6a-WH-Plot-Contours.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MadMiner version: 0.4.10\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "madminer_src_path = \"/home/shomiller/madminer\"\n",
    "sys.path.append(madminer_src_path)\n",
    "\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import logging\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "% matplotlib inline\n",
    "from matplotlib.patches import Patch\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "from madminer.fisherinformation import FisherInformation\n",
    "from madminer.fisherinformation import project_information, profile_information\n",
    "\n",
    "from madminer.sampling import SampleAugmenter\n",
    "\n",
    "from madminer.plotting import plot_fisher_information_contours_2d\n",
    "from madminer.plotting import plot_distribution_of_information\n",
    "from madminer.plotting import plot_fisherinfo_barplot\n",
    "from madminer.plotting import plot_distributions\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from pandas import DataFrame\n",
    "\n",
    "import madminer.__version__\n",
    "print( 'MadMiner version: {}'.format(madminer.__version__) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MadMiner output\n",
    "logging.basicConfig(\n",
    "    format='%(asctime)-5.5s %(name)-20.20s %(levelname)-7.7s %(message)s',\n",
    "    datefmt='%H:%M',\n",
    "    level=logging.INFO\n",
    ")\n",
    "\n",
    "# Output of all other modules (e.g. matplotlib)\n",
    "for key in logging.Logger.manager.loggerDict:\n",
    "    if \"madminer\" not in key:\n",
    "        logging.getLogger(key).setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set some default matplotlib Plotting Options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['text.usetex'] = True\n",
    "#mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}']\n",
    "mpl.rcParams['figure.figsize'] = (5,5)\n",
    "mpl.rcParams['axes.labelsize'] = 20\n",
    "mpl.rcParams['xtick.labelsize'] = 16\n",
    "mpl.rcParams['ytick.labelsize'] = 16\n",
    "\n",
    "mpl.rcParams['font.size'] = 16\n",
    "mpl.rcParams['legend.fontsize'] = 16\n",
    "\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "mpl.rcParams['font.style'] = 'normal'\n",
    "mpl.rcParams['font.serif'] = 'Times New Roman'\n",
    "\n",
    "#mpl.rcParams['savefig.bbox'] = 'standard'\n",
    "\n",
    "mpl.rcParams['mathtext.fontset'] = 'cm'\n",
    "mpl.rcParams['axes.formatter.use_mathtext'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Color List for Plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_sally_met = '#3972E5'\n",
    "col_stxs_5bins = '#A5000D'\n",
    "col_imp_stxs = '#418C1C'\n",
    "\n",
    "#col_rate = '#BFBDC1'\n",
    "col_rate = '#747373'\n",
    "\n",
    "col_sally_full = '#FB4F14'\n",
    "col_parton = '#070600'\n",
    "\n",
    "col_stxs_3bins = '#30BCED'\n",
    "col_stxs_6bins = '#149911'\n",
    "col_sally_ptw = '#5B1865' #palatinate purple\n",
    "\n",
    "col_sally_2d = '#D84797'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = [ 'cHbox', 'cHD', 'cHW', 'cHq3' ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Files:\n",
    "\n",
    "Saving the paths for all our lhe data files, for easy use later on.\n",
    "\n",
    "Note, lhedata are saved in the format:\n",
    "`data/{channel}_lhedata_{observables}.h5`\n",
    "while the `SALLY` models are saved in:\n",
    "`models/sally_ensemble_{channel}_{observables}/`\n",
    "\n",
    "here `channel` can be e.g, `wph_mu_wbkgs` or `wmh_e_wbkgs` (or take out the backgrounds for the background free runs), while `observables` can be `full`, `met`, `ptw` or `2d`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Signal + Backgrounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_mu_wbkgs_full = 'data/wph_mu_wbkgs_sigsystonly_lhedata_full.h5'\n",
    "lhedatafile_wph_mu_wbkgs_met = 'data/wph_mu_wbkgs_sigsystonly_lhedata_met.h5'\n",
    "lhedatafile_wph_mu_wbkgs_ptw = 'data/wph_mu_wbkgs_sigsystonly_lhedata_ptw.h5'\n",
    "lhedatafile_wph_mu_wbkgs_2d = 'data/wph_mu_wbkgs_sigsystonly_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_e_wbkgs_full = 'data/wph_e_wbkgs_sigsystonly_lhedata_full.h5'\n",
    "lhedatafile_wph_e_wbkgs_met = 'data/wph_e_wbkgs_sigsystonly_lhedata_met.h5'\n",
    "lhedatafile_wph_e_wbkgs_ptw = 'data/wph_e_wbkgs_sigsystonly_lhedata_ptw.h5'\n",
    "lhedatafile_wph_e_wbkgs_2d = 'data/wph_e_wbkgs_sigsystonly_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wmh_mu_wbkgs_full = 'data/wmh_mu_wbkgs_sigsystonly_lhedata_full.h5'\n",
    "lhedatafile_wmh_mu_wbkgs_met = 'data/wmh_mu_wbkgs_sigsystonly_lhedata_met.h5'\n",
    "lhedatafile_wmh_mu_wbkgs_ptw = 'data/wmh_mu_wbkgs_sigsystonly_lhedata_ptw.h5'\n",
    "lhedatafile_wmh_mu_wbkgs_2d = 'data/wmh_mu_wbkgs_sigsystonly_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wmh_e_wbkgs_full = 'data/wmh_e_wbkgs_sigsystonly_lhedata_full.h5'\n",
    "lhedatafile_wmh_e_wbkgs_met = 'data/wmh_e_wbkgs_sigsystonly_lhedata_met.h5'\n",
    "lhedatafile_wmh_e_wbkgs_ptw = 'data/wmh_e_wbkgs_sigsystonly_lhedata_ptw.h5'\n",
    "lhedatafile_wmh_e_wbkgs_2d = 'data/wmh_e_wbkgs_sigsystonly_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Signal Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_mu_full = 'data/wph_mu_smeftsim_lhedata_full.h5'\n",
    "lhedatafile_wph_mu_met = 'data/wph_mu_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wph_mu_ptw = 'data/wph_mu_smeftsim_lhedata_ptw.h5'\n",
    "lhedatafile_wph_mu_2d = 'data/wph_mu_smeftsim_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_e_full = 'data/wph_e_smeftsim_lhedata_full.h5'\n",
    "lhedatafile_wph_e_met = 'data/wph_e_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wph_e_ptw = 'data/wph_e_smeftsim_lhedata_ptw.h5'\n",
    "lhedatafile_wph_e_2d = 'data/wph_e_smeftsim_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wmh_mu_full = 'data/wmh_mu_smeftsim_lhedata_full.h5'\n",
    "lhedatafile_wmh_mu_met = 'data/wmh_mu_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wmh_mu_ptw = 'data/wmh_mu_smeftsim_lhedata_ptw.h5'\n",
    "lhedatafile_wmh_mu_2d = 'data/wmh_mu_smeftsim_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wmh_e_full = 'data/wmh_e_smeftsim_lhedata_full.h5'\n",
    "lhedatafile_wmh_e_met = 'data/wmh_e_smeftsim_lhedata_met.h5'\n",
    "lhedatafile_wmh_e_ptw = 'data/wmh_e_smeftsim_lhedata_ptw.h5'\n",
    "lhedatafile_wmh_e_2d = 'data/wmh_e_smeftsim_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Backgrounds Only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_mu_backgrounds_only_full = 'data/wph_mu_backgrounds_only_lhedata_full.h5'\n",
    "lhedatafile_wph_mu_backgrounds_only_met = 'data/wph_mu_backgrounds_only_lhedata_met.h5'\n",
    "lhedatafile_wph_mu_backgrounds_only_ptw = 'data/wph_mu_backgrounds_only_lhedata_ptw.h5'\n",
    "lhedatafile_wph_mu_backgrounds_only_2d = 'data/wph_mu_backgrounds_only_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wph_e_backgrounds_only_full = 'data/wph_e_backgrounds_only_lhedata_full.h5'\n",
    "lhedatafile_wph_e_backgrounds_only_met = 'data/wph_e_backgrounds_only_lhedata_met.h5'\n",
    "lhedatafile_wph_e_backgrounds_only_ptw = 'data/wph_e_backgrounds_only_lhedata_ptw.h5'\n",
    "lhedatafile_wph_e_backgrounds_only_2d = 'data/wph_e_backgrounds_only_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wmh_mu_backgrounds_only_full = 'data/wmh_mu_backgrounds_only_lhedata_full.h5'\n",
    "lhedatafile_wmh_mu_backgrounds_only_met = 'data/wmh_mu_backgrounds_only_lhedata_met.h5'\n",
    "lhedatafile_wmh_mu_backgrounds_only_ptw = 'data/wmh_mu_backgrounds_only_lhedata_ptw.h5'\n",
    "lhedatafile_wmh_mu_backgrounds_only_2d = 'data/wmh_mu_backgrounds_only_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lhedatafile_wmh_e_backgrounds_only_full = 'data/wmh_e_backgrounds_only_lhedata_full.h5'\n",
    "lhedatafile_wmh_e_backgrounds_only_met = 'data/wmh_e_backgrounds_only_lhedata_met.h5'\n",
    "lhedatafile_wmh_e_backgrounds_only_ptw = 'data/wmh_e_backgrounds_only_lhedata_ptw.h5'\n",
    "lhedatafile_wmh_e_backgrounds_only_2d = 'data/wmh_e_backgrounds_only_lhedata_2d.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Computed Fisher Informations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_outfile = './fisher_info/fi_sally_wbkgs.npz'\n",
    "fi_sally_wbkgs_cov_outfile = './fisher_info/fi_sally_wbkgs_cov.npz'\n",
    "\n",
    "fi_sally_wph_mu_wbkgs_full_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][0]\n",
    "fi_sally_wph_e_wbkgs_full_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][1]\n",
    "fi_sally_wmh_mu_wbkgs_full_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][2]\n",
    "fi_sally_wmh_e_wbkgs_full_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][3]\n",
    "fi_sally_wph_mu_wbkgs_met_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][4]\n",
    "fi_sally_wph_e_wbkgs_met_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][5]\n",
    "fi_sally_wmh_mu_wbkgs_met_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][6]\n",
    "fi_sally_wmh_e_wbkgs_met_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][7]\n",
    "fi_sally_wph_mu_wbkgs_ptw_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][8]\n",
