+1b9++2TUCAAAAANAajG/yN2zYoAEDBjT6mJ/97GeaM2dO3G2/+MUv9Mwzz8TdtnTpUo0cObK5JQIAAAAA0CqMb/J79eqlV199Ne62ZcuWadq0aTrvvPMafH5RUZGGDRum++67b79tmZmZjtUJAAAAAEBLM77JT0lJ0dixY/e7f8+ePfqf//kfZWdn67LLLmvw+V9++aUuueSSuGMAAAAAAGAS31547+6771Z5ebny8/MbfMy2bdu0ZcsWDRo0SLZta9u2bYrFYq1YJQAAAAAAzjF+Jj+e1atX67HHHtOzzz6rrl27Nvi4oqIiSdKCBQv061//Wps2bVLnzp11zjnnaMaMGerbt2+Dz7VtWzt37mxyjcnJyUpOTm7y8wEAAAAAZqisrFRlZWWTn2/bdsKP9WWTf/fdd+sHP/iBLr/88kYf9+WXX0qS3n33Xf3iF7/QUUcdpeXLlys/P1+LFy/WqlWr1KVLl7jPLS0tbfQNhAOZMmWKpk6d2uTnAwAAAADMMH36dE2bNq1Vvpbvmvzly5frpZde0iuvvKJAoPGzEXr27KlQKKRrr71Wxx57rCTpyiuv1IknnqjLL79cjzzyiO699964z+3Tp49WrVrV5DqZxQcAAACAtuGOO+7Q5MmTm/z8wYMHq7S0NKHH+q7J/93vfqe+ffvq/PPPP+BjzznnHJ1zzjn73X/ppZfqpptu0pIlSxp8rmVZDc7yAwAAAABQq7mna1uWlfBjfXXhva1bt+qll15Sdna2gsFgk8exLEv9+/fX5s2bHawOAAAAAICW5asm/7nnntOePXt09dVXJ/T4SZMm6frrr9/vftu29a9//UuDBw92ukQAAAAAAFqMr5r8P//5z0pPT0+4Oe/cubOefPJJvfPOO/vcP3v2bG3atEkXXXRRS5QJAAAAAECL8M05+bt379bChQv1k5/8JO72nTt3au7cuerbt69+/OMfS5JuvvlmPffccxo7dqyuueYaHX744VqxYoVeeOEFXXDBBbrssstacxcAAAAAAGgW38zkL1iwQNXV1Ro9enTc7Vu3blVeXp5mzJhRd1+PHj20YMECXX755Xr11Vd17733au3atXrkkUf05z//+aAubgAAAAAAgNt8M5N/3nnnybbtBrdnZGTE3X744Ydrzpw5LVgZAAAAAACtwzcz+QAAAAAAtHW+mckHsK/5Kza4XUJCxg1Pd7sEAAAAwDeYyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AABgrKKiIuXn50uS8vPzVVRU5HJFAAC4iyYfAAAYafbs2Ro0aJDmzZsnSZo3b54GDRqkOXPmuFsYAAAuSnK7AAAt444/rnS7hISMG57udgkADFRUVKQJEyYoFovV3ReNRiVJubm5GjNmjDIzM90qDwAA1zCTDwAAjDNr1ixZlhV3m2VZKigoaOWKAADwBmbymygSiSgrKyvutlAopFAo1MoVAQDQdhQXF8u27bjbbNtWcXFx6xYEAEAzhcNhhcPhuNsikUjC49DkN1FqaqoKCwvdLgMAgDYpIyOj0Zn8jIyM1i0IAIBmamyyOD09XSUlJQmNw3J9AABgnPHjxzc6k5+bm9vKFQEA4A00+QAAwDgDBw5UQUGBAoGAgsGgJCkYDCoQCKigoICL7gEA2iyafByUtLQ0TZkyRWlpaW6XAgBo43JycrRmzRplZ2dLkrKzs7VmzRrl5OS4WxgAAC6iycdBSUtL09SpU2nyAQCekJmZqby8PElSXl4eM/gAgDaPJh8AAAAAAJ/g6voAAPhETXmFrN273S7jgJJSUhq8Mj4AAGgemnwAAHzildGnqCYW/4rzXnLFv9apXadObpcBAIAvsVwfAAAAAACfYCYfAACDBTt0rLt98Yplsg7t7mI1DaspL9dLg49xuwwAAHyPJh8AAIPVP7c9qWOKAiyDBwCgTWO5PgAA2EesvFwb+vbThr79FCsvd7scAABwEGjyAQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ9IcrsAU0UiEWVlZcXdFgqFFAqFWrkiAAAAAICpwuGwwuFw3G2RSCThcWjymyg1NVWFhYVulwEAAAAA8IHGJovT09NVUlKS0Dg0+QAA+IRdUa5YecdmjxMrL497uznsiop6/7AdGRMAAOyPJh8AAJ/YOOpk2TGHxxw63JFxovUae3vPHqlzZ0fGBQAA++LCewAAAAAA+AQz+QAAGMzq+P3y/LSVn0gdnFmuXzuD33vlCgVSUpo9ZvWWLdKIkc0eBwAANI4mHwAAg1nW97cDKR2lDs1vyOsLpKQ40uRbDowBAAAOjOX6AAAAAAD4BE0+AAAAAAA+QZMPAAAAAIBP0OQDAAAAAOATNPkAAAAAAPgETT4AAAAAAD5Bkw8AAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ARNPgAAAAAAPkGTDwAA9hFISVF6yTdKL/lGgZQUt8tBKyoqKlJ+fr4kKT8/X0VFRS5XBAA4WEluFwAATkpLS9OUKVOUlpbmdilA69tT4XYFDausUFLA2nvbtt2tBXHNnj1bEyZMkGXtzWnevHmaO3euCgoKlJOT425xAICE0eQD8JW0tDRNnTrV7TIAd1xzptsVNKi9pHGje0uSqqr2uFsM9lNUVKQJEyYoFovV3ReNRiVJubm5GjNmjDIzM90qDwBwEFiuDwAA0MbNmjWrbgb/P1mWpYKCglauCADQVMzkN1EkElFWVlbcbaFQSKFQqJUrAgC0SckdpfmL3a7igKpKS9T+lkvcLgMNKC4ult3AaRS2bau4uLh1CwKANigcDiscDsfdFolEEh6HJr+JUlNTVVhY6HYZQIOyT5ngdgkJKnO7AMBsliV1MODieMkd3K4AjcjIyGh0Jj8jI6N1CwKANqixyeL09HSVlJQkNA7L9QEAANq48ePHNzqTn5ub28oVAQCaiiYfAACgjRs4cKAKCgoUCAQUDAYlScFgUIFAQAUFBVx0DwAMQpMPAAAA5eTkaM2aNcrOzpYkZWdna82aNXx8HgAYhiYfAAAAkqTMzEzl5eVJkvLy8pjBBwAD0eQDAAAAAOATNPkAAAAAAPgETT4AAAAAAD5Bkw8AAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ARNPgAAAAAAPkGTDwAAAACAT9DkAwAAAADgEzT5AAAAAAD4BE0+AAAAAAA+QZMPAAAAAIBP0OQDAAAAAOATNPkAAAAAAPhEktsFmCoSiSgrKyvutlAopFAo1MoVAQAAAABMFQ6HFQ6H426LRCIJj0OT30SpqakqLCx0uwzfmL9ig6PjrVu99yD4++qI1ljOjg0AAAAATmtssjg9PV0lJSUJjcNyfQAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh++dGiPXvrJtTfr0B693C4FAAAAAFpNktsFAC2hW89UXXLdZLfLAAAAAIBWxUw+AAAAAAA+4ZuZ/NLSUsVisf3uT0pKUu/evRt8Xk1NjX7zm9/oL3/5i9asWaMTTjhBP//5z3XppZe2ZLkAAAAAADjOFzP51dXV6t+/v/r167ffn7PPPrvR506aNEl33nmnevbsqUmTJikSieiyyy7TSy+91ErVAwAAAADgDF/M5BcXFysajerWW2/Vaaedts+2Qw45pMHnRSIRzZw5U1dffbXmzJkjy7J06623auTIkXrggQd0ySWXtHTpAAAAAAA4xhdN/pdffilJuuSSS3TiiScm/Lz58+crGo1q8uTJsixL0t43BX7+85/rtttuU2FhobKyslqkZgAAAAAAnOarJv/oo49WTU2Ndu/era5dux7weUuWLFH37t117LHH7nP/mWeeWbedJh9oIbYtq0qq3r3b7UoaVVNeofZuFwEAAAAkyBdNflFRkVJSUnTDDTfoxRdfVE1Njfr166ebbrpJN998s4LBYNznRSIR9e7du24Wv1ZaWpokadOmTQ1+Tdu2tXPnzibXnJycrOTk5CY/HzCdVSUN/HlMz2uA26U0Kilgadzohi/eCQAAABxIZWWlKisrm/x827YTfqwvmvwvv/xS5eXlWrdunWbMmKH27dvr+eef12233ab169fr8ccfj/u8SCSi7t2773d/t27dJDXe5JeWlia0WqAhU6ZM0dSpU5v8fAAAAACAGaZPn65p06a1ytfyRZN/6qmn6qSTTtJdd92lDh06SJLGjx+vCy64QE888YTy8vI0cODA/Z5nWVbcd0RqP4qvurq6wa/Zp08frVq1qsk1M4sPfO+SVV8oKSXF7TLisrdvlW4cK0kKdujocjUAAAAw0R133KHJkyc3+fmDBw9WaWlpQo/1RZN/zz337HdfIBDQjTfeqNdff11Lly6N2+T36tUr7mz9tm3bJEm9eze8RNeyLHXp0qUZVQOolZSSonadOjV7nFh5uUoHHi1J6lO0RgEH3jiIVe6pu/2fp/YAAAAAiWju6doH8/9QXzT5DTn88MMlSZs3b467PTU1VStXrlQsFlMgEKi7f+PGjZIab/IBAPCS+Ss2uF1Co5I2l+lSt4sAAKANCBz4Id62bt06jR07Vi+//PJ+24qLiyXtXdoQz6hRo7Rt2zatXLlyn/vfffddSdLxxx/vbLEAAAAAALQg45v8ww8/XJ988onuuusu7dq1q+7+6upqPfLII+rVq5dGjRoV97njxo1TMBjUY489Vndu/s6dO/X73/9eI0aM2O+j9QAAAAAA8DLjl+sHAgH97ne/06WXXqrjjjtOV155paLRqF555RV9/vnnmj9/vrp06aKdO3dq7ty56tu3r3784x9L2rtc/7rrrlM4HNbOnTs1bNgw/eUvf1FxcbFeffVVl/cMAAAAAICDY/xMviRdcsklevXVV9WzZ0/97ne/05NPPqm0tDQtWrRIl112mSRp69atysvL04wZM/Z57m9/+1vdf//9Ki0t1YwZM9SrVy+99tpr+uEPf+jGrgAAAAAA0GTGz+TXGjt2rMaOHdvg9oyMjLgfl9euXTvddddduuuuu1qyPAAAABjC6xeyrDVueLrbJQDwIF/M5AMAAAAAAJp8AAAAAAB8wzfL9QEYpt7ZM3ZFhWKW1ewhY+XlcW83h13hzDgAAABAa6DJB+CKpKrvb5cdO0xBB5r8+jYOHe7IOFZA6juyuyNjAQAAAC2N5foAAAAAAPgEM/kAXNd7yYdq16NHs8eJlZfXzeD3XrlCgZSUZo+pPRVS7pmSJKtjx+aPBwAAALQgZvIBuM5KSVHAoT+1nBovkPJ9Y+/wGQUA2piysjJNnTpVZWVlbpfiCya9nts2R/TSk49p2+aI26U0yqTXFEDDaPIBAABaQVlZmaZNm0YD5RCTXs/tWzbpT0/9Vtu3bHK7lEaZ9JoCaBhNPgAAAAAAPkGTDwAAAACAT9DkAwAAAADgEzT5AAAAAAD4BB+h10SRSERZWVlxt4VCIYVCoVauCAAAAABgqnA4rHA4HHdbJJL4p3PQ5DdRamqqCgsL3S4DAAAAAOADjU0Wp6enq6SkJKFxaPIB+EYgJUXpJd+4XQYAAADgGpp8AO6wpaSAtfd2ZYW0p9zdehqyp8LtCgAcQFpamqZMmaK0tDS3SwEAwHU0+QBc0S4qjRvde+8/bhzrbjEAjJaWlqapU6e6XQYAAJ7A1fUBAAAAAPAJZvIBuK7q0ZfUvk9ft8s4sOSOblcAAAAANIomH4D7kjtIHVLcrgIAAAAwHsv1AQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ5LcLsBUkUhEWVlZcbeFQiGFQqFWrggAAAAAYKpwOKxwOBx3WyQSSXgcmvwmSk1NVWFhodtlAAAAAAB8oLHJ4vT0dJWUlCQ0Dsv1AQAAAADwCZp8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8giYfAAAAddLS0jRlyhSlpaW5XQoAoAmS3C4AAAAA3pGWlqapU6e6XQYAoIlo8gG4bsyD72pT5x5ul9Go4unnu10CAAAAcEAs1wcAAAAAwCdo8gEAAAAA8AmW6zdRJBJRVlZW3G2hUEihUKiVKwIAAAAAmCocDiscDsfdFolEEh6HJr+JUlNTVVhY6HYZAAAAAAAfaGyyOD09XSUlJQmNw3J9AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCJh8AAAAAAJ+gyQcAAABgjLKyMk2dOlVlZWVul9IoU+qE/9DkAwAAADBGWVmZpk2b5vnm2ZQ64T80+QAAAAAA+ARNPgAAAAAAPkGTDwAAAACATyS5XQAAoG2av2KD2yUkZNzwdLdLAAAASBgz+QAAAAAA+ARNPgAAAAAAPkGTDwAAAACAT9DkAwAAAADgEzT5AA4oqVKa+PMkTfx5kpIq3a4GAAAAQENo8gEAAAAA8Ak+Qq+JIpGIsrKy4m4LhUIKhUKtXBEAAAAAwFThcFjhcDjutkgkkvA4NPlNlJqaqsLCQrfLAAAAAAD4QGOTxenp6SopKUloHJbrAwAAAADgEzT5AAAAAAD4BE0+AAAAAAA+QZMPAAAAAIBP0OQDAAAAAOATNPkAAAAAAPgETT4AAAAAAD5Bkw8AAAAAgE8kuV0AAABoW2rKK2Tt3u12GQlJSkmRZVlulwEAQMJo8gEAQKt6ZfQpqonZjowVkHRacrIkaUFlpWKOjPq9K/61Tu06dXJ4VAAAWg7L9QEAAAAA8Alm8gGfsW1b2rNHVqUzs2SSZFXWv23LiYWr9ccE0Aa071B38+IVy2Qd2t2RYe3ycm0aNkKSdOmqL2SlpDR7zJrycr00+JhmjwMAgBt80+R//fXX+uUvf6mPPvpI27Zt0w9+8AP94he/0KWXXtrouXS7du3S9u3b427r3r27Uhz4zwLQqvbsUfTC0Rro4JABSdq7GlZH3hhzZDlsUsCSRjswEAAj1P9dnNQxRQGHlsDH6o/bqZMC/N4GALRxvliuX1paqmHDhunNN9/UT37yE912222qqanR5Zdfrvvuu6/R5z7yyCPq169f3D+vv/56K+0BAAAAAADN54uZ/AceeEDffvutPvroIx1//PGSpDvvvFMXX3yx7rvvPmVnZ2vAgAFxn1tUVKQ+ffpo5syZ+2077rjjWrRuoKV99URAseTmj5NUKZ12y/dj1jgwZsfdkl7ee7s6qX3zBwQAAADgjyb/ww8/1HHHHVfX4EtSMBjU5MmT9corr+jDDz9ssMn/8ssvdeyxx2rs2LGtVS7QamLJkp3c/DPo65/dbydbsh1o8u3qev/g46mANsWuKFesvKMjY8XKy+Pebg67oqLeP5y7vgkAAK3BF01+dXX1Pg1+rZqaGknS5s2b4z7Ptm19+eWXGjNmjCRp+/bt6ty5s5KSfPGyAADgSRtHnSzb6c+6k7Rx6HBHxonWa+ztPXukzp0dGRcAgNbgi3PyP//8cz311FP73f/cc89JkoYMGRL3eZs3b9bOnTu1atUqHXHEEerWrZsOOeQQ/dd//ZdWr17dojUDAAAAAOA0X05Zx2Ix3X777Zo1a5aGDh2qM844I+7jvvzyS0nSe++9p0mTJmnYsGFas2aNHnvsMY0cOVJffPGFDj/88LjPtW1bO3fubHKNycnJSk52YM0zAAAGiCV//xF6aSs/kTo4t1y/dga/98oVjlxdv3rLFmnEyGaPAwBArcrKSlVWNv0zpO2DOH3Md03+Rx99pFAopGXLlqlfv37605/+pEAg/oKFDh06KBQK6ZJLLtHpp59ed/95552nUaNG6e6779bcuXPjPre0tFRdu3Ztcp1TpkzR1KlTm/x8oEH1fgAkVe57Pn1TJVXGv92sMaucGQeAIepdeyOQ0lHq4PxH3QVSUhxp8i0+hg8A4LDp06dr2rRprfK1fNPkV1dXa9q0aZo+fbpisZiys7M1Y8YMdevWrcHnjBw5UiNH7v9O/YknnqgTTjhBS5YsafC5ffr00apVq5pcL7P4aCmBqj2Kfnf7Z5OTFHT4onY/u8WZHxtWQJJBE2XzV2xwu4SEjBue7nYJAAAA+A933HGHJk+e3OTnDx48WKWlpQk91hdNfk1Njf77v/9br7/+uo466igVFBTUXUyvqQ4//PC65fzxWJalLl26NOtrAADQJu2pOPBjEh6rfN/bTlxtqLJCSYHv3iDl6voAAAc093Rt6yAm7nzR5D/wwAN6/fXX9cMf/lAvvPCCDj300ISft3LlSs2fP1/BYHCfbcXFxRo8eHALVAu0nuem16i6S/Nn8pMqv5/Bf+bRGtU4sBClXbV05/y9t/cktW/+gADMcc2Zzo0VrdeE55wpBZv/M6+9pHGje0uSqqr2NHs8AABak/FNfjQa1VNPPaVevXrpr3/960G9O9KrVy+9+OKLOuecc5Sbm1t3/zvvvKOlS5fqwQcfbImSgVZTkyxHGvKWGNOqP9vm8CkFAAAAQFtlfJO/fv16lZaW6qSTTtLTTz8d9zFnnXWWBg8erCeeeEKHHHKIfvazn0mSrrrqKj311FOaOHGiFixYoB/84Adas2aN5s2bp+OOO04333xza+4KAAC+FW2XrD/e/rIuG9rH0XEDkpy+EkVVaYna33KJw6MCANA6jG/yN2/eLElavHixFi9eHPcxs2fP1uDBg5WXl6fDDz+8rsnv2LGj3njjDd13333661//qhdffFEDBw7Ubbfdprvvvlvt27OEGAAAR1iWou07tMhV9R1X7+P+AAAwjfFN/gknnJDwZwbGe1yPHj00Y8YMzZgxw+nSACRo3Mk3qLqd21UcyFK3CwAAAAAOyPgmH8B/sO26q0K3q5FU3fwhk+qN0a76P86nb6L2NZyHDwAAADiNJh/wmWBNpS797qrQetmZMWNRW6XaJkm6/dmeCjhw9WoAAAAAznPi02QBAAAAAIAHMJMP+NgjF21Sebfmj5NUKV29fO+Piwev2uz4x/JV85MIAAAAcAT/tQZ8rDrJVnW75i+tt2P1xmwn1Xj+InkAAABA28RyfQAAAAAAfIImHwAAAAAAn6DJBwAAAADAJ2jyAQAAWlhRUZHy8/MlSfn5+SoqKnK5IrOZ9HqWrV+nN5+fLUl68/nZKlu/zuWK4jPpNQXQOC68BxyEO/640u0SDqjXri261O0iAAB1Zs+erQkTJsiy9l4Idd68eZo7d64KCgqUk5PjbnEGMun1fP+VF/T0ff9T9+8P/vayPvjby7r2nt/otAsvc7GyfZn0mgI4MGbyAQAAWkhRUZEmTJigWCymaDQqSYpGo4rFYsrNzdXatWtdrtAsJr2eZevX6en7/kd2LCY7tvdjampvP3XvbdrokRl9k15TAImhyQcAAGghs2bNqpsd/U+WZamgoKCVKzKbSa/ngldeaLTW9195oZUris+k1xRAYliu30SRSERZWVlxt4VCIYVCoVauCAAAeE1xcbFs2467zbZtFRcXt25BhjPp9dxc+k2jtW4u/aaVK4rPpNcU8LtwOKxwOBx3WyQSSXgcmvwmSk1NVWFhodtlAAAAD8vIyGh0ljQjI6N1CzKcSa9nzz79ZFmW4rXPlmWpZ59+rV5TPCa9poDfNTZZnJ6erpKSkoTGYbk+AABACxk/fnyjs6S5ubmtXJHZTHo9T7vo8kZrPf2iy1u5ovhMek0BJIYmH8AB1SRLM5+u0cyna1ST7HY1AGCOgQMHqqCgQIFAQMFgUJIUDAYVCARUUFCgzMxMlys0i0mvZ1r/Abr2nt/ICgQUCOz9L7cVCMgKBHTtPb9R7/4DXK5wL5NeUwCJockHAABoQTk5OVqzZo2ys7MlSdnZ2VqzZg0fTdZEJr2ep114mR790/sac/7FkqRTzr9Yj/7pfU99fJ5k1msK4MBo8gEAAFpYZmam8vLyJEl5eXnMjjaTSa9n7/4DdM4V10iSzrniGs/M4P8nk15TAI2jyQcAAAAAwCe4uj4AuGDb5ojeefk5nXXxT9WtZ6rb5TSorKxMM2fO1MSJE5WWluZ2OY1q66/pHX9c6dhYklSza6t2rXhDnYefq6TO3R0bd9zwdMfGAgA4w5Tf96bU6TZm8gHABdu3bNKfnvqttm/Z5HYpjSorK9O0adNUVlbmdikHxGvqrOiurdqxaL6iu7a6XQoAoIWZ8rvJlDrdxkw+APhIxh1/c3S8yo1rJUnn53+g5N7O/kKdftlQR8cDAAAAM/kAAAAAAPgGTT4AAAAAAD5Bkw8AAAAAgE/Q5AMAAAAA4BM0+QAAAAAA+ARNPgAAAAAAPsFH6AEAXHHHH1c6Ol7tx/3lv/Wlkj+LOTbuuOHpjo0FAADQ0pjJBwAAAADAJ2jyAQAAAADwCZp8AAAAAAB8gnPyASABppw/DgAAgLaNJr+JIpGIsrKy4m4LhUIKhUKtXBEAAAAAwFThcFjhcDjutkgkkvA4NPlNlJqaqsLCQrfLAAAAAAD4QGOTxenp6SopKUloHM7JBwAAAADAJ2jyAQAAAADwCZbrwxOcvqgZADgl446/OTpe7UUXz8//QMm9yxwdGwAAgJl8AAAAAAB8giYfAAAAAACfoMkHAAAAAMAnaPIBAAAAAPAJmnwAAAAAAHyCq+sDAIBW4/SnFbSEXru26CO3iwAAoImYyQcAAAAAwCdo8gEAAAAA8AmafAAAAAAAfIImHwAAAAAAn+DCe/CE7FMmuF1CQuZ98Hu3SwAAAACABjGTDwAAAMAIRUVFys/PlyTl5+erqKjI5YriM6VO+BMz+cBBMGHFQcpWW9qQ6nYZAIB6YuXlipxz7t7bFRUuVwOYafbs2ZowYYIsy5IkzZs3T3PnzlVBQYFycnLcLa4eU+qEf9HkAwAANKCmvELW7t3NHscuL6+7Hd2zR9UOjFlfUkpKXUMB+FFRUZEmTJigWCxWd180GpUk5ebmasyYMcrMzHSrvDqm1Al/o8n3ufkrNrhdAgAAxnpl9CmqidnNHicgqft3TfhbF1+iL5PaNXvM+q741zq169TJ0TEBL5k1a1aDb2RZlqWCggJNnz69lavanyl1wt9o8psoEokoKysr7rZQKKRQKNTKFQEAgDbLbv4bEYCXFRcXy27g+9y2bRUXF7duQQ0wpU54UzgcVjgcjrstEokkPA5NfhOlpqaqsLDQ7TIAAIDDqpPa190e3b6d7FgjDz4IhdXVkqTh7drpB+3aH+DRBxaVtKiqSpJk79kjde7c7DEBr8rIyGh0hjwjI6N1C2qAKXXCmxqbLE5PT1dJSUlC49DkA4CPOH1xyLK11SqYI50//D6lZTq7vJiPpIRn1fsPetCyZDt8qntAloJOnD/P7D3akPHjx+vhhx+Ou822beXm5rZyRfGZUif8jSYfAACgnj31ZvIvvfjXqgg0/w2u5JoqTZl/myTpf84KKanPUc0es9OebxV66Vd7/0HDD58bOHCgCgoKlJubK8uyFI1GFQwGZdu2CgoKPHMxO1PqhL/R5ANAAkyaIQfQTPVm2T/81yOODBmL2nrzu9uzS+dpZGWKI+NqdG9JUlXVHmfGAzwsJydHY8aM0QMPPKA5c+YoOztbd911l+caZ1PqhH8F3C4AAAAAABKRmZmpvLw8SVJeXp5nG2dT6oQ/MZMPAABQT4XVToOPmuromB1qKvXrxZMkST/tP16BvoOaPWav3f/WgpL8Zo8DAPAXmnwgEbYtq8rZIZMqpfG37F2mPevRatUkOzOuVenMOADQZlmWKqzmX/2+vor27TV+7J3SnF9oZ7sUJQeaP36Fxak+AID90eQDCbCqpIE/d+gzlL4TkKTvGvsjb4zJqdGTApY02qHBAABwSMYdf3N0vMqNayVJ5+d/oOTeZY6OPf2yoY6OBwCtiXPygURw0WIAAAAABmAmH0hAUr2l+qPbt1fQ4fHHtHduWahV7607m5WcAAAAQJtCkw8cpKCkYL2PV3JkTAfH22coh+sEAAAA4G00+UAi7O/OdZf0x/tqVH1I85vnpErpytv3HoJ/eLDGsQvvtauxdNuLe2/XOHvdKAAeln3KBEfHK1tbrYI50vnD71NapnPLguZ98HvHxjJNsHN3dR09TsHO3R0fe8yD72pT5x6Oj+tlLfl6Ou3QHr30k2tv1qE9erldSqPS0tI0ZcoUpaWluV0KgGagyQcS0C4qjRvde+8/XnNmzFjUVqm2SZJufbGnAsEWmHVnIh8APCOpc3cdespP3S7DN0x6Pbv1TNUl1012u4wDSktL09SpU90uA0AzceE9AAAAAAB8gpl8n7vjjyvdLiEh2ae4XUHiHrlok8q7OTRYbu2NzQ4NuK9qjnAAAACgTaEFAA5SdZKt6nasgwcAwK9MmSQZNzzd7RIAeBDL9QEAAAAA8AmafAAAAAAAfILl+gAAAICBMu74m9slJKR4+vlulwC0KTT5TRSJRJSVlRV3WygUUigUauWKAAAAAACmCofDCofDcbdFIpGEx6HJb6LU1FQVFha6XYb5bFvtaqpkVdqODZlUKY2/pZ0kadaj1apJbv6YVmXzxwAAAACAhjQ2WZyenq6SkpKExqHJh6vaVVfqnmeul+Y4N2ZAkr5r7I+8MaaYA2MmBSxptAMDAQAAwPdi5eWKnHPu3tsVFS5Xg7aGJh+u6lBdubeBdlD9q0kmBSxnmvwgH5kHAAAAwPto8n2ssrJS2z94Tl1PukxWUju3y4mro12tcaN7OzpmLGqrdPk2SdKlo1IVcLhBtx18KWuqbS364y6NvqyzktrxRoJXkZMhojX7/g3Pqam2Pf97qa3rUF2pt+dPkiSdddkjquzQ2eWK0BC7plo7Fv+R48kA1dXVbpeARlRWVmr69Om64447lJzswHm+HkCT72OVlZXasWi+upzwY0d++Nf/xX/2uBna084fB8HBqkl2rsmLVtv64A+7NerHnWgePYyczGB/19zbDjf5xcljHR3v4/bScZL+1v4XGuHgj9G75ewbpi0hWm07+nsJ30uqqVK76uZfQKZdzfdjJO3ZrVjQ2Zyqk9pLFj9HnWBHqzmeDFFVVeV2CWhEZWWlpk2bpsmTJ9Pkw9ts21b17t2S9p73HggEmz1m/V/87WoqFW32iHv/U1LrkYs2qbybA2NWSlcv3/ut/eBVmx258F591Rw1AACPufkvd6sm2vyL2AYkqV17SdJtL/5SyYFAo48/WPdcFVZ1ckdHxwQA7It2xadqdu/Wy8cMkST98rlfqKMDv6T3XtBub8d8+7OTHLyg3d7Zp+okW9UOzJJWt5NmPs1yXQBA23HpqFRHxql/ytuPj++lrh2c/a/ib6t3axNNPgC0KJp8n7IrKhT87oJ2wYDlyMXtuKAdAADeUZ3U3u0SDtrC5PFqn+zE/yBaVkbla26XAABNRpPvV1V7dOmoVE342xZdOipVXdo1f7m+SRe0AwDA77a176zhA+5wdMzkmiq9tPwuSdLZ/fNU0+nQZo/Zo2Kr3to4s9njAAASQ5MPz3DygnYAAPheIKBtyYc4O2aydPLlj+mb316mfp0OUyA5pdlDOnFBQABA4mjy24Bz+0zU1m7pzR6nQ02lXl1+myTpuIF3ak+Sc1e0G3fyDVzQDgAAAACaibaqDdhjtVNFoPnn7dnW91ftrbDaa48DY9aqZqk+AAC+V1NjyXLgI8PtakubnusrSer10xJZ7Zr/yQL1tauu5OP+ABiLJt9DwuGwQqGQ4+OOHXmPYn2bf06+JM08U1r22m5deqazdS57bbdGju3UJsdsKabsvyljmsKkff92+Ws65DhnP4O+JYQ/kkInuF3FgZmUfUtoie+ntjxmSwh/vUW9lvRw7KP+Rn83QfDnOT0duRhwfWMqfqaFP5/t6Mf9mZJTS9R52A+vd3TMyo1rJUnn53+g5N5ljo17W3pxi/xfvCW0VN/gNFPqbAlu7jtNvoeYchAse628BZqytjtmSzFl/00Z0xQm7fu3H//NiP/whpea0uSbk31LaInvp7Y8ZksIr/u3Cs862pGxWvpiwPe9s0bdHP64P1NyMuF7NNi5uwIduyjYubtjY0rO/1+8VzComzsfot6pznzEZX2m9A2m1NkSaPIdUFxcrClTpuj999+XJJ122mm69957lZGR0ejzdu/erdtuu03vvfeeNm7cqJEjR2rChAkaPXq0kpKS1Lt375YvHgAAeE7XlFJlnzLB0TGffG6z42M+9LS3z3kz8aP+JKlH5TZHxkmuqdL8v94rJUvjtpeq0qHXI1pVoW8kXXFiSMmHOLNiUzLjezSpc3cFU7oqyeEmf+2mXcq442+OjNWhulJvB4Oa3KWrziz4TFWdNjgybq3mXxITfuaLJn/r1q065ZRT9O233+rqq6+WJD3zzDN6//339cknn6h79/g/AGzb1iWXXKK///3vdfe9/fbbevvttyVJxxxzjD7//POW3wEAaIOcbqDWf16puX+Qzhn6kPr/wLkLg778e8eGAlqE08dSZXlMv/mtdMXJNyo5JdD8AWPSr8slfVCp2CDn/l/V5xjpB0/H9PkxpY4t168uDyp5/WBJ0uubZzsyZixqq/S72wtK8h1bdbCzOqqukrqUSLE+zl2TwIpJ7XY6e42DQCwmxWJSwIHvJwAH5Ismf86cOdqwYYPeeecdnXnmmZKksWPH6pxzztG8efM0adKkuM9bsWJFXYN/66236rTTTtO///1v3XTTTcrKytKDDz7YavsAoO3YWlKjpa/uliQtfXW3xlzWWd37eu/HsSl1Sntr/fiNCknSx29UqHO3oCdrLfq3lP/R3tv5H0l3jpEGHuZuTfG0dPaONY/1tMTs45PPOTpc2xWQKjpLsqRAssNnzzs8phMXBWxt/R6S2ttOXeegvcYrTYtuqnL0Ogc9dm7UgO3rtbt952aP1VIrIyQpEotK0agUdG5lhAmKioqUn58vScrPz9edd96pgQMHulzV/kyp0wu89z+gJpg7d66GDRtW1+BL0g9/+EMdc8wxeu655xps8ufOnSvLsupm9E888URJ0tKlS/X000/r2GOPbZX6AbQdn7xVrr/N2Cl9N5Hz2bt79Nk7ezR2UhcN/S/vLL4zpU5p/1q/+OcefbHAe7XOXiFNeLWuTM1bKc1dKRVcKOUMc7OyfZmUPeC0pI4xVR35ubZ8ZGn6xRFnxqySxi3fu1z90f/epBoHetKU7Zau+fvedwh/fHwvdWnX/Ka0Ja9zcN87O/TepqccGaulVkZIUlbNv9W5Yps2de7h2JiOi0bVq2Kbvq2pVlXJN80e7pkX/qjrbvuf7383zZ2ruXPnauYjv9HVl13a7PFtA+qs+vZbSVIsGm1mld5hfJO/Z88erVy5UjfffPN+284880zl5+erpqZGSUn77+qSJUvUp08flZSU6Oijj1ZNTY12796tM888U+FwWCtWrNjnjQO0nJ4VUd330UZHx3y5Qo6P+aTa1ju7cNbWkhr9bcZO2bak7yZd7O+mSl6bsVP9jmmv7n3c/7FsSp2SObUW/Xtvgx+rN9lWe4Hx3L9KY/pLmc6eWtokprye2JfTv0N3Vkq/kfSr5ZvUxbkzX/Syc0O1mEBQat85JjsYVHl355rHmU/X6MnrNuu63j0dGa+6k7PL6fE9J6/FUDfm7q2qsZ1pIHtUbtPrm2cra+M3an9j8y5mWLSrUte9vWafVRvR2N5/TZx8i05//Slldm7eDwHLgDrbV+/NpiZSJnXr1pxSPcOybQfW97joX//6lzIyMvTwww/rtttu22fb9OnTdeedd6qsrCzuBfQGDBigWCymLVu26KKLLtKLL76ompoapaamKhKJ6LnnntOVV165z3Osep+XGnD4vKJYLObsmLGYYvruI14dvOisHZMsh0+psmNSwOGPoo3ZLTOmo/tuS7btfEZSy+XUJsd0KCc7gbWPza3biX1vjTprv44JtTrx88m263rmuCw1/+O4nfj51OKvJz/z2vzvUBPGrB3X89l/dzyZ8JrG7Ob/jPtPtsNjWrYU097TFkzgRK313s+Ny4kf1UbV6fHeLhb7/pf0gVp449+Oj0T2LqXqFuddl9r7Nm3aFLfJj0Qi6tq1q8rLy7Vu3TrNmDFD7du3V0FBgSKRiGbOnLlfk19f/RfaKS0xpn2gI6MpYzpf5j4zXF4esyX2vSUyklqo1rY8ZgvltM/XcKDultj3lvoaptTaEj9L6rP13fdXc8cx5PXkZ57zY5ryO9SUMSWyd1pLTCs6OWbtUK3wY9QxLV2rUz+qjanTkN4uEcY3+bUz6/Hezah9Uaur419FxbIs9ezZUxMmTNBdd92lDh06SJJGjRqlIUOG6IMPPlBRUVHcCzoEAoH93jiwbXufehq7bcV56zESiSjVwc/RjMViKisrU1pamqPvIjldp0ljbtq0Sb169XJsvNqM+vTpE/d7ojlMeU3bck6mvJ4tMaZETm1xTNu2VVpa6vjvperq6rrvp3btnPuoLhNe05YYs6VycrrOlspdars5tdRr6vTPe1OO+crKSm3ZskU9evRQcrKD577IjP8/SWYcS63VM9XvVw+mb6y9HYlEEn/TwDZccXGxLcl++OGH99t2//3325LsDRs2xH1uRkaGfd555+13/8KFC2vfELKfe+65fbb17dvXlmT37dvXmR2oZ/DgwY6Ot2PHDluSvWPHDkfHdbrOtjxmS2Vk22bsvyljciw5P2ZLjEtO3h+zpTJavny5Lclevny5o+Oa8Jq2xJimHEstlbttt92cTDmWTKlzwYIFtiR7wYIFjo5r22Z8j5oypik/8w6mDzXltJMG1b47Ulpaut+2jRv3XoSmoXekUlNTG32eJG3evNmJMl0R/e4KkVEfXSnSb8jIDORkBnLyPjIyAzmZgZy8r3bW1a0l20iMH48lzzf5tm3rgw8+0O7du/fbNmHCBD333HMaPHiw3n333f22v/vuuxo+fHiDy3gGDx6sTz75RLNnz97vecHvPh9z8ODBDuyFO2qXdzi9DBzOISMzkJMZyMn7yMgM5GQGcvI+MjKDH3PydJP/zDPPqF+/fjrjjDO0Y8eO/ba//PLLuvbaa/Xll1/q008/1VtvvVW37c0331RhYaGuueaaBscPhUKSpNtuu027du2SJG3YsEEvvPCCunfvrl69emnUqFEO71XrqT2nxOkrRcI5ZGQGcjIDOXkfGZmBnMxATt7nx+bRj/x4LHn2wnt33nmnHnroIdm2rf79+6tjx477PeZXv/qVXnvtNS1YsECSNHbsWF1//fWKRqOaO3euhg4dqquvvlqSVFJSoj//+c8aNGiQzj77bEnScccdpxEjRujjjz9Wnz59NGzYMH322WfauXOnJGn+/Pnq0qVLK+2x8/y49MRvyMgM5GQGcvK+tpRRWVmZysrK9ru/oqJCH3/88X73p6WlKS0trTVKO6C2lJPJyMn7WK5vBj8eS558u+KTTz7Rgw8+qE6dOumPf/yj1q1bF/cj8m655Ra99957evvtt9WrVy9VV1dr/vz5+utf/6of//jHeuedd9S1a1dJUlFRkfLy8vTss8/WPd+yLP3zn//UT37yE0WjUS1cuFDl5eU68cQTtWjRIl122WWtts8tgXcPvY+MzEBOZiAn72tLGc2cOVPHHXfcfn+Ki4vj3j9z5ky3S67TlnIyGTl5HxmZwY85eXIm/5FHHpFlWXrqqad0ySWXHPDxZ555pp5++mn993//t84++2w999xz+z3m9NNPj/sxe506ddLLL7/sSN3NVXv6gFNaaumJ03W25TFbcnmQCftvypgcS86P2RLjkpP3xzRtSWRz9n/ixIm68MIL97lv1apVuuqqq/Tss8/ud82f5szim5JTS/0saQkcT84yJXun62zJ5tGE71FTxvTjzzzLjtf5umzYsGEqLS1VJBI5qIOid+/e6tu3r5YvX95itaWnp6ukpER9+/bVhg0bWuzrOGHbtm3q3r27tm7dGnclBNxHRmYgJzOQk/e1VEYff/yxjjvuOC1fvlwjRoxwbFynmVKnKceSKa9nS2mJnEx5TU2p8/3339cZZ5yh9957T6effrrb5aABpvzMO5g+1JNv/X311Vc68sgjD/pdryOPPFJfffVVC1VlHj8uPfEbMjIDOZmBnLyPjMxATmYgJ+8jIzP4MSdPLtdv166dysvLD/p527Zti7skvyVEIhFlZWXF3RYKhTyxLMmkZVxtFRmZgZzMQE7eR0ZmICczkJP3+bF59CMvHUvhcFjhcDjutkgkkvA4nmzyDz/8cH3++efavXu3OnXqlNBzysvLtWbNGh199NEtXN1eqampKiwsbJWv1VR+vFKk35CRGcjJDOTkfWRkBnIyAzl5H1fXN4OXjqXGJotrl+snwv23K+I499xzFYvF9H//938JP+fxxx+Xbds666yzWrAys/DuofeRkRnIyQzk5H1kZAZyMgM5eR8ZmcGPOXmyyZ84caKSk5N1xx136LXXXjvg499++23dfffdCgQCmjhxYitUaAYvLT1BfGRkBnIyAzl5HxmZgZzMQE7e58fm0Y/8eCx5ck8OP/xwPfroo4pGo7rooot07rnn6u2339amTZvqHrNjxw4tWbJEl19+uc455xxFo1Hdeuut+sEPfuBi5d5SU1Ozz9/wHjIyAzmZgZy8j4zMQE5mICfv89IycDTMj8eSJ8/Jl6Trr79eknTLLbfozTff1D/+8Q9JUseOHdWhQwdt27ZNkuoutHfrrbfqwQcfdKdYjwoGg/v8De8hIzOQkxnIyfvIyAzkZAZy8j4/zhD7kR+PJU9/x11//fX6/PPPdeONN+qII46QZVkqLy/X1q1bZdu2evfurYsvvliLFi3Sww8/7Ha5ntOhQwdNmTJFHTp0cLsUNICMzEBOZiAn7yMjM5CTGcjJ+5KTk/f5G97kx2PJszP5tY444gg9/vjjkqTq6mqtW7dOu3bt0sCBA3XIIYe4XJ23BYNBnXPOOb56V8pvyMgM5GQGcvI+MjIDOZmBnLyPmXwz+PFY8nyTX1+7du101FFHuV2GMYLBoLKysnz1Des3ZGQGcjIDOXkfGZmBnMxATt7nx2XgfuTHY8moJh8Hx7Isde3a1e0y0AgyMgM5mYGcvI+MzEBOZiAn7+Pq+mbw47HkybUjwWCwyX+SklrnfYtIJKKsrKy4f8LhcKvUcCA1NTVauHChr64U6TdkZAZyMgM5eR8ZmYGczEBO3ufHq7b7kZeOpXA43GCPGYlEEh7HkzP5tm3LsiwFg0F16tTJ7XLiSk1NVWFhodtlNCoYDGr48OG+WnriN2RkBnIyAzl5HxmZgZzMQE7ex3J9M3jpWAqFQgqFQnG3paenq6SkJKFxPNnkJycnq7KyUrFYTMOGDdMVV1yhiy++WD169HC7NKNYluXZN0mwFxmZgZzMQE7eR0ZmICczkJP3sVzfDH48ljy5XH/z5s169tlndf7552vx4sW64YYblJaWpnPPPVdz5szR9u3b3S7RCDU1NXr//fc9sfQE8ZGRGcjJDOTkfWRkBnIyAzl5H8v1zeDHY8mTTX7nzp115ZVX6pVXXlEkElFBQYHOPvtsvfPOOxo/frx69+6tiy66SH/4wx+0a9cut8v1rGAwqJNOOskTS08QHxmZgZzMQE7eR0ZmICczkJP3sVzfDH48ljzZ5NfXtWtX5eTk6I033tDGjRs1c+ZMjRkzRn/729+UnZ2tXr166dJLL9XLL7+siooKt8v1HD99s/oVGZmBnMxATt5HRmYgJzOQE+AMvx1Lnm/y6+vevbt+/vOf6+2331Zpaany8/N1/PHH609/+pMuu+wy9erVSz/96U/dLtMzotGoFi5cqGg06nYpaAAZmYGczEBO3kdGZiAnM5CT99VmQ0be5sdjyagmv75evXrphhtu0IIFC1RcXKzzzz9fu3fv1vPPP+92aZ4RDAY1ZswY370z5SdkZAZyMgM5eR8ZmYGczEBO3sdyfTP48Vjy5NX1E7V48WLNnz9fL774ojZt2iRJ6t+/v8tVeUs0GvXVN6wfkZEZyMkM5OR9ZGQGcjIDOQHO8NuxZNxM/sqVK3X77bdrwIABGjNmjJ544glZlqW8vDx9+OGHWrdundslekY0GtXixYt9tfTEb8jIDORkBnLyPjIyAzmZgZy8j+X6ZvDjsWTETH5RUZHmz5+v559/XmvWrJFt23Xn519xxRU69dRTFQgY935Fi0tKStLpp5/udhloBBmZgZzMQE7eR0ZmICczkJP3JSUl7fM3vMmPx5Jnv+M2bNig559/Xs8//7xWrFgh27Z1yCGHKDs7W1dccYXOPvtsVw+YSCSirKysuNtCoZBCoVArV7Q/27ZVXl6ulJQUWZbldjmIg4zMQE5mICfvIyMzkJMZyMn7bNve5294k5eOpXA4rHA4HHdbJBJJeBxPNvmnnnqqFi1aJEnq0KGDLr30Uo0bN04/+tGPlJyc7HJ1e6WmpqqwsNDtMhoVjUa1YsUKjRo1incQPYqMzEBOZiAn7yMjM5CTGcjJ+1iubwYvHUuNTRanp6erpKQkoXE8+RNh4cKFsixL7du31+jRo1VVVaVnnnlGzzzzzAGfa1mWXn755Vao0vuSkpI0ZswYt8tAI8jIDORkBnLyPjIyAzmZgZy8j+X6ZvDjseTZ7zjbtlVZWam33377oJ7n9hILL7FtWzt37lSXLl14XTyKjMxATmYgJ+8jIzOQkxnIyftYrm8GPx5Lnmzy33vvPbdL8IVoNKrCwkIdf/zxvIPoUWRkBnIyAzl5HxmZgZzMQE7ex3J9M/jxWPLkXvTt21eZmZlul2G8pKQknXTSSW6XgUaQkRnIyQzk5H1kZAZyMgM5eR/L9c3gx2PJk587N2jQIA0fPlwPPPCAVq9e7XY5xrJtW1u3bmWJkIeRkRnIyQzk5H1kZAZyMgM5eR/L9c3gx2PJk01+WVmZrrvuOi1YsEBDhgzRkCFDdO+993r+avZeE41G9dVXX7FEyMPIyAzkZAZy8j4yMgM5mYGcvI/l+mbw47HkySa/Z8+emjhxov7xj39o48aNmjRpkhYvXqxhw4Zp8ODBuueee/TZZ5+5XabnJSUl+ercEj8iIzOQkxnIyfvIyAzkZAZy8j6W65vBj8eSJ5v8+g477DBNmDBBb7zxhiKRiH75y1/q448/1siRI3X00Ufrrrvu0ooVK9wu05NisZg2bdqkWCzmdiloABmZgZzMQE7eR0ZmICczkJP31WZDRt7mx2PJ801+fd26dVNOTo5ee+01bdq0Sb/61a/0xRdf6OSTT1ZmZqZuv/12LVu2zO0yPcO2bW3YsMFX55f4DRmZgZzMQE7eR0ZmICczkJP3cU6+Gfx4LBm7JqFr167Kzs5Wdna2vv32W7366qt6+eWXddppp2n37t0t/vUjkYiysrLibguFQgqFQi1ew4EEg0GNGDHC7TLQCDIyAzmZgZy8j4zMQE5mICfvCwaD+/wNb/LSsRQOhxUOh+Nui0QiCY9jbJNf3yGHHKIrr7xS5eXlOuuss1rla6ampnr+QoCxWEyRSESpqakKBIxatNFmkJEZyMkM5OR9ZGQGcjIDOXkfy/XN4KVjqbHJ4vT0dJWUlCQ0jlFNfnV1tcrKyuJu27p1q371q18pGAzqhhtuaOXKvMm2bW3evFm9evVyuxQ0gIzMQE5mICfvIyMzkJMZyMn7WK5vBj8eS0Y0+TU1Nbrppps0e/ZsVVVVNfg427Z13nnntWJl3hYMBnXssce6XQYaQUZmICczkJP3kZEZyMkM5OR9LNc3gx+PJSPW9sycOVNPPvmkLMvSqFGjdNhhh8m2bZ144ok6+eSTlZKSItu2dfHFF2vu3LnN+loVFRXKzMzUhAkTHKrePbFYTN988w1LhDyMjMxATmYgJ+8jIzOQkxnIyftYrm8GPx5LRjT58+bNk2VZWrRokRYtWqS///3vkqRHH31UH3zwgTZu3KizzjpLq1evVqdOnZr1taZPn66vvvrKibJdZ9u2du7cyRIhDyMjM5CTGcjJ+8jIDORkBnLyPpbrm8GPx5IRTf66des0ZMgQDR8+XJI0YsQI9ezZs+7j8jp16qSnn35aq1ev1sMPP9zkr7NmzRo99NBDjtTsBcFgUMcccwxLhDyMjMxATmYgJ+8jIzOQkxnIyftYrm8GPx5LRjT5O3fuVPfu3fe5b+TIkVq6dGndvzMyMjR48GC99dZbTfoatm3r+uuv1+DBg5tVq5fEYjEVFxf7aumJ35CRGcjJDOTkfWRkBnIyAzl5H8v1zeDHY8mIJr9Pnz5at27dPvcde+yxeuedd/a5LyUlRStXrmzS13juuef03nvv6f/+7/+aXKfX2LatyspKXy098RsyMgM5mYGcvI+MzEBOZiAn72O5vhn8eCwZ0eSPGDFC69ev14wZM+pe/CFDhmjjxo36+OOPJUmVlZX65JNP1LVr14Mef9u2bZo8ebImTJigk046ydHa3RQMBnX00Uf7aumJ35CRGcjJDOTkfWRkBnIyAzl5H8v1zeDHY8mIJv+2225TMBjU5MmTdfHFF0uSTjrpJFmWpSuvvFL333+/LrjgAlVVVenUU0896PHvuOMOxWIxPfjggwk/JxaLaefOndq+fbu2b9+unTt3atu2bQnfrqysVE1NTd2bFge6bdv2frclNXq7qqpKa9euVU1NjaLRaF3didyuXa4SjUYP6nZL71Pt7UT3w+v7FI1G9eWXX9bV54d9Iicz9omczNgnv+UUi8VUVFRU93G4Tu5TLXLydk5O71Nt3eTk3D7VbnNjnw4mp/q1ejWn6urquq/jp++9g8nJhH2q7ZlaOqfy8vK4/eHWrVu1Y8eOA96u3b9EGNHkn3DCCXr33Xf1wx/+UF26dJEkDRgwQHfffbe+/PJLTZkyRW+//bbS09N13333HdTYS5Ys0VNPPaWHH35Yhx12WMLPKysrU9euXdWtWzd169ZNXbt2Vffu3RO+PX36dK1YsULbtm2TJC1dulQ7d+6sq6m8vFyStHDhQlVVVSkajWrhwoWKRqOqqqrSwoULJe39ZlmyZImkvdcuqL1OwbZt2/TJJ59Ikv7973/XncYQiUT0xRdfSJJKSkq0evVqSdL69eu1du1aSdLXX3+tr7/+WpK0du1arV+/XpK0evVqlZSUSJK++OILRSIRSdLKlSu1ZcsWSWrxfVqxYoUkacuWLb7Zp9qv76d9Iicz9omczNgnv+VUUVFR9/vJqX0qLi5WLS/nVFvnxo0b22ROLbFPkjieHM5JkjZt2uTaPiWSU219Xs9p1apVdbf99r3np+Np+fLlrbJPt9xyS9z+8LDDDtOhhx56wNulpaVKlGUfzFsCHvThhx9q0aJF6tOnj8477zx169Yt4efW1NRo5MiR6tSpkz744AMFAnvf87AsS7m5ufr973+/33PS09NVUlKitLQ0rV69uu6dmUAgoGg0KsuyErrdsWNHBYNBBYNBWZalmpqaRm9Le3+J1b+dlJQk27YPeDsWi8m2bQWDwYRvN2WfAoHAAfeDfWKf2Cf2iX1in5zep6VLl+qEE07Q8uXLNXToUM/u0/Lly3XCCSdo6dKlGj58eJvLyel9+vjjjzVy5EgtW7ZMI0aM8MU+uZ3TJ598ouOOO05Lly7VyJEjPbtPy5Yt0/HHH6/ly5dr2LBhns1p6dKlOvHEE7Vs2TINHTqU7702vk/l5eWqqqpq8j4NGTJEpaWl6tu3rzZs2KDGJDW61SPWr1+vjh07qmfPnvttO/nkk3XyySdLkjZv3qzS0lL16dMnoXFnz56tlStX6m9/+9t+74zs3r1bGzZsUOfOnXXooYfu99xAIFC3qsAJSUlJTbptWVaDty3L0po1a5SZmVn3zV37Rkait2ufl+jtpu5HovtUe/tg98Or+xSLxfTVV1/tk5Hp+0ROZuwTOZmxT37LKRqNau3atcrMzPTNPv3n7cZqr/+7ON7vZa/skyk5WZZV93ci2XA8JbZP9bd5+Xg6mH1yK6fa79FYLOabffJrTrU9U0vuU0pKilJSUtRUtd9PiTBiuX5GRoZycnIO+Lgbb7xRI0eOTHjc2qUT559/vvr161f3R5Kef/559evXT/fff3+TavYCy7KUnJx8UN8QaF1kZAZyMgM5eR8ZmYGczEBO3lf/jSh4lx+PpaQDP8Qd9c8LkvaeO/Gf99W3fft2ff7553XnaCTiyiuvjPumwAUXXKD/+q//0k033aQjjjgi8aI9JhAIKCMjw+0y0AgyMgM5mYGcvI+MzEBOZiAn76ud3a0/ywvv8eOx5NkmPyMjY593v/75z39qwIABjT7Htm0NGzYs4a9x1FFH6aijjoq7rX///ho7dmzCY3lRNBrV6tWrNWjQoP2WYcEbyMgM5GQGcvI+MjIDOZmBnLwvGo3u8ze8yY/Hkmeb/FNPPbWuyV+wYIG6d++uIUOGNPqcbt266Z577mmN8oxgWZa6dOniq6UnfkNGZiAnM5CT95GRGcjJDOTkfSzXN4MfjyXPNvnvv/9+3e1AIKATTjhBr7/+eqt8bcM/cKBOIBCou8YAvImMzEBOZiAn7yMjM5CTGcjJ+1iubwY/HktGfMe99957euihhw74uOrqatXU1LRCRWaIRqP69NNPWSLkYWRkBnIyAzl5HxmZgZzMQE7ex3J9M/jxWDKiyT/ttNMOuFRfksaMGaNTTz21FSoyg2VZ6tmzp6+WnvgNGZmBnMxATt5HRmYgJzM4nVNRUZHy8/MlSfn5+SoqKnJk3LaM5fpm8OPPPM8u1/9PRUVFev7557Vu3bq427du3aply5b5bqlFcwQCAaWlpbldBhpBRmYgJzOQk/eRkRnIyQxO5jR79mxNmDChrsmZN2+e5s6dq4KCgoQ+xhrxsVzfDH78mWdEk//555/rlFNO0c6dO+vOl7csa59z5y3LUocOHTRt2rRWqSkSiSgrKyvutlAopFAo1Cp1NCYajWrlypUaOnSob64U6TdkZAZyMgM5eR8ZmYGczOBUTkVFRZowYYJisdg+Y0tSbm6uxowZo8zMzGbX2xaxXN8MXvqZFw6HFQ6H426LRCIJj2NEk//II49ox44d+ulPf6qrr75ab7zxhmbMmKG5c+cqLS1NixYt0q9//Wtdd911+tnPftYqNaWmpqqwsLBVvlZTWZal9PR0Xy098RsyMgM5mYGcvI+MzEBOZnAqp1mzZjU4hmVZKigo0PTp05v1NdoqluubwUs/8xqbLE5PT1dJSUlC4xjR5C9atEg9evTQ7NmzlZSUpOOPP16PP/64OnbsqLPOOktnnXWWMjIylJubq8suu0wnnnii2yV7QiAQUK9evdwuA40gIzOQkxnIyfvIyAzkZAanciouLm7wk6Vs21ZxcXGzv0ZbxXJ9M/jxZ54R33ElJSUaPHiwkpL2vidx6KGHaujQoVq2bFndY37605+qe/fu+t3vfudSld5TU1OjpUuX8okDHkZGZiAnM5CT95GRGcjJDE7llJGR0ehMfkZGRrPGb8tqs+FY8jY//swzosnv1KmTKisr97lv6NChWrRoUd2/g8GgjjrqKC1durS1y/OsYDCoI4880vVzS9AwMjIDOZmBnLyPjMxATmZwKqfx48c3OpOfm5vbrPHbstpsOJa8zY8/84xo8vv376+VK1dq06ZNdfcNGTJEixcv1vbt2+vuW79+/T6Paessy1L37t09cX4J4iMjM5CTGcjJ+8jIDORkBqdyGjhwoAoKChQIBPZpSgOBgAoKCrjoXjNwTr4Z/Pgzz4gmf9y4caqsrNSpp56qF198UZJ07LHHKhqN6oknnpBt23r33Xf1zTff8IOonpqaGi1evNhXS0/8hoyap6ysTB9//HHCf8rKypr0dcjJDOTkfWRkBnIyg5M55eTkaM2aNcrOzpYkZWdna82aNXx8XjOxXN8MfvyZZ8SF966//nq99957euONNzR79mxdeumlOvXUU5WZmakpU6bovvvuU01NjSzL0g033OB2uZ4RDAaVlZXlq6UnfkNGzTNz5syD+tjMKVOmaOrUqQf9dcjJDOTkfWRkBnIyg9M5ZWZmKi8vT3PmzFFeXh4TZw5gub4Z/Pgzz4gmv1OnTnrttde0dOlS7dy5U5LUrl07/f3vf9fEiRP14Ycfqn///rruuus0YcIEl6v1Dsuy1LVrV7fLQCPIqHkmTpyoCy+8cJ/7Vq1apauuukrPPvusBg8evM+2tLS0Jn0dcjIDOXkfGZmBnMxATt7Hcn0z+PFYMqLJl/a++CeccMI+9x1xxBF66623XKrI+2pqarRkyRKNGjWq7pMJ4C1k1DxpaWkNNu6DBw/WiBEjHPk65GQGcvI+MjIDOZmBnLyP5fpm8OOx5I+9cEEkElFWVlbcbaFQSKFQqJUr2l8wGNTw4cN9tfTEb8jIDORkBnLyPjIyAzmZgZy8j+X6ZvDSsRQOhxUOh+Nui0QiCY/j+Sa/qqpKf/nLX7RixQp99dVX+uqrr2RZlo488kgdeeSRGjFihC688EK1b9++VetKTU1VYWFhq37Ng2VZljp16uR2GWgEGZmBnMxATt5HRmYgJzOQk/exXN8MXjqWGpssTk9PV0lJSULjePrq+i+88IL69++vcePG6eGHH9ZLL72kFStW6OOPP9aLL76ohx56SJdffrkyMjLqrrqP79XU1Oj9999niZCHkZEZyMkM5OR9ZGQGcjIDOXkfy/XN4MdjybMz+b/97W916623yrZt9e7dW+PGjdOAAQPUv39/SdL69eu1bt06/eEPf9DGjRt1xRVXqLS0VJMmTXK5cu8IBoM66aSTPLH0BPGRkRnIyQzk5H1OZFRWVrbfx2GuWrVqn7/ra+zaHYiPY8kM5OR9LNc3gx+PJU82+Z999pluu+02SXub/euuu07JyclxH/vrX/9a4XBYt956q2699VadccYZOvbYY1uzXE/z0zerX5GRGcjJDOTkfc3NqLGPzrzqqqv2u6+pH53Z1nEsmYGcAGf47Vjy5HL9xx9/XLFYTHfddZcmTZrUYIMvScnJyZo8ebLuuOMORaNRPf74461YqbdFo1EtXLhQ0WjU7VLQADIyAzmZgZy8z4mMJk6cqOXLlyf8Z+LEiQ7uQdvAsWQGcvK+2mzIyNv8eCx5cib/nXfeUTAY1J133pnwc371q1/poYce0jvvvNOClZklGAxqzJgxvntnyk/IyAzkZAZy8j4nMmL5fcvjWDIDOXkfy/XN4MdjyZMz+SUlJTr66KPVoUOHhJ/TsWNHDRo0aL/z9No6P70j5VdkZAZyMgM5eR8ZmYGczEBOgDP8dix5ssmvrq5Wv379Dvp56enpqq6uboGKzBSNRrV48WLffdP6CRmZgZzMQE7eR0ZmICczkJP3sVzfDH48ljy5XF9q2udJ8hmU+0pKStLpp5/udhloBBmZgZzMQE7eR0ZmICczkJP3JSUl7fM3vMmPx5InZ/LhDNu2tXv3btm27XYpaAAZmYGczEBO3kdGZiAnM5CT99VmQ0be5sdjybNvK1VUVGj9+vUH/ZzWEolElJWVFXdbKBRSKBRqtVoaEo1GtWLFCo0aNYp3ED2KjMxATmYgJ+8jIzOQkxnIyftYrm8GLx1L4XBY4XA47rZIJJLwOJ79ifDPf/5TAwYMcLuMBqWmpqqwsNDtMhqVlJSkMWPGuF0GGkFGZiAnM5CT95GRGcjJDOTkfSzXN4OXjqXGJovT09NVUlKS0DieXa5v23aT/uB7tm1rx44dvC4eRkZmICczkJP3kZEZyMkM5OR9LNc3gx+PJU82+bFYrMl/WA7zvWg0qsLCQl4TDyMjM5CTGcjJ+8jIDORkBnLyPpbrm8GPxxJrR3wsKSlJJ510kttloBFkZAZyMgM5eR8ZmYGczEBO3sdyfTP48Vjy5Ew+nGHbtrZu3eqrpSd+Q0ZmICczkJP3kZEZyMkM5OR9LNc3gx+PJZp8H4tGo/rqq698tfTEb8jIDORkBnLyPjIyAzmZgZy8j+X6ZvDjscTaER9LSkrS8ccf73YZaAQZmYGczEBO3kdGZiAnM5CT97Fc3wx+PJaYyfexWCymTZs2KRaLuV0KGkBGZiAnM5CT95GRGcjJDOTkfbXZkJG3+fFYosn3Mdu2tWHDBl+dX+I3ZGQGcjIDOXkfGZmBnMxATt7HOflm8OOxxNoRHwsGgxoxYoTbZaARZGQGcjIDOXkfGZmBnMxATt4XDAb3+Rve5MdjiSa/iSKRiLKysuJuC4VCCoVCrVzR/mKxmCKRiFJTUxUIsGjDi8jIDORkBnLyPjIyAzmZgZy8j+X6ZvDSsRQOhxUOh+Nui0QiCY9Dk99EqampKiwsdLuMRtm2rc2bN6tXr15ul4IGkJEZyMkM5OR9ZGQGcjIDOXkfy/XN4KVjqbHJ4vT0dJWUlCQ0Dk2+jwWDQR177LFul4FGkJEZyMkM5OR9ZGQGcjIDOXkfy/XN4MdjibU9PhaLxfTNN9+wRMjDyMgM5GQGcvI+MjIDOZmBnLyP5fpm8OOxRJMv6Y033tCYMWPUrVs39e7dW+eee66WLl3qdlnNZtu2du7cyRIhDyMjM5CTGcjJ+8jIDORkBnLyPpbrm8GPx1Kbb/L/8pe/6LzzztO2bds0adIkjR8/Xp988olGjx6tTz/91O3ymiUYDOqYY45hiZCHkZEZyMkM5OR9ZGQGcjIDOXkfy/XN4Mdjqc03+XfffbfS09O1bNkyTZ06Vb/+9a+1ePFiBQIBTZ8+3e3ymiUWi6m4uNhXS0/8hozMQE5mICfvIyMzkJMZyMn7WK5vBj8eS226ya+qqlJhYaEuuOACdezYse7+jIwMDRkyRF988YWL1TWfbduqrKz01dITvyEjM5CTGcjJ+8jIDORkBnLyPpbrm8GPx1Kbvrp+TU2NZs6cqeHDh+9zv23b2r59u4YMGeJSZc4IBoM6+uij3S4DjSAjM5CTGcjJ+8jIDORkBnLyPpbrm8GPx1KbnslPSUnRhAkTdNxxx0mSvvrqKy1cuFA///nPtXbtWl155ZUuV9g8sVhMa9eu9dXSE78hIzOQkxnIyfvIyAzkZAZy8j6W65vBj8dSm57J/09Dhw7V7t27JUmTJ0/WxRdf3OBjY7GYdu7cWffNEAgEFI1GZVlWQrc7duyoYDCoYDAoy7JUU1PT6G1Jikaj+9xOSkqSbdsN3q6pqamr1bZtBYPBhG83ZZ8CgcAB96O5+1R720/7VP8XgF/2yc2catUuuSInb+bE8dQ2cwoEAnW/n9q3b++LfTqYnKLRaN33Z+34XtwnU3KqvxQ6kWw4ng68T7Vqt3n5eKpfq5dzqv0eramp8c33nt+Op9qeqaVzKi8vV1VVVZP36WBOJ2jTM/n/6cUXX1RBQYHGjh2rxx57THfeeWeDjy0rK1PXrl3VrVs3devWTV27dlX37t0Tvj19+nStWLFC27ZtkyQtXbpUO3fulCQtWbJE5eXlkqSFCxeqqqpK0WhUCxcuVDQaVVVVlRYuXChJKi8v15IlSyRJO3furPvov23btmnlypXKzMzU1q1btXLlSklSJBKpu9ZASUmJVq9eLUlav3691q5dK0n6+uuv9fXXX0uS1q5dq/Xr10uSVq9erZKSEknSF198oUgkIklauXKltmzZIkktvk8rVqyQJG3ZssUX+7Rjxw7t2LFDgUDAN/vkhZwkadeuXeTk8Zw4ntpeToFAQIcddljdfvhhnw4mp+LiYknSxo0bPb1PJuUkiePJ4ZwkadOmTa7sU6I51dbn9ZxWrVolae//Sfz0vZdoTqbs0/Lly5WZmakdO3a06D7dcsstcfvDww47TIceeugBb5eWlipRhcQ5jwAAUnpJREFUlu2nKww4xLZtnX/++frHP/6hsrIy9ezZs25benq6SkpKlJaWptWrV3t6Jr+6ulrr1q3TEUccoUAgYOy7a358x7D2dk1NjYqKinTUUUfJsixf7JPbOa1YsULHHXecli1bpuOOO46cPJoTx1PbzMmyLBUVFemII47w9AxxS+W0fPlynXDCCVq6dKmGDx/u2X0yJaePP/5YI0eO1LJlyzRixAiOJwf26ZNPPtFxxx2npUuXauTIkZ49npYtW6bjjz9ey5cv17Bhwzyb00cffaRRo0bp//2//6cRI0b45nvPb8dTbc905JFHSpJnZ/KHDBmi0tJS9e3bVxs2bFBj2vRy/aKiIr3zzju64IIL1Ldv37r7LcvS+eefrzfeeENff/31Pk1+rUAgoC5dujhWS1JSUpNuW5bV6O3k5GQFg0EFAoG6uuvvw4Fu1x4Uid5u6n4czD41ZT+8uk+BQEAdO3asO8j9sE9eyKm2Dqf2iZzM2Cdy8v4+xWIxdejQwVf79J+3G6u9tp7/vO21fTIlp9qf85ZlJZQNx1Ni+1R/m5ePp4PZJ7dyqn+c+2Wf/JhTUtLenikQCDS5Z0pkn1JSUpSSkqKmqv2Zl4g2vVz/m2++0fXXX69//vOf+23bsWOHJO3T/JsmEAgoIyNjn286eAsZmYGczEBO3kdGZiAnM5CT98VrGOE9fjyW/LMnTTB48GAFg0HNnTt3nwt47N69W3PmzNGAAQOMbvKj0ai++OILRaNRt0tBA8jIDORkBnLyPjIyAzmZgZy8rzYbMvI2Px5LSQd+iH+lpaXphhtuUH5+vk499VT96Ec/0p49ezR//nz961//0ptvvnlQyyK8xrIsdenSxeh98DsyMgM5mYGcvI+MzEBOZiAn76t/Sgm8y4/HUptu8iXpscceU0ZGhubMmaPp06ere/fuGjFihP785z/r2GOPdbu8ZgkEAurXr5/bZaARZGQGcjIDOXkfGZmBnMxATt7Hcn0z+PFYavPfcUlJSZo8ebI+/fRT7d69W998841eeeUV4xt8ae/Sk08//dRXS0/8hozMQE5mICfvIyMzkJMZyMn7WK5vBj8eS22+yfczy7LUs2dPXy098RsyMgM5mYGcvI+MzEBOZiAn72O5vhn8eCy1+eX6fhYIBJSWluZ2GWgEGZmBnMxATt5HRmYgJzOQk/exXN8MfjyW+I5rokgkoqysrLh/wuGw2+VJ2rv05OOPP/bV0hO/ISMzkJMZyMn7yMgM5GQGcvI+luubwUvHUjgcbrDHjEQiCY/DTH4TpaamqrCw0O0yGmVZltLT03219MRvyMgM5GQGcvI+MjIDOZmBnLyP5fpm8NKxFAqFFAqF4m5LT09XSUlJQuPQ5PtYIBBQr1693C4DjSAjM5CTGcjJ+8jIDORkBnLyPpbrm8GPxxLfcT5WU1OjpUuXqqamxu1S0AAyMgM5mYGcvI+MzEBOZiAn76vNhoy8zY/HEk2+jwWDQR155JEKBoNul4IGkJEZyMkM5OR9ZGQGcjIDOXlfbTZk5G1+PJZYru9jlmWpe/fubpeBRpCRGcjJDOTkfWRkBnIyAzl5H+fkm8GPxxIz+T5WU1OjxYsX+2rpid+QkRnIyQzk5H1kZAZyMgM5eR/L9c3gx2OJJt/HgsGgsrKyfLX0xG/IyAzkZAZy8j4yMgM5mYGcvI/l+mbw47HEcn0fsyxLXbt2dbsMNIKMzEBOZiAn7yMjM5CTGcjJ+1iubwY/HkvM5PtYTU2NFi5c6KulJ35DRmYgJzOQk/eRkRnIyQzk5H0s1zeDH48lmnwfCwaDGj58uK+WnvgNGZmBnMxATt5HRmYgJzOQk/exXN8MfjyWWK7fRJFIRFlZWXG3hUIhhUKhVq5of5ZlqVOnTm6XgUZ4NaOysjKVlZUl/Pi0tDSlpaW1YEXu8mpO2Bc5eR8ZmYGczEBO3sdyfTN46VgKh8MKh8Nxt0UikYTHoclvotTUVBUWFrpdRqNql56MGTNGSUlE7UVezWjmzJmaNm1awo+fMmWKpk6d2nIFJaioqEj5+fmSpPz8fN15550aOHBgs8f1ak7YFzl5HxmZgZzMQE7ex3J9M3jpWGpssjg9PV0lJSUJjcNPBB8LBoM66aSTfLX0xG+8mtHEiRN14YUX7nPfqlWrdNVVV+nZZ5/V4MGD99nmhVn82bNna8KECXXvls+bN09z585VQUGBcnJymjW2V3PCvsjJ+8jIDORkBnLyPpbrm8GPxxJNvs/56ZvVr7yYUWPL7wcPHqwRI0a0ckWNKyoq0oQJExSLxerui0ajkqTc3FyNGTNGmZmZzfoaXswJ+yMn7yMjM3g9p5ZauWUar+cEmMJvxxIX3vOxaDSqhQsX1jU78B4ycsasWbMaPN/NsiwVFBQ0a3xyMgM5eR8ZmcHrOc2ePVuDBg3SvHnzJO1duTVo0CDNmTPH3cJamddzwvcTDmTkbX48lmjyfSwYDGrMmDG+e2fKT8jIGcXFxbJtO+4227ZVXFzcrPHJyQzk5H1kZAYv51R/5Vb9BioWiyk3N1dr1651ucLW4+WcsBfL9c3gx2OJJt/n/PSOlF+RUfNlZGQ0OpOfkZHR7K9BTmYgJ+8jIzN4NaeWXrllGq/mBJjGb8cSTb6PRaNRLV682HfftH5CRs4YP358ozP5ubm5zRqfnMxATt5HRmbwck4tvXLLJF7OCXuxXN8MfjyWaPJ9LCkpSaeffrrrHwWBhpGRMwYOHKiCggIFAoF9lsYFAgEVFBQ0+6J75GQGcvI+MjKDl3NqjZVbpvByTtirNhsy8jY/Hks0+T5m27Z2797d4DvecB8ZOScnJ0dr1qxRdna2JCk7O1tr1qxp9sfnSeRkCnLyPjIyg5dzaumVWybxck7YqzYbMvI2Px5LNPk+Fo1GtWLFCl8tPfEbMnJWZmam8vLyJEl5eXnNnsGvRU5mICfvIyMzeDmnll65ZRIv54S9WK5vBj8eS/5Zk9DKIpGIsrKy4m4LhUIKhUKtXNH+kpKSNGbMGLfLQCPIyAzkZAZy8j4yMoPXc8rJydGYMWP0wAMPaM6cOcrOztZdd93Vphp8yfs5geX6pvDSsRQOhxUOh+Nui0QiCY/Dd1wTpaamqrCw0O0yGmXbtnbu3KkuXbo0eP4a3EVGZiAnM5CT95GRGUzIqXbl1pw5cxxduWUSE3Jq61iubwYvHUuNTRan///27jyuqSvvH/gnCVVEBXdEcMQd3MVal6LWpXvrdNparQ+oFbsyOtNFZ6pjXVprp7bTxdKnLrg7avexra192ql2UFFEa60iQpWqgBELiIoFc3N+f/hLxpiAFwjknJvP+/Xi5fXem5vv4cNJcnJPbiIikJubq+s4nK5vYJqm4fDhw4aaemI0zEgNzEkNzEl+zEgNzEkNzEl+nK6vBiP2JZ7JN7CAgAAMGjTI12VQJZiRGpiTGpiT/JiRGpiTGpiT/DhdXw1G7Es8k29gQggUFhZyipDEmJEamJMamJP8mJEamJMamJP8OF1fDUbsSxzkG5imafj5558NNfXEaJiRGpiTGpiT/JiRGpiTGpiT/DhdXw1G7EucO2JgAQEB6N+/v6/LoEowIzUwJzUwJ/kxIzUwJzUwJ/lxur4ajNiXeCbfwOx2O86cOQO73e7rUqgCzEgNzEkNzEl+zEgNzEkNzEl+jmyYkdyM2Jc4yDcwIQROnTplqM+XGA0zUgNzUgNzkh8zUgNzUgNzkh8/k68GI/Ylzh0xMIvFgpiYGF+XQZVgRmpgTmpgTvJjRmpgTmpgTvKzWCwu/5KcjNiXeCbfwOx2O/Lz8w019cRomJEamJMamJP8mJEamJMamJP8OF1fDUbsSzyTX01WqxXdunXzuC0xMRGJiYl1XJE7IQQKCgrQqlUrX5dSq/Lz85Gfn697/7CwMISFhdViRfr5S0aqY05qYE7yY0ZqYE5qYE7y43R9NcjUl5KSkpCUlORxm9Vq1X0cDvKrKTQ0FIcPH/Z1GZWyWCzo1auXr8uodUuWLMG8efN07z9nzhzMnTu39gqqAn/JSHXMSQ3MSX7+nFFWVhYWL14MAFi8eDFmzpyJzp07+7gqz/w5J5UwJ/lxur4aZOpLlZ0sjoiIQG5urq7jcJBvYHa7Hbm5uQgPD4fZbNxPZjz++OMYPXq0y7qMjAzExcVh3bp1iI6Odtkmy1l8wH8yUh1zUgNzkp+/ZrRy5UpMmTIFJpMJALB27VqsWbMGycnJmDRpkm+L88Bfc1INc5Ifp+urwYh9iYN8AxNCoKSkBG3atPF1KbWqsun30dHRUl9Iw18yUh1zUgNzkp8/ZpSVlYUpU6a4vMjXNA0AkJCQgNjYWHTq1MlX5XnkjzmpiDnJj9P11WDEvmSMtypq6NixYxgzZgzatWuH4OBgDB48GO+//77yHdJisaB79+6cIiQxZqQG5qQG5iQ/f8xoxYoVzjP41zKZTEhOTq7jiq7PH3NSEXOSH6frq8GIfcnvB/l5eXno06cPtm7divvvvx/Tp0+HzWbD2LFj8eKLL/q6vBqx2+3IycnhFCGJMSM1MCc1MCf5+WNGOTk5FZ40EEIgJyenbgvSwR9zUhFzkh+n66vBiH3J76frL1iwAOfPn8eePXvQv39/AMDMmTPxwAMP4MUXX0R8fDzat2/v4yqrRwiBsrIy5WckGBkzUgNzUgNzkp8/ZhQZGVnpmfzIyMi6LUgHf8xJRcxJfpyurwYj9iW/P5O/c+dO9OvXzznAB65M2XjmmWdgs9mwc+dOH1ZXMxaLBV27djXU1BOjYUZqYE5qYE7y88eMJk+eXOmZ/ISEhDqu6Pr8MScVMSf5cbq+GozYl/x+kH/58mWPF2az2WwAgIKCgrouyWvsdjuys7MNNfXEaJiRGpiTGpiT/Pwxo86dOyM5ORlms9nlBb/ZbEZycrJ0F90D/DMnFTEn+XG6vhqM2Jf8fpD/008/YenSpW7r169fDwDo2bNnXZdEREREBjJp0iRkZmYiPj4eABAfH4/MzEwpvz6PiIjU5/eD/GvZ7XbMmDEDK1asQO/evTF8+PAK9yspKUFxcTGKi4tRUlKCoqIi3ctlZWWw2WzOKXzXWxZCuC0DqHTZbrc7zxA4vq7HbrfrWna8k6VpWpWWa7tNjmU97bi23TK2yfF5TLPZrDsbX7QpIyMDixcvBgC89dZbyMrK8lpOtdEmRx3+llNt9icV2sSc5G+T2WxGx44dXc5uqd4mvTl16NABTz31FAAgMTHReb0fGdukUk6OutmfvNcmxzZftKkqOV1dq6w5OY5tMpkM9bdXlZxUaJPdfmXMVNs5lZaWehwfFhYW4ty5c9ddvvp17vVwkH+VPXv2YMCAAVi0aBHatm2Ljz/+GGaz519Rfn4+QkJC0LRpUzRt2hQhISFo1qyZ7uWFCxdi//79KCoqAgCkpaWhpKQEAJCamorS0lIAQEpKCsrLy6FpGlJSUqBpGsrLy5GSkgLgyh9LamoqAKCkpARpaWkAgKKiIuzbtw+ZmZk4c+YMDhw4AACwWq04dOgQACA3NxdHjhwBAJw4cQLZ2dkArnyl4LFjxwAA2dnZOHHiBADgyJEjyM3NBQAcOnQIVqsVAHDgwAGcPXsWAGq9Tfv37wcAnD179rptAoCTJ09K3aZff/0V33//PTRN09UmX+SUnJyMHj16YO3atQCAdevWISoqCkuXLvVKTrXRJgC4cOGCX+VU2/1JhTYxJ/nbpGkaDhw4gH379hmmTVXJyXEl/dOnT0vdJpVyAsD+5OWcAODMmTM+aZPenBz1yZ6To97i4mJD/e3pzUmVNu3ZsweZmZn49ddfa7VNzz77rMfxYfPmzdGkSZPrLufl5UE3QaK8vFzMmjVLmM1mAUDEx8eLwsJCj/uGh4cLACIsLEycO3dOFBUViaKiInHu3DlRWFioe/m3334Tly9fFna7XQghrrtst9vdloUQlS6XlZWJ48ePi8uXLwubzSaEEELTNF3LmqYJIYSw2WxVWq7tNjmWr9eO9PR0AUDs2bNH6jbZbDbx888/O+uTLafMzExnv7j2x2w2i4yMjBrlVBttcmS/d+9ev8mptvuTKm1iTvK3SdM0cezYMVFWVmaYNlUlpz179ggAIi0tTeo2qZLT3r17nY/37E/eaZPjOTQtLc0nbdKbU1pamgAg0tPTpc4pNTXV+XrUSH97enNSpU2OMZPNZqvVNl28eNHj+PDXX38VxcXF111u06aNACDCw8PF9fj9V+jZbDbcd9992LJlC7p06YLk5GTExsZe93ZmsxnBwcFeqyMgIKBayyaTqcLlevXquX01z9UzE/QsX32VST3L1W2H3jY5lvXUfnVtsrbJYrGgQ4cOuJYsOa1cubLSr35avXo1Fi5cWKOcaqNNjjqq0tZra1cpp+q0SYac/K0/VadNRszp6q+lNUqb9OZ09XOS7M9PKuTkeJw3mUy6smF/0temq7fJ3J+q0iZf5XTDDTc478cobTJiTt4YM+lpU1BQEIKCglBdFb0m98Tvp+svWLAAW7ZswW233Ybdu3frGuCrQtM0HDp0yDmdjeQje0Y5OTkVfv5HCOGcemp0sudEVzAn+TEjNTAnNTAn+WlXfaSE5GXEvhRw/V2MS9M0LF26FK1atcLmzZtRv359X5fkVSaTCcHBwVV614fqluwZRUZGVnom/9p3PY1K9pzoCuYkP2akBuakBuYkv6tnm5C8jNiX/HqQf+LECeTl5WHQoEFYtmyZx31GjhyJ6OjoOq7MO8xmM9q2bevrMqgSsmc0efJkvPrqqx63CSGQkJBQxxX5huw50RXMSX7MSA3MSQ3MSX6OKdwVXcib5GDEvuTXg/yCggIAwK5du7Br1y6P+6xcuVLZQb5j6kn37t3dPmtFcpA9o86dOyM5ORkJCQkwmUzQNA0WiwVCCCQnJzu/otHoZM+JrmBO8mNGamBOamBO8uN0fTUYsS/59dtKN910k/N7Eyv6mTRpkq/LrDaTyYSWLVsaauqJ0aiQ0aRJk5CZmYn4+HgAQHx8PDIzM5XuG1WlQk7EnFTAjNTAnNTAnOTH6fpqMGJf8utBvtGZzWaEhYVxipDEVMmoU6dOmDp1KgBg6tSpfnMG30GVnPwdc5IfM1IDc1IDc5Ifp+urwYh9yTgtITeapmHfvn2cIiQxZqQG5qQG5iQ/ZqQG5qQG5iQ/TtdXgxH7kl9/Jr8mrFYrunXr5nFbYmIiEhMT67gidyaTCREREYaaemI0zEgNzEkNzEl+zEgNzEkNzEl+nK6vBpn6UlJSEpKSkjxus1qtuo/DQX41hYaG4vDhw74uo1JmsxmtWrXydRlUCWakBuakBuYkP2akBuakBuYkP07XV4NMfamyk8URERHIzc3VdRz+xRmYzWZDWloabDabr0uhCjAjNTAnNTAn+TEjNTAnNTAn+TmyYUZyM2Jf4iDfwCwWCzp27GiYr4IwImakBuakBuYkP2akBuakBuYkP0c2zEhuRuxLnK5vYCaTCc2aNfN1GVQJZqQG5qQG5iQ/ZqQG5qQG5iQ/fiZfDUbsSzyTb2A2mw27du0y1NQTo2FGNZOfn499+/a5/GRkZAAAMjIy3Lbl5+dX636YkxqYk/yYkRqYkxqYk/w4XV8NRuxLPJNvYBaLBd26dTPU1BOjYUY1s2TJEsybN8/jtri4OLd1c+bMwdy5c6t8P8xJDcxJfsxIDcxJDcxJfpyurwYj9iUO8g3MZDIhJCTE12VQJZhRzTz++OMYPXq07v3DwsKqdT/MSQ3MSX7MSA3MSQ3MSX6crq8GI/YlDvINzGazITU1FQMHDkRAAKP2tfz8fLfp4jabDQcPHkTPnj3dMgoLC6v2oNRf1NXviH1JDcxJfsxIDcxJDcxJfpyurwYj9iVjtII8slgs6Nu3r6GmnqissqnlnlR3ajl5H/uSGpiT/JiRGpiTGpiT/DhdXw1G7Esc5BuYyWRCw4YNfV0G/X+eppZnZGQgLi4O69atQ3R0tMs2nsWXB/uSGpiT/JiRGpiTGpiT/DhdXw1G7Esc5FeT1WpFt27dPG5LTExEYmJiHVfkzmazISUlBbGxsYaZeqIyT1PLHdO3OnfujJiYGF+URTqwL6mBOcmPGamBOamBOcmP0/XVIFNfSkpKQlJSksdtVqtV93H4iFBNoaGhOHz4sK/LqJTFYsGgQYMMNfXEaDiNSw3sS2pgTvJjRmpgTmpgTvLj6zw1yNSXKjtZHBERgdzcXF3HMXuzKJKPDH+sREbAvqQG5iQ/ZqQG5qQG5kTkHUbrSxzkG5imaUhJSYGmab4uhSrgyIYZyY19SQ3MSX7MSA3MSQ3MSX58nacGI/YlDvINzGKxIDY21nDvTF1PVlYWFi9eDABYvHgxsrKyfFxRxTiNSw3+2pdUw5zkx4zUwJzUwJzkx9d5ajBiX+Ig3+CM9I6UHitXrkRUVBTWrl0LAFi7di2ioqKwatUq3xZGyvO3vqQq5iQ/ZqQG5qQG5kTkHUbrSxzkG5imadi1a5fh/mgrkpWVhSlTpsBut7tMj7Lb7UhISEB2draPK3THaVxq8Le+pCrmJD9mpAbmpAbmJD++zlODEfsSB/kGFhAQgFtuucXnXwVRV1asWFHh95CaTCYkJyfXcUXX58jGXzJSlb/1JVUxJ/kxIzUwJzUwJ/nxdZ4ajNiXOMg3MCEELl68CCGEr0upEzk5ORW2VQiBnJycui1IB0e9/pKRqvytL6mKOcmPGamBOamBOcmPr/PUYMS+xEG+gWmahv379xtq6kllIiMjKz2THxkZWbcF6SDrNK78/Hzs27fP5ScjIwMAkJGR4bYtPz/fxxXXLn/rS6piTvJjRmpgTmpgTvKT9XUeuTJiXzLOnARyExAQgNjYWF+XUWcmT56MV1991eM2IQQSEhLquKLrk3Ua15IlSzBv3jyP2+Li4tzWzZkzB3Pnzq3lqnzH3/qSqpiT/JiRGpiTGpiT/GR9nUeujNiX+BdXTVarFd26dfO4LTExEYmJiXVckTshBEpKShAcHFzhGW4j6dy5M5KTk5GQkACTyQRN02CxWCCEQHJyMjp16uTrEt3IOo3r8ccfx+jRo3XvHxYWVovV+J6/9SVVMSf5MSM1MCc1MCf5yfo6j1zJ1JeSkpKQlJTkcZvVatV9HA7yqyk0NBSHDx/2dRmV0jQNhw8fRv/+/f3mHcRJkyYhNjYWCxYswKpVqxAfH49Zs2ZJOcAH5J3GFRYWZviBe1X4Y19SEXOSHzNSA3NSA3OSn6yv88iVTH2pspPFERERyM3N1XUcfibfwAICAjBo0CCf/7HWtU6dOmHq1KkAgKlTp0o7wAc4jUsV/tqXVMOc5MeM1MCc1MCc5MfXeWowYl8yTkvIjRACRUVFaNq0qc+nnpBnnMalBvYlNTAn+TEjNTAnNdQ0p/z8fLcL5159kd1rcZZf1fF1nhqM+JjHM/kGpmkafv75Z04RkhincamBfUkNzEl+zEgNzEkNNc1pyZIl6Nevn8uP4+K6cXFxbtuWLFnizfL9Al/nqcGIj3k8k29gAQEB6N+/v6/LoEpwGpca2JfUwJzkx4zUwJzUUNOceJHd2sfXeWow4mMe/+IMzG634+zZs2jRogXMZk7akE1WVhbefvttAMDbb7+NWbNmoXPnzj6uijxhX1IDc5IfM1IDc1JDTXPi9PvaZ7fbXf4lORnxMc8YrSCPhBA4deoUPwckoZUrVyIqKgrr1q0DAKxbtw5RUVFYtWqVbwsjj9iX1MCc5MeM1MCc1MCc5MfP5KvBiH2Jg3wDs1gsiImJgcVi8XUpdJWsrCxMmTIFdrvd5bNadrsdCQkJyM7O9nGFdC32JTUwJ/kxIzUwJzUwJ/k5smFGcjNiX+Ig38Dsdjvy8/M5RUgyK1asqPDKnSaTCcnJyXVcEV0P+5IamJP8mJEamJMamJP8OF1fDUbsSxzkG5gQAgUFBYaaemIEOTk5FWYihEBOTk7dFkTXxb6kBuYkP2akBuakBuYkP07XV4MR+xIvvFdNVqsV3bp187gtMTERiYmJdVyRO4vFgl69evm6DLpGZGRkpWfyIyMj67Ygui72JTUwJ/kxIzUwJzUwJ/lxur4aZOpLSUlJSEpK8rjNarXqPg4H+dUUGhqKw4cP+7qMStntduTm5iI8PNwwV4o0gsmTJ+PVV1/1uE0IgYSEhDquiK6HfUkNzEl+zEgNzEkNzEl+nK6vBpn6UmUniyMiIpCbm6vrOHxEMDAhBEpKSgw19cQIOnfujOTkZJjNZpd3eM1mM5KTk9GpUycfV0jXYl9SA3OSHzNSA3NSA3OSH6frq8GIfYln8g3MYrGge/fuvi6DPJg0aRJiY2OxYMECrFq1CvHx8Zg1axYH+JJiX1IDc5IfM1IDc1IDc5JLfn4+8vPzXdYdPXrU+e+1U/bDwsIQFhZWZ/VRxYzYl3gm/ypCCLRv39753eWqs9vtyMnJ4RQhSXXq1Mk5HScxMZEDfImxL6mBOcmPGamBOamBOcllyZIl6Nevn8tPXFwcACAuLs5t25IlS3xcMTkYsS/xTP5VPv/8c0Nd2VwIgbKyMkNNPTEaTuNSA/uSGpiT/JiRGpiTGpiTXB5//HGMHj3aZZ2maTh58iTatm3r8Uw+ycGIfcnvB/mlpaX4/PPP8Z///AcrVqzwdTleZbFY0LVrV1+XQZXgVVfVwL6kBuYkP2akBuakBuYkl4qm3/fv398H1VBVGLEv+f10/RMnTmDs2LF45513UFpa6utyvMputyM7O9tQU0+MhlddVQP7khqYk/yYkRqYkxqYk/yYkRqMmJPfD/K7dOmCS5cu4dKlS9i6dauvyyEiIiIiIiKqNr+frm82mxEYGAgAqFevno+r8S6z2cyLuUnO8V2cvv5OTqoc+5IamJP8mJEamJMamJP8mJEajJgTRxbVZLfbUVJSguLiYhQXF6OkpARFRUW6l8vKymCz2ZwXeLjeshDCbRlApcvl5eXIzMzE5cuXoWmas249y47pKpqmVWm5ttvkWNbTDkdtMrfJ8a/j/vwxJxXaZLPZkJGRwZwkbxNzkr9NmqbhyJEjKC8vN0ybmJNv2+SomzkZo01VyclB5jY5XovbbDZD/e1VJScV2lRXOZWWlnocHxYWFuLcuXPXXXa0Tw8O8qspPz8fISEhaNq0KZo2bYqQkBA0a9ZM9/LChQuxf/9+FBUVAQDS0tJQUlICAEhNTXVeHyAlJQXl5eXQNA0pKSnQNA3l5eVISUkBcOWPJTU1FQBQUlKCtLQ0AEBRURF++OEH1K9fH4WFhThw4AAAwGq14tChQwCA3NxcHDlyBMCVaxNkZ2cDAI4dO4Zjx44BALKzs3HixAkAwJEjR5CbmwsAOHToEKxWKwDgwIEDOHv2LADUepv2798PADh79ux12wQAJ0+elLpN58+fBwCYTCZdbTJiTiq0qbi4GFarlTlJ3ibmJH+bTCYTNE3DDz/8YJg2VSUnxzf4nD59Wuo2qZQTAPYnBXLydn86c+YMHGRuU3p6OurXr4/i4mJD/e3pzUmVNtVVTs8++6zH8WHz5s3RpEmT6y7n5eVBL5OoylsCBrdt2zYMHz4ca9eudX6v5bUiIiKQm5uLsLAwHDlyxPnOjNlshqZpMJlMupYbNGgAi8UCi8UCk8kEm81W6TJw5Uns6uWAgAAIIa67bLfbIYSAxWLRvVydNpnN5uu2ozbalJubi7y8PJd2HD16FHFxcVizZg26d+/u0qZWrVqhTZs2UrQpPT0dN954I9LT09GnTx9D58Q2sU1sE9vk721KT0/HTTfdhLS0NPTt29cQbfJlTvv27cONN96IvXv3IiYmRpo2Wa1WnDp1Sneb2rRpg5YtWxo2p9po0969e9G/f3+Pr59UbZMRc2KbXJdLS0tRXl5e7Tb17NkTeXl5CA8Px6lTp1AZv/9MfnWZzWYEBwd77XgBAQHVWjaZTBUum0wmHDp0CFFRUc4/7qs/+61n2XE7vcvVbYfeNjmWr65x2bJlmDdvHjyZMGGC27o5c+Zg7ty5UrTp6qk8Vc1GtZxUbpPdbkdmZqZLX1K9TcxJjTYZLSdNuzK9OCoqyjBtuna5stqvfi729LwsS5tUyclkMjn/1ZNNXfWnJUuWVPi6xJOrX5dU1lZVc6pKm65dVr1NV78WN0qbmFP12xQUFISgoCBUl+MxT4+A6+9CqjKZTAgODq7SH4SKHn/8cYwePVr3/p6+w9RXrn6BQvLyl76kOuYkP2akBuZUM55el2RkZCAuLg7r1q1DdHS0y7bqvi5hTvJjRmowYk4c5BuY2WxG27ZtfV1GrQsLC5Nq4F6R/Px85Ofnu6zLzMx0/nv1O4CAOu3yB/7Sl1THnOTHjNTAnGqmsufv6OhoxMTEeOV+/CUnT6+fMjIyXP69mkyvn/wlI9UZMScO8g1M0zQcOnQI3bt3d5kqQr5R2fQ9T9eAuHb6HvkO+5IamJP8mJEaZMxJ5YFebZExp9qg8usnf8lIdUbMiYN8AzOZTGjZsqWhpp6ozNP0Pbvdjl9//RXNmzf3eCaf5MC+pAbmJD9mpAYZc1J5oFdbZMypNqj+sUx/yEh1RsyJg/yr3HLLLVX6/kHZmc1mqR7o/J0/nFUwKvYlNTAn+TEjNciYk8oDvdoiY061QeXXT/6SkeqMmBMH+QamaRoOHDiA3r17G2bqidEwIzUwJzUwJ/kxIzXImJPKA73aImNO5IoZqcGIOZmvvwupymQyISIiwlBTT4yGGamBOamBOcmPGamBOamBOcmPGanBiDnxTH41Wa1WdOvWzeO2xMREJCYm1nFF7sxmM1q1auXrMqgSzEgNzEkNzEl+zEgNzEkNzEl+zEgNMuWUlJSEpKQkj9usVqvu43CQX02hoaE4fPiwr8uolM1mw/79+9G3b18EBDBqGTEjNTAnNTAn+TEjNTAnNTAn+TEjNciUU2UniyMiIpCbm6vrOPxrMzCLxYKOHTsa5rMlRsSM1MCc1MCc5OdPGan8lW/+lJPKmJP8mJEajJgTB/kGZjKZ0KxZM1+XQZVgRmpgTmpgTvLzp4xU/so3f8pJZcxJfsxIDUbMiYN8A7PZbEhLS0P//v19PvWEPGNGamBOamBO8vOnjFT+yjd/ykllzEl+zEgNRszJGK0gjywWC7p162aoqSdGw4zUwJzUwJzk508ZyTT9vqr8KSeVMSf5MSM1GDEnDvINzGQyISQkxNdlUCWYkRqYkxqYk/yYkRqYkxqYk/yYkRqMmJPZ1wVQ7bHZbEhJSYHNZvN1KVQBZqQG5qQG5iQ/ZqQG5qQG5iQ/ZqQGI+bEQb6BWSwW9O3b11BTT4yGGamBOamBOcmPGamBOamBOcmPGanBiDlxur6BmUwmNGzY0NdlUCWYkRqYkxqYk/yYkRqYkxqYk/yYkRqMmBPP5BuYzWbDtm3bDDX1xGiYkRqYkxqYk/yYkRqYkxqYk/yYkRqMmJNJCCF8XYRKIiIikJubi4CAAHTu3NnjPomJiUhMTKzjytwJIVBeXo569erBZDL5uhzygBmpgTmpgTnJjxmpgTl53759+9CvXz+kp6cjJibGK8dkTvJjRmqQKaekpCQkJSV53JaVlQWbzYbw8HCcOnWq0uNwun41hYaG4vDhw74u47qM9NkSo2JGamBOamBO8mNGamBOamBO8mNGapAlp8pOFjtONuvB6foGpmkaUlJSoGmar0uhCjAjNTAnNTAn+TEjNTAnNTAn+TEjNRgxJ07XryLHOyh6pkn4mhACmqbBYrH4fOoJecaM1MCc1MCc5MeM1MCcvK+2puszJ7kxIzWoklNVxqE8k29wRnpHyqiYkRqYkxqYk/yYkRqYkxqYk/yYkRqMlhMH+QamaRp27dpluD9aI2FGamBOamBO8mNGamBOamBO8mNGajBiTpyuX0UqTdcnIiIiIt/KysrCyy+/jFWrVmHSpEmYOXNmhd/QRERUEU7XJwBXPl9y8eJF8H0ceTEjNTAnNTAn+TEjNTAn71m5ciWioqKwdu1aAMDatWsRFRWFVatW1fjYzEl+zEgNRsyJg3wD0zQN+/fvN9TUE6NhRmpgTmpgTvJjRmpgTt6RlZWFKVOmwG63O3+XmqbBbrcjISEB2dnZNTo+c5IfM1KDEXPidP0q4nR9IiIiIrqe559/HosWLfI4cLBYLJg+fToWLlzog8qISEWcrk8Arkw9OXfunKGmnhgNM1IDc1IDc5IfM1IDc/KOnJycCn+HQgjk5OTU6PjMSX7MSA1GzImD/GqyWq3o1q2bx5+kpCRflwfgytSTw4cPG2rqidEwIzUwJzUwJ/kxIzUwJ++IjIys8Du3TSYTIiMja3R85iQ/ZqQGmXJKSkqqcIxptVp1H4fT9auI0/WJiIiI6HqysrIQFRUFu93uts1sNiMzMxOdOnXyQWVEpCJO1ycAV6aeFBYWGmrqidEwIzUwJzUwJ/kxIzUwJ+/o3LkzkpOTYTabYbFYAFz5LL7ZbEZycnKNB/jMSX7MSA1GzImDfAPTNA0///yzFFNPyDNmpAbmpAbmJD9mpAbm5D2TJk1CZmYm4uPjAQDx8fHIzMzEpEmTanxs5iQ/ZqQGI+bEQb6BaZqGL774wlB/sEbDjNTAnNTAnOTHjNTAnLyrU6dOmDp1KgBg6tSpXpuiz5zkx4zUYMSc+Jn8KlLpM/nFxcVo2rQpioqK0KRJE1+XQx4wIzUwJzUwJ/kxIzUwJ+/bt28f+vXrh/T0dMTExHjlmMxJfsxIDarkxM/kEwA4P1fC93HkxYzUwJzUwJzkx4zUwJzUwJzkx4zUYMScOMg3sKsv8kJyYkZqYE5qYE7yY0ZqYE5qYE7yY0ZqMGJOHOQbmOMrWzx9dQvJgRmpgTmpgTnJjxmpgTmpgTnJjxmpwYg5cZBvYEacemI0zEgNzEkNzEl+zEgNzEkNzEl+zEgNRsyJg3wDM+LUE6NhRmpgTmpgTvJjRmpgTmpgTvJjRmowYk4Bvi5AVVarFd26dfO4LTExEYmJiXVckTsjTj0xGmakBuakBuYkP2akBuakBuYkP2akBplySkpKQlJSksdtVqtV93E4yK+m0NBQHD582NdlVMqIU0+MhhmpgTmpgTnJjxmpgTnVTH5+PvLz813WZWRkuPx7tbCwMISFhVX5fpiT/JiRGmTKqbKTxY6v0NOD0/UlUtG7NtVVW1NPvF2nPx+zNqcHqdB+VY7JvuT9Y9bGcZmT/MfkY54ax/T3vlTT4y5ZsgT9+vVz+YmLiwMAxMXFuW1bsmRJte7H33NS4Zh8zFPjmCr1Jd0EVUl4eLgAIMLDw71+7OjoaK8er6ioSAAQRUVFXj2ut+v052PWVkZCqNF+VY7JvuT9Y9bGcZmT/MfkY54ax/T3vlTT4+bl5Yn09HS3n8jISI/r8/LyqnU//p6TCsfkY54ax1SlL1VlHMrp+gBycnIwZ84cbNu2DQAwbNgwzJ8/H5GRkT6tq6aERFNPyDNmpAbmpAbmJD9mpAbmVDMVTb9v0KABYmJivHY/zEl+zEgNRszJ7wf5hYWFGDJkCM6fP48JEyYAAFavXo1t27bhhx9+QLNmzXxcYfUZ8UqRRsOM1MCc1MCc5MeM1MCc1MCc5MeM1GDEnPz+M/mrVq3CqVOn8PHHH+Ptt9/G22+/jQ8++AAnT57E2rVrfV1ejch0pUjyjBmpwds5ZWVl4fnnn0dubi6ef/55ZGVleeW43qZKncCVWufMmQMAmDNnjrS1qvI7ra06+ZinBuakBuYkP1Uy4nOTGjlVhd8P8tesWYM+ffpgxIgRznW33XYbunfvjvXr1/uwMiIyopUrVyIqKgqLFi1CSUkJFi1ahKioKKxatcrXpblQpU7gv7UuXrwYALB48WIpa1Xld6pKnUREVHOqPOarUqcs/HqQ/9tvv+HAgQMYPny427YRI0YgLS0NNpvNB5V5h9lsdvmX5MOM1OCtnLKysjBlyhTY7XZomgYA0DQNdrsdCQkJyM7OrnGt3qBKnYA6tbLOK/iYpwbmpAbmJD/ZM+Jz0xWy51Qdfv2ZfKvVCgAeL47iWHf27Fm0bt3auf7MmTMArnwHanh4uMtthBAwmUy6lh3/v7aeiIiImjbLyTHlJCoqyqt/tN6uU6VjnjlzRomMAHV+p/6Qk+OJ6MKFCxVOBbPb7ejduzcaNWoEs9lc7c+F1eT36Xjy1FunxWKp0d9tTXJS5XdalTobNmyIgICAav9OVciej3lqHFOl1w/efrx3YE7y5+TPGQHeqbWkpKTSx/yYmBgEBwfX6D68Uee5c+dqtc666ktXX9ivKuNGx7Jj7OoYj1bGJIx0GcEq2rNnDwYMGIBly5ZhypQpLtvee+89PPnkkzhw4AB69erlXG+xWAz1eQ0iIiIiIiJSg9lsds5oqIhfn8m/+p2RazkG8pcvX3ZZHxgYiN9++w0WiwUtW7as8X0TERERERGR8dXk/HpBQQE0TUNgYOB19/XrQX6rVq0AAMXFxW7bioqKAMBlqj4AXLx4sdbrIiIiIiIiIqoO41xdoBpCQ0MBAHl5eW7bTp8+DeC/bwQQERERERERyc6vP5MPAH369IEQAgcOHHBZ3717d9SvXx/79u3zUWVEREREREREVePXZ/IBYMKECfjxxx/xzTffONdt3boVhw8fxiOPPOLDyoiIiIiIiIiqxu/P5BcWFqJ37964cOECJk6cCE3TsGbNGrRv3x7bt29HSEiIr0skIiIiIiIi0sXvB/kAcPz4cbzwwgvYvn07TCYThg8fjtdffx3Nmzf3dWlEREREREREunGQT0RERERERGQQfv+ZfNXl5eXh1KlTbj+ObwcArnzt31/+8hf07t0bzZs3x+jRo/Hvf//bh1X7D5vN5jGfq38KCwsBAL/++muF+2ia5uOWGJcQAu3bt8e6devctuntOzabDQsXLsSAAQPQpEkT3Hbbbfjggw/qony/UFlGFy5cwDPPPIPo6GgEBQWhd+/emD9/PsrLy132u3TpUoX96/z583XVFEOrLCe9j29CCLz33nsYNmwYQkJCMHToULz33ns1+l5hclVRTvn5+ZU+V+Xn5zv3vXDhQoX7lZaW1nWTDOPYsWMYM2YM2rVrh+DgYAwePBjvv/++y9+/3ucb9qXaoyen8vJyvPjii+jVqxcaNmyI6OhoPPPMM27PN5cvX66wLzm+zpuqR09OxcXFFf7+r30d8cEHH+C2225DkyZNMGDAACxcuBA2m62um6WfIGWVl5cLi8UiALj9dO/eXQghhN1uF3fddZcAIB5++GExa9Ys0bFjRxEQECB27tzp4xYY3/Hjxz3mc/XPxIkThRBCDBs2rMJ9CgoKfNsQA9u8ebMAINauXeuyvip956mnnhIAxN133y1eeOEF0atXLwFAfPDBB3XZFMOqKCNN00T//v0FABEXFydefvll8Yc//EEAECNGjBCapjn3XblyZYX9a9GiRXXdJEOqKCch9D++vfrqqwKAGDJkiJg7d66IjY1lRl5WUU7t2rWr9LmqXbt2zn3nzJlT4X583Kue3Nxc0bhxY9G4cWPx5z//WcyfP9/5+DZv3jznfnqfb9iXaofenB544AEBQIwePVq8/PLLIj4+XpjNZhEdHS1KS0ud+3333XcV9qXExERfNNEQ9OY0ceLECn//aWlpzv3ef/99AUD06dNHvPDCC87XhzJnxEG+wo4ePSoAiOeee0589tlnLj/btm0TQgiRnp4uAIgXXnjBebtTp06JFi1aiPvuu89XpfuNixcvumXj+HG8SNq0aZMQQog2bdqI22+/3eO+ZWVlPm6JsVy8eFFs2rRJ/PGPfxRBQUEeX/Dq7TunT58WFotFTJgwQdjtdiGEECUlJaJLly6iT58+ddMgA9KTkeNJ99oXra+//rrb/jNnzhQBAQFi8+bNbv0rOzu7TtpkRHpyEkLf41tZWZlo1qyZGD58uLh8+bIQ4sqb2UOHDhUtWrQQ5eXlddo2I9GT0zfffOMxn3Xr1ol69eqJJ5980rnv+PHjRZs2bTzun5eXV9fNMwTH4H3Pnj3OdTabTfz+978XAQEB4tixY7qfb9iXao+enPbs2eNxAPjRRx8JAOLFF190rlu6dKkAIFavXu3Wlw4ePFhn7TIaPTkJIcTgwYNFnz59PD6WFRUVCSGunPTp2bOniI6OFufPn3euGz9+vAgICJD2RBwH+Qr7/PPPBQCRmppa4T5/+tOfhNlsFmfPnnVZn5iYKOrVqycKCwtru0zy4NKlSyI6OlrEx8cLIYQ4f/68ACBeeeUVH1fmHzIyMtzesb32Ba/evvPGG28IAOKHH35w2W/RokUCgDh06FDtNsag9GT05z//WQAQJSUlLuvLyspEvXr1xJQpU5zrHnzwQREVFVUntfsTPTnpfXz75JNPBADx6aefuqz/4IMPBADxxRdfeL1+f6Enp4pMnjxZdO3aVVy8eNG57sYbbxR33HFHbZXrl/r06SP69evntn779u0CgFi3bp3u5xv2pdqjJ6c333xTAPA4SG/fvr0YNWqU8//PPfecaNCggcvMM6o5PTkJIUSLFi3EE088Uemx9u/fLwCIN99802V9WlqaACCSkpK8V7gX8TP5Cjt69CgAoGvXrrDZbDh37pzbPqmpqc7PE19txIgRKC8vx/79++ukVnI1e/ZslJaWYvHixQCArKwsAEBUVBSEECgsLOTn5mpRly5dcOnSJVy6dAlbt271uI/evpOamopmzZqhV69ebvs5tlPV6cnowoUL6NGjBxo3buyy/vLlyzCZTCgoKHCuO3r0KKKiogAA58+fd/usHVWPnpz0Pr45+srw4cNd1rMv1ZyenDz54osvsGbNGqxfvx5BQUEAACGES38qLi6W+3Opirh8+TJiYmLc1jt+twUFBbqfb9iXao+enIqLixEeHo727du77COEgKZpbs9NXbt2hdlsRmlpKa9p4SV6cioqKsLZs2edz01FRUWw2+1ut6moP8XExCAkJETa/sRBvsKysrIQFBSEp556Cg0aNECTJk3wu9/9Dq+99przQkZWqxVhYWFut3WsO3PmTJ3WTMCRI0fwj3/8AwsXLkRISAiA/74Ifv/999GkSRM0b94czZs3x6OPPori4mIfVmtMZrMZgYGBCAwMRL169Tzuo7fvWK1WtG7dGiaTqdL9qGr0ZLRs2TIcPHjQbf3HH3+MsrIy9OzZEwBgt9uRnZ2NgoIC9OzZE8HBwWjUqBEGDhwo7ZOzKvTkpPfxzWq1IigoCMHBwS63b9q0KerVq8e+VAN6crpWeXk5pk6dismTJ6Nfv37O9QUFBSgpKUFGRgY6dOiApk2bonHjxrj11ltx5MiR2mqC4f30009YunSp2/r169cDAHr27Kn7+YZ9qfboyWnOnDk4deoUGjZs6LJPSkoKTpw44XxuAq48PmqahsGDB6Nhw4Zo3LgxevbsiS1bttRuQwxOT06O56bt27ejdevWaNasGUJCQvDggw8iNzfXeRur1QoAbq8JzWYzQkNDpe1PHOQr7OjRoygtLcXx48fx1ltvYdmyZejSpQumT5+Op59+GsCVP8ymTZu63daxTtY/TCObPXs2evTogbFjxzrXOWZlfP/993j++eexbt06PPDAA1i+fDmGDx/OsyQ+oLfvsI/J5aOPPsKUKVMQFBSExx57DMCVbyEpLS3Fjh07EBsbi7Vr12L+/Pk4fvw4br75ZuzZs8fHVRub3se3ivqSyWRC06ZN2Zfq2LJly5CXl4fZs2e7rHfk+d133+HBBx/E+vXrMWPGDKSmpuLGG2/EL7/84otyDcdut2PGjBlYsWIFevfujeHDh9f4eYl9yfs85eRJSkoK7r33XpjNZkybNg0AoGkasrOzcfDgQbRr1w6rVq3C66+/jkuXLuHuu+/GRx99VJdNMTRPOTkey/7973/jySefxPr16/HYY49h8+bNuOmmm1BSUgLgv4P8Jk2auB1X5v4U4OsCqPqGDh2KQYMGYdasWQgMDAQATJ48Gffeey/eeecdTJ06FSaTyeO0SMd0lMuXL9dpzf4uPT0dH374If71r3/BbP7ve2wdO3bE1KlTMX36dLRt2xYA8D//8z/o3Lkz/vKXv2DNmjWYPHmyr8r2S3r7DvuYHPLy8vDcc89hw4YNCAwMxMaNG519qby8HImJiRgxYgTuv/9+523GjRuH6OhoPPfcc/j+++99Vbrh6X18q6gvAVf6E/tS3bl48SJefPFFPPXUU4iIiHDZFhgYiMTERDz44IO45ZZbnOvvuusuDBw4ELNnz8aaNWvquGJj2bNnDxITE7F37160bdsWH3/8Mcxmc42flxz7si95R0U5Xa24uBizZ8/Gu+++CwB499130b9/fwBXPnL22GOPoXfv3nj00Uedt3nkkUcQFRWFp59+Gvfff7/bzA2qmopyatmyJRITE/HYY485PwIzfvx4DBgwAGPHjsVrr72G+fPnV/r7l7o/+eRKAFSrtmzZIgCI9evXi8jISHHXXXe57ZOSkuJy4QmqG3FxcSI8PFzYbDZd+1+4cEEAEI8++mgtV+a/HF9fc+1FqPT2nVtuuUV069bNbb9Tp04JAOKll16qncL9SEUZOWzcuFE0adJEABADBgyo0sUOH3roIVG/fn3dfZIqdr2crnXt49ukSZNEUFCQ2352u10EBAS4XEiRqk9PTsuXLxcAxJEjR6p07Jtuukl07ty5piX6rfLycjFr1ixhNpsFABEfH+9ygWS9zzfsS7Xrejk5fPvttyI8PFwAEFFRUSIlJUX3fcyYMUMAELm5ud4s3a/ozeladrtdhIaGiltvvVUIIcTcuXMFAHHmzBm3fTt16uRyIUWZcLq+AbVr1w7Alc/NhYaGIi8vz22f06dPAwBat25dp7X5s8LCQnz44YeIj4+HxWLRdZuGDRuiZcuWLhdpobqht++EhoYiPz/f7WIt7GN1Y+HChRg3bhxsNhuSk5Oxc+dOdOvWTfft27Vrh7KyMly4cKEWqyRPrn18Cw0NRWlpqdtFZAsLC2Gz2diX6tCyZcswcOBAdO3atUq3a9euHZ+vqslms+G+++7DggUL0KlTJ/znP//BmjVrXKbd632+YV+qPXpyAq589nvkyJGwWq34+9//jgMHDuDmm2/WfT9Xv5anqtObkycmkwm/+93vXJ6bALi9JrTb7Thz5oy0/YmDfEUdP34c99xzj8fP6+Tk5AAAoqOjMXDgQBw4cABnz5512eff//43brjhBvTu3bsuyiVcecD/7bffMGHCBJf1Qgg8/PDDmDt3rtttLly4gIKCAkRHR9dRleSgt+8MHDgQRUVFOHDggNt+AJzT8sj7tm3bhpkzZ6J9+/ZITU3F5MmT3aZKAsDy5ctxzz33eLyIZU5ODsLCwpwXwSTvqsrj28CBAwH8t+84sC/VrYMHD2L37t1uz1UOCxYswEMPPeS8wO/VcnJy+HxVTQsWLMCWLVtw2223Yffu3YiNjXXbR+/zDftS7dGT09GjRzF58mS0aNEC3377LWbMmOHxgpebN2/GPffc4/xs+NVycnJgMpnQpUuXWmmH0enJ6U9/+hOefPJJt/VCCPzyyy/XfW5KT09HSUmJvP3JxzMJqJo0TRPh4eGia9eu4vz588715eXlYvjw4aJVq1bi3LlzIj09XQAQf/vb35z7nDx5UrRo0ULcf//9vijdbw0fPlxERER43DZ+/HhRv3598dNPP7msnz17tgAgdu7cWRcl+qWKpq7q7TunT58WFotFxMXFCbvdLoQQ4ty5c6JLly4iJiambhphcBVlNHbsWAFA7N+/v9Lbf/31125ZCiHEoUOHRL169a77HbmkT0U56X18KysrE82bNxfDhg0Tly9fFkJceU4bMmSIaNWqlSgvL6+bhhjc9abrz5s3TwAQ2dnZHrcvXbpUABDLly93Wf/NN98IAOKVV17xes1GZ7PZRJs2bUSrVq3Eb7/9VuF+ep9v2Jdqh96c/vKXvwgA4tNPP630eJmZmQKAS55CXMm5efPm4o477vBa7f5Eb04zZ84UAMQ333zjsj45OVkAEBs3bhRCXJm+37NnTxEVFeUcc9ntdvHwww+LG264QRQUFNReY2qAF95TlNlsxptvvokxY8agX79+GD9+PDRNw7/+9S/89NNP2LBhA4KDg9G3b1/cfffdWLBgAXJycvC73/0OGzduhM1mc7tiLtWeixcvIiUlxeWiX1ebN28etmzZgtjYWOe7vykpKdiyZQumTp2KQYMG1XHFpLfvhIaG4oknnkBSUhJKSkrQp08ffPrpp8jJycFnn33mwxYY365du5x9JSUlxW17VFQURo0ahZEjR+Lee+/FSy+9hEOHDmHAgAE4efIkVq9ejdatW2PBggU+qN5/6H18q1evHv76179i+vTpGDVqFIYPH47/+7//w44dO7By5UrccMMNPm6Jf/j6668RGhqKDh06eNweFxeHpUuX4vHHH8f27dvRo0cPZGZmYu3atejXr5/z231IvxMnTiAvLw+DBg3CsmXLPO4zcuRIREdH63q+YV+qHXpz2rVrF2644Qbk5OTgnXfecdsnPDwcf/jDH9ClSxf88Y9/xDvvvIP8/HyMGjUKBQUFWLt2Lex2OxYvXlzbTTIkvTk9/fTTWL9+Pe655x488sgjaNeuHfbv349Nmzbh3nvvxUMPPQTgyvT92bNn46GHHsLQoUMxevRo7NmzB19++SXmzZuHFi1a1GXz9PP1uwxUM5999pm4+eabRZMmTUTLli3F7bffLnbt2uWyz4ULF8T06dNFjx49RLNmzcQ999wj0tPTfVSxf/riiy8EAPH2229XuM/BgwfFH/7wB9G6dWvRqFEjMWjQILFq1SqXd3fJ+yo7q6W375SXl4uXXnpJ3HjjjSIkJESMGjVKfP3113VRvl+oKKMGDRoIABX+TJw40bnvhQsXxJw5c0Tnzp1FYGCg6Nq1q3jqqadEUVFR3TbGwCrrS3of3+x2u3j33XdFbGysCA4OFkOGDBH//Oc/66oJfqGynIqLi4XFYrnuTL+CggIxbdo0ERkZKQIDA0XPnj3FzJkzxaVLl2qrbEPbvXt3pY9lAMTKlSuFEPqfb9iXvE9vTtHR0ZXuM2zYMOcxy8vLxZtvvim6desmGjRoINq3by/i4+N5wb0aqEp/ysnJERMnThQREREiKChI9OvXT7z22mseL8a7adMmMXLkSBESEiL69+8v3nrrLalfo5uEqOA7NoiIiIiIiIhIKbzwHhEREREREZFBcJBPREREREREZBAc5BMREREREREZBAf5RERERERERAbBQT4RERERERGRQXCQT0RERERERGQQHOQTERERERERGQQH+UREREREREQGwUE+ERERERERkUFwkE9ERERERERkEBzkExERERERERkEB/lERERERESkW05ODkwmE1JSUmq0D9UODvKJiIiIiIiIDIKDfCIiIiIiIiKD4CCfiIiIiIiIqqy4uBgPPvggmjVrhh49emD58uXV2oe8i4N8IiIiIiIiqrKEhAQ0atQISUlJ6NmzJx599FGsW7euyvt4W1ZWFho2bIiffvrJbZumadiwYQMeeOABtGvXDoGBgWjdujUGDx6MP/3pTzh27JhXasjMzITJZEJAQAAKCgoq3ffcuXOoX78+TCYTjhw5grZt29bod2QSQohq35qIiIiIiIj8Sk5ODtq3b4+7774bn332GUwmE4QQuOuuu3Dq1CkcPHhQ1z61QQiBUaNGISIiAqtXr3bZlpWVhQcffBA//vgjAKBBgwbo1asXLl26hOzsbJSWlsJsNmPGjBl4+eWXYTKZalTLgAEDsGfPHiQlJeGpp56qcL+1a9diwoQJiImJQXp6OpYvX46ZM2fiyJEjaNasWZXvl2fyiYiIiIiIqMrGjRvnHAibTCbExcXh8OHDuHz5cpX28aYPP/wQ3333HebMmeOy/tixYxg8eDB+/PFHREdHY+fOnSgpKUFqaioOHDiAM2fOICkpCfXr18crr7yCefPm1biW+Ph4AMA///nPSvf74IMPAADjx48HAEycOBFBQUGYO3dute6Xg3wiIiIiIiKqstatW7v8v02bNrDb7SgsLKzSPt705ptv4pZbbkGHDh2c64QQ+J//+R+cPXsWd955J/bu3YtBgwYhICDAuU/Dhg3x1FNP4fPPPwcALFy4EGfPnq1RLWPHjkVAQAB27NiBnJwcj/ucO3cOW7duhclkwtixYwEAN9xwAyZMmIAVK1bg3LlzVb5fDvKJiIiIiIj8xKpVq2AymbBx40akpaVh5MiRaNSoEZo0aYIxY8bAarUCAJKTkzFs2DAEBwejffv2mD59OkpLS12O5djX4dSpU6hXrx5atmxZpX28Zd++fdi5cyfi4uJc1n/99ddITU1F/fr18b//+78ICgqq8BgjRozAc889hzvvvBMHDhxw275z5048+OCD6NixIxo1aoS+ffviH//4h9vvBgBatmyJO++8EwCwceNGj/f32Wefoby8HEOHDkVERIRzfVxcHC5evIiVK1fqavvVOMgnIiIiIiLyM6mpqRgyZAiOHj2Knj174tKlS/jwww9xzz33YObMmZgyZQrOnj2LDh06ICcnB6+99hr+9re/uRxj06ZNcFziTQiBNWvWYMCAATCbzVXax1scA+l7773X4/rHHnsM7dq1u+5xFi1ahE8//RQjR450Wf/6669j6NCh+OijjxAQEIDo6GhkZGTg2WefxahRo1BcXOx2LMeU/Q0bNni8r2un6jt06dIF0dHRFd6uMhzkExERERER+Zm33noLs2bNwi+//IJdu3YhPT0d9evXx969e/H666/jyy+/xKFDh/DDDz9g2bJlAICPPvrI5Rjp6elISEjAxo0bMW7cOHz77beYPXt2lffxlm+//RadOnVymyWwY8cOAED//v2rfeyDBw9ixowZaNWqFXbs2IHMzEykpaXhxIkTuPXWW7Fr1y6P7br33nsREhKCH3/8EYcOHXLZVlJSgq1btyIgIAAPPPCA220HDhyI9PR0lJSUVKlWDvKJiIiIiIj8TP/+/fG3v/3NeUa9R48ezjPX06ZNwx133OHcNyEhAQ0bNsSJEydcjrF582acPn0aTzzxBLKysvDRRx/h1ltvrfI+3lBUVIT9+/fjpptuctuWn58PAIiKivJ4W5PJ5PHnlltuce7zwgsvwG63Y/ny5Rg8eLBzfatWrbBp0ya0aNECS5YswaVLl1yOHRgYiDFjxgBwvwDfZ599hrKyMtxxxx1o3ry5W10DBgyApmn4/vvv9f0S/r+A6+9CRERERERERjJkyBC3r4hr0aIFAGDo0KEu600mE5o3b46LFy8CACIjI51T8Lds2eLx+Hr28aZjx45BCOFxOn55eTkAwGKxeLzt7bff7vL/S5cuuQ2s9+zZg+DgYLd9AaBp06YYPnw4PvjgA6SnpyM2NtZle3x8PJYvX44NGzbgpZdecv7eK5qq7xAZGQngylf/VQUH+URERERERH6mcePGbuscg8/KtsnKcYE/T2fEW7ZsidzcXGRlZSEmJsZt+1dffeXy/x9++AF9+/Z1/v/ChQvIy8sDAJcr8nvi6Yr8sbGxaNeuHY4fP47du3dj4MCBOH/+PL766isEBQVh9OjRHo/laMu1Fy+8Hk7XJyIiIiIioiqZO3euxynuERERmDRpknOKfF0pKioCAAQHB7tt69evH4Arg3c9fvzxR5f/X758GQAQFBSE22+/vdIfT/dvNpudV/x3TNl3TNW/77770LBhQ491hISEAECVv26QZ/KJiIiIiIioWr788kvncnl5OQ4dOoSFCxfil19+wXfffVdndTRr1gwAPF6k7r777sPmzZvx7rvv4plnnrnu1/d9+umnLv9v2rQpmjdvDpvNhi+//LJasxri4+OxYMECbNq0Cf/4xz+cU/UffvjhCm9z7tw5AP9tm148k09ERERERETVcscddzh/Ro8ejeeffx7PPPMMtm3bVuWrwtdEaGgoAODXX3912zZ+/Hi0a9cOJSUlmDZtmvMz+p58/PHH+OSTT9zW9+nTB+fOncPu3bvdttntdkyZMgVjxoxBWVmZx+N27doV/fv3x5kzZ7B582Z8+eWXaNasGW677bYKa3G0xdE2vTjIJyIiIiIiIq8JCQlBw4YNERgYCAAQQuCdd95B79690ahRIwwePBjbt293uU1ZWRmmTp2KNm3aoGvXrliyZAmeeOIJTJo0Sdd9dujQASaTCb/88ovbtvr162PDhg2oV68eNm7ciMGDByM9PR2apjn3KS0txZw5czB27Fh07NjR7RgvvPACAGDSpEn46aefnOsvX76MWbNmITk52XlfFYmPjwcA/PGPf0RZWRnGjBmDevXqVbi/oy2dO3eurOluOMgnIiIiIiKiarlw4YLzp6ioCDt27EBSUhISEhKcA9iXX34ZzzzzDH7/+99j6dKlaNCgAW6//Xbs37/feZxHHnkE69evx1//+lfMmDEDr776apWuyt+kSRPExMR4PNMOAIMGDcLXX3+NVq1aIT09HTfeeCOCg4MxYMAAdOvWDY0bN8b8+fMxYsQIpKWluV3Ab+jQoXjuueeQmZmJ3r17o3v37hg2bBjCw8PxyiuvoHPnzkhKSqq0xnHjxiEgIMB5vYKKrqrvsHv3bpjNZgwZMkT37wHgIJ+IiIiIiIiqqXHjxs6fZs2aITY2Fh06dMAbb7wB4Mrnyl955RX8/e9/x/z58zF+/Hhs3boVHTp0wKJFiwAAR48exYYNG7B69WpMmzYNCQkJ2Lx5M06ePFmlWkaMGIGff/4ZBQUFHrcPGzYM2dnZWLRoEW6++WYEBgZi3759KCoqwujRo/HJJ5/gq6++QtOmTTFw4EC32y9atAhffPEF7rzzTpw/fx779u1DmzZtMH/+fKSlpaFVq1aV1teyZUvccccdAICIiAi3r9q7VmpqKm688UbnBfj0MgnHlxcSERERERER6TB37lzMmzcP//nPf5zrNE1DWloaZs6cib///e94+umnsWPHDsTGxiIrKwutW7d27jtv3jx899132Lt3L9atW4cpU6agtLQUZvN/z0NHRUVh4MCBWLVqla6a9u3bh379+mH58uVISEjwWlt9ISsrC126dMEbb7yBP//5z1W6La+uT0RERERERNVy7dnoYcOGYf/+/di2bRuefvpp59l4T58rd1zl/tSpU2jZsqXLAB+Ay5sCesTExGDw4MFYu3at8oP8devWoWHDhnjkkUeqfFsO8omIiIiIiMhrunbtig8//BDAfwfqX3/9NRo0aOBx/9atW6OgoAB2u91loH/mzBlERkZW6b6ffvppjBkzBtnZ2ejUqVP1GuBjNpsNa9asweTJk6s8VR/gZ/KJiIiIiIjIi+rVq4fTp08DAHr06IH69eujvLwcsbGxiI2Nxc0334yvvvoKO3fuBAD069cPZWVl+PLLL53HyMzMREZGRpXv+4EHHsDw4cMxf/587zTGB1avXo0LFy5gzpw51bo9z+QTERERERGR17Rq1QqFhYUoLy9HixYtMG3aNEycOBF//etfERERga+++gqrV6/Gp59+CgDo2bMn7rvvPkyYMAHz5s1Dw4YN8eqrr6JTp04wmUxVum+TyYT33nsPffv2xYwZM9CjR49aaGHt+e233zBv3jy88cYbblf414uDfCIiIiIiIvKa9u3bQ9M0LF68GM8++ywWLlyIJk2aIDk5Gb/88gu6du2K999/H7///e+dt1m3bh2mTZuGefPmoU2bNliwYAE++eQTNG3atMr336VLF1y8eNGbTaozgYGBOHHiRI2OwavrExERERERkc9omoZLly4hKCjI5TP5I0eOxKhRo/D888/7sDr18DP5RERERERE5DN5eXlo3Lgxdu3a5VxXWFiIHTt2oGfPnj6sTE08k09EREREREQ+NXz4cBQVFeG1114DAMyfPx/Hjx/H8ePHERDAT5lXBc/kExERERERkU9t2LAB0dHRGDduHMaNG4cGDRpg+/btHOBXA8/kExERERERERkEz+QTERERERERGQQH+UREREREREQGwUE+ERERERERkUFwkE9ERERERERkEBzkExERERERERkEB/lEREREREREBsFBPhEREREREZFBcJBPREREREREZBAc5BMREREREREZBAf5RERERERERAbBQT4RERERERGRQXCQT0RERERERGQQ/w/qElIsbaQcqQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fail\n",
-      "\n",
-      "Cutflow:\n",
-      "          BDTPreselection   TriggerEffs        QCD SF  \\\n",
-      "HHbbVV      3.080614e+00  1.777757e+00  1.777757e+00   \n",
-      "QCD         2.297340e+06  1.194347e+06  9.723930e+05   \n",
-      "TT          1.393617e+05  9.016284e+04  9.016284e+04   \n",
-      "ST          9.979986e+03  6.717597e+03  6.717597e+03   \n",
-      "V+Jets      6.347558e+04  3.886090e+04  3.886090e+04   \n",
-      "Diboson     9.467148e+02  5.956462e+02  5.956462e+02   \n",
-      "Data        1.108730e+06  1.108730e+06  1.108730e+06   \n",
-      "\n",
-      "         bbFatJetParticleNetMD_Txbb > 0.8  bbFatJetParticleNetMD_Txbb < 0.987  \n",
-      "HHbbVV                       1.777757e+00                        9.603467e-01  \n",
-      "QCD                          9.723930e+05                        8.930165e+05  \n",
-      "TT                           9.016284e+04                        8.370478e+04  \n",
-      "ST                           6.717597e+03                        6.254504e+03  \n",
-      "V+Jets                       3.886090e+04                        3.132061e+04  \n",
-      "Diboson                      5.956462e+02                        4.275990e+02  \n",
-      "Data                         1.108730e+06                        1.024006e+06  \n",
-      "Trigger SF Unc.: 0.006190808302948323\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "({'pass': Hist(\n",
-       "    StrCategory(['HHbbVV', 'Data', 'ST', 'QCD', 'TT', 'Diboson', 'V+Jets', 'HHbbVV_txbb_down', 'HHbbVV_txbb_up'], name='Sample'),\n",
-       "    Regular(20, 50, 250, name='bbFatJetMsd', label='$m^{bb}_{Reg}$ (GeV)'),\n",
-       "    storage=Weight()) # Sum: WeightedSum(value=130.471, variance=178.899) (WeightedSum(value=158.637, variance=213.229) with flow),\n",
-       "  'fail': Hist(\n",
-       "    StrCategory(['HHbbVV', 'Data', 'ST', 'QCD', 'TT', 'Diboson', 'V+Jets'], name='Sample'),\n",
-       "    Regular(20, 50, 250, name='bbFatJetMsd', label='$m^{bb}_{Reg}$ (GeV)'),\n",
-       "    storage=Weight()) # Sum: WeightedSum(value=1.63673e+06, variance=1.82098e+07) (WeightedSum(value=2.03873e+06, variance=2.23626e+07) with flow)},\n",
-       " {'pass': {'trig_unc': 0.02519890558032111},\n",
-       "  'fail': {'trig_unc': 0.006190808302948323}})"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "postprocessing.get_templates(\n",
+    "h, tsysts = postprocessing.get_templates(\n",
     "    events_dict,\n",
     "    bb_masks,\n",
     "    year,\n",
-    "    selection_regions[year],\n",
-    "    shape_var,\n",
-    "    shape_bins,\n",
-    "    blind_window,\n",
-    "    plot_dir=plot_dir,\n",
+    "    nonres_sig_keys,\n",
+    "    selection_regions,\n",
+    "    nonres_shape_vars,\n",
+    "    systematics,\n",
+    "    bg_keys=bg_keys,\n",
+    "    plot_dir=f\"{plot_dir}/templates\",\n",
     "    prev_cutflow=cutflow,\n",
     "    weight_shifts={},\n",
     "    jshift=\"\",\n",
+    "    plot_shifts=False,\n",
     "    show=True,\n",
     ")"
    ]
@@ -618,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -628,6 +412,13 @@
     "    with open(f\"templates/Feb28/{year}_templates.pkl\", \"rb\") as f:\n",
     "        templates_dict[year] = pickle.load(f)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -646,7 +437,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.11"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/postprocessing/PostProcessRes.ipynb b/src/HHbbVV/postprocessing/PostProcessRes.ipynb
new file mode 100644
index 00000000..7852c750
--- /dev/null
+++ b/src/HHbbVV/postprocessing/PostProcessRes.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import utils\n",
+    "import plotting\n",
+    "import postprocessing\n",
+    "import corrections\n",
+    "\n",
+    "from utils import CUT_MAX_VAL\n",
+    "from hh_vars import (\n",
+    "    years,\n",
+    "    data_key,\n",
+    "    qcd_key,\n",
+    "    bg_keys,\n",
+    "    samples,\n",
+    "    nonres_sig_keys,\n",
+    "    # res_samples,\n",
+    "    # res_sig_keys,\n",
+    "    nonres_samples,\n",
+    "    txbb_wps,\n",
+    "    jec_shifts,\n",
+    "    jmsr_shifts,\n",
+    ")\n",
+    "from postprocessing import res_shape_vars\n",
+    "\n",
+    "from collections import OrderedDict\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle, json\n",
+    "from pandas.errors import SettingWithCopyWarning\n",
+    "from hist import Hist\n",
+    "\n",
+    "import os\n",
+    "from copy import deepcopy\n",
+    "from inspect import cleandoc\n",
+    "import warnings\n",
+    "\n",
+    "# ignore these because they don't seem to apply\n",
+    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_samples = OrderedDict()\n",
+    "\n",
+    "res_mps = [\n",
+    "    (1000, 125),\n",
+    "    # (1400, 125),\n",
+    "    (1400, 150),\n",
+    "    # (1800, 125),\n",
+    "    # (1800, 150),\n",
+    "    (1800, 190),\n",
+    "    (2200, 125),\n",
+    "    # (2200, 150),\n",
+    "    # (2200, 190),\n",
+    "    # (2200, 250),\n",
+    "    # (3000, 125),\n",
+    "    # (3000, 150),\n",
+    "    # (3000, 190),\n",
+    "    (3000, 250),\n",
+    "    # (3000, 350),\n",
+    "]\n",
+    "\n",
+    "for mX, mY in res_mps:\n",
+    "    res_samples[\n",
+    "        f\"X[{mX}]->H(bb)Y[{mY}](VV)\"\n",
+    "    ] = f\"NMSSM_XToYH_MX{mX}_MY{mY}_HTo2bYTo2W_hadronicDecay\"\n",
+    "\n",
+    "res_sig_keys = list(res_samples.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# del nonres_samples[\"VBFHHbbVV\"]\n",
+    "samples = nonres_samples | samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_split_points = [\n",
+    "    [\n",
+    "        (1000, 125),\n",
+    "        (1400, 125),\n",
+    "        (1800, 125),\n",
+    "        (2200, 125),\n",
+    "        (3000, 125),\n",
+    "    ],\n",
+    "    [\n",
+    "        (1400, 150),\n",
+    "        (1800, 150),\n",
+    "        (1800, 190),\n",
+    "        (2200, 150),\n",
+    "        (2200, 190),\n",
+    "        (2200, 250),\n",
+    "    ],\n",
+    "    [\n",
+    "        (3000, 125),\n",
+    "        (3000, 150),\n",
+    "        (3000, 190),\n",
+    "        (3000, 250),\n",
+    "        (3000, 350),\n",
+    "    ],\n",
+    "]\n",
+    "\n",
+    "sig_splits = [\n",
+    "    [\"HHbbVV\", \"VBFHHbbVV\"] + [f\"X[{mX}]->H(bb)Y[{mY}](VV)\" for (mX, mY) in mps]\n",
+    "    for mps in sig_split_points\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sig_splits = [res_sig_keys]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../\"\n",
+    "samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
+    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Mar10_2\"\n",
+    "year = \"2017\"\n",
+    "\n",
+    "date = \"23Aug21\"\n",
+    "plot_dir = f\"../../../plots/PostProcessing/{date}/\"\n",
+    "templates_dir = f\"templates/{date}/\"\n",
+    "for i in range(len(sig_splits)):\n",
+    "    _ = os.system(f\"mkdir -p {plot_dir}/ControlPlots/{year}/sigs{i}/\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/cutflows/\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/wshifts\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/jshifts\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/hists2d\")\n",
+    "_ = os.system(f\"mkdir -p {templates_dir}\")\n",
+    "\n",
+    "selection_regions = postprocessing.get_res_selection_regions(year)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Both Jet's Regressed Mass above 50, electron veto\n",
+    "filters = [\n",
+    "    [\n",
+    "        (\"('ak8FatJetParticleNetMass', '0')\", \">=\", 50),\n",
+    "        (\"('ak8FatJetParticleNetMass', '1')\", \">=\", 50),\n",
+    "        # (\"('nGoodElectrons', '0')\", \"==\", 0),\n",
+    "    ],\n",
+    "]\n",
+    "systematics = {year: {}}\n",
+    "\n",
+    "# save cutflow as pandas table\n",
+    "cutflow = pd.DataFrame(index=list(samples.keys()) + list(res_samples.keys()))\n",
+    "\n",
+    "# utils.remove_empty_parquets(samples_dir, year)\n",
+    "events_dict = utils.load_samples(signal_samples_dir, res_samples, year, filters)\n",
+    "events_dict |= utils.load_samples(samples_dir, samples, year, filters)\n",
+    "\n",
+    "utils.add_to_cutflow(events_dict, \"Preselection\", \"weight\", cutflow)\n",
+    "\n",
+    "print(\"\")\n",
+    "# print weighted sample yields\n",
+    "for sample in events_dict:\n",
+    "    tot_weight = np.sum(events_dict[sample][\"weight\"].values)\n",
+    "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "higgs_samples = OrderedDict(\n",
+    "    [\n",
+    "        (\"Hbb\", \"*HToBB\"),\n",
+    "        # (\"HWW\", (\"*HToWW\", \"*HToNonbb\")),\n",
+    "        # (\"HH\", (\"VBF_HHTobbVV_CV_1_C2V_1_C3_1\", \"GluGluToHHTo4B_node_cHHH1_preUL\")),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "events_dict |= utils.load_samples(samples_dir, higgs_samples, year, filters)\n",
+    "\n",
+    "cutflow = pd.DataFrame(\n",
+    "    index=list(samples.keys()) + list(res_samples.keys()) + list(higgs_samples.keys())\n",
+    ")\n",
+    "utils.add_to_cutflow(events_dict, \"Preselection\", \"weight\", cutflow)\n",
+    "\n",
+    "print(\"\")\n",
+    "# print weighted sample yields\n",
+    "for sample in events_dict:\n",
+    "    tot_weight = np.sum(events_dict[sample][\"weight\"].values)\n",
+    "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "bins = np.arange(-20, 61, 5)\n",
+    "plt.hist(events_dict[\"ST\"][\"weight\"], bins, histtype=\"step\", label=\"ST\")\n",
+    "plt.hist(events_dict[\"TT\"][\"weight\"], bins, histtype=\"step\", label=\"TT\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.ylabel(\"# Events\")\n",
+    "plt.xlabel(\"Weights\")\n",
+    "plt.legend()\n",
+    "plt.savefig(f\"{plot_dir}/sttt_weights.pdf\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scale factors and bb VV assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "postprocessing.apply_weights(events_dict, year, cutflow)\n",
+    "bb_masks = postprocessing.bb_VV_assignment(events_dict)\n",
+    "cutflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sample, events in events_dict.items():\n",
+    "    if \"ak8FatJetParTMD_THWWvsT\" not in events:\n",
+    "        h4qvst = (events[\"ak8FatJetParTMD_probHWW3q\"] + events[\"ak8FatJetParTMD_probHWW4q\"]) / (\n",
+    "            events[\"ak8FatJetParTMD_probHWW3q\"]\n",
+    "            + events[\"ak8FatJetParTMD_probHWW4q\"]\n",
+    "            + events[\"ak8FatJetParTMD_probQCD\"]\n",
+    "            + events[\"ak8FatJetParTMD_probT\"]\n",
+    "        )\n",
+    "\n",
+    "        events_dict[sample] = pd.concat(\n",
+    "            [events, pd.concat([h4qvst], axis=1, keys=[\"ak8FatJetParTMD_THWWvsT\"])], axis=1\n",
+    "        )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Control Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# {var: (bins, label)}\n",
+    "control_plot_vars = {\n",
+    "    # \"MET_pt\": ([50, 0, 300], r\"$p^{miss}_T$ (GeV)\"),\n",
+    "    # \"DijetEta\": ([50, -8, 8], r\"$\\eta^{jj}$\"),\n",
+    "    # \"DijetPt\": ([50, 0, 750], r\"$p_T^{jj}$ (GeV)\"),\n",
+    "    # \"DijetMass\": (\n",
+    "    #     # list(range(800, 1400, 100)) + [1400, 1600, 2000, 3000, 4400],\n",
+    "    #     [40, 600, 4500],\n",
+    "    #     r\"$m^{jj}$ (GeV)\",\n",
+    "    # ),\n",
+    "    # \"bbFatJetEta\": ([50, -2.4, 2.4], r\"$\\eta^{bb}$\"),\n",
+    "    # \"bbFatJetPt\": ([50, 300, 1500], r\"$p^{bb}_T$ (GeV)\"),\n",
+    "    # \"bbFatJetParticleNetMass\": ([40, 52.5, 252.5], r\"$m^{bb}_{reg}$ (GeV)\"),\n",
+    "    # \"bbFatJetMsd\": ([50, 0, 300], r\"$m^{bb}_{msd}$ (GeV)\"),\n",
+    "    # \"bbFatJetParticleNetMD_Txbb\": ([50, 0.8, 1], r\"$T^{bb}_{Xbb}$\"),\n",
+    "    # \"VVFatJetEta\": ([50, -2.4, 2.4], r\"$\\eta^{VV}$\"),\n",
+    "    # \"VVFatJetPt\": ([50, 300, 1500], r\"$p^{VV}_T$ (GeV)\"),\n",
+    "    # \"VVFatJetParticleNetMass\": (\n",
+    "    #     # list(range(50, 110, 10)) + list(range(110, 200, 15)) + [200, 220, 250],\n",
+    "    #     [20, 50, 250],\n",
+    "    #     r\"$m^{VV}_{reg}$ (GeV)\",\n",
+    "    # ),\n",
+    "    # \"VVFatJetMsd\": ([40, 50, 250], r\"$m^{VV}_{msd}$ (GeV)\"),\n",
+    "    # \"VVFatJetParticleNet_Th4q\": ([50, 0, 1], r\"Prob($H \\to 4q$) vs Prob(QCD) (Non-MD)\"),\n",
+    "    # \"VVFatJetParTMD_THWW4q\": (\n",
+    "    #     [50, 0, 1],\n",
+    "    #     r\"Prob($H \\to VV \\to 4q$) vs Prob(QCD) (Mass-Decorrelated)\",\n",
+    "    # ),\n",
+    "    # \"VVFatJetParTMD_probT\": ([50, 0, 1], r\"Prob(Top) (Mass-Decorrelated)\"),\n",
+    "    # \"VVFatJetParTMD_THWWvsT\": (\n",
+    "    #     [50, 0, 1],\n",
+    "    #     r\"$T^{VV}_{HWW}$\",\n",
+    "    # ),\n",
+    "    # \"bbFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{bb}_T / p_T^{jj}$\"),\n",
+    "    # \"VVFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{VV}_T / p_T^{jj}$\"),\n",
+    "    # \"VVFatJetPtOverbbFatJetPt\": ([50, 0.4, 2.0], r\"$p^{VV}_T / p^{bb}_T$\"),\n",
+    "    \"nGoodMuons\": ([3, 0, 3], r\"# of Muons\"),\n",
+    "    \"nGoodElectrons\": ([3, 0, 3], r\"# of Electrons\"),\n",
+    "    \"nGoodJets\": ([5, 0, 5], r\"# of AK4 B-Jets\"),\n",
+    "}\n",
+    "\n",
+    "# hists = postprocessing.control_plots(\n",
+    "#     events_dict,\n",
+    "#     bb_masks,\n",
+    "#     nonres_sig_keys + res_sig_keys,\n",
+    "#     control_plot_vars,\n",
+    "#     f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "#     year,\n",
+    "#     sig_splits=sig_splits,\n",
+    "#     # bg_keys=bg_keys + list(higgs_samples.keys()),\n",
+    "#     bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "#     show=True,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection, _ = utils.make_selection(\n",
+    "    {\n",
+    "        \"VVFatJetParTMD_THWWvsT\": [0.8, CUT_MAX_VAL],\n",
+    "        \"bbFatJetParticleNetMD_Txbb\": [0.98, CUT_MAX_VAL],\n",
+    "        \"bbFatJetParticleNetMass\": [110, 145],\n",
+    "    },\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    ")\n",
+    "cutstr = f\"pass_noveto\"\n",
+    "\n",
+    "postprocessing.control_plots(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    nonres_sig_keys + res_sig_keys,\n",
+    "    control_plot_vars,\n",
+    "    f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "    year,\n",
+    "    sig_splits=sig_splits[:1],\n",
+    "    hists={},\n",
+    "    # bg_keys=bg_keys + list(higgs_samples.keys()),\n",
+    "    # bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "    bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\"],\n",
+    "    sig_scale_dict={key: 10 for key in nonres_sig_keys + res_sig_keys},\n",
+    "    selection=selection,\n",
+    "    cutstr=cutstr,\n",
+    "    show=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sb1, sb2 in [[0, 300], [75, 180], [92.5, 162.5]]:\n",
+    "    selection, _ = utils.make_selection(\n",
+    "        {\n",
+    "            \"VVFatJetParTMD_THWWvsT\": [0.8, CUT_MAX_VAL],\n",
+    "            \"bbFatJetParticleNetMD_Txbb\": [0.98, CUT_MAX_VAL],\n",
+    "            \"bbFatJetParticleNetMass\": [[sb1, 110], [145, sb2]],\n",
+    "        },\n",
+    "        events_dict,\n",
+    "        bb_masks,\n",
+    "    )\n",
+    "    cutstr = f\"sidebands_{sb1}_{sb2}\"\n",
+    "\n",
+    "    postprocessing.control_plots(\n",
+    "        events_dict,\n",
+    "        bb_masks,\n",
+    "        nonres_sig_keys + res_sig_keys,\n",
+    "        control_plot_vars,\n",
+    "        f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "        year,\n",
+    "        sig_splits=sig_splits,\n",
+    "        hists={},\n",
+    "        # bg_keys=bg_keys + list(higgs_samples.keys()),\n",
+    "        bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "        selection=selection,\n",
+    "        cutstr=cutstr,\n",
+    "        show=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall LP SF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "sel, cf = utils.make_selection(\n",
+    "    selection_regions[\"lpsf\"].cuts, events_dict, bb_masks, prev_cutflow=cutflow\n",
+    ")\n",
+    "\n",
+    "sf_table = OrderedDict()\n",
+    "\n",
+    "for sig_key in tqdm(res_sig_keys):\n",
+    "    systematics[sig_key] = {}\n",
+    "    # calculate only for current year\n",
+    "    events_dict[sig_key] = postprocessing.postprocess_lpsfs(events_dict[sig_key])\n",
+    "    lp_sf, unc, uncs = postprocessing.get_lpsf(events_dict[sig_key], sel[sig_key])\n",
+    "    # print(f\"BDT LP Scale Factor for {sig_key}: {lp_sf:.2f} ± {unc:.2f}\")\n",
+    "    # print(uncs)\n",
+    "\n",
+    "    systematics[sig_key][\"lp_sf\"] = lp_sf\n",
+    "    systematics[sig_key][\"lp_sf_unc\"] = unc / lp_sf\n",
+    "\n",
+    "    sf_table[sig_key] = {\"SF\": f\"{lp_sf:.2f} ± {unc:.2f}\", **uncs}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_df = pd.DataFrame(index=nonres_sig_keys + res_sig_keys)\n",
+    "\n",
+    "for key in sf_table[sig_key]:\n",
+    "    sf_df[key] = [sf_table[skey][key] for skey in nonres_sig_keys + res_sig_keys]\n",
+    "\n",
+    "sf_df.to_clipboard()\n",
+    "sf_df"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Templates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection_regions = postprocessing.get_res_selection_regions(\"2017\", txbb_wp=\"HP\", thww_wp=0.8)\n",
+    "del selection_regions[\"fail\"], selection_regions[\"failBlinded\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = postprocessing.get_templates(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    year,\n",
+    "    # nonres_sig_keys + res_sig_keys[:10],\n",
+    "    res_sig_keys,\n",
+    "    selection_regions,\n",
+    "    res_shape_vars[:1],\n",
+    "    systematics,\n",
+    "    templates_dir,\n",
+    "    bg_keys=[\"QCD\", \"TT\", \"V+Jets\", \"Diboson\", \"Hbb\"],\n",
+    "    plot_dir=f\"{plot_dir}/templates/\",\n",
+    "    prev_cutflow=cutflow,\n",
+    "    # sig_splits=sig_splits[:2],\n",
+    "    weight_shifts={},\n",
+    "    jshift=\"\",\n",
+    "    plot_shifts=False,\n",
+    "    pass_ylim=70,\n",
+    "    fail_ylim=40000,\n",
+    "    blind_pass=True,\n",
+    "    show=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_templates(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    args.year,\n",
+    "    sig_keys,\n",
+    "    selection_regions,\n",
+    "    shape_vars,\n",
+    "    systematics,\n",
+    "    template_dir,\n",
+    "    bg_keys=bg_keys,\n",
+    "    plot_dir=plot_dir,\n",
+    "    prev_cutflow=cutflow,\n",
+    "    # sig_splits=sig_splits,\n",
+    "    weight_shifts=weight_shifts,\n",
+    "    jshift=jshift,\n",
+    "    blind_pass=True if args.resonant else False,\n",
+    "    show=False,\n",
+    "    plot_shifts=args.plot_shifts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates = {}\n",
+    "\n",
+    "for jshift in [\"\"] + jec_shifts + jmsr_shifts:\n",
+    "    print(jshift)\n",
+    "    ttemps, tsyst = postprocessing.get_templates(\n",
+    "        events_dict,\n",
+    "        bb_masks,\n",
+    "        year,\n",
+    "        nonres_sig_keys + res_sig_keys,\n",
+    "        res_selection_regions[year],\n",
+    "        res_shape_vars,\n",
+    "        bg_keys=[\"QCD\", \"TT\", \"V+Jets\"],\n",
+    "        plot_dir=plot_dir if jshift == \"\" else \"\",\n",
+    "        prev_cutflow=cutflow,\n",
+    "        sig_splits=sig_splits,\n",
+    "        weight_shifts=postprocessing.weight_shifts,\n",
+    "        jshift=jshift,\n",
+    "        pass_ylim=7,\n",
+    "        fail_ylim=40000,\n",
+    "        blind_pass=True,\n",
+    "        show=False,\n",
+    "        plot_shifts=False,\n",
+    "    )\n",
+    "\n",
+    "    templates = {**templates, **ttemps}\n",
+    "    if jshift == \"\":\n",
+    "        systematics[year] = tsyst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{templates_dir}/{year}_templates.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(templates, f)\n",
+    "\n",
+    "with open(f\"{templates_dir}/systematics.json\", \"w\") as f:\n",
+    "    json.dump(systematics, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"templates/Apr10//2017_templates.pkl\", \"rb\") as f:\n",
+    "    templates = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(templates[\"pass\"].axes[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotting.hist2ds(\n",
+    "    templates,\n",
+    "    f\"{plot_dir}/templates/hists2d/\",\n",
+    "    regions=[\"pass\", \"fail\", \"passBlinded\", \"failBlinded\"],\n",
+    "    region_labels=selection_regions_label,\n",
+    "    samples=[\"Data\", \"TT\", \"V+Jets\", \"X[3000]->H(bb)Y[190](VV)\"],\n",
+    "    # fail_zlim=5e3,\n",
+    "    # pass_zlim=1.0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "systematics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates_dict = {}\n",
+    "\n",
+    "for year in years:\n",
+    "    with open(f\"templates/{date}/{year}_templates.pkl\", \"rb\") as f:\n",
+    "        templates_dict[year] = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates = []\n",
+    "for year in years:\n",
+    "    with open(f\"templates/Apr7//{year}_templates.pkl\", \"rb\") as f:\n",
+    "        templates.append(pickle.load(f))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "15adc7883e707560d0d9727709639b8fe3f3cff1f197d2d643742923ff23a29c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/postprocessing/PostProcessResNew.ipynb b/src/HHbbVV/postprocessing/PostProcessResNew.ipynb
new file mode 100644
index 00000000..4d6ce5c8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/PostProcessResNew.ipynb
@@ -0,0 +1,806 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import utils\n",
+    "import plotting\n",
+    "import postprocessing\n",
+    "import corrections\n",
+    "\n",
+    "from utils import CUT_MAX_VAL\n",
+    "from hh_vars import (\n",
+    "    years,\n",
+    "    data_key,\n",
+    "    qcd_key,\n",
+    "    bg_keys,\n",
+    "    samples,\n",
+    "    nonres_sig_keys,\n",
+    "    # res_samples,\n",
+    "    # res_sig_keys,\n",
+    "    nonres_samples,\n",
+    "    txbb_wps,\n",
+    "    jec_shifts,\n",
+    "    jmsr_shifts,\n",
+    "    LUMI,\n",
+    ")\n",
+    "from postprocessing import res_shape_vars, new_filters, old_filters\n",
+    "\n",
+    "from collections import OrderedDict\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle, json\n",
+    "import hist\n",
+    "from hist import Hist\n",
+    "\n",
+    "import os\n",
+    "from copy import deepcopy\n",
+    "from inspect import cleandoc\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_samples = OrderedDict()\n",
+    "\n",
+    "res_mps = [(900, 80), (1200, 190), (2000, 125), (3000, 250), (4000, 150)]\n",
+    "\n",
+    "for mX, mY in res_mps:\n",
+    "    res_samples[f\"X[{mX}]->H(bb)Y[{mY}](VV)\"] = f\"NMSSM_XToYHTo2W2BTo4Q2B_MX-{mX}_MY-{mY}\"\n",
+    "\n",
+    "res_sig_keys = list(res_samples.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# del nonres_samples[\"VBFHHbbVV\"]\n",
+    "nonres_sig_keys = [\"HHbbVV\", \"VBFHHbbVV\"]\n",
+    "nonres_samples = {key: nonres_samples[key] for key in nonres_sig_keys}\n",
+    "\n",
+    "bg_keys = [\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Diboson\"]\n",
+    "samples = {key: samples[key] for key in [\"Data\"] + bg_keys}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../\"\n",
+    "samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
+    "nonres_signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Jun10\"\n",
+    "res_signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/23Aug22_5xhy\"\n",
+    "# samples_dir = \"/eos/uscms/store/user/rkansal/bbVV/skimmer/Feb24\"\n",
+    "# nonres_signal_samples_dir = \"/eos/uscms/store/user/cmantill/bbVV/skimmer/Jun10/\"\n",
+    "# res_signal_samples_dir = \"/eos/uscms/store/user/rkansal/bbVV/skimmer/Apr11/\"\n",
+    "year = \"2018\"\n",
+    "\n",
+    "date = \"23Aug23\"\n",
+    "plot_dir = f\"../../../plots/PostProcessing/{date}/\"\n",
+    "templates_dir = f\"templates/{date}/\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/ControlPlots/{year}\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/cutflows\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/wshifts\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/jshifts\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/hists2d\")\n",
+    "_ = os.system(f\"mkdir -p {templates_dir}\")\n",
+    "\n",
+    "selection_regions = postprocessing.get_res_selection_regions(year)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removing 1430 events\n",
+      "Loaded NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80              : 144441 entries\n",
+      "Removing 1873 events\n",
+      "Loaded NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190            : 210988 entries\n",
+      "Removing 2717 events\n",
+      "Loaded NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125            : 292440 entries\n",
+      "Removing 2795 events\n",
+      "Loaded NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250            : 321055 entries\n",
+      "Removing 2673 events\n",
+      "Loaded NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150            : 327318 entries\n",
+      "Removing 2014 events\n",
+      "Loaded GluGluToHHTobbVV_node_cHHH1                       : 188941 entries\n",
+      "Removing 277 events\n",
+      "Loaded VBF_HHTobbVV_CV_1_C2V_1_C3_1                      : 17539 entries\n",
+      "Loaded JetHT_Run2018A                                    : 376541 entries\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raghav/Documents/CERN/hhbbww/HHbbVV/src/HHbbVV/postprocessing/utils.py:151: UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the bool dtype, use '&' instead.\n",
+      "  hem_cut = np.any((events[\"ak8FatJetEta\"] > -3.2) * (events[\"ak8FatJetEta\"] <-1.3) * (events[\"ak8FatJetPhi\"] > -1.57) * (events[\"ak8FatJetPhi\"] <-0.87 ), axis=1)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removing 13922 events\n",
+      "Loaded JetHT_Run2018D                                    : 833289 entries\n",
+      "Removing 2895 events\n",
+      "Loaded JetHT_Run2018C                                    : 171794 entries\n",
+      "Loaded JetHT_Run2018B                                    : 175146 entries\n",
+      "Removing 0 events\n",
+      "Loaded QCD_HT300to500                                    : 23 entries\n",
+      "Removing 0 events\n",
+      "Loaded QCD_HT200to300                                    : 0 entries\n",
+      "Removing 3351 events\n",
+      "Loaded QCD_HT700to1000                                   : 197372 entries\n",
+      "Removing 1762 events\n",
+      "Loaded QCD_HT1000to1500                                  : 122855 entries\n",
+      "Removing 455 events\n",
+      "Loaded QCD_HT2000toInf                                   : 73168 entries\n",
+      "Removing 1390 events\n",
+      "Loaded QCD_HT1500to2000                                  : 134020 entries\n",
+      "Removing 340 events\n",
+      "Loaded QCD_HT500to700                                    : 18460 entries\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raghav/Documents/CERN/hhbbww/HHbbVV/src/HHbbVV/postprocessing/utils.py:163: UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the bool dtype, use '&' instead.\n",
+      "  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removing 7609 events\n",
+      "Loaded TTToSemiLeptonic                                  : 557747 entries\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/raghav/Documents/CERN/hhbbww/HHbbVV/src/HHbbVV/postprocessing/utils.py:163: UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the bool dtype, use '&' instead.\n",
+      "  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removing 10387 events\n",
+      "Loaded TTToHadronic                                      : 749409 entries\n",
+      "Removing 290 events\n",
+      "Loaded ST_tW_top_5f_inclusiveDecays                      : 26038 entries\n",
+      "Removing 197 events\n",
+      "Loaded ST_tW_antitop_5f_inclusiveDecays                  : 19168 entries\n",
+      "Removing 277 events\n",
+      "Loaded ST_s-channel_4f_leptonDecays                      : 19853 entries\n",
+      "Removing 643 events\n",
+      "Loaded ST_t-channel_antitop_4f_InclusiveDecays           : 42693 entries\n",
+      "Removing 0 events\n",
+      "Loaded WJetsToQQ_HT-200to400                             : 0 entries\n",
+      "Removing 0 events\n",
+      "Loaded ZJetsToQQ_HT-200to400                             : 0 entries\n",
+      "Removing 21 events\n",
+      "Loaded ZJetsToQQ_HT-400to600                             : 685 entries\n",
+      "Removing 2276 events\n",
+      "Loaded WJetsToQQ_HT-800toInf                             : 133078 entries\n",
+      "Removing 1559 events\n",
+      "Loaded ZJetsToQQ_HT-600to800                             : 75028 entries\n",
+      "Removing 563 events\n",
+      "Loaded WJetsToQQ_HT-600to800                             : 25485 entries\n",
+      "Removing 4971 events\n",
+      "Loaded ZJetsToQQ_HT-800toInf                             : 294481 entries\n",
+      "Removing 6 events\n",
+      "Loaded WJetsToQQ_HT-400to600                             : 213 entries\n",
+      "Removing 13 events\n",
+      "Loaded WW                                                : 583 entries\n",
+      "Removing 17 events\n",
+      "Loaded ZZ                                                : 949 entries\n",
+      "Removing 42 events\n",
+      "Loaded WZ                                                : 2247 entries\n",
+      "\n",
+      "Pre-selection X[900]->H(bb)Y[80](VV) yield: 21.71\n",
+      "Pre-selection X[1200]->H(bb)Y[190](VV) yield: 33.48\n",
+      "Pre-selection X[2000]->H(bb)Y[125](VV) yield: 43.99\n",
+      "Pre-selection X[3000]->H(bb)Y[250](VV) yield: 48.29\n",
+      "Pre-selection X[4000]->H(bb)Y[150](VV) yield: 49.21\n",
+      "Pre-selection HHbbVV yield: 4.17\n",
+      "Pre-selection VBFHHbbVV yield: 0.08\n",
+      "Pre-selection Data yield: 1556770.00\n",
+      "Pre-selection QCD yield: 3003204.29\n",
+      "Pre-selection TT yield: 214598.05\n",
+      "Pre-selection ST yield: 15007.66\n",
+      "Pre-selection V+Jets yield: 84306.66\n",
+      "Pre-selection Diboson yield: 1339.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "systematics = {year: {}}\n",
+    "\n",
+    "# save cutflow as pandas table\n",
+    "cutflow = pd.DataFrame(index=list(samples.keys()) + list(res_samples.keys()))\n",
+    "\n",
+    "# utils.remove_empty_parquets(samples_dir, year)\n",
+    "events_dict = utils.load_samples(res_signal_samples_dir, res_samples, year, new_filters)\n",
+    "events_dict |= utils.load_samples(nonres_signal_samples_dir, nonres_samples, year, new_filters)\n",
+    "events_dict |= utils.load_samples(samples_dir, samples, year, new_filters)\n",
+    "\n",
+    "utils.add_to_cutflow(events_dict, \"Preselection\", \"weight\", cutflow)\n",
+    "\n",
+    "print(\"\")\n",
+    "# print weighted sample yields\n",
+    "for sample in events_dict:\n",
+    "    tot_weight = np.sum(events_dict[sample][\"weight\"].values)\n",
+    "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "higgs_samples = OrderedDict(\n",
+    "    [\n",
+    "        (\"Hbb\", \"*HToBB\"),\n",
+    "        # (\"HWW\", (\"*HToWW\", \"*HToNonbb\")),\n",
+    "        # (\"HH\", (\"VBF_HHTobbVV_CV_1_C2V_1_C3_1\", \"GluGluToHHTo4B_node_cHHH1_preUL\")),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "events_dict |= utils.load_samples(samples_dir, higgs_samples, year, filters)\n",
+    "\n",
+    "cutflow = pd.DataFrame(\n",
+    "    index=list(samples.keys()) + list(res_samples.keys()) + list(higgs_samples.keys())\n",
+    ")\n",
+    "utils.add_to_cutflow(events_dict, \"Preselection\", \"weight\", cutflow)\n",
+    "\n",
+    "print(\"\")\n",
+    "# print weighted sample yields\n",
+    "for sample in events_dict:\n",
+    "    tot_weight = np.sum(events_dict[sample][\"weight\"].values)\n",
+    "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scale factors and bb VV assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "postprocessing.apply_weights(events_dict, year, cutflow)\n",
+    "bb_masks = postprocessing.bb_VV_assignment(events_dict)\n",
+    "# postprocessing.derive_variables(events_dict)\n",
+    "cutflow"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Control Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = list(events_dict.keys())\n",
+    "weight_key = \"finalWeight\"\n",
+    "\n",
+    "control_plot_2d_vars = [\n",
+    "    {\n",
+    "        f\"{jet}FatJetPhi\": ([40, -3.5, 3.5], rf\"$\\varphi^{{{jet}}}$\"),\n",
+    "        f\"{jet}FatJetEta\": ([40, -3, 3], rf\"$\\eta^{{{jet}}}$\"),\n",
+    "    }\n",
+    "    for jet in [\"bb\", \"VV\"]\n",
+    "]\n",
+    "\n",
+    "hists2d = []\n",
+    "\n",
+    "for vars2d in control_plot_2d_vars:\n",
+    "    h = Hist(\n",
+    "        hist.axis.StrCategory(samples, name=\"Sample\"),\n",
+    "        *[hist.axis.Regular(*bins, name=var, label=label) for var, (bins, label) in vars2d.items()],\n",
+    "        storage=hist.storage.Weight(),\n",
+    "    )\n",
+    "\n",
+    "    for sample in samples:\n",
+    "        events = events_dict[sample]\n",
+    "\n",
+    "        fill_data = {var: utils.get_feat(events, var, bb_masks[sample]) for var in vars2d}\n",
+    "        weight = events[weight_key].values.squeeze()\n",
+    "\n",
+    "        # if selection is not None:\n",
+    "        #     sel = selection[sample]\n",
+    "        #     fill_data[var] = fill_data[var][sel]\n",
+    "        #     weight = weight[sel]\n",
+    "\n",
+    "        if len(weight):\n",
+    "            h.fill(Sample=sample, **fill_data, weight=weight)\n",
+    "\n",
+    "    hists2d.append(h)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "\n",
+    "plot_keys = [\"Data\", \"QCD\", \"TT\", \"HHbbVV\", \"X[3000]->H(bb)Y[250](VV)\"]\n",
+    "\n",
+    "fig, axs = plt.subplots(\n",
+    "    len(plot_keys),\n",
+    "    2,\n",
+    "    figsize=(20, 8 * len(plot_keys)),\n",
+    "    gridspec_kw={\"wspace\": 0.25, \"hspace\": 0.25},\n",
+    ")\n",
+    "\n",
+    "for j, key in enumerate(plot_keys):\n",
+    "    for i in range(2):\n",
+    "        ax = axs[j][i]\n",
+    "        hep.hist2dplot(hists2d[i][key, ...], cmap=\"turbo\", ax=ax)\n",
+    "        hep.cms.label(\n",
+    "            \"Work in Progress\", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax\n",
+    "        )\n",
+    "        ax.set_title(key, y=1.07)\n",
+    "        ax._children[0].colorbar.set_label(\"Events\")\n",
+    "\n",
+    "plt.savefig(f\"{plot_dir}/ControlPlots/{year}/HEM2d.pdf\", bbox_inches=\"tight\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# {var: (bins, label)}\n",
+    "control_plot_vars = {\n",
+    "    \"MET_pt\": ([40, 0, 320], r\"$p^{miss}_T$ (GeV)\"),\n",
+    "    # \"DijetEta\": ([50, -8, 8], r\"$\\eta^{jj}$\"),\n",
+    "    # \"DijetPt\": ([50, 0, 750], r\"$p_T^{jj}$ (GeV)\"),\n",
+    "    # \"DijetMass\": ([50, 500, 3000], r\"$m^{jj}$ (GeV)\"),\n",
+    "    \"bbFatJetPhi\": ([40, -3.5, 3.5], r\"$\\varphi^{bb}$\"),\n",
+    "    \"bbFatJetEta\": ([40, -3, 3], r\"$\\eta^{bb}$\"),\n",
+    "    \"bbFatJetPt\": ([40, 300, 2300], r\"$p^{bb}_T$ (GeV)\"),  # TODO: increase bin widths, x max\n",
+    "    # \"bbFatJetParticleNetMass\": ([50, 0, 300], r\"$m^{bb}_{reg}$ (GeV)\"),\n",
+    "    # \"bbFatJetMsd\": ([50, 0, 300], r\"$m^{bb}_{msd}$ (GeV)\"),\n",
+    "    # \"bbFatJetParticleNetMD_Txbb\": ([50, 0.8, 1], r\"$p^{bb}_{Txbb}$\"),\n",
+    "    \"VVFatJetPhi\": ([40, -3.5, 3.5], r\"$\\varphi^{VV}$\"),\n",
+    "    \"VVFatJetEta\": ([40, -3, 3], r\"$\\eta^{VV}$\"),\n",
+    "    \"VVFatJetPt\": ([40, 300, 2300], r\"$p^{VV}_T$ (GeV)\"),\n",
+    "    # \"VVFatJetParticleNetMass\": ([50, 0, 300], r\"$m^{VV}_{reg}$ (GeV)\"),\n",
+    "    # \"VVFatJetMsd\": ([50, 0, 300], r\"$m^{VV}_{msd}$ (GeV)\"),\n",
+    "    # \"VVFatJetParticleNet_Th4q\": ([50, 0, 1], r\"Prob($H \\to 4q$) vs Prob(QCD) (Non-MD)\"),\n",
+    "    # \"VVFatJetParTMD_THWW4q\": (\n",
+    "    #     [50, 0, 1],\n",
+    "    #     r\"Prob($H \\to VV \\to 4q$) vs Prob(QCD) (Mass-Decorrelated)\",\n",
+    "    # ),\n",
+    "    # \"VVFatJetParTMD_probT\": ([50, 0, 1], r\"Prob(Top) (Mass-Decorrelated)\"),\n",
+    "    # \"bbFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{bb}_T / p_T^{jj}$\"),\n",
+    "    # \"VVFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{VV}_T / p_T^{jj}$\"),\n",
+    "    # \"VVFatJetPtOverbbFatJetPt\": ([50, 0.4, 2.0], r\"$p^{VV}_T / p^{bb}_T$\"),\n",
+    "    # \"nGoodMuons\": ([3, 0, 3], r\"# of Muons\"),\n",
+    "    # \"nGoodElectrons\": ([3, 0, 3], r\"# of Electrons\"),\n",
+    "    # \"nGoodJets\": ([5, 0, 5], r\"# of AK4 B-Jets\"),\n",
+    "    # \"BDTScore\": ([50, 0, 1], r\"BDT Score\"),\n",
+    "}\n",
+    "\n",
+    "hists = postprocessing.control_plots(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    nonres_sig_keys + res_sig_keys,\n",
+    "    control_plot_vars,\n",
+    "    f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "    year,\n",
+    "    bg_keys=bg_keys,\n",
+    "    sig_scale_dict={\"HHbbVV\": 1e5, \"VBFHHbbVV\": 2e6} | {key: 2e4 for key in res_sig_keys},\n",
+    "    # bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "    show=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection, _ = utils.make_selection(\n",
+    "    {\n",
+    "        \"VVFatJetParTMD_THWWvsT\": [0.8, CUT_MAX_VAL],\n",
+    "        \"bbFatJetParticleNetMD_Txbb\": [0.98, CUT_MAX_VAL],\n",
+    "        \"bbFatJetParticleNetMass\": [110, 145],\n",
+    "    },\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    ")\n",
+    "cutstr = f\"pass_noveto\"\n",
+    "\n",
+    "postprocessing.control_plots(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    nonres_sig_keys + res_sig_keys,\n",
+    "    control_plot_vars,\n",
+    "    f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "    year,\n",
+    "    sig_splits=sig_splits[:1],\n",
+    "    hists={},\n",
+    "    # bg_keys=bg_keys + list(higgs_samples.keys()),\n",
+    "    # bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "    bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\"],\n",
+    "    sig_scale_dict={key: 10 for key in nonres_sig_keys + res_sig_keys},\n",
+    "    selection=selection,\n",
+    "    cutstr=cutstr,\n",
+    "    show=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sb1, sb2 in [[0, 300], [75, 180], [92.5, 162.5]]:\n",
+    "    selection, _ = utils.make_selection(\n",
+    "        {\n",
+    "            \"VVFatJetParTMD_THWWvsT\": [0.8, CUT_MAX_VAL],\n",
+    "            \"bbFatJetParticleNetMD_Txbb\": [0.98, CUT_MAX_VAL],\n",
+    "            \"bbFatJetParticleNetMass\": [[sb1, 110], [145, sb2]],\n",
+    "        },\n",
+    "        events_dict,\n",
+    "        bb_masks,\n",
+    "    )\n",
+    "    cutstr = f\"sidebands_{sb1}_{sb2}\"\n",
+    "\n",
+    "    postprocessing.control_plots(\n",
+    "        events_dict,\n",
+    "        bb_masks,\n",
+    "        nonres_sig_keys + res_sig_keys,\n",
+    "        control_plot_vars,\n",
+    "        f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "        year,\n",
+    "        sig_splits=sig_splits,\n",
+    "        hists={},\n",
+    "        # bg_keys=bg_keys + list(higgs_samples.keys()),\n",
+    "        bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "        selection=selection,\n",
+    "        cutstr=cutstr,\n",
+    "        show=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall LP SF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "sel, cf = utils.make_selection(\n",
+    "    selection_regions[\"lpsf\"].cuts, events_dict, bb_masks, prev_cutflow=cutflow\n",
+    ")\n",
+    "\n",
+    "sf_table = OrderedDict()\n",
+    "\n",
+    "for sig_key in tqdm(res_sig_keys):\n",
+    "    systematics[sig_key] = {}\n",
+    "    # calculate only for current year\n",
+    "    events_dict[sig_key] = postprocessing.postprocess_lpsfs(events_dict[sig_key])\n",
+    "    lp_sf, unc, uncs = postprocessing.get_lpsf(events_dict[sig_key], sel[sig_key])\n",
+    "    # print(f\"BDT LP Scale Factor for {sig_key}: {lp_sf:.2f} ± {unc:.2f}\")\n",
+    "    # print(uncs)\n",
+    "\n",
+    "    systematics[sig_key][\"lp_sf\"] = lp_sf\n",
+    "    systematics[sig_key][\"lp_sf_unc\"] = unc / lp_sf\n",
+    "\n",
+    "    sf_table[sig_key] = {\"SF\": f\"{lp_sf:.2f} ± {unc:.2f}\", **uncs}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sf_df = pd.DataFrame(index=nonres_sig_keys + res_sig_keys)\n",
+    "\n",
+    "for key in sf_table[sig_key]:\n",
+    "    sf_df[key] = [sf_table[skey][key] for skey in nonres_sig_keys + res_sig_keys]\n",
+    "\n",
+    "sf_df.to_clipboard()\n",
+    "sf_df"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Templates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection_regions = postprocessing.get_res_selection_regions(\"2017\", txbb_wp=\"HP\", thww_wp=0.8)\n",
+    "del selection_regions[\"fail\"], selection_regions[\"failBlinded\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h = postprocessing.get_templates(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    year,\n",
+    "    # nonres_sig_keys + res_sig_keys[:10],\n",
+    "    res_sig_keys,\n",
+    "    selection_regions,\n",
+    "    res_shape_vars[:1],\n",
+    "    systematics,\n",
+    "    templates_dir,\n",
+    "    bg_keys=[\"QCD\", \"TT\", \"V+Jets\", \"Diboson\", \"Hbb\"],\n",
+    "    plot_dir=f\"{plot_dir}/templates/\",\n",
+    "    prev_cutflow=cutflow,\n",
+    "    # sig_splits=sig_splits[:2],\n",
+    "    weight_shifts={},\n",
+    "    jshift=\"\",\n",
+    "    plot_shifts=False,\n",
+    "    pass_ylim=70,\n",
+    "    fail_ylim=40000,\n",
+    "    blind_pass=True,\n",
+    "    show=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_templates(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    args.year,\n",
+    "    sig_keys,\n",
+    "    selection_regions,\n",
+    "    shape_vars,\n",
+    "    systematics,\n",
+    "    template_dir,\n",
+    "    bg_keys=bg_keys,\n",
+    "    plot_dir=plot_dir,\n",
+    "    prev_cutflow=cutflow,\n",
+    "    # sig_splits=sig_splits,\n",
+    "    weight_shifts=weight_shifts,\n",
+    "    jshift=jshift,\n",
+    "    blind_pass=True if args.resonant else False,\n",
+    "    show=False,\n",
+    "    plot_shifts=args.plot_shifts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates = {}\n",
+    "\n",
+    "for jshift in [\"\"] + jec_shifts + jmsr_shifts:\n",
+    "    print(jshift)\n",
+    "    ttemps, tsyst = postprocessing.get_templates(\n",
+    "        events_dict,\n",
+    "        bb_masks,\n",
+    "        year,\n",
+    "        nonres_sig_keys + res_sig_keys,\n",
+    "        res_selection_regions[year],\n",
+    "        res_shape_vars,\n",
+    "        bg_keys=[\"QCD\", \"TT\", \"V+Jets\"],\n",
+    "        plot_dir=plot_dir if jshift == \"\" else \"\",\n",
+    "        prev_cutflow=cutflow,\n",
+    "        sig_splits=sig_splits,\n",
+    "        weight_shifts=postprocessing.weight_shifts,\n",
+    "        jshift=jshift,\n",
+    "        pass_ylim=7,\n",
+    "        fail_ylim=40000,\n",
+    "        blind_pass=True,\n",
+    "        show=False,\n",
+    "        plot_shifts=False,\n",
+    "    )\n",
+    "\n",
+    "    templates = {**templates, **ttemps}\n",
+    "    if jshift == \"\":\n",
+    "        systematics[year] = tsyst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"{templates_dir}/{year}_templates.pkl\", \"wb\") as f:\n",
+    "    pickle.dump(templates, f)\n",
+    "\n",
+    "with open(f\"{templates_dir}/systematics.json\", \"w\") as f:\n",
+    "    json.dump(systematics, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f\"templates/Apr10//2017_templates.pkl\", \"rb\") as f:\n",
+    "    templates = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(templates[\"pass\"].axes[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotting.hist2ds(\n",
+    "    templates,\n",
+    "    f\"{plot_dir}/templates/hists2d/\",\n",
+    "    regions=[\"pass\", \"fail\", \"passBlinded\", \"failBlinded\"],\n",
+    "    region_labels=selection_regions_label,\n",
+    "    samples=[\"Data\", \"TT\", \"V+Jets\", \"X[3000]->H(bb)Y[190](VV)\"],\n",
+    "    # fail_zlim=5e3,\n",
+    "    # pass_zlim=1.0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "systematics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates_dict = {}\n",
+    "\n",
+    "for year in years:\n",
+    "    with open(f\"templates/{date}/{year}_templates.pkl\", \"rb\") as f:\n",
+    "        templates_dict[year] = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "templates = []\n",
+    "for year in years:\n",
+    "    with open(f\"templates/Apr7//{year}_templates.pkl\", \"rb\") as f:\n",
+    "        templates.append(pickle.load(f))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "15adc7883e707560d0d9727709639b8fe3f3cff1f197d2d643742923ff23a29c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/postprocessing/PostProcessVBF.ipynb b/src/HHbbVV/postprocessing/PostProcessVBF.ipynb
new file mode 100644
index 00000000..643cca60
--- /dev/null
+++ b/src/HHbbVV/postprocessing/PostProcessVBF.ipynb
@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import utils\n",
+    "import plotting\n",
+    "import postprocessing\n",
+    "import corrections\n",
+    "from collections import OrderedDict\n",
+    "\n",
+    "from utils import CUT_MAX_VAL, ShapeVar\n",
+    "from hh_vars import (\n",
+    "    years,\n",
+    "    data_key,\n",
+    "    qcd_key,\n",
+    "    bg_keys,\n",
+    "    samples,\n",
+    "    nonres_sig_keys,\n",
+    "    nonres_samples,\n",
+    "    txbb_wps,\n",
+    "    jec_shifts,\n",
+    "    jmsr_shifts,\n",
+    ")\n",
+    "from postprocessing import nonres_shape_vars, Region\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "# from pandas.errors import SettingWithCopyWarning\n",
+    "from hist import Hist\n",
+    "\n",
+    "import os\n",
+    "from copy import deepcopy\n",
+    "from inspect import cleandoc\n",
+    "import warnings\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 16})\n",
+    "\n",
+    "# ignore these because they don't seem to apply\n",
+    "# warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MAIN_DIR = \"../../../\"\n",
+    "samples_dir = f\"{MAIN_DIR}/../data/skimmer/anava/Test\"\n",
+    "# samples_dir = \"/eos/uscms/store/user/anava/bbVV/skimmer/Test/\"\n",
+    "year = \"2017\"\n",
+    "\n",
+    "date = \"23Sep2\"\n",
+    "plot_dir = f\"../../../plots/PostProcessing/{date}/\"\n",
+    "templates_dir = f\"templates/{date}\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/cutflows/\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/ControlPlots/{year}/\")\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}/templates/\")\n",
+    "# _ = os.system(f\"mkdir -p {plot_dir}/templates/wshifts\")\n",
+    "# _ = os.system(f\"mkdir -p {plot_dir}/templates/jshifts\")\n",
+    "# _ = os.system(f\"mkdir -p {templates_dir}\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filters = postprocessing.new_filters\n",
+    "systematics = {year: {}}\n",
+    "\n",
+    "# save cutflow as pandas table\n",
+    "cutflow = pd.DataFrame(index=list(samples.keys()) + list(nonres_samples.keys()))\n",
+    "\n",
+    "# utils.remove_empty_parquets(samples_dir, year)\n",
+    "events_dict = utils.load_samples(samples_dir, nonres_samples, year, filters)\n",
+    "events_dict |= utils.load_samples(samples_dir, samples, year, filters)\n",
+    "\n",
+    "utils.add_to_cutflow(events_dict, \"Preselection\", \"weight\", cutflow)\n",
+    "\n",
+    "cutflow"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Scale factors and bb VV assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "postprocessing.apply_weights(events_dict, year, cutflow)\n",
+    "bb_masks = postprocessing.bb_VV_assignment(events_dict)\n",
+    "# events_dict[sig_key] = postprocessing.postprocess_lpsfs(events_dict[sig_key])\n",
+    "cutflow"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Control plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# {var: (bins, label)}\n",
+    "control_plot_vars = [\n",
+    "    # ShapeVar(var=\"MET_pt\", label=r\"$p^{miss}_T$ (GeV)\", bins=[50, 0, 300]),\n",
+    "    # ShapeVar(var=\"DijetEta\", label=r\"$\\eta^{jj}$\", bins=[30, -8, 8]),\n",
+    "    # ShapeVar(var=\"DijetPt\", label=r\"$p_T^{jj}$ (GeV)\", bins=[30, 0, 750]),\n",
+    "    # ShapeVar(var=\"DijetMass\", label=r\"$m^{jj}$ (GeV)\", bins=[30, 600, 4000]),\n",
+    "    # ShapeVar(var=\"bbFatJetEta\", label=r\"$\\eta^{bb}$\", bins=[30, -2.4, 2.4]),\n",
+    "    ShapeVar(\n",
+    "        var=\"bbFatJetPt\", label=r\"$p^{bb}_T$ (GeV)\", bins=[30, 300, 1500], significance_dir=\"right\"\n",
+    "    ),\n",
+    "    ShapeVar(\n",
+    "        var=\"bbFatJetParticleNetMass\",\n",
+    "        label=r\"$m^{bb}_{reg}$ (GeV)\",\n",
+    "        bins=[20, 50, 250],\n",
+    "        significance_dir=\"bin\",\n",
+    "    ),\n",
+    "    # ShapeVar(var=\"bbFatJetMsd\", label=r\"$m^{bb}_{msd}$ (GeV)\", bins=[50, 0, 300]),\n",
+    "    # ShapeVar(var=\"bbFatJetParticleNetMD_Txbb\", label=r\"$T^{bb}_{Xbb}$\", bins=[50, 0.8, 1]),\n",
+    "    # ShapeVar(var=\"VVFatJetEta\", label=r\"$\\eta^{VV}$\", bins=[30, -2.4, 2.4]),\n",
+    "    # ShapeVar(var=\"VVFatJetPt\", label=r\"$p^{VV}_T$ (GeV)\", bins=[30, 300, 1500]),\n",
+    "    # ShapeVar(var=\"VVParticleNetMass\", label=r\"$m^{VV}_{reg}$ (GeV)\", bins=[20, 50, 250]),\n",
+    "    # ShapeVar(var=\"VVFatJetMsd\", label=r\"$m^{VV}_{msd}$ (GeV)\", bins=[40, 50, 250]),\n",
+    "    # ShapeVar(var=\"VVFatJetParticleNet_Th4q\", label=r\"Prob($H \\to 4q$) vs Prob(QCD) (Non-MD)\", bins=[50, 0, 1]),\n",
+    "    # ShapeVar(var=\"VVFatJetParTMD_THWW4q\", label=r\"Prob($H \\to VV \\to 4q$) vs Prob(QCD) (Mass-Decorrelated)\", bins=[50, 0, 1]),\n",
+    "    # ShapeVar(var=\"VVFatJetParTMD_probT\", label=r\"Prob(Top) (Mass-Decorrelated)\", bins=[50, 0, 1]),\n",
+    "    # ShapeVar(var=\"VVFatJetParTMD_THWWvsT\", label=r\"$T^{VV}_{HWW}$\", bins=[50, 0, 1]),\n",
+    "    # ShapeVar(var=\"bbFatJetPtOverDijetPt\", label=r\"$p^{bb}_T / p_T^{jj}$\", bins=[50, 0, 40]),\n",
+    "    # ShapeVar(var=\"VVFatJetPtOverDijetPt\", label=r\"$p^{VV}_T / p_T^{jj}$\", bins=[50, 0, 40]),\n",
+    "    # ShapeVar(var=\"VVFatJetPtOverbbFatJetPt\", label=r\"$p^{VV}_T / p^{bb}_T$\", bins=[50, 0.4, 2.0]),\n",
+    "    # ShapeVar(var=\"nGoodMuons\", label=r\"# of Muons\", bins=[3, 0, 3]),\n",
+    "    # ShapeVar(var=\"nGoodElectrons\", label=r\"# of Electrons\", bins=[3, 0, 3]),\n",
+    "    # ShapeVar(var=\"nGoodJets\", label=r\"# of AK4 B-Jets\", bins=[5, 0, 5]),\n",
+    "    # ShapeVar(var=\"BDTScore\", label=r\"BDT Score\", bins=[50, 0, 1]),\n",
+    "]\n",
+    "\n",
+    "hists = postprocessing.control_plots(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    [\"HHbbVV\", \"VBFHHbbVV\", \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\", \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\"],\n",
+    "    control_plot_vars,\n",
+    "    f\"{plot_dir}/ControlPlots/{year}/\",\n",
+    "    year,\n",
+    "    bg_keys=bg_keys,\n",
+    "    # bg_keys=[\"QCD\", \"TT\", \"ST\", \"V+Jets\", \"Hbb\"],\n",
+    "    sig_scale_dict={\n",
+    "        \"HHbbVV\": 1e5,\n",
+    "        \"VBFHHbbVV\": 2e5,\n",
+    "        \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\": 2e3,\n",
+    "        \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\": 2e3,\n",
+    "    },\n",
+    "    plot_significance=True,\n",
+    "    show=True,\n",
+    "    log=False,\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Templates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add actual cuts here\n",
+    "selection_regions = {\n",
+    "    \"pass\": postprocessing.Region(\n",
+    "        cuts={\n",
+    "            \"bbFatJetParticleNetMD_Txbb\": [0.99, CUT_MAX_VAL],\n",
+    "        },\n",
+    "        label=\"Pass\",\n",
+    "    ),\n",
+    "    \"fail\": postprocessing.Region(\n",
+    "        cuts={\n",
+    "            \"bbFatJetParticleNetMD_Txbb\": [-CUT_MAX_VAL, 0.99],\n",
+    "        },\n",
+    "        label=\"Fail\",\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "# variable to plot\n",
+    "shape_vars = [\n",
+    "    postprocessing.ShapeVar(\n",
+    "        \"bbFatJetParticleNetMass\",\n",
+    "        r\"$m^{bb}_\\mathrm{Reg}$ (GeV)\",\n",
+    "        [20, 50, 250],\n",
+    "        reg=True,\n",
+    "        blind_window=[100, 150],\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "h, tsysts = postprocessing.get_templates(\n",
+    "    events_dict,\n",
+    "    bb_masks,\n",
+    "    year,\n",
+    "    nonres_sig_keys,\n",
+    "    selection_regions,\n",
+    "    shape_vars,\n",
+    "    systematics,\n",
+    "    bg_keys=bg_keys,\n",
+    "    plot_sig_keys=[\n",
+    "        \"HHbbVV\",\n",
+    "        \"VBFHHbbVV\",\n",
+    "        \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\",\n",
+    "        \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\",\n",
+    "    ],\n",
+    "    sig_scale_dict={\n",
+    "        \"HHbbVV\": 1e4,\n",
+    "        \"VBFHHbbVV\": 1e5,\n",
+    "        \"qqHH_CV_1_C2V_0_kl_1_HHbbVV\": 2e2,\n",
+    "        \"qqHH_CV_1_C2V_2_kl_1_HHbbVV\": 2e2,\n",
+    "    },\n",
+    "    plot_dir=f\"{plot_dir}/templates\",\n",
+    "    prev_cutflow=cutflow,\n",
+    "    weight_shifts={},\n",
+    "    jshift=\"\",\n",
+    "    plot_shifts=False,\n",
+    "    lpsfs=False,\n",
+    "    show=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "15adc7883e707560d0d9727709639b8fe3f3cff1f197d2d643742923ff23a29c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/postprocessing/PostProcessXHY.ipynb b/src/HHbbVV/postprocessing/PostProcessXHY.ipynb
deleted file mode 100644
index d346eae6..00000000
--- a/src/HHbbVV/postprocessing/PostProcessXHY.ipynb
+++ /dev/null
@@ -1,319 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import utils\n",
-    "import plotting\n",
-    "import postprocessing\n",
-    "import corrections\n",
-    "\n",
-    "from utils import CUT_MAX_VAL\n",
-    "from hh_vars import (\n",
-    "    years,\n",
-    "    sig_keys,\n",
-    "    data_key,\n",
-    "    qcd_key,\n",
-    "    bg_keys,\n",
-    "    samples,\n",
-    "    res_samples,\n",
-    "    res_sig_keys,\n",
-    "    txbb_wps,\n",
-    "    jec_shifts,\n",
-    "    jmsr_shifts,\n",
-    ")\n",
-    "from postprocessing import (\n",
-    "    shape_var,\n",
-    "    shape_bins,\n",
-    "    blind_window,\n",
-    "    selection_regions,\n",
-    "    selection_regions_label,\n",
-    "    # selection_regions_year,\n",
-    ")\n",
-    "\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import pickle\n",
-    "from pandas.errors import SettingWithCopyWarning\n",
-    "from hist import Hist\n",
-    "\n",
-    "import os\n",
-    "from copy import deepcopy\n",
-    "from inspect import cleandoc\n",
-    "import warnings\n",
-    "\n",
-    "# ignore these because they don't seem to apply\n",
-    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MAIN_DIR = \"../../../\"\n",
-    "samples_dir = f\"{MAIN_DIR}/../data/skimmer/Feb24\"\n",
-    "signal_samples_dir = f\"{MAIN_DIR}/../data/skimmer/Mar2\"\n",
-    "year = \"2017\"\n",
-    "\n",
-    "plot_dir = \"../../../plots/PostProcessing/Mar6\"\n",
-    "templates_dir = \"templates/Mar6/\"\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}\")\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}/cutflows/\")\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}/templates/\")\n",
-    "# _ = os.system(f\"mkdir -p {templates_dir}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load samples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Both Jet's Regressed Mass above 50, electron veto\n",
-    "filters = [\n",
-    "    [\n",
-    "        (\"('ak8FatJetParticleNetMass', '0')\", \">=\", 50),\n",
-    "        (\"('ak8FatJetParticleNetMass', '1')\", \">=\", 50),\n",
-    "        # (\"('nGoodElectrons', '0')\", \"==\", 0),\n",
-    "    ],\n",
-    "]\n",
-    "systematics = {}\n",
-    "\n",
-    "# save cutflow as pandas table\n",
-    "cutflow = pd.DataFrame(index=list(samples.keys()) + list(res_samples.keys()))\n",
-    "\n",
-    "# utils.remove_empty_parquets(samples_dir, year)\n",
-    "events_dict = utils.load_samples(signal_samples_dir, res_samples, year, filters)\n",
-    "events_dict |= utils.load_samples(samples_dir, samples, year, filters)\n",
-    "\n",
-    "utils.add_to_cutflow(events_dict, \"BDTPreselection\", \"weight\", cutflow)\n",
-    "\n",
-    "print(\"\")\n",
-    "# print weighted sample yields\n",
-    "for sample in events_dict:\n",
-    "    tot_weight = np.sum(events_dict[sample][\"weight\"].values)\n",
-    "    print(f\"Pre-selection {sample} yield: {tot_weight:.2f}\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Scale factors and bb VV assignment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "postprocessing.apply_weights(events_dict, year, cutflow)\n",
-    "bb_masks = postprocessing.bb_VV_assignment(events_dict)\n",
-    "cutflow"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# {var: (bins, label)}\n",
-    "control_plot_vars = {\n",
-    "    \"MET_pt\": ([50, 0, 300], r\"$p^{miss}_T$ (GeV)\"),\n",
-    "    \"DijetEta\": ([50, -8, 8], r\"$\\eta^{jj}$\"),\n",
-    "    \"DijetPt\": ([50, 0, 750], r\"$p_T^{jj}$ (GeV)\"),\n",
-    "    \"DijetMass\": ([50, 500, 3200], r\"$m^{jj}$ (GeV)\"),\n",
-    "    \"bbFatJetEta\": ([50, -2.4, 2.4], r\"$\\eta^{bb}$\"),\n",
-    "    \"bbFatJetPt\": ([50, 300, 1500], r\"$p^{bb}_T$ (GeV)\"),\n",
-    "    \"bbFatJetParticleNetMass\": ([50, 0, 300], r\"$m^{bb}_{reg}$ (GeV)\"),\n",
-    "    \"bbFatJetMsd\": ([50, 0, 300], r\"$m^{bb}_{msd}$ (GeV)\"),\n",
-    "    \"bbFatJetParticleNetMD_Txbb\": ([50, 0.8, 1], r\"$p^{bb}_{Txbb}$\"),\n",
-    "    \"VVFatJetEta\": ([50, -2.4, 2.4], r\"$\\eta^{VV}$\"),\n",
-    "    \"VVFatJetPt\": ([50, 300, 1500], r\"$p^{VV}_T$ (GeV)\"),\n",
-    "    \"VVFatJetParticleNetMass\": ([50, 0, 300], r\"$m^{VV}_{reg}$ (GeV)\"),\n",
-    "    \"VVFatJetMsd\": ([50, 0, 300], r\"$m^{VV}_{msd}$ (GeV)\"),\n",
-    "    \"VVFatJetParticleNet_Th4q\": ([50, 0, 1], r\"Prob($H \\to 4q$) vs Prob(QCD) (Non-MD)\"),\n",
-    "    \"VVFatJetParTMD_THWW4q\": (\n",
-    "        [50, 0, 1],\n",
-    "        r\"Prob($H \\to VV \\to 4q$) vs Prob(QCD) (Mass-Decorrelated)\",\n",
-    "    ),\n",
-    "    \"VVFatJetParTMD_probT\": ([50, 0, 1], r\"Prob(Top) (Mass-Decorrelated)\"),\n",
-    "    \"bbFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{bb}_T / p_T^{jj}$\"),\n",
-    "    \"VVFatJetPtOverDijetPt\": ([50, 0, 40], r\"$p^{VV}_T / p_T^{jj}$\"),\n",
-    "    \"VVFatJetPtOverbbFatJetPt\": ([50, 0.4, 2.0], r\"$p^{VV}_T / p^{bb}_T$\"),\n",
-    "    \"nGoodMuons\": ([3, 0, 3], r\"# of Muons\"),\n",
-    "    \"nGoodElectrons\": ([3, 0, 3], r\"# of Electrons\"),\n",
-    "    \"nGoodJets\": ([5, 0, 5], r\"# of AK4 B-Jets\"),\n",
-    "}\n",
-    "\n",
-    "hists = postprocessing.control_plots(\n",
-    "    events_dict,\n",
-    "    bb_masks,\n",
-    "    sig_keys + res_sig_keys,\n",
-    "    control_plot_vars,\n",
-    "    plot_dir,\n",
-    "    year,\n",
-    "    show=True,\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overall BDT SF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sel, cf = utils.make_selection(\n",
-    "    selection_regions[year][\"BDTOnly\"], events_dict, bb_masks, prev_cutflow=cutflow\n",
-    ")\n",
-    "lp_sf, unc, uncs = postprocessing.get_lpsf(events_dict[sig_key], sel[sig_key])\n",
-    "print(f\"BDT Scale Factor: {lp_sf:.2f} ± {unc:.2f}\")\n",
-    "print(uncs)\n",
-    "systematics[\"lp_sf_unc\"] = unc / lp_sf\n",
-    "cf"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Templates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "postprocessing.get_templates(\n",
-    "    events_dict,\n",
-    "    bb_masks,\n",
-    "    year,\n",
-    "    selection_regions[year],\n",
-    "    shape_var,\n",
-    "    shape_bins,\n",
-    "    blind_window,\n",
-    "    plot_dir=plot_dir,\n",
-    "    prev_cutflow=cutflow,\n",
-    "    weight_shifts={},\n",
-    "    jshift=\"\",\n",
-    "    show=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "templates = {}\n",
-    "\n",
-    "for jshift in [\"\"] + jec_shifts + jmsr_shifts:\n",
-    "    print(jshift)\n",
-    "    ttemps, tsyst = postprocessing.get_templates(\n",
-    "        events_dict,\n",
-    "        bb_masks,\n",
-    "        year,\n",
-    "        selection_regions[year],\n",
-    "        shape_var,\n",
-    "        shape_bins,\n",
-    "        blind_window,\n",
-    "        plot_dir=plot_dir,\n",
-    "        prev_cutflow=cutflow,\n",
-    "        weight_shifts=postprocessing.weight_shifts,\n",
-    "        jshift=jshift,\n",
-    "        show=False,\n",
-    "    )\n",
-    "\n",
-    "    templates = {**templates, **ttemps}\n",
-    "    systematics = {**systematics, **tsyst}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "systematics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "templates_dict = {}\n",
-    "\n",
-    "for year in years:\n",
-    "    with open(f\"templates/Feb28/{year}_templates.pkl\", \"rb\") as f:\n",
-    "        templates_dict[year] = pickle.load(f)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "python310",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.8"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "15adc7883e707560d0d9727709639b8fe3f3cff1f197d2d643742923ff23a29c"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/HHbbVV/postprocessing/TopAnalysis.ipynb b/src/HHbbVV/postprocessing/TopAnalysis.ipynb
new file mode 100644
index 00000000..09eaec17
--- /dev/null
+++ b/src/HHbbVV/postprocessing/TopAnalysis.ipynb
@@ -0,0 +1,812 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import utils\n",
+    "import plotting\n",
+    "import numpy as np\n",
+    "import warnings\n",
+    "import pandas as pd\n",
+    "\n",
+    "# from pandas.core.common import SettingWithCopyWarning\n",
+    "from hh_vars import data_key\n",
+    "\n",
+    "# ignore these because they don't seem to apply\n",
+    "# warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
+    "\n",
+    "from PyPDF2 import PdfMerger\n",
+    "\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 16})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dir = \"../../../plots/ttsfs/23Aug24_JMEOR_plots\"\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "samples = {\n",
+    "    \"QCD\": \"QCD\",\n",
+    "    # \"Single Top\": \"ST\",\n",
+    "    \"Top\": [\"TTToSemiLeptonic\", \"ST\"],\n",
+    "    \"TTbar\": [\"TTTo2L2Nu\", \"TTToHadronic\"],\n",
+    "    \"W+Jets\": \"WJets\",\n",
+    "    \"Diboson\": [\"WW\", \"WZ\", \"ZZ\"],\n",
+    "    \"Data\": \"SingleMuon\",\n",
+    "}\n",
+    "\n",
+    "top_matched_key = \"TT Top Matched\"\n",
+    "\n",
+    "# data_dir = \"../../../../data/ttsfs/Dec22/\"\n",
+    "data_dir = \"../../../../data/ttsfs/23Jun9/\"\n",
+    "year = \"2018\"\n",
+    "\n",
+    "# filters = [(\"('ak8FatJetPt', '0')\", \">=\", 500)]\n",
+    "filters = None\n",
+    "\n",
+    "events_dict = utils.load_samples(data_dir, samples, year, filters=filters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = \"TTToSemiLeptonic\"\n",
+    "utils.get_cutflow(f\"{data_dir}/{year}/{sample}/pickles\", year, sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# old ones\n",
+    "sample = \"TTToSemiLeptonic\"\n",
+    "utils.get_cutflow(f\"{data_dir}//{sample}/pickles\", year, sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = \"QCD_HT300to500\"\n",
+    "utils.get_cutflow(f\"{data_dir}/{year}/{sample}/pickles\", year, sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = \"QCD_HT300to500\"\n",
+    "utils.get_cutflow(f\"{data_dir}//{sample}/pickles\", year, sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events_dict[top_matched_key] = events_dict[\"Top\"].loc[events_dict[\"Top\"][\"top_matched\"][0] == 1]\n",
+    "events_dict[\"TT W Matched\"] = events_dict[\"Top\"].loc[events_dict[\"Top\"][\"w_matched\"][0] == 1]\n",
+    "events_dict[\"TT Unmatched\"] = pd.concat(\n",
+    "    [events_dict[\"TTbar\"], events_dict[\"Top\"].loc[events_dict[\"Top\"][\"unmatched\"][0] == 1]]\n",
+    ")\n",
+    "# del events_dict[\"Top\"]\n",
+    "# del events_dict[\"TTbar\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# normalize scale factors to average to 1\n",
+    "for key in [\"lp_sf\", \"lp_sf_lnN\", \"lp_sf_sys_down\", \"lp_sf_sys_up\"]:\n",
+    "    # cut off at 10\n",
+    "    events_dict[top_matched_key].loc[:, key] = np.minimum(\n",
+    "        events_dict[top_matched_key].loc[:, key].values, 10\n",
+    "    )\n",
+    "    mean_lp_sfs = np.mean(np.nan_to_num(events_dict[top_matched_key][key]), axis=0)\n",
+    "    events_dict[top_matched_key].loc[:, key] = (\n",
+    "        np.nan_to_num(events_dict[top_matched_key].loc[:, key]) / mean_lp_sfs\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams.update({\"font.size\": 24})\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "_ = plt.hist(\n",
+    "    events_dict[top_matched_key][\"lp_sf\"][10].values,\n",
+    "    np.logspace(-4, 2, 101, base=10),\n",
+    "    histtype=\"step\",\n",
+    ")\n",
+    "plt.xscale(\"log\")\n",
+    "# plt.yscale(\"log\")\n",
+    "plt.xlabel(\"LP SF\")\n",
+    "plt.title(\"Scale factor distribution\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for key in events_dict:\n",
+    "    events_dict[key] = events_dict[key][events_dict[key][\"ak8FatJetPt\"][0] >= 500]\n",
+    "    events_dict[key] = events_dict[key][events_dict[key][\"ak8FatJetMsd\"][0] >= 125]\n",
+    "    events_dict[key] = events_dict[key][events_dict[key][\"ak8FatJetMsd\"][0] <= 225]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events_dict2 = {}\n",
+    "for key in events_dict:\n",
+    "    events_dict2[key] = events_dict[key][\n",
+    "        (events_dict[key][\"ak8FatJetParticleNetMD_Txqc\"][0] >= 0.91)\n",
+    "        & (events_dict[key][\"ak8FatJetPt\"][0] >= 300)\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events = events_dict[top_matched_key]\n",
+    "sj_matching_unc = (\n",
+    "    (np.sum(events[\"lp_sf_unmatched_quarks\"]) / (len(events) * 3))\n",
+    "    + (np.sum(events[\"lp_sf_double_matched_event\"]) / (len(events)))\n",
+    ").values[0]\n",
+    "sj_pt_unc = (np.sum(events[\"lp_sf_num_sjpt_gt350\"]) / (len(events))).values[0] * 0.21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sj_matching_unc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sj_pt_unc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_samples = [\n",
+    "    \"QCD\",\n",
+    "    \"Diboson\",\n",
+    "    # \"Single Top\",\n",
+    "    \"W+Jets\",\n",
+    "    \"TT Unmatched\",\n",
+    "    \"TT W Matched\",\n",
+    "    top_matched_key,\n",
+    "]\n",
+    "\n",
+    "bg_colours = {\n",
+    "    \"QCD\": \"lightblue\",\n",
+    "    \"Single Top\": \"darkblue\",\n",
+    "    \"TT Unmatched\": \"darkgreen\",\n",
+    "    \"TT W Matched\": \"green\",\n",
+    "    \"TT Top Matched\": \"orange\",\n",
+    "    \"W+Jets\": \"darkred\",\n",
+    "    \"Diboson\": \"red\",\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pre plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# {var: (bins, label)}\n",
+    "plot_vars = {\n",
+    "    \"ak8FatJetMass\": ([30, 50, 200], r\"$m_{SD}$ (GeV)\"),\n",
+    "    \"ak8FatJetParticleNetMass\": ([30, 50, 200], r\"$m_{reg}$ (GeV)\"),\n",
+    "    \"ak8FatJetPt\": ([30, 0, 1200], r\"$p_T$ (GeV)\"),\n",
+    "    \"MET_pt\": ([30, 0, 200], r\"MET (GeV)\"),\n",
+    "    # \"ak8FatJetnPFCands\": ([20, 0, 120], r\"# of PF Candidates\"),\n",
+    "    # \"ak8FatJetParticleNet_Th4q\": ([20, 0.6, 1], r\"ParticleNet $T_{H4q}$ Non-MD\"),\n",
+    "    # \"ak8FatJetParTMD_THWW4q\": ([20, 0.6, 1], r\"ParT $T_{HWW4q}$ MD\"),\n",
+    "    # \"tau21\": ([20, 0.04, 0.8], r\"$\\tau_{21}$\"),\n",
+    "    # \"tau32\": ([20, 0.2, 1], r\"$\\tau_{32}$\"),\n",
+    "    # \"tau43\": ([20, 0.42, 1], r\"$\\tau_{43}$\"),\n",
+    "    # \"tau42\": ([20, 0, 1], r\"$\\tau_{42}$\"),\n",
+    "    # \"tau41\": ([20, 0, 1], r\"$\\tau_{41}$\"),\n",
+    "}\n",
+    "\n",
+    "pre_hists = {}\n",
+    "\n",
+    "for var, (bins, label) in plot_vars.items():\n",
+    "    if var not in pre_hists:\n",
+    "        pre_hists[var] = utils.singleVarHistNoMask(\n",
+    "            events_dict2, var, bins, label, weight_key=\"weight\"\n",
+    "        )\n",
+    "\n",
+    "merger_pre_plots = PdfMerger()\n",
+    "\n",
+    "for var, var_hist in pre_hists.items():\n",
+    "    name = f\"{plot_dir}/pre_{var}.pdf\"\n",
+    "    plotting.ratioHistPlot(\n",
+    "        var_hist,\n",
+    "        year,\n",
+    "        [],\n",
+    "        plot_samples,\n",
+    "        # bg_err=None,\n",
+    "        name=name,\n",
+    "        bg_colours=bg_colours,\n",
+    "        bg_order=plot_samples,\n",
+    "        ratio_ylims=[0.6, 1.3],\n",
+    "    )\n",
+    "    merger_pre_plots.append(name)\n",
+    "\n",
+    "merger_pre_plots.write(f\"{plot_dir}/PrePlots.pdf\")\n",
+    "merger_pre_plots.close()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Post plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "post_hists = {}\n",
+    "post_hists_err = {}\n",
+    "uncs_dict = {}\n",
+    "\n",
+    "events = events_dict[top_matched_key]\n",
+    "\n",
+    "for var, (bins, label) in plot_vars.items():\n",
+    "    # if var not in post_hists:\n",
+    "    toy_hists = []\n",
+    "    for i in range(events[\"lp_sf\"].shape[1]):\n",
+    "        toy_hists.append(\n",
+    "            np.histogram(\n",
+    "                events[var][0].values.squeeze(),\n",
+    "                np.linspace(*bins[1:], bins[0] + 1),\n",
+    "                weights=events[\"weight\"][0].values * events[\"lp_sf\"][i].values,\n",
+    "            )[0]\n",
+    "        )\n",
+    "\n",
+    "    sys_up_down = []\n",
+    "    for key in [\"lp_sf_sys_up\", \"lp_sf_sys_down\"]:\n",
+    "        sys_up_down.append(\n",
+    "            np.histogram(\n",
+    "                events[var][0].values.squeeze(),\n",
+    "                np.linspace(*bins[1:], bins[0] + 1),\n",
+    "                weights=events[\"weight\"][0].values * events[key][0].values,\n",
+    "            )[0]\n",
+    "        )\n",
+    "\n",
+    "    nom_vals = toy_hists[0]  # first column are nominal values\n",
+    "\n",
+    "    uncs = {\n",
+    "        \"stat_unc\": np.minimum(nom_vals, np.std(toy_hists[1:], axis=0)),  # cap at 100% unc\n",
+    "        \"syst_rat_unc\": np.minimum(nom_vals, (np.abs(sys_up_down[0] - sys_up_down[1])) / 2),\n",
+    "        \"syst_sjm_unc\": nom_vals * sj_matching_unc,\n",
+    "        \"syst_sjpt_unc\": nom_vals * sj_pt_unc,\n",
+    "    }\n",
+    "\n",
+    "    uncs_dict[var] = uncs\n",
+    "\n",
+    "    unc = np.linalg.norm(list(uncs.values()), axis=0)\n",
+    "\n",
+    "    thist = deepcopy(pre_hists[var])\n",
+    "    top_matched_key_index = np.where(np.array(list(thist.axes[0])) == top_matched_key)[0][0]\n",
+    "    thist.view(flow=False)[top_matched_key_index, :].value = nom_vals\n",
+    "    post_hists[var] = thist\n",
+    "    post_hists_err[var] = unc\n",
+    "\n",
+    "\n",
+    "merger_post_plots = PdfFileMerger()\n",
+    "\n",
+    "for var, var_hist in post_hists.items():\n",
+    "    name = f\"{plot_dir}/post_{var}.pdf\"\n",
+    "    plotting.ratioLinePlot(\n",
+    "        var_hist,\n",
+    "        plot_samples,\n",
+    "        year,\n",
+    "        bg_err=post_hists_err[var],\n",
+    "        name=name,\n",
+    "    )\n",
+    "    merger_post_plots.append(name)\n",
+    "\n",
+    "merger_post_plots.write(f\"{plot_dir}/PostPlots.pdf\")\n",
+    "merger_post_plots.close()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Post LnN Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "post_lnN_hists = {}\n",
+    "post_lnN_hists_err = {}\n",
+    "uncs_lnN_dict = {}\n",
+    "\n",
+    "events = events_dict[top_matched_key]\n",
+    "\n",
+    "for var, (bins, label) in plot_vars.items():\n",
+    "    if var not in post_lnN_hists:\n",
+    "        toy_hists = []\n",
+    "        for i in range(events[\"lp_sf_lnN\"].shape[1]):\n",
+    "            toy_hists.append(\n",
+    "                np.histogram(\n",
+    "                    events[var][0].values.squeeze(),\n",
+    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
+    "                    weights=events[\"weight\"][0].values * events[\"lp_sf_lnN\"][i].values,\n",
+    "                )[0]\n",
+    "            )\n",
+    "\n",
+    "        sys_up_down = []\n",
+    "        for key in [\"lp_sf_sys_up\", \"lp_sf_sys_down\"]:\n",
+    "            sys_up_down.append(\n",
+    "                np.histogram(\n",
+    "                    events[var][0].values.squeeze(),\n",
+    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
+    "                    weights=events[\"weight\"][0].values * events[key][0].values,\n",
+    "                )[0]\n",
+    "            )\n",
+    "\n",
+    "        nom_vals = toy_hists[0]  # first column are nominal values\n",
+    "\n",
+    "        uncs = {\n",
+    "            \"stat_unc\": np.minimum(nom_vals, np.std(toy_hists[1:], axis=0)),  # cap at 100% unc\n",
+    "            \"syst_rat_unc\": np.minimum(nom_vals, (np.abs(sys_up_down[0] - sys_up_down[1])) / 2),\n",
+    "            \"syst_sjm_unc\": nom_vals * sj_matching_unc,\n",
+    "            \"syst_sjpt_unc\": nom_vals * sj_pt_unc,\n",
+    "        }\n",
+    "\n",
+    "        uncs_lnN_dict[var] = uncs\n",
+    "\n",
+    "        unc = np.linalg.norm(list(uncs.values()), axis=0)\n",
+    "\n",
+    "        thist = deepcopy(pre_hists[var])\n",
+    "        top_matched_key_index = np.where(np.array(list(thist.axes[0])) == top_matched_key)[0][0]\n",
+    "        thist.view(flow=False)[top_matched_key_index, :].value = nom_vals\n",
+    "        post_lnN_hists[var] = thist\n",
+    "\n",
+    "        post_lnN_hists_err[var] = unc\n",
+    "\n",
+    "\n",
+    "# merger_post_plots = PdfFileMerger()\n",
+    "\n",
+    "# for var, var_hist in post_lnN_hists.items():\n",
+    "#     name = f\"{plot_dir}/postlnN_{var}.pdf\"\n",
+    "#     plotting.ratioLinePlot(\n",
+    "#         var_hist,\n",
+    "#         plot_samples,\n",
+    "#         year,\n",
+    "#         bg_err=post_lnN_hists_err[var],\n",
+    "#         name=name,\n",
+    "#     )\n",
+    "#     merger_post_plots.append(name)\n",
+    "\n",
+    "# merger_post_plots.write(f\"{plot_dir}/PostLnNPlots.pdf\")\n",
+    "# merger_post_plots.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binn = -1\n",
+    "tvar = \"ak8FatJetParTMD_THWW4q\"\n",
+    "pre_vals = pre_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
+    "nom_vals = post_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
+    "unc = post_hists_err[tvar]\n",
+    "print(\"SF: \", nom_vals[binn] / pre_vals[binn])\n",
+    "print(\"Uncs: \", {key: val[binn] / nom_vals[binn] * 100 for key, val in uncs_dict[tvar].items()})\n",
+    "print(\"Combined: \", unc[binn] / nom_vals[binn] * 100)\n",
+    "print(\"Abs: \", unc[binn] / pre_vals[binn])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "binn = -1\n",
+    "tvar = \"ak8FatJetParTMD_THWW4q\"\n",
+    "pre_vals = pre_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
+    "nom_vals = post_lnN_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
+    "unc = post_lnN_hists_err[tvar]\n",
+    "print(\"SF: \", nom_vals[binn] / pre_vals[binn])\n",
+    "print(\"Uncs: \", {key: val[binn] / nom_vals[binn] * 100 for key, val in uncs_lnN_dict[tvar].items()})\n",
+    "print(\"Combined: \", unc[binn] / nom_vals[binn] * 100)\n",
+    "print(\"Abs: \", unc[binn] / pre_vals[binn])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_vals = pre_hists[tvar][\"Data\", ...].values()\n",
+    "pre_MC_vals = (\n",
+    "    pre_hists[tvar][sum, :].values()\n",
+    "    - data_vals\n",
+    "    - pre_hists[tvar][\"TTbar\", :].values()\n",
+    "    - pre_hists[tvar][\"TTSL\", :].values()\n",
+    ")\n",
+    "post_lnN_MC_vals = (\n",
+    "    post_lnN_hists[tvar][sum, :].values()\n",
+    "    - data_vals\n",
+    "    - post_lnN_hists[tvar][\"TTbar\", :].values()\n",
+    "    - post_lnN_hists[tvar][\"TTSL\", :].values()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chisquare(mc, data):\n",
+    "    return np.sum(np.square(data - mc) / data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lb = 5\n",
+    "print(\"Pre chi2:\", chisquare(pre_MC_vals[-lb:], data_vals[-lb:]))\n",
+    "print(\"Post chi2:\", chisquare(post_lnN_MC_vals[-lb:], data_vals[-lb:]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"QCD\",\n",
+    "\"Diboson\",\n",
+    "\"Single Top\",\n",
+    "\"W+Jets\",\n",
+    "\"TT Unmatched\",\n",
+    "\"TT W Matched\",\n",
+    "top_matched_key,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "post_lnN_hists[tvar][\"Data\", :].values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    post_lnN_hists[tvar][sum, :].values()\n",
+    "    - post_lnN_hists[tvar][\"Data\", :].values()\n",
+    "    - post_lnN_hists[tvar][\"TTbar\", :].values()\n",
+    "    - post_lnN_hists[tvar][\"TTSL\", :].values()\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ratio plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tvar = \"ak8FatJetParTMD_THWW4q\"\n",
+    "\n",
+    "# plt.figure(figsize=(12, 12))\n",
+    "# hists = pre_hists[tvar]\n",
+    "# bg_tot = np.sum(hists[plot_samples, :].values(), axis=0)\n",
+    "# mcdata_ratio = (bg_tot + 1e-5) / hists[data_key, :].values()\n",
+    "# _ = plt.hist(mcdata_ratio - 1, np.linspace(-0.5, 0.5, 10), histtype='step')\n",
+    "\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "hists = post_hists[tvar]\n",
+    "bg_tot = np.sum(hists[plot_samples, :].values(), axis=0)\n",
+    "data_tot = hists[data_key, :].values()\n",
+    "unc = post_hists_err[tvar]\n",
+    "mcdata_ratio = (bg_tot) / data_tot\n",
+    "_ = plt.hist(((bg_tot - data_tot) / (unc))[10:], np.linspace(-6.5, 4.5, 23), histtype=\"step\")\n",
+    "plt.xlabel(\"(MC - Data) / Unc.\")\n",
+    "plt.savefig(f\"{plot_dir}/pull_hist.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotting.ratioLinePlot(\n",
+    "    post_hists[tvar],\n",
+    "    plot_samples,\n",
+    "    year,\n",
+    "    bg_err=post_hists_err[tvar],\n",
+    "    name=f\"{plot_dir}/post_ak8FatJetParTMD_THWW4q_pulls.pdf\",\n",
+    "    pulls=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cut_dict = {}\n",
+    "\n",
+    "for key in events_dict:\n",
+    "    cut_dict[key] = events_dict[key][events_dict[key][\"tau42\"][0] <= 0.3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# {var: (bins, label)}\n",
+    "plot_vars = {\n",
+    "    \"ak8FatJetParTMD_THWW4q\": ([20, 0.6, 1], r\"ParT $T_{HWW4q}$ MD\"),\n",
+    "}\n",
+    "\n",
+    "pre_hists_cut = {}\n",
+    "\n",
+    "for var, (bins, label) in plot_vars.items():\n",
+    "    if var not in pre_hists_cut:\n",
+    "        pre_hists_cut[var] = utils.singleVarHistNoMask(\n",
+    "            cut_dict, var, bins, label, weight_key=\"weight\"\n",
+    "        )\n",
+    "\n",
+    "merger_pre_plots = PdfFileMerger()\n",
+    "\n",
+    "for var, var_hist in pre_hists_cut.items():\n",
+    "    name = f\"{plot_dir}/pre_{var}_tau42_cut.pdf\"\n",
+    "    plotting.ratioLinePlot(\n",
+    "        var_hist,\n",
+    "        plot_samples,\n",
+    "        year,\n",
+    "        bg_err=None,\n",
+    "        name=name,\n",
+    "    )\n",
+    "    merger_pre_plots.append(name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "post_lnN_cut_hists = {}\n",
+    "post_lnN_cut_hists_err = {}\n",
+    "uncs_lnN_cut_dict = {}\n",
+    "\n",
+    "events = cut_dict[top_matched_key]\n",
+    "\n",
+    "for var, (bins, label) in plot_vars.items():\n",
+    "    if var not in post_lnN_cut_hists:\n",
+    "        toy_hists = []\n",
+    "        for i in range(events[\"lp_sf_lnN\"].shape[1]):\n",
+    "            toy_hists.append(\n",
+    "                np.histogram(\n",
+    "                    events[var][0].values.squeeze(),\n",
+    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
+    "                    weights=events[\"weight\"][0].values * events[\"lp_sf_lnN\"][i].values,\n",
+    "                )[0]\n",
+    "            )\n",
+    "\n",
+    "        sys_up_down = []\n",
+    "        for key in [\"lp_sf_sys_up\", \"lp_sf_sys_down\"]:\n",
+    "            sys_up_down.append(\n",
+    "                np.histogram(\n",
+    "                    events[var][0].values.squeeze(),\n",
+    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
+    "                    weights=events[\"weight\"][0].values * events[key][0].values,\n",
+    "                )[0]\n",
+    "            )\n",
+    "\n",
+    "        nom_vals = toy_hists[0]  # first column are nominal values\n",
+    "\n",
+    "        uncs = {\n",
+    "            \"stat_unc\": np.minimum(nom_vals, np.std(toy_hists[1:], axis=0)),  # cap at 100% unc\n",
+    "            \"syst_rat_unc\": np.minimum(nom_vals, (np.abs(sys_up_down[0] - sys_up_down[1])) / 2),\n",
+    "            \"syst_sjm_unc\": nom_vals * sj_matching_unc,\n",
+    "            \"syst_sjpt_unc\": nom_vals * sj_pt_unc,\n",
+    "        }\n",
+    "\n",
+    "        uncs_lnN_cut_dict[var] = uncs\n",
+    "\n",
+    "        unc = np.linalg.norm(list(uncs.values()), axis=0)\n",
+    "\n",
+    "        thist = deepcopy(pre_hists[var])\n",
+    "        top_matched_key_index = np.where(np.array(list(thist.axes[0])) == top_matched_key)[0][0]\n",
+    "        thist.view(flow=False)[top_matched_key_index, :].value = nom_vals\n",
+    "        post_lnN_cut_hists[var] = thist\n",
+    "\n",
+    "        post_lnN_cut_hists_err[var] = unc\n",
+    "\n",
+    "\n",
+    "merger_post_plots = PdfFileMerger()\n",
+    "\n",
+    "for var, var_hist in post_lnN_cut_hists.items():\n",
+    "    name = f\"{plot_dir}/postlnN_{var}_cut.pdf\"\n",
+    "    plotting.ratioLinePlot(\n",
+    "        var_hist,\n",
+    "        plot_samples,\n",
+    "        year,\n",
+    "        bg_err=post_lnN_cut_hists_err[var],\n",
+    "        name=name,\n",
+    "    )\n",
+    "    merger_post_plots.append(name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mass_hist = utils.singleVarHistNoMask(\n",
+    "    events_dict, \"ak8FatJetMass\", [20, 125, 225], r\"$m_{SD}$\", weight_key=\"weight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plotting.ratioHistPlot(\n",
+    "    mass_hist,\n",
+    "    [\"QCD\", \"Diboson\", \"Single Top\", \"W+Jets\", \"TT Unmatched\", \"TT W Matched\", top_matched_key],\n",
+    "    f\"{plot_dir}/\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('python310')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31a7b1cb5f073f7a7d37b3db504c6954ce2b88e0f82e412b65ad0b5f2dd17394"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/postprocessing/TrainBDT.py b/src/HHbbVV/postprocessing/TrainBDT.py
index d0c193b5..a8e8deb4 100644
--- a/src/HHbbVV/postprocessing/TrainBDT.py
+++ b/src/HHbbVV/postprocessing/TrainBDT.py
@@ -17,12 +17,14 @@
 
