From a9078ca7c0404bdcee26d5be5a0c8e8db503487f Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Wed, 22 Jan 2025 18:46:07 -0500
Subject: [PATCH 1/7] added h_bb code from jake's fork

---
 analyses/h_bb/h_bb.ipynb | 1324 ++++++++++++++++++++++++++++++++++++++
 analyses/h_bb/h_bb.py    |  468 ++++++++++++++
 2 files changed, 1792 insertions(+)
 create mode 100644 analyses/h_bb/h_bb.ipynb
 create mode 100644 analyses/h_bb/h_bb.py

diff --git a/analyses/h_bb/h_bb.ipynb b/analyses/h_bb/h_bb.ipynb
new file mode 100644
index 0000000..3906450
--- /dev/null
+++ b/analyses/h_bb/h_bb.ipynb
@@ -0,0 +1,1324 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d04a60b8-f482-47b7-9924-9f33bb410263",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uproot\n",
+    "import hist\n",
+    "from hist import Hist\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import mplhep as hep\n",
+    "from scipy.optimize import curve_fit\n",
+    "from matplotlib.patches import Rectangle\n",
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "bb005f9c-aab1-47c7-8713-9ab01f601a8f",
+   "metadata": {},
+   "source": [
+    "First we open the ROOT file using uproot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "389e4150-c583-4833-a4bc-c218c5b5ecb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['wzp6_ee_eeH_Hbb_ecm240;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/meta;1', 'wzp6_ee_mumuH_Hbb_ecm240;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/meta;1', 'wzp6_ee_tautauH_Hbb_ecm240;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/meta;1', 'wzp6_ee_nunuH_Hbb_ecm240;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/meta;1', 'wzp6_ee_qqH_Hbb_ecm240;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/meta;1', 'wzp6_ee_ssH_Hbb_ecm240;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/meta;1', 'wzp6_ee_ccH_Hbb_ecm240;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/meta;1', 'wzp6_ee_bbH_Hbb_ecm240;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/meta;1', 'wzp6_ee_eeH_Hcc_ecm240;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/meta;1', 'wzp6_ee_mumuH_Hcc_ecm240;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/meta;1', 'wzp6_ee_tautauH_Hcc_ecm240;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/meta;1', 'wzp6_ee_nunuH_Hcc_ecm240;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/meta;1', 'wzp6_ee_qqH_Hcc_ecm240;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/meta;1', 'wzp6_ee_ssH_Hcc_ecm240;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/meta;1', 'wzp6_ee_ccH_Hcc_ecm240;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/meta;1', 'wzp6_ee_bbH_Hcc_ecm240;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/meta;1', 'wzp6_ee_eeH_Hgg_ecm240;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/meta;1', 'wzp6_ee_mumuH_Hgg_ecm240;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/meta;1', 'wzp6_ee_tautauH_Hgg_ecm240;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/meta;1', 'wzp6_ee_nunuH_Hgg_ecm240;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/meta;1', 'wzp6_ee_qqH_Hgg_ecm240;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/meta;1', 'wzp6_ee_ssH_Hgg_ecm240;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/meta;1', 'wzp6_ee_ccH_Hgg_ecm240;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/meta;1', 'wzp6_ee_bbH_Hgg_ecm240;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/meta;1', 'p8_ee_WW_ecm240;1', 'p8_ee_WW_ecm240/muons_all_p_cut0;1', 'p8_ee_WW_ecm240/muons_all_theta_cut0;1', 'p8_ee_WW_ecm240/muons_all_phi_cut0;1', 'p8_ee_WW_ecm240/muons_all_q_cut0;1', 'p8_ee_WW_ecm240/muons_all_no_cut0;1', 'p8_ee_WW_ecm240/electrons_all_p_cut0;1', 'p8_ee_WW_ecm240/electrons_all_theta_cut0;1', 'p8_ee_WW_ecm240/electrons_all_phi_cut0;1', 'p8_ee_WW_ecm240/electrons_all_q_cut0;1', 'p8_ee_WW_ecm240/electrons_all_no_cut0;1', 'p8_ee_WW_ecm240/cutFlow_mumu;1', 'p8_ee_WW_ecm240/cutFlow_ee;1', 'p8_ee_WW_ecm240/cutFlow_nunu;1', 'p8_ee_WW_ecm240/cutFlow_qq;1', 'p8_ee_WW_ecm240/cutFlow_ss;1', 'p8_ee_WW_ecm240/cutFlow_cc;1', 'p8_ee_WW_ecm240/cutFlow_bb;1', 'p8_ee_WW_ecm240/missingEnergy_nOne;1', 'p8_ee_WW_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_WW_ecm240/ee_recoil_m_nOne;1', 'p8_ee_WW_ecm240/mumu_p_nOne;1', 'p8_ee_WW_ecm240/ee_p_nOne;1', 'p8_ee_WW_ecm240/zmumu_m_nOne;1', 'p8_ee_WW_ecm240/zee_m_nOne;1', 'p8_ee_WW_ecm240/meta;1', 'p8_ee_ZZ_ecm240;1', 'p8_ee_ZZ_ecm240/muons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/cutFlow_mumu;1', 'p8_ee_ZZ_ecm240/cutFlow_ee;1', 'p8_ee_ZZ_ecm240/cutFlow_nunu;1', 'p8_ee_ZZ_ecm240/cutFlow_qq;1', 'p8_ee_ZZ_ecm240/cutFlow_ss;1', 'p8_ee_ZZ_ecm240/cutFlow_cc;1', 'p8_ee_ZZ_ecm240/cutFlow_bb;1', 'p8_ee_ZZ_ecm240/missingEnergy_nOne;1', 'p8_ee_ZZ_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/ee_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/mumu_p_nOne;1', 'p8_ee_ZZ_ecm240/ee_p_nOne;1', 'p8_ee_ZZ_ecm240/zmumu_m_nOne;1', 'p8_ee_ZZ_ecm240/zee_m_nOne;1', 'p8_ee_ZZ_ecm240/meta;1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "f_mumu = uproot.open(\"/home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root\")\n",
+    "print(f_mumu.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dfe61ba9-8ed1-412a-be7a-2d3852df41e9",
+   "metadata": {},
+   "source": [
+    "<h1>Cutflow</h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1a9f682f-e53d-43c6-9c34-faa2df4536bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "analyses = ['mumu', 'ee', 'nunu', 'qq', 'ss', 'cc', 'bb']\n",
+    "\n",
+    "Zprods = ['ee', 'mumu', 'tautau', 'nunu', 'qq', 'ss', 'cc', 'bb']\n",
+    "bb_sig = [f'wzp6_ee_{i}H_Hbb_ecm240' for i in Zprods]\n",
+    "cc_sig = [f'wzp6_ee_{i}H_Hcc_ecm240' for i in Zprods]\n",
+    "gg_sig = [f'wzp6_ee_{i}H_Hgg_ecm240' for i in Zprods]\n",
+    "\n",
+    "cols = 3\n",
+    "rows = int(np.ceil(len(analyses) / cols))\n",
+    "\n",
+    "# for cut labels\n",
+    "cuts = {}\n",
+    "cuts['mumu'] = ['Decay Products', 'OS Muons', 'H Recoil', 'Z Momentum', 'Z Mass', 'B Tag']\n",
+    "cuts['ee']   = ['Decay Products', 'OS Electrons', 'H Recoil', 'Z Momentum', 'Z Mass', 'B Tag']\n",
+    "cuts['nunu'] = ['Decay Products', 'Dijet Momentum', 'Dijet Mass', 'B Tag']\n",
+    "cuts['qq']   = ['Decay Products', 'Z Momentum', 'H Mass',  'ML Pairing', 'Hbb Tag', 'Zqq Tag']\n",
+    "cuts['ss']   = ['Decay Products', 'Z Momentum', 'H Mass',  'ML Pairing', 'Hbb Tag', 'Zss Tag']\n",
+    "cuts['cc']   = ['Decay Products', 'Z Momentum', 'H Mass',  'ML Pairing', 'Hbb Tag', 'Zcc Tag']\n",
+    "cuts['bb']   = ['Decay Products', 'Z Momentum', 'H Mass',  'ML Pairing', 'Hbb Tag', 'Zbb Tag']\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 14})\n",
+    "fig, ax = plt.subplots(rows, cols, figsize=(cols * 5, rows * 5))\n",
+    "\n",
+    "for i in range(rows):\n",
+    "    for j in range(cols):\n",
+    "        if i*cols + j < len(analyses):\n",
+    "            p = analyses[i*cols + j]\n",
+    "        else:\n",
+    "            break\n",
+    "        part = f_mumu[f'wzp6_ee_{p}H_Hbb_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        maxi = np.nonzero(part.values())[0][-1]\n",
+    "        WW   = f_mumu[f'p8_ee_WW_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        Hbb = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)])\n",
+    "        Hcc = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in cc_sig])\n",
+    "        Hgg = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in gg_sig])\n",
+    "        \n",
+    "        ax[i][j].stairs(part.values(), label=f'Z{p}')\n",
+    "        ax[i][j].stairs(WW.values(), label=f'WW bkg')\n",
+    "        ax[i][j].stairs(ZZ.values(), label=f'ZZ bkg')\n",
+    "        ax[i][j].stairs(Hbb.values(), label='Other Hbb')\n",
+    "        ax[i][j].stairs(Hcc.values(), label='Hcc')\n",
+    "        ax[i][j].stairs(Hgg.values(), label='Hgg')\n",
+    "        \n",
+    "        ax[i][j].legend()\n",
+    "        ax[i][j].set_yscale('log')\n",
+    "        ax[i][j].set_title(f'Cuts for Z{p}')\n",
+    "        ax[i][j].set_ylabel('Events')\n",
+    "        ax[i][j].set_xlabel('')\n",
+    "        ax[i][j].set_xticks(ticks=list(range(1, len(cuts[p]) + 1)),\n",
+    "                   labels=cuts[p],\n",
+    "                   rotation=-45, ha='left')\n",
+    "        ax[i][j].set_xlim((0, maxi + 1))\n",
+    "        ax[i][j].set_ylim(10, 2e8)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa54617e-b976-4fa3-baab-2705fcca554d",
+   "metadata": {},
+   "source": [
+    "Graph cuts specifically for Z->leps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc2a7a9c-0819-48fe-854b-fd0230ccb32a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1600x650 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prods = ['mumu', 'ee', 'nunu', 'qq']\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 15})\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(16,6.5))\n",
+    "\n",
+    "for i in range(3):\n",
+    "        p = prods[i]\n",
+    "        \n",
+    "        part = f_mumu[f'wzp6_ee_{p}H_Hbb_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        maxi = np.nonzero(part.values())[0][-1]\n",
+    "        WW   = f_mumu[f'p8_ee_WW_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        Hbb = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)])\n",
+    "        Hcc = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in cc_sig])\n",
+    "        Hgg = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in gg_sig])\n",
+    "        \n",
+    "        ax[i].stairs(part.values(), label=f'Z{p}')\n",
+    "        ax[i].stairs(WW.values(), label='WW bkg')\n",
+    "        ax[i].stairs(ZZ.values(), label='ZZ bkg')\n",
+    "        ax[i].stairs(Hbb.values(), label='Other Hbb')\n",
+    "        ax[i].stairs(Hcc.values(), label='Hcc')\n",
+    "        ax[i].stairs(Hgg.values(), label='Hgg')\n",
+    "        \n",
+    "        ax[i].legend(loc='upper right')\n",
+    "        ax[i].set_yscale('log')\n",
+    "        ax[i].set_ylim(10, 2e8)\n",
+    "        ax[i].set_title(f'ZH->{p}bb')\n",
+    "        ax[i].set_ylabel('Events')\n",
+    "        ax[i].set_xlabel('')\n",
+    "        ax[i].set_xticks(ticks=list(range(1, len(cuts[p]) + 1)),\n",
+    "                   labels=cuts[p],\n",
+    "                   rotation=-45, ha='left')\n",
+    "        ax[i].set_xlim((0, maxi + 1))\n",
+    "\n",
+    "plt.suptitle('Cuts on Z->Leptons')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9282a4b5-9954-4918-9482-033146ec0d5c",
+   "metadata": {},
+   "source": [
+    "Graph cuts specifically for Z->quarks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ffba1ed4-007f-487d-b11f-14909a31a843",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "prods = ['qq', 'ss', 'cc', 'bb']\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 14})\n",
+    "fig, ax = plt.subplots(2, 2, figsize=(12,12))\n",
+    "\n",
+    "for i in range(2):\n",
+    "    for j in range(2):\n",
+    "        p = prods[i*2 + j]\n",
+    "        \n",
+    "        part = f_mumu[f'wzp6_ee_{p}H_Hbb_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        maxi = np.nonzero(part.values())[0][-1]\n",
+    "        WW   = f_mumu[f'p8_ee_WW_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/cutFlow_{p}'].to_hist()\n",
+    "        Hbb = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)])\n",
+    "        Hcc = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in cc_sig])\n",
+    "        Hgg = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in gg_sig])\n",
+    "        \n",
+    "        ax[i][j].stairs(part.values(), label=f'Z{p}')\n",
+    "        ax[i][j].stairs(WW.values(), label='WW bkg')\n",
+    "        ax[i][j].stairs(ZZ.values(), label='ZZ bkg')\n",
+    "        ax[i][j].stairs(Hbb.values(), label='Other Hbb')\n",
+    "        ax[i][j].stairs(Hcc.values(), label='Hcc')\n",
+    "        ax[i][j].stairs(Hgg.values(), label='Hgg')\n",
+    "        \n",
+    "        ax[i][j].legend(loc='upper right')\n",
+    "        ax[i][j].set_yscale('log')\n",
+    "        ax[i][j].set_ylim(10, 2e8)\n",
+    "        ax[i][j].set_title(f'ZH->{p}bb')\n",
+    "        ax[i][j].set_ylabel('Events')\n",
+    "        ax[i][j].set_xlabel('')\n",
+    "        ax[i][j].set_xticks(ticks=list(range(1, len(cuts[p]) + 1)),\n",
+    "                   labels=cuts[p],\n",
+    "                   rotation=-45, ha='left')\n",
+    "        ax[i][j].set_xlim((0, maxi + 1))\n",
+    "\n",
+    "plt.suptitle('Cuts on Z->Quarks')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b7d4350e-e264-4807-8211-728a74ae296e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = 'nunu'\n",
+    "\n",
+    "cuts['nunu'] = ['$n_\\\\mu = n_e = 0$\\n$102 < E_{miss} < 110$', '$40 < p_H < 58$', '$115 < m_H < 128$', 'B Tag > 0.5']\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 12})\n",
+    "plt.figure(figsize=(7, 5))\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "part = f_mumu[f'wzp6_ee_{p}H_Hbb_ecm240/cutFlow_{p}'].to_hist()\n",
+    "maxi = np.nonzero(part.values())[0][-1]\n",
+    "WW   = f_mumu[f'p8_ee_WW_ecm240/cutFlow_{p}'].to_hist()\n",
+    "ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/cutFlow_{p}'].to_hist()\n",
+    "Hbb = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)])\n",
+    "Hcc = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in cc_sig])\n",
+    "Hgg = reduce(lambda a, b : a + b, [f_mumu[x + f'/cutFlow_{p}'].to_hist() for x in gg_sig])\n",
+    "\n",
+    "plt.stairs(part.values(), label='ZH$\\\\rightarrow\\\\nu\\\\overline{\\\\nu}b\\\\overline{b}$', linewidth=3)\n",
+    "plt.stairs(WW.values(), label='WW bkg', linewidth=3)\n",
+    "plt.stairs(ZZ.values(), label='ZZ bkg', linewidth=3)\n",
+    "plt.stairs(Hbb.values(), label='Other Hbb', linewidth=3)\n",
+    "plt.stairs(Hcc.values(), label='Hcc', linewidth=3)\n",
+    "plt.stairs(Hgg.values(), label='Hgg', linewidth=3)\n",
+    "\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.yscale('log')\n",
+    "plt.ylim(10, 2e8)\n",
+    "plt.title('CutFlow for $e^-e^+ \\\\rightarrow ZH \\\\rightarrow \\\\nu\\\\overline{\\\\nu}b\\\\overline{b}$')\n",
+    "plt.ylabel('Events')\n",
+    "plt.xlabel('')\n",
+    "plt.xticks(ticks=list(range(1, len(cuts[p]) + 1)),\n",
+    "           labels=cuts[p],\n",
+    "           rotation=-45, ha='left')\n",
+    "plt.xlim(0, maxi + 1)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad744852-7d77-4c2f-8afd-051e7fea93f2",
+   "metadata": {},
+   "source": [
+    "<h1>Misc Graphs</h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dfd250a2-37ac-4fba-9635-d27b350e3fa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['wzp6_ee_eeH_Hbb_ecm240;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/meta;1', 'wzp6_ee_mumuH_Hbb_ecm240;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/meta;1', 'wzp6_ee_tautauH_Hbb_ecm240;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/meta;1', 'wzp6_ee_nunuH_Hbb_ecm240;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/meta;1', 'wzp6_ee_qqH_Hbb_ecm240;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/meta;1', 'wzp6_ee_ssH_Hbb_ecm240;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/meta;1', 'wzp6_ee_ccH_Hbb_ecm240;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/meta;1', 'wzp6_ee_bbH_Hbb_ecm240;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/meta;1', 'wzp6_ee_eeH_Hcc_ecm240;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/meta;1', 'wzp6_ee_mumuH_Hcc_ecm240;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/meta;1', 'wzp6_ee_tautauH_Hcc_ecm240;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/meta;1', 'wzp6_ee_nunuH_Hcc_ecm240;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/meta;1', 'wzp6_ee_qqH_Hcc_ecm240;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/meta;1', 'wzp6_ee_ssH_Hcc_ecm240;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/meta;1', 'wzp6_ee_ccH_Hcc_ecm240;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/meta;1', 'wzp6_ee_bbH_Hcc_ecm240;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/meta;1', 'wzp6_ee_eeH_Hgg_ecm240;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/meta;1', 'wzp6_ee_mumuH_Hgg_ecm240;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/meta;1', 'wzp6_ee_tautauH_Hgg_ecm240;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/meta;1', 'wzp6_ee_nunuH_Hgg_ecm240;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/meta;1', 'wzp6_ee_qqH_Hgg_ecm240;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/meta;1', 'wzp6_ee_ssH_Hgg_ecm240;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/meta;1', 'wzp6_ee_ccH_Hgg_ecm240;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/meta;1', 'wzp6_ee_bbH_Hgg_ecm240;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/meta;1', 'p8_ee_WW_ecm240;1', 'p8_ee_WW_ecm240/muons_all_p_cut0;1', 'p8_ee_WW_ecm240/muons_all_theta_cut0;1', 'p8_ee_WW_ecm240/muons_all_phi_cut0;1', 'p8_ee_WW_ecm240/muons_all_q_cut0;1', 'p8_ee_WW_ecm240/muons_all_no_cut0;1', 'p8_ee_WW_ecm240/electrons_all_p_cut0;1', 'p8_ee_WW_ecm240/electrons_all_theta_cut0;1', 'p8_ee_WW_ecm240/electrons_all_phi_cut0;1', 'p8_ee_WW_ecm240/electrons_all_q_cut0;1', 'p8_ee_WW_ecm240/electrons_all_no_cut0;1', 'p8_ee_WW_ecm240/cutFlow_mumu;1', 'p8_ee_WW_ecm240/cutFlow_ee;1', 'p8_ee_WW_ecm240/cutFlow_nunu;1', 'p8_ee_WW_ecm240/cutFlow_qq;1', 'p8_ee_WW_ecm240/cutFlow_ss;1', 'p8_ee_WW_ecm240/cutFlow_cc;1', 'p8_ee_WW_ecm240/cutFlow_bb;1', 'p8_ee_WW_ecm240/missingEnergy_nOne;1', 'p8_ee_WW_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_WW_ecm240/ee_recoil_m_nOne;1', 'p8_ee_WW_ecm240/mumu_p_nOne;1', 'p8_ee_WW_ecm240/ee_p_nOne;1', 'p8_ee_WW_ecm240/zmumu_m_nOne;1', 'p8_ee_WW_ecm240/zee_m_nOne;1', 'p8_ee_WW_ecm240/meta;1', 'p8_ee_ZZ_ecm240;1', 'p8_ee_ZZ_ecm240/muons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/cutFlow_mumu;1', 'p8_ee_ZZ_ecm240/cutFlow_ee;1', 'p8_ee_ZZ_ecm240/cutFlow_nunu;1', 'p8_ee_ZZ_ecm240/cutFlow_qq;1', 'p8_ee_ZZ_ecm240/cutFlow_ss;1', 'p8_ee_ZZ_ecm240/cutFlow_cc;1', 'p8_ee_ZZ_ecm240/cutFlow_bb;1', 'p8_ee_ZZ_ecm240/missingEnergy_nOne;1', 'p8_ee_ZZ_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/ee_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/mumu_p_nOne;1', 'p8_ee_ZZ_ecm240/ee_p_nOne;1', 'p8_ee_ZZ_ecm240/zmumu_m_nOne;1', 'p8_ee_ZZ_ecm240/zee_m_nOne;1', 'p8_ee_ZZ_ecm240/meta;1']\n"
+     ]
+    },
+    {
+     "ename": "KeyInFileError",
+     "evalue": "not found: 'hmuons_m' (with any cycle number)\n\n    Available keys: 'meta;1', 'mumu_p_nOne;1', 'muons_all_p_cut0;1', 'muons_all_q_cut0;1', 'cutFlow_mumu;1', 'cutFlow_ee;1', 'cutFlow_qq;1', 'cutFlow_ss;1', 'cutFlow_cc;1', 'cutFlow_bb;1', 'zmumu_m_nOne;1', 'zee_m_nOne;1'...\n\nin file /home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyInFileError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhmuons_m\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(f_mumu\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[0;32m----> 4\u001b[0m ZZ \u001b[38;5;241m=\u001b[39m \u001b[43mf_mumu\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mp8_ee_ZZ_ecm240/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mg\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mto_hist()\n\u001b[1;32m      5\u001b[0m hists \u001b[38;5;241m=\u001b[39m [f_mumu[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwzp6_ee_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mH_Hbb_ecm240/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_hist() \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m z_products]\n\u001b[1;32m      6\u001b[0m hists\u001b[38;5;241m.\u001b[39mappend(ZZ)\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2095\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2094\u001b[0m         last \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m-> 2095\u001b[0m         step \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2097\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, uproot\u001b[38;5;241m.\u001b[39mbehaviors\u001b[38;5;241m.\u001b[39mTBranch\u001b[38;5;241m.\u001b[39mHasBranches):\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items[i:])]\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2112\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step\n\u001b[1;32m   2111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2062\u001b[0m, in \u001b[0;36mReadOnlyDirectory.key\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m last\n\u001b[1;32m   2061\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cycle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2062\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2063\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2064\u001b[0m     )\n\u001b[1;32m   2065\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2066\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2067\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39mcycle, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2068\u001b[0m     )\n",
+      "\u001b[0;31mKeyInFileError\u001b[0m: not found: 'hmuons_m' (with any cycle number)\n\n    Available keys: 'meta;1', 'mumu_p_nOne;1', 'muons_all_p_cut0;1', 'muons_all_q_cut0;1', 'cutFlow_mumu;1', 'cutFlow_ee;1', 'cutFlow_qq;1', 'cutFlow_ss;1', 'cutFlow_cc;1', 'cutFlow_bb;1', 'zmumu_m_nOne;1', 'zee_m_nOne;1'...\n\nin file /home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root"
+     ]
+    }
+   ],
+   "source": [
+    "z_products = ['ee', 'mumu', 'tautau', 'nunu', 'qq', 'ss', 'cc', 'bb']\n",
+    "g = 'hmuons_m'\n",
+    "print(f_mumu.keys())\n",
+    "ZZ = f_mumu[f'p8_ee_ZZ_ecm240/{g}'].to_hist()\n",
+    "hists = [f_mumu[f'wzp6_ee_{i}H_Hbb_ecm240/{g}'].to_hist() for i in z_products]\n",
+    "hists.append(ZZ)\n",
+    "z_products.append('ZZ')\n",
+    "hep.histplot(hists, label=z_products)#, stack=True)\n",
+    "#plt.xlim(115,140)\n",
+    "plt.legend()\n",
+    "plt.xlim(100, 150)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "37d1b978-3ee5-4a71-bac0-2873164090ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p = 'nunu'\n",
+    "g = f'missingEnergy_nOne'\n",
+    "\n",
+    "mumu =  f_mumu[f'wzp6_ee_{p}H_Hbb_ecm240/{g}'].to_hist()\n",
+    "WW   = f_mumu[f'p8_ee_WW_ecm240/{g}'].to_hist()\n",
+    "ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/{g}'].to_hist()\n",
+    "rest = reduce(lambda a, b : a + b, [f_mumu[x + f'/{g}'].to_hist() for x in filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)])\n",
+    "plt.rcParams.update({'font.size': 15})\n",
+    "hep.histplot([mumu, WW, ZZ, rest], label=['ZH$\\\\rightarrow\\\\nu\\\\overline{\\\\nu}b\\\\overline{b}$', 'WW bkg', 'ZZ bkg', 'Other Hbb'], yerr=False)\n",
+    "plt.vlines([102, 110], [10, 10], [2e7, 2e7], color='black', label='Cuts')\n",
+    "#hep.histplot(mumu, ZZ, yerr=False)\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.title(\"ZH$\\\\rightarrow\\\\nu\\\\overline{\\\\nu}b\\\\overline{b}$ Missing Energy\")\n",
+    "plt.ylabel(\"Events\")\n",
+    "plt.xlabel(\"Missing Energy (GeV)\")\n",
+    "plt.yscale('log')\n",
+    "plt.xlim(80,130)\n",
+    "plt.ylim(10, 2e7)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c1c44a5d-4a51-4f2d-b6b5-83f9b987d527",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyInFileError",
+     "evalue": "not found: 'Zss_prob_nOne' (with any cycle number)\n\n    Available keys: 'ee_p_nOne;1', 'zee_m_nOne;1', 'mumu_p_nOne;1', 'zmumu_m_nOne;1', 'ee_recoil_m_nOne;1', 'missingEnergy_nOne;1', 'mumu_recoil_m_nOne;1', 'meta;1', 'cutFlow_ee;1', 'cutFlow_nunu;1', 'cutFlow_qq;1', 'cutFlow_ss;1'...\n\nin file /home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyInFileError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mss\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      2\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mZ\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mp\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_prob_nOne\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 4\u001b[0m mumu \u001b[38;5;241m=\u001b[39m  \u001b[43mf_mumu\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwzp6_ee_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mp\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43mH_Hbb_ecm240/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mg\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mto_hist()\n\u001b[1;32m      5\u001b[0m WW   \u001b[38;5;241m=\u001b[39m f_mumu[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp8_ee_WW_ecm240/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_hist()\n\u001b[1;32m      6\u001b[0m ZZ   \u001b[38;5;241m=\u001b[39m f_mumu[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp8_ee_ZZ_ecm240/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_hist()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2095\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2094\u001b[0m         last \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m-> 2095\u001b[0m         step \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2097\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, uproot\u001b[38;5;241m.\u001b[39mbehaviors\u001b[38;5;241m.\u001b[39mTBranch\u001b[38;5;241m.\u001b[39mHasBranches):\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items[i:])]\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2112\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step\n\u001b[1;32m   2111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2062\u001b[0m, in \u001b[0;36mReadOnlyDirectory.key\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m last\n\u001b[1;32m   2061\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cycle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2062\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2063\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2064\u001b[0m     )\n\u001b[1;32m   2065\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2066\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2067\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39mcycle, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2068\u001b[0m     )\n",
+      "\u001b[0;31mKeyInFileError\u001b[0m: not found: 'Zss_prob_nOne' (with any cycle number)\n\n    Available keys: 'ee_p_nOne;1', 'zee_m_nOne;1', 'mumu_p_nOne;1', 'zmumu_m_nOne;1', 'ee_recoil_m_nOne;1', 'missingEnergy_nOne;1', 'mumu_recoil_m_nOne;1', 'meta;1', 'cutFlow_ee;1', 'cutFlow_nunu;1', 'cutFlow_qq;1', 'cutFlow_ss;1'...\n\nin file /home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root"
+     ]
+    }
+   ],
+   "source": [
+    "p = 'ss'\n",
+    "g = f'Z{p}_prob_nOne'\n",
+    "\n",
+    "mumu =  f_mumu[f'wzp6_ee_{p}H_Hbb_ecm240/{g}'].to_hist()\n",
+    "WW   = f_mumu[f'p8_ee_WW_ecm240/{g}'].to_hist()\n",
+    "ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/{g}'].to_hist()\n",
+    "rest = reduce(lambda a, b : a + b, [f_mumu[x + f'/{g}'].to_hist() for x in filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)])\n",
+    "plt.rcParams.update({'font.size': 15})\n",
+    "hep.histplot([mumu, WW, ZZ, rest], label=[f'Z{p}', 'WW bkg', 'ZZ bkg', 'Other Hbb'], yerr=False)\n",
+    "plt.vlines([102, 110], [10, 10], [2e7, 2e7], color='black', label='Cuts')\n",
+    "#hep.histplot(mumu, ZZ, yerr=False)\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.title(\"Znunu Missing Energy\")\n",
+    "plt.ylabel(\"Events\")\n",
+    "plt.xlabel(\"Missing Energy (GeV)\")\n",
+    "plt.yscale('log')\n",
+    "plt.xlim(0,1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f9b6c8f2-9946-4a40-bcf4-e03c013ec9ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/submit/aniketkg/.local/lib/python3.10/site-packages/mplhep/utils.py:481: RuntimeWarning: All sumw are zero!  Cannot compute meaningful error bars\n",
+      "  return np.abs(method_fcn(self.values, variances) - self.values)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g = f'zee_m_nOne'\n",
+    "\n",
+    "mumu = f_mumu[f'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nOne'].to_hist()\n",
+    "ee   = f_mumu[f'wzp6_ee_eeH_Hbb_ecm240/zee_m_nOne'].to_hist()\n",
+    "WW   = f_mumu[f'p8_ee_WW_ecm240/{g}'].to_hist()\n",
+    "ZZ   = f_mumu[f'p8_ee_ZZ_ecm240/{g}'].to_hist()\n",
+    "rest = reduce(lambda a, b : a + b, [f_mumu[x + f'/{g}'].to_hist() for x in filter(lambda x : x != 'wzp6_ee_eeH_Hbb_ecm240' and x != 'wzp6_ee_mumuH_Hbb_ecm240', bb_sig)])\n",
+    "plt.rcParams.update({'font.size': 15})\n",
+    "hep.histplot([ee, mumu, WW, ZZ, rest], label=['Zee', 'Zmumu', 'WW bkg', 'ZZ bkg', 'Other Hbb'], yerr=False)\n",
+    "plt.vlines([85, 95], [10, 10], [1e5, 1e5], color='black', label='Cuts')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.title(\"Reconstructed Z Mass\")\n",
+    "plt.ylabel(\"Events\")\n",
+    "plt.xlabel(\"Mass (GeV)\")\n",
+    "plt.yscale('log')\n",
+    "plt.xlim(70, 110)\n",
+    "plt.ylim(10, 1e5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "bcf780a3-4c64-4692-ad04-e19d6096a8d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/submit/aniketkg/.local/lib/python3.10/site-packages/mplhep/utils.py:481: RuntimeWarning: All sumw are zero!  Cannot compute meaningful error bars\n",
+      "  return np.abs(method_fcn(self.values, variances) - self.values)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAHCCAYAAAAKFAY/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABqpklEQVR4nO3de1xU1d4/8M9wmeGOgjdQEMUL3lNESrLMW6EeS/GWnsq7nTrHytSyTDliXk+W9fyqR+to9aTlBY/H1C4aliASghcU8IaKomaCDCgDzMD6/cHMjmFmYGaY4ebn/XrNK2at9d17bTbTfF177bVlQggBIiIiIoJDfXeAiIiIqKFgYkRERESkxcSIiIiISIuJEREREZEWEyMiIiIiLSZGRERERFpMjIiIiIi0nOq7A41JeXk5bty4AU9PT8hksvruDhEREZlBCIHCwkL4+/vDwaH6MSEmRha4ceMGAgIC6rsbREREZIVr166hXbt21bZhYmQBT09PABW/WC8vr3ruDREREZmjoKAAAQEB0vd4dZgYWUB3+czLy4uJERERUSNjzjQYTr4mIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLS4jhERERml0Wig0WjquxtEepycnODkZL/0hYkRERHpKSoqwp07d3D//v367gqRUe7u7mjRogXc3Nxsvm0mRkREJCktLcW1a9fg7OwMPz8/KBQKPjSbGgwhBEpKSpCXl4dr166hQ4cOkMvlNt0HEyMiIpLcvn0bjo6OaN++PRwdHeu7O0QGXF1d4enpicuXL+P27ds1PhTWUpx8TUREACr+NV5UVARvb28mRdSgOTo6wtvbG0VFRRBC2HTbTIyIiAgAoFarUVZWBldX1/ruClGNXF1dUVZWBrVabdPtMjEiIiIAQHl5OQBwtIgaBd3fqe7v1laYGBERkR5OtqbGwF5/p0yMiGysqFSDoDf3IejNfSgq5RowRESNCRMjIhsSQuglQ0WlGptPDCQiIvthYkRkQyp1GfqvOCS977/iEFTqsnrsEVHDx1FWakiYGBERERFpMTEiIiIi0mJiREREZIHBgwdDJpNZ9KLGg48EISIiskCvXr2g0dQ8F+rEiRPSSuLUeDAxIiIissBHH31UY5v9+/dj9OjRAIDNmzfbu0tkQ7yURkREZEPXrl3D888/DyEEXnnlFYwdO7a+u0QWYGJERERkIxqNBpMmTUJubi4GDBiAtWvXGrS5cuUKZDIZAgICTG6nefPmkMlkyM/Pr3UcWYaJERERkY28+eabSExMRLNmzfDtt99CLpcbtDl9+jQA4KGHHjK6jStXriA/Px/t27dHs2bNah1HluEcIyIiMosQwi4LllZdLd4eXJ0d7X532N69e7F+/XoAwJYtWxAUFGS0XU0JzqlTpwAAffr0sUkcWYaJERERmUWlLkP3pT/YdR+VV463pfTlT8JNbr+vvOzsbEybNg1CCMyfPx9PP/20yba6BKZv375G60+ePAnAMMGxNo4sw0tpREREtaBWqzFx4kTk5eUhPDwcq1evrrZ9TSM/phIca+PIMhwxIiIis7g6OyJ9+ZM2325RqUYaKTq+ZKhdRnZcnR1tvk2dN954A0lJSfDx8cH27dvh7Oxssm1RUREuXrwILy8vdOjQwWgb3chQ5QTI2jiyHBMjIiIyi0wms+vlKABwkzvZfR+2tGfPHrz//vuQyWTYsmULAgMDq21/5swZlJeXo0+fPkbnPBUUFODKlSvw8PBAx44dax1HlrPqUtp//vMfzJ07F6GhofDz84NcLkezZs0wcOBAbNiwAaWlpQYx0dHRNS6ZnpmZaXKfGRkZmDp1Kvz8/ODi4oLg4GAsWLCgxlsSc3JyMGfOHAQEBEChUCAwMBBz585FTk6ONYdOREQEoOIusGnTpgEAXn/9dfzlL3+pMaamUZ3k5GQIIdC7d2+9BMjaOLKcVWn5v/71LyQkJEChUMDf3x99+vTBzZs3kZiYiMTERHz11Vc4ePCg0dsFAwICTGbUbm5uRsvj4uIwatQoqFQqtGzZEj169EBmZibee+897N69G0ePHkXr1q0N4tLT0zFo0CDk5eXB29sbPXv2xKVLl7Bx40bs2rUL8fHxCAkJseZXQERED7DS0lJMnDgR+fn5GDhwIFatWmVWnG6eUJcuXYzWf/fddwBMzy+yNI4sZ9WI0axZsxAXF4fCwkJkZWUhOTkZ169fR2JiItq1a4eUlBS8/fbbRmNnzJiB+Ph4oy9jCVNhYSEmTZoElUqFefPmIScnBykpKcjOzkZERASysrIwc+ZMg7iysjJMmDABeXl5iIqKwo0bN5CSkoKcnByMGzcOubm5mDRpEsrLy635FRAR0QNs4cKFSE5Ohq+vL7755hs4OZk3zqAb+SksLDSou379Or744gsApu9IszSOLGdVYjRt2jQMHjzYYILZww8/LK3h8J///KfWnQOATz/9FH/88Qe6deuG9evXS/v09fXF1q1b4eTkhH379iE1NVUvLjY2Funp6fD19cXmzZul0Sh3d3ds2bIFvr6+OH36NPbs2WOTfhIR0YMhNjYWH374IWQyGb788stqV6KuKi0tDUDFd9ulS5ek8tTUVIwaNQp3794FAPj7+6OkpKTWcWQ5m9+ur7s0VVRUZJPtxcbGAqhIxhwd9e8qCAwMxLBhwwAAO3fuNBo3ceJEeHp66tV5enpiwoQJAIAdO3bYpJ9ERPRgmDFjBgDA1dUVK1euxKOPPlrj68SJE8jOzpZWpr5//z66du2KTp06oWXLlggNDZWmpwDA9OnTsXLlSgCwOo6sY/PEKDExEQDQr18/o/VxcXGYMGEChgwZgvHjx2Pt2rW4deuW0bYajQYpKSkAgIiICKNtdOVJSUl65ceOHbMqjoiIqDpKpRJAxQBAQkKCWS+lUildDgsLC8O+ffsQFhaGnJwcODs746WXXsJPP/2E119/He7u7vD390dkZCQAWB1H1rHJPZFlZWW4efMm/vvf/+LNN9+Eu7u7yYlov/76q977Xbt2ITo6Gh9//LE0u1/nypUrUKvVAGDy9kNd+YULF6Sy0tJSZGdnmxWn24exdSdKSkr0hiQLCgqMbouIiB4cQgir4t59910AQM+ePREeHi4NJFQ2f/58zJ8/X69MN/Ha0jiyTq1GjD744APIZDI4OTkhICAAL7/8MoYOHYpjx45hwIABem39/Pzw1ltvITk5Gbm5uVKmHRkZCZVKhRkzZmDv3r16MbprpkDFE4ON0ZVXbqtUKqVJ1TXFlZeXm0x4Vq1aBW9vb+llyXVkIiKiynQjPz179qyTOLJOrRKjtm3bIiIiAgMGDJBul4+Li8O2bdtQVqb/oMG5c+fi3XffRf/+/eHj4wNXV1cMHDgQ+/btw9ixYyGEwGuvvaaXiRcXF0s/G3tCMQAoFAoAgEqlsiquamxlixcvhlKplF7Xrl0z2o6IiKznJnfCldWjcGX1qEa1uKOldCM/vXr1qpM4sk6tEqMJEyYgPj4eSUlJuHXrFo4dO4agoCCsXLkSf//7383ahkwmk54rc+nSJekPAABcXFykn40tGglAutTl6upqVVzV2MoUCgW8vLz0XkRERJZSqVS4cOGCtECxvePIejadfB0eHo79+/dDoVBg48aNuHr1qllxXbp0gY+PDwDg4sWLUnnly2CVL5VVpiuv3Nbb2xsODg5mxTk4ODDhISIiu9I90qNbt24Gd1jbI46sZ/O70vz9/fHQQw+hvLxcui5qDt3kZ41GI5UFBQVJ5VlZWUbjdOWdO3eWyuRyubRYZE1xlfdBRERkD2FhYRBCGKy5Z684sp7NEyPgz+SmcpJTnTt37uD27dsAgHbt2knlTk5O0m3/CQkJRmN15eHh4XrluveWxhEREdGDy+aJ0ZUrV6SRInOXJl+/fj2EEPD29kZYWJhe3bhx4wAAW7ZsMZjQnZ2djYMHDwIAoqKijMZt377dYAn1wsJCaWHH8ePHm9VHIiIiavosToxSUlKwbNkyo5eovv/+e0RGRkKj0WDkyJHSRLGzZ8/ipZdewtmzZ/XaFxcXY+XKlVizZg0A4I033jC4i+zFF19EixYtkJGRgfnz50vrGuXm5mLKlCnQaDSIjIxEaGioXlxUVBRCQkKQm5uL6dOnSytx379/H9OnT0dubi569uyJZ555xtJfARERETVRMmHhSlWHDx/GE088AQBo06YN2rVrJy2omJ+fD6Dimuj+/fvRokULAMDJkyfRt29fAEDLli2l+T8ZGRlSwjJz5kxs2rQJMpnMYJ+HDh3C6NGjUVxcLMXrYoOCgpCYmIg2bdoYxJ05cwaPPfYY7t69C29vb3Tq1AkXL16EUqmEj48Pjhw5gu7du5t97AUFBfD29oZSqeSEbTKqqFSD7kt/0CtLX/5kk74FmZqO4uJiXL58GR06dNC7u5eoIbLk79WS72+LR4z69OmDDRs2YMyYMXB3d0dmZiYyMzPh6uqKyMhIbN68GUePHpWSIqBignNMTAwiIyPh4eGBc+fOIS0tDT4+Phg/fjy+//57fPbZZ0aTIgAYOnQojh8/jsmTJ0MmkyEtLQ2tW7fG/PnzkZqaajQpAioWwzp16hRmzZoFDw8PpKWlwcPDA7Nnz8apU6csSoqIiIio6bN4xOhBxhEjqglHjKgx44gRNSYNZsSIiIiIqKliYkRERESkxcSIiIiISIuJEREREZEWEyMiIqpfpfeBaO+KV+n9+u4NPeCYGBERERFpMTEiIiJ6QAUFBUEmk+HKlSsWxQ0ePBgymQyHDx+2S7/qExMjIiIiM5w/fx4ymQwODg7Izc012uaLL76ATCaDTCaTnslZ1Y0bN6Q2V65csdt2yTpMjIiIiMzQpUsXtG7dGkIIJCQkGG0THx8v/XzkyBGjbXTl7dq1Q1BQkN22S9ZhYkRERGSmQYMGATCdnMTHx6NZs2Zo1qxZtW0qb8ue2yXLMTEiIiIyky7pqDyCo3Pnzh1kZmZi4MCBeOSRR3D69GkUFBQYtNMlNo899pjdt0uWY2JERERkJl0Ck5KSgqKiIr06XVLz6KOPIiIiAuXl5Th69Khem4KCAqSlpelty57btcQPP/yAwYMHw9vbG15eXhg+fLjJ0anqfPPNN3B2doa7uzt+/PFHvbrbt29j7ty58Pf3h4uLC0JCQrBq1SpoNJoGM6GbiREREZGZ+vTpA29vb6jVaiQlJenVVU5gHn30UQCGl8aOHj2K8vJy+Pr6onv37nbfrrm++eYbREZGIi0tDV26dIFcLsfBgwcxePBgk5O9jdm0aROmTp0qJUUjRoyQ6q5fv46wsDBs3LgRd+7cQffu3SGEwFtvvYXx48db3Gd7YWJERETmEaJiAUabvyqNkJQW2WcfQtjkV+Dg4ICBAwcCMLzsdeTIEcjlcoSFhWHAgAFwdnY22gaoSHJkMpndt2uud955B6+99hpu3bqF5ORk3Lp1C4sWLUJ5eTlmzZqFmzdv1riNf/3rX5gzZw58fX0RFxeHiIgIvfoXX3wR2dnZ6N+/P7KyspCamopz587h119/xS+//GIwClZfnOq7A0RE1Eioi4CV/vbdx7862We7b90A5O422dSgQYNw