    "fi_sally_wph_e_wbkgs_ptw_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][9]\n",
    "fi_sally_wmh_mu_wbkgs_ptw_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][10]\n",
    "fi_sally_wmh_e_wbkgs_ptw_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][11]\n",
    "fi_sally_wph_mu_wbkgs_2d_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][12]\n",
    "fi_sally_wph_e_wbkgs_2d_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][13]\n",
    "fi_sally_wmh_mu_wbkgs_2d_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][14]\n",
    "fi_sally_wmh_e_wbkgs_2d_mean = np.load(fi_sally_wbkgs_outfile)['arr_0'][15]\n",
    "\n",
    "\n",
    "fi_sally_wph_mu_wbkgs_full_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][0] \n",
    "fi_sally_wph_e_wbkgs_full_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][1]\n",
    "fi_sally_wmh_mu_wbkgs_full_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][2]\n",
    "fi_sally_wmh_e_wbkgs_full_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][3]\n",
    "fi_sally_wph_mu_wbkgs_met_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][4]\n",
    "fi_sally_wph_e_wbkgs_met_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][5]\n",
    "fi_sally_wmh_mu_wbkgs_met_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][6]\n",
    "fi_sally_wmh_e_wbkgs_met_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][7]\n",
    "fi_sally_wph_mu_wbkgs_ptw_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][8]\n",
    "fi_sally_wph_e_wbkgs_ptw_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][9]\n",
    "fi_sally_wmh_mu_wbkgs_ptw_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][10]\n",
    "fi_sally_wmh_e_wbkgs_ptw_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][11]\n",
    "fi_sally_wph_mu_wbkgs_2d_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][12]\n",
    "fi_sally_wph_e_wbkgs_2d_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][13]\n",
    "fi_sally_wmh_mu_wbkgs_2d_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][14]\n",
    "fi_sally_wmh_e_wbkgs_2d_covariance = np.load(fi_sally_wbkgs_cov_outfile)['arr_0'][15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_nobkgs_outfile = './fisher_info/fi_sally_nobkgs.npz'\n",
    "fi_sally_nobkgs_cov_outfile = './fisher_info/fi_sally_nobkgs_cov.npz'\n",
    "\n",
    "fi_sally_wph_mu_full_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][0]\n",
    "fi_sally_wph_e_full_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][1]\n",
    "fi_sally_wmh_mu_full_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][2]\n",
    "fi_sally_wmh_e_full_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][3]\n",
    "fi_sally_wph_mu_met_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][4]\n",
    "fi_sally_wph_e_met_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][5]\n",
    "fi_sally_wmh_mu_met_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][6]\n",
    "fi_sally_wmh_e_met_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][7]\n",
    "fi_sally_wph_mu_2d_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][8]\n",
    "fi_sally_wph_e_2d_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][9]\n",
    "fi_sally_wmh_mu_2d_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][10]\n",
    "fi_sally_wmh_e_2d_mean = np.load(fi_sally_nobkgs_outfile)['arr_0'][11]\n",
    "\n",
    "fi_sally_wph_mu_full_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][0]\n",
    "fi_sally_wph_e_full_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][1]\n",
    "fi_sally_wmh_mu_full_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][2]\n",
    "fi_sally_wmh_e_full_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][3]\n",
    "fi_sally_wph_mu_met_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][4]\n",
    "fi_sally_wph_e_met_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][5]\n",
    "fi_sally_wmh_mu_met_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][6]\n",
    "fi_sally_wmh_e_met_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][7]\n",
    "fi_sally_wph_mu_2d_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][8]\n",
    "fi_sally_wph_e_2d_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][9]\n",
    "fi_sally_wmh_mu_2d_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][10]\n",
    "fi_sally_wmh_e_2d_covariance = np.load(fi_sally_nobkgs_cov_outfile)['arr_0'][11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_other_outfile = './fisher_info/fi_sally_other.npz'\n",
    "fi_sally_other_cov_outfile = './fisher_info/fi_sally_other_cov.npz'\n",
    "\n",
    "fi_wph_mu_wbkgs_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][0]\n",
    "fi_wph_e_wbkgs_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][1]\n",
    "fi_wmh_mu_wbkgs_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][2]\n",
    "fi_wmh_e_wbkgs_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][3]\n",
    "fi_wph_mu_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][4]\n",
    "fi_wph_e_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][5]\n",
    "fi_wmh_mu_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][6]\n",
    "fi_wmh_e_rate_mean = np.load(fi_sally_other_outfile)['arr_0'][7]\n",
    "fi_wph_mu_truth_mean = np.load(fi_sally_other_outfile)['arr_0'][8]\n",
    "fi_wph_e_truth_mean = np.load(fi_sally_other_outfile)['arr_0'][9]\n",
    "fi_wmh_mu_truth_mean = np.load(fi_sally_other_outfile)['arr_0'][10]\n",
    "fi_wmh_e_truth_mean = np.load(fi_sally_other_outfile)['arr_0'][11]\n",
    "\n",
    "fi_wph_mu_wbkgs_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][0]\n",
    "fi_wph_e_wbkgs_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][1]\n",
    "fi_wmh_mu_wbkgs_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][2]\n",
    "fi_wmh_e_wbkgs_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][3]\n",
    "fi_wph_mu_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][4]\n",
    "fi_wph_e_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][5]\n",
    "fi_wmh_mu_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][6]\n",
    "fi_wmh_e_rate_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][7]\n",
    "fi_wph_mu_truth_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][8]\n",
    "fi_wph_e_truth_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][9]\n",
    "fi_wmh_mu_truth_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][10]\n",
    "fi_wmh_e_truth_covariance = np.load(fi_sally_other_cov_outfile)['arr_0'][11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D. Combining the Information In Different Channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean = fi_sally_wph_mu_wbkgs_met_mean + fi_sally_wph_e_wbkgs_met_mean + fi_sally_wmh_mu_wbkgs_met_mean + fi_sally_wmh_e_wbkgs_met_mean\n",
    "fi_sally_wbkgs_met_covariance = fi_sally_wph_mu_wbkgs_met_covariance + fi_sally_wph_e_wbkgs_met_covariance + fi_sally_wmh_mu_wbkgs_met_covariance + fi_sally_wmh_e_wbkgs_met_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_full_mean = fi_sally_wph_mu_wbkgs_full_mean + fi_sally_wph_e_wbkgs_full_mean + fi_sally_wmh_mu_wbkgs_full_mean + fi_sally_wmh_e_wbkgs_full_mean\n",
    "fi_sally_wbkgs_full_covariance = fi_sally_wph_mu_wbkgs_full_covariance + fi_sally_wph_e_wbkgs_full_covariance + fi_sally_wmh_mu_wbkgs_full_covariance + fi_sally_wmh_e_wbkgs_full_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean = fi_sally_wph_mu_wbkgs_ptw_mean + fi_sally_wph_e_wbkgs_ptw_mean + fi_sally_wmh_mu_wbkgs_ptw_mean + fi_sally_wmh_e_wbkgs_ptw_mean\n",
    "fi_sally_wbkgs_ptw_covariance = fi_sally_wph_mu_wbkgs_ptw_covariance + fi_sally_wph_e_wbkgs_ptw_covariance + fi_sally_wmh_mu_wbkgs_ptw_covariance + fi_sally_wmh_e_wbkgs_ptw_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_2d_mean = fi_sally_wph_mu_wbkgs_2d_mean + fi_sally_wph_e_wbkgs_2d_mean + fi_sally_wmh_mu_wbkgs_2d_mean + fi_sally_wmh_e_wbkgs_2d_mean\n",
    "fi_sally_wbkgs_2d_covariance = fi_sally_wph_mu_wbkgs_2d_covariance + fi_sally_wph_e_wbkgs_2d_covariance + fi_sally_wmh_mu_wbkgs_2d_covariance + fi_sally_wmh_e_wbkgs_2d_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_met_mean = fi_sally_wph_mu_met_mean + fi_sally_wph_e_met_mean + fi_sally_wmh_mu_met_mean + fi_sally_wmh_e_met_mean\n",
    "fi_sally_met_covariance = fi_sally_wph_mu_met_covariance + fi_sally_wph_e_met_covariance + fi_sally_wmh_mu_met_covariance + fi_sally_wmh_e_met_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_full_mean = fi_sally_wph_mu_full_mean + fi_sally_wph_e_full_mean + fi_sally_wmh_mu_full_mean + fi_sally_wmh_e_full_mean\n",
    "fi_sally_full_covariance = fi_sally_wph_mu_full_covariance + fi_sally_wph_e_full_covariance + fi_sally_wmh_mu_full_covariance + fi_sally_wmh_e_full_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_2d_mean = fi_sally_wph_mu_2d_mean + fi_sally_wph_e_2d_mean + fi_sally_wmh_mu_2d_mean + fi_sally_wmh_e_2d_mean\n",
    "fi_sally_2d_covariance = fi_sally_wph_mu_2d_covariance + fi_sally_wph_e_2d_covariance + fi_sally_wmh_mu_2d_covariance + fi_sally_wmh_e_2d_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_truth_mean = fi_wph_mu_truth_mean + fi_wph_e_truth_mean + fi_wmh_mu_truth_mean + fi_wmh_e_truth_mean\n",