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import roc_curve, auc
+from sklearn.preprocessing import LabelEncoder
+
 import xgboost as xgb
 
 import utils
 import plotting
 
-from hh_vars import years, sig_key, data_key, jec_shifts, jmsr_shifts, jec_vars, jmsr_vars
+from hh_vars import years, data_key, jec_shifts, jmsr_shifts, jec_vars, jmsr_vars
 
 from copy import deepcopy
 
@@ -32,25 +34,33 @@
 
 
 weight_key = "finalWeight"
+sig_key = "HHbbVV"
+bg_keys = ["QCD", "TT", "V+Jets"]
+training_keys = [sig_key] + bg_keys
+
+# if doing multiclass classification, encode each process separately
+label_encoder = LabelEncoder()
+label_encoder.fit(training_keys)
 
-# only vars used for training
+
+# only vars used for training, ordered by importance
 bdtVars = [
-    "MET_pt",
-    "DijetEta",
-    "DijetPt",
-    "DijetMass",
-    "bbFatJetPt",
-    "VVFatJetEta",
-    "VVFatJetPt",
-    "VVFatJetParticleNetMass",
     # "VVFatJetParTMD_THWW4q",
-    "VVFatJetParTMD_probQCD",
-    "VVFatJetParTMD_probT",
     "VVFatJetParTMD_probHWW3q",
+    "VVFatJetParTMD_probQCD",
     "VVFatJetParTMD_probHWW4q",
-    "bbFatJetPtOverDijetPt",
+    "VVFatJetParticleNetMass",
+    "DijetMass",
+    "VVFatJetParTMD_probT",
     "VVFatJetPtOverDijetPt",
+    "DijetPt",
+    "bbFatJetPt",
+    "VVFatJetPt",
     "VVFatJetPtOverbbFatJetPt",
+    "MET_pt",
+    "bbFatJetPtOverDijetPt",
+    "VVFatJetEta",
+    "DijetEta",
 ]
 
 
@@ -107,10 +117,13 @@ def get_X(data_dict: Dict[str, pd.DataFrame], jec_shift: str = None, jmsr_shift:
     return pd.concat(X, axis=0), mc_vars
 
 
-def get_Y(data_dict: Dict[str, pd.DataFrame]):
+def get_Y(data_dict: Dict[str, pd.DataFrame], multiclass: bool = False):
     Y = []
     for year, data in data_dict.items():
-        Y.append((data["Dataset"] == sig_key).astype(int))
+        if multiclass:
+            Y.append(pd.DataFrame(label_encoder.transform(data["Dataset"])))
+        else:
+            Y.append((data["Dataset"] == sig_key).astype(int))
 
     return pd.concat(Y, axis=0)
 
@@ -127,11 +140,30 @@ def remove_neg_weights(data: pd.DataFrame):
     return data[data[weight_key] > 0]
 
 
-def equalize_weights(data: pd.DataFrame):
-    """Scales signal such that total signal = total background"""
+def equalize_weights(
+    data: pd.DataFrame, equalize_sig_bg: bool = True, equalize_per_process: bool = False
+):
+    """
+    If `equalize_sig_bg`: scales signal such that total signal = total background
+    If `equalize_per_process`: scales each background process separately to be equal as well
+        If `equalize_sig_bg` is False: signal scaled to match the individual bg process yields
+        instead of total.
+    """
     sig_total = np.sum(data[data["Dataset"] == sig_key][weight_key])
-    bg_total = np.sum(data[data["Dataset"] != sig_key][weight_key])
-    data[weight_key].loc[data["Dataset"] == sig_key] *= bg_total / sig_total
+
+    if equalize_per_process:
+        qcd_total = np.sum(data[data["Dataset"] == "QCD"][weight_key])
+        for bg_key in bg_keys:
+            if bg_key != "QCD":
+                total = np.sum(data[data["Dataset"] == bg_key][weight_key])
+                data[weight_key].loc[data["Dataset"] == bg_key] *= qcd_total / total
+
+        if not equalize_sig_bg:
+            data[weight_key].loc[data["Dataset"] == sig_key] *= qcd_total / sig_total
+
+    if equalize_sig_bg:
+        bg_total = np.sum(data[data["Dataset"] != sig_key][weight_key])
+        data[weight_key].loc[data["Dataset"] == sig_key] *= bg_total / sig_total
 
 
 def load_data(data_path: str, year: str, all_years: bool):
@@ -144,67 +176,98 @@ def load_data(data_path: str, year: str, all_years: bool):
 
 
 def main(args):
+    global bdtVars
+
     classifier_params = {
-        "max_depth": 3,
+        "max_depth": args.max_depth,
+        "min_child_weight": args.min_child_weight,
         "learning_rate": 0.1,
-        "n_estimators": 400,
+        "n_estimators": args.n_estimators,
         "verbosity": 2,
         "n_jobs": 4,
         "reg_lambda": 1.0,
     }
 
-    data_dict = load_data(args.data_path, args.year, args.all_years)
-    training_data_dict = {
-        year: data[
-            (data["Dataset"] == sig_key)
-            | (data["Dataset"] == "QCD")
-            | (data["Dataset"] == "TT")
-            | (data["Dataset"] == "V+Jets")
-        ]
-        for year, data in data_dict.items()
-    }
+    if args.rem_feats:
+        bdtVars = bdtVars[: -args.rem_feats]
 
-    print("Training samples:", np.unique(list(training_data_dict.values())[0]["Dataset"]))
+    print("BDT features:\n", bdtVars)
 
-    if args.test:
-        data_dict = {
-            year: pd.concat(
-                (data[:50], data[1000000:1000050], data[2000000:2000050], data[-50:]), axis=0
-            )
-            for year, data in data_dict.items()
-        }
-        # 100 signal, 100 bg events
+    data_dict = load_data(args.data_path, args.year, args.all_years)
+
+    if not args.inference_only:
         training_data_dict = {
-            year: pd.concat((data[:100], data[-100:]), axis=0)
-            for year, data in training_data_dict.items()
+            year: data[
+                # select only signal and `bg_keys` backgrounds for training - rest are only inferenced
+                np.sum(
+                    [data["Dataset"] == key for key in training_keys],
+                    axis=0,
+                ).astype(bool)
+            ]
+            for year, data in data_dict.items()
         }
 
-    if args.equalize_weights:
-        for year, data in training_data_dict.items():
-            print(
-                (
-                    f"Pre-equalization {year} sig total: "
-                    f'{np.sum(data[data["Dataset"] == sig_key][weight_key])}'
-                )
-            )
+        training_samples = np.unique(list(training_data_dict.values())[0]["Dataset"])
+        print("Training samples:", training_samples)
 
-            equalize_weights(data)
-
-            print(
-                (
-                    f"Post-equalization {year} sig total: "
-                    f'{np.sum(data[data["Dataset"] == sig_key][weight_key])}'
+        if args.test:
+            # get a sample of different processes
+            data_dict = {
+                year: pd.concat(
+                    (data[:50], data[1000000:1000050], data[2000000:2000050], data[-50:]), axis=0
+                )
+                for year, data in data_dict.items()
+            }
+            # 100 signal, 100 bg events
+            training_data_dict = {
+                year: pd.concat(
+                    (
+                        data[:150],
+                        data[
+                            np.sum(data["Dataset"] == sig_key) : np.sum(data["Dataset"] == sig_key)
+                            + 50
+                        ],
+                        data[
+                            np.sum(data["Dataset"] == "V+Jets")
+                            - 50 : np.sum(data["Dataset"] == "V+Jets")
+                        ],
+                        data[-50:],
+                    ),
+                    axis=0,
+                )
+                for year, data in training_data_dict.items()
+            }
+
+        if args.equalize_weights or args.equalize_weights_per_process:
+            for year, data in training_data_dict.items():
+                for key in training_keys:
+                    print(
+                        (
+                            f"Pre-equalization {year} {key} total: "
+                            f'{np.sum(data[data["Dataset"] == key][weight_key])}'
+                        )
+                    )
+
+                equalize_weights(data, args.equalize_weights, args.equalize_weights_per_process)
+
+                for key in training_keys:
+                    print(
+                        (
+                            f"Post-equalization {year} {key} total: "
+                            f'{np.sum(data[data["Dataset"] == key][weight_key])}'
+                        )
+                    )
+
+                print("")
+
+        if len(training_samples) > 0:
+            train, test = {}, {}
+            for year, data in training_data_dict.items():
+                train[year], test[year] = train_test_split(
+                    remove_neg_weights(data) if not args.absolute_weights else data,
+                    test_size=args.test_size,
+                    random_state=args.seed,
                 )
-            )
-
-    train, test = {}, {}
-
-    for year, data in training_data_dict.items():
-        train[year], test[year] = train_test_split(
-            remove_neg_weights(data) if not args.absolute_weights else data,
-            test_size=args.test_size,
-            random_state=args.seed,
-        )
 
     if args.evaluate_only or args.inference_only:
         model = xgb.XGBClassifier()
@@ -214,8 +277,8 @@ def main(args):
         model = train_model(
             get_X(train),
             get_X(test),
-            get_Y(train),
-            get_Y(test),
+            get_Y(train, args.multiclass),
+            get_Y(test, args.multiclass),
             get_weights(train, args.absolute_weights),
             get_weights(test, args.absolute_weights),
             args.model_dir,
@@ -225,10 +288,10 @@ def main(args):
         )
 
     if not args.inference_only:
-        evaluate_model(model, args.model_dir, test)
+        evaluate_model(model, args.model_dir, test, multiclass=args.multiclass)
 
     if not args.evaluate_only:
-        do_inference(model, args.model_dir, data_dict)
+        do_inference(model, args.model_dir, data_dict, multiclass=args.multiclass)
 
 
 def train_model(
@@ -273,6 +336,7 @@ def evaluate_model(
     model_dir: str,
     test: Dict[str, pd.DataFrame],
     txbb_threshold: float = 0.98,
+    multiclass: bool = False,
 ):
     """ """
     print("Evaluating model")
@@ -281,6 +345,7 @@ def evaluate_model(
     weights_test = get_weights(test)
 
     preds = model.predict_proba(get_X(test))
+    preds = preds[:, 0] if multiclass else preds[:, 1]
 
     var_labels = [var_label_map[var][1] for var in bdtVars]
 
@@ -298,15 +363,15 @@ def evaluate_model(
 
     sig_effs = [0.15, 0.2]
 
-    fpr, tpr, thresholds = roc_curve(Y_test, preds[:, 1], sample_weight=weights_test)
+    fpr, tpr, thresholds = roc_curve(Y_test, preds, sample_weight=weights_test)
     plotting.rocCurve(
         fpr,
         tpr,
-        auc(fpr, tpr),
+        # auc(fpr, tpr),
         sig_eff_lines=sig_effs,
-        title="ROC Curve",
+        title=None,
         plotdir=model_dir,
-        name="bdtroccurve",
+        name="bdtroc",
     )
 
     np.savetxt(f"{model_dir}/fpr.txt", fpr)
@@ -318,7 +383,7 @@ def evaluate_model(
         print(f"Threshold at {sig_eff} sig_eff: {thresh:0.4f}")
 
     if txbb_threshold > 0:
-        preds_txbb_thresholded = preds[:, 1].copy()
+        preds_txbb_thresholded = preds.copy()
         preds_txbb_thresholded[_txbb_thresholds(test, txbb_threshold)] = 0
 
         fpr_txbb_threshold, tpr_txbb_threshold, thresholds_txbb_threshold = roc_curve(
@@ -328,11 +393,11 @@ def evaluate_model(
         plotting.rocCurve(
             fpr_txbb_threshold,
             tpr_txbb_threshold,
-            auc(fpr_txbb_threshold, tpr_txbb_threshold),
+            # auc(fpr_txbb_threshold, tpr_txbb_threshold),
             sig_eff_lines=sig_effs,
-            title=f"ROC Curve Including Txbb > {txbb_threshold} Cut",
+            title=f"Including Txbb > {txbb_threshold} Cut",
             plotdir=model_dir,
-            name="bdtroccurve_txbb_cut",
+            name="bdtroc_txbb_cut",
         )
 
         np.savetxt(f"{model_dir}/fpr_txbb_threshold.txt", fpr_txbb_threshold)
@@ -349,6 +414,7 @@ def do_inference(
     model_dir: str,
     data_dict: Dict[str, pd.DataFrame],
     jec_jmsr_shifts: bool = True,
+    multiclass: bool = False,
 ):
     """ """
     import time
@@ -359,13 +425,21 @@ def do_inference(
         year_data_dict = {year: data}
         os.system(f"mkdir -p {model_dir}/inferences/{year}")
 
+        sample_order = list(pd.unique(data["Dataset"]))
+        value_counts = data["Dataset"].value_counts()
+        sample_order_dict = OrderedDict([(sample, value_counts[sample]) for sample in sample_order])
+
+        with open(f"{model_dir}/inferences/{year}/sample_order.txt", "w") as f:
+            f.write(str(sample_order_dict))
+
         print("Running inference")
         X = get_X(year_data_dict)
         model.get_booster().feature_names = bdtVars
 
         start = time.time()
-        preds = model.predict_proba(X)[:, 1]
+        preds = model.predict_proba(X)
         print(f"Finished in {time.time() - start:.2f}s")
+        preds = preds[:, :-1] if multiclass else preds[:, 1]  # save n-1 probs to save space
         np.save(f"{model_dir}/inferences/{year}/preds.npy", preds)
 
         if jec_jmsr_shifts:
@@ -374,7 +448,8 @@ def do_inference(
                 X, mcvars = get_X(year_data_dict, jec_shift=jshift)
                 # have to change model's feature names since we're passing in a dataframe
                 model.get_booster().feature_names = mcvars
-                preds = model.predict_proba(X)[:, 1]
+                preds = model.predict_proba(X)
+                preds = preds[:, :-1] if multiclass else preds[:, 1]
                 np.save(f"{model_dir}/inferences/{year}/preds_{jshift}.npy", preds)
 
             for jshift in jmsr_shifts:
@@ -382,7 +457,8 @@ def do_inference(
                 X, mcvars = get_X(year_data_dict, jmsr_shift=jshift)
                 # have to change model's feature names since we're passing in a dataframe
                 model.get_booster().feature_names = mcvars
-                preds = model.predict_proba(X)[:, 1]
+                preds = model.predict_proba(X)
+                preds = preds[:, :-1] if multiclass else preds[:, 1]
                 np.save(f"{model_dir}/inferences/{year}/preds_{jshift}.npy", preds)
 
 
@@ -397,17 +473,16 @@ def do_inference(
     )
     parser.add_argument(
         "--model-dir",
-        default="./",
+        default="testBDT",
         help="directory in which to save model and evaluation output",
         type=str,
     )
     parser.add_argument(
         "--year",
-        default="",
-        choices=["2016", "2016APV", "2017", "2018"],
+        choices=["2016", "2016APV", "2017", "2018", "all"],
         type=str,
+        required=True,
     )
-    utils.add_bool_arg(parser, "all-years", "Combine data from all years", default=False)
     utils.add_bool_arg(parser, "load-data", "Load pre-processed data if done already", default=True)
     utils.add_bool_arg(
         parser, "save-data", "Save pre-processed data if loading the data", default=True
@@ -419,9 +494,14 @@ def do_inference(
         help="Num events per sample to train on - if 0 train on all",
         type=int,
     )
-    utils.add_bool_arg(
-        parser, "preselection", "Apply preselection on events before training", default=True
-    )
+
+    parser.add_argument("--max-depth", default=6, help="xgboost param", type=int)
+    parser.add_argument("--min-child-weight", default=1, help="xgboost param", type=int)
+    parser.add_argument("--n-estimators", default=1000, help="xgboost param", type=int)
+
+    parser.add_argument("--rem-feats", default=3, help="remove N lowest importance feats", type=int)
+
+    utils.add_bool_arg(parser, "multiclass", "Classify each background separtely", default=True)
 
     utils.add_bool_arg(
         parser, "use-sample-weights", "Use properly scaled event weights", default=True
@@ -433,7 +513,13 @@ def do_inference(
         default=True,
     )
     utils.add_bool_arg(
-        parser, "equalize-weights", "Equalise signal and background weights", default=True
+        parser, "equalize-weights", "Equalise total signal and background weights", default=True
+    )
+    utils.add_bool_arg(
+        parser,
+        "equalize-weights-per-process",
+        "Equalise each backgrounds' weights too",
+        default=False,
     )
 
     parser.add_argument(
@@ -450,7 +536,9 @@ def do_inference(
 
     args = parser.parse_args()
 
-    if args.equalize_weights:
+    if args.equalize_weights or args.equalize_weights_per_process:
         args.use_sample_weights = True  # sample weights are used before equalizing
 
+    args.all_years = True if args.year == "all" else False
+
     main(args)
diff --git a/src/HHbbVV/postprocessing/VV_analysis.ipynb b/src/HHbbVV/postprocessing/VV_analysis.ipynb
index 199a348a..e6436e4e 100644
--- a/src/HHbbVV/postprocessing/VV_analysis.ipynb
+++ b/src/HHbbVV/postprocessing/VV_analysis.ipynb
@@ -1,8 +1,16 @@
 {
  "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Investigating LP Reweighting"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +21,7 @@
     "import warnings\n",
     "import pandas as pd\n",
     "from pandas.errors import SettingWithCopyWarning\n",
-    "from hh_vars import samples, sig_key, data_key\n",
+    "from hh_vars import nonres_samples, samples, nonres_sig_keys, data_key\n",
     "\n",
     "# ignore these because they don't seem to apply\n",
     "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
@@ -35,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,13 +53,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Loaded GluGluToHHTobbVV_node_cHHH1                       : 236504 entries\n",
       "Pre-selection GluGluToHHTobbVV yield: 5.12\n"
      ]
     }
@@ -69,16 +78,19 @@
     "    ],\n",
     "]\n",
     "\n",
-    "events_dict = utils.load_samples(signal_samples_dir, {sig_key: samples[sig_key]}, year, filters)\n",
+    "sig_key = nonres_sig_keys[0]\n",
+    "events_dict = utils.load_samples(\n",
+    "    signal_samples_dir, {sig_key: nonres_samples[sig_key]}, year, filters\n",
+    ")\n",
     "tot_weight = np.sum(events_dict[sig_key][\"weight\"]).values[0]\n",
-    "print(f\"Pre-selection {samples[sig_key]} yield: {tot_weight:.2f}\")\n",
+    "print(f\"Pre-selection {nonres_samples[sig_key]} yield: {tot_weight:.2f}\")\n",
     "\n",
     "cutflow = pd.DataFrame(index=[sig_key])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/src/HHbbVV/postprocessing/bash_scripts/ControlPlots.sh b/src/HHbbVV/postprocessing/bash_scripts/ControlPlots.sh
new file mode 100755
index 00000000..c0be40d0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/bash_scripts/ControlPlots.sh
@@ -0,0 +1,11 @@
+MAIN_DIR="../../.."
+TAG=23Aug23_JME_OR_plots
+
+for year in 2016APV 2016 2017 2018
+do
+    python -u postprocessing.py --control-plots --year $year --resonant --HEM2D \
+    --data-dir "${MAIN_DIR}/../data/skimmer/Feb24" \
+    --signal-data-dirs "${MAIN_DIR}/../data/skimmer/Jun10" "${MAIN_DIR}/../data/skimmer/23Aug22_5xhy" \
+    --sig-samples  'HHbbVV' 'VBFHHbbVV' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250' 'NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150' \
+    --plot-dir "${MAIN_DIR}/plots/PostProcessing/$TAG"
+done
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/corrections.py b/src/HHbbVV/postprocessing/corrections.py
index 312ecb72..2059a85d 100644
--- a/src/HHbbVV/postprocessing/corrections.py
+++ b/src/HHbbVV/postprocessing/corrections.py
@@ -80,11 +80,17 @@ def apply_txbb_sfs(
     bb_pt = utils.get_feat(events, "bbFatJetPt", bb_mask)
 
     for var in ["up", "down"]:
-        events[f"{weight_key}_txbb_{var}"] = events[weight_key] * txbb_sf_lookups[year][var](
-            bb_txbb, bb_pt
-        )
+        if len(events[weight_key]):
+            events[f"{weight_key}_txbb_{var}"] = events[weight_key] * txbb_sf_lookups[year][var](
+                bb_txbb, bb_pt
+            )
+        else:
+            events[f"{weight_key}_txbb_{var}"] = events[weight_key]
 
-    events[weight_key] = events[weight_key] * txbb_sf_lookups[year]["nom"](bb_txbb, bb_pt)
+    if len(events[weight_key]):
+        events[weight_key] = events[weight_key] * txbb_sf_lookups[year]["nom"](bb_txbb, bb_pt)
+    else:
+        events[weight_key] = events[weight_key]
 
 
 trig_effs = {}
@@ -124,7 +130,6 @@ def _get_uncorr_trig_eff_unc_per_sample(
     total_errs = []
 
     for jet, h in hists.items():
-        # print(jet)
         h.fill(
             jet1txbb=utils.get_feat(events, f"{jet}FatJetParticleNetMD_Txbb", bb_mask),
             jet1pt=utils.get_feat(events, f"{jet}FatJetPt", bb_mask),
@@ -132,20 +137,10 @@ def _get_uncorr_trig_eff_unc_per_sample(
             weight=utils.get_feat(events, f"{weight_key}_noTrigEffs"),
         )
 
-        # print(h)
-
-        # print(h.values(flow=True) * np.nan_to_num(effs.view(flow=True)))
-
         total = np.sum(h.values(flow=True) * np.nan_to_num(effs.view(flow=True)))
-
-        # print(f"{total = }")
-
         totals.append(total)
 
         total_err = np.linalg.norm((h.values(flow=True) * np.nan_to_num(errs)).reshape(-1))
-
-        # print(f"{total_err = }")
-
         total_errs.append(total_err)
 
     total = np.sum(totals)
@@ -171,8 +166,6 @@ def get_uncorr_trig_eff_unc(
             if not len(events) or (sel is not None and not len(events[sel[sample]])):
                 continue
 
-            # print(sample)
-
             total, total_err = _get_uncorr_trig_eff_unc_per_sample(
                 events[sel[sample]] if sel is not None else events,
                 bb_masks[sample][sel[sample]] if sel is not None else bb_masks[sample],
@@ -180,17 +173,167 @@ def get_uncorr_trig_eff_unc(
                 weight_key,
             )
 
-            # print(f"Sample {total = }")
-            # print(f"Sample {total_err = }")
-
             totals.append(total)
             total_errs.append(total_err)
 
     total = np.sum(totals)
     total_err = np.linalg.norm(total_errs)
 
-    # print("")
-    # print(f"{total = }")
-    # print(f"{total_err = }")
-
     return total, total_err
+
+
+def postprocess_lpsfs(
+    events: pd.DataFrame,
+    num_jets: int = 2,
+    num_lp_sf_toys: int = 100,
+    save_all: bool = True,
+    weight_key: str = "finalWeight",
+):
+    """
+    (1) Splits LP SFs into bb and VV based on gen matching.
+    (2) Sets defaults for unmatched jets.
+    (3) Cuts of SFs at 10 and normalises.
+    """
+
+    # for jet in ["bb", "VV"]:
+    for jet in ["VV"]:
+        # temp dict
+        td = {}
+
+        # defaults of 1 for jets which aren't matched to anything - i.e. no SF
+        for key in ["lp_sf_nom", "lp_sf_sys_down", "lp_sf_sys_up"]:
+            td[key] = np.ones(len(events))
+
+        td["lp_sf_toys"] = np.ones((len(events), num_lp_sf_toys))
+
+        # defaults of 0 - i.e. don't contribute to unc.
+        for key in ["lp_sf_double_matched_event", "lp_sf_unmatched_quarks", "lp_sf_num_sjpt_gt350"]:
+            td[key] = np.zeros(len(events))
+
+        # lp sfs saved for both jets
+        if events["lp_sf_sys_up"].shape[1] == 2:
+            # ignore rare case (~0.002%) where two jets are matched to same gen Higgs
+            events.loc[np.sum(events[f"ak8FatJetH{jet}"], axis=1) > 1, f"ak8FatJetH{jet}"] = 0
+            jet_match = events[f"ak8FatJetH{jet}"].astype(bool)
+
+            # fill values from matched jets
+            for j in range(num_jets):
+                offset = num_lp_sf_toys + 1
+                td["lp_sf_nom"][jet_match[j]] = events["lp_sf_lnN"][jet_match[j]][j * offset]
+                td["lp_sf_toys"][jet_match[j]] = events["lp_sf_lnN"][jet_match[j]].loc[
+                    :, j * offset + 1 : (j + 1) * offset - 1
+                ]
+
+                for key in [
+                    "lp_sf_sys_down",
+                    "lp_sf_sys_up",
+                    "lp_sf_double_matched_event",
+                    "lp_sf_unmatched_quarks",
+                    "lp_sf_num_sjpt_gt350",
+                ]:
+                    td[key][jet_match[j]] = events[key][jet_match[j]][j]
+
+        # lp sfs saved only for hvv jet
+        elif events["lp_sf_sys_up"].shape[1] == 1:
+            # ignore rare case (~0.002%) where two jets are matched to same gen Higgs
+            jet_match = np.sum(events[f"ak8FatJetH{jet}"], axis=1) == 1
+
+            # fill values from matched jets
+            td["lp_sf_nom"][jet_match] = events["lp_sf_lnN"][jet_match][0]
+            td["lp_sf_toys"][jet_match] = events["lp_sf_lnN"][jet_match].loc[:, 1:]
+
+            for key in [
+                "lp_sf_sys_down",
+                "lp_sf_sys_up",
+                "lp_sf_double_matched_event",
+                "lp_sf_unmatched_quarks",
+                "lp_sf_num_sjpt_gt350",
+            ]:
+                td[key][jet_match] = events[key][jet_match].squeeze()
+
+        else:
+            raise ValueError("LP SF shapes are invalid")
+
+        for key in ["lp_sf_nom", "lp_sf_toys", "lp_sf_sys_down", "lp_sf_sys_up"]:
+            # cut off at 10
+            td[key] = np.minimum(td[key], 10)
+            # normalise
+            td[key] = td[key] / np.mean(td[key], axis=0)
+
+        # add to dataframe
+        if save_all:
+            td = pd.concat(
+                [pd.DataFrame(v.reshape(v.shape[0], -1)) for k, v in td.items()],
+                axis=1,
+                keys=[f"{jet}_{key}" for key in td.keys()],
+            )
+
+            events = pd.concat((events, td), axis=1)
+        else:
+            key = "lp_sf_nom"
+            events[f"{jet}_{key}"] = td[key]
+
+    return events
+
+
+def get_lpsf(
+    events: pd.DataFrame, sel: np.ndarray = None, VV: bool = True, weight_key: str = "finalWeight"
+):
+    """
+    Calculates LP SF and uncertainties in current phase space. ``postprocess_lpsfs`` must be called first.
+    Assumes bb/VV candidates are matched correctly - this is false for <0.1% events for the cuts we use.
+    """
+
+    jet = "VV" if VV else "bb"
+    if sel is not None:
+        events = events[sel]
+
+    tot_matched = np.sum(np.sum(events[f"ak8FatJetH{jet}"].astype(bool)))
+
+    weight = events[weight_key].values
+    tot_pre = np.sum(weight)
+    tot_post = np.sum(weight * events[f"{jet}_lp_sf_nom"][0])
+    lp_sf = tot_post / tot_pre
+
+    uncs = {}
+
+    # difference in yields between up and down shifts on LP SFs
+    uncs["syst_unc"] = np.abs(
+        (
+            np.sum(events[f"{jet}_lp_sf_sys_up"][0] * weight)
+            - np.sum(events[f"{jet}_lp_sf_sys_down"][0] * weight)
+        )
+        / 2
+        / tot_post
+    )
+
+    # std of yields after all smearings
+    uncs["stat_unc"] = (
+        np.std(np.sum(weight[:, np.newaxis] * events[f"{jet}_lp_sf_toys"].values, axis=0))
+        / tot_post
+    )
+
+    # fraction of subjets > 350 * 0.21 measured by CASE
+    uncs["sj_pt_unc"] = (np.sum(events[f"{jet}_lp_sf_num_sjpt_gt350"][0]) / tot_matched) * 0.21
+
+    if VV:
+        num_prongs = events["ak8FatJetHVVNumProngs"][0]
+
+        sj_matching_unc = np.sum(events[f"{jet}_lp_sf_double_matched_event"][0])
+        for nump in range(2, 5):
+            sj_matching_unc += (
+                np.sum(events[f"{jet}_lp_sf_unmatched_quarks"][0][num_prongs == nump]) / nump
+            )
+
+        uncs["sj_matching_unc"] = sj_matching_unc / tot_matched
+    else:
+        num_prongs = 2
+        uncs["sj_matching_unc"] = (
+            (np.sum(events[f"{jet}_lp_sf_unmatched_quarks"][0]) / num_prongs)
+            + np.sum(events[f"{jet}_lp_sf_double_matched_event"][0])
+        ) / tot_matched
+
+    tot_rel_unc = np.linalg.norm([val for val in uncs.values()])
+    tot_unc = lp_sf * tot_rel_unc
+
+    return lp_sf, tot_unc, uncs
diff --git a/src/HHbbVV/postprocessing/hh_vars.py b/src/HHbbVV/postprocessing/hh_vars.py
index cbc29ba0..b5e545e9 100644
--- a/src/HHbbVV/postprocessing/hh_vars.py
+++ b/src/HHbbVV/postprocessing/hh_vars.py
@@ -19,45 +19,176 @@
 
 # order is important for loading BDT preds
 # label: selector
+# when loading samples, assigns all sample names which start with `selector` to the `label` dataframe
+# if `selector` starts with `*`, assigns all sample names which have `selector` in it
 samples = OrderedDict(
     [
-        ("HHbbVV", "GluGluToHHTobbVV"),
         ("QCD", "QCD"),
         ("TT", "TT"),
         ("ST", "ST"),
         ("V+Jets", ("WJets", "ZJets")),
         ("Diboson", ("WW", "WZ", "ZZ")),
+        # break this down into production modes for combination
+        # https://gitlab.cern.ch/hh/naming-conventions#single-h-backgrounds
+        ("ggFHbb", "GluGluHToBB"),
+        ("VBFHbb", "VBFHToBB"),
+        ("ZHbb", "ZH_HToBB"),
+        ("WHbb", ("WminusH_HToBB", "WplusH_HToBB")),
+        ("ggZHbb", "ggZH_HToBB"),
+        ("ttHbb", "ttHToBB"),
+        # ("HWW", ("*HToWW", "*HToNonbb")),
+        # ("HH", ("GluGluToHHTo4B_node_cHHH1_preUL")),
         ("Data", "JetHT"),
     ]
 )
 
-res_samples = OrderedDict([])
-# for mX in [600, 1000, 1600, 2000, 3000]:
-#     for mY in [100, 450, 800, 1000, 1800, 2400]:
-#         res_samples[f"X{mX}->H(bb)Y{mY}(VV)"] = f"NMSSM_XToYH_MX{mX}_MY{mY}_HTo2bYTo2W_hadronicDecay"
 
+data_key = "Data"
+qcd_key = "QCD"
+bg_keys = [key for key in samples.keys() if key != data_key]
+hbb_bg_keys = [key for key in samples.keys() if "Hbb" in key]
+
+# change HHbbVV to hbbhVV
+nonres_samples = OrderedDict(
+    [
+        ("HHbbVV", "GluGluToHHTobbVV_node_cHHH1"),
+        ("ggHH_kl_2p45_kt_1_HHbbVV", "GluGluToHHTobbVV_node_cHHH2p45"),
+        ("ggHH_kl_5_kt_1_HHbbVV", "GluGluToHHTobbVV_node_cHHH5"),
+        ("ggHH_kl_0_kt_1_HHbbVV", "GluGluToHHTobbVV_node_cHHH0"),
+        # ("qqHH_CV_1_C2V_1_kl_1_HHbbVV", "VBF_HHTobbVV_CV_1_C2V_1_C3_1"),
+        ("VBFHHbbVV", "VBF_HHTobbVV_CV_1_C2V_1_C3_1"),
+        ("qqHH_CV_1_C2V_0_kl_1_HHbbVV", "VBF_HHTobbVV_CV_1_C2V_0_C3_1"),
+        ("qqHH_CV_1p5_C2V_1_kl_1_HHbbVV", "VBF_HHTobbVV_CV_1_5_C2V_1_C3_1"),
+        ("qqHH_CV_1_C2V_1_kl_2_HHbbVV", "VBF_HHTobbVV_CV_1_C2V_1_C3_2"),
+        ("qqHH_CV_1_C2V_2_kl_1_HHbbVV", "VBF_HHTobbVV_CV_1_C2V_2_C3_1"),
+        ("qqHH_CV_1_C2V_1_kl_0_HHbbVV", "VBF_HHTobbVV_CV_1_C2V_1_C3_0"),
+        ("qqHH_CV_0p5_C2V_1_kl_1_HHbbVV", "VBF_HHTobbVV_CV_0_5_C2V_1_C3_1"),
+    ]
+)
+nonres_sig_keys = list(nonres_samples.keys())
+
+res_samples = OrderedDict([])
 
 res_mps = [
-    (600, 100),
     (1000, 100),
-    # (1000, 450),
+    (1000, 125),
+    (1000, 150),
+    (1000, 190),
+    (1000, 250),
+    (1000, 60),
+    (1000, 80),
+    (1200, 100),
+    (1200, 150),
+    (1200, 190),
+    (1200, 250),
+    (1200, 60),
+    (1200, 80),
+    (1400, 100),
+    (1400, 125),
+    (1400, 150),
+    (1400, 190),
+    (1400, 250),
+    (1400, 60),
+    (1400, 80),
+    (1600, 100),
+    (1600, 125),
+    (1600, 150),
+    (1600, 190),
+    (1600, 250),
+    (1600, 60),
+    (1600, 80),
+    (1800, 100),
+    (1800, 125),
+    (1800, 150),
+    (1800, 190),
+    (1800, 250),
+    (1800, 60),
+    (1800, 80),
     (2000, 100),
-    # (2000, 450),
-    (2000, 1000),
+    (2000, 125),
+    (2000, 150),
+    (2000, 190),
+    (2000, 250),
+    (2000, 60),
+    (2000, 80),
+    (2200, 100),
+    (2200, 125),
+    (2200, 150),
+    (2200, 190),
+    (2200, 250),
+    (2200, 60),
+    (2200, 80),
+    (2400, 100),
+    (2400, 125),
+    (2400, 150),
+    (2400, 190),
+    (2400, 250),
+    (2400, 60),
+    (2400, 80),
+    (2600, 100),
+    (2600, 125),
+    (2600, 150),
+    (2600, 190),
+    (2600, 250),
+    (2600, 80),
+    (2800, 100),
+    (2800, 125),
+    (2800, 150),
+    (2800, 190),
+    (2800, 250),
+    (2800, 60),
+    (2800, 80),
     (3000, 100),
-    # (3000, 450),
-    # (3000, 1000),
+    (3000, 125),
+    (3000, 150),
+    (3000, 190),
+    (3000, 250),
+    (3000, 60),
+    (3000, 80),
+    (3500, 100),
+    (3500, 125),
+    (3500, 150),
+    (3500, 190),
+    (3500, 250),
+    (3500, 60),
+    (3500, 80),
+    (4000, 100),
+    (4000, 125),
+    (4000, 150),
+    (4000, 190),
+    (4000, 250),
+    (4000, 60),
+    (4000, 80),
+    (600, 100),
+    (600, 125),
+    (600, 150),
+    (600, 250),
+    (600, 60),
+    (600, 80),
+    (700, 100),
+    (700, 125),
+    (700, 150),
+    (700, 250),
+    (700, 60),
+    (700, 80),
+    (800, 100),
+    (800, 125),
+    (800, 150),
+    (800, 250),
+    (800, 60),
+    (800, 80),
+    (900, 100),
+    (900, 125),
+    (900, 150),
+    (900, 250),
+    (900, 60),
+    (900, 80),
 ]
 
 for mX, mY in res_mps:
-    res_samples[f"X{mX}->H(bb)Y{mY}(VV)"] = f"NMSSM_XToYH_MX{mX}_MY{mY}_HTo2bYTo2W_hadronicDecay"
+    res_samples[f"X[{mX}]->H(bb)Y[{mY}](VV)"] = f"NMSSM_XToYHTo2W2BTo4Q2B_MX-{mX}_MY-{mY}"
 
-sig_keys = ["HHbbVV"]
 res_sig_keys = list(res_samples.keys())
-data_key = "Data"
-qcd_key = "QCD"
-bg_keys = [key for key in samples.keys() if key not in sig_keys + [data_key]]
-
 
 # from https://cms.cern.ch/iCMS/jsp/db_notes/noteInfo.jsp?cmsnoteid=CMS%20AN-2021/005
 txbb_wps = {
diff --git a/src/HHbbVV/postprocessing/plotting.py b/src/HHbbVV/postprocessing/plotting.py
index f7b614b8..693a3bd9 100644
--- a/src/HHbbVV/postprocessing/plotting.py
+++ b/src/HHbbVV/postprocessing/plotting.py
@@ -9,6 +9,7 @@
 import matplotlib.pyplot as plt
 import mplhep as hep
 import matplotlib.ticker as mticker
+import matplotlib
 
 plt.style.use(hep.style.CMS)
 hep.style.use("CMS")
@@ -16,16 +17,27 @@
 formatter.set_powerlimits((-3, 3))
 plt.rcParams.update({"font.size": 20})
 
+import hist
 from hist import Hist
-from hist.intervals import ratio_uncertainty
+from hist.intervals import ratio_uncertainty, poisson_interval
 
 from typing import Dict, List, Union, Tuple
 from numpy.typing import ArrayLike
 
-from hh_vars import LUMI, sig_keys, res_sig_keys, data_key
+from hh_vars import LUMI, data_key, hbb_bg_keys
 
 from copy import deepcopy
 
+
+bg_order = ["Diboson", "HH", "HWW", "Hbb", "ST", "V+Jets", "TT", "QCD"]
+
+sample_label_map = {
+    "HHbbVV": "ggF HHbbVV",
+    "VBFHHbbVV": "VBF HHbbVV",
+    "qqHH_CV_1_C2V_0_kl_1_HHbbVV": r"VBF HHbbVV ($\kappa_{2V} = 0$)",
+    "qqHH_CV_1_C2V_2_kl_1_HHbbVV": r"VBF HHbbVV ($\kappa_{2V} = 2$)",
+}
+
 colours = {
     "darkblue": "#1f78b4",
     "lightblue": "#a6cee3",
@@ -34,27 +46,135 @@
     "darkred": "#A21315",
     "orange": "#ff7f00",
     "green": "#7CB518",
+    "mantis": "#81C14B",
+    "forestgreen": "#2E933C",
+    "darkgreen": "#064635",
     "purple": "#9381FF",
+    "slategray": "#63768D",
+    "deeppurple": "#36213E",
+    "ashgrey": "#ACBFA4",
+    "canary": "#FFE51F",
+    "arylideyellow": "#E3C567",
+    "earthyellow": "#D9AE61",
+    "satinsheengold": "#C8963E",
+    "flax": "#EDD382",
+    "vanilla": "#F2F3AE",
+    "dutchwhite": "#F5E5B8",
 }
+
 bg_colours = {
     "QCD": "lightblue",
     "TT": "darkblue",
     "V+Jets": "green",
     "ST": "orange",
-    "Diboson": "purple",
+    "Diboson": "canary",
+    "Hbb": "lightred",
+    "HWW": "deeppurple",
+    "HH": "ashgrey",
 }
+
 sig_colour = "red"
 
 sig_colours = [
     "#23CE6B",
+    "#7F2CCB",
     "#ffbaba",
     "#ff7b7b",
     "#ff5252",
-    "#EDB458",
     "#a70000",
+    "#885053",
+    "#3C0919",
 ]
 
-bg_order = ["Diboson", "ST", "V+Jets", "TT", "QCD"]
+
+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()
+
+    h.view()[-1] = hbb_hist
+
+    return h, bg_keys
+
+
+def _process_samples(sig_keys, bg_keys, bg_colours, sig_scale_dict, variation):
+    # set up samples, colours and labels
+    bg_keys = [key for key in bg_order if key in bg_keys]
+    bg_colours = [colours[bg_colours[sample]] for sample in bg_keys]
+    bg_labels = deepcopy(bg_keys)
+
+    if sig_scale_dict is None:
+        sig_scale_dict = OrderedDict([(sig_key, 1.0) for sig_key in sig_keys])
+    else:
+        sig_scale_dict = deepcopy(sig_scale_dict)
+
+    sig_labels = OrderedDict()
+    for sig_key, sig_scale in sig_scale_dict.items():
+        label = sig_key if sig_key not in sample_label_map else sample_label_map[sig_key]
+
+        if sig_scale == 1:
+            label = label
+        elif sig_scale <= 100:
+            label = f"{label} $\\times$ {sig_scale:.0f}"
+        else:
+            label = f"{label} $\\times$ {sig_scale:.1e}"
+
+        sig_labels[sig_key] = label
+
+    # set up systematic variations if needed
+    if variation is not None:
+        wshift, shift, wsamples = variation
+        skey = {"up": " Up", "down": " Down"}[shift]
+
+        for i, key in enumerate(bg_keys):
+            if key in wsamples:
+                bg_keys[i] += f"_{wshift}_{shift}"
+                bg_labels[i] += skey
+
+        for sig_key in list(sig_scale_dict.keys()):
+            if sig_key in wsamples:
+                new_key = f"{sig_key}_{wshift}_{shift}"
+                sig_scale_dict[new_key] = sig_scale_dict[sig_key]
+                sig_labels[new_key] = sig_labels[sig_key] + skey
+                del sig_scale_dict[sig_key], sig_labels[sig_key]
+
+    return bg_keys, bg_colours, bg_labels, sig_scale_dict, sig_labels
+
+
+def _divide_bin_widths(hists, data_err):
+    """Divide histograms by bin widths"""
+    edges = hists.axes[1].edges
+    bin_widths = edges[1:] - edges[:-1]
+
+    if data_err is None:
+        data_err = (
+            np.abs(poisson_interval(hists[data_key, ...]) - hists[data_key, ...]) / bin_widths
+        )
+
+    hists = hists / bin_widths[np.newaxis, :]
+    return hists, data_err
 
 
 def _fill_error(ax, edges, down, up, scale=1):
@@ -69,59 +189,107 @@ def _fill_error(ax, edges, down, up, scale=1):
     )
 
 
+def _asimov_significance(s, b):
+    """Asimov estimate of discovery significance (with no systematic uncertainties).
+    See e.g. https://www.pp.rhul.ac.uk/~cowan/atlas/cowan_atlas_15feb11.pdf.
+    Or for more explanation: https://www.pp.rhul.ac.uk/~cowan/stat/cowan_munich16.pdf
+    """
+    return np.sqrt(2 * ((s + b) * np.log(1 + (s / b)) - s))
+
+
 def ratioHistPlot(
     hists: Hist,
     year: str,
     sig_keys: List[str],
     bg_keys: List[str],
+    sig_colours: List[str] = sig_colours,
     bg_colours: Dict[str, str] = bg_colours,
-    sig_colours: str = sig_colours,
     sig_err: Union[ArrayLike, str] = None,
     data_err: Union[ArrayLike, bool, None] = None,
     title: str = None,
     blind_region: list = None,
     name: str = "",
-    sig_scale_dict: float = None,
+    sig_scale_dict: OrderedDict[str, float] = None,
+    ylim: int = None,
     show: bool = True,
     variation: Tuple = None,
+    plot_data: bool = True,
+    bg_order: List[str] = bg_order,
+    log: bool = False,
+    ratio_ylims: List[float] = [0, 2],
+    divide_bin_width: bool = False,
+    plot_significance: bool = False,
+    significance_dir: str = "right",
+    axrax: Tuple = None,
 ):
     """
     Makes and saves a histogram plot, with backgrounds stacked, signal separate (and optionally
     scaled) with a data/mc ratio plot below
-    """
 
-    bg_keys = [key for key in bg_order if key in bg_keys]
-    bg_colours = [colours[bg_colours[sample]] for sample in bg_keys]
-    bg_labels = deepcopy(bg_keys)
-
-    if sig_scale_dict is None:
-        sig_scale_dict = OrderedDict([(sig_key, 1.0) for sig_key in sig_keys])
+    Args:
+        hists (Hist): input histograms per sample to plot
+        year (str): datataking year
+        sig_keys (List[str]): signal keys
+        bg_keys (List[str]): background keys
+        sig_colours (Dict[str, str], optional): dictionary of colours per signal. Defaults to sig_colours.
+        bg_colours (Dict[str, str], optional): dictionary of colours per background. Defaults to bg_colours.
+        sig_err (Union[ArrayLike, str], optional): plot error on signal.
+          if string, will take up down shapes from the histograms (assuming they're saved as "{sig_key}_{sig_err}_{up/down}")
+          if 1D Array, will take as error per bin
+        data_err (Union[ArrayLike, bool, None], optional): plot error on data.
+          if True, will plot poisson error per bin
+          if array, will plot given errors per bin
+        title (str, optional): plot title. Defaults to None.
+        blind_region (list): [min, max] range of values which should be blinded in the plot
+          i.e. Data set to 0 in those bins
+        name (str): name of file to save plot
+        sig_scale_dict (Dict[str, float]): if scaling signals in the plot, dictionary of factors
+          by which to scale each signal
+        ylim (optional): y-limit on plot
+        show (bool): show plots or not
+        variation (Tuple): Tuple of
+          (wshift: name of systematic e.g. pileup, shift: up or down, wsamples: list of samples which are affected by this)
+        plot_data (bool): plot data
+        bg_order (List[str]): order in which to plot backgrounds
+        ratio_ylims (List[float]): y limits on the ratio plots
+        divide_bin_width (bool): divide yields by the bin width (for resonant fit regions)
+        plot_significance (bool): plot Asimov significance below ratio plot
+        significance_dir (str): "Direction" for significance. i.e. a > cut ("right"), a < cut ("left"), or per-bin ("bin").
+        axrax (Tuple): optionally input ax and rax instead of creating new ones
+    """
+    # 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)
 
-    sig_labels = OrderedDict(
-        [
-            (sig_key, f"{sig_key} $\\times$ {sig_scale:.1e}" if sig_scale != 1 else sig_key)
-            for sig_key, sig_scale in sig_scale_dict.items()
-        ]
+    bg_keys, bg_colours, bg_labels, sig_scale_dict, sig_labels = _process_samples(
+        sig_keys, bg_keys, bg_colours, sig_scale_dict, variation
     )
 
-    if variation is not None:
-        wshift, shift, wsamples = variation
-        skey = {"up": " Up", "down": " Down"}[shift]
+    pre_divide_hists = hists
+    if divide_bin_width:
+        hists, data_err = _divide_bin_widths(hists, data_err)
 
-        for i, key in enumerate(bg_keys):
-            if key in wsamples:
-                bg_keys[i] += f"_{wshift}_{shift}"
-                bg_labels[i] += skey
+    # set up plots
+    if axrax is not None:
+        if plot_significance:
+            raise RuntimeError("Significance plots with input axes not implemented yet.")
 
-        if sig_key in wsamples:
-            sig_key = f"{sig_key}_{wshift}_{shift}"
-            sig_label += skey
+        ax, rax = axrax
+        ax.sharex(rax)
+    elif plot_significance:
+        fig, (ax, rax, sax) = plt.subplots(
+            3, 1, figsize=(12, 18), gridspec_kw=dict(height_ratios=[3, 1, 1], hspace=0), sharex=True
+        )
+    else:
+        fig, (ax, rax) = plt.subplots(
+            2, 1, figsize=(12, 14), gridspec_kw=dict(height_ratios=[3, 1], hspace=0), sharex=True
+        )
 
-    fig, (ax, rax) = plt.subplots(
-        2, 1, figsize=(12, 14), gridspec_kw=dict(height_ratios=[3, 1], hspace=0), sharex=True
-    )
+    # plot histograms
+    y_label = r"Events / Bin Width (GeV$^{-1}$)" if divide_bin_width else "Events"
+    ax.set_ylabel(y_label)
 
-    ax.set_ylabel("Events")
+    # background samples
     hep.histplot(
         [hists[sample, :] for sample in bg_keys],
         ax=ax,
@@ -130,14 +298,18 @@ def ratioHistPlot(
         label=bg_labels,
         color=bg_colours,
     )
-    hep.histplot(
-        [hists[sig_key, :] * sig_scale for sig_key, sig_scale in sig_scale_dict.items()],
-        ax=ax,
-        histtype="step",
-        label=list(sig_labels.values()),
-        color=sig_colours,
-    )
 
+    # signal samples
+    if len(sig_scale_dict):
+        hep.histplot(
+            [hists[sig_key, :] * sig_scale for sig_key, sig_scale in sig_scale_dict.items()],
+            ax=ax,
+            histtype="step",
+            label=list(sig_labels.values()),
+            color=sig_colours[: len(sig_keys)],
+        )
+
+    # plot signal errors
     if type(sig_err) == str:
         scolours = {"down": colours["lightred"], "up": colours["darkred"]}
         for skey, shift in [("Up", "up"), ("Down", "down")]:
@@ -149,8 +321,9 @@ def ratioHistPlot(
                 yerr=0,
                 ax=ax,
                 histtype="step",
-                label=f"{sig_key} {skey}",
-                color=scolours[shift],
+                label=[f"{sig_key} {skey}" for sig_key in sig_scale_dict],
+                alpha=0.6,
+                color=sig_colours[: len(sig_keys)],
             )
     elif sig_err is not None:
         for sig_key, sig_scale in sig_scale_dict.items():
@@ -162,27 +335,84 @@ def ratioHistPlot(
                 sig_scale,
             )
 
-    hep.histplot(
-        hists[data_key, :], ax=ax, yerr=data_err, histtype="errorbar", label=data_key, color="black"
-    )
+    # plot data
+    if plot_data:
+        hep.histplot(
+            hists[data_key, :],
+            ax=ax,
+            yerr=data_err,
+            histtype="errorbar",
+            label=data_key,
+            color="black",
+        )
+
+    if log:
+        ax.set_yscale("log")
+
     ax.legend()
-    ax.set_ylim(0)
 
-    bg_tot = sum([hists[sample, :] for sample in bg_keys])
-    yerr = ratio_uncertainty(hists[data_key, :].values(), bg_tot.values(), "poisson")
+    y_lowlim = 0 if not log else 1e-3
+    if ylim is not None:
+        ax.set_ylim([y_lowlim, ylim])
+    else:
+        ax.set_ylim(y_lowlim)
+
+    # plot ratio below
+    if plot_data:
+        bg_tot = sum([pre_divide_hists[sample, :] for sample in bg_keys])
+        yerr = ratio_uncertainty(pre_divide_hists[data_key, :].values(), bg_tot.values(), "poisson")
+
+        hep.histplot(
+            pre_divide_hists[data_key, :] / (bg_tot.values() + 1e-5),
+            yerr=yerr,
+            ax=rax,
+            histtype="errorbar",
+            color="black",
+            capsize=4,
+        )
+    else:
+        rax.set_xlabel(hists.axes[1].label)
 
-    hep.histplot(
-        hists[data_key, :] / (bg_tot.values() + 1e-5),
-        yerr=yerr,
-        ax=rax,
-        histtype="errorbar",
-        color="black",
-        capsize=4,
-    )
     rax.set_ylabel("Data/MC")
-    rax.set_ylim([0, 2])
+    rax.set_ylim(ratio_ylims)
     rax.grid()
 
+    if plot_significance:
+        bg_tot = sum([pre_divide_hists[sample, :] for sample in bg_keys]).values()
+        sigs = [pre_divide_hists[sig_key, :].values() for sig_key in sig_scale_dict]
+
+        if significance_dir == "left":
+            bg_tot = np.cumsum(bg_tot[::-1])[::-1]
+            sigs = [np.cumsum(sig[::-1])[::-1] for sig in sigs]
+            sax.set_ylabel(r"Asimov Sign. for $\leq$ Cuts")
+        elif significance_dir == "right":
+            bg_tot = np.cumsum(bg_tot)
+            sigs = [np.cumsum(sig) for sig in sigs]
+            sax.set_ylabel(r"Asimov Sign. for $\geq$ Cuts")
+        elif significance_dir == "bin":
+            sax.set_ylabel("Asimov Sign. per Bin")
+        else:
+            raise RuntimeError(
+                'Invalid value for ``significance_dir``. Options are ["left", "right", "bin"].'
+            )
+
+        edges = pre_divide_hists.axes[1].edges
+        hep.histplot(
+            [(_asimov_significance(sig, bg_tot), edges) for sig in sigs],
+            ax=sax,
+            histtype="step",
+            label=[
+                sig_key if sig_key not in sample_label_map else sample_label_map[sig_key]
+                for sig_key in sig_scale_dict
+            ],
+            color=sig_colours[: len(sig_keys)],
+        )
+
+        sax.legend(fontsize=12)
+        sax.set_yscale("log")
+        sax.set_ylim([1e-7, 10])
+        sax.set_xlabel(hists.axes[1].label)
+
     if title is not None:
         ax.set_title(title, y=1.08)
 
@@ -198,6 +428,123 @@ def ratioHistPlot(
         hep.cms.label(
             "Work in Progress", data=True, lumi=f"{LUMI[year] / 1e3:.0f}", year=year, ax=ax
         )
+
+    if axrax is None:
+        if len(name):
+            plt.savefig(name, bbox_inches="tight")
+
+        if show:
+            plt.show()
+        else:
+            plt.close()
+
+
+def ratioLinePlot(
+    hists: Hist,
+    bg_keys: List[str],
+    year: str,
+    bg_colours: Dict[str, str] = bg_colours,
+    sig_colour: str = sig_colour,
+    bg_err: Union[np.ndarray, str] = None,
+    data_err: Union[ArrayLike, bool, None] = None,
+    title: str = None,
+    blind_region: list = None,
+    pulls: bool = False,
+    name: str = "",
+    sig_scale: float = 1.0,
+    show: bool = True,
+):
+    """
+    Makes and saves a histogram plot, with backgrounds stacked, signal separate (and optionally
+    scaled) with a data/mc ratio plot below
+    """
+    plt.rcParams.update({"font.size": 24})
+
+    fig, (ax, rax) = plt.subplots(
+        2, 1, figsize=(12, 14), gridspec_kw=dict(height_ratios=[3, 1], hspace=0), sharex=True
+    )
+
+    bg_tot = np.sum(hists[bg_keys, :].values(), axis=0)
+    plot_hists = [hists[sample, :] for sample in bg_keys]
+
+    ax.set_ylabel("Events")
+    hep.histplot(
+        plot_hists + [sum(plot_hists)],
+        ax=ax,
+        histtype="step",
+        label=bg_keys + ["Total"],
+        color=[colours[bg_colours[sample]] for sample in bg_keys] + ["black"],
+        yerr=0,
+    )
+
+    if bg_err is not None:
+        ax.fill_between(
+            np.repeat(hists.axes[1].edges, 2)[1:-1],
+            np.repeat(bg_tot - bg_err, 2),
+            np.repeat(bg_tot + bg_err, 2),
+            color="black",
+            alpha=0.2,
+            hatch="//",
+            linewidth=0,
+        )
+
+    if sig_key in hists:
+        hep.histplot(
+            hists[sig_key, :] * sig_scale,
+            ax=ax,
+            histtype="step",
+            label=f"{sig_key} $\\times$ {sig_scale:.1e}" if sig_scale != 1 else sig_key,
+            color=colours[sig_colour],
+        )
+    hep.histplot(
+        hists[data_key, :], ax=ax, yerr=data_err, histtype="errorbar", label=data_key, color="black"
+    )
+    ax.legend(ncol=2)
+    ax.set_ylim(0, ax.get_ylim()[1] * 1.5)
+
+    data_vals = hists[data_key, :].values()
+
+    if not pulls:
+        datamc_ratio = data_vals / (bg_tot + 1e-5)
+
+        if bg_err == "ratio":
+            yerr = ratio_uncertainty(data_vals, bg_tot, "poisson")
+        elif bg_err is None:
+            yerr = 0
+        else:
+            yerr = datamc_ratio * (bg_err / (bg_tot + 1e-8))
+
+        hep.histplot(
+            hists[data_key, :] / (sum([hists[sample, :] for sample in bg_keys]).values() + 1e-5),
+            yerr=yerr,
+            ax=rax,
+            histtype="errorbar",
+            color="black",
+            capsize=4,
+        )
+        rax.set_ylabel("Data/MC")
+        rax.set_ylim(0.5, 1.5)
+        rax.grid()
+    else:
+        mcdata_ratio = bg_tot / (data_vals + 1e-5)
+        yerr = bg_err / data_vals
+
+        hep.histplot(
+            (sum([hists[sample, :] for sample in bg_keys]) / (data_vals + 1e-5) - 1) * (-1),
+            yerr=yerr,
+            ax=rax,
+            histtype="errorbar",
+            color="black",
+            capsize=4,
+        )
+        rax.set_ylabel("(Data - MC) / Data")
+        rax.set_ylim(-0.5, 0.5)
+        rax.grid()
+
+    if title is not None:
+        ax.set_title(title, y=1.08)
+
+    hep.cms.label("Work in Progress", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax)
     if len(name):
         plt.savefig(name, bbox_inches="tight")
 
@@ -207,15 +554,72 @@ def ratioHistPlot(
         plt.close()
 
 
+def hist2ds(
+    hists: Dict[str, Hist],
+    plot_dir: str,
+    regions: List[str] = None,
+    region_labels: Dict[str, str] = None,
+    samples: List[str] = None,
+    fail_zlim: float = None,
+    pass_zlim: float = None,
+    show: bool = True,
+):
+    """
+    2D hists for each region and sample in ``hists``.
+
+    Args:
+        hists (Dict[str, Hist]): dictionary of hists per region.
+        plot_dir (str): directory in which to save plots.
+        regions (List[str], optional): regions to plot. Defaults to None i.e. plot all in hists.
+        region_labels (Dict[str, str], optional): Optional labels for each region in hists.
+        samples (List[str], optional): samples to plot. Defaults to None i.e. plot all in hists.
+        fail_zlim (float, optional): fail region plots upper limit. Defaults to None.
+        pass_zlim (float, optional): pass region plots upper limit. Defaults to None.
+        show (bool, optional): show plot or close. Defaults to True.
+    """
+    if samples is None:
+        samples = list(list(hists.values())[0].axes[0])
+
+    if regions is None:
+        regions = list(hists.keys())
+
+    for region in regions:
+        h = hists[region]
+        region_label = region_labels[region] if region_labels is not None else region
+        pass_region = region.startswith("pass")
+        lim = pass_zlim if pass_region else fail_zlim
+        for sample in samples:
+            if sample == "Data" and region == "pass":
+                continue
+
+            if "->H(bb)Y" in sample and not pass_region:
+                continue
+
+            if lim is not None:
+                norm = matplotlib.colors.LogNorm(vmax=lim)
+            else:
+                norm = matplotlib.colors.LogNorm()
+
+            plt.figure(figsize=(12, 12))
+            hep.hist2dplot(hists[region][sample, ...], cmap="turbo", norm=norm)
+            plt.title(f"{sample} in {region_label} Region")
+            plt.savefig(f"{plot_dir}/{region}_{sample}_2d.pdf", bbox_inches="tight")
+
+            if show:
+                plt.show()
+            else:
+                plt.close()
+
+
 def rocCurve(
     fpr,
     tpr,
-    auc,
+    auc=None,
     sig_eff_lines=[],
     # bg_eff_lines=[],
     title=None,
-    xlim=[0, 0.4],
-    ylim=[1e-6, 1e-2],
+    xlim=[0, 0.8],
+    ylim=[1e-6, 1],
     plotdir="",
     name="",
 ):
@@ -223,7 +627,8 @@ def rocCurve(
     line_style = {"colors": "lightgrey", "linestyles": "dashed"}
 
     plt.figure(figsize=(12, 12))
-    plt.plot(tpr, fpr, label=f"AUC: {auc:.2f}")
+
+    plt.plot(tpr, fpr, label=f"AUC: {auc:.2f}" if auc is not None else None)
 
     for sig_eff in sig_eff_lines:
         y = fpr[np.searchsorted(tpr, sig_eff)]
@@ -231,10 +636,40 @@ def rocCurve(
         plt.vlines(x=sig_eff, ymin=0, ymax=y, **line_style)
 
     plt.yscale("log")
-    plt.xlabel("Signal Eff.")
-    plt.ylabel("BG Eff.")
+    plt.xlabel("Signal efficiency")
+    plt.ylabel("Background efficiency")
     plt.title(title)
-    plt.legend()
+
+    if auc is not None:
+        plt.legend()
+
     plt.xlim(*xlim)
     plt.ylim(*ylim)
+    hep.cms.label(data=False, rlabel="")
     plt.savefig(f"{plotdir}/{name}.pdf", bbox_inches="tight")
+
+
+def plot_HEM2d(hists2d: List[Hist], plot_keys: List[str], year: str, name: str, show: bool = False):
+    fig, axs = plt.subplots(
+        len(plot_keys),
+        2,
+        figsize=(20, 8 * len(plot_keys)),
+        gridspec_kw={"wspace": 0.25, "hspace": 0.25},
+    )
+
+    for j, key in enumerate(plot_keys):
+        for i in range(2):
+            ax = axs[j][i]
+            hep.hist2dplot(hists2d[i][key, ...], cmap="turbo", ax=ax)
+            hep.cms.label(
+                "Work in Progress", data=True, lumi=round(LUMI[year] * 1e-3), year=year, ax=ax
+            )
+            ax.set_title(key, y=1.07)
+            ax._children[0].colorbar.set_label("Events")
+
+    plt.savefig(name, bbox_inches="tight")
+
+    if show:
+        plt.show()
+    else:
+        plt.close()
diff --git a/src/HHbbVV/postprocessing/postprocessing.py b/src/HHbbVV/postprocessing/postprocessing.py
index 6ae85ef6..0380c685 100644
--- a/src/HHbbVV/postprocessing/postprocessing.py
+++ b/src/HHbbVV/postprocessing/postprocessing.py
@@ -10,14 +10,19 @@
 Author(s): Raghav Kansal, Cristina Mantilla Suarez
 """
 
+from dataclasses import dataclass, field
 from collections import OrderedDict
+
 import os
 import sys
 import pickle, json
+import itertools
 
 import numpy as np
 import pandas as pd
-from pandas.errors import SettingWithCopyWarning
+
+# from pandas.errors import SettingWithCopyWarning
+import hist
 from hist import Hist
 
 import utils
@@ -29,31 +34,57 @@
 from textwrap import dedent
 
 import corrections
+from corrections import postprocess_lpsfs, get_lpsf
 from hh_vars import (
     years,
-    sig_keys,
+    nonres_sig_keys,
     res_sig_keys,
     data_key,
     qcd_key,
     bg_keys,
     samples,
+    nonres_samples,
+    res_samples,
     txbb_wps,
     jec_shifts,
     jmsr_shifts,
 )
-from utils import CUT_MAX_VAL
+from utils import CUT_MAX_VAL, ShapeVar
 
 from pprint import pprint
 from copy import deepcopy
 import warnings
 
+import argparse
+
 
 # ignore these because they don't seem to apply
-warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
+# warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
+
+
+@dataclass
+class Syst:
+    samples: List[str] = None
+    years: List[str] = field(default_factory=lambda: years)
+    label: str = None
+
 
+@dataclass
+class Region:
+    cuts: Dict = None
+    label: str = None
+
+
+# Both Jet's Regressed Mass above 50, electron veto included in new samples
+new_filters = [
+    [
+        ("('ak8FatJetParticleNetMass', '0')", ">=", 50),
+        ("('ak8FatJetParticleNetMass', '1')", ">=", 50),
+    ],
+]
 
-# Both Jet's Regressed Mass above 50, electron veto
-filters = [
+# TODO: remove electron veto!!
+old_filters = [
     [
         ("('ak8FatJetParticleNetMass', '0')", ">=", 50),
         ("('ak8FatJetParticleNetMass', '1')", ">=", 50),
@@ -62,256 +93,455 @@
 ]
 
 # {var: (bins, label)}
-control_plot_vars = {
-    "MET_pt": ([50, 0, 250], r"$p^{miss}_T$ (GeV)"),
-    "DijetEta": ([50, -8, 8], r"$\eta^{jj}$"),
-    "DijetPt": ([50, 0, 750], r"$p_T^{jj}$ (GeV)"),
-    "DijetMass": ([50, 500, 3000], r"$m^{jj}$ (GeV)"),
-    "bbFatJetEta": ([50, -2.4, 2.4], r"$\eta^{bb}$"),
-    "bbFatJetPt": ([50, 300, 1300], r"$p^{bb}_T$ (GeV)"),
-    "bbFatJetParticleNetMass": ([50, 0, 300], r"$m^{bb}_{reg}$ (GeV)"),
-    "bbFatJetMsd": ([50, 0, 300], r"$m^{bb}_{msd}$ (GeV)"),
-    "bbFatJetParticleNetMD_Txbb": ([50, 0.8, 1], r"$p^{bb}_{Txbb}$"),
-    "VVFatJetEta": ([50, -2.4, 2.4], r"$\eta^{VV}$"),
-    "VVFatJetPt": ([50, 300, 1300], r"$p^{VV}_T$ (GeV)"),
-    "VVFatJetParticleNetMass": ([50, 0, 300], r"$m^{VV}_{reg}$ (GeV)"),
-    "VVFatJetMsd": ([50, 0, 300], r"$m^{VV}_{msd}$ (GeV)"),
-    "VVFatJetParticleNet_Th4q": ([50, 0, 1], r"Prob($H \to 4q$) vs Prob(QCD) (Non-MD)"),
-    "VVFatJetParTMD_THWW4q": (
-        [50, 0, 1],
-        r"Prob($H \to VV \to 4q$) vs Prob(QCD) (Mass-Decorrelated)",
+control_plot_vars = [
+    ShapeVar(var="MET_pt", label=r"$p^{miss}_T$ (GeV)", bins=[50, 0, 300]),
+    ShapeVar(var="DijetEta", label=r"$\eta^{jj}$", bins=[30, -8, 8]),
+    ShapeVar(var="DijetPt", label=r"$p_T^{jj}$ (GeV)", bins=[30, 0, 750]),
+    ShapeVar(var="DijetMass", label=r"$m^{jj}$ (GeV)", bins=[30, 600, 4000]),
+    ShapeVar(var="bbFatJetEta", label=r"$\eta^{bb}$", bins=[30, -2.4, 2.4]),
+    ShapeVar(
+        var="bbFatJetPt", label=r"$p^{bb}_T$ (GeV)", bins=[30, 300, 1500], significance_dir="right"
     ),
-    "VVFatJetParTMD_probT": ([50, 0, 1], r"Prob(Top) (Mass-Decorrelated)"),
-    "bbFatJetPtOverDijetPt": ([50, 0, 40], r"$p^{bb}_T / p_T^{jj}$"),
-    "VVFatJetPtOverDijetPt": ([50, 0, 40], r"$p^{VV}_T / p_T^{jj}$"),
-    "VVFatJetPtOverbbFatJetPt": ([50, 0.4, 2.0], r"$p^{VV}_T / p^{bb}_T$"),
-    "nGoodMuons": ([3, 0, 3], r"# of Muons"),
-    "nGoodElectrons": ([3, 0, 3], r"# of Electrons"),
-    "nGoodJets": ([5, 0, 5], r"# of AK4 B-Jets"),
-    "BDTScore": ([50, 0, 1], r"BDT Score"),
-}
-
-bdt_cut = 0.99
-
-# {label: {cutvar: [min, max], ...}, ...}
-selection_regions_year = {
-    "pass": {
-        "BDTScore": [bdt_cut, CUT_MAX_VAL],
-        "bbFatJetParticleNetMD_Txbb": ["HP", CUT_MAX_VAL],
-    },
-    "fail": {
-        "bbFatJetParticleNetMD_Txbb": [0.8, "HP"],
-    },
-    "BDTOnly": {
-        "BDTScore": [bdt_cut, CUT_MAX_VAL],
-    },
-}
-
-selection_regions_label = {"pass": "Pass", "fail": "Fail", "BDTOnly": "BDT Cut", "top": "Top"}
-
-selection_regions = {}
+    ShapeVar(
+        var="bbFatJetParticleNetMass",
+        label=r"$m^{bb}_{reg}$ (GeV)",
+        bins=[20, 50, 250],
+        significance_dir="bin",
+    ),
+    ShapeVar(var="bbFatJetMsd", label=r"$m^{bb}_{msd}$ (GeV)", bins=[50, 0, 300]),
+    ShapeVar(var="bbFatJetParticleNetMD_Txbb", label=r"$T^{bb}_{Xbb}$", bins=[50, 0.8, 1]),
+    ShapeVar(var="VVFatJetEta", label=r"$\eta^{VV}$", bins=[30, -2.4, 2.4]),
+    ShapeVar(var="VVFatJetPt", label=r"$p^{VV}_T$ (GeV)", bins=[30, 300, 1500]),
+    ShapeVar(var="VVParticleNetMass", label=r"$m^{VV}_{reg}$ (GeV)", bins=[20, 50, 250]),
+    ShapeVar(var="VVFatJetMsd", label=r"$m^{VV}_{msd}$ (GeV)", bins=[40, 50, 250]),
+    # ShapeVar(var="VVFatJetParticleNet_Th4q", label=r"Prob($H \to 4q$) vs Prob(QCD) (Non-MD)", bins=[50, 0, 1]),
+    # ShapeVar(var="VVFatJetParTMD_THWW4q", label=r"Prob($H \to VV \to 4q$) vs Prob(QCD) (Mass-Decorrelated)", bins=[50, 0, 1]),
+    # ShapeVar(var="VVFatJetParTMD_probT", label=r"Prob(Top) (Mass-Decorrelated)", bins=[50, 0, 1]),
+    ShapeVar(var="VVFatJetParTMD_THWWvsT", label=r"$T^{VV}_{HWW}$", bins=[50, 0, 1]),
+    ShapeVar(var="bbFatJetPtOverDijetPt", label=r"$p^{bb}_T / p_T^{jj}$", bins=[50, 0, 40]),
+    ShapeVar(var="VVFatJetPtOverDijetPt", label=r"$p^{VV}_T / p_T^{jj}$", bins=[50, 0, 40]),
+    ShapeVar(var="VVFatJetPtOverbbFatJetPt", label=r"$p^{VV}_T / p^{bb}_T$", bins=[50, 0.4, 2.0]),
+    # ShapeVar(var="nGoodMuons", label=r"# of Muons", bins=[3, 0, 3]),
+    # ShapeVar(var="nGoodElectrons", label=r"# of Electrons", bins=[3, 0, 3]),
+    # ShapeVar(var="nGoodJets", label=r"# of AK4 B-Jets", bins=[5, 0, 5]),
+    # ShapeVar(var="BDTScore", label=r"BDT Score", bins=[50, 0, 1]),
+]
 
-for year in years:
-    sr = deepcopy(selection_regions_year)
 
-    for region in sr:
-        for cuts in sr[region]:
-            for i in range(2):
-                if sr[region][cuts][i] == "HP":
-                    sr[region][cuts][i] = txbb_wps[year]["HP"]
+def get_nonres_selection_regions(
+    year: str,
+    txbb_wp: str = "MP",
+    bdt_wp: float = 0.998,
+):
+    pt_cuts = [300, CUT_MAX_VAL]
+    txbb_cut = txbb_wps[year][txbb_wp]
+
+    return {
+        # {label: {cutvar: [min, max], ...}, ...}
+        "pass": Region(
+            cuts={
+                "bbFatJetPt": pt_cuts,
+                "VVFatJetPt": pt_cuts,
+                "BDTScore": [bdt_wp, CUT_MAX_VAL],
+                "bbFatJetParticleNetMD_Txbb": [txbb_cut, CUT_MAX_VAL],
+            },
+            label="Pass",
+        ),
+        "fail": Region(
+            cuts={
+                "bbFatJetPt": pt_cuts,
+                "VVFatJetPt": pt_cuts,
+                "bbFatJetParticleNetMD_Txbb": [0.8, txbb_cut],
+            },
+            label="Fail",
+        ),
+        "lpsf": Region(
+            cuts={  # cut for which LP SF is calculated
+                "BDTScore": [bdt_wp, CUT_MAX_VAL],
+            },
+            label="LP SF Cut",
+        ),
+    }
 
-    selection_regions[year] = sr
 
+def get_res_selection_regions(
+    year: str, mass_window: List[float] = [110, 145], txbb_wp: str = "HP", thww_wp: float = 0.96
+):
+    pt_cuts = [300, CUT_MAX_VAL]
+    mwsize = mass_window[1] - mass_window[0]
+    mw_sidebands = [
+        [mass_window[0] - mwsize / 2, mass_window[0]],
+        [mass_window[1], mass_window[1] + mwsize / 2],
+    ]
+    txbb_cut = txbb_wps[year][txbb_wp]
+
+    return {
+        # "unblinded" regions:
+        "pass": Region(
+            cuts={
+                "bbFatJetPt": pt_cuts,
+                "VVFatJetPt": pt_cuts,
+                "bbFatJetParticleNetMass": mass_window,
+                "bbFatJetParticleNetMD_Txbb": [txbb_cut, CUT_MAX_VAL],
+                "VVFatJetParTMD_THWWvsT": [thww_wp, CUT_MAX_VAL],
+            },
+            label="Pass",
+        ),
+        "fail": Region(
+            cuts={
+                "bbFatJetPt": pt_cuts,
+                "VVFatJetPt": pt_cuts,
+                "bbFatJetParticleNetMass": mass_window,
+                "bbFatJetParticleNetMD_Txbb": [0.8, txbb_cut],
+                "VVFatJetParTMD_THWWvsT": [-CUT_MAX_VAL, thww_wp],
+            },
+            label="Fail",
+        ),
+        # "blinded" validation regions:
+        "passBlinded": Region(
+            cuts={
+                "bbFatJetPt": pt_cuts,
+                "VVFatJetPt": pt_cuts,
+                "bbFatJetParticleNetMass": mw_sidebands,
+                "bbFatJetParticleNetMD_Txbb": [txbb_cut, CUT_MAX_VAL],
+                "VVFatJetParTMD_THWWvsT": [thww_wp, CUT_MAX_VAL],
+            },
+            label="Validation Pass",
+        ),
+        "failBlinded": Region(
+            cuts={
+                "bbFatJetPt": pt_cuts,
+                "VVFatJetPt": pt_cuts,
+                "bbFatJetParticleNetMass": mw_sidebands,
+                "bbFatJetParticleNetMD_Txbb": [0.8, txbb_cut],
+                "VVFatJetParTMD_THWWvsT": [-CUT_MAX_VAL, thww_wp],
+            },
+            label="Validation Fail",
+        ),
+        # cut for which LP SF is calculated
+        "lpsf": Region(
+            cuts={"VVFatJetParTMD_THWWvsT": [thww_wp, CUT_MAX_VAL]},
+            label="LP SF Cut",
+        ),
+    }
 
-del year  # creates bugs later
 
-scan_regions = {}
+# fitting on bb regressed mass for nonresonant
+nonres_shape_vars = [
+    ShapeVar(
+        "bbFatJetParticleNetMass",
+        r"$m^{bb}_\mathrm{Reg}$ (GeV)",
+        [20, 50, 250],
+        reg=True,
+        blind_window=[100, 150],
+    )
+]
 
-for bdtcut in np.arange(0.97, 1, 0.002):
-    for bbcut in np.arange(0.97, 1, 0.002):
-        cutstr = f"bdtcut_{bdtcut}_bbcut_{bbcut}"
-        scan_regions[cutstr] = {
-            "passCat1": {
-                "BDTScore": [bdtcut, CUT_MAX_VAL],
-                "bbFatJetParticleNetMD_Txbb": [bbcut, CUT_MAX_VAL],
-            },
-            "fail": {
-                "bbFatJetParticleNetMD_Txbb": [0.8, bbcut],
-            },
-        }
 
+# fitting on VV regressed mass + dijet mass for resonant
+res_shape_vars = [
+    ShapeVar(
+        "VVFatJetParticleNetMass",
+        r"$m^{VV}_\mathrm{Reg}$ (GeV)",
+        list(range(50, 110, 10)) + list(range(110, 200, 15)) + [200, 220, 250],
+        reg=False,
+    ),
+    ShapeVar(
+        "DijetMass",
+        r"$m^{jj}$ (GeV)",
+        list(range(800, 1400, 100)) + [1400, 1600, 2000, 3000, 4400],
+        reg=False,
+    ),
+]
 
-# bb msd is final shape var
-shape_var = ("bbFatJetParticleNetMass", r"$m^{bb}_{Reg}$ (GeV)")
-shape_bins = [20, 50, 250]  # num bins, min, max
-blind_window = [100, 150]
+nonres_scan_cuts = ["txbb", "bdt"]
+res_scan_cuts = ["txbb", "thww"]
 
+nonres_sig_keys_ggf = [
+    "HHbbVV",
+    "ggHH_kl_2p45_kt_1_HHbbVV",
+    "ggHH_kl_5_kt_1_HHbbVV",
+    "ggHH_kl_0_kt_1_HHbbVV",
+]
 
 # TODO: check which of these applies to resonant as well
 weight_shifts = {
-    "pileup": sig_keys + res_sig_keys + bg_keys,
-    "PDFalphaS": sig_keys,
-    "ISRPartonShower": sig_keys + ["V+Jets"],
-    "FSRPartonShower": sig_keys + ["V+Jets"],
-}
-
-weight_labels = {
-    "pileup": "Pileup",
-    "PDFalphaS": "PDF",
-    "ISRPartonShower": "ISR Parton Shower",
-    "FSRPartonShower": "FSR Parton Shower",
+    "pileup": Syst(samples=nonres_sig_keys + res_sig_keys + bg_keys, label="Pileup"),
+    # "PDFalphaS": Syst(samples=nonres_sig_keys, label="PDF"),
+    # "QCDscale": Syst(samples=nonres_sig_keys, label="QCDscale"),
+    "ISRPartonShower": Syst(samples=nonres_sig_keys_ggf + ["V+Jets"], label="ISR Parton Shower"),
+    "FSRPartonShower": Syst(samples=nonres_sig_keys_ggf + ["V+Jets"], label="FSR Parton Shower"),
+    "L1EcalPrefiring": Syst(
+        samples=nonres_sig_keys + res_sig_keys + bg_keys,
+        years=["2016APV", "2016", "2017"],
+        label="L1 ECal Prefiring",
+    ),
+    # "top_pt": ["TT"],
 }
 
 
 def main(args):
-    if not (args.control_plots or args.templates or args.scan):
-        print("You need to pass at least one of --control-plots, --templates, or --scan")
-        return
-
-    # make plot, template dirs if needed
-    _make_dirs(args)
-
-    systs_file = f"{args.template_dir}/systematics.json"
-
-    # save cutflow as pandas table
-    cutflow = pd.DataFrame(index=list(samples.keys()))
-    systematics = _check_load_systematics(systs_file)
+    shape_vars, scan, scan_cuts, scan_wps = _init(args)
+    sig_keys, sig_samples, bg_keys, bg_samples = _process_samples(args)
+    all_samples = sig_keys + bg_keys
+    _make_dirs(args, scan, scan_cuts, scan_wps)  # make plot, template dirs if needed
+    cutflow = pd.DataFrame(index=all_samples)  # save cutflow as pandas table
 
     # utils.remove_empty_parquets(samples_dir, year)
-    events_dict = utils.load_samples(args.data_dir, samples, args.year, filters)
-    utils.add_to_cutflow(events_dict, "BDTPreselection", "weight", cutflow)
+    events_dict = _load_samples(args, bg_samples, sig_samples, cutflow)
 
-    print("")
-    # print weighted sample yields
     for sample in events_dict:
         tot_weight = np.sum(events_dict[sample]["weight"].values)
         print(f"Pre-selection {sample} yield: {tot_weight:.2f}")
 
-    apply_weights(events_dict, args.year, cutflow)
     bb_masks = bb_VV_assignment(events_dict)
-    cutflow.to_csv(f"{args.plot_dir}/cutflows/bdt_cutflow.csv")
+
+    # trigger effs and QCD scale (if not already from processor)
+    apply_weights(events_dict, args.year, cutflow)
     print("\nCutflow\n", cutflow)
 
-    print("\nLoading BDT predictions")
-    bdt_preds_dir = f"{args.data_dir}/inferences/" if args.bdt_preds == "" else args.bdt_preds
-    load_bdt_preds(
-        events_dict,
-        args.year,
-        bdt_preds_dir,
-        list(samples.keys()),
-        jec_jmsr_shifts=True,
-    )
-    print("Loaded BDT preds\n")
+    # THWW score vs Top (if not already from processor)
+    derive_variables(events_dict)
+
+    if args.plot_dir != "":
+        cutflow.to_csv(f"{args.plot_dir}/preselection_cutflow.csv")
 
+    print("\nCutflow\n", cutflow)
+
+    # Load BDT Scores
+    if not args.resonant:
+        print("\nLoading BDT predictions")
+        load_bdt_preds(
+            events_dict,
+            args.year,
+            args.bdt_preds_dir,
+            jec_jmsr_shifts=True,
+        )
+        print("Loaded BDT preds\n")
+
+    # Control plots
     if args.control_plots:
         print("\nMaking control plots\n")
+
         control_plots(
-            events_dict, bb_masks, control_plot_vars, f"{args.plot_dir}/control_plots/", show=False
+            events_dict,
+            bb_masks,
+            sig_keys,
+            control_plot_vars,
+            f"{args.plot_dir}/ControlPlots/",
+            args.year,
+            bg_keys=args.bg_keys,
+            sig_scale_dict={"HHbbVV": 1e5, "VBFHHbbVV": 2e6} | {key: 2e4 for key in res_sig_keys},
+            # sig_splits=sig_splits,
+            show=False,
         )
 
-    # BDT LP SF
-    if "lp_sf" not in systematics:
-        if args.lp_sf_all_years:
-            for sig_key in sig_keys:
-                systematics[sig_key] = get_lpsf_all_years(
-                    events_dict, sig_key, args.data_dir, bdt_preds_dir, list(samples.keys())
+    if args.templates:
+        for wps in scan_wps:  # if not scanning, this will just be a single WP
+            cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)]) if scan else ""
+            template_dir = f"{args.template_dir}/{cutstr}/{args.templates_name}/"
+
+            cutargs = {f"{cut}_wp": wp for cut, wp in zip(scan_cuts, wps)}
+            selection_regions = (
+                get_nonres_selection_regions(args.year, **cutargs)
+                if not args.resonant
+                else get_res_selection_regions(args.year, **cutargs)
+            )
+
+            print(sig_keys)
+            # load pre-calculated systematics and those for different years if saved already
+            systs_file = f"{template_dir}/systematics.json"
+            systematics = _check_load_systematics(systs_file, args.year)
+
+            # Lund plane SFs
+            lpsfs(
+                events_dict,
+                bb_masks,
+                sig_keys,
+                sig_samples,
+                cutflow,
+                selection_regions["lpsf"],
+                systematics,
+                args.lp_sf_all_years,
+                args.bdt_preds_dir,
+                template_dir,
+                systs_file,
+                args.signal_data_dirs[0],
+            )
+
+            # Check for 0 weights - would be an issue for weight shifts
+            check_weights(events_dict)
+
+            print("\nMaking templates")
+            templates = {}
+
+            jshifts = [""] + jec_shifts + jmsr_shifts if args.do_jshifts else [""]
+            for jshift in jshifts:
+                print(jshift)
+                plot_dir = (
+                    f"{args.plot_dir}/templates/{cutstr}/" f"{'jshifts/' if jshift != '' else ''}"
+                    if args.plot_dir != ""
+                    else ""
                 )
-        else:
-            for sig_key in sig_keys:
-                systematics[sig_key] = {}
-                # calculate only for current year
-                events_dict[sig_key] = postprocess_lpsfs(events_dict[sig_key])
-                sel, cf = utils.make_selection(
-                    selection_regions[args.year]["BDTOnly"],
+                temps = get_templates(
                     events_dict,
                     bb_masks,
+                    args.year,
+                    sig_keys,
+                    selection_regions,
+                    shape_vars,
+                    systematics,
+                    template_dir,
+                    bg_keys=bg_keys,
+                    plot_dir=plot_dir,
                     prev_cutflow=cutflow,
+                    # sig_splits=sig_splits,
+                    weight_shifts=weight_shifts,
+                    jshift=jshift,
+                    blind_pass=True if args.resonant else False,
+                    show=False,
+                    hists_HEM2d=args.HEM2d,
+                    plot_shifts=args.plot_shifts,
                 )
-                lp_sf, unc, uncs = get_lpsf(events_dict[sig_key], sel[sig_key])
-                print(f"BDT LP Scale Factor for {sig_key}: {lp_sf:.2f} ± {unc:.2f}")
-                print(uncs)
+                templates = {**templates, **temps}
 
-                systematics[sig_key]["lp_sf"] = lp_sf
-                systematics[sig_key]["lp_sf_unc"] = unc / lp_sf
-
-    with open(systs_file, "w") as f:
-        json.dump(systematics, f)
+            print("\nSaving templates")
+            save_templates(
+                templates, f"{template_dir}/{args.year}_templates.pkl", args.resonant, shape_vars
+            )
 
-    # scale signal by LP SF
-    for wkey in ["finalWeight", "finalWeight_noTrigEffs"]:
-        events_dict[sig_key][wkey] *= systematics["lp_sf"]
+            with open(systs_file, "w") as f:
+                json.dump(systematics, f)
 
-    utils.add_to_cutflow(events_dict, "LP SF", "finalWeight", cutflow)
 
-    # Check for 0 weights - would be an issue for weight shifts
-    check_weights(events_dict)
+def _init(args):
+    if not (args.control_plots or args.templates or args.scan):
+        print("You need to pass at least one of --control-plots, --templates, or --scan")
+        return
 
-    if args.templates:
-        print("\nMaking templates")
+    if not args.resonant:
+        scan = len(args.nonres_txbb_wp) > 1 or len(args.nonres_bdt_wp) > 1
+        scan_wps = list(itertools.product(args.nonres_txbb_wp, args.nonres_bdt_wp))
+        scan_cuts = nonres_scan_cuts
+        shape_vars = nonres_shape_vars
+    else:
+        scan = len(args.res_txbb_wp) > 1 or len(args.res_thww_wp) > 1
+        scan_wps = list(itertools.product(args.res_txbb_wp, args.res_thww_wp))
+        scan_cuts = res_scan_cuts
+        shape_vars = res_shape_vars
+
+    return shape_vars, scan, scan_cuts, scan_wps
+
+
+def _process_samples(args, BDT_sample_order: List[str] = None):
+    sig_samples = res_samples if args.resonant else nonres_samples
+
+    if not args.resonant and BDT_sample_order is None:
+        with open(f"{args.bdt_preds_dir}/{args.year}/sample_order.txt", "r") as f:
+            BDT_sample_order = list(eval(f.read()).keys())
+
+    if args.read_sig_samples:
+        # read all signal samples in directory
+        read_year = args.year if args.year != "all" else "2017"
+        read_samples = os.listdir(f"{args.signal_data_dirs[0]}/{args.year}")
+        sig_samples = OrderedDict()
+        for sample in read_samples:
+            if sample.startswith("NMSSM_XToYHTo2W2BTo4Q2B_MX-"):
+                mY = int(sample.split("-")[-1])
+                mX = int(sample.split("NMSSM_XToYHTo2W2BTo4Q2B_MX-")[1].split("_")[0])
+
+                sig_samples[f"X[{mX}]->H(bb)Y[{mY}](VV)"] = sample
+
+    if args.sig_samples is not None:
+        for sig_key, sample in list(sig_samples.items()):
+            if sample not in args.sig_samples:
+                del sig_samples[sig_key]
+
+        # can load in nonres samples for control plots
+        for sample in args.sig_samples:
+            if sample in nonres_samples:
+                sig_samples[sample] = nonres_samples[sample]
+
+        # re-order acording to input ordering
+        tsig_samples = OrderedDict()
+        for sample in args.sig_samples:
+            if sample in sig_samples:
+                # if sample is a key, get it directly
+                tsig_samples[sample] = sig_samples[sample]
+            else:
+                # else if it is a value, have to find corresponding key
+                key = [key for key, value in sig_samples.items() if value == sample][0]
+                tsig_samples[key] = sample
 
-        templates = {}
+        sig_samples = tsig_samples
 
-        for jshift in [""] + jec_shifts + jmsr_shifts:
-            print(jshift)
-            ttemps, tsyst = get_templates(
-                events_dict,
-                bb_masks,
-                args.year,
-                selection_regions[args.year],
-                shape_var,
-                shape_bins,
-                blind_window,
-                plot_dir=args.plot_dir,
-                prev_cutflow=cutflow,
-                weight_shifts=weight_shifts,
-                jshift=jshift,
-                show=False,
-            )
+    bg_samples = deepcopy(samples)
+    for bg_key, sample in list(bg_samples.items()):
+        if bg_key not in args.bg_keys and bg_key != data_key:
+            del bg_samples[bg_key]
 
-            templates = {**templates, **ttemps}
-            if jshift == "":
-                systematics[args.year] = tsyst
+    if not args.resonant:
+        for key in sig_samples.copy():
+            if key not in BDT_sample_order:
+                del sig_samples[key]
 
-        print("\nSaving templates")
-        save_templates(templates, blind_window, f"{args.template_dir}/{args.year}_templates.pkl")
+        for key in bg_samples.copy():
+            if key not in BDT_sample_order:
+                del bg_samples[key]
 
-        with open(systs_file, "w") as f:
-            json.dump(systematics, f)
+    if not args.data:
+        try:
+            del bg_samples[data_key]
+        except:
+            print(f"no key {data_key}")
 
-    if args.scan:
-        os.system(f"mkdir -p {args.template_file}")
+    sig_keys = list(sig_samples.keys())
+    bg_keys = list(bg_samples.keys())
 
-        templates = {}
+    print("BDT Sample Order: ", BDT_sample_order)
+    print("Sig keys: ", sig_keys)
+    # print("Sig samples: ", sig_samples)
+    print("BG keys: ", bg_keys)
+    # print("BG Samples: ", bg_samples)
 
-        for cutstr, region in scan_regions.items():
-            print(cutstr)
-            templates[cutstr] = get_templates(
-                events_dict,
-                bb_masks,
-                region,
-                shape_var,
-                shape_bins,
-                blind_window,
-                plot_dir=args.plot_dir,
-                prev_cutflow=cutflow,
-                cutstr=cutstr,
-            )
-            save_templates(templates[cutstr], blind_window, f"{args.template_dir}/{cutstr}.pkl")
+    return sig_keys, sig_samples, bg_keys, bg_samples
 
 
-def _make_dirs(args):
+def _make_dirs(args, scan, scan_cuts, scan_wps):
     if args.plot_dir != "":
         args.plot_dir = f"{args.plot_dir}/{args.year}/"
-        os.system(f"mkdir -p {args.plot_dir}/cutflows/")
+        os.system(f"mkdir -p {args.plot_dir}")
+
         if args.control_plots:
-            os.system(f"mkdir -p {args.plot_dir}/control_plots/")
+            os.system(f"mkdir -p {args.plot_dir}/ControlPlots/")
+
         os.system(f"mkdir -p {args.plot_dir}/templates/")
 
+        if scan:
+            for wps in scan_wps:
+                cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)])
+                os.system(f"mkdir -p {args.plot_dir}/templates/{cutstr}/")
+                os.system(f"mkdir -p {args.plot_dir}/templates/{cutstr}/wshifts")
+                os.system(f"mkdir -p {args.plot_dir}/templates/{cutstr}/jshifts")
+        else:
+            os.system(f"mkdir -p {args.plot_dir}/templates/wshifts")
+            os.system(f"mkdir -p {args.plot_dir}/templates/jshifts")
+
+            if args.resonant:
+                os.system(f"mkdir -p {args.plot_dir}/templates/hists2d")
+
     if args.template_dir != "":
-        os.system(f"mkdir -p {args.template_dir}")
+        if scan:
+            for wps in scan_wps:
+                cutstr = "_".join([f"{cut}_{wp}" for cut, wp in zip(scan_cuts, wps)])
+                os.system(
+                    f"mkdir -p {args.template_dir}/{cutstr}/{args.templates_name}/cutflows/{args.year}/"
+                )
+        else:
+            os.system(f"mkdir -p {args.template_dir}/{args.templates_name}/cutflows/{args.year}/")
 
 
-def _check_load_systematics(systs_file: str):
+def _check_load_systematics(systs_file: str, year: str):
     if os.path.exists(systs_file):
         print("Loading systematics")
         with open(systs_file, "r") as f:
@@ -319,14 +549,45 @@ def _check_load_systematics(systs_file: str):
     else:
         systematics = {}
 
+    if year not in systematics:
+        systematics[year] = {}
+
     return systematics
 
 
+def _load_samples(args, samples, sig_samples, cutflow, filters=None):
+    if filters is None:
+        filters = old_filters if args.old_processor else new_filters
+
+    events_dict = {}
+    for d in args.signal_data_dirs:
+        events_dict = {**events_dict, **utils.load_samples(d, sig_samples, args.year, filters)}
+
+    if args.data_dir:
+        events_dict = {
+            **events_dict,
+            **utils.load_samples(args.data_dir, samples, args.year, filters),
+        }
+
+    print("Samples: ", list(events_dict.keys()))
+
+    utils.add_to_cutflow(events_dict, "Pre-selection", "weight", cutflow)
+
+    print("")
+    # print weighted sample yields
+    wkey = "finalWeight" if "finalWeight" in list(events_dict.values())[0] else "weight"
+    for sample in events_dict:
+        tot_weight = np.sum(events_dict[sample][wkey].values)
+        print(f"Pre-selection {sample} yield: {tot_weight:.2f}")
+
+    return events_dict
+
+
 def apply_weights(
     events_dict: Dict[str, pd.DataFrame],
     year: str,
     cutflow: pd.DataFrame = None,
-    weight_key: str = "finalWeight",
+    qcd_sf: bool = True,
 ):
     """
     Applies (1) 2D trigger scale factors, (2) QCD scale facotr.
@@ -348,11 +609,13 @@ def apply_weights(
         np.nan_to_num(effs_txbb.view(flow=False), 0), np.squeeze(effs_txbb.axes.edges)
     )
 
+    weight_key = "finalWeight"
+
     for sample in events_dict:
         events = events_dict[sample]
         if sample == data_key:
             events[weight_key] = events["weight"]
-        else:
+        elif f"{weight_key}_noTrigEffs" not in events:
             fj_trigeffs = ak8TrigEffsLookup(
                 events["ak8FatJetParticleNetMD_Txbb"].values,
                 events["ak8FatJetPt"].values,
@@ -367,13 +630,13 @@ def apply_weights(
         utils.add_to_cutflow(events_dict, "TriggerEffs", weight_key, cutflow)
 
     # calculate QCD scale factor
-    if qcd_key in events_dict:
+    if qcd_sf and qcd_key in events_dict:
         trig_yields = cutflow["TriggerEffs"]
         non_qcd_bgs_yield = np.sum(
             [
                 trig_yields[sample]
                 for sample in events_dict
-                if sample not in {*sig_keys, qcd_key, data_key, *res_sig_keys}
+                if sample not in {*nonres_sig_keys, qcd_key, data_key, *res_sig_keys}
             ]
         )
         QCD_SCALE_FACTOR = (trig_yields[data_key] - non_qcd_bgs_yield) / trig_yields[qcd_key]
@@ -405,175 +668,177 @@ def bb_VV_assignment(events_dict: Dict[str, pd.DataFrame]) -> Dict[str, pd.DataF
     return bb_masks
 
 
-def postprocess_lpsfs(
-    events: pd.DataFrame,
-    num_jets: int = 2,
-    num_lp_sf_toys: int = 100,
-    save_all: bool = True,
-    weight_key: str = "finalWeight",
-):
-    """
-    (1) Splits LP SFs into bb and VV based on gen matching.
-    (2) Sets defaults for unmatched jets.
-    (3) Cuts of SFs at 10 and normalises.
-    """
-
-    for jet in ["bb", "VV"]:
-        # ignore rare case (~0.002%) where two jets are matched to same gen Higgs
-        events.loc[np.sum(events[f"ak8FatJetH{jet}"], axis=1) > 1, f"ak8FatJetH{jet}"] = 0
-        jet_match = events[f"ak8FatJetH{jet}"].astype(bool)
-
-        # temp dict
-        td = {}
-        # td["tot_matched"] = np.sum(np.sum(jet_match))
-
-        # defaults of 1 for jets which aren't matched to anything - i.e. no SF
-        for key in ["lp_sf_nom", "lp_sf_sys_down", "lp_sf_sys_up"]:
-            td[key] = np.ones(len(events))
-
-        td["lp_sf_toys"] = np.ones((len(events), num_lp_sf_toys))
-
-        # defaults of 0 - i.e. don't contribute to unc.
-        for key in ["lp_sf_double_matched_event", "lp_sf_unmatched_quarks", "lp_sf_num_sjpt_gt350"]:
-            td[key] = np.zeros(len(events))
-
-        # fill values from matched jets
-        for j in range(num_jets):
-            offset = num_lp_sf_toys + 1
-            td["lp_sf_nom"][jet_match[j]] = events["lp_sf_lnN"][jet_match[j]][j * offset]
-            td["lp_sf_toys"][jet_match[j]] = events["lp_sf_lnN"][jet_match[j]].loc[
-                :, j * offset + 1 : (j + 1) * offset - 1
-            ]
+def derive_variables(events_dict: Dict[str, pd.DataFrame]):
+    """Add HWW vs (QCD + Top) discriminant"""
+    for sample, events in events_dict.items():
+        if "VVFatJetParTMD_THWWvsT" in events or "ak8FatJetParTMD_THWWvsT" in events:
+            continue
 
-            for key in [
-                "lp_sf_sys_down",
-                "lp_sf_sys_up",
-                "lp_sf_double_matched_event",
-                "lp_sf_unmatched_quarks",
-                "lp_sf_num_sjpt_gt350",
-            ]:
-                td[key][jet_match[j]] = events[key][jet_match[j]][j]
-
-        for key in ["lp_sf_nom", "lp_sf_toys", "lp_sf_sys_down", "lp_sf_sys_up"]:
-            # cut off at 10
-            td[key] = np.minimum(td[key], 10)
-            # normalise
-            td[key] = td[key] / np.mean(td[key], axis=0)
-
-        # add to dataframe
-        if save_all:
-            td = pd.concat(
-                [pd.DataFrame(v.reshape(v.shape[0], -1)) for k, v in td.items()],
-                axis=1,
-                keys=[f"{jet}_{key}" for key in td.keys()],
-            )
+        if "ak8FatJetParTMD_probT" not in events:
+            warnings.warn(f"ParT variables missing in {sample}!")
+            continue
 
-            events = pd.concat((events, td), axis=1)
-        else:
-            key = "lp_sf_nom"
-            events[f"{jet}_{key}"] = td[key]
+        h4qvst = (events["ak8FatJetParTMD_probHWW3q"] + events["ak8FatJetParTMD_probHWW4q"]) / (
+            events["ak8FatJetParTMD_probHWW3q"]
+            + events["ak8FatJetParTMD_probHWW4q"]
+            + events["ak8FatJetParTMD_probQCD"]
+            + events["ak8FatJetParTMD_probT"]
+        )
 
-    return events
+        events_dict[sample] = pd.concat(
+            [events, pd.concat([h4qvst], axis=1, keys=["ak8FatJetParTMD_THWWvsT"])], axis=1
+        )
 
 
-def get_lpsf(
-    events: pd.DataFrame, sel: np.ndarray = None, VV: bool = True, weight_key: str = "finalWeight"
+def load_bdt_preds(
+    events_dict: Dict[str, pd.DataFrame],
+    year: str,
+    bdt_preds_dir: str,
+    jec_jmsr_shifts: bool = False,
 ):
-    """Calculates LP SF and uncertainties in current phase space. ``postprocess_lpsfs`` must be called first."""
-
-    jet = "VV" if VV else "bb"
-    if sel is not None:
-        events = events[sel]
-
-    tot_matched = np.sum(np.sum(events[f"ak8FatJetH{jet}"].astype(bool)))
-
-    weight = events[weight_key].values
-    tot_pre = np.sum(weight)
-    tot_post = np.sum(weight * events[f"{jet}_lp_sf_nom"][0])
-    lp_sf = tot_post / tot_pre
-
-    uncs = {}
+    """
+    Loads the BDT scores for each event and saves in the dataframe in the "BDTScore" column.
+    If ``jec_jmsr_shifts``, also loads BDT preds for every JEC / JMSR shift in MC.
 
-    # difference in yields between up and down shifts on LP SFs
-    uncs["syst_unc"] = np.abs(
-        (
-            np.sum(events[f"{jet}_lp_sf_sys_up"][0] * weight)
-            - np.sum(events[f"{jet}_lp_sf_sys_down"][0] * weight)
-        )
-        / 2
-        / tot_post
-    )
+    Args:
+        bdt_preds (str): Path to the bdt_preds .npy file.
+        bdt_sample_order (List[str]): Order of samples in the predictions file.
 
-    # std of yields after all smearings
-    uncs["stat_unc"] = (
-        np.std(np.sum(weight[:, np.newaxis] * events[f"{jet}_lp_sf_toys"].values, axis=0))
-        / tot_post
-    )
+    """
+    with open(f"{bdt_preds_dir}/{year}/sample_order.txt", "r") as f:
+        sample_order_dict = eval(f.read())
 
-    # fraction of subjets > 350 * 0.21 measured by CASE
-    uncs["sj_pt_unc"] = (np.sum(events[f"{jet}_lp_sf_num_sjpt_gt350"][0]) / tot_matched) * 0.21
+    bdt_preds = np.load(f"{bdt_preds_dir}/{year}/preds.npy")
 
-    if VV:
-        num_prongs = events["ak8FatJetHVVNumProngs"][0]
+    multiclass = len(bdt_preds.shape) > 1
 
-        sj_matching_unc = np.sum(events[f"{jet}_lp_sf_double_matched_event"][0])
-        for nump in range(2, 5):
-            sj_matching_unc += (
-                np.sum(events[f"{jet}_lp_sf_unmatched_quarks"][0][num_prongs == nump]) / nump
-            )
+    if jec_jmsr_shifts:
+        shift_preds = {
+            jshift: np.load(f"{bdt_preds_dir}/{year}/preds_{jshift}.npy")
+            for jshift in jec_shifts + jmsr_shifts
+        }
 
-        uncs["sj_matching_unc"] = sj_matching_unc / tot_matched
-    else:
-        num_prongs = 2
-        uncs["sj_matching_unc"] = (
-            (np.sum(events[f"{jet}_lp_sf_unmatched_quarks"][0]) / num_prongs)
-            + np.sum(events[f"{jet}_lp_sf_double_matched_event"][0])
-        ) / tot_matched
+    i = 0
+    for sample, num_events in sample_order_dict.items():
+        if sample in events_dict:
+            events = events_dict[sample]
+            assert num_events == len(
+                events
+            ), f"# of BDT predictions does not match # of events for sample {sample}"
+
+            if not multiclass:
+                events["BDTScore"] = bdt_preds[i : i + num_events]
+            else:
+                events["BDTScore"] = bdt_preds[i : i + num_events, 0]
+                events["BDTScoreQCD"] = bdt_preds[i : i + num_events, 1]
+                events["BDTScoreTT"] = bdt_preds[i : i + num_events, 2]
+                events["BDTScoreVJets"] = 1 - np.sum(bdt_preds[i : i + num_events], axis=1)
+
+            if jec_jmsr_shifts and sample != data_key:
+                for jshift in jec_shifts + jmsr_shifts:
+                    if not multiclass:
+                        events["BDTScore_" + jshift] = shift_preds[jshift][i : i + num_events]
+                    else:
+                        events["BDTScore_" + jshift] = shift_preds[jshift][i : i + num_events, 0]
+                        events["BDTScoreQCD_" + jshift] = shift_preds[jshift][i : i + num_events, 1]
+                        events["BDTScoreTT_" + jshift] = shift_preds[jshift][i : i + num_events, 2]
+                        events["BDTScoreVJets_" + jshift] = 1 - np.sum(
+                            shift_preds[jshift][i : i + num_events], axis=1
+                        )
 
-    tot_rel_unc = np.linalg.norm([val for val in uncs.values()])
-    tot_unc = lp_sf * tot_rel_unc
+        i += num_events
 
-    return lp_sf, tot_unc, uncs
+    assert i == len(bdt_preds), f"# events {i} != # of BDT preds {len(bdt_preds)}"
 
 
 def get_lpsf_all_years(
-    full_events_dict: Dict[str, pd.DataFrame],
     sig_key: str,
     data_dir: str,
-    bdt_preds_dir: str,
-    bdt_sample_order: List[str],
+    samples: Dict,
+    lp_region: Region,
+    bdt_preds_dir: str = None,
 ):
     print("Getting LP SF for all years combined")
     events_all = []
     sels_all = []
 
+    # (column name, number of subcolumns)
+    load_columns = [
+        ("weight", 1),
+        ("weight_noTrigEffs", 1),
+        ("ak8FatJetPt", 2),
+        ("ak8FatJetMsd", 2),
+        ("ak8FatJetHVV", 2),
+        ("ak8FatJetHVVNumProngs", 1),
+        ("ak8FatJetParticleNetMD_Txbb", 2),
+        ("VVFatJetParTMD_THWWvsT", 1),
+        ("lp_sf_lnN", 101),
+        ("lp_sf_sys_down", 1),
+        ("lp_sf_sys_up", 1),
+        ("lp_sf_double_matched_event", 1),
+        ("lp_sf_unmatched_quarks", 1),
+        ("lp_sf_num_sjpt_gt350", 1),
+    ]
+
+    # nonresonant samples use old skimmer #TODO: update!!!
+    if bdt_preds_dir is not None:
+        load_columns.remove(("weight_noTrigEffs", 1))
+        load_columns.remove(("VVFatJetParTMD_THWWvsT", 1))
+
+        load_columns += [
+            ("ak8FatJetParTMD_probHWW3q", 2),
+            ("ak8FatJetParTMD_probHWW4q", 2),
+            ("ak8FatJetParTMD_probQCD", 2),
+            ("ak8FatJetParTMD_probT", 2),
+        ]
+
+    # reformat into ("column name", "idx") format for reading multiindex columns
+    column_labels = []
+    for key, num_columns in load_columns:
+        for i in range(num_columns):
+            column_labels.append(f"('{key}', '{i}')")
+
     for year in years:
-        events_dict = utils.load_samples(data_dir, {sig_key: samples[sig_key]}, year, filters)
+        events_dict = utils.load_samples(
+            data_dir, {sig_key: samples[sig_key]}, year, new_filters, column_labels
+        )
 
         # print weighted sample yields
-        tot_weight = np.sum(events_dict[sig_key]["weight"].values)
-        # print(f"Pre-selection {year} yield: {tot_weight:.2f}")
+        wkey = "finalWeight" if "finalWeight" in list(events_dict.values())[0] else "weight"
+        tot_weight = np.sum(events_dict[sig_key][wkey].values)
 
-        apply_weights(events_dict, year)
         bb_masks = bb_VV_assignment(events_dict)
+
+        if "finalWeight_noTrigEffs" not in events_dict[sig_key]:
+            apply_weights(events_dict, year)
+            derive_variables(events_dict)
+
         events_dict[sig_key] = postprocess_lpsfs(events_dict[sig_key])
 
-        # load bdt preds for sig only
-        bdt_preds = np.load(f"{bdt_preds_dir}/{year}/preds.npy")
-        i = 0
-        for sample in bdt_sample_order:
-            if sample != sig_key:
-                i += len(full_events_dict[sample])
-                continue
-            else:
-                events = events_dict[sample]
-                num_events = len(events)
-                events["BDTScore"] = bdt_preds[i : i + num_events]
-                break
+        if bdt_preds_dir is not None:
+            with open(f"{bdt_preds_dir}/{year}/sample_order.txt", "r") as f:
+                sample_order_dict = eval(f.read())
+
+            # load bdt preds for sig only
+            bdt_preds = np.load(f"{bdt_preds_dir}/{year}/preds.npy")
+            multiclass = len(bdt_preds.shape) > 1
+            i = 0
+            for sample, num_events in sample_order_dict.items():
+                if sample != sig_key:
+                    i += num_events
+                    continue
+                else:
+                    events = events_dict[sample]
+                    assert num_events == len(
+                        events
+                    ), f"# of BDT predictions does not match # of events for sample {sample}"
+                    if not multiclass:
+                        events["BDTScore"] = bdt_preds[i : i + num_events]
+                    else:
+                        events["BDTScore"] = bdt_preds[i : i + num_events, 0]
+                    break
 
-        sel, _ = utils.make_selection(
-            selection_regions[args.year]["BDTOnly"], events_dict, bb_masks
-        )
+        sel, _ = utils.make_selection(lp_region.cuts, events_dict, bb_masks)
 
         events_all.append(events_dict[sig_key])
         sels_all.append(sel[sig_key])
@@ -581,64 +846,150 @@ def get_lpsf_all_years(
     events = pd.concat(events_all, axis=0)
     sel = np.concatenate(sels_all, axis=0)
 
-    lp_sf, unc, uncs = get_lpsf(events, sel)
-
-    print(f"BDT LP Scale Factor: {lp_sf:.2f} ± {unc:.2f}")
-    print(uncs)
-
-    return {"lp_sf": lp_sf, "lp_sf_unc": unc / lp_sf}
+    return get_lpsf(events, sel)
 
 
-def load_bdt_preds(
+def lpsfs(
     events_dict: Dict[str, pd.DataFrame],
-    year: str,
-    bdt_preds_dir: str,
-    bdt_sample_order: List[str],
-    jec_jmsr_shifts: bool = False,
+    bb_masks: Dict[str, pd.DataFrame],
+    sig_keys: List[str],
+    sig_samples: Dict[str, str],
+    cutflow: pd.DataFrame,
+    lp_selection_region: Region,
+    systematics: Dict,
+    all_years: bool = False,
+    bdt_preds_dir: str = None,
+    template_dir: str = None,
+    systs_file: str = None,
+    data_dir: str = None,
 ):
     """
-    Loads the BDT scores for each event and saves in the dataframe in the "BDTScore" column.
-    If ``jec_jmsr_shifts``, also loads BDT preds for every JEC / JMSR shift in MC.
+    1) Calculates LP SFs for each signal, if not already in ``systematics``
+        - Does it for all years once if args.lp_sf_all_years or just for the given year
+    2) Saves them to ``systs_file`` and CSV for posterity
+    """
+    for sig_key in sig_keys:
+        if sig_key not in systematics or "lp_sf" not in systematics[sig_key]:
+            print(f"\nGetting LP SFs for {sig_key}")
 
-    Args:
-        bdt_preds (str): Path to the bdt_preds .npy file.
-        bdt_sample_order (List[str]): Order of samples in the predictions file.
+            if sig_key not in systematics:
+                systematics[sig_key] = {}
 
-    """
-    bdt_preds = np.load(f"{bdt_preds_dir}/{year}/preds.npy")
+            # SFs are correlated across all years so needs to be calculated with full dataset
+            if all_years:
+                lp_sf, unc, uncs = get_lpsf_all_years(
+                    sig_key,
+                    data_dir,
+                    sig_samples,
+                    lp_selection_region,
+                    bdt_preds_dir,
+                )
+            # Only for testing, can do just for a single year
+            else:
+                warnings.warn(f"LP SF only calculated from single year's samples", RuntimeWarning)
+                # calculate only for current year
+                events_dict[sig_key] = postprocess_lpsfs(events_dict[sig_key])
+                sel, cf = utils.make_selection(
+                    lp_selection_region.cuts,
+                    events_dict,
+                    bb_masks,
+                    prev_cutflow=cutflow,
+                )
+                lp_sf, unc, uncs = get_lpsf(events_dict[sig_key], sel[sig_key])
 
-    if jec_jmsr_shifts:
-        shift_preds = {
-            jshift: np.load(f"{bdt_preds_dir}/{year}/preds_{jshift}.npy")
-            for jshift in jec_shifts + jmsr_shifts
+            print(f"LP Scale Factor for {sig_key}: {lp_sf:.2f} ± {unc:.2f}")
+
+            systematics[sig_key]["lp_sf"] = lp_sf
+            systematics[sig_key]["lp_sf_unc"] = unc / lp_sf
+            systematics[sig_key]["lp_sf_uncs"] = uncs
+
+            if systs_file is not None:
+                with open(systs_file, "w") as f:
+                    json.dump(systematics, f)
+
+    if template_dir is not None:
+        sf_table = OrderedDict()  # format SFs for each sig key in a table
+        for sig_key in sig_keys:
+            systs = systematics[sig_key]
+            sf_table[sig_key] = {
+                "SF": f"{systs['lp_sf']:.2f} ± {systs['lp_sf'] * systs['lp_sf_unc']:.2f}",
+                **systs["lp_sf_uncs"],
+            }
+
+        print("\nLP Scale Factors:\n", pd.DataFrame(sf_table).T)
+        pd.DataFrame(sf_table).T.to_csv(f"{template_dir}/lpsfs.csv")
+
+    utils.add_to_cutflow(events_dict, "LP SF", "finalWeight", cutflow)
+
+
+def hists_HEM2d(
+    events_dict: Dict[str, pd.DataFrame],
+    bb_masks: Dict[str, pd.DataFrame],
+    weight_key: str = "finalWeight",
+    selection: Dict[str, np.ndarray] = None,
+):
+    """Create 2D hists of FatJet phi vs eta for bb and VV jets as a check for HEM cleaning."""
+    HEM2d_vars = [
+        {
+            f"{jet}FatJetPhi": ([40, -3.5, 3.5], rf"$\varphi^{{{jet}}}$"),
+            f"{jet}FatJetEta": ([40, -3, 3], rf"$\eta^{{{jet}}}$"),
         }
+        for jet in ["bb", "VV"]
+    ]
+
+    samples = list(events_dict.keys())
+    hists2d = []
+
+    for vars2d in HEM2d_vars:
+        h = Hist(
+            hist.axis.StrCategory(samples, name="Sample"),
+            *[
+                hist.axis.Regular(*bins, name=var, label=label)
+                for var, (bins, label) in vars2d.items()
+            ],
+            storage=hist.storage.Weight(),
+        )
 
-    i = 0
-    for sample in bdt_sample_order:
-        events = events_dict[sample]
-        num_events = len(events)
-        events["BDTScore"] = bdt_preds[i : i + num_events]
+        for sample in samples:
+            events = events_dict[sample]
 
-        if jec_jmsr_shifts and sample != data_key:
-            for jshift in jec_shifts + jmsr_shifts:
-                events["BDTScore_" + jshift] = shift_preds[jshift][i : i + num_events]
+            fill_data = {var: utils.get_feat(events, var, bb_masks[sample]) for var in vars2d}
+            weight = events[weight_key].values.squeeze()
 
-        i += num_events
+            if selection is not None:
+                sel = selection[sample]
+                for var in fill_data:
+                    fill_data[var] = fill_data[var][sel]
 
-    assert i == len(bdt_preds), f"# events {i} != # of BDT preds {len(bdt_preds)}"
+                weight = weight[sel]
+
+            if len(weight):
+                h.fill(Sample=sample, **fill_data, weight=weight)
+
+        hists2d.append(h)
+
+    return hists2d
 
 
 def control_plots(
     events_dict: Dict[str, pd.DataFrame],
     bb_masks: Dict[str, pd.DataFrame],
     sig_keys: List[str],
-    control_plot_vars: Dict[str, Tuple],
+    control_plot_vars: List[ShapeVar],
     plot_dir: str,
     year: str,
     weight_key: str = "finalWeight",
     hists: Dict = {},
     cutstr: str = "",
+    sig_splits: List[List[str]] = None,
+    bg_keys: List[str] = bg_keys,
+    selection: Dict[str, np.ndarray] = None,
+    sig_scale_dict: Dict[str, float] = None,
+    combine_pdf: bool = True,
+    HEM2d: bool = False,
+    plot_significance: bool = False,
     show: bool = False,
+    log: Tuple[bool, str] = "both",
 ):
     """
     Makes and plots histograms of each variable in ``control_plot_vars``.
@@ -646,43 +997,78 @@ def control_plots(
     Args:
         control_plot_vars (Dict[str, Tuple]): Dictionary of variables to plot, formatted as
           {var1: ([num bins, min, max], label), var2...}.
-
+        sig_splits: split up signals into different plots (in case there are too many for one)
+        log: True or False if plot on log scale or not - or "both" if both.
     """
 
     from PyPDF2 import PdfMerger
 
     # sig_scale_dict = utils.getSignalPlotScaleFactor(events_dict, sig_keys)
-    sig_scale_dict = {sig_key: 2e4 for sig_key in sig_keys}
-    sig_scale_dict["HHbbVV"] = 2e5
-    # print(f"{sig_scale_dict = }")
-
-    for var, (bins, label) in control_plot_vars.items():
-        # print(var)
-        if var not in hists:
-            hists[var] = utils.singleVarHist(
-                events_dict, var, bins, label, bb_masks, weight_key=weight_key
+    # sig_scale_dict = {sig_key: 5e3 for sig_key in sig_keys}
+    # sig_scale_dict["HHbbVV"] = 2e5
+
+    if sig_scale_dict is None:
+        sig_scale_dict = {sig_key: 2e5 for sig_key in sig_keys}
+
+    print(control_plot_vars)
+    print(selection)
+
+    for shape_var in control_plot_vars:
+        if shape_var.var not in hists:
+            hists[shape_var.var] = utils.singleVarHist(
+                events_dict, shape_var, bb_masks, weight_key=weight_key, selection=selection
             )
 
+    if HEM2d and year == "2018":
+        hists["HEM2d"] = hists_HEM2d(events_dict, bb_masks, weight_key, selection)
+
     with open(f"{plot_dir}/hists.pkl", "wb") as f:
         pickle.dump(hists, f)
 
-    merger_control_plots = PdfMerger()
+    if sig_splits is None:
+        sig_splits = [sig_keys]
 
-    for var, var_hist in hists.items():
-        name = f"{plot_dir}/{cutstr}{var}.pdf"
-        plotting.ratioHistPlot(
-            var_hist,
-            year,
-            sig_keys,
-            bg_keys,
-            name=name,
-            sig_scale_dict=sig_scale_dict,
-            show=show,
-        )
-        merger_control_plots.append(name)
+    do_log = [True, False] if log == "both" else [log]
+
+    for log, logstr in [(False, ""), (True, "_log")]:
+        if log not in do_log:
+            continue
+
+        for i, plot_sig_keys in enumerate(sig_splits):
+            tplot_dir = plot_dir if len(sig_splits) == 1 else f"{plot_dir}/sigs{i}/"
+            tsig_scale_dict = {key: sig_scale_dict.get(key, 1) for key in plot_sig_keys}
+
+            merger_control_plots = PdfMerger()
 
-    merger_control_plots.write(f"{plot_dir}/{year}_{cutstr}ControlPlots.pdf")
-    merger_control_plots.close()
+            for shape_var in control_plot_vars:
+                name = f"{tplot_dir}/{cutstr}{shape_var.var}{logstr}.pdf"
+                plotting.ratioHistPlot(
+                    hists[shape_var.var],
+                    year,
+                    plot_sig_keys,
+                    bg_keys,
+                    name=name,
+                    sig_scale_dict=tsig_scale_dict if not log else None,
+                    plot_significance=plot_significance,
+                    significance_dir=shape_var.significance_dir,
+                    show=show,
+                    log=log,
+                    ylim=None if not log else 1e15,
+                )
+                merger_control_plots.append(name)
+
+            if combine_pdf:
+                merger_control_plots.write(f"{tplot_dir}/{cutstr}{year}{logstr}_ControlPlots.pdf")
+
+            merger_control_plots.close()
+
+    if HEM2d and year == "2018":
+        # TODO: change plot keys?
+        name = f"{plot_dir}/HEM2d.pdf"
+        # plot keys
+        plotting.plot_HEM2d(
+            hists["HEM2d"], ["Data", "QCD", "TT", "HHbbVV", "X[3000]->H(bb)Y[250](VV)"], year, name
+        )
 
     return hists
 
@@ -701,22 +1087,42 @@ def check_weights(events_dict):
     )
 
 
+def _get_fill_data(
+    events: pd.DataFrame, bb_mask: pd.DataFrame, shape_vars: List[ShapeVar], jshift: str = ""
+):
+    return {
+        shape_var.var: utils.get_feat(
+            events,
+            shape_var.var if jshift == "" else utils.check_get_jec_var(shape_var.var, jshift),
+            bb_mask,
+        )
+        for shape_var in shape_vars
+    }
+
+
 def get_templates(
     events_dict: Dict[str, pd.DataFrame],
     bb_masks: Dict[str, pd.DataFrame],
     year: str,
     sig_keys: List[str],
-    selection_regions: Dict[str, Dict],
-    shape_var: Tuple[str],
-    shape_bins: List[float],
-    blind_window: List[float],
+    selection_regions: Dict[str, Region],
+    shape_vars: List[ShapeVar],
+    systematics: Dict,
+    template_dir: str = "",
+    bg_keys: List[str] = bg_keys,
     plot_dir: str = "",
     prev_cutflow: pd.DataFrame = None,
     weight_key: str = "finalWeight",
-    cutstr: str = "",
+    plot_sig_keys: List[str] = None,
+    sig_splits: List[List[str]] = None,
+    sig_scale_dict: Dict = None,
     weight_shifts: Dict = {},
     jshift: str = "",
-    selection_regions_label: Dict = selection_regions_label,
+    lpsfs: bool = True,
+    plot_shifts: bool = False,
+    pass_ylim: int = None,
+    fail_ylim: int = None,
+    blind_pass: bool = False,
     show: bool = False,
 ) -> Dict[str, Hist]:
     """
@@ -728,189 +1134,239 @@ def get_templates(
     (6) Saves a plot of each (if ``plot_dir`` is not "").
 
     Args:
-        selection_region (Dict[str, Dict]): Dictionary of cuts for each region
-          formatted as {region1: {cutvar1: [min, max], ...}, ...}.
-        shape_var (Tuple[str]): final shape var: (var name, var plotting label).
-        shape_bins (List[float]): binning for shape var: [num bins, min, max].
-        blind_window (List[float]): signal window to blind: [min, max] (min, max should be bin edges).
-        cutstr (str): optional string to add to plot file names.
+        selection_region (Dict[str, Dict]): Dictionary of ``Region``s including cuts and labels.
+        bg_keys (list[str]): background keys to plot.
 
     Returns:
         Dict[str, Hist]: dictionary of templates, saved as hist.Hist objects.
 