4MABvVGboqIinDhxAmFhYXBxcQEA9OvXD7/99htKS0shl8sBVD9B2l7bNUePHj3w3nvvSe+dnJywZs0aHDx4EKmpqfj000/xz3/+02T80qVLERMTg3bt2uGnn35CSEiIXv25c+ewb98+ODs7Y/v27WjXrp3ecb///vuYPn26VX23NY4YERERWUCXfCQmJqKsrAwAcOzYMajVaulSFwBERESguLgYycnJAIDS0lL89ttvAIxPkLbXds3x0ksvVVv+ww8/GK0XQuDVV19FTEwMgoODceTIEYOkCAB++uknABULQ3bo0MGgfvLkyXB1dbWq77bGESMiIjKPs1vFyIutlRb9OVK04CIgd7P9Ppxtt82wsDAoFArcu3cPJ0+eRGhoqN48IJ2IiAisX78e8fHxiIiIwPHjx1FcXAwPDw/07du3zrZrjm7dulVbfv78eaP1CxYsQGpqKnr06IGffvoJfn5+RttduHABANC7d2+j9S4uLujcuTNOnz5taddtjiNGRERkHpms4nKUzV+Vkha5m332YcW8G1MUCgUGDBgA4M+5PfHx8ZDJZHrzaqpOlNYlOY888gicnAzHJey1XXO0atXKaHnr1q0BAIWFhUbrL168CKDi0SItWrQwuf379yseDuzp6WmyTXV1dYmJERERkYUqL8hYVlaGxMREdOvWDT4+PlKbVq1aoVOnTkhISIAQQkpkqpsHZK/t1uSPP/4wWn779m0AppOWTZs2ISQkBPv27cOzzz4LjUZjtJ27e8X8rnv37pnsg6nkq64xMSIiIrKQbi5PfHw8Tp48iXv37uld7tJ59NFHkZ+fj7S0NOmuq+oSGHtttyYZGRnVlnfp0sVofatWrXDo0CF06tQJu3btwvPPP4/y8nKDdrp4U5fKSkpKpMtt9Y2JERERkYUGDhwIR0dH3L59G59//jkAGE1gdJfAPv30U+Tl5UEulyM8PLzOt1uTjz/+uNryyusRVeXv74+ff/4ZQUFB2LZtG2bMmAFRZXmE4cOHAwDi4uJw9epVg218++23UKlU1nbfppgYERERWcjT0xN9+vQBAGzevBlA9QmMrk3//v2rvfvKXtutyZkzZ7Bo0SKo1WoAgEajwVtvvYWUlBR4enrixRdfrDY+ICAAcXFxCAgIwBdffIEXX3xRLznq0qULRo0aBbVajYkTJ+LGjT8n8SckJOC1116Ds7Oz1f23JSZGREREVtBduiouLoa/v7/R29BDQkLg6+uL4uJiAObdTm+v7VYnJiYG//rXv+Dn54cBAwbAz88Pq1atgoODAzZu3Ah//5rXrwoKCsLPP/8Mf39/bNy4Ea+88ope/aefforAwED89ttvCAoKQmhoKEJCQqQVvR955BEAgKOjY62OpbaYGBEREVmh8pweY6M6ACCTyaQVravG1PV2qzN58mQcOHAAPXr0QGZmJoqLizFkyBDExcVh8uTJZm+nU6dOOHToEFq3bo2PPvoICxculOratWuH3377TVod++zZsygvL8fy5cuxc+dO6Rlx9X13mkxUvRBIJhUUFMDb2xtKpRJeXl713R1qgIpKNei+VH8htPTlT8JNziXDqOErLi7G5cuX0aFDB2mV5TpRev/PFbVtuEI1NR7l5eXw8fGBUqlEXl4emjdvXmOMJX+vlnx/c8SIiIiI6lVsbCyUSiW6d+9uVlJkT0yMiIiIyO5+//13rF27Frm5uXrl33//vTS5u6ZJ3nWB4/tERFS/5O5AtLK+e0F2plKp8MYbb+DNN99Eu3bt0KZNG1y/fh03b94EAIwaNcrkM9vqEkeMiIiIyO5atWqFZcuWYcCAASgpKcHJkydRVFSEiIgIfPrpp/jPf/5T73ekAVYmRv/5z38wd+5chIaGws/PD3K5HM2aNcPAgQOxYcMGlJaWmoxNTEzE008/jZYtW8LV1RXdu3dHTEyMdMuhKRkZGZg6dSr8/Pzg4uKC4OBgLFiwAPn5+dXG5eTkYM6cOQgICIBCoUBgYCDmzp2LnJwcaw6diIiIrODm5obo6GgcO3YMv//+O0pLS5Gfn4/4+HjMnTvX6ue82ZpVd6U9+uijSEhIgEKhgL+/P3x9fXHz5k0p2QgNDcXBgwfRrFkzvbivv/4aL7zwAsrKytC2bVu0atUKZ86cgVqtRlhYGA4fPgw3N8MnIMfFxWHUqFFQqVRo2bIlAgICkJmZiaKiInTs2BFHjx6VHnRXWXp6OgYNGoS8vDx4e3sjODgYly5dglKphK+vL+Lj4xESEmL2cfOuNKoJ70qjxqze7kojskKDuitt1qxZiIuLQ2FhIbKyspCcnIzr168jMTER7dq1Q0pKCt5++229mCtXrmDmzJkoKyvD2rVrce3aNaSmpuLChQvo2rUrkpOTsWjRIoN9FRYWYtKkSVCpVJg3bx5ycnKQkpKC7OxsREREICsrCzNnzjSIKysrw4QJE5CXl4eoqCjcuHEDKSkpyMnJwbhx45Cbm4tJkyYZfaYLERERPZisSoymTZuGwYMHGyzf/fDDD2P9+vUAKi63VbZu3TqUlJRgxIgRWLhwIWQyGQCgffv2+Pe//w0A2LhxI37//Xe9uE8//RR//PEHunXrhvXr10v79PX1xdatW+Hk5IR9+/YhNTVVLy42Nhbp6enw9fXF5s2bpZEod3d3bNmyBb6+vjh9+jT27Nljza+AiIiImiCbT77WXZrSrWAJAEII7N69GwCMju4MHDgQISEhUKvVBolKbGwsgIpkrOqkrMDAQAwbNgwAsHPnTqNxEydONFhF09PTExMmTAAA7Nixw7IDJDLB2GU0IiJqXGyeGCUmJgIA+vXrJ5VlZ2dLt+PpHnxXla48KSlJKtNoNEhJSbE4DgCOHTtmVRyRpYQQKCrVoKhUU99dISKiWrLJjNCysjLcvHkT//3vf/Hmm2/C3d0dq1atkuovXLgAANJkbWM6duyo1xaomJeke9Kvrt6cuNLSUmRnZ5sVp9uHsaf6lpSUoKSkRHpfUFBgdFv0YFOpyzhSRETURNRqxOiDDz6ATCaDk5MTAgIC8PLLL2Po0KE4duwYBgwYILW7e/cuAKBZs2bS3KKqdEuA69pW/dnUEuHG4pRKpTSpuqa48vJykwnPqlWr4O3tLb0CAgKMtiMiIqKmoVaJUdu2bREREYEBAwZIt8vHxcVh27ZtKCsrk9rp1iiSy+Umt6VQKABUrIxZNa662NrGVY2tbPHixVAqldLr2rVrJvtPVNmRRU/UdxeIiMgKtbqUNmHCBGkSM1AxX2fu3LlYuXIl8vLy8MknnwCAtL5AdQs/6i5Zubq6SmWV1yUoLS01uk6BOXHV7a9qbGUKhUIvgSKqiW7NIs43IjJfkboI4VvDAQBJU5Lg5my4nh1RXbHp5Ovw8HDs378fCoUCGzduxNWrVwH8edkqPz8fptaT1F0Kq3zpq/LPlS+V1RTn7e0NBwcHs+IcHBy4WCMREREBsMNdaf7+/njooYdQXl6OU6dOAQA6d+4MoGKU5saNG0bjsrKy9NoCQFBQkDQpWldvTpxcLkdgYKBZcZX3QURE1Bht2bIFMpkM06ZNsyju8OHDkMlkGDx4sF361RjZ5SGyGo1G77+BgYFo06YNACAhIcFojK48PDxcKnNycpJu+7ckrvJ7S+OIiIhMGTx4MGQymUUvnejoaItjDx8+XH8H+4Cy+QOcrly5Io0U9enTBwAgk8kwduxYfPLJJ/j8888xceJEvZijR48iMzMTzs7OGDNmjF7duHHjkJSUhC1btuD111/XW+QxOzsbBw8eBABERUUZxH377bfYvn071qxZo7fIY2FhobSw4/jx42105ERE1NT16tVL+kd/dU6cOIGioiJ4e3tLZYGBgSbX1qssKytLWvuv6jNHqQ4ICx0/flwsXbpUXLp0yaDuwIEDIiQkRAAQI0eO1KvLysoScrlcABBr164V5eXlQgghrly5Irp27SoAiL/97W8G21QqlaJFixYCgJg3b54oLS0VQghx584dERERIQCIyMhIgziNRiP1JSoqSty/f18IIcS9e/dEVFSUACB69uwpysrKzD52pVIpAAilUml2DDV990vUov0b34n2b3wn7peoTZYRNXQqlUqkp6cLlUpVp/u9X3pf9NzSU/Tc0lPcL71fp/u2h3379gmZTCYAiNjYWItir127Jnx9faXvPHNt3rxZABAvvPCCRfuLi4sTAMTjjz9uUVxDYMnfqyXf3xZfSissLMTy5csRHBwMPz8/hIWFoU+fPmjevDkiIyORmZmJsLAwfPHFF3pxHTp0wKZNm+Dg4IBFixYhICAA/fr1Q+fOnXHu3DmEhoZi3bp1Bvvz8vLCN998AxcXF3z44Ydo27Yt+vfvj8DAQCQkJCAoKEh61lpljo6O2LFjB5o3b45du3bB398f/fv3R9u2bbFr1y74+Pjg22+/lSZpExER1da1a9fw/PPPQwiBV155BWPHjjU7VqPRYNKkScjNzUVYWJjR70SyP4uzgj59+mDDhg0YM2YM3N3dkZmZiczMTLi6uiIyMhKbN2/G0aNH0aJFC4PY559/HkeOHMHo0aOhUqmQnp6Ojh07Ijo6GvHx8XB3dze6z6FDh+L48eOYPHkyZDIZ0tLS0Lp1a8yfPx+pqanS/KWqevbsiVOnTmHWrFnw8PBAWloaPDw8MHv2bJw6dQrdu3e39PCJiIiMqpzYDBgwAGvXrrUofvHixTh69CiaNWuG7du3V7v2X3UKCwsxf/58BAUFwcXFBR07dsTbb7+t9wxTcyiVSjz66KOQyWT4y1/+ordGIABs374dDz/8MNzd3dGiRQuMGTMGJ06caPQTui2eY9S8eXPMmzcP8+bNs2qHAwcOxN69ey2O69GjB7Zt22ZxXEBAADZt2mRxHBERkSXefPNNJCYmolmzZvj2228tSmy+++47vPfeewAq7jALCgqyqg8lJSV4/PHHcfLkSXTv3h2dO3fG2bNnsXLlSvz88884dOgQ3NxqXifqjz/+wJNPPokTJ05g8uTJ+PLLL/Xu4I6JicHSpUsBVNyN7u/vj8OHD2PgwIF45513rOp7Q8HrSERERLW0d+9erF+/HoDliU12djZeeOEFCCHw2muv4emnn7a6Hzt37sTt27dx4sQJnDlzBmlpaTh9+jQCAgJw7NgxLFu2rMZt5OTk4LHHHsOJEycwe/ZsfP3113pJ0W+//SbdYffJJ5/g+vXrSE5Oxq1btzBhwgRER0db3f+GwOZ3pRERUdMkhIBKY/wRSrVReZv22D4AuDq5mnxWZ21lZ2dj2rRpEEJg/vz5FiU2arUakyZNQl5eHsLDw7FmzZpa9UWj0eCjjz6S7goHKqaV/L//9/8wZswYfPLJJ1i6dKnendqVXbp0CcOGDcOVK1ewYMECo/Oc3n//fZSXl2PWrFl48cUXpXI3Nzd8/vnnSExMxMWLF2t1HPWJiREREZlFpVFJj+6wl8HbB9tlu/Z61IharcbEiROlxGb16tUWxb/xxhs4duwYmjdvjm+//bbWCw63bdvWaGI2evRoBAYGIjs7GwkJCXjqqacM2pw9exbDhw/HzZs3sXz5cpOXxHTL5EyfPt2gztnZGX/9618b9agRL6URERFZ6Y033kBSUhJ8fHywfft2ixKb//73v3j//fcBVFx+a9++fa3707VrV6N3W8tkMnTt2hUAcP78eYP6K1eu4LHHHsOtW7ewYcMGk0nR3bt3cefOHQBA7969jbYxVd5YcMSIiIjM4urkiqQpSTbfrkqjkkaKDk88DFcn4w/2rg17bHPPnj14//33IZPJsGXLFulRVOa4cuWK9PiOBQsWGCxubK1WrVqZrGvdujWAirvWqsrLy0NhYSEUCgV69eplchv3798HUJFoeXh4GG1j6jJdY8HEiIiIzCKTyexyOaoyVydXu+/DFionNq+//jr+8pe/mB2rm1d09+5dPPLII1i1apXN+vXHH3+YrLt9+zYA44lLv379MG7cOLzyyiv4y1/+gu+//x6PPvqoQTvdsjpCCNy/f9/oMjvGEq/GhJfSiIiILFBaWoqJEyciPz8fAwcOtDixWbhwIX777TdpoWEnJ9uNUZw7dw7l5eUG5UIInDt3DgDQpUsXo7Hz5s3D2rVrcf/+fYwcORJJSYajg82bN5fWKTx9+rTR7aSlpVnb/QaBiREREZEFFi5ciOTkZPj6+uKbb76xKLHZvXs3NmzYAJlMhi+//BIBAQE27dv169eNrhW4b98+XL16Fe7u7tU+r23hwoWIiYlBYWEhnnrqKaSmphq0GT58OICKeVFVaTQafP3119YfQAPAxIiIiMhMsbGx+PDDD61KbC5fvizdybVw4UKMGjXK5v1zcnLCP/7xD71Rm/T0dPz9738HALz44os1zgFasmQJlixZgvz8fIwYMcJgBOjVV1+FTCbDZ599preAskqlwuzZs3H58mUbHlHd4xwjIiIiM82YMQMA4OrqipUrV2LlypU1xnz00Ufo27cvXnvtNSiVSgDAL7/8YnQOj7H96fZpjvHjx+PcuXPo06cPevToAZlMhjNnzkAIgbCwMPzzn/80azsxMTEoKSnBunXrMGzYMBw+fBjdunUDAAwYMADR0dFYtmwZ5syZg3/+85/w9/fHuXPnUFJSgujoaLz99ttwdHQ0u98NCRMjIiIiM+kSm6KiIiQkJFgUk5+fL5UZm79jzLBhwyzqn0KhwC+//IKlS5ciNjYWt27dQmBgIJ599lksWbLE5DNJjVm7di1KS0uxYcMGDB06FL/88gs6d+4MAFi6dClCQkLw3nvvIS0tDUVFRXj00Ufxz3/+E7///juAxnt3GhMjIiIiMwkhrI49fPiw7TpSxbRp06S75ICK1al1ayRVZ/DgwdUe0wcffIAPPvjAaN3EiRMxceJEg3LdM9+sfd5bfeMcIyIiIrKJsrIyfPnllwBQ7STvhoyJEREREVnk888/x5EjR/TK8vLyMG3aNJw+fRr+/v4Wre3UkPBSGhER1Ss3ZzekvdC417550Bw5cgSzZs2Ch4cHgoODIYRARkYG1Go13Nzc8NVXX8HFxaW+u2kVJkZERERkkRdeeAFqtRrHjh3DpUuXUFpaCn9/fwwdOhSLFi2SnsvWGDExIiIiIos88cQTeOKJJ+q7G3bBOUZEREREWkyMiIiIiLSYGBERERFpMTEiIiI9tVnEkKiu2OvvlIkREREBgPRsK7VaXc89IaqZ7u/U1s9kY2JEREQAAGdnZygUCiiVSo4aUYMmhIBSqYRCoYCzs7NNt83b9YmISNKiRQvk5OTg+vXr8Pb2hrOzM2QyWX13iwhARUKkVquhVCpx7949tG3b1ub7YGJEZGfdl/6A9OVPwk3Ojxs1fF5eXgCAO3fuICcnp557Q2ScQqFA27Ztpb9XW+L/qYnqQFGpBq7OjvyXNzUKXl5e8PLyglqtRllZWX13h0iPo6OjzS+fVcbEiKgO9F9xiKNG1Og4Ozvb9QuIqCHi5GsiIiIiLSZGRHbg6uyI40uG1nc3iIjIQhYnRkIIxMfHY+HChXj44YfRrFkzyOVy+Pv7IyoqCnFxcUbjoqOjIZPJqn1lZmaa3G9GRgamTp0KPz8/uLi4IDg4GAsWLEB+fn61/c3JycGcOXMQEBAAhUKBwMBAzJ07l5MKya5kMhkvmxERNUIW/5/7559/xrBhwwAADg4O6NSpE9zd3XHhwgXExsYiNjYWS5YsQUxMjNH4gIAABAYGGq1zc3MzWh4XF4dRo0ZBpVKhZcuW6NGjBzIzM/Hee+9h9+7dOHr0KFq3bm0Ql56ejkGDBiEvLw/e3t7o2bMnLl26hI0bN2LXrl2Ij49HSEiIpb8CIrO4yZ2QvvxJdF/6Q313hYiIzGTViFGnTp3w8ccf486dOzh37hxSU1ORm5uLxYsXAwBWrFiB7777zmj8jBkzEB8fb/RlLGEqLCzEpEmToFKpMG/ePOTk5CAlJQXZ2dmIiIhAVlYWZs6caRBXVlaGCRMmIC8vD1FRUbhx4wZSUlKQk5ODcePGITc3F5MmTUJ5ebmlvwIiIiJqoixOjAYMGICMjAz87W9/Q/PmzaVyuVyOlStXIjIyEgCwadMmm3Tw008/xR9//IFu3bph/fr10h0Svr6+2Lp1K5ycnLBv3z6kpqbqxcXGxiI9PR2+vr7YvHmzNBrl7u6OLVu2wNfXF6dPn8aePXts0k8iIiJq/CxOjLy8vODkZPoK3PDhwwEA58+ft75XlcTGxgIApk2bZvA8lMDAQOmy3s6dO43GTZw4EZ6ennp1np6emDBhAgBgx44dNuknERERNX42vyutuLgYAODq6mq0Pi4uDhMmTMCQIUMwfvx4rF27Frdu3TLaVqPRICUlBQAQERFhtI2uPCkpSa/82LFjVsURERHRg8umt80IIaQRGFMJya+//qr3fteuXYiOjsbHH3+MadOm6dVduXJFenpux44djW5PV37hwgWprLS0FNnZ2WbF6fZhbBGzkpISlJSUSO8LCgqMbouIiIiaBpuOGG3atAknTpyAXC7Hq6++qlfn5+eHt956C8nJycjNzUVRURESEhIQGRkJlUqFGTNmYO/evXoxd+/elX6uPJ+pMl155bZKpVKaVF1TXHl5ucmEZ9WqVfD29pZeAQEB1Rw9ERERNXY2S4xSU1PxyiuvAKi4Ky04OFivfu7cuXj33XfRv39/+Pj4wNXVFQMHDsS+ffswduxYCCHw2muvQQghxeguywEVk7uNUSgUAACVSmVVXNXYyhYvXgylUim9rl27ZrQdERERNQ02SYwuX76M0aNHo7i4GFOmTMGCBQvMjpXJZFi9ejUA4NKlSzh9+rRU5+LiIv1cWlpqNF53qavynCZL4qrGVqZQKKSHKepeRERE1HTVOjG6desWhg8fjps3b2LUqFHYsmWLxU8Q79KlC3x8fAAAFy9elMorXwarfKmsMl155bbe3t5wcHAwK87BwYEJDxEREQGoZWKUl5eH4cOH49KlS3j88cexY8cOq5/ErIvTaDRSWVBQkFSelZVlNE5X3rlzZ6lMLpdLi0XWFFd5H0RERPRgszoxunfvHkaOHIkzZ84gLCwMe/fuNXlJqiZ37tzB7du3AQDt2rWTyp2cnNCvXz8AQEJCgtFYXXl4eLheue69pXFERET04LIqMSopKcHTTz+NpKQk9OjRA99//73BIoqWWL9+PYQQ8Pb2RlhYmF7duHHjAABbtmxBWVmZXl12djYOHjwIAIiKijIat337dhQWFurVFRYWSssKjB8/3up+ExERUdNicWJUVlaGyZMn4+eff0ZwcDB++uknaX6QKWfPnsVLL72Es2fP6pUXFxdj5cqVWLNmDQDgjTfeMLiL7MUXX0SLFi2QkZGB+fPnS+sa5ebmYsqUKdBoNIiMjERoaKheXFRUFEJCQpCbm4vp06ejqKgIAHD//n1Mnz4dubm56NmzJ5555hlLfwVERETURMlE5fvjzbBt2zZMmTIFQMW8nlatWhlt5+fnJ43KnDx5En379gUAtGzZUpr/k5GRISUsM2fOxKZNm4xO3D506JB015suXhcbFBSExMREtGnTxiDuzJkzeOyxx3D37l14e3ujU6dOuHjxIpRKJXx8fHDkyBF0797d7GMvKCiAt7c3lEolJ2yTpKhUg+5LfwAApC9/Em5yJ7PqiIiobljy/W3x/6Ur3+Z+4cIFvRWnK2vfvr30c1BQEGJiYnD06FFkZmbi3LlzKC0tRatWrTBy5EjMmjULTz75pMl9Dh06FMePH8eKFSvw888/Iy0tDW3btsXYsWOxZMkSk4s49uzZE6dOncLy5ctx4MABpKWloWXLlpg4cSKWLl2qN5+JiIiIyOIRowcZR4zIGI4YERE1bJZ8f9v8IbJEREREjRUTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpWZwYCSEQHx+PhQsX4uGHH0azZs0gl8vh7++PqKgoxMXFVRufmJiIp59+Gi1btoSrqyu6d++OmJgYFBcXVxuXkZGBqVOnws/PDy4uLggODsaCBQuQn59fbVxOTg7mzJmDgIAAKBQKBAYGYu7cucjJybH00ImIiKiJkwkhhCUBhw4dwrBhwwAADg4O6NSpE9zd3XHhwgXcu3cPALBkyRLExMQYxH799dd44YUXUFZWhrZt26JVq1Y4c+YM1Go1wsLCcPjwYbi5uRnExcXFYdSoUVCpVGjZsiUCAgKQmZmJoqIidOzYEUePHkXr1q0N4tLT0zFo0CDk5eXB29sbwcHBuHTpEpRKJXx9fREfH4+QkBCzj72goADe3t5QKpXw8vIyO46atqJSDbov/QEAkL78SbjJncyqIyKiumHJ97dVI0adOnXCxx9/jDt37uDcuXNITU1Fbm4uFi9eDABYsWIFvvvuO724K1euYObMmSgrK8PatWtx7do1pKam4sKFC+jatSuSk5OxaNEig/0VFhZi0qRJUKlUmDdvHnJycpCSkoLs7GxEREQgKysLM2fONIgrKyvDhAkTkJeXh6ioKNy4cQMpKSnIycnBuHHjkJubi0mTJqG8vNzSXwERERE1VcJCSqVSqNVqk/WRkZECgBgzZoxe+UsvvSQAiBEjRhjEJCQkCADC2dlZ3Lp1S69u7dq1AoDo1q2b0Gg0enVXr14VTk5OAoBISUnRq9u+fbsAIHx9fUVBQYFeXUFBgfD19RUARGxsrFnHLUTFsQMQSqXS7Bhq+u6XqEX7N74T7d/4TtwvUZtdR0REdcOS72+LR4y8vLzg5GT6csDw4cMBAOfPn6+cfGH37t0AYHR0Z+DAgQgJCYFarcaePXv06mJjYwEA06ZNg6Ojo15dYGCgdFlv586dRuMmTpwIT09PvTpPT09MmDABALBjxw6Tx0JEREQPFpvflaabRO3q6iqVZWdn4+bNmwCAiIgIo3G68qSkJKlMo9EgJSXF4jgAOHbsmFVxRERE9OCy6UxQIYQ0AlM5Iblw4QIAQKFQwN/f32hsx44d9doCFfOS1Gq1Xr05caWlpcjOzjYrTrcPZ2dngzYlJSUoKSmR3hcUFBjdFhERETUNNh0x2rRpE06cOAG5XI5XX31VKr979y4AoFmzZpDJZEZjmzdvrte26s+6enPilEqlNKm6prjy8nKTCc+qVavg7e0tvQICAoy2IyIioqbBZolRamoqXnnlFQAVd6UFBwdLdbrLa3K53GS8QqEAAKhUKoO46mJrG1c1trLFixdDqVRKr2vXrpnsPxERETV+NrmUdvnyZYwePRrFxcWYMmUKFixYoFfv4uICoOISlym6S1aV5ybp4nSxld9bElfd/qrGVqZQKPQSKCIiImraaj1idOvWLQwfPhw3b97EqFGjsGXLFoPLZbrLVvn5+RAm1pPUXQqrfOmr8s+VL5XVFOft7Q0HBwez4hwcHLhYIxEREQGoZWKUl5eH4cOH49KlS3j88cexY8cOo5OYO3fuDKBilObGjRtGt5WVlaXXFgCCgoKk7enqzYmTy+UIDAw0K67yPoiIiOjBZnVidO/ePYwcORJnzpxBWFgY9u7da/KSVGBgINq0aQMASEhIMNpGVx4eHi6VOTk5oV+/fhbHVX5vaRwRERE9uKxKjEpKSvD0008jKSkJPXr0wPfff2+wiGJlMpkMY8eOBQB8/vnnBvVHjx5FZmYmnJ2dMWbMGL26cePGAQC2bNmCsrIyvbrs7GwcPHgQABAVFWU0bvv27SgsLNSrKywslJYVGD9+fI3HS0RERA8GixOjsrIyTJ48GT///DOCg4Px008/wcfHp8a4hQsXQi6X48cff8S6deukuUZXr17FjBkzAACzZs2SRpZ0XnzxRbRo0QIZGRmYP3++tK5Rbm4upkyZAo1Gg8jISISGhurFRUVFISQkBLm5uZg+fTqKiooAAPfv38f06dORm5uLnj174plnnrH0V0BERERNlEyYmg1twrZt2zBlyhQAFfN6WrVqZbSdn5+fweM2vvzyS0yfPh3l5eVo27YtWrVqhTNnzkCtViM0NBS//PIL3N3dDbZ16NAh6a63li1bIjAwEBkZGSgqKkJQUBASExMNEioAOHPmDB577DHcvXsX3t7e6NSpEy5evAilUgkfHx8cOXIE3bt3N/vYLXk6Lz04iko16L70BwBA+vIn4SZ3MquOiIjqhiXf3xaPGFW+zf3ChQtISEgw+kpOTjaIff7553HkyBGMHj0aKpUK6enp6NixI6KjoxEfH280KQKAoUOH4vjx45g8eTJkMhnS0tLQunVrzJ8/H6mpqUaTIgDo2bMnTp06hVmzZsHDwwNpaWnw8PDA7NmzcerUKYuSIiIiImr6LB4xepBxxIiM4YgREVHDZtcRIyIiIqKmiokRkZWEELhzr1gaESIiosaPiRGRlVTqMvRfcai+u0FERDbExIiIiIhIi4kRkY0cXzIUrs6O9d0NIiKqBd4iQ2QDvOOMiKhp4IgRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKRlcWJ0+fJlbNq0CbNnz0afPn3g5OQEmUyGFStWmIyJjo6GTCar9pWZmWkyPiMjA1OnToWfnx9cXFwQHByMBQsWID8/v9q+5uTkYM6cOQgICIBCoUBgYCDmzp2LnJwcSw+biIiIHgBOlgZs2LABGzZssGpnAQEBCAwMNFrn5uZmtDwuLg6jRo2CSqVCy5Yt0aNHD2RmZuK9997D7t27cfToUbRu3dogLj09HYMGDUJeXh68vb3Rs2dPXLp0CRs3bsSuXbsQHx+PkJAQq46DiIiImiaLR4xatGiB0aNHY/ny5Thw4ACioqLMjp0xYwbi4+ONvowlTIWFhZg0aRJUKhXmzZuHnJwcpKSkIDs7GxEREcjKysLMmTMN4srKyjBhwgTk5eUhKioKN27cQEpKCnJycjBu3Djk5uZi0qRJKC8vt/TwiYiIqAmzODFasmQJ9u7di3feeQdPPfUUPDw87NEvAMCnn36KP/74A926dcP69evh7OwMAPD19cXWrVvh5OSEffv2ITU1VS8uNjYW6enp8PX1xebNm6XRKHd3d2zZsgW+vr44ffo09uzZY7e+ExERUePToCdfx8bGAgCmTZsGR0dHvbrAwEAMGzYMALBz506jcRMnToSnp6denaenJyZMmAAA2LFjh136TURERI1TnSZGcXFxmDBhAoYMGYLx48dj7dq1uHXrltG2Go0GKSkpAICIiAijbXTlSUlJeuXHjh2zKo6IiIgebBZPvq6NX3/9Ve/9rl27EB0djY8//hjTpk3Tq7ty5QrUajUAoGPHjka3pyu/cOGCVFZaWors7Gyz4nT70F2iq6qkpAQlJSXS+4KCAlOHRkRERE1AnYwY+fn54a233kJycjJyc3NRVFSEhIQEREZGQqVSYcaMGdi7d69ezN27d6WfmzdvbnS7uvLKbZVKpTSpuqa48vLyapOdVatWwdvbW3oFBASYcbRERETUWNVJYjR37ly8++676N+/P3x8fODq6oqBAwdi3759GDt2LIQQeO211yCEkGKKi4uln+VyudHtKhQKAIBKpbIqrmpsVYsXL4ZSqZRe165dq+FIiYiIqDGr18nXMpkMq1evBgBcunQJp0+flupcXFykn0tLS43G6y5zubq6WhVXNbYqhUIBLy8vvRcRERE1XfV+V1qXLl3g4+MDALh48aJUXvkyWOVLZZXpyiu39fb2hoODg1lxDg4OTHaIiIhIUu+JEQBp8rNGo5HKgoKCpPKsrCyjcbryzp07S2VyuVxaLLKmuMr7ICIiIqr3xOjOnTu4ffs2AKBdu3ZSuZOTE/r16wcASEhIMBqrKw8PD9cr1723NI6IiIgebPWeGK1fvx5CCHh7eyMsLEyvbty4cQCALVu2oKysTK8uOzsbBw8eBACDx5Lo4rZv347CwkK9usLCQmlhx/Hjx9vuQIiIiKjRs3tidPbsWbz00ks4e/asXnlxcTFWrlyJNWvWAADeeOMNg7vIXnzxRbRo0QIZGRmYP3++tK5Rbm4upkyZAo1Gg8jISISGhurFRUVFISQkBLm5uZg+fTqKiooAAPfv38f06dORm5uLnj174plnnrHTURMREVFjJBOV75E3Q0JCAp5++mnp/b1791BSUgI3Nze9O7xOnDiBgIAAnDx5En379gUAtGzZUpr/k5GRISUsM2fOxKZNmyCTyQz2d+jQIYwePRrFxcVSvC42KCgIiYmJaNOmjUHcmTNn8Nhjj+Hu3bvw9vZGp06dcPHiRSiVSvj4+ODIkSPo3r27JYeOgoICeHt7Q6lUctI2oahUg+5LfwAApC9/Em5yw/VSzWlDRET2Zcn3t8UjRmq1Grm5udJLd+t7UVGRXrnu0ldQUBBiYmIQGRkJDw8PnDt3DmlpafDx8cH48ePx/fff47PPPjOaFAHA0KFDcfz4cUyePBkymQxpaWlo3bo15s+fj9TUVKNJEQD07NkTp06dwqxZs+Dh4YG0tDR4eHhg9uzZOHXqlMVJERERETV9Fo8YPcg4YvRgKVIXIXxrxQT9pClJcHN206/niBERUaNg1xEjIiIioqaKiRERERGRFhMjIiIiIi0mRkRERERaTIyIiIiItJgYEREREWkxMSKqI92X/oA794pRVKoBV8kgImqYmBgR1aH+Kw6h+9IfoFKX1dyYiIjqHBMjIiIiIi0mRkR25OrsiONLhtZ3N4iIyExMjIjsSCaToYWHC66sHoX05U/Wd3eIiKgGTIyIiIiItPhESyILCSGgUpehqJQTqImImhomRkQWUqnL0H3pD/XdDSIisgNeSiMiIiLSYmJEVEvHlwyFq7NjfXeDiIhsgJfSiGohffmTcJPzY0RE1FRwxIiIiIhIi4kRERERkRYTIyIiIiItJkZENlKkLkKvL3qh1xe9UKQuqu/uEBGRFZgYEREREWkxMSIiIiLSYmJEREREpMXEiIiIiEiLiRERERGRFhMjIiIiIi0mRkRERERaTIyIiIiItCxOjC5fvoxNmzZh9uzZ6NOnD5ycnCCTybBixYoaYxMTE/H000+jZcuWcHV1Rffu3RETE4Pi4uJq4zIyMjB16lT4+fnBxcUFwcHBWLBgAfLz86uNy8nJwZw5cxAQEACFQoHAwEDMnTsXOTk5lhwyERERPSAsTow2bNiAOXPm4LPPPsPp06dRVlZmVtzXX3+NQYMG4b///S8UCgW6deuGixcvYunSpXjsscdQVGR8peC4uDiEhoZi69atKCsrQ48ePXDr1i289957CA0Nxe+//240Lj09Hb1798amTZtQWFiInj17oqCgABs3bkSfPn2QmZlp6aETERFRE2dxYtSiRQuMHj0ay5cvx4EDBxAVFVVjzJUrVzBz5kyUlZVh7dq1uHbtGlJTU3HhwgV07doVycnJWLRokUFcYWEhJk2aBJVKhXnz5iEnJwcpKSnIzs5GREQEsrKyMHPmTIO4srIyTJgwAXl5eYiKisKNGzeQkpKCnJwcjBs3Drm5uZg0aRLKy8stPXx6wBWpixC+rS88u70JyErruztERGRjFidGS5Yswd69e/HOO+/gqaeegoeHR40x69atQ0lJCUaMGIGFCxdCJpMBANq3b49///vfAICNGzcajP58+umn+OOPP9CtWzesX78ezs7OAABfX19s3boVTk5O2LdvH1JTU/XiYmNjkZ6eDl9fX2zevBlubm4AAHd3d2zZsgW+vr44ffo09uzZY+nhExERURNm98nXQgjs3r0bAIyO7gwcOBAhISFQq9UGiUpsbCwAYNq0aXB0dNSrCwwMxLBhwwAAO3fuNBo3ceJEeHp66tV5enpiwoQJAIAdO3ZYe1hERETUBNk9McrOzsbNmzcBABEREUbb6MqTkpKkMo1Gg5SUFIvjAODYsWNWxREREdGDzcneO7hw4QIAQKFQwN/f32ibjh076rUFKuYlqdVqvXpz4kpLS5GdnW1WnG4fukt0VZWUlKCkpER6X1BQYLQdERERNQ12HzG6e/cuAKBZs2bS3KKqmjdvrte26s+6enPilEqlNKm6prjy8vJqk51Vq1bB29tbegUEBJhsS02LEAIqjUp6r9KoIISoxx4REVFdsHtipFujSC6Xm2yjUCgAACrVn19Eldc2MhVb27iqsVUtXrwYSqVSel27ds1kW2paVBoVBm8fLL0fvH2wXqJERERNk90vpbm4uACouMRliu5ylaurq0GcLrbye0viqttf1diqFAqFXhJFRERETZvdR4x0l63y8/NNXorQXQqrfOmr8s+VL5XVFOft7Q0HBwez4hwcHODl5WXWcRAREVHTZ/fEqHPnzgAqRmlu3LhhtE1WVpZeWwAICgqSJkXr6s2Jk8vlCAwMNCuu8j6IiIiI7J4YBQYGok2bNgCAhIQEo2105eHh4VKZk5MT+vXrZ3Fc5feWxhEREdGDze6JkUwmw9ixYwEAn3/+uUH90aNHkZmZCWdnZ4wZM0avbty4cQCALVu2GDyTLTs7GwcPHgQAg8eS6OK2b9+OwsJCvbrCwkJpYcfx48dbe1hERETUBNk9MQKAhQsXQi6X48cff8S6deukuUZXr17FjBkzAACzZs2SRpZ0XnzxRbRo0QIZGRmYP3++tK5Rbm4upkyZAo1Gg8jISISGhurFRUVFISQkBLm5uZg+fbr0gNr79+9j+vTpyM3NRc+ePfHMM8/Y+ciJiIioMbE4MUpISECLFi2k1zfffAOgYs2fyuWVb23v0KEDNm3aBAcHByxatAgBAQHo168fOnfujHPnziE0NBTr1q0z2JeXlxe++eYbuLi44MMPP0Tbtm3Rv39/BAYGIiEhAUFBQdKz1ipzdHTEjh070Lx5c+zatQv+/v7o378/2rZti127dsHHxwfffvutNEmbiIiICLAiMVKr1cjNzZVeulvfi4qK9MqrXvp6/vnnceTIEYwePRoqlQrp6eno2LEjoqOjER8fD3d3d6P7Gzp0KI4fP47JkydDJpMhLS0NrVu3xvz585GammowyqTTs2dPnDp1CrNmzYKHhwfS0tLg4eGB2bNn49SpU+jevbulh05ERERNnMXrGA0ePNjqFYAHDhyIvXv3WhzXo0cPbNu2zeK4gIAAbNq0yeI4IiIiejDxWhIRERGRlt1XviZqKoQQyCsyvYI7ERE1fhwxIjJTsaYcg9Ycru9uEBGRHTExIiIiItJiYkRkpZR3hsHV2bG+u0FERDbExIjISq7OjpDJZPXdDSIisiEmRvTAK1IXodcXvdDri14oUheZbDd4x0B4hiy1evvh2/oCDvfg2e1NhG/rW+2+iIiofjAxIqolIQSK1EVQaVRSmUqjsnq9LyIiqj+8XZ+ollQaFcK3huuVDd4+GElTkuDm7FZPvSIiImtwxIiIiIhIi4kRERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRGSl2i7iqFKXcRFIIqIGhgs8Ellp8PbB1daHbw1H0pQk0/E7BgKA2QtBFqmLpIUkdTHGyhqjpnIcRNT4ccSIiIiISIuJEVEdin/zifruAhERVYOJEVEdcnV2rO8uEBFRNZgYEREREWkxMSIiIiLSYmJEREREpMXEiIiIiEiLiRGRGUQ5l/wiInoQMDEiIiIi0mJiRERERKTF6wNElegeS3F44mG9cpmDplbbs0TVx2MY245Ko7Jq2/WBj/sgosakTkeMpk2bBplMVu2ruLjYaGxiYiKefvpptGzZEq6urujevTtiYmJMttfJyMjA1KlT4efnBxcXFwQHB2PBggXIz8+3wxESERFRY1YvI0adO3dGq1atjNY5OBjmal9//TVeeOEFlJWVoW3btggICMCZM2ewdOlS7N27F4cPH4abm+G/QuPi4jBq1CioVCq0bNkSPXr0QGZmJt577z3s3r0bR48eRevWrW1+fERERNQ41csco7feegvx8fFGX3K5XK/tlStXMHPmTJSVlWHt2rW4du0aUlNTceHCBXTt2hXJyclYtGiRwT4KCwsxadIkqFQqzJs3Dzk5OUhJSUF2djYiIiKQlZWFmTNn1tUhExERUSPQ4Cdfr1u3DiUlJRgxYgQWLlwImUwGAGjfvj3+/e9/AwA2btyI33//XS/u008/xR9//IFu3bph/fr1cHZ2BgD4+vpi69atcHJywr59+5Camlq3B0REREQNVoNOjIQQ2L17NwAYHd0ZOHAgQkJCoFarsWfPHr262NhYABXzmhwd9R/cGRgYiGHDhgEAdu7caY+uExERUSNUL4nRzp078cwzz2DIkCGYPHkyPvroIyiVSoN22dnZuHnzJgAgIiLC6LZ05UlJSVKZRqNBSkqKxXFERET0YKuXydf79u3Te//tt99i2bJl2Lp1K5566imp/MKFCwAAhUIBf39/o9vq2LGjXlugYl6SWq3WqzcnrqqSkhKUlJRI7wsKCky2pcZHCAGVRgWVRmWzbR6eeBiDtw82WW/LfRERke3V6YhRcHAwVq5ciVOnTqGgoACFhYX48ccfER4ejrt37+KZZ57B8ePHpfZ3794FADRr1kyaW1RV8+bN9dpW/VlXb05cVatWrYK3t7f0CggIMPNIqTHQrQVUXSJja5GxkXW2LyIislydJkbvvPMOFi9ejN69e8PT0xMeHh4YPnw4fv31VwwYMAAlJSV44403pPa6NYqq3qlWmUKhAACoVH/+S7zy2kamYo3FVbV48WIolUrpde3aNTOOkoiIiBqrBjH5Wi6XIyYmBgBw+PBhaRTHxcUFAFBaWmoyVnepy9XVVSrTxVUXayyuKoVCAS8vL70XPXjunV+CwnNLbLKtA+MO2GQ7RERkHw0iMQKARx55BABQXl6OrKwsAH9e7srPz4cQwmicLomqfMms8s+mLpUZiyPSqfz3JsrlgDA9amkJVyfTiTgREdW/BvOsNN06Q0DFXWVAxQrZQMXozo0bN9C2bVuDOF0SpWsLAEFBQXB2doZarUZWVhb8/PzMiiPSeWLHEwCAwszlFUmRzPSoZW1VfeZZ1ee0WbodU88js/Uzy6zdXuU4HWuPmYjI1hrMiNHZs2eln9u1awegYr2hNm3aAAASEhKMxunKw8P//B+tk5MT+vXrZ3EcERERPdgaTGL03nvvAQBCQkKkkSGZTIaxY8cCAD7//HODmKNHjyIzMxPOzs4YM2aMXt24ceMAAFu2bEFZWZleXXZ2Ng4ePAgAiIqKsu2BUJPiGbIUR96MQMo7w6Qyjm4QETVddZYY/fTTT1i8eDEuX76sV65UKjFv3jxs27YNALB06VK9+oULF0Iul+PHH3/EunXrpLkfV69exYwZMwAAs2bNkkaWdF588UW0aNECGRkZmD9/vrSuUW5uLqZMmQKNRoPIyEiEhoba5Xip6XCTO8LV2bHmhkRE1OjVWWJ0//59rF69Gh07dkS7du0wYMAA9O3bF61atcJHH30EmUyGZcuW4dlnn9WL69ChAzZt2gQHBwcsWrQIAQEB6NevHzp37oxz584hNDQU69atM9ifl5cXvvnmG7i4uODDDz9E27Zt0b9/fwQGBiIhIQFBQUHSs9aIGpumtlBk5eNRaVQmb7YgIrK3OkuMQkND8fbbb2PIkCFwdHTEmTNnkJmZibZt2+L5559HYmIioqOjjcY+//zzOHLkCEaPHg2VSoX09HR07NgR0dHRiI+Ph7u7u9G4oUOH4vjx45g8eTJkMhnS0tLQunVrzJ8/H6mpqQajTESNRVNbKLLy8QzePrjJJX5E1HjU2V1pAQEBWLFihdXxAwcOxN69ey2O69Gjh3SZjoiIiKg6DWbyNVFj4eLoYnQC9uGJh+Hj4sPJ2UREjRgTIyILyWQyows1ujq5mqwjIqLGgYkRERERkRYTIyIiIiItJkZEREREWg3mWWlElZl6Dpelz+eq7fPBRHnj/4gYezaZLZ+ZBlSsPWTLbVbur636SERkjsb/f30iO5I5aODiVLcDq1UXO2yshBBQaVQGx+Pi6FKPvSIiqh4TI6IayGSyOt1f1cUOG6vKo0g6g7cP5nIGRNSgcY4RERERkRYTI6JG6MC4A/XdBSKiJomX0ohQMcEXgMGlHx03ZzekvZAmvS9SF1m8fd0E4rQX0oxOiLYEF5EkIrIPjhgRERERaXHEiAgVd1AVa8rruxtERFTPOGJEBKBYU47QmIP13Q0iIqpnTIyIiIiItJgYETUxKo0KQgi77kO3eGPlfZqrMS9aSURNHxMjoiZm8PbBdk8+VBqV3uKTlRelrIklbYmI6honXxNZoert+8bqqrsl383ZDUlTkiy+ZV9323/l5QIOTzzcqFfI1mkqx0FEjRtHjIiqcXjiYbutGeTq5Gr08RiWLt7o4ujS6B+zYc/fMxGRJThi1EBUN7pgzr+kG1Mbc+N1TP1ejD2Ly5rtVMfVydVuz0qTyWTGkwEhr/V2wreGm/V7Nvd3aMlIjrW/55q211D+ftnGNp9vtmkabeyxj8oL4tYHjhgRASgqLavvLkgeXR1X310gInpgMTGiB54od8Kg1Qn13Q0iImoAmBjRA0/moAFkpfXdDSIiagA4x4gIwJE3noCvmweAv0CgBA9ve7jW26zuzrVqCTkKM5fDM2SpXnHl6+61fahtXeMdZ0TUWHDEiAiAm9wRbnInuMmd7DbZ2iLa5EivyM6LNhIRERMjaqDMWaDQVm1EeQMcOJWVGowY2eMht1yFmohIXwP8RiAyb3VkW7WROWjM6lNdSXlnGIpKyxC520nqm72SN65CTUSkj4kREQAXp4YzeOrq7AgXJwe9hE3moEFRaRlcnTRwdXZsGJf7iIiaoIbzbUBUjxpaomGsP4PWxKH70h+Qe78URaUaFJVqGsy8I0tX6zbG1ErgRER16YEYMdq/fz/Wr1+P1NRUlJSUoGvXrpg+fTpefvllODgwNyR9Vt9NZsN9Vb3LrDBzubQidv8VB6Xy40uGwU3uCFdnV7OevZY0JQmAdatT17RNa+Kqrm7Lx4IQUX1r8lnB6tWrMWrUKBw6dAjNmzdHp06dcOrUKcybNw9jx45FebntJ7QS2VrFRGzDtZb6rzgojSKp1A1n9W4iosaqSSdGiYmJeOutt+Dg4ICtW7fi0qVLOHXqFFJTU9G6dWv897//xfr16+u7m2RHotyxvrtgMylLhyF9+ZM4vmSYQV3/FQcRGnNQr4yJEhGR5Zp0YrRixQoIITBr1iw8++yzUnmfPn2khGj16tVQq9X11UWqBXOSHplD00kOXJ0r1lrydZcbT5CqrH1k7JlrKnUZEyYiomo02cSooKAABw9W/At65syZBvUTJkyAl5cXcnNzERfHh3Zawpwv1ura8Iu5dmQymfEEycjaR1WFxhiOLNWkpvNlTrJlrF6lLmtQE8iJiIAmPPn6xIkTKC0thYuLC/r162dQ7+zsjLCwMBw6dAhJSUkYMWJEPfTSPHeVtxtUm+Fr9sCpi/VtzIkv17ji/oVlRr/sRXnF+j7mjgYdHrsfg3ePrL6RughoSF/QxhZerNJHGQA3AK7OAulLHoVKo8LgPX82d5UV6IdnvAWgYgJ3YcZqAIB753/Cwan6RR5rOl+PxnxXMTFc9hacQlaa2MYBg23o4o4segJu8j9H/5Ki4gFAezzDpfKG9jlgm4bdD7Zpeue7rshEE/3n2meffYbZs2ejS5cuOHfunNE2c+bMwaZNm/Dcc8/hyy+/NKgvKSlBSUmJ9F6pVCIwMBDXrl2Dl5eXTftbpC7CkB1DbLrNqkS5Y43JRLnGBQ5OxXbth7n2Z9/AyED/OtnOz1dz4IaG81EoggxD2rfVK6upj8Ziqouvqb25fr6aAwhnDCj5BJCVwqPLu3r1otwJ9y+8o1d37/zb0l12APCL/BU8XrpBL+4Xl5cwqn3LWvePiBqXnyf8bHDHam0VFBQgICAA+fn58Pb2rrZtkx0xunv3LgCgefPmJtvo6nRtq1q1ahX++c9/GpQHBATYoIdUkw4AAGWdbMev1nuxB/0RH/P6WGCyxni86fbm+nO7E6tpVbXur3rvOhppU1F2x+p+EVHj5Pc3+/0fubCw8MFNjIqLK0Y95HK5yTYKhQIAoFIZv5SwePFizJ8/X3pfXl6OvLw8+Pr62nxBQF02a4/RKLI/nr/Gj+ew8eM5bPzsdQ6FECgsLIS/f81XIZpsYuTi4gIAKC01XPtFR3eZzNXV+KJyCoVCSp50mjVrZpsOmuDl5cUPdCPG89f48Rw2fjyHjZ89zmFNI0U6TfautJouk1Wuq+5yGxERET04mmxi1LlzZwBAdnY2NBrjT0/PysrSa0tEREQPtiabGPXt2xfOzs4oLi5GamqqQb1arUZycjIAIDzcds+NspZCocCyZcsMLt1R48Dz1/jxHDZ+PIeNX0M4h032dn0AGDlyJA4cOIA5c+bgf//3f/Xqtm7diqlTp8LX1xc3btyodpI2ERERPRia7IgRALz99tuQyWT47LPPsG3bNqn81KlT0t1mixYtYlJEREREAJr4iBEAvPvuu1iyZAkAoGPHjvDw8MCZM2dQXl6OUaNGYc+ePXB0bDoPGiUiIiLrNfnECAC+++47vP/++0hJSYFarUbnzp0xffp0/P3vf2dSRERERJIHIjEiIiIiMkeTnmPUGOzfvx/Dhg2Dj48P3N3d0a9fP3z00UcoLy+v764RgGnTpkEmk1X70q2yXlViYiKefvpptGzZEq6urujevTtiYmJMtifrXL58GZs2bcLs2bPRp08fODk5QSaTYcWKFTXGWnuOMjIyMHXqVPj5+cHFxQXBwcFYsGAB8vPzbXRUDxZrzmF0dHSNn83MzEyT8TyHtiOEQHx8PBYuXIiHH34YzZo1g1wuh7+/P6KiohAXF1dtfIP7HAqqN6tWrRIABADRsWNH0bt3b+Hg4CAAiDFjxoiysrL67uID74UXXhAAROfOnUVERITRV0lJiUHc//3f/wlHR0cBQLRt21b07dtXODs7CwAiLCxM3L9/vx6Opml65ZVXpM9R5VdMTEy1cdaeo59//lm4uroKAKJly5aiX79+ws3NTfoc37p1yx6H2aRZcw6XLVsmAIiAgACTn82rV68ajeU5tK2DBw9K58zBwUF06dJF9O3bV3h4eEjlS5YsMRrbED+HTIzqydGjR4VMJhMODg5i69atUvnJkydF69atBQCxbt26euwhCfFnYrR582azYy5fviwUCoUAINauXSvKy8uFEEJcuXJFdO3aVQAQL7/8sp16/OCJiYkRo0ePFsuXLxcHDhwQUVFRNX6pWnuOCgoKRMuWLQUAMW/ePFFaWiqEEOLOnTsiIiJCABCjRo2yz4E2YdacQ11itGzZMov2xXNoez/99JPo1KmT+Pjjj0VeXp5UXlJSIhYvXiwlR3v37tWLa6ifQyZG9WTkyJECgJgzZ45B3ddffy0ACF9fX+mEU/2wJjF66aWXBAAxYsQIg7qEhAQBQDg7O/NfpXaiO2fVfalae47Wrl0rAIhu3boJjUajV3f16lXh5OQkAIiUlBTbHMwDypxzaG1ixHNoe0qlUqjVapP1kZGR0pWQyhrq55BzjOpBQUEBDh48CACYOXOmQf2ECRPg5eWF3NzcGq/NUsMihMDu3bsBGD+3AwcOREhICNRqNfbs2VPX3SPU7hzFxsYCqJh7VvWO1sDAQAwbNgwAsHPnTnt0nWyA59D2vLy84ORk+pn0w4cPBwCcP39eKmvIn0MmRvXgxIkTKC0thYuLC/r162dQ7+zsjLCwMABAUlJSXXePjNi5cyeeeeYZDBkyBJMnT8ZHH30EpVJp0C47Oxs3b94EAERERBjdlq6c57Z+WHuONBoNUlJSLI4j+4qLi8OECRMwZMgQjB8/HmvXrsWtW7eMtuU5rB+6SdSurq5SWUP+HJpO8chuLly4AKAiszWVZXfs2BGHDh2S2lL92rdvn977b7/9FsuWLcPWrVvx1FNPSeW686VQKODv7290Wx07dtRrS3XL2nN05coVqNVqvXpz4si+fv31V733u3btQnR0ND7++GNMmzZNr47nsO4JIbBjxw4A+olMQ/4ccsSoHty9excA0Lx5c5NtdHW6tlQ/goODsXLlSpw6dQoFBQUoLCzEjz/+iPDwcNy9exfPPPMMjh8/LrXXna9mzZpBJpMZ3SbPbf2y9hxV/tnUZ5fntu74+fnhrbfeQnJyMnJzc1FUVISEhARERkZCpVJhxowZ2Lt3r14Mz2Hd27RpE06cOAG5XI5XX31VKm/In0OOGNUD3bBidc9o0z1ZWKVS1UmfyLh33nnHoGz48OF4/PHHMWjQIPz222944403cOjQIQA8t42Bteeo8poqpmJ5buvO3LlzDcoGDhyIffv2ISoqCrt378Zrr72G0aNHS1+8PId1KzU1Fa+88goAYMWKFQgODpbqGvLnkCNG9cDFxQUAUFpaarJNSUkJAP1rstRwyOVyxMTEAAAOHz4s/cuE57bhs/Yc6eKqi+W5rX8ymQyrV68GAFy6dAmnT5+W6ngO687ly5cxevRoFBcXY8qUKViwYIFefUP+HDIxqgfmDPOZc7mN6tcjjzwCACgvL0dWVhaAP89Xfn4+hImn7fDc1i9rz1Hln019dnluG4YuXbrAx8cHAHDx4kWpnOewbty6dQvDhw/HzZs3MWrUKGzZssXgcllD/hwyMaoHnTt3BlAxK1+j0Rhto/ui1bWlhsfZ2Vn6WXcedeerpKQEN27cMBrHc1u/rD1HQUFB0jnX1ZsTR/VDd64q/z+W59D+8vLyMHz4cFy6dAmPP/44duzYoff/Sp2G/DlkYlQP+vbtC2dnZxQXFyM1NdWgXq1WIzk5GQAQHh5e190jM509e1b6uV27dgAq7jRs06YNACAhIcFonK6c57Z+WHuOnJycpOU1eG4btjt37uD27dsA/vxsAjyH9nbv3j2MHDkSZ86cQVhYGPbu3WvyclZD/hwyMaoHXl5e0gJUn3/+uUH9jh07UFBQAF9fXwwePLiOe0fmeu+99wAAISEhaNu2LYCK+Q1jx44FYPzcHj16FJmZmXB2dsaYMWPqrrMkqc05GjduHABgy5YtKCsr06vLzs6WFm6NioqyR9fJTOvXr4cQAt7e3tKacDo8h/ZRUlKCp59+GklJSejRowe+//57eHp6mmzfoD+HVq2XTbUWHx9f47PS1qxZU489pB9//FG8+eabIisrS688Pz9f/OMf/5Ce/1P5/AkhRFZWlpDL5dU+/+dvf/tbnR3Hg8acx0lYe46USqVo0aJFtc9oioyMtM+BPUBqOodnzpwRf/vb38SZM2f0ylUqlXj33Xelh3GvXLnSIJbn0PY0Go145plnBAARHBwsbty4YVZcQ/0cMjGqRytWrJC+XDt27Ch69+4tfaBHjRpl8AwYqlu7d++Wzk/btm1FWFiYeOihh6QPskwmM/mcpi+++EI6l1WfGB0aGiru3btXtwfThMXHxwtfX1/ppXsopZubm155dna2Xpy15+jgwYPCxcVFeqp3aGio9FTvoKAgcfPmzbo47CbF0nN44sQJ6bOpOweVzwMAMXPmTOmLtiqeQ9vaunWr9Hvv3LmziIiIMPoaP368QWxD/BwyMapne/fuFUOGDBHe3t7Czc1N9OnTR3zwwQdMihqA7Oxs8fbbb4shQ4aIwMBA4erqKlxcXESHDh3E888/L44dO1ZtfEJCghg9erTw8fERCoVCdO3aVURHRwuVSlVHR/BgiIuLk/6nXN3r8uXLBrHWnqMzZ86IyZMni1atWgm5XC46dOgg5s+fr/dkcTKfpefw7t27IiYmRkRGRooOHToIDw8PIZfLRbt27cT48ePF999/X+M+eQ5tZ/PmzWadv/bt2xuNb2ifQ5kQJu6TIyIiInrAcPI1ERERkRYTIyIiIiItJkZEREREWkyMiIiIiLSYGBERERFpMTEiIiIi0mJiRERERKTFxIiIiIhIi4kRERERkRYTIyIiIiItJkZERI3ExIkTIZfLkZ2dXd9dsYkhQ4agWbNmyM3Nre+uEEmYGBE1IEFBQZDJZJDJZHj99derbbthwwaprUwmq6MeNg4ffPABoqOjkZ+fX99dsZnk5GTs2LED06ZNQ2BgoMl2mZmZWLBgAfr164cWLVrA2dkZPj4+6N+/P/7+97/j8OHDNunPlClTIJPJMHXqVLPar1+/HjKZDD169JDK3nnnHSiVSqxYscImfSKyiVo9gpaIbKp9+/bSk6jbtGkjNBqNybb9+/fXe3I1/Un3e9Q9jb0pGDJkiJDJZCIrK8tovUajEa+++qpwcHAQAISDg4Po3LmzGDBggOjatatQKBTS30pYWJgoLCysVX8OHDggAAg3NzezttW3b18BQKxatUqv/OGHHxZyuVxcvXq1Vv0hshWOGBE1QF27dsWtW7dw8OBBo/Xnzp3D8ePH0bVr1zruGdWHjIwM/Pzzz3jsscfQoUMHo22mTp2KDz74AB4eHli/fj3u3LmD8+fPIykpCZmZmcjNzcWuXbvw8MMPIzk5Gffu3atVn4YPH442bdqgqKgIu3fvrrH/J06cMDrC9MILL6C0tBSbNm2qVX+IbIWJEVED9Ne//hUA8H//939G67/66isAwHPPPVdnfaL6s3HjRgDAs88+a7R+06ZN+Pbbb+Hu7o5ff/0Vr732Gpo3b67Xxt3dHePGjUNiYiL+53/+B87OzrXqk6Ojo9Sfr7/+utq2ur/XwYMHIyAgQK9u/PjxcHJywpYtW1BWVlarPhHZRH0PWRHRn3SXgH799VcREBAg3N3dxb179/TalJeXi6CgIOHq6irS09OrvZR27949ERMTI3r16iXc3NyEp6enGDBggPif//kfoVarDdrHxcUJAOLxxx8XGo1GrF69WoSEhAgXFxfRvn17sWzZMimuqKhILFmyRAQHBwuFQiE6duwo1qxZI8rLy00eX1JSkpg0aZLw9/cXzs7OolWrVmL8+PEiNTXVaPvKx7Z//34xaNAg4eHhIby8vMRTTz1lELd582a9y4tVX3FxcXrtXnjhBaP7rfx7qMvfjyn+/v4CgLh27ZpBnUajkf5u/vWvf1m87aosOUepqakCgHB0dBS3bt0yur3y8nKpf//+97+NtomIiBAAxC+//FLr/hPVFhMjogZE9wVy5MgR8eabbwoA4quvvtJr8+uvvwoA4tlnnxXXrl0zmRjdvn1b9OrVS5pv0rt3b9GtWzep/fDhw4VKpdKLqfzFHxUVJQCIbt26ia5duwqZTCYAiOnTpwuVSiXCw8OFo6Oj6N27twgKCpK2u3TpUqPHtn79emkbPj4+om/fvsLX11cAEM7OzmLXrl0GMbptfvLJJ0Imkwk/Pz/Rr18/4e7uLgAIDw8PkZGRIbXfv3+/iIiIkObT9O/fX0REREgv3Ze7LRIjW/9+TLlw4YIAIPz8/IzWJyQkSL/D/Px8i7ZdlTXnqGfPngKAeP/9941u85dffhEAhKurq1AqlUbbvPbaawKAiImJqVX/iWyBiRFRA1I5MTp79qwAIEaMGKHXZvbs2QKA2L9/f7WJke6Lu0ePHuLixYtSeXJysmjdurUAIBYtWqQXo/vid3Z2Fu3atRMnTpyQ6g4fPizkcrmQyWRizJgxolevXuLSpUtS/ddffy0ACIVCIfLy8vS2e+DAASGTyUSLFi0Mvlw/++wz4eTkJDw9PcWNGzf06nTH5ubmJjZv3iyVFxQUiKFDhwoAYtKkSSZ/j6YmX9c2MbL176c6X3zxhQAgRo8ebbR+3bp1AoDo27ev2ds0xtpztGbNGikJNUb39zp58mST+966dasAIJ588slaHQORLTAxImpAKidGQlTcyePo6Ch9GRUXF4tmzZqJVq1aCbVabTIxOn/+vPQvf2OXQLZv3y4ACHd3d1FQUCCV6774AYjdu3cbxD377LMCgJDJZEa3+/DDDwsAIjY2Vq+8X79+AoDYs2eP0eN+/fXXBQCxfPlyvXJdX/7xj38YxJw+fVoAEN7e3gZ19k6MbP37qc7y5csFADF79myj9a+++qoAIMaOHWv2No2x9hxdv35duhMuMzNTr66kpEQ0b95cABD79u0zuW/d7zUkJKRWx0BkC5x8TdSAPffccygrK8O2bdsAAN999x3y8/Px7LPPwsnJyWTcTz/9BCEEHn30UfTt29egPioqCu3atcP9+/eRkJBgUO/j44NnnnnGoPyhhx4CAPTt29fodnVlWVlZUtnVq1eRmpqKVq1aYcyYMUb7qyv/5ZdfjNbPmjXLoKxXr15wcXGBUqms8wUCbfn7qcmdO3ekfRpTWFgIoGJytTHHjh3TW+9K94qOjpba1OYctW3bFk888QQAw5sFvvvuO9y9exetWrXCiBEjTB6j7tj++OMPk22I6orp/7MSUb179tlnsXDhQnz11VeYP3++dHeP7q41U86fPw8A6N69u9F6BwcHhISE4Pr16zh//jyeeuopvfrg4GCjcS1btjSrvvKt4GlpaQCA4uJiPProo0bjiouLAQA5OTlG66vb37Vr13Dv3j34+voabWMPtvz91ET3u1EoFEbrPT09AQD37983Wu/l5YWIiAjp/cWLF/H777/rtantOXruuedw6NAhbN26FTExMVK5LlGqKZF3dXUFAKhUKpNtiOoKEyOiBqxNmzYYNmwYfvjhB/z66684cOAAQkJC0L9//2rjdF+8rVq1MtmmdevWAP4ccajMzc3NaIxuhe2a6oUQUplSqQQAFBQUGB2dqszUF6Op0RAHBweD/dUFW/5+aqIbTTG1infbtm0BAFeuXDFa3717d8THx0vvZ82ahc8//1yvTW3PUVRUFF566SVkZWXh6NGjGDhwIPLz87F//34ANS8rkZeXBwBo0aJFte2I6gIvpRE1cLovleeeew6lpaVmrV3k4eEBALh9+7bJNrpRA92Ig73o+hIREQFRMa/R5MvUl7ut1ZSgmBp9qQ+65FaXPFT1yCOPAADOnDlj9SNQanuOPDw8pEuLulGi7du3o6SkBN26dUNoaGi1+9cdm25Ejag+MTEiauDGjh0LDw8PZGdnm/1sqi5dugAA0tPTjdaXl5cjMzNTr6296C7nZWRkoLy83K770qnp2XG6EShTc1ouXrxo8z5ZSzdvKSMjw2h9eHg4AgMDoVar8dlnn1m1D1uco+effx5ARUKkVqulBMmcRF73d9qvXz+r9k1kS0yMiBo4Nzc3vP766xg6dCjmzp2L9u3b1xgzYsQIyGQyxMfH48SJEwb1sbGxuH79Otzd3fXmn9hD586d0bNnT+Tl5eHLL7+06750apqz0rFjRwDAyZMnodFo9OrKy8uxefNm+3bQAuHh4XB2dkZaWhpKSkoM6p2cnPDWW28BAKKjo3Hq1CmL92GLczRs2DC0adMGubm5+N///V/Ex8ebncj/9ttvAIBBgwZZtW8iW2JiRNQIREdH4+DBg/jkk0/Mat+pUyeMGzcOQMW/5CvfBZWamop58+YBAP7+97/b/VIaAKxZswYymQwvv/wyPvvsM4NkJCsrC++++y5iY2Ntsj9d4mPqLrc+ffrA398fN2/exLJly6RLasXFxXj11VdNjrTVBzc3N0RERKC0tBTJyclG28ydOxdRUVG4f/8+HnvsMaxfvx53797VayOEwA8//IBjx44Z3UZtz5GjoyOmTJkCAFi4cCGEEHj88ccRGBhY7fEJIXD06FE4Ojpi6NCh1bYlqgtMjIiaqE8++QS9evXCmTNn0KVLFzz00EPo0aMHQkNDcfPmTQwbNkzvlm17GjlyJD766COUlJRg9uzZ8PHxQf/+/REWFoY2bdogODgYS5YsqXZOlCUmTZoEAPjb3/6GXr16YfDgwRg8eDBOnjwJoOJLfM2aNQCAlStXonXr1ggLC0Pr1q2xefNmrFq1yib9sJWZM2cCAL799luTbb755hv84x//wL179/D666+jRYsW6NKlC8LDwxESEgJvb2889dRTOHv2LMLCwqTEWccW50h32Ux3B5s5l9GOHj2K69ev48knn4S/v3+N7YnsjYkRURPVsmVLJCYmYvny5ejWrRvOnz+Pq1evIiwsDB999BH2798PFxeXOuvPyy+/jJMnT2LWrFlo2bIlzp49iwsXLqBFixZ49tlnsWPHDmmeSm0999xz2LBhA3r37o1Lly7hl19+wS+//KI3Ofmvf/0rtm/fjtDQUBQWFiIrKwtDhw5FUlJSjZOF69r48ePRvHlzbN++3WAkR8fJyQkffvgh0tLS8Nprr6F37964c+cOUlNTcfv2bXTq1AkvvfQSDh8+jN9++w29e/c22EZtz9FDDz2EXr16AQBcXFwwfvz4Go9Nt0bX7NmzzflVENmdTNT1fa5ERGSxlStX4u2338ZXX31V4zpWjUV+fj6CgoIQGBiIkydPSssvENUn/hUSETUCr776Kvz9/fHuu+/W2d199rZhwwYolUqsXr2aSRE1GPxLJCJqBNzc3PDll19i0qRJuHHjRn13xyaaN2+O9evXY+TIkfXdFSIJL6URERERaXHEiIiIiEiLiRERERGRFhMjIiIiIi0mRkRERERaTIyIiIiItJgYEREREWkxMSIiIiLSYmJEREREpMXEiIiIiEiLiRERERGR1v8HUNW82p7ngXgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mumu =  f_mumu['wzp6_ee_mumuH_Hbb_ecm240/mumu_p_nOne'].to_hist()\n",
+    "WW   = f_mumu['p8_ee_WW_ecm240/mumu_p_nOne'].to_hist()\n",
+    "ZZ   = f_mumu['p8_ee_ZZ_ecm240/mumu_p_nOne'].to_hist()\n",
+    "hep.histplot([mumu, WW, ZZ], label=['Z$\\mu\\mu$', 'WW bkg', 'ZZ bkg'])#, stack=True)\n",