    "fi_truth_covariance = fi_wph_mu_truth_covariance + fi_wph_e_truth_covariance + fi_wmh_mu_truth_covariance + fi_wmh_e_truth_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_rate_mean = fi_wph_mu_rate_mean + fi_wph_e_rate_mean + fi_wmh_mu_rate_mean + fi_wmh_e_rate_mean\n",
    "fi_rate_covariance = fi_wph_mu_rate_covariance + fi_wph_e_rate_covariance + fi_wmh_mu_rate_covariance + fi_wmh_e_rate_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_wbkgs_rate_mean = fi_wph_mu_wbkgs_rate_mean + fi_wph_e_wbkgs_rate_mean + fi_wmh_mu_wbkgs_rate_mean + fi_wmh_e_wbkgs_rate_mean\n",
    "fi_wbkgs_rate_covariance = fi_wph_mu_wbkgs_rate_covariance + fi_wph_e_wbkgs_rate_covariance + fi_wmh_mu_wbkgs_rate_covariance + fi_wmh_e_wbkgs_rate_covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Projecting & Profiling out Systematics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No Systematics (Project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_nosyst, fi_sally_wbkgs_met_covariance_nosyst = project_information( fi_sally_wbkgs_met_mean, [0,1,2,3], covariance=fi_sally_wbkgs_met_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_full_mean_nosyst, fi_sally_wbkgs_full_covariance_nosyst = project_information( fi_sally_wbkgs_full_mean, [0,1,2,3], covariance=fi_sally_wbkgs_full_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_nosyst, fi_sally_wbkgs_ptw_covariance_nosyst = project_information( fi_sally_wbkgs_ptw_mean, [0,1,2,3], covariance=fi_sally_wbkgs_ptw_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_2d_mean_nosyst, fi_sally_wbkgs_2d_covariance_nosyst = project_information( fi_sally_wbkgs_2d_mean, [0,1,2,3], covariance=fi_sally_wbkgs_2d_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fi_sally_wbkgs_thetaw_mean_nosyst, fi_sally_wbkgs_thetaw_covariance_nosyst = project_information( fi_sally_wbkgs_thetaw_mean, [0,1,2,3], covariance=fi_sally_wbkgs_thetaw_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fi_sally_wbkgs_2dthetaw_mean_nosyst, fi_sally_wbkgs_2dthetaw_covariance_nosyst = project_information( fi_sally_wbkgs_2dthetaw_mean, [0,1,2,3], covariance=fi_sally_wbkgs_2dthetaw_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_met_mean_nosyst, fi_sally_met_covariance_nosyst = project_information( fi_sally_met_mean, [0,1,2,3], covariance=fi_sally_met_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_full_mean_nosyst, fi_sally_full_covariance_nosyst = project_information( fi_sally_full_mean, [0,1,2,3], covariance= fi_sally_full_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_2d_mean_nosyst, fi_sally_2d_covariance_nosyst = project_information( fi_sally_2d_mean, [0,1,2,3], covariance=fi_sally_2d_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_truth_mean_nosyst, fi_truth_covariance_nosyst = project_information( fi_truth_mean, [0,1,2,3], covariance= fi_truth_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_rate_mean_nosyst, fi_rate_covariance_nosyst = project_information( fi_rate_mean, [0,1,2,3], covariance= fi_rate_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_wbkgs_rate_mean_nosyst, fi_wbkgs_rate_covariance_nosyst = project_information( fi_wbkgs_rate_mean, [0,1,2,3], covariance=fi_wbkgs_rate_covariance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1E. Rotating the Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients $C_{H\\square}$ and $C_{HD}$ always enter in the combination $(C_{H\\square} - \\frac{1}{4}C_{HD})$. This implies there is an inherent flat direction, along the axis $C_{H\\square} + 4 C_{HD}$. \n",
    "\n",
    "Before analyzing the information, we want to rotate to a basis where this flat direction is manifest. Then we can profile (or project -- the results should be approximately the same) out the flat direction easily and have a $3 \\times 3$ fisher information matrix to work with in the basis $C_{H\\square} - \\frac{1}{4} C_{HD}$, $c_{HW}^{\\phantom{(3)}}$, and $C_{HQ}^{(3)}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for Rotating:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Function for rotating the fisher information, including the covariance matrix.\n",
    "\n",
    "By default, it takes the rotation angle to be `np.arctan(4)`, which rotates out the flat direction and leaves the direction $C_{H\\square} - \\frac{1}{4} C_{HD}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rotate(\n",
    "    fisher_info, covariance=None, include_flat_direction=False, include_nuisance_params=False, rotation_angle=None, axis1=0, axis2=1,\n",
    "):\n",
    "\n",
    "    # specify an angle (default = arctan(4), which rotates out the flat direction in cHD and cHbox)\n",
    "    if rotation_angle == None:\n",
    "        this_angle = np.arctan(4)\n",
    "    else: \n",
    "        this_angle = rotation_angle\n",
    "    \n",
    "    #define the rotation matrix\n",
    "    dimension = len(fisher_info)\n",
    "    this_rotation_matrix = np.zeros((dimension, dimension))\n",
    "    np.fill_diagonal(this_rotation_matrix, 1)\n",
    "    this_rotation_matrix[axis1,axis1] = np.cos(this_angle)\n",
    "    this_rotation_matrix[axis1,axis2] = -1.*np.sin(this_angle)\n",
    "    this_rotation_matrix[axis2,axis1] =  1.*np.sin(this_angle)\n",
    "    this_rotation_matrix[axis2,axis2] = np.cos(this_angle)\n",
    "    \n",
    "    #get the rotated fisher information\n",
    "    rotated_fisher_info = np.einsum('ki,lj,kl->ij', this_rotation_matrix, this_rotation_matrix, fisher_info)\n",
    "    \n",
    "    #get the rotated covariance of the fisher information\n",
    "    if covariance is None:\n",
    "        rotated_covariance = covariance \n",
    "    else:\n",
    "        rotated_covariance = np.einsum('mi,nj,ok,pl,mnop->ijkl', \n",
    "                                this_rotation_matrix, this_rotation_matrix, this_rotation_matrix, this_rotation_matrix, covariance)\n",
    "\n",
    "    # If not desired, project out the nuisance parameters\n",
    "    if not include_nuisance_params:\n",
    "        if covariance is None:\n",
    "            rotated_fisher_info = project_information(rotated_fisher_info, [0,1,2,3])\n",
    "        else:\n",
    "            rotated_fisher_info, rotated_covariance = project_information(rotated_fisher_info, [0,1,2,3], covariance=rotated_covariance)\n",
    "        \n",
    "    #return either the three physical directions or include the flat direction\n",
    "    if include_flat_direction:\n",
    "        if covariance is None:\n",
    "            return rotated_fisher_info\n",
    "        else:\n",
    "            return rotated_fisher_info, rotated_covariance\n",
    "    else:\n",
    "        return project_information(rotated_fisher_info, np.arange(1,len(rotated_fisher_info)), covariance=rotated_covariance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rotate all the F.I. (w/o Systematics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_rot, fi_sally_wbkgs_met_covariance_rot = rotate(fi_sally_wbkgs_met_mean_nosyst, covariance=fi_sally_wbkgs_met_covariance_nosyst)\n",
    "fi_sally_wbkgs_full_mean_rot, fi_sally_wbkgs_full_covariance_rot = rotate(fi_sally_wbkgs_full_mean_nosyst, covariance=fi_sally_wbkgs_full_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_rot, fi_sally_wbkgs_ptw_covariance_rot = rotate(fi_sally_wbkgs_ptw_mean_nosyst, covariance=fi_sally_wbkgs_ptw_covariance_nosyst)\n",
    "fi_sally_wbkgs_2d_mean_rot, fi_sally_wbkgs_2d_covariance_rot = rotate(fi_sally_wbkgs_2d_mean_nosyst, covariance=fi_sally_wbkgs_2d_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fi_sally_wbkgs_thetaw_mean_rot, fi_sally_wbkgs_thetaw_covariance_rot = rotate(fi_sally_wbkgs_thetaw_mean_nosyst, covariance=fi_sally_wbkgs_thetaw_covariance_nosyst)\n",
    "#fi_sally_wbkgs_2dthetaw_mean_rot, fi_sally_wbkgs_2dthetaw_covariance_rot = rotate(fi_sally_wbkgs_2dthetaw_mean_nosyst, covariance=fi_sally_wbkgs_2dthetaw_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_met_mean_rot, fi_sally_met_covariance_rot = rotate(fi_sally_met_mean_nosyst, covariance=fi_sally_met_covariance_nosyst)\n",
    "fi_sally_full_mean_rot, fi_sally_full_covariance_rot = rotate(fi_sally_full_mean_nosyst, covariance=fi_sally_full_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_2d_mean_rot, fi_sally_2d_covariance_rot = rotate(fi_sally_2d_mean_nosyst, covariance=fi_sally_2d_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_truth_mean_rot, fi_truth_covariance_rot = rotate(fi_truth_mean_nosyst, covariance=fi_truth_covariance_nosyst)\n",
    "fi_rate_mean_rot, fi_rate_covariance_rot = rotate(fi_rate_mean_nosyst, covariance=fi_rate_covariance_nosyst)\n",
    "fi_wbkgs_rate_mean_rot, fi_wbkgs_rate_covariance_rot = rotate(fi_wbkgs_rate_mean_nosyst, covariance=fi_wbkgs_rate_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Contour Plot Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_contours(\n",
    "    fisher_information_matrices,\n",
    "    fisher_information_covariances=None,\n",
    "    reference_thetas=None,\n",
    "    contour_distance=1.0,\n",
    "    xlabel=r\"$\\theta_0$\",\n",
    "    ylabel=r\"$\\theta_1$\",\n",
    "    xrange=(-1.0, 1.0),\n",
    "    yrange=(-1.0, 1.0),\n",
    "    labels=None,\n",
    "    inline_labels=None,\n",
    "    resolution=500,\n",
    "    colors=None,\n",
    "    linestyles=None,\n",
    "    linewidths=1.5,\n",