     """
-
     do_jshift = jshift != ""
     jlabel = "" if not do_jshift else "_" + jshift
-    templates, systematics = {}, {}
-
-    var = shape_var[0]
-
-    # print(selection_regions)
+    templates = {}
 
-    for label, region in selection_regions.items():
-        pass_region = label.startswith("pass")
+    for rname, region in selection_regions.items():
+        pass_region = rname.startswith("pass")
 
-        if label == "BDTOnly":
+        if rname == "lpsf":
             continue
 
         if not do_jshift:
-            print(label)
+            print(rname)
 
+        # make selection, taking JEC/JMC variations into account
         sel, cf = utils.make_selection(
-            region, events_dict, bb_masks, prev_cutflow=prev_cutflow, jshift=jshift
+            region.cuts, events_dict, bb_masks, prev_cutflow=prev_cutflow, jshift=jshift
         )
-        if not do_jshift:
-            print("\nCutflow:\n", cf)
 
-        cf.to_csv(f"{plot_dir}/cutflows/{label}_cutflow{jlabel}.csv")
+        if template_dir != "":
+            cf.to_csv(f"{template_dir}/cutflows/{year}/{rname}_cutflow{jlabel}.csv")
 
+        # trigger uncertainties
         if not do_jshift:
-            systematics[label] = {}
-
+            systematics[year][rname] = {}
             total, total_err = corrections.get_uncorr_trig_eff_unc(events_dict, bb_masks, year, sel)
+            systematics[year][rname]["trig_total"] = total
+            systematics[year][rname]["trig_total_err"] = total_err
+            print(f"Trigger SF Unc.: {total_err / total:.3f}\n")
 
-            systematics[label]["trig_total"] = total
-            systematics[label]["trig_total_err"] = total_err
-
-            # print(f"\nTrigger SF Unc.: {total_err / total:.3f}\n")
-
-        # ParticleNetMD Txbb SFs
+        # ParticleNetMD Txbb and ParT LP SFs
         sig_events = {}
         for sig_key in sig_keys:
             sig_events[sig_key] = deepcopy(events_dict[sig_key][sel[sig_key]])
             sig_bb_mask = bb_masks[sig_key][sel[sig_key]]
+
             if pass_region:
-                corrections.apply_txbb_sfs(sig_events, sig_bb_mask, year, weight_key)
+                # scale signal by LP SF
+                if lpsfs:
+                    for wkey in [weight_key, f"{weight_key}_noTrigEffs"]:
+                        sig_events[sig_key][wkey] *= systematics[sig_key]["lp_sf"]
+
+                corrections.apply_txbb_sfs(sig_events[sig_key], sig_bb_mask, year, weight_key)
+
+        # if not do_jshift:
+        #     print("\nCutflow:\n", cf)
 
         # set up samples
         hist_samples = list(events_dict.keys())
 
         if not do_jshift:
+            # set up weight-based variations
             for shift in ["down", "up"]:
                 if pass_region:
                     for sig_key in sig_keys:
                         hist_samples.append(f"{sig_key}_txbb_{shift}")
 
-                for wshift, wsamples in weight_shifts.items():
-                    for wsample in wsamples:
-                        hist_samples.append(f"{wsample}_{wshift}_{shift}")
-
-        h = (
-            Hist.new.StrCat(hist_samples, name="Sample")
-            .Reg(*shape_bins, name=var, label=shape_var[1])
-            .Weight()
+                for wshift, wsyst in weight_shifts.items():
+                    # if year in wsyst.years:
+                    # add to the axis even if not applied to this year to make it easier to sum later
+                    for wsample in wsyst.samples:
+                        if wsample in events_dict:
+                            hist_samples.append(f"{wsample}_{wshift}_{shift}")
+
+        # histograms
+        h = Hist(
+            hist.axis.StrCategory(hist_samples, name="Sample"),
+            *[shape_var.axis for shape_var in shape_vars],
+            storage="weight",
         )
 
+        # fill histograms
         for sample in events_dict:
             events = sig_events[sample] if sample in sig_keys else events_dict[sample][sel[sample]]
             if not len(events):
                 continue
 
             bb_mask = bb_masks[sample][sel[sample]]
-            fill_var = (
-                var if sample == data_key or not do_jshift else utils.check_get_jec_var(var, jshift)
+            fill_data = _get_fill_data(
+                events, bb_mask, shape_vars, jshift=jshift if sample != data_key else None
             )
-            fill_data = {var: utils.get_feat(events, fill_var, bb_mask)}
             weight = events[weight_key].values.squeeze()
             h.fill(Sample=sample, **fill_data, weight=weight)
 
             if not do_jshift:
                 # add weight variations
-                for wshift, wsamples in weight_shifts.items():
-                    if sample in wsamples:
+                for wshift, wsyst in weight_shifts.items():
+                    if sample in wsyst.samples and year in wsyst.years:
                         # print(wshift)
                         for skey, shift in [("Down", "down"), ("Up", "up")]:
-                            # reweight based on diff between up/down and nominal weights
-                            sweight = (
-                                weight
-                                * (
-                                    events[f"weight_{wshift}{skey}"][0] / events["weight_nonorm"]
-                                ).values.squeeze()
-                            )
+                            if "QCDscale" in wshift:
+                                # QCDscale7pt/QCDscale4
+                                # https://github.com/LPC-HH/HHLooper/blob/master/python/prepare_card_SR_final.py#L263-L288
+                                sweight = (
+                                    weight
+                                    * (
+                                        events[f"weight_QCDscale7pt{skey}"][0]
+                                        / events["weight_QCDscale4"]
+                                    ).values.squeeze()
+                                )
+                            else:
+                                # reweight based on diff between up/down and nominal weights
+                                sweight = (
+                                    weight
+                                    * (
+                                        events[f"weight_{wshift}{skey}"][0]
+                                        / events["weight_nonorm"]
+                                    ).values.squeeze()
+                                )
                             h.fill(Sample=f"{sample}_{wshift}_{shift}", **fill_data, weight=sweight)
 
         if pass_region:
-            # blind Higgs mass window in pass region in data
-            utils.blindBins(h, blind_window, data_key)
+            # blind signal mass windows in pass region in data
+            for i, shape_var in enumerate(shape_vars):
+                if shape_var.blind_window is not None:
+                    utils.blindBins(h, shape_var.blind_window, data_key, axis=i)
 
         if pass_region and not do_jshift:
-            # ParticleNetMD Txbb SFs
-            fill_data = {var: utils.get_feat(sig_events, var, sig_bb_mask)}
-            for shift in ["down", "up"]:
-                h.fill(
-                    Sample=f"{sig_key}_txbb_{shift}",
-                    **fill_data,
-                    weight=sig_events[f"{weight_key}_txbb_{shift}"],
-                )
-
-        templates[label + jlabel] = h
-
-        if plot_dir != "":
-            sig_scale_dict = utils.getSignalPlotScaleFactor(events_dict, sig_keys, selection=sel)
-            plot_params = {
-                "hists": h,
-                "bg_keys": bg_keys,
-                "sig_scale_dict": {key: val / 2 for key, val in sig_scale_dict.items()},
-                "show": show,
-                "year": year,
-            }
-
-            if not do_jshift:
-                title = f"{selection_regions_label[label]} Region Pre-Fit Shapes"
-            else:
-                title = f"{selection_regions_label[label]} Region {jshift} Shapes"
+            for sig_key in sig_keys:
+                if not len(sig_events[sig_key]):
+                    continue
 
-            plotting.ratioHistPlot(
-                **plot_params,
-                title=title,
-                name=f"{plot_dir}/templates/{cutstr}{label}_region_bb_mass{jlabel}.pdf",
+                # ParticleNetMD Txbb SFs
+                fill_data = _get_fill_data(
+                    sig_events[sig_key], bb_masks[sig_key][sel[sig_key]], shape_vars
+                )
+                for shift in ["down", "up"]:
+                    h.fill(
+                        Sample=f"{sig_key}_txbb_{shift}",
+                        **fill_data,
+                        weight=sig_events[sig_key][f"{weight_key}_txbb_{shift}"],
+                    )
+
+        templates[rname + jlabel] = h
+
+        # plot templates incl variations
+        if plot_dir != "" and (not do_jshift or plot_shifts):
+            if sig_scale_dict is None:
+                sig_scale_dict = {
+                    **{skey: 1 for skey in nonres_sig_keys},
+                    **{skey: 1 for skey in res_sig_keys},
+                }
+
+            title = (
+                f"{region.label} Region Pre-Fit Shapes"
+                if not do_jshift
+                else f"{region.label} Region {jshift} Shapes"
             )
 
-            if not do_jshift:
-                for wshift, wsamples in weight_shifts.items():
-                    wlabel = weight_labels[wshift]
-
-                    if wsamples == [sig_key]:
-                        plotting.ratioHistPlot(
-                            **plot_params,
-                            sig_err=wshift,
-                            title=f"{selection_regions_label[label]} Region {wlabel} Unc. Shapes",
-                            name=f"{plot_dir}/templates/{cutstr}{label}_region_bb_mass_{wshift}.pdf",
+            if sig_splits is None:
+                sig_splits = [plot_sig_keys]
+
+            for i, shape_var in enumerate(shape_vars):
+                for j, p_sig_keys in enumerate(sig_splits):
+                    split_str = "" if len(sig_splits) == 1 else f"sigs{j}_"
+                    plot_params = {
+                        "hists": h.project(0, i + 1),
+                        "sig_keys": p_sig_keys,
+                        "bg_keys": bg_keys,
+                        "sig_scale_dict": {key: sig_scale_dict[key] for key in p_sig_keys}
+                        if pass_region
+                        else None,
+                        "show": show,
+                        "year": year,
+                        "ylim": pass_ylim if pass_region else fail_ylim,
+                        "plot_data": not (rname == "pass" and blind_pass),
+                    }
+
+                    plot_name = (
+                        f"{plot_dir}/"
+                        f"{'jshifts/' if do_jshift else ''}"
+                        f"{split_str}{rname}_region_{shape_var.var}"
+                    )
+
+                    plotting.ratioHistPlot(
+                        **plot_params,
+                        title=title,
+                        name=f"{plot_name}{jlabel}.pdf",
+                    )
+
+                    if not do_jshift and plot_shifts:
+                        plot_name = (
+                            f"{plot_dir}/wshifts/" f"{split_str}{rname}_region_{shape_var.var}"
                         )
-                    else:
-                        for skey, shift in [("Down", "down"), ("Up", "up")]:
+
+                        for wshift, wsyst in weight_shifts.items():
+                            if wsyst.samples == [sig_key]:
+                                plotting.ratioHistPlot(
+                                    **plot_params,
+                                    sig_err=wshift,
+                                    title=f"{region.label} Region {wsyst.label} Unc. Shapes",
+                                    name=f"{plot_name}_{wshift}.pdf",
+                                )
+                            else:
+                                for skey, shift in [("Down", "down"), ("Up", "up")]:
+                                    plotting.ratioHistPlot(
+                                        **plot_params,
+                                        variation=(wshift, shift, wsyst.samples),
+                                        title=f"{region.label} Region {wsyst.label} Unc. {skey} Shapes",
+                                        name=f"{plot_name}_{wshift}_{shift}.pdf",
+                                    )
+
+                        if pass_region:
                             plotting.ratioHistPlot(
                                 **plot_params,
-                                variation=(wshift, shift, wsamples),
-                                title=f"{selection_regions_label[label]} Region {wlabel} Unc. {skey} Shapes",
-                                name=f"{plot_dir}/templates/{cutstr}{label}_region_bb_mass_{wshift}_{shift}.pdf",
+                                sig_err="txbb",
+                                title=rf"{region.label} Region $T_{{Xbb}}$ Shapes",
+                                name=f"{plot_name}_txbb.pdf",
                             )
 
-            if pass_region and not do_jshift:
-                plotting.ratioHistPlot(
-                    **plot_params,
-                    sig_err="txbb",
-                    title=rf"{selection_regions_label[label]} Region $T_{{Xbb}}$ Shapes",
-                    name=f"{plot_dir}/templates/{cutstr}{label}_region_bb_mass_txbb.pdf",
-                )
-
-    return templates, systematics
+    return templates
 
 
 def save_templates(
-    templates: Dict[str, Hist],
-    blind_window: List[float],
-    template_file: str,
+    templates: Dict[str, Hist], template_file: str, resonant: bool, shape_vars: List[ShapeVar]
 ):
     """Creates blinded copies of each region's templates and saves a pickle of the templates"""
 
-    from copy import deepcopy
+    if not resonant:
+        from copy import deepcopy
+
+        blind_window = shape_vars[0].blind_window
 
-    for label, template in list(templates.items()):
-        blinded_template = deepcopy(template)
-        utils.blindBins(blinded_template, blind_window)
-        templates[f"{label}Blinded"] = blinded_template
+        for label, template in list(templates.items()):
+            blinded_template = deepcopy(template)
+            utils.blindBins(blinded_template, blind_window)
+            templates[f"{label}Blinded"] = blinded_template
 
     with open(template_file, "wb") as f:
         pickle.dump(templates, f)
@@ -919,18 +1375,23 @@ def save_templates(
 
 
 if __name__ == "__main__":
-    import argparse
-
     parser = argparse.ArgumentParser()
 
     parser.add_argument(
         "--data-dir",
-        dest="data_dir",
-        default="../../../../data/skimmer/Feb24/",
+        default=None,
         help="path to skimmed parquet",
         type=str,
     )
 
+    parser.add_argument(
+        "--signal-data-dirs",
+        default=[],
+        help="path to skimmed signal parquets, if different from other data",
+        nargs="*",
+        type=str,
+    )
+
     parser.add_argument(
         "--year",
         default="2017",
@@ -939,7 +1400,7 @@ def save_templates(
     )
 
     parser.add_argument(
-        "--bdt-preds",
+        "--bdt-preds-dir",
         help="path to bdt predictions directory, will look in `data dir`/inferences/ by default",
         default="",
         type=str,
@@ -959,14 +1420,96 @@ def save_templates(
         type=str,
     )
 
+    parser.add_argument(
+        "--templates-name",
+        help="If saving templates, optional name for folder (comes under cuts directory if scanning).",
+        default="",
+        type=str,
+    )
+
+    utils.add_bool_arg(parser, "resonant", "for resonant or nonresonant", default=False)
     utils.add_bool_arg(parser, "control-plots", "make control plots", default=False)
     utils.add_bool_arg(parser, "templates", "save m_bb templates using bdt cut", default=False)
     utils.add_bool_arg(
         parser, "overwrite-template", "if template file already exists, overwrite it", default=False
     )
-    utils.add_bool_arg(parser, "scan", "Scan BDT + Txbb cuts and save templates", default=False)
-
+    utils.add_bool_arg(parser, "do-jshifts", "Do JEC/JMC variations", default=True)
+    utils.add_bool_arg(parser, "plot-shifts", "Plot systematic variations as well", default=False)
     utils.add_bool_arg(parser, "lp-sf-all-years", "Calculate one LP SF for all run 2", default=True)
 
+    parser.add_argument(
+        "--sig-samples",
+        help="specify signal samples. By default, will use the samples defined in `hh_vars`.",
+        nargs="*",
+        default=None,
+        type=str,
+    )
+
+    parser.add_argument(
+        "--bg-keys",
+        help="specify background samples",
+        nargs="*",
+        default=["QCD", "TT", "ST", "V+Jets", "Diboson"],
+        type=str,
+    )
+
+    utils.add_bool_arg(
+        parser, "read-sig-samples", "read signal samples from directory", default=False
+    )
+
+    utils.add_bool_arg(parser, "data", "include data", default=True)
+    utils.add_bool_arg(
+        parser, "HEM2d", "fatjet phi v eta plots to check HEM cleaning", default=False
+    )
+
+    utils.add_bool_arg(parser, "old-processor", "temp arg for old processed samples", default=False)
+
+    parser.add_argument(
+        "--nonres-txbb-wp",
+        help="Txbb WP for signal region. If multiple arguments, will make templates for each.",
+        default=["MP"],
+        choices=["LP", "MP", "HP"],
+        nargs="*",
+        type=str,
+    )
+
+    parser.add_argument(
+        "--nonres-bdt-wp",
+        help="BDT WP for signal region. If multiple arguments, will make templates for each.",
+        default=[0.998],
+        nargs="*",
+        type=float,
+    )
+
+    parser.add_argument(
+        "--res-txbb-wp",
+        help="Txbb WP for signal region. If multiple arguments, will make templates for each.",
+        default=["HP"],
+        choices=["LP", "MP", "HP"],
+        nargs="*",
+        type=str,
+    )
+
+    parser.add_argument(
+        "--res-thww-wp",
+        help="Thww WP for signal region. If multiple arguments, will make templates for each.",
+        default=[0.96],
+        nargs="*",
+        type=float,
+    )
+
     args = parser.parse_args()
+
+    if args.templates and args.template_dir == "":
+        print("Need to set --template-dir if making templates. Exiting.")
+        sys.exit()
+
+    if not args.signal_data_dirs:
+        args.signal_data_dirs = [args.data_dir]
+
+    if args.bdt_preds_dir == "" and not args.resonant:
+        args.bdt_preds_dir = f"{args.data_dir}/inferences/"
+    elif args.resonant:
+        args.bdt_preds_dir = None
+
     main(args)
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23Jun14/2016APV_templates.pkl
new file mode 100644
index 00000000..12594f41
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23Jun14/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23Jun14/2016_templates.pkl
new file mode 100644
index 00000000..6e9c6856
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23Jun14/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23Jun14/2017_templates.pkl
new file mode 100644
index 00000000..a3ed6233
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23Jun14/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23Jun14/2018_templates.pkl
new file mode 100644
index 00000000..cf482e07
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23Jun14/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow.csv
new file mode 100644
index 00000000..79aba87b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow.csv
@@ -0,0 +1,18 @@
+,Pre-selection,TriggerEffs,QCD SF,LP SF,bbFatJetPt ≥ 300,VVFatJetPt ≥ 300,bbFatJetParticleNetMD_Txbb ≥ 0.8,bbFatJetParticleNetMD_Txbb < 0.9734
+HHbbVV,4.212152831867275,1.992043000326517,1.992043000326517,1.992043000326517,1.9861334246299032,1.981125128881637,1.981125128881637,0.6563995518769355
+ggHH_kl_2p45_kt_1_HHbbVV,2.9745782884612515,1.5432728900245152,1.5432728900245152,1.5432728900245152,1.5389825758017326,1.5358698844856542,1.5358698844856542,0.5360067851075913
+ggHH_kl_5_kt_1_HHbbVV,3.024267581347732,1.6992983771239942,1.6992983771239942,1.6992983771239942,1.6954316100118954,1.6912509178977773,1.6912509178977773,0.7721329919863715
+ggHH_kl_0_kt_1_HHbbVV,5.525753766039689,2.5008088076536663,2.5008088076536663,2.5008088076536663,2.492400734674631,2.486021038384556,2.486021038384556,0.8321068468988357
+qqHH_CV_1_C2V_1_kl_1_HHbbVV,0.08080083387529777,0.0471359884176165,0.0471359884176165,0.0471359884176165,0.04696965505299343,0.04680782753427778,0.04680782753427778,0.016525436397830513
+qqHH_CV_1_C2V_0_kl_1_HHbbVV,37.59031628851101,30.684634831131394,30.684634831131394,30.684634831131394,30.649876720820107,30.62791174926079,30.62791174926079,8.169795927012046
+qqHH_CV_1p5_C2V_1_kl_1_HHbbVV,63.61565564660195,51.09146910173492,51.09146910173492,51.09146910173492,51.02619356994726,50.982718165453605,50.982718165453605,13.706437165458288
+qqHH_CV_1_C2V_1_kl_2_HHbbVV,0.0755132861969917,0.04912744088644729,0.04912744088644729,0.04912744088644729,0.048941276899068145,0.04880549001071947,0.04880549001071947,0.023726362019957654
+qqHH_CV_1_C2V_2_kl_1_HHbbVV,33.59757114342516,28.05713425984107,28.05713425984107,28.05713425984107,28.027153630489774,28.00840383282709,28.00840383282709,7.4574613446394595
+qqHH_CV_1_C2V_1_kl_0_HHbbVV,0.21899698228055675,0.11904051829601674,0.11904051829601674,0.11904051829601674,0.11867720764603297,0.11831599320372019,0.11831599320372019,0.03634352635978662
+qqHH_CV_0p5_C2V_1_kl_1_HHbbVV,20.109831451322012,16.609688285231236,16.609688285231236,16.609688285231236,16.590017395357748,16.57790852793485,16.57790852793485,4.455708476157183
+QCD,3052764.194451403,1301832.896386517,1396522.353647176,1396522.353647176,1391274.5382613477,1387353.1969275854,1387353.1969275854,1116331.3034096966
+TT,217539.42041251797,122304.088162081,122304.088162081,122304.088162081,121872.74207564502,121419.87436582817,121419.87436582817,98253.22915404756
+ST,15175.476848651859,9009.706988812268,9009.706988812268,9009.706988812268,8970.28989150847,8949.125923995576,8949.125923995576,7340.602605799113
+V+Jets,85869.75128605982,45001.54838412287,45001.54838412287,45001.54838412287,44839.88385294852,44728.338222447535,44728.338222447535,30486.54445856925
+Diboson,1365.2537297679432,749.3028178092068,749.3028178092068,749.3028178092068,746.9936486720342,744.0506017684028,744.0506017684028,438.91139965219475
+Data,1573587.0,1573587.0,1573587.0,1573587.0,1573587.0,1573587.0,1573587.0,1294132.0
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JER_down.csv
new file mode 100644
index 00000000..2107d596
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JER_down.csv
@@ -0,0 +1,18 @@
+,Pre-selection,TriggerEffs,QCD SF,LP SF,bbFatJetPt_JER_down ≥ 300,VVFatJetPt_JER_down ≥ 300,bbFatJetParticleNetMD_Txbb ≥ 0.8,bbFatJetParticleNetMD_Txbb < 0.9734,bbFatJetPt ≥ 300,VVFatJetPt ≥ 300
+HHbbVV,4.212152831867275,1.992043000326517,1.992043000326517,1.992043000326517,1.986315512236825,1.9815073231361668,1.9815073231361668,0.6566752885640064,,
+ggHH_kl_2p45_kt_1_HHbbVV,2.9745782884612515,1.5432728900245152,1.5432728900245152,1.5432728900245152,1.5393005051252022,1.5362611350124076,1.5362611350124076,0.5361874482182619,,
+ggHH_kl_5_kt_1_HHbbVV,3.024267581347732,1.6992983771239942,1.6992983771239942,1.6992983771239942,1.6956640860385312,1.691550309895977,1.691550309895977,0.7721836083063666,,
+ggHH_kl_0_kt_1_HHbbVV,5.525753766039689,2.5008088076536663,2.5008088076536663,2.5008088076536663,2.4928557654112016,2.486750635802229,2.486750635802229,0.8325341668963208,,
+qqHH_CV_1_C2V_1_kl_1_HHbbVV,0.08080083387529777,0.0471359884176165,0.0471359884176165,0.0471359884176165,0.04700210312789463,0.04685081431225273,0.04685081431225273,0.01653775940511957,,
+qqHH_CV_1_C2V_0_kl_1_HHbbVV,37.59031628851101,30.684634831131394,30.684634831131394,30.684634831131394,30.652749719059294,30.631414806447417,30.631414806447417,8.170244843134162,,
+qqHH_CV_1p5_C2V_1_kl_1_HHbbVV,63.61565564660195,51.09146910173492,51.09146910173492,51.09146910173492,51.03358768384827,50.99100693344389,50.99100693344389,13.709926425173961,,
+qqHH_CV_1_C2V_1_kl_2_HHbbVV,0.0755132861969917,0.04912744088644729,0.04912744088644729,0.04912744088644729,0.048962169629822375,0.04883542367546119,0.04883542367546119,0.02375722266697959,,
+qqHH_CV_1_C2V_2_kl_1_HHbbVV,33.59757114342516,28.05713425984107,28.05713425984107,28.05713425984107,28.030399250315277,28.012796591098304,28.012796591098304,7.459012303247486,,
+qqHH_CV_1_C2V_1_kl_0_HHbbVV,0.21899698228055675,0.11904051829601674,0.11904051829601674,0.11904051829601674,0.11872731289785511,0.11834849310826141,0.11834849310826141,0.03636500202086177,,
+qqHH_CV_0p5_C2V_1_kl_1_HHbbVV,20.109831451322012,16.609688285231236,16.609688285231236,16.609688285231236,16.59184125282177,16.580592054168513,16.580592054168513,4.456519324557359,,
+QCD,3052764.194451403,1301832.896386517,1396522.353647176,1396522.353647176,1391438.8555958904,1387651.584409052,1387651.584409052,1116581.2634885325,,
+TT,217539.42041251797,122304.088162081,122304.088162081,122304.088162081,121858.57397285436,121393.74802776259,121393.74802776259,98230.10818315783,,
+ST,15175.476848651859,9009.706988812268,9009.706988812268,9009.706988812268,8969.153793738911,8948.396413198747,8948.396413198747,7340.255240912415,,
+V+Jets,85869.75128605982,45001.54838412287,45001.54838412287,45001.54838412287,44847.03254687517,44740.07191338349,44740.07191338349,30495.415943414046,,
+Diboson,1365.2537297679432,749.3028178092068,749.3028178092068,749.3028178092068,746.3661198286002,743.1069179218454,743.1069179218454,438.14056891526815,,
+Data,1573587.0,1573587.0,1573587.0,1573587.0,,,1573587.0,1294132.0,1573587.0,1573587.0
diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JER_up.csv
new file mode 100644
index 00000000..49410c92
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JER_up.csv
@@ -0,0 +1,18 @@
+,Pre-selection,TriggerEffs,QCD SF,LP SF,bbFatJetPt_JER_up ≥ 300,VVFatJetPt_JER_up ≥ 300,bbFatJetParticleNetMD_Txbb ≥ 0.8,bbFatJetParticleNetMD_Txbb < 0.9734,bbFatJetPt ≥ 300,VVFatJetPt ≥ 300
+HHbbVV,4.212152831867275,1.992043000326517,1.992043000326517,1.992043000326517,1.9858583403863088,1.9806416646179514,1.9806416646179514,0.6562134311157858,,
+ggHH_kl_2p45_kt_1_HHbbVV,2.9745782884612515,1.5432728900245152,1.5432728900245152,1.5432728900245152,1.5386357184830675,1.535408402703747,1.535408402703747,0.5358210583216941,,
+ggHH_kl_5_kt_1_HHbbVV,3.024267581347732,1.6992983771239942,1.6992983771239942,1.6992983771239942,1.6951316637944287,1.690940773491271,1.690940773491271,0.7718118198440417,,
+ggHH_kl_0_kt_1_HHbbVV,5.525753766039689,2.5008088076536663,2.5008088076536663,2.5008088076536663,2.492096175198671,2.485480165346472,2.485480165346472,0.8318864959688351,,
+qqHH_CV_1_C2V_1_kl_1_HHbbVV,0.08080083387529777,0.0471359884176165,0.0471359884176165,0.0471359884176165,0.04697039942054864,0.04680466317679371,0.04680466317679371,0.016526376475821086,,
+qqHH_CV_1_C2V_0_kl_1_HHbbVV,37.59031628851101,30.684634831131394,30.684634831131394,30.684634831131394,30.645634816019943,30.623662868410257,30.623662868410257,8.167979857987252,,
+qqHH_CV_1p5_C2V_1_kl_1_HHbbVV,63.61565564660195,51.09146910173492,51.09146910173492,51.09146910173492,51.02173174042651,50.97817866746364,50.97817866746364,13.706030761543719,,
+qqHH_CV_1_C2V_1_kl_2_HHbbVV,0.0755132861969917,0.04912744088644729,0.04912744088644729,0.04912744088644729,0.04892080808919949,0.04878741527115518,0.04878741527115518,0.023709633426953702,,
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JES_down.csv
new file mode 100644
index 00000000..bfff4882
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JES_down.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JES_up.csv
new file mode 100644
index 00000000..c1b30b8f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JES_up.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMR_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMR_down.csv
new file mode 100644
index 00000000..79aba87b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMR_down.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMR_up.csv
new file mode 100644
index 00000000..79aba87b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMR_up.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMS_down.csv
new file mode 100644
index 00000000..79aba87b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMS_down.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMS_up.csv
new file mode 100644
index 00000000..79aba87b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/fail_cutflow_JMS_up.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow.csv
new file mode 100644
index 00000000..bdef8745
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JER_down.csv
new file mode 100644
index 00000000..fd703044
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JER_down.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JER_up.csv
new file mode 100644
index 00000000..78cf4d99
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JER_up.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JES_down.csv
new file mode 100644
index 00000000..acb24e50
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JES_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JES_up.csv
new file mode 100644
index 00000000..e98f782c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JES_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMR_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMR_down.csv
new file mode 100644
index 00000000..fb834643
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMR_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMR_up.csv
new file mode 100644
index 00000000..26454008
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMR_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..a11bcecd
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMS_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMS_up.csv
new file mode 100644
index 00000000..6f7b441f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/cutflows/pass_cutflow_JMS_up.csv
@@ -0,0 +1,18 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23Jun14/lpsfs.csv
new file mode 100644
index 00000000..bccd81bb
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/lpsfs.csv
@@ -0,0 +1,12 @@
+,SF,syst_unc,stat_unc,sj_pt_unc,sj_matching_unc
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+ggHH_kl_2p45_kt_1_HHbbVV,0.81 ± 0.16,0.19030900317205782,0.028993648707240898,0.02369631732222509,0.055326181786903586
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+ggHH_kl_0_kt_1_HHbbVV,0.83 ± 0.17,0.18916508189844478,0.03639952205096509,0.018916476313437285,0.054929321018307005
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diff --git a/src/HHbbVV/postprocessing/templates/23Jun14/systematics.json b/src/HHbbVV/postprocessing/templates/23Jun14/systematics.json
new file mode 100644
index 00000000..8cedcc71
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23Jun14/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13/2016APV_templates.pkl
new file mode 100644
index 00000000..21ae4ff6
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13/2016_templates.pkl
new file mode 100644
index 00000000..56418dbb
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13/2017_templates.pkl
new file mode 100644
index 00000000..539c1e96
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13/2018_templates.pkl
new file mode 100644
index 00000000..b0c5391a
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow_JER_down.csv
new file mode 100644
index 00000000..daee575b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow_JER_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow_JER_up.csv
new file mode 100644
index 00000000..3aab61f3
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13/fail_cutflow_JER_up.csv
@@ -0,0 +1,8 @@
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new file mode 100644
index 00000000..640384a7
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new file mode 100644
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new file mode 100644
index 00000000..433ad485
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new file mode 100644
index 00000000..433ad485
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new file mode 100644
index 00000000..433ad485
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13/lpsfs.csv
new file mode 100644
index 00000000..2cd96eca
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow.csv
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index 00000000..8b3508fa
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new file mode 100644
index 00000000..f00460ad
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new file mode 100644
index 00000000..79772867
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow_JES_down.csv
new file mode 100644
index 00000000..0cbf6fc7
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow_JES_up.csv
new file mode 100644
index 00000000..73724e61
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new file mode 100644
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new file mode 100644
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--- /dev/null
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new file mode 100644
index 00000000..10b255a2
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13/pass_cutflow_JMS_up.csv
new file mode 100644
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diff --git a/src/HHbbVV/postprocessing/templates/23May13/systematics.json b/src/HHbbVV/postprocessing/templates/23May13/systematics.json
new file mode 100644
index 00000000..24b9d7b8
--- /dev/null
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13MP/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
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new file mode 100644
index 00000000..fe4da5d8
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new file mode 100644
index 00000000..bcb8b569
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow_JES_down.csv
new file mode 100644
index 00000000..636e83a0
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow_JES_up.csv
new file mode 100644
index 00000000..d57ecf27
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow_JMR_down.csv b/src/HHbbVV/postprocessing/templates/23May13MP/fail_cutflow_JMR_down.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
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new file mode 100644
index 00000000..aeaff4e0
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new file mode 100644
index 00000000..aeaff4e0
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new file mode 100644
index 00000000..aeaff4e0
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13MP/lpsfs.csv
new file mode 100644
index 00000000..d5c29b47
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow.csv
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index 00000000..da20e9e9
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new file mode 100644
index 00000000..b9d32210
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new file mode 100644
index 00000000..3043add8
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new file mode 100644
index 00000000..aade4700
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new file mode 100644
index 00000000..7a76e1b2
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new file mode 100644
index 00000000..7746213c
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new file mode 100644
index 00000000..30bc791a
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..d1efbb99
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow_JMS_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow_JMS_up.csv
new file mode 100644
index 00000000..f1bd5106
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13MP/pass_cutflow_JMS_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13MP/systematics.json b/src/HHbbVV/postprocessing/templates/23May13MP/systematics.json
new file mode 100644
index 00000000..9333cbad
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13MP/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13Nonres/2017_templates.pkl
new file mode 100644
index 00000000..539c1e96
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13Nonres/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow.csv
new file mode 100644
index 00000000..f4fedc0b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JER_down.csv
new file mode 100644
index 00000000..1d4ba299
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JER_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JER_up.csv
new file mode 100644
index 00000000..14f1618a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JER_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JES_down.csv
new file mode 100644
index 00000000..e6ce4167
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JES_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JES_up.csv
new file mode 100644
index 00000000..bcd4678e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JES_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMR_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMR_down.csv
new file mode 100644
index 00000000..f4fedc0b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMR_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMR_up.csv
new file mode 100644
index 00000000..f4fedc0b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMR_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMS_down.csv
new file mode 100644
index 00000000..f4fedc0b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMS_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMS_up.csv
new file mode 100644
index 00000000..f4fedc0b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/fail_cutflow_JMS_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/lpsfs.csv
new file mode 100644
index 00000000..2cd96eca
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow.csv
new file mode 100644
index 00000000..11d7b529
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow.csv
@@ -0,0 +1,8 @@
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new file mode 100644
index 00000000..40ea4bcf
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JER_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JER_up.csv
new file mode 100644
index 00000000..3d6d169d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JER_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JES_down.csv
new file mode 100644
index 00000000..1057c6a8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JES_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JES_up.csv
new file mode 100644
index 00000000..2e0193fd
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JES_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMR_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMR_down.csv
new file mode 100644
index 00000000..97ca4068
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMR_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMR_up.csv
new file mode 100644
index 00000000..2d2bcb4e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMR_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..aa5e7d37
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMS_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMS_up.csv
new file mode 100644
index 00000000..5b34f7db
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/pass_cutflow_JMS_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13Nonres/systematics.json b/src/HHbbVV/postprocessing/templates/23May13Nonres/systematics.json
new file mode 100644
index 00000000..014eb23e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13Nonres/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2018_templates.pkl
new file mode 100644
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/fail_cutflow.csv
new file mode 100644
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/lpsfs.csv
new file mode 100644
index 00000000..dba446c0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/pass_cutflow.csv
new file mode 100644
index 00000000..36d25b0c
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/systematics.json
new file mode 100644
index 00000000..3e918190
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.4/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2016APV_templates.pkl differ
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/lpsfs.csv
new file mode 100644
index 00000000..d5b2b46f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.6/pass_cutflow.csv
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2016APV_templates.pkl differ
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/fail_cutflow.csv
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--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/lpsfs.csv
new file mode 100644
index 00000000..c6f84bcf
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/pass_cutflow.csv
new file mode 100644
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--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.8/systematics.json
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--- /dev/null
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/fail_cutflow.csv
new file mode 100644
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--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/lpsfs.csv
new file mode 100644
index 00000000..5ea810de
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/pass_cutflow.csv
new file mode 100644
index 00000000..5846ebef
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/systematics.json
new file mode 100644
index 00000000..4d2aab2a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/lpsfs.csv
new file mode 100644
index 00000000..7b8d25ce
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/pass_cutflow.csv
new file mode 100644
index 00000000..081f3f99
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/pass_cutflow.csv
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index 00000000..ef15be6b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.96/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
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@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/lpsfs.csv
new file mode 100644
index 00000000..2cd96eca
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/pass_cutflow.csv
new file mode 100644
index 00000000..8b3508fa
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/systematics.json
new file mode 100644
index 00000000..24b9d7b8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.99/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/lpsfs.csv
new file mode 100644
index 00000000..7654ada8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/pass_cutflow.csv
new file mode 100644
index 00000000..7ca2e4be
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/systematics.json
new file mode 100644
index 00000000..1e36e6e7
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.994/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/lpsfs.csv
new file mode 100644
index 00000000..3d3da9de
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/pass_cutflow.csv
new file mode 100644
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new file mode 100644
index 00000000..c8a7779c
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+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.995/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/fail_cutflow.csv
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index 00000000..433ad485
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/lpsfs.csv
new file mode 100644
index 00000000..c2e3f3c9
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/systematics.json
new file mode 100644
index 00000000..9a6ec651
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.997/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/lpsfs.csv
new file mode 100644
index 00000000..a11aec83
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/pass_cutflow.csv
new file mode 100644
index 00000000..945356d3
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/systematics.json
new file mode 100644
index 00000000..f0b8fb8a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9975/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/lpsfs.csv
new file mode 100644
index 00000000..d5c29b47
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/pass_cutflow.csv
new file mode 100644
index 00000000..4c9d9835
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/pass_cutflow.csv
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new file mode 100644
index 00000000..be30d355
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.998/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/lpsfs.csv
new file mode 100644
index 00000000..42d7a1a8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/pass_cutflow.csv
new file mode 100644
index 00000000..97eae28a
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/systematics.json
new file mode 100644
index 00000000..1593537c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9985/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/lpsfs.csv
new file mode 100644
index 00000000..39b0e2c4
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/pass_cutflow.csv
new file mode 100644
index 00000000..706e15a3
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/systematics.json
new file mode 100644
index 00000000..267eba85
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.9988/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/lpsfs.csv
new file mode 100644
index 00000000..8c538250
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/pass_cutflow.csv
new file mode 100644
index 00000000..04862a20
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/systematics.json
new file mode 100644
index 00000000..3033baee
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_HP_bdt_0.999/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/lpsfs.csv
new file mode 100644
index 00000000..dba446c0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/pass_cutflow.csv
new file mode 100644
index 00000000..950cab7c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/systematics.json
new file mode 100644
index 00000000..945e3ea4
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.4/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/lpsfs.csv
new file mode 100644
index 00000000..d5b2b46f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/pass_cutflow.csv
new file mode 100644
index 00000000..ec1b77d9
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/systematics.json
new file mode 100644
index 00000000..72cec243
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.6/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JER_down.csv
new file mode 100644
index 00000000..2661e89c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JER_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JER_up.csv
new file mode 100644
index 00000000..82d4cfad
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JER_up.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JES_down.csv
new file mode 100644
index 00000000..8c52c954
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JES_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JES_up.csv
new file mode 100644
index 00000000..229ddd8e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JES_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JMS_up.csv
new file mode 100644
index 00000000..138b665f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/fail_cutflow_JMS_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/lpsfs.csv
new file mode 100644
index 00000000..c6f84bcf
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow.csv
new file mode 100644
index 00000000..e6b19338
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JER_down.csv
new file mode 100644
index 00000000..a6f05adb
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JER_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JER_up.csv
new file mode 100644
index 00000000..c27f52aa
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JES_down.csv
new file mode 100644
index 00000000..c1503318
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JES_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JES_up.csv
new file mode 100644
index 00000000..168b7e6b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JES_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JMS_up.csv
new file mode 100644
index 00000000..0784377c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/pass_cutflow_JMS_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/systematics.json
new file mode 100644
index 00000000..bee49710
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.8/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow.csv
@@ -0,0 +1,8 @@
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index 00000000..2661e89c
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow_JER_up.csv
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index 00000000..82d4cfad
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/fail_cutflow_JES_down.csv
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index 00000000..8c52c954
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index 00000000..229ddd8e
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index 00000000..138b665f
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index 00000000..138b665f
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow.csv
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index 00000000..03d12163
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new file mode 100644
index 00000000..29861e99
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JES_down.csv
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index 00000000..17fab61a
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JES_up.csv
new file mode 100644
index 00000000..740928e0
--- /dev/null
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index 00000000..af68603c
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMR_up.csv
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index 00000000..da1d4f8e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMR_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..9b2fde82
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMS_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/pass_cutflow_JMS_up.csv
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--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/systematics.json
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index 00000000..6b3b8a67
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/fail_cutflow.csv
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index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/fail_cutflow_JER_down.csv
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index 00000000..2661e89c
--- /dev/null
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index 00000000..82d4cfad
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index 00000000..229ddd8e
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index 00000000..138b665f
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index 00000000..2853647a
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JER_up.csv
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index 00000000..2687689b
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JES_down.csv
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index 00000000..89affbf6
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JES_up.csv
new file mode 100644
index 00000000..4feab5b7
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMR_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMR_down.csv
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index 00000000..ff61e260
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMR_up.csv
new file mode 100644
index 00000000..c7c6b1f6
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMR_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..48a80051
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMS_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMS_up.csv
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index 00000000..f505b0dd
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/pass_cutflow_JMS_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/systematics.json
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index 00000000..01aac7ba
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.96/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow.csv
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index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow_JER_down.csv
new file mode 100644
index 00000000..2661e89c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow_JER_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/fail_cutflow_JER_up.csv
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index 00000000..82d4cfad
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index 00000000..138b665f
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/lpsfs.csv
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index 00000000..201038a2
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index 00000000..61eb61be
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JES_down.csv
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index 00000000..ebc59b3c
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index 00000000..02701268
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index 00000000..7848e130
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMR_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMR_up.csv
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index 00000000..a8e9917d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..12a6a3ac
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMS_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/pass_cutflow_JMS_up.csv
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--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.99/systematics.json
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--- /dev/null
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/fail_cutflow.csv
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index 00000000..e6f3a08d
--- /dev/null
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new file mode 100644
index 00000000..2661e89c
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/fail_cutflow_JER_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/fail_cutflow_JER_up.csv
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index 00000000..82d4cfad
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/fail_cutflow_JES_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/fail_cutflow_JES_down.csv
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index 00000000..8c52c954
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index 00000000..b805492c
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index 00000000..ba82a39f
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JES_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JES_up.csv
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new file mode 100644
index 00000000..ff4d8358
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMR_down.csv
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new file mode 100644
index 00000000..749ba3d3
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMR_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..96301e9d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMS_down.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMS_up.csv
new file mode 100644
index 00000000..cc56c1ee
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/pass_cutflow_JMS_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/systematics.json
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--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.994/systematics.json
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/lpsfs.csv
new file mode 100644
index 00000000..3d3da9de
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/pass_cutflow.csv
new file mode 100644
index 00000000..02ebdf3c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/pass_cutflow.csv
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new file mode 100644
index 00000000..2820c22e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.995/systematics.json
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/lpsfs.csv
new file mode 100644
index 00000000..c2e3f3c9
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/pass_cutflow.csv
new file mode 100644
index 00000000..7acb7189
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/systematics.json
new file mode 100644
index 00000000..2b17509c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.997/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/lpsfs.csv
new file mode 100644
index 00000000..a11aec83
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/pass_cutflow.csv
new file mode 100644
index 00000000..39bc84f1
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/systematics.json
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index 00000000..27bd5b74
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9975/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/lpsfs.csv
new file mode 100644
index 00000000..d5c29b47
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/pass_cutflow.csv
new file mode 100644
index 00000000..d8540c34
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/systematics.json
new file mode 100644
index 00000000..e69d357a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.998/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/lpsfs.csv
new file mode 100644
index 00000000..42d7a1a8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/pass_cutflow.csv
new file mode 100644
index 00000000..4cc6c2cf
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/systematics.json
new file mode 100644
index 00000000..af61b34e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9985/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/lpsfs.csv
new file mode 100644
index 00000000..39b0e2c4
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/pass_cutflow.csv
new file mode 100644
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--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/systematics.json
new file mode 100644
index 00000000..27661c6d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.9988/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/2018_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/fail_cutflow.csv
new file mode 100644
index 00000000..e6f3a08d
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/lpsfs.csv
new file mode 100644
index 00000000..8c538250
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/lpsfs.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/pass_cutflow.csv
new file mode 100644
index 00000000..17e8d5f9
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/systematics.json
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index 00000000..741e6460
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_LP_bdt_0.999/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/lpsfs.csv
new file mode 100644
index 00000000..dba446c0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/pass_cutflow.csv
new file mode 100644
index 00000000..49ab1cbb
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/systematics.json
new file mode 100644
index 00000000..6293b25d
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.4/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
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@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/lpsfs.csv
new file mode 100644
index 00000000..d5b2b46f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/pass_cutflow.csv
new file mode 100644
index 00000000..0ba07171
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/systematics.json
new file mode 100644
index 00000000..5b3bc98c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.6/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2016APV_templates.pkl differ
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/lpsfs.csv
new file mode 100644
index 00000000..c6f84bcf
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/pass_cutflow.csv
new file mode 100644
index 00000000..8f249e0f
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/systematics.json
new file mode 100644
index 00000000..b7dcedef
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.8/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2016APV_templates.pkl differ
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/lpsfs.csv
new file mode 100644
index 00000000..5ea810de
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/pass_cutflow.csv
new file mode 100644
index 00000000..82cb8d18
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/systematics.json
new file mode 100644
index 00000000..6170d345
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2017_templates.pkl
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index 00000000..8784a434
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
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@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/lpsfs.csv
new file mode 100644
index 00000000..7b8d25ce
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/pass_cutflow.csv
new file mode 100644
index 00000000..1385afe1
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/systematics.json
new file mode 100644
index 00000000..c721a48c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.96/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/lpsfs.csv
new file mode 100644
index 00000000..2cd96eca
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/pass_cutflow.csv
new file mode 100644
index 00000000..5538a422
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/systematics.json
new file mode 100644
index 00000000..9c4e84de
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.99/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2018_templates.pkl
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index 00000000..2f20ceb5
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/fail_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/lpsfs.csv
new file mode 100644
index 00000000..7654ada8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/pass_cutflow.csv
new file mode 100644
index 00000000..2992938a
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/systematics.json
new file mode 100644
index 00000000..dd04631b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.994/systematics.json
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2018_templates.pkl
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index 00000000..0df8c162
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/lpsfs.csv
new file mode 100644
index 00000000..3d3da9de
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/pass_cutflow.csv
new file mode 100644
index 00000000..c9c90498
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/systematics.json
new file mode 100644
index 00000000..81ad3177
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.995/systematics.json
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2016_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/lpsfs.csv
new file mode 100644
index 00000000..c2e3f3c9
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/pass_cutflow.csv
new file mode 100644
index 00000000..4736a5d8
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/systematics.json
new file mode 100644
index 00000000..d3b3fdfb
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.997/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2017_templates.pkl
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index 00000000..7afb5980
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2018_templates.pkl
new file mode 100644
index 00000000..bbdb3208
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/lpsfs.csv
new file mode 100644
index 00000000..a11aec83
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/pass_cutflow.csv
new file mode 100644
index 00000000..801163a5
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/systematics.json
new file mode 100644
index 00000000..4a872dd4
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9975/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2017_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2018_templates.pkl
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index 00000000..485b62ff
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/lpsfs.csv
new file mode 100644
index 00000000..d5c29b47
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/pass_cutflow.csv
new file mode 100644
index 00000000..da20e9e9
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/systematics.json
new file mode 100644
index 00000000..9333cbad
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.998/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/lpsfs.csv
new file mode 100644
index 00000000..42d7a1a8
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/pass_cutflow.csv
new file mode 100644
index 00000000..00f79d28
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/systematics.json
new file mode 100644
index 00000000..78ff3b25
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9985/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2016APV_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2017_templates.pkl
new file mode 100644
index 00000000..8e7bcecd
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2018_templates.pkl
new file mode 100644
index 00000000..494bf7ab
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/lpsfs.csv
new file mode 100644
index 00000000..39b0e2c4
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/pass_cutflow.csv
new file mode 100644
index 00000000..d87eda33
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/pass_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/systematics.json
new file mode 100644
index 00000000..bdb2e282
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.9988/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2016APV_templates.pkl
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2016_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2018_templates.pkl
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index 00000000..b79f7ab8
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/fail_cutflow.csv
new file mode 100644
index 00000000..aeaff4e0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/lpsfs.csv
new file mode 100644
index 00000000..8c538250
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/pass_cutflow.csv
new file mode 100644
index 00000000..206babe0
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/pass_cutflow.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/systematics.json b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/systematics.json
new file mode 100644
index 00000000..b71b3bb4
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May13NonresScan/txbb_MP_bdt_0.999/systematics.json
@@ -0,0 +1 @@
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\ No newline at end of file
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/23May14HP/2016_templates.pkl
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index 00000000..19ff956e
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May14HP/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/23May14HP/2017_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May14HP/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/23May14HP/2018_templates.pkl
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/23May14HP/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/fail_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May14HP/fail_cutflow.csv
new file mode 100644
index 00000000..433ad485
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May14HP/fail_cutflow.csv
@@ -0,0 +1,8 @@
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/fail_cutflow_JER_down.csv b/src/HHbbVV/postprocessing/templates/23May14HP/fail_cutflow_JER_down.csv
new file mode 100644
index 00000000..daee575b
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May14HP/fail_cutflow_JER_down.csv
@@ -0,0 +1,8 @@
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new file mode 100644
index 00000000..3aab61f3
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new file mode 100644
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new file mode 100644
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new file mode 100644
index 00000000..433ad485
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/lpsfs.csv b/src/HHbbVV/postprocessing/templates/23May14HP/lpsfs.csv
new file mode 100644
index 00000000..42d7a1a8
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow.csv
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new file mode 100644
index 00000000..74ac2d8f
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new file mode 100644
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new file mode 100644
index 00000000..495466a5
--- /dev/null
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new file mode 100644
index 00000000..6538de33
--- /dev/null
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new file mode 100644
index 00000000..5244d028
--- /dev/null
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new file mode 100644
index 00000000..96fa357c
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow_JMS_down.csv b/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow_JMS_down.csv
new file mode 100644
index 00000000..7105b970
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow_JMS_down.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow_JMS_up.csv b/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow_JMS_up.csv
new file mode 100644
index 00000000..79ce4d00
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May14HP/pass_cutflow_JMS_up.csv
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diff --git a/src/HHbbVV/postprocessing/templates/23May14HP/systematics.json b/src/HHbbVV/postprocessing/templates/23May14HP/systematics.json
new file mode 100644
index 00000000..1593537c
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/23May14HP/systematics.json
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\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/23May4/passBlinded_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May4/passBlinded_cutflow.csv
new file mode 100644
index 00000000..e8decd12
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/23May4/pass_cutflow.csv b/src/HHbbVV/postprocessing/templates/23May4/pass_cutflow.csv
new file mode 100644
index 00000000..0c0c248c
--- /dev/null
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diff --git a/src/HHbbVV/postprocessing/templates/Apr10/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr10/2016APV_templates.pkl
new file mode 100644
index 00000000..516a2a27
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr10/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr10/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr10/2016_templates.pkl
new file mode 100644
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr10/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr10/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr10/2017_templates.pkl
new file mode 100644
index 00000000..e8ba252d
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr10/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr10/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr10/2018_templates.pkl
new file mode 100644
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Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr10/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr10/lpsfs.csv b/src/HHbbVV/postprocessing/templates/Apr10/lpsfs.csv
new file mode 100644
index 00000000..6909a6e9
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Apr10/lpsfs.csv
@@ -0,0 +1,2 @@
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diff --git a/src/HHbbVV/postprocessing/templates/Apr10/systematics.json b/src/HHbbVV/postprocessing/templates/Apr10/systematics.json
new file mode 100644
index 00000000..91afa19e
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Apr10/systematics.json
@@ -0,0 +1 @@
+{"X[3000]->H(bb)Y[190](VV)": {"lp_sf": 0.7373364788334175, "lp_sf_unc": 0.4352690545344123}, "2016APV": {"pass": {"trig_total": 5.635846837800307, "trig_total_err": 0.1749919103763648}, "fail": {"trig_total": 24987.71801161629, "trig_total_err": 71.23569238462065}, "passBlinded": {"trig_total": 4.030520568425114, "trig_total_err": 0.0704143494691382}, "failBlinded": {"trig_total": 25636.00561223495, "trig_total_err": 59.2318361968727}}, "2016": {"pass": {"trig_total": 3.065567884187113, "trig_total_err": 0.17337022144548214}, "fail": {"trig_total": 21185.535909271384, "trig_total_err": 66.62848856655776}, "passBlinded": {"trig_total": 3.1359448964581422, "trig_total_err": 0.07936929910205542}, "failBlinded": {"trig_total": 21940.8733898546, "trig_total_err": 58.37090056091921}}, "2017": {"pass": {"trig_total": 6.021269060603105, "trig_total_err": 0.42363879437317525}, "fail": {"trig_total": 34421.77091274184, "trig_total_err": 81.47143214266579}, "passBlinded": {"trig_total": 10.411132995348193, "trig_total_err": 0.13486354219490437}, "failBlinded": {"trig_total": 36016.5381623723, "trig_total_err": 69.83075454481455}}, "2018": {"pass": {"trig_total": 11.11623907109742, "trig_total_err": 0.6332396212996775}, "fail": {"trig_total": 39138.14951916459, "trig_total_err": 85.76491936433258}, "passBlinded": {"trig_total": 7.0156882440192545, "trig_total_err": 0.12910042005593822}, "failBlinded": {"trig_total": 41602.06520195484, "trig_total_err": 74.76060784112092}}}
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/Apr4/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr4/2017_templates.pkl
new file mode 100644
index 00000000..4571dae1
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr4/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr4/systematics.json b/src/HHbbVV/postprocessing/templates/Apr4/systematics.json
new file mode 100644
index 00000000..837caff3
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Apr4/systematics.json
@@ -0,0 +1 @@
+{"HHbbVV": {"lp_sf": 0.7463381219818481, "lp_sf_unc": 0.16039560204303066}, "X[1000]->H(bb)Y[125](VV)": {"lp_sf": 0.7390027273995707, "lp_sf_unc": 0.16226767615171173}, "X[1400]->H(bb)Y[125](VV)": {"lp_sf": 0.7390133491512622, "lp_sf_unc": 0.10637414561847838}, "X[1400]->H(bb)Y[150](VV)": {"lp_sf": 0.7575306813038684, "lp_sf_unc": 0.1021857957135389}, "X[1800]->H(bb)Y[125](VV)": {"lp_sf": 0.7324224997108651, "lp_sf_unc": 0.14718008631624366}, "X[1800]->H(bb)Y[150](VV)": {"lp_sf": 0.741004932943581, "lp_sf_unc": 0.14834202185211395}, "X[1800]->H(bb)Y[190](VV)": {"lp_sf": 0.7324778046747054, "lp_sf_unc": 0.17013386033627714}, "X[2200]->H(bb)Y[125](VV)": {"lp_sf": 0.793303447077326, "lp_sf_unc": 0.2134474832119846}, "X[2200]->H(bb)Y[150](VV)": {"lp_sf": 0.7333743302801837, "lp_sf_unc": 0.24482337613683836}, "X[2200]->H(bb)Y[190](VV)": {"lp_sf": 0.712738273391245, "lp_sf_unc": 0.29721149696264015}, "X[2200]->H(bb)Y[250](VV)": {"lp_sf": 0.7319386334915634, "lp_sf_unc": 0.2658555983693373}, "X[3000]->H(bb)Y[125](VV)": {"lp_sf": 0.7505044663331026, "lp_sf_unc": 0.37400966287838944}, "X[3000]->H(bb)Y[150](VV)": {"lp_sf": 0.769697151679857, "lp_sf_unc": 0.3407140369816917}, "X[3000]->H(bb)Y[190](VV)": {"lp_sf": 0.7288718970365345, "lp_sf_unc": 0.4301469622765102}, "X[3000]->H(bb)Y[250](VV)": {"lp_sf": 0.7058431831685202, "lp_sf_unc": 0.49749582446143126}, "X[3000]->H(bb)Y[350](VV)": {"lp_sf": 0.9429081271667437, "lp_sf_unc": 0.430447137025332}, "2017": {"pass": {"trig_total": 43.15314193649918, "trig_total_err": 0.9874982279907853}, "fail": {"trig_total": 45144.965940041606, "trig_total_err": 107.34391407848038}, "passBlinded": {"trig_total": 19.59291037133775, "trig_total_err": 0.15068889432490343}, "failBlinded": {"trig_total": 48567.06568396235, "trig_total_err": 94.05183267395269}}}
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/Apr7/2016APV_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr7/2016APV_templates.pkl
new file mode 100644
index 00000000..cfa94e22
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr7/2016APV_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr7/2016_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr7/2016_templates.pkl
new file mode 100644
index 00000000..7a778368
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr7/2016_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr7/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr7/2017_templates.pkl
new file mode 100644
index 00000000..d9f192fc
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr7/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr7/2018_templates.pkl b/src/HHbbVV/postprocessing/templates/Apr7/2018_templates.pkl
new file mode 100644
index 00000000..278795b7
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Apr7/2018_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Apr7/lpsfs.csv b/src/HHbbVV/postprocessing/templates/Apr7/lpsfs.csv
new file mode 100644
index 00000000..6909a6e9
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Apr7/lpsfs.csv
@@ -0,0 +1,2 @@
+,SF,syst_unc,stat_unc,sj_pt_unc,sj_matching_unc
+X[3000]->H(bb)Y[190](VV),0.74 ± 0.32,0.2890926764027163,0.031991944745726336,0.3158717420174845,0.07131712523525913
diff --git a/src/HHbbVV/postprocessing/templates/Apr7/systematics.json b/src/HHbbVV/postprocessing/templates/Apr7/systematics.json
new file mode 100644
index 00000000..cfa67260
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Apr7/systematics.json
@@ -0,0 +1 @@
+{"X[3000]->H(bb)Y[190](VV)": {"lp_sf": 0.7373364788334175, "lp_sf_unc": 0.4352690545344123}, "2017": {"pass": {"trig_total": 7.918978856101321, "trig_total_err": 0.4537272030772028}, "fail": {"trig_total": 39892.16474366249, "trig_total_err": 92.31633324833442}, "passBlinded": {"trig_total": 12.872690094785929, "trig_total_err": 0.12449104844056788}, "failBlinded": {"trig_total": 44280.02401683124, "trig_total_err": 84.76082407453758}}, "2016APV": {"pass": {"trig_total": 6.654262914497089, "trig_total_err": 0.1862514184089576}, "fail": {"trig_total": 29182.300234188337, "trig_total_err": 81.19887525562555}, "passBlinded": {"trig_total": 6.38453140643344, "trig_total_err": 0.08459253556571955}, "failBlinded": {"trig_total": 31558.128162737066, "trig_total_err": 69.30148098727163}}, "2016": {"pass": {"trig_total": 4.101151200578493, "trig_total_err": 0.184625767964644}, "fail": {"trig_total": 24762.543178577474, "trig_total_err": 76.2088653589862}, "passBlinded": {"trig_total": 4.042340119087536, "trig_total_err": 0.10918479637001308}, "failBlinded": {"trig_total": 26795.84305882992, "trig_total_err": 69.32241205866303}}, "2018": {"pass": {"trig_total": 12.894963022503557, "trig_total_err": 0.6675401445194743}, "fail": {"trig_total": 45305.604279687584, "trig_total_err": 97.34111253082085}, "passBlinded": {"trig_total": 9.628614481780112, "trig_total_err": 0.1332114909869311}, "failBlinded": {"trig_total": 51075.35443209496, "trig_total_err": 89.9856733969254}}}
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/Mar24/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/Mar24/2017_templates.pkl
new file mode 100644
index 00000000..2edf6412
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Mar24/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Mar24/systematics.json b/src/HHbbVV/postprocessing/templates/Mar24/systematics.json
new file mode 100644
index 00000000..6cce1968
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Mar24/systematics.json
@@ -0,0 +1 @@
+{"HHbbVV": {"lp_sf": 0.7398819221312001, "lp_sf_unc": 0.16283816599413997}, "X[1000]->H(bb)Y[125](VV)": {"lp_sf": 0.7381843897909458, "lp_sf_unc": 0.16320690238042973}, "X[1400]->H(bb)Y[125](VV)": {"lp_sf": 0.7360006527756711, "lp_sf_unc": 0.10615540339225933}, "X[1400]->H(bb)Y[150](VV)": {"lp_sf": 0.7580473506110272, "lp_sf_unc": 0.10065754090912249}, "X[1800]->H(bb)Y[125](VV)": {"lp_sf": 0.7327312649242111, "lp_sf_unc": 0.14561852219659716}, "X[1800]->H(bb)Y[150](VV)": {"lp_sf": 0.7378678860628718, "lp_sf_unc": 0.14971905152700934}, "X[1800]->H(bb)Y[190](VV)": {"lp_sf": 0.7329480898931854, "lp_sf_unc": 0.16903369661848308}, "X[2200]->H(bb)Y[125](VV)": {"lp_sf": 0.7955666102753871, "lp_sf_unc": 0.2124073730033089}, "X[2200]->H(bb)Y[150](VV)": {"lp_sf": 0.728456956395807, "lp_sf_unc": 0.24672602176163788}, "X[2200]->H(bb)Y[190](VV)": {"lp_sf": 0.7074723256762374, "lp_sf_unc": 0.2986638289700003}, "X[2200]->H(bb)Y[250](VV)": {"lp_sf": 0.7275312381871016, "lp_sf_unc": 0.2681379425344931}, "X[3000]->H(bb)Y[125](VV)": {"lp_sf": 0.7498346768725543, "lp_sf_unc": 0.37413563269359024}, "X[3000]->H(bb)Y[150](VV)": {"lp_sf": 0.7657741645167321, "lp_sf_unc": 0.3415675551583506}, "X[3000]->H(bb)Y[190](VV)": {"lp_sf": 0.7271658826234707, "lp_sf_unc": 0.4361611804019981}, "X[3000]->H(bb)Y[250](VV)": {"lp_sf": 0.7060976690344886, "lp_sf_unc": 0.4963003198555397}, "X[3000]->H(bb)Y[350](VV)": {"lp_sf": 0.9318117433299862, "lp_sf_unc": 0.428192949216294}, "2017": {"pass": {"trig_total": 37.49945606615274, "trig_total_err": 0.9151445402117205}, "fail": {"trig_total": 697.7594000015749, "trig_total_err": 1.6642548399391783}, "passBlinded": {"trig_total": 18.005663889959827, "trig_total_err": 0.18543881412108446}, "failBlinded": {"trig_total": 617.4325054123767, "trig_total_err": 1.2180296479221948}}}
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/templates/Mar28/2017_templates.pkl b/src/HHbbVV/postprocessing/templates/Mar28/2017_templates.pkl
new file mode 100644
index 00000000..90d05bb9
Binary files /dev/null and b/src/HHbbVV/postprocessing/templates/Mar28/2017_templates.pkl differ
diff --git a/src/HHbbVV/postprocessing/templates/Mar28/systematics.json b/src/HHbbVV/postprocessing/templates/Mar28/systematics.json
new file mode 100644
index 00000000..7320bfea
--- /dev/null
+++ b/src/HHbbVV/postprocessing/templates/Mar28/systematics.json
@@ -0,0 +1 @@
+{"HHbbVV": {"lp_sf": 0.7398819221312001, "lp_sf_unc": 0.16283816599413997}, "X[1000]->H(bb)Y[125](VV)": {"lp_sf": 0.7381843897909458, "lp_sf_unc": 0.16320690238042973}, "X[1400]->H(bb)Y[125](VV)": {"lp_sf": 0.7360006527756711, "lp_sf_unc": 0.10615540339225933}, "X[1400]->H(bb)Y[150](VV)": {"lp_sf": 0.7580473506110272, "lp_sf_unc": 0.10065754090912249}, "X[1800]->H(bb)Y[125](VV)": {"lp_sf": 0.7327312649242111, "lp_sf_unc": 0.14561852219659716}, "X[1800]->H(bb)Y[150](VV)": {"lp_sf": 0.7378678860628718, "lp_sf_unc": 0.14971905152700934}, "X[1800]->H(bb)Y[190](VV)": {"lp_sf": 0.7329480898931854, "lp_sf_unc": 0.16903369661848308}, "X[2200]->H(bb)Y[125](VV)": {"lp_sf": 0.7955666102753871, "lp_sf_unc": 0.2124073730033089}, "X[2200]->H(bb)Y[150](VV)": {"lp_sf": 0.728456956395807, "lp_sf_unc": 0.24672602176163788}, "X[2200]->H(bb)Y[190](VV)": {"lp_sf": 0.7074723256762374, "lp_sf_unc": 0.2986638289700003}, "X[2200]->H(bb)Y[250](VV)": {"lp_sf": 0.7275312381871016, "lp_sf_unc": 0.2681379425344931}, "X[3000]->H(bb)Y[125](VV)": {"lp_sf": 0.7498346768725543, "lp_sf_unc": 0.37413563269359024}, "X[3000]->H(bb)Y[150](VV)": {"lp_sf": 0.7657741645167321, "lp_sf_unc": 0.3415675551583506}, "X[3000]->H(bb)Y[190](VV)": {"lp_sf": 0.7271658826234707, "lp_sf_unc": 0.4361611804019981}, "X[3000]->H(bb)Y[250](VV)": {"lp_sf": 0.7060976690344886, "lp_sf_unc": 0.4963003198555397}, "X[3000]->H(bb)Y[350](VV)": {"lp_sf": 0.9318117433299862, "lp_sf_unc": 0.428192949216294}, "2017": {"pass": {"trig_total": 37.49945606615274, "trig_total_err": 0.9151445402117205}, "fail": {"trig_total": 38984.14257955842, "trig_total_err": 94.64825353208238}, "passBlinded": {"trig_total": 18.005663889959827, "trig_total_err": 0.18543881412108446}, "failBlinded": {"trig_total": 36065.9012974348, "trig_total_err": 69.8344441147203}}}
\ No newline at end of file
diff --git a/src/HHbbVV/postprocessing/utils.py b/src/HHbbVV/postprocessing/utils.py
index d9e9a084..81c67ff1 100644
--- a/src/HHbbVV/postprocessing/utils.py
+++ b/src/HHbbVV/postprocessing/utils.py
@@ -4,6 +4,7 @@
 Author: Raghav Kansal
 """
 
+from dataclasses import dataclass
 import time
 import contextlib
 from os import listdir
@@ -16,15 +17,52 @@
 
 from typing import Dict, List, Union
 
-from coffea.analysis_tools import PackedSelection
+import hist
 from hist import Hist
 
-from hh_vars import sig_keys, res_sig_keys, data_key, jec_shifts, jmsr_shifts, jec_vars, jmsr_vars
+from hh_vars import (
+    nonres_sig_keys,
+    res_sig_keys,
+    data_key,
+    jec_shifts,
+    jmsr_shifts,
+    jec_vars,
+    jmsr_vars,
+)
 
 MAIN_DIR = "./"
 CUT_MAX_VAL = 9999.0
 
 
+@dataclass
+class ShapeVar:
+    """Class to store attributes of a variable to make a histogram of.
+
+    Args:
+        var (str): variable name
+        label (str): variable label
+        bins (List[int]): bins
+        reg (bool, optional): Use a regular axis or variable binning. Defaults to True.
+        blind_window (List[int], optional): if blinding, set min and max values to set 0. Defaults to None.
+        significance_dir (str, optional): if plotting significance, which direction to plot it in.
+          See more in plotting.py:ratioHistPlot(). Options are ["left", "right", "bin"]. Defaults to "right".
+    """
+
+    var: str = None
+    label: str = None
+    bins: List[int] = None
+    reg: bool = True
+    blind_window: List[int] = None
+    significance_dir: str = "right"
+
+    def __post_init__(self):
+        # create axis used for histogramming
+        if self.reg:
+            self.axis = hist.axis.Regular(*self.bins, name=self.var, label=self.label)
+        else:
+            self.axis = hist.axis.Variable(self.bins, name=self.var, label=self.label)
+
+
 def add_bool_arg(parser, name, help, default=False, no_name=None):
     """Add a boolean command line argument for argparse"""
     varname = "_".join(name.split("-"))  # change hyphens to underscores
@@ -117,19 +155,52 @@ def get_cutflow(pickles_path, year, sample_name):
 
 
 def check_selector(sample: str, selector: Union[str, List[str]]):
-    if isinstance(selector, list) or isinstance(selector, tuple):
-        for s in selector:
+    if not (isinstance(selector, list) or isinstance(selector, tuple)):
+        selector = [selector]
+
+    for s in selector:
+        if s.startswith("*"):
+            if s[1:] in sample:
+                return True
+        else:
             if sample.startswith(s):
                 return True
-    else:
-        if sample.startswith(selector):
-            return True
 
     return False
 
 
+def _hem_cleaning(sample, events):
+    if sample.startswith("JetHT") or sample.startswith("SingleMuon"):
+        if sample.endswith("2018C") or sample.endswith("2018D"):
+            hem_cut = np.any(
+                (events["ak8FatJetEta"] > -3.2)
+                * (events["ak8FatJetEta"] < -1.3)
+                * (events["ak8FatJetPhi"] > -1.57)
+                * (events["ak8FatJetPhi"] < -0.87),
+                axis=1,
+            )
+            print(f"Removing {np.sum(hem_cut)} events")
+            return events[~hem_cut]
+        else:
+            return events
+    else:
+        hem_cut = np.any(
+            (events["ak8FatJetEta"] > -3.2)
+            * (events["ak8FatJetEta"] < -1.3)
+            * (events["ak8FatJetPhi"] > -1.57)
+            * (events["ak8FatJetPhi"] < -0.87),
+            axis=1,
+        ) * (np.random.rand(len(events)) < 0.632)
+        print(f"Removing {np.sum(hem_cut)} events")
+        return events[~hem_cut]
+
+
 def load_samples(
-    data_dir: str, samples: Dict[str, str], year: str, filters: List = None
+    data_dir: str,
+    samples: Dict[str, str],
+    year: str,
+    filters: List = None,
+    columns: List = None,
 ) -> Dict[str, pd.DataFrame]:
     """
     Loads events with an optional filter.
@@ -152,7 +223,6 @@ def load_samples(
     events_dict = {}
 
     for label, selector in samples.items():
-        # print(f"Finding {label} samples")
         events_dict[label] = []
         for sample in full_samples_list:
             if not check_selector(sample, selector):
@@ -167,12 +237,14 @@ def load_samples(
                 continue
 
             # print(f"Loading {sample}")
-            events = pd.read_parquet(f"{data_dir}/{year}/{sample}/parquet", filters=filters)
+            events = pd.read_parquet(
+                f"{data_dir}/{year}/{sample}/parquet", filters=filters, columns=columns
+            )
             not_empty = len(events) > 0
             pickles_path = f"{data_dir}/{year}/{sample}/pickles"
 
             if label != data_key:
-                if label in sig_keys + res_sig_keys:
+                if label in nonres_sig_keys + res_sig_keys:
                     n_events = get_cutflow(pickles_path, year, sample)["has_4q"]
                 else:
                     n_events = get_nevents(pickles_path, year, sample)
@@ -183,7 +255,17 @@ def load_samples(
                             print(f"WARNING: {sample} has not been scaled by its xsec and lumi")
 
                     events["weight_nonorm"] = events["weight"]
-                    events["weight"] /= n_events
+
+                    if "weight_noTrigEffs" in events and not np.all(
+                        np.isclose(events["weight"], events["weight_noTrigEffs"], rtol=1e-5)
+                    ):
+                        events["finalWeight"] = events["weight"] / n_events
+                        events["finalWeight_noTrigEffs"] = events["weight_noTrigEffs"] / n_events
+                    else:
+                        events["weight"] /= n_events
+
+            if year == "2018":
+                events = _hem_cleaning(sample, events)
 
             if not_empty:
                 events_dict[label].append(events)
@@ -233,6 +315,10 @@ def get_feat(events: pd.DataFrame, feat: str, bb_mask: pd.DataFrame = None):
         return events["ak8" + feat[2:]].values[bb_mask ^ feat.startswith("VV")].squeeze()
 
 
+def get_feat_first(events: pd.DataFrame, feat: str):
+    return events[feat][0].values.squeeze()
+
+
 def make_vector(events: dict, name: str, bb_mask: pd.DataFrame = None, mask=None):
     """
     Creates Lorentz vector from input events and beginning name, assuming events contain
@@ -270,12 +356,16 @@ def make_vector(events: dict, name: str, bb_mask: pd.DataFrame = None, mask=None
         )
 
 
-def blindBins(h: Hist, blind_region: List, blind_sample: str = None):
+# TODO: extend to multi axis using https://stackoverflow.com/a/47859801/3759946 for 2D blinding
+def blindBins(h: Hist, blind_region: List, blind_sample: str = None, axis=0):
     """
     Blind (i.e. zero) bins in histogram ``h``.
     If ``blind_sample`` specified, only blind that sample, else blinds all.
     """
-    bins = h.axes[1].edges
+    if axis > 0:
+        raise Exception("not implemented > 1D blinding yet")
+
+    bins = h.axes[axis + 1].edges
     lv = int(np.searchsorted(bins, blind_region[0], "right"))
     rv = int(np.searchsorted(bins, blind_region[1], "left") + 1)
 
@@ -290,12 +380,9 @@ def blindBins(h: Hist, blind_region: List, blind_sample: str = None):
 
 def singleVarHist(
     events_dict: Dict[str, pd.DataFrame],
-    var: str,
-    bins: list,
-    label: str,
+    shape_var: ShapeVar,
     bb_masks: Dict[str, pd.DataFrame],
     weight_key: str = "finalWeight",
-    blind_region: List = None,
     selection: Dict = None,
 ) -> Hist:
     """
@@ -304,9 +391,7 @@ def singleVarHist(
     Args:
         events (dict): a dict of events of format
           {sample1: {var1: np.array, var2: np.array, ...}, sample2: ...}
-        var (str): variable inside the events dict to make a histogram of
-        bins (list): bins in Hist format i.e. [num_bins, min_value, max_value]
-        label (str): label for variable (shows up when plotting)
+        shape_var (ShapeVar): ShapeVar object specifying the variable, label, binning, and (optionally) a blinding window.
         weight_key (str, optional): which weight to use from events, if different from 'weight'
         blind_region (list, optional): region to blind for data, in format [low_cut, high_cut].
           Bins in this region will be set to 0 for data.
@@ -315,7 +400,13 @@ def singleVarHist(
     """
     samples = list(events_dict.keys())
 
-    h = Hist.new.StrCat(samples, name="Sample").Reg(*bins, name=var, label=label).Weight()
+    h = Hist(
+        hist.axis.StrCategory(samples, name="Sample"),
+        shape_var.axis,
+        storage="weight",
+    )
+
+    var = shape_var.var
 
     for sample in samples:
         events = events_dict[sample]
@@ -335,6 +426,52 @@ def singleVarHist(
         if len(fill_data[var]):
             h.fill(Sample=sample, **fill_data, weight=weight)
 
+    if shape_var.blind_window is not None:
+        blindBins(h, shape_var.blind_window, data_key)
+
+    return h
+
+
+def singleVarHistNoMask(
+    events_dict: Dict[str, pd.DataFrame],
+    var: str,
+    bins: list,
+    label: str,
+    weight_key: str = "finalWeight",
+    blind_region: List = None,
+    selection: Dict = None,
+) -> Hist:
+    """
+    Makes and fills a histogram for variable `var` using data in the `events` dict.
+
+    Args:
+        events (dict): a dict of events of format
+          {sample1: {var1: np.array, var2: np.array, ...}, sample2: ...}
+        var (str): variable inside the events dict to make a histogram of
+        bins (list): bins in Hist format i.e. [num_bins, min_value, max_value]
+        label (str): label for variable (shows up when plotting)
+        weight_key (str, optional): which weight to use from events, if different from 'weight'
+        blind_region (list, optional): region to blind for data, in format [low_cut, high_cut].
+          Bins in this region will be set to 0 for data.
+        selection (dict, optional): if performing a selection first, dict of boolean arrays for
+          each sample
+    """
+    samples = list(events_dict.keys())
+
+    h = Hist.new.StrCat(samples, name="Sample").Reg(*bins, name=var, label=label).Weight()
+
+    for sample in samples:
+        events = events_dict[sample]
+        fill_data = {var: get_feat_first(events, var)}
+        weight = events[weight_key].values.squeeze()
+
+        if selection is not None:
+            sel = selection[sample]
+            fill_data[var] = fill_data[var][sel]
+            weight = weight[sel]
+
+        h.fill(Sample=sample, **fill_data, weight=weight)
+
     if blind_region is not None:
         blindBins(h, blind_region, data_key)
 
@@ -365,7 +502,7 @@ def make_selection(
     bb_masks: Dict[str, pd.DataFrame],
     weight_key: str = "finalWeight",
     prev_cutflow: dict = None,
-    selection: dict = None,
+    selection: Dict[str, np.ndarray] = None,
     jshift: str = "",
     MAX_VAL: float = CUT_MAX_VAL,
 ):
@@ -377,13 +514,14 @@ def make_selection(
         events (dict): a dict of events of format {sample1: {var1: np.array, var2: np.array, ...}, sample2: ...}
         weight_key (str): key to use for weights. Defaults to 'finalWeight'.
         prev_cutflow (dict): cutflow from previous cuts, if any. Defaults to None.
-        selection (dict): cutflow from previous selection, if any. Defaults to None.
+        selection (dict): previous selection, if any. Defaults to None.
         MAX_VAL (float): if abs of one of the cuts equals or exceeds this value it will be ignored. Defaults to 9999.
 
     Returns:
         selection (dict): dict of each sample's cut boolean arrays.
         cutflow (dict): dict of each sample's yields after each cut.
     """
+    from coffea.analysis_tools import PackedSelection
 
     if selection is None:
         selection = {}
@@ -404,28 +542,51 @@ def make_selection(
         else:
             selection[sample] = PackedSelection()
 
-        for var, brange in var_cuts.items():
+        for var, branges in var_cuts.items():
             if jshift != "" and sample != data_key:
                 var = check_get_jec_var(var, jshift)
 
-            if brange[0] > -MAX_VAL:
-                add_selection(
-                    f"{var} > {brange[0]}",
-                    get_feat(events, var, bb_mask) > brange[0],
-                    selection[sample],
-                    cutflow[sample],
-                    events,
-                    weight_key,
-                )
-            if brange[1] < MAX_VAL:
+            if isinstance(branges[0], list):
+                # OR the cuts
+                sels = []
+                selstrs = []
+                for brange in branges:
+                    sels.append(
+                        (get_feat(events, var, bb_mask) >= brange[0])
+                        * (get_feat(events, var, bb_mask) < brange[1])
+                    )
+                    selstrs.append(f"{brange[0]} ≤ {var} < {brange[1]}")
+                sel = np.sum(sels, axis=0).astype(bool)
+                selstr = " or ".join(selstrs)
+
                 add_selection(
-                    f"{var} < {brange[1]}",
-                    get_feat(events, var, bb_mask) < brange[1],
+                    selstr,
+                    sel,
                     selection[sample],
                     cutflow[sample],
                     events,
                     weight_key,
                 )
+            else:
+                brange = branges
+                if brange[0] > -MAX_VAL:
+                    add_selection(
+                        f"{var} ≥ {brange[0]}",
+                        get_feat(events, var, bb_mask) >= brange[0],
+                        selection[sample],
+                        cutflow[sample],
+                        events,
+                        weight_key,
+                    )
+                if brange[1] < MAX_VAL:
+                    add_selection(
+                        f"{var} < {brange[1]}",
+                        get_feat(events, var, bb_mask) < brange[1],
+                        selection[sample],
+                        cutflow[sample],
+                        events,
+                        weight_key,
+                    )
 
         selection[sample] = selection[sample].all(*selection[sample].names)
 
@@ -506,3 +667,70 @@ def getSignalPlotScaleFactor(
             )
 
     return sig_scale_dict
+
+
+def mxmy(sample):
+    mY = int(sample.split("-")[-1])
+    mX = int(sample.split("NMSSM_XToYHTo2W2BTo4Q2B_MX-")[1].split("_")[0])
+
+    return (mX, mY)
+
+
+def merge_dictionaries(dict1, dict2):
+    merged_dict = dict1.copy()
+    merged_dict.update(dict2)
+    return merged_dict
+
+
+# from https://gist.github.com/kdlong/d697ee691c696724fc656186c25f8814
+def rebin_hist(h, axis_name, edges):
+    if type(edges) == int:
+        return h[{axis_name: hist.rebin(edges)}]
+
+    ax = h.axes[axis_name]
+    ax_idx = [a.name for a in h.axes].index(axis_name)
+    if not all([np.isclose(x, ax.edges).any() for x in edges]):
+        raise ValueError(
+            f"Cannot rebin histogram due to incompatible edges for axis '{ax.name}'\n"
+            f"Edges of histogram are {ax.edges}, requested rebinning to {edges}"
+        )
+
+    # If you rebin to a subset of initial range, keep the overflow and underflow
+    overflow = ax.traits.overflow or (
+        edges[-1] < ax.edges[-1] and not np.isclose(edges[-1], ax.edges[-1])
+    )
+    underflow = ax.traits.underflow or (
+        edges[0] > ax.edges[0] and not np.isclose(edges[0], ax.edges[0])
+    )
+    flow = overflow or underflow
+    new_ax = hist.axis.Variable(edges, name=ax.name, overflow=overflow, underflow=underflow)
+    axes = list(h.axes)
+    axes[ax_idx] = new_ax
+
+    hnew = hist.Hist(*axes, name=h.name, storage=h._storage_type())
+
+    # Offset from bin edge to avoid numeric issues
+    offset = 0.5 * np.min(ax.edges[1:] - ax.edges[:-1])
+    edges_eval = edges + offset
+    edge_idx = ax.index(edges_eval)
+    # Avoid going outside the range, reduceat will add the last index anyway
+    if edge_idx[-1] == ax.size + ax.traits.overflow:
+        edge_idx = edge_idx[:-1]
+
+    if underflow:
+        # Only if the original axis had an underflow should you offset
+        if ax.traits.underflow:
+            edge_idx += 1
+        edge_idx = np.insert(edge_idx, 0, 0)
+
+    # Take is used because reduceat sums i:len(array) for the last entry, in the case
+    # where the final bin isn't the same between the initial and rebinned histogram, you
+    # want to drop this value. Add tolerance of 1/2 min bin width to avoid numeric issues
+    hnew.values(flow=flow)[...] = np.add.reduceat(h.values(flow=flow), edge_idx, axis=ax_idx).take(
+        indices=range(new_ax.size + underflow + overflow), axis=ax_idx
+    )
+    if hnew._storage_type() == hist.storage.Weight():
+        hnew.variances(flow=flow)[...] = np.add.reduceat(
+            h.variances(flow=flow), edge_idx, axis=ax_idx
+        ).take(indices=range(new_ax.size + underflow + overflow), axis=ax_idx)
+    return hnew
diff --git a/src/HHbbVV/processors/GenSelection.ipynb b/src/HHbbVV/processors/GenSelection.ipynb
index 0491fbfb..cbc5e2ad 100644
--- a/src/HHbbVV/processors/GenSelection.ipynb
+++ b/src/HHbbVV/processors/GenSelection.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,6 +68,7 @@
     "TAU_PDGID = 15\n",
     "vTAU_PDGID = 16\n",
     "\n",
+    "G_PDGID = 22\n",
     "Z_PDGID = 23\n",
     "W_PDGID = 24\n",
     "HIGGS_PDGID = 25\n",
@@ -92,99 +93,533 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15SubJet_nBHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15SubJet_nCHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15_nBHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15_nCHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDBvLV2 in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDCvBV2 in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDCvLV2 in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_nBHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_nCHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch SubJet_nBHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch SubJet_nCHadrons in <TTree 'Events' (1975 branches) at 0x7fe338e51a80>, taking first instance\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P4 = {\n",
+    "    \"eta\": \"Eta\",\n",
+    "    \"phi\": \"Phi\",\n",
+    "    \"mass\": \"Mass\",\n",
+    "    \"pt\": \"Pt\",\n",
+    "}\n",
+    "\n",
+    "\n",
+    "PAD_VAL = -99999\n",
+    "\n",
+    "\n",
+    "skim_vars = {\n",
+    "    \"FatJet\": {\n",
+    "        **P4,\n",
+    "        \"msoftdrop\": \"Msd\",\n",
+    "        \"particleNetMD_QCD\": \"ParticleNetMD_QCD\",\n",
+    "        \"particleNetMD_Xbb\": \"ParticleNetMD_Xbb\",\n",
+    "        \"particleNet_H4qvsQCD\": \"ParticleNet_Th4q\",\n",
+    "        \"particleNet_mass\": \"ParticleNetMass\",\n",
+    "    },\n",
+    "    \"GenHiggs\": P4,\n",
+    "    \"other\": {\"MET_pt\": \"MET_pt\"},\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def pad_val(\n",
+    "    arr: ak.Array,\n",
+    "    target: int,\n",
+    "    value: float = PAD_VAL,\n",
+    "    axis: int = 0,\n",
+    "    to_numpy: bool = True,\n",
+    "    clip: bool = True,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    pads awkward array up to ``target`` index along axis ``axis`` with value ``value``,\n",
+    "    optionally converts to numpy array\n",
+    "    \"\"\"\n",
+    "    ret = ak.fill_none(ak.pad_none(arr, target, axis=axis, clip=clip), value, axis=axis)\n",
+    "    return ret.to_numpy() if to_numpy else ret\n",
+    "\n",
+    "\n",
+    "def add_selection(\n",
+    "    name: str,\n",
+    "    sel: np.ndarray,\n",
+    "    selection: PackedSelection,\n",
+    "    cutflow: dict,\n",
+    "    isData: bool,\n",
+    "    genWeights: ak.Array = None,\n",
+    "):\n",
+    "    \"\"\"adds selection to PackedSelection object and the cutflow dictionary\"\"\"\n",
+    "    if isinstance(sel, ak.Array):\n",
+    "        sel = sel.to_numpy()\n",
+    "\n",
+    "    selection.add(name, sel.astype(bool))\n",
+    "    cutflow[name] = (\n",
+    "        np.sum(selection.all(*selection.names))\n",
+    "        if isData\n",
+    "        # add up genWeights for MC\n",
+    "        else np.sum(genWeights[selection.all(*selection.names)])\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "events = nanoevents.NanoEventsFactory.from_root(\n",
-    "    \"../../../../data/2017_UL_nano/NMSSM_XToYH_MX1000_MY400_HTo2bYTo2W_hadronicDecay/nano_mc2017_101.root\",\n",
+    "    # \"../../../../data/2017_UL_nano/NMSSM_XToYH_MX1000_MY400_HTo2bYTo2W_hadronicDecay/nano_mc2017_101.root\",\n",
+    "    # \"../../../../data/2017_UL_nano/NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190/nano_mc2016pre_13.root\",\n",
+    "    # \"/eos/uscms//store/user/lpcpfnano/rkansal/v2_3/2016/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190/230323_193051/0000/nano_mc2016post_1-3.root\",\n",
+    "    \"../../../../data/nano_files/v2_3/2018/VBF_HHTobbVV_CV_1_C2V_1_C3_1/nano_mc2017_1-1.root\",\n",
     "    schemaclass=nanoevents.NanoAODSchema,\n",
     ").events()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isData = False\n",
+    "signGenWeights = None if isData else np.sign(events[\"genWeight\"])\n",
+    "n_events = len(events) if isData else int(np.sum(signGenWeights))\n",
+    "selection = PackedSelection()\n",
+    "\n",
+    "cutflow = {}\n",
+    "cutflow[\"all\"] = len(events)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "genparts = events.GenPart[events.GenPart.hasFlags(GEN_FLAGS)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "genqs = events.GenPart[\n",
+    "    (np.abs(events.GenPart.pdgId) <= b_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<NanoEventsArray [<event 1:1:2>, ... <event 1:4:398>] type='400 * event'>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "events"
+    "genvs = events.GenPart[\n",
+    "    ((np.abs(events.GenPart.pdgId) == W_PDGID) | (np.abs(events.GenPart.pdgId) == Z_PDGID))\n",
+    "    * events.GenPart.hasFlags(GEN_FLAGS)\n",
+    "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "skim_vars = P4\n",
+    "fatjets = events.FatJet\n",
+    "\n",
+    "higgs = events.GenPart[\n",
+    "    (abs(events.GenPart.pdgId) == HIGGS_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)\n",
+    "]\n",
+    "GenHiggsVars = {f\"GenHiggs{key}\": higgs[var].to_numpy() for (var, key) in skim_vars.items()}\n",
+    "is_bb = abs(higgs.children.pdgId) == b_PDGID\n",
+    "has_bb = ak.sum(ak.flatten(is_bb, axis=2), axis=1) == 2\n",
+    "\n",
+    "bb = ak.flatten(higgs.children[is_bb], axis=2)\n",
+    "GenbbVars = {f\"Genbb{key}\": pad_val(bb[var], 2, axis=1) for (var, key) in skim_vars.items()}\n",
+    "\n",
+    "# gen Y and kids\n",
     "Ys = events.GenPart[(abs(events.GenPart.pdgId) == Y_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)]\n",
+    "GenYVars = {f\"GenY{key}\": Ys[var].to_numpy() for (var, key) in skim_vars.items()}\n",
     "is_VV = (abs(Ys.children.pdgId) == W_PDGID) + (abs(Ys.children.pdgId) == Z_PDGID)\n",
     "has_VV = ak.sum(ak.flatten(is_VV, axis=2), axis=1) == 2\n",
     "\n",
+    "add_selection(\"has_bbVV\", has_bb * has_VV, selection, cutflow, False, signGenWeights)\n",
+    "\n",
     "VV = ak.flatten(Ys.children[is_VV], axis=2)\n",
+    "GenVVVars = {f\"GenVV{key}\": VV[var][:, :2].to_numpy() for (var, key) in skim_vars.items()}\n",
+    "\n",
     "VV_children = VV.children\n",
+    "\n",
+    "# iterate through the children in photon scattering events to get final daughter quarks\n",
+    "for i in range(5):\n",
+    "    photon_mask = ak.any(ak.flatten(abs(VV_children.pdgId), axis=2) == G_PDGID, axis=1)\n",
+    "    if not np.any(photon_mask):\n",
+    "        break\n",
+    "\n",
+    "    # use a where condition to get next layer of children for photon scattering events\n",
+    "    VV_children = ak.where(photon_mask, ak.flatten(VV_children.children, axis=3), VV_children)\n",
+    "\n",
     "quarks = abs(VV_children.pdgId) <= b_PDGID\n",
-    "all_q = ak.all(ak.all(quarks, axis=2), axis=1)"
+    "all_q = ak.all(ak.all(quarks, axis=2), axis=1)\n",
+    "add_selection(\"all_q\", all_q, selection, cutflow, False, signGenWeights)\n",
+    "\n",
+    "V_has_2q = ak.count(VV_children.pdgId, axis=2) == 2\n",
+    "has_4q = ak.values_astype(ak.prod(V_has_2q, axis=1), bool)\n",
+    "add_selection(\"has_4q\", has_4q, selection, cutflow, False, signGenWeights)\n",
+    "\n",
+    "Gen4qVars = {\n",
+    "    f\"Gen4q{key}\": ak.to_numpy(\n",
+    "        ak.fill_none(\n",
+    "            ak.pad_none(ak.pad_none(VV_children[var], 2, axis=1, clip=True), 2, axis=2, clip=True),\n",
+    "            PAD_VAL,\n",
+    "        )\n",
+    "    )\n",
+    "    for (var, key) in skim_vars.items()\n",
+    "}\n",
+    "\n",
+    "# fatjet gen matching\n",
+    "Hbb = ak.pad_none(higgs, 1, axis=1, clip=True)[:, 0]\n",
+    "HVV = ak.pad_none(Ys, 1, axis=1, clip=True)[:, 0]\n",
+    "\n",
+    "bbdr = fatjets[:, :2].delta_r(Hbb)\n",
+    "vvdr = fatjets[:, :2].delta_r(HVV)\n",
+    "\n",
+    "match_dR = 0.8\n",
+    "Hbb_match = bbdr <= match_dR\n",
+    "HVV_match = vvdr <= match_dR\n",
+    "\n",
+    "# overlap removal - in the case where fatjet is matched to both, match it only to the closest Higgs\n",
+    "Hbb_match = (Hbb_match * ~HVV_match) + (bbdr <= vvdr) * (Hbb_match * HVV_match)\n",
+    "HVV_match = (HVV_match * ~Hbb_match) + (bbdr > vvdr) * (Hbb_match * HVV_match)\n",
+    "\n",
+    "VVJets = ak.pad_none(fatjets[HVV_match], 1, axis=1)[:, 0]\n",
+    "quarkdrs = ak.flatten(VVJets.delta_r(VV_children), axis=2)\n",
+    "num_prongs = ak.sum(quarkdrs < match_dR, axis=1)\n",
+    "\n",
+    "GenMatchingVars = {\n",
+    "    \"ak8FatJetHbb\": pad_val(Hbb_match, 2, axis=1),\n",
+    "    \"ak8FatJetHVV\": pad_val(HVV_match, 2, axis=1),\n",
+    "    \"ak8FatJetHVVNumProngs\": ak.fill_none(num_prongs, PAD_VAL).to_numpy(),\n",
+    "}\n",
+    "\n",
+    "genbb, gen4q = bb, ak.flatten(VV_children, axis=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fatjet_idx = 0\n",
+    "ak8_pfcands = events.FatJetPFCands\n",
+    "ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == fatjet_idx]\n",
+    "pfcands0 = events.PFCands[ak8_pfcands.pFCandsIdx]\n",
+    "\n",
+    "fatjet_idx = 1\n",
+    "ak8_pfcands = events.FatJetPFCands\n",
+    "ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == fatjet_idx]\n",
+    "pfcands1 = events.PFCands[ak8_pfcands.pFCandsIdx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sel = np.prod(\n",
+    "    pad_val(\n",
+    "        (fatjets.pt > 250) * (np.abs(fatjets.eta) < 2.4) * (fatjets.particleNet_mass >= 50),\n",
+    "        2,\n",
+    "        False,\n",
+    "        axis=1,\n",
+    "    ),\n",
+    "    axis=1,\n",
+    ").astype(bool)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = plt.hist(ak.count(pfcands0[sel].pdgId, axis=1), np.linspace(-0.5, 20.5, 22), histtype=\"step\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = plt.hist(ak.count(pfcands1[sel].pdgId, axis=1), np.linspace(-0.5, 20.5, 22), histtype=\"step\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = plt.hist(\n",
+    "    ak.sum(pfcands0[sel].pdgId == 22, axis=1) / ak.count(pfcands1[sel].pdgId, axis=1),\n",
+    "    np.linspace(0.8, 1, 101),\n",
+    "    histtype=\"step\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = plt.hist(\n",
+    "    ak.sum(pfcands0.pdgId == 22, axis=1) / ak.count(pfcands0.pdgId, axis=1),\n",
+    "    np.linspace(0, 1, 101),\n",
+    "    histtype=\"step\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.where(ak.count(pfcands1[sel].pdgId, axis=1) <= 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_idx = np.where(ak.count(pfcands1[sel].pdgId, axis=1) <= 3)[0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfcands1[sel][event_idx].pdgId"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pfcands0[sel][event_idx].pt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VV_children[sel][event_idx].pdgId"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "VV_children[sel][event_idx].children.children.pdgId"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak.flatten(ak.flatten(VV_children[sel][event_idx].children.pdgId, axis=-1), axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak.flatten(\n",
+    "    ak.flatten(ak.flatten(VV_children[sel][event_idx].children.children.pdgId, axis=-1), axis=-1),\n",
+    "    axis=-1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak.flatten(\n",
+    "    ak.flatten(\n",
+    "        ak.flatten(\n",
+    "            ak.flatten(VV_children[sel][event_idx].children.children.children.pdgId, axis=-1),\n",
+    "            axis=-1,\n",
+    "        ),\n",
+    "        axis=-1,\n",
+    "    ),\n",
+    "    axis=-1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in events.FatJet[597][1].delta_r(events.PFCands[597][events.FatJetPFCands[597].pFCandsIdx]):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events.FatJet[sel][event_idx][1].delta_r(VV_children[sel][event_idx])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events.FatJet[597][1].delta_r(Hbb[597])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events.FatJet[597][1].delta_r(HVV[597])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events.FatJet[597][1].delta_r(VV_children[597])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "events.FatJet[597][1].delta_r(pfcands1[597])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gen4q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fatjet_idx = 0\n",
+    "ak8_pfcands = events.FatJetPFCands\n",
+    "ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == fatjet_idx]\n",
+    "pfcands0 = events.PFCands[ak8_pfcands.pFCandsIdx]\n",
+    "\n",
+    "fatjet_idx = 1\n",
+    "ak8_pfcands = events.FatJetPFCands\n",
+    "ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == fatjet_idx]\n",
+    "pfcands1 = events.PFCands[ak8_pfcands.pFCandsIdx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sel = np.prod(\n",
+    "    pad_val(\n",
+    "        (fatjets.pt > 250) * (np.abs(fatjets.eta) < 2.4) * (fatjets.particleNet_mass >= 50),\n",
+    "        2,\n",
+    "        False,\n",
+    "        axis=1,\n",
+    "    ),\n",
+    "    axis=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.min(ak.count(pfcands1[sel].pt, axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fatjet_idx = 0\n",
+    "ak8_pfcands = events.FatJetPFCands\n",
+    "ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == fatjet_idx]\n",
+    "pfcands0 = events.PFCands[ak8_pfcands.pFCandsIdx]\n",
+    "\n",
+    "fatjet_idx = 1\n",
+    "ak8_pfcands = events.FatJetPFCands\n",
+    "ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == fatjet_idx]\n",
+    "pfcands1 = events.PFCands[ak8_pfcands.pFCandsIdx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"../corrections/jec_compiled.pkl\", \"rb\") as filehandler:\n",
+    "    jmestuff = pickle.load(filehandler)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Array [[24, 22], [-24]] type='2 * option[var * ?int32[parameters={\"__doc__\": \"P...'>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "VV_children.pdgId[~all_q][0]"
+    "jmestuff"
    ]
   },
   {
@@ -211,7 +646,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.11"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/processors/GenSelection.py b/src/HHbbVV/processors/GenSelection.py
index ec5425dd..f3239785 100644
--- a/src/HHbbVV/processors/GenSelection.py
+++ b/src/HHbbVV/processors/GenSelection.py
@@ -30,6 +30,7 @@
 TAU_PDGID = 15
 vTAU_PDGID = 16
 
+G_PDGID = 22
 Z_PDGID = 23
 W_PDGID = 24
 HIGGS_PDGID = 25
@@ -75,12 +76,22 @@ def gen_selection_HYbbVV(
     GenVVVars = {f"GenVV{key}": VV[var][:, :2].to_numpy() for (var, key) in skim_vars.items()}
 
     VV_children = VV.children
+
+    # iterate through the children in photon scattering events to get final daughter quarks
+    for i in range(5):
+        photon_mask = ak.any(ak.flatten(abs(VV_children.pdgId), axis=2) == G_PDGID, axis=1)
+        if not np.any(photon_mask):
+            break
+
+        # use a where condition to get next layer of children for photon scattering events
+        VV_children = ak.where(photon_mask, ak.flatten(VV_children.children, axis=3), VV_children)
+
     quarks = abs(VV_children.pdgId) <= b_PDGID
     all_q = ak.all(ak.all(quarks, axis=2), axis=1)
     add_selection("all_q", all_q, selection, cutflow, False, signGenWeights)
 
     V_has_2q = ak.count(VV_children.pdgId, axis=2) == 2
-    has_4q = ak.values_astype(ak.prod(V_has_2q, axis=1), np.bool)
+    has_4q = ak.values_astype(ak.prod(V_has_2q, axis=1), bool)
     add_selection("has_4q", has_4q, selection, cutflow, False, signGenWeights)
 
     Gen4qVars = {
@@ -174,6 +185,15 @@ def gen_selection_HHbbVV(
     # checking that each V has 2 q children
     VV_children = VV.children
 
+    # iterate through the children in photon scattering events to get final daughter quarks
+    for i in range(5):
+        photon_mask = ak.any(ak.flatten(abs(VV_children.pdgId), axis=2) == G_PDGID, axis=1)
+        if not np.any(photon_mask):
+            break
+
+        # use a where condition to get next layer of children for photon scattering events
+        VV_children = ak.where(photon_mask, ak.flatten(VV_children.children, axis=3), VV_children)
+
     quarks = abs(VV_children.pdgId) <= b_PDGID
     all_q = ak.all(ak.all(quarks, axis=2), axis=1)
     add_selection("all_q", all_q, selection, cutflow, False, signGenWeights)
@@ -493,6 +513,7 @@ def tagger_gen_H_matching(
         genVars = {**genVars, **genVVars, **genVstarVars, **genLabelVars}
 
     elif "qq" in decays:
+        print("qq")
         children_mask = get_pid_mask(
             matched_higgs_children,
             [g_PDGID, b_PDGID, c_PDGID, s_PDGID, d_PDGID, u_PDGID],
@@ -515,20 +536,32 @@ def tagger_gen_H_matching(
             + ((ak.sum(daughters_pdgId == g_PDGID, axis=1) == 2)) * 7
         )
 
+        bs_decay = (ak.sum(daughters_pdgId == b_PDGID, axis=1) == 1) * (
+            ak.sum(daughters_pdgId == s_PDGID, axis=1) == 1
+        )
+
+        print(bs_decay)
+        print(np.sum(bs_decay))
+
         genLabelVars = {
             "fj_nprongs": nprongs,
             "fj_H_bb": to_label(decay == 1),
             "fj_H_cc": to_label(decay == 3),
             "fj_H_qq": to_label(decay == 5),
             "fj_H_gg": to_label(decay == 7),
+            "fj_H_bs": to_label(bs_decay),
         }
 
+        print(genLabelVars)
+
         genVars = {**genVars, **genLabelVars}
 
         # select event only if any of the q/g decays are within jet radius
         matched_qs_mask = ak.any(fatjets.delta_r(daughters) < jet_dR, axis=1)
         matched_mask = matched_higgs_mask & matched_qs_mask
 
+        breakpoint()
+
     return matched_mask, genVars
 
 
diff --git a/src/HHbbVV/processors/JetHTTriggerEfficienciesProcessor.py b/src/HHbbVV/processors/JetHTTriggerEfficienciesProcessor.py
index d768f44a..3e48cafe 100644
--- a/src/HHbbVV/processors/JetHTTriggerEfficienciesProcessor.py
+++ b/src/HHbbVV/processors/JetHTTriggerEfficienciesProcessor.py
@@ -10,45 +10,44 @@
 from .common import HLTs
 
 
-class JetHTTriggerEfficienciesProcessor(processor.ProcessorABC):
-    """Accumulates two 2D (pT, msd) histograms from all input events: 1) before triggers, and 2) after triggers"""
+muon_HLTs = {
+    "2016": ["IsoMu24", "IsoTkMu24", "Mu50"],
+    "2017": ["IsoMu27", "Mu50"],
+    "2018": ["IsoMu24", "Mu50"],
+}
+
+# same selection as in AN-2020-201
 
-    muon_HLTs = {
-        "2016": ["IsoMu24", "IsoTkMu24", "Mu50"],
-        "2017": ["IsoMu27", "Mu50"],
-        "2018": ["IsoMu24", "Mu50"],
-    }
+muon_selection = {
+    "Id": "tight",
+    "pt": 30,
+    "eta": 2.4,
+    "pfIsoId": 4,  # tight PF isolation
+    "count": 1,
+}
 
-    # same selection as in AN-2020-201
+ak8_jet_selection = {
+    "eta": 2.4,
+    "delta_r_muon": 1.5,
+}
 
-    muon_selection = {
-        "Id": "tight",
-        "pt": 30,
-        "eta": 2.4,
-        "pfIsoId": 4,  # tight PF isolation
-        "count": 1,
-    }
+# # bins, min, max
+msd_bins = (15, 0, 300)
 
-    ak8_jet_selection = {
-        "eta": 2.4,
-        "delta_r_muon": 1.5,
-    }
+# edges
+pt_bins = [250, 275, 300, 325, 350, 375, 400, 450, 500, 600, 800, 1000]
+tagger_bins = [0.0, 0.9, 0.95, 0.98, 1.0]
 
-    # # bins, min, max
-    msd_bins = (15, 0, 300)
 
-    # edges
-    pt_bins = [250, 275, 300, 325, 350, 375, 400, 450, 500, 600, 800, 1000]
-    tagger_bins = [0.0, 0.9, 0.95, 0.98, 1.0]
+class JetHTTriggerEfficienciesProcessor(processor.ProcessorABC):
+    """Accumulates two 2D (pT, msd) histograms from all input events: 1) before triggers, and 2) after triggers"""
 
     def __init__(self, ak15=False):
         super(JetHTTriggerEfficienciesProcessor, self).__init__()
-
         self.ak15 = ak15
 
     def process(self, events):
         """Returns pre- (den) and post- (num) trigger 2D (pT, msd) histograms from input NanoAOD events"""
-
         year = events.metadata["dataset"][:4]
 
         selection = PackedSelection()
@@ -65,7 +64,7 @@ def process(self, events):
 
         # passing single-muon triggers
         muon_triggered = np.any(
-            np.array([events.HLT[trigger] for trigger in self.muon_HLTs[year]]),
+            np.array([events.HLT[trigger] for trigger in muon_HLTs[year]]),
             axis=0,
         )
 
@@ -79,14 +78,14 @@ def process(self, events):
 
         # muon
         muon_selector = (
-            (muon[f"{self.muon_selection['Id']}Id"])
-            * (muon.pt > self.muon_selection["pt"])
-            * (np.abs(muon.eta) < self.muon_selection["eta"])
-            * (muon.pfIsoId >= self.muon_selection["pfIsoId"])
+            (muon[f"{muon_selection['Id']}Id"])
+            * (muon.pt > muon_selection["pt"])
+            * (np.abs(muon.eta) < muon_selection["eta"])
+            * (muon.pfIsoId >= muon_selection["pfIsoId"])
         )
 
         muon_selector = muon_selector * (
-            ak.count(events.Muon.pt[muon_selector], axis=1) == self.muon_selection["count"]
+            ak.count(events.Muon.pt[muon_selector], axis=1) == muon_selection["count"]
         )
         muon = ak.pad_none(muon[muon_selector], 1, axis=1)[:, 0]
 
@@ -94,8 +93,8 @@ def process(self, events):
         add_selection("muon", muon_selector, *selection_args)
 
         # ak8 jet selection
-        fatjet_selector = (np.abs(fatjets.eta) < self.ak8_jet_selection["eta"]) * (
-            np.abs(fatjets.delta_r(muon)) > self.ak8_jet_selection["delta_r_muon"]
+        fatjet_selector = (np.abs(fatjets.eta) < ak8_jet_selection["eta"]) * (
+            np.abs(fatjets.delta_r(muon)) > ak8_jet_selection["delta_r_muon"]
         )
 
         fatjets = ak.pad_none(fatjets[fatjet_selector], num_jets, axis=1)[:, :num_jets]
@@ -110,10 +109,10 @@ def process(self, events):
 
         # initialize histograms
         h = (
-            Hist.new.Var(self.tagger_bins, name="jet1txbb", label="$T_{Xbb}$ Score")
-            .Var(self.tagger_bins, name="jet1th4q", label="$T_{H4q}$ Score")
-            .Var(self.pt_bins, name="jet1pt", label="$p_T$ (GeV)")
-            .Reg(*self.msd_bins, name="jet1msd", label="$m_{SD}$ (GeV)")
+            Hist.new.Var(tagger_bins, name="jet1txbb", label="$T_{Xbb}$ Score")
+            .Var(tagger_bins, name="jet1th4q", label="$T_{H4q}$ Score")
+            .Var(pt_bins, name="jet1pt", label="$p_T$ (GeV)")
+            .Reg(*msd_bins, name="jet1msd", label="$m_{SD}$ (GeV)")
             .Double()
         )
 
@@ -144,90 +143,108 @@ def postprocess(self, accumulator):
         return accumulator
 
 
-class JetHT3DTriggerEfficienciesProcessor(processor.ProcessorABC):
+class JetHT4DTriggerEfficienciesProcessor(processor.ProcessorABC):
     """Accumulates two 2D (pT, msd) histograms from all input events: 1) before triggers, and 2) after triggers"""
 
-    def __init__(self, ak15=True):
-        super(JetHT3DTriggerEfficienciesProcessor, self).__init__()
-
-        self.muon_HLTs = {2017: ["IsoMu27", "Mu50"]}
-
-        self.HLTs = {
-            2017: [
-                "PFJet500",
-                "AK8PFJet400",
-                "AK8PFJet500",
-                "AK8PFJet360_TrimMass30",
-                "AK8PFJet380_TrimMass30",
-                "AK8PFJet400_TrimMass30",
-                "AK8PFHT750_TrimMass50",
-                "AK8PFHT800_TrimMass50",
-                "PFHT1050",
-                # 'AK8PFJet330_PFAK8BTagCSV_p17'
-            ]
-        }
-
+    def __init__(self, ak15=False):
+        super(JetHT4DTriggerEfficienciesProcessor, self).__init__()
         self.ak15 = ak15
 
     def process(self, events):
         """Returns pre- (den) and post- (num) trigger 3D (jet 2 pT, jet 1 pT, jet 1 msd) histograms from input NanoAOD events"""
+        year = events.metadata["dataset"][:4]
+
+        selection = PackedSelection()
+
+        cutflow = OrderedDict()
+        cutflow["all"] = len(events)
+
+        selection_args = (selection, cutflow, True)
+
+        # objects
+        num_jets = 2
+        muon = events.Muon
+        fatjets = events.FatJet
 
         # passing single-muon triggers
         muon_triggered = np.any(
-            np.array([events.HLT[trigger] for trigger in self.muon_HLTs[2017]]), axis=0
+            np.array([events.HLT[trigger] for trigger in muon_HLTs[year]]),
+            axis=0,
         )
 
-        fatjets = events.FatJetAK15 if self.ak15 else events.FatJet
+        # passing our triggers
+        bbVV_triggered = np.any(
+            np.array(
+                [events.HLT[trigger] for trigger in HLTs[year] if trigger in events.HLT.fields]
+            ),
+            axis=0,
+        )
 
-        # does event have a fat jet
-        fatjet1bool = ak.any(fatjets.pt, axis=1)
+        # muon
+        muon_selector = (
+            (muon[f"{muon_selection['Id']}Id"])
+            * (muon.pt > muon_selection["pt"])
+            * (np.abs(muon.eta) < muon_selection["eta"])
+            * (muon.pfIsoId >= muon_selection["pfIsoId"])
+        )
 
-        # for denominator select events which pass the muon triggers and contain at least one fat jet
-        den_selection = fatjet1bool * muon_triggered
+        muon_selector = muon_selector * (
+            ak.count(events.Muon.pt[muon_selector], axis=1) == muon_selection["count"]
+        )
+        muon = ak.pad_none(muon[muon_selector], 1, axis=1)[:, 0]
 
-        # denominator
-        jet2varbins = [0, 250, 300, 350, 400, 500, 750, 1000]
+        muon_selector = ak.any(muon_selector, axis=1)
+        add_selection("muon", muon_selector, *selection_args)
 
-        den = (
-            Hist.new.Var(jet2varbins, name="jet2pt", label="AK15 Fat Jet 2 $p_T$ (GeV)")
-            .Reg(50, 0, 1000, name="jet1pt", label="AK15 Fat Jet 1 $p_T$ (GeV)")
-            .Reg(15, 0, 300, name="jet1msd", label="AK15 Fat Jet 1 MassSD (GeV)")
-            .Double()
-        ).fill(
-            jet1pt=fatjets.pt[den_selection][:, 0].to_numpy(),
-            jet1msd=fatjets.msoftdrop[den_selection][:, 0].to_numpy(),
-            jet2pt=ak.fill_none(
-                ak.pad_none(fatjets.pt[den_selection][:, 1:2], 1, axis=1), 0
-            ).to_numpy()[
-                :, 0
-            ],  # putting events with no fat jet 2 in the low pT bin
+        # ak8 jet selection
+        eta_cut = np.abs(fatjets.eta) < ak8_jet_selection["eta"]
+        dRmuon_cut = np.abs(fatjets.delta_r(muon)) > ak8_jet_selection["delta_r_muon"]
+        fatjet_selection = np.any(dRmuon_cut, axis=1) * (
+            ak.count(fatjets[eta_cut].pt, axis=1) >= num_jets
         )
+        add_selection("ak8_jet", fatjet_selection, *selection_args)
 
-        # numerator
+        fatjets = ak.pad_none(fatjets[eta_cut], num_jets, axis=1)[:, :num_jets]
 
-        # passing all HLT triggers
-        bbVV_triggered = np.any(
-            np.array([events.HLT[trigger] for trigger in self.HLTs[2017]]), axis=0
+        fatjets.txbb = fatjets.particleNetMD_Xbb / (
+            fatjets.particleNetMD_QCD + fatjets.particleNetMD_Xbb
         )
 
-        num_selection = fatjet1bool * muon_triggered * bbVV_triggered
-
-        num = (
-            Hist.new.Var(jet2varbins, name="jet2pt", label="AK15 Fat Jet 2 $p_T$ (GeV)")
-            .Reg(50, 0, 1000, name="jet1pt", label="AK15 Fat Jet 1 $p_T$ (GeV)")
-            .Reg(15, 0, 300, name="jet1msd", label="AK15 Fat Jet 1 MassSD (GeV)")
+        # initialize histograms
+        h = (
+            Hist.new.Var(tagger_bins, name="jet1txbb", label="$T_{Xbb}$ Score")
+            .Var(pt_bins, name="jet1pt", label="$p_T$ (GeV)")
+            .Reg(*msd_bins, name="jet1msd", label="$m_{SD}$ (GeV)")
+            .Var(tagger_bins, name="jet2txbb", label="$T_{Xbb}$ Score")
+            .Var(pt_bins, name="jet2pt", label="$p_T$ (GeV)")
+            .Reg(*msd_bins, name="jet2msd", label="$m_{SD}$ (GeV)")
             .Double()
-        ).fill(
-            jet1pt=fatjets.pt[num_selection][:, 0].to_numpy(),
-            jet1msd=fatjets.msoftdrop[num_selection][:, 0].to_numpy(),
-            jet2pt=ak.fill_none(
-                ak.pad_none(fatjets.pt[num_selection][:, 1:2], 1, axis=1), 0
-            ).to_numpy()[
-                :, 0
-            ],  # putting events with no fat jet 2 in the low pT bin
         )
 
-        return {"den": den, "num": num}
+        select = selection.all(*selection.names)
+
+        selections = {
+            # select events which pass the muon triggers and selection
+            "den": select * muon_triggered,
+            # add our triggers
+            "num": select * muon_triggered * bbVV_triggered,
+        }
+
+        hists = {}
+
+        for key, selection in selections.items():
+            hists[key] = h.copy().fill(
+                jet1txbb=fatjets.txbb[selection][:, 0].to_numpy(),
+                jet1pt=fatjets.pt[selection][:, 0].to_numpy(),
+                jet1msd=fatjets.msoftdrop[selection][:, 0].to_numpy(),
+                jet2txbb=fatjets.txbb[selection][:, 1].to_numpy(),
+                jet2pt=fatjets.pt[selection][:, 1].to_numpy(),
+                jet2msd=fatjets.msoftdrop[selection][:, 1].to_numpy(),
+            )
+
+        hists["cutflow"] = cutflow
+
+        return hists
 
     def postprocess(self, accumulator):
         return accumulator
diff --git a/src/HHbbVV/processors/TTScaleFactorsSkimmer.py b/src/HHbbVV/processors/TTScaleFactorsSkimmer.py
index 903edf18..167755b7 100644
--- a/src/HHbbVV/processors/TTScaleFactorsSkimmer.py
+++ b/src/HHbbVV/processors/TTScaleFactorsSkimmer.py
@@ -34,6 +34,8 @@
 from .corrections import (
     add_pileup_weight,
     add_lepton_weights,
+    add_btag_weights,
+    add_pileupid_weights,
     add_top_pt_weight,
     get_jec_jets,
     get_lund_SFs,
@@ -52,8 +54,9 @@
     "GluGluHToWWTo4q_M-125": gen_selection_HH4V,
 }
 
-# deepcsv medium WP's https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation
-btagWPs = {"2016APV": 0.6001, "2016": 0.5847, "2017": 0.4506, "2018": 0.4168}
+# btag medium WP's https://twiki.cern.ch/twiki/bin/viewauth/CMS/BtagRecommendation
+# btagWPs = {"2016APV": 0.6001, "2016": 0.5847, "2017": 0.4506, "2018": 0.4168}  # for deepCSV
+btagWPs = {"2016APV": 0.2598, "2016": 0.2489, "2017": 0.3040, "2018": 0.2783}  # deepJet
 
 
 num_prongs = 3
@@ -83,14 +86,16 @@ class TTScaleFactorsSkimmer(ProcessorABC):
         "FatJet": {
             **P4,
             "msoftdrop": "Msd",
+            "particleNet_mass": "ParticleNetMass",
             "particleNetMD_QCD": "ParticleNetMD_QCD",
             "particleNetMD_Xbb": "ParticleNetMD_Xbb",
             "particleNet_H4qvsQCD": "ParticleNet_Th4q",
             "nConstituents": "nPFCands",
         },
+        "Jet": P4,
         "FatJetDerived": ["tau21", "tau32", "tau43", "tau42", "tau41"],
         "GenHiggs": P4,
-        "other": {"MET_pt": "MET_pt"},
+        "other": {"MET_pt": "MET_pt", "MET_phi": "MET_phi"},
     }
 
     muon_selection = {
@@ -104,20 +109,24 @@ class TTScaleFactorsSkimmer(ProcessorABC):
     }
 
     ak8_jet_selection = {
-        "pt": 400.0,
-        "msd": [125, 250],
+        "pt": 200.0,
+        # "msd": [125, 250],
         "eta": 2.5,
         "delta_phi_muon": 2,
-        "jetId": 2,  # tight ID bit
+        "jetId": "tight",
     }
 
     ak4_jet_selection = {
-        "pt": 25,
+        "pt": 30,  # from JME-18-002
         "eta": 2.4,
         "delta_phi_muon": 2,
-        "jetId": 2,
+        "jetId": "tight",
         "puId": 4,  # loose pileup ID
         "btagWP": btagWPs,
+        "ht": 250,
+        "num": 2,
+        "closest_muon_dr": 0.4,
+        "closest_muon_ptrel": 25,
     }
 
     met_selection = {"pt": 50}
@@ -128,12 +137,14 @@ class TTScaleFactorsSkimmer(ProcessorABC):
 
     top_matchings = ["top_matched", "w_matched", "unmatched"]
 
-    def __init__(self, xsecs={}):
+    def __init__(self, xsecs={}, inference: bool = True):
         super(TTScaleFactorsSkimmer, self).__init__()
 
         # TODO: Check if this is correct
         self.XSECS = xsecs  # in pb
 
+        self._inference = inference
+
         # find corrections path using this file's path
         package_path = str(pathlib.Path(__file__).parent.parent.resolve())
         with gzip.open(package_path + "/corrections/corrections.pkl.gz", "rb") as filehandler:
@@ -214,24 +225,20 @@ def process(self, events: ak.Array):
 
         # triggers
         # OR-ing HLT triggers
-        if isData or not isData:  # CASE uses triggers for MC too
-            HLT_triggered = np.any(
-                np.array(
-                    [
-                        events.HLT[trigger]
-                        for trigger in self.HLTs[year]
-                        if trigger in events.HLT.fields
-                    ]
-                ),
-                axis=0,
-            )
-            add_selection("trigger", HLT_triggered, *selection_args)
+        HLT_triggered = np.any(
+            np.array(
+                [events.HLT[trigger] for trigger in self.HLTs[year] if trigger in events.HLT.fields]
+            ),
+            axis=0,
+        )
+        add_selection("trigger", HLT_triggered, *selection_args)
 
         # objects
+        num_ak4_jets = 2
         num_jets = 1
         muon = events.Muon
         fatjets = get_jec_jets(events, year) if not isData else events.FatJet
-        ak4_jets = events.Jet
+        ak4_jets = get_jec_jets(events, year, fatjets=False) if not isData else events.Jet
         met = events.MET
 
         # at least one good reconstructed primary vertex
@@ -277,8 +284,8 @@ def process(self, events: ak.Array):
         add_selection("met", met_selection * metfilters, *selection_args)
 
         # leptonic W selection
-        # add_selection("lepW", (met + muon).pt >= self.lepW_selection["pt"], *selection_args)
-        add_selection("lepW", met.pt + muon.pt >= self.lepW_selection["pt"], *selection_args)
+        add_selection("lepW", (met + muon).pt >= self.lepW_selection["pt"], *selection_args)
+        # add_selection("lepW", met.pt + muon.pt >= self.lepW_selection["pt"], *selection_args)
 
         # ak8 jet selection
         fatjet_selector = (
@@ -287,9 +294,7 @@ def process(self, events: ak.Array):
             # * (fatjets.msoftdrop < self.ak8_jet_selection["msd"][1])
             * (np.abs(fatjets.eta) < self.ak8_jet_selection["eta"])
             * (np.abs(fatjets.delta_phi(muon)) > self.ak8_jet_selection["delta_phi_muon"])
-            * (
-                fatjets.jetId & self.ak8_jet_selection["jetId"] == self.ak8_jet_selection["jetId"]
-            )  # tight ID
+            * fatjets.isTight
         )
 
         leading_fatjets = ak.pad_none(fatjets[fatjet_selector], num_jets, axis=1)[:, :num_jets]
@@ -299,26 +304,58 @@ def process(self, events: ak.Array):
         add_selection("ak8_jet", fatjet_selector, *selection_args)
 
         # ak4 jet
-        # leading_btag_jet = ak.flatten(
-        #     ak.pad_none(events.Jet[ak.argsort(events.Jet.btagDeepB, axis=1)[:, -1:]], 1, axis=1)
-        # )
-
-        ak4_jet_selector = (
-            (
-                ak4_jets.jetId & self.ak4_jet_selection["jetId"] == self.ak4_jet_selection["jetId"]
-            )  # tight ID
-            # * (ak4_jets.puId >= self.ak4_jet_selection["puId"])
-            * (ak4_jets.puId % 2 == 1)
+
+        # save the selection without btag for applying btag SFs
+        ak4_jet_selector_no_btag = (
+            ak4_jets.isTight
+            # pileup ID should only be applied for pT < 50 jets (https://twiki.cern.ch/twiki/bin/view/CMS/PileupJetIDUL)
+            * ((ak4_jets.puId % 2 == 1) + (ak4_jets.pt >= 50))
             * (ak4_jets.pt > self.ak4_jet_selection["pt"])
             * (np.abs(ak4_jets.eta) < self.ak4_jet_selection["eta"])
             * (np.abs(ak4_jets.delta_phi(muon)) < self.ak4_jet_selection["delta_phi_muon"])
-            * (ak4_jets.btagDeepB > self.ak4_jet_selection["btagWP"]["2018"])
         )
 
-        ak4_jet_selector = ak.any(ak4_jet_selector, axis=1)
-        add_selection("ak4_jet", ak4_jet_selector, *selection_args)
+        ak4_jet_selector = ak4_jet_selector_no_btag * (
+            ak4_jets.btagDeepFlavB > self.ak4_jet_selection["btagWP"][year]
+        )
+
+        ak4_selection = (
+            (ak.any(ak4_jet_selector, axis=1))
+            * (ak.sum(ak4_jet_selector_no_btag, axis=1) >= self.ak4_jet_selection["num"])
+            * (ak.sum(ak4_jets[ak4_jet_selector].pt, axis=1) >= 250)
+        )
+
+        add_selection("ak4_jet", ak4_selection, *selection_args)
+
+        # 2018 HEM cleaning
+        # https://indico.cern.ch/event/1249623/contributions/5250491/attachments/2594272/4477699/HWW_0228_Draft.pdf
+        if year == "2018":
+            check_fatjets = events.FatJet[:, :2]
+            hem_cleaning = (
+                ((events.run >= 319077) & isData)  # if data check if in Runs C or D
+                # else for MC randomly cut based on lumi fraction of C&D
+                | ((np.random.rand(len(events)) < 0.632) & ~isData)
+            ) & (
+                ak.any(
+                    (
+                        (leading_fatjets.pt > 30.0)
+                        & (leading_fatjets.eta > -3.2)
+                        & (leading_fatjets.eta < -1.3)
+                        & (leading_fatjets.phi > -1.57)
+                        & (leading_fatjets.phi < -0.87)
+                    ),
+                    -1,
+                )
+                | ((events.MET.phi > -1.62) & (events.MET.pt < 470.0) & (events.MET.phi < -0.62))
+            )
+
+            add_selection("hem_cleaning", ~np.array(hem_cleaning).astype(bool), *selection_args)
 
         # select vars
+        ak4JetVars = {
+            f"ak4Jet{key}": pad_val(ak4_jets[ak4_jet_selector], num_ak4_jets, axis=1)
+            for (var, key) in self.skim_vars["Jet"].items()
+        }
 
         ak8FatJetVars = {
             f"ak8FatJet{key}": pad_val(leading_fatjets[var], num_jets, axis=1)
@@ -336,7 +373,7 @@ def process(self, events: ak.Array):
             for (var, key) in self.skim_vars["other"].items()
         }
 
-        skimmed_events = {**skimmed_events, **ak8FatJetVars, **otherVars}
+        skimmed_events = {**skimmed_events, **ak4JetVars, **ak8FatJetVars, **otherVars}
 
         # particlenet h4q vs qcd, xbb vs qcd
 
@@ -347,6 +384,28 @@ def process(self, events: ak.Array):
             axis=1,
         )
 
+        skimmed_events["ak8FatJetParticleNetMD_Txqq"] = pad_val(
+            fatjets.particleNetMD_Xqq / (fatjets.particleNetMD_QCD + fatjets.particleNetMD_Xqq),
+            num_jets,
+            -1,
+            axis=1,
+        )
+
+        skimmed_events["ak8FatJetParticleNetMD_Txcc"] = pad_val(
+            fatjets.particleNetMD_Xcc / (fatjets.particleNetMD_QCD + fatjets.particleNetMD_Xcc),
+            num_jets,
+            -1,
+            axis=1,
+        )
+
+        skimmed_events["ak8FatJetParticleNetMD_Txqc"] = pad_val(
+            (fatjets.particleNetMD_Xcc + fatjets.particleNetMD_Xqq)
+            / (fatjets.particleNetMD_QCD + fatjets.particleNetMD_Xqq + fatjets.particleNetMD_Xcc),
+            num_jets,
+            -1,
+            axis=1,
+        )
+
         # calc weights
 
         if isData:
@@ -354,7 +413,18 @@ def process(self, events: ak.Array):
         else:
             skimmed_events["genWeight"] = events.genWeight.to_numpy()
             add_pileup_weight(weights, year, events.Pileup.nPU.to_numpy())
+            # includes both ID and trigger SFs
             add_lepton_weights(weights, year, muon)
+            add_btag_weights(weights, year, ak4_jets[ak4_jet_selector_no_btag])
+            add_pileupid_weights(weights, year, ak4_jets[ak4_jet_selector_no_btag], events.GenJet)
+
+            if year in ("2016APV", "2016", "2017"):
+                weights.add(
+                    "L1EcalPrefiring",
+                    events.L1PreFiringWeight.Nom,
+                    events.L1PreFiringWeight.Up,
+                    events.L1PreFiringWeight.Dn,
+                )
 
             if dataset.startswith("TTTo"):
                 add_top_pt_weight(weights, events)
@@ -367,10 +437,17 @@ def process(self, events: ak.Array):
                     * self.LUMI[year]
                     * weights.weight()
                 )
+
+                skimmed_events["weight_nobtagSFs"] = (
+                    np.sign(skimmed_events["genWeight"])
+                    * self.XSECS[dataset]
+                    * self.LUMI[year]
+                    * weights.partial_weight(exclude=["btagSF"])
+                )
             else:
-                skimmed_events["weight"] = np.sign(skimmed_events["genWeight"])
+                skimmed_events["weight"] = np.sign(skimmed_events["genWeight"]) * weights.weight()
 
-        if dataset in ["TTToSemiLeptonic", "TTToSemiLeptonic_ext1"]:
+        if dataset in ["SingleTop", "TTToSemiLeptonic", "TTToSemiLeptonic_ext1"]:
             match_dict, gen_quarks = ttbar_scale_factor_matching(
                 events, leading_fatjets[:, 0], selection_args
             )
@@ -404,23 +481,24 @@ def process(self, events: ak.Array):
         }
 
         # apply HWW4q tagger
-        print("pre-inference")
-
-        pnet_vars = runInferenceTriton(
-            self.tagger_resources_path,
-            events[selection.all(*selection.names)],
-            num_jets=1,
-            in_jet_idx=fatjet_idx[selection.all(*selection.names)],
-            jets=ak.flatten(leading_fatjets[selection.all(*selection.names)]),
-            all_outputs=False,
-        )
+        if self._inference:
+            print("pre-inference")
+
+            pnet_vars = runInferenceTriton(
+                self.tagger_resources_path,
+                events[selection.all(*selection.names)],
+                num_jets=1,
+                in_jet_idx=fatjet_idx[selection.all(*selection.names)],
+                jets=ak.flatten(leading_fatjets[selection.all(*selection.names)]),
+                all_outputs=False,
+            )
 
-        print("post-inference")
+            print("post-inference")
 
-        skimmed_events = {
-            **skimmed_events,
-            **{key: value for (key, value) in pnet_vars.items()},
-        }
+            skimmed_events = {
+                **skimmed_events,
+                **{key: value for (key, value) in pnet_vars.items()},
+            }
 
         if len(skimmed_events["weight"]):
             df = self.to_pandas(skimmed_events)
diff --git a/src/HHbbVV/processors/TaggerInference.py b/src/HHbbVV/processors/TaggerInference.py
index 6b19cb81..f58b14ff 100644
--- a/src/HHbbVV/processors/TaggerInference.py
+++ b/src/HHbbVV/processors/TaggerInference.py
@@ -6,6 +6,8 @@
 
 from typing import Dict, Union
 
+import warnings
+
 import numpy as np
 import numpy.ma as ma
 from numpy.typing import ArrayLike
@@ -51,8 +53,8 @@ def get_pfcands_features(
     preselected_events: NanoEventsArray,
     jet_idx: Union[int, ArrayLike],
     jet: FatJetArray = None,
-    fatjet_label: str = "FatJetAK15",
-    pfcands_label: str = "FatJetAK15PFCands",
+    fatjet_label: str = "FatJet",
+    pfcands_label: str = "FatJetPFCands",
     normalize: bool = True,
 ) -> Dict[str, np.ndarray]:
     """
@@ -177,14 +179,6 @@ def get_pfcands_features(
 
         feature_dict[var] = a
 
-    # shouldn't be necessary now that -1 is replaced by 999 above
-    # if normalize:
-    #     var = "pfcand_normchi2"
-    #     info = tagger_vars["pf_features"]["var_infos"][var]
-    #     # finding what -1 transforms to
-    #     chi2_min = -1 - info["median"] * info["norm_factor"]
-    #     feature_dict[var][feature_dict[var] == chi2_min] = info["upper_bound"]
-
     return feature_dict
 
 
@@ -193,8 +187,8 @@ def get_svs_features(
     preselected_events: NanoEventsArray,
     jet_idx: Union[int, ArrayLike],
     jet: FatJetArray = None,
-    fatjet_label: str = "FatJetAK15",
-    svs_label: str = "JetSVsAK15",
+    fatjet_label: str = "FatJet",
+    svs_label: str = "JetSVs",
     normalize: bool = True,
 ) -> Dict[str, np.ndarray]:
     """
@@ -335,7 +329,7 @@ def get_lep_features(
     tagger_vars: dict,
     preselected_events: NanoEventsArray,
     jet_idx: int,
-    fatjet_label: str = "FatJetAK15",
+    fatjet_label: str = "FatJet",
     muon_label: str = "Muon",
     electron_label: str = "Electron",
     normalize: bool = True,
@@ -542,6 +536,103 @@ def _do_inference(
         return request.as_numpy(out_name)
 
 
+def _derive_vars_new_tagger(
+    jet_outputs: np.ndarray, jet_label: str, all_outputs: bool, tagger_vars: dict
+):
+    if len(jet_outputs):
+        derived_vars = {
+            f"{jet_label}FatJetParTMD_probQCD": np.sum(jet_outputs[:, 309:314], axis=1),
+            f"{jet_label}FatJetParTMD_probT": np.sum(jet_outputs[:, :17], axis=1),
+            **{
+                f"{jet_label}FatJetParTMD_probH{l}": jet_outputs[:, 17 + i]
+                for i, l in enumerate(["bb", "cc", "ss", "qq", "bc", "bs", "cs", "gg"])
+            },
+        }
+
+        pnet_vars_all = {}
+
+        if all_outputs:
+            for i, output_name in enumerate(tagger_vars["output_names"]):
+                pnet_vars_all[f"{jet_label}FatJetParTMD_{output_name}"] = jet_outputs[:, i]
+
+        pvars = {**derived_vars, **pnet_vars_all}
+
+    else:
+        derived_vars = {
+            f"{jet_label}FatJetParTMD_probQCD": np.array([]),
+            f"{jet_label}FatJetParTMD_probT": np.array([]),
+            **{
+                f"{jet_label}FatJetParTMD_probH{l}": np.array([])
+                for i, l in enumerate(["bb", "cc", "ss", "qq", "bc", "bs", "cs", "gg"])
+            },
+        }
+        pnet_vars_all = {}
+
+        if all_outputs:
+            for i, output_name in enumerate(tagger_vars["output_names"]):
+                pnet_vars_all[f"{jet_label}FatJetParTMD_{output_name}"] = np.array([])
+
+        pvars = {**derived_vars, **pnet_vars_all}
+
+    return pvars
+
+
+def _derive_vars(jet_outputs: np.ndarray, jet_label: str, all_outputs: bool, tagger_vars: dict):
+    if len(jet_outputs):
+        derived_vars = {
+            f"{jet_label}FatJetParTMD_probQCD": np.sum(jet_outputs[:, 23:28], axis=1),
+            f"{jet_label}FatJetParTMD_probHWW3q": np.sum(jet_outputs[:, 0:3], axis=1),
+            f"{jet_label}FatJetParTMD_probHWW4q": np.sum(jet_outputs[:, 3:6], axis=1),
+            f"{jet_label}FatJetParTMD_probT": np.sum(jet_outputs[:, 28:37], axis=1),
+        }
+
+        derived_vars[f"{jet_label}FatJetParTMD_THWW4q"] = (
+            derived_vars[f"{jet_label}FatJetParTMD_probHWW3q"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probHWW4q"]
+        ) / (
+            derived_vars[f"{jet_label}FatJetParTMD_probHWW3q"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probHWW4q"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probQCD"]
+        )
+
+        derived_vars[f"{jet_label}FatJetParTMD_THWWvsT"] = (
+            derived_vars[f"{jet_label}FatJetParTMD_probHWW3q"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probHWW4q"]
+        ) / (
+            derived_vars[f"{jet_label}FatJetParTMD_probHWW3q"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probHWW4q"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probQCD"]
+            + derived_vars[f"{jet_label}FatJetParTMD_probT"]
+        )
+
+        pnet_vars_all = {}
+
+        if all_outputs:
+            for i, output_name in enumerate(tagger_vars["output_names"]):
+                pnet_vars_all[f"{jet_label}FatJetParTMD_{output_name}"] = jet_outputs[:, i]
+
+        pvars = {**derived_vars, **pnet_vars_all}
+
+    else:
+        derived_vars = {
+            f"{jet_label}FatJetParTMD_probQCD": np.array([]),
+            f"{jet_label}FatJetParTMD_probHWW3q": np.array([]),
+            f"{jet_label}FatJetParTMD_probHWW4q": np.array([]),
+            f"{jet_label}FatJetParTMD_probT": np.array([]),
+            f"{jet_label}FatJetParTMD_THWW4q": np.array([]),
+            f"{jet_label}FatJetParTMD_THWWvsT": np.array([]),
+        }
+        pnet_vars_all = {}
+
+        if all_outputs:
+            for i, output_name in enumerate(tagger_vars["output_names"]):
+                pnet_vars_all[f"{jet_label}FatJetParTMD_{output_name}"] = np.array([])
+
+        pvars = {**derived_vars, **pnet_vars_all}
+
+    return pvars
+
+
 def runInferenceTriton(
     tagger_resources_path: str,
     events: NanoEventsArray,
@@ -549,13 +640,49 @@ def runInferenceTriton(
     in_jet_idx: ArrayLike = None,
     jets: FatJetArray = None,
     ak15: bool = False,
-    all_outputs: bool = True,
+    all_outputs: bool = False,
+    jet_label: str = None,
+    new_tagger: bool = False,
 ) -> dict:
+    """Runs inference with the triton server.
+
+    Args:
+        tagger_resources_path (str): Path to server, model configs.
+        events (NanoEventsArray): Nano events.
+        num_jets (int, optional): # jets for which to run inference. Defaults to 2.
+        in_jet_idx (ArrayLike, optional): run inference for a specific jet per event. Only possible
+          if ``num_jets`` is 1. Defaults to None.
+        jets (FatJetArray, optional): give the jets to inference directly. ``in_jet_idx`` should be
+          specified to get the correct pfcands/svs matching. Defaults to None.
+        ak15 (bool, optional): AK15 or AK8 jets Defaults to False.
+        all_outputs (bool, optional): Get all ParT outputs (i.e., incl. fine-tuning neurons).
+          Defaults to False.
+        jet_label (str, optional): custom label for output jets. Defaults to 'ak8' or 'ak15'.
+
+    Returns:
+        dict: output probabilities / discriminants per event
+    """
+    if in_jet_idx is not None and num_jets > 1:
+        raise ValueError("Can't give in_jet_idx for num_jets != 1")
+
+    if jets is not None and in_jet_idx is None:
+        warnings.warn(
+            "Input jets given without in_jet_idx - pfcands and svs matching will likely be incorrect!"
+        )
+
     total_start = time.time()
 
-    jet_label = "ak15" if ak15 else "ak8"
+    if jet_label is None:
+        jet_label = "ak15" if ak15 else "ak8"
 
-    with open(f"{tagger_resources_path}/triton_config_{jet_label}.json") as f:
+    if new_tagger:
+        config_name = "triton_config_new_tagger_ak8"
+    elif not ak15:
+        config_name = "triton_config_ak8"
+    else:
+        config_name = "triton_config_ak15"
+
+    with open(f"{tagger_resources_path}/{config_name}.json") as f:
         triton_config = json.load(f)
 
     with open(f"{tagger_resources_path}/{triton_config['model_name']}.json") as f:
@@ -573,7 +700,13 @@ def runInferenceTriton(
     tagger_inputs = []
     feature_dicts = []
     for j in range(num_jets):
-        jet_idx = in_jet_idx if in_jet_idx is not None else j
+        if in_jet_idx is None:
+            jet_idx = j
+            jets = ak.pad_none(events[fatjet_label], 2, axis=1)[:, jet_idx]
+        else:
+            jet_idx = in_jet_idx
+            if jets is None:
+                jets = ak.pad_none(events[fatjet_label], 2, axis=1)[np.arange(len(events)), jet_idx]
 
         feature_dict = {
             **get_pfcands_features(tagger_vars, events, jet_idx, jets, fatjet_label, pfcands_label),
@@ -600,69 +733,44 @@ def runInferenceTriton(
             }
         )
 
-    # return feature_dicts
-
     # run inference for both fat jets
     tagger_outputs = []
     for jet_idx in range(num_jets):
         print(f"Running inference for Jet {jet_idx + 1}")
         start = time.time()
-        tagger_outputs.append(triton_model(tagger_inputs[jet_idx]))
+        out = triton_model(tagger_inputs[jet_idx])
         time_taken = time.time() - start
         print(f"Inference took {time_taken:.1f}s")
 
-    pnet_vars_list = []
-
-    for jet_idx in range(num_jets):
-        if len(tagger_outputs[jet_idx]):
-            derived_vars = {
-                f"{jet_label}FatJetParTMD_probQCD": np.sum(
-                    tagger_outputs[jet_idx][:, 23:28], axis=1
-                ),
-                f"{jet_label}FatJetParTMD_probHWW3q": np.sum(
-                    tagger_outputs[jet_idx][:, 0:3], axis=1
-                ),
-                f"{jet_label}FatJetParTMD_probHWW4q": np.sum(
-                    tagger_outputs[jet_idx][:, 3:6], axis=1
-                ),
-                f"{jet_label}FatJetParTMD_probT": np.sum(tagger_outputs[jet_idx][:, 28:37], axis=1),
-            }
-
-            derived_vars[f"{jet_label}FatJetParTMD_THWW4q"] = (
-                derived_vars[f"{jet_label}FatJetParTMD_probHWW3q"]
-                + derived_vars[f"{jet_label}FatJetParTMD_probHWW4q"]
-            ) / (
-                derived_vars[f"{jet_label}FatJetParTMD_probHWW3q"]
-                + derived_vars[f"{jet_label}FatJetParTMD_probHWW4q"]
-                + derived_vars[f"{jet_label}FatJetParTMD_probQCD"]
+        if triton_config["num_reg"] > 0:
+            # separate class and regression outputs
+            out_cls, out_reg = (
+                out[:, : -triton_config["num_reg"]],
+                out[:, -triton_config["num_reg"] :],
             )
+            # apply softmax if not already included in model
+            if triton_config["softmax"] == "False":
+                out_cls = softmax(out_cls, axis=1)
 
-            pnet_vars_all = {}
+            out = np.concatenate((out_cls, out_reg), axis=1)
+        elif triton_config["softmax"] == "False":
+            out = softmax(out, axis=1)
 
-            if all_outputs:
-                for i, output_name in enumerate(tagger_vars["output_names"]):
-                    pnet_vars_all[f"{jet_label}FatJetParTMD_{output_name}"] = tagger_outputs[
-                        jet_idx
-                    ][:, i]
+        tagger_outputs.append(out)
 
-            pvars = {**derived_vars, **pnet_vars_all}
-            pnet_vars_list.append(pvars)
+    pnet_vars_list = []
 
+    for jet_idx in range(num_jets):
+        if new_tagger:
+            pnet_vars_list.append(
+                _derive_vars_new_tagger(
+                    tagger_outputs[jet_idx], jet_label, all_outputs, tagger_vars
+                )
+            )
         else:
-            derived_vars = {
-                f"{jet_label}FatJetParTMD_probQCD": np.array([]),
-                f"{jet_label}FatJetParTMD_probHWW3q": np.array([]),
-                f"{jet_label}FatJetParTMD_probHWW4q": np.array([]),
-                f"{jet_label}FatJetParTMD_THWW4q": np.array([]),
-            }
-            pnet_vars_all = {}
-
-            if all_outputs:
-                for i, output_name in enumerate(tagger_vars["output_names"]):
-                    pnet_vars_all[f"{jet_label}FatJetParTMD_{output_name}"] = np.array([])
-
-            pvars = {**derived_vars, **pnet_vars_all}
-            pnet_vars_list.append(pvars)
+            pnet_vars_list.append(
+                _derive_vars(tagger_outputs[jet_idx], jet_label, all_outputs, tagger_vars)
+            )
 
     print(f"Total time taken: {time.time() - total_start:.1f}s")
 
diff --git a/src/HHbbVV/processors/TaggerInputSkimmer.py b/src/HHbbVV/processors/TaggerInputSkimmer.py
index aa5e9391..4d529b0c 100644
--- a/src/HHbbVV/processors/TaggerInputSkimmer.py
+++ b/src/HHbbVV/processors/TaggerInputSkimmer.py
@@ -12,7 +12,7 @@
 from coffea.processor import ProcessorABC, dict_accumulator
 from coffea.analysis_tools import PackedSelection
 
-from .utils import add_selection_no_cutflow
+from .utils import add_selection_no_cutflow, PAD_VAL
 from .TaggerInference import (
     get_pfcands_features,
     get_svs_features,
@@ -364,8 +364,8 @@ def process(self, events: ak.Array):
             # selection
             selection = PackedSelection()
             # preselection_cut = (fatjets.pt > 200) * (fatjets.pt < 1500)
-            # preselection_cut = fatjets.pt > 200
-            # add_selection_no_cutflow("preselection", preselection_cut, selection)
+            preselection_cut = fatjets.pt > 200
+            add_selection_no_cutflow("preselection", preselection_cut, selection)
 
             # variables
             FatJetVars = {
@@ -487,6 +487,7 @@ def process(self, events: ak.Array):
             events[selection.all(*selection.names)],
             num_jets=self.num_jets,
             ak15=False,
+            new_tagger=True,
         )
 
         for jet_idx in range(self.num_jets):
diff --git a/src/HHbbVV/processors/XHYProcessor.py b/src/HHbbVV/processors/XHYProcessor.py
new file mode 100644
index 00000000..70f43c00
--- /dev/null
+++ b/src/HHbbVV/processors/XHYProcessor.py
@@ -0,0 +1,235 @@
+"""
+Get # of quarks per HVV fatjet for resonant samples.
+
+Author(s): Raghav Kansal
+"""
+
+import numpy as np
+import awkward as ak
+import pandas as pd
+
+from coffea import processor
+from coffea.analysis_tools import Weights, PackedSelection
+import vector
+from hist import Hist
+
+import pathlib
+import pickle
+import gzip
+import os
+
+from typing import Dict
+from collections import OrderedDict
+
+from .GenSelection import gen_selection_HYbbVV
+from .utils import pad_val, add_selection, concatenate_dicts, P4
+from .corrections import (
+    get_jec_key,
+    get_jec_jets,
+    get_jmsr,
+)
+
+import logging
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+class XHYProcessor(processor.ProcessorABC):
+    """
+    Histograms of quarks captured per YVV fatjet.
+    """
+
+    ak8_jet_selection = {
+        "pt": 300.0,
+        "eta": 2.4,
+        "VVmsd": 50,
+        "VVparticleNet_mass": 50,
+        "bbparticleNet_mass": 50,
+        "bbFatJetParticleNetMD_Txbb": 0.8,
+    }
+
+    jecs = {
+        "JES": "JES_jes",
+        "JER": "JER",
+    }
+
+    # key is name in nano files, value will be the name in the skimmed output
+    skim_vars = {
+        "FatJet": {
+            **P4,
+            "msoftdrop": "Msd",
+            "particleNetMD_QCD": "ParticleNetMD_QCD",
+            "particleNetMD_Xbb": "ParticleNetMD_Xbb",
+            "particleNet_H4qvsQCD": "ParticleNet_Th4q",
+            "particleNet_mass": "ParticleNetMass",
+        },
+        "GenHiggs": P4,
+        "other": {"MET_pt": "MET_pt"},
+    }
+
+    def __init__(self):
+        super(XHYProcessor, self).__init__()
+
+        self._accumulator = processor.dict_accumulator({})
+
+    @property
+    def accumulator(self):
+        return self._accumulator
+
+    def process(self, events: ak.Array):
+        """Runs event processor for different types of jets"""
+
+        year = events.metadata["dataset"].split("_")[0]
+        year_nosuffix = year.replace("APV", "")
+        dataset = "_".join(events.metadata["dataset"].split("_")[1:])
+        isData = False
+
+        # only signs for HH
+        # TODO: check if this is also the case for HY
+        gen_weights = np.sign(events["genWeight"])
+
+        n_events = np.sum(gen_weights)
+        selection = PackedSelection()
+        weights = Weights(len(events), storeIndividual=True)
+
+        cutflow = OrderedDict()
+        cutflow["all"] = n_events
+
+        selection_args = (selection, cutflow, isData, gen_weights)
+
+        num_jets = 2
+        fatjets, jec_shifted_vars = get_jec_jets(events, year, isData, self.jecs)
+
+        # change to year with suffix after updated JMS/R values
+        jmsr_shifted_vars = get_jmsr(fatjets, num_jets, year_nosuffix)
+
+        skimmed_events = {}
+
+        vars_dict, _ = gen_selection_HYbbVV(events, fatjets, selection, cutflow, gen_weights, P4)
+        skimmed_events = {**skimmed_events, **vars_dict}
+
+        # FatJet vars
+
+        ak8FatJetVars = {
+            f"ak8FatJet{key}": pad_val(fatjets[var], num_jets, axis=1)
+            for (var, key) in self.skim_vars["FatJet"].items()
+        }
+
+        # JMSR vars
+
+        for var in ["msoftdrop", "particleNet_mass"]:
+            key = self.skim_vars["FatJet"][var]
+            for shift, vals in jmsr_shifted_vars[var].items():
+                # overwrite saved mass vars with corrected ones
+                label = "" if shift == "" else "_" + shift
+                ak8FatJetVars[f"ak8FatJet{key}{label}"] = vals
+
+        # particlenet xbb vs qcd
+
+        fatjets["Txbb"] = fatjets.particleNetMD_Xbb / (
+            fatjets.particleNetMD_QCD + fatjets.particleNetMD_Xbb
+        )
+
+        ak8FatJetVars["ak8FatJetParticleNetMD_Txbb"] = pad_val(
+            fatjets["Txbb"],
+            num_jets,
+            axis=1,
+        )
+
+        # bb VV assignment
+
+        bb_mask = (
+            ak8FatJetVars["ak8FatJetParticleNetMD_Txbb"][:, 0]
+            >= ak8FatJetVars["ak8FatJetParticleNetMD_Txbb"][:, 1]
+        )
+        bb_mask = np.stack((bb_mask, ~bb_mask)).T
+
+        ######################
+        # Selection
+        ######################
+
+        # pt, eta cuts: check if jet passes pT cut in any of the JEC variations
+        cuts = []
+
+        for pts in jec_shifted_vars["pt"].values():
+            cut = np.prod(
+                pad_val(
+                    (pts > self.ak8_jet_selection["pt"])
+                    * (np.abs(fatjets.eta) < self.ak8_jet_selection["eta"]),
+                    num_jets,
+                    False,
+                    axis=1,
+                ),
+                axis=1,
+            )
+            cuts.append(cut)
+
+        add_selection("ak8_pt_eta", np.any(cuts, axis=0), *selection_args)
+
+        # mass cuts: check if jet passes mass cut in any of the JMS/R variations
+        cuts = []
+
+        for shift in jmsr_shifted_vars["msoftdrop"]:
+            msds = jmsr_shifted_vars["msoftdrop"][shift]
+            pnetms = jmsr_shifted_vars["particleNet_mass"][shift]
+
+            # TODO: change to cut on regressed mass only
+            cut = (
+                (msds[~bb_mask] >= self.ak8_jet_selection["VVmsd"])
+                | (pnetms[~bb_mask] >= self.ak8_jet_selection["VVparticleNet_mass"])
+            ) * (pnetms[bb_mask] >= self.ak8_jet_selection["bbparticleNet_mass"])
+            cuts.append(cut)
+
+        add_selection("ak8_mass", np.any(cuts, axis=0), *selection_args)
+
+        # Txbb pre-selection cut
+
+        txbb_cut = (
+            ak8FatJetVars["ak8FatJetParticleNetMD_Txbb"][bb_mask]
+            >= self.ak8_jet_selection["bbFatJetParticleNetMD_Txbb"]
+        )
+        add_selection("ak8bb_txbb", txbb_cut, *selection_args)
+
+        # 2018 HEM cleaning
+        # https://indico.cern.ch/event/1249623/contributions/5250491/attachments/2594272/4477699/HWW_0228_Draft.pdf
+        if year == "2018":
+            hem_cleaning = (
+                ((events.run >= 319077) & isData)  # if data check if in Runs C or D
+                # else for MC randomly cut based on lumi fraction of C&D
+                | ((np.random.rand(len(events)) < 0.632) & ~isData)
+            ) & (
+                ak.any(
+                    (
+                        (events.Jet.pt > 30.0)
+                        & (events.Jet.eta > -3.2)
+                        & (events.Jet.eta < -1.3)
+                        & (events.Jet.phi > -1.57)
+                        & (events.Jet.phi < -0.87)
+                    ),
+                    -1,
+                )
+                | ((events.MET.phi > -1.62) & (events.MET.pt < 470.0) & (events.MET.phi < -0.62))
+            )
+
+            add_selection("hem_cleaning", ~hem_cleaning, *selection_args)
+
+        # reshape and apply selections
+        sel_all = selection.all(*selection.names)
+
+        skimmed_events = {
+            key: value.reshape(len(events), -1)[sel_all] for (key, value) in skimmed_events.items()
+        }
+
+        # initialize histograms
+        h = Hist.new.Var(
+            [0, 1, 2, 3, 4, 5], name="numquarks", label="Number of quarks in AK8 Jet"
+        ).Double()
+
+        if len(skimmed_events["ak8FatJetHVVNumProngs"]):
+            h.fill(numquarks=skimmed_events["ak8FatJetHVVNumProngs"].squeeze())
+
+        return {year: {dataset: {"h": h, "cutflow": cutflow}}}
+
+    def postprocess(self, accumulator):
+        return accumulator
diff --git a/src/HHbbVV/processors/__init__.py b/src/HHbbVV/processors/__init__.py
index ba270cb4..7ecd1b57 100644
--- a/src/HHbbVV/processors/__init__.py
+++ b/src/HHbbVV/processors/__init__.py
@@ -2,3 +2,4 @@
 from .bbVVSkimmer import *
 from .TaggerInputSkimmer import *
 from .TTScaleFactorsSkimmer import *
+from .XHYProcessor import *
diff --git a/src/HHbbVV/processors/bbVVSkimmer.py b/src/HHbbVV/processors/bbVVSkimmer.py
index d99d8874..4aed2d2c 100644
--- a/src/HHbbVV/processors/bbVVSkimmer.py
+++ b/src/HHbbVV/processors/bbVVSkimmer.py
@@ -12,34 +12,38 @@
 import vector
 
 import pathlib
-import pickle
-import gzip
+import pickle, json, gzip
 import os
 
 from typing import Dict
 from collections import OrderedDict
 
-from .GenSelection import gen_selection_HHbbVV, gen_selection_HH4V, gen_selection_HYbbVV
+from .GenSelection import gen_selection_HHbbVV, gen_selection_HH4V, gen_selection_HYbbVV, G_PDGID
 from .TaggerInference import runInferenceTriton
-from .utils import pad_val, add_selection, concatenate_dicts, P4
+from .utils import pad_val, add_selection, concatenate_dicts, select_dicts, P4
 from .corrections import (
     add_pileup_weight,
     add_VJets_kFactors,
+    add_top_pt_weight,
     add_ps_weight,
     add_pdf_weight,
     add_scalevar_7pt,
+    add_trig_effs,
     get_jec_key,
     get_jec_jets,
     get_jmsr,
     get_lund_SFs,
 )
-from .common import LUMI, HLTs, btagWPs
+from .common import LUMI, HLTs, btagWPs, jec_shifts, jmsr_shifts
+from . import common
 
 
 # mapping samples to the appropriate function for doing gen-level selections
 gen_selection_dict = {
     "HTo2bYTo2W": gen_selection_HYbbVV,
+    "XToYHTo2W2BTo4Q2B": gen_selection_HYbbVV,
     "GluGluToHHTobbVV_node_cHHH": gen_selection_HHbbVV,
+    "VBF_HHTobbVV": gen_selection_HHbbVV,
     "jhu_HHbbWW": gen_selection_HHbbVV,
     "GluGluToBulkGravitonToHHTo4W_JHUGen": gen_selection_HH4V,
     "GluGluToHHTo4V_node_cHHH1": gen_selection_HH4V,
@@ -53,14 +57,6 @@
 logger.setLevel(logging.INFO)
 
 
-def update(events, collections):
-    """Return a shallow copy of events array with some collections swapped out"""
-    out = events
-    for name, value in collections.items():
-        out = ak.with_field(out, value, name)
-    return out
-
-
 class bbVVSkimmer(processor.ProcessorABC):
     """
     Skims nanoaod files, saving selected branches and events passing preselection cuts
@@ -83,25 +79,59 @@ class bbVVSkimmer(processor.ProcessorABC):
             "particleNet_mass": "ParticleNetMass",
         },
         "GenHiggs": P4,
-        "other": {"MET_pt": "MET_pt"},
+        "other": {"MET_pt": "MET_pt", "MET_phi": "MET_phi"},
     }
 
-    ak8_jet_selection = {
+    preselection = {
         "pt": 300.0,
         "eta": 2.4,
         "VVmsd": 50,
-        "VVparticleNet_mass": 50,
-        "bbparticleNet_mass": 50,
+        "VVparticleNet_mass": [50, 250],
+        "bbparticleNet_mass": [92.5, 162.5],
         "bbFatJetParticleNetMD_Txbb": 0.8,
         "jetId": 2,  # tight ID bit
+        "DijetMass": 800,  # TODO
+        # "nGoodElectrons": 0,
     }
 
-    jecs = {
-        "JES": "JES_jes",
-        "JER": "JER",
-    }
-
-    def __init__(self, xsecs={}, save_ak15=False, save_systematics=True, inference=True):
+    jecs = common.jecs
+
+    # only the branches necessary for templates and post processing
+    min_branches = [
+        "ak8FatJetPhi",
+        "ak8FatJetEta",
+        "ak8FatJetPt",
+        "ak8FatJetMsd",
+        "ak8FatJetParticleNetMass",
+        "DijetMass",
+        "ak8FatJetHbb",
+        "ak8FatJetHVV",
+        "ak8FatJetHVVNumProngs",
+        "ak8FatJetParticleNetMD_Txbb",
+        "VVFatJetParTMD_THWWvsT",
+        "MET_pt",
+        "MET_phi",
+        "nGoodElectrons",
+        "nGoodMuons",
+    ]
+
+    for shift in jec_shifts:
+        min_branches.append(f"ak8FatJetPt_{shift}")
+        min_branches.append(f"DijetMass_{shift}")
+
+    for shift in jmsr_shifts:
+        min_branches.append(f"ak8FatJetParticleNetMass_{shift}")
+        min_branches.append(f"DijetMass_{shift}")
+
+    def __init__(
+        self,
+        xsecs={},
+        save_ak15=False,
+        save_systematics=True,
+        inference=True,
+        save_all=True,
+        vbf_search=False,
+    ):
         super(bbVVSkimmer, self).__init__()
 
         self.XSECS = xsecs  # in pb
@@ -113,15 +143,27 @@ def __init__(self, xsecs={}, save_ak15=False, save_systematics=True, inference=T
         # run inference
         self._inference = inference
 
+        # save all branches or only necessary ones
+        self._save_all = save_all
+
+        # search for VBF production events
+        self._vbf_search = vbf_search
+
         # for tagger model and preprocessing dict
         self.tagger_resources_path = (
             str(pathlib.Path(__file__).parent.resolve()) + "/tagger_resources/"
         )
 
+        # MET filters
+        # https://twiki.cern.ch/twiki/bin/view/CMS/MissingETOptionalFiltersRun2
+        package_path = str(pathlib.Path(__file__).parent.parent.resolve())
+        with open(package_path + "/data/metfilters.json", "rb") as filehandler:
+            self.metfilters = json.load(filehandler)
+
         self._accumulator = processor.dict_accumulator({})
 
         logger.info(
-            f"Running skimmer with inference {self._inference} and systematics {self._systematics}"
+            f"Running skimmer with inference {self._inference} and systematics {self._systematics} and save all {self._save_all} and VBF search {self._vbf_search}"
         )
 
     def to_pandas(self, events: Dict[str, np.array]):
@@ -167,7 +209,11 @@ def process(self, events: ak.Array):
 
         isData = "JetHT" in dataset
         isQCD = "QCD" in dataset
-        isSignal = "GluGluToHHTobbVV" in dataset or "HTo2bYTo2W" in dataset
+        isSignal = (
+            "GluGluToHHTobbVV" in dataset
+            or "XToYHTo2W2BTo4Q2B" in dataset
+            or "VBF_HHTobbVV" in dataset
+        )
 
         if isSignal:
             # only signs for HH
@@ -268,6 +314,13 @@ def process(self, events: ak.Array):
                     **self.getDijetVars(ak8FatJetVars, bb_mask, mass_shift=label),
                 }
 
+        # VBF ak4 Jet vars (pt, eta, phi, M, nGoodJets)
+        if self._vbf_search:
+            ak4JetVars = {
+                **self.getVBFVars(events, ak8FatJetVars, bb_mask, isGen="VBF_HHTobbVV" in dataset)
+            }
+            skimmed_events = {**skimmed_events, **ak4JetVars}
+
         otherVars = {
             key: events[var.split("_")[0]]["_".join(var.split("_")[1:])].to_numpy()
             for (var, key) in self.skim_vars["other"].items()
@@ -303,13 +356,10 @@ def process(self, events: ak.Array):
         for pts in jec_shifted_vars["pt"].values():
             cut = np.prod(
                 pad_val(
-                    (pts > self.ak8_jet_selection["pt"])
-                    * (np.abs(fatjets.eta) < self.ak8_jet_selection["eta"])
+                    (pts > self.preselection["pt"])
+                    * (np.abs(fatjets.eta) < self.preselection["eta"])
                     # tight ID
-                    * (
-                        fatjets.jetId & self.ak8_jet_selection["jetId"]
-                        == self.ak8_jet_selection["jetId"]
-                    ),
+                    * (fatjets.jetId & self.preselection["jetId"] == self.preselection["jetId"]),
                     num_jets,
                     False,
                     axis=1,
@@ -327,26 +377,37 @@ def process(self, events: ak.Array):
             msds = jmsr_shifted_vars["msoftdrop"][shift]
             pnetms = jmsr_shifted_vars["particleNet_mass"][shift]
 
-            # TODO: change to cut on regressed mass only
-            cut = (
-                (msds[~bb_mask] >= self.ak8_jet_selection["VVmsd"])
-                | (pnetms[~bb_mask] >= self.ak8_jet_selection["VVparticleNet_mass"])
-            ) * (pnetms[bb_mask] >= self.ak8_jet_selection["bbparticleNet_mass"])
+            if self._save_all:
+                cut = (
+                    (pnetms[~bb_mask] >= self.preselection["VVparticleNet_mass"][0])
+                    + (msds[~bb_mask] >= self.preselection["VVparticleNet_mass"][0])
+                ) * (pnetms[bb_mask] >= 50)
+            else:
+                cut = (
+                    (pnetms[~bb_mask] >= self.preselection["VVparticleNet_mass"][0])
+                    * (pnetms[~bb_mask] < self.preselection["VVparticleNet_mass"][1])
+                    * (pnetms[bb_mask] >= self.preselection["bbparticleNet_mass"][0])
+                    * (pnetms[bb_mask] < self.preselection["bbparticleNet_mass"][1])
+                )
+
             cuts.append(cut)
 
         add_selection("ak8_mass", np.any(cuts, axis=0), *selection_args)
 
+        # TODO: dijet mass: check if dijet mass cut passes in any of the JEC or JMC variations
+
         # Txbb pre-selection cut
 
         txbb_cut = (
             ak8FatJetVars["ak8FatJetParticleNetMD_Txbb"][bb_mask]
-            >= self.ak8_jet_selection["bbFatJetParticleNetMD_Txbb"]
+            >= self.preselection["bbFatJetParticleNetMD_Txbb"]
         )
         add_selection("ak8bb_txbb", txbb_cut, *selection_args)
 
         # 2018 HEM cleaning
         # https://indico.cern.ch/event/1249623/contributions/5250491/attachments/2594272/4477699/HWW_0228_Draft.pdf
         if year == "2018":
+            check_fatjets = events.FatJet[:, :2]
             hem_cleaning = (
                 ((events.run >= 319077) & isData)  # if data check if in Runs C or D
                 # else for MC randomly cut based on lumi fraction of C&D
@@ -354,95 +415,77 @@ def process(self, events: ak.Array):
             ) & (
                 ak.any(
                     (
-                        (events.Jet.pt > 30.0)
-                        & (events.Jet.eta > -3.2)
-                        & (events.Jet.eta < -1.3)
-                        & (events.Jet.phi > -1.57)
-                        & (events.Jet.phi < -0.87)
+                        (check_fatjets.pt > 30.0)
+                        & (check_fatjets.eta > -3.2)
+                        & (check_fatjets.eta < -1.3)
+                        & (check_fatjets.phi > -1.57)
+                        & (check_fatjets.phi < -0.87)
                     ),
                     -1,
                 )
                 | ((events.MET.phi > -1.62) & (events.MET.pt < 470.0) & (events.MET.phi < -0.62))
             )
 
-            add_selection("hem_cleaning", ~hem_cleaning, *selection_args)
+            add_selection("hem_cleaning", ~np.array(hem_cleaning).astype(bool), *selection_args)
 
-        #########################
-        # Veto variables
-        #########################
+        # MET filters
 
-        electrons, muons = events.Electron, events.Muon
+        metfilters = np.ones(len(events), dtype="bool")
+        metfilterkey = "data" if isData else "mc"
+        for mf in self.metfilters[year][metfilterkey]:
+            if mf in events.Flag.fields:
+                metfilters = metfilters & events.Flag[mf]
 
-        # selection from https://github.com/cmantill/boostedhiggs/blob/e7dc206de17fd108a5e1abcb7d76a52ccb636599/boostedhiggs/hwwprocessor.py#L185-L224
+        add_selection("met_filters", metfilters, *selection_args)
 
-        good_muons = (
-            (muons.pt > 30)
-            & (np.abs(muons.eta) < 2.4)
-            & (np.abs(muons.dz) < 0.1)
-            & (np.abs(muons.dxy) < 0.05)
-            & (muons.sip3d <= 4.0)
-            & muons.mediumId
-        )
-        n_good_muons = ak.sum(good_muons, axis=1)
-
-        good_electrons = (
-            (electrons.pt > 38)
-            & (np.abs(electrons.eta) < 2.4)
-            & ((np.abs(electrons.eta) < 1.44) | (np.abs(electrons.eta) > 1.57))
-            & (np.abs(electrons.dz) < 0.1)
-            & (np.abs(electrons.dxy) < 0.05)
-            & (electrons.sip3d <= 4.0)
-            & (electrons.mvaFall17V2noIso_WP90)
-        )
-        n_good_electrons = ak.sum(good_electrons, axis=1)
+        # remove weird jets which have <4 particles (due to photon scattering?)
+        pfcands_sel = []
 
-        bbjet = ak.pad_none(fatjets[ak.argsort(fatjets.Txbb, ascending=False)], 1, axis=1)[:, 0]
+        for i in range(num_jets):
+            ak8_pfcands = events.FatJetPFCands
+            ak8_pfcands = ak8_pfcands[ak8_pfcands.jetIdx == i]
+            pfcands = events.PFCands[ak8_pfcands.pFCandsIdx]
+            pfcands_sel.append(ak.count(pfcands.pdgId, axis=1) < 4)
 
-        goodjets = (
-            (events.Jet.pt > 30)
-            & (np.abs(events.Jet.eta) < 5.0)
-            & events.Jet.isTight
-            & (events.Jet.puId > 0)
-            & (events.Jet.btagDeepFlavB > btagWPs["deepJet"][year]["M"])
-            & (np.abs(events.Jet.delta_r(bbjet)) > 0.8)
-        )
-        n_good_jets = ak.sum(goodjets, axis=1)
+        add_selection("photon_jets", ~np.sum(pfcands_sel, axis=0).astype(bool), *selection_args)
+
+        #########################
+        # Veto variables
+        #########################
+
+        electrons, muons = events.Electron, events.Muon
 
         # selection from https://github.com/jennetd/hbb-coffea/blob/85bc3692be9e0e0a0c82ae3c78e22cdf5b3e4d68/boostedhiggs/vhbbprocessor.py#L283-L307
         # https://indico.cern.ch/event/1154430/#b-471403-higgs-meeting-special
 
         goodelectron = (
-            (events.Electron.pt > 10)
+            (events.Electron.pt > 20)
             & (abs(events.Electron.eta) < 2.5)
+            & (events.Electron.pfRelIso04_all < 0.4)
             & (events.Electron.cutBased >= events.Electron.LOOSE)
         )
         nelectrons = ak.sum(goodelectron, axis=1)
 
         goodmuon = (
-            (events.Muon.pt > 10)
+            (events.Muon.pt > 15)
             & (abs(events.Muon.eta) < 2.4)
-            & (events.Muon.pfRelIso04_all < 0.25)
+            & (events.Muon.pfRelIso04_all < 0.4)
             & events.Muon.looseId
         )
         nmuons = ak.sum(goodmuon, axis=1)
 
-        ntaus = ak.sum(
-            (
-                (events.Tau.pt > 20)
-                & (abs(events.Tau.eta) < 2.3)
-                & (events.Tau.rawIso < 5)
-                & (events.Tau.idDeepTau2017v2p1VSjet)
-            ),
-            axis=1,
-        )
+        skimmed_events["nGoodMuons"] = nmuons.to_numpy()
+        skimmed_events["nGoodElectrons"] = nelectrons.to_numpy()
 
-        skimmed_events["nGoodMuons"] = n_good_muons.to_numpy()
-        skimmed_events["nGoodElectrons"] = n_good_electrons.to_numpy()
-        skimmed_events["nGoodJets"] = n_good_jets.to_numpy()
+        ######################
+        # Remove branches
+        ######################
 
-        skimmed_events["nGoodMuonsv2"] = nmuons.to_numpy()
-        skimmed_events["nGoodElectronsv2"] = nelectrons.to_numpy()
-        skimmed_events["nGoodTausv2"] = ntaus.to_numpy()
+        # if not saving all, save only necessary branches
+        if not self._save_all:
+            skimmed_events = {
+                key: val for key, val in skimmed_events.items() if key in self.min_branches
+            }
 
         ######################
         # Weights
@@ -456,6 +499,12 @@ def process(self, events: ak.Array):
             add_pileup_weight(weights, year, events.Pileup.nPU.to_numpy())
             add_VJets_kFactors(weights, events.GenPart, dataset)
 
+            # if dataset.startswith("TTTo"):
+            #     # TODO: need to add uncertainties and rescale yields (?)
+            #     add_top_pt_weight(weights, events)
+
+            # TODO: figure out which of these apply to VBF, single Higgs, ttbar etc.
+
             if "GluGluToHHTobbVV" in dataset or "WJets" in dataset or "ZJets" in dataset:
                 add_ps_weight(weights, events.PSWeight)
 
@@ -477,9 +526,24 @@ def process(self, events: ak.Array):
                     events.L1PreFiringWeight.Dn,
                 )
 
-            # TODO: trigger SFs here once calculated properly
+            if not self._save_all:
+                add_trig_effs(weights, fatjets, year, num_jets)
+
+            # xsec and luminosity and normalization
+            # this still needs to be normalized with the acceptance of the pre-selection (done in post processing)
+            if dataset in self.XSECS or "XToYHTo2W2BTo4Q2B" in dataset:
+                # 1 fb xsec for now for resonant signal
+                xsec = self.XSECS[dataset] if dataset in self.XSECS else 1e-3  # in pb
 
-            systematics = [""]
+                weight_norm = xsec * LUMI[year]
+            else:
+                logger.warning("Weight not normalized to cross section")
+                weight_norm = 1
+
+            if self._save_all:
+                systematics = [""]
+            else:
+                systematics = ["", "notrigeffs"]
 
             if self._systematics:
                 systematics += list(weights.variations)
@@ -490,26 +554,19 @@ def process(self, events: ak.Array):
                 if systematic in weights.variations:
                     weight = weights.weight(modifier=systematic)
                     weight_name = f"weight_{systematic}"
-                else:
+                elif systematic == "":
                     weight = weights.weight()
                     weight_name = "weight"
+                elif systematic == "notrigeffs":
+                    weight = weights.partial_weight(exclude=["trig_effs"])
+                    weight_name = "weight_noTrigEffs"
 
-                # this still needs to be normalized with the acceptance of the pre-selection (done in post processing)
-                if dataset in self.XSECS or "HTo2bYTo2W" in dataset:
-                    # 1 fb xsec for now for resonant signal
-                    xsec = self.XSECS[dataset] if dataset in self.XSECS else 1e-3  # in pb
-                    skimmed_events[weight_name] = (
-                        xsec * LUMI[year] * weight  # includes genWeight (or signed genWeight)
-                    )
-
-                    if systematic == "":
-                        # to check in postprocessing for xsec & lumi normalisation
-                        skimmed_events["weight_noxsec"] = weight
-                else:
-                    logger.warning("Weight not normalized to cross section")
-                    skimmed_events[weight_name] = weight
+                # includes genWeight (or signed genWeight)
+                skimmed_events[weight_name] = weight * weight_norm
 
-        print(cutflow)
+                if systematic == "":
+                    # to check in postprocessing for xsec & lumi normalisation
+                    skimmed_events["weight_noxsec"] = weight
 
         # reshape and apply selections
         sel_all = selection.all(*selection.names)
@@ -519,64 +576,93 @@ def process(self, events: ak.Array):
             for (key, value) in skimmed_events.items()
         }
 
+        bb_mask = bb_mask[sel_all]
+
         ################
         # Lund plane SFs
         ################
 
-        if isSignal and len(skimmed_events["weight"]):
-            genbb = genbb[sel_all]
-            genq = genq[sel_all]
-
-            sf_dicts = []
-
-            for i in range(num_jets):
-                bb_select = skimmed_events["ak8FatJetHbb"][:, i].astype(bool)
-                VV_select = skimmed_events["ak8FatJetHVV"][:, i].astype(bool)
-
-                # selectors for Hbb jets and HVV jets with 2, 3, or 4 prongs separately
-                selectors = {
-                    # name: (selector, gen quarks, num prongs)
-                    "bb": (bb_select, genbb, 2),
-                    **{
-                        f"VV{k}q": (
-                            VV_select * (skimmed_events["ak8FatJetHVVNumProngs"].squeeze() == k),
-                            genq,
-                            k,
-                        )
-                        for k in range(2, 4 + 1)
-                    },
-                }
+        if isSignal and self._systematics:
+            # TODO: remember to add new LP variables
+            items = [
+                ("lp_sf_lnN", 101),
+                ("lp_sf_sys_down", 1),
+                ("lp_sf_sys_up", 1),
+                ("lp_sf_double_matched_event", 1),
+                ("lp_sf_unmatched_quarks", 1),
+                ("lp_sf_num_sjpt_gt350", 1),
+            ]
+
+            if len(skimmed_events["weight"]):
+                genbb = genbb[sel_all]
+                genq = genq[sel_all]
+
+                sf_dicts = []
+                lp_num_jets = num_jets if self._save_all else 1
+
+                for i in range(lp_num_jets):
+                    if self._save_all:
+                        bb_select = skimmed_events["ak8FatJetHbb"][:, i].astype(bool)
+                        VV_select = skimmed_events["ak8FatJetHVV"][:, i].astype(bool)
+                    else:
+                        # only do VV jets
+                        bb_select = np.zeros(len(skimmed_events["ak8FatJetHbb"])).astype(bool)
+                        # exactly 1 jet is matched (otherwise those SFs are ignored in post-processing anyway)
+                        VV_select = np.sum(skimmed_events["ak8FatJetHVV"], axis=1) == 1
+
+                    # selectors for Hbb jets and HVV jets with 2, 3, or 4 prongs separately
+                    selectors = {
+                        # name: (selector, gen quarks, num prongs)
+                        "bb": (bb_select, genbb, 2),
+                        **{
+                            f"VV{k}q": (
+                                VV_select
+                                * (skimmed_events["ak8FatJetHVVNumProngs"].squeeze() == k),
+                                genq,
+                                k,
+                            )
+                            for k in range(2, 4 + 1)
+                        },
+                    }
+
+                    selected_sfs = {}
+
+                    for key, (selector, gen_quarks, num_prongs) in selectors.items():
+                        if np.sum(selector) > 0:
+                            sel_events = events[sel_all][selector]
+                            selected_sfs[key] = get_lund_SFs(
+                                sel_events,
+                                i
+                                if self._save_all
+                                else skimmed_events["ak8FatJetHVV"][selector][:, 1],
+                                num_prongs,
+                                gen_quarks[selector],
+                                trunc_gauss=False,
+                                lnN=True,
+                            )
 
-                selected_sfs = {}
-
-                for key, (selector, gen_quarks, num_prongs) in selectors.items():
-                    if np.sum(selector) > 0:
-                        sel_events = events[sel_all][selector]
-                        selected_sfs[key] = get_lund_SFs(
-                            sel_events,
-                            i,
-                            num_prongs,
-                            gen_quarks[selector],
-                            trunc_gauss=False,
-                            lnN=True,
-                        )
-                        items = selected_sfs[key].items()
+                    sf_dict = {}
 
-                sf_dict = {}
+                    # collect all the scale factors, fill in 1s for unmatched jets
+                    for key, shape in items:
+                        arr = np.ones((np.sum(sel_all), shape))
 
-                # collect all the scale factors, fill in 0s for unmatched jets
-                for key, val in items:
-                    arr = np.ones((np.sum(sel_all), val.shape[1]))
+                        for select_key, (selector, _, _) in selectors.items():
+                            if np.sum(selector) > 0:
+                                arr[selector] = selected_sfs[select_key][key]
 
-                    for select_key, (selector, _, _) in selectors.items():
-                        if np.sum(selector) > 0:
-                            arr[selector] = selected_sfs[select_key][key]
+                        sf_dict[key] = arr
 
-                    sf_dict[key] = arr
+                    sf_dicts.append(sf_dict)
 
-                sf_dicts.append(sf_dict)
+                sf_dicts = concatenate_dicts(sf_dicts)
 
-            sf_dicts = concatenate_dicts(sf_dicts)
+            else:
+                print("No signal events selected")
+                sf_dicts = {}
+                for key, shape in items:
+                    arr = np.ones((np.sum(sel_all), shape))
+                    sf_dicts[key] = arr
 
             skimmed_events = {**skimmed_events, **sf_dicts}
 
@@ -588,16 +674,30 @@ def process(self, events: ak.Array):
         if self._inference:
             # apply HWW4q tagger
             pnet_vars = {}
-            pnet_vars = runInferenceTriton(
-                self.tagger_resources_path,
-                events[sel_all],
-                ak15=False,
-                all_outputs=False,
-            )
-            skimmed_events = {
-                **skimmed_events,
-                **{key: value for (key, value) in pnet_vars.items()},
-            }
+            if self._save_all:
+                # run on both jets
+                pnet_vars = runInferenceTriton(
+                    self.tagger_resources_path,
+                    events[sel_all],
+                    ak15=False,
+                    all_outputs=False,
+                )
+                skimmed_events = {**skimmed_events, **pnet_vars}
+            else:
+                # run only on VV candidate jet
+                pnet_vars = runInferenceTriton(
+                    self.tagger_resources_path,
+                    events[sel_all],
+                    num_jets=1,
+                    in_jet_idx=bb_mask[:, 0].astype(int),
+                    ak15=False,
+                    all_outputs=False,
+                    jet_label="VV",
+                )
+                skimmed_events = {
+                    **skimmed_events,
+                    **{key: val for (key, val) in pnet_vars.items() if key in self.min_branches},
+                }
 
         df = self.to_pandas(skimmed_events)
         fname = events.behavior["__events_factory__"]._partition_key.replace("/", "_") + ".parquet"
@@ -608,6 +708,268 @@ def process(self, events: ak.Array):
     def postprocess(self, accumulator):
         return accumulator
 