+    "plt.xlabel(\"Momentum (GeV)\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e1f2e2fd-9ead-40d9-87ed-6ff6958e2f30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAHCCAYAAADYRAxnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABW/ElEQVR4nO3deVxU9f4/8NewDPuOqCCIKIpImiDpldyuS7nkhlua+3q1q2na6tdUzFLLsu61burN7GqJpdfKstKrXkEkA0MJUEkRxJ1lQNZh+Pz+4DdzGWcGZmAOm6/n4zGPB5zP533mcw5HePk5Z86RCSEEiIiIiEgSFo09ACIiIqKWjGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJMSwRURERCQhq8YeQHNSWVmJmzdvwsnJCTKZrLGHQ0REREYQQqCwsBDe3t6wsGj4eSaGLRPcvHkTvr6+jT0MIiIiqoOsrCy0a9euwd+XYcsETk5OAKp+WM7Ozo08GiIiIjJGQUEBfH19NX/HGxrDlgnUpw6dnZ0ZtoiIiJqZxroEiBfIExEREUmIYYuIiIhIQgxbRERERBKqc9i6e/cuVq5ciW7dusHe3h62trbo2LEjFixYgPT0dIN1cXFxGDNmDFq1agU7OzsEBwcjKioKpaWlNb5famoqpk2bhrZt22rea+XKlcjPz6+xLjs7GwsWLICvry9sbGzg5+eHhQsXIjs7uy6bTURERGQSmRBCmFp06dIl9O/fH3fv3oW1tTUCAgJgbW2N9PR0lJaWwt7eHt9//z0GDBigVbd3717MnDkTKpUKPj4+8PLyQnJyMpRKJcLDw3Hy5EnY29vrvN+JEycwcuRIlJSUoFWrVvD19UVaWhqKi4sREBCAM2fOoHXr1jp1KSkp6NevH3Jzc+Hi4oKOHTvijz/+gEKhgIeHB2JiYhAUFGT0dhcUFMDFxQUKhYIXyBMRETUTjf73W9TB4MGDBQAREREhsrKyNMvv378vRo8eLQCIDh06iMrKSk3btWvXhI2NjQAgNm/erGnLyMgQXbp0EQDEkiVLdN6roKBAtGrVSgAQS5cuFeXl5Zr3ioiIEADEyJEjdeoqKipEcHCwACAiIyNFUVGREEKIBw8eiPHjxwsAonv37kKlUhm93QqFQgAQCoXC6BoiIiJqXI3999vksFVUVCQsLCwEAHHhwgWd9tzcXCGTyQQAkZKSolm+ePFiAUAMGzZMpyY2NlYAENbW1uL27dtabZs3bxYARNeuXUVFRYVW2/Xr14WVlZUAIBISErTaoqOjBQDh4eEhCgoKtNoKCgqEh4eHACAOHjxo9LY39g+LiIiITNfYf79Nvs9WeXk5KisrAQABAQE67W5ubnB3d0dOTg4qKirUs2c4dOgQAGDu3Lk6NX379kVQUBDS0tJw+PBhLFiwQNN28OBBAMCsWbNgaWmpVefn54chQ4bg6NGj+OqrrxAaGqpTN2nSJJ2bmDk5OWHixIn4+OOPceDAAYwbN87U3UBERHoIIaBUKjV/J4jMzcrKClZWzes2oSaP1tXVFb6+vsjKysKZM2cwdOhQrfZLly4hJycHrq6uCAwMBABkZmbi1q1bAICIiAi9642IiEBaWhri4+M1YauiogIJCQm11h09ehTx8fFay8+ePVtr3ccff6xTR0REplOpVLh//z4KCwuhVCobezjUwjk4OMDT01Pvdd5NUZ2i4YYNGzBz5kzMmTMH77//PgYOHAgrKyucPXsWL7zwAmQyGTZv3gxbW1sAwJUrVwAANjY28Pb21rtO9SyZui8AZGRkaP7R6ptFM1RXXl6OzMxMo+rU72Ftba3Tp6ysDGVlZZrvCwoK9K6LiOhRplKpkJWVhbKyMri4uMDR0RGWlpaNdrduarmEECgrK0Nubi6ysrLQoUMHyOXyxh5WreoUtmbMmAFHR0dERUVhwoQJWm3du3fH999/j6efflqzLC8vD0DVrJihf3xubm5afR/+Wt1uTJ1CodBMYddWV1lZiYKCAnh4eOj0eeutt7Bu3Tq99UREVOX+/fsoKyuDn58f7OzsGns41MLZ2dnByckJ165dw927dxvlwdKmqtN9toQQuHr1KnJycmBpaYlOnTohODgYcrkcycnJ+OSTT5Cbm6vpr76HVk3p08bGBgBQUlKiU1dTbX3rHq6t7tVXX4VCodC8srKyDI6fiOhRJIRAYWEhXFxcGLSowVhaWsLFxQXFxcUQpt/BqsHVKWwtWrQIq1atgq+vL9LT03HlyhX8/vvvyMrKwogRI3Do0CEMGjQIKpUKADSnE8vLyw2uU326rvo/VnVdTbX1rXu4tjobGxvNQ6f58GkiIl1KpRJKpRKOjo6NPRR6xNjZ2UGlUjWLawRNDltJSUnYsWMHrK2t8eWXX8Lf31/T5uXlhb1798LT0xMXLlxAdHQ0gP+dssvPzzeYQNWnAauf9qv+dfXThLXVubi4wMLCwqg6CwsLhigiojpSX7Lx8KfFiaSmPuaawydfTQ5bsbGxEEKgc+fO8PX11Wl3dnbGE088AQD49ddfAUDzqcSysjLcvHlT73qvXr2q1RcA/P39NReuq9uNqZPL5fDz8zOqrvp7EBFR3fBieGpozemYMzlsFRYW1tpHPXulvnbKz88Pbdq0AVAV1vRRL+/du7dmmZWVlebeWabUVf/e1DqSnhACxeUVBl/3H5TC/5Uj8H/lCO4/KK2xb3M4V09ERI82kz+NqJ5Bunz5MrKysnRmtwoKCnDu3DkAQOfOnQFUpc9x48bho48+wq5duzBp0iStmjNnziAtLQ3W1tYYPXq0Vtv48eMRHx+P3bt348UXX9Saqs7MzMSxY8cAAJGRkTp1+/fvR3R0NDZt2qR1Y9PCwkIcOHAAAHQ+TUnSK1GqELzmR6P69tpwvMb2lPVPwV7evG5uR0REjxaTZ7aGDRsGT09PKJVKTJkyBRkZGZq2u3fvYtq0abh//z5sbW21gsyqVasgl8vx008/YcuWLZoZievXr2POnDkAgHnz5mlmwNQWLVoET09PpKamYsWKFZoL4XJycjB16lRUVFRg+PDhCAsL06qLjIxEUFAQcnJyMHv2bBQXFwMAioqKMHv2bOTk5CAkJARjx441dRcQEVEjKy6v0MyAF5dXNPZwiGokE3U4D/PDDz9g/PjxKC0thaWlJQICAmBtbY309HSUl5fDysoKO3fuxMyZM7Xq9uzZg9mzZ6OyshI+Pj7w8vJCcnIylEolwsLCcOrUKTg4OOi83/HjxzFq1CiUlpaiVatW8PPzQ2pqKoqLi+Hv74+4uDidkAYAycnJ6N+/P/Ly8uDi4oJOnTohPT0dCoUC7u7uOH36NIKDg43e7kZ/angLUVxeoZnZ+nX1ENjLLXXa1TNav64erDNzVVyuQq8NVTOanNkialylpaW4du0aOnTooPVJcKlV/z3C3wOPJlOOvcb++12nWz8MHz4cSUlJWLBgATp06IDMzEykp6ejbdu2mD59OuLj43WCFlB1M9TTp09j1KhRKCkpQUpKCgICArB27VrExMToDVoAMHjwYPz666+YMmUKZDIZLl68iNatW2PFihVITEzUG7QAICQkBElJSZg3bx4cHR1x8eJFODo6Yv78+UhKSjIpaJE07OWWsJdbab08HW2R8fZIZLw9Ep6OtjrtD4czIiKipqzO/xXo3Lkz/vGPf5hc17dvX3z77bcm13Xr1g1ffPGFyXW+vr7YsWOHyXVERETmMnDgQJw6dcqkGn4AqOXgvCsREZHEHnvsMVRU1H5t2fnz51FcXAwXF5cGGBU1FIYtIiIiiX344Ye19vn+++8xatQoAMCnn34q9ZCoAdXpmi0iIiIyn6ysLMyYMQNCCCxbtgzjxo1r7CGRGTFsERERNaKKigpMnjwZOTk5eOKJJ7B582adPhkZGZDJZHqf3KLm5uYGmUyG/Pz8eteReTFsERERNaJXXnkFcXFxcHV1xf79+yGXy3X6XLhwAQDw+OOP611HRkYG8vPz0b59e7i6uta7jsyL12wREZFkhBAoUarMvt7qNzKV8qamdtaWkj6D79tvv8XWrVsBALt374a/v7/efrWFpqSkJABAjx49zFJH5sWwRUREkjHl8Vx1VdtjvepDyhumZmZmYtasWRBCYMWKFRgzZozBvupQ1LNnT73tv/32GwDd0FTXOjIvnkYkIiJqYEqlEpMmTUJubi569+6Nt99+u8b+tc1QGQpNda0j8+LMFhERScbO2hIp658y+3pre6yXudhZS/PEipdffhnx8fFwd3dHdHQ0rK2tDfYtLi5Geno6nJ2d0aFDB7191DNY1UNVXevI/Bi2iIhIMjKZTPLnFqof5dVcHD58GO+99x5kMhl2794NPz+/GvsnJyejsrISPXr00Hv9WEFBATIyMuDo6IiAgIB615H58TQiERFRA8nIyMCsWbMAAC+++CKeeeaZWmtqm306d+4chBDo3r27Vqiqax2ZH8MWERFRAygvL8ekSZOQn5+Pvn374q233jKqTn3dVefOnfW2f/fddwAMX69lah2ZH8MWERFRA1i1ahXOnTsHDw8PfPnll7CyMu7Up3qGqrCwUKftxo0b+OyzzwAY/iSiqXVkfgxbREREEjt48CA++OADyGQy7Nmzp8Y7uj/s4sWLAICPP/4Yf/zxh2Z5YmIiRo4ciby8PACAt7c3ysrK6l1H5td8rigkIiJqpubMmQMAsLOzw8aNG7Fx48Zaaz788EN4eHho7vD+4MEDdOnSBf7+/lAoFLh//z7Cw8Ph7e2NmzdvYvbs2ViyZAnWrVuHzMzMOtWRNDizRUREJDGFQgGg6nYMsbGxRr0UCoXmVGB4eDiOHDmC8PBwZGdnw9raGosXL8bPP/+MF198EQ4ODvD29sbw4cMBoM51JA3ObBEREUlMCFGnujfffBMAEBISgt69eyMuLk6nz4oVK7BixQqtZeqL402tI2lwZouIiKiJUs9QhYSENEgdSYMzW0RE1OzYy62Q8fbIxh6G5NQzVI899liD1JE0OLNFRETUBJWUlODKlSuwtbVFx44dJa8j6TBsERERNUHqx+107doVlpbGP6OxrnUkHZ5GJCIiaoLCw8PrdGF9XetIOpzZIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiKj5KS8C1rpUvcqLGns0RDVi2CIiIiKSEMMWERHRI2j37t2QyWSYNWuWSXUnT56ETCbDwIEDJRlXS8SwRUREJKGBAwdCJpOZ9FJbu3atybUnT55svI0lvawaewBEREQt2WOPPYaKiopa+50/fx7FxcVwcXHRLPPz80NERESttVevXsWtW7cAAK6urnUeK0mDYYuIiEhCH374Ya19vv/+e4waNQoA8Omnn2qWz5kzB3PmzKmx9saNG3j88ccBAEuXLtV8TU0HTyMSERE1oqysLMyYMQNCCCxbtgzjxo0zuraiogKTJ09GTk4OwsPDsWXLFglHSnXFsEVERNRIqoelJ554Aps3bzap/tVXX8WZM2fg6uqK6OhoyOXyOo2jsLAQK1asgL+/P2xtbREQEIDXX38dxcXFJq1HoVDgySefhEwmwzPPPIPS0lKt9ujoaPTp0wcODg7w9PTE6NGjcf78+RZ/0T3DFhERUSN55ZVXEBcXB1dXV+zfv9+ksPTdd9/h3XffBVD1yUJ/f/86jaGsrAwDBgzA+++/D0dHRwQGBiIjIwMbN27E4MGDjQ5c9+7dw6BBgxAbG4spU6bg4MGDsLW11bRHRUVh8uTJiI+Ph6urKzp06ICTJ0+ib9++OHPmTJ3G3lzwmi0iIpKOEIDStNkRo5QX6//a3KztgWqfDjSnb7/9Flu3bgVgeljKzMzEzJkzIYTA8uXLMWbMmDqP46uvvkLr1q1x/vx59OjRAwCQnJyMESNG4OzZs3jjjTdqPT2ZnZ2NIUOGIC0tDfPnz8fHH38MC4v/zef88ssvmk9Wbt++HQsXLoRMJkNxcTEWLVqEtWvX1nn8zQHDFhERSUdZDGz0lvY93ukk3bpfuwnIHcy+2szMTMyaNQtCCKxYscKksKRUKjF58mTk5uaid+/e2LRpU73GUlFRgQ8//FATtAAgJCQEf//73zF69Gh89NFHWLNmDZycnPTW//HHHxgyZAgyMjKwcuVKvcHsvffeQ2VlJebNm4dFixZpltvb22PXrl2Ii4tDenp6vbajKeNpRCIiogakVCoxadIkTVh6++23Tap/+eWXcfbsWbi5uWH//v2wtrau13h8fHz0hr1Ro0bBz88PRUVFiI2N1Vv7+++/o1+/fsjIyMD69esNzoAdO3YMADB79mydNmtrazz33HP12IKmjzNbREQkHWv7qtkhcysv/t+M1sp0QG5v/vcAqsZvZi+//DLi4+Ph7u6O6Ohok8LSN998g/feew9A1anH9u3b13s8Xbp00TrlpyaTydClSxdkZmbi8uXLePrpp7XaMzIy0L9/f+Tl5WHbtm1YunSp3vXn5eXh/v37AIDu3bvr7WNoeUvBsEVERNKRySQ5DadFbi/9e5jJ4cOH8d5770Emk2H37t3w8/MzujYjI0PzaJ2VK1di9OjRZhmTl5eXwbbWrVsDqPq04sNyc3NRWFgIGxsbPPbYYwbXUVRU9aBwmUwGR0dHvX0MnaJsKXgakYiIqAFUD0svvvginnnmGaNr1ddp5eXl4U9/+hPeeusts43r3r17Btvu3r0LQH8YCg0NxbZt21BWVoZnnnkGMTExetfh4FAVhIUQmuD1MH1hriVh2CIiIpJYeXk5Jk2ahPz8fPTt29fksLRq1Sr88ssvcHd3x/79+2FlZb4TU5cuXUJlZaXOciEELl26BADo3Lmz3tqlS5di8+bNKCoqwogRIxAfH6/Tx83NDZ6engCACxcu6F3PxYsX6zr8ZoFhi4iISGKrVq3CuXPn4OHhgS+//NKksHTo0CFs27YNMpkMe/bsga+vr1nHduPGDXz77bc6y48cOYLr16/DwcGhxuczrlq1ClFRUSgsLMTTTz+NxMREnT5Dhw4FUHWd2cMqKiqwd+/eum9AM8CwRUREJKGDBw/igw8+qFNYunbtmuYTfKtWrcLIkSPNPj4rKyv89a9/1ZpdSklJwfPPPw8AWLRoUa3XVK1evRqrV69Gfn4+hg0bpjNT9cILL0Amk2Hnzp3YsWOHZnlJSQnmz5+Pa9eumXGLmh5eIE9ERCQh9YOk7ezssHHjRmzcuLHWmg8//BA9e/bE8uXLoVAoAACnTp3Ck08+adT71fbw6uomTJiAS5cuoUePHujWrRtkMhmSk5MhhEB4eDjWrVtn1HqioqJQVlaGLVu2YMiQITh58iS6du0KAHjiiSewdu1avPHGG1iwYAHWrVsHb29vXLp0CWVlZVi7di1ef/11WFpaGj3u5oRhi4iISELqsFRcXGzwflWGavLz8zXL9F0Ppc+QIUNMGp+NjQ1OnTqFNWvW4ODBg7h9+zb8/Pzw7LPPYvXq1ZoL3I2xefNmlJeXY9u2bRg8eDBOnTqFwMBAAMCaNWsQFBSEd999FxcvXkRxcTGefPJJrFu3Dnfu3AHQcj+VyLBFREQkISFEnWtPnjxpvoE8ZNasWZpPRwJVd3lX38OrJgMHDqxxm95//328//77etsmTZqESZMm6SxXP+Oxrs93bOp4zRYRERE1GpVKhT179gBAjRfiN2cMW0RERCS5Xbt24fTp01rLcnNzMWvWLFy4cAHe3t4m3XusOeFpRCIian7kDsBaRWOPgkxw+vRpzJs3D46OjujYsSOEEEhNTYVSqYS9vT0+//xz2NraNvYwJcGwRURERJKbOXMmlEolzp49iz/++APl5eXw9vbG4MGD8dJLL6FLly6NPUTJMGwRERGR5AYNGoRBgwY19jAaBa/ZIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVERPVWnxt3EtVFczrmGLaIiKjOrKyqPmdVVlbWyCOhR41SqQSAZvE8RYYtIiKqMysrKzg4OCA3Nxcqlaqxh0OPCCEEFAoFbGxsYG1t3djDqRVv/UBERPXi6emJrKwsXLt2DS4uLrCzs4OlpSVkMlljD41aGCEElEolFAoFHjx4AB8fn8YeklEYtoiIqF7s7e3RoUMH3L17F3l5ebh//35jD4laOBsbG/j4+MDZ2bmxh2IUhi0iIqo3uVyOdu3aaWYeKisrG3tI1EJZWlo2i1OH1TFsERGR2chkMsjl8sYeBlGTwgvkiYiIiCTEsEVEREQkIYYtIiIiIgnVK2ypVCrs2LEDAwYMgKenJ2xtbdG+fXuMHTsWhw8f1lsTFxeHMWPGoFWrVrCzs0NwcDCioqJQWlpa43ulpqZi2rRpaNu2LWxtbdGxY0esXLkS+fn5NdZlZ2djwYIF8PX1hY2NDfz8/LBw4UJkZ2fXdbOJiIiIjFbnsJWXl4cnn3wSCxYswOnTp+Hp6YmQkBAolUocPnwYn3/+uU7N3r170a9fP3zzzTewsbFB165dkZ6ejjVr1qB///4oLi7W+14nTpxAWFgY9u3bB5VKhW7duuH27dt49913ERYWhjt37uitS0lJQffu3bFjxw4UFhYiJCQEBQUF+OSTT9CjRw+kpaXVdfOJiIiIjFKnsFVZWYnRo0fj7NmzGD9+PDIzM5GWloZff/0VN2/eRFZWFpYuXapVk5GRgblz50KlUmHz5s3IyspCYmIirly5gi5duuDcuXN46aWXdN6rsLAQkydPRklJCZYuXYrs7GwkJCQgMzMTERERuHr1KubOnatTp1KpMHHiROTm5iIyMhI3b95EQkICsrOzMX78eOTk5GDy5Mn8eDIRERFJqk5h65NPPkFMTAwGDRqEAwcOoF27dlrt7dq1Q//+/bWWbdmyBWVlZRg2bBhWrVqlubNw+/bt8c9//lOz3odnqT7++GPcu3cPXbt2xdatWzX31vDw8MC+fftgZWWFI0eOIDExUavu4MGDSElJgYeHBz799FPY29sDABwcHLB79254eHjgwoULBk93EhEREZlDncLWtm3bAABRUVGwsKh9FUIIHDp0CAD0zkL17dsXQUFBmlOQ1R08eBAAMGvWLJ2HTfr5+WHIkCEAgK+++kpv3aRJk+Dk5KTV5uTkhIkTJwIADhw4UOv4iYiIiOrK5LB15coVpKWlwd3dHX379sXhw4fx3HPPYfDgwZgyZQp27typ8/T3zMxM3Lp1CwAQERGhd73q5fHx8ZplFRUVSEhIMLkOAM6ePVunOiIiIiJzMvkO8urwExQUhOnTp2Pv3r1a7fv378e7776Lo0ePon379gCqAhpQ9Swjb29vvesNCAjQ6gtUXeelVCq12o2pKy8vR2ZmplF16vfQd+v/srIyreBYUFCgd11EREREhpg8s6WeoTp37hz27t2LefPmISMjA6WlpTh27BgCAgKQlpaGyMhIzcXneXl5AABXV1eDT4F3c3PT6vvw1+p2Y+oUCoXmvWurq6ysNBii3nrrLbi4uGhevr6+evsRERERGWJy2CoqKgIAKJVK9OvXDzt27ED79u1hY2ODwYMH4+DBg5DJZEhISMCRI0cAQHMPrZqel2VjYwMAKCkp0Syrfu8tQ7X1rXu4trpXX30VCoVC88rKyjI4fiIiIiJ9TA5btra2mq+XLVum096jRw8MGjQIAHD06FGtmvLycoPrVZ+us7Oz0/tehmrrW/dwbXU2NjZwdnbWehERERGZwuSwVf20XFBQkN4+Xbt2BVB1PVT1mvz8fAgh9NaoTwNWX3/1r6ufJqytzsXFRfMpydrqLCwsGKKIiIhIMiaHrS5dumi+rn4qrjr1cpVKBQAIDAwEUDWbdPPmTb01V69e1eoLAP7+/poL19XtxtTJ5XL4+fkZVVf9PYiIiIjMzeSw1bNnT81putqCjI+PD4Cq+2G1adMGABAbG6u3Rr28d+/emmVWVlYIDQ01ua7696bWEREREZmTyWHLwcEBI0aMAAB89tlnOu23b9/Gjz/+CAD485//DACQyWQYN24cAGDXrl06NWfOnEFaWhqsra0xevRorbbx48cDAHbv3q2ZKVPLzMzEsWPHAACRkZF666Kjo1FYWKjVVlhYqLmZ6YQJE2rbZCIiIqI6q9Md5NesWQNLS0t8+eWXWoErPz8fs2bNQklJCQICAjR3aQeAVatWQS6X46effsKWLVs0125dv34dc+bMAQDMmzdPMwOmtmjRInh6eiI1NRUrVqzQ3HcrJycHU6dORUVFBYYPH46wsDCtusjISAQFBSEnJwezZ8/WPOS6qKgIs2fPRk5ODkJCQjB27Ni67AIiIiIio8iEoSvWa/Hxxx9j8eLFEELAz88PXl5eSElJQXFxMTw9PfHzzz/j8ccf16rZs2cPZs+ejcrKSvj4+MDLywvJyclQKpUICwvDqVOn4ODgoPNex48fx6hRo1BaWopWrVrBz88PqampKC4uhr+/P+Li4nRCGgAkJyejf//+yMvLg4uLCzp16oT09HQoFAq4u7vj9OnTCA4ONnqbCwoK4OLiAoVCwYvq66G4vALBa6pmP1PWPwV7uWn31q1vPRERPVoa++93nWa2gKoZp1OnTuGZZ55BcXExLly4AC8vLyxZsgS//fabTtACgBkzZuD06dMYNWoUSkpKkJKSgoCAAKxduxYxMTF6gxYADB48GL/++iumTJkCmUyGixcvonXr1lixYgUSExP1Bi0ACAkJQVJSEubNmwdHR0dcvHgRjo6OmD9/PpKSkkwKWkRERER1UeeZrUdRYyfjloIzW0RE1JAa++93nWe2iIiIiKh2DFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJMSwRURERCQhhi0iIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpKQVWMPgJoXIQRKlKoa+xSXV6DXhuMAgF9XD4a93Oqh9prriYiIWhKGLTJJiVKF4DU/Gt1fHbqIiIgeVTyNSERERCQhzmxRnf26egjs5Zb1Woeddf3qiYiImjqGLaoze7mlzvVYREREpI2nEYmIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJMSwRURERCQhhi0iIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikpBZwtbq1ashk8kgk8mwYcMGg/3i4uIwZswYtGrVCnZ2dggODkZUVBRKS0trXH9qaiqmTZuGtm3bwtbWFh07dsTKlSuRn59fY112djYWLFgAX19f2NjYwM/PDwsXLkR2dnZdNpOIiIjIZPUOW6mpqdiyZUut/fbu3Yt+/frhm2++gY2NDbp27Yr09HSsWbMG/fv3R3Fxsd66EydOICwsDPv27YNKpUK3bt1w+/ZtvPvuuwgLC8OdO3f01qWkpKB79+7YsWMHCgsLERISgoKCAnzyySfo0aMH0tLS6rXdRERERMaoV9gSQmDhwoWwtrbGn//8Z4P9MjIyMHfuXKhUKmzevBlZWVlITEzElStX0KVLF5w7dw4vvfSSTl1hYSEmT56MkpISLF26FNnZ2UhISEBmZiYiIiJw9epVzJ07V6dOpVJh4sSJyM3NRWRkJG7evImEhARkZ2dj/PjxyMnJweTJk1FZWVmfzSciIiKqVb3C1q5du3D69GmsWbMGvr6+Bvtt2bIFZWVlGDZsGFatWgWZTAYAaN++Pf75z38CAD755BOdWaqPP/4Y9+7dQ9euXbF161ZYW1sDADw8PLBv3z5YWVnhyJEjSExM1Ko7ePAgUlJS4OHhgU8//RT29vYAAAcHB+zevRseHh64cOECDh8+XJ/NJyIiIqpVncPWvXv38PLLLyM4OBjLly832E8IgUOHDgGA3lmovn37IigoCEqlUif8HDx4EAAwa9YsWFpaarX5+flhyJAhAICvvvpKb92kSZPg5OSk1ebk5ISJEycCAA4cOFDrdhIRERHVR53D1vLly5Gbm4vt27drZpz0yczMxK1btwAAERERevuol8fHx2uWVVRUICEhweQ6ADh79myd6oiIiIjMrU5h6/jx49i7dy+ee+45DBgwoMa+V65cAQDY2NjA29tbb5+AgACtvkDVdV5KpVKr3Zi68vJyZGZmGlVX/T2IiIiIpGBlakFpaSkWLVoEFxcXvPPOO7X2z8vLAwC4urpqrtV6mJubm1bfh79WtxtTp1AoNBe+11ZXWVmJgoICeHh46O1XVlaGsrIyzfcFBQV6+xEREREZYvLM1oYNG5Ceno4333wTrVu3rrW/+h5acrncYB8bGxsAQElJiU5dTbX1rXu49mFvvfUWXFxcNK+aPgRAREREpI9JYUt9T63Q0FD85S9/MarG1tYWQNXpPUPUs0d2dnY6dTXV1rfu4dqHvfrqq1AoFJpXVlaWwb5ERERE+ph0GnHx4sWoqKjARx99BAsL43Ka+pRdfn4+hBB6TyWqTwNWP+1X/eu8vDy0bdvWqDoXFxdYWFigsrJS6/SivjoLCws4OzsbHLuNjY3WLBgRERGRqUya2Tp//jxkMhlGjx6NNm3aaL32798PANi0aRPatGmD8PBwAEBgYCCAqtmkmzdv6l3v1atXtfoCgL+/v+ZTjup2Y+rkcjn8/PyMqqv+HkRERERSMPmaLZVKhTt37ui81NdKPXjwAHfu3MG9e/cAVN0Pq02bNgCA2NhYvetUL+/du7dmmZWVFUJDQ02uq/69qXVERERE5mZS2FKfCtT3mjlzJgAgKioKQghkZGQAAGQyGcaNGweg6o7zDztz5gzS0tJgbW2N0aNHa7WNHz8eALB7926oVCqttszMTBw7dgwAEBkZqbcuOjoahYWFWm2FhYWam5lOmDDBlM0nIiIiMlm9H0RtjFWrVkEul+Onn37Cli1bIIQAAFy/fh1z5swBAMybN08zA6a2aNEieHp6IjU1FStWrNDcEysnJwdTp05FRUUFhg8fjrCwMK26yMhIBAUFIScnB7Nnz9Y85LqoqAizZ89GTk4OQkJCMHbsWIm3nIiIiB51DRK2OnTogB07dsDCwgIvvfQSfH19ERoaisDAQFy6dAlhYWHYsmWLTp2zszO+/PJL2Nra4oMPPoCPjw969eoFPz8/xMbGwt/fX/NsxeosLS1x4MABuLm54euvv4a3tzd69eoFHx8ffP3113B3d8f+/fuNvsifiIiIqK4aLG3MmDEDp0+fxqhRo1BSUoKUlBQEBARg7dq1iImJgYODg966wYMH49dff8WUKVMgk8lw8eJFtG7dGitWrEBiYqLObJhaSEgIkpKSMG/ePDg6OuLixYtwdHTE/PnzkZSUhODgYCk3l4iIiAgAIBPqc3pUq4KCAri4uEChUNR4y4iWrLi8AsFrfgQApKx/CvZykx9C0CLGQEREzUdj//3meTQiIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJMSwRURERCQhhi0iIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJMSwRURERCQhhi0iIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJGRy2BJCICYmBqtWrUKfPn3g6uoKuVwOb29vREZG4sSJEzXWx8XFYcyYMWjVqhXs7OwQHByMqKgolJaW1liXmpqKadOmoW3btrC1tUXHjh2xcuVK5Ofn11iXnZ2NBQsWwNfXFzY2NvDz88PChQuRnZ1t6qZTE1RcrkJxeUW9XkKIxt4MIiJqwWTCxL80x48fx5AhQwAAFhYW6NSpExwcHHDlyhU8ePAAALB69WpERUXp1O7duxczZ86ESqWCj48PvLy8kJycDKVSifDwcJw8eRL29vY6dSdOnMDIkSNRUlKCVq1awdfXF2lpaSguLkZAQADOnDmD1q1b69SlpKSgX79+yM3NhYuLCzp27Ig//vgDCoUCHh4eiImJQVBQkNHbXlBQABcXFygUCjg7Oxtd15IUl1cgeM2PAICU9U/BXm7VqGMwh8baDiIiahiN/fe7TjNbnTp1wvbt23H//n1cunQJiYmJyMnJwauvvgoA2LBhA7777jutuoyMDMydOxcqlQqbN29GVlYWEhMTceXKFXTp0gXnzp3DSy+9pPN+hYWFmDx5MkpKSrB06VJkZ2cjISEBmZmZiIiIwNWrVzF37lydOpVKhYkTJyI3NxeRkZG4efMmEhISkJ2djfHjxyMnJweTJ09GZWWlqbuAiIiIyHjCRAqFQiiVSoPtw4cPFwDE6NGjtZYvXrxYABDDhg3TqYmNjRUAhLW1tbh9+7ZW2+bNmwUA0bVrV1FRUaHVdv36dWFlZSUAiISEBK226OhoAUB4eHiIgoICrbaCggLh4eEhAIiDBw8atd1CVG07AKFQKIyuaWmKypSi/cvfifYvfyeKygwfB1KqrKwURWXKer3uFZY2+nYQEVHDaOy/3ybPbDk7O8PKyvApl6FDhwIALl++XD3Q4dChQwCgdxaqb9++CAoKglKpxOHDh7XaDh48CACYNWsWLC0ttdr8/Pw0pzS/+uorvXWTJk2Ck5OTVpuTkxMmTpwIADhw4IDBbaGmSSaTwV5uVc+XZe1vREREZAZm/zSi+kJ3Ozs7zbLMzEzcunULABAREaG3Tr08Pj5es6yiogIJCQkm1wHA2bNn61RHREREZE5mDVtCCM1MUfWQc+XKFQCAjY0NvL299dYGBARo9QWqrvNSKpVa7cbUlZeXIzMz06i66u9BREREZG5m/QjWjh07cP78ecjlcrzwwgua5Xl5eQAAV1dXyGQyvbVubm5afR/+Wt1uTJ1CodBc+F5bXWVlJQoKCuDh4aHTp6ysDGVlZZrvCwoK9K6LiIiIyBCzzWwlJiZi2bJlAKo+jdixY0dNm/rUolwuN1hvY2MDACgpKdGpq6m2vnUP11b31ltvwcXFRfPy9fU1OH4iIiIifcwStq5du4ZRo0ahtLQUU6dOxcqVK7XabW1tAVSd3jNEPYNU/VovdV1NtfWte7i2uldffRUKhULzysrKMjh+IiIiIn3qfRrx9u3bGDp0KG7duoWRI0di9+7dOqcK1afs8vPzIYTQeypRfRqw+mm/6l/n5eWhbdu2RtW5uLjAwsIClZWVWqcX9dVZWFgYvMGZjY2N1gwYERERkanqNbOVm5uLoUOH4o8//sCAAQNw4MABWFtb6/QLDAwEUDWbdPPmTb3runr1qlZfAPD399esT91uTJ1cLoefn59RddXfg4iIiMjc6hy2Hjx4gBEjRiA5ORnh4eH49ttvDZ6O8/PzQ5s2bQAAsbGxevuol/fu3VuzzMrKCqGhoSbXVf/e1DoiIiIic6pT2CorK8OYMWMQHx+Pbt264ejRozo3Dq1OJpNh3LhxAIBdu3bptJ85cwZpaWmwtrbG6NGjtdrGjx8PANi9ezdUKpVWW2ZmJo4dOwYAiIyM1FsXHR2NwsJCrbbCwkLNLSomTJhQ6/YSERER1ZXJYUulUmHKlCn4z3/+g44dO+Lnn3+Gu7t7rXWrVq2CXC7HTz/9hC1btkD8/+dfX79+HXPmzAEAzJs3TzMDprZo0SJ4enoiNTUVK1as0NwTKycnB1OnTkVFRQWGDx+OsLAwrbrIyEgEBQUhJycHs2fPRnFxMQCgqKgIs2fPRk5ODkJCQjB27FhTdwERERGR0WRCnXqM9MUXX2Dq1KkAqq6T8vLy0tuvbdu2Oo/C2bNnD2bPno3Kykr4+PjAy8sLycnJUCqVCAsLw6lTp+Dg4KCzruPHj2s+7diqVSv4+fkhNTUVxcXF8Pf3R1xcnE5IA4Dk5GT0798feXl5cHFxQadOnZCeng6FQgF3d3ecPn0awcHBRm97Yz81vCkoLq9A8JofAQAp65+Cvdyst2prMC1lO4iIqHaN/ffb5Jmt6rdMuHLlCmJjY/W+zp07p1M7Y8YMnD59GqNGjUJJSQlSUlIQEBCAtWvXIiYmRm/QAoDBgwfj119/xZQpUyCTyXDx4kW0bt0aK1asQGJiot6gBQAhISFISkrCvHnz4OjoiIsXL8LR0RHz589HUlKSSUGLiIiIqC5Mntl6lDV2Mm4KWsqMUEvZDiIiql1j//02+4OoiYiIiOh/GLaIiIiIJMSwRURERCQhhi0iIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQwxYRERGRhBi2iIiIiCTEsEVEREQkIYYtIiIiIgkxbBERERFJiGGLiIiISEIMW0REREQSsmrsARA1tuJyVb3q7awtIZPJzDQaIiJqaRi26JHXa8OxetWnrH8K9nL+UyIiIv14GpGIiIhIQvzvOD2S7KwtkbL+qTrXF5er6j0jRkREjwaGLXokyWQynvojIqIGwdOIRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiIiIpIQn1dCVE/F5ap6r8PO2hIymcwMoyEioqaGYYuonszxQOqU9U/xWY1ERC0UTyMSERERSYj/lSaqAztrS6Ssf6pe6yguV5llVoyIiJo2hi2iOpDJZDztR0RERuFpRCIiIiIJMWwRERERSYhhi4iIiEhCvOiEqAngvbqIiFouhi2iJoD36iIiarl4GpGIiIhIQvxvMFEj4b26iIgeDQxbRI2E9+oiIno08Dc9UQtR34vseYE9EZE0GLaIWoj6nk7kBfZERNLgBfJEREREEuJ/Y4masfpeZM8L7ImIpMewRdSMmfMie95YlYhIGgxbRASAN1YlIpIKr9kiIiIikhD/C0r0CDP3jVV5+wkiIl0MW0SPMHPfWJW3nyAi0sXTiEREREQS4n8hiahezHn7CX4ikohaIoYtIqoXc56K5Cciiagl4m8kImpReJE+ETU1DFtE1KjM/YnI+s6O/bp6COzllvVaBwMbEVXHsEVEjcrcn4isL3OcyqxvYGNYI2pZms5vOCKiOmpqz4jk7BoRVcewRUTNXn1nx8x9KrO+OLtG1LIwbBHRI88cpzJb2uwaP9VJZD78l0REZAYtbXbNHPc8qy/OrlFLwbD1iBFCoERZ91+iTeEXMFFL1NRm18w5y1ZX5rh2rb4Y+MgcGLbMpbwI2Ohd9fVrNwG5Q5NcR4lSheA1Pxost0MpUm3nAAC6lv4TJbA1+xgkr+cYOIaWOAYj1lFrYDPDGOr7O8KUekOBryHHYDDwlRfB/h0/AEDxyswa96XewNYUjimOocE8EmHr+++/x9atW5GYmIiysjJ06dIFs2fPxpIlS2BhwcdDEhEBRs6MlRcB71R9mbB6SN3+uNZQb+5r1+qr5sBX9XXYhmOmB7byCtj//y+LyysAVNQ4Ds6wNW8tPmy9/fbbePXVVwEAAQEBcHR0RFJSEpYuXYpjx47h0KFDj2zg0v8LwPhfpHbWjTu9T0TmZdypzP+128utAJNPfdZc3xIDn751mRLWAPMENh31rTdxHY9yYGzRYSsuLg6vvfYaLCws8K9//QvPPvssACApKQlPPfUUvvnmG2zduhUrV65s5JE2Dnu5pZ5frPX9RUpEVHctJfA1xcBm7npT1/Eoz/C16L+kGzZsgBAC8+fP1wQtAOjRowe2bt2KadOm4e2338ayZctgbW3diCMlIqKmwhyBr9bAZsTsXFM7pVpfjTnDV7W88bTYsFVQUIBjx6p+sHPnztVpnzhxIv7yl78gJycHJ06cwLBhwxp6iCYz6pOEtRx0/DQhEZH0ag9stc/OmSOw1ai+9Uaso6nM8FWWFZttDHXRYsPW+fPnUV5eDltbW4SGhuq0W1tbIzw8HMePH0d8fLxJYav4gQJWFkJ7YXnx/0LOgwJAXpdz3zWvo7hchX6bT9S4CjuUIfH/H2dPbjiCEtjo6aN+vyLoHALlxfq/NkV918ExcAwcgzTr4Bia1RhkgOZvgoGVaL6yRzlM/5Ne3/ra12FnLZCy+skayouBD6q+THipLyDX3eLa/vbZoUzv19WpUGp4DA1AJoQQtXdrfnbu3In58+ejc+fOuHTpkt4+CxYswI4dOzB9+nTs2bNHp72srAxlZf/7wSkUCvj5+SFruSOcbZrfOWMiIqJHUUGZgO97D5Cfnw8XF5cGf/8WO7OVl5cHAHBzczPYR92m7vuwt956C+vWrdNZ7vveAzOMkIiIiBpSTk4Ow5Y5lZZWTRnK5XKDfWxsqk6xlZSU6G1/9dVXsWLFCs33+fn5aN++PTIzMxvlh9WSFBQUwNfXF1lZWXB2dm7s4TRr3Jfmwf1oPtyX5sN9aR7qM1Pu7u6N8v4tNmzZ2lZduFReXm6wj/oUoZ2dnd52GxsbTSCrzsXFhQe9mTg7O3Nfmgn3pXlwP5oP96X5cF+aR2PdV7PF3s2ztlOE1dtqOtVIREREVB8tNmwFBgYCADIzM1FRof+TgVevXtXqS0RERGRuLTZs9ezZE9bW1igtLUViYqJOu1KpxLlz5wAAvXv3NmqdNjY2eOONN/SeWiTTcF+aD/eleXA/mg/3pflwX5pHY+/HFnvrBwAYMWIEfvjhByxYsAD/+Mc/tNr27duHadOmwcPDAzdv3qzxQnoiIiKiumqxM1sA8Prrr0Mmk2Hnzp344osvNMuTkpI0nzJ86aWXGLSIiIhIMi16ZgsA3nzzTaxevRoAEBAQAEdHRyQnJ6OyshIjR47E4cOHYWlpWctaiIiIiOqmxYctAPjuu+/w3nvvISEhAUqlEoGBgZg9ezaef/55Bi0iIiKS1CMRtoiIiIgaS4u+Zkvt2rVr2LFjB+bPn48ePXrAysoKMpkMGzZsMFhz/vx5rFmzBgMGDICnpyesra3h5eWF4cOH49ChQ7W+Z2pqKqZNm4a2bdvC1tYWHTt2xMqVK5Gfn2/GLWt4Dbkvd+/eDZlMVuPr6NGjUmxmg6jLvjxx4gSWLl2KP/3pT/Dx8YGNjQ2cnJwQFhaGqKgoFBYW1vieLfG4bMj9yGPSODt37tTsj3nz5tXYtyUek0DD7suWfFzWZT+uXbu21v2RlpZmsF6KY7LF3kG+um3btmHbtm1G9//jjz8QGhqq+b5Dhw7w9/fH1atXcfToURw9ehQzZ87EP//5T713oz1x4gRGjhyJkpIStGrVCt26dUNaWhreffddHDp0CGfOnEHr1q3Nsm0NraH3JQB4eXkZvBdac74hran7EgB27dqFvXv3wsrKCt7e3ujevTvu3buH8+fPIzExEZ9++ilOnjwJPz8/ndqWelw29H4EeEzW5N69e3j55ZeN6ttSj0mg4fcl0DKPy/rsR19fX4P/hu3t7fUul+yYFI+AqKgoMWrUKLF+/Xrxww8/iMjISAFAREVF6e1/5coV0bZtW7Fp0yZx8+ZNzXKVSiU+/PBDIZPJBADx4Ycf6tQWFBSIVq1aCQBi6dKlory8XAghxP3790VERIQAIEaOHCnNhjaAhtyXn376qQAgZs6cKdXmNCpT96UQQnz11Vfihx9+EMXFxVrLf//9d9G9e3cBQIwYMUKnriUflw25H3lM1m7atGnCwsJCjBw5UgAQc+fO1duvJR+TQjTsvmzJx2Vd9uMbb7whAIg33njDpPeS8ph8JMLWw2bOnFnjD6ukpEQUFRUZrF+0aJEAILp3767TtnnzZgFAdO3aVVRUVGi1Xb9+XVhZWQkAIiEhoX4b0URIuS9b8i8QfWrbl7X55ZdfBABhaWkpSkpKtNoepeNSyv3IY7JmP//8swAg/vKXv2j+4BkKCI/SMSmEtPvyUToujdmPdQ1bUh6Tj8Q1W6aytbU1OMUIAMOGDQMAXL58Waft4MGDAIBZs2bpfNLRz88PQ4YMAQB89dVX5hpuk1affUmmCQoKAgCoVCrNQ9bVeFwar6b9SIaVlpbiL3/5C7y8vLBx48Za+/OYNMzUfUnmIeUx+Uhcs2VupaWlAAA7Ozut5RUVFUhISAAARERE6K2NiIjA0aNHER8fL+0gmwlD+7K6pKQkTJ06Fbdv34azszN69uyJ5557Dh07dmyoYTYLcXFxAKruJ+fi4qJZzuPSNIb2Y3U8JnVt2LAB6enp+Oyzz+Dq6lpjXx6TNTNlX1bH41LbiRMn8PvvvyMnJwfu7u544oknMGPGDLRp00anr9THJMNWHURHRwPQ/YFkZGRAqVQCqPpFrY96+ZUrVyQcYfNhaF9W99tvv+G3337TfH/48GFERUVh3bp1eP3116UeYpMmhMCdO3dw/PhxrFq1ClZWVti6datWHx6XtTNmP1bHY1JbamoqtmzZgn79+mHGjBm19ucxaZip+7I6Hpfa/vvf/2p9//XXX2Pt2rXYvn07Zs2apdUm9THJ04gm+umnn/Dvf/8bALBq1Sqttry8PM3Xhj75oV5eve+jqqZ9CQCurq7461//itjYWNy5cwelpaU4f/48pk+fDpVKhdWrV+Nvf/tbA4+6afj3v/8NmUwGCwsLtG3bFs899xw6d+6MkydPYsyYMVp9eVwaZsp+BHhM6iOEwMKFC1FZWYnt27cbVcNjUr+67EuAx+XD2rZti9deew3nzp1DTk4OiouLERsbi+HDh6OkpARz5szBt99+q1Uj9THJsGWCzMxMTJs2DQCwePFi9O/fX6tdfUoMgMHnLaqfOF5SUiLRKJuH2vYlAIwdOxYffPAB+vbtCy8vL9jY2ODxxx/Hnj178MILLwAAVq9eXeu9pVoiDw8PREREoE+fPvDx8YFMJsMvv/yCPXv26BxbPC4NM2U/Ajwm9dm1axdOnz6NF154ASEhIUbV8JjUry77EuBx+bCFCxfizTffRK9eveDu7g47Ozv07dsXR44cwbhx4yCEwPLlyyGq3dNd6mOSYctIubm5GD58OO7fv4+BAwfqPcVga2ur+bq8vFzvetQX3NZ0jVJLZ8y+rM26detgY2MDhUKB//znPxKMsmnr168fYmJiEBcXhxs3buD3339Hnz598Mknn2D8+PFafXlcGmbKfqzNo3hMqu8D1a5dO7zxxhtG1/GY1FXXfVmbR/G4NEQmk+Htt98GUHUPyAsXLmjapD4mGbaM8ODBA4wYMQIpKSkICwvDN998o0m41VWfejQ0zahe3lxvMFdfxu7L2jg7O6Nbt24AgPT0dHMPs9np2rUrvv32W7Ru3RpHjx5FTEyMpo3HpfFq2o+1eRSPyZdeegm5ubl477334OjoaHQdj0lddd2XtXkUj8uadO7cGe7u7gC094fUxyTDVi3KysowZswYxMfHIzg4GEePHoWTk5Pevv7+/rC2tgYAXL16VW8f9XJDd/ltyUzZl8ZQ7+uKigpzDbFZc3BwwMCBAwEAiYmJmuU8Lk1jaD8a41E7Js+fPw8AeP7559GmTRut1zvvvAMA2Ldvn2aZGo9JXXXdl8Z41I7L2ujbH1IfkwxbNaioqMCkSZPwn//8BwEBAfj555/h6elpsL+VlZXm0TSxsbF6+6iX9+7d2/wDbsJM3Ze1UalUuHTpEgCgXbt25hpms6f+5VH9lwiPS9Pp24+1eZSPyTt37ui8ioqKAFRd36JepsZj0jBT92VtHuXjUp/79+/j7t27ALT3h9THJMOWAUIIzJo1C9988w28vb1x7NgxeHt711qnvs5j9+7dUKlUWm2ZmZk4duwYACAyMtL8g26i6rova7Jr1y7k5+fD0tJSMwvxqFMoFDhx4gQA4PHHH9dq43FpvJr2Y00exWPyt99+g6h6EonOS33d0dy5czXLquMxqa0++7Imj+JxWZOtW7dCCAEXFxeEh4drtUl5TDJsGbBs2TLs3bsXnp6eOHbsGDp06GBU3aJFi+Dp6YnU1FSsWLFCc9+OnJwcTJ06FRUVFRg+fDjCwsKkHH6TUpd9WVBQgGeffRa//PKL1nKVSoUdO3Zg2bJlAKp++fj4+Egy7qbm5s2beOGFF/D777/rtJ09exZPP/00cnNz8dhjj2HAgAFa7Twu/6eu+5HHpHnxmDQPHpfafv/9dyxevFjn33dpaSk2btyITZs2AQBefvllnU8dSnpMmvyAn2YoJiZGeHh4aF42NjYCgLC3t9danpmZKYQQ4syZMwKAACB8fX1FRESEwZc+x44dE7a2tgKAaNWqlQgLCxP29vYCgPD39xe3bt1qyM03q4bal3l5eZo6V1dX0bNnTxEeHi5cXV01y4cPH67z7LrmxNR9ee3aNc22u7u7i9DQUNGzZ0/h6empWd6xY0eRnp6u9/1a6nHZUPuRx6TuvqxJbc/zE6LlHpNCNNy+bOnHpan78fz585rtVh9T1Y8r9X6srKzU+35SHZOPRNg6ceKEZifX9Lp27ZpJ/WvKqsnJyWLKlCnCy8tLyOVy0aFDB7FixQqRm5vbQFstjYbal+Xl5WLz5s1i7NixolOnTsLZ2VlYW1uLNm3aiJEjR4r9+/cb/MfSXJi6L0tKSsQ//vEPMWnSJNG5c2fh4uIirKysRKtWrcSf//xn8cEHH4ji4uIa37MlHpcNtR95TOruy5oYE7aEaJnHpBANty9b+nFp6n7My8sTUVFRYvjw4aJDhw7C0dFRyOVy0a5dOzFhwgRx9OjRWt9TimNSJoQJJ3+JiIiIyCS8ZouIiIhIQgxbRERERBJi2CIiIiKSEMMWERERkYQYtoiIiIgkxLBFREREJCGGLSIiIiIJMWwRERERSYhhi4iIiEhCDFtEREREEmLYIiJJZGRkQCaTwd/fX6fN398fMpkMGRkZDT6u5m7SpEmQy+XIzMxs7KGYxZ///Ge4uroiJyensYdCJBmGLaImQB0+qr9sbW3RoUMHPPfcczh37lxjD7HRrV27VrNvWrdujYqKCoN9c3JyIJfLNf13797dcAOV0Llz53DgwAHMmjULfn5+BvulpaVh5cqVCA0NhaenJ6ytreHu7o5evXrh+eefx8mTJ80ynqlTp0Imk2HatGlG9d+6dStkMhm6deumWfZ///d/UCgU2LBhg1nGRNQUMWwRNSGBgYGIiIhAREQEAgMDcfv2bezduxd/+tOf8Pnnnzf28ExibW2NLl26oGPHjmZf9927d/HTTz8ZbP/yyy+hVCrN/r6N7ZVXXoFMJsOrr76qt12lUmH58uXo1q0b3n33XSQlJcHd3R2hoaHw8vJCcnIy/v73v2PQoEF44okn8ODBg3qNZ8aMGQCAf//730at61//+hcAYPr06ZplgwYNQp8+fbB9+/YWM1tH9DCGLaIm5LXXXkNMTAxiYmJw8eJF3Lx5ExMmTIBKpcKSJUuQl5fX2EM0mo+PD9LS0nD8+HGzrrdLly4AUGP4/PzzzyGTyRAYGGjW925Mqamp+M9//oP+/fujQ4cOevtMmzYN77//PhwdHbF161bcv38fly9fRnx8PNLS0pCTk4Ovv/4affr0wblz5+odtoYOHYo2bdqguLgYhw4dqnX858+f1zsTNnPmTJSXl2PHjh31Gg9RU8WwRdSEubm5YdeuXXBwcEBhYWGNszmPioiICPj7++Pw4cMoLCzUaU9PT0d8fDwGDBhQ46m25uaTTz4BADz77LN623fs2IH9+/fDwcEB//3vf7F8+XK4ublp9XFwcMD48eMRFxeHv/3tb7C2tq7XmCwtLTXj2bt3b4191eF44MCB8PX11WqbMGECrKyssHv3bqhUqnqNiagpYtgiauKcnZ3RuXNnADB4QfmPP/6I0aNHo3Xr1rCxsUG7du0we/Zs/PHHHwbXW1xcjHfeeQd9+vSBq6sr7O3tERgYiOnTp+PUqVM6/YuKirBhwwZ0794dDg4OcHZ2Ru/evfH3v/9d7/VTNV0gXx/qmZGSkhJ8/fXXOu3qP+rPPfecwXWUlJTgiy++wJQpU9ClSxc4OjrC0dERjz/+ODZs2ICioiK9dTk5OVi5ciWCgoJga2sLBwcH+Pv74+mnn8b27dt1+sfExGDcuHFo06aN5rqprl27Yt68eTh79qxJ2x0dHQ0AGDlypE6bSqXCm2++CQBYt24devToUev6lixZAg8PD71tv/zyC6ZMmQIfHx/I5XK0bt0aEydOxPnz53X6qk8JHjt2DHfu3NG7PiEE9u3bp9W/Ok9PT/Tu3Rs3btxAbGxsrWMnanYEETW69u3bCwDi008/1dvepUsXAUB88MEHOm3Lli0TAAQA4eXlJXr27CmcnZ0FAOHs7CxiY2N1aq5fvy66du2qqQsMDBShoaHC3d1dABADBgzQ6n/37l3x2GOPCQDCwsJCdO/eXat+6NChoqSkRKvm2rVrAoBo3769we29du2asbtIvPHGGwKAmDt3rkhLSxMAxODBg3X6dezYUdja2or8/HwxePBgvfv19OnTAoCwsrIS7dq1E7169RKBgYHCyspKABChoaGiuLhYqyY/P1907NhRABByuVwEBweL0NBQ4eXlJWQymXBxcdHq/+9//1tYWFgIAMLDw0OEhoaKoKAg4eDgIACIZcuWGb3tV65cEQBE27Zt9bbHxsYKAMLa2lrk5+cbvV59tm7dKmQymQAg3N3dRc+ePYWHh4dm/V9//bVOTUhIiAAg3nvvPb3rPHXqlAAg7OzshEKh0Ntn+fLlAoCIioqq1/iJmiKGLaImoKawdfnyZU0I+O9//6vV9vHHHwsAokOHDuLEiROa5RUVFWLDhg0CgGjXrp1WEKqoqBBhYWECgOjVq5dISUnRWuf58+fF9u3btZZFRkYKAKJbt24iPT1ds/zcuXOidevWAoB46aWXtGqkDFtCCBEeHi4sLCzEjRs3NH3UoWPSpElCCGEwbGVkZIjo6GhRWFiotfzWrVtiwoQJAoBYu3atVts777wjAIhhw4aJnJwcrbbr16/rBA11ANm+fbuoqKjQLK+srBQnTpwQ33zzjdHb/tlnnwkAYtSoUXrbt2zZIgCInj17Gr1OfX744Qchk8mEp6enTqjauXOnsLKyEk5OTuLmzZtabZs2bdIcT/rMnz9fABBTpkwx+N779u0TAMRTTz1Vr20gaooYtoiaAH1hS6FQiJ9//lkEBwcLACIiIkKrpqysTLRp00ZYWlqKxMREvetVh6Q9e/ZolkVHR2tmwe7fv1/r2C5fvqyZ6dD3Pur1OTg4iIKCAs1yqcPWtm3bBACxadMmTZ9FixYJAJogYyhs1aS4uFjI5XIRGBiotXzhwoUCgDh8+LBR67GxsRFubm5Gv29N1q9fLwCI+fPn621/4YUXBAAxbty4er1PaGhojdv44osvCgBi/fr1Wstv3LihmcVLS0vTaisrKxNubm4CgDhy5IjB9z5x4oQAIIKCguq1DURNEa/ZImpCZs+erbk3lIuLC4YOHYq0tDRMnjwZ3377rVbfuLg43L59G6GhoejZs6fe9Y0ePRoAtK7BOnz4MABgzpw5Bq/Zqe7nn3+GEAJPPvmk3veJjIxEu3btUFRU1KDX2zz77LOwsrLS3E6gvLwc0dHR8PT0xNNPP11rfWVlJQ4fPowlS5Zg+PDh6NevH5588kkMHToUMpkMV65cQXFxsaa/+qLuQ4cO1XiPr+r98/Pz8fPPP9dxC//n/v37AAB3d3e97eoPCjg4OOhtP3v2rM593GQyGdauXavpc/36dSQmJsLLy0tz3DxM3/EEVH3ydNCgQQD+d3sHte+++w55eXnw8vLCsGHDDG6jetvu3btnsA9Rc2XV2AMgov8JDAyEl5cXhBC4ffs2rl69Cmtra4SHh+t8suzixYsAqi5Ef/LJJ/WuLz8/HwCQnZ2tWZaamgoA6NOnj1Fjunz5MgAgODhYb7uFhQWCgoJw48YNXL582aigYw6tWrXCsGHD8P333yMpKQnXrl1Dbm4ulixZUuun7PLz8zFixAjExcXV2C8vLw/29vYAqoLwli1bsHv3bvzwww94+umn0a9fPwwaNAgBAQE6tcuXL8eSJUswbNgwhIWFYciQIXjyyScxYMAAODk5mbStpaWlAAAbGxu97er1Gbqw39nZGREREZrv09PTdS5mVx9PpaWlBo8n9TiqH09q06dPx/Hjx7Fv3z5ERUVplqvDlzocG2JnZweg6sMLRC0NwxZRE/Laa69h1qxZmu9jY2MxduxYrFy5Eq1bt9b6hJ1CoQBQNRNQ22xA9T9gBQUFAABXV1ejxqS+F5OXl5fBPq1btwYAvbdikNL06dPx/fff41//+heuXbumWVabFStWIC4uDl26dMHGjRvRp08feHp6Qi6XAwDatWuH7OxsrRujent7Iy4uDv/3f/+HI0eO4LPPPsNnn30GoCq4bt26FX/60580/RcvXgwnJye8++67SEhIQEJCAjZt2gRbW1tMnz4dW7ZsgYuLi1HbqZ71UYfnh/n4+AAw/GnV4OBgxMTEaL6fN28edu3apdVHfTwVFBTUOkOpLxBFRkZi8eLFuHr1Ks6cOYO+ffsiPz8f33//PYDafy65ubkAqj6ZSNTS8DQiURMWERGhudHjsmXLNEEJABwdHQFU3chSVF1/afBV/fEs6lkQQ3+4H6Z+n7t37xrso54lMXXGpr7GjBkDZ2dnfP755/juu+8QGBiI3r1711hTUVGhuY3C4cOHMX78eHh7e2uCVkVFBW7fvq23tmvXrvjqq6+Qn5+PEydOYO3atQgKCsLZs2cxbNgwnbAzffp0/Pbbb7h16xa+/PJLzJ07F1ZWVtixY0eNt6Z4mDroqgPJw9QhLzk52eif68PUP+eIiIhajyd9oc7R0RFjx44F8L/ZrOjoaJSVlaFr164ICwur8f3V29aqVas6jZ+oKWPYImrixo4diz59+iA3Nxdbt27VLFef1ktOTjZpfern0hl7nyf1Pb5SUlL0tldWViItLU2rb0Oxs7PD+PHjcefOHZSVlRkVYO7du4eioiK4u7tr7kZfXXJycq031rSxscHAgQPxxhtvIDk5GREREXjw4AG++OILvf3btGmDyZMnY+fOnYiPj4eFhQW+++473Lp1y6jtfPzxxwH87xTww3r37g0/Pz8olUrs3LnTqHU+TH08paamorKysk7rUD++Jzo6GkqlUu/jeQxRH1+hoaF1em+ipoxhi6gZeOWVVwAAH3zwgea0Xr9+/eDp6YmkpCSTHiysnn345z//aXCmpLphw4ZBJpMhJiZG700tDx48iBs3bsDBwUHruqCGsmDBAgwePBiDBw826o+6+tqggoICvafDNm/ebNL7W1paIjw8HABw8+bNWvsHBwdrTh8a0x+oClPW1ta4ePEiysrKdNqtrKzw2muvAah6YHdSUpKxw9cIDAxESEgIcnNzsWfPHpPrAWDIkCFo06YNcnJy8I9//AMxMTFGP6j6l19+AVB1XBO1NAxbRM3A6NGj0bVrV+Tl5eGjjz4CANja2mL9+vUAgIkTJ+LQoUMQQmjVJScn4+WXX9a6Bmfs2LHo1asX7t69ixEjRuDSpUtaNUlJSZr3AIBOnTph/PjxAKpmLq5evappS0xMxNKlSwEAzz//fIOfRgSqTqEdO3YMx44dM/jMwOpcXV3RrVs3VFRUYPny5SgvLwdQdRf2TZs2Yf/+/ZpTitW9/vrr2LVrl85puuTkZM1pSfWsTEFBAaZMmYKTJ09qzRKpVCp88MEHyMvLg4ODg96ZNX3s7e0RERGB8vJynDt3Tm+fhQsXIjIyEkVFRejfvz+2bt2q8yxNIQR+/PFHg7OamzZtgkwmw5IlS7Bz506dT11evXoVb775Jg4ePKi33tLSElOnTgUArFq1CkIIox6bJITAmTNnYGlpicGDB9fYl6hZasj7TBCRfrXdQV4IIXbt2iUAiDZt2mjdpPSVV17R3Mnd3d1dhIeHa90NHoD44YcftNZ1/fp1zV3pAYjOnTuLsLAwzZ3Ca7qDvKWlpejRo4fm/l8AxJAhQxr0DvLGMnSfrW+++UbrLum9evUSnp6eAoD4v//7P73jGzNmjOYO+p06dRJPPPGE6NSpk2YfDBo0SCiVSiGEEHl5eZrlDg4OokePHlrvIZPJxI4dO4zeDiGE+PzzzwUA8fzzzxvso1QqxV//+lfNPa8sLCxEYGCgeOKJJ0SXLl2Ek5OTZlzh4eEiKSlJZx1/+9vfhKWlpQAgnJycRFhYmOjVq5fm5rUAxEcffWRwDOfPn9f0AyB27dpV67bFxMQIAGLEiBHG7QyiZoZhi6gJMCZslZWVCW9vbwFA/P3vf9dqi42NFVOnThW+vr5CLpcLd3d30b17dzFnzhxx5MgRUV5errO+Bw8eiLfeekuEhoYKR0dHYW9vLwIDA8XMmTN17lSv7r9+/XoREhIi7OzshIODgwgPDxcffvih3vU35bAlhBBHjx4Vffv2FXZ2dsLJyUn06dNH/Otf/zI4vnPnzolXXnlF9O7dW7Rp00bI5XLh4+MjBgwYIPbs2aMJWkJU3aX/888/F9OnTxdBQUHCxcVF2NnZic6dO4vnnntO/Pbbb0Zvg1pJSYlwc3MTXl5eWu+lz++//y6WL18uHn/8ceHm5iasrKyEm5ub6Nmzp1i8eLE4efJkjfUXL14U8+bNEwEBAcLW1la4uLiIbt26iWeffVYcOHBAFBUV1VivDua2trYGH89T3ZIlSwQAcejQoVr7EjVHMiEeOu9ARERN0saNG/H666/j888/N+nTjE1Zfn4+/P394efnh99++w0WFry6hVoeHtVERM3ECy+8AG9vb7z55pt1/sRgU7Nt2zYoFAq8/fbbDFrUYvHIJiJqJuzt7bFnzx5MnjzZ6E8yNnVubm7YunUrRowY0dhDIZIMTyMSERERSYgzW0REREQSYtgiIiIikhDDFhEREZGEGLaIiIiIJMSwRURERCQhhi0iIiIiCTFsEREREUmIYYuIiIhIQgxbRERERBJi2CIiIiKS0P8D6rqB7mbMP7sAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#zmumu_m_nOne\n",
+    "mumu =  f_mumu['wzp6_ee_mumuH_Hbb_ecm240/mumu_recoil_m_nOne'].to_hist()\n",
+    "#ee   =  f_mumu['wzp6_ee_eeH_Hbb_ecm240/muons_all_p_cut0'].to_hist()\n",
+    "#WW   = f_mumu['p8_ee_WW_ecm240/muons_all_p_cut0'].to_hist()\n",
+    "ZZ   = f_mumu['p8_ee_ZZ_ecm240/zmumu_m_nOne'].to_hist()\n",
+    "hep.histplot([mumu, ZZ], label=['Z$\\mu\\mu$', 'ZZ bkg'])\n",
+    "#hep.histplot(mumu, label='Z$\\mu\\mu$')\n",
+    "#hep.histplot(ee, label='Zee')\n",
+    "plt.legend()\n",
+    "plt.xlim((120,150))\n",
+    "#plt.ylim((0,50000))\n",
+    "#plt.yscale('log')\n",
+    "plt.xlabel(\"Recoil Mass (GeV)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18c3b0ef-72db-41c1-9386-6d7624469af2",
+   "metadata": {},
+   "source": [
+    "<h1>Flavour Tagging Scan</h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "419e8ab9-a374-43cd-b9a5-eb61cfe66193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = uproot.open(\"/home/submit/jakedlee/FCCAnalyzer/end_of_h_bb.root\")\n",
+    "\n",
+    "def minmax(data):\n",
+    "    min_val = np.nanmin(data)\n",
+    "    max_val = np.nanmax(data)\n",
+    "    return (data - min_val) / (max_val - min_val)\n",
+    "\n",
+    "Zprods = ['ee', 'mumu', 'tautau', 'nunu', 'qq', 'ss', 'cc', 'bb']\n",
+    "bb_sig = [f'wzp6_ee_{i}H_Hbb_ecm240' for i in Zprods]\n",
+    "cc_sig = [f'wzp6_ee_{i}H_Hcc_ecm240' for i in Zprods]\n",
+    "gg_sig = [f'wzp6_ee_{i}H_Hgg_ecm240' for i in Zprods]\n",
+    "\n",
+    "def unnormalize(sample, hist, L=7200000):\n",
+    "    # w = L * sigma / Nevents\n",
+    "    w = L * df[sample + '/meta'].values()[2] / df[sample + '/meta'].values()[1]\n",
+    "    return df[sample + '/' + hist].values() / w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "20addab0-db0b-4820-9d11-89480a387f0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyInFileError",
+     "evalue": "not found: 'scanProb_muons0' (with any cycle number)\n\n    Available keys: 'Zcc_prob_nOne;1', 'Zss_prob_nOne;1', 'zmuons_h_m;1', 'zmuons_h_p;1', 'Hbb_prob_nOne;1', 'Zbb_prob_nOne;1', 'Zqq_prob_nOne;1', 'zbb_z_m;1', 'zbb_h_m;1', 'zcc_z_m;1', 'zcc_h_m;1', 'zss_z_m;1', 'zss_h_m;1'...\n\nin file /home/submit/jakedlee/FCCAnalyzer/end_of_h_bb.root",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyInFileError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[16], line 20\u001b[0m\n\u001b[1;32m     17\u001b[0m bkg_raw \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m0\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat64)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m3\u001b[39m):\n\u001b[0;32m---> 20\u001b[0m     part    \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(part, \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwzp6_ee_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mp\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43mH_Hbb_ecm240/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mg\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues()[:\u001b[38;5;241m20\u001b[39m])\n\u001b[1;32m     21\u001b[0m     bkg     \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(bkg, reduce(\u001b[38;5;28;01mlambda\u001b[39;00m a, b : a \u001b[38;5;241m+\u001b[39m b, [df[x \u001b[38;5;241m+\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mvalues()[:\u001b[38;5;241m20\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m bkg_samples]))\n\u001b[1;32m     22\u001b[0m     bkg_raw \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(bkg_raw, reduce(\u001b[38;5;28;01mlambda\u001b[39;00m a, b : a \u001b[38;5;241m+\u001b[39m b, [calc_dbkg(x, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m bkg_samples]))\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2095\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2094\u001b[0m         last \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m-> 2095\u001b[0m         step \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2097\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, uproot\u001b[38;5;241m.\u001b[39mbehaviors\u001b[38;5;241m.\u001b[39mTBranch\u001b[38;5;241m.\u001b[39mHasBranches):\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items[i:])]\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2112\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step\n\u001b[1;32m   2111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2062\u001b[0m, in \u001b[0;36mReadOnlyDirectory.key\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m last\n\u001b[1;32m   2061\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cycle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2062\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2063\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2064\u001b[0m     )\n\u001b[1;32m   2065\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2066\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2067\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39mcycle, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2068\u001b[0m     )\n",
+      "\u001b[0;31mKeyInFileError\u001b[0m: not found: 'scanProb_muons0' (with any cycle number)\n\n    Available keys: 'Zcc_prob_nOne;1', 'Zss_prob_nOne;1', 'zmuons_h_m;1', 'zmuons_h_p;1', 'Hbb_prob_nOne;1', 'Zbb_prob_nOne;1', 'Zqq_prob_nOne;1', 'zbb_z_m;1', 'zbb_h_m;1', 'zcc_z_m;1', 'zcc_h_m;1', 'zss_z_m;1', 'zss_h_m;1'...\n\nin file /home/submit/jakedlee/FCCAnalyzer/end_of_h_bb.root"
+     ]
+    }
+   ],
+   "source": [
+    "p = 'mumu'\n",
+    "g = f\"scanProb_{'muons' if p == 'mumu' else 'electrons'}\"\n",
+    "\n",
+    "plt.rcParams.update({'font.size': 16})\n",
+    "\n",
+    "bkg_samples = list(filter(lambda x : x != f'wzp6_ee_{p}H_Hbb_ecm240', bb_sig)) + cc_sig + gg_sig + ['p8_ee_WW_ecm240', 'p8_ee_ZZ_ecm240']\n",
+    "L    = 7200000\n",
+    "\n",
+    "def calc_dbkg(a, b):\n",
+    "    Nsel = unnormalize(a, b)[:20]\n",
+    "    Ntot = df[a + '/meta'].values()[1]\n",
+    "    sigm = df[a + '/meta'].values()[2]\n",
+    "    return (sigm * L * np.sqrt(Nsel * (Ntot - Nsel) / Ntot**3))**2\n",
+    "\n",
+    "part    = np.array([0], dtype=np.float64)\n",
+    "bkg     = np.array([0], dtype=np.float64)\n",
+    "bkg_raw = np.array([0], dtype=np.float64)\n",
+    "\n",
+    "for i in range(3):\n",
+    "    part    = np.append(part, df[f'wzp6_ee_{p}H_Hbb_ecm240/{g}{i}'].values()[:20])\n",
+    "    bkg     = np.append(bkg, reduce(lambda a, b : a + b, [df[x + f'/{g}{i}'].values()[:20] for x in bkg_samples]))\n",
+    "    bkg_raw = np.append(bkg_raw, reduce(lambda a, b : a + b, [calc_dbkg(x, f'{g}{i}') for x in bkg_samples]))\n",
+    "\n",
+    "def uncert(i):\n",
+    "    Nobs = part[i] + bkg[i]\n",
+    "    Nbkg = bkg[i]\n",
+    "    A    = part[i] / (L * df[f'wzp6_ee_{p}H_Hbb_ecm240/meta'].values()[2])\n",
+    "    E    = 1\n",
+    "    \n",
+    "    dNobs = 1 / np.sqrt(Nobs)\n",
+    "    dNbkg = np.sqrt(bkg_raw[i])\n",
+    "    dA    = np.sqrt(A * (1 - A) / df[f'wzp6_ee_{p}H_Hbb_ecm240/meta'].values()[1])\n",
+    "    dL    = 0.3e-6\n",
+    "    \n",
+    "    xsec  = (Nobs - Nbkg) / (A*E*L)\n",
+    "    dxsec = np.sqrt((dNbkg/(A*E*L))**2 + (dNobs/(A*E*L))**2 + (dA*xsec/A)**2 + (dL*xsec/L)**2)\n",
+    "    \n",
+    "    return dxsec\n",
+    "\n",
+    "x = [0.005*i + 0.7 for i in range(0, 60)]\n",
+    "y = [uncert(i) for i in range(0, 60)] \n",
+    "\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(6, 8))\n",
+    "\n",
+    "ax[0].plot(x, y)\n",
+    "ax[0].set_title(f'Z{p} Cross Section Uncertainty')\n",
+    "ax[0].set_ylabel('Uncertainty (pb)')\n",
+    "ax[0].set_xlabel('B Tag Threshold')\n",
+    "#ax[0].set_xlim((0.7, 0.985))\n",
+    "\n",
+    "ax[1].plot(x, part[part != 0], label=f'Z{p}')\n",
+    "ax[1].plot(x, bkg[part != 0], label=f'bkg')\n",
+    "ax[1].set_title('Events vs Cut Threshold')\n",
+    "ax[1].set_yscale('log')\n",
+    "ax[1].set_ylabel('Events')\n",
+    "ax[1].set_xlabel('B Tag Threshold')\n",
+    "#ax[1].set_xlim((0.7, 0.985))\n",
+    "ax[1].legend(loc='lower left')\n",
+    "\n",
+    "#plt.suptitle(f'B Tag Cut on ZH->{p}bb')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "63030869-9f49-4ac7-a30e-88b911522342",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = 'cc'\n",
+    "\n",
+    "bb = [f'wzp6_ee_{i}H_Hbb_ecm240' for i in filter(lambda j : j != p, ['mumu', 'bb', 'cc', 'ss', 'qq'])]\n",
+    "cc = [f'wzp6_ee_{i}H_Hcc_ecm240' for i in ['mumu', 'bb', 'cc', 'ss', 'qq']]\n",
+    "gg = [f'wzp6_ee_{i}H_Hgg_ecm240' for i in ['mumu', 'bb', 'cc', 'ss', 'qq']]\n",
+    "bkg_samples = bb + cc + gg + ['p8_ee_WW_ecm240', 'p8_ee_ZZ_ecm240']\n",
+    "\n",
+    "#xbkg = reduce(np.append, [minmax(df[f'{i}/Hbb_prob_vs_Z{p}_prob'].values()[0]) for i in bkg_samples])\n",
+    "#ybkg = reduce(np.append, [minmax(df[f'{i}/Hbb_prob_vs_Z{p}_prob'].values()[1]) for i in bkg_samples])\n",
+    "\n",
+    "xsig = minmax(df[f'wzp6_ee_{p}H_Hbb_ecm240/Hbb_prob_vs_Z{p}_prob'].values()[0])\n",
+    "ysig = minmax(df[f'wzp6_ee_{p}H_Hbb_ecm240/Hbb_prob_vs_Z{p}_prob'].values()[1])\n",
+    "\n",
+    "def uncert(cutH, cutZ):\n",
+    "    L    = 7200000\n",
+    "    \n",
+    "    Nsig = xsig[~(np.isnan(xsig)) & ~(np.isnan(ysig)) & (xsig > cutH) & (ysig > cutZ)].size\n",
+    "    nsig = L * df[f'wzp6_ee_{p}H_Hbb_ecm240/meta'].values()[2] * Nsig / df[f'wzp6_ee_{p}H_Hbb_ecm240/meta'].values()[1]\n",
+    "    \n",
+    "    nbkg  = 0\n",
+    "    dNbkg = 0\n",
+    "    for i in bkg_samples:\n",
+    "        xbkg = minmax(df[f'{i}/Hbb_prob_vs_Z{p}_prob'].values()[0])\n",
+    "        ybkg = minmax(df[f'{i}/Hbb_prob_vs_Z{p}_prob'].values()[1])\n",
+    "        \n",
+    "        temp = xbkg[~(np.isnan(xbkg)) & ~(np.isnan(ybkg)) & (xbkg > cutH) & (ybkg > cutZ)].size\n",