    "    alphas=1.0,\n",
    "    alphas_uncertainties=0.25,\n",
    "    scale_x = 1.,\n",
    "    scale_y = 1.,\n",
    "):\n",
    "\n",
    "    # Input data\n",
    "    fisher_information_matrices = np.asarray(fisher_information_matrices)\n",
    "\n",
    "    n_matrices = fisher_information_matrices.shape[0]\n",
    "\n",
    "    if fisher_information_matrices.shape != (n_matrices, 2, 2):\n",
    "        raise RuntimeError(\n",
    "            \"Fisher information matrices have shape {}, not (n, 2,2)!\".format(fisher_information_matrices.shape)\n",
    "        )\n",
    "\n",
    "    if fisher_information_covariances is None:\n",
    "        fisher_information_covariances = [None for _ in range(n_matrices)]\n",
    "\n",
    "    if reference_thetas is None:\n",
    "        reference_thetas = [None for _ in range(n_matrices)]\n",
    "\n",
    "    d2_threshold = contour_distance ** 2.0\n",
    "\n",
    "    # Line formatting\n",
    "    if colors is None:\n",
    "        colors = [\"C\" + str(i) for i in range(10)] * (n_matrices // 10 + 1)\n",
    "    elif not isinstance(colors, list):\n",
    "        colors = [colors for _ in range(n_matrices)]\n",
    "\n",
    "    if linestyles is None:\n",
    "        linestyles = [\"solid\", \"dashed\", \"dotted\", \"dashdot\"] * (n_matrices // 4 + 1)\n",
    "    elif not isinstance(linestyles, list):\n",
    "        linestyles = [linestyles for _ in range(n_matrices)]\n",
    "\n",
    "    if not isinstance(linewidths, list):\n",
    "        linewidths = [linewidths for _ in range(n_matrices)]\n",
    "\n",
    "    if not isinstance(alphas, list):\n",
    "        alphas = [alphas for _ in range(n_matrices)]\n",
    "\n",
    "    if not isinstance(alphas_uncertainties, list):\n",
    "        alphas_uncertainties = [alphas_uncertainties for _ in range(n_matrices)]\n",
    "\n",
    "    # Grid\n",
    "    xi = np.linspace(xrange[0], xrange[1], resolution)\n",
    "    yi = np.linspace(yrange[0], yrange[1], resolution)\n",
    "    xx, yy = np.meshgrid(xi, yi, indexing=\"xy\")\n",
    "    xx, yy = xx.flatten(), yy.flatten()\n",
    "    thetas = np.vstack((xx, yy)).T\n",
    "\n",
    "    # Theta from reference thetas\n",
    "    d_thetas = []\n",
    "    for reference_theta in reference_thetas:\n",
    "        if reference_theta is None:\n",
    "            d_thetas.append(thetas)\n",
    "        else:\n",
    "            d_thetas.append(thetas - reference_theta)\n",
    "    d_thetas = np.array(d_thetas)  # Shape (n_matrices, n_thetas, n_parameters)\n",
    "\n",
    "    # Calculate Fisher distances\n",
    "    fisher_distances_squared = np.einsum(\"mni,mij,mnj->mn\", d_thetas, fisher_information_matrices, d_thetas)\n",
    "    fisher_distances_squared = fisher_distances_squared.reshape((n_matrices, resolution, resolution))\n",
    "\n",
    "    # Calculate uncertainties of Fisher distances\n",
    "    fisher_distances_squared_uncertainties = []\n",
    "    for d_theta, inf_cov in zip(d_thetas, fisher_information_covariances):\n",
    "        if inf_cov is None:\n",
    "            fisher_distances_squared_uncertainties.append(None)\n",
    "            continue\n",
    "\n",
    "        var = np.einsum(\"ni,nj,ijkl,nk,nl->n\", d_theta, d_theta, inf_cov, d_theta, d_theta)\n",
    "\n",
    "        uncertainties = (var ** 0.5).reshape((resolution, resolution))\n",
    "        fisher_distances_squared_uncertainties.append(uncertainties)\n",
    "\n",
    "#        logger.debug(\"Std: %s\", uncertainties)\n",
    "\n",
    "    # Plot results\n",
    "    fig, ax = plt.subplots()\n",
    "#    fig = plt.figure(figsize=(5.0, 5.0))\n",
    "\n",
    "    # Error bands\n",
    "    for i in range(n_matrices):\n",
    "        if fisher_information_covariances[i] is not None:\n",
    "            d2_up = fisher_distances_squared[i] + fisher_distances_squared_uncertainties[i]\n",
    "            d2_down = fisher_distances_squared[i] - fisher_distances_squared_uncertainties[i]\n",
    "            band = (d2_up > d2_threshold) * (d2_down < d2_threshold) + (d2_up < d2_threshold) * (d2_down > d2_threshold)\n",
    "\n",
    "            plt.contourf(xi, yi, band, [0.5, 2.5], colors=colors[i], alpha=alphas_uncertainties[i])\n",
    "\n",
    "    # Predictions\n",
    "    for i in range(n_matrices):\n",
    "        cs = ax.contour(\n",
    "            xi,\n",
    "            yi,\n",
    "            fisher_distances_squared[i],\n",
    "            np.array([d2_threshold]),\n",
    "            colors=colors[i],\n",
    "            linestyles=linestyles[i],\n",
    "            linewidths=linewidths[i],\n",
    "            alpha=alphas[i],\n",
    "            label=None if labels is None else labels[i],\n",
    "        )\n",
    "\n",
    "        if inline_labels is not None and inline_labels[i] is not None and len(inline_labels[i]) > 0:\n",
    "            plt.clabel(cs, cs.levels, inline=True, fontsize=12, fmt={d2_threshold: inline_labels[i]})\n",
    "\n",
    "    # Legend and decorations\n",
    "    if labels is not None:\n",
    "        ax.legend()\n",
    "\n",
    "    # Scale the x and y axes by some factor\n",
    "    ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_x))\n",
    "    ax.xaxis.set_major_formatter(ticks_x)\n",
    "    ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y))\n",
    "    ax.yaxis.set_major_formatter(ticks_y)\n",
    "    \n",
    "    # Set Limits\n",
    "    ax.set_xlim(xrange)\n",
    "    ax.set_ylim(yrange)\n",
    "    \n",
    "    # Set Labels\n",
    "    ax.set_xlabel(xlabel)\n",
    "    ax.set_ylabel(ylabel)\n",
    "\n",
    "    #ax.tight_layout()\n",
    "    fig.subplots_adjust(left=0.18,bottom=0.16,top=0.94,right=0.96, hspace=0.0, wspace=0.0)\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project For Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_dw, fi_sally_wbkgs_met_covariance_dw = project_information(fi_sally_wbkgs_met_mean_rot, [0,1], fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_full_mean_dw, fi_sally_wbkgs_full_covariance_dw = project_information(fi_sally_wbkgs_full_mean_rot, [0,1], fi_sally_wbkgs_full_covariance_rot)\n",
    "\n",
    "fi_sally_met_mean_dw, fi_sally_met_covariance_dw = project_information(fi_sally_met_mean_rot, [0,1], fi_sally_met_covariance_rot)\n",
    "fi_sally_full_mean_dw, fi_sally_full_covariance_dw = project_information(fi_sally_full_mean_rot, [0,1], fi_sally_full_covariance_rot)\n",
    "\n",
    "fi_truth_mean_dw, fi_truth_covariance_dw = project_information(fi_truth_mean_rot, [0,1], fi_truth_covariance_rot)\n",
    "fi_rate_mean_dw, fi_rate_covariance_dw = project_information(fi_rate_mean_rot, [0,1], fi_rate_covariance_rot)\n",
    "fi_wbkgs_rate_mean_dw, fi_wbkgs_rate_covariance_dw = project_information(fi_wbkgs_rate_mean_rot, [0,1], fi_wbkgs_rate_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_dq, fi_sally_wbkgs_met_covariance_dq = project_information(fi_sally_wbkgs_met_mean_rot, [0,2], fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_full_mean_dq, fi_sally_wbkgs_full_covariance_dq = project_information(fi_sally_wbkgs_full_mean_rot, [0,2], fi_sally_wbkgs_full_covariance_rot)\n",
    "\n",
    "fi_sally_met_mean_dq, fi_sally_met_covariance_dq = project_information(fi_sally_met_mean_rot, [0,2], fi_sally_met_covariance_rot)\n",
    "fi_sally_full_mean_dq, fi_sally_full_covariance_dq = project_information(fi_sally_full_mean_rot, [0,2], fi_sally_full_covariance_rot)\n",
    "\n",
    "fi_truth_mean_dq, fi_truth_covariance_dq = project_information(fi_truth_mean_rot, [0,2], fi_truth_covariance_rot)\n",
    "fi_rate_mean_dq, fi_rate_covariance_dq = project_information(fi_rate_mean_rot, [0,2], fi_rate_covariance_rot)\n",
    "fi_wbkgs_rate_mean_dq, fi_wbkgs_rate_covariance_dq = project_information(fi_wbkgs_rate_mean_rot, [0,2], fi_wbkgs_rate_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_wq, fi_sally_wbkgs_met_covariance_wq = project_information(fi_sally_wbkgs_met_mean_rot, [1,2], fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_full_mean_wq, fi_sally_wbkgs_full_covariance_wq = project_information(fi_sally_wbkgs_full_mean_rot, [1,2], fi_sally_wbkgs_full_covariance_rot)\n",
    "\n",
    "fi_sally_met_mean_wq, fi_sally_met_covariance_wq = project_information(fi_sally_met_mean_rot, [1,2], fi_sally_met_covariance_rot)\n",
    "fi_sally_full_mean_wq, fi_sally_full_covariance_wq = project_information(fi_sally_full_mean_rot, [1,2], fi_sally_full_covariance_rot)\n",
    "\n",
    "fi_truth_mean_wq, fi_truth_covariance_wq = project_information(fi_truth_mean_rot, [1,2], fi_truth_covariance_rot)\n",
    "fi_rate_mean_wq, fi_rate_covariance_wq = project_information(fi_rate_mean_rot, [1,2], fi_rate_covariance_rot)\n",
    "fi_wbkgs_rate_mean_wq, fi_wbkgs_rate_covariance_wq = project_information(fi_wbkgs_rate_mean_rot, [1,2], fi_wbkgs_rate_covariance_rot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each set of operators, we'll show two plots: one with, and one without backgrounds \n",
    "(just to avoid making the plots too cluttery). \n",
    "For comparison's sake, we'll keep the axes the same size on each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "legend_elements = [ \n",