+    def getVBFVars(
+        self,
+        events: ak.Array,
+        ak8FatJetVars: Dict,
+        bb_mask: np.ndarray,
+        isGen: bool = False,
+        skim_vars: dict = None,
+        pt_shift: str = None,
+        mass_shift: str = None,
+    ) -> Dict:
+        """
+        Computes selections on VBF jet candidates based on B2G-22-003.
+        Sorts jets by pt after applying base cuts and fatjet exclusion.
+        Stores nGoodVBFJets, an int list encoding cuts, and kinematic variables of VBF jet candidates.
+
+        TODO:
+            - Implement the use of pt_shift, mass_shift, skim_vars.
+            - Study selection parameters that provide the best sensitivity.
+            - Decide best way to compute vbf variables (vector or by hand)
+
+        Args:
+            events (ak.Array): Event array.
+            ak8FatJetVars (Dict): AK8 Fat Jet variables.
+            bb_mask (np.ndarray): BB mask array.
+            isGen (bool, optional): Flag for generation-level. Defaults to False.
+            skim_vars (dict, optional): Skim variables. Defaults to None.
+            pt_shift (str, optional): PT shift. Defaults to None.
+            mass_shift (str, optional): Mass shift. Defaults to None.
+
+        Returns:
+            Dict: VBF variables dictionary.
+
+        """
+        vbfVars = {}
+        jets = events.Jet
+
+        # AK4 jets definition: 5.4 B2G-22-003
+        ak4_jet_mask = (
+            (jets.pt > 25)
+            & (np.abs(jets.eta) < 4.7)
+            & (jets.jetId != 1)
+            & ((jets.pt > 50) | ((jets.puId == 6) | (jets.puId == 7)))
+        )
+
+        # VBF selections: 7.1.4 B2G-22-003
+
+        # Mask for electron/muon overlap
+        electrons, muons = events.Electron[events.Electron.pt < 5], events.Muon[events.Muon.pt < 7]
+        e_pairs = ak.cartesian([jets, electrons], nested=True)
+        e_pairs_mask = np.abs(e_pairs.slot0.delta_r(e_pairs.slot1)) < 0.4
+        m_pairs = ak.cartesian([jets, muons], nested=True)
+        m_pairs_mask = np.abs(m_pairs.slot0.delta_r(m_pairs.slot1)) < 0.4
+
+        electron_muon_overlap_mask = ~(
+            ak.any(e_pairs_mask, axis=-1) | ak.any(m_pairs_mask, axis=-1)
+        )
+
+        # Fatjet Definition for ∆R calculations: same definition as in getDijetVars() (not included so we can study its effects in the output.)
+        ptlabel = pt_shift if pt_shift is not None else ""
+        mlabel = mass_shift if mass_shift is not None else ""
+        bbJet = vector.array(
+            {
+                "pt": ak8FatJetVars[f"ak8FatJetPt{ptlabel}"][bb_mask],
+                "phi": ak8FatJetVars["ak8FatJetPhi"][bb_mask],
+                "eta": ak8FatJetVars["ak8FatJetEta"][bb_mask],
+                "M": ak8FatJetVars[f"ak8FatJetParticleNetMass{mlabel}"][bb_mask],
+            }
+        )
+
+        VVJet = vector.array(
+            {
+                "pt": ak8FatJetVars[f"ak8FatJetPt{ptlabel}"][~bb_mask],
+                "phi": ak8FatJetVars["ak8FatJetPhi"][~bb_mask],
+                "eta": ak8FatJetVars["ak8FatJetEta"][~bb_mask],
+                "M": ak8FatJetVars[f"ak8FatJetMsd{mlabel}"][~bb_mask],
+                # TODO: change this to ParticleNetMass for next run
+                # "M": ak8FatJetVars[f"ak8FatJetParticleNetMass{mlabel}"][~bb_mask],
+            }
+        )
+
+        fatjet_overlap_mask = (np.abs(jets.delta_r(bbJet)) > 1.2) & (
+            np.abs(jets.delta_r(VVJet)) > 0.8
+        )  # this might not work due to types
+
+        # Apply base masks, sort, and calculate vbf dijet (jj) cuts
+        vbfJets_mask = ak4_jet_mask & electron_muon_overlap_mask & fatjet_overlap_mask
+        vbfJets = jets[vbfJets_mask]
+
+        vbfJets_sorted_pt = vbfJets[ak.argsort(vbfJets.pt, ascending=False)]
+        # this is the only which does not guarantee two guys. in the other sorts, the entries are specifically None.
+        vbfJets_sorted_pt = ak.pad_none(vbfJets_sorted_pt, 2, clip=True)
+
+        # pt sorted eta and dijet mass mask
+        vbf1 = vector.array(
+            {
+                "pt": ak.flatten(pad_val(vbfJets_sorted_pt[:, 0:1].pt, 1, axis=1)),
+                "phi": ak.flatten(pad_val(vbfJets_sorted_pt[:, 0:1].phi, 1, axis=1)),
+                "eta": ak.flatten(pad_val(vbfJets_sorted_pt[:, 0:1].eta, 1, axis=1)),
+                "M": ak.flatten(pad_val(vbfJets_sorted_pt[:, 0:1].mass, 1, axis=1)),
+            }
+        )
+
+        vbf2 = vector.array(
+            {
+                "pt": ak.flatten(pad_val(vbfJets_sorted_pt[:, 1:2].pt, 1, axis=1)),
+                "phi": ak.flatten(pad_val(vbfJets_sorted_pt[:, 1:2].phi, 1, axis=1)),
+                "eta": ak.flatten(pad_val(vbfJets_sorted_pt[:, 1:2].eta, 1, axis=1)),
+                "M": ak.flatten(pad_val(vbfJets_sorted_pt[:, 1:2].mass, 1, axis=1)),
+            }
+        )
+
+        jj = vbf1 + vbf2
+
+        mass_jj_cut_sorted_pt = jj.mass > 500
+        eta_jj_cut_sorted_pt = np.abs(vbf1.eta - vbf2.eta) > 4.0
+
+        vbfJets_mask_sorted_pt = vbfJets_mask  # * mass_jj_cut_sorted_pt * eta_jj_cut_sorted_pt # uncomment these last two to include dijet cuts
+        n_good_vbf_jets_sorted_pt = ak.fill_none(ak.sum(vbfJets_mask_sorted_pt, axis=1), 0)
+
+        # add vbf gen quark info
+        if isGen:  # add | True when debugging with local files
+            vbfGenJets = events.GenPart[events.GenPart.hasFlags(["isHardProcess"])][:, 4:6]
+
+            vbfVars[f"vbfptGen"] = pad_val(vbfGenJets.pt, 2, axis=1)
+            vbfVars[f"vbfetaGen"] = pad_val(vbfGenJets.eta, 2, axis=1)
+            vbfVars[f"vbfphiGen"] = pad_val(vbfGenJets.phi, 2, axis=1)
+            vbfVars[f"vbfMGen"] = pad_val(vbfGenJets.mass, 2, axis=1)
+
+            jet_pairs = ak.cartesian({"reco": vbfJets_sorted_pt[:, 0:2], "gen": vbfGenJets[:, 0:2]})
+
+            # Calculate delta eta and delta phi for each pair
+            delta_eta = jet_pairs["reco"].eta - jet_pairs["gen"].eta
+            delta_phi = np.pi - np.abs(np.abs(jet_pairs["reco"].phi - jet_pairs["gen"].phi) - np.pi)
+
+            # Calculate delta R for each pair
+            delta_R = np.sqrt(delta_eta**2 + delta_phi**2)
+
+            # Apply a mask for a low delta R value
+            mask_low_delta_R = delta_R < 0.4
+            num_per_event = ak.sum(mask_low_delta_R, axis=-1)  # miscounts 0's since some are empty
+
+            # Combine masks with logical 'and' operation
+            total_mask = n_good_vbf_jets_sorted_pt > 1
+
+            # set event that fail to have 0 for num of events.
+            num_per_event = ak.where(total_mask, num_per_event, 0)
+            vbfVars[f"vbfNumMatchedGen"] = num_per_event.to_numpy()
+
+            # adds data about R1 R2 selection efficiencies.
+            graphingR1R2 = False
+            if (
+                graphingR1R2 == True
+            ):  # compute fatjet mask many times, execute dijet selection and associated cuts. add to datafram label with R1 R2 info in it and ngoodjets for this configuration
+                for R1 in np.arange(0.3, 2, 0.1):
+                    for R2 in np.arange(0.3, 2, 0.1):
+                        fatjet_overlap_mask = (np.abs(jets.delta_r(bbJet)) > R1) & (
+                            np.abs(jets.delta_r(VVJet)) > R2
+                        )
+
+                        # compute n_good_vbf_jets + incorporate eta_jj > 4.0
+                        vbfJets_mask = (
+                            ak4_jet_mask & electron_muon_overlap_mask & fatjet_overlap_mask
+                        )
+                        # vbfJets_mask = fatjet_overlap_mask # this is for unflitered events
+                        vbfJets = jets[vbfJets_mask]
+                        vbfJets_sorted_pt = vbfJets[ak.argsort(vbfJets.pt, ascending=False)]
+                        vbfJets_sorted_pt = ak.pad_none(vbfJets_sorted_pt, 2, clip=True)
+                        jj_sorted_pt = vbfJets_sorted_pt[:, 0:1] + vbfJets_sorted_pt[:, 1:2]
+                        mass_jj_cut_sorted_pt = jj_sorted_pt.mass > 500
+                        eta_jj_cut_sorted_pt = (
+                            np.abs(vbfJets_sorted_pt[:, 0:1].eta - vbfJets_sorted_pt[:, 1:2].eta)
+                            > 4.0
+                        )
+                        vbfJets_mask_sorted_pt = (
+                            vbfJets_mask * mass_jj_cut_sorted_pt * eta_jj_cut_sorted_pt
+                        )
+                        num_sorted_pt = ak.fill_none(ak.sum(vbfJets_mask_sorted_pt, axis=1), 0)
+
+                        # here we can print some information about the jets so that we can study the selections a bit.
+                        # jet_pairs = ak.cartesian({"reco": vbfJets, "gen": vbfGenJets[:,0:2]}) # this is only for unfiltered events
+                        jet_pairs = ak.cartesian(
+                            {"reco": vbfJets_sorted_pt[:, 0:2], "gen": vbfGenJets[:, 0:2]}
+                        )
+
+                        # Calculate delta eta and delta phi for each pair
+                        delta_eta = jet_pairs["reco"].eta - jet_pairs["gen"].eta
+                        delta_phi = np.pi - np.abs(
+                            np.abs(jet_pairs["reco"].phi - jet_pairs["gen"].phi) - np.pi
+                        )
+
+                        # Calculate delta R for each pair
+                        delta_R = np.sqrt(delta_eta**2 + delta_phi**2)
+
+                        # Apply a mask for a low delta R value
+                        mask_low_delta_R = delta_R < 0.4
+                        num_per_event = ak.sum(
+                            mask_low_delta_R, axis=-1
+                        )  # miscounts 0's since some are empty
+
+                        # Combine masks with logical 'and' operation
+                        total_mask = num_sorted_pt > 1
+
+                        # set event that fail to have 0 for num of events.
+                        num_per_event = ak.where(total_mask, num_per_event, 0)
+                        vbfVars[f"vbfR1{R1:.1f}R2{R2:.1f}"] = num_per_event.to_numpy()
+
+                        # note later when we have the pd dataframe, we need to apply mask, then turn data into graph.
+                        # and df = df[selection_mask & (df[('nGoodMuons', 0)] == 0) & (df[('nGoodElectrons', 0)] == 0) & (df[('nGoodVBFJets', 0)] >= 1)& (df[('nGoodJets', 0)] == 0)]
+
+        vbfVars[f"vbfpt"] = pad_val(vbfJets_sorted_pt.pt, 2, axis=1)
+        vbfVars[f"vbfeta"] = pad_val(vbfJets_sorted_pt.eta, 2, axis=1)
+        vbfVars[f"vbfphi"] = pad_val(vbfJets_sorted_pt.phi, 2, axis=1)
+        vbfVars[f"vbfM"] = pad_val(vbfJets_sorted_pt.mass, 2, axis=1)
+
+        # int list representing the number of passing vbf jets per event
+        vbfVars[f"nGoodVBFJets"] = n_good_vbf_jets_sorted_pt.to_numpy()
+
+        adding_bdt_vars = True
+        if adding_bdt_vars == True:
+            # Adapted from HIG-20-005 ggF_Killer 6.2.2
+            # https://coffeateam.github.io/coffea/api/coffea.nanoevents.methods.vector.PtEtaPhiMLorentzVector.html
+            # https://coffeateam.github.io/coffea/api/coffea.nanoevents.methods.vector.LorentzVector.html
+            # Adding variables defined in HIG-20-005 that show strong differentiation for VBF signal events and background
+
+            # seperation between both ak8 higgs jets
+            vbfVars[f"vbf_dR_HH"] = VVJet.deltaR(bbJet)
+
+            vbfVars[f"vbf_dR_j0_HVV"] = vbf1.deltaR(VVJet)
+            vbfVars[f"vbf_dR_j1_HVV"] = vbf2.deltaR(VVJet)
+            vbfVars[f"vbf_dR_j0_Hbb"] = vbf1.deltaR(bbJet)
+            vbfVars[f"vbf_dR_j1_Hbb"] = vbf2.deltaR(bbJet)
+            vbfVars[f"vbf_dR_jj"] = vbf1.deltaR(vbf2)
+            vbfVars[f"vbf_Mass_jj"] = jj.M
+            vbfVars[f"vbf_dEta_jj"] = np.abs(vbf1.eta - vbf2.eta)
+
+            # Subleading VBF-jet cos(θ) in the HH+2j center of mass frame:
+            # https://github.com/scikit-hep/vector/blob/main/src/vector/_methods.py#L916
+            system_4vec = vbf1 + vbf2 + VVJet + bbJet
+            j1_CMF = vbf1.boostCM_of_p4(system_4vec)
+
+            # Leading VBF-jet cos(θ) in the HH+2j center of mass frame:
+            thetab1 = 2 * np.arctan(np.exp(-j1_CMF.eta))
+            thetab1 = np.cos(thetab1)  # 12
+            vbfVars[f"vbf_cos_j1"] = np.abs(thetab1)
+
+            # Subleading VBF-jet cos(θ) in the HH+2j center of mass frame:
+            j2_CMF = vbf2.boostCM_of_p4(system_4vec)
+            thetab2 = 2 * np.arctan(np.exp(-j2_CMF.eta))
+            thetab2 = np.cos(thetab2)
+            vbfVars[f"vbf_cos_j2"] = np.abs(thetab2)
+
+            # H1-centrality * H2-centrality:
+            delta_eta = vbf1.eta - vbf2.eta
+            avg_eta = (vbf1.eta + vbf2.eta) / 2
+            prod_centrality = np.exp(
+                -np.power((VVJet.eta - avg_eta) / delta_eta, 2)
+                - np.power((bbJet.eta - avg_eta) / delta_eta, 2)
+            )
+            vbfVars[f"vbf_prod_centrality"] = prod_centrality
+
+        return vbfVars
+
     def getDijetVars(
         self, ak8FatJetVars: Dict, bb_mask: np.ndarray, pt_shift: str = None, mass_shift: str = None
     ):
diff --git a/src/HHbbVV/processors/common.py b/src/HHbbVV/processors/common.py
index d1f89540..7b46eb02 100644
--- a/src/HHbbVV/processors/common.py
+++ b/src/HHbbVV/processors/common.py
@@ -58,7 +58,7 @@
         #
         "PFHT1050",
         #
-        "HLT_AK8PFJet330_TrimMass30_PFAK8BTagCSV_p17_v",
+        "AK8PFJet330_TrimMass30_PFAK8BTagCSV_p17_v",
     ],
 }
 
@@ -87,3 +87,53 @@
         },
     }
 }
+
+
+jecs = {
+    "JES": "JES_jes",
+    "JER": "JER",
+}
+
+jmsr = {
+    "JMS": "JMS",
+    "JMR": "JMR",
+}
+
+jec_shifts = []
+for key in jecs:
+    for shift in ["up", "down"]:
+        jec_shifts.append(f"{key}_{shift}")
+
+jmsr_shifts = []
+for key in jmsr:
+    for shift in ["up", "down"]:
+        jmsr_shifts.append(f"{key}_{shift}")
+
+# variables affected by JECs
+jec_vars = [
+    "bbFatJetPt",
+    "VVFatJetPt",
+    "DijetEta",
+    "DijetPt",
+    "DijetMass",
+    "bbFatJetPtOverDijetPt",
+    "VVFatJetPtOverDijetPt",
+    "VVFatJetPtOverbbFatJetPt",
+    "BDTScore",
+]
+
+
+# variables affected by JMS/R
+jmsr_vars = [
+    "bbFatJetMsd",
+    "bbFatJetParticleNetMass",
+    "VVFatJetMsd",
+    "VVFatJetParticleNetMass",
+    "DijetEta",
+    "DijetPt",
+    "DijetMass",
+    "bbFatJetPtOverDijetPt",
+    "VVFatJetPtOverDijetPt",
+    "VVFatJetPtOverbbFatJetPt",
+    "BDTScore",
+]
diff --git a/src/HHbbVV/processors/corrections.py b/src/HHbbVV/processors/corrections.py
index ac46210b..227d6464 100644
--- a/src/HHbbVV/processors/corrections.py
+++ b/src/HHbbVV/processors/corrections.py
@@ -1,3 +1,14 @@
+"""
+Collection of utilities for corrections and systematics in processors.
+
+Loosely based on https://github.com/jennetd/hbb-coffea/blob/master/boostedhiggs/corrections.py
+
+Most corrections retrieved from the cms-nanoAOD repo:
+See https://cms-nanoaod-integration.web.cern.ch/commonJSONSFs/
+
+Authors: Raghav Kansal, Cristina Suarez
+"""
+
 import os
 from typing import Dict, List, Tuple, Union
 import numpy as np
@@ -6,11 +17,11 @@
 import correctionlib
 import awkward as ak
 
+from coffea import util as cutil
 from coffea.analysis_tools import Weights
 from coffea.lookup_tools.dense_lookup import dense_lookup
 from coffea.nanoevents.methods.nanoaod import MuonArray, JetArray, FatJetArray, GenParticleArray
 from coffea.nanoevents.methods.base import NanoEventsArray
-
 from coffea.nanoevents.methods import vector
 
 ak.behavior.update(vector.behavior)
@@ -18,7 +29,7 @@
 import pathlib
 
 from . import utils
-from .utils import P4
+from .utils import P4, pad_val
 
 
 package_path = str(pathlib.Path(__file__).parent.parent.resolve())
@@ -26,13 +37,15 @@
 
 """
 CorrectionLib files are available from: /cvmfs/cms.cern.ch/rsync/cms-nanoAOD/jsonpog-integration - synced daily
+See https://cms-nanoaod-integration.web.cern.ch/commonJSONSFs/
 """
 pog_correction_path = "/cvmfs/cms.cern.ch/rsync/cms-nanoAOD/jsonpog-integration/"
 pog_jsons = {
     "muon": ["MUO", "muon_Z.json.gz"],
     "electron": ["EGM", "electron.json.gz"],
     "pileup": ["LUM", "puWeights.json.gz"],
-    "jec": ["JME", "fatJet_jerc.json.gz"],
+    "btagging": ["BTV", "btagging.json.gz"],
+    "jmar": ["JME", "jmar.json.gz"],
 }
 
 
@@ -172,6 +185,8 @@ def add_systs(systlist, qcdcorr, ewkcorr, vpt):
 def add_ps_weight(weights, ps_weights):
     """
     Parton Shower Weights (FSR and ISR)
+    "Default" variation: https://twiki.cern.ch/twiki/bin/view/CMS/HowToPDF#Which_set_of_weights_to_use
+    i.e. scaling ISR up and down
     """
 
     nweights = len(weights.weight())
@@ -301,6 +316,78 @@ def add_scalevar_3pt(weights, var_weights):
     weights.add("QCDscale3pt", nom, up, down)
 
 
+def _btagSF(cset, jets, flavour, wp="M", algo="deepJet", syst="central"):
+    j, nj = ak.flatten(jets), ak.num(jets)
+    corrs = cset[f"{algo}_comb"] if flavour == "bc" else cset[f"{algo}_incl"]
+    sf = corrs.evaluate(
+        syst,
+        wp,
+        np.array(j.hadronFlavour),
+        np.array(abs(j.eta)),
+        np.array(j.pt),
+    )
+    return ak.unflatten(sf, nj)
+
+
+def _btag_prod(eff, sf):
+    num = ak.fill_none(ak.prod(1 - sf * eff, axis=-1), 1)
+    den = ak.fill_none(ak.prod(1 - eff, axis=-1), 1)
+    return num, den
+
+
+def add_btag_weights(
+    weights: Weights,
+    year: str,
+    jets: JetArray,
+    wp: str = "M",
+    algo: str = "deepJet",
+):
+    ul_year = get_UL_year(year)
+    cset = correctionlib.CorrectionSet.from_file(get_pog_json("btagging", year))
+    efflookup = cutil.load(package_path + f"/corrections/btag_effs/btageff_deepJet_M_{year}.coffea")
+
+    lightJets = jets[jets.hadronFlavour == 0]
+    bcJets = jets[jets.hadronFlavour > 0]
+
+    lightEff = efflookup(lightJets.pt, abs(lightJets.eta), lightJets.hadronFlavour)
+    bcEff = efflookup(bcJets.pt, abs(bcJets.eta), bcJets.hadronFlavour)
+
+    lightSF = _btagSF(cset, lightJets, "light", wp, algo)
+    bcSF = _btagSF(cset, bcJets, "bc", wp, algo)
+
+    lightnum, lightden = _btag_prod(lightEff, lightSF)
+    bcnum, bcden = _btag_prod(bcEff, bcSF)
+
+    weight = np.nan_to_num((1 - lightnum * bcnum) / (1 - lightden * bcden), nan=1)
+    weights.add("btagSF", weight)
+
+
+def add_pileupid_weights(weights: Weights, year: str, jets: JetArray, genjets, wp: str = "L"):
+    """Pileup ID scale factors
+    https://twiki.cern.ch/twiki/bin/view/CMS/PileupJetIDUL#Data_MC_Efficiency_Scale_Factors
+
+    Takes ak4 jets which already passed the pileup ID WP.
+    Only applies to jets with pT < 50 GeV and those geometrically matched to a gen jet.
+    """
+
+    # pileup ID should only be used for jets with pT < 50
+    jets = jets[jets.pt < 50]
+    # check that there's a geometrically matched genjet (99.9% are, so not really necessary...)
+    jets = jets[ak.any(jets.metric_table(genjets) < 0.4, axis=-1)]
+
+    sf_cset = correctionlib.CorrectionSet.from_file(get_pog_json("jmar", year))["PUJetID_eff"]
+    # save offsets to reconstruct jagged shape
+    offsets = jets.pt.layout.content.offsets
+    # correctionlib < 2.3 doesn't accept jagged arrays (but >= 2.3 needs awkard v2)
+    sfs = sf_cset.evaluate(ak.flatten(jets.eta), ak.flatten(jets.pt), "nom", "L")
+    # reshape flat effs
+    sfs = ak.Array(ak.layout.ListOffsetArray64(offsets, ak.layout.NumpyArray(sfs)))
+    # product of SFs across arrays, automatically defaults empty lists to 1
+    sfs = ak.prod(sfs, axis=1)
+
+    weights.add("pileupIDSF", sfs)
+
+
 # for scale factor validation region selection
 lepton_corrections = {
     "trigger_noniso": {
@@ -388,6 +475,7 @@ def add_top_pt_weight(weights: Weights, events: NanoEventsArray):
     with open(package_path + "/corrections/jec_compiled.pkl", "rb") as filehandler:
         jmestuff = pickle.load(filehandler)
 
+    ak4jet_factory = jmestuff["jet_factory"]
     fatjet_factory = jmestuff["fatjet_factory"]
 except:
     print("Failed loading compiled JECs")
@@ -404,18 +492,30 @@ def _add_jec_variables(jets: JetArray, event_rho: ak.Array) -> JetArray:
 
 
 def get_jec_jets(
-    events: NanoEventsArray, year: str, isData: bool = False, jecs: Dict[str, str] = None
+    events: NanoEventsArray,
+    year: str,
+    isData: bool = False,
+    jecs: Dict[str, str] = None,
+    fatjets: bool = True,
 ) -> FatJetArray:
     """
     Based on https://github.com/nsmith-/boostedhiggs/blob/master/boostedhiggs/hbbprocessor.py
     Eventually update to V5 JECs once I figure out what's going on with the 2017 UL V5 JER scale factors
 
+    See https://cms-nanoaod-integration.web.cern.ch/commonJSONSFs/summaries/
+
     If ``jecs`` is not None, returns the shifted values of variables are affected by JECs.
     """
 
     jec_vars = ["pt"]  # vars we're saving that are affected by JECs
+    if fatjets:
+        jets = events.FatJet
+        jet_factory = fatjet_factory
+    else:
+        jets = events.Jet
+        jet_factory = ak4jet_factory
 
-    apply_jecs = not (not ak.any(events.FatJet.pt) or isData)
+    apply_jecs = not (not ak.any(jets.pt) or isData)
 
     import cachetools
 
@@ -424,29 +524,27 @@ def get_jec_jets(
     corr_key = f"{get_vfp_year(year)}mc"
 
     # fatjet_factory.build gives an error if there are no fatjets in event
-    if not apply_jecs:
-        fatjets = events.FatJet
-    else:
-        fatjets = fatjet_factory[corr_key].build(
-            _add_jec_variables(events.FatJet, events.fixedGridRhoFastjetAll), jec_cache
+    if apply_jecs:
+        jets = jet_factory[corr_key].build(
+            _add_jec_variables(jets, events.fixedGridRhoFastjetAll), jec_cache
         )
 
-    # return fatjets only if no jecs given
+    # return only fatjets if no jecs given
     if jecs is None:
-        return fatjets
+        return jets
 
     jec_shifted_vars = {}
 
     for jec_var in jec_vars:
-        tdict = {"": fatjets[jec_var]}
+        tdict = {"": jets[jec_var]}
         if apply_jecs:
             for key, shift in jecs.items():
                 for var in ["up", "down"]:
-                    tdict[f"{key}_{var}"] = fatjets[shift][var][jec_var]
+                    tdict[f"{key}_{var}"] = jets[shift][var][jec_var]
 
         jec_shifted_vars[jec_var] = tdict
 
-    return fatjets, jec_shifted_vars
+    return jets, jec_shifted_vars
 
 
 jmsr_vars = ["msoftdrop", "particleNet_mass"]
@@ -508,7 +606,7 @@ def get_jmsr(
             smearing = np.random.normal(size=mass.shape)
             # scale to JMR nom, down, up (minimum at 0)
             jmr_nom, jmr_down, jmr_up = [
-                (smearing * max(jmrValues[mkey][year][i] - 1, 0) + 1) for i in range(3)
+                ((smearing * max(jmrValues[mkey][year][i] - 1, 0)) + 1) for i in range(3)
             ]
             jms_nom, jms_down, jms_up = jmsValues[mkey][year]
 
@@ -526,6 +624,31 @@ def get_jmsr(
     return jmsr_shifted_vars
 
 
+def add_trig_effs(weights: Weights, fatjets: FatJetArray, year: str, num_jets: int = 2):
+    """Add the trigger efficiencies we measured in SingleMuon data"""
+    with open(f"{package_path}/corrections/trigEffs/{year}_combined.pkl", "rb") as filehandler:
+        combined = pickle.load(filehandler)
+
+    # sum over TH4q bins
+    effs_txbb = combined["num"][:, sum, :, :] / combined["den"][:, sum, :, :]
+
+    ak8TrigEffsLookup = dense_lookup(
+        np.nan_to_num(effs_txbb.view(flow=False), 0), np.squeeze(effs_txbb.axes.edges)
+    )
+
+    # TODO: confirm that these should be corrected pt, msd values
+    fj_trigeffs = ak8TrigEffsLookup(
+        pad_val(fatjets.Txbb, num_jets, axis=1),
+        pad_val(fatjets.pt, num_jets, axis=1),
+        pad_val(fatjets.msoftdrop, num_jets, axis=1),
+    )
+
+    # combined eff = 1 - (1 - fj1_eff) * (1 - fj2_eff)
+    combined_trigEffs = 1 - np.prod(1 - fj_trigeffs, axis=1)
+
+    weights.add("trig_effs", combined_trigEffs)
+
+
 def _get_lund_arrays(events: NanoEventsArray, fatjet_idx: Union[int, ak.Array], num_prongs: int):
     """
     Gets the ``num_prongs`` subjet pTs and Delta and kT per primary LP splitting of fatjets at
diff --git a/src/HHbbVV/processors/tagger_resources/2023May30_ak8_MD_inclv8_part_2reg_manual.json b/src/HHbbVV/processors/tagger_resources/2023May30_ak8_MD_inclv8_part_2reg_manual.json
new file mode 100644
index 00000000..d2a267d6
--- /dev/null
+++ b/src/HHbbVV/processors/tagger_resources/2023May30_ak8_MD_inclv8_part_2reg_manual.json
@@ -0,0 +1,784 @@
+{
+  "output_names": [
+    "label_Top_bWcs",
+    "label_Top_bWqq",
+    "label_Top_bWc",
+    "label_Top_bWs",
+    "label_Top_bWq",
+    "label_Top_bWev",
+    "label_Top_bWmv",
+    "label_Top_bWtauev",
+    "label_Top_bWtaumv",
+    "label_Top_bWtauhv",
+    "label_Top_Wcs",
+    "label_Top_Wqq",
+    "label_Top_Wev",
+    "label_Top_Wmv",
+    "label_Top_Wtauev",
+    "label_Top_Wtaumv",
+    "label_Top_Wtauhv",
+    "label_H_bb",
+    "label_H_cc",
+    "label_H_ss",
+    "label_H_qq",
+    "label_H_bc",
+    "label_H_bs",
+    "label_H_cs",
+    "label_H_gg",
+    "label_H_ee",
+    "label_H_mm",
+    "label_H_tauhtaue",
+    "label_H_tauhtaum",
+    "label_H_tauhtauh",
+    "label_H_WW_cscs",
+    "label_H_WW_csqq",
+    "label_H_WW_qqqq",
+    "label_H_WW_csc",
+    "label_H_WW_css",
+    "label_H_WW_csq",
+    "label_H_WW_qqc",
+    "label_H_WW_qqs",
+    "label_H_WW_qqq",
+    "label_H_WW_csev",
+    "label_H_WW_qqev",
+    "label_H_WW_csmv",
+    "label_H_WW_qqmv",
+    "label_H_WW_cstauev",
+    "label_H_WW_qqtauev",
+    "label_H_WW_cstaumv",
+    "label_H_WW_qqtaumv",
+    "label_H_WW_cstauhv",
+    "label_H_WW_qqtauhv",
+    "label_H_WxWx_cscs",
+    "label_H_WxWx_csqq",
+    "label_H_WxWx_qqqq",
+    "label_H_WxWx_csc",
+    "label_H_WxWx_css",
+    "label_H_WxWx_csq",
+    "label_H_WxWx_qqc",
+    "label_H_WxWx_qqs",
+    "label_H_WxWx_qqq",
+    "label_H_WxWx_csev",
+    "label_H_WxWx_qqev",
+    "label_H_WxWx_csmv",
+    "label_H_WxWx_qqmv",
+    "label_H_WxWx_cstauev",
+    "label_H_WxWx_qqtauev",
+    "label_H_WxWx_cstaumv",
+    "label_H_WxWx_qqtaumv",
+    "label_H_WxWx_cstauhv",
+    "label_H_WxWx_qqtauhv",
+    "label_H_WxWxStar_cscs",
+    "label_H_WxWxStar_csqq",
+    "label_H_WxWxStar_qqqq",
+    "label_H_WxWxStar_csc",
+    "label_H_WxWxStar_css",
+    "label_H_WxWxStar_csq",
+    "label_H_WxWxStar_qqc",
+    "label_H_WxWxStar_qqs",
+    "label_H_WxWxStar_qqq",
+    "label_H_WxWxStar_csev",
+    "label_H_WxWxStar_qqev",
+    "label_H_WxWxStar_csmv",
+    "label_H_WxWxStar_qqmv",
+    "label_H_WxWxStar_cstauev",
+    "label_H_WxWxStar_qqtauev",
+    "label_H_WxWxStar_cstaumv",
+    "label_H_WxWxStar_qqtaumv",
+    "label_H_WxWxStar_cstauhv",
+    "label_H_WxWxStar_qqtauhv",
+    "label_H_ZZ_bbbb",
+    "label_H_ZZ_bbcc",
+    "label_H_ZZ_bbss",
+    "label_H_ZZ_bbqq",
+    "label_H_ZZ_cccc",
+    "label_H_ZZ_ccss",
+    "label_H_ZZ_ccqq",
+    "label_H_ZZ_ssss",
+    "label_H_ZZ_ssqq",
+    "label_H_ZZ_qqqq",
+    "label_H_ZZ_bbb",
+    "label_H_ZZ_bbc",
+    "label_H_ZZ_bbs",
+    "label_H_ZZ_bbq",
+    "label_H_ZZ_ccb",
+    "label_H_ZZ_ccc",
+    "label_H_ZZ_ccs",
+    "label_H_ZZ_ccq",
+    "label_H_ZZ_ssb",
+    "label_H_ZZ_ssc",
+    "label_H_ZZ_sss",
+    "label_H_ZZ_ssq",
+    "label_H_ZZ_qqb",
+    "label_H_ZZ_qqc",
+    "label_H_ZZ_qqs",
+    "label_H_ZZ_qqq",
+    "label_H_ZZ_bbee",
+    "label_H_ZZ_bbmm",
+    "label_H_ZZ_bbe",
+    "label_H_ZZ_bbm",
+    "label_H_ZZ_bee",
+    "label_H_ZZ_bmm",
+    "label_H_ZZ_bbtauhtaue",
+    "label_H_ZZ_bbtauhtaum",
+    "label_H_ZZ_bbtauhtauh",
+    "label_H_ZZ_btauhtaue",
+    "label_H_ZZ_btauhtaum",
+    "label_H_ZZ_btauhtauh",
+    "label_H_ZZ_ccee",
+    "label_H_ZZ_ccmm",
+    "label_H_ZZ_cce",
+    "label_H_ZZ_ccm",
+    "label_H_ZZ_cee",
+    "label_H_ZZ_cmm",
+    "label_H_ZZ_cctauhtaue",
+    "label_H_ZZ_cctauhtaum",
+    "label_H_ZZ_cctauhtauh",
+    "label_H_ZZ_ctauhtaue",
+    "label_H_ZZ_ctauhtaum",
+    "label_H_ZZ_ctauhtauh",
+    "label_H_ZZ_ssee",
+    "label_H_ZZ_ssmm",
+    "label_H_ZZ_sse",
+    "label_H_ZZ_ssm",
+    "label_H_ZZ_see",
+    "label_H_ZZ_smm",
+    "label_H_ZZ_sstauhtaue",
+    "label_H_ZZ_sstauhtaum",
+    "label_H_ZZ_sstauhtauh",
+    "label_H_ZZ_stauhtaue",
+    "label_H_ZZ_stauhtaum",
+    "label_H_ZZ_stauhtauh",
+    "label_H_ZZ_qqee",
+    "label_H_ZZ_qqmm",
+    "label_H_ZZ_qqe",
+    "label_H_ZZ_qqm",
+    "label_H_ZZ_qee",
+    "label_H_ZZ_qmm",
+    "label_H_ZZ_qqtauhtaue",
+    "label_H_ZZ_qqtauhtaum",
+    "label_H_ZZ_qqtauhtauh",
+    "label_H_ZZ_qtauhtaue",
+    "label_H_ZZ_qtauhtaum",
+    "label_H_ZZ_qtauhtauh",
+    "label_H_ZxZx_bbbb",
+    "label_H_ZxZx_bbcc",
+    "label_H_ZxZx_bbss",
+    "label_H_ZxZx_bbqq",
+    "label_H_ZxZx_cccc",
+    "label_H_ZxZx_ccss",
+    "label_H_ZxZx_ccqq",
+    "label_H_ZxZx_ssss",
+    "label_H_ZxZx_ssqq",
+    "label_H_ZxZx_qqqq",
+    "label_H_ZxZx_bbb",
+    "label_H_ZxZx_bbc",
+    "label_H_ZxZx_bbs",
+    "label_H_ZxZx_bbq",
+    "label_H_ZxZx_ccb",
+    "label_H_ZxZx_ccc",
+    "label_H_ZxZx_ccs",
+    "label_H_ZxZx_ccq",
+    "label_H_ZxZx_ssb",
+    "label_H_ZxZx_ssc",
+    "label_H_ZxZx_sss",
+    "label_H_ZxZx_ssq",
+    "label_H_ZxZx_qqb",
+    "label_H_ZxZx_qqc",
+    "label_H_ZxZx_qqs",
+    "label_H_ZxZx_qqq",
+    "label_H_ZxZx_bbee",
+    "label_H_ZxZx_bbmm",
+    "label_H_ZxZx_bbe",
+    "label_H_ZxZx_bbm",
+    "label_H_ZxZx_bee",
+    "label_H_ZxZx_bmm",
+    "label_H_ZxZx_bbtauhtaue",
+    "label_H_ZxZx_bbtauhtaum",
+    "label_H_ZxZx_bbtauhtauh",
+    "label_H_ZxZx_btauhtaue",
+    "label_H_ZxZx_btauhtaum",
+    "label_H_ZxZx_btauhtauh",
+    "label_H_ZxZx_ccee",
+    "label_H_ZxZx_ccmm",
+    "label_H_ZxZx_cce",
+    "label_H_ZxZx_ccm",
+    "label_H_ZxZx_cee",
+    "label_H_ZxZx_cmm",
+    "label_H_ZxZx_cctauhtaue",
+    "label_H_ZxZx_cctauhtaum",
+    "label_H_ZxZx_cctauhtauh",
+    "label_H_ZxZx_ctauhtaue",
+    "label_H_ZxZx_ctauhtaum",
+    "label_H_ZxZx_ctauhtauh",
+    "label_H_ZxZx_ssee",
+    "label_H_ZxZx_ssmm",
+    "label_H_ZxZx_sse",
+    "label_H_ZxZx_ssm",
+    "label_H_ZxZx_see",
+    "label_H_ZxZx_smm",
+    "label_H_ZxZx_sstauhtaue",
+    "label_H_ZxZx_sstauhtaum",
+    "label_H_ZxZx_sstauhtauh",
+    "label_H_ZxZx_stauhtaue",
+    "label_H_ZxZx_stauhtaum",
+    "label_H_ZxZx_stauhtauh",
+    "label_H_ZxZx_qqee",
+    "label_H_ZxZx_qqmm",
+    "label_H_ZxZx_qqe",
+    "label_H_ZxZx_qqm",
+    "label_H_ZxZx_qee",
+    "label_H_ZxZx_qmm",
+    "label_H_ZxZx_qqtauhtaue",
+    "label_H_ZxZx_qqtauhtaum",
+    "label_H_ZxZx_qqtauhtauh",
+    "label_H_ZxZx_qtauhtaue",
+    "label_H_ZxZx_qtauhtaum",
+    "label_H_ZxZx_qtauhtauh",
+    "label_H_ZxZxStar_bbbb",
+    "label_H_ZxZxStar_bbcc",
+    "label_H_ZxZxStar_bbss",
+    "label_H_ZxZxStar_bbqq",
+    "label_H_ZxZxStar_cccc",
+    "label_H_ZxZxStar_ccss",
+    "label_H_ZxZxStar_ccqq",
+    "label_H_ZxZxStar_ssss",
+    "label_H_ZxZxStar_ssqq",
+    "label_H_ZxZxStar_qqqq",
+    "label_H_ZxZxStar_bbb",
+    "label_H_ZxZxStar_bbc",
+    "label_H_ZxZxStar_bbs",
+    "label_H_ZxZxStar_bbq",
+    "label_H_ZxZxStar_ccb",
+    "label_H_ZxZxStar_ccc",
+    "label_H_ZxZxStar_ccs",
+    "label_H_ZxZxStar_ccq",
+    "label_H_ZxZxStar_ssb",
+    "label_H_ZxZxStar_ssc",
+    "label_H_ZxZxStar_sss",
+    "label_H_ZxZxStar_ssq",
+    "label_H_ZxZxStar_qqb",
+    "label_H_ZxZxStar_qqc",
+    "label_H_ZxZxStar_qqs",
+    "label_H_ZxZxStar_qqq",
+    "label_H_ZxZxStar_bbee",
+    "label_H_ZxZxStar_bbmm",
+    "label_H_ZxZxStar_bbe",
+    "label_H_ZxZxStar_bbm",
+    "label_H_ZxZxStar_bee",
+    "label_H_ZxZxStar_bmm",
+    "label_H_ZxZxStar_bbtauhtaue",
+    "label_H_ZxZxStar_bbtauhtaum",
+    "label_H_ZxZxStar_bbtauhtauh",
+    "label_H_ZxZxStar_btauhtaue",
+    "label_H_ZxZxStar_btauhtaum",
+    "label_H_ZxZxStar_btauhtauh",
+    "label_H_ZxZxStar_ccee",
+    "label_H_ZxZxStar_ccmm",
+    "label_H_ZxZxStar_cce",
+    "label_H_ZxZxStar_ccm",
+    "label_H_ZxZxStar_cee",
+    "label_H_ZxZxStar_cmm",
+    "label_H_ZxZxStar_cctauhtaue",
+    "label_H_ZxZxStar_cctauhtaum",
+    "label_H_ZxZxStar_cctauhtauh",
+    "label_H_ZxZxStar_ctauhtaue",
+    "label_H_ZxZxStar_ctauhtaum",
+    "label_H_ZxZxStar_ctauhtauh",
+    "label_H_ZxZxStar_ssee",
+    "label_H_ZxZxStar_ssmm",
+    "label_H_ZxZxStar_sse",
+    "label_H_ZxZxStar_ssm",
+    "label_H_ZxZxStar_see",
+    "label_H_ZxZxStar_smm",
+    "label_H_ZxZxStar_sstauhtaue",
+    "label_H_ZxZxStar_sstauhtaum",
+    "label_H_ZxZxStar_sstauhtauh",
+    "label_H_ZxZxStar_stauhtaue",
+    "label_H_ZxZxStar_stauhtaum",
+    "label_H_ZxZxStar_stauhtauh",
+    "label_H_ZxZxStar_qqee",
+    "label_H_ZxZxStar_qqmm",
+    "label_H_ZxZxStar_qqe",
+    "label_H_ZxZxStar_qqm",
+    "label_H_ZxZxStar_qee",
+    "label_H_ZxZxStar_qmm",
+    "label_H_ZxZxStar_qqtauhtaue",
+    "label_H_ZxZxStar_qqtauhtaum",
+    "label_H_ZxZxStar_qqtauhtauh",
+    "label_H_ZxZxStar_qtauhtaue",
+    "label_H_ZxZxStar_qtauhtaum",
+    "label_H_ZxZxStar_qtauhtauh",
+    "label_QCD_bb",
+    "label_QCD_cc",
+    "label_QCD_b",
+    "label_QCD_c",
+    "label_QCD_others",
+    "mreg1",
+    "mreg2"
+  ],
+  "input_names": [
+    "pf_features",
+    "pf_vectors",
+    "pf_mask",
+    "sv_features",
+    "sv_vectors",
+    "sv_mask"
+  ],
+  "pf_features": {
+    "var_names": [
+      "pfcand_pt_log_nopuppi",
+      "pfcand_e_log_nopuppi",
+      "pfcand_etarel",
+      "pfcand_phirel",
+      "pfcand_abseta",
+      "pfcand_charge",
+      "pfcand_isEl",
+      "pfcand_isMu",
+      "pfcand_isGamma",
+      "pfcand_isChargedHad",
+      "pfcand_isNeutralHad",
+      "pfcand_VTX_ass",
+      "pfcand_lostInnerHits",
+      "pfcand_normchi2",
+      "pfcand_quality",
+      "pfcand_dz",
+      "pfcand_dzsig",
+      "pfcand_dxy",
+      "pfcand_dxysig",
+      "pfcand_btagEtaRel",
+      "pfcand_btagPtRatio",
+      "pfcand_btagPParRatio",
+      "pfcand_btagSip3dVal",
+      "pfcand_btagSip3dSig",
+      "pfcand_btagJetDistVal"
+    ],
+    "var_infos": {
+      "pfcand_pt_log_nopuppi": {
+        "median": 1,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_e_log_nopuppi": {
+        "median": 1.3,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_etarel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_phirel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_abseta": {
+        "median": 0.6,
+        "norm_factor": 1.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_charge": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isEl": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isMu": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isGamma": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isChargedHad": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isNeutralHad": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_VTX_ass": {
+        "median": 4,
+        "norm_factor": 0.3,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_lostInnerHits": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_normchi2": {
+        "median": 5,
+        "norm_factor": 0.2,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_quality": {
+        "median": 0,
+        "norm_factor": 0.2,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_dz": {
+        "median": 0,
+        "norm_factor": 180,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_dzsig": {
+        "median": 0,
+        "norm_factor": 0.9,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_dxy": {
+        "median": 0.0,
+        "norm_factor": 300,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_dxysig": {
+        "median": 0,
+        "norm_factor": 1.0,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_btagEtaRel": {
+        "median": 1.5,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_btagPtRatio": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_btagPParRatio": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_btagSip3dVal": {
+        "median": 0,
+        "norm_factor": 100,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_btagSip3dSig": {
+        "median": 0,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_btagJetDistVal": {
+        "median": 0,
+        "norm_factor": 40,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      }
+    },
+    "var_length": 128
+  },
+  "pf_vectors": {
+    "var_names": [
+      "pfcand_px",
+      "pfcand_py",
+      "pfcand_pz",
+      "pfcand_energy"
+    ],
+    "var_infos": {
+      "pfcand_px": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_py": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_pz": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_energy": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      }
+    },
+    "var_length": 128
+  },
+  "pf_mask": {
+    "var_names": [
+      "pfcand_mask"
+    ],
+    "var_infos": {
+      "pfcand_mask": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      }
+    },
+    "var_length": 128
+  },
+  "sv_features": {
+    "var_names": [
+      "sv_pt_log",
+      "sv_mass",
+      "sv_etarel",
+      "sv_phirel",
+      "sv_abseta",
+      "sv_ntracks",
+      "sv_normchi2",
+      "sv_dxy",
+      "sv_dxysig",
+      "sv_d3d",
+      "sv_d3dsig"
+    ],
+    "var_infos": {
+      "sv_pt_log": {
+        "median": 4,
+        "norm_factor": 0.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_mass": {
+        "median": 1.2,
+        "norm_factor": 0.3,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_etarel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_phirel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_abseta": {
+        "median": 0.5,
+        "norm_factor": 1.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_ntracks": {
+        "median": 3,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_normchi2": {
+        "median": 0.8,
+        "norm_factor": 0.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_dxy": {
+        "median": 0.4,
+        "norm_factor": 0.25,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_dxysig": {
+        "median": 7,
+        "norm_factor": 0.02,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_d3d": {
+        "median": 0.5,
+        "norm_factor": 0.2,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_d3dsig": {
+        "median": 7,
+        "norm_factor": 0.02,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      }
+    },
+    "var_length": 10
+  },
+  "sv_vectors": {
+    "var_names": [
+      "sv_px",
+      "sv_py",
+      "sv_pz",
+      "sv_energy"
+    ],
+    "var_infos": {
+      "sv_px": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_py": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_pz": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_energy": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      }
+    },
+    "var_length": 10
+  },
+  "sv_mask": {
+    "var_names": [
+      "sv_mask"
+    ],
+    "var_infos": {
+      "sv_mask": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      }
+    },
+    "var_length": 10
+  }
+}
\ No newline at end of file
diff --git a/src/HHbbVV/processors/tagger_resources/ak8_MD_vminclv2ParT_manual_fixwrap.json b/src/HHbbVV/processors/tagger_resources/Dec22_ak8_MD_vminclv2ParT_manual_fixwrap.json
similarity index 100%
rename from src/HHbbVV/processors/tagger_resources/ak8_MD_vminclv2ParT_manual_fixwrap.json
rename to src/HHbbVV/processors/tagger_resources/Dec22_ak8_MD_vminclv2ParT_manual_fixwrap.json
diff --git a/src/HHbbVV/processors/tagger_resources/triton_config_ak8.json b/src/HHbbVV/processors/tagger_resources/triton_config_ak8.json
index 72dc3d67..eb1448c9 100644
--- a/src/HHbbVV/processors/tagger_resources/triton_config_ak8.json
+++ b/src/HHbbVV/processors/tagger_resources/triton_config_ak8.json
@@ -1,5 +1,7 @@
 {
-  "model_name": "ak8_MD_vminclv2ParT_manual_fixwrap",
-  "model_url": "triton+grpc://67.58.49.52:8001/ak8_MD_vminclv2ParT_manual_fixwrap/1",
-  "batch_size": 256
+  "model_name": "Dec22_ak8_MD_vminclv2ParT_manual_fixwrap",
+  "model_url": "triton+grpc://67.58.49.48:8001/ak8_MD_vminclv2ParT_manual_fixwrap/1",
+  "batch_size": 192,
+  "softmax": "True",
+  "num_reg": 0
 }
diff --git a/src/HHbbVV/processors/tagger_resources/triton_config_new_tagger_ak8.json b/src/HHbbVV/processors/tagger_resources/triton_config_new_tagger_ak8.json
new file mode 100644
index 00000000..0e3ef87b
--- /dev/null
+++ b/src/HHbbVV/processors/tagger_resources/triton_config_new_tagger_ak8.json
@@ -0,0 +1,7 @@
+{
+  "model_name": "2023May30_ak8_MD_inclv8_part_2reg_manual",
+  "model_url": "triton+grpc://67.58.49.48:8001/ak8_MD_inclv8_part_2reg_manual/1",
+  "batch_size": 192,
+  "softmax": "False",
+  "num_reg": 2
+}
diff --git a/src/HHbbVV/processors/utils.py b/src/HHbbVV/processors/utils.py
index 5b1ad619..2b1e7c03 100644
--- a/src/HHbbVV/processors/utils.py
+++ b/src/HHbbVV/processors/utils.py
@@ -84,3 +84,17 @@ def concatenate_dicts(dicts_list: List[Dict[str, np.ndarray]]):
         }
     else:
         return dicts_list[0]
+
+
+def select_dicts(dicts_list: List[Dict[str, np.ndarray]], sel: np.ndarray):
+    """given a list of dicts of numpy arrays, select the entries per array across the lists according to ``sel``"""
+    return {
+        key: np.stack(
+            [
+                dicts_list[i][key].reshape(dicts_list[i][key].shape[0], -1)
+                for i in range(len(dicts_list))
+            ],
+            axis=1,
+        )[sel]
+        for key in dicts_list[0]
+    }
diff --git a/src/HHbbVV/resonant_studies/QuarkFractions.ipynb b/src/HHbbVV/resonant_studies/QuarkFractions.ipynb
new file mode 100644
index 00000000..19025b51
--- /dev/null
+++ b/src/HHbbVV/resonant_studies/QuarkFractions.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "import matplotlib.ticker as mticker\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
+    "formatter.set_powerlimits((-3, 3))\n",
+    "plt.rcParams.update({\"font.size\": 20})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_dir = \"../../../plots/XHY/Apr12\"\n",
+    "_ = os.system(f\"mkdir -p {plot_dir}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = \"2017\"\n",
+    "\n",
+    "with open(f\"/eos/uscms/store/user/rkansal/bbVV/xhy/Apr4/{year}_combined.pkl\", \"rb\") as f:\n",
+    "    out = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mxmy(sample):\n",
+    "    mY = int(sample.split(\"-\")[-1])\n",
+    "    mX = int(sample.split(\"NMSSM_XToYHTo2W2BTo4Q2B_MX-\")[1].split(\"_\")[0])\n",
+    "\n",
+    "    return mX, mY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out[year][\"NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100\"][\"2017\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acceptances = np.array(\n",
+    "    [\n",
+    "        [\n",
+    "            *mxmy(s),\n",
+    "            sdict[year][s][\"cutflow\"][\"ak8bb_txbb\"] / sdict[year][s][\"cutflow\"][\"has_4q\"],\n",
+    "        ]\n",
+    "        for s, sdict in out[year].items()\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "fraction_4qs = np.array(\n",
+    "    [\n",
+    "        [\n",
+    "            *mxmy(s),\n",
+    "            sdict[year][s][\"h\"].values()[4] / sdict[year][s][\"h\"].sum(flow=True),\n",
+    "        ]\n",
+    "        for s, sdict in out[year].items()\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "fraction_3q4qs = np.array(\n",
+    "    [\n",
+    "        [\n",
+    "            *mxmy(s),\n",
+    "            sum(sdict[year][s][\"h\"].values()[3:]) / sdict[year][s][\"h\"].sum(flow=True),\n",
+    "        ]\n",
+    "        for s, sdict in out[year].items()\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scatter2d(arr, title, name):\n",
+    "    fig, ax = plt.subplots(figsize=(14, 12))\n",
+    "    mappable = plt.scatter(arr[:, 1], arr[:, 0], s=150, c=arr[:, 2], cmap=\"turbo\")\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel(r\"$m_Y$ (GeV)\")\n",
+    "    plt.ylabel(r\"$m_X$ (GeV)\")\n",
+    "    plt.colorbar(mappable)\n",
+    "    plt.savefig(name, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter2d(acceptances, \"Preselection Acceptance\", f\"{plot_dir}/acceptance.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter2d(fraction_4qs, \"Fraction of Y(VV) jets containing 4 quarks\", f\"{plot_dir}/fraction_4q.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f4q_gt50 = np.copy(fraction_4qs)\n",
+    "f4q_gt50[:, 2][f4q_gt50[:, 2] < 0.5] = 0\n",
+    "scatter2d(\n",
+    "    fraction_4qs[fraction_4qs[:, 2] >= 0.5],\n",
+    "    \"Fraction of Y(VV) jets containing 4 quarks > 0.5\",\n",
+    "    f\"{plot_dir}/fraction_4q_gt50.pdf\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scatter2d(\n",
+    "    fraction_3q4qs,\n",
+    "    \"Fraction of Y(VV) jets containing 3 or 4 quarks\",\n",
+    "    f\"{plot_dir}/fraction_3q4q.pdf\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_samples = [\n",
+    "    f\"NMSSM_XToYH_MX{int(mx)}_MY{int(my)}_HTo2bYTo2W_hadronicDecay\"\n",
+    "    for mx, my, frac in fraction_4qs\n",
+    "    if (frac >= 0.5 and my >= 120)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/scale_factors/VV_reweighting.ipynb b/src/HHbbVV/scale_factors/VV_reweighting.ipynb
index 87e5f51d..0056984e 100644
--- a/src/HHbbVV/scale_factors/VV_reweighting.ipynb
+++ b/src/HHbbVV/scale_factors/VV_reweighting.ipynb
@@ -2,20 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed loading compiled JECs\n"
      ]
     }
    ],
@@ -60,23 +54,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -84,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,36 +74,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJetAK15SubJet_nBHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJetAK15SubJet_nCHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJetAK15_nBHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJetAK15_nCHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJet_btagDDBvLV2 in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJet_btagDDCvBV2 in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJet_btagDDCvLV2 in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJet_nBHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch FatJet_nCHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch SubJet_nBHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/python39/lib/python3.9/site-packages/coffea/nanoevents/mapping/uproot.py:75: UserWarning: Found duplicate branch SubJet_nCHadrons in <TTree 'Events' (1813 branches) at 0x7f9ce407a790>, taking first instance\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
-   "source": [
-    "plot_dir = \"../../../plots/ScaleFactors/Feb1\"\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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",
+    "    # \"/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",
+    "    \"/eos/uscms//store/user/lpcpfnano/rkansal/v2_3/2016APV/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190/230323_173705/0000/nano_mc2016pre_13.root\",\n",
+    "    # \"../../../../data/2017_UL_nano/NMSSM_XToYH_MX1000_MY400_HTo2bYTo2W_hadronicDecay/nano_mc2017_101.root\",\n",
     "    schemaclass=nanoevents.NanoAODSchema,\n",
     ").events()"
    ]
@@ -138,23 +125,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def pad_val(\n",
     "    arr: ak.Array,\n",
@@ -193,23 +166,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "isData = False\n",
     "signGenWeights = None if isData else np.sign(events[\"genWeight\"])\n",
@@ -222,7 +181,8 @@
     "preselection_cut_vals = {\"pt\": 250, \"msd\": 20}\n",
     "num_jets = 2\n",
     "\n",
-    "fatjets = corrections.get_jec_jets(events, \"2018\")\n",
+    "# fatjets = corrections.get_jec_jets(events, \"2018\")\n",
+    "fatjets = events.FatJet\n",
     "\n",
     "preselection_cut = np.prod(\n",
     "    pad_val(\n",
@@ -255,23 +215,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "d_PDGID = 1\n",
     "u_PDGID = 2\n",
@@ -300,6 +246,7 @@
     "GEN_FLAGS = [\"fromHardProcess\", \"isLastCopy\"]\n",
     "\n",
     "FILL_NONE_VALUE = -99999\n",
+    "PAD_VAL = -99999\n",
     "\n",
     "skim_vars = {\n",
     "    \"eta\": \"Eta\",\n",
@@ -309,6 +256,14 @@
     "}"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nonresonant"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -378,25 +333,295 @@
     "}"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resonant"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# gen higgs and kids\n",
+    "higgs = events.GenPart[\n",
+    "    (abs(events.GenPart.pdgId) == HIGGS_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)\n",
+    "]\n",
+    "GenHiggsVars = {f\"GenHiggs{key}\": higgs[var].to_numpy() for (var, key) in skim_vars.items()}\n",
+    "is_bb = abs(higgs.children.pdgId) == b_PDGID\n",
+    "has_bb = ak.sum(ak.flatten(is_bb, axis=2), axis=1) == 2\n",
+    "\n",
+    "bb = ak.flatten(higgs.children[is_bb], axis=2)\n",
+    "# GenbbVars = {f\"Genbb{key}\": pad_val(bb[var], 2, axis=1) for (var, key) in skim_vars.items()}\n",
+    "\n",
+    "# gen Y and kids\n",
+    "Ys = events.GenPart[(abs(events.GenPart.pdgId) == Y_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)]\n",
+    "GenYVars = {f\"GenY{key}\": Ys[var].to_numpy() for (var, key) in skim_vars.items()}\n",
+    "is_VV = (abs(Ys.children.pdgId) == W_PDGID) + (abs(Ys.children.pdgId) == Z_PDGID)\n",
+    "has_VV = ak.sum(ak.flatten(is_VV, axis=2), axis=1) == 2\n",
+    "\n",
+    "add_selection(\"has_bbVV\", has_bb * has_VV, selection, cutflow, False, signGenWeights)\n",
+    "\n",
+    "VV = ak.flatten(Ys.children[is_VV], axis=2)\n",
+    "# GenVVVars = {f\"GenVV{key}\": VV[var][:, :2].to_numpy() for (var, key) in skim_vars.items()}\n",
+    "\n",
+    "VV_children = VV.children\n",
+    "quarks = abs(VV_children.pdgId) <= b_PDGID\n",
+    "all_q = ak.all(ak.all(quarks, axis=2), axis=1)\n",
+    "add_selection(\"all_q\", all_q, selection, cutflow, False, signGenWeights)\n",
+    "\n",
+    "V_has_2q = ak.count(VV_children.pdgId, axis=2) == 2\n",
+    "has_4q = ak.values_astype(ak.prod(V_has_2q, axis=1), bool)\n",
+    "add_selection(\"has_4q\", has_4q, selection, cutflow, False, signGenWeights)\n",
+    "\n",
+    "# Gen4qVars = {\n",
+    "#     f\"Gen4q{key}\": ak.to_numpy(\n",
+    "#         ak.fill_none(\n",
+    "#             ak.pad_none(\n",
+    "#                 ak.pad_none(VV_children[var], 2, axis=1, clip=True), 2, axis=2, clip=True\n",
+    "#             ),\n",
+    "#             PAD_VAL,\n",
+    "#         )\n",
+    "#     )\n",
+    "#     for (var, key) in skim_vars.items()\n",
+    "# }\n",
+    "\n",
+    "# fatjet gen matching\n",
+    "Hbb = ak.pad_none(higgs, 1, axis=1, clip=True)[:, 0]\n",
+    "HVV = ak.pad_none(Ys, 1, axis=1, clip=True)[:, 0]\n",
+    "\n",
+    "bbdr = fatjets[:, :2].delta_r(Hbb)\n",
+    "vvdr = fatjets[:, :2].delta_r(HVV)\n",
+    "\n",
+    "match_dR = 0.8\n",
+    "Hbb_match = bbdr <= match_dR\n",
+    "HVV_match = vvdr <= match_dR\n",
+    "\n",
+    "# overlap removal - in the case where fatjet is matched to both, match it only to the closest Higgs\n",
+    "Hbb_match = (Hbb_match * ~HVV_match) + (bbdr <= vvdr) * (Hbb_match * HVV_match)\n",
+    "HVV_match = (HVV_match * ~Hbb_match) + (bbdr > vvdr) * (Hbb_match * HVV_match)\n",
+    "\n",
+    "VVJets = ak.pad_none(fatjets[HVV_match], 1, axis=1)[:, 0]\n",
+    "quarkdrs = ak.flatten(VVJets.delta_r(VV_children), axis=2)\n",
+    "num_prongs = ak.sum(quarkdrs < match_dR, axis=1)\n",
+    "\n",
+    "# GenMatchingVars = {\n",
+    "#     \"ak8FatJetHbb\": pad_val(Hbb_match, 2, axis=1),\n",
+    "#     \"ak8FatJetHVV\": pad_val(HVV_match, 2, axis=1),\n",
+    "#     \"ak8FatJetHVVNumProngs\": ak.fill_none(num_prongs, PAD_VAL).to_numpy(),\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mJupyter cannot be started. Error attempting to locate Jupyter: Select an Interpreter to start Jupyter\n",
-      "\u001b[1;31mRun the following command to install 'jupyter and notebook' into the Python environment. \n",
-      "\u001b[1;31mCommand: 'python -m pip install jupyter notebook -U\n",
-      "\u001b[1;31mor\n",
-      "\u001b[1;31mconda install jupyter notebook -U'\n",
-      "\u001b[1;31mClick <a href='https://aka.ms/installJupyterForVSCode'>here</a> for more info."
-     ]
+     "data": {
+      "text/plain": [
+       "(array([410., 410., 390., 390.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 178.,\n",
+       "          0., 356.]),\n",
+       " array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5,\n",
+       "        11.5, 12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5,\n",
+       "        22.5, 23.5, 24.5]),\n",
+       " [<matplotlib.patches.Polygon at 0x7fd7b954ab00>])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(\n",
+    "    np.abs(ak.flatten(ak.flatten(ak.flatten(Ys.distinctChildren.children.pdgId)))),\n",
+    "    np.linspace(0.5, 24.5, 25),\n",
+    "    histtype=\"step\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([358., 118.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  87.]),\n",
+       " array([ 1. ,  3.3,  5.6,  7.9, 10.2, 12.5, 14.8, 17.1, 19.4, 21.7, 24. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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amhpNmjRJH374oe6++241NjYqKSlJkrRu3TotXbpUx48fV2xs7A8+biAQkMvlUmtrq5xOZ3enf0XX/v7vPb7P3vbv5wvDPQUAAL7X1f7+vqprWlpbWyVJbrdbklRbW6sLFy4oNzfXHjN27FilpaWppqZGklRTU6PMzEw7WCQpPz9fgUBABw8evJrpAACACDagu3fs7OzUokWLdNttt2n8+PGSJL/fr9jYWCUkJASNTUpKkt/vt8f8b7Bc2n5pW1fa29vV3t5u3w4EAt2dNgAAMFS3X2nx+Xz68ssv9fbbb/fkfLpUXl4ul8tlL6mpqb3+mAAAoG/pVrSUlJSosrJSO3bs0IgRI+z1Ho9H58+f16lTp4LGNzc3y+Px2GO++2miS7cvjfmusrIytba22suxY8e6M20AAGCwkKLFsiyVlJRo06ZN2r59u0aOHBm0PTs7WwMHDlRVVZW9rq6uTg0NDfJ6vZIkr9erAwcOqKWlxR6zbds2OZ1OZWRkdPm4DodDTqczaAEAAP1LSNe0+Hw+VVRU6IMPPlB8fLx9DYrL5dKgQYPkcrk0b948lZaWyu12y+l06tFHH5XX69WkSZMkSXl5ecrIyNCDDz6oVatWye/3a9myZfL5fHI4HD1/hAAAICKEFC1r166VJE2ZMiVo/euvv66HHnpIkvTSSy8pOjpaRUVFam9vV35+vl577TV7bExMjCorK7Vw4UJ5vV4NHjxYxcXFevrpp6/uSAAAQES7qu9pCRe+p+VyfE8LAKCvC+v3tAAAAPxUiBYAAGAEogUAABiBaAEAAEYgWgAAgBGIFgAAYASiBQAAGIFoAQAARiBaAACAEYgWAABgBKIFAAAYgWgBAABGIFoAAIARiBYAAGAEogUAABiBaAEAAEYgWgAAgBGIFgAAYASiBQAAGIFoAQAARiBaAACAEYgWAABgBKIFAAAYgWgBAABGIFoAAIARiBYAAGAEogUAABiBaAEAAEYgWgAAgBGIFgAAYASiBQAAGIFoAQAARiBaAACAEYgWAABgBKIFAAAYgWgBAABGIFoAAIARiBYAAGAEogUAABgh5GjZtWuXZsyYoZSUFEVFRen9998P2v7QQw8pKioqaJk2bVrQmJMnT2rOnDlyOp1KSEjQvHnzdObMmas6EAAAENlCjpa2tjZNmDBBa9asueKYadOmqampyV7eeuutoO1z5szRwYMHtW3bNlVWVmrXrl1asGBB6LMHAAD9xoBQ71BQUKCCgoLvHeNwOOTxeLrc9vXXX2vLli367LPPdMstt0iSXn31VU2fPl0vvviiUlJSQp0SAADoB3rlmpadO3cqMTFRY8aM0cKFC3XixAl7W01NjRISEuxgkaTc3FxFR0dr7969Xe6vvb1dgUAgaAEAAP1Lj0fLtGnT9Oabb6qqqkp//OMfVV1drYKCAnV0dEiS/H6/EhMTg+4zYMAAud1u+f3+LvdZXl4ul8tlL6mpqT09bQAA0MeF/PbQD5k9e7b9c2ZmprKysjRq1Cjt3LlTU6dO7dY+y8rKVFpaat8OBAKECwAA/Uyvf+T5uuuu07Bhw3TkyBFJksfjUUtLS9CYixcv6uTJk1e8DsbhcMjpdAYtAACgf+n1aPnmm2904sQJJScnS5K8Xq9OnTql2tpae8z27dvV2dmpnJyc3p4OAAAwVMhvD505c8Z+1USSjh49qn379sntdsvtdmvlypUqKiqSx+NRfX29Hn/8cY0ePVr5+fmSpHHjxmnatGmaP3++1q1bpwsXLqikpESzZ8/mk0MAAOCKQn6l5fPPP9fNN9+sm2++WZJUWlqqm2++WcuXL1dMTIz279+vX/7yl7rhhhs0b948ZWdn65///KccDoe9jw0bNmjs2LGaOnWqpk+frttvv11//vOfe+6oAABAxAn5lZYpU6bIsqwrbt+6desP7sPtdquioiLUhwYAAP0Yf3sIAAAYgWgBAABGIFoAAIARiBYAAGAEogUAABiBaAEAAEYgWgAAgBGIFgAAYASiBQAAGIFoAQAARiBaAACAEYgWAABgBKIFAAAYgWgBAABGIFoAAIARiBYAAGAEogUAABiBaAEAAEYgWgAAgBGIFgAAYASiBQAAGIFoAQAARiBaAACAEYgWAABghAHhngB6xrW//3u4pxCyfz9fGO4pAAAMwistAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADAC0QIAAIxAtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACCFHy65duzRjxgylpKQoKipK77//ftB2y7K0fPlyJScna9CgQcrNzdXhw4eDxpw8eVJz5syR0+lUQkKC5s2bpzNnzlzVgQAAgMgWcrS0tbVpwoQJWrNmTZfbV61apVdeeUXr1q3T3r17NXjwYOXn5+vcuXP2mDlz5ujgwYPatm2bKisrtWvXLi1YsKD7RwEAACLegFDvUFBQoIKCgi63WZal1atXa9myZbrnnnskSW+++aaSkpL0/vvva/bs2fr666+1ZcsWffbZZ7rlllskSa+++qqmT5+uF198USkpKVdxOAAAIFL16DUtR48eld/vV25urr3O5XIpJydHNTU1kqSamholJCTYwSJJubm5io6O1t69e7vcb3t7uwKBQNACAAD6lx6NFr/fL0lKSkoKWp+UlGRv8/v9SkxMDNo+YMAAud1ue8x3lZeXy+Vy2UtqampPThsAABjAiE8PlZWVqbW11V6OHTsW7ikBAICfWI9Gi8fjkSQ1NzcHrW9ubra3eTwetbS0BG2/ePGiTp48aY/5LofDIafTGbQAAID+pUejZeTIkfJ4PKqqqrLXBQIB7d27V16vV5Lk9Xp16tQp1dbW2mO2b9+uzs5O5eTk9OR0AABABAn500NnzpzRkSNH7NtHjx7Vvn375Ha7lZaWpkWLFunZZ5/V9ddfr5EjR+rJJ59USkqKZs6cKUkaN26cpk2bpvnz52vdunW6cOGCSkpKNHv2bD45BAAArijkaPn888/1i1/8wr5dWloqSSouLtb69ev1+OOPq62tTQsWLNCpU6d0++23a8uWLbrmmmvs+2zYsEElJSWaOnWqoqOjVVRUpFdeeaUHDgcAAESqKMuyrHBPIlSBQEAul0utra29cn3Ltb//e4/vE5f79/OF4Z4CAOAndLW/v4349BAAAADRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMEPL3tAAAgB/P1K/R6ItfS8ErLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADAC0QIAAIxAtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADAC0QIAAIxAtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACM0OPR8tRTTykqKipoGTt2rL393Llz8vl8Gjp0qIYMGaKioiI1Nzf39DQAAECE6ZVXWm688UY1NTXZyyeffGJvW7x4sTZv3qyNGzequrpajY2NmjVrVm9MAwAARJABvbLTAQPk8XguW9/a2qq//OUvqqio0J133ilJev311zVu3Djt2bNHkyZN6o3pAACACNArr7QcPnxYKSkpuu666zRnzhw1NDRIkmpra3XhwgXl5ubaY8eOHau0tDTV1NRccX/t7e0KBAJBCwAA6F96PFpycnK0fv16bdmyRWvXrtXRo0d1xx136PTp0/L7/YqNjVVCQkLQfZKSkuT3+6+4z/LycrlcLntJTU3t6WkDAIA+rsffHiooKLB/zsrKUk5OjtLT0/Xuu+9q0KBB3dpnWVmZSktL7duBQIBwAQCgn+n1jzwnJCTohhtu0JEjR+TxeHT+/HmdOnUqaExzc3OX18Bc4nA45HQ6gxYAANC/9Hq0nDlzRvX19UpOTlZ2drYGDhyoqqoqe3tdXZ0aGhrk9Xp7eyoAAMBgPf720GOPPaYZM2YoPT1djY2NWrFihWJiYnT//ffL5XJp3rx5Ki0tldvtltPp1KOPPiqv18snhwAAwPfq8Wj55ptvdP/99+vEiRMaPny4br/9du3Zs0fDhw+XJL300kuKjo5WUVGR2tvblZ+fr9dee62npwEAACJMj0fL22+//b3br7nmGq1Zs0Zr1qzp6YcGAAARjL89BAAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADAC0QIAAIxAtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADAC0QIAAIxAtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADAC0QIAAIxAtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAI4Q1WtasWaNrr71W11xzjXJycvTpp5+GczoAAKAPC1u0vPPOOyotLdWKFSv0xRdfaMKECcrPz1dLS0u4pgQAAPqwsEXLn/70J82fP18PP/ywMjIytG7dOsXFxemvf/1ruKYEAAD6sAHheNDz58+rtrZWZWVl9rro6Gjl5uaqpqbmsvHt7e1qb2+3b7e2tkqSAoFAr8yvs/1sr+wXwXrr/AFAX2Lq75Te+G/0pX1altWt+4clWr799lt1dHQoKSkpaH1SUpIOHTp02fjy8nKtXLnysvWpqam9Nkf0PtfqcM8AAHAlvfnf6NOnT8vlcoV8v7BES6jKyspUWlpq3+7s7NTJkyc1dOhQnT59WqmpqTp27JicTmcYZ9m/BQIBzkMfwHnoGzgPfQPnoW/43/MQHx+v06dPKyUlpVv7Cku0DBs2TDExMWpubg5a39zcLI/Hc9l4h8Mhh8MRtC4hIUGSFBUVJUlyOp38o+wDOA99A+ehb+A89A2ch77h0nnozissl4TlQtzY2FhlZ2erqqrKXtfZ2amqqip5vd5wTAkAAPRxYXt7qLS0VMXFxbrlllt06623avXq1Wpra9PDDz8crikBAIA+LGzRct999+n48eNavny5/H6/brrpJm3ZsuWyi3N/iMPh0IoVKy57+wg/Lc5D38B56Bs4D30D56Fv6MnzEGV193NHAAAAPyH+9hAAADAC0QIAAIxAtAAAACMQLQAAwAjGR8uaNWt07bXX6pprrlFOTo4+/fTTcE+pX3nqqacUFRUVtIwdOzbc04p4u3bt0owZM5SSkqKoqCi9//77Qdsty9Ly5cuVnJysQYMGKTc3V4cPHw7PZCPYD52Hhx566LLnx7Rp08Iz2QhVXl6uiRMnKj4+XomJiZo5c6bq6uqCxpw7d04+n09Dhw7VkCFDVFRUdNmXm+Lq/JjzMGXKlMueD4888khIj2N0tLzzzjsqLS3VihUr9MUXX2jChAnKz89XS0tLuKfWr9x4441qamqyl08++STcU4p4bW1tmjBhgtasWdPl9lWrVumVV17RunXrtHfvXg0ePFj5+fk6d+7cTzzTyPZD50GSpk2bFvT8eOutt37CGUa+6upq+Xw+7dmzR9u2bdOFCxeUl5entrY2e8zixYu1efNmbdy4UdXV1WpsbNSsWbPCOOvI82POgyTNnz8/6PmwatWq0B7IMtitt95q+Xw++3ZHR4eVkpJilZeXh3FW/cuKFSusCRMmhHsa/Zoka9OmTfbtzs5Oy+PxWC+88IK97tSpU5bD4bDeeuutMMywf/juebAsyyouLrbuueeesMynv2ppabEkWdXV1ZZl/fff/sCBA62NGzfaY77++mtLklVTUxOuaUa8754Hy7Ks//u//7N++9vfXtV+jX2l5fz586qtrVVubq69Ljo6Wrm5uaqpqQnjzPqfw4cPKyUlRdddd53mzJmjhoaGcE+pXzt69Kj8fn/Qc8PlciknJ4fnRhjs3LlTiYmJGjNmjBYuXKgTJ06Ee0oRrbW1VZLkdrslSbW1tbpw4ULQ82Hs2LFKS0vj+dCLvnseLtmwYYOGDRum8ePHq6ysTGfPng1pv0b8leeufPvtt+ro6LjsG3STkpJ06NChMM2q/8nJydH69es1ZswYNTU1aeXKlbrjjjv05ZdfKj4+PtzT65f8fr8kdfncuLQNP41p06Zp1qxZGjlypOrr6/XEE0+ooKBANTU1iomJCff0Ik5nZ6cWLVqk2267TePHj5f03+dDbGys/Ud2L+H50Hu6Og+S9MADDyg9PV0pKSnav3+/li5dqrq6Or333ns/et/GRgv6hoKCAvvnrKws5eTkKD09Xe+++67mzZsXxpkB4Td79mz758zMTGVlZWnUqFHauXOnpk6dGsaZRSafz6cvv/yS6+rC7ErnYcGCBfbPmZmZSk5O1tSpU1VfX69Ro0b9qH0b+/bQsGHDFBMTc9kV4M3NzfJ4PGGaFRISEnTDDTfoyJEj4Z5Kv3Xp3z/Pjb7nuuuu07Bhw3h+9IKSkhJVVlZqx44dGjFihL3e4/Ho/PnzOnXqVNB4ng+940rnoSs5OTmSFNLzwdhoiY2NVXZ2tqqqqux1nZ2dqqqqktfrDePM+rczZ86ovr5eycnJ4Z5KvzVy5Eh5PJ6g50YgENDevXt5boTZN998oxMnTvD86EGWZamkpESbNm3S9u3bNXLkyKDt2dnZGjhwYNDzoa6uTg0NDTwfetAPnYeu7Nu3T5JCej4Y/fZQaWmpiouLdcstt+jWW2/V6tWr1dbWpocffjjcU+s3HnvsMc2YMUPp6elqbGzUihUrFBMTo/vvvz/cU4toZ86cCfp/J0ePHtW+ffvkdruVlpamRYsW6dlnn9X111+vkSNH6sknn1RKSopmzpwZvklHoO87D263WytXrlRRUZE8Ho/q6+v1+OOPa/To0crPzw/jrCOLz+dTRUWFPvjgA8XHx9vXqbhcLg0aNEgul0vz5s1TaWmp3G63nE6nHn30UXm9Xk2aNCnMs48cP3Qe6uvrVVFRoenTp2vo0KHav3+/Fi9erMmTJysrK+vHP9BVffaoD3j11VettLQ0KzY21rr11lutPXv2hHtK/cp9991nJScnW7GxsdbPf/5z67777rOOHDkS7mlFvB07dliSLluKi4sty/rvx56ffPJJKykpyXI4HNbUqVOturq68E46An3feTh79qyVl5dnDR8+3Bo4cKCVnp5uzZ8/3/L7/eGedkTp6n9/Sdbrr79uj/nPf/5j/eY3v7F+9rOfWXFxcdavfvUrq6mpKXyTjkA/dB4aGhqsyZMnW26323I4HNbo0aOtJUuWWK2trSE9TtT/fzAAAIA+zdhrWgAAQP9CtAAAACMQLQAAwAhECwAAMALRAgAAjEC0AAAAIxAtAADACEQLAAAwAtECAACMQLQAAAAjEC0AAMAIRAsAADDC/wPxthJ0G063mwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
+   "source": [
+    "plt.hist(\n",
+    "    np.abs(\n",
+    "        ak.flatten(\n",
+    "            ak.flatten(ak.flatten(VV.children[np.abs(VV.children.pdgId) > 5].children.pdgId))\n",
+    "        )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([148.,   0.,   0.,   0., 504., 504.,   0.,   0.,   0., 296.]),\n",
+       " array([-24. , -19.2, -14.4,  -9.6,  -4.8,   0. ,   4.8,   9.6,  14.4,\n",
+       "         19.2,  24. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(ak.flatten(ak.flatten(VV.children.pdgId, axis=1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ak.any(np.abs(ddYs.distinctChildren.children.pdgId) > 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  0., 514., 527., 506., 501.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+       "          0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
+       "        148.,   0.]),\n",
+       " array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
+       "        13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.]),\n",
+       " [<matplotlib.patches.Polygon at 0x7fd7b927dba0>])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(\n",
+    "    np.abs(ak.flatten(ak.flatten(VV.distinctChildren.pdgId, axis=1))),\n",
+    "    np.linspace(0, 24, 25),\n",
+    "    histtype=\"step\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['eta',\n",
+       " 'mass',\n",
+       " 'phi',\n",
+       " 'pt',\n",
+       " 'genPartIdxMother',\n",
+       " 'pdgId',\n",
+       " 'status',\n",
+       " 'statusFlags',\n",
+       " 'genPartIdxMotherG',\n",
+       " 'distinctParentIdxG',\n",
+       " 'childrenIdxG',\n",
+       " 'distinctChildrenIdxG']"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Ys.children.fields"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "select = selection.all(*selection.names)\n",
     "presel_events = events[selection.all(*selection.names)]"
@@ -980,7 +1205,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.9.15"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/scale_factors/corrections.py b/src/HHbbVV/scale_factors/corrections.py
index 54666bf0..da4a7113 100644
--- a/src/HHbbVV/scale_factors/corrections.py
+++ b/src/HHbbVV/scale_factors/corrections.py
@@ -33,6 +33,7 @@
     "electron": ["EGM", "electron.json.gz"],
     "pileup": ["LUM", "puWeights.json.gz"],
     "jec": ["JME", "fatJet_jerc.json.gz"],
+    "jmar": ["JME", "jmar.json.gz"],
 }
 