+    "        \n",
+    "        nbkg += temp * L * df[f'{i}/meta'].values()[2] / df[f'{i}/meta'].values()[1]\n",
+    "        \n",
+    "        Nsel = temp\n",
+    "        Ntot = df[f'{i}/meta'].values()[1]\n",
+    "        \n",
+    "        dNbkg += (L*df[f'{i}/meta'].values()[2]*np.sqrt(Nsel*(Ntot - Nsel) / Ntot**3))**2 \n",
+    "    dNbkg = np.sqrt(dNbkg)\n",
+    "    \n",
+    "    A    = Nsig / df[f'wzp6_ee_{p}H_Hbb_ecm240/meta'].values()[1]\n",
+    "    E    = 1\n",
+    "    Nobs = nsig + nbkg\n",
+    "    \n",
+    "    dNobs = 1 / np.sqrt(Nobs)\n",
+    "    dA    = np.sqrt(A * (1 - A) / df[f'wzp6_ee_{p}H_Hbb_ecm240/meta'].values()[1])\n",
+    "    dL    = 0.3e-6\n",
+    "    \n",
+    "    xsec  = (Nobs - nbkg) / (A*E*L)\n",
+    "    dxsec = np.sqrt((dNbkg/(A*E*L))**2 + (dNobs/(A*E*L))**2 + (dA*xsec/A)**2 + (dL*xsec/L))\n",
+    "    \n",
+    "    return dxsec / xsec\n",
+    "\n",
+    "res = 95\n",
+    "x = np.linspace(0, 0.95, res)\n",
+    "y = np.linspace(0, 0.95, res)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "sigma = np.zeros((res, res))\n",
+    "for i in range(res):\n",
+    "    for j in range(res):\n",
+    "        sigma[i][j] = uncert(x[i], y[j])\n",
+    "sigma = np.log(sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "8ff5506d-678d-45d2-b03f-46aa096cfd96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.04042553191489361 0.020212765957446806 0.009444815406975072 0.00963828972479918\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.contourf(X, Y, sigma, levels=500, cmap='hot')\n",
+    "\n",
+    "plt.title(\"ZH$\\\\rightarrow c\\\\overline{c}b\\\\overline{b}$ Cross Section Uncertainty\\n(After ML Flavour Assignment)\")\n",
+    "plt.xlabel(\"Z$c\\\\overline{c}$ Tag Threshold\")\n",
+    "plt.ylabel(\"H$b\\\\overline{b}$ Tag Threshold\")\n",
+    "\n",
+    "[minx], [miny] = np.where(sigma == np.min(sigma))\n",
+    "print(x[minx], y[miny], uncert(x[minx], y[miny]), uncert(x[miny], y[minx]))\n",
+    "plt.scatter(y[miny], x[minx], color=(0,1,0), marker='x', linewidth=3, s=10**2)\n",
+    "\n",
+    "#plt.colorbar(label='Log Uncertainty')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "012f8da9-693b-4ffc-baab-3a69a04c345d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.013938326480856075"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "uncert(0.042, 0.011)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c44be143-c460-47d6-b308-336b02607589",
+   "metadata": {},
+   "source": [
+    "B tag X tag\n",
+    "\n",
+    "bb: 0.011 0.042\n",
+    "\n",
+    "cc: 0.029 0.134\n",
+    "\n",
+    "ss: 0.032 0.095\n",
+    "\n",
+    "qq: 0.032 0.053"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "073addb5-4ad3-4176-94cd-e84f0ca0ef5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x   = np.linspace(0, 0.95, 100)\n",
+    "sig = np.zeros(100)\n",
+    "\n",
+    "for i in range(100):\n",
+    "    sig[i] = uncert(x[i], 0.134)\n",
+    "\n",
+    "plt.plot(x, sig)\n",
+    "plt.xlim(0, 0.95)\n",
+    "plt.ylabel('Uncertainty')\n",
+    "#plt.yticks([])\n",
+    "plt.xlabel('H Dijet B Tag Cut')\n",
+    "plt.title('Cross Section Uncertainty vs H Dijet B Tagging Confidence')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# second run\n",
+    "# B: 0.67 Q: 0.80\n",
+    "# B: 0.77 S: 0.45\n",
+    "# B: 0.80 C: 0.04\n",
+    "# B: 0.60 B: 0.00"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23502822-1cec-4240-80d0-2bfc316842ab",
+   "metadata": {},
+   "source": [
+    "<h1>Fitting & Cross Section</h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7bed947f-e42e-4a4f-a439-16d6a6ee299e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "****************************************\n",
+      "Minimizer is Minuit2 / Migrad\n",
+      "Chi2                      =        25751\n",
+      "NDf                       =            7\n",
+      "Edm                       =   6.4899e-07\n",
+      "NCalls                    =          611\n",
+      "p0                        =      14105.5   +/-   338.309     \n",
+      "p1                        =       125.11                      \t (fixed)\n",
+      "p2                        =     0.329599   +/-   0.0100079   \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
+      "Info in <TCanvas::Print>: png file /home/submit/aniketkg/FCCAnalyzer_ag/analyses/h_bb/bw.png has been created\n"
+     ]
+    }
+   ],
+   "source": [
+    "import ROOT\n",
+    "\n",
+    "def breit_wigner(x, par):\n",
+    "    return par[0] * ROOT.TMath.BreitWigner(x[0], par[1], par[2])\n",
+    "\n",
+    "fIn = ROOT.TFile(\"/home/submit/jakedlee/FCCAnalyzer/output_h_bb.root\")\n",
+    "h = fIn.Get(\"wzp6_ee_mumuH_Hbb_ecm240/zmuons_final_recoil_m\")\n",
+    "xMin, xMax = 100, 140\n",
+    "\n",
+    "rel_bwTF1 = ROOT.TF1(\"rel_bwTF1\", breit_wigner, xMin, xMax, 3)\n",
+    "rel_bwTF1.SetParameters(h.Integral(), 125.11, 4.07e-3)\n",
+    "rel_bwTF1.FixParameter(1, 125.11);\n",
+    "h.Fit(\"rel_bwTF1\", \"R\")\n",
+    "\n",
+    "canvas = ROOT.TCanvas(\"canvas\", \"Canvas\", 800, 600)\n",
+    "h.Draw()\n",
+    "rel_bwTF1.Draw(\"same\")\n",
+    "canvas.SaveAs(\"/home/submit/aniketkg/FCCAnalyzer_ag/analyses/h_bb/bw.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "eb614bb1-2e8f-4361-8168-d1f381a74107",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unnormalize(sample, hist, L=7200000):\n",
+    "    # w = L * sigma / Nevents\n",
+    "    w = L * f_mumu[sample + '/meta'].values()[2] / f_mumu[sample + '/meta'].values()[1]\n",
+    "    return f_mumu[sample + '/' + hist].values() / w\n",
+    "\n",
+    "def xsec(decay_product):\n",
+    "    bkg_samples = list(filter(lambda x : x != f'wzp6_ee_{decay_product}H_Hbb_ecm240', bb_sig)) + cc_sig + gg_sig + ['p8_ee_WW_ecm240', 'p8_ee_ZZ_ecm240']\n",
+    "    L = 7200000\n",
+    "    \n",
+    "    nums = f_mumu[f'wzp6_ee_{decay_product}H_Hbb_ecm240/cutFlow_{decay_product}'].values()\n",
+    "    maxi = np.nonzero(nums)[0][-1]\n",
+    "    nsig = nums[maxi]\n",
+    "    nbkg = sum([f_mumu[x + f'/cutFlow_{decay_product}'].values()[maxi] for x in bkg_samples])\n",
+    "    nobs = nsig + nbkg\n",
+    "    A    = nums[maxi] / (L * f_mumu[f'wzp6_ee_{decay_product}H_Hbb_ecm240/meta'].values()[2])\n",
+    "    E    = 1\n",
+    "    \n",
+    "    xsec = (nobs - nbkg) / (A*E*L)\n",
+    "    \n",
+    "    dNobs = 1 / np.sqrt(nobs)\n",
+    "    dNbkg = np.sqrt(sum([(L * f_mumu[x + '/meta'].values()[2] * np.sqrt(unnormalize(x, f'cutFlow_{decay_product}')[maxi] * (f_mumu[x + '/meta'].values()[1] - unnormalize(x, f'cutFlow_{decay_product}')[maxi]) / f_mumu[x + '/meta'].values()[1]**3))**2 for x in bkg_samples]))\n",
+    "    dA    = np.sqrt(A * (1-A) / f_mumu[f'wzp6_ee_{decay_product}H_Hbb_ecm240/meta'].values()[1])\n",
+    "    dL    = 0.3e-6\n",
+    "    dxsec = np.sqrt((dNobs/(A*E*L))**2 + (dNbkg/(A*E*L))**2 + (dA*xsec/A)**2 + (dL*xsec/L)**2)\n",
+    "    #print(\"Acceptance:\", A, \"\\nEfficiency:\", E, \"\\nNsig:\", nsig, \"\\nNbkg:\", nbkg)\n",
+    "    #print(\"dNobs\", dNobs, \"dNbkg\", dNbkg, \"dA\", dA)\n",
+    "    print(f\"ee->ZH->{decay_product}bb Cross Section: {xsec*1000:.2f} +- {dxsec*1000:.3f} fb ({100*dxsec / xsec:.2f}%)\")\n",
+    "    \n",
+    "    return xsec, dxsec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ed4be0e3-8aa8-4dce-b79e-5127cb986ca5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----------------------\n",
+      "\tmumu\n",
+      "----------------------\n",
+      "ee->ZH->mumubb Cross Section: 3.94 +- 0.178 fb (4.51%)\n",
+      "----------------------\n",
+      "\tee\n",
+      "----------------------\n",
+      "ee->ZH->eebb Cross Section: 4.17 +- 0.187 fb (4.48%)\n",
+      "----------------------\n",
+      "\tnunu\n",
+      "----------------------\n",
+      "ee->ZH->nunubb Cross Section: 26.90 +- 16.773 fb (62.35%)\n",
+      "----------------------\n",
+      "\tbb\n",
+      "----------------------\n",
+      "ee->ZH->bbbb Cross Section: 17.45 +- 31.427 fb (180.09%)\n",
+      "----------------------\n",
+      "\tcc\n",
+      "----------------------\n",
+      "ee->ZH->ccbb Cross Section: 13.59 +- 29.338 fb (215.88%)\n",
+      "----------------------\n",
+      "\tss\n",
+      "----------------------\n",
+      "ee->ZH->ssbb Cross Section: 17.45 +- 28.634 fb (164.09%)\n",
+      "----------------------\n",
+      "\tqq\n",
+      "----------------------\n",
+      "ee->ZH->qqbb Cross Section: 31.07 +- 28.606 fb (92.07%)\n",
+      "----------------------\n",
+      "\tTotal\n",
+      "----------------------\n",
+      "ee->ZH->stuff Cross Section: 114.57 +- 61.384 fb (53.58%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"----------------------\\n\\tmumu\\n----------------------\")\n",
+    "x1, a = xsec(\"mumu\")\n",
+    "print(\"----------------------\\n\\tee\\n----------------------\")\n",
+    "x2, b = xsec(\"ee\")\n",
+    "print(\"----------------------\\n\\tnunu\\n----------------------\")\n",
+    "x3, c = xsec(\"nunu\")\n",
+    "print(\"----------------------\\n\\tbb\\n----------------------\")\n",
+    "x4, d = xsec(\"bb\")\n",
+    "print(\"----------------------\\n\\tcc\\n----------------------\")\n",
+    "x5, e = xsec(\"cc\")\n",
+    "print(\"----------------------\\n\\tss\\n----------------------\")\n",
+    "x6, f = xsec(\"ss\")\n",
+    "print(\"----------------------\\n\\tqq\\n----------------------\")\n",
+    "x7, g = xsec(\"qq\")\n",
+    "\n",
+    "print(\"----------------------\\n\\tTotal\\n----------------------\")\n",
+    "xtot  = x1 + x2 + x3 + x4 + x5 + x6 + x7\n",
+    "dxtot = np.sqrt(a**2 + b**2 + c**2 + d**2 + e**2 + f**2 + g**2)\n",
+    "print(f\"ee->ZH->stuff Cross Section: {1000*xtot:.2f} +- {1000*dxtot:.3f} fb ({100 * dxtot / xtot:.2f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a84a9ba1-564e-48e6-b823-25f2b521c36e",
+   "metadata": {},
+   "source": [
+    "ee->ZH->mumubb Cross Section: 3.94 +- 0.011 fb (0.29%)\n",
+    "\n",
+    "ee->ZH->eebb Cross Section: 4.17 +- 0.010 fb (0.25%)\n",
+    "\n",
+    "ee->ZH->nunubb Cross Section: 26.90 +- 0.042 fb (0.16%)\n",
+    "\n",
+    "ee->ZH->bbbb Cross Section: 17.45 +- 0.326 fb (1.87%)\n",
+    "\n",
+    "ee->ZH->ccbb Cross Section: 13.59 +- 0.162 fb (1.19%)\n",
+    "\n",
+    "ee->ZH->ssbb Cross Section: 17.45 +- 0.218 fb (1.25%)\n",
+    "\n",
+    "ee->ZH->qqbb Cross Section: 31.07 +- 0.292 fb (0.94%)\n",
+    "\n",
+    "ee->ZH->stuff Cross Section: 114.57 +- 0.516 fb (0.45%)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "ee->ZH->mumubb Cross Section: 3.94 +- 0.012 fb (0.30%)\n",
+    "ee->ZH->eebb Cross Section: 4.17 +- 0.011 fb (0.27%)\n",
+    "ee->ZH->nunubb Cross Section: 26.90 +- 0.043 fb (0.16%)\n",
+    "ee->ZH->bbbb Cross Section: 17.45 +- 0.269 fb (1.54%)\n",
+    "ee->ZH->ccbb Cross Section: 13.59 +- 0.178 fb (1.31%)\n",
+    "ee->ZH->ssbb Cross Section: 17.45 +- 0.245 fb (1.40%)\n",
+    "ee->ZH->qqbb Cross Section: 31.07 +- 0.317 fb (1.02%)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "ee->ZH->mumubb Cross Section: 0.00394 +- 0.0027432519475296957\n",
+    "ee->ZH->eebb Cross Section: 0.004171 +- 0.0024606667928812427\n",
+    "ee->ZH->nunubb Cross Section: 0.026899999999999997 +- 0.0014778926767689588\n",
+    "ee->ZH->bbbb Cross Section: 0.01745 +- 0.0183934902955624\n",
+    "ee->ZH->ccbb Cross Section: 0.013590000000000001 +- 0.012027530255229981\n",
+    "ee->ZH->ssbb Cross Section: 0.01745 +- 0.02143603607292202\n",
+    "ee->ZH->qqbb Cross Section: 0.03107 +- 0.009315990848536801"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "23d11cb3-16ff-4274-a114-037dd7458391",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.015415472779369629"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.269/17.45"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "d20c8ca3-4425-4083-a207-5fae9b574f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['wzp6_gaga_mumu_60_ecm240;1', 'wzp6_gaga_mumu_60_ecm240/meta;1', 'wzp6_gaga_mumu_60_ecm240/muons_all_p_cut0;1', 'wzp6_gaga_mumu_60_ecm240/muons_all_theta_cut0;1', 'wzp6_gaga_mumu_60_ecm240/muons_all_phi_cut0;1', 'wzp6_gaga_mumu_60_ecm240/muons_all_q_cut0;1', 'wzp6_gaga_mumu_60_ecm240/muons_all_no_cut0;1', 'wzp6_gaga_mumu_60_ecm240/electrons_all_p_cut0;1', 'wzp6_gaga_mumu_60_ecm240/electrons_all_theta_cut0;1', 'wzp6_gaga_mumu_60_ecm240/electrons_all_phi_cut0;1', 'wzp6_gaga_mumu_60_ecm240/electrons_all_q_cut0;1', 'wzp6_gaga_mumu_60_ecm240/electrons_all_no_cut0;1', 'wzp6_gaga_mumu_60_ecm240/cutFlow;1', 'wzp6_gaga_mumu_60_ecm240/muon1_p;1', 'wzp6_gaga_mumu_60_ecm240/muon2_p;1', 'wzp6_gaga_mumu_60_ecm240/zmumu_m_nOne;1', 'wzp6_gaga_mumu_60_ecm240/zmumu_m;1', 'wzp6_gaga_mumu_60_ecm240/muons_no;1', 'wzp6_gaga_mumu_60_ecm240/electrons_no;1', 'wzp6_gaga_mumu_60_ecm240/jet_p;1', 'wzp6_gaga_mumu_60_ecm240/jet_nconst;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isQ_jet0;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isQ_jet1;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isS_jet0;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isS_jet1;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isG_jet0;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isG_jet1;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isC_jet0;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isC_jet1;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isB_jet0;1', 'wzp6_gaga_mumu_60_ecm240/recojet_isB_jet1;1', 'wzp6_gaga_mumu_60_ecm240/dijet_higgs_m_reco;1', 'wzp6_gaga_mumu_60_ecm240/dijet_z_m_reco;1', 'wzp6_gaga_mumu_60_ecm240/dijet_higgs_m_mc;1', 'wzp6_gaga_mumu_60_ecm240/mumu_p_nOne;1', 'wzp6_gaga_mumu_60_ecm240/missingEnergy_nOne;1', 'wzp6_gaga_mumu_60_ecm240/mumu_recoil_m_nOne;1', 'wzp6_gaga_tautau_60_ecm240;1', 'wzp6_gaga_tautau_60_ecm240/meta;1', 'wzp6_gaga_tautau_60_ecm240/muons_all_p_cut0;1', 'wzp6_gaga_tautau_60_ecm240/muons_all_theta_cut0;1', 'wzp6_gaga_tautau_60_ecm240/muons_all_phi_cut0;1', 'wzp6_gaga_tautau_60_ecm240/muons_all_q_cut0;1', 'wzp6_gaga_tautau_60_ecm240/muons_all_no_cut0;1', 'wzp6_gaga_tautau_60_ecm240/electrons_all_p_cut0;1', 'wzp6_gaga_tautau_60_ecm240/electrons_all_theta_cut0;1', 'wzp6_gaga_tautau_60_ecm240/electrons_all_phi_cut0;1', 'wzp6_gaga_tautau_60_ecm240/electrons_all_q_cut0;1', 'wzp6_gaga_tautau_60_ecm240/electrons_all_no_cut0;1', 'wzp6_gaga_tautau_60_ecm240/cutFlow;1', 'wzp6_gaga_tautau_60_ecm240/muon1_p;1', 'wzp6_gaga_tautau_60_ecm240/muon2_p;1', 'wzp6_gaga_tautau_60_ecm240/zmumu_m_nOne;1', 'wzp6_gaga_tautau_60_ecm240/zmumu_m;1', 'wzp6_gaga_tautau_60_ecm240/muons_no;1', 'wzp6_gaga_tautau_60_ecm240/electrons_no;1', 'wzp6_gaga_tautau_60_ecm240/jet_p;1', 'wzp6_gaga_tautau_60_ecm240/jet_nconst;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isQ_jet0;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isQ_jet1;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isS_jet0;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isS_jet1;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isG_jet0;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isG_jet1;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isC_jet0;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isC_jet1;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isB_jet0;1', 'wzp6_gaga_tautau_60_ecm240/recojet_isB_jet1;1', 'wzp6_gaga_tautau_60_ecm240/dijet_higgs_m_reco;1', 'wzp6_gaga_tautau_60_ecm240/dijet_z_m_reco;1', 'wzp6_gaga_tautau_60_ecm240/dijet_higgs_m_mc;1', 'wzp6_gaga_tautau_60_ecm240/mumu_p_nOne;1', 'wzp6_gaga_tautau_60_ecm240/missingEnergy_nOne;1', 'wzp6_gaga_tautau_60_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautau_ecm240;1', 'wzp6_ee_tautau_ecm240/meta;1', 'wzp6_ee_tautau_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautau_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautau_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautau_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautau_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautau_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautau_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautau_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautau_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautau_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautau_ecm240/cutFlow;1', 'wzp6_ee_tautau_ecm240/muon1_p;1', 'wzp6_ee_tautau_ecm240/muon2_p;1', 'wzp6_ee_tautau_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautau_ecm240/zmumu_m;1', 'wzp6_ee_tautau_ecm240/muons_no;1', 'wzp6_ee_tautau_ecm240/electrons_no;1', 'wzp6_ee_tautau_ecm240/jet_p;1', 'wzp6_ee_tautau_ecm240/jet_nconst;1', 'wzp6_ee_tautau_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_tautau_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_tautau_ecm240/recojet_isS_jet0;1', 'wzp6_ee_tautau_ecm240/recojet_isS_jet1;1', 'wzp6_ee_tautau_ecm240/recojet_isG_jet0;1', 'wzp6_ee_tautau_ecm240/recojet_isG_jet1;1', 'wzp6_ee_tautau_ecm240/recojet_isC_jet0;1', 'wzp6_ee_tautau_ecm240/recojet_isC_jet1;1', 'wzp6_ee_tautau_ecm240/recojet_isB_jet0;1', 'wzp6_ee_tautau_ecm240/recojet_isB_jet1;1', 'wzp6_ee_tautau_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_tautau_ecm240/dijet_z_m_reco;1', 'wzp6_ee_tautau_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_tautau_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautau_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautau_ecm240/mumu_recoil_m_nOne;1', 'wzp6_gammae_eZ_Zmumu_ecm240;1', 'wzp6_gammae_eZ_Zmumu_ecm240/meta;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muons_all_p_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muons_all_theta_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muons_all_phi_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muons_all_q_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muons_all_no_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/electrons_all_p_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/electrons_all_theta_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/electrons_all_phi_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/electrons_all_q_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/electrons_all_no_cut0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/cutFlow;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muon1_p;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muon2_p;1', 'wzp6_gammae_eZ_Zmumu_ecm240/zmumu_m_nOne;1', 'wzp6_gammae_eZ_Zmumu_ecm240/zmumu_m;1', 'wzp6_gammae_eZ_Zmumu_ecm240/muons_no;1', 'wzp6_gammae_eZ_Zmumu_ecm240/electrons_no;1', 'wzp6_gammae_eZ_Zmumu_ecm240/jet_p;1', 'wzp6_gammae_eZ_Zmumu_ecm240/jet_nconst;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isQ_jet0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isQ_jet1;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isS_jet0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isS_jet1;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isG_jet0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isG_jet1;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isC_jet0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isC_jet1;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isB_jet0;1', 'wzp6_gammae_eZ_Zmumu_ecm240/recojet_isB_jet1;1', 'wzp6_gammae_eZ_Zmumu_ecm240/dijet_higgs_m_reco;1', 'wzp6_gammae_eZ_Zmumu_ecm240/dijet_z_m_reco;1', 'wzp6_gammae_eZ_Zmumu_ecm240/dijet_higgs_m_mc;1', 'wzp6_gammae_eZ_Zmumu_ecm240/mumu_p_nOne;1', 'wzp6_gammae_eZ_Zmumu_ecm240/missingEnergy_nOne;1', 'wzp6_gammae_eZ_Zmumu_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumu_ecm240;1', 'wzp6_ee_mumu_ecm240/meta;1', 'wzp6_ee_mumu_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumu_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumu_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumu_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumu_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumu_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumu_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumu_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumu_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumu_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumu_ecm240/cutFlow;1', 'wzp6_ee_mumu_ecm240/muon1_p;1', 'wzp6_ee_mumu_ecm240/muon2_p;1', 'wzp6_ee_mumu_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumu_ecm240/zmumu_m;1', 'wzp6_ee_mumu_ecm240/muons_no;1', 'wzp6_ee_mumu_ecm240/electrons_no;1', 'wzp6_ee_mumu_ecm240/jet_p;1', 'wzp6_ee_mumu_ecm240/jet_nconst;1', 'wzp6_ee_mumu_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_mumu_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_mumu_ecm240/recojet_isS_jet0;1', 'wzp6_ee_mumu_ecm240/recojet_isS_jet1;1', 'wzp6_ee_mumu_ecm240/recojet_isG_jet0;1', 'wzp6_ee_mumu_ecm240/recojet_isG_jet1;1', 'wzp6_ee_mumu_ecm240/recojet_isC_jet0;1', 'wzp6_ee_mumu_ecm240/recojet_isC_jet1;1', 'wzp6_ee_mumu_ecm240/recojet_isB_jet0;1', 'wzp6_ee_mumu_ecm240/recojet_isB_jet1;1', 'wzp6_ee_mumu_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_mumu_ecm240/dijet_z_m_reco;1', 'wzp6_ee_mumu_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_mumu_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumu_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumu_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_WW_ecm240;1', 'p8_ee_WW_ecm240/meta;1', 'p8_ee_WW_ecm240/muons_all_p_cut0;1', 'p8_ee_WW_ecm240/muons_all_theta_cut0;1', 'p8_ee_WW_ecm240/muons_all_phi_cut0;1', 'p8_ee_WW_ecm240/muons_all_q_cut0;1', 'p8_ee_WW_ecm240/muons_all_no_cut0;1', 'p8_ee_WW_ecm240/electrons_all_p_cut0;1', 'p8_ee_WW_ecm240/electrons_all_theta_cut0;1', 'p8_ee_WW_ecm240/electrons_all_phi_cut0;1', 'p8_ee_WW_ecm240/electrons_all_q_cut0;1', 'p8_ee_WW_ecm240/electrons_all_no_cut0;1', 'p8_ee_WW_ecm240/cutFlow;1', 'p8_ee_WW_ecm240/muon1_p;1', 'p8_ee_WW_ecm240/muon2_p;1', 'p8_ee_WW_ecm240/zmumu_m_nOne;1', 'p8_ee_WW_ecm240/zmumu_m;1', 'p8_ee_WW_ecm240/muons_no;1', 'p8_ee_WW_ecm240/electrons_no;1', 'p8_ee_WW_ecm240/jet_p;1', 'p8_ee_WW_ecm240/jet_nconst;1', 'p8_ee_WW_ecm240/recojet_isQ_jet0;1', 'p8_ee_WW_ecm240/recojet_isQ_jet1;1', 'p8_ee_WW_ecm240/recojet_isS_jet0;1', 'p8_ee_WW_ecm240/recojet_isS_jet1;1', 'p8_ee_WW_ecm240/recojet_isG_jet0;1', 'p8_ee_WW_ecm240/recojet_isG_jet1;1', 'p8_ee_WW_ecm240/recojet_isC_jet0;1', 'p8_ee_WW_ecm240/recojet_isC_jet1;1', 'p8_ee_WW_ecm240/recojet_isB_jet0;1', 'p8_ee_WW_ecm240/recojet_isB_jet1;1', 'p8_ee_WW_ecm240/dijet_higgs_m_reco;1', 'p8_ee_WW_ecm240/dijet_z_m_reco;1', 'p8_ee_WW_ecm240/dijet_higgs_m_mc;1', 'p8_ee_WW_ecm240/mumu_p_nOne;1', 'p8_ee_WW_ecm240/missingEnergy_nOne;1', 'p8_ee_WW_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240;1', 'p8_ee_ZZ_ecm240/meta;1', 'p8_ee_ZZ_ecm240/muons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/cutFlow;1', 'p8_ee_ZZ_ecm240/muon1_p;1', 'p8_ee_ZZ_ecm240/muon2_p;1', 'p8_ee_ZZ_ecm240/zmumu_m_nOne;1', 'p8_ee_ZZ_ecm240/zmumu_m;1', 'p8_ee_ZZ_ecm240/muons_no;1', 'p8_ee_ZZ_ecm240/electrons_no;1', 'p8_ee_ZZ_ecm240/jet_p;1', 'p8_ee_ZZ_ecm240/jet_nconst;1', 'p8_ee_ZZ_ecm240/recojet_isQ_jet0;1', 'p8_ee_ZZ_ecm240/recojet_isQ_jet1;1', 'p8_ee_ZZ_ecm240/recojet_isS_jet0;1', 'p8_ee_ZZ_ecm240/recojet_isS_jet1;1', 'p8_ee_ZZ_ecm240/recojet_isG_jet0;1', 'p8_ee_ZZ_ecm240/recojet_isG_jet1;1', 'p8_ee_ZZ_ecm240/recojet_isC_jet0;1', 'p8_ee_ZZ_ecm240/recojet_isC_jet1;1', 'p8_ee_ZZ_ecm240/recojet_isB_jet0;1', 'p8_ee_ZZ_ecm240/recojet_isB_jet1;1', 'p8_ee_ZZ_ecm240/dijet_higgs_m_reco;1', 'p8_ee_ZZ_ecm240/dijet_z_m_reco;1', 'p8_ee_ZZ_ecm240/dijet_higgs_m_mc;1', 'p8_ee_ZZ_ecm240/mumu_p_nOne;1', 'p8_ee_ZZ_ecm240/missingEnergy_nOne;1', 'p8_ee_ZZ_ecm240/mumu_recoil_m_nOne;1', 'wzp6_egamma_eZ_Zmumu_ecm240;1', 'wzp6_egamma_eZ_Zmumu_ecm240/meta;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muons_all_p_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muons_all_theta_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muons_all_phi_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muons_all_q_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muons_all_no_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/electrons_all_p_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/electrons_all_theta_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/electrons_all_phi_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/electrons_all_q_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/electrons_all_no_cut0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/cutFlow;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muon1_p;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muon2_p;1', 'wzp6_egamma_eZ_Zmumu_ecm240/zmumu_m_nOne;1', 'wzp6_egamma_eZ_Zmumu_ecm240/zmumu_m;1', 'wzp6_egamma_eZ_Zmumu_ecm240/muons_no;1', 'wzp6_egamma_eZ_Zmumu_ecm240/electrons_no;1', 'wzp6_egamma_eZ_Zmumu_ecm240/jet_p;1', 'wzp6_egamma_eZ_Zmumu_ecm240/jet_nconst;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isQ_jet0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isQ_jet1;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isS_jet0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isS_jet1;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isG_jet0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isG_jet1;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isC_jet0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isC_jet1;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isB_jet0;1', 'wzp6_egamma_eZ_Zmumu_ecm240/recojet_isB_jet1;1', 'wzp6_egamma_eZ_Zmumu_ecm240/dijet_higgs_m_reco;1', 'wzp6_egamma_eZ_Zmumu_ecm240/dijet_z_m_reco;1', 'wzp6_egamma_eZ_Zmumu_ecm240/dijet_higgs_m_mc;1', 'wzp6_egamma_eZ_Zmumu_ecm240/mumu_p_nOne;1', 'wzp6_egamma_eZ_Zmumu_ecm240/missingEnergy_nOne;1', 'wzp6_egamma_eZ_Zmumu_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240;1', 'wzp6_ee_nunuH_Hbb_ecm240/meta;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_nunuH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_nunuH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_no;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_nunuH_Hbb_ecm240/jet_p;1', 'wzp6_ee_nunuH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_nunuH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_nunuH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_nunuH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_nunuH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240;1', 'wzp6_ee_eeH_Hbb_ecm240/meta;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_eeH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_eeH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_no;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_eeH_Hbb_ecm240/jet_p;1', 'wzp6_ee_eeH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_eeH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_eeH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_eeH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_eeH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240;1', 'wzp6_ee_tautauH_Hbb_ecm240/meta;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_tautauH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_tautauH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_no;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_tautauH_Hbb_ecm240/jet_p;1', 'wzp6_ee_tautauH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_tautauH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_tautauH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_tautauH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_tautauH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240;1', 'wzp6_ee_ccH_Hbb_ecm240/meta;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_ccH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_ccH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_no;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_ccH_Hbb_ecm240/jet_p;1', 'wzp6_ee_ccH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_ccH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_ccH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_ccH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_ccH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240;1', 'wzp6_ee_bbH_Hbb_ecm240/meta;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_bbH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_bbH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_no;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_bbH_Hbb_ecm240/jet_p;1', 'wzp6_ee_bbH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_bbH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_bbH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_bbH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_bbH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240;1', 'wzp6_ee_qqH_Hbb_ecm240/meta;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_qqH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_qqH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_no;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_qqH_Hbb_ecm240/jet_p;1', 'wzp6_ee_qqH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_qqH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_qqH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_qqH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_qqH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240;1', 'wzp6_ee_ssH_Hbb_ecm240/meta;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_ssH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_ssH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_no;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_ssH_Hbb_ecm240/jet_p;1', 'wzp6_ee_ssH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_ssH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_ssH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_ssH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_ssH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240;1', 'wzp6_ee_mumuH_Hbb_ecm240/meta;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow;1', 'wzp6_ee_mumuH_Hbb_ecm240/muon1_p;1', 'wzp6_ee_mumuH_Hbb_ecm240/muon2_p;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_no;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_no;1', 'wzp6_ee_mumuH_Hbb_ecm240/jet_p;1', 'wzp6_ee_mumuH_Hbb_ecm240/jet_nconst;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isQ_jet0;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isQ_jet1;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isS_jet0;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isS_jet1;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isG_jet0;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isG_jet1;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isC_jet0;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isC_jet1;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isB_jet0;1', 'wzp6_ee_mumuH_Hbb_ecm240/recojet_isB_jet1;1', 'wzp6_ee_mumuH_Hbb_ecm240/dijet_higgs_m_reco;1', 'wzp6_ee_mumuH_Hbb_ecm240/dijet_z_m_reco;1', 'wzp6_ee_mumuH_Hbb_ecm240/dijet_higgs_m_mc;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_recoil_m_nOne;1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "f_mumu = uproot.open(\"/home/submit/jakedlee/FCCAnalyzer/test.root\")\n",
+    "print(f_mumu.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fbdea41c-744e-4a2e-9e20-c7ceec8e21dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unnormalize(sample, hist, L=7200000):\n",
+    "    # w = L * sigma / Nevents\n",
+    "    w = L * f_mumu[sample + '/meta'].values()[2] / f_mumu[sample + '/meta'].values()[1]\n",
+    "    return f_mumu[sample + '/' + hist].values() / w\n",
+    "\n",
+    "def xsec(decay_product):\n",
+    "    bkg_samples = list(filter(lambda x : x != f'wzp6_ee_{decay_product}H_Hbb_ecm240', bb_sig)) + ['p8_ee_WW_ecm240', 'p8_ee_ZZ_ecm240']\n",
+    "    L = 7200000\n",
+    "    \n",
+    "    nums = f_mumu[f'wzp6_ee_{decay_product}H_Hbb_ecm240/cutFlow'].values()\n",
+    "    maxi = np.nonzero(nums)[0][-1]\n",
+    "    nsig = nums[maxi]\n",
+    "    nbkg = sum([f_mumu[f'{x}/cutFlow'].values()[maxi] for x in bkg_samples])\n",
+    "    nobs = nsig + nbkg\n",
+    "    A    = nums[maxi] / (L * f_mumu[f'wzp6_ee_{decay_product}H_Hbb_ecm240/meta'].values()[2])\n",
+    "    E    = 1\n",
+    "    \n",
+    "    xsec = (nobs - nbkg) / (A*E*L)\n",
+    "    \n",
+    "    dNobs = 1 / np.sqrt(nobs)\n",
+    "    dNbkg = np.sqrt(sum([(L * f_mumu[x + '/meta'].values()[2] * np.sqrt(unnormalize(x, f'cutFlow')[maxi] * (f_mumu[x + '/meta'].values()[1] - unnormalize(x, f'cutFlow')[maxi]) / f_mumu[x + '/meta'].values()[1]**3))**2 for x in bkg_samples]))\n",
+    "    dA    = np.sqrt(A * (1-A) / f_mumu[f'wzp6_ee_{decay_product}H_Hbb_ecm240/meta'].values()[1])\n",
+    "    dL    = 0.3e-6\n",
+    "    dxsec = np.sqrt((dNobs/(A*E*L))**2 + (dNbkg/(A*E*L))**2 + (dA*xsec/A)**2 + (dL*xsec/L)**2)\n",
+    "    print(\"Acceptance:\", A, \"\\nEfficiency:\", E, \"\\nNsig:\", nsig, \"\\nNbkg:\", nbkg)\n",
+    "    print(\"dNobs\", dNobs, \"dNbkg\", dNbkg, \"dA\", dA)\n",
+    "    print(f\"ee->ZH->{decay_product}bb Cross Section: {xsec*1000:.2f} +- {dxsec*1000:.3f} fb ({100*dxsec / xsec:.2f}%)\")\n",
+    "    \n",
+    "    return xsec, dxsec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b7924c7b-e5f5-4c77-84bc-0f67d6c09ac4",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyInFileError",