    "        Line2D([0],[0], color=col_sally_full, lw=2, ls='dashed', label=r'Full Kin. (w/ $p_{\\nu}$)'),\n",
    "        Line2D([0],[0], color=col_sally_met, lw=2, ls='solid', label=r'Full Kin. (MET)'),\n",
    "        Line2D([0],[0], color=col_rate, lw=2, ls='-.', label='Rate'),\n",
    "    ]\n",
    "\n",
    "legend_elements_sig = [\n",
    "        Line2D([0],[0], color=col_parton, lw=2, ls='dotted', label=r'Parton Level'),\n",
    "        Line2D([0],[0], color=col_sally_full, lw=2, ls='dashed', label=r'Full Kin. (w/ $p_{\\nu}$)'),\n",
    "        Line2D([0],[0], color=col_sally_met, lw=2, ls='solid', label=r'Full Kin. (MET)'),\n",
    "        Line2D([0],[0], color=col_rate, lw=2, ls='-.', label='Rate'),\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $c_{H\\triangle} - c_{HW}^{\\phantom{(3)}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JbDaTmpp63tdUTWB1bbap0+ka1W4yKanGDa6rMZlMNSZVh8NRbZaGOzG3RL1a8X2FhYUs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\nIYRo7P8BlZ8hsacq+rYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f118e670a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dw = 1.0\n",
    "y_dw = 2.5\n",
    "\n",
    "fig, validation_plot_dw = plot_contours(\n",
    "    [ fi_truth_mean_dw, fi_sally_full_mean_dw, fi_sally_met_mean_dw, fi_rate_mean_dw ],\n",
    "    [ fi_truth_covariance_dw, fi_sally_full_covariance_dw, fi_sally_met_covariance_dw, fi_rate_covariance_dw ],\n",
    "    colors=[ col_parton, col_sally_full, col_sally_met, col_rate ],\n",
    "    linestyles=[ 'dotted', 'dashed', 'solid', '-.' ],\n",
    "    xrange=(-x_dw, x_dw),\n",
    "    yrange=(-y_dw, y_dw),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=10.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=legend_elements_sig, loc='lower right', frameon=False)\n",
    "\n",
    "plt.text(-.9*x_dw,.80*y_dw,r'$WH$ Only',fontsize=16)\n",
    "plt.text(-.9*x_dw,.60*y_dw,r'$C_{HQ}^{(3)} = 0$',fontsize=16)\n",
    "plt.text(-.9*x_dw,.45*y_dw,r'$L = 300\\,\\mathrm{fb}^{-1}$',fontsize=16)\n",
    "plt.savefig('plots/validation_plot_dw.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GTEwM0dHRDYq3Q3gbG7tU83hzqDu9OiIigqioKCXEzcRms7F2zRoOHz7MnDlz\nPC7CG78qYfGmQkYO9CZhrhE/n9av3TpeaOPdb0sZ0seLay/08ejaf/75JytWrCAwMJDZt9+Ol9fp\nJV2qss5NMjMzG2xVmZyc3KrnrZuV4OhfDHYxrXvccdtgMDR53BWCg4MbzJRQ1EdKyTtvv83u3buZ\neuONDBs+3KNrL/24iOfeKuSS4V14ISawTURYSsljq8xUWCX/nmX0aBbDieJiUpYuRa/XM2/ePHx8\nPCvyHYHT67LSxmRnZ2OxWBrM9zyZKLkbmsjIyCAiIoKCgoJ6xy0WC6GhofXuN5vNTs+9qeOuoAo8\nms8Xn3/O1q1bueKKKxg3bpzH1tU0yfObCtn4dSnXjOnKw1GGNpsJl/F9OT/st+cMn93Dc7JSWVnJ\nsmXLKC4u5o6FCzEGBnps7Y6EEmI3cMSH6wpQVFQU8fHxjT7P3dBEeHh4vRBEeno6kZGRzvuio6Nr\n5QWnp6fXugA0dbwpao55AjXktLn8sHMn7733HiNHjuSaKVM8tq61SvK3Jflk7avk1vHdWr3nb03M\nxTYS0ywM6OnF9Ms9lzOsaRpvrFvHwYMHmTV7Nv379/fY2h0NNSrJhVFJjqq2RYsWYTQanQKWk5Pj\nTAFzVLu1FjWHi+bn5wONV9Y5hpc2VlnX2PHs7Gzi4uLIzMwkPj7eKf5paWkYDAan19/Y8xW12b9/\nP0mvv07ffv2IiYnx2OTl8kqNe17L5+cDVm642Ie4qLb7ZiKl5M5X8vn590pWPtDDo1kZ77zzDl9+\n8QXX33ADl112mcfWbUuaOypJCXEHnVmnOL2wWCw8v3gxPj4+3H3PPR6bvFxSrrHw5Tz2Hanilsu7\nce8NrVct1xCfZJfyz7UWFk7x5baJnp3KvPP77/nj4EGuvfZaj67bljRXiDtsaEIIEQvkAkYAKWWT\nu19CiEhgjJSybasrFIom8PPzY+y4cYSFhXlMhC0nbPxtqZncY1X8a2YAEaM938LyZBwvtJGYVsh5\nAzpx6/juHlu3rKyMrl27MuqCC06p5u7u0CGzJoQQCUC2lDKtWoBDqkW2scebqoU7BlA7RooOg9Vq\npbCwEJ1Ox+TJk+nRo4dH1s0rsnHXq/nkHrGSMMfY5iIspWTRBgs2DR77S4DHGgcdPHiQp558kl27\ndnlkvVOFDinEQLSUsma9bTp2kW0QKWWGlDIRqF+7q1C0I2+99RYvvfgi5eXlHlvziLmKhS/nccxi\n47kFgVwIPGbEAAAgAElEQVQyvO1bP27aVso3eyq461o/+nswSyIwMJARI0Zw9tlne2zNU4EOF5oQ\nQjRUpmYGVNsuxSnHZZddxtlnn+2xPrl/HK/i3tfzKa3Q+E9MIOcNaJ1m603Z8J+3CxlzTmduvsQz\nOb1lZWV4eXnRtWtXpt9yi0fWPJXocEKMPSZcd36OBUAIYZBSqqoBRYfn8OHD9O7d2/njCXKPWvnr\n6/nYNHhpYRDn9G3dvhENUWWT/GtdAV56wd89NGC0qqqKlStWABBzxx2nfktLF+iIoQkD1Rt0NXAI\nc/PqbxWKdmTP7t288PzzbP/2W4+t+ctBK3e/mo8Q8Mqdge0iwgDL04vZdcDKI9MNHuldIaUkNTWV\nnJwcLrzoojNShKFjCnFDHq9DgN2eNCmEiBZCZAohMo8fP+7ucgpFLQ4dOsTq1avp06cPF4we7ZE1\ndx2o5N7X8+jcSfDqXUGt3kGtMX74rYKVGSe4Oryrx4aMfvLJJ2RnZXHV5MlndCvUjijEZupnPhgA\nPBGWkFImSynDpZThntrBVigACgoKSE5Kwtvbm7nz5tG5s/u9eH/8rZJ7X8/H10fHq3cF0i+ofaKJ\nJ8o0HllZQK8APX+70TO5yju2bycjPZ0xF17IpEmTPLLmqUqHE2IpZTb1vWIj0HZdyxWKFlJWVsay\nlBQ0TSMmJgY/P/eHZX6XU8F9yfkE+up49c4gehvbR4SllDyzsRDLCY1/zgygWxf3ZWPv3r2kpaUx\nZMgQbr755jM2JOGgwwlxNcl18oYjAGeDBiFE8MnyihWKtsRms7F61Sr+/PNPZs2eTS8PbM5l/lrB\nX5PyOStAzyt3BbXKqPvm8mFmGenflTHvKl9GeCBL4+iRI6xetYqePXty26xZ6PXt99o6Ch0xawIp\nZZwQIrZabIOBHCllWo2HmIAoIA2cKW8mIBIwCiFygIxq71qhaDWklGxMS+PXX39l2vTpDBkyxO01\nt/9SzgNLzfQ06Hl5YaDHG6y3hAPHq3juzUJGh3gza5L71XNFRUWkpKTg7e3NvPnz6dq1bQtROiod\nUogBqgs0GjuWDCTXuJ2NvZij0ecoFK3Bli1b2LFjB6aICMaMGeP2et/sKeehFDO9AvQk3xtEQPf2\nE+HKKslDS+2ZGv+41TPVc106d2bgoEGMHz9etU2tQYcVYoXiVOD888+nvLzcI/Pmtu0uJ3aZmYDu\nOpb+tQf+3do3cvjqe0X8kWfj6TkBbodGbDYbVVVVdO7cmRkzZnjIwtOHjhojVig6NPn5+UgpCQoK\n4uqrr3Z7s2nr7nIeWmamt1HP6gd7trsIf/VzORu+LCHqsm5cPsL98MGa1at59ZVXqKys9IB1px9K\niBWKFmKxWHjh+ef55JNPPLLe1mpPuK9R3yE84WMFNv65toB+QXruutb97A+A/v3707t3b7y9274k\n+1RAhSYUihbi7+/PJJOJUSNHur3WthoivOSvPVo8X05WWdFeewiZfxisFeAXiP7aBYjzxrpkT5VN\n8n9r7HVTixcE4u3lnqdfXFyMr68vEyZOdGud0x0lxApFM6msqKCouJigoCDGjx/v9nrf7LGLcKCv\nrkUiLHN2on3zIfgaYOAIZNYnoGng3QX2ZmLb9i5iwi3o//pSi21K+rCYH/db+dfMALeLR/bv309y\nUhLTb7mFUaNGubXW6Y4SYoWiGWiaxtp16/h9/34ejo93u5vat7/YsyN6V4cjmhJhKSXy+y1oK/8F\n+38GHz9E3HL0Iy9FLvkO0clexScryrC9/hCMvAztx6/RnX9Js236elc5az87wdSxPm73N87Ly2P5\nsmX4+/szePBgt9Y6E1BCrFA0gZSSd955h10//8zUqVPdFuHMvRU8sNRM7wA9S+5thgjn/ogtYQ4c\n+x38AtHd/i+EaQaiu73U2CHCAKJzV7z++jLaj1+3yKajBVX8Y00BQ/p48Vc3xy2VlJSQsnQpAHPn\nzfPYRJLTGSXECkUTfPnll3z91VdcfvnlXHLppW6tlb2vggdS8ull0LPk3qBmbcxpe7OhtBhddAIi\nYkYt4W0M+cOXoNmgGR6xtUry2KoCAP49y0jnTq7Hha1WK8uXL8disRBzxx0em0hyuqOEWKE4CT/+\n+CPvvvMOwcHBTHFziOX3uRU8mGKmX6AXLy0MxNBIsYa0VSHfTULbk4luynxE38Holv2I6NT8jAO5\n9W3w8QMebfKx/1hTwM8HrDw5O4Cz3Zi2oWka6//7X37fv5+Zt93GwIEDXV7rTEMJsULRCL///jvr\n1q6l/4ABzF+wAJ3O9bSyH/dX8uBSMz399bx4R+NlyzL3R2zPzIcjuYgLJyOGX4TQt+zPVEoJluPQ\na2CTj/10Zxlbfixn/PldmDDSvbjwhx98wM6dO5kyZYranGshSogVigaoudk0Z84cOnVyvQfw7j8q\n+duSfIy+Ol5aGEigX30RllVWbK/cD5+nQeeu6GKXIcZe61qhSN4hKClENCHEv/9ZxVPrLYwY0Il/\nzQxo+XlqsG3bNrZs2cLYsWO5wgMZJWcaSogVijpUVlSwLCUFKSXz5s+ne3fXm938etjKfUn5+Pno\neOmOoEanWmjb3oMv30JcdiO6+U8hfF0XRu39FAB0ETMbfUxphcYjK814ewn+PctIJzfzhc/q2ZPQ\n0FBumDr1jG9p6QpKiBWKOnh37sy4cePo26+fW5tNv1XPmOvqLXjpjkDOCqgtwvaUtM+Qem/0l92I\nHHohokdfd81HHvsd/HvAgOENH5eSxNRCfj9WxfPRgW71kSgrK6Nr164Eh4QQHBLi8jpnOqrEWaGo\nRtM0zPn5AFx62WUMGjTI5bUc05b1Ovugzz6BtX0eWVqM9lw02r+mw277bDtPiDCA7pp56F/Z2qhn\n+vymQj75roz5k30Zc47rU0QKCwt59pln+PrrlqXKKeqjhFihqGbLZ5+xePFi8qvF2FWOmKu457U8\nbBJevCOwXiaC/O0nbHdfgtz6DrqZj6KLut+t89Vau8oKgOjecIvJH3+r5M2tpYwY0IlZE93rL9yt\nWzeGn3ceIcHBbq2jUKEJhcJJaFgYCIHR6Pqw8OOFNu55PZ+ySslLC+sP+tQ+34j2yn3Q3YD+iU0u\n94RoCGmrwhYTjgiPaDB/OL/IxqOr7IUkzy0IROdif2GbzUZFRQU+Pj7cfPPN7pqtQHnECgVHjx7l\n1717MRgMTJw40eXNJnOxjXtfz8dyQuP56IZH3sv8w3D2uegXf+pREQaQW9+F/MOI0PqDOKtskr+v\nLuBEuWTR7UZ8u7r2py+lZOPGjbz80ktUVFS4a7KiGiXEijOaw4cP8/JLL7F9+3a31ikq1bg/OZ+j\nBTaenW9keP//FV/IgmNoX7wJgP6me9A/m44weLbiTGoa2ponoUc/xJir6h1/6d0iduZW8nCUP4P7\nuJ6Kt3nzZnZs386oUaM8MqVaYUeFJhRnLBaLhZSlS+nSpQvXXnedy+uUlGs8sCSf/ceqSJxn5ILg\n/wmUzNmJ7V/ToaIUccEVCL/AVknvklvfgWO/o7v/dUSdwpOPskpJ/bKE6Zd348pQH5fPkZWVxccf\nfURoaChXXlVf7BWuozxixRlJeXk5y1JSqKioYO68efj7u9bopsIqiVtuZs9BK4/fFsBF5/6vIZC2\n9R1s8deBdxf0i95H+AV6yvxaSCnRVj8JPc5GXDq11rFfDlp5eoOF0SHebjV5z9m3j9QNGwgJCSFq\n2jSVK+xhlEesOOOw2WysXrWKY8eOMW/+fPr06ePSOlU2yd2v5bHrdyv/d6uBK87/X4mwlvaCPVQw\ndAz6h1cgDD09ZX49hBCIGfHoeg9C1BhNbzlhI36FGUM3HU/cFoCX3jXxPHbsGCtXriQwMJBZs2fj\n5aVkw9Ood1RxRiGl5M2NG9m7dy9R06ZxzjnnuLSOTZM8vs7Cz79beehmf64Kq/2VX+YdQlwRie7u\nF5rVLc1VpK0Kuetb9JffVOv+KpvkvuR88opsJN0T1Ghvi6YoLi4mZelS9F5ezJs/Hx8f10MbisZR\nQqw4o/j000/Zvn07k0wmLrzwQpfWkFLyzMZCMr4v484pvtw4zt5vV54ohMM5iHNC0cUkArT6V3ht\n5b+QezIRi96t1RzolfeK2Huoir/fYmDY2a7NiausrGT5smWcOHGChQsXupXWpzg5SogVZww//vgj\nH334IaNDQ7nKxc0mKSWvvlfEO9+UMmtSd2ZO9LXfn3cY2+O3gOVP9EmZiK7uFUs0y5bfdyPfX4qY\n+JdaIvxhZinrv7BPYL5mjOserF6vx8/Pj0kmE2f37+8JkxWNoIRYccYwaNAgLr3sMqZMmeKyp7r6\n0xOs3VLCTeN8iLm6WoQP7LFnRpQWoXt4ZduIsKZhW3wHeHdFd9v/eg7vOlBJQqqF0BBv7rnO9c25\n8vJyunTpwu1z5njCXEUTqKwJxWmPxWLBZrPRvXt3brjhBpc3m97aWsLrHxRzZWhX/najP0II5K5v\nsMVOhsoy9E+9i27U5R62vmHk5rXw+y50cx93ZmPkF9k354y+ep6Y5frm3NdffcVzzz6LxWLxpMmK\nk6CEWHFaU1lZyWuvvsr6//7XrXXSvyvj2TcLuWR4Z/5+i8FZHqx9+l/o5o/+2QzEoBGeMLlJpKah\nbXgeBp6HMM0AoLJKEr/CTHGZJGGukYBGpn80hwEDBzJ06FD8/Fz3qBUtQ4UmFKc13t7eRERE0POs\ns1xe45s95Ty+roBRg7z59ywjXnqBrCxHeHdBd8czUFqM8Gu7jSz58zb0z38GZSfsXrmUPLuxkJ9+\nt/LvWQEMcbFy7kRxMd19fenXrx/9IiM9bLXiZCiPWHFaomkaR48eBSB8zBj6u7jZ9ONvlcSvKCCk\ndycS59oHa2ofpGC7fwKyMA/h1altRXj/z8gqK/K3n5xtMzd8WcJ720uZE9GdiaNcG3eUn5/Pc889\nx2effeZJcxXNRAmx4rTkvXff5YXnnycvL8/lNXKOWHkwJZ+e/joWLzDSvavOXqiR/DCi7xBog025\nmkjzUXul3teb0FV3V9v+SzkvvVPE5SO6MO9KX5fWLS0tJWXpUmw2G+edd54nTVY0EyXEitOOr778\nki+//JJx48YRFBTk0hqH86u4Pzmfzp0EL8QEEtBdh23tU2hrnkRcfjO62BSEd5emF/IQUkq0l+8D\nWxW6G+8G4MDxKh5bXcCgXl78360Gl9paVlVVsXLFCsxmM7fffjs9e7ZeBaCicVwWYiGEiuQrOhw/\n/fQT77zzDueNGOFyIx9zsY37kvOpsEpeiAmkt9EL+W4SMvV5RMRt6O57FeHlegczV5AfLkdmb0Y3\n6zFE38EUl2nEppjR6wQJc4z4dG75n7KUkg3r15Obm8v0W25Ro47aEXc26xKAhZ4yRKFwlwMHDrBu\n7Vr69evHrbfeik7XcnEqKdd4YKmZ44UaL94RSHB1Y3dxRSS6qkrEjfe0ecMb+cdetBX/QIyegJgy\nnyqb5LFVBRw2V/GfmMB6Y5iay8cffcR3333H1VdfzejRoz1staIluBOaGCOEuKnphykUrY85P5/l\ny5bh6+vL3Llz8fZueVlvhVXy8HIz+w5beXJ2ACP6e6F9vBJprUT4B6G76d726TomNRgSiu7elxBC\n8NK7RWzfW8FDN/szOsS1Phbbv/2WzZs3c+FFFzFh4kQPG6xoKe54xFFSyt+EEJOAfCnl954ySqFo\nCaWlpaSkpGCz2Zg3fz7