 
diff --git a/src/HHbbVV/scale_factors/top_analysis.ipynb b/src/HHbbVV/scale_factors/top_analysis.ipynb
deleted file mode 100644
index 4435c42d..00000000
--- a/src/HHbbVV/scale_factors/top_analysis.ipynb
+++ /dev/null
@@ -1,951 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import utils\n",
-    "import plotting\n",
-    "import numpy as np\n",
-    "import warnings\n",
-    "import pandas as pd\n",
-    "from pandas.core.common import SettingWithCopyWarning\n",
-    "from sample_labels import sig_key, data_key\n",
-    "\n",
-    "# ignore these because they don't seem to apply\n",
-    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
-    "\n",
-    "from PyPDF2 import PdfFileMerger\n",
-    "\n",
-    "from copy import deepcopy\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mplhep as hep\n",
-    "import matplotlib.ticker as mticker\n",
-    "\n",
-    "plt.style.use(hep.style.CMS)\n",
-    "hep.style.use(\"CMS\")\n",
-    "formatter = mticker.ScalarFormatter(useMathText=True)\n",
-    "formatter.set_powerlimits((-3, 3))\n",
-    "plt.rcParams.update({\"font.size\": 16})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_dir = \"../../../plots/ttsfs/23Feb1\"\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "_ = os.system(f\"mkdir -p {plot_dir}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Finding QCD samples\n",
-      "Loading QCD_HT300to500\n",
-      "Loaded 0 entries\n",
-      "Loading QCD_HT200to300\n",
-      "Loaded 0 entries\n",
-      "Loading QCD_HT700to1000\n",
-      "Loaded 490 entries\n",
-      "Loading QCD_HT1000to1500\n",
-      "Loaded 517 entries\n",
-      "Loading QCD_HT100to200\n",
-      "Loaded 0 entries\n",
-      "Loading QCD_HT2000toInf\n",
-      "Loaded 251 entries\n",
-      "Loading QCD_HT1500to2000\n",
-      "Loaded 585 entries\n",
-      "Loading QCD_HT50to100\n",
-      "Loaded 0 entries\n",
-      "Loading QCD_HT500to700\n",
-      "Loaded 0 entries\n",
-      "Finding Single Top samples\n",
-      "Loading ST_tW_antitop_5f_NoFullyHadronicDecays\n",
-      "Loaded 14575 entries\n",
-      "Loading ST_tW_top_5f_NoFullyHadronicDecays\n",
-      "Loaded 14842 entries\n",
-      "Loading ST_t-channel_top_4f_InclusiveDecays\n",
-      "Loaded 24887 entries\n",
-      "Loading ST_tW_top_5f_inclusiveDecays\n",
-      "Loaded 5649 entries\n",
-      "Loading ST_tW_antitop_5f_inclusiveDecays\n",
-      "Loaded 5431 entries\n",
-      "Loading ST_s-channel_4f_leptonDecays\n",
-      "Loaded 14524 entries\n",
-      "Loading ST_t-channel_top_5f_InclusiveDecays\n",
-      "Loaded 23014 entries\n",
-      "Loading ST_t-channel_antitop_4f_InclusiveDecays\n",
-      "Loaded 9837 entries\n",
-      "Loading ST_t-channel_antitop_5f_InclusiveDecays\n",
-      "Loaded 7840 entries\n",
-      "Finding TTSL samples\n",
-      "Loading TTToSemiLeptonic\n",
-      "Loaded 221952 entries\n",
-      "Finding TTbar samples\n",
-      "Loading TTTo2L2Nu\n",
-      "Loaded 196159 entries\n",
-      "Loading TTToHadronic\n",
-      "Loaded 712 entries\n",
-      "Finding W+Jets samples\n",
-      "Loading WJetsToLNu_HT-100To200\n",
-      "Loaded 0 entries\n",
-      "Loading WJetsToLNu_HT-800To1200\n",
-      "Loaded 42415 entries\n",
-      "Loading WJetsToLNu_HT-1200To2500\n",
-      "Loaded 62060 entries\n",
-      "Loading WJetsToLNu_HT-200To400\n",
-      "Loaded 75 entries\n",
-      "Loading WJetsToLNu_HT-70To100\n",
-      "Loaded 0 entries\n",
-      "Loading WJetsToLNu_HT-600To800\n",
-      "Loaded 16901 entries\n",
-      "Loading WJetsToLNu_HT-2500ToInf\n",
-      "Loaded 21091 entries\n",
-      "Loading WJetsToLNu_HT-400To600\n",
-      "Loaded 1736 entries\n",
-      "Finding Diboson samples\n",
-      "Loading WW\n",
-      "Loaded 100 entries\n",
-      "Loading ZZ\n",
-      "Loaded 19 entries\n",
-      "Loading WZ\n",
-      "Loaded 141 entries\n",
-      "Finding Data samples\n",
-      "Loading SingleMuon_Run2018A\n",
-      "Loaded 11967 entries\n",
-      "Loading SingleMuon_Run2018C\n",
-      "Loaded 5654 entries\n",
-      "Loading SingleMuon_Run2018D\n",
-      "Loaded 26219 entries\n",
-      "Loading SingleMuon_Run2018B\n",
-      "Loaded 5952 entries\n"
-     ]
-    }
-   ],
-   "source": [
-    "samples = {\n",
-    "    \"QCD\": \"QCD\",\n",
-    "    \"Single Top\": \"ST\",\n",
-    "    \"TTSL\": \"TTToSemiLeptonic\",\n",
-    "    \"TTbar\": [\"TTTo2L2Nu\", \"TTToHadronic\"],\n",
-    "    \"W+Jets\": \"WJets\",\n",
-    "    \"Diboson\": [\"WW\", \"WZ\", \"ZZ\"],\n",
-    "    \"Data\": \"SingleMuon\",\n",
-    "}\n",
-    "\n",
-    "top_matched_key = \"TT Top Matched\"\n",
-    "\n",
-    "# data_dir = \"../../../../data/ttsfs/Dec22/\"\n",
-    "data_dir = \"../../../../data/ttsfs/Jan31/\"\n",
-    "year = \"2018\"\n",
-    "\n",
-    "# filters = [(\"('ak8FatJetPt', '0')\", \">=\", 500)]\n",
-    "filters = None\n",
-    "\n",
-    "events_dict = utils.load_samples(data_dir, samples, year, filters=filters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('ak4_jet', 1073887.0),\n",
-       "             ('ak8_jet', 1798382.0),\n",
-       "             ('muon', 11213765.0),\n",
-       "             ('npvsGood', 16197479.0),\n",
-       "             ('all', 126125558),\n",
-       "             ('met', 6254499.0),\n",
-       "             ('top_has_bs', 1073061.0),\n",
-       "             ('trigger', 16197479.0),\n",
-       "             ('lepW', 6254499.0)])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_dir = \"../../../../data/ttsfs/Jan17/\"\n",
-    "sample = \"TTToSemiLeptonic\"\n",
-    "\n",
-    "# utils.get_cutflow(f\"{data_dir}/{year}/TTToSemiLeptonic/pickles\", year, \"TTToSemiLeptonic\")\n",
-    "utils.get_cutflow(f\"{data_dir}/{year}/{sample}/pickles\", year, sample)\n",
-    "\n",
-    "# import pickle\n",
-    "\n",
-    "# for i in range(68):\n",
-    "#     with open(f\"{data_dir}/{year}/{sample}/pickles/out_{i}.pkl\", \"rb\") as file:\n",
-    "#         out_dict = pickle.load(file)[year][sample]['cutflow']\n",
-    "#         print(out_dict[\"all\"])\n",
-    "# utils.get_cutflow(f\"{data_dir}/{year}/{sample}/pickles\", year, sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('ak4_jet', 214814.0),\n",
-       "             ('ak8_jet', 313658.0),\n",
-       "             ('muon', 11213765.0),\n",
-       "             ('npvsGood', 16197479.0),\n",
-       "             ('all', 126125558),\n",
-       "             ('met', 6254499.0),\n",
-       "             ('top_has_bs', 214620.0),\n",
-       "             ('trigger', 16197479.0),\n",
-       "             ('lepW', 6254499.0)])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_dir = \"../../../../data/ttsfs/Jan19/\"\n",
-    "\n",
-    "# utils.get_cutflow(f\"{data_dir}/{year}/TTToSemiLeptonic/pickles\", year, \"TTToSemiLeptonic\")\n",
-    "utils.get_cutflow(f\"{data_dir}/{year}/{sample}/pickles\", year, sample)\n",
-    "\n",
-    "# import pickle\n",
-    "\n",
-    "# with open(f\"{data_dir}/{year}/{sample}/pickles/out_1.pkl\", \"rb\") as file:\n",
-    "#     out_dict = pickle.load(file)[year][sample]['cutflow']\n",
-    "#     print(out_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "events_dict[top_matched_key] = events_dict[\"TTSL\"].loc[events_dict[\"TTSL\"][\"top_matched\"][0] == 1]\n",
-    "events_dict[\"TT W Matched\"] = events_dict[\"TTSL\"].loc[events_dict[\"TTSL\"][\"w_matched\"][0] == 1]\n",
-    "events_dict[\"TT Unmatched\"] = pd.concat(\n",
-    "    [events_dict[\"TTbar\"], events_dict[\"TTSL\"].loc[events_dict[\"TTSL\"][\"unmatched\"][0] == 1]]\n",
-    ")\n",
-    "# del events_dict[\"TTSL\"]\n",
-    "# del events_dict[\"TTbar\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# normalize scale factors to average to 1\n",
-    "for key in [\"lp_sf\", \"lp_sf_lnN\", \"lp_sf_sys_down\", \"lp_sf_sys_up\"]:\n",
-    "    # cut off at 10\n",
-    "    events_dict[top_matched_key].loc[:, key] = np.minimum(\n",
-    "        events_dict[top_matched_key].loc[:, key].values, 10\n",
-    "    )\n",
-    "    mean_lp_sfs = np.mean(np.nan_to_num(events_dict[top_matched_key][key]), axis=0)\n",
-    "    events_dict[top_matched_key].loc[:, key] = (\n",
-    "        np.nan_to_num(events_dict[top_matched_key].loc[:, key]) / mean_lp_sfs\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Scale factor distribution')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.rcParams.update({\"font.size\": 24})\n",
-    "plt.figure(figsize=(12, 12))\n",
-    "_ = plt.hist(\n",
-    "    events_dict[top_matched_key][\"lp_sf\"][10].values,\n",
-    "    np.logspace(-4, 2, 101, base=10),\n",
-    "    histtype=\"step\",\n",
-    ")\n",
-    "plt.xscale(\"log\")\n",
-    "# plt.yscale(\"log\")\n",
-    "plt.xlabel(\"LP SF\")\n",
-    "plt.title(\"Scale factor distribution\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for key in events_dict:\n",
-    "    events_dict[key] = events_dict[key][events_dict[key][\"ak8FatJetPt\"][0] >= 500]\n",
-    "    events_dict[key] = events_dict[key][events_dict[key][\"ak8FatJetMsd\"][0] >= 125]\n",
-    "    events_dict[key] = events_dict[key][events_dict[key][\"ak8FatJetMsd\"][0] <= 225]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "events = events_dict[top_matched_key]\n",
-    "sj_matching_unc = (\n",
-    "    (np.sum(events[\"lp_sf_unmatched_quarks\"]) / (len(events) * 3))\n",
-    "    + (np.sum(events[\"lp_sf_double_matched_event\"]) / (len(events)))\n",
-    ").values[0]\n",
-    "sj_pt_unc = (np.sum(events[\"lp_sf_num_sjpt_gt350\"]) / (len(events))).values[0] * 0.21"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sj_matching_unc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sj_pt_unc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_samples = [\n",
-    "    \"QCD\",\n",
-    "    \"Diboson\",\n",
-    "    \"Single Top\",\n",
-    "    \"W+Jets\",\n",
-    "    \"TT Unmatched\",\n",
-    "    \"TT W Matched\",\n",
-    "    top_matched_key,\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pre plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# {var: (bins, label)}\n",
-    "plot_vars = {\n",
-    "    \"ak8FatJetMass\": ([20, 125, 225], r\"$m_{SD}$ (GeV)\"),\n",
-    "    \"ak8FatJetPt\": ([20, 500, 1200], r\"$p_T$ (GeV)\"),\n",
-    "    \"ak8FatJetnPFCands\": ([20, 0, 120], r\"# of PF Candidates\"),\n",
-    "    \"ak8FatJetParticleNet_Th4q\": ([20, 0.6, 1], r\"ParticleNet $T_{H4q}$ Non-MD\"),\n",
-    "    \"ak8FatJetParTMD_THWW4q\": ([20, 0.6, 1], r\"ParT $T_{HWW4q}$ MD\"),\n",
-    "    \"tau21\": ([20, 0.04, 0.8], r\"$\\tau_{21}$\"),\n",
-    "    \"tau32\": ([20, 0.2, 1], r\"$\\tau_{32}$\"),\n",
-    "    \"tau43\": ([20, 0.42, 1], r\"$\\tau_{43}$\"),\n",
-    "    \"tau42\": ([20, 0, 1], r\"$\\tau_{42}$\"),\n",
-    "    \"tau41\": ([20, 0, 1], r\"$\\tau_{41}$\"),\n",
-    "}\n",
-    "\n",
-    "pre_hists = {}\n",
-    "\n",
-    "for var, (bins, label) in plot_vars.items():\n",
-    "    if var not in pre_hists:\n",
-    "        pre_hists[var] = utils.singleVarHistNoMask(\n",
-    "            events_dict, var, bins, label, weight_key=\"weight\"\n",
-    "        )\n",
-    "\n",
-    "merger_pre_plots = PdfFileMerger()\n",
-    "\n",
-    "for var, var_hist in pre_hists.items():\n",
-    "    name = f\"{plot_dir}/pre_{var}.pdf\"\n",
-    "    plotting.ratioLinePlot(\n",
-    "        var_hist,\n",
-    "        plot_samples,\n",
-    "        year,\n",
-    "        bg_err=None,\n",
-    "        name=name,\n",
-    "    )\n",
-    "    merger_pre_plots.append(name)\n",
-    "\n",
-    "merger_pre_plots.write(f\"{plot_dir}/PrePlots.pdf\")\n",
-    "merger_pre_plots.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Post plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "post_hists = {}\n",
-    "post_hists_err = {}\n",
-    "uncs_dict = {}\n",
-    "\n",
-    "events = events_dict[top_matched_key]\n",
-    "\n",
-    "for var, (bins, label) in plot_vars.items():\n",
-    "    # if var not in post_hists:\n",
-    "    toy_hists = []\n",
-    "    for i in range(events[\"lp_sf\"].shape[1]):\n",
-    "        toy_hists.append(\n",
-    "            np.histogram(\n",
-    "                events[var][0].values.squeeze(),\n",
-    "                np.linspace(*bins[1:], bins[0] + 1),\n",
-    "                weights=events[\"weight\"][0].values * events[\"lp_sf\"][i].values,\n",
-    "            )[0]\n",
-    "        )\n",
-    "\n",
-    "    sys_up_down = []\n",
-    "    for key in [\"lp_sf_sys_up\", \"lp_sf_sys_down\"]:\n",
-    "        sys_up_down.append(\n",
-    "            np.histogram(\n",
-    "                events[var][0].values.squeeze(),\n",
-    "                np.linspace(*bins[1:], bins[0] + 1),\n",
-    "                weights=events[\"weight\"][0].values * events[key][0].values,\n",
-    "            )[0]\n",
-    "        )\n",
-    "\n",
-    "    nom_vals = toy_hists[0]  # first column are nominal values\n",
-    "\n",
-    "    uncs = {\n",
-    "        \"stat_unc\": np.minimum(nom_vals, np.std(toy_hists[1:], axis=0)),  # cap at 100% unc\n",
-    "        \"syst_rat_unc\": np.minimum(nom_vals, (np.abs(sys_up_down[0] - sys_up_down[1])) / 2),\n",
-    "        \"syst_sjm_unc\": nom_vals * sj_matching_unc,\n",
-    "        \"syst_sjpt_unc\": nom_vals * sj_pt_unc,\n",
-    "    }\n",
-    "\n",
-    "    uncs_dict[var] = uncs\n",
-    "\n",
-    "    unc = np.linalg.norm(list(uncs.values()), axis=0)\n",
-    "\n",
-    "    thist = deepcopy(pre_hists[var])\n",
-    "    top_matched_key_index = np.where(np.array(list(thist.axes[0])) == top_matched_key)[0][0]\n",
-    "    thist.view(flow=False)[top_matched_key_index, :].value = nom_vals\n",
-    "    post_hists[var] = thist\n",
-    "    post_hists_err[var] = unc\n",
-    "\n",
-    "\n",
-    "merger_post_plots = PdfFileMerger()\n",
-    "\n",
-    "for var, var_hist in post_hists.items():\n",
-    "    name = f\"{plot_dir}/post_{var}.pdf\"\n",
-    "    plotting.ratioLinePlot(\n",
-    "        var_hist,\n",
-    "        plot_samples,\n",
-    "        year,\n",
-    "        bg_err=post_hists_err[var],\n",
-    "        name=name,\n",
-    "    )\n",
-    "    merger_post_plots.append(name)\n",
-    "\n",
-    "merger_post_plots.write(f\"{plot_dir}/PostPlots.pdf\")\n",
-    "merger_post_plots.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Post LnN Plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "post_lnN_hists = {}\n",
-    "post_lnN_hists_err = {}\n",
-    "uncs_lnN_dict = {}\n",
-    "\n",
-    "events = events_dict[top_matched_key]\n",
-    "\n",
-    "for var, (bins, label) in plot_vars.items():\n",
-    "    if var not in post_lnN_hists:\n",
-    "        toy_hists = []\n",
-    "        for i in range(events[\"lp_sf_lnN\"].shape[1]):\n",
-    "            toy_hists.append(\n",
-    "                np.histogram(\n",
-    "                    events[var][0].values.squeeze(),\n",
-    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
-    "                    weights=events[\"weight\"][0].values * events[\"lp_sf_lnN\"][i].values,\n",
-    "                )[0]\n",
-    "            )\n",
-    "\n",
-    "        sys_up_down = []\n",
-    "        for key in [\"lp_sf_sys_up\", \"lp_sf_sys_down\"]:\n",
-    "            sys_up_down.append(\n",
-    "                np.histogram(\n",
-    "                    events[var][0].values.squeeze(),\n",
-    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
-    "                    weights=events[\"weight\"][0].values * events[key][0].values,\n",
-    "                )[0]\n",
-    "            )\n",
-    "\n",
-    "        nom_vals = toy_hists[0]  # first column are nominal values\n",
-    "\n",
-    "        uncs = {\n",
-    "            \"stat_unc\": np.minimum(nom_vals, np.std(toy_hists[1:], axis=0)),  # cap at 100% unc\n",
-    "            \"syst_rat_unc\": np.minimum(nom_vals, (np.abs(sys_up_down[0] - sys_up_down[1])) / 2),\n",
-    "            \"syst_sjm_unc\": nom_vals * sj_matching_unc,\n",
-    "            \"syst_sjpt_unc\": nom_vals * sj_pt_unc,\n",
-    "        }\n",
-    "\n",
-    "        uncs_lnN_dict[var] = uncs\n",
-    "\n",
-    "        unc = np.linalg.norm(list(uncs.values()), axis=0)\n",
-    "\n",
-    "        thist = deepcopy(pre_hists[var])\n",
-    "        top_matched_key_index = np.where(np.array(list(thist.axes[0])) == top_matched_key)[0][0]\n",
-    "        thist.view(flow=False)[top_matched_key_index, :].value = nom_vals\n",
-    "        post_lnN_hists[var] = thist\n",
-    "\n",
-    "        post_lnN_hists_err[var] = unc\n",
-    "\n",
-    "\n",
-    "merger_post_plots = PdfFileMerger()\n",
-    "\n",
-    "for var, var_hist in post_lnN_hists.items():\n",
-    "    name = f\"{plot_dir}/postlnN_{var}.pdf\"\n",
-    "    plotting.ratioLinePlot(\n",
-    "        var_hist,\n",
-    "        plot_samples,\n",
-    "        year,\n",
-    "        bg_err=post_lnN_hists_err[var],\n",
-    "        name=name,\n",
-    "    )\n",
-    "    merger_post_plots.append(name)\n",
-    "\n",
-    "merger_post_plots.write(f\"{plot_dir}/PostLnNPlots.pdf\")\n",
-    "merger_post_plots.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SF:  0.9454609916441871\n",
-      "Uncs:  {'stat_unc': 7.671583522379341, 'syst_rat_unc': 11.47522796374204, 'syst_sjm_unc': 6.821087463337595, 'syst_sjpt_unc': 5.308039407385124}\n",
-      "Combined:  16.286085075718603\n",
-      "Abs:  0.15397858145690504\n"
-     ]
-    }
-   ],
-   "source": [
-    "binn = -1\n",
-    "tvar = \"ak8FatJetParTMD_THWW4q\"\n",
-    "pre_vals = pre_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
-    "nom_vals = post_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
-    "unc = post_hists_err[tvar]\n",
-    "print(\"SF: \", nom_vals[binn] / pre_vals[binn])\n",
-    "print(\"Uncs: \", {key: val[binn] / nom_vals[binn] * 100 for key, val in uncs_dict[tvar].items()})\n",
-    "print(\"Combined: \", unc[binn] / nom_vals[binn] * 100)\n",
-    "print(\"Abs: \", unc[binn] / pre_vals[binn])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SF:  0.9401747401835858\n",
-      "Uncs:  {'stat_unc': 7.714717971612027, 'syst_rat_unc': 11.539748885216932, 'syst_sjm_unc': 6.859439890843695, 'syst_sjpt_unc': 5.337884530712665}\n",
-      "Combined:  15.744204462622047\n",
-      "Abs:  0.14802303340042935\n"
-     ]
-    }
-   ],
-   "source": [
-    "binn = -1\n",
-    "tvar = \"ak8FatJetParTMD_THWW4q\"\n",
-    "pre_vals = pre_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
-    "nom_vals = post_lnN_hists[tvar].view(flow=False)[top_matched_key_index, :].value\n",
-    "unc = post_lnN_hists_err[tvar]\n",
-    "print(\"SF: \", nom_vals[binn] / pre_vals[binn])\n",
-    "print(\"Uncs: \", {key: val[binn] / nom_vals[binn] * 100 for key, val in uncs_dict[tvar].items()})\n",
-    "print(\"Combined: \", unc[binn] / nom_vals[binn] * 100)\n",
-    "print(\"Abs: \", unc[binn] / pre_vals[binn])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Ratio plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/boost_histogram/_internal/hist.py:863: UserWarning: List indexing selection is experimental. Removed bins are not placed in overflow.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tvar = \"ak8FatJetParTMD_THWW4q\"\n",
-    "\n",
-    "# plt.figure(figsize=(12, 12))\n",
-    "# hists = pre_hists[tvar]\n",
-    "# bg_tot = np.sum(hists[plot_samples, :].values(), axis=0)\n",
-    "# mcdata_ratio = (bg_tot + 1e-5) / hists[data_key, :].values()\n",
-    "# _ = plt.hist(mcdata_ratio - 1, np.linspace(-0.5, 0.5, 10), histtype='step')\n",
-    "\n",
-    "plt.figure(figsize=(12, 12))\n",
-    "hists = post_hists[tvar]\n",
-    "bg_tot = np.sum(hists[plot_samples, :].values(), axis=0)\n",
-    "data_tot = hists[data_key, :].values()\n",
-    "unc = post_hists_err[tvar]\n",
-    "mcdata_ratio = (bg_tot) / data_tot\n",
-    "_ = plt.hist(((bg_tot - data_tot) / (unc))[10:], np.linspace(-6.5, 4.5, 23), histtype=\"step\")\n",
-    "plt.xlabel(\"(MC - Data) / Unc.\")\n",
-    "plt.savefig(f\"{plot_dir}/pull_hist.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/boost_histogram/_internal/hist.py:863: UserWarning: List indexing selection is experimental. Removed bins are not placed in overflow.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plotting.ratioLinePlot(\n",
-    "    post_hists[tvar],\n",
-    "    plot_samples,\n",
-    "    year,\n",
-    "    bg_err=post_hists_err[tvar],\n",
-    "    name=f\"{plot_dir}/post_ak8FatJetParTMD_THWW4q_pulls.pdf\",\n",
-    "    pulls=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cut_dict = {}\n",
-    "\n",
-    "for key in events_dict:\n",
-    "    cut_dict[key] = events_dict[key][events_dict[key][\"tau42\"][0] <= 0.3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/boost_histogram/_internal/hist.py:863: UserWarning: List indexing selection is experimental. Removed bins are not placed in overflow.\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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zwbkl7XRq7/vUZR1OX8jSPutvcNcdxbnDcGU3vQLwGmd/ANX3KeP5klsKXOhTj64+5/ovOc2vRUs4Lj7vlxE+aSWBh9z37CGcLpeTcYK6d/HzIznAtje7ec8NsN6zraCmmSTrIL5NgHLV3PUHQ32+09WrBDCcs40DJ4Cn8BOo5/ZcElwQv9bNc0uAus51198bxLFd4eYdm1XeYOsXoFwEZ6dxPI77vZSD/4U3vgqwPtInT1bL+ADb8EyFebtP2tduWsAfIDhXzixOF+LMjyPUL+pwXTgbRPoudfNgu/F+ttsQp7+ib9oVbv7z06X/Jd0L1LNMyGSfUTi/KJeQtpU1s+UYQdyQAP9BfH83LYmzLb6eX6cJPvluctMOAOV90j3Tax3GzxROOF2dPPWs6ZP+X5+6Z2jt5mxAfCHOl9cP7vOJQJls/A9L4gS/Z4BKPumeL89TQCc/5d5y1z8X5H6q+Bzn9enWGZxZKgZztuViEz4/NDM7Hzj3QPCcjxc8/6d02/d8QD3qkz7eTZtPuis2bhnPl8aWdOs8gX+GG3jg/GjYgfOF18vPeYjg7BderE+650ZlGaYkxRlkluy7vezm16IlHBfgPvd1/mqQ+T0NGdcEkdfTat8iwHrPTaj83rTQT35L5kF8VIByniDol1Cfb586nQN85fOZ/T6ZtwLn6lySRZCMMxWi5/O/ZIA8niu4U4M4vpvdvBmuzuakfkGU91wpuD2H5b3xVYD1VTj7Q8HfzaJ8F78Nb0Ccu41F7vNG7vPdgc65m6+Um29jVsehga0552+0dj2cluP88ANO0FrJfd4F58XTKV2+rGamycA6014mA//nDh5pAbTBuaTWnbN9LH2VxRlUck129uX2w/yn+/Q/1n3FcvZ8+vaJ9wxoHWet/dMn/Sn3cZT1M9DVOoN1DuC0xpwP7HVXefqPP2Ot/SFdvargBP9HcC45T3PLTweG+tQzGBfhjGJfb53ppjzauY9jrLVfZyzm7bP+g591/rT1+XuKyXRyIX4CrrLWHvdJC3g+cLo7NcUJ/B9Lf/zWWmuMWYnzOjwfwO1HPxTnR8ow6/QbTF9mDc6PRu/sP+6597zG7khfDmcAdD2cFp4F6Q/MOgPavnPrUcHdZlmcLmMANxljnrPWnvIpc7vvNrKbX6QwMsZUBmKAI9bamQGyeWZC2eiWqY3zQ3mXzTgWqDxnJ3FYG0QVtrj5u5FucgDjjIPyjOfaGsS2gtEM57srvQvdxyyn2SwI7nmchXP8v+M0Nvgbl+Yrv8+lb1fUQNNip/jkzYrnO7xyDuvj5fav/xb43VrbPkC2be5jno7X87DOoO99OMfT3zqTVWTXJzgDYKPdMVU3uOmTs9ie5xweyCQPoIGtueFvxHuGwZR5xToDJXwDdM8b2DeI32at3ZXL/Ry31q621iZYa++01l6AE8yN95M9/QDbYHRzy32P86Hm8bv7eA6AMaY9TneOE8A4TybjzEPr6d4wxt8O3B8Knnlt/3DTynJ2asUP/BRr5/P3PM5+MLQj++8Tz7Z8A9VSOMErOC37mZX7JsD69DwfbqdwPvDTLz/h/NC7C2htrfV+2AdxPoa6j/+XyYdNmnOMM1IfnKs+OwOU8fxI853C0zNF1zb8z6w0wn0c6A42zbDgXMoFd3C5+2Nlnps2EvjFGJNgjIk1xtRMv4Ps5hcppI7ijCuZYYzJMPmCce5fcaX71DMBwtM4gaC/Oa1vxOlWsNI6U9VmZZL7ONB3QLqrO07DyOJMPh+y684A6fe5j9lu1MoncTjfe5txPouzCuAhn8+ltTYFJ46pQtoGIV+egZVrgtikZ6rEYAZAZ8pt/KoCtDN+5mJ3z0dH92kwPy5zai3OzHk9/a00xjxhjJnhxisZWGv/4Ozg1ZvJelYaD88P7d8yzeXuREvOLsWcR8YuJu8FWbY3TuuB79LAXRfvZ7sN3XVP+KQdwelGsMon7QOffaTfxgSfdZfgBD2epWcQdTb47z9/Thbl0nSn4eyl2VvS5evmpn/rPvcM5E1Il8/TF25BJvus6+Y5jdvHG+cNb4F1Acr80+eYfsRp4fkNn65L2XhtvOGWu98nrbWbthM/t2LGCW4tTkBcIsj9THPLfJqD12/A84ETnJ9019fLZBvz3Dx/dZ97XovXZVJmnZunm0/a3920MX7yV/TzmstsucinbCTwb5wvKt88qTgtJFXT7Stb+bVoKYwLZweOf0vaMSK+40cSOduV8XI3bRdwgU/+yzg7kDzYMVAlONu17R+ezzKcBq4dbvoN2TgWS+bdaSzOdIMlfPbv+Z78vbC8Zzk7BuzqbJTJ1bkkuD7xD7h51uNzh1Cc2OIunBb4Pwhi0g73s/o08HWQx5dp/YCP3fVf4jMeDeeq0VPuug1k426yfl5b3vgqQJ5Obp7tQId0/5vb3POzj0zGQwJ9fd5fFqdxLUMMkK6MZ3zBs1keR6hf3OG8cDYg8SyngPOCKPdKunIWt+80mQfxPdOldyJtH/bhPvtIv40JPuteSrcuTd/tTOr9gJ/tVs6ijDeI52wg+zPp+oPhdOGxOC1C53I2iGyeLp/nC+qNTPbp6Uuf5JM23E17K0AZT/+6eZwdNOzZ17Rsvi48wWxnnzTP7a399i30edPOzcZ+PB/kj2anflmdD5zuQBanX7/fHxQ4AcEBN59nEKjnC9/v7Do4P3xP4QTFFXzSp+Dnh527rou7bnku3qcG52rSnTjz93peuwl5kV+LlsK04HR19AzuP4kz00wSZwdn/49031PAmz75k3Cuinle908E2I+nP3CfdOk9fT6/9+C0Znq6bszAZ5axII7FEjiIP4oz+NbiBOwrODvzTgqZNCbkwTn2e+wB8pbACW6tW+/M+lbfmlfnkuCC+BKcndDhDE6AuQynC6rnO+DabJyXZThXz7McQ5ZV/XDuS7DfzXMU52rACs5+7xwAWuXif5gmvsokX7zPOd/g/g8946f+xKdBKkD5CM5O0GFxuqdmVbd4N2/WDaz59SIvDgtpZ/fwLO+Qya8snFbivenK/ODnn+e7NHTXRfp8GFjOTi/oWdr4bCf9Nib4rEs/043FbU3N4njfSlfmYGbH6pbxDeI/DrQv9w1rcWZG8JyDDNMm4kzLZfGZxSbdet+Zb+72SZ/gpsUFKOd5Uzb0SbuQsz/OagX5miiD86F7mrSDcT2DSP8WoNy/3fVZTuPm5q/l83/I9EMkQPmA54OzgfOeTMo/4ub5BifoLe1TH7+t95ydlvV/6dK3uenN/JSJJYsgHudHXxPO/hCujfMDt3mA/E+621yak/xatBT2Bedq2gM4gc9BnOD2K5wrjv5mQimBM/bkG5yrvAdxZibpl8k+PO/3oX7WtcGZtvYgTlD3A87sNwEH82Wyj21+0hfjfLd4ZtX5Eadv9wac78UL8/n8Bjx2P3lrkPZ7M7MlPq/OJUEOHHU/v/vj3Dl+G07AnIzzXVs/q+NLty3PZ2WGCSdyUj+cbiUvu/U5hBO4fw38H7mc7tfnnDcMIm93nJ4Im3EC959wfvg2CnJfz2Vzf3Pc/WQ542G+vciLw4LT4rHezxtxHH7mUseZJ3yVn/yP+eSJ97O+oc963/m3fVvhj/q+qf1sY4LPutqknV/b4gSet+H/Az4C5wP+dKBtZnKOPEH8fnefv/l7YeJ86Xi265k3PcOoe5z5jy3uaO906wxnL6VuI21rr2emmdZ+yp3rrttHxllYPNNB/T3I10R7N39yuvRv3fQeAcp97q4P6nIrZ2f4OUUOLidmcT58u4o19LM+irOtIVf6pHv+bxmmYcRpUfLMFf+OT7rnC+4P/LT6c/Zy5iH8XPXBmUXoD/c8NHTTPNOVzglw7J71Y3KSX4sWLVpwujPeGup6FKYFpzHlDH7mTdcS9Dk8B+cH6fhg8mtgay5YZzDcIJwA2Nc9OAPjJhtjnjfG/NcYsxCnhbhdurw/49wYKFjLff72nV0oyQY5etpa+xvOiGlfpXCuImw1xkx175j5ojHmfZyA+F2cYN7jJM6dVoNVDae15yWbdoYUT52O4bQygDM7zCqcKbbSW+w+RhtjHjPOXUeNMeZinJaDkW7dBlt3lhNjTCROq/px0o3yd3kGpay27rvIx3j38Q4/g4v88Teo1TOI9Ax+Bgi52+3gPs3uoNZk99wFLYjz8StOiwPAe+4d5jDG1DDGDMX5v1TF6dL0hU+5r9zHUcaYS9wypdwyX+D86IW0x+g5X6utM3g7vbU4g3QrAZOMMbXc7UYYYwbgXL6tjDOD0Ta3zO/uY3djTD/P/80YU9EYMwKnNTIV+CiH+UWkGHM/hzrhNOKJy1q7GWfqy1h3Bh3Jvhtxrui/ElTuUP/qKAoLzkBVT3/D7Cy7SNeFgKxb4m8KsK1n0m0nYEu8u74cae9OmZ3lNHBzkOcmxafcQaBiJnl/88mb2YCdcT75Trjb9TzfS8Y+mt3ddX5vnMDZltaRftZV4WwL8mVBHK/nBhx/9Unr4KYFGlTrmet/ezZec1+Qw9bhrM6Hm+dSzvbF9L064lleIV3LOc7l0UPpynjO3cucHdjTyKeMpxtRwAE8OLMR+b6OfvPZrsUZSOx7N996PvvytPLv4my/xlPAnTnNr0WLluK94FyJ/L9Q16MwLpy9W+41oa5LuC04vQlWA18GW0Yt8XnAWjsf51f5nGwU+xinK8PGbO5ueYB0f1PzBWSd1ttrcLqfZKcl9wecYPbD7OzPNdZaeyST9Z55ZrfhDDQN5F6cecxX4PQbK4nTZ+5JnH7N89Llz9A6HmD96vQrrDNFlKcugaYzy2pfnrSkAGWy1QrvthR7tpmTKdSyOh9Ya5fjXD2YgvP/qIwzQn8C0NFa+6BN13JunZbwVjgzuXjul/AFzuvsbZxuXD9Zn6kug6zLMpyuaJNw5rau4tblI5ybZt1trU31yb8Tp1//azgzzZTCadlYA7yOczOnt3KaX0SKN2ttNWvtY6GuR2Hkfne8AzxljFGMmT0DcL7r7ssin5dniinJI8aYjsC1QC+cQaw1cFqLf8fpurASeN9a+3OA8vE4waivRvZsVwGMMTtwWg99VbPWHvTJk/4fO9FaOzTAPqsA17t1jsIZNFkJpwXyEE7Xim9xBnYsSR+8iWTFGPMvnKngXrTW/i3U9RERkfzhxhQ/4Myc5u8+JJKO2/3oe+Bta+2LQZdTEC8iuWGMKYczWh+cW4SnpFt/Lk7f0co4V5+SC7aGIiIiRY8udYhIbh3HmTe+AXC/7yVUY0wLnJl3KgP/VQAvIiKSN9QSLyK5Zox5FHjefforTj/683ACe3DGgAy21qafyUlERERyQEG8iOSaO9h2IM6g4+Y4sx9twLkJy2Sc0fb6sBEREckjCuJFRERERMKMJuPPJj+zvoiIiIiI5Atrrd+bTWpgq4iIiIhImFEQn0M5vSPXnXfemeOybdu2Dcl+c1s+VGV1vgrufIXrMet8FVzZ3J6zcDxmnS+dr/w+5nD8jtT5Cn7JioJ4EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMJMoZxi0hgTAVwKVAS+ttYeCHGV8kxMTExY7jc35UNVNjd0vgp23+FYNrfC8Zh1vgqubG6F4zHrfBV8+VDsV+cr74T0Zk/GmDrAY0A5a+3tbloFYAHQ3s12BrjHWvtWaGqZlmee+FCct3bt2rF69eoC32+40vnKHp2v7NH5yj6ds+zR+coena/s0fnKnlCcL+dm6IHniQ9ZS7wxpj6wBqgKfOez6h9AB+APYAvQBnjTGJNsrV1V0PUMJC4uLkNaTExMSFsSRERERCT8JCYmkpiYmK0yoexO8y+gGrAceMonPRawQLS1dp0x5j7gVeAB4NYCr2UACQkJoa6CiIiIiBQB/hqC33or804ooRzY2h2nq8z11tr5AMaYi4D6OP3g17n5EoCTQNuQ1FJEREREpJAJZRDfANhtrd3jk9bFfZznSbDWngD2AvUKsG4iIiIiIoVWKIP4A0CldGkxOF1pvkqXXgE4VhCVKsz89cOXwHS+skfnK3t0vrJP5yx7dL6yR+cre3S+sqcwnq+QzU5jjJkPXAYMsNYmGmPqAVuBFKCGtfaUm68zTr/576y1rUNSWR+hnJ1GRERERIqHrGanCWVL/OuAASYbYz4CFrvPp1hrTxljyhtj/gnMxGmd/yxkNRURERERKURC2RJvcAL5v/ok/wK0t9buMcY0wGmZB9gEdLLW/l7A1cxALfEiIiIikt+yaokP6c2eAIwxHYFOwGHgU2vtH256LeBlnDnkx1lrj4askj4UxIuIiIhIfiv0QXy4URAvIiIiIvmt0PaJN8YsNMa8F2Ted4wx7+d3nUREAlmwaS8LNu0NdTVEiqx3vr6Od76+LtTVEAkbobxjazSwOci83YHa+VcVEREREZHwUWBBvDFmCDAkXXJdY8zCLIrWBs4H9uVLxUREREREwkxBdqdpiNP67lksUC5dmr/lArf8awVQRxERESmGhg4dijEmqCU6Opr4+Pig8zds2DDgfq+55hqMMYwfPz7DOmst9erVwxjDO++847d8gwYNMMawdu3agPuYMGGCty4ffPBBpufhvffe8+Z9//2C78m8ePFijDFMmDChwPbpOT+LFy8usH3mhYLsTjMBZy54cOaDXwj8CtwaRNnd1toN+VOtnPF3566YmBhiYmJCUBsRKW5WrVrFf//7XxYtWsSuXbsoW7YsTZs25eqrr2b48OGcc845fsudOnWKt99+m8mTJ/PTTz9x9OhRzj//fDp06MDjjz9Oo0aN0uTftm1bhjSAEiVKUKdOHXr16sU//vEPmjdvni/HKVJQevXqRWRkZJq0iRMnkpKSwvDhw9OkN23alKZNm2ZI//TTT9m1axexsbFUqnT2pvRVq1YNuN/o6Gg+++wzVq5cybBhw9Ks27hxI7/88gsA8+bN4/bbb0+zfteuXezYsYPKlSvTsmXLoI5zypQp3HLLLQHXT506NajtZCY+Pp6nnnqKrVu3ZvoDRs5KTEwkMTExW2UKLIi31m4HtnueuyNuj1lrlxRUHfJSQkJCqKsgIsVQamoqDz30EGPGjAGgSZMmDBgwgAMHDrB69WqeeOIJXnnlFT7++GN69+6dpuyuXbvo168f69ato0qVKnTu3Jlq1arx008/8e677/L+++8zbdo0+vfvn2G/derU4dprr/U+9+xv4sSJTJ48mY8++ijNepFwExsbS2xsbJq0WbNmkZKSwmuv+e8McOWVV6Z5/v3337Nr1y5GjhwZdPAaHR0NwMqVKzOsmz9/PgClS5dm/vz5nDlzhhIlznaiSEpKAqBbt25ERERkua+KFSvy5ZdfcuTIESpWrJhh/eHDh5kzZw4VK1bkyJEjQdVf8oa/huC33nor0zIhm53GWlvCWtssVPsXEQlHDzzwAGPGjKFRo0asXr2aTZs2MXnyZObOncv+/fsZNWoUhw4dol+/fmmCghMnTtC3b1/WrVvH3Xffzb59+/jiiy+YNGkSq1evZs6cORhjiI2NZefOnRn227RpU1577TXvMnnyZDZt2sQHH3yAMYZbbrmFvXs1e49IdrVs2ZLKlSvzww8/cOjQoTTr5s2bR5kyZRg2bBgHDhzI0GXm66+/BqBHjx5B7SsmJoYTJ04EbPH9/PPPOXnypHoVhImQBfEiIpI9ixcvZty4cZx33nl8++23tG3bNs36EiVK8Mgjj/DRRx9x+vRpBg8ezOnTpwF49dVX+f777xk8eDDjxo2jZMm0F2Ivv/xyRo4cyR9//MHEiRODrtPNN9/MqFGjOHbsGC+++GLuD1KkmImIiKB79+5Ya70t6wCnT59m0aJFdO3alWuuuQaAuXPnpimb3SC+V69eVKlShSlTpvhdP3XqVKpWreq9OpDezJkziY6OpmbNmpQtW5bGjRtz1113ebv8ADRs2JCnnnoKgEaNGqW5ImGt5Y033qBbt25UrFiRRo0acdttt6Up72vhwoX07duXKlWqUL9+fR544AG/Vwg+//xzrrjiCmrUqEGNGjW46qqrWLp0aYZ8x44d49FHH6VVq1ZUqlSJHj168Oabb4btvX9CGsQbY/5ijPnWGHPCGJOaxXI6lHUVEQk1T5D8n//8hypVqgTM95e//IUePXqwefNmZsyYgbWWN998kxIlSvDEE094byCSXmxsLB07dmTbtm3ZqtewYcOoUqUKr7/+OqmpqdkqKyJnu9SsWLHCm7Z69WoOHz5Mr1696N69O6VLl2bevHne9adPn2bVqlVERkbSunXroPZTunRprrnmGmbPnp0hGD569CizZ89mwIABlCpVKkPZGTNmMGDAANasWUOPHj247rrrsNby3//+lz59+ngbDAYPHkz79u0B5zNl8ODB3m0MHTqUe+65h127djFgwADq1avH+PHj6datW4arEHPmzCEmJobTp08TExPD8ePHGTNmDA899FCafP/5z3/o378/3333Hb169aJTp04sXLiQyy67jE8++cSb79ixY3Tp0oVRo0Zx5MgRYmJiOHnyJHfffTcvv/xyUOevsAnZPPHGmCuBKTiz1Pj/RklXJH9rJCJF3Zpf/uCPYyczpP/+56ksy54+47TUTP3u1yzznlM+4xdglXKlaXtelawrGcDx48dZsGABFStW5NZbM58PwBjDPffcw5IlS/jiiy+47LLL2Lp1K506daJJkyYBy9WuXdvbspcdkZGRdOjQgblz57Jr1y7q1auX7W1I0fDFj0/w2+EfMqT7S0vv5OmjADwz94IsckLtShf7TbvyoqeDqGXh469fvKc/fO/evalQoQKXXnopy5Yt4+jRo1SoUIH//e9/HDt2jCuuuCLDlbXMDBw4kIkTJzJr1iwGDRrkTZ89ezbHjh3j+uuvZ8+ePRnKJSQkULp0adauXZvmc+SOO+7gnXfe4ccff6Rly5aMHDmSEiVKsGrVqjRjA+bPn897773Hddddx4cffkiZMmUAeOmll3jkkUcYN24cjz32mHe7H330EZ999pm3a8/evXu5+OKL+fTTT719xTdu3Eh8fDy9e/dm+vTp3n7+GzdupHv37tx9991cfvnlVKlShbFjx5KcnMygQYOYMGECpUuXBuC///0vd911V9DnrzAJZUv8393HZUBLoKTbTz7gEsK6ioiE1M8//8zx48e54IIL0gxsC+Siiy4C4IcffmDzZue+eo0bN863+p133nneeopI9ni6d3z99decOXMGcPrDV6lShTZt2gBOl7dTp06xZIkzH0h2u9J49O7dm8qVK2foUjN16lQqV65Mr169/JYbPHgwkyZNytAQ0KpVKwAOHjyY6X7HjBlDiRIlePnll70BPMDw4cNp164du3fvTpP/mmuuSdM3v2bNmvTo0YMDBw5408aNG8fp06d544030gzUbdasGfHx8Rw8eJBFixYBziDRyMhIxo4d6w3gwZltsEOHDpnWvbAK5R1bW+C0wt9krf0thPUQkWIiNy3hCzY5gzZ7Na2ZR7XJnt9//x3A73SP/njyHTx4kO3bnYnBPIF2fqhRowYAv/2mj/PiLDct4e98fR0At3eanlfVCRuefvGzZs3ixx9/pGHDhqxcuZL+/ft7Z525/PLL+ec//8m8efO48sorva322Q3iy5QpwzXXXMPHH3/snaXm2LFjfP7551x33XVpAlxfN910k/fvlJQU1q1bx9KlSwPO3JPehg0baNSoEQ0aNEiTXrZsWVatWpUhf+fOnTOkpZ8CdP369ZQqVYrRo0dnyLtr1y7Aacjo378/W7dupXv37lSrVi1NPmMMl19+Od98801Qx1GYhDKILwHsVQAvIpI1z5dXsEHyr7863X4qVapE9erVAdi3L/9ufL1//37AmYpSRLIvOjqaWbNmsXLlSnbu3MmpU6fStIpHRUVRvXp17+DWr7/+mvLly9OuXbts72vgwIG899573i41c+fOJSUlheuvvz5gmW3btjFy5EiWL1/Opk2bKFWqFC1btqR+/foBB6b62rFjR4bB+JmpWTPrBpMdO3Zw6tQpxo0bFzDPkSNHOHDgAKmpqdSuXdtvnnD93AplF5W1QE1jTOUQ1kFEJCw0aNCAEiVKsGFDcPe9W79+PXD2pjQAW7ZsybLcK6+8wogRI7Id8HumpQz2SoGIpOU7uNW3P7xHiRIl6N27Nz/++CPr1q1j06ZNdOnSxe8g1Kz06dOHSpUqebvUTJs2jYoVK9KnTx+/+U+fPk337t2ZOnUqAwYM4KuvvuLIkSOsWrWKO++8M6h91qpVy/tjPxiBBuD7ql27NlWqVOHMmTNYa/0uzz//PDVq1KB06dIBG0HC9QpiKIP4Z3C604TnKBQRkQJUpUoVOnbsyL59+9LMUOFrzZo1nDrlDNL1TBPZt29f6tatS+XKlfnmm2+8l5j9OX78OI899hgTJ07M9A6T6aWkpPDNN99QoUKFsG3REgm1qKgoKlWqxMqVK5k3bx5169alWbO0t9O5/PLLAXjmmWeA7Hel8fB0qZk9ezYHDx70DiAtW7as3/xJSUns3LmTxx9/nOeff56uXbt6u90EO5tV06ZN2bJlS4bPIGstbdq0oW/fvtk+jqZNm/LHH3/4baBYvXo1zz33HNu3byciIoImTZqwatWqNH3qPftfsGBBtvddGITyZk8LgZuBocaYT40x3Y0x1bIqJyJSXI0YMQKABx98kBMnTqRZt2/fPnr06EHfvn2ZNGkS06dPp27dugwaNIgSJUowbNgwjh8/ztNPB243+eijjzh+/DjR0dFB3f3RY/z48fzxxx/cc8892SonImdFRETQrVs3NmzYwP/+9z969+6doTXa01I+depUIOdBPDhdao4fP86DDz7IoUOHMu1Kc/jwYYAMLelr16719ok/fvx4hnK+n1NxcXGcPn2aBx98kJMnz84S9tFHH7F27VrvYPzsiIuLA+Dee+8lJSXFm37w4EGGDh3Ks88+6x2vc9ddd5GSksK9996bpl7vvPNOmqk9w0qgyw/5vQAb3eUAkBrEcjpUdU1Xb+ucNhEpTuZv3GPnb9wT0jqcOXPGXnfddRawbdq0sevXr0+zfsaMGbZUqVIWsMYYO3fuXO+6AwcO2Dp16ljAPvDAA/bEiRNpyi5btsxWrVrVli5d2n733Xfe9K1bt1rA9ujRw2+dPvzwQ1u2bFlbrlw5u2dPaM+PhLe3V15r3155bairkUaDBg2y9Z3fo0cPC9itW7fmaH+jRo2ynjjjvffe85vnoosusoAtW7asPX78eFDbHT9+vAXspEmTvGnHjx+3lSpVsoCtUKGCPXr0aMD8f/zxh61Tp441xti2bdvaIUOG2OjoaFuiRAkbExNjAdu8eXP76aefWmutff755y1gr7zySvvCCy9Ya53Pr/79+1vANmnSxMbGxtorrrjCGmNsnTp17P79+6211i5atMgCdvz48RmOY8iQIRn+HyNGjLCArVOnjr3pppvszTffbKtVq2YBO3ny5DTH2759ewvYRo0a2VtuucVeeumlFvAew6JFi4I6nwXFJ+b0G5OGsjtNE3c5B2cO+KwWTTEpIsWaMYb333+fQYMG8e2333LxxRdzySWXcMsttzBo0CAeeOABb3ea0qVLs3fvXm/ZqlWrMm/ePOrVq8err75K7dq1ufzyyxkyZAiXXnopXbt25ciRI4wZM4aWLVtm2PemTZu49957vcugQYO44IILuPnmm0lNTeX9998PaiCaiATme6fUnj17+s3j6VLTqVOnNFM1ZleZMmW4+uqrAbjyyispX758wLyVK1dm3rx59O/fn+3bt/P5559Tvnx5vvjiC2bOnMk999zDgQMHvNNM3nzzzbRv35758+d7b7hkjGHGjBm88MILVK1alenTp7NhwwZuu+02Vq9enWHWmGC98sorfPDBBzRt2pQ5c+Ywf/582rVrx5IlS9LMqFOmTBm++uor/v73v1OpUiVmzpxJiRIleOONN3jiiSdytO9QMzZMbzUbKsYYpzle502kWAn1FJPpLVq0iLfffpulS5eyZ88eKlasSMOGDbnmmmsYOHAgDz30ELNnz+bNN9/kr3/9q7dcSkoKr732GtOmTWPTpk2cOnWKJk2a0KpVKx5//PEMfXC3bdvmd7BqiRIlOPfcc+nVqxf//Oc/ufDCC/P9mKVoK85TTIr44+lOZa31O8pXQXw2eYJ4f6OxY2Ji0tyYQESKjsIWxGclNTWVUaNGceedd+a4hUukICmIl+IsMTGRxMTENGmeO9MW6iDeOD81LgHOAypaaz82xhhbGCqXjlriRURERCS/ZdUSH9J+5saYEsaYEcA+nHnjE4EP3dWNjTGbjDH3h6p+IiIiIiKFUagHi74NvARUBVKA0+nWnw+8Yox5t6ArJiIiIiJSWIUsiDfGXAsMxQnebwaqADs86621W4ArgYPAEGNMv4KvpYiIiIhI4RPKlvi/4sx/+bC19iN//d+ttV8Ct+JMMXlfAddPRERERKRQCtnAVmPMLqA6EGmtPemmbQIaW2sj0uU9BPxhrW1Q8DVNSwNbRURERCS/FeaBreWBg54APgvHcLrbiIiIiIgUeyVDuO+1QHdjTE1r7d5AmYwxtYCawNfZ2bgxph7wHNAJOBfYBEwDRllrj/vkux1oHWAz31prNahWRERERAqVUAbxK4AewH+AjHdOOmsUTt/5b4LdsDGmNrAOp/V+IbAU6ACMBHoYY/r49MG/AyfQ9+djQEG8iIiIiBQqoewTXwGnNf58YCbwBvAWUA+4GGgOPAB0B3YDLa21B4Lc9uvA3cAwa+0EN60kMBm4HrjaWpvopu/DaXHvG+S21SdepBjae/1AAGpOnRLimogUUeOjncdhi0NZC5FCo9D2ibfWHsUJqH8CBgBf4gTwAD/gdH3pAfwK3BRsAO/qDvwGvOezv9PAaPdpOwBjTBWcwbWbcnYUIiIiIiIFL5TdabDWrjPGtAJuA/oDzYAGOAH4JpyuMGOttceyuenDOK3rZ9Klp7iPld3HJu6jgngRERERCRuhvmMr1tpUa+1b1tprrLUXWmvLW2vPt9ZeYa19IQcBPNbaLtbawb5pxrkmMch9utx99ATx9YwxS4wxh40xW40xHxtjzs/5UYmI5L3FixdjjMmwlCpVisaNG3Pvvffy66+/pikzYcIEjDFER0d704wxDB06tGArL1LIDR061O/7y98SHR1NfHx80PkbNmwYcL8NGzYMejvx8fEFdj781bFp06aZdie21tK4cWOMMTRp0iRgvvwUHR2d6fnODw0bNkzzGVtQQtYSb4x5DJhkrd2Zz/sZBFyDMwNNM5x+99Pc1Z5X2MPAd8AMnD76NwD9jTE9rbVJ+Vk/EZHsuuCCC+jdu7f3+W+//caqVasYN24ckyZNYvbs2XTp0gWA5s2bM3z4cJo2bRqq6oqEhV69ehEZGZkmbeLEiaSkpDB8+PA06U2bNqVp06YZ0j/99FN27dpFbGwslSpV8qZXrVo14H4HDx7MwYMHvc8PHz7MpEmTqFOnDtdee22avB06dMj2ceWlzZs389133xEVFeV3/dq1a9m6dWuu92OMYciQIUyYMCHX2yrKQtmd5hngaWPMQmAiMN1a+2c+7OdS4Eb37z+B7Th3gAWn684J4Alr7ShPAWPMdTiB/pvGmLZ+uuXQrl27oCsQFxdHXFxczmovIpJOp06deO2119KkWWt56aWXePTRR7nhhhvYsmULZcqUoVOnTnTqFGgCLhHxiI2NJTY2Nk3arFmzSElJyfB+87jyyivTPP/+++/ZtWsXI0eODLo1eOTIkWmeb9u2jUmTJtG0adOA+w2FyMhIUlJSmDJlSsAgfurUqQBUqFChAGsWXhISEkhISMiTbYV6iskuQC+gJ/C6MWYq8J61dnFe7cRae68x5mGcVvcncH48VAcetNbeiZ/pLa21040xM3AG3DYGNqfPs3r16ryqoohIrhljeOSRR/jtt994+eWXeeedd7jnnntCXS0RKSJq1arFRRddxJQpU3jmmWe8M6d4WGuZNm0aXbp0ydCtT87KTsNu+nOcXihnp+mKMxvNQ0ASEAkMBRa4/dKfMsbkSYcqa+0Ja+0PwK04AfldxpiILIp5bi51YV7UQUSkINx///2UKFHC24K3bdu2gH1pk5KSuPbaa6lVqxYNGzb0tuCnd+jQIe677z5atmxJZGQkUVFRjBgxgiNHjmTIO3fuXHr37k3NmjWpWLEibdu2ZeLEiRn60Z44cYInnniCdu3aERkZyUUXXcTtt9/Onj170uTz9Onftm0bn3zyCa1ataJs2bI0atSIf/zjH/z5Z35cwBUp/H777TeGDh1K8+bNqVixIh06dCA+Pp6TJ0+mydewYUPuuOMOfvrpJ2JjY6lfvz7nnXce1157LT/++GO29nn99dezadMm1q1bl2Hd999/z8aNG7n++uv9lt2zZw8PPfQQTZs2pVy5clSvXp0uXbrwwQcfePN43u/gdGUyxqTpUrN9+3Zuvvlmzj//fCpVqkSHDh2YPHmy3376J06cYMSIEVx00UVUrFiRzp07s2DBggz5Dh06xP3330+rVq0oX748F1xwAfHx8aSkpGTIu3TpUq644gpq1KjB+eefz5AhQ/Kk+1COWWsLxcLZgP5r4Iy7pALLcG7IVDnI7ZwPfAQMCrB+Ks7No6rhtMhXDJDvQTdf93Tp1jltIlKc7PnL9XbPX64PaR0WLVpkATtkyJBM8zVr1syWLFnSnj592m7dutUC9sknn/SuB2zbtm1tuXLlbJMmTewtt9xi27RpYwFbqVIlu2bNGm/ePXv22EaNGlnARkVF2djYWBsVFWUB27hxY7tv3z5v3hkzZljAlilTxvbq1cvGxsbaGjVqWMBOnDjRm+/YsWO2bdu2FrDNmjWzt956q+3SpYsFbM2aNe369eu9ecePH28BGx8fb0uWLGn79Oljb7nlFlu9enUL2Lvvvjv3J1YKh3d7OEsh0qBBg2x95/fo0cMCduvWrTnep+c926NHj4B5fvzxR+97q0uXLvbWW2+1zZo1s4Bt3769PX78eJpj6NGjh61cubI977zz7KBBg2zXrl0tYCtUqGC/+uqrLOvUoEEDe/7559uff/7ZAvZf//pXhjxPPvmkBey2bdu8+T1OnDhh27dvbwHbpk0bO2zYMNu9e3dbtmxZC9gPPvjAWmvtypUr7fDhwy1gL7jgAjt8+HC7cuVKa621q1evtuecc44tXbq0vfzyy+3NN9/s/Rx49dVXvfvq0aOHPffcc+3ll19uzzvvPHvzzTfb6OhoC9iyZcvajRs3evPu3LnT+/nWrVs3Gxsba5s3b24B26FDB3v06FFv3kmTJtmIiAhbqlQp27t3bztw4EBbvXp1W6dOHXvOOedk+v/KKZ+Y03/MG2hFKBegPvAITgu9J5g/GmTZc92Dnhxg/VpgP1DKffwJ96ZX6fJ96G6nerp0BfEiYer3J570BuO+yy/NL8xy2Vmvgd1Zr0FQef3t4/cnnsx1/YMN4nv27On9Mg0UxAP2lltusSdPnrTWWnvmzBk7evRoC9hevXp58957770WsE8//bQ9c+aMN298fLwF7EMPPeTN26tXL1uqVCm7bds2b9qvv/5qS5YsaS+77DJv2osvvmgBGxcXZ0+fPu1N9wTs1113XYa0cuXK2aVLl3rT9+zZY6tXr26rV68e5NmTAvHFA2eDcd/l/ypnvcRHOEswef3t44sH8vxwCmsQ379/fwvYSZMmedNOnTplb7/9dgvYMWPGeNM9x9CzZ0975MgRb/q0adO8Qb/nvR2Ib1Derl0726xZswxlLr74YtuhQ4cM+a21NikpyQL2tttuS1Nu69atNiIiwl577bVptpX+c+7MmTP20ksvtWXKlLHLli3zpu/fv9/Wr1/fVqhQwZ44ccJae/Z/EB0dbQ8fPuzN++9//9sCduzYsd60wYMH2xIlSthZs2Z5006fPm0feeQR7+eetdYePnzY1qhRw0ZGRtoVK1Z48+7Zs8decsklWf6/ciqrID7kU0z6Y63dASwC5gN7cAailg2y+B5gB3CdMaa17wpjzDAgCvjCWnsKZ/Bqc+D+dPmuAG4Cplhr9+f8SERECl6NGjUA53J7IOXLl2f06NGUKlUKcPpe3n///XTs2JEFCxbwyy+/cObMGd5++22aNm3KP//5T+9lbmMM//rXv2jYsCEJCQneS9l79+4lIiKCsmXPflzXqVOHFStW8MILL3jTEhISqFy5MqNGjSIi4mzPxqFDh9K1a1emT5/O/v1pP3pvu+02unbt6n1es2ZNoqOjM+QTKep+++03Zs2aRc+ePbn11lu96SVLluTFF18kMjLS78DJMWPGpJl957rrruMvf/kLq1atIjk5Oej9Dxw4kI0bN/K///3Pm7Z+/Xp++OGHgF1pqlatyhtvvMG//vWvNP2869WrxznnnJNmdh5/1q5dy/Lly4mNjeXSSy/1plerVo1HH32UJk2asHlz2uGLr7zyChUrVkxTb8D7mXHgwAEmTZrEoEGDuOqqq7z5IiIiePbZZ6lTpw5Tpjh36P7ss8/Yt28f9913H507d/bmrVmzZprPtoIW0ps9+TLGlAC6AtcB1wLneVYBW3C6yGTJWmuNMQ/iBOjfGGPm4QT2FwIdcQL8h9zs/wKuAEYbY27CaZU/H+eOr9t98olIEVBlZHyOy+693vkCqDl1St5UJh95vqTq1KnDmTMZJtcCoG3btlSvXj1NmjGGfv36kZSUxObNmzlz5gzHjx+nR48eaYJtcAKGbt26MWnSJPbu3UutWrUYMGAATz/9NO3atWPo0KH069ePtm3b0r59e2+506dP8/PPPxMdHZ1mCj6Pyy67jGXLlrFly5Y09fMN4D00A0Yh1G90zsuOj3Yehy3Og4oUXZs2Ofen7NmzZ4Z1VapUoXXr1qxevRprrTdgrlevHhdffHGG/H379mXatGls2rSJ1q1bZ1jvz/XXX8/f//53PvnkE1q2bAnAtGnOzN1/+ctf/JZp0qSJd954ay3btm1j1apVfPrpp0H9EN+wYQMAPXr0yLBu+PDhGab6LFeuHK1atUqTln760I0bN3rrcu+992bYrjGGH3/8EWut95xfccUVGfL17NkzywGo+SWkQbwxpjTQGydovwann7rnTGwHPgE+ttZ+m53tWmd2mR44s9G0AqoAG4Hngf9Ya4+4+fYbYzq7+S7HaaXfBLwKPGmtPZSb4xMRCYWdO3dSqlQp6taty86d/m/FUbt27UzT9+zZ421Rr1WrVqZ5f/nlF2rVqsW///1vatSowdixY3nmmWd45plnqFChAldffTVPP/00559/Pvv37+f06dNBbbNjx47e9ED5RYqbXbt2AZm/L48dO8bBgwepVq0a4Pyg96du3boAAT8n/GncuDFt27ZlypQpPP300xhjmDp1Km3atKFx48Z+y1hreffdd3nvvfdITk7m8OHD1K1bl7Zt22YIrv3ZsWMHAOeee25QdaxRo0aWgbVnm8uXL2f58uUB8x0/fpzdu3cD/j83S5cu7b36WdBC1p3GGDMZ2AckArfjDDLdBYwGOltrG1lr/57dAN7DWvuVtbaPtbautbaCtba1tfYfngDeJ98ua+3d1rlLbDlrbUtr7QgF8CISjnbs2MGWLVto0qRJhtZzX+lngfH45ZdfAGdGC88Xf6C8nnTPF1vJkiW577772LBhAxs2bCAhIYGuXbsyefJkOnfuzMGDB6levTolS5YMepseoWrpEilsgnlflilThnPOOcebtnfvXr95MwtOM+PbpWbLli0kJycH7EoD8Pbbb3PHHXdgjOHll1/m119/5ZdffmHmzJneHxqZ8fxgCbb7XDCfF55jHj16dKZjLcuVK0e9evUA/10UT548GbJufaHsE38jUBHYC4zD6cJS31r7kNVdUkVEcuTVV18lNTXV7+VhX2vWrMnQD9VayxdffEGJEiVo1qwZdevWpWzZsixdupTU1NQ0eU+fPs3SpUupUKECtWvX5ujRo8THx/Phhx9ijKFZs2bceeedfPnllzz88MPs27eP5cuXU7JkSRo3bsyaNWv8TlG5ePFiAN1hViQAz3vD817x9ccff7B27VrOP/98SpQ4G+Jt376dbdu2Zcg/Z86cNNsMlqd/+ZQpU7LsSgPwySefUKlSJebNm8ftt9/u/SFy7Ngx7w+JzHjqt2zZsgzr3n//fc477zzvsQTLs81Vq1ZlWGet5dVXX/VOf9msWTMAvvzyywx5lyxZErDbYn4LZRCfgHOjp7rW2vustcusZ3SUiIhki7WWl19+mdGjR1O3bl1uv/32TPOnpKQwYsQITp8+7S3/4osvsmbNGoYMGcI555xDREQEd9xxBxs2bOCFF17wDmC11vLMM8/w888/c+edd2KMoVy5cowaNYr7778/Tauftdb73PPFHRcXx++//86jjz6a5sfBhAkTWLJkCQMGDAjZ5WmRwq527dr079+f+fPn8+GHH3rTT58+zSOPPMLhw4cz3EzozJkz3H///Rw/ftybNn36dKZMmULr1q1p27ZtturQuHFj2rRpw5QpU5g6dSotW7b0Brr+HD58mJMnT3L48GFv2smTJ3nooYc4ceJEmnp5nDhxwvt3p06daNGiBe+88w5JSWfbef/8809efPFF9uzZk+1jqF27NldffTWTJ09m7ty5ada99dZbjBgxgu3btwPQv39/6tSpw5gxY1ixYoU33759+/jb3/6Wrf3mqcwuIeTVgtPK3j4X5V8HphVEXYOoi6aYFCmGCtM88Z75kz3LX/7yF+80chUrVrTLly/3lgk0xWR0dLQ1xnjnaffME9+gQQO7fft2b17feeLbtGljBw8e7J0n/vzzz7f79+/35r3//vu9c73feuut9sYbb7TnnXeeBWyfPn2800n6zhN/4YUX2tjY2DTzxG/YsMG7Tc8Uk4sWLcpwPoYMGaLP46JE88Rba4OfJ94zR3rXrl1tbGysveCCC7zzm3umW/QcQ7169Wy1atVsw4YN7S233GK7detmAVu+fHm7ZMmSLOuUfspIa6199tlnvVMgjhw5MtP8nmllq1WrZgcMGGBvuukmW6dOHduwYUPbokULW6JECXvTTTd5PyPKlStnq1atah999FH7zTffWGudz7/y5cvb0qVL2yuuuMIOGTLE1qtXzwL2ueee8+6rR48etkGDBgHPq+9n4YYNG2zNmjUtYC+77DJ72223eefQb9u2rU1JSfHm/eijj2zJkiVtqVKlbJ8+feyNN95oa9asaWvXrm3btGlTpKeYXAy8H2ilMWaxMSbjbQLP6gMMyOM6iYiEpQ0bNjBu3DjvMnPmTADuvvtufvzxR7p06ZLlNvr27csXX3xB06ZN+eKLLzhy5Ai33347a9asoX79+t58NWvWJDk5mfvuu49Tp04xdepUUlNTeeCBB1i7dm2a/qwvvPACo0aNombNmsycOZMvvviCc845h2effZbp06d7++iXLVuW5cuX8/jjj1O+fHmmT5/OgQMHGDZsGOvWrcu0RU9E4MILL2TdunUMHTqUffv2MX36dCIjI3nyySdZunQppUuXTpO/cePGrFq1ijZt2rBw4UI2b97MNddcw6pVq+jevXuO6uDpUgOZd6UBePDBB3nxxRepXr06c+bM4aeffuKmm27iu+++491336VZs2Zp7h779NNPc+bMGcaNG+ftBhQdHc2aNWu46qqr+OGHH/j000+pU6cOH3/8MY8++miOjqFZs2asW7eO2267jd9++43Jkydz4MABnnjiCRYuXJhmBqwbb7yRxYsX06tXL5KTk/n666/p27cvK1asyPaYgrxibAH0YDHGnAE2W2v9fjIbYzYBja21fkdhZbW+IBljnOZ49fwRKVbCaYpJkbCkKSbzRcOGDWnYsKHfPvRSuHkG6Fpr/Y7ULZQ3exIRERERkcAKzc2ewk36QSMAMTExxMTEhKA2IpLf1AIvks/UAi/FWGJiIomJidkqo+402aTuNCIiIhIu1J0mfGXVnUYt8SIiIiJFlL/54aVoUJ94EREREZEwoyBeRERERCTMKIgXEREREQkzBdknvpIxZnCgdQDGmFjAX+f9SvlWKxERERGRMFOQs9PkZkcG57azmp1GRERERIq8wjI7zQ5yF8SLiIiIiIirQFriixK1xIsUTzcmrATg47jOIa6JSNEUHR0NoPnMRVxZtcRrYKuIiIiISJhREC8iIiIiEmYUxIuIhJnVq1dzww03cOGFF1KuXDkaNGhAz549ee+99zh9+nSavBMmTMAY4+2qkJ88+8qr7hDx8fEYY4JaGjZsmCf7lOJt6NChQb/mCuI9lV50dLRe6+JVkFNMiohILr388ss8/PDDRERE0KNHD7p06cK+fftYuXIlQ4YMISEhgYULF1K6dGkAmjdvzvDhw2natGmIa559HTp0YPjw4WnSPv30U3bt2kVsbCyVKp2dfbhq1aoFXT0pgnr16kVkZGSatIkTJ5KSkpLhtZjd99S2bdto1KgRTz75JPHx8bmtqoiCeBGRcJGcnMyjjz5K3bp1WbBgARdccIF33Z9//sl9993Hu+++yzPPPMPIkSMB6NSpE506dQpVlXPlyiuv5Morr0yT9v3337Nr1y5GjhypFknJc7GxscTGxqZJmzVrFikpKbz22mshqpWIfwricyguLi5DWkxMDDExMSGojYgUB3PnziU1NZUnn3wyTQAPUL58ed544w2mTJnC7NmzvUG8iIgUfomJiSQmJmarjPrE51BCQkKGRQG8iOSn7du3A1ClShW/60uXLs2YMWO4+eabvWnbtm3DGJPm8n10dDTR0dH8+eefPPDAA9SuXZsKFSrQoUMHPvvsswzb3b17N0OGDKFx48bUrVuXwYMHs2fPHvr370+TJk2yrPeGDRu48cYbady4MRUrVqRDhw5MmDAh36bqPXPmDK+88gpdunShUqVKNG3alBtvvJHNmzenyRcfH0/JkiU5fvw48fHxdOjQgcqVK9OtWzfefvttTSUsQTlx4gRPPPEE7dq1IzIykosuuojbb7+dPXv2ePPEx8fTqFEjAJ566qkMY0dmzpxJdHQ0NWvWpGzZsjRu3Ji77rqLX375paAPR0IkJiYmQ1yZFQXxIiJh4pJLLgHgscce47PPPiM1NTVDnqFDh/Lggw9mua3U1FSuvvpqPvnkE6Kjo+nevTurV6/m2muvZeXKld58P//8M23btuW9996jXr169OjRgwULFnDZZZexd+/eLPczd+5c2rRpQ2JiIhdffDFXXXUV27dvZ9iwYTzwwAPZOPrgWGsZMGAADz30EL/++iv9+/enfv36TJs2jaioKJYuXZoh/8CBA3nuueeoVq0avXv35ocffuDOO+9kxIgReV4/KVqOHz/OpZdeyjPPPMORI0e49tprOeecc3j33Xdp2bIlGzZsAJzxHZ5uOu3bt2f48OHUrVsXgBkzZjBgwADWrFlDjx49uO6667DW8t///pc+ffpkGKwu4qHuNCJSbDyV+AM//nY4Q7q/tPT+POF8kV7y1Jws815Uu5LftCdjLg6iloENGzaM999/n+XLl3PNNdfQoEEDrrrqKnr27EmPHj2oXr160NtasWIFnTp14qeffvK27I8fP57bbruN6dOn07mzc1OrJ554gl27dvHxxx9zww03AHDo0CH69u1LUlIS559/fsB9nDx5kuHDh1O5cmWWLVtG48aNveUHDBjA2LFjGThwIN26dcvhGclo+vTpJCYm0r9/fz7++GPKly8PwPz587niiit48MEHWbVqlfcmKmfOnGHx4sV89dVXdOjQAYBff/2VPn36MHbsWO68805atGiRZ/Ur6kaMGEFycnKGdH9p6aWkpACBrzT5ioqK8ps2evToLMvmpXHjxrFmzRri4uJ4/fXXiYiIAJyZmoYNG8Zjjz3GtGnTuPLKK7nooouYNGkSV155ZZorYwkJCZQuXZq1a9emubJ1xx138M477/Djjz/SsmXLAj0uCQ9qiRcRCRNlypRh4cKFTJgwgb59+7Jv3z5ef/11rr/+emrUqEHHjh2ZOHFiUN1Azpw5w6uvvpomYPIE6fv27QNg//79TJ48md69e3vXAVSuXJnnnnsuy318/vnnbN68mZEjR3oDeE/5N954A4CpU6cGdezBSkhIoESJEowdO9YbwAP07t2bQYMGsWbNGr799ts0Ze655x5vAA9Qt25dXnjhBay1vPXWW3laPylaEhISqFy5MqNGjfIG8OBcEevatSvTp09n//79mW5j8ODBTJo0KUPXtFatWgFw8ODBvK+4FAlqiReRYiM3LeE3JjhdTD6O65xX1cmR0qVLM2TIEIYMGcLJkyf5+uuvWbBgAe+//z7ffPMN33zzDUuWLOHdd9/NdDtly5alTZs2adIqVKiQ5vnGjRux1vqdD7tr166UKJF5O9D69esBmDdvXoaWWGstxhh++OGHTLeRXZs2baJx48Z+Z6657LLLeP/999m8eTNt27b1pvft2zdD3t69e2OMYdOmTXlav6IuNy3hntdZXt1nIL+dPn2an3/+mejo6DTTnXpcdtllLFu2jC1btmR6leymm27y/p2SksK6detYunSpZsORLCmIFxEJU6VLl6Z79+50796df//738yYMYM777zT2y2ma9euAcvWrFkzyyB8586dANSqVSvDupIlS1KjRo1My+/YsQOATz75JGCeI0eOZLqN7Nq1axft2rXzu6527doAGQYL1qlTJ0PesmXLUr16de85EElv//79nD592u/7A9K+3jp27BhwO9u2bWPkyJEsX76cTZs2UapUKVq2bEn9+vU1sFUype40IiJhon79+gED1IiICP7yl7/wyCOPAJCUlJTptjx9wjNz7rnnAvgdwJqamsqBAwcyLe8JYpKTk7HW+l2yqmd21alTJ82sIL486Z56efg7vpMnT3LgwIEMeUU8qlevTsmSJbP9evN1+vRpunfvztSpUxkwYABfffUVR44cYdWqVdx55535Um8pOhTEi4iEiebNm/Pdd995W7j9OXHiBAD16tXL9f6aNWsGwFdffZVh3TfffJPlrBmeO1quWrUqw7q9e/fy3HPPZZgtJreaNm3K1q1bvdNx+vJ000h/p80lS5ZkyLtgwQLOnDkTlne6lYJRsmRJGjduzJo1a/xeUQr0evOVlJTEzp07efzxx3n++efp2rWr927L27Zty49qSxGiIF5EJEzExsZy+vRpbrjhBrZs2ZJh/Xfffce4ceOIjIzMkxlfateuzdVXX82cOXOYNm2aNz0lJYV//OMfWZa/+uqrqV27Nk899VSagOTMmTM89NBD/POf/0wzGDAvxMXFkZqayv3338+xY8e86fPmzeP999+ndevWafrDA7zyyiv873//8z7/9ddfefjhhwFnhhCRQOLi4vj999959NFH00z5OmHCBJYsWcKAAQMydDvz/NAGOHzYmRkr/eDXtWvXevvEHz9+PL+qL2FOfeJFRMJEbGwsX3/9Na+//jrNmzenU6dONGrUCGstW7ZsYeXKlZQqVYqpU6fmWTeQ559/nuXLlzNw4EB69OhBnTp1WLp0KXXr1qVTp06cOnUqYNkKFSrw6quvcsstt9CiRQt69epFtWrVWL58ORs3buTuu+/2TmWZV6677jr69+/PZ599xoUXXsill17K7t27Wbx4MeXLl2fMmDEZxgI0atSIjh070qtXL0qXLs3ChQv5448/uP/++2ndunWe1k+KluHDhzN58mTefPNNlixZQrt27diyZQsrVqygZs2aPP/88968ntmSPvzwQ4wx3HbbbXTp0oU6derw4osvsnDhQlq0aMH27dv56quvuOqqq0hMTOTBBx/k+PHjDBgwIERHKYWVWuJFRMLIuHHjWLp0KVdffTX79+9n6tSpzJkzh9OnT/PAAw+wZcsWrr766jzbX/PmzVmzZg3XX38969evJykpiQEDBjB//nxOnTrl7TcfyMCBA1m5ciU9e/bk22+/ZerUqVSuXJnx48czduzYoPrmZ4cxhpkzZ/Lyyy9Tp04dEhMT2bp1K9dffz3Jycl+B/t+/PHHPPLII2zfvp25c+dy0UUX8dZbbxX4nOMSfsqWLcvy5ct5/PHHKV++PNOnT+fAgQMMGzaMdevWebukAdSoUYMHH3yQgwcPMm7cOPbv30/lypWZN28e/fv3Z/v27Xz++eeUL1+eL774gpkzZ3LPPfdw4MABTTMpfhndVjp7jDEW0O24RYqZwjLFZEHytPCXL18+wwwuJ06coFatWgwcODBs51KPj4/nqaeeYuvWrX6npJSCFW5TTIrkN08jh7XWb2uHutOIiIhfxhiuvPJKUlJS2LBhAxUrVvSue+WVVzh06BA33nhjCGsoIlJ8KYgXEQlCcWqB9/XYY48xbNgwWrZsSd++falTpw5JSUl88cUXtG/fnl69eoW6ilJEqAVeJHsUxOdQXFxchrSYmBhiYmJCUBsRkfwxdOhQqlatyksvvcS0adM4fvw4559/Pvfeey/x8fF53qddRKQ4SkxMJDExMVtl1Cc+m9QnXkRERETyW1Z94jU7jYiIiIhImFEQLyIiIiISZhTEi4iIiIiEGQXxIiIiIiJhRkG8iIiIiEiYURAvIiIiIhJmFMSLiAQhMTqaRPe28CKSP+Lj40NdBZGwoSBeRERECoWnnnoq1FUQCRsK4kVEREREwoyCeBGRMPDKK69gjGHIkCF+18fGxmKMITY21u/6IUOGYIzhlVdeyZP6REdHY4xh27ZtebI9kcJi8eLFGGMyLKVKlaJx48bce++9/Prrr6GupgglQ10BERHJWrTbH3/lypUZ1llrmT9/PgDz5s3DWuu9XbfH119/DUCPHj3yt6JBio6OZtu2bfoRIIXWBRdcQO/evb3Pf/vtN1atWsW4ceOYNGkSs2fPpkuXLjnadsOGDWnYsCGLFy/Oo9pKcaQgXkQkDLRs2ZIqVaqwadMm9u/fT/Xq1b3rfvzxR3bv3k3p0qXZs2cP//vf/2jZsqV3/YEDB9i4cSOVKlWiVatWoai+SNjp1KkTr732Wpo0ay0vvfQSjz76KDfccANbtmyhTJkyIaqhFHcK4nMoLi4uQ1pMTAwxMTEhqI2IFHURERF069aNxMREVq5cmeazZt68eQDcc889jB49mnnz5qUJ4r/55hsAunXrRkRERMFWXKQIMcbwyCOP8Ntvv/Hyyy/zzjvvcM8994S6WlIEJCYmkpiYmK0y6hOfQwkJCRkWBfAikp8CdamZP38+5cqV47HHHiMiIoK5c+emWV+QXWk2bNjAjTfeSOPGjalYsSIdOnRgwoQJWGsB2LZtG8YYlixZwvbt2zHGMHToUG/5uXPn0rt3b2rWrEnFihVp27YtEydO9JaXoik1NZVZs2YBMGvWLFJTU0Nco8zdf//9lChRIk1L/ZEjR4iPj+fCCy+kQoUKnHPOObRp04axY8dy5swZ4Gx/++3bt7NkyRKMMWmm1fzuu+8YOHAg9erVo0yZMtStW5err77a+x6WoismJiZDXJkVBfEiImHCE8SvWLHCm3bq1CmWLFlCt27dqFGjBp07d+arr77i+PHj3jxZBfHGGCZMmJDr+s2dO5c2bdqQmJjIxRdfzFVXXcX27dsZNmwYDzzwAAAVK1Zk+PDh1KlTh8jISIYPH06vXr0AmDlzJn379mXZsmW0bNmSa6+9lp07dzJ06FAmTZqU6/pJ4ZSamkrfvn0ZNGgQAIMGDaJv376FOpBv0KABTZo0YdOmTd56Dh482DtF5g033ED79u3ZvHkz999/P8899xwAdevWZfjw4URGRlKnTh2GDx9Ohw4dANi6dSvR0dFMnz6diy66iJtvvpkaNWrw+eef06tXLzZu3Biag5VCS91pRKTYWDFiBAeSkzOk+0tL71RKCgATqlTJMm+1qCi/aV1Gj86ybGZatWpF5cqVWbVqFadOnaJUqVIkJSWRkpLiHYDXp08fli1bxrJly+jduzdnzpwhKSmJyMhI2rRpk6v9Z+bkyZMMHz6cypUrs2zZMho3bgzAoUOHGDBgAGPHjmXgwIF069aN1157je+//55t27alackcO3YspUqVYsOGDTRo0ACAXbt20aBBAyZMmMDgwYPzrf4SOrNnz/a+jgFSUlJISkpi9uzZ9O/fP8S1C+y8885j48aN/PLLL5QrV44ZM2bQu3dv5syZQ4kSThvpwYMHueCCC5g6dSqPPfYYTZs25bXXXmPWrFk0bNgwzet/ypQp/PHHH7z33ntpZpl6//33iY2N5csvv6RZs2YFfpxSeKklXkQkTHj6xf/555+sW7cOONsf3tOaffnllwN4u9Rs2LCBQ4cOcemll1KyZP6123z++eds3ryZkSNHegN4gMqVK/PGG28AMHXq1Ey3sXfvXiIiIihbtqw3rU6dOqxYsYIXXnghfyouBSI+Pt7vtI3GGGJiYrwBvEdKSgoxMTEByxSGO7vWqFEDcGatsdbyxhtv8OKLL3oDeICqVatSv359Dh48mOX2OnXqxBtvvMHAgQPTpHsGowezDSle1BIvIsVGblrCE92uLDEhnhIuOjqaWbNmsXLlStq2bcv8+fOpWrUqUW7rf7t27ahSpYo3uPf0n/ftSvPqq6+yadOmNNudNGkSq1ev9j5/4IEHaNq0adD1Wr9+PeD8qEhOd2XDM+XlDz/8kOk2BgwYwNNPP027du0YOnQo/fr1o23btrRv3z7oekjhFB8fHzDwnjVrFoMGDUoTyEdGRjJ58uRC3RK/f/9+wPmhWatWLe666y7Aeb3v2rWL1atXM2fOHL799lvvlaXMdO/ene7duwNON7n169ezYsWKPOnqJkWTgngRkTDiO7h18ODBJCUlce2113pb/0qWLEmvXr2YNm0ae/fu9dsf/tNPP2XJkiVptrtw4UIWLlzofX799dfTtGlTdu/eTUREhLfVMZAdO3YA8MknnwTMc+TIkUy38e9//5saNWowduxYnnnmGZ555hkqVKjA1VdfzdNPP83555+faXkJT/369aNjx47eLjWRkZF07NiRfv36hbpqmdq5cyelSpWibt26gDOm47XXXmPt2rUcOHCA6tWr065dO2rXrh3U9o4ePcqzzz7L7Nmz+f777zl16hTNmjXL1o9pKV7UnUZEJIxERUVRqVIlVqxYwZIlS0hNTU1zQxpw+sWDM2vN119/Tbly5WjXrp13/eLFi7HWeheA8ePHp0nz/FioXbt2mrIef/75Z5rnnkAlOTk5zXZ8l6SkpEyPrWTJktx3331s2LCBDRs2kJCQQNeuXZk8eTKdO3dWd4IiKiIigjlz5jB58mQAJk+ezJw5cwr1dKg7duxgy5YtNGnSxDsj1IABA9i/fz/x8fFs2bKFvXv3Mnv27KD7scfFxfGf//yHFi1a8Mknn3Do0CHWr1/P2LFj8/loJFwpiBcRCSOefvHbtm3zztji6Q/v4Qnip02bxvfff0+XLl0oXbp0jvZXpUoV9u7d650iD5zuAr/88gslSpSgVq1aAN7WwlWrVmXYxt69e3nuuedYunRpwP0cPXqU+Ph4PvzwQ4wxNGvWjDvvvJMvv/yShx9+mH379rF8+fIcHYMUfhEREd6uM/379y/UATw4XdJSU1O59957AWdQKsCcOXO49957ady4McYYrLVB3ZX4xIkTzJw5k379+jFx4kSuueYaKlasCKC7GktARTaIN8bUM8Z8YIzZYow5aoxJNsY8YYwpmy6fMcbcZYz5wRhz3Biz0xjzojGmfKjqLiKSGU8r+dSpU6lfv36GbiaNGzemSZMmfPrpp1hrczU/fKtWrTh+/DgfffSRN+2LL77gt99+o0WLFpQrVw6Aq6++mtq1a/PUU0+lCTrOnDnDQw89xD//+c8MgdmJEye8f5crV45Ro0Zx//33s3fvXm+6tdb7vE6dOjk+DpG8YK3l5ZdfZvTo0dStW5fbb78dgMOHDwNn+8mDM3Xm//3f/7F9+3aOHz+e4V4Hvq//U6dO8eeff3Lw4ME0P5j379/P3//+d4A008aKQBHtE2+MqQ2sA6oAC4GlQAdgJNDDGNPHnn03PQ38C9gNzABaAg8DbYwxl1trTxds7UVEMucJ4q219O7dG2NMhjyXX345r7/+OpC7mzw9/fTT9OjRg8GDB/PRRx9RsmRJPv/8cwBefPFFb74KFSrw6quvcsstt9CiRQt69epFtWrVWL58ORs3buTuu++mc+fO3vzly5dn9+7dDB8+nOjoaAYOHMgdd9zBmDFjuOSSS7j88ss5deoUy5cv55dffqFPnz7ewbsiBeHrr7/2trQD7N69m9WrV7N9+3YqVqzIJ598QpkyZQBnXvhPPvmETp060bVrV6pVq8bKlSs5dOgQnTt3ZuXKlQwYMICEhARq1apF+fLlWbt2LQ8//DBXXHEFffr04aqrrmLWrFk0bdqUzp07c+TIEebPn0/Hjh2JjIzkrbfeolKlSjz22GOhOiVS2ATquxjOC/A6YIGhPmklgSlueoybVgM4BfwAVHLTDDDZzXeVn21b57SJSHHyWY8e9rMePUJdDWuttadOnbKVKlWygP3ggw/85pkxY4YFbJkyZeyxY8dytb9ly5bZnj172mrVqtlq1arZXr162WXLlvnNu3r1ahsTE2PPO+88W7FiRdu+fXs7fvx4e/r06TT55s+fbxs0aGDLlCljR4wYYa219vjx43bUqFG2RYsWtmLFirZixYr2kksusc8++6w9cuRIro5BwkNh+H5dtGiR9XzX+y4lS5a0DRo0sHfffbfduXNnhnITJkywLVu2tOXLl7fNmze3d955p/3tt9/sli1bbNu2bW2TJk3srl27rLXWfvDBB7ZWrVq2bNmy9qWXXrLWWnvgwAF7zz332Hr16tnIyEjbrVs3O2bMGJuammrfeecdW7t2bfvXv/61QM+FhJZPzOk33jW2CN7K2hjzPVAVOM9ae8Yn/VJgGTDSWvukMeZ+4FXgWmvtDJ98tXBa5j+21t6UbttOJF8Ez5uIBFZYppgUKco8/chFBO9VVmttxsutFNHuNMBh4FvfAN7lmYS2svt4FU5L/HzfTNbaPcaYVcBVxhhj9YkiIiIiIoVIkQzirbVd0qcZ5+fMIPepZ4qDOsAv1tqU9PmBjUB7oAJng38RKabUAi+S/5588slQV0EkbBTJIN6XMWYQcA3QGmgGvAVMc1fXAnYGKHrAfayJnyDe37zJgcTFxREXFxd0fhERkeIo0F1dRYqKhIQEEhIS8mRbRbJPvC9jzGvAcPfpn8D/Ac9Za1ONMaeAFdbaDFM3GGP+AzwGdLTWfuOTrj7xIiIiIpKvsuoTX2Tnifew1t4LlAVaAInAM4BnXrTfgYoBinr6zf+Rn/UTEREREcmuIh/EA1hrT1hrfwBuBTYDdxljIoA9wDkBinnSdxdAFUVEREREglbkgnhjzPnGmI/cvvBpWOfGTd/htMxXBn4DzjPG+GuNvwCn+82R/KyviIiIiEh2FbkgHjgK3AhcHWD9+TiDVn8HPscZ3NvbN4M7T3xb4AtNLykiIiIihU1RDOL3ADuA64wxrX1XGGOGAVGcDc4/BE4Dz3ha492pKEe7Rd4toDqLiIiIiAStSM5OY4y5DmcaydPAPJzA/kKgI06A39Zau9/N65mF5jdgCdDKzbsA6GutTU23bc1OIyIiIiL5KqvZaYpkEA9gjOkOPAFcBFTBuXnTHOA/1tojPvkMcA9wL9AQ+MEn359+tqsgXqQY6vO3uwCYN+rNENdERESKg6yC+CJ7sydr7VdAnyDyWWCcu4iIiIiIFHpFsU+8iIiIiEiRpiBeRCQMDB06FGNMUEt0dDTx8fFB52/YsGGm+/Zsa8KECQHzbNu2zbvv4sAYw9ChQwtsf57zGx8fX2D7FJHCrch2pxERKUp69epFZGRkmrSJEyeSkpLC8OHD06Q3bdqUpk2bZkj/9NNP2bVrF7GxsVSqVMmbXrVq1fyreCG1ePFiLrvsMsaPH1+gwbiISF5REC8iEgZiY2OJjY1NkzZr1ixSUlJ47bXX/Ja58sor0zz//vvv2bVrFyNHjsyy9V1ERAo3dacREREREQkzCuJzKC4uLsOSmJgY6mqJiBQaEyZMwBjDtm3b+OSTT2jVqhVly5alUaNG/OMf/+DPP8/O4rt48WKMMSxbtozJkyfTo0cPKleuTPfu3Zk6dSoAkydPpkuXLkRGRtK8eXM++OCDDPv87rvvGDhwIPXq1aNMmTLUrVuXq6++mq+//tqbZ+jQoVx22WUADBs2zFtHj0WLFnHllVdSo0YNatWqRb9+/UhKSvJ7jDt27OCGG26gbt261KhRg2uvvZYtW7ZkyLdhwwZuvPFGGjduTMWKFenQoQMTJkzIMF2xtZaEhAS6du1KpUqVaN26NX//+99JSUkJ/sSLSNhJTEzMEFdmRd1pcighISHUVRARCQsTJ07kmWee4bLLLuOSSy5hzpw5PP/88xw+fJjXX389Q96PP/6Yvn370q1bNz7//HOWL1/O8OHDSUhI4IorrqBXr14kJiYyePBgWrZsySWXXALA1q1biY6O5vDhw/Tu3ZvevXuzdu1aPv/8cxYsWMDatWtp1qwZvXr14tChQ8yYMYOePXty4YUXUrFiRQDefvtt/vrXv1KlShV69uzJ6dOnveVXrFhBu3btvHXdvn073bt3p3LlyvTu3ZtVq1YxY8YMfvrpJ77//ntKlnS+YufOncu1116LtZZevXpRoUIFFi1axLBhw/j2228ZM2aMd5t33303//3vfznnnHPo06cPf/75J6+++ipffPFFfv+bRCSEYmJiiImJSZP21ltvZVpGQbyIFBsPv/Ey637emCH9uy0Z09JLOea0Gte8rmeWeVud3yxDWsvGzXjp7oeCqGXR8/zzz7No0SK6du0KwN69e7n44ouZMmVKhiD+448/5ptvvqF58+YAjBo1ikcffZTXX3+dr776ii5dugDw8ssv8/DDD7No0SJvED9lyhT++OMP3nvvvTTjB95//31iY2P58ssvadasGbGxsdSrV48ZM2YQGxvrHdh64MABHn74YZo0acKiRYuoU6cOAF9//TVdu3blscceY+7cud7tLl68mL/97W88//zzGGNITU2lT58+LFq0iPXr19OiRQtOnjzJ8OHDqVy5MsuWLaNx48YAHDp0iAEDBjB27FgGDhxIt27dSEpK4r///S8tWrRg7ty51K5dG4BVq1bRq1evvP63iEiYU3caERHJV7fddps3gAeoWbMm0dHR7N+/P0PeuLg4bwAPcNVVVwFw7bXXegN4gCuuuAKAgwcPetM6derEG2+8wcCBA9Nss1WrVhny+jNx4kQOHz7Mv//9b28A79lubGwsf/75J2fOnElzHCNHjvTeVTEiIoLrrrsOwHtsn3/+OZs3b2bkyJHeAB6gcuXKvPHGGwDe7kJvv/02AC+99JI3gAdo374999xzT6Z1F5HiRy3xIlJs5KYlvM/f7gJg3qg386o6xYZvAO9RoUIFv3kvvPDCNM/Lly8PkCaw90331b17d7p37w7AqVOnWL9+PStWrMh0fntfGzZsAKBHjx4Z1o0fPz5DWps2bShbtmyatPTTgK5fvx6AefPmkZycnGadtRZjDD/88AMAmzZtomTJkvTsmfFqT58+fXj++eeDOg4RKR4UxIuISL6qVatW0HkjIiKyle7r6NGjPPvss8yePZvvv/+eU6dO0axZM5o2bRrUvnfs2JGt+tasWTPobX7yyScB8xw5cgSA3bt3U7NmTW9fel++VwZEREDdaUREJAtVqlQBnH7cgXjWnXPOORnWebqb5Le4uDj+85//0KJFCz755BMOHTrE+vXrGTt2bFDlPcH7gQMHgsofzHF5usUkJydjrfW7eGa+qVevHnv37uX06dMZtvPbb78FVScRKT4UxIuISKaioqIAWLFiRcA8X331FUCa2VsK0okTJ5g5cyb9+vVj4sSJXHPNNd4ZZ3ynj8yMp8V+2bJlGdYNHz6cevXqZfpDJrNtrlq1KsO6vXv38txzz7F06VIAmjVrxunTp1m4cGGGvAsWLMjWfkWk6FMQLyIimerYsSMXXHABn3zyCQkJCWkGdwIsXbqUxx9/nAoVKnDDDTeEpI6nTp3izz//5ODBg2nqt3//fv7+978DcPz48QzlTpw44f07NjaWMmXK8O9//5vdu3d70zdu3MjEiROpXr06lStXzla9rr76amrXrs1TTz2V5sfEmTNneOihh/jnP//p7SrkmRf6oYceYteuXd68a9asCXhXXhEpvtQnXkREMlWuXDk+/vhjevXqxV//+ldeeuklWrduTenSpVm/fj2rVq2iTJkyTJw4Mej+53ktMjKSq666ilmzZtG0aVM6d+7MkSNHmD9/Ph07diQyMpK33nqLSpUq8dhjj3kHxr722mvs2LGDhx9+mPr16/P000/z6KOP0qJFCy677DIiIiKYNWsWx44dy1EgXaFCBV599VVuueUWWrRoQa9evahWrRrLly9n48aN3H333XTu3BlwZtG5//77GTNmjHf/J06cYMGCBVx66aUsX748T8+ZiIQ3tcSLiEiWWrVqxZYtW/jXv/5FtWrVWLBgATNmzCA1NZW77rqLjRs3cuONN4a0jhMnTuSee+7h1KlTzJw5k99//53nnnuO+fPn8+qrr1KmTBnvQNM2bdpw0003sXnzZt58803v3WP/9re/kZiYSKtWrVi4cCGLFy+mR48efPPNN1x66aU5qtfAgQNZuXIlPXv25Ntvv2Xq1KlUrlyZ8ePHM3bs2DR960ePHs3bb7/NRRddxLx589i5cyf3338/M2fODGpwr4gUHyb9LZ8lc8YYC2S4VbaIFG2aYlJERAqS5we+tdbvKHq1xIuIiIiIhBm1xGeTWuJFREREJL+pJV5EREREpIjR7DQ55JkKzFdMTAwxMTEhqI2IiIiIhKvExEQSExOzVUbdabJJ3WlEREREJL+pO42IiIiISBGjIF5EREREJMwoiBcRERERCTMK4kVEREREwoyCeBERERGRMKMgXkREREQkzCiIFxEREREJMwriRURERETCjIJ4EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMKMgngRERERkTBTMtQVCFdxcXEZ0mJiYoiJiQlBbUREREQkXCUmJpKYmJitMsZam0/VKZqMMRZA501ERERE8osxBgBrrfG3Xt1pRERERETCjIJ4EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMKMgngRERERkTCjIF5EREREJMwoiBcRERERCTMK4kVEREREwoyCeBERERGRMKMgXkREREQkzCiIFxEREREJMwriRURERETCTMlQVyBcxcXFZUiLiYkhJiYmBLURERERkXCVmJhIYmJitsoYa20+VadoMsZYAJ03EREREckvxhgArLXG33p1pxERERERCTMK4kVEREREwoyCeBERERGRMKMgXkREREQkzCiIFxEREREJMwriRURERETCTJGdJ94YUwl4GugFNAK2AvOBJ621h3zyPQrUD7CZedbamfldVxERERGR7CiS88QbY8oD/wMaA6uBH4AWQFtgB9DWWrvfzfsbcG6ATT1vrf1Hum1rnngRERERyVfFdZ74B3AC+OeADtbaoUB74EGcVvdRAMaYSJwAPsFaa/ws//C/eRERERGR0CmqQfxVwDHgP9ZtMncfXwW+B2KM8/PmfDf/ppDUUkREREQkB4pqEN8Q2GqtTfFNdAP5rUA14BygqbtKQbyIiIiIhI2iGsTfDNyaPtEYUwrojtNK/wfQxF3VyhizyhiTYozZYIx52xhTq8BqKyIiIiKSDUVydhpr7Vfp04wxEcBrQGVggrX2jDHGE8Q/BXwNTAMuAW4HrjHGdLDWbvW3j3bt2gVdn7i4OOLi4rJ3ECIiIiJSpCQkJJCQkJAn2yqSs9OkZ4w5D3gTp6/8LpzBrr8aY+YCXYA4a+2Hbl4DjABeBj6z1l6TbluanUZERERE8lVWs9MU6SDeGFMCZ6aap4EKwHfAjdbaDVmUM8C3QEuggrX2uM86BfEiIiIikq+K6xSTGGPq4Nzc6WXAAI8DHbMK4ME7APZrnPPTNIvsIiIiIiIFqkj2iTfGVAbm4NzgKQm4wVq7I12eUjj9449Za4/62cxJ9/GQn3UiIiIiIiFTVFvin8YJ4D8AotMH8K7KwG5gavoVbnea9kAKsDMf6ykiIiIikm1FLog3xpQGbgEOA3f59mf3Za3dDywArjDGXJtu9R1AZ+ANq87vIiIiIlLIFLmBrcaYxsAWnJs6fZFJ1idxbvi0GqdVfiGwA7gYpxX+O6Br+htGaWCriIiIiOS3Yjc7jTGmA04/+Kw0stZuM8Y0BeKBrkB14Cfgc+D/rLUn/GxfQbyIiIiI5KtiF8TnNwXxIiIiEu7i4+OJj48PdTUkEwri85iCeBEREQl3xhjFMoVcsZ0nXkRERESkqFIQLyIiIiISZhTEi4iIiIiEGQXxIiIiIiJhpmSoKxCu4uLiMqTFxMQQExMTgtqIiIiISLhKTEwkMTExW2U0O002aXYaERERCXeanabw0+w0IiIiIiJFjIJ4EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMKMgngRERERkTCjIF5EREREJMwoiBcRERERCTMK4kVEREREwoyCeBERERGRMKMgXkREREQkzCiIFxEREREJMwriRURERETCTMlQVyBcxcXFZUiLiYkhJiYmBLURERERkXCVmJhIYmJitsoYa20+VadoMsZYAJ03ERERCVfGGMUyhZwxBgBrrfG3Xt1pRERERETCjIJ4EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMKMgngRERERkTCjIF5EREREJMwoiBcRERERCTMK4kVEREREwoyCeBERERGRMKMgXkREREQkzCiIFxEREREJMwriRURERETCTMlQVyBcxcXFZUiLiYkhJiYmBLURERERkXCVmJhIYmJitsoYa20+VadoMsZYAJ03ERERCVfGGMUyhZwxBgBrrfG3Xt1pRERERETCjIJ4EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMKMgngRERERkTCjIF5EREREJMwoiBcRERERCTMK4kVEREREwoyCeBERERGRMKMgXkREREQkzCiIFxERkZCLj48PdRVEwoqx1oa6DmHFGGMBdN5ERETyjjFG360FSOe78DPGAGCtNf7WlyzQ2hQhcXFxGdJiYmKIiYkJQW1EREREJFwlJiaSmJiYrTJqic8mtcSLiIjkPbUMFyyd78Ivq5Z49YkXEREREQkzCuJFRERERMKMgngRERERkTCjIF5EREREJMwoiBcRERERCTMK4kVEREREwkyRDeKNMZWMMa8aY743xhx1H0cbYyqny1fKGPMvY8wWY8wJY8xm97nm0BcRERGRQqlIzhNvjCkP/A9oDKwGfgBaAG2BHUBba+1+N+/7wC3AViAJ6Aw0ACZZawf72bbmiRcREcljmre8YOl8F37FdZ74B3AC+OeADtbaoUB74EGgPjAKwBjTCieAXwRcYK0dBDQDlgKxxpiLC77qIiIiIiKZK6pB/FXAMeA/1v2Z6T6+CnwPxBjn580QN/9D1tpTbr6TwMNuemyB1lpEREREJAhFNYhvCGy11qb4JrqB/FagGnAOTrD/K/BduvJrgL3uehERERGRQqWoDt68GTiSPtEYUwrojtNK/wdQB1hl03UKs9aeMcZsAprnf1VFRERERLKnSAbx1tqv0qcZYyKA14DKwASgLBAJ/B5gMweAasaYktba0/lUVRERERGRbCuSQXx6xpjzgDdxusfsAh7H6U4DkBKgmCe9Kk7XmjTatWsX9P7j4uKIi4sLOr+IiIhIURQfH098fHyoqxEyCQkJJCQk5Mm2iuQUkx7GmBI4M9U8DVTA6ft+o7V2gzsN5VHgU2vtdX7KzsIJ+staa0/4pGuKSRERkTymKQ8LVqjOt/7PwSuuU0xijKkDzAdeBgxO63tHa+0GAGvtnzit7ecE2MQ5wO++AbyIiIiISGFQJLvTuHdlnYNzg6ck4AZr7Q4/WX8DLjDGGN/BrW4LflNgd0HUV0REREQkO4pqS/zTOAH8B0B0gAAe4HOgNhCVLr0tUMNdLyIiIiJSqBS5PvHGmNI4Lewlgbrp54pPl7cVkIxzx9a+1tpTxpgywDygG3CxtfbHdGXUJ15ERCSPqa90wVKf+MIvqz7xRbE7zXk4M8psBZ7znAA/nrTWfmeM+RBnXvn1xpgkoDPOzaImpA/gRUREREQKg6LYEt8Bpx98VhpZa7e5N4D6B3AbUAv4FpgJvOJvfni1xIuIiOQ9tdAWLLXEF35ZtcQXuSA+vymIFxERyXsK7gqWgvjCr9hOMSkiIiIiUlQpiBcRERERCTMK4kVERESKidTUVGbNmgXArFmzSE1NDXGNJKfUJz6b1CdeREQk76mvdP5LTU2lb9++JCUlkZKSQmRkJB07dmTOnDlEREQUSB30fw6e+sSLiIiICLNnz/YG8AApKSkkJSUxe/bsENdMcqIozhNfIOLi4jKkxcTEEBMTE4LaiIiIiGRu7dq1HD16NE3a0aNHSU5Opn///iGqlQAkJiaSmJiYrTLqTpNN6k4jIiKS99TNIv/NmjWLQYMGeVviASIjI5k8eXKBBfH6PwdP3WlEREREhH79+tGxY0ciIyMBvH3i+/XrF+KaSU6oJT6b1BIvIiKS99RCWzBSU1OZPXs2MTExJCYm0q9fvwIb1Ar6P2eH7tiaxxTEi4iI5D0FdwVLd2wt/NSdRkRERESkiFEQLyIiIiISZhTEi4iIiIiEGQXxIiIiIiJhRkG8iIiIiEiYURAvIiIiIhJmFMSLiIiIiIQZBfEiIiIiImFGQbyIiIiISJhREC8iIiIiEmZKhroC4SouLi5DWkxMDDExMSGojYiIiIiEq8TERBITE7NVxlhr86k6RZMxxgLovImIiOQdY4y+WwtQqM63/s/BM8YAYK01/tarO42IiIiISJhREC8iIiIiEmYUxIuIiIiIhBkF8SIiIiIiYUZBvIiIiIhImFEQLyIiIiISZhTEi4iIiIiEGQXxIiIiIiJhRkG8iIiIiEiYURAvIiIiIhJmFMSLiIiIiIQZBfEiIiIiImFGQbyIiIiISJgpGeoKhKu4uLgMaTExMcTExISgNiIiIiISrhITE0lMTMxWGWOtzafqFE3GGAug8yYiIpJ3jDH6bi1AoTrf+j8HzxgDgLXW+Fuv7jQiIiIiImFGQbyIiIiISJhREC8iIiIiEmYUxIuIiIiIhBkF8SIiIiIiYUZBvIiIiBRr8fHxxWq/xVVRO9+aYjKbNMWkiIhI3gvl1IPFcbpFHXPhpykmRURERESKGAXxIiIiIiJhpmSoKyAiIiIi4WHEiBEkJyfnahvR0dE5KhcVFcXo0aNzte+iRC3xIiIiEjKpqanMmjULgFmzZpGamhriGklmkpOTcx3Eh9N+CzMNbM0mDWwVERHJG6mpqfTt25ekpCRSUlKIjIykY8eOzJkzh4iIiAKrhwZ5Bs/Tir548eKw2m9u9h0qGtgqIiIihdLs2bO9ATxASkoKSUlJzJ49O8Q1Eyn81Cc+h+Li4jKkxcTEEBMTE4LaiIiIhJ+1a9dy9OjRNGlHjx4lOTmZ/v37h6hWIgUvMTGRxMTEbJVREJ9DCQkJoa6CiIhIWGvdujUVKlTwtsQDVKhQgaioqNBVSiQE/DUEv/XWW5mWUXcaERERCYl+/frRsWNHIiMjAbx94vv16xfimokUfgriRUREJCQiIiKYM2cOkydPBmDy5MkFPqhVJFypO42IiIiETEREhLf/u/rBiwRPQbyIiIhImAnVTZeSk5M1ZqGQUHcaERERkTATqpsfRUVFKYgvJNQSLyIiIhKGoqKicnXTpdzcOElCr1gE8caYR4C7rLVN/Kx7FKgfoOg8a+3MfK2cSB6Kj48nPj4+1NUQERGRfFbkg3hjTFngtkyyPAicG2BdCqAgXsLGU089pSBeRESkGCiSfeKN4zxjzADgS+DCAPkicQL4BGut8bP8owCrLSIiIiISlKLaEh8B7Awi3/nu46Z8rIuIiIiISJ4qki3xwBlgoM+yP0C+pu6jgngRERGRfJKamsqsWbMAmDVrFqmpqSGuUfgrkkG8tfaMtXaqZwGOBsjqGejayhizyhiTYozZYIx52xhTq4CqKyIiIlJkpaam0rdvXwYNGgTAoEGD6Nu3rwL5XCqSQXw2eIL4p4DTwDScgP924HtjTKNQVUxERESkKJg9ezZJSUmkpKQAkJKSQlJSErNnzw5xzcJbUe0TH6z6OEF7nLX2Q3AGxQIjgJeB0cA1/gq2a9cu6J3ExcURFxeXy6qKiIiIhJ+1a9dy9GjaThFHjx4lOTmZ/v37h6hWoZGQkEBCQkKebKtYB/HW2sv9pFljzGhgMNDfGFPWWns8fb7Vq1cXQA1FREREwlvr1q2pUKGCtyUeoEKFCsXyzq/Zadh12pUDK+7dafyy1lrga5zz0zSL7CIiIiISQL9+/ejYsSORkZEAREZG0rFjR/r16xfimoW3YtsSb4wpBVQGjllr/Q18Pek+Hiq4WomIiIgUYrNHwO7kbBWJAOYMssy+uCExY75n8m0N6dfyFBHv9Qp+I7uToXRktvZb1BXnlvjKwG5gavoVbr/49jh3bA1mvnkRERGRsPHkk0/mrODu5GwH8QARJQz9o6oB0D+qGhElMu8qksHJFGcRr2LbEm+t3W+MWQBcYYy51lr7qc/qO4DOwCi3a42IiIhIkREfH5/zwudGwbDFOSt7m8lZ2eer5Gx/RVixDeJdw4HVwHRjzEJgB3AxTiv8d8DIENZNREREgjBixAiSk5NztY3o6OgclYuKimL06NG52rdIThTn7jRYazfjBOwf4swZfwPOORkJdLTW6rqNiIhIIZecnJzrID6c9isCxaQl3lrbMJN1m4BbCq42IiIikteioqJYvHhxjsoaY3JUNqet9yJ5oVi3xIuIiIiIhCMF8SIiIiIiYUZBvIiIiHjlatYSESkwCuJFRETE66mnngp1FUQkCAriRURERETCjIJ4EREREZEwUyymmMwPcXFxGdJiYmKIiYkJQW1EREREirbkHSm5ntazsN7UKzExkcTExGyVURCfQwkJCaGugoiIiEixEFUvMmT7LogbevlrCH7rrbcyLaMgXkREREQKtdE3N3H+GLY4x9soajf1Up94EREREZEwoyBeRERERCTMKIgXEREREQkzCuJFRERERMKMgngRERERkTCj2WlEREREcig5OTlXs5fktGxycjJRUVE53q+EPwXxIiIiIjkQyiA6KipKQXwxpyBeREREwtvBzXAyBcZH53wbOSg7uhXQNwr6jc7RLnM6b7kIKIgXERGRcHcyxVkK2u7kgt+nx+wRodn/7mQ4N6rg9ysZKIgXERGR8Fc6Mud387zN5Kxsblr+c2t3cmgC6nOjFMQXEgriJd/Fx8cTHx8f6mpIEaXXlxRVem1Lls6NyvkPFwl7xlob6jqEFWOMBbjzzjszrIuJiSEmJqbA61TYGWPQ66xgFMdzXRyPWYqHUL22w3G/0c2rALB4/R8Fu29PS3wOA+lcnetc7jtUQnWuc7NvzwxC+Tl+ITExkcTExDRpb731FgDWWuOvjFricyghISHUVRARERGRIsBfQ7AniA9EQbyIiEgeGzFiBMnJybneTk7nEI+KimL06NG53r+IFF66Y6uIiEgeS05OzpMgPtz2LSIFRy3xIiIi+SAqKipXfWhzOod4bu4eKiLhQy3xIiIiIiJhRkG8iIiIiEiYURAvIiIiIhJmFMSLiIiIiIQZBfEiIiIiImFGs9OIiIiISOG3O/nsnVtzKifldydD6cjc7TcfKIgXERGRXMuLG1zldHrM5B0pRNUvfEGW5KFzo0K375Mpodt3JhTEi4iISK55bjIVFRVV4PuOqh9JVL3sB/GpqanMnj0bgFmzZtGvXz8iIiLyunqFZr9hrd/oHBc9e76XMKvGI9k/389XyfG+85OCeBEREckTubnBVU5vbgXkqItEamoqffv2JSkpCYBBgwbRsWNH5syZk68Bdaj2W1wV5fOtga0iIiJS7MyePZukpCRSUpyuEikpKSQlJXlbyIvafourony+1RKfQ3FxcRnSYmJiiImJCUFtREREwtuTTz5ZoPtbu3YtR48eTZN29OhRkpOT6d+/f5Hbb3EVLuc7MTGRxMTEbJVREJ9DCQkJoa6CiEiBio+PJz4+vtjtWwpGQf9/W7duTYUKFbwttAAVKlTI9z79odpvcRUu59tfQ/Bbb72VaRljrc3POhU5xhgLoPMWPGOMzlcBKY7nujgec6iE8lyH2/85uuV5cDKFxX+PyvE2zG1LsO/2yP6+n0+G0pEsXvdLzvabw3PtmVkmx/3ac8PTJ35Y8Pv27SudkpJCZGRk9vtKj492ph/MxswpqWcsfV9aR9LPh0k5cYbIMiXo2LgScx5uSUQJE/R2vPvNxjEXBgX9Xs6L/3N08yoALF7/R/5V1A9jnNeDtdbvC0Mt8SIiUmTlxbSHOZG8eTdR5xb4bh2FdDq8wiYiIoI5c+Ywe/ZsYmJimDx5cvZnLcnBtIcRJQxzHm7J7HUHiRnzPZP/ehH9WlbNXgDv2Xcop10ME3nyfy6k1BKfTWqJz75wa0ELZ8XxXBfHYw6VcGyJj46ODs20h7uTiaoXyeh5OWsNh1wccy5bDYtLS7yvUL22i+PnV1h+jqglXkREpODlZtrDHMvtXSVFRLKgKSZFRERERMKMgngRERERkTCjIF5EREREJMwoiBcRERERCTMK4kVEREREwoyCeBERERGRMKMgXqQISE1NZdasWQDMmjWL1NTUENdIREQkoyeffDLUVSgyFMSLhDnPLaUHDRoEwKBBg+jbt68CeRERKXTi4+NDXYUiQ0G8SJibPXs2SUlJpKQ4t1pPSUkhKSmJ2bNnh7hmIiIikl90x9YciouLy5AWExNDTExMCGojxdnatWs5evRomrSjR4+SnJxM//79Q1QrERERCVZiYiKJiYnZKqMgPocSEhJCXQURAFq3bk2FChW8LfEAFSpUICoqKnSVEhERkaD5awh+6623Mi2jIF4kzPXr14+OHTt6u9RERkbSsWNH+vXrF+qqiUhxcnAznEyB8dEFv+/dyXBuVMHvVySEFMSLhLmIiAjmzJnD7NmziYmJYfLkyfTr14+IiIgCrUd8fLwGLBUQnWvJSvKOFKKjo3NcPidlkzfvJurcHO8yd86NUhAvxY6x1oa6DmHFGGMBdN6CZ4zR+SogoTzXodp3cXx9FcdzndN9e4LRxYsX522FsuJpjR6W8/3m9JhH9DmP5J0pOQ5qlyxZQo8ePbJfcHcyUfUiGT3vlxztN5SK43uqOMrx50jzKgAsXv9H3lYoC8YYAKy1xt96tcSLiEi+GzFiBMnJybnaRo5ah5OTi934kNE3N3H+yOEPCGNMzn70hKIbjUgxpikmRUQk3yUnJ+c6iM+JqKioYhfEi0jxoJZ4EREpEFFRUTnu1pLj1mERkSKqWLTEG2MeMcZsDrCulDHmX8aYLcaYE8aYze5z/cARERERkUKpyAfxxpiywG2ZZBkPPAMYYDrO1YlngHfzv3YiIiIiItlXJIN44zjPGDMA+BK4MEC+VsAtwCLgAmvtIKAZsBSINcZcXEBVFhEREREJWpEM4oEIYCfwKZDZPFlD3MeHrLWnAKy1J4GH3fTYfKuhiIiIiEgOFdUg/gww0GfZHyDfVcCvwHfp0tcAe931IiIiRV5qaiqzZs0CYNasWaSmpoa4RiKSmSIZxFtrz1hrp3oW4GiArHWAjTbdzP/W2jPAJqB2PldVREQk5FJTU+nbty+DBg0CYNCgQfTt21eBvEghViSD+GAYY8oDkcDvAbIcAKpplhoRESnqZs+eTVJSEikpKQCkpKSQlPT/7d17nJxlff//14cNISRLOCMnEZHzcQPoSj1kPW5Xd9V6atdDtVj267e0X2m11dYqeGgrWi391a+2yddCbXU9YnUX11TBBFBZRFiOCSGEGEIIIYSwbJZls5PP74/7njDZ7GGue2bnvu+Z9/PxmMfAfZjrmvfcSa655rqva5CBgYGUayYi02nkBuqh8fPINPuL2w8jGlqzlwsvvLDsgnp6eujp6QmqnIiISK3ccccd7Ny594/WO3fuZGhoiM7OzpRqJVJ/li1bxrJly6ryWo3ciC/2wB80zf6D4+enptp52223Vb1CIiJSZQOXwZah2pe7ZQiObql9uQktWbKERYsW7emJB1i0aJFWuxWpspCOXTObcX/DDqdx91Gi3vZDpznkUOBJd3+2drUSEZGq2jKUTiP+6JZcNeI7OjpobW2lubkZgObmZlpbW+no6Ei5ZiIynUbuiQd4FDjNzKz05lYz2w84BdiSWs2Eyy67jKGhoVTKbmlp4aqrrkqlbBGpsqNb4I9Wpl2LTGtqamLFihUMDAzQ1dVFb28vHR0dNDU1pV01EZlGw/bEx64jmoGmZdL2C4Aj4/2SkqGhoVQa8WmVKyKSpqampj3j3zs7O9WAF8m4Ru+Jvwa4DPiimbW7+y4zOwD4Yrz/6rQqJpGWlhZWrlxZ0zLb2tpqWp5IrVTj162kfz6GhoY0vlpEpIoauife3e8Evgm8ClhjZt8E1gCvAK5x9/vSrJ9U5oorrki7ClLH8nh9pfkrU0tLSy4b8Xn8nEWkMdikdY7qkpltACbc/eQp9u0PfAy4GHgecDvwQ+Cf3H1iiuMdoBFyqxYzS5RXsccvaU98WuWmKel7znPZjVZuJWWn9WeqGhKXfXVb9JxwTLzec+3KTVMj/j3SiBL/3Xn6IQCsXLOjuhWaRXF2GnefcpqahhhO4+4nzrBvF/CZ+CEiIiIiknkNPZxGRERERCSP1IgXEREREcmZhhhOIyKSJWnNEqMZYhrIlqHnxqgnkeTcnK1SK5J36omXOVMoFOjv7wegv7+fQqGQco2knuT5+kprlpi8zhDTaCq+ttNaLTZnq9SK5J164mVOFAoF2tvbGRwcBKC7u5vW1lZWrFhR/gIi29fB+Eg6vUnzm5OXKXOuKtdXyipZA8HMcjl7ksyuKtd2x1WVVeJiy+UMMyKNRo14mRMDAwMMDg4yMjICwMjICIODgwwMDOxZEXBW4yPRo9bSKFOCVOX6ktoauCz6glyJBhjioWtbRMqlRnxCPT09+2zr6uqiq6srhdpkzx133MHOnTv32rZz506GhobC/iGa35y8Ryhpb9KVhyQrT2qmateX1M6WoXQa1Dkb4qFrW6Qx9fX10dfXF3SOGvEJLVu2LO0qZNqSJUtYtGjRnt4kgEWLFmk8rlSFrq+cOrql9l/Kc0bXtkhjmqojePny5TOeo0a8zImOjg5aW1v3/Czc3NxMa2srHR0daVdN6oCur4TSGtICuRvWkhZd2yJSLs1OI3OiqamJFStW0NvbC0Bvb2+ubjqUbNP1lVBxSEsacjasJS26tkWkXObuadchV8zMAZRb+cwsUV5tpx8CwMo1O3JVbpqSvuc8l53HcotzvFcyO02isou96AmHpej6aoyy03zPaVHWjSFv7QIzA8Ddbar96okXEREREckZNeJFRESkoV1++eUNVW6jqre81YgXERGRhnbFFVc0VLmNqt7yViNeRERERCRn1IgXEREREckZzRMvIqm67LLLGBoaqug1irO9hGppaeGqq66qqGwREZE0qCdeRFI1NDRUcSM+T+XmUaFQoL+/H4D+/n4KhUJDlC0ikmXqiReR1LW0tFQ0Z3qSc5P23lfF9nUwPpJ89VNIdm6CVVMLhQLt7e0MDg4C0N3dTWtra00WIEqzbBGRrFMjXmQKQxtHUmvkaYhHAxgfiR61lmDV1IGBAQYHBxkZieo7MjLC4OAgAwMDdHZ2Vr+OGSlbRCTr1IhPqKenZ59tXV1ddHV1pVAbqaaW5zenVraGdzSQ+c2JV07lYkt+bqA77riDnTt37rVt586dDA0NzXlDOs2yRURqqa+vj76+vqBz1IhPaNmyZWlXQebIVe86OfqPGjWSSqU6xENkCkuWLGHRokV7esMBFi1aREtLS12XLSJSS1N1BC9fvnzGc3Rjq4iITKujo4PW1laam6NfqJqbm2ltbaWjo6OuyxYRyTo14kVEZFpNTU2sWLGC3t5eAHp7e2t2Y2maZYuIZJ2G04jUkcsvvzztKkgdampq2jMGvdZj0dMsW0Qky9QTL1JHrrjiirSrICIiIjWgRryIiIiISM6oES8iIiIikjNqxIuIiIiI5Iwa8SIiIiIiOaNGvIiIiIhIzqgRLyIiIiKSM2rEi0hFCoUC/f39APT391MoFFKuUf1S1iIiUqTFnkSEyy67jKGhoeDz3J277rqL4eFhAN785jezePFizj33XMysrNcYGhqipaUluOxGUygUaG9vZ3BwEIDu7m5aW1u1gqmISINST7yIMDQ0lKgRv337doaHh9m9ezcAu3fvZnh4mO3bt5f9Gi0tLWrEl2FgYIDBwUFGRkYAGBkZYXBwkIGBgZRrJiIiaVBPfEI9PT37bOvq6qKrqyuF2kg9GRoaoq2treZltrS0sHLlyqDzPvOZz3D55Zfvtc3d+f3f/33+9m//too1lDvuuIOdO3futW3nzp0MDQ3R2dmZUq1ERKQa+vr66OvrCzpHjfiEli1blnYVpA6l1SOdtDd8yZIlLFq0aE/vMMCiRYty07Ne6RempOcObRyh5YTmoHPynrWIiExvqo7g5cuXz3iOGvEiGXLVVVelXYUgHR0dtLa27hnm0dzcTGtrKx0dHeW/yMBlsGWosopc3RZ8Ssv+6yCwIV0tLSc00/L8sLKrkrVIGSb/uiYi2aRGfE4kvfGwWlpaWnLXwJS519TUxIoVKxgYGKCrq4ve3l46OjrCbrTcMhQ9jm6Zo1pO7apXj0Rl/tHKROebWfDwoz0SfOmoStYiZbjiiivSroKIlEGN+Jwo3niYxk/naX55SM2WoUQNrao4ugU6rkqn7ASampr2jMlOPDa7gsY0F1uyc9P6fCtQlaxFRKQuqBGfI0luPKyGtra2VMYOJxk3XBU17hHeS6XDSkRERKQhqBEvs0rtZssE44arIs1e8Bz2DouIiEjtqREvs6p0LHziscNq0IqIiIhMSYs9iYiIiIjkjHriRaRxVXoDc9JzU5iNR0RE6osa8SLSmNJsRB/doka8iIhURI14EanOgkuQrGc6rV7pSm9gTjq1pYiISBVoTLzUnUKhQH9/PwD9/f0UCoWUa5QDxQWX0pCzXmldX41Bn7OIZJ164qWuFAoF2tvbGRwcBKC7u5vW1lZWrFihlS1nU8mCS9AQPdO6vhqDPmcRyQP1xMucu/zyy2tW1sDAAIODg4yMjAAwMjLC4OAgAwMDNauD1JauL6m2rHzOtby2RSR/1BOfUE9Pzz7burq66OrqSqE22XbFFVfUrKw77riDnTt37rVt586dDA0NaZn6OqXrS6otK59zLa9tEUlXX18ffX19QeeoEZ/QsmXL0q5CkCuuuKIh/kFYsmQJixYt2tODBrBo0aKarzrbKHk3mqxcXzK39DmLSK1N1RG8fPnyGc8xd5/LOtUdM3OAWufW1tYGkGzlU6JVU3P3WRdnOgkYZ106lnVkZITm5uZUxrImzvvqtnRmaymWWcGY9lxeY4Fyf33ltNxal52Vz1lEsqHt9EMAWLlmR03LNTMA3N2m2q+e+LzYvg7GR9JZmCYtCRqzTU1NrFixgoGBAbq6uujt7aWjoyM///CmNUtLzmaISUvury8piz5nEckD9cQHSq0n/vRDYHyElZ94eaLz7eJV+L8vrW6lauHolsTzeTdKr2FWNNp7bsTrS+9ZRBqReuKlcvObkw93aIDp/0REREQahaaYFBERERHJGTXiRURERERyRsNpADP7K+CEaXb/1N1/WMv6iIiIiIjMRI34yJ8DR0+zbwRQI15EREREMqPhG/Fm1kzUgF/m7v8r7fqIiIiIiMxGY+LhRfHzA6nWQkRERESkTGrEwynxsxrxIiIzuPzyyxuybBGRLFIjHk6On88zs1+b2YiZ3W9m/8/MnpdqzUREMuSKK65oyLJFRLJIjfjnGvGfAiaA7wM7gQ8A95jZC9OqmIiIiIjIVBr+xlaiqSV3Aj3u/k0Ai9a5vQz4EnAV8ObJJ1144YVlF9DT00NPT08VqioiIiIiebVs2TKWLVtWldcyd6/KC9WbuCF/O3AusMjdx+LtDlDr3NpOPwSAlWt2JDrfzGpe57Sl+Z6Vd/3T9dUYlLWIVNoGSypqioK721T7NZxmGh79rX0LUUanzHK4iIiIiEjNNHQj3sz2N7MjzGzRNIeMx89P1apOIiIiIiKzaehGPHAwsAX43uQd8XCaFxOt2PpwjeslIiIiIjKthm7Eu/s24Hrgd83s9ybt/mPgIuCrrgGRIiIiIpIhmp0GLgVuA641sxuAjcBZRL3wdwKfTrFuIiIiIiL7aOieeAB3X0fUYP8m0Zzx7yTK5dNAq7uPpFg9EREREZF9qCcecPcHgHenXQ8RERERkXI0fE+8iFTH5ZdfnnYVGoayFhERLfYUSIs95YcW45G5pM+4MehzFhEt9iQiIiIiIlWhRryIiIiISM6oES8iIiIikjNqxIuIiIiI5IymmEyop6dnn21dXV10dXWlUBsRERERyau+vj76+vqCztHsNIE0O01+aHYamUv6jBuDPmcR0ew0IiIiIiJSFWrEi4iIiIjkjBrxIiIiIiI5o0a81C0tTS9zSddXY9DnLCJZpRtbA+nGVimH8hYREakPurFVRERERESqQo14EREREZGcUSNeRERERCRn1IgXEREREckZNeJFRERERHJGjfg6VygU6O/vB6C/v59CoZByjeqb8hYREZFaUCO+jhUKBdrb2+nu7gagu7ub9vZ2NSzniPIWERGRWlEjvo4NDAwwODjIyMgIACMjIwwODjIwMJByzeqT8hYREZFamZd2BfKqp6dnn21dXV10dXWlUJup3XHHHezcuXOvbTt37mRoaIjOzs6UalW/lLeIiIgk0dfXR19fX9A5asQntGzZsrSrMKslS5awaNGiPT3DAIsWLaKlpSW9StUx5S0iIiJJTNURvHz58hnP0XCaOtbR0UFrayvNzc0ANDc309raSkdHR8o1q0/KW0RERGpFjfg61tTUxIoVK+jt7QWgt7eXFStW0NTUlHLN6pPyFhERkVoxd0+7DrliZg5Q69zaTj8EgJVrdiQ638xqXudGprxFRETqQ6VtsKTMDAB3t6n2qydeRERERCRn1IgXEREREckZNeJFRERERHJGjXgRERERkZxRI15EREREJGfUiBcRERERyRk14kVEREREckaNeBERERGRnFEjXkREREQkZ+alXYG86unp2WdbV1cXXV1dKdRGRERERPKqr6+Pvr6+oHNMS8OHMTMHqHVulS75a2Y1r3MjU94iIiL1odI2WFJmBoC721T7NZxGRERERCRn1IgXEREREckZNeJFRERERHJGjXgRERERkZxRI15EREREJGfUiBcRERERyRk14kVEREREckaNeBERERGRnFEjvkFcfvnlaVehoShvERERmUtasTVQXldsFREREZFwWV2xdV5NayMiIlJDn+q7l/seHU6l7DOPWczlXWelUraI1D8NpxERkbp136PDqTTi0ypXRBqHeuJFRKSunXnMYr7dc1FNy/z9Zb+qaXki0njUiE+op6dnn21dXV10dXWlUBsRERERyau+vj76+vqCzlEjPqFly5alXQURERERqQNTdQQvX758xnM0Jl5EREREJGfUE58jm3eMp12FXFm2bNmUw55kamnl9eGvfom71q+tebkA5550Kl/833+R6FxdX+GUWRjlFabR8vrxfZ/g0eF7E5+/6r8fYulbXpjo3GMWn8UbzvxM4rKTqPT9VmJ8YiePZfA+dTXic+RRNeKD5PEv9DQbtHd/57pU8rpr/VrufHAt573o1JqWe+eDleWcx+srTZ/qu5d//rt/4nrOqWm59z06zJnHLK5pmdWS9BrL6xfjSqXxZzLNKUyPP/JXLJy/gRMPPzfR+Tf+cEOiRvyWlBrSjw7fy5bhezl6ce2nbd3Nbh4b3l3zcmejRrxIhqTZoB17dqymZZY670Wn8tMv/GtNy3zdX36wpuWVSrORlZZ7Nw/z9M7tNS/3zGMW57YRn1Raf48sOvJGJhbfzNduubmm5RY9Nba55mUWpxJN4xrb+cw4vvNIuq5uSnT+v2wxuv4x/NxrO2Bi0QZ4aaJiK3L04rP4wEuvrXm5X2N/QI14kcxLs4FV/Ic3jQbt4C/vrmmZjSytRlaannrqEcwnaj7V44e/+iVuvnEtr7uxpsVy7+ZhDj7kOKC277cojb9HPvSNs1nY/FRNyyzaMnwvuwrP1Lzcu+68lqd2PML2kdo34g86bQvDOO8YTdaUe3D3Lt4xuiX4vHN3jXHg8C6uf2BronKTevKZXczbb8qFSxuWGvE58cyzz7Lbd6fWe5jmT6S19tR+3+eE07fQvODAmpd9Zgsc2LSw5uVK7aXRyErT733pxdx8zyhfu+WtNS13YvFaFh25m52Pv7Km5Z535q856shf8qFvDCR+jYe3P8iHvnF28HnHnzrKjqcPr/lc9QftX2B46/7cdvNRNS0X4MyWtTzz7FjN/41cv+726D+OPb+m5T7HmH9WsuEldvuaROfu3O9hHE9UZiUe2bqZZ8dr/xkDFHbvJoW3PCs14nNiV6GAu3Pv5tqPvXti2zpuvOt2em++reZln3TcC7n5839X0zIXNT/FwuZxTn7eeTUtF2DDE3cxOj5W83987908zPhE9n4qlPpx+KEjNM0rcFeF9yKEOmDhk7zguEP557+q7RemD/7neSw+aDvP7qxpsQAceeQu9tv/WUZqfBuV2/40pdSqGNtVYPduav5v5IGLT+SYo17AT7/whZqWC/AX/3URo/vtz2GnvC/R+fMOWJXoXNvvBsZxXnNKbb+sff+WMcaerf2vLQCeyteW2WW+EW9mHwE+6O4nl3n8McDHZzjks+6+peT4I4Argd8Hil2gjwFfBq5094lEFZ9GX19fBQtCGa9Y+meJztx8580ce97LE517153X8tg9tyY6F2Bs0yMsOP644POefHIT9216JHG5lbjnxv34hy8mH3eX9HP+8A9ey32/3MLiU5KVm/RzfnpsF+O7xjn23e9KVjDJP+ennnqEA55OvsJl0vd87+ZhdjxwV+JenbWbfpv43DsfXMuxzE90LlT290ha586bN5/CRBP3DSX7e+jxBzdw5ItODD7vzJab2XxP8tZs0ve8qPlI1t4K//33dyYu+xf/dCH//O7wzpMP/+C1DK+/h999Q7KG5dBNj9LyimOCz3tw207uWHVg4kZlJf9O7Z6/mqZ56+l+e7IhHg/++nFe9OIjE537yB3Jv6lV8mfqwN0TPHj7kyw4PXHxiew248kHn0j8q1rS62vx4mE2rDJ++qXkX8iT5t36/eVU0hVfWdtvepluxJvZAuDiwNPOBS6dYf+XgS3x6x8E3AQU/wjsBOYDRwOfBU4D/jCw/BlV+kEmHU/a03M1y3r+MmGpF9HT05N4gauk5x777nexY23yLw+VeOiOpys6v5LP+ZE7n2TgC7X9nF++7lRuXXlDojKLxh5J1oh//atHWdO3nuOP/ESiclc/sJqXvHZF8Hkdr9vK9esfS1Rmpd7U/iy/+d49if8R/PrX7mDrkVcnOve7X9+QSiN+UfORNM17KPEQop6eHpYlOPdrt7yVoe/ekahMqOw9r79tW+JyK/Hs+Incf8tqfvcNyc6/6xdbEjWymuxkNt7+W85O2glxV/JG/AFNp7CfDSW+wfS2+x7kzDe9KPi8LcP3cttdtf+SCPD8XcPc+JvHuOlLyf69uHDZokRtirdceSD33TTOYaesTlTuzf2P8swRO4LPm5jYn9W/Su/f5iyWm7lGvJkZcBxwIXAZcAbwYMBLFHvsf8fdZ+vau5jnGvA/B9qBY4FfA4cA7zWzK909nfmUhPHxnYl7h7c99FCic9uXjlDYNU5fW1uicgF+u3ZtovOb3/oQTc/U/vf3mz//d1x4wwpu+8Y3E79G0i9rX7vlrWz62QPJ//FdOD/RuQ/u/xCLD5qfuFF54c9vS3zuJ65rh6axxFPTPTk6nujcA/Z/iK1PP5uoTMmPx5/6ADufeTzxLB6DR/XwgZcm67R57Ic9LEva2XTb8xKfCxdxyxeGav6ev3bLWxkdvyHxF/L7t96R+NzHD0thrBbwyK1nsOnhHfz4Z0sSnb/jkV2Jzp336EaeXbeGK09Lfv/BnY9t5MpV4b9uuWdxME0GG/FAE/BwBecXG/EPlHHs+4AxYAHwF+6+C/itmX0B+Hx8zHuBj1VQn6poaipg5qn8RQGw8ckNic9N6vVLH+OWTc/w8tcm60X74S9GeUOCcw8/eJym3c59W5OPrdyRsJHVNDaKLRhP5XPetvPBiq6RpGVvGb6XhfMPrfk/vh/+wWuBZD1JlRodL/DsrmY2PZ5ssZSdz3yOTY+H/7V02OK/ZmJ3OX811o/hdesY2bmVz//rSYnOv3P1Nj7/rz8LPm/hkWM0PTOaqEzJj2MWn8XC+cl/6anEkdsXccBY7acG/sYzz/CJ3fDthJ0+STt8/vGMC/jBk0/BYYmKrUhW58TJYiN+N/COkv//auD5pwA7gCdmOigeO78EeDo+tnTg4nVEjfhngDeSgUa8pXgFbXrsNp7csiNxz3TSXukz/2Ajd8/fxanPJus5PMB3Jzr32QcnWDDSxO0f/r+JygXY9p+f4/b3hl82Zz34HvYbrf2NO8Pr1jExOsoTQ0OJX2PsiScSnb8/4Ntq/w/RgVs3Me/ppxL36mz5bfIeoYX/exsHjI8mHx6XsMfyb/9tS0XvOWkvFkTveb/xZ2v+98j4qRvY76lCojIrsf/6Ceyp3RXdpL7+8Z2Jzk9r8aFGdMiyp1lw67Mc9bFkayAsWJv83PF7t2NPjyT+M/XU/fcnOnf07nvYtjN5uUn/LB/yyDr2P+xwPnr/7YnKBXiwp4ePJvgC8f0X7pe4zLlkWf2JoMjMNgATATe2ron/80dEXwaOAO4BvgF8xd13x8ddANwGjAO/cPdXl7zG/vH2JwDc/YiSfdkOTERERETqhrtP2ZWbza8WCZlZE3AS0Q2pPcDdwE/ibf8CfCcecw/wvPh5PvBk6evEw2qKXRmHm1kWf7EQERERkQZVV4144Hii4Tj3Aue6+5vc/R1EjfobgbcBb4mPPbTkvJEpXmuE6Jd+mGIElrsnelxyySWJzz3rrLNSKbfS89M6V3nVLq+8vmflVbtzK80sj+9ZeSmvuX7Pefw3UnmV/5hNXTXi3f237r7A3c92940l23cA/yf+37fFz6W97wdN8XIHA8WBlOmsIz3JggUL0q5CriivMMorjPIKp8zCKK8wyiuM8gqTxbzqqhE/i7uJblQ9I/7/4gTR4+zdK18cE7+IaGb/J909E3OyjaVwF3qeKa8wyiuM8gqnzMIorzDKK4zyCpPFvOqqEW9mB5nZEfHY+Mkc2MVzveqPxs/PAqeVjJWHaPgNwIHEC0NlQRa/BWaZ8gqjvMIor3DKLIzyCqO8wiivMFnMq64a8cAHgMeBt0+x7xRgMXAfgLtvBoaIbmw9BmgpObYjfj6QaLrJTMjit8AsU15hlFcY5RVOmYVRXmGUVxjlFSaLeQU34s1svpmdM82+/xM/zq68aol8n+jG1s+a2ZEl9TqIaHaaAlA6Qeg1wAHxf3/RzPY3sxcAHyEaZgOQbG3zaVSy7O4BBxww+0FzUG6l56d1rvIKU0lelZadx3OVV7i0/kzm8VxQXqEaLa9Kz1deYdJsU0yn7Hnizewo4B+BNwMr3f3NUxzzXaC4ZONq4E/dfWVFFZxmnngzOwX4ELDd3T9Zsv0jwBeIFnxaQbTQ1iuIets/6e6fKTn2IODXPDd8ZifRjDTz4/+/xt3/aFK5DukswXvuuedy11131bzcvFJeYZRXGOUVTpmFUV5hlFcY5RUmjbyKI729knnizewi4A7g3UQzueye5tBfAJuIGs5nAj81s3dMc2yljgMuBf5w0vYvAu8E1gCvB15NtNhTR2kDHsDdnwZeSdQjP0o0fGZefO5fAZfMUd0TmTdP09WHUF5hlFcY5RVOmYVRXmGUVxjlFSaLec1aIzNbAHybqCd7NfC3wMBUx7r7VcBVZnYh8BXgQuDfzeyX7v5Ikgq6+4nTbF9J9GVh8nYHvhs/ynn9rcAfxY9MKxRqv3R4nimvMMorjPIKp8zCKK8wyiuM8gqTxbzK+VrxZ0SLKP0aeI27T7Uw0l7c/TYzeylwM9AKfBD4RCUVzZqenp59tnV1dc3ZuCeA/fart/uQ55byCqO8wiivcMosjPIKo7zCKK8wc51XX18ffX19QeeU04h/HdH0jB8rpwFf5O67zewy4BbggqBa5cCyZctmP6jK0hiHn2fKK4zyCqO8wimzMMorjPIKo7zCzHVeU3UEL1++fMZzyvlacQrRGPgbE9TpdmAifg2p0Hve8560q5AryiuM8gqjvMIpszDKK4zyCqO8wmQxr1lnpzGzMeBxd39+ogLMHgUOcfcDk5yfNWnOTjM8PMzixYtrXm5eKa8wyiuM8gqnzMIorzDKK4zyCpNGXtWYneYx4NCEhR8GPA94Msn5srfh4eG0q5AryiuM8gqjvMIpszDKK4zyCqO8wmQxr3Ia8fcBB8bTTIZ6Sfy8NsG5MskRRxyRdhVyRXmFUV5hlFc4ZRZGeYVRXmGUV5gs5lVOI/57RFM5/r0V+/XLYGb7AR8juin2h8mqJ6U2b96cdhVyRXmFUV5hlFc4ZRZGeYVRXmGUV5gs5lXOmPj5wN3AyUQN+j9x9ydmOedg4MtEi0NtBU6JF1bKvTTHxLs7Ad+jGp7yCqO8wiivcMosjPIKo7zCKK8waeRV8Zh4dx8naow/Dbwd+K2ZfdHMXmxmh5cU1Gxm55rZ3wAPAe8CCsDF9dKAT9v999+fdhVyRXmFUV5hlFc4ZRZGeYVRXmGUV5gs5jVrT/yeA83OB74JnEo0RKZoFBgDDis9HNhO1ID/UXWqmg3FnvhLLrlkn31zvdiTiIiIiNSfqRZ7Ks4TP11PfNmNeAAzOwD4QPxYMs1hW4BrgH9298fKfvGcSHM4zerVqznjjDNqXm5eKa8wyiuM8gqnzMIorzDKK4zyCpNGXrMNpwlqxE964UOB84AjgPlE00g+4O7rEr1gTqTZiBcRERGRxlCNeeKn5O5PuvtKd/+eu3/T3QfqvQGftjVr1qRdhVxRXmGUVxjlFU6ZhVFeYZRXGOUVJot5Je6Jb1SanSZcW1sbK1eurHm5ec0rLcorjPIKp8zCKK8wyiuM8gqTxdlp5pXxAj+uQj3c3d9YhddpaA899BAnnXRS2tUoW6FQYGBggFWrVtHf309HRwdNTU01Kz9veaVNeYVRXuGUWRjlFUZ5hVFeYbKYVznzxO/mudlokn4FcXevXettDqXZEz82NsaCBQtqXm4ShUKB9vZ2BgcHGRkZobm5mdbWVlasWFGzhnye8soC5RVGeYVTZmGUVxjlFUZ5hUkjr4p74ie5HbgWeKSyakkS27Zt4/jjj0+7GmUZGBjY04AHGBkZYXBwkIGBATo7O2tShzzllQXKK4zyCqfMwiivMMorjPIKk8W8ymnEfw54G3AKcD7R1JK/Ar4PfN/dN85d9aTU4sWL065CWdra2li1atU+20dGRujq6mLp0qU1GSOfl7yyQnmFUV7hlFkY5RVGeYVRXmGymFc5K7b+jbufBpwLfAa4D/gd4B+Bh8zsFjP7iJlla6BQHRobG0u7CmVZuXIlfX19NDc377W9ubmZvr6+mt3kmpe8skJ5hVFe4ZRZGOUVRnmFUV5hsphX2cNp3P0e4B7gcjM7DXh7/HhJ/LjSzIaA7wHXunv21qetop6enn22zfWKrfvtl3hG0Jrr6OigtbV1nzHxHR0dNatDnvLKAuUVRnmFU2ZhlFcY5RVGeYWZ67ymWrF1NhVPMRn3wL+daMjNi+PNTtRj/12iITf3VlRIhqR5Y+uOHTs45JBDal5uUsXZabq6uujr66v57DR5yyttyiuM8gqnzMIorzDKK4zyCpNGXnO22FORu69398+7eyvwAuAviMbMnwlcDtxlZqsrLUdgdHQ07SoEaWpqorOzk6VLl9LZ2VnTBjzkL6+0Ka8wyiucMgujvMIorzDKK0wW85qzxZ7M7OXA14huiNUUk1UwOjrKwoULa15uXimvMMorjPIKp8zCNFpelS4M2Gh5VUp5hUkjrznviZ9U2IvM7GNm9htgFVEDHiB7a9Xm0NatW9OuQq4orzDKK4zyCqfMwjRKXoVCgf7+/j0LAxYKhUSv0yh5VYvyCpPFvKoxJv5k4B3x4zyeWxCqOCb+exoTXx0TExPMmxc6tX/jUl5hlFcY5RVOmYVphLyquTBgI+RVTcorTBp5zUlPvJmdYmZ/Y2Z3APcDfwe0EM9eA5zl7me7+6fqqQGftvXr16ddhVxRXmGUVxjlFU6ZhWmEvGZaGDBUI+RVTcorTBbzKrsn3sxO5bke93N4rsf9Tp7rcV87F5XMkjR74kVEROrFdAsDFtVqYUCRrKq4J97M/tbM7gRWA58mWvTpDuBvgFPdfYm7/30jNODTtnq1JvkJobzCKK8wyiucMgtT73lVe2HAes+r2pRXmCzmNWtPvJntJpr33Yga798Fgn9TcPfvJKlg1hR74i+55JJ99s31Yk8iIiL1pJpj4kXybKrFnpYvXw5M3xMf2ohPPIZEU0xWbvXq1Zxxxhk1LzevlFcY5RVGeYVTZmEaJa9qLQzYKHlVi/IKk0Zesw2nKacRv5IKGu9F7v6qSl8jCzQmXkREpPoqnSdepN5U3IhPm5l9BPigu58ccM6FROP3zwEOAu4C/g34ppe8YTM7Bvj4DC/1WXffMum1U2vEr1u3jpNPLjuGhqe8wiivMMornDILo7zCKK8wyitMGnlVoyf+74Fr3f22qtduFma2ALgdmF9uI97M3gD0AePAT4GdwKuA5wFfcfdLS45tB34yw8ud4e57LVSVZiN+fHyc+fPn17zcvFJeYZRXGOUVTpmFUV5hlFcY5RUmjbyqMU/8a4BbzexhM/sXM3uVmc3Z+HaLHG9mbyFqYJc9ACmu1xeBCeCl7v4md+8GTgNuAv7EzNpKTil+Mfgdd7cpHplaaXbLli2zHyR7KK8wyiuM8gqnzMIorzDKK4zyCpPFvGZtxLt7K3A88DngdOB/gK1mdo2ZvSnuLa+mJuBh4AfA0sBzXxjX8dvufmdxo7s/Bfx5/L+dJccXG/EPJKtqbR122GFpVyFXlFcY5RVGeYVTZmGUVxjlFUZ5hcliXmWt2Orum939/7r764CjiBrEi4FeYJuZXWtm7zGzQ6tQp908t6jUO4BtAeeeGD/fPcW+h+LnU0u2nQLsAJ4IqmFKiivaSXmUVxjlFUZ5hVNmYZRXGOUVRnmFyWJeZTXiS7n7k+7+dXd/K3AE8B5gGPhn4DEz+6mZ/W8zOzZJhdx9t7t/r/ggGtNerruIxr//1xT7Xh0/P1Ky7WTgMeBKM3vIzJ42s1+Z2Z+aWXA2c01j18IorzDKK4zyCqfMwiivMMorjPIKk8W8Kmqouvsz7v7f7v5+ohtHO4A1RKu5Pmxmt5jZR83s1Jlep1rcfau7r3T3R0u3m9kLgX+I//db8bYm4CSi8fI9RL33P4m3/QvwHSveUSAiIiIikiFV62129wl3v97d/ww4AbgIuAF4P5DaWrXxbDW/IOp1vwa4Md51PNHQnXuBc+ObYN9B1Ki/EXgb8JbpXnfJkiW0tLTQ0tLCkiVLOPfcc7ngggs455xzuPDCCzn77LP3PC9btow1a9bg7qxfv56xsTE2bdrE8PAwW7duZdu2bezYsYPNmzczOjrKhg0bmJiYYO3atcBzS/0++OCDQDTN0fj4OBs3bmRkZIQtW7awfft2tm/fzpYtWxgZGWHjxo2Mj4+zbt26vV6j+Lx27VomJibYsGEDo6OjbN68mR07drBt2za2bt3K8PAwmzZtYmxsjPXr1+PurFmzZq/XqMZ7Kj7PxXvasmVL3b2nufycnn766bp7T3P5OY2Pj9fde5rrz+nRRx+tu/c0l5/T8PBw3b2nufycSv9M1st7msvPaXR0tO7eUx4+p6uuuorzzjuP888/f0/bsbTNWPo8mzmfJ97MXg/sdPdfJDx/AzARMk98fN7RwFd5riH+L8BH3H28jHPPA4aAb7j7eybtS22KyeKS1FIe5RVGeYVRXuGUWRjlFUZ5hXnFK17BTTfdlHY1ciON66saU0xWUvhi4Fpg2VyWM0W5nUTDY94C3Ae8xt3/TzkN+NjdwDMETG9ZC9u3b0+7CrmivMIorzDKK5wyC6O8wiiv8hQKBfr7+7n55pvp7++nUCikXaVcyOL1lbgn3sxeBnwEOJtoWsipHAksAh5x9+cnLGcDAT3xcc//j+M6fQb4jLvvmuK4g4ADgCfdvTBpnxHNWvMbd3/1pH1a7CknlFcY5RVGeYVTZmGUVxjlNbtCoUB7ezuDg4N7epZbW1tZsWIFTU1ztgRQXcjrYk9TvWgLcD3wJuBFRFM7TvVYBKwF3peknAT1OgD4BlAA3u7un5yqAR/7APA48PYp9p1CNIXmfXNS0YQ2btyYdhVyRXmFUV5hlFc4ZRZGeYVRXrMbGBjY04CHaIjI4OAgAwMDKdcs+7J4fSXqiTeza4A/BPqATwALgeuAXwGXAgcD7wL+Cviku382cQUDeuLN7K3A94F/d/cPzHLs84ENwHqiFVsfj7cfBHyPaKXa8939rknnpdYTLyIiIpJEW1sbq1atmnb/0qVLWblyZe0qJLOaqzHxFwEO/C93v8vdbyGanvFUd/9tvO1jwJeAT8Q991VlZqeY2ZfN7NMlm4vlHBPvm+rxIQB3fxj4KNGsNWvN7Ftm9m3gfuD1wKcmN+DTVrzzWcqjvMIorzDKK5wyC6O8wiivma1cuZK+vr59bs5sbm6mr69PDfhZZPH6StoTPwI87e7HlGz7X8BXgMPc/al42zFEiytd4+4XJ6rgND3xZtYG/Bz4rbufGG/7CvC/Z3nJVe7eFh9vRMNp/oJoaskCcAfwJXf/yTT1UU+8iIiI5I7GxOfLXPXEj7Pvzax3AAZcUNwQL7q0DXhxwnJw9xOnGkoTL+pkxQZ8vO1P4m0zPdpKjnd3/667X+Tuh7n7ke7++uka8GnL4rfALFNeYZRXGOUVTpmFUV5hlNfsmpqaWLFiBb29vQD09vaqAV+mLF5fSXvi7wLOAl7k7hvibQuBp4G/dvfPlxz7GLDI3eti8tZiT/wll1yyz76uri66urpqXicRERGREG1tbRpCkyF9fX309fXttW358uXA9D3xSRvx/0g0BOUW4DJ3vzXevoZoFdQWdx83s/OB24D73T1Tc64nleZwmrVr13LqqafWvNy8Ul5hlFcY5RVOmYVRXmGUVxjlFSaNvGYbTpO0EX8ccDvRPPC7gfnuvtvM/hn4M+Be4E6gHTgM+Ed3/2iSN5A1aTbiJyYmmDdvXs3LzSvlFUZ5hVFe4ZRZGOUVRnmFUV5h0shrTsbEu/sjwPnA1USrmxZ9iqgBfxbRFJOHA78B/iFJObK3TZs2pV2FXFFeYZRXGOUVTpmFUV5hlFcY5RUmi3klXrF12hc0mw/8HnAC0XSNP3H38aoWkqI0e+JHR0dZuHBhzcvNK+UVRnmFUV7hlFkY5RVGeYVRXmHSyGuuZqeZlruPu/u33f0L7v6jemrAp23Hjh1pVyFXlFcY5RVGeYVTZmGUVxjlFUZ5hcliXoka8Wa23sxuKPPYH5vZ9EuESdn0jTmM8gqjvMIor3DKLIzyCqO8wiivMFnMK+kI/ROBidkOMrMm4GzgiITlSImJiVkjlxLKK4zyCqO8wimzMMorjPIKo7zCZDGvshrxZvYh4EOTNp9oZutnOfVIYCGwOUHdZJLdu3enXYVcUV5hlFcY5RVOmYVRXmGUVxjlFSaLeZXbE38IUe97kcfnnjjFsZPtJpq1Riq0YMGCtKuQK8orjPIKo7zCKbMwyiuM8gqjvMJkMa9yG/FXAdfE/23AeuC3QFsZ5z7u7qOhFcu6np6efbbN9Yqtw8PDLF68eM5ev94orzDKK4zyCqfMwiivMMorjPIKM9d5TbVi62ySLva0AXjI3V8VfHLOpTnF5NjYWCa/CWaV8gqjvMIor3DKLIzyCqO8wiivMGnkNVeLPZ1YbgPezM4ws+OTlCN727xZtxaEUF5hlFcY5RVOmYVRXmGUVxjlFSaLeVV9sae9Xtxsf+Bx4AF3f/GcFVRDafbEu/ueb2UyO+UVRnmFUV7hlFkY5RVGeYVRXmHSyGvOFnsys1PM7J/M7KdmdsNUD2ANsJjyboCVWdx///1pVyFXlFcY5RVGeYVTZmGUVxjlFUZ5hcliXknHxJ8M3AIcSnSjK0Qz1kz1TeFJ4E/dvTdpJbMkzZ54EREREWkMc9UT/yHgMOBW4C3Au4BR4AbgVfG2b8fHfqpeGvBpW716ddpVyBXlFSavebW1taVSbl7zSpMyC6O8wiivMMorTBbzStoTfy9wOnCKu6+Pt30fONXdzyk57j+A3wPOc/eHqlPldKknXiQbCoUCAwMDdHV10dfXR0dHB01NTWlXS0REpCrmqif+OOCJYgM+thI4w8wWlmz7DNAM/GXCcqTEmjVr0q5CriivMHnKq1Ao0N7eTnd3NwDd3d20t7dTKBRqVoc85ZUVyiyM8gqjvMIorzBZzCtpT/wwsNPdjynZ9nJgFfBKd/9FyfYngQfd/cIq1Dd1xZ74Sy65ZJ99c73Yk+4kD6O8wuQpr/7+frq7uxkZGdmzrbm5md7eXjo7O2tShzzllRXKLIzyCqO8wiivMHOd11SLPS1fvrxY9pQFVzqc5hh33xpvWwzsAC5z9/+v5NjNwGJ3bw4uKIPSHE6zfv16TjrppJqXm1fKK0xe8mpra2PVqlXT7l+6dCkrV66c83rkJa8sUWZhlFcY5RVGeYVJI6+5Gk5zM9FMNMvM7Oi4gGFgA/DuksJPAo4GHk1YjpQ49thj065CriivMHnJa+XKlfT19dHcvHe/QHNzM319fTVpwEN+8soSZRZGeYVRXmGUV5gs5pW0Ef9F4BngTcAmMyu+zirgQjNbYWafBVYQTT15fcU1FbZt25Z2FXJFeYXJU14dHR20trbuacg3NzfT2tpKR0dHzeqQp7yyQpmFUV5hlFcY5RUmi3klasS7+1rgd4CfA0+X7PokUa/764C/Bl4EbAQ+XVk1BWDx4sVpVyFXlFeYPOXV1NTEihUr6O2NZq/t7e1lxYoVNZ2dJk95ZYUyC6O8wiivMMorTBbzSrxiq7vf6e6vcfdD3X13vO1hYAlRA/5fgcuA8919czUq2+jGxsbSrkKuKK8wecurqamJzs5Oli5dSmdnZ82nl8xbXlmgzMIorzDKK4zyCpPFvOZV+wXd/XHgymq/rsB++yX+ztWQlFeYvOZVqzHwk+U1rzQpszDKK4zyCqO8wmQxr+BGvJm1Au8EXgKcBBxKNO59B/Ag0Squ33H3W6tXTQGYN6/q37nqmvIKo7zCKK9wyiyM8gqjvMIorzBZzKvsrxVmdraZ/QL4JdEwmZcBxwALgAPj/3458OfAr8zsJjM7s+o1bmCjo6NpVyFXlFcY5RVGeYVTZmGUVxjlFUZ5hcliXmV9rTCzVwLXAYviTbcQrdC6KX448HzgeKANeClRI/8WM+soXfxJkjvkkEPSrkKuKK8wyiuM8gqnzMIorzDKK4zyCpPFvGZtxJvZocB3iRrwNwEfdfdbZjnnIuBzwCuA75nZGe6+o/LqZkdPT88+2+Z6xdatW7dy4oknztnr1xvlFUZ5hVFe4ZRZGOUVRnmFUV5h5jqvqVZsnc2sK7aa2SeBK4BBYKm7j5f1wmbzieaNfwlwhbt/JqhmGZXmiq0TExOZHJOVVcorjPIKo7zCKbMwyiuM8gqjvMKkkVc1Vmx9E9FwmcvLbcDHBY4TNf4NeEu558n01q9fn3YVckV5hVFeYZRXOGUWRnmFUV5hlFeYLOZVTk/8duBg4PDQITFmdgiwHXjK3Q9NVEGzjwAfdPeTA855IdE0l68F5gO3E30J+fkUxx5BNPSnC1gM3At8zt2/N81rp9YTLyIiIiKNoRo98YcAm5KMaY/P2UTUOA5mZguAiwPPOY7oxtu3AfcANwAXAD8zs9dNOvYgonH+HyBaWfY6olVmv2tmQeXWwurVq9OuQq4orzDKK4zyCqfMwiivMMorjPIKk8W8yumJ3w2sc/dTExVg9gBwkruXtZyiRV87jgMuJJrKcinwYLk98Wb2T/F5f+zuX4u3XUDUWL/X3V9ccuyHgKuAz7j7J+NtLwB+DewGjnf3iUmvr554EREREZlT1eiJr7Um4GHgB0QN+LKZ2f7Au4E7iw14AHf/DXA1cKGZnV5yyvuIhvt8uuTY3wJfAJ5HNBwnM7L4LTDLlFcY5RVGeYVTZmGUVxjlFUZ5hcliXlnsid8PeGvJpq8SjamftSc+ntryl8Dfu/vHJ+17A9FwmY+4+xfN7BhgM/BNd3/3pGPPJBob/2V3/7NJ+9QTLyIiIiJzarae+HLnyllsZn+YsA5B4+HdfTew56ZSM/vHgNOPjZ/vn2Lf2vj5mDKOfWDSsZmwbt06Tj657Pt7G57yClNpXm1tbaxcubJ6Fco4XV/hlFkY5RVGeYVRXmGymFe5w2mOJBqOkuRxZHWrPKPnxc9PTrHvifj5qNmOdfddwHDJsftYsmQJLS0ttLS0sGTJEs4991wuuOACzjnnHC688ELOPvvsPc/Lli1jzZo1uDvr169nbGyMTZs2MTw8zNatW9m2bRs7duxg8+bNjI6OsmHDBiYmJli7NvreUfwJZ2xsDIgupPHxcTZu3MjIyAhbtmxh+/btbN++nS1btjAyMsLGjRsZHx9n3bp1e71G8Xnt2rVMTEywYcMGRkdH2bx5Mzt27GDbtm1s3bqV4eFhNm3axNjYGOvXr8fdWbNmzV6vUY33VHyei/c0f/78untPc/k5HXnkkYne0+OPP05vby+rVq3iu9/9Lg899FBm3tNcfk4nnHBC3b2nuf6c5s2bV3fvaS4/p8MPP7zu3tNcfk6lfybr5T3N5ed0zDHH1N17msvP6cADD6zKe7rqqqs477zzOP/88/e0HUvbjKXPsylnOM0GonniK+LuL0xyXlz+RJnDaT4OfBZ49eTpJOPx8uPAgLu/wczeDfwXcLG7Xz3Faz1CNIznzEnbUxtOs3HjRk444YSal5tXyitMkrwKhQLt7e0MDg4yMjJCc3Mzra2trFixgqamskbQ5Zaur3DKLIzyCqO8wiivMGnkVfFwGnc/sbpVmlPFXvWDpth3cPy8o4xji8f/tjrVqo7DDjss7SrkivIKkySvgYGBPQ14gJGREQYHBxkYGKCzs7PaVcwUXV/hlFkY5RVGeYVRXmGymFcWZ6epxGPx81QLSxW3bZnt2LjXflHJsZlQbChJeZRXmNC82tra6Orq2ue8kZERurq6aGtrq2LtskfXVzhlFkZ5hVFeYZRXmCzmVW+N+Efj59On2Hda/LwlwbGZMH/+/LSrkCvKK0xoXitXrqSvr4/m5ua9tjc3N9PX11f3N7nq+gqnzMIorzDKK4zyCpPFvOqtEX8bsA14wxT7OuLnHwO4+2ZgCGg3s8nDiorHXjcHdRSpGx0dHbS2tu5pyBfHxHd0dMxypoiIiFSirhrx7j5OdLPquWZ2cXF7vGLrHwG3uvs9JadcQzSc5pMlx74A+AhRT/2KGlS7bOPj42lXIVeUV5gkeTU1NbFixQp6e3sB6O3tbYibWkHXVxLKLIzyCqO8wiivMFnMa9bZadI23ew0ZnYK8CFgu7uXNsKPB24HDgduBJ4GXgMsAF7v7teXHHsQ8Gui4TO3Eq0U+2qihv373f0/pqhParPTFGf/kPIorzCV5tVo88Tr+gqnzMIorzDKK4zyCpNGXrPNTpPnnvjjgEuBvRahcvdNwEuBHwAtQCvwE6JpJ6+fdOzTwCuJ5rM/iaix/yvgbVM14NO2ffv2tKuQK8orTKV5NVIDHnR9JaHMwiivMMorjPIKk8W8Mt8TnzVp9sSPj49n8saKrFJeYZRXGOUVTpmFSSuvvP6qpusrjPIKk0ZeFc8TL1Pr6enZZ1tXVxddXV1zVubGjRszt+RvlimvMMorjPIKp8zC1DqvQqHAwMAAq1ator+/n46Ojlzd36LrK4zyCjPXefX19dHX1xd0jnriA6XZEy8iIjIXGnn1ZamtvP7Sk4Z6HhPfcFavXp12FXJFeYVRXmGUVzhlFqaWec20+nJe6PoKU+u8CoUC/f39e37pKRQKNS2/Ulm8vtQTH0g98SIiUk/a2tpYtWrVtPuXLl2qnlOpiH7pSUY98XUki98Cs0x5hVFeYZRXOGUWplZ51cvqy7q+wuiXnjBZvL7UEx9IPfEiIlJv1FMqc0W/9CSnnvg6snbt2rSrkCvKK4zyCqO8wimzMLXMqx5WX87j9dXW1pZa2bXKq15+6cni9aWe+EBp9sRPTEwwb55mBS2X8gqjvMIor3DKLExaeeV19pA8XV/F6Ty7urro6+tLZTrPWuZVD7/0pHF9qSe+jmzatCntKuSK8gqjvMIor3DKLExaeeWxAQ/5ub6KDdru7m4Auru7aW9vr/lsLbXMqx5+6cni9aWe+EDFnvhLLrlkn31zvdjT6OgoCxcunLPXrzfKK4zyCqO8wimzMMorTF7y6u/vp7u7e89NnhANLent7aWzs7Nm9Ugrr7z+0jPXeU212NPy5cuB6Xvi1YgPlOZwms2bN3PsscfWvNy8Ul5hlFcY5RVOmYVRXmHykFeWbvLMQ15ZkkZeGk5TR/LQw5AlyiuM8grzzne+M+0q5I6usTDKK0we8srSTZ55yCtLspiXGvE5MjExkXYVckV5hVFe5SmuOviLX/wil6sOpknXWBjlFSYveXV0dNDa2rqnIV+8ybOjo6Om9chLXtVUyWxAWcxLjfgc2b17d9pVyBXlFUZ5zS4rN6Tlla6xMMorTF7yyspNnnnJqxqKnS+rVq1K3PmSxbzUiM+RBQsWpF2FXFFeYZTX7Oph1cE06RoLo7zC5CmvpqYmOjs7Wbp0KZ2dnanM0pKnvCpRrc6XLOalRnyODA8Pp12FXFFeYZTXzNra2ujq6tprRgmIGvJdXV2pLtqSF7rGwiivMHnMK81ZWvKYVxLV6nzJYl6anSZQmrPTjI2NZfKbYFYprzDKa3ZZmRour3SNhVFeYZRXmEbIq5qzAaWRl2anqSObN29Ouwq5orzCKK/ZZeWGtLzSNRZGeYVRXmEaIa9qzgaUxbzUEx8ozZ54d9/zrUxmp7zCKK/yZGG59LRUukiLrrEwyiuM8grTKHkVx8QXh9QUO19CbyZOI6/ZeuLViA+U5oqta9as4fTTT5+z1683yiuM8grzkpe8hFtvvTXtatREtb646BoLo7zCVJJXXlcRrUQjXV/V+DtsrvPSiq01kGZPvIhIrVWrF0skixr5l7VGlLcvaxoTX0dWr16ddhVyRXmFUV5hGiWvak6r2SiZVYvyChOaV6Ov+9CI11clDfgs5qWe+EDqiReRRlHNmR1EskazTUnWqSe+jqxZsybtKuSK8gqjvMI0Ql7VnNkBGiOzalJeYULy0roPur5CZTEv9cQH0uw0+aG8wiivMI2SVzXHxDdKZtWivMKE5tXoPfG6vsJkcXYa9cTnyEMPPZR2FXJFeYVRXmEaJa+mpiZWrFhBb28vAL29vYlvam2UzKpFeYUJzavR133Q9RUmi3mpJz6QVmzND+UVRnmFacS8Kp3ZoREzq4TyCpMkr0aenUbXVxit2CoV2bZtW9pVyBXlFUZ5hWnEvCq9ibURM6uE8gqTJK+mpiY6OztZunQpnZ2dDdOAB11fobKY17y0K5BXPT09+2yb68WeFi9ePGevXY+UVxjlFUZ5hVNmYZRXmEryasRZlnR9hZnrvKZa7Gk2Gk4TKM3hNFu3buWoo46qebl5pbzCKK8wyiucMgujvMIorzDKK0waeWk4TR3Zbz99XCGUVxjlFUZ5hVNmYZRXGOUVRnmFyWJemRxOY2b7A38FXAwcDzwMXA1c6e4TVS7rc0DzNLu/7e43VbO8Ssybl8mPK7OUVxjlFUZ5hVNmYZRXGOUVRnmFyWJe2ftaEbka+CxgwLVEXzY+C/z7TCeZ2fvNzMt4nBgffyDwUeDSaR5nzdH7S2R0dDTtKuSK8gqjvMKkmVdeF6LRNRZGeYVRXmGUV5gs5pW5rxVmdh7wbuDnQLu77zKz+cDPgPea2ZXufu80p68B/u8ML/8eYBewPf7/k+Lnv3H3f6i89nPrkEMOSbsKuaK8wiivMGnkVZwOb9WqVfT39+duOrxKMqt0ess80p/JMMorjPIKk8W8stgT/774+S/cfReAu48DH463v3e6E939Fnf/06kewM3AwcD73X04PuXk+PmB6r+N6tu6dWvaVcgV5RVGeYWpdV7FlVO7u7sB6O7upr29nUKhUNN6VCJJZoVCgf7+/j1fXPL0fiulP5NhlFcY5RUmi3llbnYaM7sfWAQ830sqZ2b7AY8CW939nMDXPA64G/gvd/8/Jds/AnwBaHH3O8t8rdRmp5mYmMjkmKysUl5hlFeYWudVD0vEh2ZW/OIyODjIyMjInhU1k64Ymzf6MxlGeYVRXmHSyCuPs9McC6z1Sa1kd99N1GN+TILX/DfgaaKbZUsVe+JfY2b3mNlOM7vbzL5kZgclKGdOrV+/Pu0q5IryCqO8wtQyr7a2Nrq6uvZqwAOMjIzQ1dWVmzHyoZkNDAzsacBD9H4HBwcZGBiYi+pljv5MhlFeYZRXmCzmlameeDNbCOwErnX3t02x/4fAm4D9y52lxsxeBdwAvM/dvz5p38+A1xCNk/8l0Sw4LwZOA9YD57v7U5POSa0nXkQaVz30xIdoa2tj1apV0+5funRpw42RF5HGkree+EPj55Fp9he3H1bOi1n07q8kGkrzjSkOeT7RTa6/6+5t7v5eohlpriK66fXT0732kiVLaGlpoaWlhSVLlnDuuedywQUXcM4553DhhRdy9tln73letmwZa9aswd1Zv349Y2NjbNq0ieHhYbZu3cq2bdvYsWMHmzdvZnR0lA0bNjAxMcHatWsBWL16NQA33RTNdrlu3TrGx8fZuHEjIyMjbNmyhe3bt7N9+3a2bNnCyMgIGzduZHx8nHXr1u31GsXntWvXMjExwYYNGxgdHWXz5s3s2LGDbdu2sXXrVoaHh9m0aRNjY2OsX78ed2fNmjV7vUY13lPxeS7e06233lp372kuP6e77rqr7t7TXH5OxUet3tNFF11ES0sLixYtAmDRokWcc845dHR05OZzGhwcLPtz+upXv0pfXx8LFy6k1MKFC/nOd77Dt771rUy8p7m89u688866e09z+TmV/pmsl/c0l5/TPffcU3fvaS4/p9tuu60q7+mqq67ivPPO4/zzz9/TdixtM5Y+zyarPfE/cPe3TrG/H3gjsMDdny3j9d4C/AD4fXf/TkA9FgAbgAl3P37SPvXEi0gqirPTdHV10dfXl7vZaUI1+ph4EWlsueqJd/dRot72Q6c55FDgyXIa8LFLiXrafxhYjzHgDuA4M1sccu5cKn7Lk/IorzDKK0waeTU1NdHZ2cnSpUvp7OzMXUM2NLOmpiZWrFhBb28vAL29vQ3VgNefyTDKK4zyCpPFvDLVEw9gZmuJVlA9borZabYA29z9zDJe52SiG2H/pXRGmpL9C+JyRuJG++T9PwQ6gEXFqS7j7eqJFxGpsUacJ15EGluueuJj1xHNQNMyafsFwJHx/nL8cfz8n9PsPx14HPinyTvMbH9gCdEsObsm709LcQyXlEd5hVFeYZRXuEoya8QGvK6xMMorjPIKk8W8stgTfx4wxN4rth4A/BR4BXCWu99XxuvcRzRd5eHuvs/qIPFNr6uBE4E2d78l3r4f0Q2tHwcudfevTDovtZ748fFx5s+fX/Ny80p5hVFeYZRXOGUW5pWvfCU33nhj2tXIDV1fYZRXmDTyyl1PfLzo0jeBVwFrzOybwBqiBvw1xQa8mR1uZl82sy9Pfg0zOwE4A/jlVA34uBwHLgaagJvNrN/M/gO4i6gB/z9E88tnxpYtW9KuQq4orzDKK4zyCqfMylNcpfamm25quFVqK6HrK4zyCpPFvDLXiI+9H/gkUf3eAjxCtFDTJSXHHER04+qlU5zfHj/fPFMh7v5L4KXAj4HzgbcCw8CfAx3TfQFIy2GHlTWzpsSUVxjlFUZ5hVNmsyvOyNPd3Q1Ad3c37e3tasiXQddXGOUVJot5ZbIR7+673P0z7v5Cd1/o7i939y+ULvDk7hvc3ab6icHdl8f7/r6Msn7j7m9y92Pd/SB3/x13vypeITZTJq/WKDNTXmGUVxjlFU6Zza7RV6mthK6vMMorTBbzmpd2BfKqp6dnn21dXV10dXXNWZkauxZGeYVRXmGUVzhlNrPpVqkdGRmhq6tLq9TOQtdXGOUVZq7z6uvro6+vL+iczN3YmnVp3ti6ffv2TP6ck1XKK4zyCqO8wimz2fX399Pd3b1Xr19zczO9vb10dnamWLPs0/UVRnmFSSOv3N3YKtMbHx9Puwq5orzCKK8wyiucMptdR0cHra2tNDc3A+xZpbajoyPlmmWfrq8wyitMFvNSIz5Hin+pS3mUVxjlFUZ5hVNms2v0VWoroesrjPIKk8W81IjPke3bt6ddhVxRXmGUVxjlFU6ZlaepqYnOzk5aW1vp7OxUA75Mur7CKK8wWcxLY+IDabGn/FBeYZRXGOUVTpmFUV5hlFcY5RVGiz1JRTZu3Jh2FXJFeYVRXmGUVzhlFkZ5hVFeYZRXmCzmpZ74QGn2xIuIiIhIY1BPfB1ZvXp12lXIFeUVRnmFUV7hlFkY5RVGeYVRXmGymJd64gOpJ15ERERE5tpsPfFasTWhNFZsXb16NWecccacvX69UV5hlFcY5RVOmYVRXmGUVxjlFWau89KKrTWgnngRERERmWsaE19H1q5dm3YVcuUlL3lJ2lXIFV1fYZRXOGUWRnmFUV5hlFeYLOalnvhAafbET0xMMG+eRkDNplAoMDAwQFdXF319fXR0dGixlDLo+gqjvMIpszDKK4zyCqO8wqSRl3ri68imTZvSrkLmFQoF2tvb6e7uBqC7u5v29nYKhULKNcs+XV9hlFc4ZRZGeYVRXmGUV5gs5qVGfI4cddRRaVch8wYGBhgcHGRkZASAkZERBgcHGRgYSLlm2afrK4zyCqfMwiivMMorjPIKk8W81IjPkR07dqRdhUxra2ujq6trTwO+aGRkhK6uLtra2tKpWE7o+gqjvMIpszDKK4zyCqO8wmQxLzXic2ThwoVpVyHTVq5cSV9fH83NzXttb25upq+vj5UrV6ZTsZzQ9RVGeYVTZmGUVxjlFUZ5hcliXmrE58jExETaVci8jo4OWltb9zTkm5ubaW1tpaOjI+WaZZ+urzDKK5wyC6O8wiivMMorTBbz0m3JCaWx2NPu3bvn7LXrRVNTEytWrNgzO01vb69mpymTrq8wyiucMgujvMIorzDKK8xc56XFnmogzSkmh4eHWbx4cc3LzauXv/zl3HzzzWlXIzd0fYVRXuGUWRjlFUZ5hVFeYdLIS1NM1pHh4eG0q5Ar3/rWt9KuQq7o+gqjvMIpszDKK4zyCqO8wmQxL/XEB0qzJ35sbIwFCxbUvNy8Ul5hlFcY5RVOmYVRXmGUVxjlFSaNvNQTX0c2b96cdhVyRXmFUV5hlFc4ZRZGeYVRXmGUV5gs5qWe+EBp9sS7+55vZTI75RVGeYVRXuGUWRjlFUZ5hVFeYdLISz3xdeT+++9Puwq5orzCKK8wyiucMgujvMIorzDKK0wW81IjPkduvPHGtKuQK8orjPIKo7zCKbMwyiuM8gqjvMJkMS8NpwmU5nCas88+m3vuuafm5eaV8gqjvMIor3DKLIzyCqO8wiivMGnkpeE0dUR3kYdRXmGUVxjlFU6ZhVFeYZRXGOUVJot5acXWhNJYsXVsbGzOXrseKa8wyiuM8gqnzMIorzDKK4zyCjPXeSVZsVWN+ISWLVtW8zKz+C0wy5RXGOUVRnmFU2ZhlFcY5RVGeYWZ67ym6ghevnz5jOdoOE2OPPvss2lXIVeUVxjlFUZ5hVNmYZRXGOUVRnmFyWJemWzEm9n+ZvZxM3vQzJ41s3Xx/8/6y4GZHWBmX57hceak4xeZ2RfN7GEzGzOze8zsgzZHk4GG/lRSav78+amUW+n5aZ2rvMJUklelZefxXOUVLq0/k5Wce/bZZ6dSLuQzL11ftTu30vOVV5g02xTTyWQjHrga+CxgwLVEw34+C/x7GeeeCFw6w+OE4oHxl4LrgL8AhoH/Bo4Avgp8qhpvZLJKPshdu3alUm6l56d1rvIKU0lelZadx3OVV7i0/kwmObdQKNDf38+9995Lf38/hUKhJuWWylNeaZ8LjZdXpecrrzBptimmk7lGvJmdB7wb+Dlwmrt3A6cCNwHvNbOzZnmJk+Pnd7m7TfH4ScmxbwSWAv8JnO3ufwCcDtwPfMzMDqviW6tYU1NT2lXIFeUVRnmFUV7h8pJZoVCgvb2d7u5uALq7u2lvb0/UkK9EXvLKCuUVRnmFyWJemWvEA++Ln//C3XcBuPs48OF4+3tnOb/YiH+gzLJ2A3/u8cTv7r4D+ASwP/DO8qs99zSnfxjlFUZ5hVFe4fKS2cDAAIODg4yMjAAwMjLC4OAgAwMDNa1HXvLKCuUVRnmFyWJeWWzEvxF4BLhz0vbfAFvj/TM5JX6esRFvZvsBbwBucfcnJu3+H6BQRlkiIlJH2tra6Orq2tOALxoZGaGrq4u2trZ0KiYiMknmVmw1s6eBX7v7q6fYdzNwursfMcP5PwFeClwJ/BFwHLAG+CHwubhXHzM7HNgGXO3uF0/xOpuALe5+4aTt2QpMREREROpWLlZsNbOFQDPw5DSHPAEcPsssNScDBxMNiXmIqPF+BNGNqjea2f7xcc+Ln2cq66jyay8iIiIiUhtZW+zp0Ph5ZJr9xe2HEQ2t2UvcuD8aeBjodPe74u0LgP8gGuP+p8A/lVnWKZM3TvdtSERERESkVjLVE89zveIHTbP/4Pj5qal2uvuEuze7+wnFBny8fQz4M2AX8LaAsnaUWW8RERERkZrJVCPe3UeJesAPneaQQ4En3T142Sx33wo8CJwRb3qs5DWnK2tLaDkiIiIiInMtU4342KPAaZNXTI1nkzmFGRrWZrbQzI4ws+mW1RrnuV78J+P/P32K1zmEaMy8GvEiIiIikjlZbMRfBxwDtEzafgFwZLx/Oq8HHgf+fPIOMzuYaNGo+wDcfTfwY6DVzCbPdvM6oGmWskREREREUpHFRvw18fMXizPJmNkBwBfj7VfPcO4Kop72vzSz4qJPxD3z/wwsAL4yqSwDvlTs+TezQ4HPAM8CvRW+FxERERGRqstcI97d7wS+CbwKWGNm3ySa5/0VwDXufh9E87yb2ZfN7Msl5z4DXAIcDtxlZt83s/+Kz38f8P/c/cclxV0H3Ei0CuxdZvYtop7604B/cPft1XxvZra/mX3czB40s2fNbF38/2XNEmRm+5nZpWZ2t5k9E59/pZk1T3HsIjP7opk9bGZjZnaPmX1w8jClLKtxXp8rXk9TPF5R/XdXfZXmFVhWw19fgWU1/PVlZs83s6+b2XozGzazQTN7/1TXjK6v4Lx0fZmdYmbfMbMNZva4mV1nZu/S9TXt+SF55f76KmVmHzGzdYHnvDDOa7uZjZjZjWb2qmmOPcLM/p+ZPWZR2+M2M3t7dWo/ibtn7gHsz3PzvI8CNwN/CcwrOeZEwKO3sM/5rwGuJ5qGcgewEngP8eJWk45tBr5EtErs0/F5fzzVsVV4X/8V13k9US//hvj/v17GuQYsLzn/v4hWsXXgp8B+JcfOi9+zA/cC3yIa3+/Ap9P+fDOY14HFa2maxwfTzmIu8wLeP8v7Lz5O1PWVKK+Gvr7ic08gug9pN9Evpl8Hfhuf/6VJxzb09ZUgL11fUaffTuAZ4EfAtcBwfP5f6vqqKK+6uL5K3s8Cos7adQHnHEc0GUqBqOP3R3F+BeB1k449CFgdZ/Nr4Hvxn2UHLq76+0k70EZ5AOfFH+INwP7xtvnxBeHAWbOc/4r4uG8CTfE2A/4t3v62kmPfXPzDzHOr8h5C9IvEOHBY2nlkLK+z4m1/nfb7TiMvohWOvzzDYwfRvSaLdX0lyquhr6/42G/Fx3WVbDuQqIPGgTNKtjf09ZUgr4a+vogaZQ/Ef+7OLtl+ErCRqHOuWddX4rzq4foy4HjgLTz3BS6kEf9P8TkfKNl2AVEn868nHfshJn0hBF5A1Km8hZLO6Kq8t7TDbZQHUW+/Ay2Ttr843v65Wc7/D6Jvfs2Tth8P/Ay4rGTbtUTfEA+fdOw7yMk35xrnVfxL/e1pv++08prhdf8gPv+Nur4S59Xw1xfRAnxrptjezaQeKl1fwXk19PVF1KvswOVT7PujeN8lur4S51UP19c89v0FoaxGPNHIkK3A0BT7/m/8WqeXbLsdeIJJjXWi0SQO/G4131vmxsTXsTcSDdm5c9L23xBdIG+c7kSLVpx9B/Ajd99rhVl33+Tur3X3q+Jj9wPeANzi7k9Meqn/IfrLa9qyMqQmecWKK/M+UGmlU5Q4r+mY2XFEN4L/i7tfF29r+OtrOlPlFdP1FV0XPsX23fHzYtD1VaKsvGKNfn2dHT//eop9v4ifXwG6vmJl5xWrh+trN1GboPjYFnDuhUw/M2Jx2xsBzOwYYAnwE3efmOnYalEjvnaOBdZ6/JWsyKOpLh8gmlZzOscR/ZS6xswONbN3mNnfm9mfmNlpk449FDgAuH/yi7j7U0Q/58xUVlbUKi+A4kxGr4lvcNpp0c2wXzKz6Vb0zZpK8prOvxH9tPpXJdt0fU1vqrxA1xfAz4HTzayzuMHMDgQuJfoH9ufxZl1fkXLzAl1fxcbSoin2HRg/F8/X9RWWF9TB9eXuu939e8UH0a/05To2ft7nmgHWxs/HlHHsA5OOrQo14mvAzBYS3UD75DSHPAEcPsNd5cUP/XCin2q+A/w10U8595jZX5Yc+7z4eaayjiqz6qmocV7w3F9SnyP6hn4t0U9ofw4MWbTGQGZVIa+pXvNVRD0Gn3D3sZJdur6mfs3p8gJdXwCXAb8EfmRmA2b2daKbvy4C3uXRrGSg66voMsrLC3R93RM/d0yx763xc/HfBF1fYXlBzq+vKpjpmin+mnPUbMe6+y6im4eren2pEV8bh8bPI9PsL24/bJr9R8fPf0b0ze98op9TX0H0je/zZvbqgLImL26VNbXMC+D5wHaisWpt7v5eopt5riK62efTCd5DLVWa117iKcauBO4GvpGgrHq/vvYyS16g6wviMahEN5j9LtG0vi8gahSU/rSt6ytSbl6g6+sXwCDwPjP7qJk9z8yONrMPE3XewHNDk3R9heUF+b++KjVT3sVtR5RxbHF7Va8vNeJro/itbLqfnorfZJ+a5XXWA2929zvc/Wl3v5nnbsYp9i6XU9aOWWucrlrmhbuf5u6Hu/sNJdsKRH+hPQa8LcF7qKVq5VX0ZqIbpD4b5xBa1o4yy0lLLfNq+OvLzJqIVsd+D/BJosboYqKpgB8DVpjZ6wPK2lFuxVNSy7wa/vqKh5D8IdEvFZ8jGhLzKNEX64/Hhz0aUNaOciqdolrmVQ/XV6VmyruY9Y4yji0ev2OafYmoEV8D7j5K9A3s0GkOORR40t2fnWb/lvj5usk/1bv7aqLGavFmlcdKXnO6srZMsy8TapzXTPUYA+4AjjOzxbMdn5Yq5DXZpUQ9Lz+cYp+ur33NlNdM9WiU6+uNwMuAf3b3z7j7xvhL9Q3xvgLwqfhYXV9hec1Uj0a5vnD3tUQ3IL4T+Dui4UgtREM/4LlGqa4vgvKaqR65uL6qYKZrprhty2zHmtn+RPchVPX6qvrKhDKtR4HTzMxKb0aJ75Y/hZk/2OK+Z6bZ/zTRGDmIvgmOA6dPPsjMDiEaszX5jvYsqkle8Uw2zcDIFGOZIcpy1wyvlRWV5LWHmZ0MvJZohpWp/hHQ9VVitrx0fQFwRvx8/eQd7v6Imd1H9PM86PqCgLx0fT0nfv/fjR/F898Q/2fxBkRdX7Fy8qqj66sSxS80+1wzQHGijC0Jjq0K9cTXznVEN4u0TNp+AdNPX1S0gWg6qd+Jx9/uEX8DPo34ZpX4p7IfA61mNnns1euAplnKyoqa5EX0h+1xosUcmHTs/kTTRa2Nb0rJskryKvXH8fN/TrVT19c+ZswLXV8Q/fIF0axRe4kbHccSrc6t6ytSdl7o+sLMDjezn5nZ5FmhAH6faPjkN0DXF4TlRf1cX5W4jeg+lDdMsa94c/CPAdx9MzAEtE9xY3Hx2OpeX56Bifgb4cHUK6wdwHMrrJ05y/mfYtJCFER3iF8db39fyfbi4gylK9IdSrQi3Rj5XZGu6nkR3ThWzOWlJcfuB3w2PvZP0s5jrvMqeZ37iMbsNc1wTMNfX+XmpevLIWps7CT6cv38SRlcEZ//eV1f4Xnp+tpz/ur4z+GLSra9gaiX+KeTjm3o6yskr3q5vqZ4/xtItmJr6SJrxRVbBycd+6H42Mkrtj4GbEYrtub3QfTt1oEHgW8S9aY4cHXJMYcTL98+6dyjiFbxc+BX8V9Aa+P//1HxL6P42HnAqnjf3URLeD/KNKu0ZfVRw7x+J/7LawLoJ1rt9Z742BXM0KDN0qOSvOJ9J8TH/3iWchr++grMq+GvL6KVIHcTNU5/GGdwb3z+XcBCXV+J89L1Ba+Pj30qfp0b4kx2ULKapq6vRHnVxfU16T1tYIpGPNFQpC9T0gCPtx9PtIhWgWiNhh/FfzYLwGsmHXsQ0RcfJ5oF6HtE90w5JZ2tVXsvaYfZSA+inuBPxH/YRoGbiWZJmVdyzInxh+1TnH8c8K/xX+JPA7cQfevbb4pjm4mWZn4kPvZ6op/+bS7eWx3kdUH8B3NzfOwviW722efYrD6qkNcl8b6/KaMsXV9heen6gpfEGWwkujHvNuBvgQW6virOS9cXvAq4iWgu7m3Atynpadb1VVFeub++Jr2fDUzdiG+L89owxb6TiBrkTxL1qn8fWDrN6x8F/DvRUKQniYbQvHUu3kvxpyQREREREckJ3dgqIiIiIpIzasSLiIiIiOSMGvEiIiIiIjmjRryIiIiISM6oES8iIiIikjNqxIuIiIiI5Iwa8SIiIiIiOaNGvIiIiIhIzqgRLyIiIiKSM2rEi4iISN0xsw1m5tM8njGzITO7xsxOqkFdrpihLjM92mr5mgJm1laS1U4zW1jGOYvN7NmS8146af9M1+KTZvYbM7vczA4Lqeu80DcnIiIikiOPA8OTth0FnBc/3mlmf+ju35vDOmwHHpxi+3HAAuDJ+JjJnqnxa8reFgIdwPdnOa4TmF/G602+Fg8AjgfOjx+Xmtk73H1VOZUzdy/nOBEREZHcMLMNwAuAP3L3aybtM+BC4N+Bs4FtwKnu/mSN67gSWAp8yt2vyOprNpL4l4qfl2z6trv/wSznfB94a8mmi9z9lpL9G5j+WjwMeAnwqfh5ND7/rtnqquE0IiIi0lA88muihtcu4AjgD9OtlWTMBqJe804zO3C6g8xsEVFv/TbgkdBC3H27u/+E6IvXTUS9//8Wf9GckRrxIiIi0pDc/QHgtvh/z0izLpI5zwI/BIqN9Ol0AAcCPwAmkhbm7mPAR+P/fWn8mJEa8SIiItLIir2nJ5RuNLP9zOz3zOynZvbb+MbFR83sV2Z26eTeWTN7f3yj4kfM7CAz+08zGzaza2r1RqTqvhs/v2OGY94eP1d8T4W7/wooDsN552zHqxEvIiIijeyF8fOGSds/DVwLvBZ4HtFNiQcT9ZB+GfiOmTVN8XoHxOe9B2gGnqp+laVGfgo8zTRDauJtbyS6ifjnk/cntDJ+fslsB6oRLyIiIg3JzF4CXBD/71DJ9hOAj8f/eylwkLsfDxxE1PM6RjQjyblTvOxfAqcDXcBCd//QnFRe5lw8xOVHRF/GfneKQ14f7/uhu++qUrGb4udZpz5VI15EREQaipkdZWZ/QDTmGaJe+P8sOeT8+Pl/3P0rxQaauxfc/fvAT+L9Z0/x8gcDb3f3/rgRKPlWHCYz1ZCat006phqKjfhDZztQjXgRERGpZ1dPXmAHeAzoBY4GtgLvdffS+dN/BOwPvGGa1ywOrZhqOM3d7j5YpbrPmXihq2nnGTezlfF0lcQLEbmZvXLSMQfGC2c9ZWb7T9r36vicj1d6/jT1+yszq8WXpBXACNBVOqTGzOYDbyKaweZnVSzPJz1PS414ERERqWePEy2KNPlxPXAlcI6731x6grvvdvcJdy9Y5EQze7OZfdbMBoH2Gcr77dy8jVRdHz+/fNL2pUQLSy0GLpq0r3js9VU4fy9mdh7w2XIqXqn4y10/0bCZ0s/9NUS/uvS5+7NVLPL58fOO2Q7Uiq0iIiJSz/5q8gI75TCzNwIfAZYQNdYAdgK3E30JeNE0p061Smre3Ur03ic3wtuJhiIdRDTV4o0l+15G1IP9G8AqPH8PM1sAfIPovoRyVkmthu8Cf0A0pOa/421Vm5VmkuPj56lW492LeuJFRERESpjZu4l6X19GNLTmfcBZwMHu/krg5hlOn3UYRN64+zhRA/tlk2bk+V2i+wP+h5KhR/ExLwVWufuuSs+fVJ1/IBrG9NXJ9TSzBWb2RTNbZ2aPm9n3zOy0eFhOW8K3T1zHUaIhNQvioT9vJvpisqKC151KW/x862wHqhEvIiIisrdPxM8fcPc/dPevu/t97l6Itx883Yl5Y2bNUz3Yd7z/9UTDXs6OzzuBaBaenwADwLlmdlx87NnxsddX8XzM7DXAnxBN31l6D0PRtcCfAt8E/izeduMUxwVx91GiL3UHEf16sBQ4HOifdC9FRczsIp5b5Ok7sx2vRryIiIjI3ooLP+1zg6qZHQ28qrbVmVNPT/OYPPRl8rj2dqIVSn/Oc73RxZVNXxY/31Ct883sUOAa4NPuvtcQm3j/K+Pze9z9k+7+LaLhL0P7vuVESmepKc5K8/0qvTZmdgDwufh/f8UU195kasSLiIiI7O3++PnPzWwhREM8zOwNRIvxFHviT0mhbtX2imkeQ5OOuwt4It4H0VCYX7r7sLtvBW5j70b4E8DdVTz/K0TTL145zft4GTBONF4eAHd34D+mf+tBfkzU+/8m4K1EY/IHKn1RMzvUzNqJrqtXEg3R+V9x3WekRryIiIjI3j4TP38Q2G5mDxM14K4jarwVZ0b5GzOr5vSCNefuN0/1YNJKs+6+m6hn/OXxmPDX8Nx8+RA1aF8X73sZcEN8TsXnm9lbgd8jGkqzIB7uMz/aZc3x1I8nAFvcfWLSW3y4gnhK3/9Ooob8QcBRwI/dfSTwZT4fj9cvPh4muhH6J0TDaB4H3uDud8/4KjE14kVERERKuPu1RD3FPyeaB3wRsAr4GPAS4Aqi4RVPAWvTqWUqrgeOA36f6NeI0ps6B4gauO8AXsDeQ2kqPf9c4ACimYGKw33+mqgh/zTwA6L5/o82s8kzLx4T+B5n8t2S/04ylOZIolmNio/jia6vIeBTwOnuXvYYfiujt15ERERE6oiZXQO8z91tmv0rAdy9rWTbycADwDqiG0+PKektbyJqSG8HTgZOc/e1k14z0fnxTbAnsLeLiW5wfTXRnOonEa3A+z53/3p8ngF9wBuBV7n7yrIDygHNEy8iIiIi5XgQ2EjUyP6vScNlCmb2P0TzqW8iaqxX5Xx33xift4eZvRbYXVyoy8zuI5pTfpmZnQSsJhqC8zuVvums0nAaEREREZlVfLNlcZaZqeZH/3H8fMNUN2ZWev4sddsNvJZoesk/Br5MNE1md8jr5ImG04iIiIhIXTKzC4FfU4fDadQTLyIiIiKSM2rEi4iIiIjkjBrxIiIiIiI5ozHxIiIiIiI5o554EREREZGcUSNeRERERCRn1IgXEREREckZNeJFRERERHJGjXgRERERkZxRI15EREREJGfUiBcRERERyRk14kVEREREckaNeBERERGRnPn/AbwbCqGNUO0+AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 864x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# {var: (bins, label)}\n",
-    "plot_vars = {\n",
-    "    \"ak8FatJetParTMD_THWW4q\": ([20, 0.6, 1], r\"ParT $T_{HWW4q}$ MD\"),\n",
-    "}\n",
-    "\n",
-    "pre_hists_cut = {}\n",
-    "\n",
-    "for var, (bins, label) in plot_vars.items():\n",
-    "    if var not in pre_hists_cut:\n",
-    "        pre_hists_cut[var] = utils.singleVarHistNoMask(\n",
-    "            cut_dict, var, bins, label, weight_key=\"weight\"\n",
-    "        )\n",
-    "\n",
-    "merger_pre_plots = PdfFileMerger()\n",
-    "\n",
-    "for var, var_hist in pre_hists_cut.items():\n",
-    "    name = f\"{plot_dir}/pre_{var}_tau42_cut.pdf\"\n",
-    "    plotting.ratioLinePlot(\n",
-    "        var_hist,\n",
-    "        plot_samples,\n",
-    "        year,\n",
-    "        bg_err=None,\n",
-    "        name=name,\n",
-    "    )\n",
-    "    merger_pre_plots.append(name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/boost_histogram/_internal/hist.py:863: UserWarning: List indexing selection is experimental. Removed bins are not placed in overflow.\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/Documents/CERN/hhbbww/HHbbVV/src/HHbbVV/scale_factors/plotting.py:216: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
-      "  if bg_err == \"ratio\":\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "post_lnN_cut_hists = {}\n",
-    "post_lnN_cut_hists_err = {}\n",
-    "uncs_lnN_cut_dict = {}\n",
-    "\n",
-    "events = cut_dict[top_matched_key]\n",
-    "\n",
-    "for var, (bins, label) in plot_vars.items():\n",
-    "    if var not in post_lnN_cut_hists:\n",
-    "        toy_hists = []\n",
-    "        for i in range(events[\"lp_sf_lnN\"].shape[1]):\n",
-    "            toy_hists.append(\n",
-    "                np.histogram(\n",
-    "                    events[var][0].values.squeeze(),\n",
-    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
-    "                    weights=events[\"weight\"][0].values * events[\"lp_sf_lnN\"][i].values,\n",
-    "                )[0]\n",
-    "            )\n",
-    "\n",
-    "        sys_up_down = []\n",
-    "        for key in [\"lp_sf_sys_up\", \"lp_sf_sys_down\"]:\n",
-    "            sys_up_down.append(\n",
-    "                np.histogram(\n",
-    "                    events[var][0].values.squeeze(),\n",
-    "                    np.linspace(*bins[1:], bins[0] + 1),\n",
-    "                    weights=events[\"weight\"][0].values * events[key][0].values,\n",
-    "                )[0]\n",
-    "            )\n",
-    "\n",
-    "        nom_vals = toy_hists[0]  # first column are nominal values\n",
-    "\n",
-    "        uncs = {\n",
-    "            \"stat_unc\": np.minimum(nom_vals, np.std(toy_hists[1:], axis=0)),  # cap at 100% unc\n",
-    "            \"syst_rat_unc\": np.minimum(nom_vals, (np.abs(sys_up_down[0] - sys_up_down[1])) / 2),\n",
-    "            \"syst_sjm_unc\": nom_vals * sj_matching_unc,\n",
-    "            \"syst_sjpt_unc\": nom_vals * sj_pt_unc,\n",
-    "        }\n",
-    "\n",
-    "        uncs_lnN_cut_dict[var] = uncs\n",
-    "\n",
-    "        unc = np.linalg.norm(list(uncs.values()), axis=0)\n",
-    "\n",
-    "        thist = deepcopy(pre_hists[var])\n",
-    "        top_matched_key_index = np.where(np.array(list(thist.axes[0])) == top_matched_key)[0][0]\n",
-    "        thist.view(flow=False)[top_matched_key_index, :].value = nom_vals\n",
-    "        post_lnN_cut_hists[var] = thist\n",
-    "\n",
-    "        post_lnN_cut_hists_err[var] = unc\n",
-    "\n",
-    "\n",
-    "merger_post_plots = PdfFileMerger()\n",
-    "\n",
-    "for var, var_hist in post_lnN_cut_hists.items():\n",
-    "    name = f\"{plot_dir}/postlnN_{var}_cut.pdf\"\n",
-    "    plotting.ratioLinePlot(\n",
-    "        var_hist,\n",
-    "        plot_samples,\n",
-    "        year,\n",
-    "        bg_err=post_lnN_cut_hists_err[var],\n",
-    "        name=name,\n",
-    "    )\n",
-    "    merger_post_plots.append(name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mass_hist = utils.singleVarHistNoMask(\n",
-    "    events_dict, \"ak8FatJetMass\", [20, 125, 225], r\"$m_{SD}$\", weight_key=\"weight\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotting.ratioHistPlot(\n",
-    "    mass_hist,\n",
-    "    [\"QCD\", \"Diboson\", \"Single Top\", \"W+Jets\", \"TT Unmatched\", \"TT W Matched\", top_matched_key],\n",
-    "    f\"{plot_dir}/\",\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('python310')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31a7b1cb5f073f7a7d37b3db504c6954ce2b88e0f82e412b65ad0b5f2dd17394"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/HHbbVV/scale_factors/top_reweighting.ipynb b/src/HHbbVV/scale_factors/top_reweighting.ipynb
index 93b3aa45..99872f2c 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": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,49 +63,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15SubJet_nBHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15SubJet_nCHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15_nBHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJetAK15_nCHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDBvLV2 in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDCvBV2 in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_btagDDCvLV2 in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_nBHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch FatJet_nCHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch SubJet_nBHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n",
-      "/Users/raghav/mambaforge/envs/python310/lib/python3.10/site-packages/coffea/nanoevents/mapping/uproot.py:86: UserWarning: Found duplicate branch SubJet_nCHadrons in <TTree 'Events' (1937 branches) at 0x7fa7000e3eb0>, taking first instance\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "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",
-    "    # \"/eos/uscms/store/user/lpcpfnano/drankin/v2_2/2017/TTbar/TTToSemiLeptonic_TuneCP5_13TeV-powheg-pythia8/TTToSemiLeptonic_ext1/211112_132937/0000/nano_mc2017_100.root\",\n",
-    "    \"../../../../data/nano_files/v2_3/2018/TTToSemiLeptonic/nano_mc2018_101.root\",\n",
+    "    \"/eos/uscms/store/user/lpcpfnano/drankin/v2_2/2017/TTbar/TTToSemiLeptonic_TuneCP5_13TeV-powheg-pythia8/TTToSemiLeptonic_ext1/211112_132937/0000/nano_mc2017_100.root\",\n",
+    "    # \"../../../../data/nano_files/v2_3/2018/TTToSemiLeptonic/nano_mc2018_101.root\",\n",
+    "    # \"../../../../data/nano_files/v2_3/2018/QCD_HT700to1000/nano_mc2018_10.root\",\n",
     "    # \"root://cmsxrootd.fnal.gov///store/user/lpcpfnano/cmantill/v2_3/2018/TTbar/TTToSemiLeptonic_TuneCP5_13TeV-powheg-pythia8/TTToSemiLeptonic/220808_151244/0000/nano_mc2018_1-196.root\",\n",
     "    schemaclass=nanoevents.NanoAODSchema,\n",
     ").events()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -116,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,14 +143,16 @@
     "n_events = len(presel_events) if isData else int(np.sum(signGenWeights))\n",
     "selection = PackedSelection()\n",
     "weights = Weights(len(events), storeIndividual=True)\n",
+    "year = \"2018\"\n",
     "\n",
     "cutflow = {}\n",
-    "cutflow[\"presel\"] = len(presel_events)"
+    "cutflow[\"presel\"] = len(presel_events)\n",
+    "selection_args = (selection, cutflow, isData, signGenWeights)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,12 +179,16 @@
     "btagWPs = {\"2016APV\": 0.6001, \"2016\": 0.5847, \"2017\": 0.4506, \"2018\": 0.4168}\n",
     "\n",
     "ak4_jet_selection = {\n",
-    "    \"pt\": 25,\n",
+    "    \"pt\": 30,  # from JME-18-002\n",
     "    \"eta\": 2.4,\n",
     "    \"delta_phi_muon\": 2,\n",
-    "    \"jetId\": nanoaod.Jet.TIGHT,\n",
+    "    \"jetId\": \"tight\",\n",
     "    \"puId\": 4,  # loose pileup ID\n",
     "    \"btagWP\": btagWPs,\n",
+    "    \"ht\": 250,\n",
+    "    \"num\": 2,\n",
+    "    \"closest_muon_dr\": 0.4,\n",
+    "    \"closest_muon_ptrel\": 25,\n",
     "}\n",
     "\n",
     "met_selection = {\"pt\": 50}\n",
@@ -244,7 +223,9 @@
     "muon_selector = muon_selector * ak.any(\n",
     "    np.abs(muon.delta_r(trigObj_muon)) <= muon_selection[\"delta_trigObj\"],\n",
     "    axis=1,\n",
-    ")"
+    ")\n",
+    "\n",
+    "add_selection(\"muon\", muon_selector, *selection_args)"
    ]
   },
   {
@@ -255,7 +236,7 @@
    "source": [
     "# met\n",
     "met = events.MET\n",
-    "met_selection = met.pt >= met_selection[\"pt\"]\n",
+    "met_sel = met.pt >= met_selection[\"pt\"]\n",
     "\n",
     "# metfilters = np.ones(len(events), dtype=\"bool\")\n",
     "# metfilterkey = \"data\" if isData else \"mc\"\n",
@@ -263,16 +244,82 @@
     "#     if mf in events.Flag.fields:\n",
     "#         metfilters = metfilters & events.Flag[mf]\n",
     "\n",
-    "add_selection(\"met\", met_selection * metfilters, *selection_args)\n",
+    "# add_selection(\"met\", met_selection * metfilters, *selection_args)\n",
+    "add_selection(\"met\", met_sel, *selection_args)\n",
     "\n",
     "# leptonic W selection\n",
-    "# add_selection(\"lepW\", (met + muon).pt >= self.lepW_selection[\"pt\"], *selection_args)\n",
-    "add_selection(\"lepW\", met.pt + muon.pt >= 100, *selection_args)"
+    "add_selection(\"lepW\", (met + muon).pt >= lepW_selection[\"pt\"], *selection_args)\n",
+    "# add_selection(\"lepW\", met.pt + muon.pt >= 100, *selection_args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ak4_jets = events.Jet\n",
+    "\n",
+    "ak4_jet_selector_no_btag = (\n",
+    "    ak4_jets.isTight\n",
+    "    # pileup ID should only be applied for pT < 50 jets (https://twiki.cern.ch/twiki/bin/view/CMS/PileupJetIDUL)\n",
+    "    * ((ak4_jets.puId % 2 == 1) + (ak4_jets.pt >= 50))\n",
+    "    * (ak4_jets.pt > ak4_jet_selection[\"pt\"])\n",
+    "    * (np.abs(ak4_jets.eta) < ak4_jet_selection[\"eta\"])\n",
+    "    * (np.abs(ak4_jets.delta_phi(muon)) < ak4_jet_selection[\"delta_phi_muon\"])\n",
+    ")\n",
+    "\n",
+    "ak4_jet_selector = ak4_jet_selector_no_btag * (\n",
+    "    ak4_jets.btagDeepFlavB > ak4_jet_selection[\"btagWP\"][year]\n",
+    ")\n",
+    "\n",
+    "ak4_selection = (\n",
+    "    (ak.any(ak4_jet_selector, axis=1))\n",
+    "    * (ak.sum(ak4_jet_selector_no_btag, axis=1) >= ak4_jet_selection[\"num\"])\n",
+    "    * (ak.sum(ak4_jets[ak4_jet_selector].pt, axis=1) >= 250)\n",
+    ")\n",
+    "\n",
+    "# add_selection(\"ak4_jet\", ak.any(ak4_jet_selector, axis=1), *selection_args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pileup ID effect\n",
+    "\n",
+    "# unmatched\n",
+    "np.sum(top_match_dict[\"unmatched\"] * ak4_selection[select])\n",
+    "print(166 / 170)\n",
+    "\n",
+    "# w matched\n",
+    "np.sum(top_match_dict[\"w_matched\"] * ak4_selection[select])\n",
+    "print(10 / 10)\n",
+    "\n",
+    "# top matched\n",
+    "np.sum(top_match_dict[\"top_matched\"] * ak4_selection[select])\n",
+    "print(21 / 21)\n",
+    "\n",
+    "# qcd (removing muon selection, otherwise there are no events)\n",
+    "print(6762 / 7168)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sel = selection.all(*selection.names)\n",
+    "muon_j_dr = muon.delta_r(events.Jet[events.Jet.isTight])\n",
+    "min_muon_j_dr = ak.min(muon_j_dr, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -314,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,20 +442,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "_ = plt.hist(jmr_nom.reshape(-1), np.linspace(0, 2, 101), histtype=\"step\")\n",
     "_ = plt.hist(jmr_up.reshape(-1), np.linspace(0, 2, 101), histtype=\"step\")\n",
@@ -417,30 +453,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f9c1266f2b0>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "_ = plt.hist(mass[mass > 30], np.linspace(0, 300, 101), histtype=\"step\", label=\"Pre\")\n",
     "_ = plt.hist(tdict[\"\"][mass > 30], np.linspace(0, 300, 101), histtype=\"step\", label=\"Post\")\n",
@@ -458,250 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['area',\n",
-       " 'btagCSVV2',\n",
-       " 'btagDDBvLV2',\n",
-       " 'btagDDCvBV2',\n",
-       " 'btagDDCvLV2',\n",
-       " 'btagDeepB',\n",
-       " 'btagHbb',\n",
-       " 'deepTagMD_H4qvsQCD',\n",
-       " 'deepTagMD_HbbvsQCD',\n",
-       " 'deepTagMD_TvsQCD',\n",
-       " 'deepTagMD_WvsQCD',\n",
-       " 'deepTagMD_ZHbbvsQCD',\n",
-       " 'deepTagMD_ZHccvsQCD',\n",
-       " 'deepTagMD_ZbbvsQCD',\n",
-       " 'deepTagMD_ZvsQCD',\n",
-       " 'deepTagMD_bbvsLight',\n",
-       " 'deepTagMD_ccvsLight',\n",
-       " 'deepTag_H',\n",
-       " 'deepTag_QCD',\n",
-       " 'deepTag_QCDothers',\n",
-       " 'deepTag_TvsQCD',\n",
-       " 'deepTag_WvsQCD',\n",
-       " 'deepTag_ZvsQCD',\n",
-       " 'eta',\n",
-       " 'mass',\n",
-       " 'msoftdrop',\n",
-       " 'n2b1',\n",
-       " 'n3b1',\n",
-       " 'particleNetMD_QCD',\n",
-       " 'particleNetMD_Xbb',\n",
-       " 'particleNetMD_Xcc',\n",
-       " 'particleNetMD_Xqq',\n",
-       " 'particleNet_H4qvsQCD',\n",
-       " 'particleNet_HbbvsQCD',\n",
-       " 'particleNet_HccvsQCD',\n",
-       " 'particleNet_QCD',\n",
-       " 'particleNet_TvsQCD',\n",
-       " 'particleNet_WvsQCD',\n",
-       " 'particleNet_ZvsQCD',\n",
-       " 'particleNet_mass',\n",
-       " 'phi',\n",
-       " 'pt',\n",
-       " 'rawFactor',\n",
-       " 'tau1',\n",
-       " 'tau2',\n",
-       " 'tau3',\n",
-       " 'tau4',\n",
-       " 'lsf3',\n",
-       " 'jetId',\n",
-       " 'subJetIdx1',\n",
-       " 'subJetIdx2',\n",
-       " 'electronIdx3SJ',\n",
-       " 'muonIdx3SJ',\n",
-       " 'nConstituents',\n",
-       " 'DDX_tau1_flightDistance2dSig',\n",
-       " 'DDX_tau1_trackEtaRel_0',\n",
-       " 'DDX_tau1_trackEtaRel_1',\n",
-       " 'DDX_tau1_trackEtaRel_2',\n",
-       " 'DDX_tau1_trackSip3dSig_0',\n",
-       " 'DDX_tau1_trackSip3dSig_1',\n",
-       " 'DDX_tau1_vertexDeltaR',\n",
-       " 'DDX_tau1_vertexEnergyRatio',\n",
-       " 'DDX_tau1_vertexMass',\n",
-       " 'DDX_tau2_flightDistance2dSig',\n",
-       " 'DDX_tau2_trackEtaRel_0',\n",
-       " 'DDX_tau2_trackEtaRel_1',\n",
-       " 'DDX_tau2_trackEtaRel_3',\n",
-       " 'DDX_tau2_trackSip3dSig_0',\n",
-       " 'DDX_tau2_trackSip3dSig_1',\n",
-       " 'DDX_tau2_vertexEnergyRatio',\n",
-       " 'DDX_tau2_vertexMass',\n",
-       " 'DDX_trackSip2dSigAboveBottom_0',\n",
-       " 'DDX_trackSip2dSigAboveBottom_1',\n",
-       " 'DDX_trackSip2dSigAboveCharm',\n",
-       " 'DDX_trackSip3dSig_0',\n",
-       " 'DDX_trackSip3dSig_1',\n",
-       " 'DDX_trackSip3dSig_2',\n",
-       " 'DDX_trackSip3dSig_3',\n",
-       " 'DDX_z_ratio',\n",
-       " 'Proba',\n",
-       " 'btagDeepB_b',\n",
-       " 'btagDeepB_bb',\n",
-       " 'btagDeepL',\n",
-       " 'DDX_jetNSecondaryVertices',\n",
-       " 'DDX_jetNTracks',\n",
-       " 'nBHadrons',\n",
-       " 'nCHadrons',\n",
-       " 'genJetAK8Idx',\n",
-       " 'hadronFlavour',\n",
-       " 'genJetAK8IdxG',\n",
-       " 'subJetIdx1G',\n",
-       " 'subJetIdx2G',\n",
-       " 'subJetIdxG',\n",
-       " 'pFCandsIdxG',\n",
-       " 'pt_raw',\n",
-       " 'mass_raw',\n",
-       " 'pt_gen',\n",
-       " 'event_rho',\n",
-       " 'pt_orig',\n",
-       " 'mass_orig',\n",
-       " 'jet_energy_correction',\n",
-       " 'pt_jec',\n",
-       " 'mass_jec',\n",
-       " 'jet_energy_resolution',\n",
-       " 'jet_energy_resolution_scale_factor',\n",
-       " 'jet_resolution_rand_gauss',\n",
-       " 'jet_energy_resolution_correction',\n",
-       " 'pt_jer',\n",
-       " 'mass_jer',\n",
-       " 'JER',\n",
-       " 'jet_energy_uncertainty_AbsoluteStat',\n",
-       " 'JES_AbsoluteStat',\n",
-       " 'jet_energy_uncertainty_AbsoluteScale',\n",
-       " 'JES_AbsoluteScale',\n",
-       " 'jet_energy_uncertainty_AbsoluteSample',\n",
-       " 'JES_AbsoluteSample',\n",
-       " 'jet_energy_uncertainty_AbsoluteFlavMap',\n",
-       " 'JES_AbsoluteFlavMap',\n",
-       " 'jet_energy_uncertainty_AbsoluteMPFBias',\n",
-       " 'JES_AbsoluteMPFBias',\n",
-       " 'jet_energy_uncertainty_Fragmentation',\n",
-       " 'JES_Fragmentation',\n",
-       " 'jet_energy_uncertainty_SinglePionECAL',\n",
-       " 'JES_SinglePionECAL',\n",
-       " 'jet_energy_uncertainty_SinglePionHCAL',\n",
-       " 'JES_SinglePionHCAL',\n",
-       " 'jet_energy_uncertainty_FlavorQCD',\n",
-       " 'JES_FlavorQCD',\n",
-       " 'jet_energy_uncertainty_TimePtEta',\n",
-       " 'JES_TimePtEta',\n",
-       " 'jet_energy_uncertainty_RelativeJEREC1',\n",
-       " 'JES_RelativeJEREC1',\n",
-       " 'jet_energy_uncertainty_RelativeJEREC2',\n",
-       " 'JES_RelativeJEREC2',\n",
-       " 'jet_energy_uncertainty_RelativeJERHF',\n",
-       " 'JES_RelativeJERHF',\n",
-       " 'jet_energy_uncertainty_RelativePtBB',\n",
-       " 'JES_RelativePtBB',\n",
-       " 'jet_energy_uncertainty_RelativePtEC1',\n",
-       " 'JES_RelativePtEC1',\n",
-       " 'jet_energy_uncertainty_RelativePtEC2',\n",
-       " 'JES_RelativePtEC2',\n",
-       " 'jet_energy_uncertainty_RelativePtHF',\n",
-       " 'JES_RelativePtHF',\n",
-       " 'jet_energy_uncertainty_RelativeBal',\n",
-       " 'JES_RelativeBal',\n",
-       " 'jet_energy_uncertainty_RelativeSample',\n",
-       " 'JES_RelativeSample',\n",
-       " 'jet_energy_uncertainty_RelativeFSR',\n",
-       " 'JES_RelativeFSR',\n",
-       " 'jet_energy_uncertainty_RelativeStatFSR',\n",
-       " 'JES_RelativeStatFSR',\n",
-       " 'jet_energy_uncertainty_RelativeStatEC',\n",
-       " 'JES_RelativeStatEC',\n",
-       " 'jet_energy_uncertainty_RelativeStatHF',\n",
-       " 'JES_RelativeStatHF',\n",
-       " 'jet_energy_uncertainty_PileUpDataMC',\n",
-       " 'JES_PileUpDataMC',\n",
-       " 'jet_energy_uncertainty_PileUpPtRef',\n",
-       " 'JES_PileUpPtRef',\n",
-       " 'jet_energy_uncertainty_PileUpPtBB',\n",
-       " 'JES_PileUpPtBB',\n",
-       " 'jet_energy_uncertainty_PileUpPtEC1',\n",
-       " 'JES_PileUpPtEC1',\n",
-       " 'jet_energy_uncertainty_PileUpPtEC2',\n",
-       " 'JES_PileUpPtEC2',\n",
-       " 'jet_energy_uncertainty_PileUpPtHF',\n",
-       " 'JES_PileUpPtHF',\n",
-       " 'jet_energy_uncertainty_PileUpMuZero',\n",
-       " 'JES_PileUpMuZero',\n",
-       " 'jet_energy_uncertainty_PileUpEnvelope',\n",
-       " 'JES_PileUpEnvelope',\n",
-       " 'jet_energy_uncertainty_SubTotalPileUp',\n",
-       " 'JES_SubTotalPileUp',\n",
-       " 'jet_energy_uncertainty_SubTotalRelative',\n",
-       " 'JES_SubTotalRelative',\n",
-       " 'jet_energy_uncertainty_SubTotalPt',\n",
-       " 'JES_SubTotalPt',\n",
-       " 'jet_energy_uncertainty_SubTotalScale',\n",
-       " 'JES_SubTotalScale',\n",
-       " 'jet_energy_uncertainty_SubTotalAbsolute',\n",
-       " 'JES_SubTotalAbsolute',\n",
-       " 'jet_energy_uncertainty_SubTotalMC',\n",
-       " 'JES_SubTotalMC',\n",
-       " 'jet_energy_uncertainty_Total',\n",
-       " 'JES_Total',\n",
-       " 'jet_energy_uncertainty_TotalNoFlavor',\n",
-       " 'JES_TotalNoFlavor',\n",
-       " 'jet_energy_uncertainty_TotalNoTime',\n",
-       " 'JES_TotalNoTime',\n",
-       " 'jet_energy_uncertainty_TotalNoFlavorNoTime',\n",
-       " 'JES_TotalNoFlavorNoTime',\n",
-       " 'jet_energy_uncertainty_FlavorZJet',\n",
-       " 'JES_FlavorZJet',\n",
-       " 'jet_energy_uncertainty_FlavorPhotonJet',\n",
-       " 'JES_FlavorPhotonJet',\n",
-       " 'jet_energy_uncertainty_FlavorPureGluon',\n",
-       " 'JES_FlavorPureGluon',\n",
-       " 'jet_energy_uncertainty_FlavorPureQuark',\n",
-       " 'JES_FlavorPureQuark',\n",
-       " 'jet_energy_uncertainty_FlavorPureCharm',\n",
-       " 'JES_FlavorPureCharm',\n",
-       " 'jet_energy_uncertainty_FlavorPureBottom',\n",
-       " 'JES_FlavorPureBottom',\n",
-       " 'jet_energy_uncertainty_TimeRunA',\n",
-       " 'JES_TimeRunA',\n",
-       " 'jet_energy_uncertainty_TimeRunB',\n",
-       " 'JES_TimeRunB',\n",
-       " 'jet_energy_uncertainty_TimeRunC',\n",
-       " 'JES_TimeRunC',\n",
-       " 'jet_energy_uncertainty_TimeRunD',\n",
-       " 'JES_TimeRunD',\n",
-       " 'jet_energy_uncertainty_CorrelationGroupMPFInSitu',\n",
-       " 'JES_CorrelationGroupMPFInSitu',\n",
-       " 'jet_energy_uncertainty_CorrelationGroupIntercalibration',\n",
-       " 'JES_CorrelationGroupIntercalibration',\n",
-       " 'jet_energy_uncertainty_CorrelationGroupbJES',\n",
-       " 'JES_CorrelationGroupbJES',\n",
-       " 'jet_energy_uncertainty_CorrelationGroupFlavor',\n",
-       " 'JES_CorrelationGroupFlavor',\n",
-       " 'jet_energy_uncertainty_CorrelationGroupUncorrelated',\n",
-       " 'JES_CorrelationGroupUncorrelated',\n",
-       " 'jet_energy_uncertainty_jes',\n",
-       " 'JES_jes']"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fatjets.fields"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,8 +490,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# fatjets = events.FatJet  # CHANGE TO JEC JETS\n",
-    "fatjets = corrections.get_jec_jets(events, \"2018\")\n",
+    "fatjets = events.FatJet  # CHANGE TO JEC JETS\n",
+    "# fatjets = corrections.get_jec_jets(events, \"2018\")\n",
     "\n",
     "num_jets = 1\n",
     "\n",
@@ -781,7 +553,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "select = ak.fill_none(muon_selector * fatjet_selector * ak4_jet_selector, False)\n",
+    "# select = ak.fill_none(muon_selector * fatjet_selector * ak4_jet_selector, False)\n",
+    "select = ak.fill_none(muon_selector * fatjet_selector, False)\n",
     "presel_events = events[select]\n",
     "leading_fatjets = leading_fatjets[select]\n",
     "fatjet_idx = fatjet_idx[select]"
@@ -916,16 +689,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "had_bs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "had_ws_children = had_ws.children"
+    "top_match_dict = {\n",
+    "    \"top_matched\": had_w_jet_match * had_b_jet_match,\n",
+    "    \"w_matched\": had_w_jet_match * ~had_b_jet_match,\n",
+    "    \"unmatched\": ~had_w_jet_match,\n",
+    "}"
    ]
   },
   {
@@ -934,11 +702,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "top_match_dict = {\n",
-    "    \"top_matched\": had_w_jet_match * had_b_jet_match,\n",
-    "    \"w_matched\": had_w_jet_match * ~had_b_jet_match,\n",
-    "    \"unmatched\": ~had_w_jet_match,\n",
-    "}"
+    "np.sum(top_match_dict[\"top_matched\"])"
    ]
   },
   {
@@ -1048,7 +812,7 @@
    "outputs": [],
    "source": [
     "gen_quarks = ak.concatenate(\n",
-    "    [had_bs[merged_top_jet_match][:, :1], had_ws_children[merged_top_jet_match][:, :2]], axis=1\n",
+    "    [had_bs[merged_top_jet_match][:, :1], had_ws.children[merged_top_jet_match][:, :2]], axis=1\n",
     ")"
    ]
   },
@@ -1107,7 +871,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f = uproot.open(\"../corrections/ratio_ktjets_nov7.root\")\n",
+    "f = uproot.open(\"../corrections/ratio_feb20.root\")\n",
     "\n",
     "# 3D histogram: [subjet_pt, ln(0.8/Delta), ln(kT/GeV)]\n",
     "ratio_nom = f[\"ratio_nom\"].to_numpy()\n",
@@ -1117,7 +881,18 @@
     "\n",
     "zero_vals = ratio_nom == 0\n",
     "ratio_nom[zero_vals] = 1\n",
-    "ratio_nom_errs[zero_vals] = 0"
+    "ratio_nom_errs[zero_vals] = 0\n",
+    "\n",
+    "ratio_shape = f[\"ratio_nom\"].values().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_pt_bin = ratio_nom_edges[0][-2]"
    ]
   },
   {
@@ -1133,6 +908,64 @@
     "_ = plt.colorbar()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": [],
+   "source": [
+    "pt_extrap_lookups = {\"params\": [], \"errs\": [], \"sys_up_params\": [], \"sys_down_params\": []}\n",
+    "max_fparams = 4  # max order (+1) of functions\n",
+    "\n",
+    "\n",
+    "def _np_pad(arr: np.ndarray, target: int = max_fparams):\n",
+    "    return np.pad(arr, ((0, target - len(arr))))\n",
+    "\n",
+    "\n",
+    "for i in range(ratio_shape[1]):\n",
+    "    for key in pt_extrap_lookups:\n",
+    "        pt_extrap_lookups[key].append([])\n",
+    "\n",
+    "    for j in range(ratio_shape[2]):\n",
+    "        func = f[\"pt_extrap\"][f\"func_{i + 1}_{j + 1}\"]\n",
+    "        pt_extrap_lookups[\"params\"][-1].append(\n",
+    "            _np_pad(func._members[\"fFormula\"]._members[\"fClingParameters\"])\n",
+    "        )\n",
+    "        pt_extrap_lookups[\"errs\"][-1].append(_np_pad(func._members[\"fParErrors\"]))\n",
+    "        pt_extrap_lookups[\"sys_up_params\"][-1].append(\n",
+    "            _np_pad(\n",
+    "                f[\"pt_extrap\"][f\"func_sys_tot_up_{i + 1}_{j + 1}\"]\n",
+    "                ._members[\"fFormula\"]\n",
+    "                ._members[\"fClingParameters\"]\n",
+    "            )\n",
+    "        )\n",
+    "        pt_extrap_lookups[\"sys_down_params\"][-1].append(\n",
+    "            _np_pad(\n",
+    "                f[\"pt_extrap\"][f\"func_sys_tot_down_{i + 1}_{j + 1}\"]\n",
+    "                ._members[\"fFormula\"]\n",
+    "                ._members[\"fClingParameters\"]\n",
+    "            )\n",
+    "        )\n",
+    "\n",
+    "for key in pt_extrap_lookups:\n",
+    "    pt_extrap_lookups[key] = dense_lookup(np.array(pt_extrap_lookups[key]), ratio_nom_edges[1:])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1156,8 +989,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kappa = (ratio_nom + ratio_nom_errs) / ratio_nom\n",
-    "ratio_nom * np.power(kappa, rand_noise)"
+    "# 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()"
    ]
   },
   {
@@ -1166,12 +1003,63 @@
    "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()"
+    "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",
+    "hpt_sjpt = flat_subjet_pt[high_pt_sel]  # change this to 1/sjpt for next iteration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# store polynomial orders for pT extrapolation\n",
+    "sj_pt_orders = np.array([np.power(hpt_sjpt, i) for i in range(max_fparams)]).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clip_max, clip_min = 10, 0.1\n",
+    "pt_lookup = pt_extrap_lookups[\"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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ = plt.hist(\n",
+    "    np.log10(np.maximum(np.sum(params * sj_pt_orders, axis=1), 0.1)),\n",
+    "    np.linspace(-1, 1, 21),\n",
+    "    histtype=\"step\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratio_nom_vals = ratio_smeared_lookups[0](flat_subjet_pt, flat_logD, flat_logkt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratio_nom_vals[high_pt_sel] = pt_extrap_vals"
    ]
   },
   {
@@ -1181,6 +1069,7 @@
    "outputs": [],
    "source": [
     "sf_vals = []\n",
+    "\n",
     "# could be parallelised but not sure if memory / time trade-off is worth it\n",
     "for i, ratio_nom_lookup in enumerate(ratio_smeared_lookups):\n",
     "    ratio_nom_vals = ratio_nom_lookup(flat_subjet_pt, flat_logD, flat_logkt)\n",
@@ -1204,6 +1093,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -1339,7 +1229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.11"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/src/HHbbVV/tagger/high_mass_check.ipynb b/src/HHbbVV/tagger/high_mass_check.ipynb
new file mode 100644
index 00000000..d5dcb467
--- /dev/null
+++ b/src/HHbbVV/tagger/high_mass_check.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\n",
+    "import awkward as ak\n",
+    "from coffea import nanoevents\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplhep as hep\n",
+    "\n",
+    "plt.style.use(hep.style.CMS)\n",
+    "hep.style.use(\"CMS\")\n",
+    "plt.rcParams.update({\"font.size\": 16})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Electron_genPartIdx => GenPart\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Electron_jetIdx => Jet\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Electron_photonIdx => Photon\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for FatJet_genJetAK8Idx => GenJetAK8\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for FatJet_subJetIdx1 => SubJet\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for FatJet_subJetIdx2 => SubJet\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for FsrPhoton_muonIdx => Muon\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Jet_electronIdx1 => Electron\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Jet_electronIdx2 => Electron\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Jet_genJetIdx => GenJet\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Jet_muonIdx1 => Muon\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Jet_muonIdx2 => Muon\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Muon_fsrPhotonIdx => FsrPhoton\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Muon_genPartIdx => GenPart\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Muon_jetIdx => Jet\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Photon_electronIdx => Electron\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Photon_genPartIdx => GenPart\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Photon_jetIdx => Jet\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Tau_genPartIdx => GenPart\n",
+      "  warnings.warn(\n",
+      "/uscms_data/d3/rkansal/mambaforge/envs/coffea23/lib/python3.9/site-packages/coffea/nanoevents/schemas/nanoaod.py:215: RuntimeWarning: Missing cross-reference index for Tau_jetIdx => Jet\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "events = nanoevents.NanoEventsFactory.from_root(\n",
+    "    \"/uscms/home/rkansal/lpcdihiggsboost/MINIAOD/ParTSamples/v1/BulkGravitonToHHTo4W_MX-Var_MH-260to650_JHUVariableWMass/20UL17MiniAODv2/*.root\",\n",
+    "    \"Events\",\n",
+    "    permit_dask=True,\n",
+    "    schemaclass=nanoevents.NanoAODSchema,\n",
+    ").events()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HIGGS_PDGID = 25\n",
+    "GRAV_PDGID = 39\n",
+    "\n",
+    "GEN_FLAGS = [\"fromHardProcess\", \"isLastCopy\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "higgs = events.GenPart[\n",
+    "    (abs(events.GenPart.pdgId) == HIGGS_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)\n",
+    "]\n",
+    "\n",
+    "grav = events.GenPart[\n",
+    "    (abs(events.GenPart.pdgId) == GRAV_PDGID) * events.GenPart.hasFlags(GEN_FLAGS)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = np.array(higgs.mass.compute())[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(m, np.arange(260, 660, 20), histtype=\"step\")\n",
+    "plt.xlabel(r\"$m_H$ (GeV)\")\n",
+    "plt.ylabel(\"Number of events\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = np.array(grav.mass.compute())[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plt.hist(g[m < 360], np.arange(600, 7400, 400), histtype=\"step\")\n",
+    "plt.title(r\"$m_X$ distribution for $m_H < 360 GeV$\")\n",
+    "plt.xlabel(r\"$m_X$ (GeV)\")\n",
+    "plt.ylabel(\"Number of events\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = plt.hist(g[m >= 550], np.arange(200, 14000, 800), histtype=\"step\")\n",
+    "plt.title(r\"$m_X$ distribution for $m_H >= 550 GeV$\")\n",
+    "plt.xlabel(r\"$m_X$ (GeV)\")\n",
+    "plt.ylabel(\"Number of events\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python39",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/triton/ParticleTransformer.py b/src/HHbbVV/triton/ParticleTransformer.py
index a8b2b0b5..feb9e254 100644
--- a/src/HHbbVV/triton/ParticleTransformer.py
+++ b/src/HHbbVV/triton/ParticleTransformer.py
@@ -363,7 +363,8 @@ def forward(self, x, x_cls=None, padding_mask=None, attn_mask=None):
             tgt_len, bsz = x.size(0), x.size(1)
             x = x.view(tgt_len, bsz, self.num_heads, self.head_dim)
             x = torch.einsum("tbhd,h->tbdh", x, self.c_attn)
-            x = x.reshape(tgt_len, bsz, self.embed_dim)
+            # need to change this to avoid a bug for batch sizes > 1
+            x = x.reshape(tgt_len, -1, self.embed_dim)
         if self.post_attn_norm is not None:
             x = self.post_attn_norm(x)
         x = self.dropout(x)
diff --git a/src/HHbbVV/triton/ParticleTransformerHidden.py b/src/HHbbVV/triton/ParticleTransformerHidden.py
index 614bb0ae..a05d9d7c 100644
--- a/src/HHbbVV/triton/ParticleTransformerHidden.py
+++ b/src/HHbbVV/triton/ParticleTransformerHidden.py
@@ -364,6 +364,7 @@ def forward(self, x, x_cls=None, padding_mask=None, attn_mask=None):
             x = x.view(tgt_len, bsz, self.num_heads, self.head_dim)
             # x = torch.einsum('tbhd,h->tbdh', x, self.c_attn)
             x = x.permute(0, 1, 3, 2) * self.c_attn.reshape(1, 1, 1, -1)  # rewrite einsum
+            # need to change this to avoid a bug for batch sizes > 1
             x = x.reshape(tgt_len, -1, self.embed_dim)
         if self.post_attn_norm is not None:
             x = self.post_attn_norm(x)
diff --git a/src/HHbbVV/triton/README.md b/src/HHbbVV/triton/README.md
index 9625bf7a..5c3b940f 100644
--- a/src/HHbbVV/triton/README.md
+++ b/src/HHbbVV/triton/README.md
@@ -1,5 +1,24 @@
 ## Triton-server
 
-- [PART data file](https://github.com/colizz/weaver-core-dev/blob/7140323a1202b7cb12e68f75177a5066280e9dcb/weaver/data_new/incl/ak8_MD_vminclv2ParT_manual_fixwrap.yaml)
+See READMEs in model directories for links to models and/or configs.
+
+To export to onnx:
+
+```bash
+modeldir="../../hhbbww/HHbbVV/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/"
+
+# based on this command https://github.com/colizz/weaver-core-dev/blob/d038cd502d1b4a8ab3deefa9c3022bd3b812baf5/cmd_stage2.md?plain=1#L979-L1010
+python weaver/train.py --train-mode hybrid \
+-o loss_gamma 5 -o fc_params '[(512,0.1)]' -o embed_dims '[64,256,64]' -o pair_embed_dims '[32,32,32]' --use-amp \
+--data-config weaver/data_new/inclv7plus/ak8_MD_inclv8_part_2reg_manual.yaml \
+--network-config weaver/networks/example_ParticleTransformer2023Tagger_hybrid.py \
+--model-prefix $modeldir/net_best_epoch_state.pt \
+--export-onnx $modeldir/model.onnx
+```
+
+For the 2023May30 model I had to:
+
+1. Bypass the if statement here https://github.com/colizz/weaver-core-dev/blob/d038cd502d1b4a8ab3deefa9c3022bd3b812baf5/weaver/networks/ParticleTransformer2023.py#L586 (just `return output` instead) - torch complained about comparing a tensor to a Python boolean.
+2. Remove the softmax part https://github.com/colizz/weaver-core-dev/blob/d038cd502d1b4a8ab3deefa9c3022bd3b812baf5/weaver/networks/ParticleTransformer2023.py#L583-L584 (doesn't make sense since regression outputs are included).
+3. Rewrite this einsum statement https://github.com/colizz/weaver-core-dev/blob/d038cd502d1b4a8ab3deefa9c3022bd3b812baf5/weaver/networks/ParticleTransformer2023.py#L448C14-L448C14 -> https://github.com/colizz/weaver-core-dev/blob/d038cd502d1b4a8ab3deefa9c3022bd3b812baf5/weaver/networks/example_ParticleTransformerTagger_hybrid_outputWithHidNeurons.py#L337 - onnx opset v11 doesn't support einsum.
 