+     "evalue": "not found: 'cutFlow' (with any cycle number)\n\n    Available keys: 'cutFlow_ee;1', 'cutFlow_qq;1', 'cutFlow_ss;1', 'cutFlow_cc;1', 'cutFlow_bb;1', 'cutFlow_mumu;1', 'cutFlow_nunu;1', 'meta;1', 'mumu_p_nOne;1', 'ee_p_nOne;1', 'zmumu_m_nOne;1', 'zee_m_nOne;1', 'muons_all_p_cut0;1'...\n\nin file /home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyInFileError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mxsec\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmumu\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[25], line 10\u001b[0m, in \u001b[0;36mxsec\u001b[0;34m(decay_product)\u001b[0m\n\u001b[1;32m      7\u001b[0m bkg_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mfilter\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x : x \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwzp6_ee_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdecay_product\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mH_Hbb_ecm240\u001b[39m\u001b[38;5;124m'\u001b[39m, bb_sig)) \u001b[38;5;241m+\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp8_ee_WW_ecm240\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp8_ee_ZZ_ecm240\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      8\u001b[0m L \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m7200000\u001b[39m\n\u001b[0;32m---> 10\u001b[0m nums \u001b[38;5;241m=\u001b[39m \u001b[43mf_mumu\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwzp6_ee_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mdecay_product\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43mH_Hbb_ecm240/cutFlow\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalues()\n\u001b[1;32m     11\u001b[0m maxi \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnonzero(nums)[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     12\u001b[0m nsig \u001b[38;5;241m=\u001b[39m nums[maxi]\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2095\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2094\u001b[0m         last \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m-> 2095\u001b[0m         step \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2097\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, uproot\u001b[38;5;241m.\u001b[39mbehaviors\u001b[38;5;241m.\u001b[39mTBranch\u001b[38;5;241m.\u001b[39mHasBranches):\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items[i:])]\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2112\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step\n\u001b[1;32m   2111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2062\u001b[0m, in \u001b[0;36mReadOnlyDirectory.key\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m last\n\u001b[1;32m   2061\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cycle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2062\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2063\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2064\u001b[0m     )\n\u001b[1;32m   2065\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2066\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2067\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39mcycle, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2068\u001b[0m     )\n",
+      "\u001b[0;31mKeyInFileError\u001b[0m: not found: 'cutFlow' (with any cycle number)\n\n    Available keys: 'cutFlow_ee;1', 'cutFlow_qq;1', 'cutFlow_ss;1', 'cutFlow_cc;1', 'cutFlow_bb;1', 'cutFlow_mumu;1', 'cutFlow_nunu;1', 'meta;1', 'mumu_p_nOne;1', 'ee_p_nOne;1', 'zmumu_m_nOne;1', 'zee_m_nOne;1', 'muons_all_p_cut0;1'...\n\nin file /home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root"
+     ]
+    }
+   ],
+   "source": [
+    "xsec('mumu')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e783fe75-1277-40d4-9a31-8107b8362a9e",
+   "metadata": {},
+   "source": [
+    "<h1>Analysis of WW Background</h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "61bec6a9-91f5-4c85-96ea-b11486abb34f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['wzp6_ee_nunuH_Hbb_ecm240;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_nunuH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_nunuH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_nunuH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_nunuH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_nunuH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_nunuH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_nunuH_Hbb_ecm240/meta;1', 'wzp6_ee_eeH_Hbb_ecm240;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_eeH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_eeH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_eeH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_eeH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_eeH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_eeH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_eeH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_eeH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_eeH_Hbb_ecm240/meta;1', 'wzp6_ee_tautauH_Hbb_ecm240;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_tautauH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_tautauH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_tautauH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_tautauH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_tautauH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_tautauH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_tautauH_Hbb_ecm240/meta;1', 'wzp6_ee_ccH_Hbb_ecm240;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_ccH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_ccH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_ccH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_ccH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_ccH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_ccH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_ccH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_ccH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_ccH_Hbb_ecm240/meta;1', 'wzp6_ee_bbH_Hbb_ecm240;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_bbH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_bbH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_bbH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_bbH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_bbH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_bbH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_bbH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_bbH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_bbH_Hbb_ecm240/meta;1', 'wzp6_ee_qqH_Hbb_ecm240;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_qqH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_qqH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_qqH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_qqH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_qqH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_qqH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_qqH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_qqH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_qqH_Hbb_ecm240/meta;1', 'wzp6_ee_ssH_Hbb_ecm240;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_ssH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_ssH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_ssH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_ssH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_ssH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_ssH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_ssH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_ssH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_ssH_Hbb_ecm240/meta;1', 'wzp6_ee_mumuH_Hbb_ecm240;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hbb_ecm240/missingEnergy;1', 'wzp6_ee_mumuH_Hbb_ecm240/cosThetaMiss_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/acolinearity_mumu;1', 'wzp6_ee_mumuH_Hbb_ecm240/acolinearity_ee;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nocat;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmuons_m;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmuons_p;1', 'wzp6_ee_mumuH_Hbb_ecm240/zelectrons_m;1', 'wzp6_ee_mumuH_Hbb_ecm240/zelectrons_p;1', 'wzp6_ee_mumuH_Hbb_ecm240/zneutrinos_m;1', 'wzp6_ee_mumuH_Hbb_ecm240/zneutrinos_p;1', 'wzp6_ee_mumuH_Hbb_ecm240/meta;1', 'p8_ee_WW_ecm240;1', 'p8_ee_WW_ecm240/muons_all_p_cut0;1', 'p8_ee_WW_ecm240/muons_all_theta_cut0;1', 'p8_ee_WW_ecm240/muons_all_phi_cut0;1', 'p8_ee_WW_ecm240/muons_all_q_cut0;1', 'p8_ee_WW_ecm240/muons_all_no_cut0;1', 'p8_ee_WW_ecm240/electrons_all_p_cut0;1', 'p8_ee_WW_ecm240/electrons_all_theta_cut0;1', 'p8_ee_WW_ecm240/electrons_all_phi_cut0;1', 'p8_ee_WW_ecm240/electrons_all_q_cut0;1', 'p8_ee_WW_ecm240/electrons_all_no_cut0;1', 'p8_ee_WW_ecm240/cutFlow_mumu;1', 'p8_ee_WW_ecm240/cutFlow_ee;1', 'p8_ee_WW_ecm240/cutFlow_nunu;1', 'p8_ee_WW_ecm240/cutFlow_qq;1', 'p8_ee_WW_ecm240/missingEnergy;1', 'p8_ee_WW_ecm240/cosThetaMiss_nOne;1', 'p8_ee_WW_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_WW_ecm240/ee_recoil_m_nOne;1', 'p8_ee_WW_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'p8_ee_WW_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'p8_ee_WW_ecm240/mumu_p_nOne;1', 'p8_ee_WW_ecm240/ee_p_nOne;1', 'p8_ee_WW_ecm240/acolinearity_mumu;1', 'p8_ee_WW_ecm240/acolinearity_ee;1', 'p8_ee_WW_ecm240/zmumu_m_nOne;1', 'p8_ee_WW_ecm240/zee_m_nOne;1', 'p8_ee_WW_ecm240/zmumu_m_nocat;1', 'p8_ee_WW_ecm240/zmuons_m;1', 'p8_ee_WW_ecm240/zmuons_p;1', 'p8_ee_WW_ecm240/zelectrons_m;1', 'p8_ee_WW_ecm240/zelectrons_p;1', 'p8_ee_WW_ecm240/zneutrinos_m;1', 'p8_ee_WW_ecm240/zneutrinos_p;1', 'p8_ee_WW_ecm240/meta;1', 'p8_ee_ZZ_ecm240;1', 'p8_ee_ZZ_ecm240/muons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/cutFlow_mumu;1', 'p8_ee_ZZ_ecm240/cutFlow_ee;1', 'p8_ee_ZZ_ecm240/cutFlow_nunu;1', 'p8_ee_ZZ_ecm240/cutFlow_qq;1', 'p8_ee_ZZ_ecm240/missingEnergy;1', 'p8_ee_ZZ_ecm240/cosThetaMiss_nOne;1', 'p8_ee_ZZ_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/ee_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/missingEnergy_vs_zmumu_recoil_m;1', 'p8_ee_ZZ_ecm240/zmumu_recoil_m_vs_missingEnergy;1', 'p8_ee_ZZ_ecm240/mumu_p_nOne;1', 'p8_ee_ZZ_ecm240/ee_p_nOne;1', 'p8_ee_ZZ_ecm240/acolinearity_mumu;1', 'p8_ee_ZZ_ecm240/acolinearity_ee;1', 'p8_ee_ZZ_ecm240/zmumu_m_nOne;1', 'p8_ee_ZZ_ecm240/zee_m_nOne;1', 'p8_ee_ZZ_ecm240/zmumu_m_nocat;1', 'p8_ee_ZZ_ecm240/zmuons_m;1', 'p8_ee_ZZ_ecm240/zmuons_p;1', 'p8_ee_ZZ_ecm240/zelectrons_m;1', 'p8_ee_ZZ_ecm240/zelectrons_p;1', 'p8_ee_ZZ_ecm240/zneutrinos_m;1', 'p8_ee_ZZ_ecm240/zneutrinos_p;1', 'p8_ee_ZZ_ecm240/meta;1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = uproot.open(\"/home/submit/jakedlee/FCCAnalyzer/uncut_h_bb.root\")\n",
+    "print(df.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e6a9b7f4-fb47-4bec-b337-709e7d4ecc16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hep.histplot(df['p8_ee_WW_ecm240/missingEnergy'].to_hist())\n",
+    "plt.xlim(0, 50)\n",
+    "plt.vlines(30, 0, 800000, color='orange')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "6376aabf-3435-430b-b2f8-ac4079e29dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hep.histplot(df['p8_ee_WW_ecm240/mumu_recoil_m_nOne'].to_hist())\n",
+    "plt.xlim(100, 150)\n",
+    "plt.vlines([122, 127], [0, 0], [25000, 25000], colors=['orange', 'orange'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b70a467b-cde0-40f6-ad86-2dd8f7240178",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "me = df['p8_ee_WW_ecm240/missingEnergy_vs_zmumu_recoil_m'].values()[0]\n",
+    "re = df['p8_ee_WW_ecm240/zmumu_recoil_m_vs_missingEnergy'].values()[0]\n",
+    "\n",
+    "plt.scatter(me, re, marker='.')\n",
+    "\n",
+    "plt.title(\"Uncut WW Background Events\")\n",
+    "plt.xlabel(\"Missing Energy\")\n",
+    "plt.ylabel(\"Recoil Mass\")\n",
+    "\n",
+    "plt.gca().add_patch(Rectangle((0, 122), 30, 5, edgecolor='orange', fill=False, label='Cuts'))\n",
+    "\n",
+    "plt.xlim(0, 200)\n",
+    "plt.ylim(0, 200)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93ae5f13-6f51-42ef-80d5-054f3b54b069",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "FCC-ee",
+   "language": "python",
+   "name": "fcc-ee"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/analyses/h_bb/h_bb.py b/analyses/h_bb/h_bb.py
new file mode 100644
index 0000000..57c98ab
--- /dev/null
+++ b/analyses/h_bb/h_bb.py
@@ -0,0 +1,468 @@
+import functions
+import helpers
+import ROOT
+import argparse
+import logging
+
+import helper_jetclustering
+import helper_flavourtagger
+from examples.FCCee.weaver.config import collections, njets
+
+logger = logging.getLogger("fcclogger")
+
+parser = functions.make_def_argparser()
+args = parser.parse_args()
+functions.set_threads(args)
+
+functions.add_include_file("analyses/higgs_mass_xsec/functions.h")
+functions.add_include_file("analyses/higgs_mass_xsec/functions_gen.h")
+
+
+# define histograms
+
+bins_m = (250, 0, 250)
+bins_p = (200, 0, 200)
+bins_m_zoom = (200, 110, 130) # 100 MeV
+
+
+bins_theta = (500, 0, 5)
+bins_phi = (400, -4, 4)
+
+bins_count = (100, 0, 100)
+bins_pdgid = (60, -30, 30)
+bins_charge = (10, -5, 5)
+
+bins_resolution = (10000, 0.95, 1.05)
+bins_resolution_1 = (20000, 0, 2)
+
+jet_energy = (1000, 0, 100) # 100 MeV bins
+dijet_m = (2000, 0, 200) # 100 MeV bins
+visMass = (2000, 0, 200) # 100 MeV bins
+missEnergy  = (2000, 0, 200) # 100 MeV bins
+
+dijet_m_final = (500, 50, 100) # 100 MeV bins
+
+bins_cos = (100, -1, 1)
+bins_aco = (1000,-360,360)
+bins_cosThetaMiss = (10000, 0, 1)
+
+bins_prob = (400, 0, 2)
+bins_pfcand = (200, -10, 10)
+
+# setup clustering and flavour taggingv helper_jetclustering. helper_flavourtagger.
+# 2 jets
+
+jet2Cluster = helper_jetclustering.ExclusiveJetClusteringHelper(2, "rps_no_leps")
+jet2Flavour = helper_flavourtagger.JetFlavourHelper(collections, jet2Cluster.jets, jet2Cluster.constituents)
+# 4 jets
+
+jet4Cluster = helper_jetclustering.ExclusiveJetClusteringHelper(4, "ReconstructedParticles")
+jet4Flavour = helper_flavourtagger.JetFlavourHelper(collections, jet4Cluster.jets, jet4Cluster.constituents)
+
+path = "/home/submit/aniketkg/FCCAnalyzer_ag/data/flavourtagger/fccee_flavtagging_edm4hep_wc_v1"
+urlpath = "https://fccsw.web.cern.ch/fccsw/testsamples/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/fccee_flavtagging_edm4hep_wc_v1"
+jet2Flavour.load(f"{path}.json", f"{path}.onnx")
+jet4Flavour.load(f"{path}.json", f"{path}.onnx")
+
+def get_file_path(url, filename):
+    import os
+    if os.path.exists(filename):
+        return os.path.abspath(filename)
+    else:
+        import urllib.request
+        urllib.request.urlretrieve(url, os.path.basename(url))
+        return os.path.basename(url)
+
+weaver_preproc = get_file_path("{}.json".format(urlpath), "{}.json".format(path))
+weaver_model = get_file_path("{}.onnx".format(urlpath), "{}.onnx".format(path))
+
+def build_graph(df, dataset):
+
+    logging.info(f"build graph {dataset.name}")
+    results, cols = [], []
+
+    df = df.Define("weight", "1.0")
+    weightsum = df.Sum("weight")
+    df = helpers.defineCutFlowVars(df) # make the cutX=X variables
+    
+    # define collections
+    df = df.Alias("Particle0", "Particle#0.index")
+    df = df.Alias("Particle1", "Particle#1.index")
+    df = df.Alias("MCRecoAssociations0", "MCRecoAssociations#0.index")
+    df = df.Alias("MCRecoAssociations1", "MCRecoAssociations#1.index")
+
+
+    # muons
+    df = df.Alias("Muon0", "Muon#0.index")
+    df = df.Define("muons_all", "FCCAnalyses::ReconstructedParticle::get(Muon0, ReconstructedParticles)")
+    df = df.Define("muons_all_p", "FCCAnalyses::ReconstructedParticle::get_p(muons_all)")
+    df = df.Define("muons_all_theta", "FCCAnalyses::ReconstructedParticle::get_theta(muons_all)")
+    df = df.Define("muons_all_phi", "FCCAnalyses::ReconstructedParticle::get_phi(muons_all)")
+    df = df.Define("muons_all_q", "FCCAnalyses::ReconstructedParticle::get_charge(muons_all)")
+    df = df.Define("muons_all_no", "FCCAnalyses::ReconstructedParticle::get_n(muons_all)")
+
+    df = df.Define("muons", "FCCAnalyses::ReconstructedParticle::sel_p(25)(muons_all)")
+    df = df.Define("muons_p", "FCCAnalyses::ReconstructedParticle::get_p(muons)")
+    df = df.Define("muons_theta", "FCCAnalyses::ReconstructedParticle::get_theta(muons)")
+    df = df.Define("muons_phi", "FCCAnalyses::ReconstructedParticle::get_phi(muons)")
+    df = df.Define("muons_q", "FCCAnalyses::ReconstructedParticle::get_charge(muons)")
+    df = df.Define("muons_no", "FCCAnalyses::ReconstructedParticle::get_n(muons)")
+
+    
+    # electrons
+    df = df.Alias("Electron0", "Electron#0.index")
+    df = df.Define("electrons_all", "FCCAnalyses::ReconstructedParticle::get(Electron0, ReconstructedParticles)")
+    df = df.Define("electrons_all_p", "FCCAnalyses::ReconstructedParticle::get_p(electrons_all)")
+    df = df.Define("electrons_all_theta", "FCCAnalyses::ReconstructedParticle::get_theta(electrons_all)")
+    df = df.Define("electrons_all_phi", "FCCAnalyses::ReconstructedParticle::get_phi(electrons_all)")
+    df = df.Define("electrons_all_q", "FCCAnalyses::ReconstructedParticle::get_charge(electrons_all)")
+    df = df.Define("electrons_all_no", "FCCAnalyses::ReconstructedParticle::get_n(electrons_all)")
+
+    df = df.Define("electrons", "FCCAnalyses::ReconstructedParticle::sel_p(25)(electrons_all)")
+    df = df.Define("electrons_p", "FCCAnalyses::ReconstructedParticle::get_p(electrons)")
+    df = df.Define("electrons_theta", "FCCAnalyses::ReconstructedParticle::get_theta(electrons)")
+    df = df.Define("electrons_phi", "FCCAnalyses::ReconstructedParticle::get_phi(electrons)")
+    df = df.Define("electrons_q", "FCCAnalyses::ReconstructedParticle::get_charge(electrons)")
+    df = df.Define("electrons_no", "FCCAnalyses::ReconstructedParticle::get_n(electrons)")
+
+    # lepton kinematic histograms
+    results.append(df.Histo1D(("muons_all_p_cut0", "", *bins_p), "muons_all_p"))
+    results.append(df.Histo1D(("muons_all_theta_cut0", "", *bins_theta), "muons_all_theta"))
+    results.append(df.Histo1D(("muons_all_phi_cut0", "", *bins_phi), "muons_all_phi"))
+    results.append(df.Histo1D(("muons_all_q_cut0", "", *bins_charge), "muons_all_q"))
+    results.append(df.Histo1D(("muons_all_no_cut0", "", *bins_count), "muons_all_no"))
+
+    results.append(df.Histo1D(("electrons_all_p_cut0", "", *bins_p), "electrons_all_p"))
+    results.append(df.Histo1D(("electrons_all_theta_cut0", "", *bins_theta), "electrons_all_theta"))
+    results.append(df.Histo1D(("electrons_all_phi_cut0", "", *bins_phi), "electrons_all_phi"))
+    results.append(df.Histo1D(("electrons_all_q_cut0", "", *bins_charge), "electrons_all_q"))
+    results.append(df.Histo1D(("electrons_all_no_cut0", "", *bins_count), "electrons_all_no"))
+
+
+    #########
+    ### CUT 0: all events
+    #########
+    results.append(df.Histo1D(("cutFlow_mumu", "", *bins_count), "cut0"))
+    results.append(df.Histo1D(("cutFlow_ee", "", *bins_count), "cut0"))
+    results.append(df.Histo1D(("cutFlow_nunu", "", *bins_count), "cut0"))
+    results.append(df.Histo1D(("cutFlow_qq", "", *bins_count), "cut0"))
+    results.append(df.Histo1D(("cutFlow_ss", "", *bins_count), "cut0"))
+    results.append(df.Histo1D(("cutFlow_cc", "", *bins_count), "cut0"))
+    results.append(df.Histo1D(("cutFlow_bb", "", *bins_count), "cut0"))
+    
+
+    #########
+    ### CUT 1: select Z decay product
+    #########
+    df = df.Define("missingEnergy_rp", "FCCAnalyses::missingEnergy(240., ReconstructedParticles)")
+    df = df.Define("missingEnergy", "missingEnergy_rp[0].energy")
+    results.append(df.Histo1D(("missingEnergy_nOne", "", *missEnergy), "missingEnergy"))
+    
+    select_mumu = "muons_no == 2 && electrons_no == 0 && missingEnergy < 30"
+    select_ee   = "muons_no == 0 && electrons_no == 2 && missingEnergy < 30"
+    select_nunu = "muons_no == 0 && electrons_no == 0 && missingEnergy > 102 && missingEnergy < 110"
+    select_qq   = "muons_no == 0 && electrons_no == 0 && missingEnergy < 35"
+    
+    df_mumu   = df.Filter(select_mumu)
+    df_ee     = df.Filter(select_ee)
+    df_nunu   = df.Filter(select_nunu)
+    df_quarks = df.Filter(select_qq)
+    
+    results.append(df_mumu.Histo1D(("cutFlow_mumu", "", *bins_count), "cut1"))
+    results.append(df_ee.Histo1D(("cutFlow_ee", "", *bins_count), "cut1"))
+    results.append(df_nunu.Histo1D(("cutFlow_nunu", "", *bins_count), "cut1"))
+    results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut1"))
+    results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut1"))
+    results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut1"))
+    results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut1"))
+
+
+    #########
+    ### CUT 2: we want to detect Z->mumu/ee (so we don't have Z->qq interfering with measurement of H->bb)
+    ###        Z->qq and Z->nunu do not get the next few cuts
+    #########
+
+    # build the Z resonance based on the available leptons. Returns the best lepton pair compatible with the Z mass and recoil at 125 GeV
+    # technically, it returns a ReconstructedParticleData object with index 0 the di-lepton system (Z), index and 2 the leptons of the pair
+    
+    # muons
+    df_mumu = df_mumu.Define("hbuilder_result", "FCCAnalyses::resonanceBuilder_mass_recoil(91.2, 125, 0, 240, false)(muons, MCRecoAssociations0, MCRecoAssociations1, ReconstructedParticles, Particle, Particle0, Particle1)")
+    df_mumu = df_mumu.Filter("hbuilder_result.size() > 0")
+    
+    results.append(df_mumu.Histo1D(("cutFlow_mumu", "", *bins_count), "cut2"))
+
+    df_mumu = df_mumu.Define("zmumu", "ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData>{hbuilder_result[0]}") # the Z
+    df_mumu = df_mumu.Define("zmumu_tlv", "FCCAnalyses::makeLorentzVectors(zmumu)") # the muons
+    df_mumu = df_mumu.Define("zmumu_leps", "ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData>{hbuilder_result[1],hbuilder_result[2]}")
+    df_mumu = df_mumu.Define("zmumu_leps_tlv", "FCCAnalyses::makeLorentzVectors(zmumu_leps)")
+
+    df_mumu = df_mumu.Define("zmumu_m", "FCCAnalyses::ReconstructedParticle::get_mass(zmumu)[0]")
+    df_mumu = df_mumu.Define("zmumu_p", "FCCAnalyses::ReconstructedParticle::get_p(zmumu)[0]")
+    df_mumu = df_mumu.Define("zmumu_recoil", "FCCAnalyses::ReconstructedParticle::recoilBuilder(240)(zmumu)")
+    df_mumu = df_mumu.Define("zmumu_recoil_m", "FCCAnalyses::ReconstructedParticle::get_mass(zmumu_recoil)[0]")
+
+    # electrons
+    df_ee = df_ee.Define("hbuilder_result", "FCCAnalyses::resonanceBuilder_mass_recoil(91.2, 125, 0, 240, false)(electrons, MCRecoAssociations0, MCRecoAssociations1, ReconstructedParticles, Particle, Particle0, Particle1)")
+    df_ee = df_ee.Filter("hbuilder_result.size() > 0")
+    
+    results.append(df_ee.Histo1D(("cutFlow_ee", "", *bins_count), "cut2"))
+
+    df_ee = df_ee.Define("zee", "ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData>{hbuilder_result[0]}") # the Z
+    df_ee = df_ee.Define("zee_tlv", "FCCAnalyses::makeLorentzVectors(zee)") # the electrons
+    df_ee = df_ee.Define("zee_leps", "ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData>{hbuilder_result[1],hbuilder_result[2]}")
+    df_ee = df_ee.Define("zee_leps_tlv", "FCCAnalyses::makeLorentzVectors(zee_leps)")
+
+    df_ee = df_ee.Define("zee_m", "FCCAnalyses::ReconstructedParticle::get_mass(zee)[0]")
+    df_ee = df_ee.Define("zee_p", "FCCAnalyses::ReconstructedParticle::get_p(zee)[0]")
+    df_ee = df_ee.Define("zee_recoil", "FCCAnalyses::ReconstructedParticle::recoilBuilder(240)(zee)")
+    df_ee = df_ee.Define("zee_recoil_m", "FCCAnalyses::ReconstructedParticle::get_mass(zee_recoil)[0]")
+
+
+
+    #########
+    ### CUT 3: recoil cut (H mass)
+    #########
+    results.append(df_mumu.Histo1D(("mumu_recoil_m_nOne", "", *bins_m), "zmumu_recoil_m"))
+    df_mumu = df_mumu.Filter("zmumu_recoil_m > 123 && zmumu_recoil_m < 132")
+    results.append(df_mumu.Histo1D(("cutFlow_mumu", "", *bins_count), "cut3"))
+    
+    results.append(df_ee.Histo1D(("ee_recoil_m_nOne", "", *bins_m), "zee_recoil_m"))
+    df_ee = df_ee.Filter("zee_recoil_m > 123 && zee_recoil_m < 132")
+    results.append(df_ee.Histo1D(("cutFlow_ee", "", *bins_count), "cut3"))
+    
+    # graphs for inspecting the WW background
+    #results.append(df_mumu.Graph("missingEnergy", "zmumu_recoil_m"))
+    #results.append(df_mumu.Graph("zmumu_recoil_m", "missingEnergy"))
+    
+
+    #########
+    ### CUT 4: momentum
+    #########
+    results.append(df_mumu.Histo1D(("mumu_p_nOne", "", *bins_p), "zmumu_p"))
+    df_mumu = df_mumu.Filter("zmumu_p > 45 && zmumu_p < 55")
+    results.append(df_mumu.Histo1D(("cutFlow_mumu", "", *bins_count), "cut4"))
+
+    results.append(df_ee.Histo1D(("ee_p_nOne", "", *bins_p), "zee_p"))
+    df_ee = df_ee.Filter("zee_p > 45 && zee_p < 55")
+    results.append(df_ee.Histo1D(("cutFlow_ee", "", *bins_count), "cut4"))
+
+
+    #########
+    ### CUT 5: cut on Z mass
+    #########
+    results.append(df_mumu.Histo1D(("zmumu_m_nOne", "", *bins_m), "zmumu_m"))
+    df_mumu = df_mumu.Filter("zmumu_m > 85 && zmumu_m < 95")
+    results.append(df_mumu.Histo1D(("cutFlow_mumu", "", *bins_count), "cut5"))
+
+    results.append(df_ee.Histo1D(("zee_m_nOne", "", *bins_m), "zee_m"))
+    df_ee = df_ee.Filter("zee_m > 85 && zee_m < 95")
+    results.append(df_ee.Histo1D(("cutFlow_ee", "", *bins_count), "cut5"))
+
+
+    for leps, df in [("muons", df_mumu), ("electrons", df_ee), ("neutrinos", df_nunu)]:
+        # define PF candidates collection by removing the leptons
+        if leps != "neutrinos":
+            df = df.Define("rps_no_leps", f"FCCAnalyses::ReconstructedParticle::remove(ReconstructedParticles, {leps})")
+        else:
+            df = df.Alias("rps_no_leps", "ReconstructedParticles")
+        
+        # clustering
+        df = jet2Cluster.define(df)
+        df = df.Define("jet_tlv", "FCCAnalyses::makeLorentzVectors(jet_px, jet_py, jet_pz, jet_e)")
+        
+        # calculate dijet m and p
+        df = df.Define("dijet", "jet_tlv[0] + jet_tlv[1]")
+        df = df.Define("dijet_m", "dijet.M()")
+        df = df.Define("dijet_p", "dijet.P()")
+        
+        # for neutrinos, cut on Higgs mass and momentum
+        if leps == "neutrinos":
+            results.append(df.Histo1D((f"z{leps}_h_m_nOne", "", *bins_m), "dijet_m"))
+            results.append(df.Histo1D((f"z{leps}_h_p_nOne", "", *bins_m), "dijet_p"))
+            
+            df = df.Filter("dijet_p > 40 && dijet_p < 58")
+            results.append(df.Histo1D(("cutFlow_nunu", "", *bins_count), "cut2"))
+            
+            df = df.Filter("dijet_m > 115 && dijet_m < 128")
+            results.append(df.Histo1D(("cutFlow_nunu", "", *bins_count), "cut3"))
+        
+        # flavour tagging
+
+        df = jet2Flavour.define_and_inference(df)
+        
+        # cut on b jet confidence
+        df = df.Filter("recojet_isB[0] > 0.5 && recojet_isB[1] > 0.5")
+        
+        if leps != "neutrinos":
+            results.append(df.Histo1D((f"cutFlow_{'mumu' if leps == 'muons' else 'ee'}", "", *bins_count), "cut6"))
+            
+            # store the final recoil mass of ee and mumu for a fit
+            results.append(df.Histo1D((f"z{leps}_final_recoil_m", "", *bins_m), f"z{'mumu' if leps == 'muons' else 'ee'}_recoil_m"))
+        else:
+            results.append(df.Histo1D(("cutFlow_nunu", "", *bins_count), "cut4"))
+        
+        # store final dijet mass and momentum
+        results.append(df.Histo1D((f"z{leps}_h_m", "", *bins_m), "dijet_m"))
+        results.append(df.Histo1D((f"z{leps}_h_p", "", *bins_m), "dijet_p"))
+
+
+    if False:
+        # jet analysis for the case of 2 jets (Z -> leps)
+        
+
+
+
+        # Z->qq analyses
+        # clustering
+        df_quarks = jet4Cluster.define(df_quarks)
+        df_quarks = df_quarks.Define("jet_tlv", "FCCAnalyses::makeLorentzVectors(jet_px, jet_py, jet_pz, jet_e)")
+
+        # pair jets based on distance to Z and H masses
+        df_quarks = df_quarks.Define("zh_min_idx", """
+                FCCAnalyses::Vec_i min{0, 0, 0, 0};
+                float distm = INFINITY;
+                for (int i = 0; i < 3; i++)
+                    for (int j = i + 1; j < 4; j++)
+                        for (int k = 0; k < 3; k++) {
+                            if (i == k || j == k) continue;
+                            for (int l = k + 1; l < 4; l++) {
+                                if (i == l || j == l) continue;
+                                float distz = (jet_tlv[i] + jet_tlv[j]).M() - 91.2;
+                                float disth = (jet_tlv[k] + jet_tlv[l]).M() - 125;
+                                if (distz*distz/91.2 + disth*disth/125 < distm) {
+                                    distm = distz*distz/91.2 + disth*disth/125;
+                                    min[0] = i; min[1] = j; min[2] = k; min[3] = l;
+                                }
+                            }
+                        }
+                return min;""")
+
+        # compute Z and H masses and momenta
+        df_quarks = df_quarks.Define("z_dijet", "jet_tlv[zh_min_idx[0]] + jet_tlv[zh_min_idx[1]]")
+        df_quarks = df_quarks.Define("h_dijet", "jet_tlv[zh_min_idx[2]] + jet_tlv[zh_min_idx[3]]")
+
+        df_quarks = df_quarks.Define("z_dijet_m", "z_dijet.M()")
+        df_quarks = df_quarks.Define("z_dijet_p", "z_dijet.P()")
+        df_quarks = df_quarks.Define("h_dijet_m", "h_dijet.M()")
+        df_quarks = df_quarks.Define("h_dijet_p", "h_dijet.P()")
+
+        results.append(df_quarks.Histo1D(("quarks_z_m_nOne", "", *bins_m), "z_dijet_m"))
+        results.append(df_quarks.Histo1D(("quarks_z_p_nOne", "", *bins_m), "z_dijet_p"))
+        results.append(df_quarks.Histo1D(("quarks_h_m_nOne", "", *bins_m), "h_dijet_m"))
+        results.append(df_quarks.Histo1D(("quarks_h_p_nOne", "", *bins_m), "h_dijet_p"))
+
+        # filter on Z momentum
+        df_quarks = df_quarks.Filter("z_dijet_p > 45 && z_dijet_p < 56")
+        results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut2"))
+        results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut2"))
+        results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut2"))
+        results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut2"))
+
+        # filter on H mass
+        df_quarks = df_quarks.Filter("h_dijet_m > 122 && h_dijet_m < 128")
+        results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut3"))
+        results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut3"))
+        results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut3"))
+        results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut3"))
+
+
+        # flavour tagging
+        df_quarks = jet4Flavour.define_and_inference(df_quarks)
+
+        # get tag confidence
+        df_quarks = df_quarks.Define("Hbb_prob", "std::min(recojet_isB[zh_min_idx[2]], recojet_isB[zh_min_idx[3]])")
+        results.append(df_quarks.Histo1D(("Hbb_prob_nOne", "", *bins_prob), "Hbb_prob"))
+
+        df_quarks = df_quarks.Define("Zbb_prob", "std::min(recojet_isB[zh_min_idx[0]], recojet_isB[zh_min_idx[1]])")
+        df_quarks = df_quarks.Define("Zcc_prob", "std::min(recojet_isC[zh_min_idx[0]], recojet_isC[zh_min_idx[1]])")
+        df_quarks = df_quarks.Define("Zss_prob", "std::min(recojet_isS[zh_min_idx[0]], recojet_isS[zh_min_idx[1]])")
+        df_quarks = df_quarks.Define("Zqq_prob", "std::min(recojet_isQ[zh_min_idx[0]], recojet_isQ[zh_min_idx[1]])")
+
+        results.append(df_quarks.Histo1D(("Zbb_prob_nOne", "", *bins_prob), "Zbb_prob"))
+        results.append(df_quarks.Histo1D(("Zcc_prob_nOne", "", *bins_prob), "Zcc_prob"))
+        results.append(df_quarks.Histo1D(("Zss_prob_nOne", "", *bins_prob), "Zss_prob"))
+        results.append(df_quarks.Histo1D(("Zqq_prob_nOne", "", *bins_prob), "Zqq_prob"))
+
+        # sort by most likely tag
+        df_quarks = df_quarks.Define("Zbb_like", "recojet_isB[zh_min_idx[0]] + recojet_isB[zh_min_idx[1]]")
+        df_quarks = df_quarks.Define("Zcc_like", "recojet_isC[zh_min_idx[0]] + recojet_isC[zh_min_idx[1]]")
+        df_quarks = df_quarks.Define("Zss_like", "recojet_isS[zh_min_idx[0]] + recojet_isS[zh_min_idx[1]]")
+        df_quarks = df_quarks.Define("Zqq_like", "recojet_isQ[zh_min_idx[0]] + recojet_isQ[zh_min_idx[1]]")
+
+        df_quarks = df_quarks.Define("best_tag", """
+                if (Zbb_like > Zcc_like && Zbb_like > Zss_like && Zbb_like > Zqq_like) {
+                    return 0;
+                } else if (Zcc_like > Zss_like && Zcc_like > Zqq_like) {
+                    return 1;
+                } else if (Zss_like > Zqq_like) {
+                    return 2;
+                } else {
+                    return 3;
+                } """)
+
+        # sort by maximum likelihood tag
+        df_bb = df_quarks.Filter("best_tag == 0")
+        df_cc = df_quarks.Filter("best_tag == 1")
+        df_ss = df_quarks.Filter("best_tag == 2")
+        df_qq = df_quarks.Filter("best_tag == 3")
+
+        results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut4"))
+        results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut4"))
+        results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut4"))
+        results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut4"))
+
+        results.append(df_bb.Graph("Hbb_prob", "Zbb_prob"))
+        results.append(df_cc.Graph("Hbb_prob", "Zcc_prob"))
+        results.append(df_ss.Graph("Hbb_prob", "Zss_prob"))
+        results.append(df_qq.Graph("Hbb_prob", "Zqq_prob"))
+
+        # make sure there are two b jets
+        df_qq = df_qq.Filter("Hbb_prob > 0.032")
+        df_ss = df_ss.Filter("Hbb_prob > 0.032")
+        df_cc = df_cc.Filter("Hbb_prob > 0.029")
+        df_bb = df_bb.Filter("Hbb_prob > 0.011")
+
+        results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut5"))
+        results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut5"))
+        results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut5"))
+        results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut5"))
+
+        # check that the Z jets are the right type
+        df_bb = df_bb.Filter("Zbb_prob > 0.042")
+        df_cc = df_cc.Filter("Zcc_prob > 0.134")
+        df_ss = df_ss.Filter("Zss_prob > 0.095")
+        df_qq = df_qq.Filter("Zqq_prob > 0.053")
+
+        results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut6"))
+        results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut6"))
+        results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut6"))
+        results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut6"))
+
+        # make final mass and momentum histograms
+        for q, df in [("bb", df_bb), ("cc", df_cc), ("ss", df_ss), ("qq", df_qq)]:
+            results.append(df.Histo1D((f"z{q}_z_m", "", *bins_m), "z_dijet_m"))
+            results.append(df.Histo1D((f"z{q}_h_m", "", *bins_m), "h_dijet_m"))
+            results.append(df.Histo1D((f"z{q}_z_p", "", *bins_m), "z_dijet_p"))
+            results.append(df.Histo1D((f"z{q}_h_p", "", *bins_m), "h_dijet_p"))
+
+
+    
+    return results, weightsum
+
+
+if __name__ == "__main__":
+
+    datadict = functions.get_datadicts() # get default datasets
+
+    Zprods = ["ee", "mumu", "tautau", "nunu", "qq", "ss", "cc", "bb"] 
+    bb_sig = [f"wzp6_ee_{i}H_Hbb_ecm240" for i in Zprods]
+    cc_sig = [f"wzp6_ee_{i}H_Hcc_ecm240" for i in Zprods]
+    gg_sig = [f"wzp6_ee_{i}H_Hgg_ecm240" for i in Zprods]
+    
+    quark_test = ["wzp6_ee_qqH_Hbb_ecm240", "wzp6_ee_ssH_Hbb_ecm240", "wzp6_ee_ccH_Hbb_ecm240", "wzp6_ee_bbH_Hbb_ecm240"]
+    
+    datasets_bkg = ["p8_ee_WW_ecm240", "p8_ee_ZZ_ecm240", "wzp6_ee_mumu_ecm240", "wzp6_ee_tautau_ecm240", "wzp6_egamma_eZ_Zmumu_ecm240", "wzp6_gammae_eZ_Zmumu_ecm240", "wzp6_gaga_mumu_60_ecm240", "wzp6_gaga_tautau_60_ecm240", "wzp6_ee_nuenueZ_ecm240"]
+
+    datasets_to_run = bb_sig + cc_sig + gg_sig + datasets_bkg[:2]
+    
+    result = functions.build_and_run(datadict, datasets_to_run, build_graph, f"end_of_h_bb.root", args, norm=True, lumi=7200000)