dfVu+aWXTJE+8UUDWvkr+71YDY8/1Rnv9IeQnq9B16Ya4ov3SuUT/oXg9\n+TYAm7aVOHsLX3dRN5fW27t3Lxs3buScc87hpptuUi0tOwAue8RSyt+q/90M/CaEuFnFjRVtjZSS\nVStXkp+f7/Jmk5SSR1aY+XRnOfdc78dVo7ugJcUiP1mFuPmviMvbZy6b9s0H2F68B1lRCkDmrxU8\n92YhY4d25m43ypf9/f0ZNmwYt82ahV7veuGHwnO47BELIfyklEUAUspCYKMQYrQQIkBK+anHLFQo\nToIQgnGXXMJFF1/s8mbTiowTfPlzBaYLunDL5d3QkuKQH69E3HwvupmPtovHKI/9jvbiPdAnGHRe\nHDhexaMrzfTv4cXjtwWgdyFDory8nIN//IEQQvWQ6GC4EyOOF0L4CiEGCiEuEEJMBAYBYUKIj4UQ\nF3jIRoWiQRxj70eOHOnyZtM735Sw5KNiJod15Z8zAuDPA8gv0hBT70Y38+/tI8LWCmzPzAdA/+AS\niq1ePLQ0H71O8Mw8I926tPzPtrKigtdefZVdu3YRMniwp01WuIk7MeI4ILb6/xagAMit/v9vgAlQ\ncWNFq7Bz507WrV3LHQsXMmjQIJfW+OrnchLTCrl4aGcemV6dh3vWAPQvfG4fwtlOsVMt5e+w73t0\nD6+kKmgAjyyxDyV98Q7XMyS8OnVi2PDhDBw40LPGKjyCO0KcCCwCwoFcR8xYoWgLzjnnHCZOnOh6\n6fL+Sh5bXcC5/Trx71kB6NIWo3XyRnfTPYieZ3vY2uYj848gv3jTniZ30dU8u6GQ7H2VPPYXA6OC\nW54hIaWkpKSE7t27M3ny5FawWOEJ3Nmse1hKWVi9WUd1aEKhaFXy8vKwWq107dqVqyZPdmmzaf8x\nKw+l5NPDX8ez8410+TgZ7Y2nkX/8gpSyFaxuPiKwN/oXtqCb+QjrtpTw7vZSZpu6c3W4aw3et3z2\nGc8+8wwFZrOHLVV4Eo+UOEspf5NSfiqEmKRiw4rWorCwkNdfe41169a5vEZekY27X8tHSli8IBD/\nbW+gLXsMMe46+3y5dgpHyKJ8tI9WIKVE9DybL3ZZefW9IiaO6sKCq1zrIfH999/zwQcfMGTIEPwN\nBg9brPAk7pQ4P1jn9kAgHwgXQrymUtkUnqS8vJzXXn2V0tJSTCaTS2uUlGs8sCSfsgppr0jb+yHa\naw8gRk9Ad/9rtcYNtSXSVoX2zAJ7bPhILrv/qOSfay0M79+Jx/4S4FIPid9++43/vvEGgwYNYtr0\n6S5VGSraDnd+O4lCiHwhhE0IYcO+Ufcp8DAQAsR7wkCFwmazsXbNGgoKCpg1ezZ9+/Zt8RrWKskj\nKwvIPVrFk7MDGHq2NxQXwNAL0cUtb9VJy02hLf8/5I9folv4LMe6DiA2xUxAdx0J1W03W8rx48dZ\nsXw5RqOR2+fMoVOntu2LoWg57rgAyUASYFEbdYrWQkrJprfeYs+ePdwcGcnQoUNdWuPpVAs79lbw\nyHQDF4XY/Q/dVbMQphmIdixq0NLXIN9bgrguhrJx03jo5TzKrZL/3BGI0bfldp04cYKUpUsRQjB3\n3jx8fFwfHqpoO9zxiOOklN8pEVa0Jls++4xvvvmGCRMncvHFF7u0RvJHxXyYWcaCyb5M6XsE250X\no+38HKBdRVgWHENLikOMGo922z/4+6oC9h+r4slZAc5mQy3BarWyfPlyCgsLmTN3rsstQBVtjzse\n8STgTU8Z4i5CiFjs4REjgJQyuX0tUriLY7PpggsucDn1atO2ElZmnOD6i32YHVaC7eFpUFmG6NHP\nw9a2HBFwFmLmo4hJf2Hx2yV8+0sFD0f5c+G5Le9zrGkab6xbxx8HDnDbrFkMGDCgFSxWtBbuVtZN\n8JglbiCESACypZRp1QIcIoRQQ7dOYf44cMDtzaatu8t5dqO9N8MDV+vR/n0rFOWh//s6RJ/2670r\nTxQ6PXL91DtZu8OLt78pZdak7lx/sWuNfIQQnN2/P9dedx3nn3++J81VtAHueMQCeEYIMRrIBjKA\n9Y4ubEKIC9qwI1u0lDKuxu107JV/aW10foWHOatXL8aOG4fJZHJps2nPH5U8tqqAIX078fitvuie\nnYncvwvdo2sQQ9qv966ssqIlzkXu2Y54fQefHvDntfeLmXRBF6Inu5amVl5eTpcuXZgwoUP4RQoX\ncDdGHC6l1GPPkAgD0qqzKD7G3ji+1RFChDZwtxl7ibXiFKOkpITy8nK8vb254YYb6Nat5R7iEXMV\nD6WY8e+m45l5Rny66KBnf3QLn0UX1n4fCykl2msPIn/4Al1MIj8WBvDEGwWMHOjN329xLU1t9+7d\nLHrqKQ4dOtQKFivaCncq6zbX+H8GkCqlHIw9Rruk+t+2wIhdeGtiARBC1MtiF0JECyEyhRCZx48f\nbwv7FM1ESsnKFStYsmQJmqa5tEZxmX3CRmWV5Ln5RgI7VyL0XugXPosuYqaHLW4ZMnUxcvM6xLQH\nODgyithlZs4y6F1OUwM466yzGDp0KD3UxtwpjTsFHXUr6CTYW2JKKdOwp7e1BQbqi75DmOtdDKSU\nydWefHiPHj1a3ThF8xFCMMlkYsKECS7FhK1VkvgVZg7mVbHodiMD9r2H7d7LkMcOtIK1LUP+kom2\n7mnEFVEUXvsgf1uSj04neG5BIP7dXBjpVFKCpmkYjUb+cuuteHduvzxohfu4E5qIOdlBKeUSN9Zu\nCZYG7nMIsCqwP0U4euQIAOeeey4jRoxo8fMducLZ+yp5ZLqB0bYf0f5zDxh7gfEsT5vbcs4JQ3f/\na1RGP0/s8gLyizSemWukX1DLt2nKysp47dVXefPNDpO0pHATt4S4Oh68QwixCHs1nZM2LHE2Y/eK\na2IAkFI2JNKKDsbWr79m8eLF7Nu3z+U1lqef4MPMMuZf5ctVZ+dje2oWBPZGH7+yXavmZM5O5IE9\nCCGQl93MP9aXsOcPK/+cYeC8AS2fq1dVVcWqlSvJy8vjggtUW5fTBXc9YiP2TbkQ/ifM64UQ82mj\nEmcpZTb1vWIj9iwORQdn165dbNq0iWHDhhEcHOzSGh9llbL042KuCe/K7ZeC7bGboKIE/aNrEX6B\nHra4+cgjudge/wu25xeiaRqL3yrkq5/LuW+qP1ec37Xl60lJWloa+/btIzIqisGqwftpgzubdUsc\n8WAp5TQppREYgl0Ap/G/pvFtQXKdvOEI7OXXig7MwYMHWbN6NX379mXGjBkuxYW/y6ngqfUWQgd7\nExdlQFRZET3PRhe3AnH2Oa1gdfOQ5qPY/jkNNBv6B5JZu6WEt7aWMmN8NyIvdS1XOP2TT8jKzOTK\nK68kPDzcwxYr2hOPtpuSUuZir25bIoR4yJNrN3HeOCFEbLUYBwM51RuGig5KgdnMspQUunfvzpy5\nc13abPrjeBXxK8z0MXrx1GwjXnoQfkZ0j29s18nE8oQF2+O3QOFx9I+/xSfH+vLa+xYiRndl4RTX\nInaZO3aQnp5OeHg4pogID1usaG9qCbEQ4nXsI48+kVJ+5ubabVpiLKVMbMvzKVynrKyMlJQUrFYr\nMTEx+Pm1XJwKSzQeWJqPTgienW+k+84P0N5fau+k5hvQClY3Hy31eTi4F93f15HJeTy5Pp/Qwd48\neovBpVzhfb/+SmpqKoOHDOHmyMh2vcgoWoda3wWllHdgj/dudhRmuNrovXqys0JRi5qbTbNvv52z\nevVq8RqVVZJ7XsvjaIGNhDlG+pbloP3nbmRlOXRueezV0+hmxKN//E329biEuGVmBvT04unbjXh7\ntVxAjx49ysqVK/H392fWrFl4ebVPz2RF69JQUM4MREgp9VLKq2qWKQsh/Ks34hQKl9j01lvs27eP\nqGnTXNpsklKSkGph35Eq/n6LgRE9y7EtmgVdfNDHLUd4t7xhjieQtipsa59CFhcgvLtwtFc4DyzJ\np2tnweIFgXTv6tp2jI+PD4MGDWLhnXfStWv7X2QUrUPd0MQiIFZKWdTQg6WUhUKIVCHE09jDF5+2\nhZGK04ew8HB69OhBWFiYS89ftfl/aWoRo7uiPX07HDuA/vE3EYG9PWtsM5E2G9qL9yI/T0X2GUzh\nmJu5PzmfyirJ63cH0cO/5a02Kysr0ev1+Pn5MXfevFawWtGRqHuZDmlMhB1UZ0o8DASogaGK5mLO\nzwdg0KBBXDF+vEtrfLqzjKQPi7lydFfmRHSHgmPI335CN/sfiPPGetDa5iM1De31B5Gfp6Kb8QgV\n4yJ5KMXM0QIbiXONDHKhr7CmaaxZvZpVq1a1+zBTRdtQV4jzm/tEKeVG6hRxKBQN8ceBAyQmJpKV\nleXyGrsOVPLEGxZGDOhE/HQDQgiEsRf6F7YgrjtpkWerIaVEWxqPTF+DiLof7ab7eGx1Abv/sPKv\nmQGMCnatkESn0zFs+HCGDh2qNubOEOoKcUsb9WyoO0RUoahLWVkZEyZMYPjw4S49/0+LjYeXmzH6\n6nh6jhHvkjx7PNZagfDxbT+xKjYjMzMQU+9C/OVhnk61sHV3BQ/e7FrBBthHHQGMHTuWsWPbx8tX\ntD11hbigehpzs6jOjFCXbEWDFBUVceLECfR6PVdNnuzSZlNZhUbsMjOlFZLEuUYCugm0F+5Evv0a\nHN3veaObgZQSqWkIv0D0z2Wgm/0Pkj48wQc7yph7ZXemjnWtYOOHnTt5etEiDvz+u4ctVnR06gpx\nEi2vSGurdpeKU4jKigqWL1tGcnIyg1wsXdY0yRP/tbDvsP2rfkjvTshNLyO/34Ju3pOIs8/1sNVN\nI6VEW/5/aK/cZxdj3wA2fFnC6k9PMHWsD/OudK25+/79+3njjTfo1bs3vfv08bDVio5O3Tzi74BC\nIcRTLVjDtb8yxWmLpmmsWbOGQ4cOMXnyZJdKlwGWflzMlh/Kues6Py4Z3gW5Nwtt7SLEuOsRV97m\nYaubRkqJlvIo8p3XoWt3EIJPskv5z9tFjD+/Cw/c5O9SmCQvL48Vy5djMBiYM2eOSxNJFKc2Df2F\nLACmCSH+21QHteowhgpNKJxIKdm0aRO7d+9m6tSpLseF078rY0XGCa690IdbLu+G1DRsL98Pxl7o\n7lzc5nFhqWloyXHI95YgrotBN+9Jvv2lgifesBAa4s0/ZgSgd6FqrqSkhJSlS5FSMnfePJcmkihO\nfeqV6VTnCl8JfAL8JoRIApKllPtrPq664i4ViGoLQxWnBl98/jnbtm7liiuuYNwll7i0xq4DlTz5\n3wYYgt4AACAASURBVAJGDfLmoZvtXqb201b0Dy2B8lJEd38PW9002tJHkB8uR9x4D7pZj/HzASuP\nrCwguJeXyxM2rFYrK5Yvx2KxEB0TgxpUcObSYL2klDJXCBEGLAUeBuKEEBbsDX1ygVDsIYmH23BA\nqKKD88MPP/D+++8zcuRIrpkyxaU1jhfaMyQC/fQ8dXsAnbwE0nwUAGnJQ3e+a+LuLuKiqxH+QYhp\nD7D/WBUPLs0n0FfH89GBdOvS8tCLpmmsX7+e/fv3M3PmTAYNGtQKVitOFRr9BFUXbkQB4dgFuQD7\ngNAw4DsgXEr5TJtYqejw7N+/nzfWraN///7c8pe/uBQXrrBKHl5upqS8OkOiux5pOY7tnkuRP33V\n5iIsK8vRMtMB0I26At30BzlaYOO+5Hy8vQQvxARi9G151RzAG+vWsfP777lmyhRGqQbvZzxNdhCp\nbrzePhnzilMCi8XCiuXL8ff353YXN5uklCzaYGH3H1YW3V6dISElWlIslJegG3d9K1h+EnvKTqAt\nmo386SvEy1sRfUIwF9u4Pzmf8krJK3cG0TfQ9QY8w4cPx2azMd7FKkPF6YVq5aRwGz8/Py666CLC\nx4yhe/fuLq2xbksJn2SXsWCyr7MYQm59B7ntPXQzH0UMGOZJk0+KLMrH9sStkLMT3T0vIfqEcKJM\n429LzByzaPwnxsjgPq5lNpSXl9OlSxdGh4YyOjTUw5YrTlXcGZWkOMOxWq0UFRWh0+m4+pprXN5s\n2ra7nFffL2LiqC7cbrILuSwyo736AISMQtx4tyfNPiny+CFsj1wH+39G9/AKdBOmUWGVxC4zk3PE\nylOzAxg5yLXS5cOHD7PoqafYvWuXh61WnOooIVa4zKa33uKlF1+koqLC5TV+/7OKf6wpYHBvLx6t\n7iEBwKFfQejQ3/0CQt92X9y0d5PAfBT9Pzegu3AyVTbJ31eZ2flbJf93awBjh7neZtNgMHDu0KH0\n6dvXgxYrTgdUaELhMpdceil9+/WjswtjjgBOlGnELTPjpRc8PcdI187/8wtkVRX65T+22QRmWXYC\nuW8nYsxV6KbeiTD2QtMk/37Dwte7KnjoZn8iRrvWP6K8vBy9Xo+Pjw+33nqrhy1XnA4oj1jRYnZs\n307Ovn306dOHcePGubSGpkkeX1fAofwqnpwdQG+j3SeQVVa0T/+L1GxtJsLa129ju2MMwteA7vxL\nEMZeSCl57q1CPvmujDuu8eXGca4VWthsNlatXMmyZctUS0tFoyghVrSIPXv2kJqayu7du91aJ+WT\nYr7aVcFfb/BndMj/BFd+kIL24j0IWl+0pJRom15Be2Y+9AkG4/8ayyd9WGyfujyhO7MmudY/QkrJ\nxrQ0fv31V0JHj1YtLRWNokITimZz6NAh1qxeTe/evd2aJPz5j2UsT7eXL998iY/zflmYh7ZuESJ0\nImLk5Z4wuVFklRVtSTzy45WIcdeju+8V55il1Z8Ws2rzCW642Ic7p7gmwgCbN29mx44dmEwmxlx4\noadMV5yGKCFWNAuLxcKylBS6dOnC3Hnz6NLFtU2r345aeeINC8P7d6rXJMf2yt+gshzdnCda3XuU\nH6TYRfime9DN/DuiugDlza0lvPZ+MWGDvXnwZtea+ABkZWXx8UcfERoaypVXXeVJ0xWnIUqIFU1S\nXl5OSkoKFRUV3HX33fj7u9brobhM4+HlZjp3Ejw1u3Z/Bnk4BzI/gfArEWef4ynT6yGltE/3uHou\nut7B6MZc6Tz2YWYpz24sZMSATjwfHehSEx+AnH37SN2wgZCQEKKmTVMhCUWTqBix4qTYbDZWr1rF\nn8eOMWv2bHr3dm1Ap6ZJnlhXwGGzjSdnB9DTULs0WNv5JQT1Qb/wWU+Y3bAN332G7a6LkUX5iE7e\ntUR4yw9lPLXeQvgQb15aGISX3jXxPHbsGCtXriQwMJBZs2fj5aV8HUXTKCFWNIqUkjfffJO9e/dy\nc2Qk55zjuqe6IuMEX+2q4N7r/bigoVlu1nJ7FVvAWW5Y3DBSSrS3XkZ74hb+v707j2+yyvcH/jld\noOxpWkAWQRNAcEAlLShemUFJlRlFvZIWBxSR0gQXXjMD2sidO15ng0lVVHBLtC2CcsX03hHRcUkc\n91GkjTrWjWvDT2TRliYp3dMk5/dHk0yaJm2aPMkTku/79epLs53n5CH59vT7nPM9cLuB7s4+j3/0\ndRfufdaOOWdn4y+3RldJDQBaW1tR+fTTyMzKQun69Rg5cuTgLyIElJogA3jnnXfw8cGDWLp0KRbG\ncLHpH191ofKNViwrGAHVZf2ngXkOvgqcdW5civrwzjZ4Hv01+Af7wS5djoyNO8BG/GsZtuXbbtxT\nbYPsrGw8WJaHkcOjH5scP3YMubm5uOaaayCV0sY1JHIUiElY559/Ptrb2nDVsmVRt3HslAu/f653\n5Zy2WNIvX8pPnYBHdyuw6BpgofAXtTxV94L/4wAy1twL9u939jl+/XdOlFfZMCUvCw+rpRgzIrog\n7PF44Orpwew5czB7TuJqYpDUQYGY9GOz2ZCbm4sJEybg6muuibqdLqcHv33GBgDYtjb0n/ye13cB\n3IPMNb+L+jih8O5O8MMWZKy+B/ynNyBj3mV9Hv/mmBObDM3IHZ2BRzbkQTI6unKWAHDgwAEcsVpx\n+x13YNiwYbF2naQhyhGTPhwOBx5+6CGY3ngjpnY456ioacG3J1y4b3UuJocoGcndLvDXdoEVKMEm\nTo/peP42O9vgfuROuP9wY+/qPMmEfkG44WQPfq23YdSIDOy8LQ/5Y6MPwgAwa+ZMzJ49m4IwiRqN\niEkf48aNw5LLL8dFF14YUzsvftiB1+o6UXrlmLCFcvg/3wNa7WBX/DKmY/nb+79P4P7LWsB2Eqx4\nEzLm9l9+/V2jC7/SNyM7C9i5IQ9n5Ub/FWhvb8eoUaMw5/zzMSfKvfkIAWhETLycTidszc1gjOGK\nK66ANC8v6ra+POrEQy+2YNHs4bi1KHx9Yv7+fmD4SLDC6FfpAd5VcvsegFu7DGAMmX/8KzJX3dOv\natuxUy5sfOIUOAd2bMjD1Pzog/DR777Dtq1b8c/PPoup74QAFIgJei827d27FzsffRRdXV0xtdXS\n7sFvd9uRPzYT/7U6FxkDLIrI3Pgw2N2V/qXF0fJ8+g48r+0C5l6GzIffBpvbf/bFSZsLG59sRo+r\nNwifMzH6Leubm5tRVVWFUaNHQyaTxdBzQnpRaoLg5QMH8EV9Pa697rqoly4D/6qoZjvtxpMb8zF2\n5MC/5z2ff4DMQmVUx+LdnfBU/Q584nSwrGxkPvIu2NjQU8Z+tLux8YlmdHR5sGNDPuSTog/CHR0d\nqHz6aXg8HpSWlmL0mOhrURDiQyPiNPf+++/jvffew2WLF2Px4sUxtfXMm2348Otu/Pr6cZhz9sAX\nrnjDZ/A883vw7w8P6Ricc3gOvQH3hgW9tSK6O5F57YawQbipxY2NT55CS4cHD6nzcN7U6IOwy+XC\nk088AZvNhrW33ooJEyZE3RYhgWhEnMbq6+vx0v79+MncuVi+fHlMbdX+XzcqX2/FlfNH4PpFg68o\n47YfgW8/AbraIz4GP1IP92O/Ab79FBg/FRl//Gu/GRGBmk/3joRtrb1B+Pxp0c9q8Hg82LdvH06e\nPInVq1dTSoIIigJxmvr+6FHsfe45TJ06FatWrUJGRvR/HJ067cZvn7Fh2oQslBdHVrGMjfTuTddm\nRyQLij2ffwDPY78BWpqQse6PYL8oBcsKP7q1tbpRtuMUmk+7sWNDHuadE9vUstdffx2ffvIJfv7z\nn+Oi+fNjaouQYBSI05DNZkNVVRVGjx6NW9