-- [Original Hybrid model](https://github.com/colizz/weaver-core-dev/blob/7140323a1202b7cb12e68f75177a5066280e9dcb/weaver/networks/example_ParticleTransformerTagger_hybrid_outputWithHidNeurons.py)
diff --git a/src/HHbbVV/triton/export_and_check.ipynb b/src/HHbbVV/triton/export_and_check.ipynb
new file mode 100644
index 00000000..c0f9ed16
--- /dev/null
+++ b/src/HHbbVV/triton/export_and_check.ipynb
@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional, List, Dict\n",
+    "\n",
+    "import numpy as np\n",
+    "import scipy\n",
+    "\n",
+    "import tritonclient.grpc as triton_grpc\n",
+    "import tritonclient.http as triton_http\n",
+    "\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from https://github.com/lgray/hgg-coffea/blob/triton-bdts/src/hgg_coffea/tools/chained_quantile.py\n",
+    "class wrapped_triton:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        model_url: str,\n",
+    "    ) -> None:\n",
+    "        fullprotocol, location = model_url.split(\"://\")\n",
+    "        _, protocol = fullprotocol.split(\"+\")\n",
+    "        address, model, version = location.split(\"/\")\n",
+    "\n",
+    "        self._protocol = protocol\n",
+    "        self._address = address\n",
+    "        self._model = model\n",
+    "        self._version = version\n",
+    "\n",
+    "    def __call__(self, input_dict: Dict[str, np.ndarray]) -> np.ndarray:\n",
+    "        if self._protocol == \"grpc\":\n",
+    "            client = triton_grpc.InferenceServerClient(url=self._address, verbose=False)\n",
+    "            triton_protocol = triton_grpc\n",
+    "        elif self._protocol == \"http\":\n",
+    "            client = triton_http.InferenceServerClient(\n",
+    "                url=self._address,\n",
+    "                verbose=False,\n",
+    "                concurrency=12,\n",
+    "            )\n",
+    "            triton_protocol = triton_http\n",
+    "        else:\n",
+    "            raise ValueError(f\"{self._protocol} does not encode a valid protocol (grpc or http)\")\n",
+    "\n",
+    "        # Infer\n",
+    "        inputs = []\n",
+    "\n",
+    "        for key in input_dict:\n",
+    "            input = triton_protocol.InferInput(key, input_dict[key].shape, \"FP32\")\n",
+    "            input.set_data_from_numpy(input_dict[key])\n",
+    "            inputs.append(input)\n",
+    "\n",
+    "        output = triton_protocol.InferRequestedOutput(\"softmax\")\n",
+    "\n",
+    "        request = client.infer(\n",
+    "            self._model,\n",
+    "            model_version=self._version,\n",
+    "            inputs=inputs,\n",
+    "            outputs=[output],\n",
+    "        )\n",
+    "\n",
+    "        out = request.as_numpy(\"softmax\")\n",
+    "\n",
+    "        return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size = 4\n",
+    "# pfs = 100\n",
+    "# svs = 7\n",
+    "pfs = 128\n",
+    "svs = 10\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# input_dict = {\n",
+    "#     \"pf_points\": np.random.rand(batch_size, 2, pfs).astype(\"float32\"),\n",
+    "#     \"pf_features\": np.random.rand(batch_size, 19, pfs).astype(\"float32\"),\n",
+    "#     \"pf_mask\": (np.random.rand(batch_size, 1, pfs) > 0.2).astype(\"float32\"),\n",
+    "#     \"sv_points\": np.random.rand(batch_size, 2, svs).astype(\"float32\"),\n",
+    "#     \"sv_features\": np.random.rand(batch_size, 11, svs).astype(\"float32\"),\n",
+    "#     \"sv_mask\": (np.random.rand(batch_size, 1, svs) > 0.2).astype(\"float32\"),\n",
+    "# }\n",
+    "\n",
+    "input_dict = {\n",
+    "    \"pf_features\": np.random.rand(batch_size, 25, pfs).astype(\"float32\"),\n",
+    "    \"pf_vectors\": np.random.rand(batch_size, 4, pfs).astype(\"float32\"),\n",
+    "    \"pf_mask\": (np.random.rand(batch_size, 1, pfs) > 0.2).astype(\"float32\"),\n",
+    "    \"sv_features\": np.random.rand(batch_size, 11, svs).astype(\"float32\"),\n",
+    "    \"sv_vectors\": np.random.rand(batch_size, 4, svs).astype(\"float32\"),\n",
+    "    \"sv_mask\": (np.random.rand(batch_size, 1, svs) > 0.2).astype(\"float32\"),\n",
+    "}\n",
+    "\n",
+    "# input_dict = {\n",
+    "#     \"pf_points__0\": np.random.rand(batch_size, 2, pfs).astype(\"float32\"),\n",
+    "#     \"pf_features__1\": np.random.rand(batch_size, 19, pfs).astype(\"float32\"),\n",
+    "#     \"pf_mask__2\": (np.random.rand(batch_size, 1, pfs) > 0.2).astype(\"float32\"),\n",
+    "#     \"sv_points__3\": np.random.rand(batch_size, 2, svs).astype(\"float32\"),\n",
+    "#     \"sv_features__4\": np.random.rand(batch_size, 11, svs).astype(\"float32\"),\n",
+    "#     \"sv_mask__5\": (np.random.rand(batch_size, 1, svs) > 0.2).astype(\"float32\"),\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model_url = \"triton+grpc://ailab01.fnal.gov:8001/particlenet_hww/1\"\n",
+    "# model_url = \"triton+grpc://prp-gpu-1.t2.ucsd.edu:8001/particlenet_hww/1\"\n",
+    "# model_url = \"triton+grpc://67.58.49.48:8001/ak8_MD_vminclv2ParT_manual_fixwrap/1\"\n",
+    "model_url = \"triton+grpc://67.58.49.48:8001/2023May30_ak8_MD_inclv8_part_2reg_manual/1\"\n",
+    "triton_model = wrapped_triton(model_url)\n",
+    "for i in tqdm(range(1)):\n",
+    "    output = triton_model(input_dict)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnx\n",
+    "import onnxruntime as ort\n",
+    "\n",
+    "\n",
+    "model_dir = (\n",
+    "    \"models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/\"\n",
+    ")\n",
+    "\n",
+    "onnx_model = onnx.load(model_dir + \"model.onnx\")\n",
+    "onnx.checker.check_model(onnx_model)\n",
+    "\n",
+    "ort_sess = ort.InferenceSession(model_dir + \"model.onnx\")\n",
+    "outputs = ort_sess.run(None, input_dict)[0]\n",
+    "print(\"ONNX outputs:\", outputs)\n",
+    "print(\"Shape:\", outputs.shape)\n",
+    "print(\"Softmax applied:\", np.allclose(np.sum(outputs, axis=1), 1, atol=1e-5))\n",
+    "\n",
+    "# Print Result\n",
+    "# predicted, actual = classes[outputs[0][0].argmax(0)], classes[y]\n",
+    "# print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnx\n",
+    "import onnxruntime as ort\n",
+    "\n",
+    "\n",
+    "model_dir = (\n",
+    "    \"models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/\"\n",
+    ")\n",
+    "\n",
+    "onnx_model = onnx.load(model_dir + \"model.onnx\")\n",
+    "onnx.checker.check_model(onnx_model)\n",
+    "\n",
+    "ort_sess = ort.InferenceSession(model_dir + \"model.onnx\")\n",
+    "outputs = ort_sess.run(None, input_dict)\n",
+    "print(\"ONNX outputs:\", outputs[0])\n",
+    "print(\"Shape:\", outputs[0].shape)\n",
+    "print(\"Softmax applied:\", np.allclose(np.sum(outputs[0], axis=1), 1, atol=1e-5))\n",
+    "\n",
+    "# Print Result\n",
+    "# predicted, actual = classes[outputs[0][0].argmax(0)], classes[y]\n",
+    "# print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"ONNX cls output sums:\", np.sum(outputs[0][:, :-2], axis=1))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2023May30 Model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from ParticleTransformer import ParticleTransformerTagger\n",
+    "\n",
+    "part_model = ParticleTransformerTagger(\n",
+    "    pf_input_dim=25,\n",
+    "    sv_input_dim=11,\n",
+    "    num_classes=314,  # one dim for regression\n",
+    "    # network configurations\n",
+    "    pair_input_dim=4,\n",
+    "    embed_dims=[128, 512, 128],\n",
+    "    pair_embed_dims=[64, 64, 64],\n",
+    "    num_heads=8,\n",
+    "    num_layers=8,\n",
+    "    num_cls_layers=2,\n",
+    "    block_params=None,\n",
+    "    cls_block_params={\"dropout\": 0, \"attn_dropout\": 0, \"activation_dropout\": 0},\n",
+    "    fc_params=[],\n",
+    "    activation=\"gelu\",\n",
+    "    # misc\n",
+    "    trim=True,\n",
+    "    for_inference=True,\n",
+    ").cpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dir = (\n",
+    "    \"models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/\"\n",
+    ")\n",
+    "part_model.load_state_dict(\n",
+    "    torch.load(model_dir + \"net_best_epoch_state.pt\", map_location=torch.device(\"cpu\"))\n",
+    ")\n",
+    "_ = part_model.eval()\n",
+    "\n",
+    "data_config = {\n",
+    "    \"input_names\": [\"pf_features\", \"pf_vectors\", \"pf_mask\", \"sv_features\", \"sv_vectors\", \"sv_mask\"],\n",
+    "    \"input_shapes\": {\n",
+    "        \"pf_features\": (-1, 25, pfs),\n",
+    "        \"pf_vectors\": (-1, 4, pfs),\n",
+    "        \"pf_mask\": (-1, 1, pfs),\n",
+    "        \"sv_features\": (-1, 11, svs),\n",
+    "        \"sv_vectors\": (-1, 4, svs),\n",
+    "        \"sv_mask\": (-1, 1, svs),\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "model_info = {\n",
+    "    \"input_names\": list(data_config[\"input_names\"]),\n",
+    "    \"input_shapes\": {k: ((1,) + s[1:]) for k, s in data_config[\"input_shapes\"].items()},\n",
+    "    \"output_names\": [\"softmax\"],\n",
+    "    \"dynamic_axes\": {\n",
+    "        **{k: {0: \"N\", 2: \"n_\" + k.split(\"_\")[0]} for k in data_config[\"input_names\"]},\n",
+    "        **{\"softmax\": {0: \"N\"}},\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "inputs = tuple(\n",
+    "    torch.ones(model_info[\"input_shapes\"][k], dtype=torch.float32)\n",
+    "    for k in model_info[\"input_names\"]\n",
+    ")\n",
+    "torch.onnx.export(\n",
+    "    part_model,\n",
+    "    inputs,\n",
+    "    model_dir + \"ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model.onnx\",\n",
+    "    input_names=model_info[\"input_names\"],\n",
+    "    output_names=model_info[\"output_names\"],\n",
+    "    dynamic_axes=model_info.get(\"dynamic_axes\", None),\n",
+    "    opset_version=11,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_tensors = [torch.Tensor(val) for key, val in input_dict.items()]\n",
+    "in_tensors[2] = in_tensors[2].bool()\n",
+    "in_tensors[5] = in_tensors[5].bool()\n",
+    "\n",
+    "out = part_model(*in_tensors)\n",
+    "print(out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dec22 Model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from ParticleTransformerHidden import ParticleTransformerTagger\n",
+    "\n",
+    "part_model = ParticleTransformerTagger(\n",
+    "    pf_input_dim=25,\n",
+    "    sv_input_dim=11,\n",
+    "    num_classes=37 + 1,  # one dim for regression\n",
+    "    # network configurations\n",
+    "    pair_input_dim=4,\n",
+    "    embed_dims=[128, 512, 128],\n",
+    "    pair_embed_dims=[64, 64, 64],\n",
+    "    num_heads=8,\n",
+    "    num_layers=8,\n",
+    "    num_cls_layers=2,\n",
+    "    block_params=None,\n",
+    "    cls_block_params={\"dropout\": 0, \"attn_dropout\": 0, \"activation_dropout\": 0},\n",
+    "    fc_params=[],\n",
+    "    activation=\"gelu\",\n",
+    "    # misc\n",
+    "    trim=True,\n",
+    "    for_inference=True,\n",
+    ").cpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "part_model.load_state_dict(torch.load(\"net_best_epoch_state.pt\", map_location=torch.device(\"cpu\")))\n",
+    "_ = part_model.eval()\n",
+    "\n",
+    "data_config = {\n",
+    "    \"input_names\": [\"pf_features\", \"pf_vectors\", \"pf_mask\", \"sv_features\", \"sv_vectors\", \"sv_mask\"],\n",
+    "    \"input_shapes\": {\n",
+    "        \"pf_features\": (-1, 25, pfs),\n",
+    "        \"pf_vectors\": (-1, 4, pfs),\n",
+    "        \"pf_mask\": (-1, 1, pfs),\n",
+    "        \"sv_features\": (-1, 11, svs),\n",
+    "        \"sv_vectors\": (-1, 4, svs),\n",
+    "        \"sv_mask\": (-1, 1, svs),\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "model_info = {\n",
+    "    \"input_names\": list(data_config[\"input_names\"]),\n",
+    "    \"input_shapes\": {k: ((1,) + s[1:]) for k, s in data_config[\"input_shapes\"].items()},\n",
+    "    \"output_names\": [\"softmax\"],\n",
+    "    \"dynamic_axes\": {\n",
+    "        **{k: {0: \"N\", 2: \"n_\" + k.split(\"_\")[0]} for k in data_config[\"input_names\"]},\n",
+    "        **{\"softmax\": {0: \"N\"}},\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "inputs = tuple(\n",
+    "    torch.ones(model_info[\"input_shapes\"][k], dtype=torch.float32)\n",
+    "    for k in model_info[\"input_names\"]\n",
+    ")\n",
+    "torch.onnx.export(\n",
+    "    part_model,\n",
+    "    inputs,\n",
+    "    \"model.onnx\",\n",
+    "    input_names=model_info[\"input_names\"],\n",
+    "    output_names=model_info[\"output_names\"],\n",
+    "    dynamic_axes=model_info.get(\"dynamic_axes\", None),\n",
+    "    opset_version=11,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_tensors = [torch.Tensor(val) for key, val in input_dict.items()]\n",
+    "in_tensors[2] = in_tensors[2].bool()\n",
+    "in_tensors[5] = in_tensors[5].bool()\n",
+    "\n",
+    "out = part_model(*in_tensors)\n",
+    "print(out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31a7b1cb5f073f7a7d37b3db504c6954ce2b88e0f82e412b65ad0b5f2dd17394"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/model.onnx b/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/model.onnx
new file mode 100644
index 00000000..14352a08
Binary files /dev/null and b/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/model.onnx differ
diff --git a/src/HHbbVV/triton/net_best_epoch_state.pt b/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/net_best_epoch_state.pt
similarity index 100%
rename from src/HHbbVV/triton/net_best_epoch_state.pt
rename to src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/net_best_epoch_state.pt
diff --git a/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/preprocess.json b/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/preprocess.json
new file mode 100644
index 00000000..3cc93568
--- /dev/null
+++ b/src/HHbbVV/triton/models/ak8_MD_vminclv2ParT_manual_fixwrap_hidden/preprocess.json
@@ -0,0 +1,505 @@
+{
+  "output_names": [
+    "label_H_WqqWqq_0c",
+    "label_H_WqqWqq_1c",
+    "label_H_WqqWqq_2c",
+    "label_H_WqqWq_0c",
+    "label_H_WqqWq_1c",
+    "label_H_WqqWq_2c",
+    "label_H_WqqWev_0c",
+    "label_H_WqqWev_1c",
+    "label_H_WqqWmv_0c",
+    "label_H_WqqWmv_1c",
+    "label_H_WqqWtauev_0c",
+    "label_H_WqqWtauev_1c",
+    "label_H_WqqWtaumv_0c",
+    "label_H_WqqWtaumv_1c",
+    "label_H_WqqWtauhv_0c",
+    "label_H_WqqWtauhv_1c",
+    "label_H_bb",
+    "label_H_cc",
+    "label_H_ss",
+    "label_H_qq",
+    "label_H_tauhtaue",
+    "label_H_tauhtaum",
+    "label_H_tauhtauh",
+    "label_QCD_bb",
+    "label_QCD_cc",
+    "label_QCD_b",
+    "label_QCD_c",
+    "label_QCD_others",
+    "label_Top_bWqq_0c",
+    "label_Top_bWqq_1c",
+    "label_Top_bWq_0c",
+    "label_Top_bWq_1c",
+    "label_Top_bWev",
+    "label_Top_bWmv",
+    "label_Top_bWtauhv",
+    "label_Top_bWtauev",
+    "label_Top_bWtaumv"
+  ],
+  "input_names": [
+    "pf_features",
+    "pf_vectors",
+    "pf_mask",
+    "sv_features",
+    "sv_vectors",
+    "sv_mask"
+  ],
+  "pf_features": {
+    "var_names": [
+      "pfcand_pt_log_nopuppi",
+      "pfcand_e_log_nopuppi",
+      "pfcand_etarel",
+      "pfcand_phirel",
+      "pfcand_abseta",
+      "pfcand_charge",
+      "pfcand_isEl",
+      "pfcand_isMu",
+      "pfcand_isGamma",
+      "pfcand_isChargedHad",
+      "pfcand_isNeutralHad",
+      "pfcand_VTX_ass",
+      "pfcand_lostInnerHits",
+      "pfcand_normchi2",
+      "pfcand_quality",
+      "pfcand_dz",
+      "pfcand_dzsig",
+      "pfcand_dxy",
+      "pfcand_dxysig",
+      "pfcand_btagEtaRel",
+      "pfcand_btagPtRatio",
+      "pfcand_btagPParRatio",
+      "pfcand_btagSip3dVal",
+      "pfcand_btagSip3dSig",
+      "pfcand_btagJetDistVal"
+    ],
+    "var_infos": {
+      "pfcand_pt_log_nopuppi": {
+        "median": 1,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_e_log_nopuppi": {
+        "median": 1.3,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_etarel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_phirel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_abseta": {
+        "median": 0.6,
+        "norm_factor": 1.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "pfcand_charge": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isEl": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isMu": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isGamma": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isChargedHad": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_isNeutralHad": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "pfcand_VTX_ass": {
+        "median": 4,
+        "norm_factor": 0.3,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
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+      }
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+  },
+  "sv_mask": {
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+      }
+    },
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+  }
+}
\ No newline at end of file
diff --git a/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/README.md b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/README.md
new file mode 100644
index 00000000..8111cee2
--- /dev/null
+++ b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/README.md
@@ -0,0 +1,3 @@
+- [Data config](https://github.com/colizz/weaver-core-dev/blob/dev-incl-train/weaver/data_new/inclv7plus/ak8_MD_inclv8_part_2reg_manual.yaml)
+
+- [Original Hybrid model](https://github.com/colizz/weaver-core-dev/blob/7140323a1202b7cb12e68f75177a5066280e9dcb/weaver/networks/example_ParticleTransformerTagger_hybrid_outputWithHidNeurons.py)
diff --git a/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model.onnx b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model.onnx
new file mode 100644
index 00000000..ccbd7338
Binary files /dev/null and b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model.onnx differ
diff --git a/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model_softmax.onnx b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model_softmax.onnx
new file mode 100644
index 00000000..c8f99345
Binary files /dev/null and b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/model_softmax.onnx differ
diff --git a/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/net_best_epoch_state.pt b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/net_best_epoch_state.pt
new file mode 100644
index 00000000..83d508f2
Binary files /dev/null and b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/net_best_epoch_state.pt differ
diff --git a/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/preprocess.json b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/preprocess.json
new file mode 100644
index 00000000..4cbd3c88
--- /dev/null
+++ b/src/HHbbVV/triton/models/model_2023May30/ak8_MD_inclv8_part_2reg_manual.useamp.lite.gm5.ddp-bs768-lr6p75e-3/preprocess.json
@@ -0,0 +1,467 @@
+{
+  "output_names": null,
+  "input_names": [
+    "pf_features",
+    "pf_vectors",
+    "pf_mask",
+    "sv_features",
+    "sv_vectors",
+    "sv_mask"
+  ],
+  "pf_features": {
+    "var_names": [
+      "pfcand_pt_log_nopuppi",
+      "pfcand_e_log_nopuppi",
+      "pfcand_etarel",
+      "pfcand_phirel",
+      "pfcand_abseta",
+      "pfcand_charge",
+      "pfcand_isEl",
+      "pfcand_isMu",
+      "pfcand_isGamma",
+      "pfcand_isChargedHad",
+      "pfcand_isNeutralHad",
+      "pfcand_VTX_ass",
+      "pfcand_lostInnerHits",
+      "pfcand_normchi2",
+      "pfcand_quality",
+      "pfcand_dz",
+      "pfcand_dzsig",
+      "pfcand_dxy",
+      "pfcand_dxysig",
+      "pfcand_btagEtaRel",
+      "pfcand_btagPtRatio",
+      "pfcand_btagPParRatio",
+      "pfcand_btagSip3dVal",
+      "pfcand_btagSip3dSig",
+      "pfcand_btagJetDistVal"
+    ],
+    "var_infos": {
+      "pfcand_pt_log_nopuppi": {
+        "median": 1,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
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+        "pad": 0
+      },
+      "pfcand_e_log_nopuppi": {
+        "median": 1.3,
+        "norm_factor": 0.5,
+        "replace_inf_value": 0,
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+        "upper_bound": 5,
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+      "pfcand_etarel": {
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+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
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+      },
+      "pfcand_phirel": {
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+      },
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+        "norm_factor": 1.6,
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+      },
+      "pfcand_VTX_ass": {
+        "median": 4,
+        "norm_factor": 0.3,
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+        "lower_bound": -1e+32,
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+      },
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+    },
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+  },
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+      "pfcand_px",
+      "pfcand_py",
+      "pfcand_pz",
+      "pfcand_energy"
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+    },
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+  },
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+  },
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+      "sv_etarel",
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+      "sv_ntracks",
+      "sv_normchi2",
+      "sv_dxy",
+      "sv_dxysig",
+      "sv_d3d",
+      "sv_d3dsig"
+    ],
+    "var_infos": {
+      "sv_pt_log": {
+        "median": 4,
+        "norm_factor": 0.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_mass": {
+        "median": 1.2,
+        "norm_factor": 0.3,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_etarel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_phirel": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_abseta": {
+        "median": 0.5,
+        "norm_factor": 1.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_ntracks": {
+        "median": 3,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_normchi2": {
+        "median": 0.8,
+        "norm_factor": 0.6,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_dxy": {
+        "median": 0.4,
+        "norm_factor": 0.25,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_dxysig": {
+        "median": 7,
+        "norm_factor": 0.02,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_d3d": {
+        "median": 0.5,
+        "norm_factor": 0.2,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      },
+      "sv_d3dsig": {
+        "median": 7,
+        "norm_factor": 0.02,
+        "replace_inf_value": 0,
+        "lower_bound": -5,
+        "upper_bound": 5,
+        "pad": 0
+      }
+    },
+    "var_length": 10
+  },
+  "sv_vectors": {
+    "var_names": [
+      "sv_px",
+      "sv_py",
+      "sv_pz",
+      "sv_energy"
+    ],
+    "var_infos": {
+      "sv_px": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_py": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_pz": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      },
+      "sv_energy": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      }
+    },
+    "var_length": 10
+  },
+  "sv_mask": {
+    "var_names": [
+      "sv_mask"
+    ],
+    "var_infos": {
+      "sv_mask": {
+        "median": 0,
+        "norm_factor": 1,
+        "replace_inf_value": 0,
+        "lower_bound": -1e+32,
+        "upper_bound": 1e+32,
+        "pad": 0
+      }
+    },
+    "var_length": 10
+  }
+}
\ No newline at end of file
diff --git a/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/README.md b/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/README.md
new file mode 100644
index 00000000..6dbf0b77
--- /dev/null
+++ b/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/README.md
@@ -0,0 +1,3 @@
+- [Data config](https://github.com/colizz/weaver-core-dev/blob/7140323a1202b7cb12e68f75177a5066280e9dcb/weaver/data_new/incl/ak8_MD_vminclv2ParT_manual_fixwrap.yaml)
+
+- [Original Hybrid model](https://github.com/colizz/weaver-core-dev/blob/7140323a1202b7cb12e68f75177a5066280e9dcb/weaver/networks/example_ParticleTransformerTagger_hybrid_outputWithHidNeurons.py)
diff --git a/src/HHbbVV/triton/model.onnx b/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/model.onnx
similarity index 100%
rename from src/HHbbVV/triton/model.onnx
rename to src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/model.onnx
diff --git a/src/HHbbVV/triton/model_old.onnx b/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/model_old.onnx
similarity index 100%
rename from src/HHbbVV/triton/model_old.onnx
rename to src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/model_old.onnx
diff --git a/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/net_best_epoch_state.pt b/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/net_best_epoch_state.pt
new file mode 100644
index 00000000..6801c022
Binary files /dev/null and b/src/HHbbVV/triton/models/model_Dec22/ak8_MD_vminclv2ParT_manual_fixwrap/net_best_epoch_state.pt differ
diff --git a/src/HHbbVV/triton/triton_check.ipynb b/src/HHbbVV/triton/triton_check.ipynb
deleted file mode 100644
index 20adb774..00000000
--- a/src/HHbbVV/triton/triton_check.ipynb
+++ /dev/null
@@ -1,692 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional, List, Dict\n",
-    "\n",
-    "import numpy as np\n",
-    "\n",
-    "import tritonclient.grpc as triton_grpc\n",
-    "import tritonclient.http as triton_http"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from https://github.com/lgray/hgg-coffea/blob/triton-bdts/src/hgg_coffea/tools/chained_quantile.py\n",
-    "class wrapped_triton:\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        model_url: str,\n",
-    "    ) -> None:\n",
-    "        fullprotocol, location = model_url.split(\"://\")\n",
-    "        _, protocol = fullprotocol.split(\"+\")\n",
-    "        address, model, version = location.split(\"/\")\n",
-    "\n",
-    "        self._protocol = protocol\n",
-    "        self._address = address\n",
-    "        self._model = model\n",
-    "        self._version = version\n",
-    "\n",
-    "    def __call__(self, input_dict: Dict[str, np.ndarray]) -> np.ndarray:\n",
-    "        if self._protocol == \"grpc\":\n",
-    "            client = triton_grpc.InferenceServerClient(url=self._address, verbose=False)\n",
-    "            triton_protocol = triton_grpc\n",
-    "        elif self._protocol == \"http\":\n",
-    "            client = triton_http.InferenceServerClient(\n",
-    "                url=self._address,\n",
-    "                verbose=False,\n",
-    "                concurrency=12,\n",
-    "            )\n",
-    "            triton_protocol = triton_http\n",
-    "        else:\n",
-    "            raise ValueError(f\"{self._protocol} does not encode a valid protocol (grpc or http)\")\n",
-    "\n",
-    "        # Infer\n",
-    "        inputs = []\n",
-    "\n",
-    "        for key in input_dict:\n",
-    "            input = triton_protocol.InferInput(key, input_dict[key].shape, \"FP32\")\n",
-    "            input.set_data_from_numpy(input_dict[key])\n",
-    "            inputs.append(input)\n",
-    "\n",
-    "        output = triton_protocol.InferRequestedOutput(\"softmax\")\n",
-    "\n",
-    "        request = client.infer(\n",
-    "            self._model,\n",
-    "            model_version=self._version,\n",
-    "            inputs=inputs,\n",
-    "            outputs=[output],\n",
-    "        )\n",
-    "\n",
-    "        out = request.as_numpy(\"softmax\")\n",
-    "\n",
-    "        return out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 1\n",
-    "# pfs = 100\n",
-    "# svs = 7\n",
-    "pfs = 128\n",
-    "svs = 10\n",
-    "\n",
-    "# input_dict = {\n",
-    "#     \"pf_points\": np.random.rand(batch_size, 2, pfs).astype(\"float32\"),\n",
-    "#     \"pf_features\": np.random.rand(batch_size, 19, pfs).astype(\"float32\"),\n",
-    "#     \"pf_mask\": (np.random.rand(batch_size, 1, pfs) > 0.2).astype(\"float32\"),\n",
-    "#     \"sv_points\": np.random.rand(batch_size, 2, svs).astype(\"float32\"),\n",
-    "#     \"sv_features\": np.random.rand(batch_size, 11, svs).astype(\"float32\"),\n",
-    "#     \"sv_mask\": (np.random.rand(batch_size, 1, svs) > 0.2).astype(\"float32\"),\n",
-    "# }\n",
-    "\n",
-    "input_dict = {\n",
-    "    \"pf_features\": np.random.rand(batch_size, 25, pfs).astype(\"float32\"),\n",
-    "    \"pf_vectors\": np.random.rand(batch_size, 4, pfs).astype(\"float32\"),\n",
-    "    \"pf_mask\": (np.random.rand(batch_size, 1, pfs) > 0.2).astype(\"float32\"),\n",
-    "    \"sv_features\": np.random.rand(batch_size, 11, svs).astype(\"float32\"),\n",
-    "    \"sv_vectors\": np.random.rand(batch_size, 4, svs).astype(\"float32\"),\n",
-    "    \"sv_mask\": (np.random.rand(batch_size, 1, svs) > 0.2).astype(\"float32\"),\n",
-    "}\n",
-    "\n",
-    "# input_dict = {\n",
-    "#     \"pf_points__0\": np.random.rand(batch_size, 2, pfs).astype(\"float32\"),\n",
-    "#     \"pf_features__1\": np.random.rand(batch_size, 19, pfs).astype(\"float32\"),\n",
-    "#     \"pf_mask__2\": (np.random.rand(batch_size, 1, pfs) > 0.2).astype(\"float32\"),\n",
-    "#     \"sv_points__3\": np.random.rand(batch_size, 2, svs).astype(\"float32\"),\n",
-    "#     \"sv_features__4\": np.random.rand(batch_size, 11, svs).astype(\"float32\"),\n",
-    "#     \"sv_mask__5\": (np.random.rand(batch_size, 1, svs) > 0.2).astype(\"float32\"),\n",
-    "# }"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "input_dict[\"pf_features\"].itemsize"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "16000"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum((val.itemsize * val.size) for val in input_dict.values())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1.4366233e-03 2.7789180e-03 1.4810998e-03 ... 1.3419604e-03\n",
-      "  5.3415606e-03 1.0536162e-03]\n",
-      " [5.8962428e-04 2.6495671e-03 2.1507419e-03 ... 3.6234359e-04\n",
-      "  3.9466871e-05 1.1135796e-02]\n",
-      " [1.8111541e-03 6.1054532e-03 4.3359781e-03 ... 4.5285108e-03\n",
-      "  4.9895938e-03 4.3286658e-03]\n",
-      " ...\n",
-      " [2.6687947e-03 5.2311928e-03 2.8158592e-03 ... 3.7654082e-03\n",
-      "  1.0825735e-02 2.2049018e-03]\n",
-      " [2.7392500e-03 2.7328818e-03 1.0838965e-03 ... 2.2412995e-03\n",
-      "  1.9178806e-03 1.2975576e-03]\n",
-      " [1.9648573e-03 4.6280641e-03 3.3738450e-03 ... 3.7505146e-04\n",
-      "  7.2018191e-04 2.7100766e-02]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# model_url = \"triton+grpc://ailab01.fnal.gov:8001/particlenet_hww/1\"\n",
-    "# model_url = \"triton+grpc://prp-gpu-1.t2.ucsd.edu:8001/particlenet_hww/1\"\n",
-    "model_url = \"triton+grpc://67.58.49.52:8001/ak8_MD_vminclv2ParT_manual_fixwrap/1\"\n",
-    "# model_url = \"triton+grpc://localhost:8001/particlenet_hww_ul_4q_3q/1\"\n",
-    "# model_url = \"triton+grpc://67.58.49.52:8001/particlenet_hww_ul_4q_3q/1\"\n",
-    "triton_model = wrapped_triton(model_url)\n",
-    "output = triton_model(input_dict)\n",
-    "print(output)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[array([[2.40560714e-03, 4.73927893e-03, 4.42884630e-03, 7.70280650e-03,\n",
-      "        7.44594727e-03, 4.89646792e-02, 2.44864757e-04, 1.84084594e-04,\n",
-      "        3.34733166e-03, 5.55951335e-03, 2.30525038e-04, 5.30063233e-04,\n",
-      "        1.57437345e-03, 1.06911850e-03, 2.14584306e-05, 1.38277428e-05,\n",
-      "        2.32464038e-02, 5.26734889e-02, 9.97571088e-03, 1.04071777e-02,\n",
-      "        3.24207067e-05, 3.28148562e-05, 7.48393745e-07, 7.66754225e-02,\n",
-      "        9.75757986e-02, 1.65003791e-01, 7.47575983e-02, 1.00998513e-01,\n",
-      "        1.26040773e-02, 4.62465100e-02, 6.31558970e-02, 1.60297364e-01,\n",
-      "        6.35669276e-04, 1.13147628e-02, 3.94817034e-04, 9.87346284e-04,\n",
-      "        4.52140672e-03],\n",
-      "       [1.02613051e-03, 3.39163793e-03, 2.07269844e-03, 8.19048751e-03,\n",
-      "        1.25318337e-02, 4.28423323e-02, 6.32008596e-04, 6.25702844e-04,\n",
-      "        1.71326788e-03, 1.57124607e-03, 5.84401190e-04, 2.10922817e-03,\n",
-      "        1.14760280e-03, 5.08062483e-04, 1.14137052e-04, 8.69628202e-05,\n",
-      "        1.43004835e-01, 4.31774035e-02, 8.73050466e-03, 9.02655907e-03,\n",
-      "        1.45769263e-05, 3.80198912e-07, 4.15077039e-07, 5.90641461e-02,\n",
-      "        3.09982877e-02, 8.30990672e-02, 2.94297803e-02, 2.74373926e-02,\n",
-      "        4.21226285e-02, 1.29914016e-01, 2.06379488e-01, 9.11063701e-02,\n",
-      "        4.21749521e-03, 4.15934157e-03, 2.49783974e-03, 5.44063095e-03,\n",
-      "        1.03111682e-03],\n",
-      "       [1.66845182e-03, 3.64692812e-03, 2.89837131e-03, 4.86101676e-03,\n",
-      "        7.48188701e-03, 1.40794534e-02, 9.03459208e-04, 5.26467687e-04,\n",
-      "        2.02787109e-03, 1.55750872e-03, 3.14598088e-04, 5.91911084e-04,\n",
-      "        1.07947027e-03, 3.05540278e-04, 4.22612320e-05, 2.96632879e-05,\n",
-      "        5.50755933e-02, 4.36562598e-02, 5.99780446e-03, 8.71451851e-03,\n",
-      "        2.17835313e-05, 8.61926401e-06, 4.27868457e-07, 9.02741998e-02,\n",
-      "        7.14722201e-02, 1.86128020e-01, 6.56804517e-02, 6.22666441e-02,\n",
-      "        3.44388224e-02, 7.51931965e-02, 1.05264127e-01, 1.22886643e-01,\n",
-      "        4.66966070e-03, 1.13344304e-02, 1.43859303e-03, 6.60695415e-03,\n",
-      "        6.85623521e-03],\n",
-      "       [4.62662056e-03, 7.83766061e-03, 4.27037384e-03, 2.01767706e-03,\n",
-      "        2.24544550e-03, 6.52226200e-03, 6.22416555e-04, 1.64934585e-03,\n",
-      "        3.05514634e-02, 8.17193165e-02, 3.25232482e-04, 3.42901098e-03,\n",
-      "        9.88645386e-03, 4.62830858e-03, 2.20900802e-06, 7.18750107e-06,\n",
-      "        3.09956577e-02, 7.39605129e-02, 1.03556328e-02, 1.61585175e-02,\n",
-      "        1.37844609e-05, 2.62309564e-04, 6.64427091e-08, 3.93500216e-02,\n",
-      "        4.16316763e-02, 3.23371440e-01, 8.53620991e-02, 9.06055272e-02,\n",
-      "        6.37303432e-03, 2.40937900e-02, 2.08340436e-02, 2.99450755e-02,\n",
-      "        1.62140757e-03, 3.63662280e-02, 6.53141615e-05, 1.40187982e-03,\n",
-      "        6.89101219e-03],\n",
-      "       [2.03629909e-03, 3.15504754e-03, 1.71309232e-03, 2.29708035e-03,\n",
-      "        2.39835400e-03, 6.03561243e-03, 1.54996669e-04, 1.23658392e-04,\n",
-      "        1.07710389e-02, 7.09561491e-03, 1.81801792e-04, 9.05116263e-04,\n",
-      "        1.09113259e-02, 1.86760747e-03, 3.02845388e-06, 6.00468229e-06,\n",
-      "        1.14395060e-01, 6.80042058e-02, 1.49212694e-02, 2.08507776e-02,\n",
-      "        3.21558014e-06, 2.38054636e-05, 2.97100264e-08, 5.11101894e-02,\n",
-      "        2.69963164e-02, 1.98006168e-01, 7.73117244e-02, 9.26602259e-02,\n",
-      "        1.68176554e-02, 5.41842990e-02, 1.02826640e-01, 8.60600099e-02,\n",
-      "        7.84013944e-04, 1.38756111e-02, 4.92379710e-04, 2.19748821e-03,\n",
-      "        8.82323179e-03],\n",
-      "       [2.63676280e-03, 6.06680289e-03, 2.61350255e-03, 6.36544265e-03,\n",
-      "        7.56744714e-03, 4.82675806e-02, 1.05008869e-04, 1.23717589e-04,\n",
-      "        1.32966079e-02, 2.92983353e-02, 8.67534036e-05, 6.88784232e-04,\n",
-      "        4.77317069e-03, 2.55475705e-03, 7.38689141e-06, 1.30460821e-05,\n",
-      "        1.34585416e-02, 6.27045482e-02, 9.64016002e-03, 1.29977288e-02,\n",
-      "        1.14714576e-05, 8.77021375e-05, 2.60991186e-07, 7.38220736e-02,\n",
-      "        9.11559612e-02, 1.41583651e-01, 8.99177194e-02, 6.93340972e-02,\n",
-      "        1.05566159e-02, 4.37865630e-02, 7.43102282e-02, 1.22026905e-01,\n",
-      "        5.21351583e-04, 5.20420112e-02, 1.01869373e-04, 4.79609822e-04,\n",
-      "        6.99579995e-03],\n",
-      "       [2.19087978e-03, 3.23095312e-03, 1.49549206e-03, 1.28474133e-02,\n",
-      "        7.49933533e-03, 1.14605520e-02, 3.56867473e-04, 1.32475878e-04,\n",
-      "        5.62349148e-03, 2.78597814e-03, 1.64682046e-04, 1.71040010e-04,\n",
-      "        1.69672316e-03, 4.03090526e-04, 4.78661968e-05, 2.36598516e-05,\n",
-      "        8.32541436e-02, 2.19073035e-02, 7.09187146e-03, 1.14430049e-02,\n",
-      "        4.83168042e-05, 3.79924204e-05, 1.96322685e-06, 1.08277187e-01,\n",
-      "        2.30791755e-02, 1.68793306e-01, 3.30831185e-02, 6.27726912e-02,\n",
-      "        3.64046171e-02, 5.83925880e-02, 1.90397322e-01, 1.00474246e-01,\n",
-      "        2.79459194e-03, 2.67766379e-02, 2.18242058e-03, 2.98561528e-03,\n",
-      "        9.67142545e-03],\n",
-      "       [2.60602473e-03, 6.06194045e-03, 6.48480467e-03, 5.24291350e-03,\n",
-      "        6.60770806e-03, 2.93504205e-02, 1.40409567e-04, 1.17491698e-04,\n",
-      "        3.11701819e-02, 3.09709739e-02, 7.95513479e-05, 3.12839460e-04,\n",
-      "        8.74308869e-03, 3.68170021e-03, 4.41717639e-05, 3.66688473e-05,\n",
-      "        6.22019544e-02, 1.28152937e-01, 4.04050248e-03, 7.28918752e-03,\n",
-      "        3.56937699e-06, 5.69726799e-05, 2.66550273e-07, 4.27597389e-02,\n",
-      "        1.01912670e-01, 1.32168725e-01, 1.18109092e-01, 9.89402831e-02,\n",
-      "        9.14235692e-03, 4.03584167e-02, 3.69383506e-02, 4.38604988e-02,\n",
-      "        1.94850640e-04, 3.02860625e-02, 4.43454715e-04, 4.01076773e-04,\n",
-      "        1.10881403e-02],\n",
-      "       [9.72755661e-04, 2.83560762e-03, 2.09250348e-03, 7.08562974e-03,\n",
-      "        1.99222248e-02, 1.70437302e-02, 6.27118628e-04, 4.44679637e-04,\n",
-      "        7.59567250e-04, 6.22394960e-04, 2.68107106e-04, 7.24267273e-04,\n",
-      "        4.60452429e-04, 2.34048755e-04, 1.98413749e-04, 1.55608912e-04,\n",
-      "        7.50128031e-02, 3.14960107e-02, 5.07137878e-03, 4.78138635e-03,\n",
-      "        9.16231329e-06, 4.43051817e-07, 4.91673404e-07, 4.33409438e-02,\n",
-      "        5.18652536e-02, 1.26850814e-01, 6.27304837e-02, 7.75047541e-02,\n",
-      "        3.53763327e-02, 9.05281901e-02, 1.76466465e-01, 1.30844772e-01,\n",
-      "        6.25297474e-03, 8.51495564e-03, 7.06573855e-03, 8.18227977e-03,\n",
-      "        3.65726859e-03],\n",
-      "       [1.18808018e-03, 2.60169245e-03, 1.44573965e-03, 1.61221239e-03,\n",
-      "        2.26570200e-03, 6.82756910e-03, 1.22456395e-05, 1.75701007e-05,\n",
-      "        1.45553514e-01, 2.46136382e-01, 5.47590980e-06, 3.54246076e-05,\n",
-      "        1.04971025e-02, 5.45290019e-03, 5.14718386e-06, 7.95967026e-06,\n",
-      "        1.66995171e-02, 3.97547148e-02, 3.19603574e-03, 6.24249410e-03,\n",
-      "        9.24222513e-06, 4.82693547e-03, 3.04615833e-06, 1.89753268e-02,\n",
-      "        1.23844156e-02, 6.41736761e-02, 1.92459356e-02, 2.05983259e-02,\n",
-      "        3.28172417e-03, 9.90303326e-03, 2.93984395e-02, 9.32308938e-03,\n",
-      "        6.60474543e-05, 2.99182653e-01, 1.61686854e-04, 4.12492227e-05,\n",
-      "        1.88677423e-02]], dtype=float32)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import onnx\n",
-    "import onnxruntime as ort\n",
-    "\n",
-    "onnx_model = onnx.load(\"model.onnx\")\n",
-    "onnx.checker.check_model(onnx_model)\n",
-    "\n",
-    "ort_sess = ort.InferenceSession(\"model.onnx\")\n",
-    "outputs = ort_sess.run(None, input_dict)\n",
-    "print(outputs)\n",
-    "\n",
-    "# Print Result\n",
-    "# predicted, actual = classes[outputs[0][0].argmax(0)], classes[y]\n",
-    "# print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 166)"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "outputs[0].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "from ParticleTransformerHidden import ParticleTransformerTagger\n",
-    "\n",
-    "part_model = ParticleTransformerTagger(\n",
-    "    pf_input_dim=25,\n",
-    "    sv_input_dim=11,\n",
-    "    num_classes=37 + 1,  # one dim for regression\n",
-    "    # network configurations\n",
-    "    pair_input_dim=4,\n",
-    "    embed_dims=[128, 512, 128],\n",
-    "    pair_embed_dims=[64, 64, 64],\n",
-    "    num_heads=8,\n",
-    "    num_layers=8,\n",
-    "    num_cls_layers=2,\n",
-    "    block_params=None,\n",
-    "    cls_block_params={\"dropout\": 0, \"attn_dropout\": 0, \"activation_dropout\": 0},\n",
-    "    fc_params=[],\n",
-    "    activation=\"gelu\",\n",
-    "    # misc\n",
-    "    trim=True,\n",
-    "    for_inference=True,\n",
-    ").cpu()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/amp/autocast_mode.py:198: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
-      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n",
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/nn/functional.py:5046: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  assert embed_dim == embed_dim_to_check, \\\n",
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/nn/functional.py:5053: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  assert head_dim * num_heads == embed_dim, f\"embed_dim {embed_dim} not divisible by num_heads {num_heads}\"\n",
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/nn/functional.py:5059: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  assert key.shape == value.shape, f\"key shape {key.shape} does not match value shape {value.shape}\"\n",
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/nn/functional.py:5093: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  if attn_mask.shape != correct_3d_size:\n",
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/nn/functional.py:5155: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  assert key_padding_mask.shape == (bsz, src_len), \\\n",
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/nn/functional.py:4849: TracerWarning: Converting a tensor to a Python float might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  q = q / math.sqrt(E)\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n",
-      "WARNING: The shape inference of prim::Constant type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.\n"
-     ]
-    }
-   ],
-   "source": [
-    "part_model.load_state_dict(torch.load(\"net_best_epoch_state.pt\", map_location=torch.device(\"cpu\")))\n",
-    "_ = part_model.eval()\n",
-    "\n",
-    "data_config = {\n",
-    "    \"input_names\": [\"pf_features\", \"pf_vectors\", \"pf_mask\", \"sv_features\", \"sv_vectors\", \"sv_mask\"],\n",
-    "    \"input_shapes\": {\n",
-    "        \"pf_features\": (-1, 25, pfs),\n",
-    "        \"pf_vectors\": (-1, 4, pfs),\n",
-    "        \"pf_mask\": (-1, 1, pfs),\n",
-    "        \"sv_features\": (-1, 11, svs),\n",
-    "        \"sv_vectors\": (-1, 4, svs),\n",
-    "        \"sv_mask\": (-1, 1, svs),\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "model_info = {\n",
-    "    \"input_names\": list(data_config[\"input_names\"]),\n",
-    "    \"input_shapes\": {k: ((1,) + s[1:]) for k, s in data_config[\"input_shapes\"].items()},\n",
-    "    \"output_names\": [\"softmax\"],\n",
-    "    \"dynamic_axes\": {\n",
-    "        **{k: {0: \"N\", 2: \"n_\" + k.split(\"_\")[0]} for k in data_config[\"input_names\"]},\n",
-    "        **{\"softmax\": {0: \"N\"}},\n",
-    "    },\n",
-    "}\n",
-    "\n",
-    "inputs = tuple(\n",
-    "    torch.ones(model_info[\"input_shapes\"][k], dtype=torch.float32)\n",
-    "    for k in model_info[\"input_names\"]\n",
-    ")\n",
-    "torch.onnx.export(\n",
-    "    part_model,\n",
-    "    inputs,\n",
-    "    \"model.onnx\",\n",
-    "    input_names=model_info[\"input_names\"],\n",
-    "    output_names=model_info[\"output_names\"],\n",
-    "    dynamic_axes=model_info.get(\"dynamic_axes\", None),\n",
-    "    opset_version=11,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[ 1.5299e-03,  2.9319e-03,  1.9769e-03,  5.4878e-03,  7.8053e-03,\n",
-      "          1.2931e-02,  1.3580e-04,  6.4316e-05,  5.6223e-03,  3.5439e-03,\n",
-      "          5.7096e-05,  7.1246e-05,  2.4305e-03,  7.1927e-04,  4.5472e-05,\n",
-      "          2.3293e-05,  7.5924e-02,  3.5818e-02,  8.9561e-03,  9.3922e-03,\n",
-      "          4.7991e-06,  2.2116e-05,  4.7249e-07,  9.0073e-02,  4.4058e-02,\n",
-      "          1.9110e-01,  5.3661e-02,  8.5326e-02,  2.5597e-02,  6.6441e-02,\n",
-      "          1.0755e-01,  1.1822e-01,  5.6389e-04,  2.4701e-02,  1.2583e-03,\n",
-      "          8.0306e-04,  1.5152e-02,  1.1746e+02, -1.4494e+00, -3.8877e+00,\n",
-      "         -4.1724e-01, -2.1862e+00,  4.6684e-01,  2.2150e-01,  2.5049e-01,\n",
-      "          1.4588e+00,  5.7455e-01,  6.8124e-01, -2.3397e+00, -7.7287e-01,\n",
-      "          7.0747e-01,  1.9500e+00, -3.2054e-01, -3.3750e-01, -3.7859e-01,\n",
-      "         -3.5869e-01,  8.4656e-01,  2.2947e+00, -5.7608e-01,  1.1714e+00,\n",
-      "         -9.2651e-02, -8.0103e-01,  2.5926e-02,  1.4265e+00, -2.4823e+00,\n",
-      "          1.6136e+00,  1.8963e+00,  6.2551e+00,  6.8286e-01, -3.8628e-01,\n",
-      "         -1.1575e+00,  6.8476e-01,  8.5872e-01, -7.8506e-01,  2.7799e+00,\n",
-      "          4.6129e-01,  7.1114e-01, -3.3843e-02, -5.5794e-01, -2.9919e-01,\n",
-      "         -1.4846e+00,  4.3219e-01, -1.4646e-01,  1.8458e+00, -2.4080e-01,\n",
-      "          1.0279e+00, -1.6629e+00, -7.0566e-01, -1.8657e-01, -7.4921e-02,\n",
-      "         -1.1353e+00,  1.0703e-01,  2.0712e-01, -2.2964e+00,  1.6433e+00,\n",
-      "          8.9940e-02,  3.7805e-01, -2.2146e+00, -5.3493e-01, -4.0156e-02,\n",
-      "         -5.8408e-01, -8.0333e-01, -2.4586e+00,  9.8550e-02, -3.2820e+00,\n",
-      "         -1.4683e+00,  7.9389e+00,  6.2665e-01,  1.6939e-01,  1.1479e+00,\n",
-      "         -3.5398e-01,  3.5816e-01, -1.0650e+00, -1.2613e+00, -1.1165e+00,\n",
-      "         -1.5212e-01, -7.9048e-01, -2.9893e-02, -1.7999e+00,  4.1555e-01,\n",
-      "         -9.1506e+00,  2.9598e-01, -1.1166e-01, -2.2689e+00, -4.0743e-01,\n",
-      "          2.9964e+00,  1.1030e+00, -4.7359e+00,  1.6196e-01,  9.1396e-01,\n",
-      "          1.6023e+00,  9.6509e-01,  1.3732e-02, -1.0029e+00,  2.4971e+00,\n",
-      "          1.9553e+00, -1.7973e+00, -1.4033e+00, -7.1935e-01, -2.5925e-01,\n",
-      "         -2.3469e-01, -8.4961e-01, -5.0476e-01, -6.1761e-01, -9.6350e-01,\n",
-      "         -1.0327e+00,  5.3326e-01,  3.7144e+00,  5.2717e-01,  7.0532e-01,\n",
-      "          2.7586e-01, -7.5351e-01,  9.4370e-01,  2.7028e-01, -9.8784e-02,\n",
-      "         -3.4679e-01,  1.8617e-01, -3.3025e-01,  5.8429e-02,  1.1500e-01,\n",
-      "          2.0636e-01,  4.2734e-01,  5.0767e-02, -1.4037e+00,  2.5175e+00,\n",
-      "          1.6230e+00]], grad_fn=<CatBackward0>)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/python310/lib/python3.10/site-packages/torch/amp/autocast_mode.py:198: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
-      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n"
-     ]
-    }
-   ],
-   "source": [
-    "in_tensors = [torch.Tensor(val) for key, val in input_dict.items()]\n",
-    "in_tensors[2] = in_tensors[2].bool()\n",
-    "in_tensors[5] = in_tensors[5].bool()\n",
-    "\n",
-    "out = part_model(*in_tensors)\n",
-    "print(out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.52987312e-03,  2.93191988e-03,  1.97693706e-03,\n",
-       "         5.48777496e-03,  7.80526781e-03,  1.29312817e-02,\n",
-       "         1.35801572e-04,  6.43163876e-05,  5.62225794e-03,\n",
-       "         3.54393735e-03,  5.70958327e-05,  7.12459951e-05,\n",
-       "         2.43050908e-03,  7.19271193e-04,  4.54720794e-05,\n",
-       "         2.32931434e-05,  7.59244114e-02,  3.58182155e-02,\n",
-       "         8.95608403e-03,  9.39224847e-03,  4.79914434e-06,\n",
-       "         2.21154678e-05,  4.72491791e-07,  9.00733918e-02,\n",
-       "         4.40584160e-02,  1.91101983e-01,  5.36605977e-02,\n",
-       "         8.53259340e-02,  2.55966950e-02,  6.64411336e-02,\n",
-       "         1.07548602e-01,  1.18220098e-01,  5.63886017e-04,\n",
-       "         2.47012507e-02,  1.25828688e-03,  8.03059200e-04,\n",
-       "         1.51520669e-02,  1.17462372e+02, -1.44942522e+00,\n",
-       "        -3.88765955e+00, -4.17238146e-01, -2.18617630e+00,\n",
-       "         4.66837943e-01,  2.21496135e-01,  2.50485390e-01,\n",
-       "         1.45877016e+00,  5.74553132e-01,  6.81239963e-01,\n",
-       "        -2.33971190e+00, -7.72875249e-01,  7.07470357e-01,\n",
-       "         1.94998372e+00, -3.20540398e-01, -3.37501228e-01,\n",
-       "        -3.78589600e-01, -3.58688295e-01,  8.46563578e-01,\n",
-       "         2.29472423e+00, -5.76084495e-01,  1.17144680e+00,\n",
-       "        -9.26510617e-02, -8.01026106e-01,  2.59260591e-02,\n",
-       "         1.42652380e+00, -2.48230267e+00,  1.61363983e+00,\n",
-       "         1.89631426e+00,  6.25512600e+00,  6.82864428e-01,\n",
-       "        -3.86280149e-01, -1.15748513e+00,  6.84761107e-01,\n",
-       "         8.58720064e-01, -7.85056114e-01,  2.77994680e+00,\n",
-       "         4.61292177e-01,  7.11136281e-01, -3.38433571e-02,\n",
-       "        -5.57939529e-01, -2.99192607e-01, -1.48460102e+00,\n",
-       "         4.32187229e-01, -1.46460190e-01,  1.84582233e+00,\n",
-       "        -2.40803346e-01,  1.02788234e+00, -1.66289389e+00,\n",
-       "        -7.05655456e-01, -1.86570972e-01, -7.49205500e-02,\n",
-       "        -1.13534272e+00,  1.07026987e-01,  2.07119957e-01,\n",
-       "        -2.29636812e+00,  1.64329708e+00,  8.99407342e-02,\n",
-       "         3.78047019e-01, -2.21461058e+00, -5.34928858e-01,\n",
-       "        -4.01555151e-02, -5.84082127e-01, -8.03329110e-01,\n",
-       "        -2.45860362e+00,  9.85501930e-02, -3.28197074e+00,\n",
-       "        -1.46827316e+00,  7.93893337e+00,  6.26650393e-01,\n",
-       "         1.69391245e-01,  1.14793801e+00, -3.53978753e-01,\n",
-       "         3.58162254e-01, -1.06499791e+00, -1.26129329e+00,\n",
-       "        -1.11649430e+00, -1.52122587e-01, -7.90478647e-01,\n",
-       "        -2.98935622e-02, -1.79986322e+00,  4.15549010e-01,\n",
-       "        -9.15061665e+00,  2.95982957e-01, -1.11661263e-01,\n",
-       "        -2.26893806e+00, -4.07427788e-01,  2.99639583e+00,\n",
-       "         1.10301161e+00, -4.73590517e+00,  1.61961943e-01,\n",
-       "         9.13960874e-01,  1.60228312e+00,  9.65091646e-01,\n",
-       "         1.37326941e-02, -1.00292659e+00,  2.49710369e+00,\n",
-       "         1.95532024e+00, -1.79733491e+00, -1.40328312e+00,\n",
-       "        -7.19354153e-01, -2.59252250e-01, -2.34689966e-01,\n",
-       "        -8.49607408e-01, -5.04762590e-01, -6.17605746e-01,\n",
-       "        -9.63499486e-01, -1.03271711e+00,  5.33264279e-01,\n",
-       "         3.71438241e+00,  5.27171910e-01,  7.05322683e-01,\n",
-       "         2.75861830e-01, -7.53513157e-01,  9.43696380e-01,\n",
-       "         2.70279795e-01, -9.87844542e-02, -3.46789420e-01,\n",
-       "         1.86171070e-01, -3.30248594e-01,  5.84291182e-02,\n",
-       "         1.14996746e-01,  2.06357971e-01,  4.27338094e-01,\n",
-       "         5.07665314e-02, -1.40367317e+00,  2.51749849e+00,\n",
-       "         1.62298036e+00]], dtype=float32)"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[ 8.1155e-04,  2.0606e-03,  1.5020e-03,  2.6562e-03,  9.1622e-03,\n",
-       "          3.2599e-03,  1.4781e-03,  9.5434e-04,  3.3028e-04,  6.9261e-04,\n",
-       "          8.2008e-04,  9.9679e-04,  1.7369e-04,  2.3844e-04,  3.0242e-04,\n",
-       "          3.9385e-04,  4.7146e-03,  1.8696e-03,  1.0828e-03,  8.7911e-04,\n",
-       "          1.6338e-04,  4.5681e-05,  9.7771e-06,  1.5454e-01,  3.1454e-02,\n",
-       "          3.7411e-02,  1.8826e-02,  9.6202e-02,  1.1759e-02,  4.5734e-02,\n",
-       "          2.2030e-01,  1.1428e-01,  6.0745e-02,  5.3709e-02,  2.1316e-02,\n",
-       "          4.7606e-02,  5.1513e-02,  4.4246e+01, -2.1041e-02, -3.4620e+00,\n",
-       "         -5.6576e-02,  2.3017e-01, -4.3493e-01, -4.8431e-01,  5.6465e-02,\n",
-       "         -5.0499e-01,  8.3038e-02,  2.3239e-01, -1.3889e+00,  1.7120e+00,\n",
-       "          1.3771e-01, -1.2663e+00,  6.6230e-01,  7.4898e-02, -3.1588e-01,\n",
-       "          3.6870e-01, -6.8668e-01,  1.6357e+00, -8.1999e-01,  2.1713e-01,\n",
-       "          6.4083e-01,  8.7478e-01,  9.4118e-01, -4.9381e-01, -1.2368e+00,\n",
-       "          1.0730e+00,  1.7268e+00,  3.7660e+00, -6.7726e-01,  8.4824e-01,\n",
-       "          4.6144e-01,  2.0225e-01,  1.7881e+00,  1.9420e-01,  1.8283e+00,\n",
-       "          9.1964e-01,  5.5956e-01, -4.7744e-01, -5.5663e-01, -5.8612e-01,\n",
-       "         -1.4102e+00,  2.4762e-01,  6.0904e-01,  3.9106e-01,  5.6764e-01,\n",
-       "          1.3071e-01,  2.4557e-01, -4.7081e-02,  2.8010e-01,  4.1005e-01,\n",
-       "         -3.8123e-01, -2.4296e-01,  6.7043e-02, -1.6497e+00,  8.9188e-01,\n",
-       "         -3.1135e-01, -1.0625e-02, -3.0718e+00, -5.4417e-01,  2.4900e-02,\n",
-       "          4.6762e-01,  1.0117e-01, -3.3451e+00, -5.2773e-01, -4.1818e+00,\n",
-       "         -2.4967e-01,  7.9518e+00, -2.8008e-01, -3.0822e-01,  3.9351e-01,\n",
-       "         -2.2870e-01, -4.7540e-02, -2.3264e-01, -3.3452e-01, -2.4542e-01,\n",
-       "         -1.0442e+00,  7.3891e-01,  5.5050e-01, -1.6255e+00, -4.6427e-02,\n",
-       "         -6.1029e+00, -6.0916e-01, -7.7775e-01, -2.9953e+00, -7.7228e-01,\n",
-       "          9.6793e-01, -9.4810e-01, -1.9821e+00,  1.9788e-01, -1.3584e-01,\n",
-       "          2.5486e+00,  2.9703e-01,  2.3074e-01, -1.8084e-01, -4.3078e-01,\n",
-       "          6.9299e-01, -1.9842e+00, -7.0539e-01, -2.4653e-01, -9.6330e-01,\n",
-       "         -7.8405e-01, -8.5037e-01, -9.2285e-01, -5.4372e-01,  1.0601e+00,\n",
-       "          6.9960e-02, -8.5179e-01,  4.2882e+00,  6.4952e-02, -3.8099e-01,\n",
-       "          1.5795e+00,  8.3206e-02,  6.9494e-01, -5.4985e-01,  2.3210e-02,\n",
-       "          6.0039e-01, -2.0198e-01, -2.7308e-01, -2.6412e-01,  5.0381e-01,\n",
-       "         -5.9945e-01,  2.0151e-02,  3.9549e-01, -5.9732e-01,  2.6158e+00,\n",
-       "          1.5727e+00]], grad_fn=<CatBackward0>)"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31a7b1cb5f073f7a7d37b3db504c6954ce2b88e0f82e412b65ad0b5f2dd17394"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/src/binder/plotTF.ipynb b/src/binder/plotTF.ipynb
new file mode 100644
index 00000000..127571a6
--- /dev/null
+++ b/src/binder/plotTF.ipynb
@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bernstein poly's:\n",
+    "\n",
+    "\n",
+    "def bpoly10(x):\n",
+    "    return 1 - x\n",
+    "\n",
+    "\n",
+    "def bpoly11(x):\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "def bpoly20(x):\n",
+    "    return np.power(1 - x, 2)\n",
+    "\n",
+    "\n",
+    "def bpoly21(x):\n",
+    "    return 2 * x * (1 - x)\n",
+    "\n",
+    "\n",
+    "def bpoly22(x):\n",
+    "    return np.power(x)\n",
+    "\n",
+    "\n",
+    "bpoly2dlist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = [[0.857, 2.31], [0.534, -7.93e-3], [-3.223e-4, -1.28e-3]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bpoly2d(x, y, params):\n",
+    "    bpoly10 = 1 - y\n",
+    "    bpoly11 = y\n",
+    "\n",
+    "    bpoly20 = np.power(1 - x, 2)\n",
+    "    bpoly21 = 2 * x * (1 - x)\n",
+    "    bpoly22 = np.power(x, 2)\n",
+    "\n",
+    "    polylist = [\n",
+    "        [bpoly20 * bpoly10, bpoly20 * bpoly11],\n",
+    "        [bpoly21 * bpoly10, bpoly21 * bpoly11],\n",
+    "        [bpoly22 * bpoly10, bpoly22 * bpoly11],\n",
+    "    ]\n",
+    "\n",
+    "    out = np.zeros(x.shape)\n",
+    "\n",
+    "    for i in range(3):\n",
+    "        for j in range(2):\n",
+    "            out += params[i][j] * polylist[i][j]\n",
+    "\n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx, yy = np.meshgrid(np.linspace(0, 1, 101), np.linspace(0, 1, 101))\n",
+    "zz = bpoly2d(xx, yy, params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.contourf(xx, yy, zz, np.linspace(-0.1, 2.9, 31), cmap=\"turbo\")\n",
+    "plt.colorbar(cmap=\"turbo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python310",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/binder/plotTrigEffs.ipynb b/src/binder/plotTrigEffs.ipynb
index 6dc19517..24eea4cb 100644
--- a/src/binder/plotTrigEffs.ipynb
+++ b/src/binder/plotTrigEffs.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fe3bbf63-2195-417f-8e52-ebcae3bb061f",
    "metadata": {},
    "outputs": [],
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "d0d59f13",
    "metadata": {},
    "outputs": [],
@@ -32,32 +32,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "342e20fd",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Hist(\n",
-       "  Variable([0, 0.9, 0.95, 0.98, 1], name='jet1txbb', label='$T_{Xbb}$ Score'),\n",
-       "  Regular(50, 0, 1000, name='jet1pt', label='$p_T$ (GeV)'),\n",
-       "  Regular(15, 0, 300, name='jet1msd', label='$m_{SD}$ (GeV)'),\n",
-       "  storage=Double()) # Sum: 106.0 (110.0 with flow)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "outs[\"num\"][:, sum, :, :]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "b9e2924b",
    "metadata": {},
    "outputs": [],
@@ -68,72 +53,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "e984e377",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.  , 0.9 , 0.95, 0.98, 1.  ])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "jet1txbbbins"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "34dd2634",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2VWpqqsvH8Ouvv2r06NEOHxYEBASoRYsWDnV4M8yaNUsDBw7U2bNnHdZbLBY1btxYISEhTn0SEhJ08803F9vP6rp163TVVVfp8OHDktLLRCxatMjhxqeeUNKur5nLmZw+fdqtOspWq9Wh3rfVatXBgwcd2hTkeL28vBzOlfDwcCUlJbncHwAAlBDFPPMZAIBCV7VqVYev9aalpbnc99ChQw5fu73ssssKHE+bNm0cxjxy5IjLfVNTUx361qxZ06lN3759HdqsWbPGrfjq16/v0P/s2bNu9c+vI0eOmFatWjns+4UXXiiU8geuxpM5lvyUrcgwe/Zsh7Fuu+22bI8rLCzMeHt7O5SLyOl8zTyel5eXQ5wLFy40ycnJ9raJiYnmyy+/NCEhIU5lB7788stsx89ctiLz+AEBAWbGjBkmISHB3vbixYvm22+/dXr/JJmXX345369bVlar1Vx++eU5llBo3LixGTt2rPn+++8d4ssvm81m+vTp47CPgIAA8+mnn5pz587Z26Wmpppff/3VXHbZZQ5tq1evbiIjI53GjY6Odig3IMnUqVPHfPLJJw4/bzabzezYscMMGTLE6VhfeumlHOPOXLaibt26JiAgwP783XffNUeOHMnx52rr1q0OpQwkmauuusqsXbvWJCYm2tvFxMSY//u//3Mqt+Ht7e1W2Q5P+Oqrrxxirl27ttm4cWOh7KukXV+z/nuyZcsWl/vu3bvX6bzKejxjxoxxucxJdq6++mqH/n/99Zdb/QEAQPEjeQwAKNOyJlv9/f3d6n/27FmPJRAzZE3gZU5CuaJmzZoO/a1Wq8P2bt26OWx3N5GTNQHmTnI7vzZv3mwCAwPt+/Tx8TGzZs0q9P3mxpPJY5vNZgYOHJhr0vb8+fOmZcuWDh8MHD9+PMcxs0uedujQwcTFxeXYJzY21in5Wrt2bXPhwgWntpmTxxmPSy65xBw7dizH8S9cuOCUXPP19TURERF5v0guOnTokLn00ktzTCBn3m+PHj3Myy+/bDZu3GhSUlLc3tfcuXMdxgwNDc315yElJcWh3q4kM2nSJKd2Dz74oEObZs2amfDw8Fxj+eCDD5yStDnFkjl5nPFo3769OXXqVK77sFqtTq/t22+/7XSNySw+Pt4MHjzYoU+3bt1y7eMpNpvNTJo0yWHfzZs3NwcPHiy0fZa06+vDDz/sMN64ceNc7pu1pr8ks3z5coc2w4cPd9i+bNkyt+IbMGBAgZLtAACg+FG2AgBQpmX9imytWrXc6l+9enX5+PjkOF5BY/L29s6zlm1WWb9mnrWubEGPOev4hf014yVLlqhXr16Kjo6WlP6a//DDDxo1alSh7rcoWSwWffbZZ6pXr5593WOPPebwlfEJEyZo37599uUZM2a49RXxqlWr6ttvv3WoMZqVv7+/VqxYIX9/f/u606dPa+HChS4dw+LFi3XJJZfk2CajVEDTpk3t61JTUzVjxgzXDsIFjRs31h9//KFRo0Y5lYvILDU1Vb/99pteeuklde3aVQEBAerfv7/mzJnjUi1mY4ymTJnisG7evHlq2LBhjn18fX31f//3f/L29ravy/raxsbG6ssvv7Qv+/j4aPHixdmWgsjs0Ucf1d13321ftlqt+vjjj/M8Dim9xMbXX3+toKCgXNv99NNP2r17t3152LBhGj9+vLy8cv6ToVq1apo1a5aaN29uX7dhwwaHEieFISUlRSNGjNCkSZPs666++mpt3LhRTZo0KbT9lrTr62233eaw/P777zvVKc7Ohg0bnM5vSUpMTMw1vuI+XgAAUPRIHgMAyrSC/uFrsVgc+ng6eVyrVi1ZLBa3+uf1x3hp+WPfGKP33nvP4cZql1xyiTZu3KjevXsXyj6LU0BAgEMt53Pnzmn06NGy2Wxat26d/vvf/9q33Xvvvbr99tvdGv/xxx/PNbGboW7dunrxxRcd1i1atCjPfkOGDNHll1+eZ7vKlSvr7bffdnt8d/j5+WnWrFmKiIjQf//7X1111VUOCdvsJCQkaPny5Ro5cqRCQkL01FNPOdX0zeyvv/5yqD19ww03qEePHnnGdskll6h79+725b179+r8+fP25aVLlzokr++55x61bds2z3El6bXXXnO4XsybN8+lfv369VOLFi3ybPfZZ5/Zn1eoUEEfffSRS9enqlWrOt0gcM2aNS7Flh+xsbG64YYb9NVXX9nXDRs2TL/88ku2NZw9qaRdX3v37q0uXbrYl9PS0nTTTTfpp59+yra9zWbTjBkz1K9fv2xrhGe9iWZJO14AAFD0fPJuAgBA6WWz2RyW3U3UZh0j653oCxpTQePJLqaCHnNe43tCWlqaHn/8cYeZk506ddLy5csdZueWNTfccIMef/xxvf/++5LSb2T29ttv69NPP7W3adasmUMi2VUjRoxwue3IkSP1n//8x34zu02bNskYk+u54s74/fr1U2BgoH02+Z49exQfH+9wcy9PCAgI0BNPPKEnnnhC586d07p167Rq1SqtWrUq19mXcXFxeu+997Ry5Up99913DrNmM/z2228Oy1lv1pebSZMmadOmTfbl5ORk+00Fs95476677nJ53Pr166tHjx5at26dJOnkyZM6ceJEnjPUe/bsmefYGR9iZLjqqqtUo0YNl2Pr3r27fHx87OdU5rE86fDhw7r55psdbuT2/PPP65VXXsnX9dRdJe36mvGthquuusqemD1x4oRuvPFG3XTTTbr88svVunVrJSYmavfu3Vq7dq127Nhh71+1alWHhG7Wb8KUtOMFAABFj+QxAKBMy3xneyk9aeQOY4xDn6zj5Tem+Ph4ezx5Je2yio2NdRovt+W4uDhVrlzZY+MXVEJCgu6880798MMP9nX9+vXTggULPL6vkujNN9/Ur7/+qr///luS9Nxzz9m3+fj4aP78+fLz83NrzMqVK2ebAM2Jv7+/GjVqpH/++UdSeumKiIiIXEsnXHbZZS6P7+Pjo/bt29tLFxhjtGvXLnXr1s3lMdxVo0YN9e/fX/3795ckRUZGavXq1Vq1apVWrFhhT2Rntn//fnXu3Fnbtm1zKnXwxx9/OCy3bt3a5Vh69eqlXr16Zbtt69atDstt2rRxedyM9pkTs1u3bs0zeZxXSQwp/bXIfK0LDAzUjz/+6FZswcHBOn78uKT0Gdee9scff+jWW29VTEyMpPTz7NNPP9Xo0aM9vq+clMTra9u2bbVw4UINHjzYIRH8ww8/OFxns3rmmWe0e/durVy50r4ua/K4oP+GFva/JwAAoPCRPAYAlGlZ/1DN+odsXuLj4x2+xutufeKcYspIHqempiopKcmtZGHWY8jaN7tjDg4Ozvf4njjmDOHh4brlllscZr499thjmjp1ap5lB8qKypUra+7cubryyiuVmprqsO21115zqTREVo0aNXJ7RmDTpk3tyWMp/X3PKclYpUoVBQYGuj1+5rq37v7sFVRQUJCGDRumYcOGKS0tTWvXrtWcOXM0d+5ch3bnzp3T+PHjtXTpUof1ERERDssNGjTwSFwZiU8p/XWtU6eOW/2z1lzOLimelSvJ46zHu2jRogKVG0lISHD7g7HcLFq0SHfffbd95mr16tW1ePHiIi9xU1KvrzfffLM2bdqk+++/32HWe3Zq1Kihd999V/fee686d+6cazwF/Te0MP89AQAARYOaxwCAMq1ChQoON9WKi4tz+hptbrLOsnJ3Rmh2sv7x7M4f41arVefOnbMvZz2+go4vFc4xZ7j33nvtiWOLxaKpU6fq/fffLzeJ4wwdOnTQCy+84LCuZcuWGj9+fL7Ga9y4sdt9Mt/UTsr9PGncuHG+ktOujl/YfHx81Lt3b3311VfasGGD0yztb7/9Vhs3bnRYlzneihUrup3kzUnmn6/8JP2zJo9dmQnqSuyefn9sNptDreeCOHnypAYPHmxPHNevX18bNmwoltroJfn6eumll2rjxo365Zdf9Mgjj+jSSy9VrVq15OPjo3r16qlbt2569913tX//ft13332yWCyKjIx0GCPr+VWSjxcAABQNZh4DAMq8Ro0a2etj2mw2xcfHq2bNmi71zfqHsidmHzZq1EgHDhxw2IcrNzqT0mdJZr7JUXbxNGrUyGG5IDPFKlas6PaM09xkrnfZtWtXPfzwwx4buzSxWq1avXq1w7p9+/Zp9erV+UqI+fi4/ytdpUqVHJZz++p9YY/vir/++sv+vEqVKm6V6cisa9eu+v7779WxY0f7NwAkaf369eratat9OfO5WrlyZXl5lYw5FxUrVnRYTklJybOPK7FnnQXvCfHx8R4pU5A1tmeeeUaXXnppgcfNj5J8fZXSP5Tr3bu3S9cRY4zDzPXAwEDVrl3boY0nj1fy3Ax+AABQdErGb8EAABSili1bOixn/Xp2bk6dOuWw3KpVqxIfT0HGT0xMVEJCgn25RYsWHp0V3K5dO/vzDRs2qE+fPsU6I7W4vP3229neUGzUqFE6e/as2+MdPnzY7T6nT592WK5Vq1au42f+0MLT47uiZ8+e6tChgzp06KDbb7+9QGM1adJEY8aMcViXUYM6Q+Z4z54965BoLojMH1wdOXLE7dc163sdEBDgibCc3p/3339fxpgCPTx188uaNWs61HV+9NFHNWXKFLdfO08oyddXd50+fdrhwwdP/3tijHH4N6t69epl+oaoAACUVSSPAQBlXtY/fjdv3uxy37CwMIflwkgeezqegoy/ZcuWPMcviP/+97+aMGGCfXndunXq2rVrvpKfpdWWLVv00ksv2Zczf008PDxcjz/+uNtjHjp0yO1EWua6015eXgoKCsqxbXx8vM6cOZPv8SUpNDTUrf5Z1a1b1/78xIkTbpWfyU7W2tJRUVEOy1mTskeOHCnQ/jJkLiFx/vx5hxrIrsj6s5J1pmh+ZR3H3bgKU82aNbV582Z17NjRvm78+PEaM2aM0tLSijSWknx9dddvv/3msJzdDS0LcrxHjx51+BCpVatWHquBDQAAig7JYwBAmXfNNdc4LK9du9blvln/uO7Vq1eJiifrWFL61/J9fX3ty+vWrXM5sejK+AXh5eWl1157TTNnzrSXQti/f7+6dOniVlKitEpMTLTfwE1Kv6nbli1bdPXVV9vbfPXVV1q8eLFb4yYlJbmV3IyJiXFI7rZr1y7PWqQ7d+50efyUlBSHc8nf3z/fZSYyNGnSxP787NmzTh+kuCvrz0TWGZGZE5WSHErN5OWvv/7SXXfdZX9knmXeqVMnh7Z79+51eVzJeYZ01vHy69JLL3Won75161aPjOsp9erV02+//aZ+/frZ13388ccaMGCAEhMTiyyOknZ9PXLkiD755BP7I2uCOjcrV650WL7tttuc2jRv3tzhZyMsLExJSUkujV/Y/54AAICiQfIYAFDm9erVyyExtnjxYoevDufk6NGjDkmfli1bOs3Cyo8WLVo4JNJ+++03HTp0KM9+iYmJ+uabb+zL1apVyzaZXb16dYc/0o8dO+ZSgtpms+nLL790WNe/f/88++XH6NGj9cMPP6h69eqS0pOZvXr10pIlSwplfyXF448/roMHD9qXP//8c9WuXVuzZ89WlSpV7Osfeughp5mweZkzZ47LbWfOnCmr1Wpf7tmzp0fHX7JkicOMwx49ehS4ZnDfvn0dlqdMmVKg8f744w+H5TZt2jgsZ07oS9LChQtdHnvu3LmaN2+e/ZFZ1tmdWbfnJjw8XGvWrLEv161b1yGpXhCVKlVS586d7ctr1651Kj2Smz179qh79+72xyeffOKRuDKrWrWqli5dqscee8y+buXKlerRo4db5RQKoqRdX5OTk/Xwww/bH67edDM6OloLFiywL4eGhmb7QYSXl5dDwj45Odnln4Ws14wBAwa41A8AAJQwBgCAcuCuu+4ykuyPV199Nc8+Dz/8sEOfl156yWPxvPDCCw5j33fffXn2eeuttxz63HPPPTm2/fzzzx3aXn/99cZms+U6/tdff+3Qp0ePHm4fl7t2795tLrnkEvs+LRaLmTp1ap6xFoUjR444vB4NGjQo0HhZX9+s7/mHH37osL1fv365vg6Z20oy1apVMydOnMgzjhMnTphq1ao59N25c6dTuwYNGji08fLyMtu2bctz/Pj4eIf3VJJZvnx5nv3yEh4ebnx9fR3GnTNnTr7GioiIMP7+/g5jHTlyxKFNcnKyCQoKcjj+rVu35jl2cnKyadiwob1fpUqVTHx8vH17TEyMqVSpkn27r6+v2b17t0txDx8+3CHmsWPHZttu4sSJuR5bTj7++GOHfk8++aRL/YwxZuDAgQ59f/nlF5f75se0adOMxWKx769+/frZnseFoSRdX61Wq6lXr57DNXT//v159nvwwQcd4nnvvfdybPvrr786tG3durW5ePFiruNv2rTJ6fpptVrdPj4AAFD8SB4DAMqFvXv3Gi8vL/sfshUrVjRbtmzJsf3y5csd/vCtXr26OXPmjMfiOX36tFMC77vvvsux/datW03FihXtbb29vc2+fftybH/x4kWHBJYk8+GHH+bY/vDhw6ZOnToO7X/99dcCHaOrTp06ZS6//HKHfT/66KMmLS2tSPafE08mj48dO2Zq1qzpMNa5c+cc2litVnPNNdc47PPzzz/PccysyWNJ5vLLLzdnz57NsU9MTIxp166dQ5/evXtn2zZr8liSadSoUa4J6qSkJNO7d2+HPs2bN/fYe/mf//zHYWxvb2/zzjvvuDX+kSNHzGWXXeYwztChQ7Nt+8orrzi0a9KkSZ4J+oceesihz5133unU5t5773Vo06JFCxMREZHruO+9955TMv/w4cPZts1v8jgpKckEBAQ49J0xY0ae/bLG1rp16yJJFC5btsxUqVLF4Tr9888/F/p+S9r19fnnn3cY+7LLLjPnz5/Psf2MGTMc2oeEhJgLFy7k2N5ms5lu3bo59Bk/fnyOCfOYmBjTsmVLh/YzZ84s8HECAIDiQfIYAFBu3H///Q5/zPr5+ZnPPvvM4Y/m2NhY8+qrrxpvb2+Htm+++WauY2dNNEoys2fPzrXPa6+95tDex8fHvPnmmw5JxcTERPPRRx+ZqlWrOrR96KGH8jzeuXPnOsU0fvx4Ex0dbW9z8eJFM3/+fBMYGOjQ7sYbb8xzfE9KTEw0AwYMcIihX79+JiEhoUjjyMxTyeO0tDTTo0cPh7FWr16dbdvDhw87vNd+fn45Jggzj5f5g4VGjRqZb775xmFmYGJiovniiy8cZtJKMpUrVzb//PNPtuNnTh5nHr9OnTpm5syZJjEx0d724sWLZsmSJaZZs2YO41ssFvP777/n63XLTnx8vGnbtq3Ted2qVSszbdo0s3//fqeEls1mM7GxsebXX381gwYNcvrZDgoKMpGRkdnuLykpybRq1cqhfWBgoJkxY4bDz6nNZjN///23uf322x3a+vr6ZvshT2RkpNPPXN26dc3nn3/uNO6ff/5p7rjjDqdjnjhxYo6vU36Tx8YY83//939O+xoyZIj5448/TFJSkkPbrVu3Os2GlmR+/PFHl/dXUFu3bnU4r318fIokUVnY19esH96MGDEix7anT592Svq3a9fOrF271iGJf/DgQfPAAw84/YwuW7Ysz3h+++03h5neGTEdPXrU3sZqtZqVK1eapk2bOrRr06aNSU1NzXMfAACgZCJ5nE/h4eFmw4YNZsOGDSY8PNzt/lar1Rw4cMCsWbPGbNmyxcTGxro9xoULF8yuXbvM6tWrzc6dO3OdYQAASE8EZZ3hmpFAa926tWnatKnT1+IlmYEDB+Y5iy4/yeO0tDRzyy23OPWrUKGCadGihWnZsqXD19szHl26dHH5mj9mzBin/t7e3qZJkyamTZs2TklpKX12ZUxMjKsvq8ekpaWZsWPHOsTSsWPHPGdkFhZPJY9fffVVh3GeeOKJXNt/8sknDu179OiR7czazG3ef/99pzIMVatWNW3atDEtWrRwSP5mPLy8vMz8+fNzjCNz8uqVV14xjRo1cuhfqVIl06pVK9O6detszyNJ5u23387Xa5abqKgo07p162z3J6WX72jatKnp0KGDady4cbY/QxmP4ODgXGfwG2PMzp07Ta1atZz6+vr6mmbNmpm2bdua6tWrZzv+Bx98kOO4v/zyS7axZYzbvn17p9mqmZOPuSXjCpI8NsY4/RxmTjQ2btzYtGrVyumbExkPV0oCedqxY8fMpZde6hDHCy+8UOjlbwrz+upO8tgYY5YsWeKU3JVkqlSpYtq2bWuCg4ML/DP6zjvvZHtONGjQwLRt29bUqFHDaXtAQIA5ePCgy/sAAAAFk5CQYP7880+zevVqs2fPHpOSklLgMct98jijvmBuszcy+/7777NNPHTq1MmsXLkyz/5Wq9W89957TrUAvb29Td++ffP8A8YYY86ePWvGjBnj9IdKtWrVzMMPP5zr11UBoLyLiIgw3bt3zzGZlPUxePBgp9l22clP8tiY9JmUWWfc5va45pprTFRUlMvHm5qa6lTbMrdH+/btc5yJWlQ+/PBDhxIjl1xyics1YT3JE8njTZs2Ocx0bd68eZ7nk81mcyr98O677zq1y7z9hx9+MOvXr3eaWZzTo1q1auabb77JNY7MyauPP/7Y/P33304zi3N6VKhQIdfEaUElJiaasWPHOpwn7j5GjBjh8ocke/fuNU2aNHF57AoVKpj3338/z3HXr1/vNCs1r8fo0aPzrDdb0OSxzWYzb775ptMs7dwelStXNtOnT3drP5509uxZc8MNNzjENGzYMJOcnFxo+yzM66u7yWNjjPnyyy9z/bAk6/v18ccfu5Vgt9lsZtKkSS7/3DVs2NCEhYW5PD4AAOWJu/nIvERERJjhw4c7/S5Qu3ZtM2HChFxLVOWl3CePu3Tp4vKb9e677+b5S9I777yTY/+0tLQ8EwSVK1c269aty3GMyMjIPP94ady4cY5fvwQApF+P33vvvVyvp5dddpmZN2+ey39Y5zd5bEz6H+RffPGFUy3azI9mzZqZ999/P991RFesWGGuuuqqHMcPCQkxkydPLtREiztWrFjhMGuvV69eRR5DQZPH8fHxDueYl5eX2bRpk0t9jx075jCzs0KFCmbXrl0ObTLH9sMPPxhjjImOjjYjR440lStXzvZ99vPzM6NGjXJpNnfW5LEx6TMZnnzySYf6zZkfFSpUMIMGDTIHDhxw67XKr4MHD5oJEyY4zYrO6dGgQQMzfvx4s2fPHrf3deHCBfPuu+9mWws64+Hr62uGDRvm1ocd8fHxZuLEiSYkJCTHcX18fMz1119v1q9f79KYBU0eZ9i/f78ZNmxYjjPLM86pBx54IMfyKkUpJSXFqTzRF198Uej7LYzra36Sx8ak34R04MCB2c5CltK/kTBixAizd+/efB6tMRs3bjTXX399jknkgIAA8+STTzrVdQcAAP9yJx+Zl3379pnatWvn+ntw586dHcrOucNijDEqp2bPnq3Ro0dLkiZOnKhJkybl2Hb16tXq3bu3jDGqXbu2pk2bpptuukmS9P333+uJJ57QmTNnJEm//vqrrrvuOqcxJk2apMmTJ0uSOnbsqClTpuiKK65QXFycZs2apcmTJ8tms6lWrVrat2+f6tSp49DfGKNrr71Wa9eulSQNGTJEL7zwgho3bqxDhw7p1Vdf1ddffy1J6tGjh9auXSuLxVKg1wgAyjJjjLZu3aoDBw4oIiJC3t7eCgkJUbt27dSqVatiiWnPnj3atWuXIiIiZLVaFRwcrFatWqlDhw4euaYfPnxY27dv18mTJ3XhwgUFBwerSZMm6tKli7y9vT1wBJ7z559/6pZbblFERIR69uxp//cPeUtISNC6desUHh6u2NhY1a5dWw0aNFCPHj1UuXLlAo+fnJysdevW6fjx44qJiZG/v79CQ0PVs2dPVa9e3QNH4L7o6Ght27ZNEREROnfunJKSkuTn56eaNWuqXr166tixowIDAwu8H2OM/v77b/3111+KiopScnK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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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5555cfvnlmThxYgYNGpTevXtn7NixOfXUUzN9+vR6y9A217Bhw3Lvvffmf//3f3PUUUdl6NCh6dmzZ7bffvscddRR+fa3v52HH344e+65Z4uOb9mKbso/owAAAABoXfKWhpWXrdiuCy5bsbDxZSu6AjOPAQAAAACoIDwGAAAAAKCC8BgAAAAAgAo927sAAAAAAKB7KIpSe5dAM5h5DAAAAABABeExAAAAAAAVhMcAAAAAAFSw5jEAAAAAUB1FexdAc5h5DAAAAABABeExAAAAAAAVhMcAAAAAAFSw5jEAAAAAUBVFUWrvEmgGM48BAAAAAKggPAYAAAAAoILwGAAAAACACsJjAAAAAAAquGEeAAAAAFAdRXsXQHOYeQwAAAAAQAXhMQAAAAAAFYTHAAAAAABUsOYxAAAAAFAlpfYugGYw8xgAAAAAgArCYwAAAAAAKgiPAQAAAACoYM1jAAAAAKA6ivYugOYw8xgAAAAAgArCYwAAAAAAKgiPAQAAAACoYM1jAAAAAKA6rHncqZh5DAAAAABABeExAAAAAAAVhMcAAAAAAFSw5jEAAAAAUB1Fqb0roBnMPAYAAAAAoILwGAAAAACACsJjAAAAAAAqCI8BAAAAAKjghnkAAAAAQFUURXtXQHOYeQwAAAAAQAXhMQAAAAAAFYTHAAAAAABUsOYxAAAAAFAd1jzuVITH3dy8efMyYcKEBvdNnjw5kydPrnJFAAAAAB3X1KlTM3Xq1Ab3zZs3r8rV0NaW3vbXLL39bw3uW7d4WZWrqT7hcTc3bNiwPPbYY+1dBgAAAECnsKnJdqNGjcrs2bOrXBFtacBRh2bAUYc2uG/2J7+QdQuXVLmi6rLmMQAAAAAAFcw8BgAAAADaXlFa/9NVdKX30ggzjwEAAAAAqCA8BgAAAACggvAYAAAAAIAK1jwGAAAAAKqiVLR3BTSHmccAAAAAAFQQHgMAAAAAUMGyFQAA1FM3d7f2LqHTqNnhyfYuAQAA2oyZxwAAAAAAVDDzGAAAAACoDjfM61TMPAYAAAAAoILwGACADuPGPyzLPkc836S2f/7rirzlpBez3W5P5+C3z8oPf764jasDAIDuRXgMAECHsHjJunzhGwua1Pbe+1fl6JPnpF+/mlxxyZAcvH+f/OeUlwXIAADQiqx5DABAu3ry6TU547yXc+/0VVm2vMie43tvts83vrsw43fulWt/MDy9epVy8kkDs3RZXb46dWE+/L6BKZVKVagcAIBmK/w9rTMx8xgAgHbVd+tS3n5Ev3z2E4NzwD5bbbb9mjVFbvjd8rznuAHp1eu1//k4+aQBefLp2jz06Jq2LBcAALoNM48BAGhXo0b0yic+tl2S5JEnVmf6Q6s32X7e/LVZs6bIhNfNUJ6w2/rHs2bXZt+9Nh9CAwAAm2bmMQAAncrc+euSJNtt06Pe84O3W/943j/3AwAAW8bMYwAAOpXGVsmrK9b/d+3aomq1AADQTP6q1qmYeQwAQKeyw9D1M4wXLKo/w3jBwvWPhw8zPwIAAFqD8BgAgE5l6PY906tX8ugT9W+M98g/H+84QngMAACtQXgMAECn0rt3KSe8o39+9Zulqa197d89/vzXS7Pz2F7Zb283ywMAgNZgWgYAAB3agoXrMu2BVdllXK/sMq53kuTjH90uR77zxbz7P1/Ke47rn7unrcrV1y7N974+NDU1ja2KDABAu7Pmcadi5jEAAB3ajMdX55iT5+Tqa5eWn3vj/n1y889GZPHiupz+qfmZdv+qfP8bQ/OR92/TjpUCAEDXYuYxAAAdxlWX7VDx3BGH9s26l3Zt8Pkjft23GmUBAEC3ZOYxAAAAAAAVzDwGAAAAAKrDmsedipnHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAVrHgMAUE/NDk+2dwkAAEAHIDwGAAAAAKqjKLV3BTSDZSsAAAAAAKggPAYAAAAAoILwGAAAAACACtY8BgAAAACqolS0dwU0h5nHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAVrHgMAAAAAba/4509X0ZXeSyPMPAYAAAAAoILwGAAAAACACsJjAAAAAAAqCI8BAAAAAKggPAYAAAAAoILwGAAAAACACsJjAAAAAAAq9GzvAgAAAACA7qFUtHcFNIeZxwAAAAAAVBAeAwAAAABQQXgMAAAAAEAF4TEAAAAAABXcMK+bmzdvXiZMmNDgvsmTJ2fy5MlVrggAAACg45o6dWqmTp3a4L558+ZVuZpOqCi1dwXNsuSuv2TJX/7a4L51S5dWuZrqEx53c8OGDctjjz3W3mUAAAAAdAqbmmw3atSozJ49u8oV0ZYG/sthGfgvhzW4b9YFF2fd4sVVrqi6LFsBAAAAAEAF4TEAAAAAABUsWwEAAAAAVEfR3gXQHGYeAwAAAABQQXgMAAAAAEAF4TEAAAAAABWseQwAAAAAVIc1jzsVM48BAAAAAKggPAYAAAAAoILwGAAAAACACtY8BgAAAACqomTN407FzGMAAAAAACoIjwEAAAAAqCA8BgAAAACggjWPAQAAAIDqsOZxp2LmMQAAAAAAFYTHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAU3zAMAAAAAqsMN8zoVM48BAAAAAKggPAYAAAAAoILwGAAAAACACtY8BgAAAACqomTN407FzGMAAAAAACoIjwEAAAAAqCA8BgAAAACggjWPAQAAAIC2VyQpSu1dRevpBus3m3kMAAAAAEAF4TEAAAAAABWExwAAAAAAVLDmMQAAAABQHd1gneCuxMxjAAAAAAAqCI8BAAAAAKggPAYAAAAAoILwGAAAAACACm6YBwAAAABURckN8zoVM48BAAAAAKggPAYAAAAAoILwGAAAAACACtY8BgAAAACqw5rHnYrwuLurm5e6ubu1dxUdXs0OT7Z3CQAAAABQVZatAAAAAACggvAYAAAAAIAKwmPaxY1/WJZ9jni+SW3//NcVectJL2a73Z7OwW+flR/+fHEbVwcAAABAayslKRVd6Ke9P9AqEB5TdYuXrMsXvrGgSW3vvX9Vjj55Tvr1q8kVlwzJwfv3yX9OeVmADAAAAABtzA3zqJonn16TM857OfdOX5Vly4vsOb73Zvt847sLM37nXrn2B8PTq1cpJ580MEuX1eWrUxfmw+8bmFKpO3zHAwAAAADVZ+YxVdN361LefkS/fPYTg3PAPltttv2aNUVu+N3yvOe4AenV67WQ+OSTBuTJp2vz0KNr2rJcAAAAAOjWzDymakaN6JVPfGy7JMkjT6zO9IdWb7L9vPlrs2ZNkQmvm6E8Ybf1j2fNrs2+e20+hAYAAACggyjauwCaw8xjOqy589clSbbbpke95wdvt/7xvH/uBwAAAABan/CYDqux1Yzr/vkN1dq1vqoCAAAAgLYiPKbD2mHo+hnGCxbVn2G8YOH6x8OHWXUFAAAAANqK9K2bmzd/bfY6/PkG953+4W1y+oe3rW5BGxm6fc/06pU8+sSaHP9vrz3/yBPrb5S34wjDFwAAAKiuqVOnZurUqQ3umzdvXpWr6YQ62T8kX3TvX7L43rsb3Ld22dIqV1N90rdubtiQnnnkzjHtXUaDevcu5YR39M+vfrM0n5y8XXr1Wr+Qxc9/vTQ7j+2V/fZ2szwAAACguiZPnpzJkyc3uG/UqFGZPXt2lSuiLW37xsOy7RsPa3Dfs1+9KGuXLK5yRdUlPKbDWLBwXaY9sCq7jOuVXcb1TpJ8/KPb5ch3vph3/+dLec9x/XP3tFW5+tql+d7Xh6amprFVkQEAAACALWXNYzqMGY+vzjEnz8nV17425f+N+/fJzT8bkcWL63L6p+Zn2v2r8v1vDM1H3r9NO1YKAAAAAF2fmce0i6su26HiuSMO7Zt1L+3a4PNH/LpvNcoCAAAAAP5JeAwAAAAAVEWpk90wr7uzbAUAAAAAABWExwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFN8zr7mqGpWaHJ9u7CgAAAACggzHzGAAAAACACsJjAAAAAAAqWLYCAAAAAKiOor0LoDnMPAYAAAAAoILwGAAAAACACsJjAAAAAAAqWPMYAAAAAGh7RVLqSmsed6X30ggzjwEAAAAAqCA8BgAAAACggvAYAAAAAIAK1jwGAAAAAKqjG6wT3JWYeQwAAAAAQAXhMQAAAAAAFYTHAAAAAABUEB4DAAAAAFDBDfMAAAAAgOpww7xOxcxjAAAAAAAqCI8BAAAAAKggPAYAAAAAoII1jwEAAACAqihZ87hTMfMYAAAAAIAKwmMAAAAAACoIjwEAAAAAqGDNYwAAAACgOqx53KmYeQwAAAAAQAXhMQAAAAAAFYTHAAAAAABUsOYxAAAAAFAVJWsedypmHgMAAAAAUEF4DAAAAABABeExAAAAAAAVrHkMAAAAAFSHNY87FeFxO5o9e3aef/75JMmYMWMycuTIZvWvq6vL008/ndmzZ6d///7Zeeeds91227VFqQAAAADAFli4cGGeeeaZLF26NCNGjMguu+ySmpqOvTBEx66uE6mtrc0b3/jGlEqlXHjhhZtse8stt+Sggw7KqFGj8qY3vSlvetObMmrUqBx44IG5+eabN/tadXV1+cY3vpFx48Zlt912y5FHHpmDDjooQ4YMybHHHpuZM2e20rsCAAAAALbEk08+mUmTJmXIkCE58MADc+SRR2b8+PEZO3ZsLr300qxbt67VX7Moirzzne9MqVTKKaec0uLjCI9byac//elMmzZts+0uvfTSHH300bnvvvsq9k2fPj3HHHNMvva1rzXaf926dTnppJMyZcqUzJo1q2LfTTfdlP322y933nln898EAAAAANBq7rrrruy333757W9/WxESv/DCCzn77LPzzne+s9UD5O985zv59a9/vcXHER63gj/+8Y/5yle+stl2t912Wz75yU8mSbbffvtcffXVWbBgQRYsWJCf/vSnGTx4cJLkk5/8ZG699dYGj/G5z30u119/fZJk//33z+23355ly5blhRdeyEUXXZSampqsXLkyJ554YubPn986bxAAAAAAaJZXXnklJ554YlasWJGamppcfPHFeeGFF7Js2bLcdttt2W+//ZIkv/nNb3LxxRe32us+8sgjmTJlSqscS3i8hebNm5d///d/32y7oihy7rnnpiiK9OzZM3/4wx/ygQ98INttt1222267nHzyyfnDH/6Qnj3XL0N93nnnpSjqryD+8ssvl2cljxs3LnfccUeOOOKI9OvXL6NGjcpnP/vZfPOb30ySLFiwYJMzmAEAAACg6oou9LMZX/nKV/Lqq68mSb71rW/lM5/5TEaNGpV+/frlyCOPzB133JGxY8cmWb9aQWtMBF2xYkXe9773ZdWqVVt8rER4vEXq6upyyimnZN68eRk+fPgm286YMaO8rMUJJ5xQ/mZhY/vvv39OOOGEJMm0adPy6KOP1tv/85//PMuXL0+SfOITn8iAAQMqjjF58uQMHTo0SXLVVVelrq6u2e8LAAAAAGi5devW5Yc//GGSZOjQoTnttNMq2gwcODBnn312kmT58uX55S9/ucWvO2XKlDz66KObzSqbSni8Bb7xjW/kd7/7Xfr06ZPvfe97m2z729/+trx9/PHHN9pu43033XRTo8c47rjjGuxfU1OTSZMmJUnmz5+fv//975usCwAAAABoXffcc0951vGkSZPSo0ePBtttnPG9PgtsrmuvvTbf/e53UyqV8pOf/GSLjrWB8LiFpk+fnnPPPTfJ+hB5zz333GT7mTNnlrePPvroRtttvO/+++9v8Bh77713dtxxx0aPccwxxzR6DAAAAACgbTU1C9xxxx2zzz77JNmyHO/555/PqaeemiQ555xz8pa3vKXFx9qY8LgFli5dmve9732pra3NCSeckI9+9KOb7fPSSy8lSfr3759BgwY12m7QoEHp169fkvqDrCiKzJ07N0kyZsyYTb7WxsHyxscAAAAAgPZUKrrOz6ZsyAKTpmd5L7/8chYtWtTsz3Tt2rU5+eSTs2jRohx00EGtevO9nq12pG5k8uTJeeqppzJy5Mh8//vfT6lU2myfDcHvpoLjDQYPHpzly5fXG2QLFixIbW1tk44xePDg8vbGx2hIURRZsmTJZmtqzFZbbZWtttqqxf0BAAAAOorVq1dn9erVLe5fFE24ixodRt3atSnWrW35ATbx+96QBSbNz/K23XbbZpVx8cUX5+67707//v3zs5/9LL17925W/00RHjfTT37yk/zkJz9JqVTKT3/603q/3E3ZEOI2pf3gwYMza9as8s3xNu7flGNsvH/jYzRkzpw52WabbTZbU2MuuOCCXHjhhS3uDwAAANBRXHLJJbnooovauwyq5JW//Smv3P2HNjl2W2V5r3fHHXfk85//fJLkyiuvzC677NKs/psjPG6Gf/zjHzn99NOTJOedd16OOOKIJvfdMLt366233mzbDTN5V65cWdG/KcfYeCbwxsdoyIgRI/L4449vtqamvBYAAABAZ3buuedmypQpLe6/xx57ZM6cOa1YEW1p+4lvzeCDj2hx/6e/96WsXdbwv+hvqyxvY6+++mo++MEPpiiKnHzyyfn3f//3JvdtKuFxE61Zsybvf//7s2zZshxyyCG54IILmtV/yJAhmTNnTpPWLdnQZuOBNWTIkIr9m+v/+mM0pFQqZeDAgZutCQAAAKCr29LlOZuytGm3Vvzzp4Oo6dEz6bEl8Wjjv+/XZ3kbP3695mR5GxRFkY985COZPXt2xo0blyuvvLJJ/ZrLDfOa6Lzzzsv06dMzcODA/OxnP0uvXr2a1X/48OFJ1q9dvDkb2vTv37+if1OOsfH+jY8BAAAAALS9ts7ypk6dmt/85jfp0aNHfv7zn7fZ5FAzj5tgzpw5ufTSS5Mk73nPezJz5szMnDmzXpt58+aVt5966qn87ne/S5IMHTo0+++/f3bYYYck6wdDURSNfhNVFEUWLlyY5LU7LSbrB06/fv2yfPnyzQ64Df1ffwwAAAAAoO1tyAKTzYfHG2d5I0eObNLxzz777CTJ0UcfnYULF5azyIbMnj27vH/AgAF505ve1KTXSITHTbJmzZry9ve///18//vf32T7q6++OldffXWS5Pjjj8/1119f/rZhzZo1mTlzZnbfffcG+86cOTO1tbVJkgkTJtTbN3z48Dz11FOZMWNG6urqUlPT8MTxGTNmlLdffwwAAAAAoG1tPPP4oYceysSJExtsV1dXl4cffjhJMnr06AwYMKBJx1+9enWS5MYbb8yNN964ybZ/+tOf8qc//SlJ8oY3vCEPPvhgk14jsWxF1Rx44IHl7U39Qjfed+ihhzZ4jLlz52b69OktOgYAAAAAtJuiC/1sQlOzwOnTp2fu3LlJOmaOJzxugrFjx6Yoik3+PPvss+X2F1xwQfn566+/PkkyadKk8lIV1157bYqicoQVRZHrrrsuyfoF1o877rh6+48//vjy9rXXXttgrYsWLcptt92WZP0dPsePH9/yNw4AAAAANNv48ePLudytt95ab2mKjW3IApPkxBNPbPLxN5dVbpw9fuhDHyo/15xZx4nwuGpGjBiR973vfUmSe++9N//7v/9b0ea73/1u7rnnniTJySefXG96e7J+AI0ZMyZJctlll1X8stetW5fJkydn1apVSZJPfOITrf02AAAAAIAmmDJlSpL1S0yceeaZqaurq7f/gQceyDe/+c0kybhx43LCCSdUucLNEx5X0Re/+MUMGjQoSXLaaafllFNOyS9/+cv8/Oc/zwc/+MF87GMfS5IMHjw4n//85yv6b7XVVrnsssuSJKtWrcrhhx+e888/P9ddd12+//3v581vfnN+9rOfJUkOOeSQfOhDH6rSOwMAAAAANvbhD384Bx98cJL190g78sgjc9VVV+Xaa6/Npz71qRx++OFZtWpVSqVSLrvssvTu3bte/1NOOSWlUimlUikXXnhhO7wDN8yrqrFjx+aGG27IO9/5zsyfPz8//vGP8+Mf/7hem6FDh+b6668vzzB+veOPPz6XX355Pv7xj2fp0qX54he/WNHmgAMOyA033JCePf16AQAAAKA99OrVKzfccEOOPvroPPDAA7nzzjtz5513VrT51re+lUmTJrVTlZtm5nGVHXbYYXn44Ydz3nnnZcKECenXr1/69++fPffcM+eff34eeeSRRu++uMEZZ5yR++67Lx/5yEcyZsyY9O7dO4MGDcqhhx6aK664In/7298ydOjQKr0jAAAAAGiaUtF1fppihx12yD333JPLL788EydOzKBBg9K7d++MHTs2p556aqZPn57TTjutbT/0LVAqGrpzG13eqFGjMnv27IwcOTIvvvhie5cDAAAA0OnJWxq24XPp2X+bjP/YBe1dTquZ+e2LsnbZ4i79+zbzGAAAAACACsJjAAAAAAAquKMaAAAAAFAdFtDtVMw8BgAAAACggvAYAAAAAIAKwmMAAAAAACpY8xgAAAAAqIqSNY87FTOPAQAAAACoIDwGAAAAAKCCZSu6uVdmL8jbat7d3mV0eH+su6a9S6CLcd41jXMPAAAA2o/wGAAAAACoDmsedyqWrQAAAAAAoIKZx1TVwmJ+nsljWZKF6ZsBGZWdMrI0rtX7APU59wAAAIDmMvOYqllcvJoHcld6pGd2z/7ZJoPyeKZndvFsq/YB6nPuAQAAAC1h5jFVMyv/SN8MyD6ZmJpSTYZndNYWtXk+MzOiGJtSqdQqfYD6nHsAAAB0GNY87lTMPKYq6oq6vJw5GZYdU1N6bdgNz+isyLIsy+JW6QPU59wDAAAAWkp4TFWsyaoUqUv/DKz3fL9/Pl6VFa3SB6jPuQcAAAC0lPCYqlidVUmSnulV7/le2SrJ+rCqNfoA9Tn3AAAAgJYSHlMVja+Oun6hm7oGFrxpSR+gPuceAAAA0FJumEdV9E6fJEltaus9X5s1SZKt/rl/S/sA9Tn3AAAA6Ejcfr1zMfOYquidPimllOWvu9HWhhtv9UnfVukD1OfcAwAAAFpKeExV1JRqMjQjMy8vpq6oKz8/Ny9k6/TLgGzbKn2A+px7AAAAQEsJj6ma0dk1K7I0M/K3zC1m5Yni/szNrIzN7imVSqkt1uSVYm5WFMua3AfYPOceAAAA0BLCY6pmm9Lg7JvDsja1eTz3Z3EWZEIOzMjSuCTJ0izKg/lLXsrzTe4DbJ5zDwAAgA6j6EI/3YAb5lFVg0pDMyhDG9331ryrWX2ApnHuAQAAAM1l5jEAAAAAABWExwAAAAAAVLBsBQAAAADQ5kpJSl1oreDucDt5M48BAAAAAKggPAYAAAAAoILwGAAAAACACtY87ua2Hzkof3zxmvYuA7qdP9Y57wAAAOhmin/+dBVd6b00wsxjAAAAAAAqCI8BAAAAAKggPAYAAAAAoII1jwEAAACA6ugG6wR3JWYeAwAAAABQQXgMAAAAAEAF4TEAAAAAABWExwAAAAAAVHDDPAAAAACgKkpumNepCI+7uXnz5mXChAkN7ps8eXImT55c5YoAAAAAOq6pU6dm6tSpDe6bN29elauhrb0y4y955eG7G9xXu2JplaupPuFxNzds2LA89thj7V0GAAAAQKewqcl2o0aNyuzZs6tcEW1p+30Oy/b7HNbgvseuuii1yxdXuaLqsuYxAAAAAAAVzDwGAAAAAKrDmsedipnHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAVrHgMAAAAAVVGy5nGnYuYxAAAAAAAVhMcAAAAAAFQQHgMAAAAAUMGaxwAAAABAdVjzuFMx8xgAAAAAgArCYwAAAAAAKgiPAQAAAACoYM1jAAAAAKAqStY87lTMPAYAAAAAoILwGAAAAACACsJjAAAAAAAqCI8BAAAAAKjghnkAAAAAQHW4YV6nYuYxAAAAAAAVhMcAAAAAAFQQHgMAAAAAUMGaxwAAAABAdVjzuFMx8xgAAAAAgArCYwAAAAAAKgiPAQAAAACoYM1jAAAAAKAqStY87lTMPAYAAAAAoILwGAAAAACACsJjAAAAAAAqWPMYAAAAAGh7xT9/uoqu9F4aITwGgE7ubTXvbu8SOo0/1l3T3iUAAAB0GpatAAAAAACggvAYAAAAAIAKlq0AAJptYTE/z+SxLMnC9M2AjMpOGVka1+p9AAAAaD/CYwCgWRYXr+aB3JVBGZbds38W59U8nulJkUbD4Jb0AQAAup5S0Q3uMteFCI8BgGaZlX+kbwZkn0xMTakmwzM6a4vaPJ+ZGVGMTalUapU+AAAAtC9rHgMATVZX1OXlzMmw7Jia0mt/jRie0VmRZVmWxa3SBwAAgPYnPAYAmmxNVqVIXfpnYL3n+/3z8aqsaJU+AAAAtD/LVgAATbY6q5IkPdOr3vO9slWS9UFxa/QBAAC6KEsedypmHgMATdb4ysTr/wZY18DfBFvSBwAAgPYnPAYAmqx3+iRJalNb7/narEmSbPXP/VvaBwAAgPYnPAYAmqx3+qSUUpa/7iZ3G2561yd9W6UPAAAA7U94DAA0WU2pJkMzMvPyYuqKuvLzc/NCtk6/DMi2rdIHAADomkpF1/npDoTHAECzjM6uWZGlmZG/ZW4xK08U92duZmVsdk+pVEptsSavFHOzoljW5D4AAAB0PMJjAKBZtikNzr45LGtTm8dzfxZnQSbkwIwsjUuSLM2iPJi/5KU83+Q+AAAAdDw927sAAKDzGVQamkEZ2ui+t+ZdzeoDAABAxyM8BgAAAACqo5usFdxVWLYCAAAAAIAKwmMAAAAAACoIjwEAAAAAqGDNYwAAAACgKkrWPO5UhMfd3JzZc9K/NLDBfaOyc3Ys7VLlijqmP9Zd094l0MW8rebd7V1Cp+DcaxqfEwAAVM/UqVMzderUBvfNmzevytXQ1uY98Ze8/MRfG9xXu3JplaupPuFxN9c7fTKx9Pb2LgMAAACgU5g8eXImT57c4L5Ro0Zl9uzZVa6ItjRs98MybPfDGtz34DUXp3bF4ipXVF3WPAYAAAAAoIKZx1TVwmJ+nsljWZKF6ZsBGZWdMrI0rtX7APU59wAAAIDmMvOYqllcvJoHcld6pGd2z/7ZJoPyeKZndvFsq/YB6nPuAQAA0GEUXeinGzDzmKqZlX+kbwZkn0xMTakmwzM6a4vaPJ+ZGVGMTalUapU+QH3OPQAAAKAlzDymKuqKurycORmWHVNTem3YDc/orMiyLEvl4uIt6QPU59wDAAAAWkp4TFWsyaoUqUv/DKz3fL9/Pl6VFa3SB6jPuQcAAAC0lGUrqIrVWZUk6Zle9Z7vla2SrA+rWqMPUJ9zDwAAgI6k1E3WCu4qzDymKhpfHXX9FaOugVXGW9IHqM+5BwAAALSU8Jiq6J0+SZLa1NZ7vjZrkiRb/XP/lvYB6nPuAQAAAC0lPKYqeqdPSill+etutLXhxlt90rdV+gD1OfcAAACAlhIeUxU1pZoMzcjMy4upK+rKz8/NC9k6/TIg27ZKH6A+5x4AAAAdStGFfroB4TFVMzq7ZkWWZkb+lrnFrDxR3J+5mZWx2T2lUim1xZq8UszNimJZk/sAm+fcAwAAAFpCeEzVbFManH1zWNamNo/n/izOgkzIgRlZGpckWZpFeTB/yUt5vsl9gM1z7gEAAAAt0bO9C6B7GVQamkEZ2ui+t+ZdzeoDNI1zDwAAAGgu4TEAAAAA0PaKpNSV1gruSu+lEZatAAAAAACggvAYAAAAAIAKwmMAAAAAACpY8xgAAAAAqI6iGywU3IWYeQwAAAAAQAXhMQAAAAAAFYTHAAAAAABUEB4DAAAAAFDBDfOoqoXF/DyTx7IkC9M3AzIqO2VkaVyr9wHqc+4BAADQ3kpJSl3ofnml9i6gCsw8pmoWF6/mgdyVHumZ3bN/tsmgPJ7pmV0826p9gPqcewAAAEBLmHlM1czKP9I3A7JPJqamVJPhGZ21RW2ez8yMKMamVKr8vqYlfYD6nHsAAABAS5h5TFXUFXV5OXMyLDumpvTasBue0VmRZVmWxa3SB6jPuQcAAAC0lPCYqliTVSlSl/4ZWO/5fv98vCorWqUPUJ9zDwAAgA6l6EI/3YDwmKpYnVVJkp7pVe/5XtkqyfqwqjX6APU59wAAAICWEh5TFY2vjrr+a5q6Br6uaUkfoD7nHgAAANBSwmOqonf6JElqU1vv+dqsSZJs9c/9W9oHqM+5BwAAALRUz/YugO6hd/qklFKWZ3GSEeXnN9x4q0/6tkofoD7nHgAAAB1Jqa69K6A5zDymKmpKNRmakZmXF1NXvHaVmJsXsnX6ZUC2bZU+QH3OPQAAAKClhMdUzejsmhVZmhn5W+YWs/JEcX/mZlbGZveUSqXUFmvySjE3K4plTe4DbJ5zDwAAAGgJ4TFVs01pcPbNYVmb2jye+7M4CzIhB2ZkaVySZGkW5cH8JS/l+Sb3ATbPuQcAAAC0hDWPqapBpaEZlKGN7ntr3tWsPkDTOPcAAADoEIr2LoDmMPMYAAAAAIAKwmMAAAAAACoIjwEAAAAAqGDNYwAAAACgKkrWPO5UzDwGAAAAAKCCmcfd3PYjB+WPL17T3mUAAAAAAB2MmccAAAAAAFQQHgMAAAAAUMGyFQDdwMJifp7JY1mShembARmVnTKyNK7V+wAAAMAmFe6Y15mYeQzQxS0uXs0DuSs90jO7Z/9sk0F5PNMzu3i2VfsAAAAAXYuZxwBd3Kz8I30zIPtkYmpKNRme0Vlb1Ob5zMyIYmxKpVKr9AEAAAC6FjOPAbqwuqIuL2dOhmXH1JReu+QPz+isyLIsy+JW6QMAAAB0PWYeA3Rha7IqRerSPwPrPd/vn49XZUUGZNst7gMAAACbVSSlrrTkcVd6L40w8xigC1udVUmSnulV7/le2SrJ+qC4NfoAAAAAXY/wGKALa3xl4vVfj9Y18DVpS/oAAAAAXY/wGKAL650+SZLa1NZ7vjZrkiRb/XP/lvYBAAAAuh5rHndz8+bNy4QJExrcN3ny5EyePLnKFQGtqXf6pJRSlmdxkhHl5zfc9K5P+rZKHwAAgO5i6tSpmTp1aoP75s2bV+VqOqFO9o9ZX3rm7rz0zF8b3Ldm9dIqV1N9wuNubtiwYXnsscfauwygjdSUajK0GJl5eTFjivGpKa3/Bydz80K2Tr8Gb3zXkj4AAADdxaYm240aNSqzZ8+uckW0peE7vSnDd3pTg/v+fsvns2bV4ipXVF2WrQDo4kZn16zI0szI3zK3mJUnivszN7MyNrunVCqltliTV4q5WVEsa3IfAAAAoOsTHgN0cduUBmffHJa1qc3juT+LsyATcmBGlsYlSZZmUR7MX/JSnm9yHwAAAKDrs2wFQDcwqDQ0gzK00X1vzbua1QcAAABaotTJ1jzu7sw8BgAAAACggvAYAAAAAIAKwmMAAAAAACoIjwEAAAAAqOCGeQAAAABAdRTumNeZmHkMAAAAAEAFM4+7uVdmL8jbat7d3mVAt/PHumvauwQAAACATTLzGAAAAACACmYeAwAAAABVUbLkcaciPKaqFhbz80wey5IsTN8MyKjslJGlca3ep7PzOTWdzwoAAACgbVi2gqpZXLyaB3JXeqRnds/+2SaD8nimZ3bxbKv26ex8Tk3nswIAAABoO2YeUzWz8o/0zYDsk4mpKdVkeEZnbVGb5zMzI4qxKZVKrdKns/M5NZ3PCgAAAKDtmHlMVdQVdXk5czIsO6am9NqwG57RWZFlWZbFrdKns/M5NZ3PCgAAADqhogv9dAPCY6piTValSF36Z2C95/v98/GqrGiVPp2dz6npfFYAAAAAbUt4TFWszqokSc/0qvd8r2yVZH2o1xp9OjufU9P5rAAAAADalvCYqmh8Fdn1c/zrGpjr35I+nZ3Pqel8VgAAAABtyw3zqIre6ZMkqU1tvedrsyZJstU/929pn87O59R0PisAAADofErmbXUqZh5TFb3TJ6WUsvx1NyTbcIOyPunbKn06O59T0/msAAAAANqW8JiqqCnVZGhGZl5eTF1RV35+bl7I1umXAdm2Vfp0dj6npvNZAQAAALQt4TFVMzq7ZkWWZkb+lrnFrDxR3J+5mZWx2T2lUim1xZq8UszNimJZk/t0RT6npvNZAQAAALQd4TFVs01pcPbNYVmb2jye+7M4CzIhB2ZkaVySZGkW5cH8JS/l+Sb36Yp8Tk3nswIAAIDOpEjqutBPuv4Czm6YR1UNKg3NoAxtdN9b865m9emqfE5N57MCAAAAaBtmHgMAAAAAUEF4DAAAAABABeExAAAAAAAVrHkMAAAAALS9rnaPua70Xhph5jEAAAAAABXMPAZazR/rrmnvEgAAAABoJWYeAwAAAABQwcxjAAAAAKAqSt1gneCuxMxjAAAAAAAqmHlMVS0s5ueZPJYlWZi+GZBR2SkjS+NavU9n53MCAAAAoL2ZeUzVLC5ezQO5Kz3SM7tn/2yTQXk80zO7eLZV+3R2PicAAAAAOgIzj6maWflH+mZA9snE1JRqMjyjs7aozfOZmRHF2JRKpVbp09n5nAAAAIAuq7DocWdi5jFVUVfU5eXMybDsmJrSa8NueEZnRZZlWRa3Sp/OzucEAAAAQEchPKYq1mRVitSlfwbWe77fPx+vyopW6dPZ+ZwAAAAA6CiEx1TF6qxKkvRMr3rP98pWSdYHoK3Rp7PzOQEAAADQUVjzmKpofMXd9evc1KVyvZuW9OnsfE4AAABAV1YSU3QqwuN2Mnv27Dz//PNJkjFjxmTkyJHN6l9XV5enn346s2fPTv/+/bPzzjtnu+22a4tSW0Xv9EmS1Ka23vO1WZMk2eqf+7e0T2fncwIAAADomhYuXJhnnnkmS5cuzYgRI7LLLrukpqZjLwzRsavrgObNm5eLLrookyZNyvjx49O3b9/sueeeec973pPvf//7Wbdu3Sb733LLLTnooIMyatSovOlNb8qb3vSmjBo1KgceeGBuvvnmzb5+XV1dvvGNb2TcuHHZbbfdcuSRR+aggw7KkCFDcuyxx2bmzJmt9VZbVe/0SSmlLH/dzds23MytT/q2Sp/OzucEAAAA0LU8+eSTmTRpUoYMGZIDDzwwRx55ZMaPH5+xY8fm0ksv3WyeuDmLFy/OpZdemne+853Za6+90rdv3+y666458cQT8/Wvfz2rVrV8SVPhcTPccsstGT9+fC688ML89re/zZNPPpmVK1fmscceyzXXXJP//M//zMEHH5z777+/wf6XXnppjj766Nx3330V+6ZPn55jjjkmX/va1xp9/XXr1uWkk07KlClTMmvWrIp9N910U/bbb7/ceeedW/ZG20BNqSZDMzLz8mLqirry83PzQrZOvwzItq3Sp7PzOQEAAAB0HXfddVf222+//Pa3v60IiV944YWcffbZeec739niAPm+++7LhAkTcvbZZ+fXv/51Hn300axcuTJPPfVUrr/++nziE5/I3nvvnT/96U8tOr7wuIkee+yxnHTSSVm8eP1szkmTJuWyyy7Lr371q3zuc5/LhAkTkiT3339/jjrqqIoZwLfddls++clPJkm23377XH311VmwYEEWLFiQn/70pxk8eHCS5JOf/GRuvfXWBmv43Oc+l+uvvz5Jsv/+++f222/PsmXL8sILL+Siiy5KTU1NVq5cmRNPPDHz589vi49hi4zOrlmRpZmRv2VuMStPFPdnbmZlbHZPqVRKbbEmrxRzs6JY1uQ+XZHPCQAAAOiyii70sxmvvPJKTjzxxKxYsSI1NTW5+OKL88ILL2TZsmW57bbbst9++yVJfvOb3+Tiiy9u1seYrF8h4dhjj82cOXOSJIcffni+9rWv5ZprrsmXv/zlHHLIIUmSp556KkcffXTuuuuuZr9GqSgKy1Q3wXvf+9786le/SpJcccUVOf300+uFcmvWrMnHP/7xXHnllUmSt7/97fnd736XJCmKIoccckimTZuWnj17Ztq0aeXBscH999+fN77xjVm7dm0OPvjg3HPPPfWO//LLL2ennXbK8uXLM27cuDz00EMZMGBAvWNcfvnlOeuss5Ik55xzTr785S83+n5GjRqV2bNnZ6tsnX8pHbMFn0zzLChezjN5LEuzKH3TPztml4wojS3vuz93Zlz2yM6lPZvUp6vqrJ/TH+uuqerrAQAAQEeyIW8ZOXJkXnzxxfYup8PY8Ln03mpgDj38vPYup9X89c4vZs3qJY3+vs8555x89atfTbI+T5w8eXK9/UuWLMkb3vCGPPfcc+nXr1+effbZDBkypMmv/6lPfSpf+cpXkqyfkPqlL32p3hrKdXV1+cpXvpJzzz03SbLHHnvkkUceadY6y8LjJli1alUGDhyY2tra7L///rnvvvsanM25Zs2aHHDAAXnkkUeSJC+++GJGjhyZhx56KPvuu2+S5F3veleuuabhgO3d7353/u///i9J8vDDD2evvfYq77vsssvyP//zP0kaHmzJ+gExfPjwvPzyyxkyZEjmzp3b6GBor/CYrk14DAAAQHcmPG5YdwyP161bl2HDhuXVV1/N0KFDM2fOnPTo0aPiGFOnTs0ZZ5yRZP3E0A3bTTFmzJjMmjUrw4cPz9NPP52tt966ok1RFHnHO96R3//+90mSu+++O4ceemiTX8OyFU3w0EMPpba2Nsn68LexZQB69+6dY489tvz4gQceSJL89re/LT93/PHHN/o6G++76aab6u3b+BjHHXdcg/1ramoyadKkJMn8+fPz97//vdHXAgAAAADaxj333JNXX301yfrlbxsKjpP6Od/r88BNmTdvXvmeaJMmTWowOE6SUqmUE088sfy4sXu1NUZ43ATz5s0rb48ZM2aTbYcPH17eXrlyZZLUW//46KOPbrTvxvte/4vccIy99947O+64Y6PHOOaY12YRN3cwAAAAAABbrql54I477ph99tknSfOyvC3NK5uqZ7Nad1N77bVXrrrqqiTJm9/85k223Xi272677ZYkeemll5Ik/fv3z6BBgxrtO2jQoPTr1y/Lly+vN8CKosjcuXOTbH4wbBwsv/6mfQAAAADQXkpJSl1oBd2G1yZYb0MemDQtz5sxY0ZefvnlLFq0KNtuu+1mX3uHHXYo55UTJ07cZNuG8sqmEh43wU477ZSddtpps+1mzJiRX/ziF0nWr+Wyxx57JEk5+N1UcLzB4MGDs3z58noDbMGCBeVlMzZ3jMGDB5e3Nz4GAAAAAFAdG/LApPl5XlPC46FDh+aUU07ZbLvZs2dn6tSpSZK+ffvmTW9602b7bEx43EqmT5+eSZMmZe3atUmSc889N717907yWoi78UBozODBgzNr1qwsX768/NzGIfDmjrHx/o2P0ZgiRdYWtZtt15ia1KSm1PCaLQAAAACdyerVq7N69eoW9y+60Kza7qCubm3q6ta2uP+mft9tmec11dNPP51JkyZl4cKFSZLJkydn++23b9YxhMdbaMmSJbnkkkvyta99rRwcf+ADH8hpp51Wr02SRheu3thWW22VpP76Ixv6N+UYG/q//hiNWZNVuSM3bLZdY8Zlj+ycPVvcv7P4Y9017V1Cp/C2mne3dwmdhjEFAADQ8VxyySW56KKL2rsMquT5Z+/Ic8/e2ibHbss8b3NWr16dyy+/PBdccEFWrFiRJDniiCNy8cUXN/tYwuMWqq2tzfe+971ceOGFmT9/fvn5U089NVdeeWVqal67F+GQIUMyZ86cLFq0aLPH3dBm40E1ZMiQiv2b6//6YzSmd/rk0Lx9s+0aU+OeiwAAAEAXce6552bKlCkt7r/HHntkzpw5rVhRF1TX3gW8ZsyYI7Ljjoe1uP+993w9a9YsbXDf6/O8jR+/XnPzvMYURZFf/OIXOe+88/Lcc8+Vn580aVJ++tOfpk+fPs0+pvC4BR588MF85CMfyQMPPFB+bvTo0fnOd76Td7zjHRXthw8fnjlz5mTBggWbPfaGNv3796/X//X7N9f/9cdoTCml9Cz12mw7AAAAgK5uq622qjcLtLlKpU3dQo2OpqamZ2pqWh6Pbur3/fo8b1PhcXPzvIY888wz+a//+q/ceutrM6m33377XHrppfn3f//3Fo9N4XEzrFu3LhdeeGG+9KUvlZeoGDRoUM4///ycfvrpjab3O+ywQ5L1A6EoikZ/WUVRlNcg2XHHHcvP9+/fP/369cvy5cs3Gx5v6P/6Y3QUC4v5eSaPZUkWpm8GZFR2ysjSuFbvQ/dhTAEAAAAdzYY8MNn8ZNCN87yRI0c263WKosiVV16Zc845p7xERd++fTNlypR88pOfzMCBA5t1vNez5kATFUWRyZMn5/Of/3zWrl2bUqmUs846K08//XSmTJmyyWnfG75pWLNmTWbOnNlou5kzZ6a2dv3N6yZMmNDgMWbMmJG6usbn98+YMaO8/fpjtLfFxat5IHelR3pm9+yfbTIoj2d6ZhfPtmofug9jCgAAAOiINp55/NBDDzXarq6uLg8//HCS9SsbDBgwoFmv8+UvfzlnnHFGOTj+wAc+kKeeeiqf+9zntjg4ToTHTfb5z38+3/3ud5Mkw4YNy1/+8pdcdtll2XbbbTfb98ADDyxv33jjjY2223jfoYce2uAx5s6dm+nTp7foGO1tVv6RvhmQfTIxw0ujs3tpv+yQ0Xk+Mxu9O2VL+tB9GFMAAADQuZSKosv8bEpT88Dp06dn7ty5SZqf5f3kJz/JueeemyTp169fbrjhhlx99dX1gustJTxugiVLluTLX/5ykmS77bbL3Xff3axf5qRJk8pLVVx77bUNBlRFUeS6665Lsn69lOOOO67e/uOPP768fe211zb4OosWLcptt92WZP0C7ePHj29yjW2trqjLy5mTYdkxNaXXht3wjM6KLMuyLG6VPnQfxhQAAADQUY0fP76czd166631lqbY2IY8MElOPPHEJh9/3bp1+exnP5sk6dWrV2699daKPLE1CI+b4Oqrr87y5cuTJF/4whey8847N6v/iBEj8r73vS9Jcu+99+Z///d/K9p897vfzT333JMkOfnkkyu+ITjxxBMzZsyYJMlll12WBx98sN7+devWZfLkyVm1alWS5BOf+ESzamxra7IqRerSP/Wny/f75+NVWdEqfeg+jCkAAACgI5syZUqSZPXq1TnzzDMrlqJ94IEH8s1vfjNJMm7cuJxwwglNPvYf/vCHPPfcc0mSj3/843njG9/YGiVXcMO8JrjzzjvL2/369cvvfve7JvU7+OCDM2jQoCTJF7/4xfz+97/PggULctppp+Vvf/tb3vGOd6Suri433XRTrr766iTJ4MGD8/nPf77iWFtttVUuu+yynHDCCVm1alUOP/zwnHnmmTnggAOyYMGC/OhHP8rdd9+dJDnkkEPyoQ99aEvfdqtanfWhds/0qvd8r6y/g+maf+7f0j50H8YUAAAA0JF9+MMfzg9+8INMmzYtV199dV544YWccsopGThwYKZNm5Yrr7wyq1atSqlUymWXXZbevXvX63/KKafkxz/+cZLkggsuyIUXXljet3Feuf322zc5r9x7772bdVM+4XETzJs3r7zdnFD29ttvzxFHHJEkGTt2bG644Ya8853vzPz58/PjH/+4/MvfYOjQobn++uvLM4xf7/jjj8/ll1+ej3/841m6dGm++MUvVrQ54IADcsMNN6Rnz471qy01umf9Eh51qVzKoyV96D6MKQAAAOhkiqRL/a/3Zt5Lr169csMNN+Too4/OAw88kDvvvLNe6Luhzbe+9a1MmjSpWS+9cV55zjnnNLnfVVddlVNOOaXJ7S1b0QQb/zK2xGGHHZaHH3445513XiZMmJB+/fqlf//+2XPPPXP++efnkUceycSJEzd5jDPOOCP33XdfPvKRj2TMmDHp3bt3Bg0alEMPPTRXXHFF/va3v2Xo0KGtUm9r6p0+SZLa1NZ7vjZrkiRb/XP/lvah+zCmAAAAgI5uhx12yD333JPLL788EydOzKBBg9K7d++MHTs2p556aqZPn57TTjut2cdtrbxyczrW9NQO6tFHH221Yw0bNixf+MIX8oUvfKHFx3jDG96QH/zgB61WUzX0Tp+UUsryLE4yovz8hhuU9UnfVulD92FMAQAAAJ1B7969c8YZZ+SMM85oVr8f/ehH+dGPftTgvptuuqkVKts8M4+pippSTYZmZOblxdQVry0OPjcvZOv0y4Bs2yp96D6MKQAAAIC2JTymakZn16zI0szI3zK3mJUnivszN7MyNrunVCqltliTV4q5WVEsa3IfujdjCgAAADqZoug6P92A8Jiq2aY0OPvmsKxNbR7P/VmcBZmQAzOyNC5JsjSL8mD+kpfyfJP70L0ZUwAAAABtx5rHVNWg0tAMSsM39BtUGpq35l3N6gPGFAAAAEDbMPMYAAAAAIAKwmMAAAAAACpYtgIAAAAAqIpS97jPXJdh5jEAAAAAABWExwAAAAAAVBAeAwAAAABQwZrH3dz2Iwfljy9e095l0EX8sc5YAgAAADahsOhxZ2LmMQAAAAAAFYTHAAAAAABUEB4DAAAAAFDBmscAAAAAQFWU6tq7AprDzGMAAAAAACoIjwEAAAAAqCA8BgAAAACggjWPAQAAAIDqKIr2roBmMPMYAAAAAIAKwmMAAAAAACoIjwEAAAAAqCA8BgAAAACgghvmAQAAAADV4X55nYqZxwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFax4DAAAAAFVRKix63JmYeQwAAAAAQAXhMQAAAAAAFYTHAAAAAABUsOYxAAAAAND2iiRdac3jLvRWGmPmMQAAAAAAFYTHAAAAAABUEB4DAAAAAFDBmscAAAAAQHXUtXcBNIeZxwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFax4DAAAAAFVQpFQU7V1EK+pK76VhZh4DAAAAAFBBeAwAAAAAQAXLVnRz8+bNy4QJExrcN3ny5EyePLnKFQEAAAB0XFOnTs3UqVMb3Ddv3rwqV0Nbm/XytLzw8rQG962pXV7laqpPeNzNDRs2LI899lh7lwEAAADQKWxqst2oUaMye/bsKldEWxo99OCMHnpwg/v+/NClWV27tMoVVZfwGAAAAACoji51w7yuz5rHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAVrHgMAAAAA1WHN407FzGMAAAAAACoIjwEAAAAAqCA8BgAAAACggjWPAQAAAIDqqGvvAmgOM48BAAAAAKggPAYAAAAAoILwGAAAAACACtY8BgAAAACqolQU7V0CzWDmMQAAAAAAFYTHAAAAAABUEB4DAAAAAFDBmscAAAAAQHVY87hTMfMYAAAAAIAKwmMAAAAAACoIjwEAAAAAqCA8BgAAAACgghvmAQAAAABtr0jXumFeF3orjTHzGAAAAACACsJjAAAAAAAqCI8BAAAAAKhgzWMAAAAAoDq60prH3YCZxwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFax4DAAAAANVR194F0BxmHgMAAAAAUEF4DAAAAABABeExAAAAAAAVrHkMAAAAAFRFqSjauwSawcxjAAAAAAAqCI8BAAAAAKggPAYAAAAAoII1jwEAAACA6rDmcadi5jEAAAAAABWExwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFN8wDAAAAAKqgSOq60g3zutJ7aZiZxwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFax4DAAAAANVRdP11grsSM48BAAAAAKggPAYAAAAAoILwGAAAAACACtY8BgAAAADaXpGuteZxF3orjTHzGAAAAACACmYed3Pz5s3LhAkTGtw3efLkTJ48ucoVAQAAAHRcU6dOzdSpUxvcN2/evCpXQ1t7ftH9mbX4wQb3rVm3orrFtAPhcTc3bNiwPPbYY+1dBgAAAECnsKnJdqNGjcrs2bOrXBFtacy2+2fMtvs3uO/2Z7+d1euWVbmi6hIeAwAAAADV0ZXWPO4GrHkMAAAAAEAF4TEAAAAAABWExwAAAAAAVBAeAwAAAABQwQ3zAAAAAIDqqHPDvM7EzGMAAAAAACoIjwEAAAAAqCA8BgAAAACggjWPAQAAAIDqKOrauwKawcxjAAAAAAAqCI8BAAAAAKggPAYAAAAAoII1jwEAAACA6iiK9q6AZjDzGAAAAACACsJjAAAAAAAqCI8BAAAAAKhgzWMAAAAAoDrqrHncmZh5DAAAAABABeExAAAAAAAVhMcAAAAAAFSw5jEAAAAAUAVFUnSlNY+70ntpmJnHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAXhMQAAAAAAFdwwDwAAAABoe0W61g3zutBbaYyZxwAAAAAAVBAeAwAAAABQQXgMAAAAAEAFax4DAAAAANXRldY87gbMPAYAAAAAoILwGAAAAACACsJjAAAAAAAqWPMYAAAAAKiOurr2roBmMPMYAAAAAIAKwmMAAAAAACoIjwEAAAAAqGDNYwAAAACgOoqivSugGcw8BgAAAACggvAYAAAAAIAKwmMAAAAAACpY8xgAAAAAqA5rHncqZh4DAAAAAFBBeAwAAAAAQAXhMQAAAAAAFYTHAAAAAABUcMM8AAAAAKA66twwrzMx8xgAAAAAgArCYwAAAAAAKgiPAQAAAACoYM1jAAAAAKAqiqKuvUugGYTH3dy8efMyYcKEBvdNnjw5kydPrnJFAAAAAB3X1KlTM3Xq1Ab3zZs3r8rV0NZmrXo0s1Y91uC+NXUrqlxN9QmPu7lhw4blsccaPgEAAAAAqG9Tk+1GjRqV2bNnV7ki2tLoPntmdJ89G9x3x8KfZXXd8ipXVF3WPAYAAAAAoIKZxwAAAABA2yuKpK5o7ypaT9GF3ksjzDwGAAAAAKCC8BgAAAAAgArCYwAAAAAAKljzGAAAAACojm6wTnBXYuYxAAAAAAAVhMcAAAAAAFQQHgMAAAAAUEF4DAAAAABABTfMAwAAAACqo66uvSugGcw8BgAAAACggvAYAAAAAIAKwmMAAAAAACpY8xgAAAAAqI6iaO8KaAYzjwEAAAAAqCA8BgAAAACggvAYAAAAAIAK1jwGAAAAAKqiqKtr7xJoBjOPAQAAAACoIDwGAAAAAKCC8BgAAAAAgArWPAYAAAAAqqMo2rsCmsHMYwAAAAAAKgiPAQAAAACoIDwGAAAAAKCCNY8BAAAAgLZXJKnrQmsed6G30hgzjwEAAAAAqCA8BgAAAACggvAYAAAAAIAKwmMAAAAAACq4YR4AAAAAUAVFUtS1dxGtqOvfMc/MYwAAAAAAKgiPAQAAAACoIDwGAAAAAKCC8BjoslavXp0LL7wwq1evbu9SoM0Y53QHxjndgXFOd2CcA0lS1BVd5qc7EB53U0VR1PsvdEWrV6/ORRdd5C+ndGnGOd2BcU53YJzTHRjndAfyFroa4TEAAAAAABWExwAAAAAAVOjZ3gUAAAAAAN1EUdfeFdAMZh4DAAAAAFDBzONObuHChXnmmWeydOnSjBgxIrvssktqanwnAAAAAAAdSVvneMuWLctTTz2VhQsXZtiwYdl1113Tq1evLTqmlLGTevLJJzNp0qQMGTIkBx54YI488siMHz8+Y8eOzaWXXpp169a1d4kAAAAA0O21dY730ksv5YMf/GCGDBmS/fbbL0cddVT23HPPjBgxIueff35WrVrV4mObedwJ3XXXXfm3f/u3rFixomLfCy+8kLPPPjt33nlnrrvuuvTo0aMdKgQAAACASkVd0d4lVFVb53gzZ87MYYcdlldeeaVi3yuvvJIvfvGL+dOf/pTbbrst/fr1a/bxzTzuZF555ZWceOKJWbFiRWpqanLxxRfnhRdeyLJly3Lbbbdlv/32S5L85je/ycUXX9zO1bbM1KlT27uECmpqmo5YU0fVET8rNTVNR6ypo+qIn5WamqYj1tRRdcTPSk1N0xFr6qg64melpqbpiDV1VB3xs1JT03TEmmCDts7xVq9eneOOO64cHJ955pl56qmnsmLFitx77715y1vekiSZNm1aTj/99Ba9h1JRFN0r7u/kzjnnnHz1q19NklxxxRWZPHlyvf1LlizJG97whjz33HPp169fnn322QwZMqTiOCNHjsycOXMyYsSIzJ49uyq1N9WECRPy2GOPtXcZ9aipaTpaTUuWLMk222yTxYsXZ+DAge1dTj0d7bNK1NRUHa0m47x51NQ0Ha0m47x51NQ0Ha0m47x51NQ0Ha0m47x51NQ0Ha2mjpy3tKdRo0Zl9uzZ2Spb519qJrV3Oa3mrrobszorM3LkyLz44osV+1srx2vMlVdeWT7m2WefXX6tDWpra3PYYYdl2rRpKZVKefjhh7Pnnns26z2aedyJrFu3Lj/84Q+TJEOHDs1pp51W0WbgwIE5++yzkyTLly/PL3/5y6rWCAAAAADdXTVyvO9973tJkp49e+b888+v2N+rV6989rOfTZIURZGrrrqqWcdPhMedyj333JNXX301STJp0qRG10E57rjjyts33XRTVWoDAAAAgM0q6rrOzya0dY43e/bsPPjgg0mSN7/5zdl2220bbPeWt7ylvNZxS3JC4XEnMnPmzPL20Ucf3Wi7HXfcMfvss0+S5P7772/zugAAAACA17R1jtfU4/fp06e89vETTzyR5cuXN/k1EuFxp/LSSy+Vt8eMGbPJtjvuuGOS5OWXX86iRYvasiwAAAAAYCNtneO15PhJ8o9//KNJx99AeNyJzJ07t7w9aNCgTbYdPHhweXvjwQQAAAAAtK22zvGqlRP2bFZr2tXGv9yNf+kN2Xh/Q9PR58+fn2T9QBs5cmSLayqVSi3u25h58+Zl1KhRrX7cLaGmpuloNRVFkSTZY4892mSsbomO9lklamqqjlaTcd48amqajlaTcd48amqajlaTcd48amqajlaTcd48amqatqhpw1htiQ2B3obchfpWZ2XuKjrS/bmKtPy3nazJqkb3tWaO1x7H30B43IksWbKkvL311ltvsu1WW21V3l65cmXF/nXr1iVJ6urqMmfOnFaqsPXMnj27vUuooKam6Yg1dcQxnnTMz0pNTdMRazLOm05NTdMRazLOm05NTdMRazLOm05NTdMRazLOm05NTdMRa9qQu1BpdSpzqq6oNXO89jj+BsLjTmTIkCHl7UWLFtV7/Hobr4/S0ADq06dPVq1alR49emzyOJvT0b4tBgAAANgSWzLzeP78+Vm3bl369OnTihV1fjvssEN7l9CoLfl9J+uzsYbeX2vmeA15/fE3pSXH30B43IkMHz68vL1gwYJNDroFCxaUt/v371+xv7lT1AEAAACgJe677772LqHqWjPHa8rxN6Ulx9/ADfM6kY2/xdjcoFi4cGF5e0vWNAYAAAAAmqetc7yWHn/HHXds0vE3EB53Iht/o/DQQw812q6uri4PP/xwkmT06NEZMGBAm9cGAAAAAKzX1jleU4+fJDNmzEiS9OjRI7vuumuTjr+B8LgTOfDAA8vbN954Y6Ptpk+fXr6756GHHtrmdQEAAAAAr2nrHG/vvfdOr169Nnv8efPmZdq0aUmSgw46KL17927yayTC405l/PjxGT9+fJLk1ltvrTflfGPXXXddefvEE0+sSm0AAAAAwHptneMNHDgwRx11VJLkscceyxNPPNFgu+uvv758U8CW5ITC405mypQpSZLVq1fnzDPPTF1dXb39DzzwQL75zW8mScaNG5cTTjihyhUCAAAAAG2d4204fpJ87GMfy6pVq+rtf/755/PZz342yfqw+T/+4z+a+Q6SUrEheqZTqK2tzWGHHVaebn744YfnlFNOycCBAzNt2rRceeWVWbZsWUqlUm644YZMmjSp4hgLFy7MM888k6VLl2bEiBHZZZddUlPjewS6l9mzZ+f5559PkowZM8aNJel0tnQM19XV5emnn87s2bPTv3//7Lzzztluu+3aolRoN8Y5nYHrOWyecU41LFu2LE899VRWrFiRXXfdNdtvv31KpVKT+7ue05AtzfFOOeWU/PjHP06SXHDBBbnwwgvr7S+KIu9617vKs5ff8IY35LTTTsuwYcPy8MMP54orrsj8+fOTJFdccUUmT57c/DdR0Om89NJLxX777VckafCnV69exbe//e2KfjNnziyOPfbYokePHvXa77jjjsXXvva1Yu3ate3wbqDp3vrWtzY67l//c9VVVzV4jJtvvrk48MADK9ofcMABxU033VTdNwRFUVxxxRVFkuKCCy5oUvstHcPr1q0rvv71rxejR4+u179Hjx7FMcccUzzxxBNb+I6gUlPH+dq1a4vevXs3+Vr/7LPPNngc45xqmDt3bnHhhRcWxx57bLHbbrsVW2+9dTFhwoTi3e9+d/G9731vs3+3dj2nM2jpOHc9p7NYuXJlcdFFF1WMsSTFtttuW5x33nnF4sWLN3kM13M2p6U5XlEUxYc+9KFyu8b+Lr106dLN5iXnn39+UVdX16L6hced1OrVq4vLL7+8mDhxYjFo0KCid+/exdixY4tTTz21mDFjRkX7O++8s+jbt+8mB9Jxxx0nQKZDa+gP9OaEx1/72tc22++rX/1q9d8Y3dohhxzS5PB4S8fw2rVrixNOOGGT/bfeeuviz3/+cyu+Q2j6OH/mmWeafJ1vLGwwzqmGm2++udhmm202Oc7233//Yvr06Q32dz2nM9iSce56TmewbNmy4uCDD97s+BwyZEiDOUtRuJ7TdM3N8TZoSnhcFOu/hPjxj39cHHXUUcWQIUOKXr16FaNGjSre//73F3fdddcW1S487gbmz59fDB48uEhS1NTUFBdffHHxwgsvFMuWLStuu+22et9+fPazn23vcqFBK1euLEqlUpGkOP3004tbbrllkz8vvvhivf633npruf/2229fXH311cWCBQuKBQsWFD/96U/L50iS4k9/+lM7vUu6mx/+8IdN+otAUbTOGL7gggvq/c/e7bffXixbtqx44YUXiosuuqioqakpkhSDBg0qXn755TZ4x3RHzRnnv//978ttv/Od72z2Wr9ixYqKYxjntLVHH3202HrrrcvjbNKkScVll11W/OpXvyo+97nPFRMmTCjv22abbSpmjLme0xls6Th3Pacz+NjHPlYeY/vtt1/xu9/9rpgzZ06xYMGC4s477yze9ra3lffvtttuxZo1a+r1dz2nuxAedwOf/OQnyxejK664omL/4sWLi7FjxxZJin79+rkg0SE98sgj5XF88803N6tvXV1d+Rvlnj17Fvfff39Fm+nTpxc9e/YskhQHH3xwi/85B2zOokWLijvvvLP48Ic/XPTq1atJoVprjOF58+YV/fr1K5IU48aNK5YsWVJxjG9961vles4555wtfq90Xy0Z50Xx2vIWSYply5Y1+3WNc6rhPe95T72/W7/+ert69eri9NNPL7d5+9vfXt7nek5nsSXjvChcz+n4Fi1aVF5aZeeddy5Wr15d0WbdunXF4YcfXh5nt956a3mf6zndifC4i1u7dm35266hQ4c2uizFxn+4X3755VWuEjbv17/+dXmMPvXUU83q++CDD5b7vutd72q03bve9a5yu4cffnhLS4YKBx10UKP/HG1ToVprjOFvfvObm/wisSjW/wV56NChRbL+n+etW7euRe+T7q2l47woiuK///u/iyTFqFGjWvTaxjltbeXKleUvRPbff/9Gv2xevXp1sddee5XH44Z/EeV6TmewpeO8KFzP6fj+9re/lcdYY2vNFkX9/w/9+te/Xn7e9ZzupCZ0affcc09effXVJMmkSZPSo0ePBtsdd9xx5e2bbrqpKrVBc/zjH/9IkvTq1StjxoxpVt/f/va35e3jjz++0XYb73Me0BZefvnlFvVrjTG88TE2vuZvrKampnx33/nz5+fvf/97i+qle2vpOE9eu9bvtttuLepvnNPWHnroodTW1iZJ3vWud6VUKjXYrnfv3jn22GPLjx944IEkrud0Dls6zhPXczq+DWM0Sfbcc89G2+26664N9nE9pzsRHndxM2fOLG8fffTRjbbbcccds88++yRJ7r///javC5prwx/UO++8c3r27Jl169blueeeyx133JHp06dnyZIljfZt6nmw8T7nAW1h5syZWblyZfnniSeeaHK/DVo6hjccY++9986OO+7Y6DGOOeaYRo8BTdHScZ5Uhg2rVq3K448/nttvvz0PP/xwOczY1GsnxjltZ968eeXtzX2ZPXz48PL2ypUrk7ie0zls6ThPXM/p+HbZZZdccsklueSSSzJhwoRG282ePbu8vcMOO5S3Xc/pTnq2dwG0rZdeeqm8vbk/+HfcccfMmDEjL7/8chYtWpRtt922jauDpnvyySeTrP8L6iWXXJKvfe1rWbBgQXl/z54989a3vjUXXHBBDjnkkHp9N5wH/fv3z6BBgxp9jUGDBqVfv35Zvnx5vb8MQGvZaqutNvm4MVs6houiyNy5c5M07c+CDZwHtERLx3ltbW2eeeaZcp+PfvSj+eEPf5i1a9eW2wwYMCDvfe9789nPfrbif7KMc6phr732ylVXXZUkefOb37zJthvPDtsQoLme0xls6Th3PaczmDhxYiZOnLjJNrW1tfnSl75UfnziiSeWt13P6U6Ex13chotRkk1e0JJk8ODB5e2XXnpJeEyHsmH2wu23357bb7+9Yv/atWvzu9/9Ln/4wx/yzW9+M2eeeWZ534bzYHPnQLL+PFi+fHm9L16gvW3pGF6wYEF5hk9z/yyAannuueeybt26JMlll13WYJulS5fm+9//fq677rpcf/31+Zd/+ZfyPuOcathpp52y0047bbbdjBkz8otf/CJJMmrUqOyxxx5JXM/pHLZ0nLue01nNmTMnf/nLX7JgwYI8+eST+dWvflWeefy5z30ue++9d7mt6zndiWUruriNLywbX3AasvH+5cuXt1lN0FzLly/PnDlzyo/322+//PGPf8yiRYvyyiuv5Pbbb8973/veJEldXV3OOuus3HzzzeX2G86DzZ0DG7dxDtCRbOkY9mcBncHG6wgmyfve97488MADWb58eZ577rlcf/312W+//ZKs/x+uE044IS+++GK5vXFORzF9+vT827/9W3mW5bnnnpvevXsncT2n69jUOHc9p7O688478973vjcf+9jH8o1vfCOzZ8/OoEGDcsstt+TTn/50vbau53QnwuMubuN1YLfeeutNtt34n5VuvF4VtLenn346PXr0SI8ePTJp0qTceeedeetb35ptttkmgwcPzhFHHJFf/OIX+frXv17u84lPfCJ1dXVJXjsPNncOJK+dB84BOpItHcP+LKAz2Phaf9FFF+VnP/tZ9t133/Tt2zdjxozJ8ccfn3vvvTcnnXRSkvWBw+c+97lyf+Oc9rZkyZKce+65OeSQQ8qhwAc+8IGcdtpp9dokrud0Xk0Z567ndCULFizI//zP/+SWW26p97zrOd2J8LiLGzJkSHl70aJFm2y78f6mXAChWvbZZ5+sXbs2a9euzW9+85v079+/wXZnnXVWDjjggCTJE088kQcffDDJa+fB5s6Bjds4B+hItnQM+7OAzuDMM88sX+s/+9nPplQqVbTp1atXLrvssvTr1y9J8qtf/ao86804p73U1tbmyiuvzC677JIvfelL5TF56qmn5kc/+lFqal77Xy7Xczqr5oxz13M6q/e9730piiKLFy/Ogw8+mM9+9rMZMGBAZs6cmUmTJuX6668vt3U9pzsRHndxG9/9duObizVk4/2NhXPQkfXo0aPeTQw23MBjw3mwuXNg4zbOATqSLR3D/iygKxk5cmT5BjeLFi3K008/ncQ4p308+OCDeeMb35jJkydn/vz5SZLRo0fn5ptvzve+97306tWrXnvXczqj5o7zpnI9p6MaOHBg3vCGN+Siiy7KzTffnJqamqxbty7nnHNOeT1v13O6E+FxF7fDDjuUtzd3QVq4cGF5e+TIkW1WE7Sl3Xffvbz96quvJnntPFiwYEGKomi0b1EU5fPg9Xd9hva0pWO4f//+5Zk9zfmzwHlAR9XQtd44p5rWrVuXz3zmMznooIPywAMPJFl/w6NLL700M2fOzDve8Y4G+7me05m0dJw3h+s5Hd1hhx2Wt7/97UnWr+e9YX1u13O6E+FxF7fxt1kPPfRQo+3q6ury8MMPJ1n/LfKAAQPavDZoCxv+uVuSbLPNNkleOw/WrFmTmTNnNtp35syZ5TveTpgwoQ2rhOZpjTG84RgzZsworwfekBkzZpS3nQd0VA1d6xPjnOooiiKTJ0/O5z//+axduzalUilnnXVWnn766UyZMiV9+vRptK/rOZ3Flozz5nA9p738f//f/5cPfvCD+cxnPrPZtnvssUd5e8ON3F3P6U6Ex13cgQceWN6+8cYbG203ffr0zJ07N0ly6KGHtnld0Bwnn3xy9tprr7zlLW/Z5B+qSfL444+Xt3fbbbckTT8PNt7nPKAjaY0xvOEYc+fOzfTp01t0DGgry5cvz7777pu99tor55xzzmbbb7jW9+rVK2PGjCk/b5xTDZ///Ofz3e9+N0kybNiw/OUvf8lll12WbbfddrN9Xc/pLFo6zl3P6SxmzJiRq6++Ot///vc323b27Nnl7Q3ngOs53YnwuIsbP358xo8fnyS59dZb6/1zh41dd9115e2N14yFjmDffffNo48+mttuuy1/+tOfGm23cuXK/OQnP0my/p8BHXbYYUmSSZMmlW/Uce211zb4z4qKoiifB6VSKccdd1xrvw1osdYYw8cff3x5+9prr23wdRYtWpTbbrstyfoZFhv+/IC21q9fv4wcOTKPPvporrzyykb/vpIkjz76aO6+++4kyZvf/OZ6a/8Z57S1JUuW5Mtf/nKSZLvttsvdd9/drP+Rdz2nM9iSce56TmexYaLR3Llz88QTTzTabtmyZbnrrruSrA+Od9lllySu53QzBV3ed7/73SJJkaQ4+eSTi3Xr1tXbf//99xd9+vQpkhTjxo0rVq9e3U6VQsNmzZpV1NTUFEmKkSNHFrNmzapos2LFiuI//uM/ymP9vPPOq7f//e9/f3nfd77znYr+3/72t8v7P/jBD7bZe4GNPfvss+Vxd8EFF2yy7ZaO4VWrVhVjxowpkhR9+vQpHnjggXr7165dW3zgAx8oH+P73//+lrw1KGvqOP/Zz35Wbvev//qvxapVqyravPjii8VBBx1UbveHP/yh3n7jnLZ25ZVXlsfPlVde2aJjuJ7T0W3pOHc9pzPYeJweccQRxdKlSyvarFq1qvjQhz5UbnfWWWfV2+96TndRKopNrOxNl1BbW5vDDjss06ZNS5IcfvjhOeWUUzJw4MBMmzYtV155ZZYtW5ZSqZQbbrghkyZNaueKodI3v/nNfPzjH0+yfkbDRz/60ey7777p2bNnHn/88fz85z/PU089lSQ54IADctddd2Xrrbcu93/uuedywAEHlG9G8KEPfSjveMc7UldXl5tuuilXX311kmTw4MGZPn16vX82B23lueeey7hx45IkF1xwQS688MJNtt3SMXzDDTfkhBNOSJIMGDAgZ555ZvmYP/rRj8qzfw455JDcdddd6dmzZyu+W7qrpo7zurq6nHTSSbn++uuTrL8Hw3/9139lt912y5IlS/LII4/kBz/4QZYuXZokOf300zN16tSK4xjntKX3v//9+cUvfpEk+fGPf5yhQ4c2qd/BBx+cQYMGJXE9p+Pb0nHuek5nUFtbm/322y+PPvpokmTkyJH5j//4j+y6667p0aNHnnzyyfzkJz/J008/nSTZZZddcu+995av5YnrOd1Ie6fXVMdLL71U7LfffuVvrF7/06tXr+Lb3/52e5cJjaqrqyvOPvvs8gzkxn7e/va3F6+++mqDx7jrrruKIUOGNNp36NChxV//+tcqvzO6s+bMPC6K1hnDl19+edGzZ89Gj3HAAQcU8+bNa6V3CM0b50uXLi2OOuqoTV7nS6VS8clPfrJYu3Zto8cxzmkrRx555CbHZ2M/t99+e73juJ7TkbXGOHc9pzN4+umni913332zY3vixInF888/3+AxXM/pDoTH3cjq1auLyy+/vJg4cWIxaNCgonfv3sXYsWOLU089tZgxY0Z7lwdN8sQTTxT/8R//URxwwAHF9ttvX/Tu3bsYPXp08b73va+4+eabi7q6uk32nzt3bnHeeecVEyZMKPr161f079+/2HPPPYvzzz+/ePnll6v0LmC95obHRdE6Y/jBBx8sPvKRjxRjxowpevfuXQwaNKg49NBDiyuuuKJYs2bNFrwjqNTccV5XV1f88Y9/LE444YRizz33LPr371/069ev2H333YvTTz+9eOSRR5r0usY5bWHChAmtEh4Xhes5HVdrjXPXczqD5cuXF9/+9reLE044odhnn32KAQMGFNtvv33xpje9qfjwhz9cXH/99Zv8gqMoXM/p+ixbAQAAAABAhZr2LgAAAAAAgI5HeAwAAAAAQAXhMQAAAAAAFYTHAAAAAABUEB4DAAAAAFBBeAwAAAAAQAXhMQAAAAAAFYTHAAAAAABUEB4DAAAAAFBBeAwAAAAAQIWe7V0AAAAAQHupq6vLzJkzc//992f+/PlZtmxZtt122wwZMiQHHnhgdtppp5RKpfYus0NYvnx57rvvvsyZMydz5szJ2rVrM3jw4IwbNy5vfOMb079///YuEWhlwmMAAADaxMaB21VXXZVTTjml/YrpZF4fVh544IH5+9//3qJjLVy4MMOGDUttbW35uTe/+c254447tqTETu/VV1/Nt771rXz3u9/NvHnzGm03cuTI/Nd//VfOOOOMDBo0aJPHfO655zJu3LgmvX6/fv0yYsSI7LLLLjnhhBNy4oknZsiQIc16Dy3x7LPPZs8998zAgQPz9NNPp1+/fptsXxRFbrrpplx11VW55ZZbsnLlygbb1dTU5KijjsoZZ5yR4447ruqB+xFHHJE///nPSZKxY8fmmWeeaVEN3/jGNzJlypTy46effjo77bRT7rvvvhx00EHZe++9M3369PTq1avVaoeOzLIVAAAA0AIf/ehHUyqV8sEPfrDNX+u+++7Ls88+26K+N954Y73gmOS2227LXnvtlYsvvniTwXGSzJ49OxdccEH22muv/OlPf2q1GpYvX55//OMfueWWW/LRj340Y8eOzVe+8pU2/V0VRZEzzzwzK1euzLnnnrvZ4Pjpp5/Osccem0mTJuW6665rNDhO1s/g/tOf/pQTTjghb33rW/Pcc8+1cvWb9oEPfKC8/dxzz+W+++5r0XGuu+668vZBBx2UnXbaKcn6L3COO+64PPzww7n88su3rFjoRMw8BgAAoMsYO3Zsnn/++STrZ1iOHTu2TV5nzZo1ueaaa9rk2I35v//7v3zyk59sUT9ec9ddd+Xoo4/O6tWry8/tuOOOOeqoo7Lbbrtl8ODBmTVrVp555pnccccdmTt3bpLkpZdeyrHHHps//OEPOfzww5v0WiNGjMjWW29d8fzatWvz8ssv1wtjV6xYkU996lO55ZZb8tvf/nazwW5LXH/99bnpppsycuTIfPSjH91k22nTpuXtb397Fi1aVH6upqYmRxxxRA455JCMGjUqK1euzOOPP55HHnkk99xzT7ndbbfdlokTJ+b222/P7rvv3urvoyEnnXRSJk+enLVr1yZJfvnLX+aggw5q1jHmzp2bu+++u/z4ve99b739F110UX7zm9/kggsuyHvf+96MHDlyywuHDk54DAAAAM30s5/9LAsXLqzqa15zzTXNDo+XLFmSP/zhD21UUeezcuXKnHzyyeXguE+fPvnSl76U0047LVtttVVF+xUrVuQ73/lOLr744ixevDirV6/O//t//y9PPPFE+vTps9nXu/rqq3PEEUc0uK8oirzwwgu5/vrr85WvfCWzZ89Oktxxxx3lkLo1l0ZYtmxZ/vu//ztJ8ulPf3qT9d97771561vfmmXLlpWf+/CHP5wvfOELGT58eIN97rnnnnzqU5/KnXfemWR9EPu2t70tM2bMyHbbbddq76MxgwcPzjve8Y7ceOONSdafL1/96lebtXTFDTfckKIoyo/f/e5319u/77775qSTTsq1116b//mf/6n6F0jQHixbAQAAAM0wY8aMnHHGGVV7vW222SZJ8ve//73ZSwHcdNNN5aB0w3G6s6uuuiovvPBC+fH3vve9/Pd//3eDwXGS9O3bN1OmTMnvf//79OjRI0ny/PPP50c/+tEW11IqlTJ69OicddZZefzxx/Ou/7+9+46K6lr/Bv6dgREBEbDkItYIKGKLvUYXKBorgnoVMMaSG8WS3NjNL9GoN2K9amKJBYmKxkREvZrEoAY7FmxRo4iIBVQwxIKCCMx5/5g1+50D0wUU8/2sNWudw9nn2XtmYP545uHZAwaIawcPHsTSpUtfeg5d69atw507d+Dq6orhw4cbHPf06VMEBweLxLFKpUJMTAzWr19vMHEMAG3btsXBgwdlFc2pqakiYV0agoODxfHt27dl1dDm2LFjhzhu164datWqVWSM9guc6OhoXLx40cqVEpUdTB4TERERERERGSFJEm7fvo09e/Zg8ODBaN68OZ49e1Zq8wcEBIhjSysdt2/frjfO35VuctDPz8/sftVt2rTBBx98IM537dpVrOtycnLCli1b4O/vL342c+ZMUY38stRqNVauXAlAU01rKFkOAFOnTpX11966dSsCAwPNmkehUGD58uV47733xM+ioqKQnJxs5cot07dvXzg4OIjzH3/80ex7Hz16hAMHDojzwi0rtFq3bg0PDw8AwIoVK6xcKVHZweQxERERERERkQFXrlyBs7MzateujT59+uCHH35AQUFBqa5BtyLVkuTxs2fP8PPPPwMA3Nzc0L59+2JfW1lz6dIlcdyxY0eL7tUdf+HChWJbk5ZKpcKqVatQrlw5AJoWG5GRkcUSe//+/UhKSgIAhIaGGhx39+5drFmzRpyPGDECQUFBFs1la2uLJUuWiHNJkrB27VoLV2wdR0dH9OvXT5xv27YNarXarHv37Nkj+iUrFArZ350uhUIhNueLioqS9YQmehOx5zERERERERG9NtLT03H48GGkpaUhPz8fdevWRf369eHj42NR79Likpubi6ysrFKfV1eDBg3QsGFDXL58WbSuMGcjwL1794oN2YKCgkTbBXPl5eXh4sWLuHPnDlJTU5GVlQV3d3dUr14dPj4+RlsY6JOeno7ExERcv34dDx48QO3ateHh4QEfHx+LNod7mTiZmZniuGLFihatv3Xr1mjatCkATYJUkqRi/5308PDA4MGDsXHjRgBAREQEPvvsMyiVL1f7p606rlmzptGk+Zo1a0QCtVy5cpg3b55V83l7e6Ndu3aIj48HAOzbt8/sWLdu3cKxY8dw79492NjYwMPDAw0aNICnp6dZ94eEhGDLli0AgLS0NBw/ftysLwpiYmLEcceOHY1uhhcaGoo5c+bg2bNn2LBhQ6m25iAqbUweExERERER0St38eJFTJ48GbGxsbINq7SaN2+OCRMmICQkpEjC7rvvvtPbw/Xtt98Wx3FxcQY3LjPGzc0Nc+bMKfLz3377DXFxcRbHs9bAgQNx+fJlAJpeq5MmTTJ5T3R0tDgeMGCA2a0DsrOzMW/ePKxcuVKWbNVlY2ODwMBATJo0CW3atDEaLzExEbNnz8b333+v972tUqUK/u///g+jR482uolbccRxd3fHrVu3AACHDh3CxIkTja5dV8OGDXH+/Hmzx1srODhYJI9v3ryJ5ORkeHl5WR0vIyNDbCI3YMAAo4noqKgocTxo0CBUrVrV6nnXrFmDq1evAgCUSqXJZPuRI0cwZcoUg32KfX19MXHiRPTq1cvovP7+/qhUqRL++usvAJrWFaaSx9nZ2di7d684N9SyQqt+/fpo2rQpLly4gIiICCaP6c0mEREREREREZUAAOIRGRlpcNz69eslGxsb2XhDjyFDhkjPnz+X3R8ZGWnyvri4uGJ9bjNnzhSxQ0NDizW2JMlfu6SkJOny5cvivHXr1ibvz8nJkSpUqCABkKpWrSrl5eVJa9euFTE6d+6s976nT59KHTp0MOu9ACDZ2NhIMTExBtexefNms9/b9u3bS9nZ2SUax9/fXzZ24cKFUkFBgcnX01wpKSkv/Xv39OlTSaFQiBibN29+qTVt3bpVxNq+fbvBcWlpabK179q166XmNZdarZZmzZpl9u/c1KlTTb5no0aNEuPd3Nyk/Px8o+NjYmLEeKVSKd27d8/kuseOHSvuSU9Pt+g5E5Ul7HlMREREREREr8y2bdswYsQI0UfYxcUF77//PubNm4elS5di+PDhcHd3F+OjoqIwePBgWeWpk5MTPDw84OHhIWvNUKtWLfFze3v70ntSJcDHxwc+Pj4AgFOnTuHmzZtGx8fGxuLp06cAgMDAQNjamvePx3PnzsWxY8fEubu7Oz799FMsXboUq1evxowZM9CuXTtxvaCgAKNGjdLbVzYxMRHDhw8X761KpUJISAgWLFiA1atX4/PPP0e9evXE+OPHj2PRokUlFgcARo8eLTufPHky2rZti3Xr1uHPP/805yUqcY6OjmJDNgA4d+7cS8XTrZA3ViWu+74DKLUe2YsXL8bMmTPFebVq1TBy5EgsXrwYCxcuRGhoKFxdXcX1+fPnm6wY1/YkBoD79+/j6NGjRsfrtqzo3Lkz3NzcTK67devW4vjgwYMmxxOVWa86e01ERERERERvJpioPL5//77k6uoqq+DNyMgoMu7JkyfSv//9b1k8Q9WutWvXFmNSUlKK+Rn9f6VdeVx4zoULFxq9f+jQoWJsbGysJEmSycrjgoIC6a233hJjgoKCpNzcXL3xo6KiZGv8448/iozRrcx0c3MTz0NXfn6+1L9/fzGuWbNmJRZHkjRVroWrj7UPpVIptWvXTpo5c6Z07NgxKS8vT28MY4qj8liSJKlLly4ixrBhw6yKoVWvXj0JgOTu7m503Lx588Sc1apVe6k5zXXp0iVJpVKJeSdOnChlZWUVGZeeni4FBweLcQqFQjp9+rTBuAUFBVKNGjXE+LCwMINjc3NzJRcXFzH222+/NWvtV69eFfeMHj3arHuIyiJWHhMREREREdErsXLlSjx8+BAA0LVrV2zYsEFvj1UnJycsWbIEI0eOFD/77LPPSm2dr4uBAweK423bthkc9+LFC+zatQsAUKlSJbN7Pd+6dQsZGRkANBvCRUREoFy5cnrHhoaGyiov9fUC1m6WBgBTp07Vu+GZjY0NZs+eLc4vX74sNmwr7jgAoFAosHv3br09stVqNeLj4zFr1ix06NABlStXRmBgIFatWiX6JJcWJycncaz9G7HG3bt3ce3aNQDGq44BiB7BAFC5cmWr57TEggULkJeXBwAYOXIkFi1ahAoVKhQZ99ZbbyEqKgpdu3YFAEiShC+++MJgXKVSieDgYHG+fft2vb8PgKZq+NGjRwA0v0dBQUFmrd3LywvOzs4AUKr9z4lKG5PHREREREREVOokScK6devE+apVq2QtJ/RZtmwZ7OzsAABXr17F3bt3S3SNrxsfHx94e3sD0LSuMJTQ/O233/D48WMAQL9+/aBSqcyKb2tri7Fjx2Ls2LGYPXs2XFxcjI7XrgUAcnNzi1zX/ZmxNgD16tXDpk2bsGnTJkRERODFixclEkfLzs4O69evx7lz5zB27FiDidInT55g586dGDNmDN5++2307t0bsbGxBucvTo6OjuJY237EGtpNFgHoTbrr0k0eV6xY0az4H374IRQKhVmP7777TnZvVlYWtm7dCkDznixdutToXEqlEt9++604P3z4sEg866ObPM7IyMChQ4f0jtNtWeHn52f2JoFKpVK8pomJiUbXQlSWMXlMREREREREpe769esi+evh4WEysQVoEmotWrQQ54V7tL7pFAqFWdXH0dHR4njAgAFmx69ZsyaWL1+O5cuXY/r06SbHm6rG1e1DvGzZMoNJUFtbWwwZMkQ8HBwcSiROYe+88w6WL1+OjIwMJCQkIDw8HH5+fuILCl2SJOGnn35C9+7dMWHCBNF/uaRkZ2eLY90qZEulp6eLY1PVxOXLl9c7f0k5ceKESPB37NhRb8VxYXXr1hU90LOzs/VWvGu98847qF+/vjj/8ccfi4xRq9XYuXOnOB80aJCZq9fQfU0fPHhg0b1EZYV5HfOJiIiIiIiIitGZM2fE8d27d81KHgOaza+07t27V+zret0NHDgQc+bMAaBJHk+aNEl2PT8/XyTDnJ2d0aVLl2Kd//nz57h8+TIiIiIMVnJq9e/fHzt27ACgSRR6eXlh7Nix6NWrF5o2bQql0rx6tuKKY4hSqUSLFi3QokULTJs2DTk5OThy5Aj27duH7du3IyUlRTZ+yZIlePz4MSIiIl5qXmOysrLEsakKcGN0k8eVKlUyOrZKlSri2NxWGVWrVpVt7qdvfkPJft3PgNOnT5v9GZCZmSmOjX0GKBQKhIaGYsaMGQA0rSuWL18uq8SPj48Xr5GtrS369etn1hq0dF/T9PR02eaeRG8KJo+JiIiIiIio1OlW6eXk5CA5OdniGLoJtr+LRo0aoX79+khMTBStK2rXri2uHzp0SCTXAgICDPYsNkWtVuPcuXM4fvw4kpKSxCMlJQVqtdqsGCEhIdi3bx82bNgAQJP4/+KLL/DFF1/A1dUVHTt2RKdOndC3b19ZdXFJxTGXvb09unXrhm7dumHBggU4ceIEZsyYgf3794sx69evx0cffWSyj7C1bt++LY7NbaOgjyWVx7rzpKam4tmzZ7L2GfqEh4cjPDzc4PX3338fUVFReq/pfgY8efIET548MTqXPqY+A4KDg0XyODMzE3FxcejWrZu4rtuywt/f3+Jez7rjdV9rojcJ21YQERERERFRqdP25H0Z1iSbyrrCrSt0W1QAmupKLUtaVmjl5+djxYoVqFGjBlq2bImPP/4Y33zzDfbu3Yvk5GSo1WpUrVoVwcHBaNWqlcm1RkZGIiYmBl5eXrJrDx8+xO7duzF58mTUr18fjRs3xsaNGyFJUonFsYZCoUC7du0QGxuLuXPnyq7997//LZY5CsvOzkZSUpI4b9asmdWxdCuITbW/aNmypTguKChAQkKC1fNq6VYJF1YanwGenp6yjR11W1dIkiRLHlvasgKA2DAPkPeMJnqTMHlMREREREREZtm9ezdatmwpHsaYqk7V7Ufbo0cPSJJk8WP+/PnF8rzKGkN9jwsKCkQyzMnJCf7+/hbFzcvLQ2BgIMaNGyfaAbRo0QLjx49HZGQk4uPjkZmZiYyMDGzZsgU+Pj4mYyoUCgQGBiIxMREXLlzArFmz0LZtW9jayv8R+tKlS/jggw/Qu3dv5Ofnl0icadOmoWvXrujatave/remnse0adPQuXNn2Vwl4ejRo7Lkt27y01Kurq7i2FSVbvPmzWV9h/fs2WP1vFrXr183eE33MyAsLMyqz4CwsDCTa9DdOC8mJkb0Wb5w4QJu3rwJAFCpVAgICLD4+ekmr3Vfa6I3CZPHREREREREZJbMzEycOXNGPIxVd/75559GY+n2CjWWYKKiGjduLCpwT548KTauO378uPjX+T59+sg2QDPHokWLRMKwTp06SEhIQEJCAr7++msMGzYMbdu2Ndk31xCFQoEmTZpgxowZiI+Px+PHj3HgwAFMmTIFtWrVEuN+/vlnLF68uETi3L59GwcOHMCBAwcQGxtr1XPo3r27OLem1Yo5vv/+e3Fct25d1K1b1+pY//jHP8SxsSpgQNPzV/cLh8jISDx//tzquZOTk2UV1IWV1mfAoEGDoFAoAGgqsQ8cOABA3rLivffes6q3tO5rqvtaE71JmDwmIiIiIiIis9jY2MjOMzIyDI4tvJGVbkUjoEmAaqWkpOitNtUnPz9fPIqrNUFZY6h1hW4LC2taVqxYsUIcb9iwAS1atDA6/mUSfg4ODvDz88P8+fORmJiIf/7zn+LaDz/8UCJxdJPLv//+u1Xr1v09LYlK0xs3bsiSxyNHjhSJT2voJjTNaavwySefiOPMzEwsWLDA6rmXLFli9LruZ4CxJHNhln4GVKtWDX5+fuJc+3vxsi0rAHny2M3NzaoYRK87Jo+JiIiIiIjILIU3k7px44bBsYWTQYUTK02aNBGbceXn58sSZobcuXMHDg4OUKlUcHV1RU5OjrlLf+MUbl2hVqtFMszR0VFWIWuOJ0+eIC0tTZw3atTI6PiMjAycOHHC4PVz587B09MTnp6eaNasGQoKCgyOLV++PCZOnCjOExMTiz0OANlrcvr0aZw/f95gLENOnToljs1p22GJvLw8hIWFITc3F4AmMT5s2LCXimlJ5TEAdOrUSdbLes6cOTh58qTF8544cQKrV682OqZ9+/bi+ObNmzh8+LDJuCdPnoRKpYJKpYK3t7fZ6wkJCRHHO3fuxKVLl3D58mUAgJ2dHfr06WN2LF26CfmX2diQ6HXG5DERERERERGZRbdSEAA2btxocOy6devEsZ2dXZEqVpVKJUvoTJ8+Hc+ePTM6/1dffYW8vDwAmuSpbs9UfUz1XS7LmjZtCk9PTwCahFp0dDRSU1MBAL169TL52hRWuLr13LlzBsc+evQIPXr0kCVyte+LlqOjI5KTk5GcnIzz588bTTQDEGsH5FXqxRUHADp27CirPg4JCTEroap17tw57N27V5xbU91tyNOnTxEaGiprpzFnzhy4u7u/VNyGDRuKY3OqexUKBSIiImBnZwdA88VO9+7dER8fb/acZ8+eRUBAgMn/JqhWrZqsTcaECROM/s2q1Wp8+eWX4nzo0KFmV2UHBQWhXLlyADQb9Y0ZM0Zc69mzJypWrGhWHF2SJInWJfXq1YNKpbI4BlFZwOQxERERERERmaVmzZqyJHBERAR++umnIuM2bdqEX3/9VZwHBQXB3t6+yLhPP/1UJFzS0tLg7++vt49sQUEB5s6dK6tkHD58uMn1llRP2tdB4dYV48aNE8fWJDWdnJzg4eEhzkeMGIHY2FiRzFOr1UhNTcWiRYvQsGFDnD17VnZ/dHS0bEM2T09PVKlSRZyPGTNGtrmYrjt37mD69OnivFOnTsUeB9B8YaG7yeKVK1fQokUL/PLLL0aTljk5OVi7di26du0qEqIeHh5m/Q6akpqaim+++QYNGjSQbX7YpUsXfPzxxy8d393dHfXq1QMgr5o2pnHjxrKWE48fP0anTp0wadIko8n2rKwshIeHo0OHDsjIyICNjY3J6uypU6eK4zNnziAgIED07daVm5uLcePGieS9jY0NhgwZYtbzAQAXFxf07NlTnB85ckQcW9uyIikpCQ8fPgQA+Pr6WhWDqCywNT2EiIiIiIiISGP27Nno1asXAE21ae/evdGvXz+0atUKBQUFOHnypCyh7OjoKKsW1NWgQQP85z//EQmk+Ph4NG3aFL169UKTJk1QpUoV3Lp1C9HR0bKqyREjRuDdd9/VG7Ny5cpiA7nAwEC0bNkSOTk5+Prrr9GmTZvieAleGwMHDkR4eDgA4MGDBwA0rRt69OhhVbzevXtj2bJlADSby3Xv3h3lypVD1apV8ddff8nahDg6OmLw4MGIiIgAAMTGxqJKlSoYMmQIIiIioFQq8eGHH2LevHkAND2G69ati8GDB8PLywtOTk7IyMjA+fPnsWPHDrx48QIAoFQqMWXKFDFPccXRGjRoEE6cOCGe561bt9CzZ0/UrVsXvr6+aNCgAVxcXJCdnY27d+8iNTUVv/zyiyxp6uzsjK1bt5q1IWFoaKjeL07y8/Px4MEDZGdnF7nm6+uL//3vf7C1LZ6Uja+vL65duyaeT40aNUzeExYWhvz8fJHAzs/Px+LFi7Fs2TL4+vqidevWqFatGuzs7JCRkYGEhATs379ffIFgb2+P6Oho3L9/HyNHjjQ4T5cuXRAWFoZVq1YBAPbs2YOGDRuiR48eorWNtg/03bt3xX0zZ860eCPBkJAQ7Ny5U/Yze3t78XlmKd1kPJPH9EaTiIiIiIiIiCwwZ84cCYDJR/ny5aWdO3cajaVWq6Vp06aZFQ+AFBwcLOXm5hqM99VXX+m9Ly4urlhfg5kzZ4rYoaGhxRpbkiTZ2pOSkvSOUavVkoeHh2xsUFCQwZhr164V4zp37lzk+rNnzyRvb2+T70HlypWlgwcPSo8ePZJq164tuxYQECDiZWVlSa1atTL7vVUqlVJERESRdRVXHN3XLTw8XCpXrpzZMbWPRo0aSWfPnjUYOyUlxeKY2oeDg4M0f/586cWLFwbjW2Pr1q1iju3bt1t0765du6RatWpZ9DyaN28uJSQkSJIkSdeuXRM/j4yM1DvHixcvpCFDhpgdf+LEiZJarbb4dcjOzpYqVKggizVw4ECL42iNHz9exLl//77VcYhed2xbQURERERERBb5/PPPsWPHDqMbVnXp0gXx8fEICAgwGkuhUCA8PBy//vprkb7Iupo3b44DBw5gy5YtonepPlOnTsXChQvRoEED2Nvbo1KlSmjSpAlcXFxMPq+ypnDrCgDo37+/1fEcHBwQFxeHUaNGwcbGpsh1Ozs7DB06FH/88Qc6d+4MZ2dn7Nu3D926dYOjoyO8vb1l1d0VKlRAXFwcJk2aBGdnZ4Pz2tnZ4aOPygP62gAAA7BJREFUPsLVq1cxYsSIIteLK46WQqHAtGnTcPHiRYwfPx6urq4Gx2q1bdsW69atw5kzZ9CsWTOT483h4OAADw8PdO/eHatWrUJKSgqmTJlS7L1zfX19oVRq0j9Hjx616N6+ffviypUrCA8PN7mJYtu2bbF27VqcOnVK/C17enoW2SyzMJVKhY0bN2Lr1q2ixYY+fn5+SEhIwKJFi8zudazL3t4eQUFBsp9Z27ICAI4dOwZA0+ZDd2NCojeNQpIk6VUvgoiIiIiIiMoetVqNS5cu4dSpU3jw4AFsbW3h7u6Od999V7YxmSVSUlJw7NgxpKenQ6lUwtvbG97e3qhdu7ZIgFHJS0tLw6VLl5CUlITnz5+jTp068PPzQ6VKlayKl5OTgytXruDOnTu4ffs2nj9/jlq1aqFOnTqoV6+eWQnc4oyjq6CgAImJiTh//jwyMzPx+PFjlC9fHi4uLqhTpw5atmxZ5r986NevH3bt2oWaNWvi5s2bVv8tXb9+HRcvXsS9e/fw6NEjVK5cGW5ubmjVqtVLb+4HaDahu3LlCk6dOoWMjAw4ODiIz4Dq1atblTQuCdevX4eXlxcAYOnSpfjkk09e8YqISg6Tx0REREREREREbzBthTgAHDx4EJ07d37FKyrbZs2ahS+//BIODg5IS0sr818uEBnDr22JiIiIiIiIiN5gXbp0EZWymzdvfsWrKdskSRKv4ZAhQ5g4pjcek8dERERERERERG8wpVKJsLAwAMC2bduQm5v7ildUdiUkJCApKQkAMHbs2Fe8GqKSx+QxEREREREREdEb7l//+hdq1KiBR48eYf369a96OWXWokWLAGg2p2zSpMkrXg1RyWPPYyIiIiIiIiKiv4GYmBj0798f1atXx/Xr11G+fPlXvaQy5ffff0fTpk3h6OiIq1evokaNGq96SUQljpXHRERERERERER/A4GBgejZsyfS0tKwevXqV72cMmfGjBkANBvmMXFMfxesPCYiIiIiIiIi+pu4ceMGGjZsCGdnZyQnJ8PR0fFVL6lMSEhIQKtWrdCoUSOcPXsWKpXqVS+JqFQweUxERERERERERERERbBtBREREREREREREREVweQxERERERERERERERXB5DERERERERERERERFcHkMREREREREREREREVweQxERERERERERERERXB5DERERERERERERERFcHkMREREREREREREREVweQxERERERERERERERXB5DERERERERERERERFfH/APqfgERTArr/AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "w, jet1txbbbins, jet1ptbins, jet1msdbins = effstxbb.to_numpy()\n",
     "\n",
@@ -162,51 +95,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "8dbc5ee8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1800x1400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "w, jet1th4qbins, jet1ptbins, jet1msdbins = effsth4q.to_numpy()\n",
     "\n",
@@ -235,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "3b5d8e21-5eba-4769-af62-f64dd63374a5",
    "metadata": {
     "tags": []
@@ -250,97 +142,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "c46cfbeb-05fc-4f67-b5ec-0257ed93d452",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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Md+A1vFGXa/wDb8CyWJ/3ux6vA6D+Xre19f4e0szrB9f7u6nzaZtS82N+L96exqQW3us/l7of1NV4jf0Pgc/w9hB/Yq0tCP/476h6jMC7ugDA+00MCNfY4ef197pmdeDpFi0W53W/JUcy1G/0RLtkZkfq0O+FMeZUvHVjVwsGE/yw3t8DGzwXi+1FPLc5Nd/J5k7LqH+az4Q2zKdGw8+rRsy/f9bakDFmLvAjvNMkvgU8He5Umxwu9r61dl1r3jZ83+bTWERE/EqDAIpIhwufe/sCdQ2g7cA51tpzW9IAMsYMMMYcFL5WeEvUDEK1s95j9S/HdnQzr6/fUPushfNs6GG8c/K74F3r+po2vk+8nEtd4/8N4GBr7QnW2h9Zax+z1r7RlmuGt8GxwIPh2+QmynVv5PG8en9/LVaVaqv2rvttMLgFZYbW+zvqHt4O1NHfi6fw9uQ3vAxoNPXPh2/YqRWL7UU8tzk1lxncr6lC4UtTbquZZ/gSjG0xqZHHO+r7F+1qAKdS16HUmsP/oe70h7wmS4mIOEgdACISD7OAk8N/rwS+bq1d0IrX/wbYAGwMD/LXKGPMfniXFIN6e/jCh7NuCE+eHh6IrTFn1vt7SSvqWctauxPv2tQL8Y62etwYc3tb3itOzq7391XW2s3RChljhndwPSrr/d1UY6b+5czqH/Zc/1z1JhtdxpiuxpjbjTG/MMac1lTZdmjvut9aJ4bPs48qPOJ9TX0K49SpUysO34uaDo2hjYwYX9/Ien9HrO+x2F7EeZtT05Bt7rx+8MagqPF/rZ2RMeZsIjss6uuQ75+19kPqjow4JXyFl5rR/y3wXPM1j6AOABHptNQBICIdKnxd6GvDkwXAJGvttiZeEk3N3h2DdxhoU35Z7++GPwr/Eb7vB1wU7cXGmCF4h5gCfGit3djyakYKj8Z+DvBI+KFfGWNmN9VAS6CaxrYl8siJhq7s4HrUP2x6WLQC4bEKDglPVhN5yPRCoOa647c3com3GtfijcT/SzrgUOAYrfutNZTGr5cO8F3qTgFY1MF1iaqDvxevhu9TaHwAuxoz6v29LMrzsdhexGubU9OQzW7B5/gQUHMO/dnGmOY+p1rhhvejTRTpyO9fzVEAaXjr+JTw9FvW2i3RX9Komg6A7U2WEhFxkDoARKSjXQJkh/++21rblnOOX6XusNXvGmNuNMZE/GA0xqQYY36A18ABb/CyvzV4n3upu8TWvcaY+iOxY4zpifcjs+aSXXe2oa4RrLVBa+10vKsTgPejd0G4cZhMag47NkRpQBpjuhhjfkrdVRWg+QG0ujTzfDSfU3do9smNHHFwAXXnuq8Oj+4O1O5h/k14chAw1xizz3nxxpiTqRulfAvRG4D1tSVLLNb9tngwfC58BGPMd6hbpyvxLoWXEB34vXgMKAr//f+MMfsMXmiMSTPG/Iy6PcjvAi9Gea9YbC/itc2pGWDQ4F2to1HW2l3AtHoPPWCMua2ZIxQwxhwJLKWJ730Hfv8gcnv+ayAn/HdrD/+Hus/ooza8VkTE10zkZW9FRGLLGPMYdT82/wB81cKXbrLWLqz3Pt/EGw265trVK/AGVtuBt9fzm9Qd0lsOHGmtrb83ueZ9bgF+F54sw/tRuRpv3IBLqPtx+yIw1cZwI2mMuRx4IpzhPeAsa21+069q9j1rRmoHeMpae3Ub3+ebwHK8juEgXv4P8RrjQ/EOUe4P7ME7SqBnuNxs4EVr7Svh97mIuh/kr+A1yFKstc+3oi6zqdtzvh64Hm/wsnS8cQH+BPQIP3+etfaFBq/vinf+dc01yHfiHVb9Gd5o48fjXfoNvGu1n2StfTdKPdqVJVbrfgvmU38d2Ansj7dsXsU7JDsDGId36bYa91prf9zC933TWjuxpfVprVh/L4wx1+KtlzVex9te7MBbJ07Eu7wlNLGtCL9Xu7cX8djmhI+KKQQygWnW2ida8JpfAr+o99DneEcHvAxstNYGw50CR+CN+D8DSMVbx/5AXWfF4PpHLcTq+9dInd8lcj0OAgNa07lmjBmKN6gpwNiWzltExBnWWt100023DrvhNdJtG24vR3mvy/D27jX1ureB4c3U6Xd4DdtG5403gnVHfB6nUNeIXos32F573m9CvXo/2c73+kUzn+1q4HC8PXc22nzxBqHb2+D59a2sRzbeXsH67xGMUp/nmniPQXiNvqby5AFnN/Ee7coSy3W/FevAdXiDDjb1/o8Bqa143zc64rvQUd8LvL3gv8Br3Df1OXwEHNuC92v39iIW79GCei4Jv9fsVrzmOryR+xvWJ4h3mkDDOn+Cd/pNJt5lLC1wUEd8/xqp7/cavMfSNnxOl4ZfWwykdfS6rZtuuumWbDedAiAiHe3g5ou0jPWuVT4Eb+Cq5cAmvEOZt+Pt7bwOGG+tbfIyWtbamXiNm7nh99iL92N0GV4nw5nW2pJY1bvBvJcB4/Gu134IdecsJ5z1rsk+Du+85bV4n8t2vIbFNcAoa+0nwM/xRnMvCt++qPceG4Dz8RpX5Xh7PFtzeS6stUXAkcCceg/X/39VjLc3Muo51eH32IQ3CNkVwEt4n/deYCPecv4pMNRaG+3Q71hlidm63wp7rbXn4n02S/H2vlbiXf99Lt7e1muttcEE1K1RsfxeWM+v8I4IehjvqhZb8Bq6K/AOub8WGGOtfa8F79fu7UWctjmvhe+Pb+kLrLWz8cbamE3dqRPgfd96UHdu/lq8xvfXrbVfWO/yfiuaeN92f/8a8Xe8cT/qT7dWzREE/7LWVjdZUkTEQToFQEQkAYwxg/D2+g2w1uY0V76zMsYcCIwCRgAleIcWr7TWFjf5wk6kwSkAV1lr5zRRvC3v+6btwFMAGsxT34s2Cn9XNuAdpj/MelchaM3r0/A6Dw7GOy2hEq+T4n/W2s9jW9vECI8dsxEYCFxkrZ2X4CqJiMSdL48AMJ6txphGL19jjDnDGPOuMabEGFNgjHnRGPP1RJUXEakvvIfsm8C/El2XZGat3WKtfcVae5+19nFr7btq/LtL34u2s95I+P8MT17WhtdXW2v/ba2dY639rbX2D9baZ11p/IdNwGv8b6PuCg0iInHVkrZsE69tdxvUlx0AeINRDWjsSWPMhXiD6RyH1xtejne5mHeNMWPjXV5EJBprbSF1I5GLCPpetNOD4ftWdwB0EjWfy6PW2qqE1kREOrMm27KNiVUb1FcdAMaY7saYbwN/bqLMfsDTeIPTjLXWjrLWHgh8H+8yTk8YY1LiVV5EpCk6B1VkX/petI219g3gY+BrxpgTEl2fZGKMycIb06OcyKtEiIjERUvask28NmZtUN80VI0xz+GNSPsXoE8TRS/B+xBm2XqXdrHWPoA3kNUIYGIcy4uIiIjEywy8Ue5/keiKJJkf4F1hZJa1dluC6yIinUwr2rKNiVkb1DcdAMA7wKPAI3gj+jbmkvB9tHO7aq4VfVocy4uIiPiOtXa5tdbEawBAiQ1r7b+BJ4GTjDFadoAxpidwM96lTO9JcHVEpHNqaVu2MTFrg6a1YeYJYa39Q83fxpgrabyHYwiwu5FBa2p6Sw6NY3kRERGReJoJnA3Mwru0Z2f3Y7y9/+dYaysTXBcR6YRa0ZZtTMzaoL7pAGiJ8OVd+uBdYzea/PD9oHiUFxER6WjW2uXUXa9dBGttAbB/ouuRLKy1twO3J7oeIiJtEes2qFMdAEAvvEyFjTy/K3yfGafyEYwxtpHXiYiIiIiIdDhrra87jZO9TdUBn2+72qAN+WkMgFhIDd+HkqS8iIiIiIiISFu1qg3q2hEABUA1sF8jz9c8XjP6a0eXjyq47ZCmno6J62/ezqP39O3w+QDccPuJzJ4dnyvqXHfddU7O6+ijj+a///1vh8/H1c/P1XlpvdC8GorXOgFufn6uzkvrheYVjdaL9rv22z3j8ns6Xr/bU/t/0eHziKd4tKlaowM/35i0QWu0+wgAY8xAY8z5xpgZxphZxpg/GWPuCE+fb4yJ2/nw1loL7MA7TCKamse3xaN8vEy9KpdVa/ZGfW7Rq6WMHL+B/qPXcdud+VHLiIiIiIhIx9PvdmmtWLdB23QEQPiyMhcBJwFDGz6Nd/3Z+uXXAa8B86y1r7dlnq2wDvimMWaUtXZlg+fGhu/Xx7F8h3l/RQUvLCrhpaWl3PHTfdeHgl1BrpyRx3OP9+eQIQGmXJ7L6MO6cPHU7vGonoiIiIiIoN/tHSHUuc66jlkbtMVHABhjuhpjbjDGrAReBa4DvgbsxGvczwUeAu4I388Nl9uO10lwHbDMGLMy/D7dWjrvVno2fH9OlOemNigTj/Id5r0PKygrD5GVGX0xzl9UwikTMpg4LoMDBwS46YYc5i4ojkfVREREREQkTL/bpZ1i1gZtUQeAMWYy8DnwMN7lBZ4BLgaGWGv7WWtPtdZeZq39vrX25+H7y6y1p1lrBwCD8Y4Y+Gv49Q8DnxtjzmrJ/FvpWaAC+Jkx5rh6GWYApwLvW2s/iWP5DjNjWjb339GHXjnRF+O6jVWMGt6ldnrUiHTWb6yKR9VERERERCRMv9ulnWLWBm22A8AYMx9YCOQBlwF9rbVXWGvnWWs3tmQm1tpN1trnrLVXAn3D75MLvGiMeb4l79FS1tpC4Aq80xveNcZ8YozZAtyPd17E1fEsnyhnnZpJYVGQrMy6q1B0z0phV1Ew5vOaMmVKzN+zs80rXlz9/FydV7y4+vm5Oq94cfXzc3Ve8eLq5+fqvOLF1c/vrFNbdFW1mMwnXr/bXRK0oaS6xYIxpo8x5uXwrU/N47Fsg7bkCICDgLOstcdba5+11pa3LkYka215+H3GAmfhHR0QU9ba54ApwHt4px9kAC8C4621n8W7fCJMOTWLnOxUikvqVsY9xSFyeqY28ao2zsvRjb7+QWteiZ5XvLj6+bk6r3hx9fNzdV7x4urn5+q84sXVz2/KqVlxm0+8frdL0usGnB6+RZwyH6s2aLODAFprj25FhVvFWrsYWNyG180B5jRT5hXglVa8Z4eWT4QhgwK88XZZ7fTK1ZUMHhRIYI1ERERERKQh/W7vPJpqy4aPsDfRngs/3+42aLsvAyjJ67zJWSxbXsbyd8rYklvFfbMLufRcjSQqIiIiIpJM9Lu99ULYpLr5RUvGAHjQGDMuHpWR9pszbw9Dj/GuANG7VypzHujL9Jk7GHPaZs6clKlLiYiIiIiIJAH9bpdEaPYUAOC7wHRjzFa8S/vNtdZ+1LHVktb46v0htX9feWEPrrywR+30WadkcdYp8Tl/SUREREREGqff7ZJoLekA2AP0AA4Efgz82BjzFfA3vM6A1R1YP5FO4brrrkt0FSQJab2QhrROSDRaLyQarRfiuhCxGXm/szHWNn2+gjEmAEwCzgemAvuFn6p54Sd41yX8e0svC9hZGWMsQHDbIYmuSkyl9Fub6CqIiIiIiMRUKG9YoqsQU6n9vwDAWtvoIHN+UNOmKskdlOiqRMgasAlI/s+32TEArLVV1tpXrLXTgL7AqcBsIB9vhMIjgN8A64wx7xhjvmeM6duRlRYRERERERGR1mn2CIBGX2hMCjAe78iAc4H+4acsEALexDsy4AVrbVF7K+oCHQEgIiIiIiKJYIy3YzrZ91A3p6ZNtXvrwERXJULPAzYDyf/5tvkygNbakLV2ubV2Bt74AOOBPwJbgVS80wYeA/KMMS8aYy6ORYVFREREREREpPXa3AFQn/W8ba39kbV2EHAccA+wEUgHzgL+Got5yb6mXpXLqjV7oz636NVSRo7fQP/R67jtzvw410xERERERESSRUw6AKJYA6wG1lE3WGBSHwrhR++vqODWWfm8tLQ06vMFu4JcOSOPB+/qwwdLBrL4tVLmLiiOcy1FRERERERiK4RNqptftOQygC1ijOkGnA1cApyGt+cfvIZ/AfD3WM1LPO99WEFZeYiszOj9OPMXlXDKhAwmjssA4KYbcpi7oJiLp3aPZzVFREREREQkCbSrAyB8icAzgIuBKUBGzVNABfAi3qH/r1hrq9szL9nXjGnZACxaFv0IgHUbqxg1vEvt9KgR6dzzcFU8qiYiIiIiIiJJptUdAMYbPnISXqP/XKBnzVN4o/+/gdfof8Faq+PNE6iwKEj/Pqm1092zUthVFExgjURERERERNov6KPD7pNJizsAjDFj8Q7vvwDYv+bh8P3HeI3+Z621uTGtobRZTnYqxSWh2uk9xSFyeqY28QoRERERERFxVbMdAMaY3wIXATUXWqxp9G8GngGesdau6pjqSXsMGRTgjbfLaqdXrq5k8KBAAmskIiIiIiIiidKSIwBuqfd3EfA88Fdr7b86pEYSM+dNzuJnd+az/J0yhg4OcN/sQmZ+b79EV0tERERERKRd/DTyfjJpyWUAq4B/AOcB/ay116nxn7zmzNvD0GPWA9C7VypzHujL9Jk7GHPaZs6clKkrAIiIiIiIiHRSxtqme06MMdnW2qL4VMdtxhgLENx2SKKrElMp/dYmugoiIiIiItIEbyx3sNaaZoomtZo21Y6tAxJdlQh9DvCGwkv2z7fZUwBa0vgPXxlgOJADdLfWLm1/1URERERERET2FWxmR7ZE15JTABpljBlkjHkGKARWAW8Bi8PPfcMYs8wYc2r7qykiIiIiIiIi7dHiywA2ZIw5Aa+x3426KwM0NAmYaIy50Vr7aFvnJSIiIiIiIlIj1HwRiaJNHQDGmP7Ai0AG8CVwK/AOkFuv2GrgAWAG8KAx5nVr7Rftq64ko1DesERXoUNobAOR2NP2QkRERCRx2noKwA+BHniN/COttS9Ya/PqF7DWlllrfwA8AqQCN7enoiIiIiIiIiLSdm3tADgdsMDN1tqyZso+EL4/ro3zkmZMvSqXVWv2Rn1u0auljBy/gf6j13Hbnflxrln7uJpLRGJP2wsREZHOJYhNqptftLUD4ODw/TstKLsufD+kjfOSRry/ooJbZ+Xz0tLSqM8X7Apy5Yw8HryrDx8sGcji10qZu6A4zrVsPVdziUjsaXshIiIi0nJt7QCoOdc/pwVle4fvd7ZxXtKI9z6soKw8RFZm9MU4f1EJp0zIYOK4DA4cEOCmG3J88cPX1VwiEnvaXoiIiIi0XFs7AFaE789uQdkJ4ftP2jgvacSMadncf0cfeuVEX4zrNlYxaniX2ulRI9JZv7EqXtVrM1dziUjsaXshIiLSOQVtct38oq0dAP/Eu/TfLGPMNxsrFL5awB144wW80sZ5SRsVFgXJyqy7QmP3rBR2FQUTWKPYcDWXiMSethciIiIiddrUAWCtfRZ4HsgE3jTG/M0Yc03N88aYC40xdwCrgIOAN6y1j8aiwtJyOdmpFJfUXSFzT3GInJ6pCaxRbLiaS0RiT9sLERERkTpp7XjtNKACuAy4GLgIaoc/fDZ8b/D2/F+zz6s7setv3r7PY2edmsmUU7NiOp8hgwK88XbdRRpWrq5k8KBATOeRCK7mEpHY0/ZCREQ6o4ULF7Jw4cJEV6NDhZov0mFeW1bBa69Gv/pQsjPWtu+EBWPMUcANwHBgGNAFWBu+/dVau6S9lXSFMcYCBLcdEtP3HXrMel78ywBG1jvPFSC/IMjwcRt44c/9GTo4wNlX5DLze/tx8dTuMZ1/R0l0rpR+a2P6fiICobxhHfK+2l6IiIg0zRjvlDhrrWmmaFKraVN9tblfoqsSYejAPCD5P9+2jgFQy1r7obX2WmvtCdbaftbaHGvtsdbay9X4j7858/Yw9Jj1APTulcqcB/oyfeYOxpy2mTMnZfqm8d+Qq7lEJPa0vRARERGJrt1HAEjLddQRANIxtEdPJPY66giARNP2QkREkp1rRwCs3dw/0VWJMGzgNiD5P982jQFgjEmx1obqTV/ektdZa//SlvmJiIiIiIiISPu0qgPAGDMauBY4GTis3lNzqBsAMOpLw8+rA0BEREREREQkAVrcAWCMuRCvoZ8O5DdS7D94HQM9w9MW+DfelQDeb3s1RURERERERDwhncneJi3qADDGXIbX+E8BdgAPRitnrR0XLn8o8Ee8IwW6Aw9Ya0tjUWFJPjr3VRLN1fPKXaTthYiIiEjiNHsVAGNMOnBXuOy/gK9Za2c19Rpr7efW2lOBPwBfBx6OQV1FREREREREpI1achnA7wAHANuAM6y1Ja14/58CXwGXGWO+0Yb6SQtMvSqXVWv2Rn1u0auljBy/gf6j13HbnY2duSEi0bj43XIxk4iIiHQ+QUxS3fyiJR0AJ+Ody3+Ptba8NW9ura2k7uiBb7e+etKU91dUcOusfF5aGv3sioJdQa6ckceDd/XhgyUDWfxaKXMXFMe5liL+4+J3y8VMIiIiItI6LekAGBq+f62N8/gofH9KG18vjXjvwwrKykNkZUZfjPMXlXDKhAwmjsvgwAEBbrohRz/oRVrAxe+Wi5lEREREpHVaMghgTQfAV02U+S2NXwZwTfj+gJZWSlpmxrRsABYti75Hb93GKkYN71I7PWpEOvc8XBWPqon4movfLRcziYiISOflp8Puk0lLOgBqfgE2WtZa+9MmXp8evu/a0kpJbBQWBenfJ7V2untWCruKggmskYgbXPxuuZhJRERERCK15BSAbeH7EW2cx/Dw/fY2vl7aKCc7leKSUO30nuIQOT1Tm3iFiLSEi98tFzOJiIiISKSWdAC8Fb6/uI3zOD98/782vl7aaMigACvXVNZOr1xdyeBBgQTWSMQNLn63XMwkIiIi7gpZk1Q3v2hJB8B8wADfM8Z8szVvbow5Gvg+3vgA/2x99aQ9zpucxbLlZSx/p4wtuVXcN7uQS8/tnuhqifiei98tFzOJiIiISKRmOwCstcuAt4FU4FljzOCWvLExpjfwDBAANob/lg42Z94ehh6zHoDevVKZ80Bfps/cwZjTNnPmpEwunqof9CJt4eJ3y8VMIiIiItI4Y21jg/fXK2TMCLxOgGxgL3AfcKe1tiRK2QDwQ+BnQE+8QQQnW2tfjVWl/coYYwGC2w5JdFViKqXf2kRXQTq5UN6wRFdBWkjbCxERkcQwxjtM3VofHa8eRU2b6sONAxNdlQhHHbQZSP7PtyVXAcBau9oYMxn4B9AX+AnwA2PMGmA13h7+AXgD/o0AuuOdNrAXmKbGv4iIiIiIiEhitagDAMBa+x9jzOHAvcBFQDfgSOCIesXq93a8BfzAWrsiBvUUERERERERkXZocQcAgLV2J3C5MeZWYCpwAnAQkAOUAjuA/wCLrLUfxLaqIiIiIiIiIhBs0Xj20lCrOgBqWGu3Ag+FbyIiIiIiIiKS5NrUASBSn6sDsLk4WJmry8pVLq6DIiIiIpI4zR43ER78r8N09PuLiIiIiIiIW0LWJNXNL1py4sQCY8w7xphJsZyxMeZkY8y7wAuxfN/OaOpVuaxaszfqc4teLWXk+A30H72O2+7Mj3PN2sfVXC5ydVm5mktEREREOqeWdACMwTtVYKkx5jNjzE+NMYPbMjNjzGBjzM+MMauAJUAqcExb3kvg/RUV3Dorn5eWlkZ9vmBXkCtn5PHgXX34YMlAFr9WytwFxXGuZeu5mstFri4rV3OJiIiISOfWbAeAtfZ/wLHAD4AewB3AV8aYT40xjxtjrjXGHGuMOdgYk22MSQnfHxx+fFq43KfAV8AsvKsG/BA41lr7cUeFc917H1ZQVh4iKzP6Ypy/qIRTJmQwcVwGBw4IcNMNOb5opLiay0WuLitXc4mIiIi4IohJqptftGgQQGutBR40xjwCnA98DxgLjASubsFb1Hwi7wIPAM9ba6tbX12pb8a0bAAWLYu+l3LdxipGDe9SOz1qRDr3PFwVj6q1i6u5XOTqsnI1l4iIiIh0bq26CkC40T4XmGuMGQBMAk4CjgP64x0hUGMPsA14D3gdeC18+UCJk8KiIP37pNZOd89KYVdRMIE1ig1Xc7nI1WXlai4RERERcVubLwNorc0F/hK+AWCM6QLsBxRaayvaXz1pj5zsVIpLQrXTe4pD5PRMbeIV/uBqLhe5uqxczSUiIiLiF0HbkuHspKGYfmrW2r3W2m1q/CeHIYMCrFxTWTu9cnUlgwcFElij2HA1l4tcXVau5hIRERERt6nbxGHnTc5i2fIylr9TxpbcKu6bXcil53ZPdLXazdVcLnJ1WbmaS0RERETcpg4Ax8yZt4ehx6wHoHevVOY80JfpM3cw5rTNnDkpk4un+rOR4mouF7m6rFzNJSIiIuJHIVKS6uYXxhvgX+LBGGMBgtsOSXRVpAVS+q1NdBViLpQ3LNFVkFZwcR0UERGRxDDGuzCbtdY/16yLoqZN9eb65GpTnTjkCyD5P1//dFWIiIiIiIiISJu1+SoAIiIiIiIiIokQJKl3tCctHQEgIiIiIiIi0gnoCIAEuP7m7fs8dtapmUw5NSsBtWk/nafsH1pWIh1D42tIImnbLonm6jbQz9+thQsXsnDhwkRXw1nvvFrCf14rTXQ12kSDAMaRq4MA+nnjKCISC67++BV/0P9hSTRXt4GufbdcGwRw6boRia5KhFMPXg0k/+cbk1MAjDGvGWPOjsV7SetNvSqXVWv2Rn1u0auljBy/gf6j13HbnflxrpmIiLSVq9t25fJXLpFE0vdKJPZiNQbAROCAGL2XtND7Kyq4dVY+Ly2NfvhJwa4gV87I48G7+vDBkoEsfq2UuQuK41xLERFpDVe37crlr1wiiaTvlUjH0SCAPvbehxWUlYfIyoy+GOcvKuGUCRlMHJfBgQMC3HRDjjaOIiJJztVtu3L5K5dIIul7JS0RwiTVzS/UAeBjM6Zlc/8dfeiVE30xrttYxajhXWqnR41IZ/3GqnhVT0RE2sDVbbtyefySSySR9L0S6TixvArAmcaY3q0ob621s2I4f2mgsChI/z6ptdPds1LYVRRMYI1ERKS9XN22K5eItJS+VyJtF9MOAGByK8pbQB0AHSgnO5XiklDt9J7iEDk9U5t4hYiIJDtXt+3KJSItpe+VAAR1MHubxLID4I/Aghi+n7TTkEEB3ni7rHZ65epKBg8KJLBGIiLSXq5u25VLRFpK3yuRtotlt8kX1tp/teYWw3lLFOdNzmLZ8jKWv1PGltwq7ptdyKXndk90tUREpB1c3bYrl4i0lL5XIm2n4yYcM2feHoYesx6A3r1SmfNAX6bP3MGY0zZz5qRMLp6qjaOIiN+4um1XLhFpKX2vpKGgTUmqm18Ya23738SYEHCjtfZP7a+Su4wxFiC47ZBEVyWmUvqtTXQVREQSKpQ3LNFVkE5M/4cl0VzdBrr23TLGu1SdtdY/16yLoqZN9eJXoxNdlQhnD/0USP7PN1ZdFU8Bq2P0XiIiIiIiIiISYzEZBNBa+51YvI+IiIiIiIhIc0I6m71NYtIBYIw5BBgavnUF1uINCqijAkRERERERESSQLs6AIwxE4A7gOMbef4jYKa19o32zMc1hyy/MtFViKmvLkp0DaSz07mHkmhaVv7h6vZCJJG0DRTxjzZ3ABhjfgj8vmYSKAPWA1XA14As4CjgVWPMzdbaP7SvqiIiIiIiIiIQTO6x9pJWm06cMMYcC/wWr+H/HnCKtTbLWjvaWvsNa20PYALwTrjMXcaYY2JVaalT8dUWttz6EBum3cHOR/+BrQ7uU6Zy83a23j6b9Vf9mm13PkWorCIBNRXxn6lX5bJqzd6ozy16tZSR4zfQf/Q6brszP841E5Fko+2FiIj4QVtHTrgBCADvAydaa19rWMBa+2/gpHCZNODGtlZSorPBINvveYbss09g4L0/pLpgN7sXvRVZprqavLv/Sva3TmDQ/T8mtUcmRYveTlCNRfzh/RUV3Dorn5eWlkZ9vmBXkCtn5PHgXX34YMlAFr9WytwFxXGupYgkA20vRETET9raAXA0YIGfWWsrGysUfu5neEcBHNXGeUkjyleuI61XT7LGjia1RybZ55xIydufRpQp+2gNXQ8ZSOZRI0jtkUnv66bS46SjE1NhEZ9478MKyspDZGVG30TOX1TCKRMymDgugwMHBLjphhz9oBfppLS9EBFJjCApSXXzi7aOAXBw+P6/LShbU+aANs5LGlG9fRfpA/vWTqcP7EvVjl0RZfZuzMOkB9h6+2wqN2+n22FD2P+6qXGuqYi/zJiWDcCiZdH36K3bWMWo4V1qp0eNSOeeh6viUTURSTLaXoiIiJ+0tatic/j+oBaUHRC+/18b5yWNCJaWY7qm106ndEvHVlRGjAMQ3F1Kybuf0uvbp3PQQ7eQ0q0L+U8tSkR1RZxRWBQkK7Nu4JnuWSnsKtp3/A0REW0vREQkmbS1A+A5vMP6r29B2avC9zrxPMZSM7thy+sGHAqV78V0CWDSUmsfM4E0Mo8+lK7DBpGS0ZWeZ46l/JMvE1FdEWfkZKdSXBKqnd5THCKnZ2oTrxCRzkrbCxGRjhGyKUl184u21vQ3wGpgujHmLmNMj4YFjDHpxphbgFuAjcDdba+mRJPWdz8qN++ona7cvJ20PvtFlundE1tVXTttgyFMepuv/igiwJBBAVauqRv+ZOXqSgYPCiSwRiKSrLS9EBGRZNLWluBpwH3AXXgN/OuNMf8CNgCVwBDgeKBfuPybwPeNiX6tRmvt/7WxHp1at5EHs3PXC5S8+yndRh5M0T+W0/2bh0eUyTp2JIXPvUb55xtIH9iXohf/TcZRhyaoxiJuOG9yFj+7M5/l75QxdHCA+2YXMvN7+zX/QhHpdLS9EBGRZNLWDoD5eFcBqGnR9wSmhB+j3uM1rmjkfUz4NeoAaAOTlkrfH19G/mMLyH/8RTKPPYyek79J8fKPKHz+dQY9cDNpvbPZ/4ZzyZ+9gOCeEjKOGkGvS09NdNVFfGfOvD38+p4Cvnp/CL17pTLngb5Mn7mDwt0hrrm0BxdP7Z7oKopIktD2QkSk4/lp5P1kYqy1zZdq+CJjnqSusd9u1trvxOq9kpkxxgIcPHdWoqsSU19ddFuiqyCdXChvWKKr0CFS+q1NdBVEnOPi9kLbChFpiZqjsa210Q/L9omaNtUzXxyT6KpEuOyQ94Hk/3zbdASAtfbqWFdERERERERERDqORoMTERERERERXwkm9472pKUTJ0REREREREQ6AR0BkACPHft0oqsQY26OAaDzRP3D1VwucvF7BW6ug1pWkmhaB0VEYk8dACIiIiIiIuIrIR3M3ib61Hxu9ccVXD95I1O//iX3/CSP6qp9L87wynO7+fYJ6zn3qK/448+3Ry0j8TP1qlxWrdkb9blFr5YycvwG+o9ex2135se5ZiL+pe+Vf2hZSaJpHRSRzkwdAD4WrLbcfv1WLp6+H0+9Opid26p57vHCiDKfrShn9l35/PKR/jyx5CC2ba7iuScKG3lH6Ujvr6jg1ln5vLS0NOrzBbuCXDkjjwfv6sMHSway+LVS5i4ojnMtRfxF3yv/0LKSRNM6KCKiDgBfW/FOGX36B5h4Vneye6Vx2ff24/UX90SUeWdZCRPOzOJrh3Ulp3ca37m5N28u1D+zRHjvwwrKykNkZUb/2s1fVMIpEzKYOC6DAwcEuOmGHP3wEGmGvlf+oWUliaZ1UMQtQZuSVDe/8E9NZR+5m6oYPCy9dnrI8C5s21wVUaZyryU1re4SGYGAYXtuZBmJjxnTsrn/jj70yon+tVu3sYpRw7vUTo8akc76jVpWIk3R98o/tKwk0bQOioioA8DXincH6VavFzsjM4XyUhtxjv9R4zP518vFbPqqkqKCav78+3z2lmsMgGRUWBQkK7Ous6Z7Vgq7ioIJrJGI/+l75R9aVpJoWgdFpDPQVQB8rHvPVHI31fVMl5aE6NrNkBao++d17ImZTL0ihx9fspnUNMOUS3uyfnVlIqorzcjJTqW4JFQ7vac4RE7P1ATWSMT/9L3yDy0rSTStgyL+EsI0X0j2oQ6ABLj3p9v3eey4SZmMPTmrVe/Tf2CAJc/XnfO/Yc1e+g8KRJQpKwkx+ZKeXPrd/QD4YHkpqz+paEOtpaMNGRTgjbfLaqdXrq5kcIPlKSKto++Vf2hZSaJpHRSXLFy4kIULFya6Gs76+PUCPn5jV6Kr0SbqAEiAm37TNybv8/WxGdw9M483XyrmyOO78cxDu5j0rR4RZQp2VHPLt7dw11MH0C0zhSfvzeeKH/SKyfwlts6bnMXP7sxn+TtlDB0c4L7Zhcz83n6JrpaIr+l75R9aVpJoWgfFJVOmTGHKlCkRjz322GMJqo17jjipF0ecFNmm+ve8fXfyJqMWjQFgYqSjw3Q2aQHDrx8dwN8e3sVVkzbQZ0AaF0zLYcn83Vw2fh0AAw9O51tXZPPDizbz40u2cOq5PTjupNYdaSAdZ868PQw9Zj0AvXulMueBvkyfuYMxp23mzEmZXDy1e4JrKOI/+l75h5aVJJrWQRH/SvSo/369CoCxtvkB4YwxIaC9I8dZa22nPuLAGGMBXls/LNFViamTBq9JdBU6RCjPreUEkNJvbaKrIJ2ci98rcPO7pWUliaZ1UCS2avbHWmt9vWO2pk31yOoTEl2VCDeM+BeQ/J9vS7sqngf2AqadNxERERERERFJgBbtkbfWXmiM6Q38HZiIdzTAtcCXHVg3ERERERERkX0EdUX7NmnxIfnW2nxjzKXARiAAfGCt/bTDaiYiIiIiIiIiMdOqbhNr7Xbg1Q6qi4iIiIiIiIh0kLYMyvcpcGasK9KZZKeUJ7oKMeXqID0iEnsa/Mo/XF1WLv7PcnVZuZpLRGIjlNxj7SWttnQAPAa8C6yPcV1EREREREREpIO0euQEa+06a+2L1tqSjqiQNG3Vx3u59Iw8TjpiK7+euYuqqn2vzvjivBLO/mYuk76+ld/cVlemoiLEr2/ZxUlHbOX0Y3J55N7d8a5+q0y9KpdVa/ZGfW7Rq6WMHL+B/qPXcdud+XGuWfu4mktEpDPTtl1ERPxAQyf6SHW15cfXFnDVd3sw//V+bN8W5K+PFUeU+fSjvfzxN7u5+9HePLesH7mbgzzzuFfm2T+XsHVzNfNf78ddD/Vi0fxSFr1QmogoTXp/RQW3zsrnpaXR61awK8iVM/J48K4+fLBkIItfK2XuguKoZZOJq7lERDozbdtFRBIjSEpS3fzCPzUVPnhnL30HpHLqlAxyeqUybUYPlrxYFlHmzWXlnHxmN4aPTGe/3ql895aeLA2XWfHeXi68IoucXqkcOaYL37ookw//E31vRSK992EFZeUhsjKjr57zF5VwyoQMJo7L4MABAW66IccXP6ZczSUi0plp2y4iIn7SljEAJEG2bKxm6LBA7fTQ4QG2bqqOKFO515IWqBsQI5AOeblBAH56Rw7Zvep+oKz9vIqjju3SwbVuvRnTsgFYtCz63pR1G6sYNbyu3qNGpHPPw1XxqFq7uJpLRKQz07ZdRET8REcA+Ejx7hAZmXWN+4xMQ1mpjRgH4LjxXXltcTnrv6yisCDIw/fsoaLce77/gWl065bCnt0hfnlzAVs2VnP61Iy452ivwqIgWfU+h+5ZKewqCiawRrHhai4Rkc5M23YRkY4RsilJdfML/9RU6N4zhbKSusZ+aUmIrt0MgXp7/MdN7MaFV2Zx/cU7uGzydkYenk7vPnWL+c2l5Zx30jaC1fDYvD70zE6Na4ZYyMlOpbgkVDu9pzhETk//5WjI1VwiIp2Ztu0iIpJM1AHgIwcMSuXLtXWHDX61pooDBkWexVFaEuLcSzNZ+t8DWPyfARx6eICvHZoOeI3/X9+yi1/dux//d18vMrP8ufiHDAqwck1l7fTK1ZUMHhRo4hX+4GouEZHOTNt2ERFJJv5sAXZSY8Z2ZXtuNUtfKqOwIMgTDxZzRoND+PN3BLn0jO18tbaKbVureeT3ezjn4kwAHr9/Nzfdns3YCd0SUf2YOW9yFsuWl7H8nTK25FZx3+xCLj23e6Kr1W6u5hIR6cy0bRcR6RhBTFLd/EIdAD4SCBh+/1hvnnxoD+dOzKPfgFS+fW13Fj5fypRxuQAcdHCAC6/M4toLdnD9RTuYfG4G4yd5Df71X1bzi5t2cdRBm2tvv7y5IJGRWmzOvD0MPWY9AL17pTLngb5Mn7mDMadt5sxJmVw81Z8/plzNJSLSmWnbLiIiycpYa5sv1fBFxjwBWGvttBaWnw3ssNb+v1bPzCHGGAvw4caBia5KTB2Z3jXRVZAWSum3NtFVEBFJqFDesERXIea0bReRljDG20ttrfXP7uooatpUd392aqKrEuGWw5YCyf/5tvUygFcDFmhRBwBwMVAJdOoOABEREREREWk/P428n0xa1AFgjBkNHBnl8W9Dkyc8GOAbQBZQ0ob6iYiIiIiIiEgMtPQIgHOAX9SbrjlvYE4r5vVWK8qKiIiIiIiISAy1tANgA7C83vSJeJ0Ay6MVjuIrIjsQOjXXzpl39dxDF88TdTGTy1z9brlI3y0REZH48tPI+8mkRR0A1tqngadrpo0xofDjJ3VQvUREREREREQkhto6csIcWnf4v3SgqVflsmrN3qjPLXq1lJHjN9B/9DpuuzM/zjWTaFxcXi5mAndziT+4uv4pl79yiYgkq5BNSaqbX7Spptbaq62134l1ZaR13l9Rwa2z8nlpaWnU5wt2BblyRh4P3tWHD5YMZPFrpcxdUBznWkoNF5eXi5nA3VziD66uf8rlr1wiIuKmmHRVGM8IY8zxxpjkuiCjw977sIKy8hBZmdEX4/xFJZwyIYOJ4zI4cECAm27I0Y+OBHJxebmYCdzNJf7g6vqnXP7KJSIibmpXB4AxZpAx5hmgEFiFN9L/4vBz3zDGLFOHQMeZMS2b++/oQ6+c6Itx3cYqRg3vUjs9akQ66zdWxat60oCLy8vFTOBuLvEHV9c/5fL4JZeISLIL2pSkuvlFm2tqjDkB+Ay4GOgBmHq3GpOAxcaY69tTSWmbwqIgWZl1i6N7Vgq7ioIJrJE0xcXl5WImcDeX+IOr659yiYiIdLw2dQAYY/oDLwIZeJf4Ox8Y0KDYauCB8DweNMYc0o56tkn41IQrjTFvG2N2GWPyjDGvGWPOaqT8GcaYd40xJcaYAmPMi8aYrzfx/q0qH2852akUl4Rqp/cUh8jpmZrAGklTXFxeLmYCd3OJP7i6/imXiIi4rr3tx9a2b6Np6xEAP8Tb678aONJa+4K1Nq9+AWttmbX2B8AjQCpwcxvn1R5/AZ4CvgGsBbYCJwALjTG/rl/QGHMhXqfGccAGoByYArxrjBnb8I1bWz4RhgwKsHJNZe30ytWVDB4USGCNpCkuLi8XM4G7ucQfXF3/lEtERFojhEmqW3Ni1H5scfu2MW3tADgdsMDN1tqyZso+EL4/ro3zahNjzGTgMryxCYZaa4+z1h4FjAZ2ALcZYw4Nl90PeBqoAsZaa0dZaw8Evg90AZ4wxqTUe+9WlU+U8yZnsWx5GcvfKWNLbhX3zS7k0nO7J7pa0ggXl5eLmcDdXOIPrq5/yiUiIq6KRfuxNe3bprS1kXpw+P6dFpRdF74f0sZ5tdWE8P2d1trcmgettauBh/GyfzP88CV4H/wsa+279co+ACwBRgAT6713a8vHzZx5exh6zHoAevdKZc4DfZk+cwdjTtvMmZMyuXiqfnQkExeXl4uZwN1c4g+urn/KJSIinUQs2o+tad82ylhrW1d1wBizBvga8DVr7fp6j4e8OtjUeo8dAGwG1ltrh7Z6Zm1kjHkI+C5wqbX22QbP/Rz4NXCTtfYPxpi3gHHAYdbazxuUvQ54FLjbWjsz/Firytd7zgIEt8V9OIQOldJvbaKr0CFCecMSXQXp5Fz9brlI2wtJJG0rRKQljPEOU7fWNn+8ehKraVP9v0+mJrgmkWYdvgCI/vm2tf3YoFyL27dN1bOtRwCsCN+f3YKyNT0Vn7RxXm21IHx/mzGmdoBCY8wIvA+uCng5/PAQYHfDhRFW00NT/3CK1pYXERERERGRzikW7ccF4fuWtG8b1dYOgH/iXe5vljGm0cMMwlcLuANvvIBX2jivNrHWLsM7p2IY8GV4tMX/Ap/iXb3gEmvtauN1hfUBChp5q/zw/SDwRl5sTXkRERERERHpnGLVfmxp+7a5+qS1tOINZv6sMeYcvMv/vWmMmQe8VvN8eITDI4DpQDbwurX20bbMq51ygT1ALyIHIdwJbAn/3Qvvcyhs5D12he8z21heREREREREYijknzMZYtl+bEn7tklt6gAImwZU4I1EeDFwEd6efoCacxIM3p7/a9oxnzYxxnwb7zIJXwBXAW/h9Yx8C/gN8JoxZjze+ARNqRnPINRkqVaUP+a0TS18K5j27Z5cd3nPFpdPBJ376h+unifq6jroai7xB20v/MPFTKB1UBLPD+vg7NmzmT17dqKr4awVz69nxfwNHT2bFrU3W9q+tdauaPRNaOMggA0qchRwAzAc73CELnjXJFwL/NVau6RdM2hbndLxGvbdgdHW2q8aPH8RMBdYDJwFVAKbrbUHR3mvA8Pv9S9r7YTwIRwtLt/gOScHART/8MM/srbQjymR2NP2QhJN66AkmmvroGuDAP7s43MSXZUIdx7xD2Dfz7c97cd6ZVrcvrXWTm6qnu2+Vr219kNr7bXW2hOstf2stTnW2mOttZcnovEfNhzvPIv3G344YfOBvcA3rdcDsgPvMIpoah7fBt4lDlpTPh6mXpXLqjV7oz636NVSRo7fQP/R67jtzvyoZZKVcvkrl4tcXFYuZgI3c7mYyWUuLi8XM7nK1WXlai6JjSApSXVrTIzajy1u3zb3ubW7AyBJ1Rw+URrtSWttNd4HFAw/tA7oYYwZFaX42PD9+nqPtbZ8h3h/RQW3zsrnpaVRY1KwK8iVM/J48K4+fLBkIItfK2XuguKOrla7KZe/crnIxWXlYiZwM5eLmVzm4vJyMZOrXF1WruaSTq297cfWtm8b1WwHgDEm2IZbtTHmK2PMi8aYk5ubRwdYg/cBjDHG7DOYQvi0hR7A/8IP1YxZEO04kqkNyrSlfId478MKyspDZGVGX4zzF5VwyoQMJo7L4MABAW66IccXG0fl8lcuF7m4rFzMBG7mcjGTy1xcXi5mcpWry8rVXNKptbf92Nr2baNacgSAacMtBe9ah5OBJcaYe1own5gJ94A8BewPPGmMqR1FzxgzDHgyPPlE+P5ZvAENf2aMOa5e2RnAqXiHWnxSbxatLd8hZkzL5v47+tArJ/piXLexilHDu9ROjxqRzvqNVR1drXZTLo9fcrnIxWXlYiZwM5eLmVzm4vJyMZOrXF1WruaS2ApZk1S3ZrSr/diG9m2jWnIVgKtaUKY+A/QEDgTOBQ4GfmSMWWStfaOV79UeNwPHAhcAZxhjVuJdWuFQvNxPWWufAbDWFhpjrgD+BrxrjPkU2A84AO9cjKvrv3FryydKYVGQ/n1Sa6e7Z6Wwq6jZo0KSnnJJorm4rFzMBG7mcjGTy1xcXi5mcpWry8rVXOKu1rQfjTF9gDnhySuttTvCf7e4fduUZjsArLVPtzRYQ8aYW4Fn8C4R+CMgbh0A1toSY8wY4Id4h1WMBMqA14E/WWsXNCj/nDGmGPglMBrvEIsXgZuiDbTQ2vKJkJOdSnFJ3dUk9hSHyOmZ2sQr/EG5JNFcXFYuZgI3c7mYyWUuLi8XM7nK1WXlai5xWyvaj92A0+v9XfP6VrVvG9OSIwDazFobMsb8Cq8D4LCOnFcj868G7gnfWlL+FeCVVrx/q8rH25BBAd54u6x2euXqSgYPCiSwRrGhXJJoLi4rFzOBm7lczOQyF5eXi5lc5eqycjWXtE7Ih+PZt6T9aK3diHdUfbTnWtW+jSYen1rNdTn6xGFeUs95k7NYtryM5e+UsSW3ivtmF3Lpud0TXa12Uy5JNBeXlYuZwM1cLmZymYvLy8VMrnJ1WbmaSyQe4tEBUHMtwi/jMK9Ob868PQw9xruCRO9eqcx5oC/TZ+5gzGmbOXNSJhdP9efGUbkk0VxcVi5mAjdzuZjJZS4uLxczucrVZeVqLpF4M9bajntzY07HG4mwH/CYtfaGDpuZDxhjLEBw2yGJrop0Uin91ia6Ch0ilDcs0VUQcY62F5JoWgcl0VxbB43xjiq3tvkh65NZTZvqhysuSnRVItz39b8Dyf/5xvwIAGPMVcaYDcaYcmAR0B+oGexARERERERERBKgI04B6AEMArrgDV7wFXCWtTavA+YlIiIiIiIiIi3QEVcB+A/e3v5dwErgP9baig6Yj4iIiIiIiHRCoeQ+0j5pxbwDwFr7PvB+rN/XJQ8UDUp0FWJqRvamRFdBWsjV8w5dO0fPZa6ugy7SspJEc3UddPF/lqvLSsRF/rt4ooiIiIiIiIi0mjoAfGbTp3u4+9z3+Nlxy5n7/z4nWBXap8zypzfxy5Pe4idHvcmj1/2Pou3eGRilhVX8ecYn3DrmTX416W3efW5rnGvfOlOvymXVmr1Rn1v0aikjx2+g/+h13HZnfpxr1j4u5nIxk/iLi+ugi5lAufyUy8VM4G4uF2lZSVNCNiWpbn7hn5oKweoQT3zvEyZNG8zPFh9PYV4FbzwVefh97ppiXn5gHd+5/3B+vmws3Xuns+gPXwHw8oPrwMDNLxzLhb8awfxZa9mxviwRUZr0/ooKbp2Vz0tLS6M+X7AryJUz8njwrj58sGQgi18rZe6C4jjXsvVczOViJvEXF9dBFzOBcvkpl4uZwN1cLtKyEuk46gDwkS/eKyS7Xxe+cWZfsvZL59QbBvPRou0RZdIz0khJDQ+IYcBa6NbDG+ph/YoiJl49iN4Du3HoN3sx+MgebPuiJN4xmvXehxWUlYfIyoy+es5fVMIpEzKYOC6DAwcEuOmGHF9s9F3M5WIm8RcX10EXM4Fy+SmXi5nA3Vwu0rIS6TgdcRUA6SD5m8rpf0hW7XT/Q7Io2FweUab3wG6cdM1B3HvBBwDk9O/KTxcfB8AtLxwLQLAqRN5XpRRsruDAkd3jVPuWmzEtG4BFy6L3+q7bWMWo4V1qp0eNSOeeh6viUbV2cTGXi5nEX1xcB13MBMpVww+5XMwE7uZykZaVtEQQXQWgLXQEgI+U76kmPSO1drpLZip7y4IR4wBsXrWHfz+zhR8/P4Y73zuBg4/O5h+/iRxt9r5L/8vd57zP6JP3p9cB3eJW/1gpLAqSlVn3he+elcKuomACaxQbLuZyMZP4i4vroIuZQLn8xMVM4G4uF2lZibSdOgB8pFuPNPaWVtdOV5QESe+WQmqgbjF+smwnoyb2ZuDIHmT0CHDytQfx8ZIdWGtry9w0bww/fn4Ma9/ZxQf/3BbXDLGQk51KcUldp8ee4hA5PVObeIU/uJjLxUziLy6ugy5mAuXyExczgbu5XKRlJdJ26gDwkV4Du7Hty7pDofK+KKHXgZF78EMhi6l/NIyFYLXFGMNTP/qUwm0VGGMYOLIHI0/qzeZVe+JU+9gZMijAyjWVtdMrV1cyeFAggTWKDRdzuZhJ/MXFddDFTKBcfuJiJnA3l4u0rAQgZE1S3fxCHQA+MuzYHIq2VbDi5e2U7Kpk6aMbOGpKv4gyoyb25n9LdrDl82LKdlfx6mMbGHXS/gDs3rGXZY9uoGh7BetX7GbF4u0MPjI7AUna57zJWSxbXsbyd8rYklvFfbMLufTc5BvLoLVczOViJvEXF9dBFzOBcvmJi5nA3Vwu0rISaTt1APhIaiCFax48nGWzN3Dnme+S078rE68axPsLtvHrk98GYMjXs5ny46/x5A8+5denvEMoBOf+bBgAF/5yBNvWlnDnGe/y9I9XcvyFB/CNM/smMlKLzZm3h6HHrAegd69U5jzQl+kzdzDmtM2cOSmTi6f6c6PvYi4XM4