From 087d607157ad0c51285b061f00e6f5d0c698f016 Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Thu, 23 Jan 2025 10:18:07 -0500
Subject: [PATCH 2/7] added Jan's tagging example

---
 analyses/examples/tag2.py        | 115 +++++++++
 analyses/examples/tag2plotter.py |  35 +++
 analyses/examples/tagfuncs.h     | 411 +++++++++++++++++++++++++++++++
 analyses/examples/tagfuncs_gen.h |  49 ++++
 4 files changed, 610 insertions(+)
 create mode 100644 analyses/examples/tag2.py
 create mode 100644 analyses/examples/tag2plotter.py
 create mode 100644 analyses/examples/tagfuncs.h
 create mode 100644 analyses/examples/tagfuncs_gen.h

diff --git a/analyses/examples/tag2.py b/analyses/examples/tag2.py
new file mode 100644
index 0000000..c7d2617
--- /dev/null
+++ b/analyses/examples/tag2.py
@@ -0,0 +1,115 @@
+
+import ROOT
+#import functions
+ROOT.TH1.SetDefaultSumw2(ROOT.kTRUE)
+
+
+
+# list of all processes
+fraction = 0.05
+processList = {
+    'wzp6_ee_nunuH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hss_ecm240':          {'fraction':fraction},
+}
+
+
+
+
+inputDir = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/"
+procDict = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/samplesDict.json"
+
+# additional/custom C++ functions
+includePaths = ["tagfuncs.h", "tagfuncs_gen.h"]
+
+
+# output directory
+outputDir   = "output/clustering_tagging/histmaker/"
+
+
+# optional: ncpus, default is 4, -1 uses all cores available
+nCPUS       = 24
+
+# scale the histograms with the cross-section and integrated luminosity
+doScale = True
+intLumi = 10800000
+
+# define histograms
+bins_m = (250, 0, 250) # 100 MeV bins
+bins_score = (100, 0, 1)
+
+
+################################################################################
+## load modules and files for jet clustering and flavor tagging
+################################################################################
+## latest particle transformer model, trainied on 9M jets in winter2023 samples
+model_name = "fccee_flavtagging_edm4hep_wc_v1"
+
+# model files needed for unit testing in CI
+url_model_dir = "https://fccsw.web.cern.ch/fccsw/testsamples/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/"
+url_preproc = "{}/{}.json".format(url_model_dir, model_name)
+url_model = "{}/{}.onnx".format(url_model_dir, model_name)
+
+# model files locally stored on /eos
+model_dir = "/eos/experiment/fcc/ee/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/"
+local_preproc = "{}/{}.json".format(model_dir, model_name)
+local_model = "{}/{}.onnx".format(model_dir, model_name)
+
+# get local file, else download from url
+def get_file_path(url, filename):
+    import os
+    if os.path.exists(filename):
+        return os.path.abspath(filename)
+    else:
+        import urllib.request
+        urllib.request.urlretrieve(url, os.path.basename(url))
+        return os.path.basename(url)
+
+weaver_preproc = get_file_path(url_preproc, local_preproc)
+weaver_model = get_file_path(url_model, local_model)
+
+from addons.ONNXRuntime.jetFlavourHelper import JetFlavourHelper
+from addons.FastJet.jetClusteringHelper import ExclusiveJetClusteringHelper
+from examples.FCCee.weaver.config import collections, njets
+
+################################################################################
+
+
+def build_graph(df, dataset):
+
+    hists, cols = [], []
+
+    df = df.Define("weight", "1.0")
+    weightsum = df.Sum("weight")
+
+
+    # define collections
+    df = df.Alias("Particle0", "Particle#0.index")
+    df = df.Alias("Particle1", "Particle#1.index")
+    df = df.Alias("MCRecoAssociations0", "MCRecoAssociations#0.index")
+    df = df.Alias("MCRecoAssociations1", "MCRecoAssociations#1.index")
+
+
+    # run jet clustering
+    njets = 2
+    jetClusteringHelper = ExclusiveJetClusteringHelper("ReconstructedParticles", njets)
+    df = jetClusteringHelper.define(df)
+
+
+    # run flavor tagging
+    jetFlavourHelper = JetFlavourHelper(collections, jetClusteringHelper.jets, jetClusteringHelper.constituents, "")
+    df = jetFlavourHelper.define(df) # define variables
+    df = jetFlavourHelper.inference(weaver_preproc, weaver_model, df) # run inference
+
+    # jetFlavourHelper adds new columns for each jet flavor (recojet_isB/C/S/...)
+    # each column is a vector of probabilities per jet to be that specific flavor
+    hists.append(df.Histo1D(("recojet_isB", "", *bins_score), "recojet_isB"))
+    hists.append(df.Histo1D(("recojet_isC", "", *bins_score), "recojet_isC"))
+    hists.append(df.Histo1D(("recojet_isS", "", *bins_score), "recojet_isS"))
+
+    # compute the invariant mass of the jets
+    df = df.Define("jet_p4", f"JetConstituentsUtils::compute_tlv_jets({jetClusteringHelper.jets})")
+    df = df.Define("event_invariant_mass", "JetConstituentsUtils::InvariantMass(jet_p4[0], jet_p4[1])")
+    hists.append(df.Histo1D(("event_invariant_mass", "", *bins_m), "event_invariant_mass"))
+    return hists, weightsum
+
diff --git a/analyses/examples/tag2plotter.py b/analyses/examples/tag2plotter.py
new file mode 100644
index 0000000..fcef59a
--- /dev/null
+++ b/analyses/examples/tag2plotter.py
@@ -0,0 +1,35 @@
+import uproot
+import matplotlib.pyplot as plt
+import mplhep as hep
+
+hep.style.use("ROOT")
+plt.style.use(hep.style.ROOT)
+
+def plot(fIn, fOut):
+    file = uproot.open(fIn)
+    score_B = file["recojet_isB"].to_hist()
+    score_C = file["recojet_isC"].to_hist()
+    score_S = file["recojet_isS"].to_hist()
+
+    fig = plt.figure()
+    ax = fig.subplots()
+
+    hep.histplot(score_B, label="b-score", ax=ax)
+    hep.histplot(score_C, label="c-score", ax=ax)
+    hep.histplot(score_S, label="s-score", ax=ax)
+
+    ax.legend(fontsize='x-small')
+    ax.set_xlabel("Tagger score")
+    ax.set_ylabel("Events")
+    ax.set_xlim(0, 1)
+    ax.set_yscale('log')
+
+    plt.savefig(f"{fOut}.png", bbox_inches="tight")
+    plt.close()  # Close the figure to avoid display in some environments
+
+
+
+if __name__ == "__main__":
+    plot("/home/submit/aniketkg/FCCAnalyzer/output/clustering_tagging/histmaker/wzp6_ee_nunuH_Hbb_ecm240.root", "nunuH_Hbb")
+    plot("/home/submit/aniketkg/FCCAnalyzer/output/clustering_tagging/histmaker/wzp6_ee_nunuH_Hcc_ecm240.root", "nunuH_Hcc")
+    plot("/home/submit/aniketkg/FCCAnalyzer/output/clustering_tagging/histmaker/wzp6_ee_nunuH_Hss_ecm240.root", "nunuH_Hss")
\ No newline at end of file
diff --git a/analyses/examples/tagfuncs.h b/analyses/examples/tagfuncs.h
new file mode 100644
index 0000000..f55c533
--- /dev/null
+++ b/analyses/examples/tagfuncs.h
@@ -0,0 +1,411 @@
+
+#ifndef FCCPhysicsFunctions_H
+#define FCCPhysicsFunctions_H
+
+namespace FCCAnalyses {
+
+
+// make Lorentz vectors for a given RECO particle collection
+Vec_tlv makeLorentzVectors(Vec_rp in) {
+    Vec_tlv result;
+    for(auto & p: in) {
+        TLorentzVector tlv;
+        tlv.SetXYZM(p.momentum.x, p.momentum.y, p.momentum.z, p.mass);
+        result.push_back(tlv);
+    }
+    return result;
+}
+
+
+// make Lorentzvectors from pseudojets
+Vec_tlv makeLorentzVectors(Vec_f jets_px, Vec_f jets_py, Vec_f jets_pz, Vec_f jets_e) {
+    Vec_tlv result;
+    for(int i=0; i<jets_px.size(); i++) {
+        TLorentzVector tlv;
+        tlv.SetPxPyPzE(jets_px[i], jets_py[i], jets_pz[i], jets_e[i]);
+        result.push_back(tlv);
+    }
+    return result;
+}
+
+
+// acolinearity between two reco particles
+float acolinearity(Vec_rp in) {
+    if(in.size() < 2) return -999;
+
+    TLorentzVector p1;
+    p1.SetXYZM(in[0].momentum.x, in[0].momentum.y, in[0].momentum.z, in[0].mass);
+
+    TLorentzVector p2;
+    p2.SetXYZM(in[1].momentum.x, in[1].momentum.y, in[1].momentum.z, in[1].mass);
+
+    TVector3 v1 = p1.Vect();
+    TVector3 v2 = p2.Vect();
+    return std::acos(v1.Dot(v2)/(v1.Mag()*v2.Mag())*(-1.));
+}
+
+// acoplanarity between two reco particles
+float acoplanarity(Vec_rp in) {
+    if(in.size() < 2) return -999;
+
+    TLorentzVector p1;
+    p1.SetXYZM(in[0].momentum.x, in[0].momentum.y, in[0].momentum.z, in[0].mass);
+
+    TLorentzVector p2;
+    p2.SetXYZM(in[1].momentum.x, in[1].momentum.y, in[1].momentum.z, in[1].mass);
+
+    float acop = abs(p1.Phi() - p2.Phi());
+    if(acop > M_PI) acop = 2 * M_PI - acop;
+    acop = M_PI - acop;
+
+    return acop;
+}
+
+// visible energy
+float visibleEnergy(Vec_rp in, float p_cutoff = 0.0) {
+    float e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        e += p.energy;
+    }
+    return e;
+}
+
+// returns missing energy vector, based on reco particles
+Vec_rp missingEnergy(float ecm, Vec_rp in, float p_cutoff = 0.0) {
+    float px = 0, py = 0, pz = 0, e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        px += -p.momentum.x;
+        py += -p.momentum.y;
+        pz += -p.momentum.z;
+        e += p.energy;
+    }
+
+    Vec_rp ret;
+    rp res;
+    res.momentum.x = px;
+    res.momentum.y = py;
+    res.momentum.z = pz;
+    res.energy = ecm-e;
+    ret.emplace_back(res);
+    return ret;
+}
+
+// calculate the visible mass of the event
+float visibleMass(Vec_rp in, float p_cutoff = 0.0) {
+    float px = 0, py = 0, pz = 0, e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        px += p.momentum.x;
+        py += p.momentum.y;
+        pz += p.momentum.z;
+        e += p.energy;
+    }
+
+    float ptot2 = std::pow(px, 2) + std::pow(py, 2) + std::pow(pz, 2);
+    float de2 = std::pow(e, 2);
+    if (de2 < ptot2) return -999.;
+    float Mvis = std::sqrt(de2 - ptot2);
+    return Mvis;
+}
+
+// calculate the missing mass, given a ECM value
+float missingMass(float ecm, Vec_rp in, float p_cutoff = 0.0) {
+    float px = 0, py = 0, pz = 0, e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        px += p.momentum.x;
+        py += p.momentum.y;
+        pz += p.momentum.z;
+        e += p.energy;
+    }
+    if(ecm < e) return -99.;
+
+    float ptot2 = std::pow(px, 2) + std::pow(py, 2) + std::pow(pz, 2);
+    float de2 = std::pow(ecm - e, 2);
+    if (de2 < ptot2) return -999.;
+    float Mmiss = std::sqrt(de2 - ptot2);
+    return Mmiss;
+}
+
+// calculate the cosine(theta) of the missing energy vector
+float get_cosTheta_miss(Vec_rp met){
+
+    float costheta = 0.;
+    if(met.size() > 0) {
+        TLorentzVector lv_met;
+        lv_met.SetPxPyPzE(met[0].momentum.x, met[0].momentum.y, met[0].momentum.z, met[0].energy);
+        costheta = fabs(std::cos(lv_met.Theta()));
+    }
+    return costheta;
+}
+
+
+
+
+// compute the cone isolation for reco particles
+struct coneIsolation {
+
+    coneIsolation(float arg_dr_min, float arg_dr_max);
+    double deltaR(double eta1, double phi1, double eta2, double phi2) { return TMath::Sqrt(TMath::Power(eta1-eta2, 2) + (TMath::Power(phi1-phi2, 2))); };
+
+    float dr_min = 0;
+    float dr_max = 0.4;
+    Vec_f operator() (Vec_rp in, Vec_rp rps) ;
+};
+
+coneIsolation::coneIsolation(float arg_dr_min, float arg_dr_max) : dr_min(arg_dr_min), dr_max( arg_dr_max ) { };
+Vec_f coneIsolation::coneIsolation::operator() (Vec_rp in, Vec_rp rps) {
+
+    Vec_f result;
+    result.reserve(in.size());
+
+    std::vector<ROOT::Math::PxPyPzEVector> lv_reco;
+    std::vector<ROOT::Math::PxPyPzEVector> lv_charged;
+    std::vector<ROOT::Math::PxPyPzEVector> lv_neutral;
+
+    for(size_t i = 0; i < rps.size(); ++i) {
+        ROOT::Math::PxPyPzEVector tlv;
+        tlv.SetPxPyPzE(rps.at(i).momentum.x, rps.at(i).momentum.y, rps.at(i).momentum.z, rps.at(i).energy);
+
+        if(rps.at(i).charge == 0) lv_neutral.push_back(tlv);
+        else lv_charged.push_back(tlv);
+    }
+
+    for(size_t i = 0; i < in.size(); ++i) {
+        ROOT::Math::PxPyPzEVector tlv;
+        tlv.SetPxPyPzE(in.at(i).momentum.x, in.at(i).momentum.y, in.at(i).momentum.z, in.at(i).energy);
+        lv_reco.push_back(tlv);
+    }
+
+    // compute the isolation (see https://github.com/delphes/delphes/blob/master/modules/Isolation.cc#L154) 
+    for (auto & lv_reco_ : lv_reco) {
+        double sumNeutral = 0.0;
+        double sumCharged = 0.0;
+
+        // charged
+        for (auto & lv_charged_ : lv_charged) {
+            double dr = coneIsolation::deltaR(lv_reco_.Eta(), lv_reco_.Phi(), lv_charged_.Eta(), lv_charged_.Phi());
+            if(dr > dr_min && dr < dr_max) sumCharged += lv_charged_.P();
+        }
+
+        // neutral
+        for (auto & lv_neutral_ : lv_neutral) {
+            double dr = coneIsolation::deltaR(lv_reco_.Eta(), lv_reco_.Phi(), lv_neutral_.Eta(), lv_neutral_.Phi());
+            if(dr > dr_min && dr < dr_max) sumNeutral += lv_neutral_.P();
+        }
+
+        double sum = sumCharged + sumNeutral;
+        double ratio= sum / lv_reco_.P();
+        result.emplace_back(ratio);
+    }
+    return result;
+}
+
+// filter reconstructed particles (in) based a property (prop) within a defined range (m_min, m_max)
+struct sel_range {
+    sel_range(float arg_min, float arg_max, bool arg_abs = false);
+    float m_min = 0.;
+    float m_max = 1.;
+    bool m_abs = false;
+    Vec_rp operator() (Vec_rp in, Vec_f prop);
+};
+
+sel_range::sel_range(float arg_min, float arg_max, bool arg_abs) : m_min(arg_min), m_max(arg_max), m_abs(arg_abs) {};
+Vec_rp sel_range::operator() (Vec_rp in, Vec_f prop) {
+    Vec_rp result;
+    //result.reserve(in.size());
+    for (size_t i = 0; i < in.size(); ++i) {
+        auto & p = in[i];
+        float val = (m_abs) ? abs(prop[i]) : prop[i];
+        //if(val > m_min && val < m_max) result.emplace_back(p);
+        if(val > m_min && val < m_max) result.push_back(p);
+    }
+    return result;
+}
+
+
+// filter reconstructed particles (in) based a property (prop) within a defined range (m_min, m_max)
+struct sel_range_idx {
+    sel_range_idx(float arg_min, float arg_max, bool arg_abs = false, Vec_i arg_idx = {});
+    float m_min = 0.;
+    float m_max = 1.;
+    bool m_abs = false;
+    Vec_i m_idx = {};
+    Vec_rp operator() (Vec_rp in, Vec_f prop);
+};
+
+sel_range_idx::sel_range_idx(float arg_min, float arg_max, bool arg_abs, Vec_i arg_idx) : m_min(arg_min), m_max(arg_max), m_abs(arg_abs), m_idx(arg_idx) {};
+Vec_rp sel_range_idx::operator() (Vec_rp in, Vec_f prop) {
+    Vec_rp result;
+    result.reserve(in.size());
+    for (size_t i = 0; i < in.size(); ++i) {
+        auto & p = in[i];
+        if(std::find(m_idx.begin(), m_idx.end(), i)!=m_idx.end()){
+            float val = (m_abs) ? abs(prop[i]) : prop[i];
+            if(val > m_min && val < m_max) result.emplace_back(p);
+        }
+        else {
+            result.emplace_back(p);
+        }
+    }
+    return result;
+}
+
+// build the Z resonance based on the available leptons. Returns the best lepton pair compatible with the Z mass and recoil at 125 GeV
+// technically, it returns a ReconstructedParticleData object with index 0 the di-lepton system, index and 2 the leptons of the pair
+struct resonanceBuilder_mass_recoil {
+    float m_resonance_mass;
+    float m_recoil_mass;
+    float chi2_recoil_frac;
+    float ecm;
+    bool m_use_MC_Kinematics;
+    resonanceBuilder_mass_recoil(float arg_resonance_mass, float arg_recoil_mass, float arg_chi2_recoil_frac, float arg_ecm, bool arg_use_MC_Kinematics);
+    Vec_rp operator()(Vec_rp legs, Vec_i recind, Vec_i mcind, Vec_rp reco, Vec_mc mc, Vec_i parents, Vec_i daugthers) ;
+};
+
+resonanceBuilder_mass_recoil::resonanceBuilder_mass_recoil(float arg_resonance_mass, float arg_recoil_mass, float arg_chi2_recoil_frac, float arg_ecm, bool arg_use_MC_Kinematics) {m_resonance_mass = arg_resonance_mass, m_recoil_mass = arg_recoil_mass, chi2_recoil_frac = arg_chi2_recoil_frac, ecm = arg_ecm, m_use_MC_Kinematics = arg_use_MC_Kinematics;}
+
+Vec_rp resonanceBuilder_mass_recoil::resonanceBuilder_mass_recoil::operator()(Vec_rp legs, Vec_i recind, Vec_i mcind, Vec_rp reco, Vec_mc mc, Vec_i parents, Vec_i daugthers) {
+    Vec_rp result;
+    result.reserve(3);
+    std::vector<std::vector<int>> pairs; // for each permutation, add the indices of the muons
+    int n = legs.size();
+
+    if(n > 1) {
+        ROOT::VecOps::RVec<bool> v(n);
+        std::fill(v.end() - 2, v.end(), true); // helper variable for permutations
+        do {
+            std::vector<int> pair;
+            rp reso;
+            reso.charge = 0;
+            TLorentzVector reso_lv; 
+            for(int i = 0; i < n; ++i) {
+                if(v[i]) {
+                    pair.push_back(i);
+                    reso.charge += legs[i].charge;
+                    TLorentzVector leg_lv;
+
+                    if(m_use_MC_Kinematics) { // MC kinematics
+                        int track_index = legs[i].tracks_begin;   // index in the Track array
+                        int mc_index = ReconstructedParticle2MC::getTrack2MC_index(track_index, recind, mcind, reco);
+                        if (mc_index >= 0 && mc_index < mc.size()) {
+                            leg_lv.SetXYZM(mc.at(mc_index).momentum.x, mc.at(mc_index).momentum.y, mc.at(mc_index).momentum.z, mc.at(mc_index).mass);
+                        }
+                    }
+                    else { // reco kinematics
+                         leg_lv.SetXYZM(legs[i].momentum.x, legs[i].momentum.y, legs[i].momentum.z, legs[i].mass);
+                    }
+                    reso_lv += leg_lv;
+                }
+            }
+
+            if(reso.charge != 0) continue; // neglect non-zero charge pairs
+            reso.momentum.x = reso_lv.Px();
+            reso.momentum.y = reso_lv.Py();
+            reso.momentum.z = reso_lv.Pz();
+            reso.mass = reso_lv.M();
+            result.emplace_back(reso);
+            pairs.push_back(pair);
+
+        } while(std::next_permutation(v.begin(), v.end()));
+    }
+    else {
+        std::cout << "ERROR: resonanceBuilder_mass_recoil, at least two leptons required." << std::endl;
+        exit(1);
+    }
+
+    if(result.size() > 1) {
+
+        Vec_rp bestReso;
+        int idx_min = -1;
+        float d_min = 9e9;
+        for (int i = 0; i < result.size(); ++i) {
+            
+            // calculate recoil
+            auto recoil_p4 = TLorentzVector(0, 0, 0, ecm);
+            TLorentzVector tv1;
+            tv1.SetXYZM(result.at(i).momentum.x, result.at(i).momentum.y, result.at(i).momentum.z, result.at(i).mass);
+            recoil_p4 -= tv1;
+
+            auto recoil_fcc = edm4hep::ReconstructedParticleData();
+            recoil_fcc.momentum.x = recoil_p4.Px();
+            recoil_fcc.momentum.y = recoil_p4.Py();
+            recoil_fcc.momentum.z = recoil_p4.Pz();
+            recoil_fcc.mass = recoil_p4.M();
+
+            TLorentzVector tg;
+            tg.SetXYZM(result.at(i).momentum.x, result.at(i).momentum.y, result.at(i).momentum.z, result.at(i).mass);
+
+            float boost = tg.P();
+            float mass = std::pow(result.at(i).mass - m_resonance_mass, 2); // mass
+            float rec = std::pow(recoil_fcc.mass - m_recoil_mass, 2); // recoil
+            float d = (1.0-chi2_recoil_frac)*mass + chi2_recoil_frac*rec;
+
+            if(d < d_min) {
+                d_min = d;
+                idx_min = i;
+            }
+
+        }
+        if(idx_min > -1) { 
+            bestReso.push_back(result.at(idx_min));
+            auto & l1 = legs[pairs[idx_min][0]];
+            auto & l2 = legs[pairs[idx_min][1]];
+            bestReso.emplace_back(l1);
+            bestReso.emplace_back(l2);
+        }
+        else {
+            std::cout << "ERROR: resonanceBuilder_mass_recoil, no mininum found." << std::endl;
+            exit(1);
+        }
+        return bestReso;
+    }
+    else {
+        auto & l1 = legs[0];
+        auto & l2 = legs[1];
+        result.emplace_back(l1);
+        result.emplace_back(l2);
+        return result;
+    }
+}
+
+
+// computes longitudinal and transversal energy balance of all particles
+Vec_f energy_imbalance(Vec_rp in) {
+    float e_tot = 0;
+    float e_trans = 0;
+    float e_long = 0;
+    for(auto &p : in) {
+        float mag = std::sqrt(p.momentum.x*p.momentum.x + p.momentum.y*p.momentum.y + p.momentum.z*p.momentum.z);
+        float cost = p.momentum.z / mag;
+        float sint =  std::sqrt(p.momentum.x*p.momentum.x + p.momentum.y*p.momentum.y) / mag;
+        if(p.momentum.y < 0) sint *= -1.0;
+        e_tot += p.energy;
+        e_long += cost*p.energy;
+        e_trans += sint*p.energy;
+    }
+    Vec_f result;
+    result.push_back(e_tot);
+    result.push_back(std::abs(e_trans));
+    result.push_back(std::abs(e_long));
+    return result;
+}
+
+Vec_f get_costheta(Vec_rp in) {
+    Vec_f result;
+    for (auto & p: in) {
+        TLorentzVector tlv;
+        tlv.SetXYZM(p.momentum.x, p.momentum.y, p.momentum.z, p.mass);
+        result.push_back(std::cos(tlv.Theta()));
+    }
+    return result;
+}
+
+
+
+}
+
+
+#endif
diff --git a/analyses/examples/tagfuncs_gen.h b/analyses/examples/tagfuncs_gen.h
new file mode 100644
index 0000000..624f222
--- /dev/null
+++ b/analyses/examples/tagfuncs_gen.h
@@ -0,0 +1,49 @@
+#ifndef FCCPhysicsFunctionsGen_H
+#define FCCPhysicsFunctionsGen_H
+
+namespace FCCAnalyses {
+
+
+
+// make Lorentz vectors for a given MC particle collection
+Vec_tlv makeLorentzVectors(Vec_mc in) {
+    Vec_tlv result;
+    for(auto & p: in) {
+        TLorentzVector tlv;
+        tlv.SetXYZM(p.momentum.x, p.momentum.y, p.momentum.z, p.mass);
+        result.push_back(tlv);
+    }
+    return result;
+}
+
+Vec_mc getRP2MC(Vec_rp in, ROOT::VecOps::RVec<int> recind, ROOT::VecOps::RVec<int> mcind, ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData> reco, ROOT::VecOps::RVec<edm4hep::MCParticleData> mc) {
+    Vec_mc result;
+    for (auto & p: in) {
+        int track_index = p.tracks_begin;
+        int mc_index = ReconstructedParticle2MC::getTrack2MC_index(track_index, recind, mcind, reco);
+        if(mc_index >= 0 && mc_index < mc.size() ) {
+            result.push_back(mc.at(mc_index));
+        }
+        else {
+            cout << "MC track not found!" << endl;
+        }
+    }
+    return result;
+}
+
+Vec_mc get_gen_pdg(Vec_mc mc, int pdgId, bool abs=true, bool stable=true) {
+   Vec_mc result;
+   for(size_t i = 0; i < mc.size(); ++i) {
+        auto & p = mc[i];
+        if(!((abs and std::abs(p.PDG) == pdgId) or (not abs and p.PDG == pdgId))) continue;
+        if(stable && p.generatorStatus != 1) continue;
+        result.emplace_back(p);
+        //if((abs and std::abs(p.PDG) == pdgId) or (not abs and p.PDG == pdgId)) result.emplace_back(p);
+   }
+   return result;
+}
+
+}
+
+
+#endif
\ No newline at end of file