eti2n+q8vNcd+zdnT3cPxpTW7kS4TPmQtkZsFzwICM\n+Vf0e5j3OMHrTPD8zw5AcQXYqHFgvyxHRkER2OiBq7/ZWt0oe+QUTp124xFNHi4MVdtiCA4ePIi/\nv/kmLr74Ylx+Rf++EhIrCsRpxtndjcqnn4bb7caG227DmBgvNm3d54ClwYn/vFEC2VmR51/ZqLHA\nJVcDH+yH64EyZFy1Fry7E6gz9eaND9cCzi5gjBRsxBhkXrshonbtbW7c8mCTPyccuPtHtPLy8nDR\nRRfh32+4gUpakrigQJxmsocNw8KFCzH17LMxcWJslc4OHe7G63WduGT2cPxiwdArjWVuehLujCww\nqbcfHjf4q1XAGCnwk0uRcfV6sPmXR7yLs73NjfWP9F6Y274+DwUzYgvCXV1dyMnJwYwZMzBjxoyY\n2iJkICydNzQsLCzktbW1YncjITjncDgcyM3NFaS95tNu3LK9CWNHZqDyV/l9dmCOFuccaGkCkwx9\nNoK9rTcd8aPDjYfK8lA4K7Yg3NLSgp07duCKpUuj3iCVEMZYHee8cLDn0fS1NPH222/jgfvvh625\nOea23B6O3++1o7XTgz+tyRUkCAPo3TkjCYIwAIwcORLnnXcezjnnnJjbImQwlJpIE/Pnz4fL5UKu\nAHVyn/17G2r/z4ktJeOGlBeOB1trQE5YgCDsdrvhdDoxYsQIFJeUCNRLQgZGI+IU19jYCI/HA4lE\ngqKiopgvNn1+xImnXmuF8qIRuGahuDtQ2FrdWOMNwtsFCMK+HUke3bkTzu5ugXpJyOCSNhAzxsoZ\nYyrGmJoxpo7wNSrGmC7efTtT/HDyJHbu2IHXXn1VkPZaOz34r+fsOCs3E9oI5wvHy6nTbpQ+fAqn\nOzx4WJ2HwpmxpyPe+vvf8fHBg5g3bx6GDY+9PUIilZSpCW8wNXHOzb7bjDEV57wmzPOVABQAigBY\nE9fT5NXS0oLKykoMGzYMl/5b7FsQcc6hMzrQ1OKGfmM+RuWI9zu80eFG2Y4mONo8eEQjzBS1Tz75\nBK+++irmz58f044khEQjWUfEal8Q9jIB0IR7MufczDmvAGCJe8/OAN3d3aiuqkJHRwfWlZZCIpHE\n3ObLH3fg7591oWzZmJiWCsfqB7sLdzx+Cq0dHDtvEyYIWxsasO/55yGTyVCyciXNFSYJl3SBmDGm\nCHG3DUB0ZbrSjNvtxrN79uDkyZO46eabMWXKlJjbPNrkwoP/24JZU7Jw0+Xh6wvH2/FmF25/rBkt\n7R7svC0PF5wbexBubGxEVVUVpFIpblm7FllZSflHIklxSReIAUjRG3gDOQCAMRb70C6Fcc6x/8UX\n8fXXX+P666/HHAE2suxxcdz3nB05wxjuL80bsL5wPB1tcuH2x06ho9uDHRvy8JPpsY/K21pbUfn0\n08jIyEDp+vUYOVLci48kfSVjIJagNxgH8gXmmOdeeS/+1TLGapuammJtLqm88/bb+PDDD7FkyRIs\nEmgRwp+fd+Dr73twT7EE48fFtrdbtKw/9OCOx06hxw08els+Zg9SYjMSTqcT1dXVaG1txfqyMuTF\nsCMJIbFKxkDsCHGfLwAHj5SHjHNu4JwXcs4Lx48fH2tzSaO+vh6vvPIKLrzwQvz8F78QpM1Prd0w\nfdqJRbOHY8kFIwRpc6i+OdaDOx5rBmPA47fnYcZkYeYtZ2ZmYvLkyVi1ejWmTZsmSJuEREvUhBhj\nzAQgsLCrBr3BNjgFIQEAznmoIE0ATJ8+HYsuvRTLly+PqaSlT3uXB/+52468MRn4w83CLIseqs//\nnxObn2ru3W05xj3mAvlqSKxQqQRpj5BYiRqIOechd41kjAUHXCkAc6jnpruWlhaMHj0aY8aMwQ03\n3CBYu/+52w5HmwdP3CnOVLW6b7tRXmlD3tgMPKLJwySpMB/VD95/H++88w7uuPNOjBs3cDlNQhIl\nGVMTAGBgjAUOV4oA6H03GGOyoMfTUk9PD5584gns27dP0Hbfre/EwW+6UTR/RMwF1aPxwZdd2PxU\nM86SZuLxO/IFC8IAMG3aNMyaNSvm8p+ECCkp5+pwzrW+lXXoTV00BC3mUAIoBlAD+Ke8KQGoAEgZ\nYw0AzJzzlJ5XnJ2djSWXXx5zOctA9jY3/vy8AzMnZ+E/ViZ+korpk078Ya8dMydn4yF1HsaNEmas\n0N7ejlGjRuHsadNwNuWESZKhMphnYBlMj8eDU6dOCb55Jeccv91tx/v1XajeND6m3Y6jseOlFjz/\nTjsukg3D/aVSwVIituZm7Ny5E0uWLMHPliwRpE1CIkFlMFPYq3/7Gx7avh1CT78zf9qFt//ZhfXL\nxiQ8COuMDjz/Tjt+Mi0b28uEC8IdHR2orKyE2+3GnPPPF6RNMnQWi3B/nArZVjCzWZxLURSIzzAf\n/uMfePvtt7FgwQLk5+cL1q6t1Y3t/9uC86dlY9WSxK2e45zj8ZdPY/9HHSiYMQyP35GPnGHCfCxd\nLhee2bULzc3NuGXtWsH/gghktVqh1WrBGENBQQEqKipQUVEBrVbrr3pntQ5eBsVisUCr1aKioiLk\n7cGOLZfL+z1Xo9GAMQaNRgOHw+G/T6MJWzVAUBaLJaL3Him9Xj/4k2IgSjDmnKftT0FBAT+TfPnl\nl/zuu+7iTz/1FHe5XIK2ffujTfyyu47zIz84BW13ID0uD9+6z84XbTrO7376FHe5PYK17fF4+HPP\nPcfv2ryZW+rqBGt3MDKZjOt0un7363Q6bjKZImpDr9dzpVIZ9nY0x25oaOhzn8lk4nUJOC92u52r\n1WrB2jOZTNxoNArWXijl5eXcbrcL0haAWh5BLKIR8Rni+PHjeHbPHkyePBk33XwzMjOFW+X21j87\n8YnVibJlY3DOxMSkJLp7OO7dY8eBgx1YqxwN3TopMgVcPv3666/jE4sFy5Ytw3xFqPIliaVWq/2j\n0cHIZLIBbw+FwWCASqXq14ZSqYQiAefFYDAIOvI2Go1QxXn+t0ajwbZt2+J6jGAUiM8ADocDVZWV\nGDlyJG5dtw7DBayVe7rDgwf/twVn52cmLCXR1unB5qea8fbnXfjVdWOh/vlYQSuevfLyy3jTbMbC\nhQtxxdKlgrUbi8AKeMEBOdIAPVQGgwElJSX9grDD4YDZbI5rrtXHZDIJFvDjdZ6CyWSyhJybQBSI\nk1xnZycqKyvhdDpRWloq+CKE+57rXbjxxzVSZGXGv6DPqdNu3PpQEz474sR9qyVY+VNhg//hw4fx\n7rvvYurUqbhhxQrRS1oG5mslEgkKCgqw1PvLweFwoLi4GLm5uYIHGa1WC4fDEbIEqs1mg16vh1ar\nBdCbEy0oKEBxcTHMZjPMZjO0Wi0MBkNMfbBarf2OX1NTg9zcXMjlcn/+mzGG4uLiPo+HGkW/8MIL\nEY2uDQYDtFotLBYLampqUFNT43+vkZLJZILmtQdDgTiJcc5h0OvR+OOPuHnNGpw1aZKg7dd9242P\nvu7GqiWjMWtK/FMSR5tcuHV7ExodbtxfKsWVCuGrnY0dOxbnnXceNBs2CJq+GQqTyYSKigoUFxf3\nubCkVCqxZcsW/22JRAKj0Sj48Q0GA/Ly8vzBOJhMJusT0Hz9slgskMlk/tuxphSsVmu/0bhKpYJO\np4NCoYBCoYBOp4NKpcLKlSv7PB7qglwko2uLxQKlUul//yqVyp/KGOiCZzDfL4pEScoFHaQXYww/\nW7IErp4ezJo1S9C2nS6OP+61I39sBtZdGf+UxBffOfEbQ+8O0k/cmT9ocXnX9eELMmXc9iAyrloD\nAPC8vhueJzb7H8sHsAYADtwDF4CsF/81xc+1aSlg/WfINlnRzci8YzsAgH/7GdiMCwd/U2EUFRWh\nvLwcAPoFMyGK9A/EZDLBaDRCIpHAZDKhuLgYJpNp0Nf5+uULnL7b4UbVkXA4HCGr2pWUlPQ5L1Kp\nFHq9HiqVCg6HI2RO3Gq1YsGCBRG9D5lMhkOHDvUbBTc0NETcd4lEQiNi8q982EUXXYTCCD6AQ3V/\njQONLR789kaJYNPFwnn/iy7c+UQzcoYxVP56vKg7fCRaQUFBQo9XVFTkD5xGoxFmsxk1NSF3GOtH\n6F8SNlvoYokSiQRKpRI1NTX+9ERtba0/d61U9t8DwheoB+ML4mazGYWF/1pHYbFYEv5vMRQ0Ik5C\n9fX1eHbPHqg1mpiumIdz7JQLr9V1QjFjGC4+L0fw9gM9sr8FxvfaMWtqNh4olUI6JrJ0QeBIdiAZ\nV60Bim7Ca6++CplcjtmzZ4dvc/ubEbUZy2g4mFod0b63cSGRSKDX61FWVgalUhn30XgwqVQadhSq\n0Wig1+uh0WigUqmgVqvxwgsvQCoNXXI8VJojHF+KJXBUbzab/Wmg4uJiFBUV+f9tfH0JFG5kHi80\nIk5Ccrkcixcvjlud3IdfbEF2JsN/rYpfeUuPp3ehxr532zFnWjYeuy0v4iAcKc452tvbkZGRgV9c\nffWAQThZyGSyPnnbeOch1Wo1ZDIZysrKYm7LarVGPLoG+r/XQCqVCmaz2T9qXrlyJXQ6XcjgZ7FY\nUFQUslBjSGazuU9A37ZtG/R6vT8wS6VS/6jbYDCEbLu5uTmhgVj0RRVi/iTbgo7m5mbudMZ3QcX7\nX3TyRZuO8wqjMBPWQ+lyevgdjzX5j9PjEm6hRqC/v/kmv++++7jNZotL+0PR0NDAy8vLOQD/worg\nRRQ+5eXlXK/Xc6PRyE0mE5dIJFytVnO73c7r6uq4UqnkALhOp+t3O9yxdTpdn2MHPqZQKDgArlKp\neF1dnb9NiUTC9Xp9v2PY7Xb/e1Gr1f73EenCkkADPd/3nn1UKlXY5w31mDqdjhuNRv95DqRQKDjn\nvYtNwh1zqO8zHES4oEP0YCjmTzIF4tOnT/M//+lPfFd1ddyO0d3j4df+/iS//g8/xC04Np928bJH\nGvmiTce57gU793jic5xPLBZ+1+bN/Nk9e7jb7Y7LMUjsgoNttG0MhUQiCXtMX/C12+3+XzqhniPU\nasBIAzGlJpKA0+lEdVUV2tracMUVV8TtODXvt6OpxQNt8bi4zBn+fz/24JYHm3D4eA/+fEsuyosl\ncZnHe+TIETz//PM499xzUbJypSA7kpD40Gq1Ma1Sq6mp8c8xjoTFYoFUKg2bD/elLWpra1FeXh7y\neUKvBowEfYJF5vF4sHfvXhw7dgyrVq+OW63clnYPDK+exvnTsnHJbOEv0H30dRfWbm9Cp5Pj8Tvy\ncXmc9rhrbGzErupqSKVS3LJ2LbKzE1sljgyNTCaDXC6PeiqYyWQKOYsiFN8MDABhc9kqlQp6vT5s\nm75FKIlY/h2IZk2I7OUDB/BFfT2uu+46zJ07N27HqahxoMcFwYu9c85hfL8dO/afxiRpJnbeloez\ncuPzsWpra0NlZSUYYyhdvx6jRo2Ky3GIsNRqNQwGQ1QzSIYy00OhUEQ0Z3ogZrNZlJkuVBhexMLw\n77/3Hvbv34/LFi/GddddF7fj/GB3QbW1ERfPGo4Hy4TbNr7HxbFllw3/+KobP52bg3tXSTByeHz+\nyOrp6cGTTz6JkydOYMOGDZg2fXpcjkOIkCItDE8jYpHU19fjpZdewk/mzsXy5cvjeqz7a1oAAOUq\n4UbDzafd+JW+GdYfXLhSMQL3/lKCDAGrpwXyeDx4ymDA0e++w5pbbqEgTFIOBWIRHD9+HHufew5T\np07FqlWr4nqx6USzCwe/6cbiuTmYmCvMPN4vvnPirspmdHZz3Fo0GmXLxgrSbjiMMeTn52PSpEmY\nN29eXI9FiBgoEItgfH4+CgsLUXTllRg2LL7LfZ99qw0ZDNj077FXbeOc46WDHbj/f1qQOyoDT/0q\nHzMnx/diWVdXF3JyclDiLQpDSCqiWRMJ1NnZie7ubgwbPhw3rFgR9y3dba1uvPxxBy6ePRzjx8U2\nGu7u4dj8tA06YwvOm5KNZ++eEPcg/OWXX+Iv27bhxIkTcT0OIWKjEXGCcM6xZ/dudHd3444770zI\n3NfHXj4NlxvYeG1so+GjTS5sfqoZx5vdWKscjdKrxgi6m0Y4EydOxKxZs5AfooIXIamEAnGCMMbw\n05/9DF2dnQkJwi43xwdfduH8admYNj76f2b9305j79ttyMxkeHC9FIvmxLdIEAC0t7djxIgRyMvL\nw6rVq+N+PELERoE4ARobGzFhwoSEFqU5+E03TndwrFkaXa3hjm4PfvuMHQe/6YbsrCw8uD5PsIt9\nAx63owOPP/YY5HI5blixIu7HIyQZUI44zg59/DEeuP9+HD58OKHHff6dNozKYbg0ihFs/XdO3PiX\nRnz8TTeWFYzArk3jExKEXS4Xdj/zDJqbm3HhhcKVoiQk2dGIOI4OHz6MmpoazJg5E3K5PGHHdbk5\nvjjag/myYUOqKeF0cWzd54DJ0oncMRl49PY8zJcLt1HpQDjnMBqNaGhowI2//CXkM2Yk5LiEJAMa\nEcfJDydPYs/u3ZgwcSLWrFmT0P3TvjnWgy4nx5xpkc9qqP/OiRv+9CPesHTi4tnD8bx2QsKCMAC8\n8frrsNTV4aply5J6JwUSnUTvijwUZrNZ7C5QII6HlpYW6PV6DBs2DKWlpcjJif8FrkBvftYJALh6\nweCbc57u8EBb1QzNjlNw9nA8sF6K7WV5GD0icR+NQx9/DLPZjAULF/p3OD7TWK1W/47EBQUFqKio\nQEVFBbRaLYqKisAYi6jwja9wjW+jy+Dbgx1bLpf3e65GowFjDBqNxl+oXaPRJKzCmMVi8b93X19z\nc3MHLPZuMBj8uzubzeaw57eiosL//ny7TlssFv/79f0b+Nry3c7Nze1znkQPxpHUykzVn3jUI+7q\n6uIPbd/Ot9xzDz927Jjg7Ufij3ttfNGm49zlDl8L2Nnj4cb32rjyP07wSzcf51uqm3lbZ+Lr+n7z\nzTe8/O67uV6v5y6XK+HHF1pwYXYfnU7HTSZTRG0EF2CPtCD7QMcOLlJvMpl4XV1dRP2JRbjavr5C\n9uGK5+v1eg6gX73gcO+xrq7Of7/JZOpXDB5An/Pvq0fsU15eHnPd5FBA9YgTz+1249k9e3DixAnc\nvGYNpkyZIko/fHnhg19393vM6eJ46aN23PCnH7H9ry2YIs3EM5vGY+taKUblJPbjcFLE9E2isA6B\nvwAAEe1JREFUqdXqsNsGBQveoieWLXsMBgNUKlW/NpRKZUJKPYar7SuRSKBSqUKWq7RYLH02/oyE\nQqHos0fdYBuNSiSSPpXdNBpNTHWTY0UX6wR04MABfP3117hhxQrMmTNHtH6cM7H3n/V1SwcyM4EL\nzhmGAwc78NkRJw4d7kZbF8fZ4zPxHyuluGT28LgUb49EU2Mjzj33XNywYkWf9M2lm5NjJd0/Hpws\nWFuBX/rgLepj2bJ+IAaDASUlJf3adjgcqK2thVQqjXswNplMKC8vD/mYLz0S/LjNZos4EAeW1/S9\nJtJfXIHHkMlkouaxKRAL6IJ58zB69GgsWrRI1H7MnJyFghnDYPqkC6ZPuvz3Z2YAc6cPw0/n5uDG\nn40SLQA7nU5kZWVh1KhRWFdaKkofEqWiosIfaCQSif9CZF1dHRwOB8rKylBTUwO73S5oMNZqtcjL\nywvZps1mg16vh8PhgMlkgtlshlarhUwm849eTSYT5HJ5TLV5fUXWw1EqlbDZbLBYLFH/Qqirq/P/\nv6+NSNsKfp5MJhvSbtFCokAsAN+IRiaXQ5bAaWrhFMzsHV1eOseFhpM9yMwAJkmzoLpsVEIvwoXi\n8XiwZ/duZGVnY82aNSGfI+RIVAy+4uSHDh2CxWLxB2KlUoktW7b4/wSWSCQwGo2C/0I0GAzIy8uD\nVquFWq3uFwx9AVen0/Xply8Yy2QyFBYWIjc3N+ZAPFhQU6vV0Ov1/u3szWYzlErlgGkc3/ltaGiA\nwWDwvzZWcrkcFouFAvGZ6Pjx43h0506oiouTatpVwcwcFMwUuxf9ZWRkYM6cOcjMzBRtRB5vRUVF\n/uAbnB+NRwoikMlkgtFohEQigclkQnFxcUS7Vvj65QtCgfnWaPvscDiQN0idEI1GA7lcPqRgGnh+\nA+fnx5rikUgkUW/pFCu6WBejiRMn4rLLLkvo8uUzVXt7OwDg0n/7N1x8ySUi9yYxEv3LuaioyB+M\njEYjzGZz2P3bggn9S8Jmsw36HJlMBoVCAYPBAIfDAalUOqRjBOaXRZ+CFgMKxFFqb29He3s7srKy\ncPU119D+aYP49NNPsW3rVnz//fdidyWhxNj/zEcikUCv16OsrCziGRtCkkqlaG5uHvR5Go0Ger0e\ntbW1MV08jHU063A4RElLABSIo9LT04Nd1dXQ6/XweDxidyfpHTlyBPuefx6TJ0/GWWedJXZ3RCWT\nyfoExXhfqVer1ZDJZCgrK4u5LavVGvHoGuj/XgMF3q9Wq2GxWGL6ZeG78BiL5uZm0QKx6IsqxPyJ\nZkGH2+3me3bv5ndt3sw//fTTIb8+3TQ2NvJ7f/c7rvvLX3hbW5vY3YmbhoYGXl5ezgH4Fx2EW6xQ\nXl7O9Xo9NxqN3GQycYlEwtVqNbfb7byuro4rlUoOgOt0un63wx3bt0AieMFDQ0MDVygUHABXqVS8\nrq7O36ZEIuF6vb7fMex2u/+9qNVq//uIdGFJoODnNzQ0cLVazQHw8vLyPufEt6DCZDJxlUrlP77J\nZOpzfhUKBdfpdP4fX3vBC0d8izZ8bfleF24hy1DfWyQQ4YIO2sV5iLs4/+2VV/DWW2/h6quvxpLL\nL49Tz1JDW1sbHt25E11dXbhz40bk5+eL3SWSYL7ZGfG+SBkrh8MBrVYr2AwMn0h3cabUxBB8+OGH\neOutt3DJokX42ZIlYncnqfX09KC6uhoOhwO3rltHQThNabVaUVesRSrcCsBEoUAcoa+/+gov/vWv\nmD17Nq6//vqUnXolBI/Hg//euxffHz2K1TfdhOnTp4vdJSISmUwGuVwu2rSwSPgWniRiyXc4FIgj\ncPz4cTz77LOYNGkSVt90U0rXRBDCK6+8gs8//xzXLF+OefPmid0dIjK1Wp3UU8vMZrOos1sAUI54\nsBzx6dOn8cjDDyMjIwN3btyIceNi35Y+1dXW1uLkiRO4Zvly