m/uLgOupgJlMtPXMwE7uZykZaVSGyY+ueGS8cyxliA+z6flOiqxNSM7E2JroJ0cin91jZfSJJCKG9YoqsgIpJQLv7PcnXb7tqyMuHzhK310fHqUdS0qa794IpEVyXCY2OeBpL/89URACIiIiIiIiKdgDoARERERERERDqBtERXQERERERERKQ1QiT1kfZJSx0ACTAla1WiqxBjGnTFL7427/pEV6FDfHmhm+ceioiIe1w9X95FWlbiIp0CICIiIiIiItIJqAPAZz75XxVTTs/nqNHb+ektu6mq2vcqDs//vYwTx+1kzBHbuf1ndWVWraxi6MC8iNt/P6iMd4QWm3pVLqvW7I363KJXSxk5fgP9R6/jtjvz41yz9nEp196Nm9j6u3vZ+NPb2fnsPGwwuE+Zrb/9Pet/cHPtbefc5xJQ07ZxaVnVcDETuJnLxUygXH7K5WImcDOXi5nAzVwuZkqUoDVJdfMLdQD4SHW15YZphVz/3UyWvrk/23KDPDG7NKLMio8q+e2dxTw8O5uXX+vN5s1B/vyYV2bzpiBTvtWVrzb3q70dPSY9EVGa9P6KCm6dlc9LS0ujPl+wK8iVM/J48K4+fLBkIItfK2XuguI417L1XMtlg0G2P/EUPU8+iQNvm0mwqIjdry/fp1xVwS4Ouuc3DPnjPQz54z3sf/EFCaht67i2rMDNTOBmLhczgXL5KZeLmcDNXC5mAjdzuZhJ/EkdAD7y7tuV9B+Qyllnd6NXrxRu/H4WC/9ZEVHm1aV7OWNyVw4bGaB371R+PLM7L73oldm0KciBB6Ymouqt8t6HFZSVh8jKjL56zl9UwikTMpg4LoMDBwS46YYcX2wgXctV/sWXpGVnk/WNI0nNyiL71JMp/WhFRJlgSQkpXbqQEggkqJZt49qyAjczgZu5XMwEyuWnXC5mAjdzuZgJ3MzlYibxJw0C6CObNgYZNrxukQ0bnsbmTZGHXO+tsKQF6g5BCQQgd6tXZvPGalZ+Ws3cw7fTpYvhkm9n8L0fZMWn8q0wY1o2AIuWRe8hXbexilHDu9ROjxqRzj0PV8Wjau3iWq7q/AIC/fvVTgf696OqYFdEmar8ArCWrb/9PVUFu+h2yFB6XXgeaT17xru6reLasgI3M4GbuVzMBMpVww+5XMwEbuZyMRO4mcvFTIkWstqX3Rb61Hxk9+4QGZl1jfvMLENpqY0YB+CbE7rwyqIKvvqymoKCEPfeXUJ5ufd8aqrhuLHpvPav/Xl8Tg7z5pbxyuKKfeaT7AqLgmTV+xy6Z6Wwq2jfc8/9xm+5QmXlpHSp+0eV0qULdu/eiHEAQhUVBPr3Y/8rL2PgL28jJSOD/L8/n4jqxpTfllVLuJgJ3MzlYiZQLj9xMRO4mcvFTOBmLhczSXLSEQA+0rNnCps21m0ISoot3boZAvX2+J84sQuXX5XBZRftIjUVLv12BmtWVwPwy1k96t4rO4XLrsjgX8v3cvqZXeMXIgZyslMpLgnVTu8pDpHTM/lPbWiO33KlZHTz9vCHhSr2YtIDmNS6OmeMGE7GiOG10/udPZnNv7oDGwphUvzb/+i3ZdUSLmYCN3O5mAmUy09czARu5nIxE7iZy8VMkpzUAZAAP/vJ7n0em3RyFyad0nRDfOCgVOY/V147vXZNNQMHRW4YSkpCXHxZBtO/5x3a/6839/LJx97hQ0/MLuXcC7qRk+M1vIoKLV27+mfEyhpDBgV44+2y2umVqysZPMhf55hH47dcab16UfL+B7XTldu2kdarV0SZ8tVrITWFbod8DYBgaRmkpvm68Q/+W1Yt4WImcDOXi5lAufzExUzgZi4XM4GbuWKdaeHSkkYHHHRFKIEj72/59ya2vLUpYfNvD3//CvepO3/bc59bc41/gOPHpbMtN8iiheUUFIR4+IESvnVO5Ot27ghx1mn5rF1TRe7WIH+4p4SLL+0GwPvvVTLrl3vYsT3ImtVVvLignFNP7xJtVkntvMlZLFtexvJ3ytiSW8V9swu59Nzuia5Wu/ktV7dhX6O6cDclK/5HsKSE3cteI+uob0SUCVVWsvPpv7F302aCZeXsfvV1Mg8flaAax47fllVLuJgJ3MzlYiZQLj9xMRO4mcvFTOBmrlhnmnJqFo/e0zfiJrFz4PhBHPfTb0bc/EIdAD4SCBgeeTybPz1YyikTdtL/gFSuuS6T+c+VM2HsTgCGHJzGFVdlcMn5u7j0wl2cc35XJk7yOgl+fUcPCgpCTDohnxumFfGDm7I47nh/dADMmbeHocesB6B3r1TmPNCX6TN3MOa0zZw5KZOLp/pzo+/nXCY1lb7TrmL3stfZcsdvSc3JoedJEyh+/wM2/+oOADIPH0X3sceR9+gTbPm/O8EYep37rQTXvG38vKwa42ImcDOXi5lAufzExUzgZi4XM4GbuVzMJMnPWGubLyUxYYyxAF9t7tdcUV8ZnKaNk198bd71ia5Ch/jywkcTXQURERGRpJba/wsArE3gsfMxUNOmuvQ/0xJdlQh/O+5xIPk/Xx0BICIiIiIiItIJqANAREREREREpBPQVQBERERERETEVxJ5FQA/0xEAIiIiIiIiIp2AjgBIgE8r3boMx+C0suYLSVJY9/0fJ7oKHSKUp0EA/SKl39pEV6FDhPKGJboKIuITrm4HXaRtu7hIHQAiIiIiIiLiKyGrg9nbQp+az3zxSRk/mrKGy49ayYM/3Ux11b6XcVz45E6mffMzLj78U379nXUU5FUBUFxUzV3f3cClR3zK1cevYu79efGufqtMvSqXVWv2Rn1u0auljBy/gf6j13Hbnflxrln7uJrLRS4uKxczucrVZaVc/snlYiZwN5f4g9Y/STR1APhIsNrymxvWc971fXhw6Qjyt1Xyzyd2RJTZsLqcZ+/L49aHB/PoG4eS3TuNv96zDYDnHtqODVkeenUEv5ozlCV/K+DTd0sSEaVJ76+o4NZZ+by0tDTq8wW7glw5I48H7+rDB0sGsvi1UuYuKI5zLVvP1VwucnFZuZjJVa4uK+XyTy4XM4G7ucQftP5JslAHgI988m4Jvfun882zcujZK40LbuzLvxcWRZTpmpFCapo3IqYxYC1k9kgF6u5rmBTolpV8q8B7H1ZQVh4iKzN63eYvKuGUCRlMHJfBgQMC3HRDji82kK7mcpGLy8rFTK5ydVkpl39yuZgJ3M0l/qD1L/ZC1iTVzS80BoCP5G3ay6BhXWunDxrWlbzNlRFl+g3qwjnX9eHmc74AYP8BAR5cNgKA86f35UdT1nL1cZ8BMPXa/fna6Iw41b7lZkzLBmDRsug9pOs2VjFqeJfa6VEj0rnn4ap4VK1dXM3lIheXlYuZXOXqslIujx9yuZgJ3M0l/qD1T5JF8u3+lUaV7A7SNaNukXXLTKWiNBQxDsCXn5ax6Ol87llwCH9dMYrDxmTyxP9tBeAvd29jwJAuPP3fkdy3eBhvLypi1QfJdwpAcwqLgmRl1vWydc9KYVdRMIE1ig1Xc7nIxWXlYiZXubqslMs/XMwE7uYSf9D6J/GiDgAfyeqZSnlpqHa6rCRIl24ppAXqNhbvLd3NmEk9+NroDLJ6pHLeDX155+XdWGv590tFnHt9H3rkpDF4eDfGnZnNu6/sTkSUdsnJTqW4pO5z2FMcIqdnahOv8AdXc7nIxWXlYiZXubqslMs/XMwE7uYSf9D613ohTFLd/EIdAD7Sb2A6m9ZW1E5vWltB34HpEWVCIe/c/xrWWoJBizGGUNA2eA5CwX2vIpDshgwKsHJN3akPK1dXMnhQIIE1ig1Xc7nIxWXlYiZXubqslMs/XMwE7uYSf9D6J/GiDgAfGX18d/K3VfLWoiJ2F1Tz3MPbmfCt7IgyY07qwTuLd7Pus3JKdlcz/5EdHDOpBwDHnNyTFx7dQcnuajasLudfCws59pSeCUjSPudNzmLZ8jKWv1PGltwq7ptdyKXndk90tdrN1VwucnFZuZjJVa4uK+XyDxczgbu5xB+0/km8qAPAR9IChp8+MoTn/7SdG09Zzf790/nWNX14ff4urpvwOQAjjsrkip/057c3buC6CZ9jQzDt9gMAuPIn/emakcINE1dzx3XrueDGvhwxzh8bljnz9jD0mPUA9O6VypwH+jJ95g7GnLaZMydlcvFUf+RoyNVcLnJxWbmYyVWuLivl8g8XM4G7ucQftP61T6JH/ffrVQCMtf47BNyvjDEWYMFXRyS6KjE1JaMs0VWQFkrptzbRVegQobxhia6CtJDWQRHp7FzdDrrItW17an/vKmHW+qi1GkVNm2rqW99NdFUiLPjmw0Dyf746AkBERERERESkE0hLdAVEREREREREWsNPh90nEx0BICIiIiIiItIJ6AiABDg4kJ/oKsRYRqIrIC3k2rlsNXQ+pX+4ug6KiLSUtoP+4d7vC+0xF3UAiIiIiIiIiM/oFIC20SkAPrPy40ouPGMH4w/P5RczC6mq2vcqDgvmlXLGuDwmHLmNWbcV1ZYpKgzyo+sKGDsyl9PH5jH/2dJ4V79Vpl6Vy6o1e6M+t+jVUkaO30D/0eu47U5/HVHhYi4XM4m/uLgOupgJlMtPuVzMBG7mcjETuJtLJJHUAeAj1dWWH04r4DvTs/jnG33Znhvk6dklEWU+/qiSP9y5hz/M3o8XXu3D1s3V/OUxr8zD9xaDgXmL+/Dz32Tzm9uL2LCuKhFRmvT+igpunZXPS0ujd1AU7Apy5Yw8HryrDx8sGcji10qZu6A4zrVsPRdzuZhJ/MXFddDFTKBcfsrlYiZwM5eLmcDdXCLJQB0APvL+O3vpOyCV08/OYL9eqVw7ozsvv1gWUebNpeWcMrkbI0am06t3KjNu6cErC8sB+N9/K7ny2iwOPCiNcRO6csQ30vlyTXUiojTpvQ8rKCsPkZUZffWcv6iEUyZkMHFcBgcOCHDTDTm+2Oi7mMvFTOIvLq6DLmYC5fJTLhczgZu5XMwE7uaS2ApZk1Q3v1AHgI9s3ljN14YHaqcPGR5gy6ZgRJm9ey1p9UZ2CKQbtm31GvnzXu7DkUd3oarKsuazKrZsCnLoqADJZsa0bO6/ow+9cqKvnus2VjFqeJfa6VEj0lm/MfmOZGjIxVwuZhJ/cXEddDETKFcNP+RyMRO4mcvFTOBuLpFkoA4AH9mz25KRUde7lJFlKCu1EeMAHH9CV15dXM76L6vYVRDkwbv3UFEeOU7AFefs5ILTd3DSaV05YKD/xoEsLAqSlVn3OXTPSmFXUbCJV/iDi7lczCT+4uI66GImUC4/cTETuJnLxUzgbi6ReFAHgI/06Ok1+GuUlli6djMEAnUbwPETu3LxlVlcc1E+F525k5FHpNO7T2rE+/xt4f7MXbQ///n3XhbOjzyFwA9yslMpLgnVTu8pDpHTM7WJV/iDi7lczCT+4uI66GImUC4/cTETuJnLxUzgbi5pnRAmqW5+oQ4AHzlwUBpfrKk7vOmLNVUcOChyY1daEuL8yzJ4/cP+LHuvHyMPDzDsUO8w/5un7yIvtxpjDIeNTufEU7qy6pPKuGaIhSGDAqxcU1fvlasrGTwo+U5laC0Xc7mYSfzFxXXQxUygXH7iYiZwM5eLmcDdXCLxoA4AHzlmbBe25wZZsrCMXQVBHn+gmMlTMyLK7NwR5ILTd/Llmiq2ba3moXv2cN4lmd5z24M89kAx2/OCfPzhXl5eWM6RR6UnIkq7nDc5i2XLy1j+Thlbcqu4b3Yhl57bPdHVajcXc7mYSfzFxXXQxUygXH7iYiZwM5eLmcDdXCLxoA4AHwkEDPc93ovHHyrh7BO3029AKldcl8U/nyvl9HF5AAw+OMAlV2Zy9QU7+c6F+Uw5L4MTJnUF4Oe/yeaL1dVMmbCdmd8r5PxLMjn97IymZpk05szbw9Bj1gPQu1cqcx7oy/SZOxhz2mbOnJTJxVP9udF3MZeLmcRfXFwHXcwEyuUnLmYCN3O5mAnczSVtl+hR//16FQBjrW2+lMSEMcYCfLLpgERXJaZGBvzRiSDuSum3NtFVkBYK5Q1LdBVERERaxLXfF8Z4jVRrfdRajaKmTXXqmz9IdFUiLD3xj0Dyf746AkBERERERESkE/DfNeBERERERESkU/PTYffJREcAiIiIiIiIiHQCOgIgAX7/06J9Hjvt5K6ccWq3+FdGxAE6r1xERERizc+/LxYuLeGlpaWJroazdr6zjvx31iW6Gm2iQQDjqGbAisKtBya6KjHVI0UdFyIiIiIiySy1/xdA8g9S15yaNtVJr/8o0VWJ8PpJfwCS//PVKQA+89H/Kjnh1O0cPDKX79+8i6qqfTtwnvl7KUcev42vjc7lxz8trC2Tc8CWfW5nX7Az3hFabOpVuaxaszfqc4teLWXk+A30H72O2+7Mj3PN2sfFXC5mAjdzuZgJ3MzlYiZQLj/lcjETuJnLxUzgZi4XM4m/qAPAR6qrLd++Jp8f3tid9//Vl625QR58tDiizAcf7uUXs3bzl8d68e4bfdm4uZqHZntlCrceGHE7b2o3Lrs4+S7h9/6KCm6dld/oYUsFu4JcOSOPB+/qwwdLBrL4tVLmLiiOWjaZuJjLxUzgZi4XM4GbuVzMBMrlp1wuZgI3c7mYCdzM5WIm8Sd1APjIv97eywH90zj3Wxn07pXKj3/Qg/kLyiPKLF5awbfO6sboUens3zuVn/+kJy/8s3yf93rvg71s2RrkwnOTrwPgvQ8rKCsPkZUZffWcv6iEUyZkMHFcBgcOCHDTDTm+2EC6mMvFTOBmLhczgZu5XMwEyuWnXC5mAjdzuZgJ3MzlYqZEC1mTVDe/UAeAj6zfUM2hI+rGbTxseICNm6ojyuytsAQCdStgIGDYsjUYUcZay//71W5+NysbY5JvZZ0xLZv77+hDr5zoq+e6jVWMGt6ldnrUiHTWb6yKV/XazMVcLmYCN3O5mAnczOViJlCuGn7I5WImcDOXi5nAzVwuZhJ/UgeAjxTtDpGZUbfIsrIMJaU2YhyAiRO68OKiMtZ+WUV+QZA7freb8opQxPvMfa6Mww4NcPio9LjVPZYKi4JkZdZ1XHTPSmFXUbCJV/iDi7lczARu5nIxE7iZy8VMoFx+4mImcDOXi5nAzVwuZpLkpMsA+kh2zxQ2bKzb419cbMnoZiL2+J9yUjemXVXF2RfsJDXVcPXlmaz6PLL3cM7fSrnr19nxqnbM5WSnUlxS16mxpzhETs/UBNYoNlzM5WImcDOXi5nAzVwuZgLl8hMXM4GbuVzMBG7mcjFTR0vywfaTlo4A8JHBB6Xx+Zq6xvxna6o46KDIDUNxSYirLstk9YoBrPpvf75+RDojDw3UPr99R5DCwhBHHu7Pvf8AQwYFWLmmsnZ65epKBg8KNPEKf3Axl4uZwM1cLmYCN3O5mAmUy09czARu5nIxE7iZy8VMkpzUAeAjJ4zrwtbcIP94sYz8giD33r+HC86JHMRv+/Yg40/dwedrqti8tZo7797NFZdm1j6/6JVyxo/r0vCtfeW8yVksW17G8nfK2JJbxX2zC7n03O6Jrla7uZjLxUzgZi4XM4GbuVzMBMrlJy5mAjdzuZgJ3MzlYiZJTuoA8JFAwPDXJ3pz7wPFjDkhjwMPSON713fn2XmlHHHcNgC+NjTAtVdnMvncnUw5fycXnZ/JaSd3q32Pl14uZ8xR/tv7P2feHoYesx6A3r1SmfNAX6bP3MGY0zZz5qRMLp7qzw2ki7lczARu5nIxE7iZy8VMoFx+4mImcDOXi5nAzVwuZoqnECapbn5hrLXNl5KYMMZYgMKtBya6KjHVI6Vb84VERERERCRhUvt/AYD1+cnzNW2q8a/enOiqRPj3yfcAyf/56ggAERERERERkU5AVwEQERERERERXwkl9472pKUjAEREREREREQ6AR0BkABlNpjoKsRUj0RXQDq9qzeNT3QVOsSTg/6d6CrE3ILSzOYL+dDUzNJEV0FEJGFS+q1NdBWkRbTHXNQBICIiIiIiIj6T5GPtJS2dAuAz//tfJaeetpORo/K4+eYiqqr2vYrD3/9exvFjdzD68O389Ge7I8o88UQpY8ftYNToPP70p5J4Vr3Vpl6Vy6o1e6M+t+jVUkaO30D/0eu47c78ONesfVzM5UKm/M92svDyBcw95RneueMtQtWhiOff/vW/efrYP+9zK8kroWBNwT6P7/h4e4KSNM2FZfXlJ2XcPGUNVx79KQ//dBPVUbaDLz21k+vHr+KyIz5h1jVfUZBXCUBxUTW/u3E9lx/5CdPGrmTe/Xnxrn6LubCsolEu/+RyMRO4mcvFTCLSMdQB4CPV1ZZrrinkxhuz+Nfy/cndFuTRRyMPO/3ww0pm3VHMY4/l8Mbrvdm8Kcjs2V6Zpcsq+Mc/ynluXi+ef64Xjz9Ryvr11YmI0qT3V1Rw66x8Xloa/ZDagl1BrpyRx4N39eGDJQNZ/FopcxcUx7mWrediLlcyhapDvDHzNUZdcThT551L6fZSVj3zaUSZcbeP54r3vlN7O/mPp9FvTH+y+mVRsrWYwaceHPF8nyP6JihNdK4sq2C15bfT13PODX24f8mh5G+r4sUndkSU2bC6nL/ft41bHh7Cw68fRnbvAH/7/TYAnn9oOzYE9y87lNufGsqSZ/P59N3kyunKsmpIufyTy8VM4GYuFzOJSMdSB4CPvP12Jf0HpPKts7vRq1cqP/h+dxb8szyizNKlFZx1VldGjQzQu3cqP/lJd/75olfmb38rY+bM7hxwQCojRgRY8kpvevdOvlXgvQ8rKCsPkZUZvW7zF5VwyoQMJo7L4MABAW66IccX/8xczOVKprz/biOzTyZDTjmYrjndOPzqI9iwdF2j5UPVIT588AOOuek4AIq3FpPVPyte1W0TV5bVp+8W06tfgHGTc+jZK43zb+zLWy8VRpTpmpFCSpp3WKAxYK0lo0cqAJnh+xopKYaM7pGPJZory6oh5fJPLhczgZu5XMwk0lIha5Lq5hfJ1/qTRm3YWM2I4XXDNgwfnsamTZEDClbshUC9kR0CAdi61Svz+efVfLSikmOP28E3jtrO3LnldO+efKvAjGnZ3H9HH3rlRK/buo1VjBrepXZ61Ih01m+silf12szFXK5kKt66h+yhObXT2UNzKM5t/AfSmhdW0+/o/mQf7L2mJLeYbR/k8vdTn+H5s+byyZ//19FVbjVXllXepkoGDetaOz1oWFe2b66MKNNvUBemXtuHn5yzlquPWcnK/5Tw7VsGAHDe9L7kbdzLtONX8aMz1zD+7ByGjsqIa4bmuLKsGlIujx9yuZgJ3MzlYiYR6VjJ1/qTRu3ebcnIrOtdysoylJbaiHP8J5yQzqJFFXz5ZTUFBUF+d3cxFeXe8/k7g3zwfhWLF/Vi7rP78ecnS3njjYq452ivwqIgWfU+h+5ZKewq8v+VFVzM5ZdMlcWVBLrV9ZwFMgJUl1XvMw4AwN49e1k97zOOmPb12sdMiqHfUf2Z+vz5nHTvKXzx4lo2vr4hHlWPGb8sq9LdQbrW29PVLTOVitJQxDgAX60sY/HT+fxuwTCe/mgUhx2dxZOztgLw13ty6T+kC099MIp7Fw3n7UWFfPZBco+H0pBfllVrKZd/uJgJ3MzlYiaRGtaapLr5hToAfKRnT0NpSd2P3OJiS7duhkCgboU76aSuXHV1JhdcWMBpp+dzxBEB+vT1Dm/t0tVww/RMevVKZdiwAGee0ZXl/6rcZz7JLic7leKSuobZnuIQOT2T6xDetnAxl18ypXdPp6qsbo9IVWkVaV3TSEnbdxO56Y0NDDjuANKz0msfO/aW4znqe2Po0qML+w3rxfDzRpD7ny1xqXus+GVZZfZMpbxePctKgnTplkJave3ge0t3M2ZSD4aOyiCzRxrn3NCHd18uwlrLWwuLOOe6PnTPSeOg4d0Ye2Y2/1lSlIAkbeeXZdVayuUfLmYCN3O5mElE2kcdAD5y0KA01qypG7RvzZoqDhoUuREvKQlx2WUZrPioL//9oC9HHJ7OoYd6ezYHDEhl7966DoSqaujSBd8ZMijAyjV1HRcrV1cyeFAggTWKDRdz+SVT1gHdKVpXdx550bpCsg7oHrXspjc2cvBpQyMe++xvK6nYXXc0zd7de0nt4q+rrPplWfUdmM7mL+o+601rK+g7MD2iTChoMSayJz5Y7T0WCjV4zkLIZzvD/LKsWku5/MPFTOBmLhcziUj7qAPAR8aNSyd3W5AXF5ZTUBDk/gdKOOecbhFltu8IceqpO1mzpoqtW4PcfXcxl17qnd969tndePTRUnbuDLJyVRWLF1dw6ildo80qqZ03OYtly8tY/k4ZW3KruG92IZeeG72x5icu5vJLpv5HD6B0eykbXl1HRWE5nzz5MUNOO3ifcpUlleR/tpPeI/ePeHz7ijz+e+97lOWXUfhVIeuXrGPQiQfFq/ox4ZdlNfr47uRvq+TtRYXsLqhm/sPbGX92TkSZoyf15J2Xi1j/WRklu6t54ZEdjJnUE4BjTu7JPx7dTsnuajasLuffCwsZc3LPRERpM78sq9ZSLv9wMRO4mcvFTCI1Ej3onwYBlA4XCBieeDyHBx4o4YQTdnLAgFSuvz6Tec+Vcdzx3mWwhh6cxtVXZXLueQWcf0EB55/fjZMneY38G67PZMiQNCaetJNrry3ktp9156ij0puaZdKYM28PQ49ZD0DvXqnMeaAv02fuYMxpmzlzUiYXT/XnPzMXc/kxU0paChN/dzKfPvUJCy6cT2a/TEZeNpqvFn3BC1Pn1Zbb8vZmcobth0mJ3MgfO3MsFUUVLDj/ed6c+SpHXPt1+h3VP94xWs2PyyotYPjJn4bwwiPb+f6pn7P/gABnX9OHN17YxfQTPwNgxDcyuXxmf+6+cQPTT/yMUMjyndsPAODymQPompnCjSd9zl3Xr+P87/bjiHHJl7MhPy6rllAu/3AxE7iZy8VMIhI7xlrbfCmJCWOMBdi6JfkbBq3RLzW5L38m7rt60/hEV6FDPDno34muQswtKM1MdBU6xNTM6NfgFhHpDFL6rU10FaQFak6Bs34asS6KmjbVmJd/muiqRPjgjN8Ayf/5+uskVREREREREen0tB+7bXQKgIiIiIiIiEgnoA4AERERERERkU5ApwCIiIiIiIiIr4RI6lPtk5Y6ABKgKOTWytovNdE1kM7OxcHyXOXqYHkuDm7o6rIS/9DAciIisadTAEREREREREQ6AXUA+MynH1dy3hk7GHv4Nm6fWURV1b7DX/5jXhmnjdvON4/M49e31ZUpKgzxg+t2cezIbZwydjvPP5vce3emXpXLqjV7oz636NVSRo7fQP/R67jtzvw416x9XMzlYiZwM5eLmcD/ub78pIybp6zhyqM/5eGfbqI6yrb9pad2cv34VVx2xCfMuuYrCvIqASguquZ3N67n8iM/YdrYlcy7Py/e1W8Vvy+rxriYy8VMIuIOa01S3fxCHQA+Ul1t+f60XUyb3p2X3ujDttwgT80uiSjz8UeV/P7OPdw3ez/++er+bN0cZM5jXpkH790DBp5fvD+/+E1P7rh9NxvWVSciSpPeX1HBrbPyeWlp9A6Kgl1BrpyRx4N39eGDJQNZ/FopcxcUx7mWrediLhczgZu5XMwEbuQKVlt+O30959zQh/uXHEr+tipefGJHRJkNq8v5+33buOXhITz8+mFk9w7wt99vA+D5h7ZjQ3D/skO5/amhLHk2n0/fTa6M4MayisbFXC5mEhERjzoAfOS9d/bSb0AqZ5zdjf16pXLDjCwWv1geUeb1pRWcOrkrh44M0Kt3Kj+4pTsvL/TKrPhvJVddm8XAg9L45oSuHPGNdL5YU5WIKE1678MKyspDZGVGXz3nLyrhlAkZTByXwYEDAtx0Q44vfni4mMvFTOBmLhczgRu5Pn23mF79AoybnEPPXmmcf2Nf3nqpMKJM14wUUtK8vQvGgLWWjB7eACyZPSIHYklJMWR0T77BWVxYVtG4mMvFTCIi4tEggD6yeWOQQ4YHaqe/NjzAlk3BiDJ791oCaXWHoATSDdu2emXmv9wHgKoqy1dfVLNlU5DDRgVINjOmZQOwaFn0PQ/rNlYxaniX2ulRI9K55+Hk68hoyMVcLmYCN3O5mAncyJW3qZJBw7rWTg8a1pXtmysjyvQb1IWp1/bhJ+d4g6L1HhDg/qWHAnDe9L7cfPYaph2/CoBvXduHoaMy4lT7lnNhWUXjYi4XM4mIe0I+Ouw+megIAB/ZsztEt4y6FT0zy1BWaiPGARh3QheWLi5n3ZdV7CoIcv/dxVSUR55Letk5+Zx3+k4mndaVAwb6rw+osChIVmbd59A9K4VdRcEmXuEPLuZyMRO4mcvFTOCPXKW7g3Stt6e1W2YqFaWhiHEAvlpZxuKn8/ndgmE8/dEoDjs6iydnbQXgr/fk0n9IF576YBT3LhrO24sK+eyDkn3mk+z8sKzawsVcLmYSEeks/Nf6c8AvflK0z2MnntyViad03bdwPT16prB5Y90/2JISS7duhkCg7p/w+IldufTKTK6+qIC0NLjw25ms/TyyV/7vC3vz+coqbv1hES/OL+Ps85JvT1FTcrJTKS4J1U7vKQ6R0zP5DndtLRdzuZgJ3MzlYibwR67MnqnkbaobaK2sJEiXbimk1du2v7d0N2Mm9ajds3/ODX34+SVfcv3/HchbC4uY+fBguuek0T0njbFnZvOfJUUcNiYr7lnaww/Lqi1czOViJhHXLFy4kIULFya6Gs7a/d5a9rz/RaKr0SbqAEiAX/02u02vO3BQKgueK6ud/nJNFQcOivyHW1oS4oLLMrn2e90BeGt5BSs/9g7zv2n6Lm75eU/6D0jlsNHpTDylK6s+qeLs89qWI1GGDArwxtt1n8PK1ZUMHpR8pzK0lou5XMwEbuZyMRP4I1ffgem8+cKu2ulNayvoOzA9okwoaDEm8lDHYLX3WCjU4DkLIR/ujPXDsmoLF3O5mEnENVOmTGHKlCkRjz322GMJqk3HsPteMCduehwzjB7HDIt4bNeSFQmqTevoFAAfOXZsF/Jyg7yysJxdBUEefaCEyVO7RZTZuSPEeafv4Ms1VeRureaBe4o5/xJvj9GO7SFmP1DM9rwg//uwkpcXlnPkUenRZpXUzpucxbLlZSx/p4wtuVXcN7uQS8/tnuhqtZuLuVzMBG7mcjET+CPX6OO7k7+tkrcXFbK7oJr5D29n/Nk5EWWOntSTd14uYv1nZZTsruaFR3YwZlJPAI45uSf/eHQ7Jbur2bC6nH8vLGTMyT0TEaVd/LCs2sLFXC5mEhHpLNQB4COBgOH+x/dj9kPFTD5xB/0HpHLVdVkseK6MU8dtB2DwwWlccmUmV1yQz9UXFnD2eRlMmOSdWvDL3/Rk7eoqJk/Ywc3fK+SCSzI44+xuTc0yacyZt4ehx6wHoHevVOY80JfpM3cw5rTNnDkpk4un+vOHh4u5XMwEbuZyMRP4L1dawPCTPw3hhUe28/1TP2f/AQHOvqYPb7ywi+knfgbAiG9kcvnM/tx94wamn/gZoZDlO7cfAMDlMwfQNTOFG0/6nLuuX8f53+3HEeOSK2Nj/LasWsrFXC5mEhHpjIxN5LETnYwxxgKs2jQg0VWJqRGBzERXQUQkoRaUurcdnJoZfQR4kXhJ6bc20VUQcUrN6WLW+nv4/Jo21egXf57oqkT49Oz/A5L/89URACIiIiIiIiKdgDoARERERERERDoBXQVAREREREREfCXJj7RPWuoASIBUNO6CiDTv7YpQ84UkKbh4vryL4xqAm8vq6k3jE12FDvEkw5ov5EMujm0QytOyEvELnQIgIiIiIiIi0gmoA8BnPv24knPO2Mmxh+fx/2YWUVW179EE8+eVcfK4HRx/5HZ+ddvuqGW+f30hz8xJ7r0gU6/KZdWavVGfW/RqKSPHb6D/6HXcdmd+nGvWPi7mcjET+D/X5x9XcM3kzZz19fX89ic7qI6yLVj83B4uOmEjU45az70/37lPmbWr9nL7d/PiVeUWcTVXQ35f/wC+/KSMm6es4cqjP+Xhn26Kuqxeemon149fxWVHfMKsa76iIK8SgOKian5343ouP/ITpo1dybz7tbw6Uv5nO1l4+QLmnvIM79zxFqHqyCOQ3v71v3n62D/vcyvJK6FgTcE+j+/4eHuCkjTP78uqM9GykqaErEmqm1+oA8BHqqstN04r5NrpWbz8xv5syw3y5OzIRvz/PqrknjuLeWB2Di+92pstm4M89VhkmZcXlrP05Yp4Vr1V3l9Rwa2z8nlpafQOioJdQa6ckceDd/XhgyUDWfxaKXMXFMe5lq3nYi4XM4EbuaqrLbddn8el03P466uD2LGtmrmPF0WUWbWigj/dVcCsR/rx9JKBbNtcxd+f8MqU7Amy9B/F3PHjHfGvfBNczVWfC+sfQLDa8tvp6znnhj7cv+RQ8rdV8eITkZ/7htXl/P2+bdzy8BAefv0wsnsH+NvvtwHw/EPbsSG4f9mh3P7UUJY8m8+n7yZfTheWV6g6xBszX2PUFYczdd65lG4vZdUzn0aUGXf7eK547zu1t5P/eBr9xvQnq18WJVuLGXzqwRHP9zmib4LSNM6FZdVZaFmJdBx1APjIf96ppP+AVM48uxv79Upl+ozuLHqxPKLMa0srOH1yVw4dGaBX71R+eEt3Fi+sK1O4K8QDfyhm/Ild4l39FnvvwwrKykNkZUZfPecvKuGUCRlMHJfBgQMC3HRDji82+i7mcjETuJHro3fK6dM/jUlnZZHdK5UrvpfDay+WRJR5a1kpE8/M4pDDupDTO41pN/fi9YVemaJdIT77XwX79U5NRPUb5Wqu+lxY/wA+fbeYXv0CjJucQ89eaZx/Y1/eeqkwokzXjBRS0ry9JsaAtZaMHt6yyewRuYxSUgwZ3ZNvubmwvPL+u43MPpkMOeVguuZ04/Crj2DD0nWNlg9Vh/jwwQ845qbjACjeWkxW/6x4VbfNXFhWnYWWlUjHUQeAj2zeWM0hw+vGbTxkeBqbNwUjylTuhbR6QzsG0iF3a12Z//v5bq6/MYve+yfvop8xLZv77+hDr5zodVy3sYpRw+s6MEaNSGf9xqp4Va/NXMzlYiZwI1fupiqGDEuvnT54eDq5myPrWLnXkppWd8haIADbc6sBOHBwgB/+an9OPSe5ftS7mqs+F9Y/gLxNlQwa1rV2etCwrmzfXBlRpt+gLky9tg8/OWctVx+zkpX/KeHbtwwA4LzpfcnbuJdpx6/iR2euYfzZOQwdlRHXDC3hwvIq3rqH7KE5tdPZQ3Mozm28MbXmhdX0O7o/2Qd7rynJLWbbB7n8/dRneP6suXzy5/91dJXbxIVl1VloWUlLWJtcN79I3lag7GP3bktGRt2P2swsQ1mpjTjHf9wJ6SxZXMG6L6vZVRDkj3cXU1HuPb/slQpKSizfOi/5fkC1RmFRkKzMus+he1YKu4qCTbzCH1zM5WIm8Eeu4t0hutXbc9ItM4XyUhtxDvaY8d1Y/nIJG7+qpKggyOO/31W7vUhWruZqDT+sfwClu4N0jVhWqVSUhiKW1Vcry1j8dD6/WzCMpz8axWFHZ/HkrK0A/PWeXPoP6cJTH4zi3kXDeXtRIZ99ULLPfJKdH5ZXZXElgW51ew8CGQGqy6r3GQcAYO+evaye9xlHTPt67WMmxdDvqP5Mff58Trr3FL54cS0bX98Qj6rHlB+WlXi0rETaTh0APtKzp6G0tO6HU0mJpVs3QyBQtwE8YWJXLrsykysvKuDcM/MZfUSA/fukUrwnxD2/2cOvftMzEVWPqZzsVIpL6n6U7CkOkdMz+Q4LbS0Xc7mYCfyRq3vPFMpK6+pYVhKiazdDWr3txXEnZnLuFT35wSW5XDNlMyMO70qvPsmVoyFXc7WGH9Y/gMyeqZSX1F9WQbp0S4lYVu8t3c2YST0YOiqDzB5pnHNDH959uQhrLW8tLOKc6/rQPSeNg4Z3Y+yZ2fxnSVECkrSPH5ZXevd0qsrq9p5WlVaR1jWNlLR9fyZuemMDA447gPSsuiNxjr3leI763hi69OjCfsN6Mfy8EeT+Z0tc6h5LflhW4tGyEmk7dQD4yIGD0vhiTXXt9BdrqjhwUOTGrqQkxIWXZfDvD/vy5nt9GXl4OsMPTWPrliAb1weZeNwORgzaxj+eK+f/fr6H7127K94x2m3IoAAr19QdRrpydSWDBwUSWKPYcDGXi5nAH7kGDAywvl4d162ppH+DOpaVhJhySQ8WvD+Y+e8MZsToLgwdkd7wrZKKq7laww/rH0Dfgels/qJuwNlNayvoOzByOYSCFmMiR04OVnuPhUINnrMQ8uEOPj8sr6wDulO0rm58hqJ1hWQd0D1q2U1vbOTg04ZGPPbZ31ZSsbtuWe/dvZfULmkNX5r0/LCsxKNlJQDWmqS6+YU6AHzkuLHpbMsN8vLCcnYVBHnkgRKmTO0WUWbnjhDnnL6TL9ZUkbs1yP33FHPBJRmMOCzA6k39a2/nXNCNn/9fDx58bL8EpWm78yZnsWx5GcvfKWNLbhX3zS7k0nOj/1DxExdzuZgJ/JHrG2O7sWNbNa+/VEJRQZC/PFTIKd+KPO+9YEc135m8mfVrK9m+tYrH793FlIt7JKjGLeNqrtbww/oHMPr47uRvq+TtRYXsLqhm/sPbGX92TkSZoyf15J2Xi1j/WRklu6t54ZEdjJnkHal2zMk9+cej2ynZXc2G1eX8e2EhY07231Fsflhe/Y8eQOn2Uja8uo6KwnI+efJjhpx28D7lKksqyf9sJ71H7h/x+PYVefz33vcoyy+j8KtC1i9Zx6ATD4pX9WPGD8tKPFpWIm2nDgAfCQQMDz2ew6MPlXDGiTsZMCCVq6/L5B/PlTFpnHdppSEHp3HZlZl8+4ICrriwgG+d140TJ3Vt5p2T35x5exh6zHoAevdKZc4DfZk+cwdjTtvMmZMyuXiqPzf6LuZyMRP4L1dawHDHo/3468OFXDZpE30HpHHxtGxenr+HC8dvBGDgwemce0VPZly0le9fkstp53bn+JMyE1zzprmaqzl+W//AW1Y/+dMQXnhkO98/9XP2HxDg7Gv68MYLu5h+4mcAjPhGJpfP7M/dN25g+omfEQpZvnP7AQBcPnMAXTNTuPGkz7nr+nWc/91+HDEu+XJG47fllZKWwsTfncynT33Cggvnk9kvk5GXjearRV/wwtR5teW2vL2ZnGH7YVIi93QdO3MsFUUVLDj/ed6c+SpHXPt1+h3VP94x2sRvy6oz07ISiQ1j/TRkoc8ZYyzA6k3++KfYUocEknc0bRE/e7ti3wG4JDmN6+pef/qCUn93mjRmamb064r72dWbxie6Ch3iyUH/TnQVOkRKv7WJrkLMhfKGJboKHcK1ZVVzWpX10/HqUdS0qYbP/1WiqxJhzXm/AJL/83XvF4uIiIiIiIiI7EMdACIiIiIiIiKdgP+GaBUREREREZFOTSeyt406ABIgYNxaXTdVFye6Ch1iUJoGk5HE0nnl/rGqameiqxBzU91cVE5y9Vx5V7l6vryI+IN7vy5FREREREREZB/qAPCZT/5XxZTT8zlq9HZ+estuqqr2PZrg+b+XceK4nYw5Yju3/6yuzKqVVQwdmBdx++8HlfGOENXH/6ti8un5fH30dn7SSK7n/l7GCeN2ctQR2/l5g1xDBuZF3JIlVzRTr8pl1Zq9UZ9b9GopI8dvoP/oddx2Z36ca9Z2LmYCN3O5kOnLT8q4ecoarjz6Ux7+6Saqo2wvXnpqJ9ePX8VlR3zCrGu+oiDP2yYUF1XzuxvXc/mRnzBt7Erm3Z8X7+pHtfLjSi48YwfjD8/lFzMLo24DF8wr5YxxeUw4chuzbiuqLVNUGORH1xUwdmQup4/NY/6zyT3KvQvrYDQu5nIxE7iZy8VM4G4uiQ1rTVLd/EIdAD5SXW25YVoh1383k6Vv7s+23CBPzI78obfio0p+e2cxD8/O5uXXerN5c5A/P+aV2bwpyJRvdeWrzf1qb0ePSU9ElAjV1ZbrpxUy/buZvBrO9XiUXHfdWcwjs7NZEs71RDjXpk1Bzv5WV9Zv7ld7S4ZcDb2/ooJbZ+Xz0tLoP84LdgW5ckYeD97Vhw+WDGTxa6XMXZDcp1e4mAnczOVKpmC15bfT13PODX24f8mh5G+r4sUndkSU2bC6nL/ft41bHh7Cw68fRnbvAH/7/TYAnn9oOzYE9y87lNufGsqSZ/P59N3E5qyutvxwWgHfmZ7FP9/oy/bcIE/PLoko8/FHlfzhzj38YfZ+vPBqH7ZuruYvj3llHr63GAzMW9yHn/8mm9/cXsSGdVWJiNIkV9bBhlzM5WImcDOXi5nA3VwiyUAdAD7y7tuV9B+Qyllnd6NXrxRu/H4WC/9ZEVHm1aV7OWNyVw4bGaB371R+PLM7L73oldm0KciBB6YmoupNeqdBru99P4sXG+RatnQvZ9bLdfPM7ixM8lwNvfdhBWXlIbIyo3/t5i8q4ZQJGUwcl8GBAwLcdENO0v8zczETuJnLlUyfvltMr34Bxk3OoWevNM6/sS9vvVQYUaZrRgopaV5PvDFgrSWjh7eNyOwRua1ISTFkdE/s9uP9d/bSd0Aqp5+dwX69Url2RndefrEsosybS8s5ZXI3RoxMp1fvVGbc0oNXFpYD8L//VnLltVkceFAa4yZ05YhvpPPlmupERGmSK+tgQy7mcjETuJnLxUzgbi6RZKBBAH1k08Ygw4bXLbJhw9PYvCkYUWZvhSUtUHcISiAAuVu9Mps3VrPy02rmHr6dLl0Ml3w7g+/9ICs+lW9Cw1zD25Dr00+reTac69JvZzAjCXI1NGNaNgCLlkXvzV63sYpRw7vUTo8akc49DyffXrz6XMwEbuZyJVPepkoGDetaOz1oWFe2b4485affoC5MvbYPPzlnLQC9BwS4f+mhAJw3vS83n72GacevAuBb1/Zh6KiMONU+us0bq/na8EDt9CHDA2xpuA3ca0mr9x87kG7YttVr5M97uQ8AVVWWdV9Us2VTkENHBUg2rqyDDbmYy8VM4GYuFzOBu7kkxtwaVz1udASAj+zeHSIjs64RnJllKC21EeeKfnNCF15ZVMFXX1ZTUBDi3rtLKC/3nk9NNRw3Np3X/rU/j8/JYd7cMl5ZXLHPfOJt9+4Qmc3kGj+hCy/Xy/X7erlSUg3Hj03njX/tzxNzcvj73DJeToJcrVVYFCSr3ufQPSuFXUXBJl6R/FzMBG7m8kum0t1ButbbI9QtM5WK0lDEOABfrSxj8dP5/G7BMJ7+aBSHHZ3Fk7O2AvDXe3LpP6QLT30winsXDeftRYV89kHJPvOJpz27LRkZdZ99RpahrME28PgTuvLq4nLWf1nFroIgD969h4ryyF8+V5yzkwtO38FJp3XlgIH+69/3yzrYWi7mcjETuJnLxUzgbi6ReFAHgI/07JlCaUndD76SYku3boZAvT3jJ07swuVXZXDZRbs4+4x8Dj8iQJ++3uGtv5zVg5/8rDs9s1M49LAAl12Rwb+WRx9YJZ5amuuKqzK45KJdTGmQ69ezenBrONdhhwX4dpLkaq2c7FSKS0K103uKQ+T0TP5TG5riYiZwM5dfMmX2TKW8Xj3LSoJ06ZYScYTQe0t3M2ZSD4aOyiCzRxrn3NCHd18uwlrLWwuLOOe6PnTPSeOg4d0Ye2Y2/1lSlIAkdXr09Br8NUpLLF0bbAPHT+zKxVdmcc1F+Vx05k5GHpFO7z6Ry+dvC/dn7qL9+c+/97JwfuQpBH7gl3WwtVzM5WImcDOXi5nA3Vwi8eB8B4AxZrAx5kljTK4xpsQY819jzNXGmH2GajTGnGGMeTdcrsAY86Ix5utNvHeryrfXwEGprK13XufaNdUMHBS5sSspCXHxZRn858M+vP1+H0YfHmD4CG9P0BOzSyksrNtYFhVaunZN/IiVAwelsqZerjVrqhkUJdcll2Xw/od9eCeca0Q41+NJmqu1hgwKsHJN3aHMK1dXMnhQ8h3G2xouZgI3c/klU9+B6Wz+ou4In01rK+g7MHLQz1DQ0nATH6z2HguFGjxnIZTgnUYHDkrjizV1h65+saaKAxtsA0tLQpx/WQavf9ifZe/1Y+ThAYYd6i2fm6fvIi+3GmMMh41O58RTurLqk+S9Ekpj/LIOtpaLuVzMBG7mcjETuJtLWifRo/635SoAsWg/tqZ9G43THQDGmNHACuAqvKyfAiOBPwP3NCh7IfAicBywASgHpgDvGmPGRnnvVpWPhePHpbMtN8iiheUUFIR4+IESvnVO14gyO3eEOOu0fNauqSJ3a5A/3FPCxZd2A+D99yqZ9cs97NgeZM3qKl5cUM6pp3eJNqu4GhvO9VI410NRcu3YEWJyONfWrUHubZDr1w1ynZYEuVrrvMlZLFtexvJ3ytiSW8V9swu59Nzuia5Wu7iYCdzM5ZdMo4/vTv62St5eVMjugmrmP7yd8WfnRJQ5elJP3nm5iPWflVGyu5oXHtnBmEk9ATjm5J7849HtlOyuZsPqcv69sJAxJ/dMRJRax4ztwvbcIEsWlrGrIMjjDxQzeWrkuAQ7dwS54PSdfLmmim1bq3nonj2cd0mm99z2II89UMz2vCAff7iXlxeWc+RRyXcllOb4ZR1sLRdzuZgJ3MzlYiZwN5e4LRbtx9a0bxvjdAcA8BcgG7gB6G+tPR44DNgC/MgY8w0AY8x+wNNAFTDWWjvKWnsg8H2gC/CEMab2s2pt+VgJBAyPPJ7Nnx4s5ZQJO+l/QCrXXJfJ/OfKmTB2JwBDDk7zDpU/fxeXXriLc87vysRJXmP613f0oKAgxKQT8rlhWhE/uCmL445PfEM5EDA8+ng2Dz9YyqQJOxlwQCrTwrnGh3MdHM510fm7uOTCXZx7fldOCuf6vzt6sKsgxMQT8rk+iXK1xJx5exh6zHoAevdKZc4DfZk+cwdjTtvMmZMyuXiq//6ZuZgJ3Mzlx0xpAcNP/jSEFx7ZzvdP/Zz9BwQ4+5o+vPHCLqaf+BkAI76RyeUz+3P3jRuYfuJnhEKW79x+AACXzxxA18wUbjzpc+66fh3nf7cfR4xLbM5AwHDf4714/KESzj5xO/0GpHLFdVn887lSTh+XB8DggwNccmUmV1+wk+9cmM+U8zI4IbwN/PlvsvlidTVTJmxn5vcKOf+STE4/O7EDG7aUH9fBlnAxl4uZwM1cLmYCd3NJ5xDD9mOL2rdN1sVaN4dPNMYcB7wLPGatva7Bc1cBTwJ3WGv/nzHmRuBB4DZr7Z0Nyr4CnAacbK19LfxYq8rXe84CfLW5X8xyJgNXe5EGpekfiUisLSjNTHQVOsQh6TsTXYWYGxnwRyeCiEhHSem3NtFViKmaI8RtS49XT1I1baqhf/+/RFclwlcX/RyI/vm2tf3YoFyL27dN1dPVthvAtPD9k1GeewYYDPwxPH1J+P4fUcq+EL4/rd5jrS0vIiIiIiIinVMs2o+tad82yn/XCWq544By+//ZO+/wpqr/j7/SJG2apLt0AWVTNsiQJXvvvUQFFERU3OLeOH6KqAyVpbiVJatsVFCQvVfZ3TNdWW0z7u+PlKa3LQgKvW2/9/U89+mT5JPbzzufc849+wjC3yU/EATBBsQWe6sOkCMIwtky7nPt+43/g72MjIyMjIyMjIyMjIzM/ya3o/14K+3b61KVZwCEA6kKhcJfoVAsVCgUJxQKRY5CodirUCieurbGonC3xBDAcJ37ZBT+jfw39jIyMjIyMjIyMjIyMjK3F6l3/b/ZUwBuY/vxptq3/0SVnAGgUCi8gEAgF9gDNATOAjHAXUBHYLBCoehbaKcCsq5zu8zCv9cWrgbdon0p+g/IvhkZAAyY4M/ACQH/bCghgcrKd970zVAVe3AMTrPULtwRgjyq5rryqkgrrxSpXbgj1Jb3DKk0XLUbpXbhtiOnv8pFg12TpHbhtnOh2zdSu/A/y+LFi1m8eLHUblRZcnccJPe3g//1Nv+5/Xgr7VtBEJzXuw9U0Q4AXD8OuNZBnAaaCYIQA6BQKCJwrb3oBTwK/PwP97p2GPMNf8hbsZ+/vs5N3kpGRkZGRkZGRkZGRqZsHn74YR5++OF/NsS9CaDMzePbux2+vdvdlO3l8Tfce+9G3Ex781batwtu9M+q6hKA4r0rk6/9OACCICQB13LJBFxTMey4f9SSXHs/ufDvrdrfVmKOW3ls8GXGtD7PJy8mYbeVPsVh7deZ3N/5AiOax/DalDgyUmwAWEwO5jyXxOhWMUzrc4k9W3PvhIv/ijPH87lvQBK9W8Yze5ahTF0bVpgYfk8ife+K5/9ecdvk5Tl55/kMereMZ9DdCSyem13O3t8awycncTomv8zPoneYadrlKuHNL/PKexll2kjN0WMF9OybRlTTZJ5+LhtbGbH66RcLbTum0rh5CrNectuEVE8qdY0cUzF1QuWPVVlUBU0njtkY0j+DNs1Teen5nDLT4KpfLHTvnE67lqm8/rLb5vQpG/VqpoiuQwcLylvCTVEVYlUWVUGXnAYrT6zKoqrpyruUQMKLC7k69V3SF/2KYHeUsimITyXx9cVcmfw2ye8tx2nJk8DTW6eqxUrmNiMoKtZ1fW5H+/FW2rc3pEp2AAiCkAfkALmCIBwq4/PjgBFoLrjOQUzDNTWjLK69n1z43Vuyv5047AJvP5LA2EeCWbK9LunJdlYvFS8luXw2j28/See1L2rw9R/18A9WsXyO63iqZR+kYTU7+XpXfZ58L5zPXk4p6hyQErtd4Plp6Ux61I+Vv0WQmmznhyXizomTR/KZ/34WHy6qxs/bI0iKt/PDUpfNL18ZSYq3s/K3CN5bGEz0ahOb1pikkHJDDhzN48XZGWzcVvY0fEOmg0kzU1jwQQgHt9Zk004zP6+tWFNV7XaBSQ9l8sRjPuzZHUJikoMvFol/60OHC3h7di5fLwlk9+/ViIt38MVil01aYoToGjncmwnjK95RY1UhViWpKprsdoFHpmYx/VEd2/6oRnKSg2WLxZqOHing/94z8vlifzbvDCY+3sFXS1w28XEOhgzTcCk+rOhq285TCinXparEqiRVRZecBitPrEpSFXUJDgepc37Af2hXas59Crshh5zov8Q2djspH32P/7CuRM57FqWvjuzoPRJ5fHNUxVjJ/O9yO9qPt9K+/Sd/qmQHQCGJgKdCoVCW/KBwgwQPXD8SwGXAV6FQNCvjPp0K/14p9t6t2t8Wjv1tplq4im6DffEPUnHvY8H8sUHcUPbWeaBUuXqgFAoQAJ2vK8x7txsZ+0gQPn5KmrXT0n2oL39tlr6wPLQ3j9AIJX2G6AgIUvLgTD+2rhcX+Lu3W+g1UEvDpp4EBiuZ8bw/2wttju7PY8wDPgQEKWnZTsPQcXqO7Cu7t1hK9h/Ow2J1oteVne1WR5vo001Lj85aakSoeeaRgAr3MPtzTz7Vw5UMH+ZNcJCSZ57Us2atVWSzZVseQwZraN5MTbVgJS+/4MPaddZS9zpwsICERAejR3qXl/s3TVWIVUmqiqa/9xQQHqFk8FBvgoI8eOwJPRvWiUeydmzLZ8AgDU2aqgkOVvLsLB82rnfZxMU5qFGj1GOhQlFVYlWSqqJLToOVJ1YlqYq6rKcuowryQ9+pOUpfHf4jumPac1JkYzkSg6ZBTXRtGqH01RH88HB8e7aVxuGbpCrGSuZ/ntvRfryV9u11qcodAL8CGlxrIUrSGdcmC9dKyJ8K/44ow3Z4CZt/Y39bSI61UauhV9HrWlFepMSLR/DDIz0Z83AgTwy/yri2Fzjxt5kHZ4UAYMsXijoHANRqBWlJ0s8ASIi1U7ehe/SjXpSapDi7yCY/X0ClLua7p4KUJNcUtxfeDaJTD3cj8sJZGw2bqO+w17fOzKn+zHs3hKCAsrPd5VgbzaLc8W3WyJMrsdLHpzhXrzpo1Mj92zaKUhMbJ55qmJ8noFaL01liothGEATeeCuH92f7Vcj1aFUhViWpKpriYh00jHJvX9MwSkV8GWlQVF6oIakwDcbH2tnzVwFtW6TSuV0aCz6reLOFqkqsSlJVdMlpsPLEqiRVUZc9NRPPmqFFrz1rhmJLyxTZ5MemoPBUu5YATHmHtM9+QaGq2J1QVTFWMrcfQahY1z9wO9qPt9K+vS5VuQNgCa6NFBYpFIqW195UKBRRhZ8BzCv8+xOQB7ysUCg6FLOdCfQFDgiCcKLYvW/V/rZgynHgrXWHTKvzwGp2itbLXzhpZf23WcxfV5tVxxrStJ2WL99JBaB1Fx1rv87EbHRw7piVHWtyyLfe7N6Gdw5jjhOtzl1R0uo8sJgFka4OXbz5bZOFqxdtZBkcfDEnm3yr6/PwGio03h7k5jh4+7kMEmPt9Bte+XaFz8p2oC/2O/joPcjMLr2OT0pycpzotG4f9XoFZrMgWv/avZsXG6KtXLhoI8Pg4P0PjVjzxKXiLyutNG6spnmzitdRczNUhljdKpVFU06J8kJXRhq8p5sXW6LzuHTRjsHgZO5HJqyF5YVSqaBDJ0927q7G0m8CWPGzhS2bKsda2GtUlljdKpVFl5wGK0+sbpXKqMthtqLQuAdRPLw9EfIKRPsAOHLMmP4+SdB9/am18Hk8vL3IWB4thbu3jcoYK5n/eW5H+/FW2rfXpcp2AAiCEAs8ies0t8OF5yQewdUrEgUsEARhU6FtFvAArlMR/i60TcD1AyYDU0rc+5bsbxd6PyUWs7vBbjE58fJWiEYZ9mwz0qGXngbNvdH7Khk3I5g/N+UiCAIzXg/DmO3g/s4XWTQ7lfa99ASGSH8QhI+fB2aTW5fZ5ERTQlenHt6MmeTDjPGp3D8omSYtvAgKcfde79pmYWzPJBx2+HJFKH7+FbtnuywC/JUYi/0OuUYnAX4VS4efnwcms7uSazQKaL0VohH/Xj01PDRZx4gxBnr3S+eulmpCQsQ6vv/RwqT7K97a/5ulMsTqVqksmvz8PDCb3GnQZBTwLpEGu/fw4v7JWiaOy2TogAxatFQTEurS8uZsX1542Qc/fw8aN1Ez8QEtu3dVvCVDN6KyxOpWqSy65DRYeWJ1q1RGXUqdN4LVnX6c1nwUXmrRCL9CrULXtjGahpF4aDX4DeyE9cRFKdy9bVTGWMn8b3Mr7UeFQhGiUCg2F14hxe5x0+3bG1FlOwAABEFYgGtKxQZcuytWB34HhguCMLOE7UpgCLAfqAdogfVAF0EQzpRx71uyvx2ERaqJPe8u5K+ezye8pnjjIKcD1+J/t6M4Ha5jP5yCwCsLq7PmRBSfrKpNvlWgTiPNnXD1lqgeqeLyefe0rUsxNqpHijsmzCYnw+/Vs/lQDTbuq0HjFp7Ub+waPd61zcI7zxt4Y24wb30ajE5fOZN1nUg1p2LcO0GfOldA7ciKNUJeq5aSczHuWJ2LsVGrlviBazI5uX+ijlNHwzh2KIyWLdU0aezWkZrmICvLScsWFWvTq1uhMsTqVqksmmpGKjkf414idD7GTs3I0mlw/EQt+w6HsOdACM1bqIlq5CpTli02k5XlrjRmZwloNBVvGcqNqCyxulUqiy45DVaeWN0qlVGXKjSQgvi0otcF8amoQsQbjauC/RBs7jQrOJwoPKUfAPovVMZYydwBhAp2/ZO7N99+9Ab6F17eJe5x0+3b61E5W0q3gCAIGwRBGCEIQg1BEEIFQegnCMK669huEQShgyAIOkEQAgVBGCYIwqUb3PuW7P8rrTrqSE+2szs6l2yDnZ8WZtBjmK/IpkMvPX9uzuXSmTyMOQ5++cJA+156AKJ/yGbeKynkZNrZv9PI+RNW7u6hv1Pu3jTtOmlITXKwfaOZLIODrxfk0L/EFP6MNAf3DUjm0vkCUhLtLPo4m+HjXb4vm5fD068H0LFbxdtM7lYYNUjP9l0Wdu21kJBk49PFWdw70kdqt0R06exFYpKDdeutZBgcfDLPxKgR4t89NdVJz75pnIuxkZBo5/8+MnL/ve7R/s1b8rinc+Vt/EPliNWtUlk0dezsSXKSg+gNVgwGJ5/PNzFshLgjMz3NyeB+GZyPsZGU6OCTOSbG3+tKpwf2FzD7zVzSUh3EnLOxfq2Vvv29yvpXFZbKEqtbpbLoktNg5YnVrVIZdXk3rYs9MwfT3ydx5JrJ/nUXPve0ENno2zfFcvwC1rNXcZisZK//E22bxhJ5fHuojLGSkYGbaz8KghArCIKi8Iot4x433b4tiyrfAVCVUKkVvLGoBj9/nsHU3pcIiVAzamoQ21dnM6mraypXkzZaHpoVwuxHE5jc9SJOJzz6RhgAIx8KJDPNzuRul/jlSwNvLqkpmmYvFSq1go+WVGP5whzG9EgiLELFxGm+bFxlYljnBABq1VUzZpIP08ek8si4VAaO1HFPL1ej8upFG289Y+DuWrFF19vPVY7zYL9ZkUu9u10bfgYHKflmfigzZqXRrl88A3vpGD+8Yj3M1GoF3ywL5NP5Rjp1TaNGdSUzpuv5eYWFth1ce03Uq6fiwSk6ho3MYPhoA2NHa+nT2105jt6cR9s2la8DoLLF6maojJrUagVfLvXniwVm+nRLJ7y6koce1rF6pZVunVxHntapq+KByVomjM7k3rGZjBitoUcvVxp8+11fDAYnvbpm8MjUbJ58Rk+HjhW/8VUZY3UzVEZdchqsPLG6GSq7LoVKSeizE8leu4v4pz9FFeyH36B7MO46QtzMOQCogv2p9shIMhavJf7Jj/Hw9iTo3r4Se37rVPZYychUFBTCTWxZKHN7UCgUAsCWy5W717UkgUqL1C7cEdp4Vr5G6j9hcJZ9nm5lJ8ij8m36+L/KVXvVPKaptkqueFYWqmIalNNf5aLBrklSu3DbudDtG6lduCN4hJ2X2oXbyrWTlwRBkH4E8D9wrU1V+/v3pHZFxNX7XgYq/u8rzwCQkZGRkZGRkZGRkZGRkfkfQO4AkJGRkZGRkZGRkZGRkZH5H6BybwEqIyMjIyMjIyMjIyMj87+HvJL9XyF3AEjApYJQqV24rTTQnZPahTtCmqPgn40qGSFK6U99kLl5psR1kdqF285nNbZJ7cIdIctZ9fZCCfDQ/rNRJUReLy8jNa+3jpbahdtOVXxeAXwTJrUHMjK3H3kJgIyMjIyMjIyMjIyMjIzM/wByB0AlI/ZkLu+POMSs9n/xw6vncNicpWx+/zaBV3v8zTOt/2ThwyfITs0vZbPkiVPs+iGxPFy+KU4cszGkfwZtmqfy0vM52Gyl5/Ss+sVC987ptGuZyusvu21On7JRr2aK6Dp0sGKM3h87VkCffuk0aZbCs89ll6nr518stO+URtMWqbz4slj70mVmOnROo0nzFD7/wlSert8SwycncTqmdDoDiN5hpmmXq4Q3v8wr71WO4xmvUdl1ZZxJZ8P9a/m5zw/sffcvnHZxebHn7T/5tv1XpS5TiglDjKHU+2nHUyVSIubIsQK69k2lbtMknngus8x89cMvZlp1TKZ+8ySefSmryCagekKpa+iY9PKWUIojxwro0TeNBk2TePK5rDI1/fiLmTYdU4hqnszzL7nLk+DqiaWu4RVA0/Wo7PnqelRFXVVRE1R+XZdPmHh92HEeu/sgX718CXsZdcFt3yTzTLfDPHLXfuZOPUtWilvv9m+Teb7XER6/+yCbllacumBVfWbJ3DkEQVGhrsqC3AFQiXDYnSx67BR9p0Xy2ua7yUrOZ+fX8SKbxBgTG+ddYdq8pry1oz2+QZ6sn3tZZHN4cxrHtlWch5rdLvDI1CymP6pj2x/VSE5ysGyx+Li6o0cK+L/3jHy+2J/NO4OJj3fw1RKXTXycgyHDNFyKDyu62raT/gg/u11gykNZPP6Ynt27qpGY7ODLRWJdhw8X8M67RpYtCWDXb8HExTlYVKh92/Y81vxqZdWKINasDGLpMjNXrtilkHJdDhzN48XZGWzcVvbxgoZMB5NmprDggxAObq3Jpp1mfl5b8Y/gqgq6nHYnv8/aSbMHWjB8xUjMqWZO/3BSZNP59S48sP/Boqv3Z/0IaxeOPkyPKdFI7b51RZ+HtJR++ZLdLnDfQxk89ZgPB3aHkpjkYMEi8W9/8HA+b8zO4bslQfz9eyix8XYWLnbZZCXWEF2jhnszcby0U93tdoEHHjLwxGN6/t4dSlKSg88XiTv8Dh0u4K3ZuSxfEshfv4cQF2/ni8Uum4zE6qJr5HBv7h1f8Y7GrAr5qiyqoq6qqAmqhi6HXWDeo+cY9HB13t/SisyUfLZ8lSyyiT9n5tfP4pm5IIoPd7bGN1jNqrmu+uLR3zL5e30GL37XlBe/b8r2b1JIjbVKIUVEVX1mychUROQOgErE+X3ZBIR70WZgCD6BnvSfUYtD0WkiGy+tEqXS1QOlUIAgCHj7urd6MGUVED3/Kk26BJar7zfi7z0FhEcoGTzUm6AgDx57Qs+GdXkimx3b8hkwSEOTpmqCg5U8O8uHjetdNnFxDmrUUErh+g35a08BERFKhg31JjhIyVNP+LB2nfghu2VbHkMGa2hWqOvFF3xYt95l88OPFl6Y5UON6koaNVKzbUswwcEVK8vuP5yHxepEryvbr9XRJvp009Kjs5YaEWqeeSSgwlWmyqIq6Eo5lIwuREedPnXRBHjTYkpLrm67fF17p93J4QUHufuZDgAYE43owyvenhG79+RTPVzFyGFagoOUPPukL6vXivPVpm15DBvsTfNmnlQLVvLaC36sWVe6grv/YD4JiQ7GjpS2A+DPPflUD1cyolDTM0/6sGateE+BzdusDC2m6eUXfPm1DE0HDuaTmOhgzEjv8nL/pqkK+aosqqKuqqgJqoaus/tyCAz3ov2gYHwD1QyZUYP9G8WDOl5aJR6qwrogIAig9XXVk3atSGPk0zUJivCiRkMtb61tgW+QurxllKKqPrNkZCoi8iaAlYj0eCsRDdyjOhENdGTEixvKwTW96T01kg/HHAEgIMKL1zfdXfT5incu0G96JOf3ZZeLzzdDXKyDhlHupNgwSkV8nENkk58noFK7p9ao1ZCU6LKJj7Vz6qSdn1uk4uWlYMJ9Wh5/UvqHQGysnUbFdDWKUhFbUlc+qIrlQk81JBbqOnPWzpGjBTw3y7Us4KEpOmY+Lr2u4syc6g9A9PayR1Mux9poFuVV9LpZI0/mfG4rD9f+E1VBlzExF/96AUWv/esFYEy6fkU2Zs05wtqG41/X9R1TkhHDuQx+6fsDSk8lDUc2osWDre602//Ilat2GjdyZ5omUWpi48QzY/LzBNSi8kJBQqI47wmCwKtv5fDxB/4oFNJO27ty1U6jRu4KeOMoNVdLlBV5eQLqYnV0T7WChESxbkEQeO2tHD6qAJrKoirkq7KoirqqoiaoGrrS4vKo0cDdaVmjoZb0BHFdMCRSw8CpEbw1yjWCHhThyftbWgEQH2Ph8nETX796GYfNSe/7wxk8vXq5+X89quozS+YOI58C8K+oWMOJMjfEkmPHS+se6fbSKcm3OET7AMSdMrLr+wReWNWGjw50pn4bP1a9dxGAY9vTsZoctB9WsbY0zclxotW5K6s6vQKzWRCtgb2nmxdbovO4dNGOweBk7kcmrFbX50qlgg6dPNm5uxpLvwlgxc8WtmzKK/V/ypvsHEGkS1+Grm5dPYmOzuPCRTsZBgcffGQs0pWR7uDAARubo4P45adAln1t5rffpdd1K2RlO9AX+w189B5kZjtu8I3KQWXQVWAsQO3tbiirtWrsFnupNZUA+bn5nFtxhpZT7yp6T+GhIKxNOMNXjabn3D5cWH+e2N+ulofrNyQ7x4lO63506fUKTCXyVY9uXqyPtnD+oo0Mg4N3P8zBmifW/fNKC00aq2nRTPrlQjk5AnrtjcuKHt00rI+2cqFQ03sf5mLNE9d8fllpoXEF0fRvqAz56t9QFXVVRU1QOXSZc+x4FZvBoNEpyTM7RfsAXD1lYsd3Kby5pjkLD7WjYVtffnz3KgC5GQWcP5zLG6ub8/zyJuz4LpkTu7PKW0YpquozS0amIiJ3AFQitH4q8szuB1GeyYGntwdKtTuMx3ek07xnMJHNfND6qun7cC2Obk3HarSzbs5l7n2roRSu3xA/Pw/MJndF1mQU8PZWiEbwuvfw4v7JWiaOy2TogAxatFQTEurqDHlzti8vvOyDn78HjZuomfiAlt27yt7cpzzx91OIdBnL0NWrp4YpU3SMHmugX/8MWhXT5aVR8OgMHcFBSqIaqhk0QMOu3RVjc8ObJcBfidHkfnjnGp0E+FW85Rq3SmXQ5enjic3iHrmymW2oNCo8VKWL/bjfrxLRoTqeenfDsf3zHWnzeDu8fL0IbBhE1KhGJO1LKBffb4S/nwcms/u3NxoFtCXyVZ+e3kydrGfomHS69UvjrpaehIaI4/PNj2am3F8x1sn7+bk6Ma5RlqbePTVMnaxn2JgMevZLL1PTdz9amFxBNP0bKkO++jdURV1VURNUDl26EnVBq8mOp7cHqmJ1wcPbM7mrVwC1m+nR+aoYPL06B7cYEAQBtZcHAx+qjm+gmur1tbTpG8Tpv3KkkCKiqj6zZGQqInIHQCUiuIY3SRfc09aSLpgJriFe5+l0uNb+FyEIOO0ChsQ80mKtvNpjH481+oN9v6aw4p0LLH78VDl5f31qRio5H+Oeyno+xk7NSPED12RyMn6iln2HQ9hzIITmLdREFU4DXrbYTFaW+4GdnSWg0Ug//TUyUsW5YrrOxdioVYau+yZqOX4klMMHQ2nZwpPGjV26qkcoyc93NwpsdvDyolJRJ1LNqRh3p8WpcwXUjpR+reF/pTLo0lf3Ifuye1Qn+3IW+upln38e93ssdfvVE7135sdT5OW4Z5zk5+Sj9JJ+1VjtWirOxrgriWdibNSqJc5XRpOTyRN1nDsawelD4dzV0pOmjd3xSU1zkJXlpFWLijFSXlLT2etoemCiljNHwzlxKIxWLdU0KaEpswJp+jdUhnz1b6iKuqqiJqgcuqrV1JB43r3/R+IFKyE1xZUDp0M8O0gQwGEHhUJBULgXBfnuOpPD7uoUkJqq+sySudMoKthVOZA+x8vcNFEd/MlOzufw5jSMmQVs/TKWdkNCRDbNewRxZEs68WeNWHJsbF0cR/OeQdRopGfhue5FV4cRYYx9rQEPL2gmkRo3HTt7kpzkIHqDFYPByefzTQwboRHZpKc5Gdwvg/MxNpISHXwyx8T4e12dHwf2FzD7zVzSUh3EnLOxfq2Vvv2lbynf09mTpGQH6zdYyTA4+Gy+iZEjxB02qWlO+vRNJybGRkKigw8/MnLfva61fUOHevPlIjPp6Q5OnbYRvSmPfn00Zf2rCsuoQXq277Kwa6+FhCQbny7O4t6RZT/QKxOVQVd42wjMqWau7rhMXpaVE18fp06/uqXsCkwFZJxJJ7hpNdH7qUdTODR3P5YMC1mXsriy9TKR3WuVl/vXpWtnLxKTHPy63kKGwcHcebmMGSHexC811UGXvmmcjbERn2jnvY9yeOBe98h49BYrXTpLX0Zco0uhprWFmj6ZZ2R0GZq6903nXIyNhEQ773+Uy/3FNG3aklehNP0bKkO++jdURV1VURNUDl2NO/iSmZLPgU0Z5Gba2PBFAh2GiMvvVj0DObjFQOwZM+YcOxsXJXJXL9da+bsHBrH16yRyMgqIPWPm8FYDrXoFlPWvypWq+sySkamIyB0AlQil2oOHFzZj66JY3h5wgIAIDb2m1GTfrym83msfAHVb+zH82bosfeI0r/feh+CEMa80kNjzG6NWK/hyqT9fLDDTp1s64dWVPPSwjtUrrXTr5DrLuk5dFQ9M1jJhdCb3js1kxGgNPXq5GsNvv+uLweCkV9cMHpmazZPP6OnQUfqKsFqt4OulAcybb6JL13SqRyh5ZLqOFSst3N3RdXpDvboqpkzWMWKUgVFjDIwZ7U3vQl0zpuuoU0dFt57pPDQti1df9qFNm4o/uvfNilzq3X0FgOAgJd/MD2XGrDTa9YtnYC8d44dXrMrUzVLZdHmoPOjxYW9OLj/B2rGr0YXpaDqxOZeiL7Bm+Ioiu4Q98QQ0DEThIe65bj+rE3nZeawdvYo/Zu2g5bS7CGsTXt4ySqFWK/h+WTBz5xtp1zWFGtVVPD7dh59WmGnZwXUUVv16aqZN0TFoZDpDRqczbrSOfr3dnW8bN1tpV4Hyklqt4LtlQXw630SHrqlUr67k0el6fl5hpnWHFMClaeoUHUNGpjNsdAbjRmvp29vdIRi92UrbCqTpZqls+epmqYq6qqImqHy6VGoPnlgYxYYvE3mp31ECw73o/2A4f61J47merg2gG7T2YezztVgwM4bneh5BcApMfLU2AAMeiiC0loZXBh5nweMxjHm+FvVbSa+xqj6zZGQqIgpBkLdPLC8UCoUAsPBcd4k9ub30152T2oU7grYC7qL9XwlRVqxTBGRuzJS4LlK7cNv5rMY2qV24Iziq4FbEAR7SHo8oI1NV+c4YLLULt53fshpL7cId4Zu7l0ntwm3l2gkxgiBU6krutTZVreUfSO2KiNjJLwIV//eVZwDIyMjIyMjIyMjIyMjIyPwPIHcAyMjIyMjIyMjIyMjIyMj8DyBvjykjIyMjIyMjIyMjIyNTuah6q+/KBXkGgIyMjIyMjIyMjIyMjIzM/wDyDAAJOJhbW2oXbithqmypXbgj9NNmSu3CbSfLaZHahTuCuor2ZT4Y8qfULtx2quJmeQAWwSG1C7edDJtJahfuCA3UVW8z1AtyrCoV9/tkSO3CbWdSg6q1WZ6MTFVG7gCQkZGRkZGRkZGRkZGRqVxU7M32KyxVc9isCpNxJoP1963np94/sWf2Hpx2p+jzv97+i+V3Ly91mVJcowNnfjnD6hGr+anPT5z67pQUEsrk4gkLzw2JYVLbk3z+Uhx2W+lRwo3L05ne5TQTW55g9kOXMKQUAGDMtvPhY1e4v9UJpnY6xYp5KeXt/nU5fCyfTn2SiWyawGPPGrCVoeu7n00065BIrWYJPPVSZpFNZpaDe6emExEVT/27Ennv45zydr9MjhwroEffNBo0TeLJ57LK1PTjL2badEwhqnkyz7+UXWQTXD2x1DV8THp5SyiTw8fyuadvCrWbJvL4c5ll6vr+FxMtOiZRt3kiz5SI1X3TMqjZKIGo1ol8MLdixKok547nMXVQHEPvusSHL6SWmc82r8xhQtcrDGtzmU9eSyvTRmqqYho8fszGgH4ZtGiWyqzncsrUtOIXC507pdOqRSqvvOy2OXXKRmSNFNF18GBBeUsok5PHCxgxIJ32LVJ4dVZ2mbpWr7DQu3MaHVul8tYrZWt/YnoWP3xjLg+X/zXDJydxOia/zM+id5hp2uUq4c0v88p7FXP0V46Vi8oQq7KoippkZGTuDHIHQCXCaXfy2/O/0XxSc0asHIE51cypH8SN+Htev4fJByYXXX3m9SG8XTj6MD3xf8Zzectl+n3Rj/5f9ufML2fIjc+VSI0bh13g/2ZcYcQjIczb2piMZBvrl6WJbK6es/LLp8k8/3kdPv+tCf7Ban78OBmAVQtTEZwwb3tjXl9ej60/ZXDyb6MUUkTY7QLjH8zgmcd9Obw7nIQkB/O+FP/eBw7n8+rsbH5cWo2Df4QTG2dn/iKX7x9+lovTCUf/jGD9z9VY9p2RXXvypJBShN0u8MBDBp54TM/fu0NJSnLw+SLx1NNDhwt4a3Yuy5cE8tfvIcTF2/liscsmI7G66Bo53Jt7x+ukkCLCbheY+JCBpx/z5dDuMBKTHEVxuMbBw/m8PjuH75cEs//3MGLjHSxY7LKZ81kughMO7Q5n7U8hfPWdid0Sx6okdrvAq9OTuHdGAN/uqEV6sp1flmaJbE4ftfLlBwbe/jKC5VsjSY63sWJZ1nXuKA1VMQ3a7QJTH8pixmM6ft9VjaRkB4sXiRtQRw4X8N67RhYv8WfHb8HExzlYsthlExfrYNgwDXEJYUVXu3aeUkgRYbcLPDY1i2kz9Gz+vRrJSQ6+XizWdexIAXPeMzJ/cQAbdwSTEO9g+RKxzeYNVrZtrlj5qTgHjubx4uwMNm4ru9FryHQwaWYKCz4I4eDWmmzaaebntdI/o4ojx8pFZYhVSaqiJhkZmTuL3AFQiUg+lIw2VEudPnXQBGho+WBLrmy9cl17p93JofmHaP9sewDOrz1P60daow/TE1AvgCHfDkEToCkv96/Lyb+NBIWp6TwoAL8gFaMfC+WvjeJGh0brgYfKNc1HoQBBEND6KgHQFf69hoeHAq2P+D0p2PVXHjUilIwepqNakJJZT/mycp14DX70Visjhmhp0cyTkGAlb7zgz6p1roe4n684e3p4KPD1kTbL/rknn+rhSkYM0xIcpOSZJ31Ys1asafM2K0MHe9O8mSfVgpW8/IIvv66zlrrXgYP5JCY6GDPSu7zcvy67CnWNKtT1/JO+rC6hK3qbleGDvWlRqOu1F/xYXRhPP7/SsfKROFYlObrXQrVwNT0H++AfpOL+xwP5bb24Erhnu5nuA/U0aOJFQLCKqc8F8duGirW2uCqmwb17CoiIUDJ0qDdBQR7MfELP+nXiRtS2bfkMGqyhaVM1wcFKnn/Bhw3rXTZxcQ5q1JS+zCvJvr0FhEcoGTjUm8AgJTNm+hC9XhyHndvy6D9IQ+OmaoKClTz1vA+bNrhtsjKdzP/ESJfuXuXt/k2z/3AeFqsTva7sPL862kSfblp6dNZSI0LNM48EVLgGmBwrF5UhViWpippkZG4WQahYV2VB3gOgEmFMMBJQN6DotX89f4xJ1y/EY1bHEN42HP+6/gBkXswk/VQ6e97bg9PmpPG4xrSY1OJOu/2PpMQVENnQ3RER2VBDarx4+mpYpBfDp4XwwojzAARHqJm3rTEAo2aE8tzQGKZ2PA3AsGkh1GumLSfvr8/lWDtNotRFr5tEeXI11i6yycsXUBfLhWpPSEhybSb2/BO+dOqbQr1WiQA8NcOHu1pIO6p35aqdRo3cmhpHqbkaJ978LC9PQO02wVOtICFRrFsQBF57K4ePPvBHoZB+/daVq3Yal9Il9jk/T0CtdvvqqYaERJf2Z2f60qVfKg3vSgLgiQoQq5Ikxdmo09DtU50oT5LibSKbgnwBlcqtUaVWkJYktpGaqpgGY2MdREW5C4KoKBVxJTTll4iNpxoSC9NfXJydkyfs/PhDKl5eCu67X8sTT0q/eVp8rJ0GxXQ1iFIRX0JXQT6oSpSBSYlum3dey2H6Y3r27a0YSxrKYuZUfwCit5c9Ans51kazKHejuFkjT+Z8XrHylRwrF5UhViWpippkZGTuLBVriErmhhQYC1Bp3U9ftVaN3WIvtQ8AQH5uPmdXnKXVtFZF7+Vl5pF6PJUhy4fQb0E/zv5yloS/E8rD9RtiznGgKdZz7a1Tkmd2itYeXzplYdO3GXy4tiHfHmlGk7Z6vp7tahh/PyeJ8DpeLD/YjLnRUeyJzuLMQelHLbNznOiK6fLRKzCZBdGayV7dNKyNthJzwUa6wcE7/5eDxer6/I33sqlfV0Xsqers2xnG6vUW9uyXdmplTo6AXutuhOj1CswlNPXopmF9tJULF21kGBy892Eu1jxxt+gvKy00bqymRbOK0UjOznGKdJUVq57dNKyLtnK+UNfsYrrefD+HenVVXDkZwd4dofy63sLe/WWvxZQKY44Tb1E+88BqFkT5rF0XLbs2G4m9VEC2wc6yjw3kWStWl3ZVTIM5OU60uhtr6trVi03ReVy8aMdgcPLRRyashbFReijo2MmTXX9WY/m3Afz8k4VN0dJPw87JEdAWi5VOr8BSQlfnrp5s3ZTH5Yt2Mg0OPvvIWJTmtm/Jw2QSGDZK+g7d/0JWtgO9rnj54kFmdsU6NUKOlYvKEKtbpSpqkpGR+W/IMwAkYO97e0u9V7NLTWp2qXnD73n6eGJMcI/428w2VBoVHqrS/Tixv8US0SECT727cqv0VNLsvmZoAjRoAjTU6lGLpH1J1OhY4z+o+e/o/JSkxLkbSxaTAy9vD1TFRlv3b8uhXS/fopH9EY+E8NqEi0x/pwZ/bchm1ue18QlQ4ROgotNAf/ZtzaZJO2lHwPz9PLhy1T3qmGt0ovVWiEaR+/b0ZvoUPQPHpKJSKnjoAT2nzrpGT1aus/Dj0mCCApUEBSoZOUTLumgrndtLt2zDz0/B1Vh3hdBoFEpp6t1Tw9TJeoaNyUClVDD5fh1nzopHG7770cK7b/uVm9//hL+fB1dibxyrPj29mTbZxuAxaaiUCqbc747V6rUWvl8aRGCgksBAJcOHaFkfbaFT+4ozFdbHz4OkOHccLCYnGm+FKJ+1765jxAP+PD0hAaVKwdB7/bh8rmJ1ZFTFNOjn50HsVXeF3GQU8C6hqUdPLyZN0TJubCYqJdx3v5azZ11p9p13fYvs/P09uP8BLbt25TNwkLRLvPz8FMQXi5XJVFpX1x4aJk6yM2mcAaUKxt+nJeasHWOukznv57L8pyApXL+tBPgrMZrcHfW5RicBfhVryYYcKxeVIVa3SlXUJHNzbNiwgQ0bNkjtxp1FwjEKy7EzWE+clc6B/4DcASABnV7u9K++51Pdh4vRF4teZ13KQl+97EZu7B+xtHywpeg9XagOR4G7kum0O1Hr1CW/Wu6E1vTkjzWZRa/jzucRWlM8Kud0CKWm6TrsrveczhKfCeCsAJ3bdWqp+GGFe0remRgbtWuJs5zR5GTKRD3PzXQ1RHb8YeXIMZd2h0NAJEtwvScltWup+Hmle7312RgbtWqJKxJGk5MHJmp5aqYPAL/9kcfR4+50lprmIDPLSasKNEW+di0lP650NxDPXjdWOp6d6Wps7fjDytHjhbFyClAyVqUn5khKeE01W1a5N6G8ElNAeKQ4/1tMTgZP8GPio4EAHNhl5tyJitOJAVUzDUZGKlm5wr2WOibGTmSkWJPJ5GTiRC2PP+4q83f9kc/x4640u2SxmdFjvAkIcHUGZ2UJaDTSL62pEani15VuXRdibNQoQ9fYiVqmF+r6c1c+J4/bSExwEHvFQY8O7g1hf11p5e+/8lmwJLB8BNwm6kSq+X2PO82eOldA7Ujpn73FkWPlojLE6lapippkbo4hQ4YwZMgQ0XtLliyRyJuqh7ZVE7StmojeM+8+IJE3t4a8BKASEd4uHHOqmSvbr5CXlceJr09Qt3/dUnYFpgIyTmdQrVk10ft1+tTh9A+nsRqsGM4biP099h9nHZQHzTv6kJFcwJ7oLHIMdlZ/nkqXoQEim7a9/Ni7OZsrZyyYcuys+TKNdr1cjea7e/vx66JUTDl2rp6z8ueGLNr1ln5kr1tnDQlJDlavN5NucPDRZ7mMGyGeHpmS6qBTnxTOxBQQn2DnnQ9zmDzRVbka1E/L3IW5ZGU7OXWmgBVrLQzuJ+30yi6dvUhMcrB2vYUMg4NP5hkZXUJTaqqD7n3TORdjIyHRzvsf5XL/ve5d1jdtyaNL54rVqOzWWUNikp01hbrmzDMypgxd9/RN5WyMjfhEO+9+lMukQl0D+3rz6UJjUaxWrTUzsK/0mxsWp3UnLWnJdn7faCTbYOf7hZn0HuYjsjGk2Zk6KI4r5/NJTbTx1VwDg8dLn5eKUxXTYKfOniQlO9iwwYrB4GT+fBPDR4hH79PSnPTvm0FMjI3ERAdzPjIx4V5XGtu/v4A338glNdVBzDkb69Za6d9fen0dOnmSnORg8wYrmQYHX843MWS4OF+kpzkZ0T+dCzE2khIdzJtjZMwELY2aqDkXF150jRjjzWvv+Fa6BiXAqEF6tu+ysGuvhYQkG58uzuLekT7//MVyRI6Vi8oQq1ulKmqSkZH5b8gdAJUID5UHPT/qyYnlJ1gzZg26MB3NJjbj4saLrBq2qsgu4a8EAhsGovAQjwA1va8pvpG+rB2/lt9n/U7bx9sS0jykvGWUQqVW8MIXdVjzZSpP9D1LtQg1Qx8K4fc1mczofgaARq113D8rnI8eu8qM7mdwOgUefL06APfPikCj8+Cxnmf5YPplRj8aRsvO0j/c1GoFP38VzJx5ubTukkzN6kqeeMSXH1aYaNretX9Bg3pqpk/xof/INAaMTmXCaB39e7sqXbNf9Uev86BFpyTGTk5n1pO+9Ogq7ZRetVrBd8uC+HS+iQ5dU6leXcmj0/X8vMJM6w4pANSvp2bqFB1DRqYzbHQG40Zr6dvb7Xf0Zitt21SMkddrqNUKflwWzNz5ubTpmkKN6kpmTvfhxxVmmndwbexXv56aaVP0DBiZxqDRaUwYraVfYazeftUfvU5Bq87JTJiSwXNPSB+rkqjUCmYviuD7zzO5v1csIREqxk0NYMvqXMZ3cZ0mUrOuJyMe8OPJcQk8NSGBviN96dhT+mMai1MV06BarWDpUn8WzjfTvWs6ERFKHp6uY+VKK507pgNQt66KSZO1jBmVydgxmYwaraFXL5em2e/6kmlw0q1LBlMfyubpZ/R07CR9B4BarWDh0gAWLTQxoLtL15SHdfy60kKvzq7R4jp1VUycpOO+MQYeGGtg2ChvuveqWHnn3/DNilzq3e3KV8FBSr6ZH8qMWWm06xfPwF46xg+X/hlVHDlWlSdWN0NV1CQjUyaComJdlQSFUJnOLKjkKBQKAWDygckSe3J7GRJ4TGoX7gj9tJn/bFTJyBMqwNqIO4C6ivZlHi2o/JXrkjTztPyzUSXEUgXzlsVZeSozt0IDtfQnJNxuLtik3/j2TlAVY1VV8Qg7L7ULMjfBtSWzglCJWqtlcK1NFbnkQ6ldERE3bRZQ8X/fqllrlpGRkZGRkZGRkZGRkZGRESFvAigjIyMjIyMjIyMjIyNTqVDIE9n/FfIMABkZGRkZGRkZGRkZGRmZ/wHkGQAScCIjQmoXbisNdalSu3BHaOpZ9XTVUFXNo3+q6t4Gey31pXbhthOoPCG1C3eEmsqq9zi1KexSu3BHSHFUvfXyHhV6tem/Z0pcF6lduCOczQyV2oXbzr5WDaV2QUZG5iapejUWGRkZGRkZGRkZGRkZmaqNvATgXyEvAahkmM8ncfbxZRwf9wmxn0Yj2EuPfFpj04l59luOjZrDhVd/wmHOK/osbd1BTj34OcfHf0Lqqn3l6foNSTiZzfxRf/FOpx2sef0kDpuzlM2e767yf71+581221j+yCFyUl26bHkOVr92knc67eCDHr+xY8GF8nb/upw8XsCIAem0b5HCq7OysdlKl1SrV1jo3TmNjq1SeeuVnDJtnpiexQ/fmMvD5X/k8LF8OvVJJrJpAo89ayjT3+9+NtGsQyK1miXw1EuZRTaZWQ7unZpORFQ89e9K5L2Pc8rb/ety5FgBPfqm0aBpEk8+l1Wmrh9/MdOmYwpRzZN5/iV3PIOrJ5a6ho9JL28Jpaiq+erU8QLGDkijS4sk3phVdqzWrjAzoHMK3VolM/sVd6yysxw8/bCBTk2T6N8phdU/VYx8deRYAV37plK3aRJPPJdZpqYffjHTqmMy9Zsn8exLbt1ZWU4emGYgslEijVsn8X9zc8vb/ety4piNIf0zaNM8lZeeL7t8W/WLhe6d02nXMpXXX3bbnD5lo17NFNF16GBBeUsok2PHCujbL52mzVJ47rmyy/ZffrHQsVMazVuk8tLLYu3Llpnp1DmNZs1T+OKLijEj4cSxAob1T+fu5im8/Px1nle/WOjZOY32LVN58+Wy4zlzehbfL68Y+Qog40w6G+5fy899fmDvu3/htIvLwT1v/8m37b8qdZlSTBhiDKXeTzsu/exAU0wSpx/7iiNjP+HKp5twXqcuePbZbzk88mNiXvkZe2Fd8OCA90td5178sbwl3DTDJydxOia/zM+id5hp2uUq4c0v88p7GeXs2b+nKmqSqVzIHQCVCMHh5PI7qwgd05Emix+mICOX1DX7RTZOm4NLb60kdGxHmn41A7WfjtRfDwCQvf8Cmb+fpuEHE2n4wUTS1h0kL0n6o+4cdiffP3GEblPr8fTGLuQk5/HX8isim+SYXHYsuMDEz1rz3NZu6IM82fap68iZvd9dJSvBwtMbuzDh47s4uj6Ro+sTpZAiwm4XeGxqFtNm6Nn8ezWSkxx8vVhcKTp2pIA57xmZvziAjTuCSYh3sHyJ2GbzBivbNudREbDbBcY/mMEzj/tyeHc4CUkO5n0pbmwcOJzPq7Oz+XFpNQ7+EU5snJ35i4wAfPhZLk4nHP0zgvU/V2PZd0Z27ZFem90u8MBDBp54TM/fu0NJSnLw+SJxpfzQ4QLemp3L8iWB/PV7CHHxdr5Y7LLJSKwuukYO9+be8ToppBRRlfPVU1MNPDhDz7rfQ0lNcvDtYnGsjh8p4JP3cvlkcSBrdoSQGG/nuyUum8/nGkEBKzaF8Nr7/rz/ejZXL9ukkFKE3S5w30MZPPWYDwd2h5KY5GBBYZ65xsHD+bwxO4fvlgTx9++hxMbbWbjYZTPns1ycToGDu8NY81M1vv7OxJ8VJF89MjWL6Y/q2PaHqwxcVqIMPHqkgP97z8jni/3ZvDOY+HgHXxWWgfFxDoYM03ApPqzoatvOUwopIux2gYceyuKxx/Ts3lWNpGQHixaJdR0+XMDsd40sWRLA778FEx/nYHGh9m3b8/j1VysrVwSxamUQS5eZuXJF2uUWdrvAo1OzePhRPVv+qEZKkoOvynheffiekQWLA9hUGKuvSzyvNm2wsnWT9GnvGk67k99n7aTZAy0YvmIk5lQzp384KbLp/HoXHtj/YNHV+7N+hLULRx+mx5RopHbfuqLPQ1pKO31fcDi5+M5qwsZ2oPni6RSk55K65oDIxmlzcOHNVYSP6UiLr2ag9teS+utBANptfkl0BXZvQnDfFlJIuSEHjubx4uwMNm4ruzPJkOlg0swUFnwQwsGtNdm008zPa41l2lYUqqImmcqJ3AFQiTAev4o62JfAbk1Q++kIH9+ZrF1nRDY5By6ga1Qd//YNUPvpiHxyIMH9WwFg2HKMiAe64Rnih3ftEBrNfxC1v7SNFIDL+w34hWloMSAcfaAX3R+px/FNySIbL60KD6VrkaNCoQABNL6uFSxXDmfRYUIt9IFe1GodQJuRNbhySPqOjX17CwiPUDJwqDeBQUpmzPQher1VZLNzWx79B2lo3FRNULCSp573YdMGt01WppP5nxjp0t2rvN0vk11/5VEjQsnoYTqqBSmZ9ZQvK9eJz3WP3mplxBAtLZp5EhKs5I0X/Fm1zvWw8/MVFzkeHgp8faQvhv7ck0/1cCUjhmkJDlLyzJM+rFkr1rV5m5Whg71p3syTasFKXn7Bl1/XWUvd68DBfBITHYwZ6V1e7pdJVc1XB/bmExqhpP9QLYFBSqbN9GHzenGs/thmpc8gbxo19SQoWMnM533ZUpivjh0qYNI0PTVqqejcTUPL1p5cjJG28bV7Tz7Vw1WMLEx/zz7py+q14rS1aVsew4qlv9de8GNNYfrz8yuZr8CnAuSrv/e4ysDBQ70JCvLgsSf0bFgnbhzu2JbPgEEamjRVExys5NlZPmxc77KJi3NQo4ZSCtdvyJ5CXcOGehMUpOTJJ3xYW6Is2LYtj8GDNTQr1PXCCz6sKyz/f/zRwqxZPlSvrqRRIzVbtwQTHCxtvPYVahp07Xn1hA8bS2jasS2vKFZBwUqemSV+pmVmOpk3t+I8rwBSDiWjC9FRp09dNAHetJjSkqvbLl/X3ml3cnjBQe5+pgMAxkQj+nB9ebl7U+Qeu4pnsC9B3Zqg9tcSMaEzhj/EdcHsAxfRNYrAv0MD1P5aaj85kGr9Wpa6l/FMAgVpuQT1aFpe7t80+w/nYbE60evKzhuro0306aalR2ctNSLUPPNIQIVvLFdFTZIjKCrWVUmQ9wCoROQnZ+Fdq1rRa02tauSnZItsrFfS8PBUEfPst1hj0/FpHknkkwOLPjPHJBI3bxOC3UG1oW0JG9upPCWUiSHeQmgDn6LXYfV9yEwQV+gDa2rp+mAdPh+3FwD/cA1Pb+wKwLDXm6ILcI8KpcQYqdM2sBw8vzHxsXYaRLmzWIMoFfFx4ml6BfmgKpYL1Z6QlOi2eee1HKY/pmff3oox7fVyrJ0mUe6NBJtEeXI1Vtx4yssXUJfQlJDk0vT8E7506ptCvVaukeSnZvhwVwvpR/SuXLXTqJFbV+MoNVdLxCovT0BdbA9FT7WChESxdkEQeO2tHD76wN/VoJaQqpyv6hdLgw2i1CSUiFV+vlAiXylILozVis0hANhsApcv2EmIc9C4mbSbY165aqdxI7fDTaLUxMaJ01Z+noBa7U5TarWChMKy4pmZPnTtl0qju1wdPDNn6GlVAfJVXKyDhsXKwIZllIH5eQIqkS53GRgfa+fUSTs/t0jFy0vBhPu0PP6k9I2xq7F2GhXTFRWlIq5keZGPuBxUQ2KhrrNn7Rw5WsDzs1xT6B+couPxx6XVFVfiedUwSlUqXxXkQfF9ZNVqSC72vHr71RweeVzPvj0V43kFYEzMxb9eQNFr/3oBGJOu36CKWXOOsLbh+Nd1fceUZMRwLoNf+v6A0lNJw5GNaPFgqzvt9g3JT87Gu7a7LuhdK7jsuqCXmrPPfov1agY+zWtS+6mBIhtBEIhfvJNaj/eT/HlVFjOn+gMQvb3s0fLLsTaaRbk7m5o18mTO59LO5vonqqImmcqJ9EMEMjeNw5SHh7e7UqfUeuG0Foj2AbBnm8n68yzVp/ak+TePodR6kfDlNgBs2WbMZxKI+mwy9d+bQPr6Q+QculTuOkqSl2vH09s9yuOpU1JgcYjWKyeezuHvH2N5bEUnXvu7N7XbBLLxg7MABER44+mtxJpjY9UrJ8iMt9ByUHi56yhJTo6AVut+qOr0CixmQbRmsnNXT7ZuyuPyRTuZBgeffWQkz+r6fPuWPEwmgWGjtOXu+/XIznGiK9Zz7aNXYCqhqVc3DWujrcRcsJFucPDO/+VgKdT0xnvZ1K+rIvZUdfbtDGP1egt79ks/XTQnR0BfLFZ6vQJzCV09umlYH23lwkUbGQYH732YizVPvP71l5UWGjdW06KZ9I2vqpqvckvkK20Z+apjVw07Nlm5ctFGpsHBgo9yi/LVNR4Ykc6Y/mn07Kehek1p+8Kzc5zotO58pS8jX/Xo5sX6aAvnC9Pfux/mYM1zxfKt93OoX1fNpZPh/LUjlF/XW9m7v+z1peVJTo4TrU5cBpbMV/d082JLdB6XLtoxGJzM/ciEtTBWSqWCDp082bm7Gku/CWDFzxa2VIDp5Tk5gkhXWeVFt66eREfncfGiHYPBwYfFyvaMdAcHD9jYFB3Ezz8F8tXXZn7/XVpdOTkCun+IVedunkWxyjQ4+OQjY1Gstm12Pa+GV6DnFUCBsQC1tzt/q7Vq7BZ7qX0AAPJz8zm34gwtp95V9J7CQ0FYm3CGrxpNz7l9uLD+PLG/XS0P16+L3ZSHUuPuiblWFyy+D4Aty0zm7rPUnNqTFt8+ilLnRdwX20X3Mew4iXedaujqh5Wb77eTrGwH+mJp1kfvQWZ25T4RqCpquuMIFeyqJMgdAJUIpV6D0+ruWXdY8vHwUqNQuSv5Hp4q/Ds0RN+4BkqdhpDh7cg9cqXos5BR7VH76fCOrIZ/5yiMR66U+j/ljcZXRb7FXcDlm+yovZUo1e7keXpHKo27h1K9qR/evmq6TavLqa0pCIIrt535LZW5Q3bjdAhM+6Y9Wn/pG2B+fq4K1DVMJgFvb4VoFK9rDw0TJ+mYNM7AyIEZNG+pplqIEmOukznv5/LW+35SuH5d/P08MJncFadcoxNtCU19e3ozfYqegWNSuadvCq1beRIW4orlynUWnnnMl6BAJc0aezJyiJZ10aWn0Zc3fn6uBtc1jEahlK7ePTVMnaxn2JgMevZL566WnoSGiKcnf/ejhcn3S7+sBqpuvvL1czX4r2E2CWhKxKpLDw3jJ+l5aFwG4wam07SlJ8ElYvXjhmr8HF2NfX/ms2G1eGZEeePv54HJ7M5XZaW/Pj29mTpZz9Ax6XTrlyZKf2vWWnnyUR8CA5U0baxm+BBvNlSIfOWB2VSsDDSWLgO79/Di/slaJo7LZOiADFq0VBMS6tL15mxfXnjZBz9/Dxo3UTPxAS27d0nfseHnpxDpMpahq2dPDZOn6Bgz1kC//hm0LKbLS6PgkRk6goKUNGyoZuAADbt2SztqXlJTWbHq1kPDfZN1PDDOwPDCWFULdT+v3vmgYj2vADx9PLFZ3COoNrMNlUaFh6p09Tfu96tEdKiOp95dzrV/viNtHm+Hl68XgQ2DiBrViKR9CeXi+/VQ6TU4yqgLepSoCwZ0bIC+cQ1UOg2hw9uRU6K+l77lOCED76KyEuCvxFiiPhLgV/GWDN0KVVGTTMVE7gCoRHiFBWC96t5Z3Ho1Hc8wf5GNupofTpt76qjgcOLh6er99qzmi1DgbhAIdicKT+lXgQTW0JJ6wT0lL/WiicAa4vXTglOAYjPUBAGcDicKhYIzv6Wy+tWTjHmvBWM/aImXTnpNADUiVVwotrb4QoyNGpHigtxkcjJ2opY/D4fyx/5QmrbwJKqxisQEB7FXHPTokEajyGR+XWnlnddyeXyatGuw69RScSbGXZk6E2Ojdi3x7200OZkyUc+lYzWIOVyd1i09adbYVaFyOAQUJeLocEjfZVq7loqzxXSdjbFRq5Y4VkaTkwcmajlzNJwTh8Jo1VJNk8buUZjUNAeZWc4KMfUaqnq+cseqrHxlNjkZPVHLb4fD2b4/jKYt1DQsjNVzMzJJSbKjUCho0tyT7n00nD4hbeOrZPo7c530N3mijnNHIzh9KJy7WnrStFCTwynOVwiu96SmZqSS88XKwPMxdmqWUQaOn6hl3+EQ9hwIoXkLNVGFyyGWLTaTleWuDGdnCWg00k9VrhWpIqaYrpgYG7XK0DVxopajR0I5dDCUli08adzYpSsiQkl+vjs+Njt4SbxsvmakShSrCzG2MmM1bqKWPYdD2X0glGYtPIlqpCIhwcHVKw66tU+jYc1k1qy08vZruTw6Vfo9Q/TVfci+nFX0OvtyFvrqPmXaxv0eS91+9UTvnfnxFHk57tkZ+Tn5KL2kLQu9wv2xxorrgl7h/iIbzxBfnCXqex7F6nu2TBN2oxVdA+lndf1b6kSqORXjLrtPnSugdqS0y7n+K1VRk0zFRO4AqET4tKyFLSOXrN1nsOWYSfllD4ElNm4JuKcRuYcvYzoVh91oJWXV3/h1aOD6rGtjUtfsx5ZlwnIplew95/Av/ExK6rUPIicljxNbkjFl5vPHoku0GhQhsmnUPYRT21JIOpuLNcfGriWXaNzDtRPv719eZNALjWl4T7Wybi8ZHTp5kpzkYPMGK5kGB1/ONzFkuLgBlp7mZET/dC7E2EhKdDBvjpExE7Q0aqLmXFx40TVijDevvePLgiXSrsHu1llDQpKD1evNpBscfPRZLuNGiKd8pqQ66NQnhTMxBcQn2HnnwxwmT3Stbx3UT8vchblkZTs5daaAFWstDO4n/ZTRLp29SExysHa9hQyDg0/mGRldQldqqoPufdM5F2MjIdHO+x/lcv+97tH+TVvy6NK54mx+VVXz1d2dvEhNcrB1g4VMg4Ol840MGi6OVXqagzH907kYYyM50c7CObmMmuCKVXqqgyXzjaSmODh+OJ/NG6y0aiNtp03XwvT3a2H6mzsvlzFlpL8ufdM4G2MjPtHOex/l8EBh+hvQ15vPFhrJLsxXq9ZaGNhX2k0oATp2dpWB0RusGAxOPp9vYtgIjcgmPc3J4H4ZnC8sAz+ZY2L8vS7fD+wvYPabuaSlOog5Z2P9Wit9+0ufxzp39iQp2cH6DVYMBgfz5psYMUL8e6emOenbN52YGBuJiQ4++sjIvfe6Yjp0qDeLFplJT3dw6rSNTZvy6NtHU9a/Kjc6dPYkJcnBpsLn1RfzTQwZUfp5Nayf+3n16RwjY+/V0riJmvPx4UXXyDHevP6OL58vlX7PkPC2EZhTzVzdcZm8LCsnvj5OnX51S9kVmArIOJNOcFNxeZd6NIVDc/djybCQdSmLK1svE9m9Vnm5XyY+LWtRkG4kc/dZbNkWkn7eW2oTv4B7GpFz+DLGU/HYjVaSV+0T1fey/j6Pb0tpdfxXRg3Ss32XhV17LSQk2fh0cRb3jiy7c6eyUBU13XGknvIvLwGQudMoVErqvjaalBV7OTNtEZ7V/Agd2R7DjhOcmrIQcI3y13p6ELGfbeL0Q1+g9Pai+oM9AQgZ2QFN9QDOzFjC5XdXE/FgT3SNqkspCQCl2oP75rVm1+JLfDLoT/zCNdwzuQ5H1ibwUd8/AKh1VwD9n4nix6eP8FG/PxCcMPilxgCkXTaz6uUTvNx0c9G16pUTEipyoVYrWLg0gEULTQzonk5EhJIpD+v4daWFXp3TAKhTV8XESTruG2PggbEGho3ypnsvaSuCN0KtVvDzV8HMmZdL6y7J1Kyu5IlHfPlhhYmm7V0b+zWop2b6FB/6j0xjwOhUJozW0b+3qyI5+1V/9DoPWnRKYuzkdGY96UuPrtLrVasVfLcsiE/nm+jQNZXq1ZU8Ol3PzyvMtO6QAkD9emqmTtExZGQ6w0ZnMG60lr693b5Hb7bSVuKGZHGqcr76dGkQSxeaGNo9lbAIJQ88rGfdSjP9O7tiVbuumgmTdEwZk86DYzMYMkpL18J89dr7/lw4Z2dIt1RmPZ7F6Ak6+g+VthNKrVbw/bJg5s430q5rCjWqq3h8ug8/rTDTsoNrY7/69dRMm6Jj0Mh0hoxOZ9xoHf0K89Vbr/qh0ym4q3My904x8OwTvnSvIPnqy6X+fLHATJ9u6YRXV/LQwzpWr7TSrZNrBLNOXRUPTNYyYXQm947NZMRoDT0KY/X2u74YDE56dc3gkanZPPmMng4dpe8AUKsVLFsawPz5Jrp2Tad6hJLp03WsWGmhQ0dX2V6vroopk3WMHGVg9BgDo0d707tQ1yPTddSpo6JHz3SmTcvilZd9aCNx2XHtefXlAhP9CmP14MM61qy00LOT+3l132QdE0cbuG+sgeGjvYtiVVHxUHnQ48PenFx+grVjV6ML09F0YnMuRV9gzfAVRXYJe+IJaBiIwkM8w6T9rE7kZeexdvQq/pi1g5bT7iKsjbSj5h4qJfVfH0XSL3s5OW0RniG+hI68m4ztJzg++XMAvKr5UufpQVz9dBMnHvwSpbcnNR/sUXSPrD0x6BpLX/+7Vb5ZkUu9u11LGYKDlHwzP5QZs9Jo1y+egb10jB9e+RrLVVGTTMVHcW2tp8ydR6FQCACtN70stSu3ldG1jkrtwh1hpM8xqV247dRQVc2pZHlC1dwkZ3F2xTub+b8y2Ef6ToQ7QU2l9FPTbzeZTmmPR7xTaCrgjuf/FXMVrcrNThogtQt3hLOZoVK7cNvZ12qV1C7I3ATK8AsACEIlOrOuDK61qWp9/pHUroiIffR5oOL/vhVjUaeMjIyMjIyMjIyMjIyMzM1SRTs/7zTyEgAZGRkZGRkZGRkZGRkZmf8B5A4AGRkZGRkZGRkZGRkZGZn/AeQlABKQeVH6nXFvJ99YO0jtwh2h9V1XpXbhthOuNEntwh3hsr1qFmU9dWelduG2kydUzVhdqILL5SvOlpa3l/QqmAav2oKlduGO4K+2SO3CHaEqrpf3CDsvtQsyN0WFXpp+61TspfYVFnkGgIyMjIyMjIyMjIyMjIzM/wBVrxv8f4z82DgMP6/CnpWNtkUzgsaNQqFUimwSP/gYW1Jy0Wt9x/YETxhT3q7eEOvFRFK/XI8tPQef9o0JfXgwCpVYR35cGilfric/NhXvqJpEPDsWpU6DM99G2tJojAfO4eGpwq9Xa4LH95RIiZiY41bmvZJMWpKdTn31zHwnHJVa3Fu59utMVi81YMp10qytN0++H05wmBqLycHnb6ayb4eRgGoqJj9Xjc79fCVS4ubwsXyenJVFQqKDwQO8+eT9ANQlNH3/i4kPP83FZBIYPtib/3vbZZOZ5eCJWVns+jMPrVbBlPv0vPiMn0RKxJw5ns97LxpISXLQvZ+WF98NLBWrDStMLJuXg8XspNdALc++6bLJy3Py0WuZ7NpmxctLwbDxeh5+xl8aIcU4czyfD14ykJJkp1s/LS/MDiqlaeMKI1/Nd2nqOVDHM2+4NX38eia7tlnw8lIwdJyeac8ESKRETFWNVVXTBHDqeAHvvJhFSpKDnv00vPxu6fJi7QozS+blYjEL9B7ozaw3/VGrFWRnOXj7hSwO7M3Hx8+DaY/7MnKCTiIlYqpi3rp0wsySV6+SkVxAuz4BPPRWJCq1eJxo0/JUor9KxZxrp1FbPQ/Prk1gmCfjGxwqdb+mHXx47buo8nL/uqSeNrBz9kFMKRbq9qhBj5faoiyma/sb+zm74Uqp702JHoJaq2LnOweJP5CKWqOk2aj6tJ/erDzdv2mGT07i3ZeCaBpV+qjM6B1mZr2VTma2kwcn+PLuy1VzxoiMjMzNIc8AqMQIDgdpS5fj16cn1V+ZhT07m5zfdpWysxsyifz4fWrPm0PteXMqXONfcDhI+r+fCBzRhTrzZmLLyCFz/V6xjc1O4vs/EjSiC3U/fwqVn46sjX8DkBW9D1taNnXmzST82bHk7jpOzh/HpZAiwmEXePuRBMY+EsyS7XVJT7azeqlBZHP5bB7ffpLOa1/U4Os/6uEfrGL5HNf52Ms+SMNqdvL1rvo8+V44n72cQkaKTQopRdjtAhMfMvD0Y74c2h1GYpKD+YuMIpuDh/N5fXYO3y8JZv/vYcTGO1iw2GUz57NcBCcc2h3O2p9C+Oo7E7v35EkhRYTdLvD8tHQmPerHyt8iSE2288OSXJHNySP5zH8/iw8XVePn7REkxdv5YanL5pevjCTF21n5WwTvLQwmerWJTWukXW5htwu88HAa98/w45ed1UlLsvPjkhyRzakjeSz4IIsPvqzGj9uqkxRv58dCTSu+yiUp3s4vO6sze2E1Nq0xs1liTVB1Y1XVNIFL19PTMnjwUR9+/S2UlGQH3y0RlxcnjuTz2fs5fLwoiNXbQ0mKt/P9UpfNF3NzUSgU/LwplNfeC+CDN7KIvSxtGQhVM2857AJzHr3I0OnhzN3SDENyAdFfpYpsYs9ZWPlZEs8srMe8nc3xC1Lz89xEAH6+0FZ0dRocSPfR0jcynXYnG5/5i7aTm3DfmoGYUiwc/f6cyKbPW+154sj4omvYwm7UvDsUn3AdB5aeQRAE7l8zkOFf9ODk6ovEH0y9zn+ThgNH83hxdgYbt5nL/NyQ6WDSzBQWfBDCwa012bTTzM9rjWXayshUNhRCxboqC3IHQCUm7/xFlP7+6Fq3Qumjx79fb8yHj4psHEYTHl5eeKgr7vnvlpNXUAX54du5GSo/HUGju2H866TIxnT4PJqoGujbRaHy0xE6Yyh+vVsDYD0Ti3//u1H56dA2isSvV2usZ65KoETMsb/NVAtX0W2wL/5BKu59LJg/Nogr9d46D5Qq16iRQuE6zUTn68qWe7cbGftIED5+Spq109J9qC9/bZb2ob1rTz7Vw5WMGqYlOEjJ80/6snqteI1m9DYrwwd706KZJ9WClbz2gh+r17ls/PzERY6HhwIfH+mLoUN78wiNUNJniI6AICUPzvRj63pxZWr3dgu9Bmpp2NSTwGAlM573Z3uhzdH9eYx5wIeAICUt22kYOk7PkX35Ukgp4vDePELC3ZqmzPRnWylNVnoO1NGwqReBwUoeed6fHRsKNR3IZ9Q1TW01DBmn58h+6TtrqmKsqqImgAN78wmLUNFviJbAICXTZvqyZb1VZPPH9jx6D/SmUaGux5/3K7I5dqiA+6fpqRGpolM3DS3u8uJijPSbLVTFvHXq71yCwjzpNCgQ3yA1Ix4NZ8/GTJGNRqtEeW3eqAIEAbQ+ylL3ijliwpBcwD1Dpd/vKP5gKvpQLQ37RaIN0NBuahPOb4m7rr3T7mTPZ8fpOstVv/DyEdedFAoFXrqKVZ/afzgPi9WJXlf2s3R1tIk+3bT06KylRoSaZx4JkDsAZGT+x5GXAFRibBkGPMPDil6rw8OwG8QPbLvBgCAIJH7wMXZDJpoG9QgaNwqVX8WYdg1QkJKJV2RI0WuvyBAKUrNENvmxqXh4qol9eSkF8Wl4N6lN2KNDAQh9eDBKP22Rbd7VFLRNapeL7zciOdZGrYbuqXi1orxIiRePXoVHejLm4UCeGH4VgJAIFUt21APAli8UdQ4AqNUK0pKkHf26ctVO40buyk/jKDVX48QV8vw8QTTF11MNCYkOAJ6d6UuXfqk0vCsJgCdm+HBXC+m3GkuItVO3oduPelFqkkrqyhdEU3zVngpSkly6Xng3iIAgd+XrwlkbrduXnoZZniTE2qgb5dZUN0pNUrxYU0G+gKrYU0CtVpCS5LKZNVus6eLZAu5qr7mzTt8EVTNWVU8TuHTVb+hOYPWj1CTcgq5fNocCYLMJXL5gIzHeTqNm0je+qmLeSo3Lp2ZD76LXNRt6kxYv7kQKjfRiyNQwXhnp2pw0OMKTuVvF0+EFQeC79+J56K1aKBTSb86Vk2AiqL67vhNU35+cxOvPtji56iI12oUSVNf1nXYPNuGne7eyrO86AFo/0IiQJtJ3bBRn5lR/AKK3lz0D4HKsjWbFlgU0a+TJnM+ln0kjIyMjHdIPvcn8a5xWKwovd6Hu4eWFkJ+P4HAUs8nDMzyMapMmUuOtV1DqtBh+rli7zzrNeXho3JUpD29PhLwCBLtbhz3bRO6eU4RM6kfdL59BqfUibdlmANQh/nh4eeIwWUle8Cu2lCx8uzQvdx0lMeU48Na6s5hW54HV7MRuc88RunDSyvpvs5i/rjarjjWkaTstX77jml7YuouOtV9nYjY6OHfMyo41OeRbneWuozjZOU70WnelzkevwGQWsBXT1LObhnXRVs5ftJFhcDD7w1ysea7P33w/h3p1VVw5GcHeHaH8ut7C3v3Sj1Qac5xodW5dWp0HFrMgilWHLt78tsnC1Ys2sgwOvpiTTb7V9Xl4DRUabw9ycxy8/VwGibF2+g2Xdq2yMdeJVntjTXd30fD7ZgtXLxaQZXCwaE5WmZreeS6DhFg7fYdJv/66SsaqCmoCyM1x4q0rXgYqsJQoLzp28WLHJgtXLtrINDhYMCeHvBLl3KSRaYwbkEaPft5Uryn9uEVVzFvmXAeaYs8rb52SPLMTu80di8unzGz9Lo33fm3MssOtaNRWzzezxaPpu381EBnlTZ2mWioC+bkFeHq704ynVoXNYsdhK/0szcst4PjPF2j/cNOi9/bOP4F/pA/TfhvBvb/058K2OBKPpJWL77eLrGwHel3x57YHmdmOG3xDRqYSIVSwq5IgdwBUYjy8vRHy3Y0nZ14+Ck+1aBNA78ZRhD0+Hc/wMJRaLQFDB5F3/gKCU9qGZHE8dBqc1mI6LPkovNSiTQA9PFX43N0I76iaKHUaAgZ3xHz8UtHnxgPnuPLEfHA4iXxnCkof6Ssfej8lFrP7d7aYnHh5K0SjXXu2GenQS0+D5t7ofZWMmxHMn5tyEQSBGa+HYcx2cH/niyyanUr7XnoCQ6St/Pr7eWA0u0u4XKMTrbdCNOLfp6c30ybrGTwmja79UrmrpSehIa6iZvVaC08/6kNgoJKmjT0ZPkTL+mjpj3ny8fPAbHLHymxyoikRq049vBkzyYcZ41O5f1AyTVp4ERTiTqO7tlkY2zMJhx2+XBGKn3/pqbHliY+vq1FyjbI1aRn9gA+PTUhl0uAkGrcUa9q9zcK4Xok4HAJf/BImuSaoorGqgpoAfP08sIh0CWhKlBf39PBm/CQ908anM2FQGk1beBIcIvb9h/Uh/LQxhH1/5rFxTdmjnOVJVcxbOl8l1mLPK6vJgZe3h2gTwIPbsmnTy5+6zXTofFUMeyScfVuyEAT3b/Hbigx6T6hWrr7fCC9fTwos7tHuArMNlUYp2gTwGhd3xhPZMQwvH/eARMzWWNpMboy3vxfBDfxp0CeSSzsTysX320WAvxJjsXyYa3QS4Cd9+SAjIyMdcgdAJUYdHERBsnt3f1tyMqqgIJGN9dx5rBcuFr12mC2gVKHwqDih9wwNJD/e3aOeH5eGOlS8I7Iq2A+nzT3FUnA4UKhdjWHjgXOkLFxL+MyRhD85Cg9v6ae+AoRFqok97+7YuHo+n/Ca4unuTgeuxf/XEAScDtc6Q6cg8MrC6qw5EcUnq2qTbxWo00jaaaK1ayk5G+OuTJ2NsVG7lrhTwmhyMmWijvNHq3PmUAR3tVTTrLFLt8MpiI6gFQRwVIC+qOqRKi6fd+u6FGOjeqRYl9nkZPi9ejYfqsHGfTVo3MKT+o1d05F3bbPwzvMG3pgbzFufBqPTS5+/qkequRRTUPT68nU1+RB9sCbr/65J4+Ze1C9c4rF7m4XZszJ44+NqvPlJtQqhCapqrKqeJoAakSouFtN1McZGjUhxw8NscjLqXh07DkWwdV84TVt40qBQ1/OPunbZVygUNG7uSfc+3pw+If3U5aqYt0IjvYg/796fIf68lZCa4mep0ymIHlcI4LQLRVP9s9NtGLPs1G0m/eyTa/hV12O45N6g0XAxB78a+jJtL/2WQFT/WqL3hBKaBQSczko0zAfUiVRzqlh6PXWugNqR0i+lkZGRkQ7pnzoy/xpNw/o4snIwHzmGw2gie9tOdG1bi2ycBQVkfPMj+XHxOCxWcnb8hq5lxTrCRtu8DraMXHL3nMKeY8aweje+XVqIbHw6NsV89CKWM7E4TFYy1+5B3851vJBh1S5CpvRHd1d9Kdy/Lq066khPtrM7Opdsg52fFmbQY5j4GL8OvfT8uTmXS2fyMOY4+OULA+17uSon0T9kM++VFHIy7ezfaeT8CSt39yi74lJedOusITHJzpr1FjIMDubMMzJmhHi2RWqqg3v6pnI2xkZ8op13P8pl0r2uCuHAvt58utBIVraTU2cKWLXWzMC+3mX9q3KlXScNqUkOtm80k2Vw8PWCHPqXmEKdkebgvgHJXDpfQEqinUUfZzN8vCsey+bl8PTrAXTsJr2Wa7TtpCEt2cGOQk3LF2TTr8Q0Y0Oag/sHJnG5UNPiuVkMm+ADwFfzs3nqtUA6VCBNUDVjVRU1AbTr5EVqkoOtGy1kGhwsXZDLwOHi8iIjzcG4AWlcPG8jOdHOwo9zGDnepT091cHSBUZSUxwcP5zPlvUWWrWRfs+Qqpi3mnbwITOlgL83ZZJrsPHrF8mlNvFr3dOffZuzuHrGginHztovk2nTy7/o84Pbs2jW0aecPb8xNdqFYkqxcH5bHJasPA4uO0PUgFql7PKNBaSeNhDWXDyIUrdbdQ4tP0tebgEZ57M5vzmWut2rl5f7t4VRg/Rs32Vh114LCUk2Pl2cxb0jK1acZGRkyhdF8albMncWhcJ1QETteXNu2z3zY+PI+HkVjqwstC1bEDR2JOZDR8jevI0ab74CQPbmbeT+tRccDrybNSVo1DA8vG9fxcOr+n+fkmm9kEDqlxuwZeTg06ExodMGk/vnSQwrfqfuF08DYPz7NOk/7sSRY0bfLoqQhwai1Go4f+9shHzxqJBvj1aEPz7iP/n02V0//6fvA8Qct/LZy8mkJdm4p78vj78dxu/rc/j+swy+2e3qsNi2MpufFmaQm+WgbTc9j78dho+/ElOug/ceT+TsUSt1Gnnx5Hvh1Grw32Y3dPT670dNHT6az5OzsohPcjB0oDdz3wtg5a8W3p+bw8l9EQB8sjCXzz434uur4PGHfXh4iquykWt08sJrWWzanoevXsETM3yYNvm/V0Ri7P+9L/P0sXzee8lAaqKDHgO0vDA7kC3rzCz5JJt1e2oA8M0XOXz3ZS56Hw/unerD2MmuDp2ujeLIs4rL0sFjdLw+578dg+UU/tsmWqeP5fPBSxmkJjnoPkDLrHeC2LrOzNJPs/n1L5emb7/I4ftFOeh9PJgw1Zcxk1yaujeOLaVp0Gg9r/1HTR634Zycihir/0pF1OTJf18nfPJYAe+8lEVKop1eA7S8PNufzessfPlJLpv2hAPw9Re5LP/ShN5Hwf1TfRg/2dWxcfG8jXdezCLmjI2AQA9GT9Tx0GO+N/p3N0We8N+XUlW0vHXV9t/T78XjJpa8GktGcgHt+wXw4JuR7NmQyar5Scz/3dUp/8eqDNZ8nowp206rrn48+GYken/X7/nu5PN0HRFEl2FBN/o3t8Re43/v2E85ZeC32Qcxplio36sG3V9sy/ktsexfdIrJG4cAELP5KmfWXWHElz1E38032dj90WEu70rCU6eizaTGtBjb4D/79En44f98j5LUu/sK67+LoGmUF9+syOXtOQYuHagDwMbtJma9lUFWjpOH7vVl9ku3v7zzCDt/2+8pc/u5NmNHEP5jBUNi7kSb6nZw9YnngIr/+8odAOVIRU2s/5Xb0QFQEbkdHQAVjdvRAVARuR0dABWR/9oBUBG5HR0AMuXD7egAqIjcjg6Aisbt6ACoiNyODoCKyJ3oAJAauQOgciB3ANxZKksHQNV7CsrIyMjIyMjIyMjIyMhUaeQxhX9H1Rw2k5GRkZGRkZGRkZGRkZGRESF3AMjIyMjIyMjIyMjIyMjI/A8gLwGQgOxlq0q951O/KT71m0rgzX/HFlGhl7n8a8KURqlduO0kO6rmml6Nomr2Zf6a2/qfjSoZ3fTnpHbhjnAyr4bULtx2Omkv/rNRJeSSLURqF247GkXBPxtVQlrrY6V24Y7Q4dhoqV247RzoL7UHMiXZsGEDGzZskNqNO4uES+0tp05jOX1Gsv//X5A7ACQgov9YqV2QkZGRkZGRkZGRkamiDBkyhCFDhojeW7JkiUTeVD20zZqibSYevDX+vU8ib24NuQOgkmNNjiN5y0psuVn4NGxOeN/RKJTKMm2NF0+TvmcbdSc9Xc5e/jN5FxNJ+3Idtowc9O0bEzJtCAqVWEd+fBppX64jPzYV76iahD0zDqVO477H5WSy1uwm/Llx5e3+dTl1vIB3XswiJclBz34aXn43ALVa3Fu5doWZJfNysZgFeg/0Ztab/qjVCrKzHLz9QhYH9ubj4+fBtMd9GTlBd53/VH6cPF7A6y/mkJTooE9/DW+861dK0+oVFr74zITZLNB/kIaX3/QtZfPE9Czad/Jk4iTpNYErVm+/mE1yop2e/b159V3/MmO16DMjFrNAn0HevPCmX1Gs3nohm/178vH182DaTB9GVYBYJZ7KZv1bx8lJttK4VziDX22OUi2eLfH395f5+5tLWHNt1GodxNA3W+Ab6j4mNPlsDruXXGDc3Lbl7f51OXc8jzkvp7mO1+yr55nZIahKxGrzyhy+nZ+JxSzQfaCema9XE9lcOJ3H9wuzeOvz8PJ2v0xiT+by8xsxZCbl0bJ3Nca90bBUrP74Np7flsdjybFTr40fE95phH+o+GjQZU+eosHd/nSdWDFmJJw+ns+7hWVg937evFRGGbh+hYml83IxF5aBz73pssnLc/Lha9n8sc2Kp5eC4eN1PPKMn0RKxFw6YWLZq1cxJOfTtk8gU96qhapEvLYsT2HTVymYc+1EtfVh6uw6BIZ5uj77JoWt36RiMTkYMi2cwdOkT4cXT1j48pV40pMLaN/Hj4ffrlkqX21cns6GZWmYch00bqtjxrs1CQrzxJht54tX4jm5x4iX1oO+44MZ+0SYRErEXD5hYvlrlzAkF9CmdyAPvFWnVKy2fZPMlq+SsOTaadjGlymz6xIQ5spb279NZts3yViNDgY+HMHAqdWlkCHCFJNE7Lwt5KfnENApilqP98OjRJ3JGpvO1XmbsVxJR9+4OvVeHo5Kp+HggPdL3c+nZS0afXBvebkvIyMjMVVz3uz/CILTQfyarwjq0JN6017ElpuN4eAfZdo68qwkb11Zvg7eJILDQdKHPxIwogu1PnsCe0YOWRv2iG1sdpLe/4GAEV2ovfBplH56sjfuBcBhziN393FSF6yWwv3rYrcLPD0tgwcf9eHX30JJSXbw3RLxsoITR/L57P0cPl4UxOrtoSTF2/l+qcvmi7m5KBQKft4UymvvBfDBG1nEXrZJIaUIu13gsalZTJuhZ/Pv1UhOcvD1YvExkMeOFDDnPSPzFwewcUcwCfEOli8R22zeYGXb5rzydP2G2O0CT0018OAMPet+DyU1ycG3i8VHJh4/UsAn7+XyyeJA1uwIITHezndLXDafzzWCAlZsCuG19/15//VsrkocK4fdyU9PHqTLQ/WZub4HOSlW9n5zSWSTEpPL7wtjGPdpO57a3AtdkBc75rmm6OcZbRzfkMCal49K4f51sdsFXp2exL0zAvh2Ry3Sk+38sjRLZHP6qJUvPzDw9pcRLN8aSXK8jRXLXDamXAfbfs3lvWdTpXC/TBx2J0seP0nvqZG8uqk9WSl57FweL7JJjDERPf8KU+c1443tHfAJ9mTDJ5dFNkc2p3FsW3p5un5D7HaBZ6cZmPyoL6t/CyM12cH3JcrAk4Vl4EeLglm5PYykeAc/FJaBP31lIjHezurfwvhgYRDRq81Er5H+2FmHXeCTRy8wZHo4H21pgSE5n01fpYhs4s5ZWPVZIk8trM8nO1viF6RmxVxXTI/szGLvegOvfN+IV79vxJZvUkiJlbY8dNgF/m/GFUY8EsK8rY3JSLaxflmayObqOSu/fJrM85/X4fPfmuAfrObHj5MBWLUwFcEJ87Y35vXl9dj6UwYn/5Z+GZ3DLjDv0XMMerg6729pRWZKPlu+ShbZxJ8z8+tn8cxcEMWHO1vjG6xmVWGsjv6Wyd/rM3jxu6a8+H1Ttn+TQmqsVQopRQgOJxffWU3Y2A40XzydgvRcUtccENk4bQ4uvLmK8DEdafHVDNT+WlJ/PQhAu80via7A7k0I7ttCCikyMv8doYJdlQS5A6ASY469iNrHH7/Gd6HS6gnu1IecM0fKtE3ZuRZdZMU8T9dy8grqID98OjdH5acjcFQ3jH+dFNmYD5/Hu2FN9G0bofLTEfLIUHx7twHAkWsm70ICSj+9FO5flwN78wmLUNFviJbAICXTZvqyZb244vDH9jx6D/SmUVNPAoOVPP68X5HNsUMF3D9NT41IFZ26aWhxlxcXY+xSSCli394CwiOUDBzqTWCQkhkzfYguoWnntjz6D9LQuKmaoGAlTz3vw6YNbpusTCfzPzHSpbtXydtLxoG9+YRGKOk/9FqsfNi83iKy+WOblT6DXLEKClYy83lftmxwx2rSND01aqno3E1Dy9aeksfqyoEM/MI0NOtfHV2gF90ebsDJzUkiG0+tEg9l4QifAhAEND5qAMxZBSSczEIfVHHiBHB0r4Vq4Wp6DvbBP0jF/Y8H8tt6cUNjz3Yz3QfqadDEi4BgFVOfC+K3Da7OmuxMB2eP5REQXPZMKSk4vz+bgDANbQaG4hPoSb9HanM4WtxB4VUsVgoFCAJ4+7on8ZmyCti04AqNuwSWq+834mBhvuo7REtAkJKpM33ZWjJfbbfSe6A3UYVl4KPP+7Gt0Obo/nzGPqAnIEhJq3ZeDBun4/C+fCmkiDj9dy6BYZ50HBSEb5Ca4Y9W5++NBpGNRuuBsjA81+Kl9XG98fuKdMY8XYPgCC9qNtTy7rpm+Aaqy1uGiJN/GwkKU9N5UAB+QSpGPxbKXxvFHWsarQcequJpUEDr68pHOl9xfvLwUKD1kT6Pnd2XQ2C4F+0HBeMbqGbIjBrs35ghsvHSKt26KIxVoZ5dK9IY+XRNgiK8qNFQy1trW+AbJG2sco9dxTPYl6BuTVD7a4mY0BnDH+J1yNkHLqJrFIF/hwao/bXUfnIg1fq1LHUv45kECtJyCepROfegkpGR+XfISwAqMQXZGXhVc08b1FQLw5aTWcrOeOksdmM2wZ36kPp7xdsMxJaaiWeke1Mmz8hQbKniikd+bAoKLxXxryyhID4N7ya1CZkxzGUfHkTIQ4PI/eMo5kPny9X3G5EQa6d+Q3cWqx+lJiFO3CjMzxdEUyzVngpSklwb9f2yORQAm03g8gUbifF2GjWTtuIRH2unQZRbU4MoFfFx4o0FC/JBVaxkUXtCUqLb5p3Xcpj+mJ59eyvOplXxsXbqR7l/2wZRahJK6MrPF0roUpCc6Irnis2u9OuKlZ2EOAeNJY5VZryFkPq+Ra9D6vuQlSAePQ2sqaPzlPosHv8nAH7h3sxc3wOAoEgdg15uzrF18cTsqjij5UlxNuo09Cx6XSfKk6R48WyLgnwBlcqdr1RqBWlJLpsatT158q0QtqzO5e+d0o8mA2TEWQlv4F4yEtFAhyFePCIcXNOb3g9F8tGYwwAEhHvx6qb2RZ+vfOcCfR+uxfn94rJTShJi7dRr6M4H9aLUJJYoAwtKlYEUlYEvvRuAf5B7nOL8WRtt2kvfIZUWl0fNhtqi1zUbepMWL+6YCInUMHhqOK+NdDXMgiI8mbPVNcoaF2Ph4nETS1+9gt0m0O+BUIZOjyg/AWWQEldAZEP3krrIhhpS48VldFikF8OnhfDCCNdzNjhCzbxtjQEYNSOU54bGMLXjaQCGTQuhXjMtUpMWl0eNBm4/ajTUkp4gzlshkRoGTo3grVGugYegCE/e39IKgPgYC5ePm/j61cs4bE563x/O4OnSLgHIT87Gu3a1otfetYLJT8kW2VivpOHhpebss99ivZqBT/Oa1H5qoMhGEATiF++k1uP9UCiq5mbOMjIyZSPPAKjEOPKseKjdFWEPTy+cBfkIxXZ6d+TnkfrbOsIr8MaDTpMVD00xHd6eCHkFCPZiOnLMGPecIviBftT+4lk8tBrSl22Swt2bJjfHibfOncW0OgUWs4DN5p4j1LGLFzs2Wbhy0UamwcGCOTnkWZ2i+0wamca4AWn06OdN9ZrS9tnl5Ahote6Kgk5fWlPnrp5s3ZTH5Yt2Mg0OPvvISJ7V9fn2LXmYTALDRklfMSxObgld2jJ0deyqYccmqztWH+UW6brGAyPSGdM/jZ79NJLHKi/XhqfWPQLnqVNRYHHgsLnTV9LpbA78eIXpP3fhxT39qdU6kM3/d0oKd28aY4l85a3zwGoWsBeLVbsuWnZtNhJ7qYBsg51lHxtKxaoiYcm14VUsVl46JfklYhV32sjuHxJ5flVb/m//PdRr68+a91279B/fnk6eyc7dwyrGmutrGHOcaHXF8lUZZWCHLhp2FuarLIODz+e481V4DRXe3h7k5jh58zkDCbF2+g+Xvuww5zrQaN1pUKNTkmd2Yi8WryunzGz9LpXZvzZl8eHWNGrrw3ezXTva52TYOH/YxDurm/LS8kZs/TaV47uzy1uGCHOOA40oX13T5I7VpVMWNn2bwYdrG/LtkWY0aavn69mJAHw/J4nwOl4sP9iMudFR7InO4sxBU6n/U96Yc+x46W4cq6unTOz4LoU31zRn4aF2NGzry4/vXgUgN6OA84dzeWN1c55f3oQd3yVzYre0nWx2Ux5KjbtjTan1wmktwFmszmTLMpO5+yw1p/akxbePotR5EffFdtF9DDtO4l2nGrr6FavckJG5JaSe8i8vAZApb5Qab5wF7lEHZ34eCrWnaBPAtD82EHBXJzz9g6Rw8abw0HvjtLpHGpyWfBReatEmgAq1Cv3djfGOikSp0+A/qCOWE5fKul2FwdfPA4vJXckwmwQ03grRBlj39PBm/CQ908anM2FQGk1beBIcIp42+cP6EH7aGMK+P/PYKPH6Vz8/BWazu4QzmQS8S2jq2kPDxEk6Jo0zMHJgBs1bqqkWosSY62TO+7m89X7F2MSrOL5+robJNcqKVZceGsZP0vPQuAzGDUynacvSsfpxQzV+jq7Gvj/z2bBaPNW5vNH4qsk3u0db88121N5K0cZyZ3emENUjlIim/nj7qukytQFntiUjCBX3Kebj54HV7M5XFpMTjbdCNIrcvruOEQ/48/SEBKYNiadRCw1BIdJPR74eWl81eWZ35T3P5MDT20MUqxPb02nWI4jIpj5ofdX0nVaLo1vTsBrtrPv4EuPfipLC9Rvi4+eBxVQ8XzlL5avOPbwZO0nP9PFpTByUWlgGunX/sc3KqJ7JOOywZEUIfv7Sx1Hnq8RaLF5WkwMvbw/RxnIHt2XRplcAdZrp0PmqGPpIOPu3ZCIIAmovDwZNDcM3SE2NBt7c3S+Ak3/lSCGlCJ2fEqupeL66pskdq/3bcmjXy5d6zbTofFWMeCSEvzdnIwgCf23IZsTDIfgEqKgV5U2ngf7s25otgRIxOj+VKG9ZTXY8S8Tq8PZM7uoVQO1menS+KgZPr87BLYaiWA18qDq+gWqq19fSpm8QpyWOlUqvwVGszuSw5OPhpRZtAujhqSKgYwP0jWug0mkIHd6OnCNXRPdJ33KckIF3lZvfMjIyFQe5A6AS4+kXRF6Ge+OhvIwUPP3E6z+tSXGk7lzLmf97htifPicvJZ4z//cMjnxpN7Epjjo0gPw49xTjgvg01KEBIhtVsB9CgbsxIzgcKNQVewVLjUgVF8+7pyZfjLFRI1JceTWbnIy6V8eOQxFs3RdO0xaeNGjs6tl//lEDKUl2FAoFjZt70r2PN6dPSLuxXI1IFReKrW2/UIYmk8nJ2Ila/jwcyh/7Q2nawpOoxioSExzEXnHQo0MajSKT+XWllXdey+XxaaWXrZQ3Ll3u37YsXWaTk9ETtfx2OJzt+8No2kJNw8JYPTcjsyhWTZp70r2PhtMnpF3iEFhDS9pF99r4tAtGAqqLR0+dTgHXqlcXgiDgdAgVejpoeE01V2LcHZ9XYgoIjxQvt7CYnAye4MeaA3VZubcOUc29qNtI+qnj1yO4pobki+7R0uQLZoJqeItsnE5xXARBwGkXMCTmkR5r5fWefzOz8e/s/zWFlbMvsORx8T4q4ZJRjwAA0yVJREFUUlA9UikqAy/F2KgeKS63zSYnI+/Vse1QdTbti6BxCzX1G7tmhP2xzcrbz2fy1txA3vk0CJ2+YlRZQiK9SDjvfobGn7dQraY4fTmdQvGshSC4NqRTKBQER3hiy3d3jNhtroamlITW9CT+gntqfNz5PEJreopsyiobrmkqmT4RwCleRSUJ1WpqSCwWq8QLVkJKxsoh7vB0xQoUCgVB4V4U5Ls7Rhx26WPlFe6PNda92af1ajpe4f4iG88QX5wF7gAIdicenu68Z8s0YTda0TWQ/vQJGRmZ8qdiPE1l/hW6Wg2wG7PJOXsMu8VExt4d+DVtI7KpO+VZmrwwlyYvzKXWhEfRhNWkyQtzUXp5X+eu5Y+2WV3shlyMe09hzzGTuXoXPl3EO9LqOzbFfOwC1jNXcZisZK39C327RhJ5fHO06+RFapKDrRstZBocLF2Qy8AS01cz0hyMG5DGxfM2khPtLPw4h5HjXWuB01MdLF1gJDXFwfHD+WxZb6FVG8+y/lW50aGTJ8lJDjZvsJJpcPDlfBNDhovTUnqakxH907kQYyMp0cG8OUbGTNDSqImac3HhRdeIMd689o4vC5ZIv2nZ3dditaEwVvONDCoRq/Q0B2P6p3MxpjBWc3KLjvpLT3WwZL47Vps3WCWPVZ27g8lNsXJqSxLmzHx2L7lAi0HitatR3UM5sz2J5HM5WHMK+HPpRaK6h0rk8c3RupOWtGQ7v280km2w8/3CTHoP8xHZGNLsTB0Ux5Xz+aQm2vhqroHB4yvezJNrNGwfQFZyPkc2p2HMLGDroqu0HSKOQ7MewRzdmkbCWSOWHBvbl8TRrGcwNRrpmX+2R9HVfkQYY15twLQFzSVS46ZdJw2pSXa2bbSQZXCwbIGRAWWUgfcOSOVSYRn45ce5jCgsA5fOy+GZ1/3p1K3iPK8AmnTwxZBSwL5NBnINNtZ9kUTnoeJZdq17+nNgcyZXz5gx59hZ/2UybXq5OrY7DAxi01fJ5GTYuHrGzMGtWbTuGVDWvyo3mnf0ISO5gD3RWeQY7Kz+PJUuQ8U+te3lx97N2Vw5Y8GUY2fNl2m06+XKV3f39uPXRamYcuxcPWflzw1ZtOstfZ5r3MGXzJR8DmzKIDfTxoYvEugwpJrIplXPQA5uMRBbGKuNixK5qzBWdw8MYuvXSeRkFBB7xszhrQZa9ZI2Vj4ta1GQbiRz91ls2RaSft5bahO/gHsakXP4MsZT8diNVpJX7cO/Q4Oiz7P+Po9vy1rl7bqMzG1HIVSsq7IgdwBUYhRKJTVHTCFj3w4uLnkfta8/Qe26k33yIBe+nC21ezeNQqUkYtYEMtfsJvbJeaiC/QkY0pncP45y5dG5AKiD/Qh9bASpX67j6mOf4OHtRdB9fST2/Mao1QrmLglm2UIjw3ukEBah4v5pPqxfZWZgZ9cxRLXqqhk/ScdDY9KZOi6dISN1dO3lquy++n4AF87ZGNY9hRdnZjLqXh39hki7/lWtVrBwaQCLFpoY0D2diAglUx7W8etKC706u46MqlNXxcRJOu4bY+CBsQaGjfKmey/NP9xZWtRqBZ8uDWLpQhNDu6cSFqHkgYf1rFtppn9n1yyb2nXVTJikY8qYdB4cm8GQUVq6Fup67X1/LpyzM6RbKrMez2L0BB39h0obK6Xag/GftePPpReYP+R3/MK96TSpHsfWxfNJ/x0ARLYKpM9Tjfnl6UN8OmAngiAw4KVmkvr9T6jUCmYviuD7zzO5v1csIREqxk0NYMvqXMZ3cU1xrVnXkxEP+PHkuASempBA35G+dOyp+4c7S4dS7cG0Bc3ZtjiW2QP3ExCuodfkmuxfm8wbvf8GoO5dfgx7th7LnjzFm3324XQKjH65wT/cWVrUagUfLwnm64W5jOyRQliEkvum+bBhlZkhnV0nUtSqq2bsJD3TxqQxfVwag0Zq6VJYBl65aOeNZzJpUyu+6HrzOcON/mW5oFJ78MznDVj3RTLP9TtBUIQXAx8MY/eadJ7qcQyAhq19GP98TT57/CJP9TiO4BR44DVXg2vQQ2GE1dIwa+BJPn3sIuOfr0mDu6Q9xUalVvDCF3VY82UqT/Q9S7UINUMfCuH3NZnM6O7ayLBRax33zwrno8euMqP7GZxOgQdfd3Uq3j8rAo3Og8d6nuWD6ZcZ/WgYLTv73OhflgsqtQdPLIxiw5eJvNTvKIHhXvR/MJy/1qTxXE/XqUkNWvsw9vlaLJgZw3M9jyA4BSa+WhuAAQ9FEFpLwysDj7Pg8RjGPF+L+q2k1eWhUlL/9VEk/bKXk9MW4RniS+jIu8nYfoLjkz8HwKuaL3WeHsTVTzdx4sEvUXp7UvPBHkX3yNoTg66xtJsZysjISIeiIq/1rGooFK6+oSYvzJXalduKrZ30G/3cCVa2Wyy1C7cdjaICzMm8AxRU0b7MX3NaS+3Cbaeb/pzULtwRTubVkNqF204n7UWpXbgjnCuoetOeNYqKc6rK7cTorFizP24XCy91l9qF286B/u9J7YLMTXBtqY4gCBV3rd9NcK1NVffjj6V2RcTlZ58FKv7vW7EXUcvIyMjIyMjIyMjIyMjIlEQex/5XVM1hMxkZGRkZGRkZGRkZGRkZGRFyB4CMjIyMjIyMjIyMjIyMzP8A8hIAGRkZGRkZGRkZGRkZmcqFvATgXyF3AEiAyvrPNpUJU3bFPV/7v7Dd3ERqF247TTSJUrtwR9hjbCi1C3cEvSrvn40qGV+ldZHahTtCNa+qtxmqo4pOEsx3qqV24baz6OQ9UrtwRxjc8JTULtwR9rVaJbULdwB5E0AZmcpC1Xy6y8jIyMjIyMjIyMjIyMjIiJBnAFQyLKlxJOxcic2UhW/d5tToMRqFUimyST+6i4yju3Da8vGp3ZjqPUaj9HSdV55xbDcZx/7EUZBHtTY9CGnTUwoZpci/Ek/mt2twZGbjfVdTAu8bgUIl1pX85qfYEpKLXuu6tCNo0mgK4hJJeXueyDb0hRl4NahdHq7fkPiTOax64wzZyXk06x3CyNcbo1SL+93+/C6W3V/HYs21UadNAKPfboJfqAZzdgGrXj/DxX2ZePuq6PVIXdqPlv6osfPHrcx/JYn0JBsd+/rw2DsRqNTi007WfW3g16UGTLkOmrbVMvP9CILD1FhMDr58M5l9O4wEVFMx6blQOvXzlUjJ9Uk6lcXmt4+Sm2KhYc8I+r/SqlTcDvxwkQPfXiQv10bN1kEMfP0ufEIr1pFVCSez+fXNU2Qn59G0dyjDXmtaSsee767y1/IrWHNt1G4TyIi3muEXqsGW52D9u2c4szMVtZcHbUfVpPfjFePc+Ywz6fz9/h7MKWYiu9eiwwud8FC5de15+08uRV8o9b2R68aSn5NP9APrRO/3XzyIkJahd9zvG5FyKpNt7xzCmGKhfs/q9H65jShWW14/wOn1V0t9b9rmQWh8Pdn5/hEu7UpCF6zhnpnNadBT+rICIO5kLr+8cZaspDxa9A5hzBtRpdLgrm/j+H15HNYcO3Xb+DPunUb4h2pENl8/eYL6dwfQZWLN8nT/usSfzGFNsbw14rUmpXT99d1Vdi+/WlS2jyqWt9a+e4YzO9NQeXnQblQN+laQvHWNvEsJZCxZhz0jG127JgQ/NLTUM7kgPpX0JesoiEtB0zCS0KfG46HVXOeO0pF62sDO2QcxpVio26MGPV5qK4rV9jf2c3bDlVLfmxI9BLVWxc53DhJ/IBW1RkmzUfVpP71Zebp/0wyfnMS7LwXRNKr0rMzoHWZmvZVOZraTByf48u7LwRJ4KCNz+1HISwD+FfIMgEqE4HRwdeNXhLTtSdR9L2IzZZN+9A+RjSU1nvTDv1Gj93jqj3samzGb9MO/AZB7+TRZMUeoO+pR6o16FMOxP8nPTpdAiRjB4SBj4bf4DuhO+OxncWRlk7ttdyk7e0YmNb+YTeTS/yNy6f8RNGm06/30TLR3typ6P3Lp/1WIxr/D7mT5zGP0mFaH56M7k52Sx67lsSKbpBgj2+Zf4oF5LXlxWxf0QZ5s/tTVcNm24BIo4KnVHRj1ZhPWzj5H+lWzFFKKcNgFZj8Sx5hHglm0vT4ZyTbWLM0Q2Vw5m8f3n6Txyhc1WfZHA/yDVXw7JxWArz9IxWp2smxXQ554L4L5LyeRkWKTQsp1cdqdrHpqHx0fbMjDa/uQm2Jl/7fixmTa+Rz+/Pwso+a2Z0Z0X3SBXvwx/7REHpeNw+7k+yeO0G1qPZ7e2IWc5Dz+Wi6u5CbH5LJjwQUmftaa57Z2Qx/kybZPzwOw97urZCVYeHpjFyZ8fBdH1ydydL30S0icdie/z9pJswdaMHzFSMypZk7/cFJk0/n1Ljyw/8Giq/dn/QhrF44+TI8p0UjtvnVFn0vd+Hfanax7eg93P9iIKb/2x5hi4dB3MSKb/m/fzbPHxhZdoz7vSmT7EHzDdez+9AQ2q52p0YPo83pbtr19CGOqRSI1bhx2J8seP0GvqbV5eVNHslLy+H15nMgmKcbI5vmXeXBeC17b3gmfYE+iP7kksjm6OZXj26R/Vl3DYXfy7RNH6D61Ls9u7EJ2spXdy6+KbJJjjGxbcJEHPruLF7Z2Qx/kxZbCvPXXd7FkJVh5dmMXJn7ciiPrkzhSAfLWNQSHg9Q5P+A/tCs15z6F3ZBDTvRfYhu7nZSPvsd/WFci5z2L0ldHdvQeiTy+Pk67k43P/EXbyU24b81ATCkWjn5/TmTT5632PHFkfNE1bGE3at4dik+4jgNLzyAIAvevGcjwL3pwcvVF4g+mSqSmbA4czePF2Rls3FZ2/cCQ6WDSzBQWfBDCwa012bTTzM9rjeXspYyMTEVC7gCoRJjiL+Kp98e/4V2otHpC2/Uh+/wRkY0l+Qq+dZriE9kQL/9gApq2x5rhGjU3nN5HWMcBePoEoAkKp8GEZ1B5+0ghRUTeuUsoA/3Q3d0SpY8e30G9sBw4LrJxGE14eHmiUJdeu2lPz0QVHFBe7t40F/dn4h+modWAMPSBnvSaXpdjm5JFNl5aJR5K1+i5QuHay8Tbx6Xx6pFsuk2uTVBNLVH3BFOrlR8pF6RdZ3z8bzPB4Wq6DvbDL0jFuMeqsXtDjshGo/NAqSqcEVCoSefrGjn6e7uR0Y8E4+OnpGk7Hd2G+rFnc245q7gxVw+k4xvmTZP+NdAFetH54SjObEkQ2ai1qlJx8/LxlMDb63N5vwG/MA0tBoSjD/Si+yP1OF4q/RXX4RKi8XVNDLtyOIsOE2qhD/SiVusA2oyswZVDmeWuoyQph5LRheio06cumgBvWkxpydVtl69r77Q7ObzgIHc/0wEAY6IRfbi+vNy9KeIOpOET5k2jfpFoAzV0mNaEmC1x17V32p3s/vQEPWfdBcDF3xNpN7kRGl9PatxVjcYDIrmwI+G63y8vLuzPwj/Mi9YDQ9EHetL3kdociRY3njyLpUEUIAjg7euenGjKKmDzgss06hJUnq7fkEv7M/EL09ByQLirbC8jb3lqlSiLlREIAt6+rrL9yuFMOk6IRB/oSe3WAbQbWYPLh7LKW8Z1sZ66jCrID32n5ih9dfiP6I5pj7iTzXIkBk2DmujaNELpqyP44eH49mwrjcM3IP5gKvpQLQ37RaIN0NBuahPO/0Pe2vPZcbrOag2Al4+4zqFQKPDSVaw9JPYfzsNidaLXlV2lXx1tok83LT06a6kRoeaZRwLkDgAZmf9x5CUAlYiCnAy8gsKLXnsFhVGQI66QB7fqCoAgCDisJkxxMejCagGQl5GEJSWOhJ0rEJwOglt2IaRtr/ITcB3saQbUEWFFrz2rh2JPN4ht0jMRBIHkNz/FnpGJJqouAfePROXviz09k4LYBBJ270ehVqHv1gG/wdLrMsRbCWvgbmiENdCTGS/eATKoppbuD9Vm3tj9APiHa5gV3RmAp9d0BMBhc5J6yYwh3kr1JtJOl0+OLaBWQ/f0wlpRGlLixSP44ZGejHo4iKeGuxpl1SLULNpRH4CCfCcqlXu5gEqtID2pYs0AyEowU62++3euVt+X7ATxaGpADR0dJjfk63v/AMA33Jvpa/uUp5v/iCHeQmgDdwdfWH0fMkvoCKyppeuDdfh83N7/Z++94+Oqzvz/951e1XuzenPvxtjYmBZ6CSFZSAJJNuxuesh+N2XTG8n+kmx2N8kSsqkEApheAriBDbgX2ZZkS5asZmnUNaMZTZ+5vz9GHmk0kguWNSNx3q+XXva988zV89Fzzrn3PPcUIFT+vvxKqA25/dvzMSaPJTW6G+wUrUiZAc/Pjb1zmKSSsYRfUkky9q6pH2YbnjtJ1opskopD33F02Rk42c9T1z+OUqOk/K5KFn1yyeV2+5xYzzhILU0MH6eVJmI9M/Von5rNzRSsyiC1JPQdvyeAYtyQZoVawXB37EcA9Le7yB7XBmaXmRiY0Aam5evZ9Kl5/OJDBwBIztbx9b+vCX/+7A8auO7BQk7ti58O8kCHk6xxdSuz1MzAhLqVmm9gwyeL+J8P7wFCdetfXwktgHnnhLplaRimOA7q1ln8PYNo8sdGxWjyM/H1Rj5reNq6kTRqOr/9KN6OHvTVRaQ/eMcMe3p+bBPqVmppErbOqRPpx59pIm9lJqnFoe+s/GQ1f7v3DX5/fWja0LKPV5JRHT+xAvj8PyYB8OrWyduM020+FoybFrCgUsPPfhNf912B4D0jS+e3EUQhRgDMIgIeF0rN2EODUqMl6PMgBwJRtgPH3qX+/77DSFcLqYtCHUq/085I12nKPvxliu/4Z/qPvo299cSM+T8VQacLhW5Ml6TTInu8yP4xXUGXG3VuFmkP/gO5P/0aCqOBwb88F/pQIaGtLCHnR/+P9C98AsfbB3AeOj7x18w4LpsPjWFszqTWqMTjDBDwBcPnztQN8+7j7Xxx8xq+v/dqipYn8+LDkUN/f3Xffv7zrj0suDaDlNzYzjF32ALoDWPNhsGowDUSxO8bm4R16riLl/8yyC9fLOapmkrmrzTw6A+6AVi23sSLfxzAaQ9wssbJ9ueseFzBqN8TS9zDPjSGsdyo1qDC6/RHxM1SP8TBvzXzib9t5KG3byZ/aRpb/+NYLNydEvewH41+rPxpjEq8E8pfZ52NPU+08dmn1/KtPddSuDyFV34SahOSc/Ro9EpcNh/P/PsxBjucLL45O+r3zDReuxe1fiw+aoMav9NP0B9djjzDHk4+Xc/if1waPicpJLKWZ3PHM3ez6RfXceqlRtp2tM6E61PiGfaiGadJY1Dhm1DmzuIe9lLzt1Nc8U9ju5TMuyKLw39txOPwYTk+QP3Lbfjd0feFmcY17D9vG9hRN8zbj5/hK8+s5Mf7rqJ4RRLPPxwaKn9say9uR4CVt8e+3I3HNeyLqFvaSerWmTob7z7RxuefvoLv7rmGouXJvDShbjltPp7+9+MMdLhYEgd16yyBERfSuHuyQq9BdkfekwO2ERx7jpP60Q8w79f/D4VeS/+fXo2Fu+fkYuvW0SdPsfrB+eFzu//nGEkFZj69407ufeoDnNrSTufh3hnxfboYsgYwGcc6SWaTgkFr7NsHgUAQO0QCYBah1OoJeD3h44DXjaTSRC0CCJC2eB1Vn/ouiSULaHv1TwAoVGrSl1+NymBCl5pFYski7O2NM+X+lCgMeoLuMV1BlwdJo45YcEg/v5zMr3wadU4mCqOBpLtvwnPiFHIwSMp9d5B8900ojAY0+TmYN67BVRd7XfpENZ6RsZusxxFArVdELD5Uu62H6qvTyZufgD5BzaZPF3HsjW5keaxD/YWnVvPFZ9ZwavcAh17qmlENEzElKnGOjD04OR0BtHopYhHAPVuGWX2NmbKFekwJSu75lzTe+fswsizzT9/Oxm4N8PErG/ndD7tZfY2Z5Iz4GoikS1DjHfGHjz0jftQ6ZUTcGnZYKNuYTXZ1MroEDWs/Vc7JrZ0RcYs1ugQVHuf48udHrY/UUbeth6qNmeTOT0SfoGbDp4upHVf+6nf08ItbdxEMyHz6z6sxJMV+moPGrMHnHHt75RvxodKpIhYBPEv7m63krMlFYxrze/X/u4Lln1uJNkFLSnkqFR+spGtvbIfLaxM0eJ3jy5wP1YQyd5bGbWeYtzYrYsrJpq8uxT3s5dEbXubN/6+Gkg05GNNivxibPkGFZ1xdcjsCaCa0gce29rHg6jTy5ydgSFBz7afncfSNXlx2Py//vIl7vlcZC9fPiT5BjWd8vKaoW9UbM8gbrVsbP13M8Td6wnWrbkcPP7/1bYIBmX/+86q4qFtnURr1yK4J92Rt5D1ZUqswrqhCV16AwqAj8aa1uI41xcLdcxKqW2PthfccdatpewcFV0TWrYY32lj+QBX6JC1pZUmUXVdA8/bYT6+5GJKTlNgdY/ftYXuQ5MTo50aBQPD+QSQAZhGaxFTcA93hY/dAN9rEyKFoA8d3M1i/HwC1MYGUhVfi7O0IHZuTkP1jDy1yMICkin3nS5Wegq9zbF6or6sbVXrkfE9XXSPuk2MLQwUdI6BSIikUDG/ZRcAxNvQtMOKcdK2AmSY1T0/3qbGhyd1NdlLzDBE2wQCjE0TPIhMMyEiSxGNfPorV4kaSJPKqE6i+OoMztbGdL59VoKGtcezBsK3RQ1Z+5INrMBApSZYhMKpJlmW+8et8njlWxc+fKcbjClJUGfuOyniSc430NY39nfuahknKM0bYyAEZJmg8G7d4ISXPQM+48tfT5CAlL3IEiRycTEcQSZKo39HDs988zod+vIh7frIYrTH2bQWAKdeM9fTYcHDr6SFMuZOvZdL+ZhvFN5REnKt/oha3zR0+9tg8KLWx1ZaUZ6K/aWwtjYGmYZLyJl+noGn7GapuLIg4Jwdlbv3ZWj7/7l3c+5dr8Ln9pJUlXU6XL4jUfD2WprG2ufuUg9QJZTAYlCc2gQT8MoOdLvraXHxv07t8qWo7+5+38OwPG/n952I/0iYlT0/PuPVYupum0jW+co3VrbodPTzzzVru+fFCPvKTRXFTt86iykzB2zH2ltvb0YMqI/JZQ5WWiOwb9zwRCCJp4ksHQGKuiYHm8XXLRuIUdat5xxkqPjAv4pw8oXzKyASD8ZPovRCKCtTUNnjDx7UnvRQWxP4ZSSCYFuQ4+5kliATALMKUV4bPYcXaWIPf6aD3wDaSKpZHGkkKeg9ux9nTjtc+RN+hHRizCwFIKltK35G38DntuPo6sTUdI6FofvQvmmF0laUEBq2MHDhKwO5g+NUdGFYvibCRvT4Gfvc3PK1nCDpdDL/2Foaloa14PI0tDD35MgHrMN7Obpz7ajAsjb2u0tUp2Lo9HH2tG8egl+2/bWHpLZHDPKuvTufYG910nhjGafOx49EWqq/OAGC4z8P2357G1uOmtcZKzWsW5i1NioGSMRZfYaTf4uPtV23YBvw89es+Nt6eGGGz6hoz77w2THO9C7stwOb/7Wf1NaEO2quPD/E//96FbdDP/u12Go+5WHl17BeiHM+8VekM97iof+MMI4Me3v1dA/NvitxSrWxDFie3dNJz0opr2MuePzRQtiF+hvAClKxOxdbt5tjrFhyDHt76bTNLbs6JsKncmEHtlm66TgzjsvnY+btmqq4Ozf1985Embv5qFeXr0mPh/pRkr8hhpGeE1m2ncQ+5OPbHoxTdUBxl53V46a/vI21+pP89R7o5+It9OPudDDUP0fLGaQo2zov6/kySvzIDe4+Thjc6cA662ft/9VTeVBBl57F7sdQNkr0wMkF6dHMzW79/EOeQh+ZdXXTXDlK8PvblsXx1MlaLmyOv9eAY9LLlt60svzUrwmbB1WnUvNHLmRN2nDYf237XyoJN6eRWmvnliWvCP6vuzOaD3yznU79aFCM1Y5SuTsXa7ebo6xYcg152TFK3qjZmcGy0bjltPt783elw2779kWZu+WolFXFWt86in1+Mf9CGY89xAsMjWJ/fiXld5N/dtHo+zqOncJ1oJeBwYX3pbQzLq2Lk8dTkrczE0e2kcUs7ziE3B35fT8WN0fXdY/fSUzdA1oS6Vbwhl4N/OoF72Et/o5XG19oo3pg7U+5PCx+82cTWnU527nZypsvHLx8d4t674uu+KxAIZhYpnoasznUkKbRb5aIv/OI9X8PZ3caZHZvx2YdILF1E7sa7GWo8TM++N6h64JvIwQCdbz2HrekYyEGMeaXkbrgLtSmRYMBP187nsDUdR6nRkrHyOlLmr75kXdZFl76YjOd0B4OPPUtgwIp++QJS7ruTkX012F7eSu5PvgaA7aWt2N/cC4EA+iXVJH/kVhQGPX7rMIN/3IynqRVlgpmEmzZiWr/qkn36/Nrtl3yN9mM2nvlOPVaLi4XXZ3LXt6o48qqFLb9u5htbQ4utHXiuk22/PY3T6qNyfRp3frMKQ5Ka7lMOnvluPV0nhzEmabjiw3lsejC6o3MxVOsufauphqNO/ucbFvq6fFz5ATOf+X4Ob71k44n/6uUPu8oB2Lp5iCd/3Yd9KMDyDSY+8/1szEkqHMMBfvK5Dk4ecVFYqeULP86hoOzSRwC8ay+/5GuMp+v4IH//QQ3DFieV1+ZwwzeWUPdaB2//70k++9oNABx9oY13f9eAy+al5MpMbvjGYvSJ0zuM16Ryn9/oHHQcs/LC2b3Kr8vk9m/N5+irXWz/TRP/b8tGAA49f4Y3f9uE0+qjfF06t32zGkOShu+s2ILPFTlPdNkdudz9o0vrgJ10XHrHtL+ujz0Pv8tIj4OCqwtZ829raXmjmaO/O8JdL9wDwOk3mml6uZHrf3VjxHedfU52//Bteo/2oE/Vs+D+xZTddunlJ117aTt0WI4PsPUHh7B3Oym7JpdrvrGck6+1s/uROj7995sBOPH3NmpfbOFDv90Y8V33sJdX/m0PXccGSC9L5PpvrwgvEHgp5GsHzm90HtqO2XjqOyexWtwsui6DD327gkOv9vD6r07z7W2h9Wn2PdfFlkdaGbH6qFqfyt3fqsCYFPmG8olv1JM/38z6+/Iv2SdP8NLffnYcs/Lsd+uwWtwsuC6TO79VTc2rFrb+pomvbdkAwIHnz7Djt804rT4q1qVxx2jd+uaKrVF1a/kdudzzo4Xv2Z/fHl93SXom4m46Q//vXsDfb8O4upq0T96G492jDD2zg4L/+VcAHHtrGXpqG4FhB4bllaQ9cAsKw/SO6LqlvPaSr9FdO8COHx7A3u2k9Jo8Nn5tBY2vt7Hvt7U88MqtADS81kr9iy3c+cjVEd/1OHzs+v8OcXpnFxqjiuX3V7HonrJL9uk/sw9d8jUmUrKqhZcey2F+hZY/Pz3M9382QPP+IgBe2erg377Xz5AtyKfuTeCHX0+b9t+vyIr91EvB+Tk7MkmWZ/fqeWf7VCU/ee99qstB89ceAuL/7ysSADPIdCQA4pHpSADEI9ORAIg3piMBEI9MdwIgXrjUBEA8Mh0JgHjkUhMA8ch0JADikelIAMQb050AiBemIwEQj1yOBECsEQmA2cFcSwCUPhxffaqmr8+OBICYAiAQCAQCgUAgEAgEAsH7AJEAEAgEAoFAIBAIBAKB4H1A/C3ZKhAIBAKBQCAQCAQCwbkQM9nfEyIBEAPUI3OrtGr65t58SoCd/XNvXvkhdWxXO79cDHvjayvB6cI2B3WZNZ7zG81Czoxc+qJ78cbLQ7HfTeVyoFX7z2802zijP7/NLOTlnhWxduGy8F9f+FusXRAIBO9jxBQAgUAgEAgEAoFAIBAI3geIEQCzjJG+dtre2Yx3xErSvAXMu/JuJIUywqandic9tTsJ+rwk5ldSsPZulBodQb+P9j3PYW2rRaFUkVaxmpxlH4iRkkjcHe30Pr8Zv3UI0/yFpN9xN5JSOantyIk6BrdvIf9zXw6fs767C+vutwm63SRvuJrkqzbNlOvnxHqih2M/exNXj52s9SUs/MoGFKoxXTUPb+PMayejvnfN5vvRJOmp/c+ddL9zGoVGRcEt1VR88tK3bbxUBur7OPCTt3H2OMjbUMiKf1uHQjWWS9z3w520vBq9GvCtz/8DxixT+Pidb2wjc1k2ZXfHx1tG64kean+2A1evg8z1xSx4aGNErI4+vI3O109Efe/qpx9AnxnaU9nW2EfzXw+y7Ps3RtnFAnuDheb/3IKnd5iUK8so+eJ1EZoAnK39NP3nG4y09JFQnUvFt25DZdTiG3bR/J9vYD3chkKnJuuWxRR87MoYKYnEdqKH2p9txz0aq+qHro7QdfzhrZPGasPTnwjHarixl+a/HmTp92+aMb8vhuGT3Zz8xTbcvXbS15VS8aVNERrrf/oG3W/UR31v7d8+hS4zYSZdPSfupk56HnkJX58N0+oqMh+8BWlCGfS099LzyEt42nrQV+ST/ZV7UBp1BD0+ev/vVRz7TyJpVCRes4y0j8RH2+481UXnr17B1z9MwpoKcv/lpihd7vZeOn/1Ku62XgyVeRT82wdRGiNH9bT9ZDOmhYWk3rxyJt2fFHd7O/3PbMZvtWJcsIC0D57jPlxfj3XLFnK/9CUAAk4nfZufxnXqFAqNhoQ1a0i+/oYZ9P7C8LS10//UM/iHrBgWLSDtng9Gaez86c/xdlnCx6YrVpP+kQ/NtKsCgeACkObWoOoZQyQAZhFyMEDztj+St/o2zNlltO58nJ7jb5G1+JqwzUhfB93H3qRow71ozSm07nqK7mNvkrviRnrrduG1DzL/g1/FM9xHy1tPoDWnkVoW2yF2ciCA5a9/IO3m2zEUl9H99ONY336L5I3XRNkGXC56X9iMyjw23HbkRB32msPkfvozBN1uuv74KMbqhWjS0mdQRTRBf5AD//535n92HanL86j5wRZOP1lD6UeXh22WfP1alnz92vBx3/52mv92GH2mmaa/HsJpGWbjXz/KSIeVIz/YgjE3kbwbKmMhBwhpeuerW1j6xTVkLM9h7/fe5OQTx6j++JKwzepvbmD1NzeEjy37znDyr0cjOv/t25o582YLmcviY0u4oD/IoW++StVn15G6LJ+jP9xCy1NHKLlvrG4s/vq1LB4fqwPtnP7bIfSZZnwOD727W2h+4jCmguRYSIhCDgQ5+Z0XKPznq0laUkDjT16l65mD5H1kLIkU9AWo/9ZzFH9mE+aqHFoeeZOuZw9S8PErOfPEHmRZZtkfP4V/2E3tV58mYWE+SUsKYqgqFKvD33yFys+uJ3VZHkd/uIXWp45QPC5WC79+HQu/fl34uP9AGy2j9crn8NC3u4XTTxzCGCexmkgwEOTYt16i7DMbSF6aT/2PX6f96UMU3rsqbFP91Ruo/upYB2vgQBvtTx2Iq86/HAjQ+dO/kfHAB9AvKKL7v55l6KXdpNy1fszG56fr4SdI/+SN6Mrz6PvT6wy9soe0D1+N9dW9+HqtFP735/FZBrD817NoslJJ2Lg4hqpCdavtR0+T/anrMC0qpOMXL9D3wl4y7h5LkAV9AVp/8BQ5n74BQ0Uult9vpf/FfWTeO9Y2Wt+pY3j3SUwLC2OgIhI5EKDnT38k9bbb0JeW0fvE49h2vkXSpsnvw/3PbEaVMHYftm7bCrJM/r99laDTieW3j6ArLkFfWjqTMs6JHAjQ8/s/kXLHbejLS+l77AlsO3aSdF1kUsk3MMi8nz2MQj03pzcKBAKBmAIwi7B3NaExJpFSvBS13kTWkusYPH0kwsbR00JSwXwScsvRJqSRVrEK11BX6Pvdp0mvuhK13oQps4i08lXYu5tjISUCV3MTqsQkzIuWojSZSLn6OuxHD09q2//qC+iLIx8obAf2knr9jaiTktFmZZP/+YdQmcwz4fo5GTh8Bn2GiZxrytAm6Sn9+Eo6t0+9T27QH+TEI7uZ/8WrQt8/2kXhnQvRJulJWZhNwc3VDNR0zZT7k9JzqAtDpomCa0vQJeupfmAp7VunLkNBf5Cjv97HsofWhs95rG5q/+8w2WvyZsLlC2LgyGisNpWHYvWxFXSdJ1YnH3mX6i+EHua9VhfW+h60KfEzD9d6pA1Nupn0jZWokwzk3XcFfW9GvhUf2tuMuTqHlCtKUScZKH3oBjJvXAQQ9aZSkiRUBs2M+T8Vg0c60GWYyN5UjibJQMnHVmI5T6waHnmXqi+E6lUoVt1oUwwz5fJFM3S4A12GmcyrK9AkGSj86Cp6dzRMaR8MBGl+9G3KP3f1DHp5fpzHW1CnJmK+cgGqRCMpd29g+J3jETaOQ43oKvIwraxAlWgk819uI/HaZaHv17eR9IFVqBKN6CsLSLxmGc761hgoicRxrAV1WgJJ6+ejSjSScc96bLsi96u3H2jEUJlHwqpyVIlGcj93CynXLw1/7h920vvETkzLSmba/UlxNTWhSkrCtCR0H0669jocR45Majvw4otRHXuFbkLbJ0kodNrL5e57wnVqVOOyJSGN11/LyOFIjQGHA4VWKzr/AoFgTiNGAMwiPPYBdMlZ4WN9chYe+2CETeaC0EOuLMv43Q6GOxsxpocWfitY+0HU+rG3sM7BLszZsX/48A32o80cexOsycrCNzgYZTfScAK/1UrypusY+PvL4fNeS1doCsFzTyMHAiStXT/p6IGZZqTLhrkoNXxsLk7B2TU8pX3bi8dJXZaLuTAFgIVf2YA2aeyhari5n5TFuZfP4QvA0TlMYvHYW9Ok4hQcXfYp7ZuerydzeQ6JRWPfOfTzd6m6fwm9h2KbzBiPs9OGKSJWqeeMVfuLx0ldmheOlTEviflf2sCZ10/Qu7v1crt7QbgtVgyFaeFjY2Eabos1wmakpQ+FRsWxLz6Os7WfhEX5lD4Uequcf+8aav75zxz48P8CkHvPSkzlWcQaZ+fEepWKs8s2pX3Hi8dJWZqHqTD0HWNeEtVf2kjn6yfo3d1y2f19L7i6rBiLxsWuKA2XZWqNnS8eJXlpPsbC1CltYoGvexBNQUb4WFuQga9nKMLG09aDpFHT/o3/w9vRi766kMzP3AZA5oO3oEwcS9R4WrvRVxfOiO/nwmsZQjdvbISZbl463h5rhI27tReFRkXzv/0Rd3sfxgUF5H3u1vDnXb99jfQPrWPkWOsMeX1u/AMDaLLG6vdU92HniRMEbFaSrrmWwVdeCZ9PuuYazvznL2j//vcASNy4EW1e/uV3/CLw9w+gzh7TqM7OwjcQqdHXPwCyTOdPf45vYBB9WQmp93wQVeLcW+RTIJgTiCkA7wkxAmAW4fc4UarHMupKtZagz4McDETZ9p14l2NPfBdHTwvpVaFhiVpzCgqVBr/HSeuuJ/HYB0gpWTZj/k9FwOVC0oy9WVRotMheD3JgTFfQ7ab/1RfJuOsepInfd9hxt54m/7NfJvdT/4x199uMNETP/51pfHYPKv3YWwSVXkPA5SPoj46X1+6m9dljlD8wNsTXkJWAUqfGa3dT8/A2RjqHyb0utjsTeIcnaDKo8Tt9BP3BSW1Pba5j/qfGpjyceasF34iPohvLZsTfC8Vnd0foUp4jVj67m9bnjlE2LlbxiN/uQakfq1dKg4bgBE2+oRH6d56k8J82suKJf0Zl1NLymx0AtP5+F/q8ZFY9+zmWPPoA/W81YDt+ZsZ1TMQ3Udd5YtX23FFKH4j92hkXg9/hQakbVx4N59Z45oUaij6+ZiZdvCACI24UunFtu16D7PYij9MRsDqwv1tL+v03UPTIQygMWvp+/xoA6owkFFoNAYeL7l89j697iIT1C2dcx0QCjom6tARdkbr81hFs79ST9cnrqPz9F1AadHT97nUAbHtOEnB6Sb560Yz7PhUBlxNJO/Z8odBqkT3R9+GBl14k7e4PgRR5Jx78+99Rp6cz73vfJ+8r/8pIzVFcp0/PmP8XQtDpQnEBGtXZWaTffx/53/13FAYD/U89Ewt3BQKB4LIhRgDEgLZ3NkedSyyoJqng3IuhqbQGPPaB8HHA50Gh0kQtAgiQUb2O5MJFdB/bwentf6L8pn8BwNpWS9s7m0nIraDips+g1MR+mzGlXo9vsD98HPS4kdSaiIV5+l97mcTVa1GnpOK3Rr5BklRqkq66GqXJhNJkwrRgEc5TjRgrqmZMw2SozVqcnWNv7fxOL0qdKmoRNoDunc2krypAbYocMtn99mmO/exN0lcWsPZ/7oz5EGxNghZH59ibcd/IWU3RucSOt1rIWp2HxhTy2evwcvQ3+9n4PzfPmL8Xitqsi3jjf95YrYyOVbyhMmsj3vj7RzwodOoITZJGReraMhKqQyNLcu5aTt3XQu1T/5snqPzOHagT9KgT9KRuqGDg7UYSF8Z26obarI1443/uWDWRNgtiNRGVSYuryxo+Dox4UUyhsW/XKVJXFKKKQ41Kow5f99gb1qDTg6RVRyyWJ2lUmFZVoq8IvS1OvuUKznz/L+HPHftP0vPISxgWl5D/g0+g0Mdep9Kkw9s9dh8KTKErYXUFxspQfUm7bTUt3/4rgRE33X/eTtEPPjrjfp8Lpd6Af2Ds+SLo8UTdhwdeeYWEtWtRp6bit1ojvu+oOULm/Q+gNBpRGo0YFy9m5Pgx9MXFMyXhvCgM+tAb/lGCbg+SRh2h0VBZgaGyInycctvNdHzvR8jBIJJCvDMTzC5efvllXn755fMbCt4TjpN1jJysi7Ub7wmRAIgB89a9t9VkNeYU+k8dCB+7Bi1ozCkRNn0n9yAplKSVr0JtSCC9ci0nXvovINT5b337KYo23kdiXuwWkpuIKiUV76ExXd6ebtQpkbrcHe0M799D/ysvhM81ff0hir7zI1RJScj+sX2d5UAApTb2D4mGnISIFf7tpwcwZE++QJdl52nK749cjLH77dMc/el2ln77ejJWzbusvl4ophxzxAr/ttNDmHImX2/hzFutzP/E2JzXEYsde8cwL98xtv9xy6uNdB/oZP1Pr798Tl8AhpwEzoxbNd7RMoA+e/Ihn927min9eOxX7D4fuqwkereM3Zicrf3osiI1aTMS8PaNTeGQ/UEkdehhWA7KRAy3kWUIRo/0mGn0OYkRK/yfK1Y9u5op+Xh8j9SYDH1OYsQK/46W/ik19r3dROHH4nOEgzozheG3asLHnvZe1JmRCy+q0xLxD4wl3+RAAEkdejRx7D9J969fIPtLd2NcGj+LyWmykhnacTR87GnrRZOVFGmTnsBI/zhd/gCSRoW314q3a5CGT/13+LOh7UdxHG1h3jfuuey+T4UqNQX7gXH3YYsFVWrkfdjT3oZ97x4GXnwxfO70v36Fwh/8cJK2IT7ai/GoUlNx7J+oMXLajOtkIygV6MtC5S0w4gSlSnT+BbOSW2+9lVtvvTXi3O9+97sYeXOZiOEUAFPFfEwVkS9vhw/ujZE3F4do0WYRCTll+EasDJ6uwedy0H10G6kThvBLkkT30e2M9LXjdQzRfWwHpsxCACw1W8lffXtcdf4BDCVl+G1W7MdqCDgcDL65DfOS5RE2BV/4CqUP/4LSh39B7qc/gzY3n9KHf4FSp8e0aCnWt9/Cb7fj6erEUXcMY1Xst5ZLW5aHq9dO145TeKwumh47SO51FVF2PocH68kekqoj51ef+ssB5n9ufdx0/gEyV+Ti7B2hfVsz7iEX9X8+wrwboh/MvQ4vA/W9pM4fm/+bXJbKR/Z8OvxTdHM5y7+yNuadf4DUZXm4ex1YImIVPd1iqljFI4lLC/D0DtO/8yQ+q5MzT+wl/ZrqCJu09eUMHWzBdvwMfrubM0/vJ3VtKJ4pV5TS+dR+/HY3I6d76dtxgpS1se+EjcWqEa/VSfNjB8iZol7ZZkmsJpK8NB93n52etxrwWp20Pb6fzGui222/w8PwyW4SquJjN42JGBYW4esfxv5uLX7bCIPP7iJhfeSwd9MV8xk50oSzvo2Aw8XQC+9iWhmK58AzO8n4xAfiqvMPYFpUiK9/GOs7dfhtI/RufoekDZFTExLWVuE40sxIXTsBh4u+53aTsKoCfVEWC1/6Vvgn+ZrF5PzTB2La+QfQl4buw46a0H3Yun0b5mWRzxd5D32F4p/9nOKf/Zzsf/kM2rx8in/2cxR6PYb5C7C9+SYBpxNPVxeOI0cwzF8QIzWToy8vxT9kw3EkpNG2dTum5ZEag14vfX95Ak97BwGnC9u2HRgXxZcOgUAguFREAmAWISmUlFz7CbqPbqfumZ+gMSaTuXAjA6cOcPypHwGQWrYSc04Zp974HfXP/5yA1828daEHC7e1l9Zdf+PQ778S/mnd9WQsJQEgKZVkf+wTDL21jbZfPIw6KYmk9RsZPnSA1v/44Xm/n7x+I+q0NNp/+R9Y/vpH0j5wC7qCwsvv+HlQqJSs+NHNNP31EG/d91d0mWaKP7KEjtdOsP2eP4fteve0kliahqSInFPpaBui5sfbeOWqX4V/ah7eNtMyIlCoFKz/yXXU/6WGv394M4ZME5X3LqLl1UZevmvszX7Xu+0kl6VGaYpXFColy390E02PH2TXRx9Dn2Gi6MNLOfP6Cd78cGSsEiaJVTyiUCmp+t6dnHliL4c/+Xu0GQnk3L2C3i21HPzYo0BoBEDZv95I0y9e5+DHH0Vp0DDvH0M7GxR+egNKnZpD9/+OE99+nvz7riBpWWEMFYVQqJQs/dHNnH78IG9/9DH0GWYKP7yUztdPsPPDfwrb9e1pJaE0fVbEaiIKlZJF37+NtscPsPf+P6HNMFNwz3Isb9Sx+97fh+3697VgimONkkpJzlc/wuBzb9P6hf9BlZ5I8m1rsb1Zw+l/+U8gNAIg63N30PO/L9LymV+i0GtI+1hoC0fvmT66/+d5Gj/4nfBP96+ej6UkIKRr3jfuoW/zuzT+y29QpyeSfscahrYf5eQ//g8AmvRE8r5wK2d+9TInP/0rFHotWQ/EfnHaqZCUSjIf+ATWHdvp+OlPUCUnk7hhI/YDB2j/0Y/O+/3UW25B0mroePjH9PzxDyRdcy2G8tiuWTMRSakk8x8fwLZ1B2d+9FOUyckkbtqAff8BOr4X0mhctADz2jV0//b3nPnBj0GSSL3r9hh7LhAIBNOLJMti+cSZQpIkGWD5p34ea1emFVtpfD58XiqVV8bXAkbTgUntibULl4Vhb+zXsrgc2OagLrNmbpZBbyB6fv5s58xQUqxduCxo1f7zG80yRhqTYu3CZUFWz81n1NNf+EqsXRC8T5FGF/CUZXlWP7yf7VOVf/8XsXYlgsZvPwTE/99XjAAQCAQCgUAgEAgEAoHgfYBIAAgEAoFAIBAIBAKBQPA+QCQABAKBQCAQCAQCgUAgeB8gtgGMAYbeQKxdmFb8urlZjI6fju1+55cDhW1uxipgmlt1KkwwrqeQvScUurk3/xpAds3NujUXUZ+ae7GSUs9vMxvR9c+9NlAgEAhijRgBIBAIBAKBQCAQCAQCwfuAuZcGn+PYrR2cOvosHpeV1Oz5lC68C4UicvXp3jNHaGvYgs/rICGliLJFd6HVJ+HzjnDq6LNY+5tQqfUUlG0ia97qGCmJxNnTTsdbm/HZh0gsXkjehruRlJG6+mp20nd0J0GfB3NBFXkb70apiVwlvfX1P2HKLSVt4bqZdH9KPKc7GPjTcwQGbBiWV5Py8TuRVJG6ur71S3wd3eFj01UrSf3kB2l74GtR19NWFZP11Qcvu98Xiqetnf6nnsE/ZMWwaAFp93wwKm6dP/053i5L+Nh0xWrSP/KhmXb1vHhaOhj883MEBq3ol80n5aPRsbJ855f4zoxpMa5fSeoDd+Nt66T7+/8dYZv5tX9BW1Y4E65Piaelg8HHng1pWjqflPvuitb0vf+coGkVqR+/O8Km738fQ1dZgvnqtTPi9/nwnD7DwB+exz9gxbB8PqkP3B6lq/Mb/x1ZrzauIO1TdxFwOBn4/fO46ppQaDWYr15F0l2x357N09rBwGisDEvnk3JvdKy6vh8ZK9O6SWL1yGPoKuIoVnNUl8vSTteWzfiGhzCXLSTnuuh7Vv/BnQwc3EnQ68FcXEX29aF7VsDrxrLtOexNdaiMZjLX30RC+aIYKRnD3dFO33Ob8duGMFYvJP3OaE1nGTlRx9D2LeR97ssANH/toSgbXXEpuQ9+5rL6fLG4LO1YXhuNW/lCsm+YWqO9qY6+d7ZQ/MCXZ9hLgUBwwczNjUIuOyIBMIuQgwHqD/yZ4upbSEwrpeHIk3Q27yK/7Oqwjds5RNPx55m/+pPo9Em0nnyDhpqnWXTFg7Q1bAVg6VVfxD3ST92BP5OQWozBlB4rSUBIV8trfyDnytsx55XRtvVxemveInP52EO5s7eD3iM7KLj2PjQJKXRsf5LewzvIXnNT2MZ6qgZb8zFMuaWxkBGFHAjQ99+PkfwPN6OrKqH/0acYfv1tEm/ZGGHn7xui4NEfIGnUEefn/eknEcd9j/wN/aLKy+32BSMHAvT8/k+k3HEb+vJS+h57AtuOnSRdtynCzjcwyLyfPYxCrZ7iSrFHDgTo/9VfSPrwLeiqShj43ZMMb9lF4k1XR9j5+wfJf+SHSBO0+PsGMaxeQtqD/zCTbp8TORCg/zd/JumeW9BVljLwf08yvHUXiTdOouk3P4rSdJaRA0dxHT6OrrJkJtw+L3IgQO8v/0rKvTehqy6m75HN2F57h6RbN0TY+fuGKPj991BMqFe2F98EWSb3Px4i6HDS85M/oK0sRF8dO31yIEDfb/5M8odCser//dSxKvj1OWJ18CjOw8fRVcRPrOakrmCA9hf+QNbVt2MsKOPMq4/Tf/At0leP3bNc3R0M7N9B7k33oUlMofP1J+nft4PM9TfR89YrBL0eyh/8d9z93XS88Ef02QWozUmx0xQI0P3YH0i7+Xb0JWX0PPU41rffInljdHIs4HLR9/xmVAmJ4XMlP4nciqvnb49hqKy63G5fFHIwQMezfyDzmtsxziuj8+XHGdj/FmlXTKLR7cLy+mZUpsRJriQQCASzGzEFYBZh7W9Gq0siPXcJGq2JgrJr6OuqibCxW9tJTC0iMaUQrT6JwsobGB5oQZaDDA+2kldyFXpjKskZFSQkF+C0d0/+y2YQx5kmNKYkksuWotKbyFxxHdZThyNsRiwtJBTOx5xfjjYxjZTq1bgHxt4Y+V0Oug+8jrkgfjrI7hPNKFMSMa5ejDLBROKtmxjZVxNhExh2oNBpojr/Udc61UZgwIrxiiWXz+GLxHWqCVVSEqZlS1CaTCRdfy0jh49E2AQcDhRabVx3/mFcrFYtRmk2kXDLNTj3HY2wCdgdKLSaSTsp/v5BVKnJM+XuBeE+2YwyOQnjyiUhTTdfg3N/TYTNuTSFPh/B9tJWdAsqZsDjC8NdfzoUqzWLUCaYSLp9IyN7JsRqtF5N7PwDKAy6CSckFHpdlN1MMjFWiTdfw8iBmgibC4mV9aWt6ObHUazmqK6RtibU5iQSK5eiMphIv+I6bCci71nOzhbMJfMxFZajSU4jeeFqPH2he5a96Thpqzeh1Bkw5hWTWLWU4cZjsZASxtXchCoxCdPipShNJpI3XYej5vCktgOvvIC+eOpEu7utBb/VimnJ8svl7ntipK0JdUISiVWhuKWtvQ5b/eQau7e/gHFefLxMEAgEgulGjACYRbicAxgSMsPHBnMm7pGBCJvk9HISU4rCx47hLowJWUiSgmUbvgRAMBjAae/B7RzElJg7I76fC89wP7qU7PCxLiUL7/BghE364qsAkGUZv8uBvaMBQ+a88Oedu54jY/m1OM6cmhmnLwB/7yCavLF4qfMy8fdF6vL3DSIHZbq+9Uv8fUPoKotJuf9OVMkJYRtZlhn62yuk3n8HkhQ/CyL5+wdQZ2eFj9XZWfgGIvX5+gdAlun86c/xDQyiLysh9Z4PokqMr7cq/r4B1LljWjS5mfj7BibYDCLLMpbv/BJ//yC6imKSP3YXquQE/H2DeNvOcOaL+5DUKkwb1pB4a2yHlYc0jZW/c2r63n+OafroXaiSQvEZ+tsLJN50Ne6TzTPq+7nw9QxMqFdZ0fWqdwhZlun8xn+H6lVVEamfuANVcgKJt22k65u/4sznHwYg4ab1aIti2w76+wbQjIuVOmfqWHV9fzRW5cWkjIvV4N9eIPHGq3E3xE+s5qour7Ufbdq4e1ZaFj5rZBlMXT52zwo4HThaG9DnhO5ZQb8fadzUPUmhwjc8NAOeT41vsB9N5pgmTWYWvsHBKLuRhhP4bVaSr76OgddejvpclmX6X3mR9Dvujqv7FYB3aELc0rPw2aI12ptP4B+2krb2OnrejNYoEAjiB0lMAXhPiBEAswi/z4VSqQ0fq1RaAgEvweDYCugqtR6NLtR57D1zmFM1m5lXcX3EdY6+82uO7PolqVkL0BlSZsb5cxBwu1CoNeFjpUZL0OdBDkSv7D5Q+y71f/wOI5YW0hZeCYDt9DECXjcpFStmzOcLITjiQtKOxUuh0yK7vcj+MV1BlxtNXhZp//wP5P78qyhMBgb/9FzEdUbePYwmLwvNvNgna8YTdLpQjNen1SJ7IuMWdLtRZ2eRfv995H/331EYDPQ/9Uws3D0nIS1jZVDSaZE9E2LldKPOzSLtn/6B3P/4WihWj43GSiGhrSgh58f/j/QvfgLH2wdwHjw+0zIiCI5ExmdSTa5RTZ++l9yffB2F0RjW5DxcS9DlxnhFfL3FCzrdSLqxWCl0minqVSbpn/0web/8N5QmAwN/eAGAoae3oM5KI/833yTnx19gZN9x3A0tMy0jgqBzkrZiklhpcrJI+8d7yX346yhMRgb/OhqrI7UE3W5McReruakr4HGh0Iwrg+e4Zw0eeZeG33wHZ2cLKUtC9yxTYQUDh3YR8LhxdrVhrTtA0OebMf8nI+iKbAMVWi2yN7o9H3jlRdLvugem6NvbDx9Ek5WNNjf+dtEJuCeJ2wSNAY+bnu0vkn3j1BoFAoFgtiMSALMIlVpPwO8JH/v9HhRKddQigB6XlWN7HqX91A6qVz1ASmbkPLwl6z/P0vVfwNp3ip6OQzPi+7lQ6vQEfWO6Al43CpVm0oV50hauo/qB75JYtIDW1/5EwOOia/er5G2Mv0XlFEY9QfeYrqDLg6RRRyyApV9QTua//SOa3EyURgPJ99yIu74JORgM2zh27se0KT4WaxyPwjBBn3tU37i4GSoryP7sP6HJykJpMJBy2824G09F6IsHorRMFat//TTqnEwURgNJd9+Epz6kJeW+O0j+0E0ojAY0+TmYr16Dq64xFlLCRJU/9ySa5peT+dCDY5o+eBOeE6cIOl1Yn/07KR/7YCxcPycKgw7ZdZ5YLSwj62ufGq1XepI//AHcdaF6NbLnKIm3XIXSbECTn4Vx9UJGDtTFQkqYKevSJLHS5Iy2FXfdhHs0VkPP/p3Uj8ZjrOamLqVWT9A77p7lcSOpJ79npS5bR/m/fBdz2QI6XvwTANnX3EnA7aTxke/R/eaLmEvnozKZZ8r9SVHo9QQ942I1iaaB114mYfVa1ClT7zlo37+XxNXxsVDjRJS6yLhNprH3zZdJXroWTdIc3VdRIBAIEFMAZhV6Qwq9HQfDx057NzpD5E3K73NxbPcjpGRWsXD1pyKGGZ44+FeK59+CVp+EKSmPlKxqHLYzZObH9u2KJiGVoRMHwsfuwW40CZEjEwZqdyMpVaRUrUJtTCB1wZWceuaXeO2DeG19nPjLD8K2QycPYO9opOimT86YhslQpafgeGcsweLr7EaVEanLVXsKSalAVxVa3CrocIJKiaQI5eYCVjsBhxNtYfy9TVGlpuLYPxY3r8WCKjWyPLpONoJSgb4sNJcyMOIEpSqsL15Qpacw8u6EWKVP0FLXiKRUhhfDCzpGwrEafmMXxiuXozQZAQg4nFPOaZ4pVGkpjOweay8m1VQ/qml0cbXgyAioVPgHhvD39tP11R+HbUd2H8Rdf4r0z94/MwKmQJ2RguPtsXm7vjM90fXq+ClQKdFXFQOheITrVVCG8UOTZRlinJBSpafgmBirtAuIlXIsVp1fi4yV68QpMj4T21jNVV2apFSsdWNtn6e/G01iZBkcrAnds5IXrkJtSiBlyZWcfuyXQGiYfP5tD6BQhR7BOl5+DF16zoz5PxnqlFTsh8a1593dqFMiNbnb2xnet4eBV14In2v+2kMUfvdHKHV6/PZhAs4RtHn5M+X2RaFJSsV6fNyzRl83mqQJbUdXO0M1e+jZ/kL4XP1PHqLiSyGNAoEgzhBTAN4TIgEwi0hMK8VT8zR9XUdJTC2h/dQOMnKXRtj0njmMVp9MyYLbo77vdQ/Tfmo7BWXX4nFZ6eusoajqxplyf0rMuWV0OJ7EeqoGU14pPQe3kVQ+ISmhUNBzaDu6lCxUBjO9h3dgzCpEn5bL4s+OrT7cvv1vGDLy42IbQF1VCYHfb2Zk/zF0lcXYXn4T45rIeMkeLwOPvUD6F+9HnZGK7dW3MCxfEP7cebgunByIN/TlpfQ/YcNxpAZ9WSm2rdsxLV8WYRP0ehnY/ByZn/4EqrQ0bNt2YFy0YIorxg5dZSmDg5sZOXAUXWUJw6/uwLBmSYSN7PEx+NenSfvCA6gzUhl+7S0My0JaPI0teNu7SP7QTQQcTpz7akj9xw/HQMkYuspSBv/0dKSm1dHlb/Dx50n73AOoM9JCmpYuQJOfQ8Hv/iNsN/DHp9EU5sXFFmy66hICv3uWkX3H0FUVY33pLYxrl0TYBD0+Bh99howvfwxVZiq2V3ZiXDEfAP3yKmyv7CT1U3cRGLAysucoaf8U2xFEuopSAkNPM3LwKLqKEmx/34FxklgNPPE86Z8Nxcr2eqj8afJzmPfoWKz6//Q02nlxEqs5qstYUIbvtSexnazBWFBK395tJFZH3rMkhYL+fdvRpWWhMprp37cDQ24hAEM17+IdHiJr4224LG24LO3k3XRvDJSMoS8pw7/5SRzHatAXlzL05jZMSyM15X/xK+H/u043MfD3l8PbAAKM1NWiLymbMZ8vFuO8Mvx/fxLbiRqM80rp3xMdt+JPjmkcaW+iZ8fLYhtAgUAw55BkWaROZgpJCi1Vsf7W/zif6ZTYh9o5dexZPC4radkLKVl4J32dR2hr2Mqqa79O0/HnsbTuifre+lv/gxF7N01Hn8Ux3IVaYyR73hryyzZN8lsujuGCS88jOXva6HhzMz7HEIkli8i76m6GTh2mZ/8bVH38m8jBAJ27nsPafAzkIKbcUnKvugu1MXIxuelMAFhXei/5Gp7THQz8MbS3vGH5AlI+fgcje2uwvrCNvJ99NfR7XtiGfcdeCATQL6ki5b7bwiuV9/x/v8d45TJMa5ee69dcMArb9Ob8PG3t9D/1DP6hIQyLF5H2obtwHDqM9bUt5H/n3wEYem0Lw+/shmAAw4L5pN51Owr99L5JCZii595eLJ7THQz+5VkCA1b0yxeQ8tE7GdlXg+2lreT+9GsA2F7civ2tveAPoF9STfI/3IrCoMc/NMzgnzbjOdWKMtFMwk0bMa1fdck+Eby0SaielvaQpkEr+uULSbn3Tkb2Hwlp+snXQ5pe2or9rT2h8re4muSP3IbCEBmf6UwAKHT+S76Gp7mD/j+8QGDAimHlfFLvv52RPUexPredvP/8fwBYn9vO8Pa9EAiiX1pF6sduQWHQEXS5GfjLK7iOnEDSa0m8+SoSrl1zyT7JrkurW56WdgYeC8XKsGwsVtaXt5L3cChW1pcjY5Xy4ehYxVNHGeJTl+nUNNyzLG1Y3tiMzz6EuXwROdfeje3EYXp3v0H5g6F7lmXbcww3HkMOBjEWlJJ97V2oTYkE3C46Xvozrq42tOnZ5NxwD7q0rPP/0nPgnoYR6+6ONvqe24zfOoRxwSLS77gbe81hhra9wbyvfjPCdrIEQNfvH8G8bAXmpdO3Ho/WOm2XAsDV1UbX65vxDQ+RULGI7OvvxlZ/mL533qDsXyI1Xs4EQN3DIqkgiA1nF+eUZXlWr3Jxtk9V8Z1fnM90Rmn43kNA/P99RQJgBpmOBEA8Mh0JgHhkOhIA8cZ0JwDihelIAMQll5gAiEemIwEQj1xqAkAwc0xHAiDemI4EQDwy3QmAeEEkAASxYs4lAL4dZwmA78+OBEB8TcQVCAQCgUAgEAgEAoFAcFkQCQCBQCAQCAQCgUAgEAjeB8y9cXACgUAgEAgEAoFAIJjTSGIm+3tCJABigL5rJNYuTCuS3xBrFy4LQY0m1i5MOz5jrD24PMgDc3Mwk8oVaw+mHykobjuzhtjujnjZMFnmnrCct+bWc8VZ5IPHY+3C5UGsASAQCGLI3HxqFggEAoFAIBAIBAKBQBCBeBUzy7CNdFLf9gpur42MpEqq5t2MQlJOarun/hFWVnwSlTL0JnvYaWHviUcjbFZWfIJkU8Fl9/t82K0dNNY+h8dtJS1zPqXz70ShiNTV23WE1lNb8XkcJKYUUTr/TnT6JAIBH031LzDQU49CoSIrfyWFZdfHSEkkzu52OreFtopKKF1I7qa7kZSRuvoP76T/0E4CPg/moipyr7kbpUbH8f98KOp6xvxSiu/+zEy5Pymurna6Xw1to2SuWEjWjdGazuI4VUf/ri0Ufir0tiPo89Lz+nPYG2tRqFQkLllD+oYPzKT7U+LqbKfn1c34bEOYKxeSedM5dDXWMbBrC/P+cUxX72vPYW8Y1bV0DWkbY6/LZWmna8torMoWknPdJOXv4E4GDu4k6PVgLq4i+/pQ+Qt43Vi2PYe9qQ6V0Uzm+ptIKF8UIyWROLtHdY3Wq5xrJ9F1aCf9B3cSHK1XOdeFdPldI3RtfRpH2ykUag0pi64gY+0NMVIyxlzUBKO6xrWBOedoA8O6RtvA2inawKIYt4EAjoF2Th94Bq/TSnLeAopWfDDqntXfepiO2jfwux2Y04soWnE3WmMSe5/816jrJWSUUL3pX2bK/UmxOTo50fYSbo+N9OQqqubdEqXpLHtq/5dVVZ9Cefb5YsTCvvpHImxWVn6KJHPsny/GY5MHOcFhPDhJJ4dKlqGQIt+F9csWGjmGHy85FFIqLYyRtwKB4LyIKQDvCZEAmEUE5SA1zU9RkXc9KeYijrc+T1vPHoqyIve8HxhuoXvwGHZXT8R5l2eIrOQFLCr+4Ey6fV7kYIC6w3+huOoWklJKaDj2JGdadlFQcnXYxu0a4lTd8yxY8Ul0uiRaTr1B4/GnWbTqQTpb38HtHGLF+q/gcvZz8uiT6A1pZOYui6GqkK62l/5A9obbMeWX0fH64/QdeouMVdeEbZzdHfQd2EH+B+5DnZjCmS1P0ndgB1lX3sTCL0dubdL+98cwF1XNtIwI5GCAzs1/IOPa2zEUlmF58XEG975F6pXXRNkG3C66/74ZlSkxfG5o/9v4rIMU//PX8A72YXnhcTTJaSQumr59o98LcjBA19N/IP36UV3PP87gnrdIXTe5rp5XN6Myj9O1L6Sr6DNfwzvQR/cLj6NOia0uORig/YU/kHX17RgLyjjz6uP0H3yL9NVjmlzdHQzs30HuTfehSUyh8/Un6d+3g8z1N9Hz1isEvR7KH/x33P3ddLzwR/TZBajNSTHTBKO6XgzVK2NBGWf+Prmu/gM7yLsxVK86X3+S/v07yFx3E337tiHLMmWf/BoBl5PWZ/4XQ14xpoIyoWmakYMB2kfbQGN+GWdef5z+Q2+RvmoSXaNtYOeWJ+k/sIPMK29iwYQ2sCMO2kAI6Wp450/MW3obiRmlNO19AsvJneRWbwrbeEaGaDn0HJVXfQqNMZmOY6/RvO9Jqjf9M2s+8rOI653a/ThJOZUzLSOCoBzgaNPfKM//ACkJRdSefpa2nt0UZa+PsBscPo1l4BgOV3fEeZdniKyUhSwsuXsm3b4ognKQY+ymjMWkkEEt+2mnkULG/vZe2UMt+1nEFRgwUcO7mOQksqT8GHouEAgE04uYAjCLGLS3oNMkkJWyAI3aSHHWeiyDtVF2Q45WFAp11HmnZwi9NmkGPL04hgaa0eoSychejEZroqDkGvosRyNs7NZ2EpOLSUwuRKtPoqjsBmyDrchyENtQCznzrkCjNZGYXEhW3kpsg6djpGYMR0cTanMSSRVLURlMZKy6DlvD4QgbZ1cL5uL5mOaVo01KI2X+atz9lqhrjXS14LNbSapcPlPuT4qzpQlVQhIJ85eiMppIXXcdw3WHJ7Xt3fIChnmlkd9vbyZpxZWojCYM+UUkLlmNs715Jlw/J1G61l+HfSpdb7yAvjBSl6u9maSVo7oKQrpcbbHVNdIWKn+JlaHyl37FddhOTCh/nS2YS+ZjKixHk5xG8sLVePpC5c/edJy01ZtQ6gwY84pJrFrKcOOxWEiJwNE+Qdea6Ho1MqFeJS9cjXtUl1Kri7ygJEWfm2HmoiYYawMTR9vA9EnawChdU7SBztE2MDHGbSCAracJrSGJtIIlqHUmcquvZaD9SISNY6ANc3oR5vQitIYk8hfdiL2/BVmOXH/A3t+K1zlE2rzYJqyHhlvQaRLJSg09XxTlbKB7IHr+/ZC9FeUkzxcuzyC6OHy+GM8QvWgxkCXlo5G0FFFJNx0RNr10kkomKVIGOsnAPMrppj1GHgsEAsHlQYwAmEW4PIOYdBnhY5M+A5dnKMquNCf05ryj78CE7w8x7LRwpu8QCoWK/PQVFGdfdXmdvgDczgGM5qzwscGcids5EGGTnFZOQnJR+Nhht2A0ZyFJCsrm34laM7a63YjdQmJK8eV3/Dx4rf3o0rLDx7q0LLy2wQibtGWhv78sywRcDhztDRiy50XYyLKMZeeL5F5zN5IkXX7Hz4HX2o82fUyTNj0Ln3Uwys7RdALfsJXUddfRt/3l8PmsG+9GaTSFjz29XRgK4iBWQ/1oM8bpysjCOzSJrlMn8A9bSV1/HX3bxnRl3hSpy93ThWFebHV5rf1oJ5S/ibFKXT6u/DkdOFob0OeEyl/Q70caN/xXUqjwDUe3NzON19qPLnVCvbKep161jelKX30tTY/9nIZHvhuyXXE1+szYvt2bi5rgvbeB+inawJw4aAMB3I4B9Inj7llJWbgdkboSsyowp4+1Ac6hLgyJ2UjjhpvLskzbkZcoWvHBmOtyegYx6s//fFGSGxrl0NG7P+L82eeLt478FIWkIi9jBcU5Gy6v0xeJixFMJISPTSTiYmSCjQMjY6O7jCRE2QgEgvhB7ALw3hAJgFmEz+8Oz7cDUCq1BIJegnJgynUAxiNJClLMhRSVfRS318aR5icx6tLITK6+nG6fF7/fFaFLpdQSCHgJBgPh+YcqtR6VWg9AT+dhTp98hYqF9wCg0ycD4PM5OX3iFdzOAcrm3zXDKqIJeFwo1GO6FBotQZ8HORCImgM7ePRdut58DnVCMjlXR07RsNYfRJeWjT4jb0b8PhcBlwvFuN0RFFotQW+kpoDHTe/WF8n7yKfxT+gwqpNSRq/jpHfri3iH+sm6KfZDRoOT6JIn0dW35UVy7/00fts5dG15Ed9QPwm3xFZXwDNB07nK35F36d4eKn/Z14bKn6mwgoFDu8i+5i48Az1Y6w6QUL54RjVMxkXpqnkXy47RerUppKvn7VfRJqdTdM9n8TtstD3/e8wl8zHmxS5hMxc1wcW3gZZZ0AYCBLwulCpt+Fip0hL0eyLvWRo9ELpn9bUeou3IS5Ss/kjEdfpbD2JIzMaYnDtjvk+F3+8OrxcEoFRqQs8X4zSdi7PPF4XlH8PttVFz6m8YdelkpsT2+WI8Prwoxz32KlERwE9QDobXAfDjQzsaNwAVavx4Z9xXgUAguJyIKQCzCLVKRyDgCR8HAh4UCvUFdf4BqgpuojzvOtQqPWZDFvnpK+kfjv3wa5VKj3+cLr/fg0Kpjnro8LisHNv/KB3NbzJ/2f2kZIzN2+vvqePgrp8jy0EWrf5n1JrYb02o1OoJesfFy+tGUmkmXVgudck6Kh/8LgklC2h75U8Rnw3W7iV10drL7e4FodTrCXrGNAU9biR1pKa+7S+TtHwtmuTUSa9hb6jl9CM/QQ4GKfjYZ1HqY783oeJCdG17maQV59bV8pufQDBIwf2x1xVV/ibRdJbUZeso/5fvYi5bQMeLfwIg+5o7CbidND7yPbrffBFz6XxUJvNMuT8lSq2ewERdU9Wrpeuo+OfvklC6gPaX/wSA9eQR0lZuQqU3okvPIbFiMcOnjkZ9dyaZi5pgEl3naQMrRtvA9glt4FDtXlLipA0EUGr0BHzjdPmmuGc5rdTveISu+u1UrP8kyTmR6xf0Nu8jo/SKGfH5fKhUuoj7cPj54gI6/wCV826mLP/6seeLjJUMDDddLnffE2o0BPCHjwP4UaCMWAQw1OH3hY/9+FAx97YEFggE72/eVwkAKcQbkiTJkhTda5Yk6UZJkvZIkuSQJGlAkqSXJElaeo7rXZT9paLXJGN39YWP7a5eDJrkC/5+a88evH5n+Njnd6JUxH4QiM6QgtM+tmDhiKMbnT6yk+X3uTi677cYTFksX/clEpLHhoj299TRePwZKhbdQ+Xij6Aa92YmlmgSU3EPjC2U5OnvRpOYEmEzcGw3g3WhoZRqYwKpi67E1TM2J9E3MkzAPRIXw3kB1EmpePrGaertRpMUqcnV2U7vlhc4+cOHaH/sN7gtHZz84UME3C7sDbVYXnmS7NvuJeeO++JinjKAJikVT2+kLnVypC53Zzu9b7xAw/cfouMvv8Hd1UHD98d0db/0JNm330v2nfehiANdmqRUPP3nLn+DNbsZOj5a/kwJpCy5Eld3qPzJskz+bQ9Q9cWHKb7vCwR9PnTpOTMnYAqidA1El8HBo7sZqh2rVymLx3QhB2HccGtZBjkY2zGEc1EThNpAz3nawMFjuxka1wamxHkbCKAzpeK0ja1T4LRZ0Jom3LO8Luq3/y+GpCwWfeArmNMipzV4XcP4vE5MKfExqkGvTcHh6g0fO1y9GLQX/nzR1r076vlCIcX++WI8eow4sIWPHdjQY5xgY2KE4XE2w1E2AoEgjpDj7OcCmO7+4/n6t5PxvkoAAJ8DJt0fTpKke4CXgDVAK+ACbgX2SJIU9erhYu2ng5SEIjw+G92DdXh9I7R0v0126oVvTzNkb6Oh4w08Pjt2Vy+WwVoykmK/onJSaiket5U+y1G8HgcdzTvIyFkSYdPTdQStPpnS6tsi5iUDtDdtp6TqFlLSK2bQ6/Njyi/DZ7dibajB73TQu39b1CJ+kqSgb/92nN3teO1D9B3cgSGnMPz5cFMtxvzYruQ9HmNhGf5hK8P1NfhHHAy8u42EBZGaij79FSq/+Qsqv/kLCj72GXTZ+VR+8xcodXoG3tlC5nW3YyqJ7YrXEzEUleEbtjJcN6rr7WhdhQ9+hYpv/4KKb/+C/I9/Bl1OPhXfHtW1awsZ19+OsTR+dBkLQppsJ0Plr2/vNhKrJ5Q/hYL+fdtxWdrxDQ/Rv28HhtxCAIZq3qVry9P4nQ7szXW4LO2Yi2M/nPdsvbI1jOmKWhxzfL0aHqJv/1i9MpcsoH//DgJuJ+6+LmwnD5NQumDmhYxjLmqCSXRN0gZynjbQHmdtIEBCZilep42B9hp8bged9dujFvHrbz2E1phE4bI7ou5ZAEOdtSRmlEadjxUp5iLc3mG6B2tDzxddu8hKvfBtP4fsbTS2v4bHa8fh7MEycJyM5Ng/X4wnmQzcuOiRO/DKHlo4STaR2xRmkMsAPQzJfbhlJ+00kjXBRiAQCN4rl6n/OGX/dko/ZDn2bwlmAkmSqoFDwNlXcypZlgOjn6UAXUAQuEaW5T2j5z8P/DdwEpgvjy7fe7H243yQAa5f/p33rMM20kl928u4vDYyk6uoKrgZy+Bxmrt2ctXCL0bYbjn0PTYt+Xp4Xp/ba6eu7UWsjnY0ahNFWevIS7v0lYfdGZc+3H7Y2sGp2mfxuK2kZS6gdP6d9HbV0Na0ldUbv8apuhewtO+J+t5VN/6Ud7Z8k2DAF3E+M3c5FYvuuSSfhiqjVzq+WJzdbXRuC+3DnlC2iNxNd2M9eZievW9Q+alvhraf2/EctlPHkOUgpvxScq6+C/Xo1nktzz5CUvUKkqumZzs53zS8yHB1ttH99834bEOYKxeRdePdDNcepn/XG5R8/psRts62Jnq3vUzhp74MQMNPv4bsi5xPmbhoJdm3/cMl+SRPQyrT1dlGzyujuqoWkXlTSNfAzjco/sIEXa1N9G17mXn/GNLV+HC0roTFK8m+/dJ0qVyX9HWcljYsb4T2YDeXLyLn2ruxnThM7+43KH8wVP4s255juPEYcjCIsaCU7GtD5S/gdtHx0p9xdbWhTc8m54Z70KVlnf+XngcpeH6bC9HVtXV0b/myReRcE6pXvbvfoOLTo/Vq+6iu0XqVfc2oLo8by5vPY2+uRaHRkbZyE6lLrrx0p+aipumIVXcbXePawJzRNrB37xtUjGsDh8e1gdkT2sDk6hUkTVMbCGCyXLowx0A7pw88g8c5RGreIgpX3MVA22HO1G5h6a3/TsvB5+hp2h31vbNbAJ5481HSipaTXjg9uxqYmy99oTqb4wz1rS/jHn2+qJx3C92Dx2nufJP1i78cYbv1wHfYtOzfw+v3uL3D1LeMe77IXkdu+qVrkw9G70RwKdjkQU5wCDdOMsijkqV0085p6lkn3QRAn9zFKY7jx0sORZRK059M2xrcPO3XFAguhLMLjsqyHPsVVS+Bs32qqm/84nymM8qJHz8ETP73fa/9x3Nxrv7tOb/3fkgASJKkAfYB+YRGPSQTmQD4LPAr4N9lWf7xhO++DtwAXCvL8vb3Yj/us0tOAMQj05EAiEemIwEQb0xHAiAemY4EQDxyqQmAeGQ6EgCCGWKOxmo6EgDxxnQkAOKR6U4AxAsiASCIFXMuAfD1OEsAPHzOBMB76j9Oxfn6t+dijj42R/EDYAnwzzBuAtgYZ1/TPT/JZ8+N/nvDJdgLBAKBQCAQCAQCgeD9yXT3H8/Xv52SOZ8AkCRpA/CvwF9kWX5mCrMiwCbL8olJPjs77rzqEuwFAoFAIBAIBAKBQPD+ZNr6jxfYv52S+FqidZqRJCkR+AvQAXx+ChsJyADap7hM/+i/Be/FXiAQCAQCgUAgEAgE04s0S2ayT2f/8UL6t+djTicAgF8DecDVsiwPT2GTSujvMDTF54Oj/56dPX2x9lHsPf6/U30URb55IfnmC1+JNxYYnN7zG81ClJ6EWLsw7QTVs3rK15TI6rk5mEnhmXtzledqGVS6RaxmC9oe5/mNZhlS4LxTPmclr4u58gLBeXn00Ud59NFHY+3GnGWoZg9DNdELkV8kl9x/HMeF9G/PyZxNAEiS9GHgPuA/ZFnedQmXOrt/z4U+3Z3Xfm3OfZfgjkAgEAgEAoFAIBDAgw8+yIMPPnhBtmcXARRcOMlLriB5yRUXZFv/k4fe66+5oP7mdPVv52QCQJKkPOB/gaPAt85jPgD4gZQpPj973vIe7acVm6eb2oFtuP12Mg2lVKduQiFF7jHc6zxNw9Db+AJu8szzKU9eB4A/6KV+YAe9rtNolUbKk64k0xgf+xDbXF3UWl7H7beRaaqgOvuGKF1neff071ld+DFUitD2Q4Ggj/ruLfQ6GlFIKvKSFlOWftVMun9BDA930HDyedxuK+np8ymvuAPFhP2he7praGnZitc7QmJSIRUVd6DTJcXG4SkYtnVwqu453G4baRnVlFXfGa2jq4bWpq34vA4Skwspq74TnT4p/Ll9uJP2028xf0n8JMPs1g4ajz2Lx2UlLWs+pQvvitLV23mE1oYtIV0pRZQuvAudPgmfd4TGY89i7W9CpdZTULaJ7ILVMVIyNcO2DhpPjMYuvZryqkliZ6mhpXksduVVd8ZdGbRbO2isfS4Uq8z5lC6I1tHbeYTWU2d1FFG64M6xWB1/FutAEyq1gYLSTWTnr4qRkqkR7UVS+PN4bC9szk7qO17F7bWRkVhJVf5NU96z9pz8LSvLPhHeknc8NS2bSTEVUpC+8nK7fF5szi7qOv+O2zdMRkI51bk3Tqlp96nfsark/oj78ImuN+gdbkQhKclLWUJp5oaZdF8gELwfmSVTAJiG/uNF9m/PydwcNwvXMLoVAvCiJEmvnf0BMkdt/j56vAboJTQ0YzLOnrcAyKF9Ey/YfjoJykEO975EceJK1ufej8tvp9V2KMLGG3BxvP91qlM2sTbnXvqcLVgcDQA0DL1NQPaxIfeTLEi9ltqBrbj9jul286IJykEOn3mW4rQ1rC/+J1z+YVoH9kfZDYy0crzrVeye3ojzbYMHcflsrC/+J5bk3kmXrZZOW3xtHRQMBjh+7K8UzNvI6jUP4fbY6Gh/O8LG7bbS0PAClVUfYtXqL6NRGzhx4qLX9bisyMEAdUceI79oIyvXPYTHbeNM6wQdLiunTjxP5cIPsXLdQ6g1RhpqQ8M4/T43PV1HaDj+dAy8nxo5GKDuwJ/JL93Iio3/GtJ1OjKx6nYNcer481Qs+TArNnwFlcZIY01IR1vDViRg2fovUrbwLppqX8Tp6IuBkqkJBgPUHn2MgsKNrFr7EB6PjY626Ng1nnyeqgUfYuXah1CrjZysi68huHIwQN2hv5BfvJEVV30Fj9vKmZZJYlX3PBWL7mHF+odQaQw0HhuN1amtSEgsW/dFyhbcSVNdfMZKtBfx214E5SA1LU9TlHElV1Z9BrfPRltv9PDQAXsLde0vYXf3THqd7qE6em2TrQU18wTlIEfaNlOUvpZ15f+E22entW9flN2Ao5XaM69gd0+4D/cfwOW1sq78n1hccBddQ8fpGoqv+7BAIBDEimnqP15w/1aSpHMOWZirCYCzzAc+MOFHP/rZ9aPHGcBpIEGSpAWTXGPt6L8t485drP20MOjuQKc0k22sQKM0UJK0CstIQ4RNj/MUqbp5pOrz0anMFCYuxzJyEoBeZxNFiStRK3Uk63LJNlbS7WycbjcvmsGRVnSqBLITqtGoDJSkXYlluD7aztmOUqGe9HxB8jI0KgPJhjzykhYzNNIxE65fMNahZnS6RDIzF6HRmCgsvJqe3qMRNsO2dpKSikhKKkSnS6So+AZs1lZkOX7mFg8NNqPVJZKRvRiNxkRBySZ6LTURNsO2dhKTi0hMLkSrS6So7AZsQyEdPq+DYVs7ao05NgKmYGigGa0+iYycJWi0JgpKr6GvsybCxj7UTmJKEYkphWj1SRRV3IBtsAVZDmIbaiW3+Cr0xlRSMipISC7Aae+OjZgpsA6Nxi4rFLt5RZvo7a6JsDlbBhPPlsHSsdjFC6FYJZKRs3gsVl2RdclubScxpXgsVuU3YBtsHY1VG7nF69EbUklJryAhqQCnY/IOWqwQ7UV8txeD9hZ06gSykuejURkpzlyPZaguym7I0YZiknsWgNfvpLl7J6nmksvt7gUx6GhFp04gO6kajcpIScaVWGyTaBppm/Q+PDTSTn7qCjQqI8nGfHJTljA40jYTrgsEAsFsYbr6jxfav52SOTkFQJblPwN/nuwzSZJagEJAJctyYPRcLrAOuBOonfCVO0b//du4c3+7SPtpwemzYtakhY/N6jScftsEG1uUTYv/IAABOYBiXM5HISlx++3T7eZF4/RZMWvTw8dmbRpOnzXK7uyw/vahyFEP87M/gEZpCB8Pu3tJMeRfHmffIy7XIEZjZvjYaMzC7RqMsElOKSMxqTB87HB0YTJlIUnxk6dzOwcxmsbpMGVG6UhJLSMxuTB87BjuwmgO6dAb0yirup3uzkMM9J2cKbfPi3tkAKN5TJfBnInbORBhk5xeTkJKUfjYMdyFMSGka/lVXwJCb26djh7czkFMibkz4vuF4nJOKIOTxC55YuzsXRjjrgwOYDRlhY8njVVaOQnJ42NlCZfB5eu+CEyIVULOzDh/gYj2Ir7bC5d3CJNu7NnKpMvA5Y1e16k0eyMAHf0Hoj47eeY1ijLXMeiY9ncF7wmndwiTbuw+bNKl4/Jao+zODutvHzgYcb4690Y0qrH7sN3VQ7JRbIYkEAguM7NnCgBcYv/xYvu35yJ+nhRiy98AN/ANSZLWnD0pSdLnCWVS9suyfOwS7KcFX9CDUhrLvCsVGgKyj+C4OPuC7igbX9ANQJpuHq3Dh/EHPVg9Fjod9QRk/3S7edH4Au6INwpKhZZA0Buh61zo1YkoFWp8ARfHu17B5R0iO3H+5XL3PeHzu1COm/+pVGoIBLwEg2Ma1Wo9Wm1o54Hu7iOcPPEsRUXXzriv5yKkQxs+Vim1UTpU43T0dB2hoe4ZCkuvm3FfLwa/z4VSNU6XagpdulFdZw7TeHQzheXXR1yn5t1fc3jXL0nLWoDOMNU0r9jg90dqVE4Su4gyaAnFrqgkvmIXitVYXZqyDJ6NVedhGo9PEqs9v+HwO/9FWtb8uIuVaC/iq8xNxBdwoVSMj8/F3bN6rCfxBzzkpMTPDj/+gDtS08XehzXj7sNnXsbpHSI7abKXXAKBQPC+JSb9x8mYkyMALhZZlockSfo48ASwR5Kk44QWY8glNBfjE5diP12oFVqcfmv42B/0opRUEYv0qBU6/LJ3nI0HtUIHQFXqRmr7t/Jmx/9h1qSSYShGq7yQ3SYuL2qlDue4tyf+0UTHVIsPTUaPvZE6y2ukGYtYNe8+VOMeOuMBtUof8eYrEPCgUKijFsNyu22cOLEZr8fOwkUfJzExvt6gqFX6iLetfr8HhTJah8dt4+Txp/F67CxYej8JSfGlYyIqtR7XhehyWWmoeRqvZ5j5Kx8gIXlexOdL130ex3AnDUeeoufMITLzls+I/xeCShWp8Vxl8GTdaOyW3E9inMXuomJ1LKRj/vL7o2O19nM4hrtoOPokPZ2HycxdNiP+XwiivYgvHRNRK/W4PGP3rHB8LuCe5Qu4OWXZxoqSj11OFy8a1cT7cODi78O9ww3Udb5GqqmIVcUfi7v7sEAgEMSSi+k/SpKUwdjb/vtlWe6deL1LQYwAGEWW5c3ArcA+oAQwAC8B62VZjpqQfrH204FenYjDO/Yw5fD1o1clnsdmAL0q4azPLM24hevmfZY12R8hIPsxq9OINXp1Eg7P2CJcDk8fek3SBX+/x95IreVVFubcyqLc2+LyoUOnT8ExMjYnfMTRg14f+dbR53Nx5PCjmIyZrFz1hbh7mAfQGVIYGTdfesTRHaXD73NRs/+3GE2ZrFj7xbh/mIeQrvFz9kfs3egMkeu0+H0uju55BIM5k+VXfTmiQ1l/6K+4XVYkScKcmEdqZjV265kZ8/9C0OsniZ0hugzWHPwtRmMmK9Z8Me46/3A2VpE6dPpJYrXvtxjMWSxf96XIWB0eH6tcUjOqsdviK1aivYhv9JqkiEXw7O5eDJrkC/qu22vF6RlkV/1/saXm+3QNHuVk52vUtDx1udy9IAyaJBzu934f7h1uoPbMqyzMu5VF+bfH5X1YIBDMPSQ5vn7Ox0X0H/VEz++fNt53CQBZlotkWZYmmx8hy/LrsiyvkWXZKMtyiizLt8uy3HyOa12U/aWSqsvHHbBjGWnAG3DSbN1PjrEywibLUEa/u41B9xncfjuttsPkGKsA6LAfo7Z/G96Ai17naWyebtINRZP9qhkl1TgPt9+OZfhEaGGk/t3kJFz4EP7m/nepzLiWdFPxZfTy0khOLsHjttHbcwyv10Fr25tkZi6JsOnpOYJOl0RZ+a1Rb8jihaSUEjxuK73dIR3tp98kI3tphE1P1xF0+iRKq25DilMdE0lKK8XjstLXdRSvx0FH0w4ycifoOnMYrT6Z0gW3R+nyuofpOLUdj8vG8FAbvV01JKREvnGONRNj19byJhlZEzRaRstg5W3xWwZTS/G4rfRZxsdqSYRNT+eRUKyqo8ug12Ono2kHHvdorCxHSUiKr1iJ9iK+STEX4fEO0z1Uh9c/QkvP22QnX9hwd7M+i+uXfDv8k5OymMrcG1lS9OHL7PW5STEV4vYN022tx+sf4XTvuxc1hL+59x0qsq8lLU4WNRQIBIJ45UL6j7Ist432VyVZls+7ouq5+reTIYV2JRDMBJIUyg19oPDL7/kaVk83df3bcAeGyTSUUZ26CcvISZqse9mQ9ykAep2naRjchS/oIc+8gPLkK4HQ0MOavlexeiyYNWksSL0Ok2aq3SguAr3uki9hdXVRZ3kNt2+YzIQKqrNuwGKro6n/HTaUfibC9vUTD3NtxVfC+w9vPfkzArIvwiY3cSELc265JJ882QmX9P2JRO7rvYDyitvp6amhtWU7V6z9NxobXqSzc2/U967e9PC0+RBUS5d8jWFrB431Z/dgX0BZ9R30Wmpoa97G6qu+yqn6F+jqiNax4YafhP9/dlGv6drXW1Zfei5zeKidU8efDenKWkjpwjvp7TxCW+NWVl/zdU4dfx5LW/RWX1fd8h+M2Ls5dexZHMNdqDVGsuetoaB00yX7pPBM74ruw7YOGuqfw+O2kp65gLLKO+jtrqG1eRtr1n+VxhMv0HUmOnYbr/vJJFd7b0xXGTx1/Fk8bitpWQsonX8nvV01tJ3ayuqrv8ap2hewtE8Sq5t+GopV7XOhWKmNZBesoaD06kv2Seme5liJ9iL8/+luL7Q9zku+hm2kk/qOV3D5bGQmVlGVfxOWoVqau3dyVfUXImy31HyfTQu/hmrcug5nqW1/kQR9DgXpKy/JHylwYXP1z4XV2Ul9599x+YbJTKikOvcDWKx1NPfs4qrKz0XYvnH8R1wz//+F78Pbav8j6j6ck7yIhXm3XpJPrx/74SV9XyAQRCJJoXZdluVLb+BjyNk+1fx//UWsXYmg7mcPAfH/9xUJgBlkOhIAcck0JADikelOAMQD0/FAH49MRwIgHpnuBEA8MFfL4HQnAOKBuRqr6UgAxBvTkQCIR0QCQCCYXkQC4PIyWxIAc/OpWSAQCAQCgUAgEAgEAkEEYhcAgUAgEAgEAoFAIBDMKi5k4T1BNGIEgEAgEAgEAoFAIBAIBO8DRAJAIBAIBAKBQCAQCASC9wFiCkAMkAetsXZhelHOjq2bLhat0xVrF6YftTrWHlwefL7z28xGVHOwifaKWM0aXHOwDQSCdkesXZh2Xnf8OdYuCAQCwcwjpgC8J8QIAIFAIBAIBAKBQCAQCN4HzMFXFnMbW6CPOtdu3PIIGaoCqnVrUUiReZw+XwcNngP4ZA+56nLKdcujrlPj3EGKKpsCTdVMuX5ObP4+6pzv4A46yFDPo9qwbhJd7TQ494V0aSso14f2TR7297PH/kKE7SrzLSSrsmbK/SmxebqpHdiOO2An01BCdcomFFLkiIleZwsNQ7vwBT3kmeZTnnwlAN6Am7rBbQy42lEq1OSbFlKatCYWMqbE5rZQ27sFt3+YTFMZ1enXRek7y7vtf2J13r3hfaPjjbkYK5vbQm3f1lB8jGVUp18brWmkmYaBnfiCbvLMCylPXQ+AP+ilvm8bvc5mtEoj5SnryTSVxUJGFDZPN7VDO0Kx0pdQnXx1tC5XCw3Wt0O6jPMpTxoXq6FtDLg7UEpq8k0LKE2Mg1h5uqnt34LbbyfTUEp12iSxcp6mYXAnvoCbPPMCylPGxWpg+1iskteRaYyTWPl6qLXtDMVKV0x1wlXRutytNNh345M95OmrKDeH4vF692+irpeiyWVVyu0z4vu5sAX6qffuwR0M3YurNGui71n+MzR6D43ei0sp0ywDwCu7qffsZTBgQSVpKFYvIk8dH/ESCAQCwdxHJABmEUE5yBHnDip0K0lV5nDMtZNWby3F2kVhG2/QzTHXLpYYNmFUJHDIuRWzL5lsdXHYptvXQo+/jRRVdixkRBGUgxxxbKXCsJpUVQ7HRt6i1XOcYt3isI036ObYyE6WGK/BqEzkkOMNzMoUsjUlOIN2stQlLDZdHUMV0QTlIIf7XqYy+SpSdfkc7X+d1uHDFCeuDNt4Ay6OD7zOkvRbMKqSONT7ImZNGtnGCppt+5BlmfW59+MNuDnY+yzJulxSdfkxVDVGUA5y2PIClWlXk2oo4Gj3q7RaD1KcvDrCbsDZRpf9BHZvX4w8PT9zMVZBOcjh7hepTNtIqr6Aoz1/j4qPN+DieO9rLMm6DaM6mUOW5zBr0sk2V9IwsJOA7GNDwadxePs53P0iibosdCpzzDTBqK6BV6hMuopUbR5HB9+g1X6Y4oQJsRp8gyWpN4di1f8SZmca2YYKmof3IwPrsz+ON+jmYO9zJGvjIFY9L1KZsiEUq76/02o7SHHShFj1vcaSjFtDsep5HrMjnWxTJQ2DuwgEfWzI+0ccvn4O97xIojZOYjX0OpXmtaFYWbfROnKUYtOysI036Oa4bTtLkj6AUZXIoaFXMatSydaX8YGsz0Rc76h1K+nagpmWEUVQDlLjeZMKzQpSlNkc97xNm6+OIs3CsI1XdnPc8w5LdBsxSGYOe3Zg8ieTrSqi2XsUgDX6W3AGh6nxvEmyMhOjIiFWkgQCgWB2IqYAvCfEFIBZxGDAgk5hJFtdjEaho0S7GIvvdIRNj7+NNFUuqapsdAojRZoFETbeoJsmzxHSlLkz7f6UDPq7Qro0JWgUekp0S7F4myNsenytIV3qnJAu7cKwjStoR680xcL1czLo7kCnNJNtrECjNFCSuBrLSEOETY+ziVTdPFJ1+ehUZgoTlodt1ArthCtKqKT4eXs+6GpDpzKTba4M6Uu5Aov9xCR2HSgV8Z1rnIuxGnS1h+JjGo1P8hosjpMRNj0jjaQa5pGqLwhpSlqBxRGKYe9IE0VJq1ArdSTr88g2V9LtaIyFlAgGPaOxMpSHdCWswuKM9KvH1USqdlyszMuwjIRsomIlSTEflRIVq6Q10eVv5BSp+nGxSliBZSQUz15nE0VJK0Ox0uWRbaqie+RULKREMOg9g05pIltfFmrbTcuxuCP96nE3k6rNJ1Wbi05potC4JMoGYMhrwRWwk60rnyn3p2Qw0I1OMpKlKkIj6ShWL8ISaI2w6fW3k6bMIUWZhU5hpFBVTbe/BQBrsJdCdTUGhZk0VS6JinQcwaEYKBEIBALB+xGRAJhFOIN2TIqk8LFJmYwraJ9gM4xJObXNCfdeijWL0Cj0l9vdCybkc3L42KRMxhWYoCswtY0rMMyAr4sd1sd4y/oEza4jM+P4eXD6rZg1qeFjszoVp98WbaMeZ6MZsylOXIXTb+XNM7/jXctj5BgrSdRmzozzF4DTZ8WsTQsfmzVpOH3WKLuy1HVUp187g55dPHMxVk6fFbPm3PGZ1GZUU0D2RwxpVqDE7R++vE5fAE6/LTIOk8ZqEpvAaKwSVoZi1fV/vNv9V3IMlSRqYhyriWVLPUms/FbM6vGxSsXpGxcrxsdKER+xCgxjVqWEj82qFJyB4Yu2kWWZk/bdVCesR5Kky+v0BeCSJ9yLFUnR9+JJbUKLD16hv5UkZQZBOYg9MIhLdpCgSEUgEAgEgplAJABmEX7Zg1IaW8VdiZoAfoJycJyNFyXjbCQ1PtkLQI+vDT9ecjSlM+f0BeAPeiN1SWoC+Cbo8kTZ+GRP6EBSkKLKZn3CPSwz3cAZbwPd3pYZ838qfMEJPis0BGQfQTkQaaMYr0uDL+gGoNH6LkZ1Mpvy/okrsz+KZaSRQXfnzAk4D76AB+W4t9yT6ZstzMVY+YLuSH8n0zRJDH2BkKY0fSGt1kP4gx6sbgud9joCsn/mBEyBL+ie0BZcZKxs72JUJbEp50GuzLwPi7ORQU+sY+VBqTh3XZosnmc1penn0Tp8eCxWjvo4idXEdnuKWEkTY+WJuE6XuwGzKoUEdfrld/oC8E28z05yL/bJXpTS2MgnlaTGR6Su/e7X2ON+hQxlAXpF/I1iEwgEgnhHirOf2YJIAMwiVJKWgDy2hZYfH0pUEW/pVJKGAGM2AdmLWtLgk700eg5SrVs7oz5fCKrRh92zhJIYE3VFag/IPtRSaChvtWEtFYZVqBVaElSpFGirGPCdmTkBU6BWaPEHx+kKhh4Ixy+ANZmNWqEDwDLSQFHCCjRKPWZNGlnGcnqcsR/Wexa1Uos/6A0f+0cf5KdaBDCemYuxUit0k8RngqaoGHrDQ+Sr0jbhC7p5s/W3nOx/kwxjCVqlceYETIFaocMvR/o8eazG2che1NJorJyNFJnHxcpQRo+zaeYETEKUv5PFKiqe42KVuglfwM2b7Y9ycvBNMgzxEittZKzkKWI1of2fOE2jw1lPvmH+5Xf4AlFPvM/iQzHhnqWWNPjHJWH8sg8VkbpW625ije5mBgJddPkip70JBAKBQHC5EAmAWYRBYcYRGJsn6AgMRb01mGhjD1jRK8y4gg6cwWF2OTbzxvAf6fI1ccK9lyPO7TPm/1QYFAmT6IpcvMqgNOMIDIaP7YGh8Lz/VvdxvKNvwgC8QQ8KKfZzzvWqRBy+/vCxw9ePXpV4ATZnF4IKTsgmyshxtNqJXpWEwzvOd28/enXiOb4Rv8zFWOnViZPEJynS5hwxlJFZmnUb1xV/gTV59xKQ/Zi1sX8Dq1cm4vANhI8dvoEpYjXRZjRW8sRYEftYqZIiy5Z3AL0qaYLNhPLnHSujsiyzNPNWriv8PGty7iUg+yKmdsQKvTIBh3+s3Xb4B9ErEyaxGRcr/wB65Vj77wk48QXdJKozLr/DF4heMmEPWsPH9qAVg2SaYGOOmNfvCFrD9+uj7p24gyNIkkSCMpUMVT7DwQEEAoFAIJgJRAJgFpGizMYtj9Dta8EbdHPac5RsdUmETaaqkH5/F4P+btzBEdq8dWSri0lQpnBDwifCPznqUqp0a1hquCZGasZIUeXgDo7Q7T2NN+jitLuGbG3kNIVMdRH9/k4GfZaQLs9xskenMgz5uznp2osn6MQeGKTb20ymel4spESQqsvHHXBgGWnEG3DSbNtPjrEywibLUEa/u51B9xncfjutw4fDNhn6Ek4PH8QXcGP39mEZaSBDXzLZr4oJqYYC3P5hLPaTIX2De8kxV8farffEXIxVqr4At9+OxTEan6F95Jgit/3MMpXR72pl0NUR0mQ9SI4pFMOO4Rpqe7fgDTjpHWnG5raQbiie7FfNKKm6PNx+BxbnaKzs+8kxVETYZOlLI2NlP0yOMWSToS/mtP0QvuD4WMVWV6o+fzRWDaPlby85pgnlz1hGv6ttLFbDh8Lx7LAfpbZ/ayhWzmZsnu74iJUmN1SvXE14gy6aHYfImbCIX5aumH7PGQa9nbgDDlpHjkbY9HhOk6rNm2nXz0mKMhuPPEK3vxWv7KbFd4xsVVGETaaqgIGAhcHA6L3YXx+28chOTvuO4w46sQb6sPhbSVLGPrkmEAgEsw45zn5mCZIszyJvZzmSJMkANyR84j1fwxroo961G1fQQaa6kGrdFVh8zTR7arjK/CEAen0dNHoOhPZUVpdTplsedZ3jrrdJVKZRoKmK+uyiUV76kG+rv5d65zu4giMhXYYrsXibaHYf5qrEjwDQ622n0bUvpEtbQZk+tO2XOzhC7cjbWP3daBQGinWLydNWnOvXXRBSwqXPybR6uqkb2Da6t3wZ1SlXYxlpoMm6lw15nwRG9/Yeejtqb3l/0EP94Fv0uU6jlDQUJ66gwLz4XL/u/KjV57e5CKxuC3W9b4T2LjeVU51+LRb7CZoGd7Oh8MEI29eb/j+uLf7i5Vlx3ec7v815iLtYAagubSSL1W2hrm90b3lj2Vh8hnazYV4oPr0jzTQM7MQXdJNnXkh5amhveV/ATU3Py1jdXZi16SxIvx7TdLxV9k5TrIa2h2KlL6U6+WoszgaabPvYkBNqX3tdp2mwvhPSZVxAeVJo+pM/6KF+aCd97tFYmZfHR6w8Fur6t4ZiZSijOu0aLI6TNFl3syH/0wD0OptpGNyJL+AJxSplHTAaq95XsHq6MGvSWZB2PSbNNCwq53Jd8iWs3h7qht/CHXCQqSumOuEqLO5TNDkOsCH9YwD0ultpsO8Ote36KsrNa8LfPzD4Ern6CnL0l96mnyVod1zyNWyBfuq9e0L3LFUBVZo1WPwtNPuOcpXhLgD6/Gdo8B7EL3vJVZdRplkKhEYD1Hv2MBwcQiNpyVOVUzxuC8H3whuOP1+yJoFAMPc5u5CqLMuzacp6FGf7VAu/9ItYuxLB8V8+BMT/31ckAGaQ6UgAxCXTkACIR6YjARB3THMCIG6YhgRAXHKJncq4ZBoSAHHJXIzVNCQA4pHpSADEGyIBIBAILgSRALi8zJYEwBx8YhEIBAKBQCAQCAQCwVxGEu+x3xNiDQCBQCAQCAQCgUAgEAjeB4gEgEAgEAgEAoFAIBAIBO8DxBSAGCB7PLF2YXqR5mgeye8/v81sY46u14AU11Ot3juBQKw9ELyfmaNrBClMxli7IBAIBILpYG7epi47c7TnJhAIBAKBQCAQCAQCgWA8YgTALMMWHKDevx+3PEKGIp8q1UoUE97A9wU6aQwcwSd7yVUWU6ZaAkBA9nPSf5De4BkUKMlVllCqWhQDFdHYgv3U+/aFdCnzqVKtnkTXGRr9h0d1lVCmDm2pFNJ1gN5Ax6iuUkrV07Ct1zRgC/RT596NOzhChrqAau0V0br8HTR4DuKTPeSqyyjXhrZt9Mpu6ty7GfRbUEkaSjSLydOUT/ZrZhSbv4861zujmuZRrb8yWpOvnQbX/pAmTQXl+hVR16kZ2U6KKpsCbfVMuX5ObP4+6pzv4A46QroM6ybX5QxtRZmrraB8dCvK8dQ4tpGiyqFAF3tdtkAfda7doXqlKqBat3YSTR00jG4bmqsup3ySbUNrnDtCsZqObUOngbmoay5qgrnZBgLYfL3UDu8MbUWpLaI64SoUUuQIq15P29j2hrrKiO0NW53HaHMexx/0UmxcQpFx6UxLEAgEAsH7FDECYBYRlIPU+HZRpKzmSs0tuOUR2gInImy8sofj/j1UqVayRvMB+oJdWAKtALQHGnDKI1ypuYVF6nV0BVroCrTEQEkkQTlIjXcnRar5XKm9DbfspC1QH2HjlT0c9+2mSrWKNdob6Qt2jtN1Eqfs4ErtbSzSrKcreJquwOkYKIkkKAc54tpBkWYh64x34g6O0OqtjbDxym6Oud6mSruGKwy30uc/g8UX8r3JUwNIXGG8jfm6tZzw7GUkaJt5IeMIykGOjGyjSLuYdeYPhjR5jkfYeINujjl3UqVfyxXmO+jzdWDxNkfYdHtP0+NrnUHPz01QDnLEsZUi3SLWJdw9ta6RnVQZruSKhDvjXldQDnLEuYMi7ULWGe+avPwF3Rxz7aJKdwVXGG+jz98RLn9n6fa10ONvm0nXz8lc1DUXNcHcbAMhpOuw9XWKjUtZn/YPuIIjtDqPRth4g26O27ZTnXAVa1Pups/bjsV9CoBeTysW1ylWJd/GqpTbaHUeZ8Qfe10CgUAw65Dj7GeWIBIAs4hBuQedZCBLOQ+NpKNYtQBLMPJhrzfYQZoiixRFJjrJQKGyiu5RmyG5lwJlGRpJR7IinTxlCUPB3lhIiWAw2I1OMpKlLBzVtTDcuT9Lb6CdNEU2KcosdJKRQlU13aM2Q8FeCpQVo7oyyFOWMhTsmXkhExgMWNBJBrLVRWgUOko0i7H4IxMuPb420lQ5pKqy0SmMFGkWhG2sgV4KNfMxKMykqXJJVGbgCFhjoGSMQX8XOoWRbE0xGoWeEt0SLN7ITkiPr5U0VS6p6pyQJt2CCBtv0E2T+zBpqryZdn9KxnSVjOpaGtW5j9KlXRhh4w26aXIdihtdgwFLSJO6OFT+tIujOow9/raQpvHlzzchVp4jpClzZ9r9KZmLuuaiJpibbSDAoLcTndJItq401F4Yl2FxN0XY9LhPk6rJJ1WTi05potCwOGzT4aynzLQKvdKMWZXK2tS70Sr0sZAiEAgEgvchIgEwi3DJDkxSUvjYJCXhkh0RNk7ZPsEmMWxTpVpFmiIn/JldHsKsSCLWuGQHJkVi+Hi8z2dxynZM43wdr71KPUFXcAizlHJ5nb4AnEE7JmVy+NikTMIVtEfayHZMinE2ijGbtcbbSFZmEJSDDAcGcQXtJChjqytKkyI5WlNw+Jw2J1y7KdYtRhNHD7xRPiuTcQUm6Aqc2+aEczfFuiVxo8sZnFBnlFPFamqbE+69FGsWxY0mmJu65qImmJttIIAzYMM8zg+zKhVnYDjaRjXeJiVsY/cPYvP18lbfX3mz7890uk6iUmhmxnmBQCAQvO8RCYBZhE/2opTGlm1QoiKAn6AcjLQZt7SDSlLjk70A6CUjSkmFT/ZS69uLU3aQrSicMf+nwocXJerwsRJ1tC4m6EKNTw7tpqCXTKO6PNT6duOU7WQrC2fM/6nwT4jFZLr8E2M6Ll5n2ed8lT3Ol8hQFaBXmC+/4+fAL3siYyWpCeCL1hRhownHqsfbil/2kaMpmzmnLwB/0ItSOp8uT5RNpC4vOdr40RXl71Tlj4maQuWvx9eGHy85mtKZc/oCmIu65qImmJttIIBP9qBUTGgvZB9BORBpE6FLgy8Yai+8QSdDPgtrUz/IyuRbaXMep8/TPnMCBAKBYI4gyfH1M1sQiwDGgDrfvqhz6YpcMpTnHjqsljQRb08C+FCgjFhQSS1p8OMLH/tlHypp7M1Cb+AM9f79pCqyWKm+FtW4h85YoUaDi/PoYoIuvBN0dVDv20eqIpuVmuvjQpdK0uCUx3T58aFEFaFLJWkIyGPbDQZkH2op8k3QGsMt2IODHHO/TZevmRx1yeV3fgpUkhbnuFj55Sk0jYtVQPailjT4ZC+N7gOsMN04oz5fCCqFBqd/7A3e2Y5LpC4tAXm8Lh9qSRvS5drPCvNNM+rz+VBJWpzBCyh/U8XKc5AVhhtm1OcLYS7qmouaYG62gQBqSYszOFl7oYyw8cu+CJuzuhSSkiJDaLSQRqEnU1tMv7eDdG3BzIkQCARznpdffpmXX3451m7MWWwtdQy31MXajfeESADEgPnq1e/pe3rJSKc8NufTLtswSKYJNiYGx81/d8hW9FJoz+PewBnq/HtZqF4bMWQ+1uglE52BsbnUdtmKQTJH2UTqsqEf1d4b6KDOt4eF6nWkKeNHl0Fhpss3Ni/UERhCr4iMl0EyMxiwhI/twaGwrhrXW1RoV6JXGElQppKhyscW6I/pw69BYabLeyp87AgORr2RMygSGPR3hY/twSH0CjOuoB1ncJhdw0+FP+viFAP+LpYar738zp8DgyKBrsA4XYGhaF1KM4O+cboCQ+iVJlyBUV22J8OfhXR1stR03eV3fgqiYjVZ+VOYGfSPK38B62isHCFNjs3hz7p8TaFYGa65/M6fg7moay5qgrnZBgLolQl0uhrCxw7/IHpVQpTNoLcz0kaZMPqZmQBjowVkgqiIfdJaIBDMLW699VZuvfXWiHO/+93vYuTN3COxaD6JRfMjzg3W742RNxeHSADMIlKkLDzyProDbaQoMmnx10YN4c9U5HPKX8NgsAeDZKYtcJJCZWg7stOBWipUy+Kq8w+QosjCI++J1DVhCH+msmCcLhNt/hMUqkKV7rT/OBXqFXHV+QdIUWbjlt+h29dCijKb095jZKuKI2wyVfNo9Bxi0N+NQWGmzVtHkWYhAB7ZyWnvUUo0i3HLI3T7WijTRm/7NZOkqHJwB9+m23uaFFU2p91HydZEPoxnqgtpdB9g0G/BoEigzV1LkW4RCcpUbkj6VNjuuHMXicq0uNgGMKRr1zhdNWRrI4dTZ6qLaHQdYNBnwaBMoM1znCLdYhJUqdyQ/I9hu+MjO0lUpsd8G8BQ+RsZK3+eo2RP6DhlqgppdE8of9qFJChTuCHhE2G74663Q7GKg63l5qKuuagJ5mYbCJCqyaU2+CYWdxOpmlyaRw6To4uc/pOlK6bRsZdBbxcGZQKtzqMUj271l6UroXWkhkRVOp6gk273aZYnxd/IKIFAIIh7ZtGw+3hCkmXxl5spJCk0O+R67b3v+Rq2YD/1/v24ZCeZinyqVCuxBFtp9h/nKu3tAPQFOmkIHMEve8lVllCmWgzANs9TBMe9dQDIURSzQL0m6vdcFNKlLyVhC/ZT79uHSx4hU1lAlWoVlmALzf5jXKW9E4C+wBka/IdHdZVSpl4CwDb336J1KYtZoF57ST5J6kvPj1kDfdS7d+MKjpCpnke19gos/tM0e2q4ynQ3AL3+Dho9B/DJXvLU5ZRplwGht2V17j3Yg4OoJS356gqKtYsuzSGl8vw258Hq76Pe+Q4u2UGmupBq/ZVYfM00uw9zVcKHAej1tdPo2h/a/1pTQZl+RdR1pjUBIEmXfAmrvzekKzgS0mW4Eou3KaQr8SMA9HrbaXTtC+nSVlCmXxl1nWlNAAQC57c5B9ZAH/Wu3biCo7HSXRGKlaeGq8wfAqDXd7b8eULlb5K95eOpUwlzU1dcapqG54O4awMByXDpCyVafT3UDe/EHXCQqSum2rwei/sUTY6DbEj/KBDa7q/BvicUL30V5abQ6L+gHKDe/g497tOoFGpKjMvJ019avF7r/s0laxIIBHMfafR5SZblS39wiiFn+1SLP/uLWLsSwdFfPwTE/99XJABmkOlIAMQl05AAiEemIwEQd0xDAiAumYYEQFxyiQkAgeCSmKPPB9ORAIg3RAJAIBBcCCIBcHmZLQmAOdjDEQgEAoFAIBAIBALBXGY2rbwfT8zNV7cCgUAgEAgEAoFAIBAIIhAJAIFAIBAIBAKBQCAQCN4HiCkAMUCec/N655qeEHMvTiAp4npKkmAic3R9DcEsQQ7G2oPLQmBBbLcRFAgEAsE0IaYAvCfE06VAIBAIBAKBQCAQCATvA8QIgFmGTR7gROAgbpykS7lUKVagmPCWsC/YxalgDT685EjFlCkjt00alodoCdazWHnlTLp+TuasruAAJwL7R3XlUaVcOYmuTk4FavDhIUdRQplyccTnw/IgLYF6FqvWzaTrU2ILDlDv349bHiFjdCvKKE2BThoDR/DJXnKVxZSplgAQkP2c9B+kN3gGBUpylSWUqi59W6/pYC7qOru9plseIUOZT5Vq9SSaztDoPzyqqYQydWiv8pCmA/QGOkY1lVKqXjzZr5lx5qKuuagJ5ma9ArA5znCi+UU8XhvpKdVUFt2KQjH5Lit7j/6alQs+jVKpAWDbnm9F2SQnFLF8/icvq88CgUAgEIBIAMwqgnKQo4F3KVcsIUXKoDa4jza5gSJpbP9gr+yhNriPxYq1GCQzRwK7MAeTyFIU4JO99MtdtAZPYpDMMVQSydzW9TblyqWkSJnUBvbQFjxJkXJsf3iv7KE2sJfFynUhXf6dmKUkshTzRnV10ho4gUFKiKGSMYJykBrfLipUy0hRZHLct5u2wAmKVPPDNl7Zw3H/Hpao12OQzBz2vYUpkES2spD2QANOeYQrNbcwIts57tuNQTKToyyKoaq5qSsoB6nx7qRCvZwURRbHfe/SFqinSLUgUpNvN0vUV2FQmDnsfRNTIHlU00mcsoMrtbcxIg9z3PcuhoCZHGVxzDTB3NQ1FzXB3KxXAEE5wLGTT1BWeCMpicXUnnqGdsu7FOZeFWE3aDtNd99RHM7uiPPXXvGDiOPjjU+Tllx+2f0WCASCuYbYBeC9IaYAzCKG5F506MlSFKCRdBQpqukOtkXY9MpnSJUySVFkopMMzFNU0i2HbHx4sMkDaCRdLNyfkrmrqwedZCBLMW9U1/xJdHWQKmWN6VJWhm3iUdfgWU3KkKZi1QIsEzUFO0hTjGkqVFaFNQ3JvRQoy9BIOpIV6eQpSxgK9sZCSgRzUddgsBudZCRLWTiqaSGWQGuETW+gnTRFNinKLHSSkUJVNd2jNkPBXgqUFaOaMshTljIU7Jl5IROYi7rmoiaYm/UKYMjWglabSFbaQjRqI0V5G+juPxZtN9yCQqk+57Ws9nbcXhtZafExYkMgEAgEcx+RAJhFOHFglBLDxyYScTESYeOSHZjG20iJuOSQjUEyU6lcTrY0b2YcvkDmrC7ZgZHxPifhwhFhM7mukE1I1wqyFYUz4u+FEPI3KXxskpLC/p7FKdsn2IxpqlKtIk2RE/7MLg9hViQRa+aiLpfswKSYvGydxSnbMY3zc7zuKvUETcEhzFLK5XX6ApiLuuaiJpib9QrA5R7EZMgMH5sMmbjcQ1F2JfnXUFl0y5TXkWWZxtbXqCi6GUkSC7QKBAKBYGYQCYBZhB8vqnGzNpSoCOAnOG6lZh9elONsVKjw4Z1RPy+WOa1LOo8ueaIudVzr8slelBerSVLjk0Oa9JIRpaTCJ3up9e3FKTviIsExF3WF6szY20cl6guoV2p8sgcAvWQa1eSh1rcbp2wnW1k4Y/5PxVzUNRc1wdysVwA+vwulQhM+Vio1BIJegsGL2znG0leDyZBJgjHn/MYCgUAgiEaOs59ZgkgAzCJUaPDjDx8H8KFAGbGgkhoNgXE2fnyoOPcQxFgzp3XJ59ElTaZLQ7yiljQEZF/4eCpNfsZs/LIPlTSmqTdwhne9ryATZKX6WtSSdmacPwdzUVeozpxHExM04Z2gqYN3PS8jyzIrNdfHXBPMTV1zURPMzXoFoFbpCQQ84eOA34NCoZ5yEcCp6Ow9SF7myul2TyAQCASCcyISALMIPUYcsi187MCGAVOkjTTBRrahlyJt4o05q0sy4SDS5yhdmCbRZZwxHy8WvWTEPs5fu2zDIE2M1URN1rCm3sAZ6vx7WaBew0L1WlRSfCRx5qIuvWTCHrSGj+2yNWqRTL1kwhGcvF71Bjqo8+1hgXotCzVXxoUmmJu65qImmJv1CkCvS8bhGluLwOHqRa9NvqhreLx2fH4nCabc6XZPIBAIBIJzIhIAs4gUKRM3TrqD7XhlNy3BE2QpIue9Z0j5DMjdDMq9uGUnbcHGuJsbP5E5rUser6uOrAnDVzMU+QzIFgaDo7oCJ+NiiOtUpEhZeGQn3YG2kCZ/bZS/mYp8BoIWBoM9UZpOB2qpUC2LmNcbD8xFXSmKSTRNGBaeqSwYp2mENv8JskdXWD/tP06FegVpyvjRBHNT11zUBHOzXgEkJxTj9tjo6T+O1zdCy5mdZKdf3CJ+fYMnSEmI7S4NAoFAMOuJ9ZD/WToFQGwDOItQSAqWKNdRHzjICQ6RKeUxT6qgK9hCc7CO9apb0EhaFijWcCJwED9ecqVishQFsXb9nMxtXeupD+znBAfIlPKZp6ikK3ia5kAt69W3hXQpr+BEYH9Il6IkKvkRTygkBUvU66n376fef4BMRT7zlFV0Bk7T7D/OVdrb0Ug6FqrWUu8/gH90v/KznRmHbKPWv5da/97wNXMUxSxQr4mRohBzUZdCUrBEs4F63z7q5X1kKguYp6ymM9BMs/8YV2nvDGlSr6Xet29UU2mkJt9uan27w9fMURazQL02RopCzEVdc1ETzM16BaBQKFlccS8nTr/IidMvk5FaTUH2lXT1HuH0mR2sW/aV816jd7Ce7PQll99ZgUAgEAgmIMnyLEpXzHIkKbRb5XWqD8faFcGFIM29ATKSQqw0/f+3d+fxct114f9f75utaUJrU5qmewqVplCWtlDaUhcELIthE1EQLW79uoEi7voFFPzqTxF/gqKAX3etWgUligoKItJCpKV0Swl0S2mSpklDatabe+/7+8c5k9w7uXO3zL0z5zOv5+NxHicz8zkzn/ec09P7ec9naZQCr0E1yLjJ+koydulFva5C133sxl/sdRUkNUBrxZHMbPQfhK021SXXvavXVZng8+//CaD/v197AEiSJEmSGiX8HXtO/HlJkiRJkqQBYAJAkiRJkqQB4BCAHsgx+6s0w2ivK9B9/T0kSe1K7NtW6LjyEuVogfdAHC8vScUo8M+khWAPAEmSJEmSBoA9ABpuTz7Kpvwch9jPaZzFuriMobaZw3fmVjbnFxhhmDM5nwuGntaj2nbWjTgey93cn5t42lBvl74ar8S49uQuNo1+joPs57Q4i4uGnnlMTI+MbeVLY7dymGHOjCfwtYuOjem+sbt4+qLnLGTVp1RiXHvGdrFpdGMd09lctOhZk8T0EF8avZXDHOLMoSfytYsmrmf+WD7KfaN38fTFVy9k1adU5LkqMKZ2e/JRNnFLfT88k3VcOsn9cBubua2+H67lgnhqj2orSVKZ7AHQYGM5xm35adbGOq6MF3KQ/WzhixPKDOch7siNrIvLuDxewE62sT239KjGkzveOEZymG35AHfmxl5Uv6MS4xrLMb4w+mnWDl3EVYtexCEO8EBOEtPYZ1k3dBnPXvTN7MytbB+rYjqcw2wbu587Rz/bi+p3VGJcVUyfYu2iJ3PV4pdwiP08MHb3hDLDeYg7Rj/DukXP5NmLX8jOsa1sH3sAaMV0H3eOW4O9H5R7rsqKqd1YjnEbN7KWC7mSazjIAbaweUKZ4TzEHWxkHZdwOc9jJ9vZng/2qMaSpH4XmX21NYUJgAbbzQ6WsZw1cS5L4wTOjycf07jfwVc4ldNZFas5IU7kvLiw7xIAxxvHMIfYk7tYxrJeVL+jEuPanTs4geWsGapjGnrykQZjy478CqfG6awaOr2KaWgd27NuVNYxLY0TelH9jkqMa3c+zAlxImuGzqtjesokMT3IqbHmaEyL1h1NAPRhTFDquSovpnbV/fBE1sQ5LI1lnM86tjOxcb+DhybeD3kS2+mv/19JktR0JgAa7AB7WcnJRx6v5GQOsG9imdzLijhaZsUkZXrteOM4MR7HuqFLWRNrF6S+M1ViXPuZWN9OMa0cXyZO5kCOi2nRZZwR5y1MhWeoxLj2515WjL/+4ms4wN4JZSaPqSpTxfRMzhhauyD1nakiz1WBMbU7wD5WctKRx5PGyMRrdgUn9d3/ryRJajoTAA12mGEWjZvGYRGLGWWEsXGzbI9wmMXjyixmMSMML2g9p1NKHO1KjGuE4Qn1nSym9rgXs5jDfRwTlBnXCMMsjmliyvaYlvR1TFDwuSospnZzux8u6ev7oSSpx7LPtoYwAdBgS1jKKCNHHo9ymCEWTZhUqfoD6vCRxyOMsJglC1rP6ZQSR7sS41rMUkamiak97uqP+v6NCcqMazFLGclpYorJYlq6oPWcrWLPVWExtTv2fjgyg/th/1+PkiQ1jQmABlvOSvay58jjvTzGclZOLBMr2ZePjSuzh+WsWLA6zkQpcbQrMa7lrGBvjo9pDyceE1NbmdzD8phYpt+UGNfyaLv+cpKYWDlJTP17/UGh56rAmNotZ0Xb/fDYe91yVrKP8ffDx/r6fihJUhOZAGiwU1jNQfbzcD7IcB7kvryLM+LcCWVWcza72M7u3MHB3M+W3MyaPhsnWkoc7UqMa1WczkH2s31sSxXT2CbWDE2s7+o4h125nUfrmB4Y29zXY5OhzLhWxekczPEx3cmatvH8q4fOYVdu49GxOqbRu/tuzH+7Ys9VYTG1q+6HB+r74SHu427OoP1+eBa7eJjd+Uh1P2Qza9rKSJLUEtlfW1Msnr6I+tVQDPF0nsOmvJlN3MxqzuJcLmRr3s+9eSdXD72EpbGMp/BsNuXN9brKT2BN9NcfVKXE0a7EuIZiiGcsupq7Rj/HJm7m9Dib8+JCto7dxz1jd/J1i7+FpbGMi4euYNPo5xhhmLPiCawZ6t+YoMy4qpi+jrtGN7KJ/+b0OIfzhtaxdexe7hm9g69b8tIqpkVXsml0YxXT0BOPaXj2m3LPVVkxtRuKIZ6eV7GJm9nELazmbM7lSdX9kLu4Ol5c3Q/zWWzilvp+eD5r4pxeV12SpKJENmjNwqaLqHJDzx96da+rogEVQ9HrKmg2osBOWuMmfVN/y9HRXldhXnxs7IZeV0GSeiKi+jswMxv9B2GrTfXM7/nNXldlgs/90ZuB/v9+7QEgSZIkSWoWf8eekwJ/XpIkSZIkSe1MAEiSJEmSNAAcAtADm8b++5jnHs8ZnBZn9qA26qjA8dc5Zl+pZilwDLZzAEiSNO82bNjAhg0bel2NedXLmfe/+uCdfPXBu3pXgePgJIAL6MgkgPGqXldFM1FgAkDqORMA6jEnAZQ0qEqbBPBZ1/bXJID//SfNmATQFo4kSZIkSQPAIQANtycfZRO3cIj9nMaZrONShtp+ud6Z29jMbfW6ymu5IJ7ao9rOTIkxQR1Xfq6O6yzWxWWTxLWVzfmFI2tgXzD0tB7VdmZKiqkbsTyWu7k/N/G0oasWsuozVtL5ain6flFYXCXGJEnqITuyz4k9ABpsLMe4jRtZy4VcyTUc5ABb2DyhzHAe4g42so5LuJznsZPtbM8He1Tj6ZUYE9Rx5adZG+u4Ml7IQfazhS9OKDOch7gjN7IuLuPyeAE72cb23NKjGk+vpJiON5aRHGZbPsCdubEX1Z+Rks5XS9H3i8LiKjEmSZKayARAg+1mB8s4kTVxDktjGeezju1M/GNpBw9xKqezKlZzQpzIeTyJ7fTvH/QlxgStuJazJs5laZzA+fHkYxpWO/jKxLjiwr5ufJUU0/HGMswh9uQulrGsF9WfkZLOV0vZ94uy4ioxJkmSmsgEQIMdYB8rOenI45WczAH2tZXZywpOPvJ4BScdU6aflBgTVHVeOa7Ok8aVe1kR4+M6tkw/KSmm443lxHgc64YuZU2sXZD6zkVJ56ul3PtFeXGVGJMkqbci+2trChMADXaYYRaNm8ZhEYsZZYSxcbNsj3CYxePKLGYJIwwvaD1no8SYYK5xLe7ruEqKqaRYOikxRu8XzYmrxJgkSWoiEwANtoSljDJy5PEoIwyxaMKkStUfUIePPK7+wFq6oPWcjRJjgsniOjyDuEZYzJIFredslBRTSbF0UmKMg3O/aH5cJcYkSVITmQBosOWsYC97jjzeyx6Ws6KtzEr28di4Mo8dU6aflBgTVHWeGNdjLGflxDKxkn05Pq5jY+8nJcVUUiydlBhjufeL8uIqMSZJUo9ln20NYQKgwU5hNQc5wMP5IMN5iPu4mzM4d0KZ1ZzFLh5mdz7CwdzPFjazpq1MPykxJmjFtb+O6yD35V2cEe1xnc0utrM7d1Rx5WbWxHk9qvH0SoqppFg6KTHGsu8XZcVVYkySJDVRZDYoXdFwEdX0EM+PV3XtPat1lW/mIPtZzdms4xK2s4V7uYur48UAPJJb+RK3H13XOy7u2ufPh76JKbqbH9uTu9iUrbiqNdi3s4V7806uHnoJUMd1ZA32J3DBUH+vgV1STN2IZWvez87cytOGrupFCNPqi/M1bsx3N/TN/aLLSoyrX2L62NgNXX9PSWqCiAAgM6PHVTkurTbV5a/7zV5XZYKNf/5moP+/XxMAC2g+EgCaR11OAEii6wkAabZMAEgaVKUlAJ79nf2VAPjsXzQjAWALR5IkSZKkAWACQJIkSZKkAbB4+iKSJEmSJPURh7LPiQkAqRPHKkuSJEkqiEMAJEmSJEkaAPYAaLhqWaVbOMR+TuNM1nEpQ22z1+/MbWzmtnpZpbVcEP25DFtLiTFBmXGVGBMYV5PiKjEmKDOuEmOSJPVOOAJgTuwB0GBjOcZt3MhaLuRKruEgB9jC5gllhvMQd7CRdVzC5TyPnWxnez7YoxpPr8SYoMy4SowJjKtJcZUYE5QZV4kxSZLURCYAGmw3O1jGiayJc1gayzifdWxn4h9LO3iIUzmdVbGaE+JEzuNJbGdLj2o8vRJjgjLjKjEmMK4mxVViTFBmXCXGJElSEzkEoMEOsI+VnHTk8UpO5gD72srsZQUnH3m8gpOOKdNPSowJyoyrxJjAuJoUV4kxQZlxlRiTJKnHHAIwJ/YAaLDDDLNoXA5nEYsZZYSxcbPXj3CYxePKLGYJIwwvaD1no8SYoMy4SowJjKtJcZUYE5QZV4kxSZLURCYAGmwJSxll5MjjUUYYYtGESZWqP6AOH3lc/YG1dEHrORslxgRlxlViTGBcTYqrxJigzLhKjEmSpCYyAdBgy1nBXvYcebyXPSxnRVuZlezjsXFlHjumTD8pMSYoM64SYwLjalJcJcYEZcZVYkySpN6Ksf7amsIEQIOdwmoOcoCH80GG8xD3cTdncO6EMqs5i108zO58hIO5ny1sZk1bmX5SYkxQZlwlxgTG1aS4SowJyoyrxJgkSWqiyHT2hIUSUa1W+fx4Vdfes1pX+WYOsp/VnM06LmE7W7iXu7g6XgzAI7mVL3F7va7y+VwQF3ft8+dDiTFBmXGVGBMYV5PiKjEmKDOufonpY2M3dP09JakJIgKAzIweV+W4tNpUV776nb2uygQ3/c1PAv3//ZoAWEDzkQCQJEkzZwJA0qAqLgHwbX2WALihGQkAhwBIkiRJkjQATABIkiRJkjQAFk9fRJIkSZKk/hGOZJ8TEwCSJOkYjpWXJKk8DgGQJEmSJGkA2AOg4apllW7hEPs5jTNZx6UMxcS8zs7cxmZuq5dVWssF8dQe1XZmSowJyoyrxJjAuJoUV4kxQblxSZLUNa5mNyf2AGiwsRzjNm5kLRdyJddwkANsYfOEMsN5iDvYyDou4XKex062sz0f7FGNp1diTFBmXCXGBMbVpLhKjAnKjUuSJPWeCYAG280OlnEia+IclsYyzmcd25n4B+AOHuJUTmdVrOaEOJHzeBLb2dKjGk+vxJigzLhKjAmMq0lxlRgTlBuXJEnqPYcANNgB9rGSk448XsnJHGBfW5m9rODkI49XcNIxZfpJiTFBmXGVGBMYV5PiKjEmKDcuSZK6yVUA5sYeAA12mGEWjcvhLGIxo4wwlmNHnhvhMIvHlVnMEkYYXtB6zkaJMUGZcZUYExhXk+IqMSYoNy5JktR7JgAabAlLGWXkyONRRhhi0YSJoqo/Cg8feVz90bh0Qes5GyXGBGXGVWJMYFxNiqvEmKDcuCRJUu85BKAHNuXNxzz3eM7gtDhzVu+znBVs5f4jj/eyh+WsaCuzkt3sGFfmsWPK9JMSY4Iy4yoxJjCuJsVVYkxQblySpIWzYcMGNmzY0OtqzK8eDgF4dNtdPLr9rt5V4DiYAOiBi+KyrrzPKazmIJ/j4XyQU1jNfdzNGZw7ocxqzuLL3M7ufITlrGALm1nLuq58/nwoMSYoM64SYwLjalJcJcYE5cYlSVo469evZ/369ROe+8AHPtCj2pRn1RlPZtUZT57w3MP3f7ZHtZmdSNdPXDAR1VQVz49Xde09q7Wib+Yg+1nN2azjErazhXu5i6vjxQA8klv5ErfXa0WfzwVxcdc+fz6UGBOUGVeJMYFxNSmuEmOC/ojrY2M3dPX9JEm9FREAZGb0uCrHpdWmes4rfqPXVZng0x/6KaD/v18TAAtoPhIAkiTNBxMAklSW0hIAV7+8vxIA//X3zUgAOAmgJEmSJEkDwDkAJEmSJEnNYk/2ObEHgCRJkiRJA8AEgCRJkiRJA8AhAJIkSZKkRglHAMyJCYCGq5aKuoVD7Oc0zmQdlzIUEzt27MxtbOa2eqmotVwQT+1RbWemxJigzLhKjAmMq0lxlRgTlBuXJEnqLYcANNhYjnEbN7KWC7mSazjIAbaweUKZ4TzEHWxkHZdwOc9jJ9vZng/2qMbTKzEmKDOuEmMC42pSXCXGBOXGJUmSes8EQIPtZgfLOJE1cQ5LYxnns47tTPwDcAcPcSqnsypWc0KcyHk8ie1s6VGNp1diTFBmXCXGBMbVpLhKjAnKjUuSpK7KPtsawgRAgx1gHys56cjjlZzMAfa1ldnLCk4+8ngFJx1Tpp+UGBOUGVeJMYFxNSmuEmOCcuOSJEm9ZwKgwQ4zzKJx0zgsYjGjjDCWY0eeG+Ewi8eVWcwSRhhe0HrORokxQZlxlRgTGFeT4ioxJig3LkmS1HsmABpsCUsZZeTI41FGGGLRhImiqj8KDx95XP3RuHRB6zkbJcYEZcZVYkxgXE2Kq8SYoNy4JEnqpsj+2prCBECDLWcFe9lz5PFe9rCcFW1lVrKPx8aVeeyYMv2kxJigzLhKjAmMq0lxlRgTlBuXJEnqPRMADXYKqznIAR7OBxnOQ9zH3ZzBuRPKrOYsdvEwu/MRDuZ+trCZNW1l+kmJMUGZcZUYExhXk+IqMSYoNy5JktR7kdmg/goNF1F1Dnl+vKpr71mtFX0zB9nPas5mHZewnS3cy11cHS8G4JHcype4vV4r+nwuiIu79vnzocSYoMy4SowJjKtJcZUYE/RHXB8bu6Gr7ydJ6q2IACAzo8dVOS6tNtXXv/j/63VVJvjPj/wM0P/frwmABTQfCQBJkuaDCQBJKosJgPnVlASAQwAkSZIkSRoAi6cvIkmSJElSH7Ej+5zYA0CSJEmSpAFgDwBJko6T4+UlSVITmACQJEmSJDVKOARgTkwANFy1VNQtHGI/p3Em67iUoZg4smNnbmMzt9VLRa3lgnhqj2o7MyXGBGXGVWJMYFxNiqvEmCRJkuaLcwA02FiOcRs3spYLuZJrOMgBtrB5QpnhPMQdbGQdl3A5z2Mn29meD/aoxtMrMSYoM64SYwLjalJcJcYkSZI0n0wANNhudrCME1kT57A0lnE+69jOxD9sd/AQp3I6q2I1J8SJnMeT2M6WHtV4eiXGBGXGVWJMYFxNiqvEmCRJ0gxl9tfWECYAGuwA+1jJSUcer+RkDrCvrcxeVnDykccrOOmYMv2kxJigzLhKjAmMq0lxlRiTJEnSfDIB0GCHGWbRuGkcFrGYUUYYy7Ejz41wmMXjyixmCSMML2g9Z6PEmKDMuEqMCYyrSXGVGJMkSdJ8MgHQYEtYyigjRx6PMsIQiyZMgFX9sXv4yOPqj+GlC1rP2SgxJigzrhJjAuNqUlwlxiRJkmYmsr+2pig+ARARr42IT0TEtojYGREfj4jXdyj7ooi4KSL2RsSuiPhwRFwyxXvPqny3LWcFe9lz5PFe9rCcFW1lVrKPx8aVeeyYMv2kxJigzLhKjAmMq0lxlRiTJEkqVzfaj7Np306m6ARARHwA+AvgOcDDwBbgauCPIuKDERHjyr4a+DBwBXA/cABYD9wUEVdN8t6zKj8fTmE1BznAw/kgw3mI+7ibMzh3QpnVnMUuHmZ3PsLB3M8WNrOmrUw/KTEmKDOuEmMC42pSXCXGJEmSytSN9uNs2rcd3yMbNGPhbETEFcBNVF/KCzNzU/38BcAHgacCP5iZ74uIVcBWYAx4XmbeVJd9A/Bu4G7gKZnVwNLZlh9XpwR4fryqa3FWa2DfzEH2s5qzWcclbGcL93IXV8eLAXgkt/Ilbq/XwD6fC+Lirn3+fCgxJigzrhJjAuNqUlz9EtPHxm7o+ntKktRNrbZhZk7bSOxnrTbVN77g13pdlQn+42M/C0z+/c61/dj2HjNu305Vz5ITAL8P/C/g+zLzD9teuwS4BbgxM58TET8C/A7wC5n5f9rK/gtwDfD8zPz3+rlZlR/3WtcTAJKk3jMBIEnqdyYA5tc0CYA5tR/bys24fTtVPUseAvCEev8f7S9k5ueB3cDT6qdeU+8/NMn7fLDeXzPuudmWlyRJkiQNpm60H2fTvu1o8XQFGuwzwAPAQ+0vRMQJwErg0fqp84E9rW4UbW6q9xeNe2625SVJkiRJXRLN6snejfbjbNq3HRWbAMjMt0zx8o8BS4D/rCdKWE01lmIyO+v9uQCzLS9JkiRJGkzdaj/OtH07XX2KTQBMpv7yfxz4FeBQvT+V6nvY3eGwVhaltW7UbMtLkiRJkgbTvLUfO7RvpzQwCYCIuIxq4oUrgMPAtZn5hYh4/DSHLqr3HWdknG35z3ae2+EYZ3E+Z8cTpi8oSQ3gZHmSJHXP+9//ft7//vf3uhq9MdPW2XHY+tBGtm7dON8fM9v2JtC5fTvdccUnACJiGfB24M1Ukx7eTvXlfL4usgsYAVZ1eIvW89vmWP4Yz47nzajukiRJktTJddddx3XXXTejsjNYIl5tzjzrcs486/IZlf3Ex3+u00vH3X4cbwbt2ykVnQCIiLOAfwEuphpb8Rbg/Zk52iqTmRkRO6i6Zkym9fy2uZSfb9Ua2LdwiP2cxpms41KGYuLiDjtzG5u5rV4Dey0XxFMXompzVmJMUGZcJcYExtW0uCRJkvpVN9uPM2nfTqfYZQAj4nHAP1F9Of8EPCUzf6/Dl3MvcFJEXDzJa1fV+/uOo/y8GMsxbuNG1nIhV3INBznAFjZPKDOch7iDjazjEi7neexkO9vzwfmu2pyVGBOUGVeJMYFxNS0uSZI0mCKzr7ZpHHf7cZbt246KTQAAbwSeDtwAvDQzd0xR9vp6/4pJXnt5W5m5lJ8Xu9nBMk5kTZzD0ljG+axjOxP/WN/BQ5zK6ayK1ZwQJ3IeT2J7xwkoe6/EmKDMuEqMCYyraXFJkiQ1QDfaj7Np33ZUcgLgB4BR4Ecyc7oJFa4HDgI/HxFXtJ6MiDcA3wxszMzbjqP8vDjAPlZy0pHHKzmZA+xrK7OXFZx85PEKTjqmTD8pMSYoM64SYwLjalpckiRJDdCN9uNs2rcdFTkHQEScCpxH9SX/6RQTXuzOzNdm5u6I+G7gL4GbIuJ2qskYzqIai/E94w+abfn5cphhFo07hYtYzCgjjOXYkXG9IxxmGcuPlFnMEkYYXojqzUmJMUGZcZUYExhX0+KSJEkDatpe9/1jNu3HiFgN/En98NrM3DHb9u1UdSkyAQCsrfcnAC+cotz21j8y84aI+B/gbcBTqdZR/DDwE5l5T/uBsy0/H5awdMKvc6OMMMSiCZN6VX/AHz7yeITDLGbpQlRvTkqMCcqMq8SYwLiaFpckSVITzKL9uJyjbdjWLzNr6/2M27edFJkAyMybgVmvc5GZ/0I1q+K8lO+25axgK/cfebyXPSxnRVuZlexmx7gyjx1Tpp+UGBOUGVeJMYFxNS0uSZKkpphJ+zEzH6CtLTvX9u1kSp4DoHinsJqDHODhfJDhPMR93M0ZnDuhzGrOYhcPszsf4WDuZwubWdNWpp+UGBOUGVeJMYFxNS0uSZI0oDL7a2uIyAZVtukiIgGeH6/q2ntW63rfzEH2s5qzWcclbGcL93IXV8eLAXgkt/Ilbq/X9T6fCyZdfaJ/lBgTlBlXiTGBcc1nXB8bu6Gr7ydJkmamNW48M7vyS3KvtNpU3/QNv9Lrqkzw8U/+AtD/368JgAU0HwkASWoSEwCSJPWGCYD51ZQEQJFzAEiSJEmSyhX+jj0nzgEgSZIkSdIAMAEgSZIkSdIAcAhADzgGVpIkSZKOg3PZzYk9ACRJkiRJGgAmACRJkiRJGgAOAZAkSZIkNUqM9boGzWQPAEmSJEmSBoAJAEmSJEmSBoBDACRJkiRJzeIqAHNiDwBJkiRJkgaACQBJkiRJkgaAQwAkSZIkSc3iCIA5sQeAJEmSJEkDwASAJEmSJEkDwCEAkiRJkqRGCVcBmBN7AEiSJEmSNABMAEiSJEmSNAAcAiBJkiRJahaHAMyJPQAkSZIkSRoAJgAkSZIkSRoADgGQJEmSJDXLWK8r0Ez2AJAkSZIkaQCYAJAkSZIkaQA4BECSJEmS1CjhKgBzYg8ASZIkSZIGgD0AeuC666475rn169ezfv36HtRGkiRJUkk2bNjAhg0bel2NYu346hd55Ktf7HU15iTSrhMLJiISwO9ckiRJ0kKKCAAyM3pclePSalN982Vv7XVVJvjozb8E9P/36xAASZIkSZIGgAkASZIkSZIGgHMASJIkSZKaxWHVc2IPAEmSJEmSBoAJAEmSJEmSBoBDACRJkiRJzTLW6wo0kz0AJEmSJEkaACYAJEmSJEkaAA4BkCRJkiQ1SrgKwJzYA0CSJEmSpAFgAkCSJEmSpAHgEABJkiRJUrM4BGBO7AEgSZIkSdIAMAEgSZIkSdIAcAiAJEmSJKlZHAIwJ/YAkCRJkiRpAJgAkCRJkiRpADgEQJIkSZLULA4BmBN7AEiSJEmSNABMAEiSJEmSNAAcAiBJkiRJapaxXlegmewBIEmSJEnSADABIEmSJEnSAHAIgCRJkiSpUcJVAObEHgCSJEmSJA0AEwCSJEmSJA0AhwBIkiRJkprFIQBzYg8ASZIkSZIGgAkASZIkSZIGgEMAJEmSJEnNMuYQgLmwB4AkSZIkSQPABIAkSZIkSQPAIQCSJEmSpGZxFYA5sQeAJEmSJEkDwASAJEmSJEkDwCEAkiRJkqRmcQjAnJgA6IHrrrvumOfWr1/P+vXre1AbSZIkSSXZsGEDGzZs6HU1irVj35fZse+eXldjTiLNnCyYiEgAv3NJkiRJCykiAMjM6HFVjkurTfXCJ/5kr6sywb/c806g/79f5wCQJEmSJGkAmACQJEmSJGkAOAeAJEmSJKlZxhxWPRf2AJAkSZIkaQCYAJAkSZIkaQA4BECSJEmS1Cw51usaNJI9ACRJkiRJGgAmACRJkiRJGgAOAZAkSZIkNUu6CsBc2ANAkiRJkqQBYAJAkiRJkqQB4BAASZIkSVKzjDkEYC7sASBJkiRJ0gAwASBJkiRJ0gBwCIAkSZIkqVlcBWBO7AEgSZIkSdIAMAEgSZIkSdIAcAiAJEmSJKlZHAIwJ/YAkCRJkiRpAJgAkCRJkiRpADgEQJIkSZLULA4BmBN7AEiSJEmSNABMAEiSJEmSNAAcAiBJkiRJapaxsV7XoJHsASBJkiRJ0gAwASBJkiRJ0gBwCIAkSZIkqVlcBWBO7AEgSZIkSdIAMAEgSZIkSdIAcAiAJEmSJKlZHAIwJ/YAkCRJkiRpAJgAkCRJkiRpADgEQJIkSZLULGMOAZgLewBIkiRJkjQATABIkiRJkjQAHAIgSZIkSWqUzLFeV6GRTAD0wHXXXXfMc+vXr2f9+vU9qI0kSZKkkmzYsIENGzb0uhrF2jH8AI8Mb+l1NeYk0vUTF0xEJIDfuSRJkqSFFBEAZGb0uCrHpdWmuubUH+h1VSb4110fAPr/+7UHgCRJkiSpWVwFYE6cBFCSJEmSpAFgAkCSJEmSpAHgEABJkiRJUrM4r9qc2ANAkiRJkqQBYAJAkiRJkqQB4BAASZIkSVKzjI31ugaNZA8ASZIkSZIGgAkASZIkSZIGgEMAJEmSJEnN4ioAc2IPAEmSJEmSBoAJAEmSJEmSBoBDACRJkiRJjZKuAjAn9gCQJEmSJGkAmACQJEmSJGkAOARAkiRJktQsrgIwJ/YAkCRJkiRpAJgAkCRJkiRpADgEQJIkSZLULGMOAZgLewBIkiRJkjQATABIkiRJkjQAHAIgSZIkSWqWHOt1DRrJHgCSJEmSJA0AEwCSJEmSJA0AEwBSH3j/+9/f6yqoD3ldqJ3XhCbjdaHJeF2odDmWfbU1hQkAqQ/4P2lNxutC7bwmNBmvC03G60LSZEwASJIkSZI0AFwFQJIkSZLULK4CMCf2ADhOEfGiiLgpIvZGxK6I+HBEXNLrekmSJEmS+kc32o7H+x4mAI5DRLwa+DBwBXA/cABYD9wUEVf1sGqSJEmSpD7RjbZjN97DBMAcRcQq4E+Bw8BVmXlxZp4NvBFYBvzfiOjZ97thwwY/q0GftVBK/f5K/ayFUur3V+pnLZRSv79SP2uhlPr9lfpZC6XU72+hPqvEa2Ih9HrW/9msAtCNtmO32p8mAObuNVRf9Dsy86bWk5n5HuBfgXXAc3tUtyJvjiV/1kIp9fsr9bMWSqnfX6mftVBK/f5K/ayFUur3V+pnLZRSvz8TAOqibrQdu9L+NAEwd6+p9x+a5LUP1vtrFqgukiRJkqT+1I22Y1fan64CMHfnA3syc9Mkr7UyMhctYH0kSZIkaTA0axWAbrQdu9L+tAfAHEREAKuBXR2K7Kz35y5MjSRJkiRJ/aYbbcdutj8jc+oJC3SsiHg88Ahwc2Y+c5LXlwEHgXsy84Jxz/tlS5IkSeqZzIxe1+F49Hubqv37nWvbsdvv0WIPgPmxqN43ql+KJEmSJGlBdaPtOOP3cA6AudkFjACrOrzeen7b+Cebnm2TJEmSpF5qYJtqTm3HeXgPwB4Ac5LVuIkdwKkdirSen/YESJIkSZLK1I22YzfbnyYA5u5e4KSIuHiS166q9/ctYH0kSZIkSf2nG23HrrQ/TQDM3fX1/hWTvPbytjKSJEmSpMHUjbZjV9qfrgIwRxFxCrC1fvjczPxM/fwbgHcDGzPz2b2qnyRJkiSp97rRduxW+9MeAHOUmbuB76aaSPGmiLgtIr5C9eVvA75nfPmIeFFE3BQReyNiV0R8OCIuWfiaayFFxC9HxD9PsT2lrbzXSaGi8lBEvH2KMrM6/14vzTfddTHbe0h9jNdFQ0XEayPiExGxLSJ2RsTHI+L1Hcp6vxgQM70uvF8Mjog4ISLeERH/HRH/ExH31efumOXh6vLeL3psNm3HiFg97r/b1XN5j+kq43YcG/BC4DPAPuBR4B+AJ7aVeTVwGEjgDuAr9b8PAlf1Oga3eb0+bq/PdaftOV4ng7EBL6nP59s7vD6r8+/1UsY2g+tixvcQr4tmb8AH6nM1DNwK3FL/O4EPUvfanMt59rpo7jbL68L7xQBswFLgC/W52gPcCNxdPx4Fvv14zrPXxbyfv5m0Hc8b99/teXN5jynr0OsvofSNakmGg8B+4Mpxz7+hPqmbgKFe19NtXs591Of9Vq+Twd2AxwGvAx6mQ0Nvtuff66X52wyvixnfQ7wumr0BV9Tn6AHgonHPXwDcVr/2v+Zynr0umrvN8rrwfjEgG/Cm+hx9FDhp3POvpFoD/hFgxVzOs9fFYGwOAZh/rwGWAe/IzJtaT2bme4B/BdYBz+1R3TS/zgCWA1+eQVmvkwJFxA1U2fk/A1ZPUXS259/rpcFmcV3M5h4CXhdN9vp6/0uZuan1ZGZ+Gbi2fvjd9d77xeB4fb2fyXXh/WJwvKrevykzH2s9mZkfBP4ReDzw1Ppp7xc6hgmA+feaev+hSV77YL2/ZoHqooV1Qb3/0gzKep2U6UbgfcDvA5+Yotxsz7/XS7PN9LqYzT0EvC6a7An1/j/aX8jMzwO7gafVT3m/GByzuS68XwyO84FDmXnnJK/dXe9b1473Cx1jca8rMADOB/aMz9yO08qsXbSA9dHCaf3P+IGIuI5qfc5FVOO2bsjMB8aV9TopUGb+VuvfEXEtnbPmsz3/Xi8NNovrYjb3EPC6aLLPUHXzfqj9hYg4AVhJNc4TvF8MktlcF94vBsd3UI3Rn0yr4d+6Zrxf6BgmAOZRRARV984tHYrsrPfnLkyNtMCeWO/fCawY9/zrgLdExI9k5p95nQy22Z5/r5eBMqN7CHhdNF1mvmWKl38MWAL8p/eLwTLT66J+7P1iQGTmf45/HBGPoxoC8p1U8wDcCnzK+4U6cQjA/DqVKsmyu8Prraztig6vq9la2fhdVDN9r6LKzL4FOAH4vxHxZLxOBt1sz7/Xy+CY6T0EvC6KUy8R+SbgV4BD9d77xYDrcF2A94uBFBHPAx4Dvkh1rj8FXJOZY3i/UAcmAHprUb0f62ktNF8+DPws1SyqH8nM3Zl5X2a+HXgbVeb+rTN4H6+TwTbb8+/1Uo5u3UPA66JRIuIyqrki3kV1zq7NzC/M4FDvFwWb5rrwfjGYdlFNzncr1ez9VwPviIiZ9PL2fjGgHAIwv3YBI1RZ2Mm0nt+2MNXRQsrMv5ji5fdSZe0vwetk0M32/Hu9DIhZ3EPA66IIEbEMeDvwZqofaW6nauR9vi7i/WIAzeC68H4xoDLzVqo14YmINcD1wA8A9wO/ivcLTcIeAPMoMxPYQdWlZjKt5/0PacBk5lepxlKd63Uy2GZ7/r1eBBPvIfVjr4uGi4izgM8BP0XV1faHgUvaGnneLwbMTK6L6Xi/GAyZuR340frhK7xfqBMTAPPvXuCkiLh4kteuqvf3LWB9tAAi4vERcW1EfFOH1xcDJwP31E95nQy22Z5/r5fCzeEeAl4XjVVP4vVPwMX1/imZ+XuZOTpJce8XA2Km14X3i8EREVdExJci4t0dirQm8GuN0/d+oWOYAJh/19f7V0zy2svbyqgc+4DfBW6IiOWTvP48qvF4t9aPvU4G22zPv9dL+WZ7DwGviyZ7I/B04AbgpZm5Y4qy3i8Gx0yvC+8Xg2Mz1YSPL6hn7W/3zHp/W733fqFjZabbPG7AKcCBerti3PNvABL4bK/r6DZv5/6P63P8V8DKcc8/gyoLP0yVzfc6GYANuLY+l2+f5LVZnX+vl3K2aa6LGd9DvC6avVGN1x0BTptBWe8XA7LN8rrwfjEgG/DJ+hy9BVg07vkLgbvq114xl/PsdTEYW9QnVfMoIr4N+EuqSRdvp5pE4yyqMTTPz8y7elg9zZOI+BqqcXtPpFpS5U6q8VNPorqJvjkz3z2uvNdJwSLiWqo/0N6Rmf97ktdndf69Xsow1XUx23tIfYzXRcNExKlU47MPAv8xRdHdmfna+hjvF4Wb7XXh/WJwRMQ6qnO9AniYqlfA1wAXUZ3L38vMHx5X3vuFJup1BmJQNqoZOj9D1U3rUeAfgCf2ul5u837eVwG/RpWR3UeVhf9b4JleJ4O1McUvvXM9/14vzd+muy5mew/xumjeBlxWXwPTbduO5zx7XTRrm8t14f1icDbgbOAPxp3r+4F/Bl7UjfPsdVH2Zg8ASZIkSZIGgJMASpIkSZI0AEwASJIkSZI0AEwASJIkSZI0AEwASJIkSZI0AEwASJIkSZI0AEwASJIkSZI0AEwASJIkSZI0AEwASJIkSZI0AEwASJIkqesi4tqIyIjIXtdlvkTEW1sxRsT+iFg5w+PWjjsuI+La+a7rfImIEyLiTRGxISI21d/D/RHx8Yj47YhYO8Wx39D2PbRveyPiixHx3oi4rEv1HYqIjRHxd9OU++aIeF9E3BkRj0bE/0TErRHxdxHxQxGxrBv1afvMX6/jHo6IU2Z4zE/Xx4xGxBn1+XgoIt7V7fqpDCYAJEmS1Ah1g/GtEfGyLrzXoojYFhGf6kbdgOXAS2dY9tu69Jk9FRHXAF8G3gV8C7CO6ns4D3gu8EZg83E0RlcATwJ+CNhYJxSOt/3yw8BlwFsnezEi1tXXxL8C1wFPBk4BVgJPB14JvBe4JyJed5x1affn9X4J8K0zPKZV7pOZuS0zDwL/B3hjRFzS5fqpACYAJEmS1BTfCLwNeHkX3uuFwJouvM943z7Dcq/u8ucuuIj4WuBvgLOokgDfC1xAlQBYC7wI+BeqxuybIuLHp3nLtZkZrQ1YDFwMfDdwI1W75Y3AB46jzmcCvwL8dWbeMcnrV9SfdTUwBrwHuApYBZwEXAL8JLC9jvtPI+I75lqfdpl5G3B7/XDa942Ic4HL64fXj3vpA8BDwPsiIrpVP5XBBIAkSZIGSkQ8DXh/F9/yXqoG4wsj4mum+ezzgWcCe4BHuliHhfabVI3iLwKXZ+YfZeY9mXkwMx/IzH/JzBcB767L/3pEnDrTN8/M0cy8MzP/jCrx8976pe89jh4gb6rr/NvtL0TEauDvqX7t3wp8U2a+MTNvyszdmfk/mXlrZv4mVaLjU0AAfxIRT59jfSbT6gXw3IiYLkHV+vX/MPDB1pOZOQy8D3gWVSJGOsIEgCRJkooXEd9UdyG/CfgCcGYX334rVYNwKdP3Tmj9+v/3wKEu1mGhXVnvfzszd09R7qeBYaqeAHPqkp6ZhzPzR4Bb66cm7b4/lYhYDnwfcE9mfnaSIr8JnE51Tr4+Mz85RX32AS8BdlCd8x+YbX2mcD2QVO206YaKvKrefzQzd7W99pf1+/xoF+umApgAkCRJ0oKrJ2P74Yj4UD1p3J6IuCUifqF9ArTWhIJU3f8BXl9PfHbfLD5yPVUX8iu6E8Ex/rreTzcMoJUA+JupCkXESRHxk/WEdVsj4uC4yfW+OyKWdjjuhIh4Y0T8V33c3oi4IyI+UPc+mOyYRfV7fjwittQT+d0dEddHxFMnKX8a8Pj64dap4sjMQ1S/3v8tVWP5ePx6vb9kDr+6fyfVr/t/3v5CRJzN0S73v52Z90z3Zpn5P8DvAjuBF0TE4snKRcSLIuLP6nOwLyLuiojfq4dQTPa+DwKt5MNrOn1+RJzF0STM9e2vZ+b9wKepeqVM+lkaTCYAJEmStKDqsdj/RtWAejnVpHGtMdbvAO6IiIu7/LG/ATxj3Pb7XX7/vwVGged36uoeEU8ELgW+Cnys0xtFxMnAzVR1fhZwBrCMo5Pr/QlwffuEePXwg89TdXF/Tn3cCuApwPcDd0XEM9qOWQp8vH7P5wLnUI3jv5CqUfyFiHhxWxV3AQfqf39/RCzqFAtAZr4pM78tMz8yVbkZ+BvgYP3vq2d5bCsx80+TvPZyqjkHDlLNETAjmfnLmXlaZl6YmSPjX4uIZRHxbuAjwOuozsGJwEXAD1Jd452SRa0kxZXReRWFV1INQTgAfLhDmX+syxQx6aS6wwSAJEmSFtr1VI3NXVTdss+hmpDvtcA2qu75/xYRJwFk5p/UE8O9rT7+j+vJ4ib9RXsymbk1M7/Q2qgmcuuazHyEqiG9mKpxNpnWr/8fyszDU7zdr1KNM38MeD3Vd3NC/VzrV/BXUo2NH+9dVDPx76L6Lk+jmr3+64E76/d4X9sxP1u/fpBqhvwzqRqql3F0nPsfjp9MLjPHONpI/Rbg1oj4wfpX6XmTmaPAV+qHz5jpcfWSfc+h6t7/hUmKfH29/2JmPnY8dRznF4A3ACNUSYUnA18DPA/4LFVviL+MiCsnOfZvOTo8pFOSoDX+/x/r3giTaQ11+KZZ1VxFMwEgSZKkBRMRr6RqcB0ArsrMP8zMr2Tmw5l5PVXD81Gq8dg/18OqzkVrGECnGdxf3Vauk6vq/c/VyY+HM/NQPcnezwD/Ub9+Wdtxzx933PWZuTMz92Xmp6gmwAO4tG0N+9Yx78rM36uXkjuQmbdQJWegOhdr2z7rp6l6cUA1W//vAV+phw68NyJeFRGPp/serPcznlCQqqv8cuDz9QR57c6r93cfT8Va6iEFP1k//N7M/MXM3JSZezLz41S9Fz5F1RY7ZonEzNwDbKgfHjMMICJOB76ufnhM9/9xPkc1OeVz2s65BpgJAEmSJC2kH67378nMze0vZuY2ql/AAV66YLXqjg9Szcj+DXUj7Yh6HPYzqJIbH5/mfX6ZKonQqXG3rd6f2PZ86/EJkxzzCeBsqob8+O7qHY/JzC/Vx5xNtazc+Ne+ClwDvIwq7j31SxcCPwTcAOyo5yL4ri4uR/dwvX/cLI5pzftwW4fXW3NOPNDpDSLiV+t5Jzpt144r/nqqhMPGehWDCerhAj/Rqlu9AkG7v6j3T4+Ii9peewVVO+4x4J871Tkz91KtUHEC0M2VCtRgJgAkSZK0kC6s9/82RZn/qvcXRcSKea5P19Sz4X8MWMTRGdpbWr/+f3Ca7v9k5gcz86/bZ9ePiNMi4jVA+5j8ltZ3+hsR8c6IeFZrfH5mjmTmQ/U2OskxP15PFPgN4ycYHHfMMb+cZ+ZYZn44M78VWEXVI+HNVL9eP0Y1fOA5wJ8C/xAR7QmLuWg1ljt1e5/MGfW+07KL++p9t1aGmMk1fguwv/73Myd5/SNA6/y39yhpXVsfysyDTK0V8xlTltLAMAEgSZKkBVE3AFvjxD/a6ddU4KbWIVTjppuk02oAM5r9vyUizoyIH42Iv6pXR3iUatm5vwRO7nDYjwL/TjVh4JuBjcDuiPhoRPxEhwnl3lbXaYhqosD/APZExCcj4hcj4skzqW+dDLglM9+VmS+lmn/gZVSTEkK1CsMvzOS9pnFOvW9f9m4qrd4Yj3Z4fUe9v7DD62Tmz9XzTkzYOHqtjvekev/zU1zjoxztfXFK+xvUCZfWtXJkGEA9rOIb64d/1am+47S+p9OnLKWBYQJAkiRJsxYRz46IH4uI7+lUZJLnlnZ4fiqz6erdD/6eagK3q+ux4ETEhcDTqJaM+8R0bxAR1wFfBt5DlUhYRTWh2+8A3w18aLLj6jH/z6ea9O13qLp/Pw54AdU691+uewaMn9DvYGZ+O3A51QSDd1F1Gf964O3AnRHxp+PHkEfEFRHxkkm6po+vy3BmfphqFYNWzD80XexTqXsztBIAt87i0FX1vtMEf5+p9+tmM1a+/h4nS5DMdrx9p2u8NdHi10bEpfW/X07Vw2QnU/cwaGkNzZjNnAkqmAkASZIkzcULgf8f+KUOr7d+pc7WE/W48VaX5Csn+0V1kq0rE7MtlHoW+X9m4vJr47v/j0x6YC0inkm1ROFy4J3AGZm5NjNflJlvqMeUTzlTfWZ+oi77RKrx+99JtVTcIqqeAd83yTH/nZk/k5lPofr1/pVUDdAx4LuA/z2u+E9RLTH3tqnqUb/vKPDe+uEpnZZInKFXc3Sugk/N4rjWL/8ndXi9tTTg44Afn8X7rmPy3hituS0m7TUwydZpScpPA/fX/24NA2jN/n/DdNdSrVW/Tr0fNGBMAEiSJGkuWg2K09vXo6+dXe/bJ1ZrNY46TkoWEadExGURcfFx1rFX2ocBzHT2f6jGdwfwX5n5U5k52XKFF7Q/ERHnRcSb621R6/l6/P5fZubLqCbmgyp50/qeW8d8zbhjdmbmhzLzu6h6Dhw5pnZXvX/ODMf1t7q4P5qZs+m63+5n6v3nM7PThH6TaU0cuGqyFzNzI0fnnfj5DpPyTVD/+v/bHV6eyTW+uL7GL4uIJR3qlVRDPgC+IyJWUS0jCDPr/g9Hf/nv6rKXai4TAJIkSZqLO+r9UiY2DltdtV9ZP7yz7bhWt+afjYiVHd77j6iWMHtTh9e7NaP8fNlANcHbsyPixVTL5O0APjmDY1vdxyed3C0insvRWe3Hfw8nUPUYeCfVMnOTaf1i/NVxn/Hr9TEvm+ExUK1OMEw1n8PvdkgAteq7EvjB+uGNncpNJSKWRMR7Odqgftss36K1asJpU5T5MaqhGycB/xURz5qiPsuovrMXdChyPdUY/2+NiEs6lHkD1TX+10xclaFd67+Xc6jO1RLgK8y8B0RrKcZtU5bSwDABIEmSpLn4DHBP/e8/iIhX1LPUX0TVaHlC/dp72o77AFXyYC1wU0S8NCJOj4jlEXFpRPwNVWP0EPDuDp998fiZ6vtNZu7jaLfyP6j3f9c2+34nt9T7b4qIH6+/0+UR8ZSI+BWq5ELrF/5viIjH179G38PRyez+OCJeGBEnRcTSiLggIt7B0W7kH63reQD4Qv3cb0XEqyNiVd3gPicifoKjy9V9dFx8d417/vXA7VEt9ffUiHhc/bkX1nMZfAa4lKrHSKeEzjEiYlFEPDkiXkc1MWFr/oA/qOcWmI3P1vundiqQmbdQTYI4Anwt8Ol6voQr6p4SK+rr8/uort+foJqH4JiGeGbeSXWdL6Ga7PKHI2JtRCyLiCdExC8Dv1EX/436l/5O9drE0WuiNXTjr6c6piWqFTSeQPXf0mx6TKhkmenm5ubm5ubm5uY26w34Oqrx6Nlh+60Ox11E1XjqdNxB4GWTHPe6cWUOAHcfR93fWr/Pp+bjPajGao+P6RsnKfNg/dq1455bQjWzfKfv5u+pxuSPf+7p9bEvphqz3+nYBH6nrQ7PqL/LqY75B2BRh/j3TnNsAl8Erp7k+G+YwbGtbZRqzomhOZynZXWMB4Gl05T9Zqpx91PVZYwqyfU44Ofaz2H9PidTzeI/1fv82gzr/6a24545w+Na3++/z/e9wK05mz0AJEmSNCeZ+SmqidA+QPVL8j7gPqpfv1+QmZP+4pvVr5qXUzWe/hnYSpVI+DzVuOonZuY/THLo9VQT5O2iaoT187jmj3B0rfqHgf+cyUGZeZiqa/lbqH613UvV/f6TwHdl5suzmgjwnfX730G91FtmfoRquMEf189/tT7+Lqqu5l+fmT/a9nm3Ui1/9x6qpMwuquELX6TqbbAeeHlO0nshM3+JqifH24F/rY85UMe7kaoB/Grgosz8r/bjZ2A/8CWqc355Zv54Zo7N9k0y8xDVhHrLmGJcfl32o1Tfx/dRTZy4heoX9D1U3+O767q8LjP/hyrOyd5nT2a+mmoJv7+lmhdgP3A31fj9Z2Xmz84whL+iSoAAfDkzPzfD455d7z8+w/IaAJE5be8RSZIkSWqsejjC+4Bfzsy39ro+CyEiPknVS+eizPxir+uj/mACQJIkSVLR6tUKvkK1EsExqyiUJiLOo+qN89HMfOF05TU4HAIgSZIkqWiZuR/4Q+CJEfHs6coX4LVUq0S0T8KpAWcPAEmSJEnFi4izgE3AP2bma3tdn/lSr5CxGdhJNV/BrOdNULnsASBJkiSpeJn5EPCLwLdHxMW9rs88+n7gbOA6G/9qZw8ASZIkSQMhIoaAzwAPZua39ro+3RYRJwD3AH/TaRUODTYTAJIkSZIkDQCHAEiSJEmSNABMAEiSJEmSNABMAEiSJEmSNABMAEiSJEmSNABMAEiSJEmSNABMAEiSJEmSNAD+H70gSyks4WWNAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import math\n",
     "\n",
@@ -371,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "28134521-7028-4b76-aa54-dc7fa8b207f3",
    "metadata": {},
    "outputs": [],
@@ -384,33 +191,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "2ded879c-ccce-46cd-979a-77b1192014e4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f24a770c100>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1296x1008 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import math\n",
     "\n",
diff --git a/src/condor/README.md b/src/condor/README.md
deleted file mode 100644
index 4b2c94fa..00000000
--- a/src/condor/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Condor scripts
-
-- [Condor scripts](#condor-scripts)
-    - [Check jobs](#check-jobs)
-    - [Combine pickles](#combine-pickles)
-
-
-### Check jobs
-
-Check that all jobs completed by going through output files:
-
-```bash
-for year in 2016APV 2016 2017 2018; do python src/condor/check_jobs.py --tag $TAG --processor trigger (--submit) --year $year; done
-```
-
-
-
-nohup version:
-
-(Do `condor_q | awk '{ print $9}' | grep -o '[^ ]*\.sh' > running_jobs.txt` first to get a list of jobs which are running.)
-
-```bash
-nohup bash -c 'for year in 2016APV 2016 2017 2018; do python src/condor/check_jobs.py --year $year --tag '"${TAG}"' --processor skimmer --submit --check-running; done' &> tmp/submitout.txt &
-```
-
-### Combine pickles
-
-Combine all output pickles into one:
-
-```bash
-for year in 2016APV 2016 2017 2018; do python src/condor/combine_pickles.py --tag $TAG --processor trigger --r --year $year; done
-```
diff --git a/src/condor/check_jobs.py b/src/condor/check_jobs.py
index 501a17aa..a670d9c0 100644
--- a/src/condor/check_jobs.py
+++ b/src/condor/check_jobs.py
@@ -19,27 +19,20 @@
 