From 1026fae3f2da577ce89304b5668ff9fbb6319a6a Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Mon, 27 Jan 2025 00:05:30 -0500
Subject: [PATCH 3/7] basic hbb fixes, generates seperate root files

---
 analyses/h_bb/h_bb.ipynb      |  22 +-
 analyses/h_bb/h_bb.py         | 410 +++++++++++++++++++++-------------
 analyses/h_bb/otherfunc_gen.h |  37 +++
 analyses/h_bb/otherfuncs.h    | 382 +++++++++++++++++++++++++++++++
 python/dataset.py             |   4 +-
 python/functions.py           |  20 +-
 python/submit.py              |   2 +-
 7 files changed, 702 insertions(+), 175 deletions(-)
 create mode 100644 analyses/h_bb/otherfunc_gen.h
 create mode 100644 analyses/h_bb/otherfuncs.h

diff --git a/analyses/h_bb/h_bb.ipynb b/analyses/h_bb/h_bb.ipynb
index 3906450..beebec7 100644
--- a/analyses/h_bb/h_bb.ipynb
+++ b/analyses/h_bb/h_bb.ipynb
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "389e4150-c583-4833-a4bc-c218c5b5ecb7",
    "metadata": {},
    "outputs": [
@@ -36,12 +36,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['wzp6_ee_eeH_Hbb_ecm240;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hbb_ecm240/meta;1', 'wzp6_ee_mumuH_Hbb_ecm240;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hbb_ecm240/meta;1', 'wzp6_ee_tautauH_Hbb_ecm240;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hbb_ecm240/meta;1', 'wzp6_ee_nunuH_Hbb_ecm240;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hbb_ecm240/meta;1', 'wzp6_ee_qqH_Hbb_ecm240;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hbb_ecm240/meta;1', 'wzp6_ee_ssH_Hbb_ecm240;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hbb_ecm240/meta;1', 'wzp6_ee_ccH_Hbb_ecm240;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hbb_ecm240/meta;1', 'wzp6_ee_bbH_Hbb_ecm240;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hbb_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hbb_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hbb_ecm240/meta;1', 'wzp6_ee_eeH_Hcc_ecm240;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hcc_ecm240/meta;1', 'wzp6_ee_mumuH_Hcc_ecm240;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hcc_ecm240/meta;1', 'wzp6_ee_tautauH_Hcc_ecm240;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hcc_ecm240/meta;1', 'wzp6_ee_nunuH_Hcc_ecm240;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hcc_ecm240/meta;1', 'wzp6_ee_qqH_Hcc_ecm240;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hcc_ecm240/meta;1', 'wzp6_ee_ssH_Hcc_ecm240;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hcc_ecm240/meta;1', 'wzp6_ee_ccH_Hcc_ecm240;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hcc_ecm240/meta;1', 'wzp6_ee_bbH_Hcc_ecm240;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hcc_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hcc_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hcc_ecm240/meta;1', 'wzp6_ee_eeH_Hgg_ecm240;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_eeH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_eeH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_eeH_Hgg_ecm240/meta;1', 'wzp6_ee_mumuH_Hgg_ecm240;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_mumuH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_mumuH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_mumuH_Hgg_ecm240/meta;1', 'wzp6_ee_tautauH_Hgg_ecm240;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_tautauH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_tautauH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_tautauH_Hgg_ecm240/meta;1', 'wzp6_ee_nunuH_Hgg_ecm240;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_nunuH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_nunuH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_nunuH_Hgg_ecm240/meta;1', 'wzp6_ee_qqH_Hgg_ecm240;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_qqH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_qqH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_qqH_Hgg_ecm240/meta;1', 'wzp6_ee_ssH_Hgg_ecm240;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_ssH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_ssH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_ssH_Hgg_ecm240/meta;1', 'wzp6_ee_ccH_Hgg_ecm240;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_ccH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_ccH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_ccH_Hgg_ecm240/meta;1', 'wzp6_ee_bbH_Hgg_ecm240;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_p_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_theta_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_phi_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_q_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/muons_all_no_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_p_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_theta_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_phi_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_q_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/electrons_all_no_cut0;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_mumu;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_ee;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_nunu;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_qq;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_ss;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_cc;1', 'wzp6_ee_bbH_Hgg_ecm240/cutFlow_bb;1', 'wzp6_ee_bbH_Hgg_ecm240/missingEnergy_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/mumu_recoil_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/ee_recoil_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/mumu_p_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/ee_p_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/zmumu_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/zee_m_nOne;1', 'wzp6_ee_bbH_Hgg_ecm240/meta;1', 'p8_ee_WW_ecm240;1', 'p8_ee_WW_ecm240/muons_all_p_cut0;1', 'p8_ee_WW_ecm240/muons_all_theta_cut0;1', 'p8_ee_WW_ecm240/muons_all_phi_cut0;1', 'p8_ee_WW_ecm240/muons_all_q_cut0;1', 'p8_ee_WW_ecm240/muons_all_no_cut0;1', 'p8_ee_WW_ecm240/electrons_all_p_cut0;1', 'p8_ee_WW_ecm240/electrons_all_theta_cut0;1', 'p8_ee_WW_ecm240/electrons_all_phi_cut0;1', 'p8_ee_WW_ecm240/electrons_all_q_cut0;1', 'p8_ee_WW_ecm240/electrons_all_no_cut0;1', 'p8_ee_WW_ecm240/cutFlow_mumu;1', 'p8_ee_WW_ecm240/cutFlow_ee;1', 'p8_ee_WW_ecm240/cutFlow_nunu;1', 'p8_ee_WW_ecm240/cutFlow_qq;1', 'p8_ee_WW_ecm240/cutFlow_ss;1', 'p8_ee_WW_ecm240/cutFlow_cc;1', 'p8_ee_WW_ecm240/cutFlow_bb;1', 'p8_ee_WW_ecm240/missingEnergy_nOne;1', 'p8_ee_WW_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_WW_ecm240/ee_recoil_m_nOne;1', 'p8_ee_WW_ecm240/mumu_p_nOne;1', 'p8_ee_WW_ecm240/ee_p_nOne;1', 'p8_ee_WW_ecm240/zmumu_m_nOne;1', 'p8_ee_WW_ecm240/zee_m_nOne;1', 'p8_ee_WW_ecm240/meta;1', 'p8_ee_ZZ_ecm240;1', 'p8_ee_ZZ_ecm240/muons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/muons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_p_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_theta_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_phi_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_q_cut0;1', 'p8_ee_ZZ_ecm240/electrons_all_no_cut0;1', 'p8_ee_ZZ_ecm240/cutFlow_mumu;1', 'p8_ee_ZZ_ecm240/cutFlow_ee;1', 'p8_ee_ZZ_ecm240/cutFlow_nunu;1', 'p8_ee_ZZ_ecm240/cutFlow_qq;1', 'p8_ee_ZZ_ecm240/cutFlow_ss;1', 'p8_ee_ZZ_ecm240/cutFlow_cc;1', 'p8_ee_ZZ_ecm240/cutFlow_bb;1', 'p8_ee_ZZ_ecm240/missingEnergy_nOne;1', 'p8_ee_ZZ_ecm240/mumu_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/ee_recoil_m_nOne;1', 'p8_ee_ZZ_ecm240/mumu_p_nOne;1', 'p8_ee_ZZ_ecm240/ee_p_nOne;1', 'p8_ee_ZZ_ecm240/zmumu_m_nOne;1', 'p8_ee_ZZ_ecm240/zee_m_nOne;1', 'p8_ee_ZZ_ecm240/meta;1']\n"
+      "[]\n"
      ]
     }
    ],
    "source": [
-    "f_mumu = uproot.open(\"/home/submit/aniketkg/FCCAnalyzer_ag/end_of_h_bb.root\")\n",
+    "f_mumu = uproot.open(\"/home/submit/aniketkg/FCCAnalyzer/end_of_h_bb.root\")\n",
     "print(f_mumu.keys())"
    ]
   },
@@ -59,9 +59,23 @@
    "id": "1a9f682f-e53d-43c6-9c34-faa2df4536bc",
    "metadata": {},
    "outputs": [
+    {
+     "ename": "KeyInFileError",
+     "evalue": "not found: 'wzp6_ee_mumuH_Hbb_ecm240' (with any cycle number)\n\n    Available keys: (none!)\n\nin file /home/submit/aniketkg/FCCAnalyzer/end_of_h_bb.root",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyInFileError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 30\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     29\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m part \u001b[38;5;241m=\u001b[39m \u001b[43mf_mumu\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwzp6_ee_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mp\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43mH_Hbb_ecm240/cutFlow_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mp\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mto_hist()\n\u001b[1;32m     31\u001b[0m maxi \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnonzero(part\u001b[38;5;241m.\u001b[39mvalues())[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     32\u001b[0m WW   \u001b[38;5;241m=\u001b[39m f_mumu[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp8_ee_WW_ecm240/cutFlow_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mp\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_hist()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2095\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2093\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2094\u001b[0m         last \u001b[38;5;241m=\u001b[39m step\n\u001b[0;32m-> 2095\u001b[0m         step \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[43m[\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m   2097\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(step, uproot\u001b[38;5;241m.\u001b[39mbehaviors\u001b[38;5;241m.\u001b[39mTBranch\u001b[38;5;241m.\u001b[39mHasBranches):\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(items[i:])]\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2112\u001b[0m, in \u001b[0;36mReadOnlyDirectory.__getitem__\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m step\n\u001b[1;32m   2111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget()\n",
+      "File \u001b[0;32m/work/submit/submit-software/conda/envs/fcc-ee/lib/python3.10/site-packages/uproot/reading.py:2062\u001b[0m, in \u001b[0;36mReadOnlyDirectory.key\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m   2060\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m last\n\u001b[1;32m   2061\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m cycle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2062\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2063\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124many\u001b[39m\u001b[38;5;124m\"\u001b[39m, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2064\u001b[0m     )\n\u001b[1;32m   2065\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2066\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m uproot\u001b[38;5;241m.\u001b[39mKeyInFileError(\n\u001b[1;32m   2067\u001b[0m         item, cycle\u001b[38;5;241m=\u001b[39mcycle, keys\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkeys(), file_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file\u001b[38;5;241m.\u001b[39mfile_path\n\u001b[1;32m   2068\u001b[0m     )\n",
+      "\u001b[0;31mKeyInFileError\u001b[0m: not found: 'wzp6_ee_mumuH_Hbb_ecm240' (with any cycle number)\n\n    Available keys: (none!)\n\nin file /home/submit/aniketkg/FCCAnalyzer/end_of_h_bb.root"
+     ]
+    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1500x1500 with 9 Axes>"
       ]
diff --git a/analyses/h_bb/h_bb.py b/analyses/h_bb/h_bb.py
index 57c98ab..833fb91 100644
--- a/analyses/h_bb/h_bb.py
+++ b/analyses/h_bb/h_bb.py
@@ -1,11 +1,14 @@
-import functions
-import helpers
+import python.functions as functions
+import python.helpers as helpers
 import ROOT
 import argparse
 import logging
 
-import helper_jetclustering
-import helper_flavourtagger
+# import helper_jetclustering
+# import helper_flavourtagger
+
+from addons.ONNXRuntime.jetFlavourHelper import JetFlavourHelper
+from addons.FastJet.jetClusteringHelper import ExclusiveJetClusteringHelper
 from examples.FCCee.weaver.config import collections, njets
 
 logger = logging.getLogger("fcclogger")
@@ -16,6 +19,64 @@
 
 functions.add_include_file("analyses/higgs_mass_xsec/functions.h")
 functions.add_include_file("analyses/higgs_mass_xsec/functions_gen.h")
+# functions.add_include_file("analyses/h_bb/otherfuncs.h")
+# functions.add_include_file("analyses/h_bb/otherfunc_gen.h")
+
+
+# list of all processes
+fraction = 0.05
+processList = {    #Hbb sigs
+    'wzp6_ee_eeH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_mumuH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_tautauH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_qqH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_ssH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_ccH_Hbb_ecm240':          {'fraction':fraction},
+    'wzp6_ee_bbH_Hbb_ecm240':          {'fraction':fraction},
+
+    #Hcc sigs
+    'wzp6_ee_eeH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_mumuH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_tautauH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_qqH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_ssH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_ccH_Hcc_ecm240':          {'fraction':fraction},
+    'wzp6_ee_bbH_Hcc_ecm240':          {'fraction':fraction},
+
+    #Hgg sigs
+    'wzp6_ee_eeH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_mumuH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_tautauH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nunuH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_qqH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_ssH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_ccH_Hgg_ecm240':          {'fraction':fraction},
+    'wzp6_ee_bbH_Hgg_ecm240':          {'fraction':fraction},
+
+    #bkgs
+    'p8_ee_WW_ecm240':          {'fraction':fraction},
+    'p8_ee_ZZ_ecm240':          {'fraction':fraction},
+    'wzp6_ee_mumu_ecm240':          {'fraction':fraction},
+    'wzp6_ee_tautau_ecm240':          {'fraction':fraction},
+    'wzp6_egamma_eZ_Zmumu_ecm240':          {'fraction':fraction},
+    'wzp6_gammae_eZ_Zmumu_ecm240':          {'fraction':fraction},
+    'wzp6_gaga_mumu_60_ecm240':          {'fraction':fraction},
+    'wzp6_gaga_tautau_60_ecm240':          {'fraction':fraction},
+    'wzp6_ee_nuenueZ_ecm240':          {'fraction':fraction},
+
+}
+
+inputDir = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/"
+procDict = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/samplesDict.json"
+
+# additional/custom C++ functions
+includePaths = ["../higgs_mass_xsec/functions.h", "../higgs_mass_xsec/functions_gen.h", "otherfuncs.h", "otherfunc_gen.h"]
+
+
+# output directory
+outputDir   = "output/hbb_tagging/histmaker/"
 
 
 # define histograms
@@ -52,17 +113,38 @@
 # setup clustering and flavour taggingv helper_jetclustering. helper_flavourtagger.
 # 2 jets
 
-jet2Cluster = helper_jetclustering.ExclusiveJetClusteringHelper(2, "rps_no_leps")
-jet2Flavour = helper_flavourtagger.JetFlavourHelper(collections, jet2Cluster.jets, jet2Cluster.constituents)
+# jet2Cluster = helper_jetclustering.ExclusiveJetClusteringHelper(2, "rps_no_leps")
+# jet2Flavour = helper_flavourtagger.JetFlavourHelper(collections, jet2Cluster.jets, jet2Cluster.constituents)
+
+njets = 2
+jet2Cluster = ExclusiveJetClusteringHelper("rps_no_leps", njets)
+jet2Flavour = JetFlavourHelper(collections, jet2Cluster.jets, jet2Cluster.constituents, "")
+
 # 4 jets
 
-jet4Cluster = helper_jetclustering.ExclusiveJetClusteringHelper(4, "ReconstructedParticles")
-jet4Flavour = helper_flavourtagger.JetFlavourHelper(collections, jet4Cluster.jets, jet4Cluster.constituents)
+# jet4Cluster = helper_jetclustering.ExclusiveJetClusteringHelper(4, "ReconstructedParticles")
+# jet4Flavour = helper_flavourtagger.JetFlavourHelper(collections, jet4Cluster.jets, jet4Cluster.constituents)
+
+njets = 4
+jet4Cluster = ExclusiveJetClusteringHelper("ReconstructedParticles", njets)
+jet4Flavour = JetFlavourHelper(collections, jet4Cluster.jets, jet4Cluster.constituents, "")
+
+# path = "/home/submit/aniketkg/FCCAnalyzer_ag/data/flavourtagger/fccee_flavtagging_edm4hep_wc_v1"
+# urlpath = "https://fccsw.web.cern.ch/fccsw/testsamples/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/fccee_flavtagging_edm4hep_wc_v1"
+# jet2Flavour.load(f"{path}.json", f"{path}.onnx")
+# jet4Flavour.load(f"{path}.json", f"{path}.onnx")
+
+model_name = "fccee_flavtagging_edm4hep_wc_v1"
 
-path = "/home/submit/aniketkg/FCCAnalyzer_ag/data/flavourtagger/fccee_flavtagging_edm4hep_wc_v1"
-urlpath = "https://fccsw.web.cern.ch/fccsw/testsamples/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/fccee_flavtagging_edm4hep_wc_v1"
-jet2Flavour.load(f"{path}.json", f"{path}.onnx")
-jet4Flavour.load(f"{path}.json", f"{path}.onnx")
+# model files needed for unit testing in CI
+url_model_dir = "https://fccsw.web.cern.ch/fccsw/testsamples/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/"
+url_preproc = "{}/{}.json".format(url_model_dir, model_name)
+url_model = "{}/{}.onnx".format(url_model_dir, model_name)
+
+# model files locally stored on /eos
+model_dir = "/eos/experiment/fcc/ee/jet_flavour_tagging/winter2023/wc_pt_13_01_2022/"
+local_preproc = "{}/{}.json".format(model_dir, model_name)
+local_model = "{}/{}.onnx".format(model_dir, model_name)
 
 def get_file_path(url, filename):
     import os
@@ -73,12 +155,12 @@ def get_file_path(url, filename):
         urllib.request.urlretrieve(url, os.path.basename(url))
         return os.path.basename(url)
 
-weaver_preproc = get_file_path("{}.json".format(urlpath), "{}.json".format(path))
-weaver_model = get_file_path("{}.onnx".format(urlpath), "{}.onnx".format(path))
+weaver_preproc = get_file_path(url_preproc, local_preproc)
+weaver_model = get_file_path(url_model, local_model)
 
 def build_graph(df, dataset):
 
-    logging.info(f"build graph {dataset.name}")
+    #logging.info(f"build graph {dataset.name}")
     results, cols = [], []
 
     df = df.Define("weight", "1.0")
@@ -268,8 +350,9 @@ def build_graph(df, dataset):
         
         # clustering
         df = jet2Cluster.define(df)
-        df = df.Define("jet_tlv", "FCCAnalyses::makeLorentzVectors(jet_px, jet_py, jet_pz, jet_e)")
-        
+        #df = df.Define("jet_tlv", "FCCAnalyses::makeLorentzVectors(jet_px, jet_py, jet_pz, jet_e)")
+        df = df.Define("jet_tlv", f"JetConstituentsUtils::compute_tlv_jets({jet2Cluster.jets})")
+
         # calculate dijet m and p
         df = df.Define("dijet", "jet_tlv[0] + jet_tlv[1]")
         df = df.Define("dijet_m", "dijet.M()")
@@ -288,7 +371,9 @@ def build_graph(df, dataset):
         
         # flavour tagging
 
-        df = jet2Flavour.define_and_inference(df)
+        #df = jet2Flavour.define_and_inference(df)
+        df = jet2Flavour.define(df) # define variables
+        df = jet2Flavour.inference(weaver_preproc, weaver_model, df) # run inference
         
         # cut on b jet confidence
         df = df.Filter("recojet_isB[0] > 0.5 && recojet_isB[1] > 0.5")
@@ -306,163 +391,166 @@ def build_graph(df, dataset):
         results.append(df.Histo1D((f"z{leps}_h_p", "", *bins_m), "dijet_p"))
 
 
-    if False:
-        # jet analysis for the case of 2 jets (Z -> leps)
-        
 
+    # jet analysis for the case of 2 jets (Z -> leps)
 
 
-        # Z->qq analyses
-        # clustering
-        df_quarks = jet4Cluster.define(df_quarks)
-        df_quarks = df_quarks.Define("jet_tlv", "FCCAnalyses::makeLorentzVectors(jet_px, jet_py, jet_pz, jet_e)")
-
-        # pair jets based on distance to Z and H masses
-        df_quarks = df_quarks.Define("zh_min_idx", """
-                FCCAnalyses::Vec_i min{0, 0, 0, 0};
-                float distm = INFINITY;
-                for (int i = 0; i < 3; i++)
-                    for (int j = i + 1; j < 4; j++)
-                        for (int k = 0; k < 3; k++) {
-                            if (i == k || j == k) continue;
-                            for (int l = k + 1; l < 4; l++) {
-                                if (i == l || j == l) continue;
-                                float distz = (jet_tlv[i] + jet_tlv[j]).M() - 91.2;
-                                float disth = (jet_tlv[k] + jet_tlv[l]).M() - 125;
-                                if (distz*distz/91.2 + disth*disth/125 < distm) {
-                                    distm = distz*distz/91.2 + disth*disth/125;
-                                    min[0] = i; min[1] = j; min[2] = k; min[3] = l;
-                                }
-                            }
-                        }
-                return min;""")
-
-        # compute Z and H masses and momenta
-        df_quarks = df_quarks.Define("z_dijet", "jet_tlv[zh_min_idx[0]] + jet_tlv[zh_min_idx[1]]")
-        df_quarks = df_quarks.Define("h_dijet", "jet_tlv[zh_min_idx[2]] + jet_tlv[zh_min_idx[3]]")
-
-        df_quarks = df_quarks.Define("z_dijet_m", "z_dijet.M()")
-        df_quarks = df_quarks.Define("z_dijet_p", "z_dijet.P()")
-        df_quarks = df_quarks.Define("h_dijet_m", "h_dijet.M()")
-        df_quarks = df_quarks.Define("h_dijet_p", "h_dijet.P()")
-
-        results.append(df_quarks.Histo1D(("quarks_z_m_nOne", "", *bins_m), "z_dijet_m"))
-        results.append(df_quarks.Histo1D(("quarks_z_p_nOne", "", *bins_m), "z_dijet_p"))
-        results.append(df_quarks.Histo1D(("quarks_h_m_nOne", "", *bins_m), "h_dijet_m"))
-        results.append(df_quarks.Histo1D(("quarks_h_p_nOne", "", *bins_m), "h_dijet_p"))
 
-        # filter on Z momentum
-        df_quarks = df_quarks.Filter("z_dijet_p > 45 && z_dijet_p < 56")
-        results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut2"))
-        results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut2"))
-        results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut2"))
-        results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut2"))
+    # Z->qq analyses
+    # clustering
+    df_quarks = jet4Cluster.define(df_quarks)
+    #df_quarks = df_quarks.Define("jet_tlv", "FCCAnalyses::makeLorentzVectors(jet_px, jet_py, jet_pz, jet_e)")
+    df_quarks = df_quarks.Define("jet_tlv", f"JetConstituentsUtils::compute_tlv_jets({jet4Cluster.jets})")
 