 
 parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--processor",
-    default="trigger",
-    help="which processor",
-    type=str,
-    choices=["trigger", "skimmer", "input", "ttsfs"],
-)
+run_utils.parse_common_args(parser)
 
-parser.add_argument("--tag", default="", help="tag for jobs", type=str)
-parser.add_argument("--year", default="2017", help="year", type=str)
 parser.add_argument("--user", default="rkansal", help="user", type=str)
 run_utils.add_bool_arg(parser, "submit-missing", default=False, help="submit missing files")
 run_utils.add_bool_arg(
     parser,
     "check-running",
     default=False,
-    help="check against running jobs as well (running_jobs.txt must be updated)",
+    help="check against running jobs as well (running_jobs.txt will be updated automatically)",
 )
 
 args = parser.parse_args()
 
+trigger_processor = args.processor.startswith("trigger")
 
 eosdir = f"/eos/uscms/store/user/{args.user}/bbVV/{args.processor}/{args.tag}/{args.year}/"
 
@@ -60,6 +53,7 @@
             if jdl.split("_")[0] == args.year and "_".join(jdl.split("_")[1:-1]) == sample
         ]
     )[-1]
+    + 1
     for sample in samples
 }
 
@@ -68,11 +62,13 @@ def print_red(s):
     return print(f"{Fore.RED}{s}{Style.RESET_ALL}")
 
 
+running_jobs = []
 if args.check_running:
+    os.system("condor_q | awk '{print $9}' > running_jobs.txt")
     with open("running_jobs.txt", "r") as f:
-        running_jobs = [s[:-1] for s in f.readlines()]
-else:
-    running_jobs = []
+        lines = f.readlines()
+
+    running_jobs = [s[:-4] for s in lines if s.endswith(".sh\n")]
 
 
 missing_files = []
@@ -82,13 +78,20 @@ def print_red(s):
 for sample in samples:
     print(f"Checking {sample}")
 
-    if args.processor != "trigger":
+    if not trigger_processor:
         if not exists(f"{eosdir}/{sample}/parquet"):
             print_red(f"No parquet directory for {sample}!")
 
             for i in range(jdl_dict[sample]):
+                if f"{args.year}_{sample}_{i}" in running_jobs:
+                    print(f"Job #{i} for sample {sample} is running.")
+                    continue
+
                 jdl_file = f"condor/{args.processor}/{args.tag}/{args.year}_{sample}_{i}.jdl"
+                err_file = f"condor/{args.processor}/{args.tag}/logs/{args.year}_{sample}_{i}.err"
+                print(jdl_file)
                 missing_files.append(jdl_file)
+                err_files.append(err_file)
                 if args.submit_missing:
                     os.system(f"condor_submit {jdl_file}")
 
@@ -107,12 +110,12 @@ def print_red(s):
         int(out.split(".")[0].split("_")[-1]) for out in listdir(f"{eosdir}/{sample}/pickles")
     ]
 
-    if args.processor == "trigger":
+    if trigger_processor:
         print(f"Out pickles: {outs_pickles}")
 
     for i in range(jdl_dict[sample]):
         if i not in outs_pickles:
-            if f"{args.year}_{sample}_{i}.sh" in running_jobs:
+            if f"{args.year}_{sample}_{i}" in running_jobs:
                 print(f"Job #{i} for sample {sample} is running.")
                 continue
 
@@ -124,7 +127,7 @@ def print_red(s):
             if args.submit_missing:
                 os.system(f"condor_submit {jdl_file}")
 
-        if args.processor != "trigger":
+        if not trigger_processor:
             if i not in outs_parquet:
                 print_red(f"Missing output parquet #{i} for sample {sample}")
 
diff --git a/src/condor/combine_pickles.py b/src/condor/combine_pickles.py
index a6010bf4..0d37fb12 100644
--- a/src/condor/combine_pickles.py
+++ b/src/condor/combine_pickles.py
@@ -36,38 +36,31 @@ def accumulate_files(files: list):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--year", help="year", type=str, required=True, choices=["2016APV", "2016", "2017", "2018"]
-    )
-    parser.add_argument(
-        "--processor",
-        default="trigger",
-        help="Trigger processor",
-        type=str,
-        choices=["trigger", "skimmer", "input", "ttsfs"],
-    )
-    parser.add_argument("--tag", type=str, required=True)
+    run_utils.parse_common_args(parser)  # year, processor, tag
     parser.add_argument("--name", default="combined", help="name of combined files", type=str)
     parser.add_argument(
-        "--inuser", default="", help="username where pickles are saved (if not you)", type=str
+        "--inuser",
+        default=os.getlogin(),
+        help="username where pickles are saved (if not you)",
+        type=str,
     )
     parser.add_argument(
         "--outuser",
-        default="",
+        default=os.getlogin(),
         help="username where combined output will be saved (if not you)",
         type=str,
     )
     run_utils.add_bool_arg(
         parser, "r", default=False, help="combine files in sub and subsubdirectories of indir"
     )
+    run_utils.add_bool_arg(
+        parser,
+        "separate-samples",
+        default=False,
+        help="combine different samples' pickles separately",
+    )
     args = parser.parse_args()
 
-    user = os.getlogin()
-    if args.inuser == "":
-        args.inuser = user
-    if args.outuser == "":
-        args.outuser = user
-
     tag_dir = f"/eos/uscms/store/user/{args.inuser}/bbVV/{args.processor}/{args.tag}"
     indir = f"{tag_dir}/{args.year}/"
 
@@ -78,23 +71,28 @@ def accumulate_files(files: list):
     print(f"Outputs directory:", outdir)
 
     files = [indir + "/" + file for file in listdir(indir) if file.endswith(".pkl")]
+    out_dict = {}
 
     if args.r:
-        dirs = [
-            indir + "/" + d + "/pickles/"
-            for d in listdir(indir)
-            if os.path.isdir(indir + "/" + d + "/pickles/")
-        ]
-        print(dirs)
-
-        for d in dirs:
-            files += [d + "/" + file for file in listdir(d) if file.endswith(".pkl")]
-            subdirs = [d + "/" + sd for sd in listdir(d) if os.path.isdir(d + "/" + sd)]
-            for sd in subdirs:
-                files += [sd + "/" + file for file in listdir(sd) if file.endswith(".pkl")]
-
-    print(f"Accumulating {len(files)} files")
-    out = accumulate_files(files)
+        samples = [d for d in listdir(indir) if os.path.isdir(indir + "/" + d + "/pickles/")]
+
+        for sample in samples:
+            print(sample)
+            pickle_path = f"{indir}/{sample}/pickles/"
+            sample_files = [
+                pickle_path + "/" + file for file in listdir(pickle_path) if file.endswith(".pkl")
+            ]
+
+            if args.separate_samples:
+                out_dict[sample] = accumulate_files(sample_files)
+            else:
+                files += sample_files
+
+    if args.separate_samples:
+        out = {args.year: out_dict}
+    else:
+        print(f"Accumulating {len(files)} files")
+        out = accumulate_files(files)
 
     with open(f"{outdir}/{args.year}_{args.name}.pkl", "wb") as f:
         pickle.dump(out, f)
diff --git a/src/condor/submit.py b/src/condor/submit.py
index 2cf3273c..99ad9cb0 100644
--- a/src/condor/submit.py
+++ b/src/condor/submit.py
@@ -31,6 +31,7 @@ def write_template(templ_file: str, out_file: str, templ_args: dict):
 
 
 def main(args):
+    username = os.environ["USER"]
     if args.site == "lpc":
         t2_local_prefix = "/eos/uscms/"
         t2_prefix = "root://cmseos.fnal.gov"
@@ -43,9 +44,11 @@ def main(args):
     elif args.site == "ucsd":
         t2_local_prefix = "/ceph/cms/"
         t2_prefix = "root://redirector.t2.ucsd.edu:1095"
-        proxy = "/home/users/rkansal/x509up_u31735"
+        if username == "rkansal":
+            proxy = "/home/users/rkansal/x509up_u31735"
+        elif username == "annava":
+            proxy = "/home/users/annava/projects/HHbbVV/test"
 
-    username = os.environ["USER"]
     local_dir = f"condor/{args.processor}/{args.tag}"
     homedir = f"/store/user/{username}/bbVV/{args.processor}/"
     outdir = homedir + args.tag + "/"
@@ -104,6 +107,8 @@ def main(args):
                     "eosoutparquet": f"{eosoutput_dir}/parquet/out_{j}.parquet",
                     "eosoutroot": f"{eosoutput_dir}/root/nano_skim_{j}.root",
                     "save_ak15": "--save-ak15" if args.save_ak15 else "--no-save-ak15",
+                    "save_all": "--save-all" if args.save_all else "--no-save-all",
+                    "vbf_search": "--vbf-search" if args.vbf_search else "--no-vbf-search",
                     "save_systematics": "--save-systematics"
                     if args.save_systematics
                     else "--no-save-systematics",
@@ -125,19 +130,11 @@ def main(args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--year", help="year", type=str, required=True)
+    run_utils.parse_common_args(parser)
     parser.add_argument("--script", default="run.py", help="script to run", type=str)
-    parser.add_argument("--tag", default="Test", help="process tag", type=str)
     parser.add_argument(
         "--outdir", dest="outdir", default="outfiles", help="directory for output files", type=str
     )
-    parser.add_argument(
-        "--processor",
-        default="trigger",
-        help="which processor",
-        type=str,
-        choices=["trigger", "skimmer", "input", "ttsfs"],
-    )
     parser.add_argument(
         "--site",
         default="lpc",
@@ -145,43 +142,18 @@ def main(args):
         type=str,
         choices=["lpc", "ucsd"],
     )
-    parser.add_argument(
-        "--samples",
-        default=[],
-        help="which samples to run",  # , default will be all samples",
-        nargs="*",
-    )
-    parser.add_argument(
-        "--subsamples",
-        default=[],
-        help="which subsamples, by default will be all in the specified sample(s)",
-        nargs="*",
-    )
-    parser.add_argument("--files-per-job", default=20, help="# files per condor job", type=int)
-    parser.add_argument("--maxchunks", default=0, help="max chunks", type=int)
-    parser.add_argument("--chunksize", default=10000, help="chunk size", type=int)
-    parser.add_argument("--label", default="AK15_H_VV", help="label", type=str)
-    parser.add_argument("--njets", default=2, help="njets", type=int)
-
     run_utils.add_bool_arg(
         parser,
         "test",
         default=False,
         help="test run or not - test run means only 2 jobs per sample will be created",
     )
+    parser.add_argument("--files-per-job", default=20, help="# files per condor job", type=int)
 
     run_utils.add_bool_arg(
         parser, "submit", default=False, help="submit files as well as create them"
     )
 
-    run_utils.add_bool_arg(
-        parser, "save-ak15", default=False, help="run inference for and save ak15 jets"
-    )
-    run_utils.add_bool_arg(
-        parser, "save-systematics", default=False, help="save systematic variations"
-    )
-    run_utils.add_bool_arg(parser, "inference", default=True, help="run inference for ak8 jets")
-
     args = parser.parse_args()
 
     main(args)
diff --git a/src/condor/submit.templ.sh b/src/condor/submit.templ.sh
index 58e058c4..4a46e5ad 100644
--- a/src/condor/submit.templ.sh
+++ b/src/condor/submit.templ.sh
@@ -9,7 +9,7 @@ mkdir outfiles
 
 # run code
 # pip install --user onnxruntime
-python -u -W ignore $script --year $year --starti $starti --endi $endi --samples $sample --subsamples $subsample --processor $processor --maxchunks $maxchunks --chunksize $chunksize --label $label --njets $njets ${save_ak15} ${save_systematics} ${inference}
+python -u -W ignore $script --year $year --starti $starti --endi $endi --samples $sample --subsamples $subsample --processor $processor --maxchunks $maxchunks --chunksize $chunksize --label $label --njets $njets ${save_ak15} ${save_systematics} ${inference} ${save_all} ${vbf_search}
 
 #move output to eos
 xrdcp -f outfiles/* $eosoutpkl
diff --git a/src/condor/submit_configs/skimmer_inputs_03_10_xhy.yaml b/src/condor/submit_configs/skimmer_inputs_03_10_xhy.yaml
new file mode 100644
index 00000000..10853d3f
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_03_10_xhy.yaml
@@ -0,0 +1,50 @@
+{
+    "Skimmer":
+        {
+            "XHYPrivate":
+                {
+                    "subsamples":
+                        [  # based on 4q quark fraction >= 0.5 and mY >= 120
+                            "NMSSM_XToYH_MX1000_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX1200_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX1200_MY150_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX1400_MY125_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX1400_MY150_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX1600_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX1600_MY150_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX1600_MY190_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX1800_MY125_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX1800_MY150_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX1800_MY190_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2000_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2000_MY150_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2000_MY190_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX2200_MY125_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX2200_MY150_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX2200_MY190_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX2200_MY250_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2400_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2400_MY150_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2400_MY190_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2400_MY250_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2600_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2600_MY150_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2600_MY190_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2600_MY250_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2600_MY300_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2800_MY125_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2800_MY150_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2800_MY190_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2800_MY250_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX2800_MY300_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX3000_MY125_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX3000_MY150_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX3000_MY190_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX3000_MY250_HTo2bYTo2W_hadronicDecay",
+                            # "NMSSM_XToYH_MX3000_MY300_HTo2bYTo2W_hadronicDecay",
+                            "NMSSM_XToYH_MX3000_MY350_HTo2bYTo2W_hadronicDecay",
+                        ],
+                    "files_per_job": 8,
+                },
+        },
+}
diff --git a/src/condor/submit_configs/skimmer_inputs_04_04_xhy.yaml b/src/condor/submit_configs/skimmer_inputs_04_04_xhy.yaml
new file mode 100644
index 00000000..a69f895e
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_04_04_xhy.yaml
@@ -0,0 +1,13 @@
+{
+    "Skimmer":
+        {
+            "XHY":
+                {
+                    "subsamples":
+                        [
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190",
+                        ],
+                    "files_per_job": 4,
+                },
+        },
+}
diff --git a/src/condor/submit_configs/skimmer_inputs_04_10_xhy.yaml b/src/condor/submit_configs/skimmer_inputs_04_10_xhy.yaml
new file mode 100644
index 00000000..77bfbdef
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_04_10_xhy.yaml
@@ -0,0 +1,127 @@
+{
+    "Skimmer":
+        {
+            "XHY":
+                {
+                    "subsamples":
+                        [
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-150",
+                            # "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80",
+                        ],
+                    "files_per_job": 8,
+                },
+        },
+}
diff --git a/src/condor/submit_configs/skimmer_inputs_23_02_17.yaml b/src/condor/submit_configs/skimmer_inputs_23_02_17.yaml
index 09e58e07..19c000ab 100644
--- a/src/condor/submit_configs/skimmer_inputs_23_02_17.yaml
+++ b/src/condor/submit_configs/skimmer_inputs_23_02_17.yaml
@@ -41,8 +41,9 @@
                         [
                             "ST_tW_antitop_5f_inclusiveDecays",
                             "ST_tW_top_5f_inclusiveDecays",
-                            "ST_s-channel_4f_hadronicDecays",
+                            # "ST_s-channel_4f_hadronicDecays",
                             "ST_t-channel_antitop_4f_InclusiveDecays",
+                            "ST_t-channel_top_4f_InclusiveDecays",
                         ],
                     "files_per_job": 20,
                     "chunksize": 100000,
diff --git a/src/condor/submit_configs/skimmer_inputs_23_04_17_higgsbgs.yaml b/src/condor/submit_configs/skimmer_inputs_23_04_17_higgsbgs.yaml
new file mode 100644
index 00000000..831d591f
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_23_04_17_higgsbgs.yaml
@@ -0,0 +1,39 @@
+{ "Skimmer": {
+            # submit script will append year to this
+            "HH": { "subsamples": [
+                            "GluGluToHHTobbVV_node_cHHH0",
+                            # "GluGluToHHTobbVV_node_cHHH1",
+                            "GluGluToHHTobbVV_node_cHHH2p45",
+                            "GluGluToHHTobbVV_node_cHHH5",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_1",
+                            "GluGluToHHTo4B_node_cHHH1_preUL",
+                        ], "files_per_job": 8, "chunksize": 10000 },
+            "HWW":
+                {
+                    "subsamples":
+                        [
+                            "GluGluHToWW_Pt-200ToInf_M-125",
+                            "VBFHToWWToAny_M-125_TuneCP5_withDipoleRecoil",
+                            "HWminusJ_HToWW_M-125",
+                            "HWplusJ_HToWW_M-125",
+                            "HZJ_HToWW_M-125",
+                            "ttHToNonbb_M125",
+                        ],
+                    "files_per_job": 20,
+                },
+            "GluGluHToBB":
+                { "subsamples": ["GluGluHToBB"], "files_per_job": 20 },
+            "HToBB":
+                {
+                    "subsamples":
+                        [
+                            "VBFHToBB",
+                            "WminusH_HToBB_WToQQ",
+                            "WplusH_HToBB_WToQQ",
+                            "ZH_HToBB_ZToQQ",
+                            "ggZH_HToBB_ZToQQ",
+                            "ttHToBB",
+                        ],
+                    "files_per_job": 20,
+                },
+        } }
diff --git a/src/condor/submit_configs/skimmer_inputs_23_06_10_higgshh.yaml b/src/condor/submit_configs/skimmer_inputs_23_06_10_higgshh.yaml
new file mode 100644
index 00000000..318dd4fc
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_23_06_10_higgshh.yaml
@@ -0,0 +1,17 @@
+{ "Skimmer": {
+            # submit script will append year to this
+            "HH": { "subsamples": [
+                            "GluGluToHHTobbVV_node_cHHH0",
+                            "GluGluToHHTobbVV_node_cHHH1",
+                            "GluGluToHHTobbVV_node_cHHH2p45",
+                            "GluGluToHHTobbVV_node_cHHH5",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_0_5_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_0_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_2_C3_1",
+                            "VBF_HHTobbVV_CV_1_5_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_0",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_2",
+                        ], "files_per_job": 8, "chunksize": 10000 },
+        } }
diff --git a/src/condor/submit_configs/skimmer_inputs_23_07_31.yaml b/src/condor/submit_configs/skimmer_inputs_23_07_31.yaml
new file mode 100644
index 00000000..deb17d16
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_23_07_31.yaml
@@ -0,0 +1,29 @@
+{
+    "Skimmer":
+        {
+            "Diboson": {"files_per_job": 20 },
+            "HWW": { "files_per_job": 20 },
+            "GluGluHToBB": { "files_per_job": 20 },
+            "HToBB": { "files_per_job": 20 },
+            "QCD": { "files_per_job": 20 },
+            "SingleTop": { "files_per_job": 20 },
+            "TTbar": { "files_per_job": 20 },
+            "TTbarBoosted": { "files_per_job": 20 },
+            "WJetsToQQ": { "files_per_job": 20 },
+            "ZJetsToQQ": { "files_per_job": 20 },
+            "HH": { "subsamples": [
+                            "GluGluToHHTobbVV_node_cHHH0",
+                            "GluGluToHHTobbVV_node_cHHH1",
+                            "GluGluToHHTobbVV_node_cHHH2p45",
+                            "GluGluToHHTobbVV_node_cHHH5",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_0_5_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_0_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_2_C3_1",
+                            "VBF_HHTobbVV_CV_1_5_C2V_1_C3_1",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_0",
+                            "VBF_HHTobbVV_CV_1_C2V_1_C3_2",
+                        ], "files_per_job": 20, "chunksize": 10000 },
+        },
+}
diff --git a/src/condor/submit_configs/skimmer_inputs_23_08_22_5xhy.yaml b/src/condor/submit_configs/skimmer_inputs_23_08_22_5xhy.yaml
new file mode 100644
index 00000000..ee7b5d9b
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_23_08_22_5xhy.yaml
@@ -0,0 +1,17 @@
+{
+    "Skimmer":
+        {
+            "XHY":
+                {
+                    "subsamples":
+                        [
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250",
+                            "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150",
+                        ],
+                    "files_per_job": 20,
+                },
+        },
+}
diff --git a/src/condor/submit_configs/skimmer_inputs_test.yaml b/src/condor/submit_configs/skimmer_inputs_test.yaml
new file mode 100644
index 00000000..7029989d
--- /dev/null
+++ b/src/condor/submit_configs/skimmer_inputs_test.yaml
@@ -0,0 +1,6 @@
+{
+    "Skimmer":
+        {
+            "QCD": { "subsamples": ["QCD_HT1000to1500"], "files_per_job": 20 },
+        },
+}
diff --git a/src/condor/submit_configs/ttsfs_inputs_12_4.yaml b/src/condor/submit_configs/ttsfs_inputs_12_4.yaml
index 29ce3da3..a5b3940e 100644
--- a/src/condor/submit_configs/ttsfs_inputs_12_4.yaml
+++ b/src/condor/submit_configs/ttsfs_inputs_12_4.yaml
@@ -1,10 +1,10 @@
 {
     "TTbarScaleFactors":
         {
-            "SingleMu2018": { "files_per_job": 20 },
-            "TTbar": { "files_per_job": 20, "subsamples": ["TTTo2L2Nu", "TTToHadronic", "TTToSemiLeptonic"]},
-            "SingleTop": { "files_per_job": 10 },
-            "QCD": { "files_per_job": 20 },
+            "SingleMu2018": { "files_per_job": 10 },
+            # "TTbar": { "files_per_job": 20, "subsamples": ["TTTo2L2Nu", "TTToHadronic", "TTToSemiLeptonic"]},
+            # "SingleTop": { "files_per_job": 10 },
+            # "QCD": { "files_per_job": 20 },
             "WJetsToLNu": { "files_per_job": 20 },
             "Diboson": { 
                 "subsamples": ["WW", "WZ", "ZZ"],
diff --git a/src/condor/submit_from_yaml.py b/src/condor/submit_from_yaml.py
index 60677d67..be90d45e 100644
--- a/src/condor/submit_from_yaml.py
+++ b/src/condor/submit_from_yaml.py
@@ -5,11 +5,17 @@
 """
 
 import os
+import sys
 import argparse
 
 import submit
 import yaml
 
+# needed to import run_utils from parent directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
+
+import run_utils
+
 
 def add_bool_arg(parser, name, help, default=False, no_name=None):
     """Add a boolean command line argument for argparse"""
@@ -27,8 +33,7 @@ def add_bool_arg(parser, name, help, default=False, no_name=None):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--year", help="year", type=str, required=True)
-    parser.add_argument("--tag", default="Test", help="process tag", type=str)
+    run_utils.parse_common_args(parser)
     parser.add_argument("--jet", default="AK8", help="jet", type=str)
     parser.add_argument(
         "--submit", dest="submit", action="store_true", help="submit jobs when created"
@@ -40,17 +45,7 @@ def add_bool_arg(parser, name, help, default=False, no_name=None):
         type=str,
         choices=["lpc", "ucsd"],
     )
-    parser.add_argument(
-        "--processor",
-        default="trigger",
-        help="which processor",
-        type=str,
-        choices=["trigger", "skimmer", "input", "ttsfs"],
-    )
     parser.add_argument("--yaml", default="", help="yaml file", type=str)
-    add_bool_arg(parser, "save-ak15", default=False, help="run inference for and save ak15 jets")
-    add_bool_arg(parser, "save-systematics", default=False, help="save systematic variations")
-    add_bool_arg(parser, "inference", default=True, help="run inference for ak8 jets")
 
     args = parser.parse_args()
 
diff --git a/src/condor/submit_pp.py b/src/condor/submit_pp.py
new file mode 100644
index 00000000..b69e8662
--- /dev/null
+++ b/src/condor/submit_pp.py
@@ -0,0 +1,240 @@
+#!/usr/bin/python
+
+"""
+Splits the total fileset and creates condor job submission files for the specified run script.
+
+Author(s): Cristina Mantilla Suarez, Raghav Kansal
+"""
+
+import argparse
+import os
+from math import ceil
+from string import Template
+import json
+
+import sys
+
+# needed to import run_utils from parent directory
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
+
+import run_utils
+
+
+def write_template(templ_file: str, out_file: str, templ_args: dict):
+    """Write to ``out_file`` based on template from ``templ_file`` using ``templ_args``"""
+
+    with open(templ_file, "r") as f:
+        templ = Template(f.read())
+
+    with open(out_file, "w") as f:
+        f.write(
+            templ.safe_substitute(
+                templ_args,
+            )
+        )
+
+
+samples = [
+    # "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1000_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1200_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1400_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-1800_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2000_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2200_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2400_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-190",
+    # "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2600_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-2800_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-150",
+    # "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3000_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-60",
+    # "NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-190",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-4000_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-700_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-800_MY-80",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-100",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-125",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-150",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-250",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-60",
+    "NMSSM_XToYHTo2W2BTo4Q2B_MX-900_MY-80",
+]
+
+
+def main(args):
+    if args.site == "lpc":
+        t2_local_prefix = "/eos/uscms/"
+        t2_prefix = "root://cmseos.fnal.gov"
+
+        try:
+            proxy = os.environ["X509_USER_PROXY"]
+        except:
+            print("No valid proxy. Exiting.")
+            exit(1)
+    elif args.site == "ucsd":
+        t2_local_prefix = "/ceph/cms/"
+        t2_prefix = "root://redirector.t2.ucsd.edu:1095"
+        proxy = "/home/users/rkansal/x509up_u31735"
+
+    username = os.environ["USER"]
+    local_dir = f"condor/postprocessing/{args.tag}"
+    homedir = f"/store/user/{username}/bbVV/templates/"
+    outdir = homedir + args.tag + "/"
+
+    # make local directory
+    logdir = local_dir + "/logs"
+    os.system(f"mkdir -p {logdir}")
+
+    # and condor directory
+    print("CONDOR work dir: " + outdir)
+    os.system(f"mkdir -p {t2_local_prefix}/{outdir}")
+
+    # and eos directory
+    eosoutput_dir = f"{t2_prefix}/{outdir}/"
+    os.system(f"mkdir -p {t2_local_prefix}/{outdir}/")
+
+    jdl_templ = "src/condor/submit_pp.templ.jdl"
+    sh_templ = "src/condor/submit_pp.templ.sh"
+
+    # submit jobs
+    nsubmit = 0
+    print("Submitting samples")
+
+    njobs = ceil(len(samples) / args.files_per_job)
+
+    for j in range(njobs):
+        run_samples = " ".join(samples[j * args.files_per_job : (j + 1) * args.files_per_job])
+
+        prefix = "templates"
+        localcondor = f"{local_dir}/{prefix}_{j}.jdl"
+        jdl_args = {"dir": local_dir, "prefix": prefix, "jobid": j, "proxy": proxy}
+        write_template(jdl_templ, localcondor, jdl_args)
+
+        localsh = f"{local_dir}/{prefix}_{j}.sh"
+        sh_args = {"samples": run_samples, "eosout": eosoutput_dir}
+        write_template(sh_templ, localsh, sh_args)
+        os.system(f"chmod u+x {localsh}")
+
+        if os.path.exists(f"{localcondor}.log"):
+            os.system(f"rm {localcondor}.log")
+
+        print("To submit ", localcondor)
+        if args.submit:
+            os.system("condor_submit %s" % localcondor)
+
+        nsubmit = nsubmit + 1
+
+    print(f"Total {nsubmit} jobs")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--tag", default="Test", help="process tag", type=str)
+    parser.add_argument(
+        "--site",
+        default="lpc",
+        help="computing cluster we're running this on",
+        type=str,
+        choices=["lpc", "ucsd"],
+    )
+    parser.add_argument("--files-per-job", default=5, help="# files per condor job", type=int)
+
+    run_utils.add_bool_arg(
+        parser, "submit", default=False, help="submit files as well as create them"
+    )
+
+    args = parser.parse_args()
+
+    main(args)
diff --git a/src/condor/submit_pp.templ.jdl b/src/condor/submit_pp.templ.jdl
new file mode 100644
index 00000000..0def1d5f
--- /dev/null
+++ b/src/condor/submit_pp.templ.jdl
@@ -0,0 +1,17 @@
+#!/usr/bin/env condor_submit
+
+executable              = $dir/${prefix}_$jobid.sh
+should_transfer_files   = YES
+transfer_input_files    = src/HHbbVV
+when_to_transfer_output = ON_EXIT_OR_EVICT
+request_memory          = 3000
+use_x509userproxy       = true
+x509userproxy           = $proxy
+
+output                  = $dir/logs/${prefix}_$jobid.out
+error                   = $dir/logs/${prefix}_$jobid.err
+log                     = $dir/logs/${prefix}_$jobid.log
+
++SingularityImage = "/cvmfs/unpacked.cern.ch/registry.hub.docker.com/coffeateam/coffea-dask:latest"
+
+Queue 1
diff --git a/src/condor/submit_pp.templ.sh b/src/condor/submit_pp.templ.sh
new file mode 100644
index 00000000..b37cb07a
--- /dev/null
+++ b/src/condor/submit_pp.templ.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+cd HHbbVV/postprocessing/
+
+# make dir for output
+mkdir condor_templates
+
+# run code
+for sample in $samples
+do
+    for year in 2016 2016APV 2017 2018
+    do 
+        python -u postprocessing.py --templates --resonant --no-data --read-sig-samples --bg-keys "" --year $year \
+        --sig-samples $sample --template-dir "condor_templates/$sample" \
+        --data-dir "/eos/uscms/store/user/rkansal/bbVV/skimmer/Apr11/"
+    done
+done
+
+#move output to eos
+xrdcp -f condor_templates/* $eosout
\ No newline at end of file
diff --git a/src/run.py b/src/run.py
index b4ccf12c..15dc27bf 100644
--- a/src/run.py
+++ b/src/run.py
@@ -99,6 +99,8 @@ def run(p: processor, fileset: dict, args):
     pickle.dump(out, filehandler)
     filehandler.close()
 
+    print(out)
+
     # need to combine all the files from these processors before transferring to EOS
     # otherwise it will complain about too many small files
     if args.processor in ["skimmer", "input", "ttsfs"]:
@@ -137,6 +139,8 @@ def main(args):
         args.njets,
         args.save_systematics,
         args.inference,
+        args.save_all,
+        args.vbf_search,
     )
 
     if len(args.files):
@@ -146,6 +150,17 @@ def main(args):
             args.processor, args.year, args.samples, args.subsamples, args.starti, args.endi
         )
 
+    ignore_files = [
+        "root://cmseos.fnal.gov///store/user/lpcpfnano/rkansal/v2_3/2016APV/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-600_MY-80/230323_175525/0000/nano_mc2016pre_36.root",
+        "root://cmseos.fnal.gov///store/user/lpcpfnano/rkansal/v2_3/2016/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-190_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-1600_MY-190/230323_195705/0000/nano_mc2016post_27.root",
+        "'root://cmseos.fnal.gov///store/user/lpcpfnano/rkansal/v2_3/2016APV/XHY/NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-80_TuneCP5_13TeV-madgraph-pythia8/NMSSM_XToYHTo2W2BTo4Q2B_MX-3500_MY-80/230323_175525/0000/nano_mc2016pre_16.root",
+    ]
+
+    for key in fileset:
+        for file in ignore_files:
+            if file in fileset[key]:
+                fileset[key].remove(file)
+
     print(f"Running on fileset {fileset}")
 
     if args.executor == "dask":
@@ -160,18 +175,9 @@ def main(args):
     # inside a dask job:  python run.py --year 2017 --processor trigger --dask
 
     parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--year", help="year", type=str, required=True, choices=["2016APV", "2016", "2017", "2018"]
-    )
+    run_utils.parse_common_args(parser)
     parser.add_argument("--starti", default=0, help="start index of files", type=int)
     parser.add_argument("--endi", default=-1, help="end index of files", type=int)
-    parser.add_argument(
-        "--processor",
-        default="trigger",
-        help="Trigger processor",
-        type=str,
-        choices=["trigger", "skimmer", "input", "ttsfs"],
-    )
     parser.add_argument(
         "--executor",
         type=str,
@@ -179,8 +185,6 @@ def main(args):
         choices=["futures", "iterative", "dask"],
         help="type of processor executor",
     )
-    parser.add_argument("--samples", default=[], help="samples", nargs="*")
-    parser.add_argument("--subsamples", default=[], help="subsamples", nargs="*")
     parser.add_argument(
         "--files", default=[], help="set of files to run on instead of samples", nargs="*"
     )
@@ -190,18 +194,6 @@ def main(args):
         default="files",
         help="sample name of files being run on, if --files option used",
     )
-    parser.add_argument("--chunksize", type=int, default=10000, help="chunk size in processor")
-    parser.add_argument("--label", default="AK8_H_VV", help="label", type=str)
-    parser.add_argument("--njets", default=2, help="njets", type=int)
-    parser.add_argument("--maxchunks", default=0, help="max chunks", type=int)
-
-    run_utils.add_bool_arg(
-        parser, "save-ak15", default=False, help="run inference for and save ak15 jets"
-    )
-    run_utils.add_bool_arg(
-        parser, "save-systematics", default=False, help="save systematic variations"
-    )
-    run_utils.add_bool_arg(parser, "inference", default=True, help="run inference for ak8 jets")
 
     args = parser.parse_args()
 
diff --git a/src/run_utils.py b/src/run_utils.py
index 0ad2f496..4bbded82 100644
--- a/src/run_utils.py
+++ b/src/run_utils.py
@@ -52,7 +52,7 @@ def get_fileset(
     get_num_files: bool = False,
     coffea_casa: str = False,
 ):
-    if processor == "trigger":
+    if processor.startswith("trigger"):
         samples = [f"SingleMu{year[:4]}"]
 
     # redirector = "root://cmsxrootd.fnal.gov//" if not coffea_casa else "root://xcache//"
@@ -112,12 +112,18 @@ def get_processor(
     njets: int = None,
     save_systematics: bool = None,
     inference: bool = None,
+    save_all: bool = None,
+    vbf_search: bool = None,
 ):
     # define processor
     if processor == "trigger":
         from HHbbVV.processors import JetHTTriggerEfficienciesProcessor
 
         return JetHTTriggerEfficienciesProcessor()
+    elif processor == "trigger4d":
+        from HHbbVV.processors import JetHT4DTriggerEfficienciesProcessor
+
+        return JetHT4DTriggerEfficienciesProcessor()
     elif processor == "skimmer":
         from HHbbVV.processors import bbVVSkimmer
 
@@ -126,6 +132,8 @@ def get_processor(
             save_ak15=save_ak15,
             save_systematics=save_systematics,
             inference=inference,
+            save_all=save_all,
+            vbf_search=vbf_search,
         )
     elif processor == "input":
         from HHbbVV.processors import TaggerInputSkimmer
@@ -134,4 +142,53 @@ def get_processor(
     elif processor == "ttsfs":
         from HHbbVV.processors import TTScaleFactorsSkimmer
 
-        return TTScaleFactorsSkimmer(xsecs=get_xsecs())
+        return TTScaleFactorsSkimmer(xsecs=get_xsecs(), inference=inference)
+    elif processor == "xhy":
+        from HHbbVV.processors import XHYProcessor
+
+        return XHYProcessor()
+
+
+def parse_common_args(parser):
+    parser.add_argument(
+        "--processor",
+        default="trigger",
+        help="Trigger processor",
+        type=str,
+        choices=["trigger", "trigger4d", "skimmer", "input", "ttsfs", "xhy"],
+    )
+
+    parser.add_argument(
+        "--year", help="year", type=str, required=True, choices=["2016APV", "2016", "2017", "2018"]
+    )
+
+    parser.add_argument(
+        "--samples",
+        default=[],
+        help="which samples to run",  # , default will be all samples",
+        nargs="*",
+    )
+    parser.add_argument(
+        "--subsamples",
+        default=[],
+        help="which subsamples, by default will be all in the specified sample(s)",
+        nargs="*",
+    )
+
+    parser.add_argument("--tag", default="Test", help="process tag", type=str)
+
+    parser.add_argument("--maxchunks", default=0, help="max chunks", type=int)
+    parser.add_argument("--chunksize", default=10000, help="chunk size", type=int)
+    parser.add_argument("--label", default="AK15_H_VV", help="label", type=str)
+    parser.add_argument("--njets", default=2, help="njets", type=int)
+
+    # REMEMBER TO PROPAGATE THESE TO SUBMIT TEMPLATE!!
+    # processor args
+    add_bool_arg(parser, "inference", default=True, help="run inference for ak8 jets")
+    # bbVVSkimmer-only args
+    add_bool_arg(parser, "save-ak15", default=False, help="run inference for and save ak15 jets")
+    add_bool_arg(parser, "save-systematics", default=False, help="save systematic variations")
+    add_bool_arg(parser, "save-all", default=True, help="save all branches")
+    add_bool_arg(
+        parser, "vbf-search", default=False, help="run selections for VBF production search"
+    )