-        # filter on H mass
-        df_quarks = df_quarks.Filter("h_dijet_m > 122 && h_dijet_m < 128")
-        results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut3"))
-        results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut3"))
-        results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut3"))
-        results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut3"))
-
-
-        # flavour tagging
-        df_quarks = jet4Flavour.define_and_inference(df_quarks)
-
-        # get tag confidence
-        df_quarks = df_quarks.Define("Hbb_prob", "std::min(recojet_isB[zh_min_idx[2]], recojet_isB[zh_min_idx[3]])")
-        results.append(df_quarks.Histo1D(("Hbb_prob_nOne", "", *bins_prob), "Hbb_prob"))
-
-        df_quarks = df_quarks.Define("Zbb_prob", "std::min(recojet_isB[zh_min_idx[0]], recojet_isB[zh_min_idx[1]])")
-        df_quarks = df_quarks.Define("Zcc_prob", "std::min(recojet_isC[zh_min_idx[0]], recojet_isC[zh_min_idx[1]])")
-        df_quarks = df_quarks.Define("Zss_prob", "std::min(recojet_isS[zh_min_idx[0]], recojet_isS[zh_min_idx[1]])")
-        df_quarks = df_quarks.Define("Zqq_prob", "std::min(recojet_isQ[zh_min_idx[0]], recojet_isQ[zh_min_idx[1]])")
-
-        results.append(df_quarks.Histo1D(("Zbb_prob_nOne", "", *bins_prob), "Zbb_prob"))
-        results.append(df_quarks.Histo1D(("Zcc_prob_nOne", "", *bins_prob), "Zcc_prob"))
-        results.append(df_quarks.Histo1D(("Zss_prob_nOne", "", *bins_prob), "Zss_prob"))
-        results.append(df_quarks.Histo1D(("Zqq_prob_nOne", "", *bins_prob), "Zqq_prob"))
-
-        # sort by most likely tag
-        df_quarks = df_quarks.Define("Zbb_like", "recojet_isB[zh_min_idx[0]] + recojet_isB[zh_min_idx[1]]")
-        df_quarks = df_quarks.Define("Zcc_like", "recojet_isC[zh_min_idx[0]] + recojet_isC[zh_min_idx[1]]")
-        df_quarks = df_quarks.Define("Zss_like", "recojet_isS[zh_min_idx[0]] + recojet_isS[zh_min_idx[1]]")
-        df_quarks = df_quarks.Define("Zqq_like", "recojet_isQ[zh_min_idx[0]] + recojet_isQ[zh_min_idx[1]]")
-
-        df_quarks = df_quarks.Define("best_tag", """
-                if (Zbb_like > Zcc_like && Zbb_like > Zss_like && Zbb_like > Zqq_like) {
-                    return 0;
-                } else if (Zcc_like > Zss_like && Zcc_like > Zqq_like) {
-                    return 1;
-                } else if (Zss_like > Zqq_like) {
-                    return 2;
-                } else {
-                    return 3;
-                } """)
-
-        # sort by maximum likelihood tag
-        df_bb = df_quarks.Filter("best_tag == 0")
-        df_cc = df_quarks.Filter("best_tag == 1")
-        df_ss = df_quarks.Filter("best_tag == 2")
-        df_qq = df_quarks.Filter("best_tag == 3")
-
-        results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut4"))
-        results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut4"))
-        results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut4"))
-        results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut4"))
-
-        results.append(df_bb.Graph("Hbb_prob", "Zbb_prob"))
-        results.append(df_cc.Graph("Hbb_prob", "Zcc_prob"))
-        results.append(df_ss.Graph("Hbb_prob", "Zss_prob"))
-        results.append(df_qq.Graph("Hbb_prob", "Zqq_prob"))
-
-        # make sure there are two b jets
-        df_qq = df_qq.Filter("Hbb_prob > 0.032")
-        df_ss = df_ss.Filter("Hbb_prob > 0.032")
-        df_cc = df_cc.Filter("Hbb_prob > 0.029")
-        df_bb = df_bb.Filter("Hbb_prob > 0.011")
-
-        results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut5"))
-        results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut5"))
-        results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut5"))
-        results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut5"))
-
-        # check that the Z jets are the right type
-        df_bb = df_bb.Filter("Zbb_prob > 0.042")
-        df_cc = df_cc.Filter("Zcc_prob > 0.134")
-        df_ss = df_ss.Filter("Zss_prob > 0.095")
-        df_qq = df_qq.Filter("Zqq_prob > 0.053")
-
-        results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut6"))
-        results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut6"))
-        results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut6"))
-        results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut6"))
-
-        # make final mass and momentum histograms
-        for q, df in [("bb", df_bb), ("cc", df_cc), ("ss", df_ss), ("qq", df_qq)]:
-            results.append(df.Histo1D((f"z{q}_z_m", "", *bins_m), "z_dijet_m"))
-            results.append(df.Histo1D((f"z{q}_h_m", "", *bins_m), "h_dijet_m"))
-            results.append(df.Histo1D((f"z{q}_z_p", "", *bins_m), "z_dijet_p"))
-            results.append(df.Histo1D((f"z{q}_h_p", "", *bins_m), "h_dijet_p"))
+    # pair jets based on distance to Z and H masses
+    df_quarks = df_quarks.Define("zh_min_idx", """
+            FCCAnalyses::Vec_i min{0, 0, 0, 0};
+            float distm = INFINITY;
+            for (int i = 0; i < 3; i++)
+                for (int j = i + 1; j < 4; j++)
+                    for (int k = 0; k < 3; k++) {
+                        if (i == k || j == k) continue;
+                        for (int l = k + 1; l < 4; l++) {
+                            if (i == l || j == l) continue;
+                            float distz = (jet_tlv[i] + jet_tlv[j]).M() - 91.2;
+                            float disth = (jet_tlv[k] + jet_tlv[l]).M() - 125;
+                            if (distz*distz/91.2 + disth*disth/125 < distm) {
+                                distm = distz*distz/91.2 + disth*disth/125;
+                                min[0] = i; min[1] = j; min[2] = k; min[3] = l;
+                            }
+                        }
+                    }
+            return min;""")
+
+    # compute Z and H masses and momenta
+    df_quarks = df_quarks.Define("z_dijet", "jet_tlv[zh_min_idx[0]] + jet_tlv[zh_min_idx[1]]")
+    df_quarks = df_quarks.Define("h_dijet", "jet_tlv[zh_min_idx[2]] + jet_tlv[zh_min_idx[3]]")
+
+    df_quarks = df_quarks.Define("z_dijet_m", "z_dijet.M()")
+    df_quarks = df_quarks.Define("z_dijet_p", "z_dijet.P()")
+    df_quarks = df_quarks.Define("h_dijet_m", "h_dijet.M()")
+    df_quarks = df_quarks.Define("h_dijet_p", "h_dijet.P()")
+
+    results.append(df_quarks.Histo1D(("quarks_z_m_nOne", "", *bins_m), "z_dijet_m"))
+    results.append(df_quarks.Histo1D(("quarks_z_p_nOne", "", *bins_m), "z_dijet_p"))
+    results.append(df_quarks.Histo1D(("quarks_h_m_nOne", "", *bins_m), "h_dijet_m"))
+    results.append(df_quarks.Histo1D(("quarks_h_p_nOne", "", *bins_m), "h_dijet_p"))
+
+    # filter on Z momentum
+    df_quarks = df_quarks.Filter("z_dijet_p > 45 && z_dijet_p < 56")
+    results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut2"))
+    results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut2"))
+    results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut2"))
+    results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut2"))
+
+    # filter on H mass
+    df_quarks = df_quarks.Filter("h_dijet_m > 122 && h_dijet_m < 128")
+    results.append(df_quarks.Histo1D(("cutFlow_bb", "", *bins_count), "cut3"))
+    results.append(df_quarks.Histo1D(("cutFlow_cc", "", *bins_count), "cut3"))
+    results.append(df_quarks.Histo1D(("cutFlow_ss", "", *bins_count), "cut3"))
+    results.append(df_quarks.Histo1D(("cutFlow_qq", "", *bins_count), "cut3"))
+
+
+    # flavour tagging
+    #df_quarks = jet4Flavour.define_and_inference(df_quarks)
+    df_quarks = jet4Flavour.define(df_quarks) # define variables
+    df_quarks = jet4Flavour.inference(weaver_preproc, weaver_model, df_quarks) # run inference
+
+
+    # get tag confidence
+    df_quarks = df_quarks.Define("Hbb_prob", "std::min(recojet_isB[zh_min_idx[2]], recojet_isB[zh_min_idx[3]])")
+    results.append(df_quarks.Histo1D(("Hbb_prob_nOne", "", *bins_prob), "Hbb_prob"))
+
+    df_quarks = df_quarks.Define("Zbb_prob", "std::min(recojet_isB[zh_min_idx[0]], recojet_isB[zh_min_idx[1]])")
+    df_quarks = df_quarks.Define("Zcc_prob", "std::min(recojet_isC[zh_min_idx[0]], recojet_isC[zh_min_idx[1]])")
+    df_quarks = df_quarks.Define("Zss_prob", "std::min(recojet_isS[zh_min_idx[0]], recojet_isS[zh_min_idx[1]])")
+    df_quarks = df_quarks.Define("Zqq_prob", "std::min(recojet_isQ[zh_min_idx[0]], recojet_isQ[zh_min_idx[1]])")
+
+    results.append(df_quarks.Histo1D(("Zbb_prob_nOne", "", *bins_prob), "Zbb_prob"))
+    results.append(df_quarks.Histo1D(("Zcc_prob_nOne", "", *bins_prob), "Zcc_prob"))
+    results.append(df_quarks.Histo1D(("Zss_prob_nOne", "", *bins_prob), "Zss_prob"))
+    results.append(df_quarks.Histo1D(("Zqq_prob_nOne", "", *bins_prob), "Zqq_prob"))
+
+    # sort by most likely tag
+    df_quarks = df_quarks.Define("Zbb_like", "recojet_isB[zh_min_idx[0]] + recojet_isB[zh_min_idx[1]]")
+    df_quarks = df_quarks.Define("Zcc_like", "recojet_isC[zh_min_idx[0]] + recojet_isC[zh_min_idx[1]]")
+    df_quarks = df_quarks.Define("Zss_like", "recojet_isS[zh_min_idx[0]] + recojet_isS[zh_min_idx[1]]")
+    df_quarks = df_quarks.Define("Zqq_like", "recojet_isQ[zh_min_idx[0]] + recojet_isQ[zh_min_idx[1]]")
+
+    df_quarks = df_quarks.Define("best_tag", """
+            if (Zbb_like > Zcc_like && Zbb_like > Zss_like && Zbb_like > Zqq_like) {
+                return 0;
+            } else if (Zcc_like > Zss_like && Zcc_like > Zqq_like) {
+                return 1;
+            } else if (Zss_like > Zqq_like) {
+                return 2;
+            } else {
+                return 3;
+            } """)
+
+    # sort by maximum likelihood tag
+    df_bb = df_quarks.Filter("best_tag == 0")
+    df_cc = df_quarks.Filter("best_tag == 1")
+    df_ss = df_quarks.Filter("best_tag == 2")
+    df_qq = df_quarks.Filter("best_tag == 3")
+
+    results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut4"))
+    results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut4"))
+    results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut4"))
+    results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut4"))
+
+    results.append(df_bb.Graph("Hbb_prob", "Zbb_prob"))
+    results.append(df_cc.Graph("Hbb_prob", "Zcc_prob"))
+    results.append(df_ss.Graph("Hbb_prob", "Zss_prob"))
+    results.append(df_qq.Graph("Hbb_prob", "Zqq_prob"))
+
+    # make sure there are two b jets
+    df_qq = df_qq.Filter("Hbb_prob > 0.032")
+    df_ss = df_ss.Filter("Hbb_prob > 0.032")
+    df_cc = df_cc.Filter("Hbb_prob > 0.029")
+    df_bb = df_bb.Filter("Hbb_prob > 0.011")
+
+    results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut5"))
+    results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut5"))
+    results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut5"))
+    results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut5"))
+
+    # check that the Z jets are the right type
+    df_bb = df_bb.Filter("Zbb_prob > 0.042")
+    df_cc = df_cc.Filter("Zcc_prob > 0.134")
+    df_ss = df_ss.Filter("Zss_prob > 0.095")
+    df_qq = df_qq.Filter("Zqq_prob > 0.053")
+
+    results.append(df_bb.Histo1D(("cutFlow_bb", "", *bins_count), "cut6"))
+    results.append(df_cc.Histo1D(("cutFlow_cc", "", *bins_count), "cut6"))
+    results.append(df_ss.Histo1D(("cutFlow_ss", "", *bins_count), "cut6"))
+    results.append(df_qq.Histo1D(("cutFlow_qq", "", *bins_count), "cut6"))
+
+    # make final mass and momentum histograms
+    for q, df in [("bb", df_bb), ("cc", df_cc), ("ss", df_ss), ("qq", df_qq)]:
+        results.append(df.Histo1D((f"z{q}_z_m", "", *bins_m), "z_dijet_m"))
+        results.append(df.Histo1D((f"z{q}_h_m", "", *bins_m), "h_dijet_m"))
+        results.append(df.Histo1D((f"z{q}_z_p", "", *bins_m), "z_dijet_p"))
+        results.append(df.Histo1D((f"z{q}_h_p", "", *bins_m), "h_dijet_p"))
 
 
     
     return results, weightsum
 
 
-if __name__ == "__main__":
+# if __name__ == "__main__":
 
-    datadict = functions.get_datadicts() # get default datasets
+#     datadict = functions.get_datadicts() # get default datasets
 
-    Zprods = ["ee", "mumu", "tautau", "nunu", "qq", "ss", "cc", "bb"] 
-    bb_sig = [f"wzp6_ee_{i}H_Hbb_ecm240" for i in Zprods]
-    cc_sig = [f"wzp6_ee_{i}H_Hcc_ecm240" for i in Zprods]
-    gg_sig = [f"wzp6_ee_{i}H_Hgg_ecm240" for i in Zprods]
+#     Zprods = ["ee", "mumu", "tautau", "nunu", "qq", "ss", "cc", "bb"] 
+#     bb_sig = [f"wzp6_ee_{i}H_Hbb_ecm240" for i in Zprods]
+#     cc_sig = [f"wzp6_ee_{i}H_Hcc_ecm240" for i in Zprods]
+#     gg_sig = [f"wzp6_ee_{i}H_Hgg_ecm240" for i in Zprods]
     
-    quark_test = ["wzp6_ee_qqH_Hbb_ecm240", "wzp6_ee_ssH_Hbb_ecm240", "wzp6_ee_ccH_Hbb_ecm240", "wzp6_ee_bbH_Hbb_ecm240"]
+#     quark_test = ["wzp6_ee_qqH_Hbb_ecm240", "wzp6_ee_ssH_Hbb_ecm240", "wzp6_ee_ccH_Hbb_ecm240", "wzp6_ee_bbH_Hbb_ecm240"]
     
-    datasets_bkg = ["p8_ee_WW_ecm240", "p8_ee_ZZ_ecm240", "wzp6_ee_mumu_ecm240", "wzp6_ee_tautau_ecm240", "wzp6_egamma_eZ_Zmumu_ecm240", "wzp6_gammae_eZ_Zmumu_ecm240", "wzp6_gaga_mumu_60_ecm240", "wzp6_gaga_tautau_60_ecm240", "wzp6_ee_nuenueZ_ecm240"]
+#     datasets_bkg = ["p8_ee_WW_ecm240", "p8_ee_ZZ_ecm240", "wzp6_ee_mumu_ecm240", "wzp6_ee_tautau_ecm240", "wzp6_egamma_eZ_Zmumu_ecm240", "wzp6_gammae_eZ_Zmumu_ecm240", "wzp6_gaga_mumu_60_ecm240", "wzp6_gaga_tautau_60_ecm240", "wzp6_ee_nuenueZ_ecm240"]
 
-    datasets_to_run = bb_sig + cc_sig + gg_sig + datasets_bkg[:2]
+#     datasets_to_run = bb_sig + cc_sig + gg_sig + datasets_bkg[:2]
     
-    result = functions.build_and_run(datadict, datasets_to_run, build_graph, f"end_of_h_bb.root", args, norm=True, lumi=7200000)
+#     result = functions.build_and_run(datadict, datasets_to_run, build_graph, f"end_of_h_bb.root", args, norm=True, lumi=7200000)
diff --git a/analyses/h_bb/otherfunc_gen.h b/analyses/h_bb/otherfunc_gen.h
new file mode 100644
index 0000000..5f361d4
--- /dev/null
+++ b/analyses/h_bb/otherfunc_gen.h
@@ -0,0 +1,37 @@
+#ifndef FCCPhysicsFunctionsGen_H
+#define FCCPhysicsFunctionsGen_H
+
+namespace FCCAnalyses {
+
+
+
+// make Lorentz vectors for a given MC particle collection
+Vec_tlv makeLorentzVectors(Vec_mc in) {
+    Vec_tlv result;
+    for(auto & p: in) {
+        TLorentzVector tlv;
+        tlv.SetXYZM(p.momentum.x, p.momentum.y, p.momentum.z, p.mass);
+        result.push_back(tlv);
+    }
+    return result;
+}
+
+Vec_mc getRP2MC(Vec_rp in, ROOT::VecOps::RVec<int> recind, ROOT::VecOps::RVec<int> mcind, ROOT::VecOps::RVec<edm4hep::ReconstructedParticleData> reco, ROOT::VecOps::RVec<edm4hep::MCParticleData> mc) {
+    Vec_mc result;
+    for (auto & p: in) {
+        int track_index = p.tracks_begin;
+        int mc_index = ReconstructedParticle2MC::getTrack2MC_index(track_index, recind, mcind, reco);
+        if(mc_index >= 0 && mc_index < mc.size() ) {
+            result.push_back(mc.at(mc_index));
+        }
+        else {
+            cout << "MC track not found!" << endl;
+        }
+    }
+    return result;
+}
+
+}
+
+
+#endif
\ No newline at end of file
diff --git a/analyses/h_bb/otherfuncs.h b/analyses/h_bb/otherfuncs.h
new file mode 100644
index 0000000..dae4094
--- /dev/null
+++ b/analyses/h_bb/otherfuncs.h
@@ -0,0 +1,382 @@
+#ifndef FCCPhysicsFunctions_H
+#define FCCPhysicsFunctions_H
+
+namespace FCCAnalyses {
+
+
+// make Lorentz vectors for a given RECO particle collection
+Vec_tlv makeLorentzVectors(Vec_rp in) {
+    Vec_tlv result;
+    for(auto & p: in) {
+        TLorentzVector tlv;
+        tlv.SetXYZM(p.momentum.x, p.momentum.y, p.momentum.z, p.mass);
+        result.push_back(tlv);
+    }
+    return result;
+}
+
+
+// make Lorentzvectors from pseudojets
+Vec_tlv makeLorentzVectors(Vec_f jets_px, Vec_f jets_py, Vec_f jets_pz, Vec_f jets_e) {
+    Vec_tlv result;
+    for(int i=0; i<jets_px.size(); i++) {
+        TLorentzVector tlv;
+        tlv.SetPxPyPzE(jets_px[i], jets_py[i], jets_pz[i], jets_e[i]);
+        result.push_back(tlv);
+    }
+    return result;
+}
+
+
+// acolinearity between two reco particles
+float acolinearity(Vec_rp in) {
+    if(in.size() < 2) return -999;
+
+    TLorentzVector p1;
+    p1.SetXYZM(in[0].momentum.x, in[0].momentum.y, in[0].momentum.z, in[0].mass);
+
+    TLorentzVector p2;
+    p2.SetXYZM(in[1].momentum.x, in[1].momentum.y, in[1].momentum.z, in[1].mass);
+
+    TVector3 v1 = p1.Vect();
+    TVector3 v2 = p2.Vect();
+    return std::acos(v1.Dot(v2)/(v1.Mag()*v2.Mag())*(-1.));
+}
+
+// acoplanarity between two reco particles
+float acoplanarity(Vec_rp in) {
+    if(in.size() < 2) return -999;
+
+    TLorentzVector p1;
+    p1.SetXYZM(in[0].momentum.x, in[0].momentum.y, in[0].momentum.z, in[0].mass);
+
+    TLorentzVector p2;
+    p2.SetXYZM(in[1].momentum.x, in[1].momentum.y, in[1].momentum.z, in[1].mass);
+
+    float acop = abs(p1.Phi() - p2.Phi());
+    if(acop > M_PI) acop = 2 * M_PI - acop;
+    acop = M_PI - acop;
+
+    return acop;
+}
+
+// visible energy
+float visibleEnergy(Vec_rp in, float p_cutoff = 0.0) {
+    float e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        e += p.energy;
+    }
+    return e;
+}
+
+// returns missing energy vector, based on reco particles
+Vec_rp missingEnergy(float ecm, Vec_rp in, float p_cutoff = 0.0) {
+    float px = 0, py = 0, pz = 0, e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        px += -p.momentum.x;
+        py += -p.momentum.y;
+        pz += -p.momentum.z;
+        e += p.energy;
+    }
+
+    Vec_rp ret;
+    rp res;
+    res.momentum.x = px;
+    res.momentum.y = py;
+    res.momentum.z = pz;
+    res.energy = ecm-e;
+    ret.emplace_back(res);
+    return ret;
+}
+
+// calculate the visible mass of the event
+float visibleMass(Vec_rp in, float p_cutoff = 0.0) {
+    float px = 0, py = 0, pz = 0, e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        px += p.momentum.x;
+        py += p.momentum.y;
+        pz += p.momentum.z;
+        e += p.energy;
+    }
+
+    float ptot2 = std::pow(px, 2) + std::pow(py, 2) + std::pow(pz, 2);
+    float de2 = std::pow(e, 2);
+    if (de2 < ptot2) return -999.;
+    float Mvis = std::sqrt(de2 - ptot2);
+    return Mvis;
+}
+
+// calculate the missing mass, given a ECM value
+float missingMass(float ecm, Vec_rp in, float p_cutoff = 0.0) {
+    float px = 0, py = 0, pz = 0, e = 0;
+    for(auto &p : in) {
+        if (std::sqrt(p.momentum.x * p.momentum.x + p.momentum.y*p.momentum.y) < p_cutoff) continue;
+        px += p.momentum.x;
+        py += p.momentum.y;
+        pz += p.momentum.z;
+        e += p.energy;
+    }
+    if(ecm < e) return -99.;
+
+    float ptot2 = std::pow(px, 2) + std::pow(py, 2) + std::pow(pz, 2);
+    float de2 = std::pow(ecm - e, 2);
+    if (de2 < ptot2) return -999.;
+    float Mmiss = std::sqrt(de2 - ptot2);
+    return Mmiss;
+}
+
+// calculate the cosine(theta) of the missing energy vector
+// float get_cosTheta_miss(Vec_rp met){
+
+//     float costheta = 0.;
+//     if(met.size() > 0) {
+//         TLorentzVector lv_met;
+//         lv_met.SetPxPyPzE(met[0].momentum.x, met[0].momentum.y, met[0].momentum.z, met[0].energy);
+//         costheta = fabs(std::cos(lv_met.Theta()));
+//     }
+//     return costheta;
+// }
+
+
+
+
+// compute the cone isolation for reco particles
+struct coneIsolation {
+
+    coneIsolation(float arg_dr_min, float arg_dr_max);
+    double deltaR(double eta1, double phi1, double eta2, double phi2) { return TMath::Sqrt(TMath::Power(eta1-eta2, 2) + (TMath::Power(phi1-phi2, 2))); };
+
+    float dr_min = 0;
+    float dr_max = 0.4;
+    Vec_f operator() (Vec_rp in, Vec_rp rps) ;
+};
+
+coneIsolation::coneIsolation(float arg_dr_min, float arg_dr_max) : dr_min(arg_dr_min), dr_max( arg_dr_max ) { };
+Vec_f coneIsolation::coneIsolation::operator() (Vec_rp in, Vec_rp rps) {
+
+    Vec_f result;
+    result.reserve(in.size());
+
+    std::vector<ROOT::Math::PxPyPzEVector> lv_reco;
+    std::vector<ROOT::Math::PxPyPzEVector> lv_charged;
+    std::vector<ROOT::Math::PxPyPzEVector> lv_neutral;
+
+    for(size_t i = 0; i < rps.size(); ++i) {
+        ROOT::Math::PxPyPzEVector tlv;
+        tlv.SetPxPyPzE(rps.at(i).momentum.x, rps.at(i).momentum.y, rps.at(i).momentum.z, rps.at(i).energy);
+
+        if(rps.at(i).charge == 0) lv_neutral.push_back(tlv);
+        else lv_charged.push_back(tlv);
+    }
+
+    for(size_t i = 0; i < in.size(); ++i) {
+        ROOT::Math::PxPyPzEVector tlv;
+        tlv.SetPxPyPzE(in.at(i).momentum.x, in.at(i).momentum.y, in.at(i).momentum.z, in.at(i).energy);
+        lv_reco.push_back(tlv);
+    }
+
+    // compute the isolation (see https://github.com/delphes/delphes/blob/master/modules/Isolation.cc#L154) 
+    for (auto & lv_reco_ : lv_reco) {
+        double sumNeutral = 0.0;
+        double sumCharged = 0.0;
+
+        // charged
+        for (auto & lv_charged_ : lv_charged) {
+            double dr = coneIsolation::deltaR(lv_reco_.Eta(), lv_reco_.Phi(), lv_charged_.Eta(), lv_charged_.Phi());
+            if(dr > dr_min && dr < dr_max) sumCharged += lv_charged_.P();
+        }
+
+        // neutral
+        for (auto & lv_neutral_ : lv_neutral) {
+            double dr = coneIsolation::deltaR(lv_reco_.Eta(), lv_reco_.Phi(), lv_neutral_.Eta(), lv_neutral_.Phi());
+            if(dr > dr_min && dr < dr_max) sumNeutral += lv_neutral_.P();
+        }
+
+        double sum = sumCharged + sumNeutral;
+        double ratio= sum / lv_reco_.P();
+        result.emplace_back(ratio);
+    }
+    return result;
+}
+
+// filter reconstructed particles (in) based a property (prop) within a defined range (m_min, m_max)
+struct sel_range {
+    sel_range(float arg_min, float arg_max, bool arg_abs = false);
+    float m_min = 0.;
+    float m_max = 1.;
+    bool m_abs = false;
+    Vec_rp operator() (Vec_rp in, Vec_f prop);
+};
+
+sel_range::sel_range(float arg_min, float arg_max, bool arg_abs) : m_min(arg_min), m_max(arg_max), m_abs(arg_abs) {};
+Vec_rp sel_range::operator() (Vec_rp in, Vec_f prop) {
+    Vec_rp result;
+    result.reserve(in.size());
+    for (size_t i = 0; i < in.size(); ++i) {
+        auto & p = in[i];
+        float val = (m_abs) ? abs(prop[i]) : prop[i];
+        if(val > m_min && val < m_max) result.emplace_back(p);
+    }
+    return result;
+}
+
+
+// // build the Z resonance based on the available leptons. Returns the best lepton pair compatible with the Z mass and recoil at 125 GeV
+// // technically, it returns a ReconstructedParticleData object with index 0 the di-lepton system, index and 2 the leptons of the pair
+// struct resonanceBuilder_mass_recoil {
+//     float m_resonance_mass;
+//     float m_recoil_mass;
+//     float chi2_recoil_frac;
+//     float ecm;
+//     bool m_use_MC_Kinematics;
+//     resonanceBuilder_mass_recoil(float arg_resonance_mass, float arg_recoil_mass, float arg_chi2_recoil_frac, float arg_ecm, bool arg_use_MC_Kinematics);
+//     Vec_rp operator()(Vec_rp legs, Vec_i recind, Vec_i mcind, Vec_rp reco, Vec_mc mc, Vec_i parents, Vec_i daugthers) ;
+// };
+
+// resonanceBuilder_mass_recoil::resonanceBuilder_mass_recoil(float arg_resonance_mass, float arg_recoil_mass, float arg_chi2_recoil_frac, float arg_ecm, bool arg_use_MC_Kinematics) {m_resonance_mass = arg_resonance_mass, m_recoil_mass = arg_recoil_mass, chi2_recoil_frac = arg_chi2_recoil_frac, ecm = arg_ecm, m_use_MC_Kinematics = arg_use_MC_Kinematics;}
+
+// Vec_rp resonanceBuilder_mass_recoil::resonanceBuilder_mass_recoil::operator()(Vec_rp legs, Vec_i recind, Vec_i mcind, Vec_rp reco, Vec_mc mc, Vec_i parents, Vec_i daugthers) {
+//     Vec_rp result;
+//     result.reserve(3);
+//     std::vector<std::vector<int>> pairs; // for each permutation, add the indices of the muons
+//     int n = legs.size();
+
+//     if(n > 1) {
+//         ROOT::VecOps::RVec<bool> v(n);
+//         std::fill(v.end() - 2, v.end(), true); // helper variable for permutations
+//         do {
+//             std::vector<int> pair;
+//             rp reso;
+//             reso.charge = 0;
+//             TLorentzVector reso_lv; 
+//             for(int i = 0; i < n; ++i) {
+//                 if(v[i]) {
+//                     pair.push_back(i);
+//                     reso.charge += legs[i].charge;
+//                     TLorentzVector leg_lv;
+
+//                     if(m_use_MC_Kinematics) { // MC kinematics
+//                         int track_index = legs[i].tracks_begin;   // index in the Track array
+//                         int mc_index = ReconstructedParticle2MC::getTrack2MC_index(track_index, recind, mcind, reco);
+//                         if (mc_index >= 0 && mc_index < mc.size()) {
+//                             leg_lv.SetXYZM(mc.at(mc_index).momentum.x, mc.at(mc_index).momentum.y, mc.at(mc_index).momentum.z, mc.at(mc_index).mass);
+//                         }
+//                     }
+//                     else { // reco kinematics
+//                          leg_lv.SetXYZM(legs[i].momentum.x, legs[i].momentum.y, legs[i].momentum.z, legs[i].mass);
+//                     }
+//                     reso_lv += leg_lv;
+//                 }
+//             }
+
+//             if(reso.charge != 0) continue; // neglect non-zero charge pairs
+//             reso.momentum.x = reso_lv.Px();
+//             reso.momentum.y = reso_lv.Py();
+//             reso.momentum.z = reso_lv.Pz();
+//             reso.mass = reso_lv.M();
+//             result.emplace_back(reso);
+//             pairs.push_back(pair);
+
+//         } while(std::next_permutation(v.begin(), v.end()));
+//     }
+//     else {
+//         std::cout << "ERROR: resonanceBuilder_mass_recoil, at least two leptons required." << std::endl;
+//         exit(1);
+//     }
+
+//     if(result.size() > 1) {
+
+//         Vec_rp bestReso;
+//         int idx_min = -1;
+//         float d_min = 9e9;
+//         for (int i = 0; i < result.size(); ++i) {
+            
+//             // calculate recoil
+//             auto recoil_p4 = TLorentzVector(0, 0, 0, ecm);
+//             TLorentzVector tv1;
+//             tv1.SetXYZM(result.at(i).momentum.x, result.at(i).momentum.y, result.at(i).momentum.z, result.at(i).mass);
+//             recoil_p4 -= tv1;
+
+//             auto recoil_fcc = edm4hep::ReconstructedParticleData();
+//             recoil_fcc.momentum.x = recoil_p4.Px();
+//             recoil_fcc.momentum.y = recoil_p4.Py();
+//             recoil_fcc.momentum.z = recoil_p4.Pz();
+//             recoil_fcc.mass = recoil_p4.M();
+
+//             TLorentzVector tg;
+//             tg.SetXYZM(result.at(i).momentum.x, result.at(i).momentum.y, result.at(i).momentum.z, result.at(i).mass);
+
+//             float boost = tg.P();
+//             float mass = std::pow(result.at(i).mass - m_resonance_mass, 2); // mass
+//             float rec = std::pow(recoil_fcc.mass - m_recoil_mass, 2); // recoil
+//             float d = (1.0-chi2_recoil_frac)*mass + chi2_recoil_frac*rec;
+
+//             if(d < d_min) {
+//                 d_min = d;
+//                 idx_min = i;
+//             }
+
+//         }
+//         if(idx_min > -1) { 
+//             bestReso.push_back(result.at(idx_min));
+//             auto & l1 = legs[pairs[idx_min][0]];
+//             auto & l2 = legs[pairs[idx_min][1]];
+//             bestReso.emplace_back(l1);
+//             bestReso.emplace_back(l2);
+//         }
+//         else {
+//             std::cout << "ERROR: resonanceBuilder_mass_recoil, no mininum found." << std::endl;
+//             exit(1);
+//         }
+//         return bestReso;
+//     }
+//     else {
+//         auto & l1 = legs[0];
+//         auto & l2 = legs[1];
+//         result.emplace_back(l1);
+//         result.emplace_back(l2);
+//         return result;
+//     }
+// }
+
+
+// computes longitudinal and transversal energy balance of all particles
+Vec_f energy_imbalance(Vec_rp in) {
+    float e_tot = 0;
+    float e_trans = 0;
+    float e_long = 0;
+    for(auto &p : in) {
+        float mag = std::sqrt(p.momentum.x*p.momentum.x + p.momentum.y*p.momentum.y + p.momentum.z*p.momentum.z);
+        float cost = p.momentum.z / mag;
+        float sint =  std::sqrt(p.momentum.x*p.momentum.x + p.momentum.y*p.momentum.y) / mag;
+        if(p.momentum.y < 0) sint *= -1.0;
+        e_tot += p.energy;
+        e_long += cost*p.energy;
+        e_trans += sint*p.energy;
+    }
+    Vec_f result;
+    result.push_back(e_tot);
+    result.push_back(std::abs(e_trans));
+    result.push_back(std::abs(e_long));
+    return result;
+}
+
+Vec_f get_costheta(Vec_rp in) {
+    Vec_f result;
+    for (auto & p: in) {
+        TLorentzVector tlv;
+        tlv.SetXYZM(p.momentum.x, p.momentum.y, p.momentum.z, p.mass);
+        result.push_back(std::cos(tlv.Theta()));
+    }
+    return result;
+}
+
+
+
+}
+
+
+#endif
\ No newline at end of file
diff --git a/python/dataset.py b/python/dataset.py
index 8b5b2ef..cd52e6f 100644
--- a/python/dataset.py
+++ b/python/dataset.py
@@ -30,8 +30,8 @@ def get_meta(self):
         else:
             f = open(fName)
             d = json.load(f)
-            self.total_files = d['total_files']
-            self.total_events = d['total_events']
+            self.total_files = d['numberOfFiles']
+            self.total_events = d['numberOfEvents']
         self.nevents_per_file = self.total_events / self.total_files
 
     def findROOTFiles(self, basedir, regex = ""):
diff --git a/python/functions.py b/python/functions.py
index 05792c6..44f081c 100644
--- a/python/functions.py
+++ b/python/functions.py
@@ -3,13 +3,13 @@
 import concurrent.futures
 import time
 import ROOT
-from dataset import Dataset
+from python.dataset import Dataset
 import argparse
 import pathlib
 import json
 import logging
 import pickle
-import submit
+import python.submit as submit
 
 logging.basicConfig(format='%(levelname)s: %(message)s')
 logger = logging.getLogger("fcclogger")
@@ -241,8 +241,9 @@ def build_and_run(datadict, datasets_to_run, build_function, output_file, args,
         if not datasetName in datadict:
             logger.warning(f"dataset {datasetName} does not exist, skipping")
             continue
-        xsec = datadict[datasetName]['xsec']
-        path = f"{datadict['basePath']}/{datadict[datasetName]['path']}"
+        xsec = datadict[datasetName]['crossSection']
+        #path = f"{datadict['basePath']}/{datadict[datasetName]['path']}"
+        path = f"{datadict[datasetName]['path']}"
         if not os.path.exists(path):
             logger.warning(f"directory {path} does not exist, skipping")
             continue
@@ -494,10 +495,15 @@ def get_datadicts(campaign="winter2023"):
     basedirs['mit'] = "/ceph/submit/data/group/cms/store/fccee/samples/"
     basedirs['fcc_eos'] = "/eos/experiment/fcc/ee/generation/DelphesEvents/"
 
+    basedirs['other'] = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents"
+
     hostname = get_hostname()
-    if "mit.edu" in hostname: basedir = basedirs['mit']
-    else: basedir = ""
-    catalog = f'{basedir}/{campaign}/catalog.json'
+    #if "mit.edu" in hostname: basedir = basedirs['mit']
+    #else: basedir = ""
+    basedir = basedirs['other']
+
+    #catalog = f'{basedir}/{campaign}/catalog.json'
+    catalog = f'{basedir}/{campaign}/IDEA/samplesDict.json'
     f = open(catalog)
     datadict = json.load(f)
     datadict['basePath'] = os.path.dirname(catalog)
diff --git a/python/submit.py b/python/submit.py
index bb7b56a..eb41152 100644
--- a/python/submit.py
+++ b/python/submit.py
@@ -3,7 +3,7 @@
 import concurrent.futures
 import time
 import ROOT
-from dataset import Dataset
+from python.dataset import Dataset
 import argparse
 import pathlib
 import json

From 927921ba056c010c093793333aa8c2e7b21e55d8 Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Tue, 28 Jan 2025 01:09:36 -0500
Subject: [PATCH 4/7] minor bug fix +comments

---
 analyses/h_bb/h_bb.py | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/analyses/h_bb/h_bb.py b/analyses/h_bb/h_bb.py
index 833fb91..6253852 100644
--- a/analyses/h_bb/h_bb.py
+++ b/analyses/h_bb/h_bb.py
@@ -1,4 +1,4 @@
-import python.functions as functions
+# import python.functions as functions      # must be commented out for running fccanalyis
 import python.helpers as helpers
 import ROOT
 import argparse
@@ -11,14 +11,22 @@
 from addons.FastJet.jetClusteringHelper import ExclusiveJetClusteringHelper
 from examples.FCCee.weaver.config import collections, njets
 
+#***************************************************
+# Notice:
+# -Currently only the use of fccanalysis run [] (with proper sourcing) works
+# -Much of the code is commented to allow for further testing, file may not work when uncommented
+# -Code takes a long time to run to completion (>1hr)
+# -Output is stored as seperate root files @ output/hbb_tagging/
+#****************************************************
+
 logger = logging.getLogger("fcclogger")
 
-parser = functions.make_def_argparser()
-args = parser.parse_args()
-functions.set_threads(args)
+# parser = functions.make_def_argparser()
+# args = parser.parse_args()
+# functions.set_threads(args)
 
-functions.add_include_file("analyses/higgs_mass_xsec/functions.h")
-functions.add_include_file("analyses/higgs_mass_xsec/functions_gen.h")
+# functions.add_include_file("analyses/higgs_mass_xsec/functions.h")
+# functions.add_include_file("analyses/higgs_mass_xsec/functions_gen.h")
 # functions.add_include_file("analyses/h_bb/otherfuncs.h")
 # functions.add_include_file("analyses/h_bb/otherfunc_gen.h")
 
@@ -538,6 +546,8 @@ def build_graph(df, dataset):
     return results, weightsum
 
 
+# Main function must be uncommented when running file using python, else keep commented
+
 # if __name__ == "__main__":
 
 #     datadict = functions.get_datadicts() # get default datasets

From 2740a2fe639cf7bc03143e28ff6a834a0345b7fc Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Fri, 31 Jan 2025 13:30:32 -0500
Subject: [PATCH 5/7] Added merging of root files

---
 analyses/h_bb/h_bb.py      |  3 +-
 analyses/h_bb/rootmerge.py | 94 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 95 insertions(+), 2 deletions(-)
 create mode 100644 analyses/h_bb/rootmerge.py

diff --git a/analyses/h_bb/h_bb.py b/analyses/h_bb/h_bb.py
index 6253852..a062516 100644
--- a/analyses/h_bb/h_bb.py
+++ b/analyses/h_bb/h_bb.py
@@ -27,8 +27,7 @@
 
 # functions.add_include_file("analyses/higgs_mass_xsec/functions.h")
 # functions.add_include_file("analyses/higgs_mass_xsec/functions_gen.h")
-# functions.add_include_file("analyses/h_bb/otherfuncs.h")
-# functions.add_include_file("analyses/h_bb/otherfunc_gen.h")
+
 
 
 # list of all processes
diff --git a/analyses/h_bb/rootmerge.py b/analyses/h_bb/rootmerge.py
new file mode 100644
index 0000000..712d551
--- /dev/null
+++ b/analyses/h_bb/rootmerge.py
@@ -0,0 +1,94 @@
+import uproot
+import subprocess
+import os
+import json
+import ROOT
+
+#####
+# Warning! Currently ignores Tgraphs(jet stuff)
+####
+
+path = "/home/submit/aniketkg/FCCAnalyzer/output/hbb_tagging/histmaker/"
+inputDir = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/"
+# get list of root files generated by hbb
+dir_list = os.listdir(path)
+
+def get_datadicts(campaign="winter2023"):
+    basedirs = {}
+    basedirs['other'] = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents"
+
+    
+    basedir = basedirs['other']
+
+    catalog = f'{basedir}/{campaign}/IDEA/samplesDict.json'
+    f = open(catalog)
+    datadict = json.load(f)
+    datadict['basePath'] = os.path.dirname(catalog)
+    return datadict
+
+datadict = get_datadicts()
+
+#creates output root file
+output_file = "analyses/h_bb/end_of_h_bb.root"
+fOut = ROOT.TFile(output_file, "RECREATE")
+fOut.Close()
+
+for s in dir_list: #for each output file
+    sfull = path + s
+    strip = s.removesuffix(".root")
+    histsToWrite = {}
+    weight = 0
+    ep = 0
+    
+
+    file = ROOT.TFile(sfull)
+    
+    a = file.GetListOfKeys()
+    
+    xsec = datadict[strip]['crossSection']
+    
+    for h in a: # for each item in root file
+        
+        event = file.Get(h.GetName())
+        
+        if event.Class_Name() == "TH1D": #if histogram, add to list
+            
+            hist = event
+            hName = event.GetName()
+            
+            if hName in histsToWrite: # merge histograms in case histogram exists
+                histsToWrite[hName].Add(hist)
+            else:
+                histsToWrite[hName] = hist
+                
+        # Get values
+        elif event.GetName() == "sumOfWeights":
+            weight = event.GetVal()
+        elif event.GetName() == "eventsProcessed":
+            ep = event.GetVal()
+        elif event.GetName() == "intLumi":
+            intlumi = event.GetVal()
+        elif event.GetName() == "crossSection":
+            xsec = event.GetVal()
+    norm = True
+    lumi = 7200000
+    # Write to output rootfile 
+    fOut = ROOT.TFile(output_file, "UPDATE")
+    fOut.cd()
+    fOut.mkdir(strip)
+    fOut.cd(strip)
+    for hist in histsToWrite.values(): #for histograms
+        
+        if norm:
+            hist.Scale(xsec*lumi/weight)
+        hist.Write()
+    h_meta = ROOT.TH1D("meta", "", 10, 0, 1)
+    h_meta.SetBinContent(1, weight)
+    h_meta.SetBinContent(2, ep)
+    h_meta.SetBinContent(3, xsec)
+    h_meta.Write()
+    fOut.cd()
+    fOut.Close()
+
+
+

From c8a07e88599df815e97ee804c30ac3496928cb67 Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Fri, 31 Jan 2025 13:48:36 -0500
Subject: [PATCH 6/7] added jan's sourcing for convenience

---
 jsetup.sh | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 jsetup.sh

diff --git a/jsetup.sh b/jsetup.sh
new file mode 100644
index 0000000..17761f5
--- /dev/null
+++ b/jsetup.sh
@@ -0,0 +1,50 @@
+if [ "${0}" != "${BASH_SOURCE}" ]; then
+  # Determinig the location of this setup script
+  export LOCAL_DIR=$(cd $(dirname "${BASH_SOURCE}") && pwd)
+
+  echo "----> Info: Setting up Key4hep stack..."
+  # Sourcing of the stack
+  if [ -n "${KEY4HEP_STACK}" ]; then
+    echo "----> Info: Key4hep stack already set up. Skipping..."
+  elif [ -f "${LOCAL_DIR}/.fccana/stackpin" ]; then
+    STACK_PATH=$(<${LOCAL_DIR}/.fccana/stackpin)
+    echo "----> Info: Sourcing pinned Key4hep stack..."
+    echo "      ${STACK_PATH}"
+    source ${STACK_PATH}
+  else
+    source /cvmfs/sw.hsf.org/key4hep/setup.sh -r 2024-03-10
+  fi
+
+  if [ -z "${KEY4HEP_STACK}" ]; then
+    echo "----> Error: Key4hep stack not setup correctly! Aborting..."
+    return 1
+  fi
+
+  echo "----> Info: Setting up environment variables..."
+  export PYTHONPATH=${LOCAL_DIR}/python:${PYTHONPATH}
+  export PYTHONPATH=${LOCAL_DIR}/install/python:${PYTHONPATH}
+  export PYTHONPATH=${LOCAL_DIR}/install/share/examples:${PYTHONPATH}
+  export PATH=${LOCAL_DIR}/bin:${PATH}
+  export PATH=${LOCAL_DIR}/install/bin:${PATH}
+  export LD_LIBRARY_PATH=${LOCAL_DIR}/install/lib:${LD_LIBRARY_PATH}
+  export CMAKE_PREFIX_PATH=${LOCAL_DIR}/install:${CMAKE_PREFIX_PATH}
+
+  export ROOT_INCLUDE_PATH=`fastjet-config --prefix`/include:${ROOT_INCLUDE_PATH}
+  export ROOT_INCLUDE_PATH=${LOCAL_DIR}/install/include:${ROOT_INCLUDE_PATH}
+
+  export ONNXRUNTIME_ROOT_DIR=`python -c "import onnxruntime; print(onnxruntime.__path__[0]+'/../../../..')" 2> /dev/null`
+  if [ -z "${ONNXRUNTIME_ROOT_DIR}" ]; then
+    echo "----> Warning: ONNX Runtime not found! Related analyzers won't be build..."
+  else
+    export LD_LIBRARY_PATH=${ONNXRUNTIME_ROOT_DIR}/lib:${LD_LIBRARY_PATH}
+  fi
+
+  export MANPATH=${LOCAL_DIR}/man:${MANPATH}
+  export MANPATH=${LOCAL_DIR}/install/share/man:${MANPATH}
+
+  export MYPYPATH=${LOCAL_DIR}/python:${MYPYPATH}
+
+  export FCCDICTSDIR=/cvmfs/fcc.cern.ch/FCCDicts:${FCCDICTSDIR}
+else
+  echo "----> Error: This script is meant to be sourced!"
+fi

From e889a361853ca9153dd6807c74b61fa0e72be594 Mon Sep 17 00:00:00 2001
From: aniketkgumd <aniketkg@terpmail.umd.edu>
Date: Fri, 31 Jan 2025 14:01:52 -0500
Subject: [PATCH 7/7] Added tgraph support

---
 analyses/h_bb/rootmerge.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/analyses/h_bb/rootmerge.py b/analyses/h_bb/rootmerge.py
index 712d551..9386b87 100644
--- a/analyses/h_bb/rootmerge.py
+++ b/analyses/h_bb/rootmerge.py
@@ -5,10 +5,11 @@
 import ROOT
 
 #####
-# Warning! Currently ignores Tgraphs(jet stuff)
+# Warning!
+# Run using python
 ####
 
-path = "/home/submit/aniketkg/FCCAnalyzer/output/hbb_tagging/histmaker/"
+path = "/home/submit/aniketkg/FCCAnalyzer/output/hbb_tagging/histmaker/" # Change to your path!!!
 inputDir = "/ceph/submit/data/group/fcc/ee/generation/DelphesEvents/winter2023/IDEA/"
 # get list of root files generated by hbb
 dir_list = os.listdir(path)
@@ -51,7 +52,7 @@ def get_datadicts(campaign="winter2023"):
         
         event = file.Get(h.GetName())
         
-        if event.Class_Name() == "TH1D": #if histogram, add to list
+        if event.Class_Name() == "TH1D" or event.Class_Name() == "TGraph": #if histogram, add to list
             
             hist = event
             hName = event.GetName()
@@ -89,6 +90,3 @@ def get_datadicts(campaign="winter2023"):
     h_meta.Write()
     fOut.cd()
     fOut.Close()
-
-
-