+suBpLVIc8S0sm4Qo0aNwkXz56Ow\nsJCC8CC6urpw/NixIVfOIiTdUWoiDLfbjbbWVmRmZmL58uWYNGmS2F1KaseOHcO2rVtx9OhRsbtC\nyBknaUfEjLFyAFYAUgDgnBsieD4ALABwiHMefkuDCLy0fz+++uor/GbTJowYMSKWptJCbm4uzjvv\nPBTQaJiQIUvKQMwY0wEwcc7NvtuMMRXnPOSyHsaYjnOuDbhdxxhDLMF4wcKFkEqlFIQH0dXV5S9p\nuWr1arG7Q8gZKVlTE2pfEPYyAQg5yY8xJgEQvKBdD2BLNAduamoCAEydOpXmCg/C5XJhV3U1qquq\nkM4XfQmJVdIFYsZYqMl8NgDKMC+RAtAxxmRBzx/yUh5rQwMefOABfPTRR0N9adrhnMNoNKKhoQGK\nggKaHUFIDJIuEKM3sAbXz3MA/tFvH5xzK4AC7399igAMaeJiY2MjqqqqMGbMGFxwwQVD7HL6eeON\nN2Cpq8OVV12VVHWYCTkTJWOOWALvBboAvsAshTcoB+Kc+0tYeYN1CYCQ0YExpgagBoBp06YBANpa\nW1H59NPIzs7GbbfdhpEjB9+GPp0d+vhjmE0mLFi4EEpluD9U0ovVaoVer0dFRQUUCgVWrlwJAP4y\nkFu2bEn6egtEPMk4Ig5VC88XmAevNA0YASwNGiH7cc4NnPNCznnh+PHj4XQ6UV1djdbWVty6bh2k\ng+wokO4OHz6MmpoazJw1CytWrKCUhJdMJoNOp4NMJsPKlStRXl6O8vJy6HQ6yOVyFBQUDLnMo8Ew\n4EQhkkJEDcSMMRNjrCHgR4nQ+V0JAHDOB/wke2db6AJHyIPZu3cvvv/+e6xavdo/QiahnTx5Es/s\n2gWJRIKbb76ZlnpHqKSkBFarFS+88MKQXhe4MSZJbaKmJjjnRaHuZ4wFB1wpBsn5MsZU6DvlTTFY\nQHY4HPiivh7XXncd5s6dO4Sep5+WlhZUVVYiJycHt91+O03rGwJfSiLSbYB8JRkj2WqIpIZkzBED\ngCFo3nAReqekAQC8MyQUvse9I2kpALM3RywFsBLAgIG4ra0Nl112GRYvXhyP95BShg8fjrPPPhtL\nlcqE5DrvvuuuIT1/ypQp+PVvftPv9fc/8ID/vocfegjHjx+PqL3A18VKq9VCpVJBpVL577NYLP6K\nZCaTCRqNxl/9y2w2+7eZr6iogEQi8RelcTgc2LZtGxYsWIBDhw6hqKiI8vQpICkDMedcyxgr945y\nZQAaghZzKAEUA6jxBl7fHimBVZ0H3dNl9OjRWH7ttUJ1OyW53W643W7k5ORgzS23iN2dM0JDQwNq\nampgs9mg1+v7bF3vU1ZWhi1btkClUkGhUKCgoAB2ux0A+gTswM0xgd7NSE0mE2QyGVQqFeRyOerq\n6uhC4Jkukm08UvUnmq2S0k11VRWv0Om40+kUuytnhOCtiux2O5fJZNxkMvV5nm9bIB+JRNJnayWj\n0chVKlWf5xiNRi6Tyfrcp1KpuF6vF6r7RGCIcKukpBwRk+RRuGAB6j//HNnZ2WJ35YwkkUigVCqh\n0+n6pBAkEgnMZjOsVmvEuWNfAfPA2r4LFiwQb8NLIhgKxCSktrY2jB49GnPnzqULmTGSSCQIrntd\nVFQEjUbjz/0OtMuy1Wrts9V7YECn/HBqSMZ5xERkR44cwbatW1FfXy92V1KCXC6Hw+HwzyN+6aWX\nUFtb2ycX7HvMt129TCbzX8yzWq1QKBT+5wduO+RwOGCxRDxbkyQpGhGTPpqamrCruhrjxo3Dueee\nK3Z3zhgOhwMvvPACrFYr9u3b57+YBvRuFVRXV4dt27ZBLpejpKQEJSUlqKiogEwmg0QigV6vh06n\nQ3FxMQBAoVCgsLAQBoOhT+rizTff9M+a8AkM6OTMRFslDbJVUjppa2vDozt3oqurC3du3Eg7LxMS\no0i3SqLUBAEA9PT0oLq6Gi0tLbh13ToKwoQkEAViAo/Hg//euxffHz2KVatXY/r06WJ3iZC0QoGY\n4JWXX8bnn3+Oa5Yvx7x588TuDiFphwJxmvvggw/w7rvvYu7cubTUmxCR0KyJNDd+/Pje+rk33kgl\nLQkRCQXiNNXV1YWcnBzMmjULs2bNErs7hKQ1Sk2kIYfDgfsrKnDw4EGxu0IIAQXitPTjDz9g8uTJ\nOIdmRxCSFCg1kUZcLhd6enqQlZWF0vXrxe4OIcSLRsRpgnMOo9GIxx97DNNoJExIUqFAnCbeeOMN\nWOrqcOFFF1FJS0KSDAXiNHDo449hNpmwYOFCLF26VOzuEEKCUCBOcYcPH0ZNTQ1mzpqFFStW0Fxh\nQpIQBeIUdvLkSex+5hlMmDgRa9asQWZmpthdIoSEQIE4RbW0tKCqshLDhw9HaWkpcnJyxO4SISQM\nCsQpymazgXOOdaWltMMvIUkurQvDM8ZaAXwjdj+SUD6AU2J3IgnReQmNzkt453HOxwz2pHRf0PFN\nJNXz0w1jrJbOS390XkKj8xIeYyyiLYAoNUEIISKjQEwIISJL90BsELsDSYrOS2h0XkKj8xJeROcm\nrS/WEUJIMkj3ETEhhIgu7QMxY0zFGNOJ3Q9CSOoYalxJ2+lrjDElAAWAIgBWkbsjCsZYOXrfuxQA\nOOdh81mMMRUAGYAaADYAagA1nPOUP3dDOU+piD4nkYs2rqTtiJhzbuacVwCwiN0XMXh/W1s45zXe\nL5bc+yUKRwpAB6ABwBEA1nT4ckVxnlIKfU6GJtq4kraBmEDNOTcH3DYB0AzymlwAcs55Lue8Jn5d\nSyrRnKdUQp+TBEjb1EQ6Y4wpQtxtA6Ac6HWccwcAR1w6lYSiPU+pgj4niUOBOD1J0fuFCuQAAMaY\nxPtF6ocxpg54ncz7J1gqi+o8pRD6nCQIBeL0JIH3wksA3xdHitCjGXNgro8xpmeMqVP8wlU05ymV\n0OckQVI+R8wYMzHGGgJ+0uLPykGE+gL5vnDBIyAAQIgLLiYAWiE7lYSGfJ5SDH1OEiTlR8Sc8yKx\n+yAmxpgJvdOJfDTo/RIFFymWAP78XnAbEgB2ALkBjzuC2k1FQzpPKYg+JwmS8oE43YX7RcQYC/4i\nSQGYQz3XqyLoyydDis+/5pxbojhPKSPK9592nxMhpHxqIhzGmMI7UV0FoIQxVh7mKnGqMgTNBy0C\noPfdYIzJfI97v1jNQa8vRnr8yTngeUoD9DkZgmjjChX9SWMBK6ZkAByBF1S8V76LfSNq75+davT+\nqSkHcChd5ogOdJ7SAX1O4o8CMSGEiCxtUxOEEJIsKBCTtOTN3Zl8FbK8uU6a2khEQakJkna8F08c\nnHOrN/hqARjTLfdLkgcFYkIIERmlJgghRGQUiAkhRGS0so6kHW+OWIN/VReToreAudb7uJ5znk41\nh4nIKBCTtOFdbPAUeuslaIKqhEkYY3p4g7JIXSRpilITJC0wxmQA6tA78i0KrhLmXZ5rRO/SVJMI\nXSRpjGZNkJTnHQnXoXfvteJBnss55ywxPSOkF42ISTp4Cr0ph7IInpsWldVIcqFATFKa98KcCoAh\nwhrCaVMpjCQPCsQk1flmP0RUupJzPqRt0AkRAuWISUpjjDUAkHLOc8XuCyHh0IiYpLqId4jwXtQj\nJOEoEJNUZ0UEgdgbhEvi3x1C+qNATFKdBUAkW2BtoeprRCwUiEmq2wZANtC+Yd4910Iu4mCMqRlj\nnDGm97XBGFMyxuq8P6owz9d5/1/t/f9yId8USS10sY6kPG8Q1AAoCl7WjN50hJVzHnL+sG+L+OBF\nHoyxOgDa4NcN8PxyAHm+ehaEBKJATNKCdzS7xXvTCu9uw5zzikFep0Jv2qIg6P6QK/C8z9f4NtMM\nuF8GoI5mb5BQqOgPSQve+cEDLm8OYyWCVtt5d/UIN9+4CKHTHBLvDyH9UCAmZGBKAAbvtvE+xQi/\nFLoEQEGI+2UDvIakOUpNEBKGN53QECLf2wCgOHgV3kDpB8aYEcChwVIhJD3RiJiQ8JQAagLv8F6M\nk4VZCq1EiFGvNz8tG6zyG0lfNH2NkPCK0T/fGzLYevXLD3tHyVsQXX6apAkaERMSxDvqVaM36FoY\nYxLOucObJ9YAsDHGFL5RccDzVQCsQflk0EiYDIZyxIQQIjJKTRBCiMgoEBNCiMgoEBNCiMgoEBNC\niMgoEBNCiMgoEBNCiMgoEBNCiMgoEBNCiMgoEBNCiMj+P71Tx8AS40krAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f118e670f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, validation_plot_wbkgs_dw = plot_contours(\n",
    "    [ fi_sally_wbkgs_full_mean_dw, fi_sally_wbkgs_met_mean_dw, fi_wbkgs_rate_mean_dw ],\n",
    "    [ fi_sally_wbkgs_full_covariance_dw, fi_sally_wbkgs_met_covariance_dw, fi_wbkgs_rate_covariance_dw ],\n",
    "    colors=[ col_sally_full, col_sally_met, col_rate ],\n",
    "    linestyles=[ 'dashed', 'solid', '-.' ],\n",
    "    xrange=(-x_dw, x_dw),\n",
    "    yrange=(-y_dw, y_dw),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=10.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=legend_elements, loc='lower right', frameon=False)\n",
    "\n",
    "plt.text(-.9*x_dw,.80*y_dw,r'$WH$ + QCD Backgrounds',fontsize=16)\n",
    "plt.text(-.9*x_dw,.60*y_dw,r'$C_{HQ}^{(3)} = 0$',fontsize=16)\n",
    "plt.text(-.9*x_dw,.45*y_dw,r'$L = 300\\,\\mathrm{fb}^{-1}$',fontsize=16)\n",
    "plt.savefig('plots/validation_plot_wbkgs_dw.eps',format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\tilde{C}}_{HD} - C_{HQ}^{(3)}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q5eXlOHr0KOYvWIDU1FSxwwk7NGLuA4lEAplMBqvVCplM1uVxh8PRZXTprkW3n9MskUig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\nV44AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1179559a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_dq = 0.4\n",
    "y_dq = 2.3\n",
    "\n",
    "validation_plot_dq = plot_contours(\n",
    "    [ fi_truth_mean_dq, fi_sally_full_mean_dq, fi_sally_met_mean_dq, fi_rate_mean_dq ],\n",
    "    [ fi_truth_covariance_dq, fi_sally_full_covariance_dq, fi_sally_met_covariance_dq, fi_rate_covariance_dq ],\n",
    "    colors=[ col_parton, col_sally_full, col_sally_met, col_rate ],\n",
    "    linestyles=[ 'dotted', 'dashed', 'solid', '-.' ],\n",
    "    xrange=(-x_dq,x_dq),\n",
    "    yrange=(-y_dq,y_dq),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=legend_elements_sig, loc='lower right', frameon=False)\n",
    "\n",
    "plt.text(-1*x_dq, 1.05*y_dq, r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(-.9*x_dq,.8*y_dq,r'$WH$ Only',fontsize=16)\n",
    "plt.text(-.9*x_dq,.6*y_dq,r'$C_{HW}^{\\phantom{(3)}} = 0$',fontsize=16)\n",
    "plt.text(-.9*x_dq,.45*y_dq,r'$L = 300\\,\\mathrm{fb}^{-1}$',fontsize=16)\n",
    "plt.savefig('plots/validation_plot_dq.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OB+fEoD+n1h9keSnkrMH+3Up0f3wZ3aXX1+8b0V3PH64K59qLw3j07SI+/PoU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\nQnSGAjMhhOjMfwNl8pPgxhAIWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1179cf6b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_plot_wbkgs_dq = plot_contours(\n",
    "    [ fi_sally_wbkgs_full_mean_dq, fi_sally_wbkgs_met_mean_dq, fi_wbkgs_rate_mean_dq ],\n",
    "    [ fi_sally_wbkgs_full_covariance_dq, fi_sally_wbkgs_met_covariance_dq, fi_wbkgs_rate_covariance_dq ],\n",
    "    colors=[ col_sally_full, col_sally_met, col_rate ],\n",
    "    linestyles=[ 'dashed', 'solid', '-.' ],\n",
    "    xrange=(-x_dq,x_dq),\n",
    "    yrange=(-y_dq,y_dq),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=legend_elements, loc='lower right', frameon=False)\n",
    "\n",
    "plt.text(-1*x_dq, 1.05*y_dq, r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(-.9*x_dq,.8*y_dq,r'$WH$ + QCD Backgrounds',fontsize=16)\n",
    "plt.text(-.9*x_dq,.6*y_dq,r'$C_{HW}^{\\phantom{(3)}} = 0$',fontsize=16)\n",
    "plt.text(-.9*x_dq,.45*y_dq,r'$L = 300\\,\\mathrm{fb}^{-1}$',fontsize=16)\n",
    "plt.savefig('plots/validation_plot_wbkgs_dq.eps',format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $C_{HW}^{\\phantom{(3)}} - C_{HQ}^{(3)}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aKlD1V4BwMsiiB7o9N2n+bLz26j8BtI7EWLdeg3++8hrmz0/CrFmzce99f8Gg\naQtQPmkY/h7wLhKqt+FDdQselx/DpYruW8NeXl5IS0vD+fPnsX//fvu+AAzTAXsIxwyYsLAwzJk7\nF1sKCnD50qUuqw4fv9CMimoj/npDaOsGwxVgmBBej7zVbZ/rli0FSF6QgtqaGsybN9/q+KCgIHzy\nySrz+82bN6H4kbvw11lL8F88hbuzS/Hy3XzMm9T1Q8OpU6di08aN2JCXh0mTJsHLy6ubrwDDWGIt\nYGZAJScnw9fXF3l5eV0et0/XOqws8ZrWemy0/BJ4D74GMnZqp+fo9XqsXKlou08Kst98x64HZElJ\nycg7eB53K97CE3NPoPHqSTz/3wp8+O1JNLd03iXB4/GQvmgRBAKBWyw6xAw8loCZARUUFIR58+Zh\n//79OHfuXKfHlVxoBj+Yh8F8L9C6atCaSvCum93ltR944F48u+xJVFVVYf78pB7FxePxQLx9cP0N\nSfA7/nfMrPwR3xT54ZHXj+Bqbed9vJMmTUJSUhL8/f17dD+GAVgCZjgwd948BAYGIi83t9Njjpxr\nhCC49duTFmlA37gP9NShLq/74INL8bcXX8bixTf2OjYfHx9oNmhw282DsbTyfRyrCMD9/ziEM1c6\nb+HGxMbi7JkzOHSo6/gYpiOWgJkBFxAQgKTkZBw5cgQnT560eUxVHTWv4UBPHwJ4PGB4TKfXLCjY\njNDQMGRlLe+XGGffKMH9H72MO089gbpGQPrmaRSf6Lw45y+//ILc9evdoiI0M3BYAmY4MXv2bISE\nhOBAJyMIGpoo/H3b+m8vnQYio0F8/GweW1JSgpdf/hvOnj3TrzESX38sWflfPO2nQEijHk8q9dhy\noM7msUtuvx1/ZZMymB5iCZjhhK+vL556+mksvtF2dwGlAM+UywylQHhkp9f66cfvceL4Mcybl9Tv\ncQoiIpAmfx+Kv0+AT+MlPP/fcvz6m97quIiICPj7+8NoNLIHcozdWAJmOBMa2jrB4urVq1Z/unvx\nYB6BQOuqQAJDOr1OfEIivv5mjUNXJwsXBGGI/gOMrduL176vx/cbrSeTNDQ04J2338YmVsCTsRNL\nwAynzpw5gxWvv44DBw5YbPf3JahrbE3AvMVSkLT7urxOUlKyw2IEWheY//yzVfjobm/Mqt2Ct35t\nxJp8yyTs5+eHyMGDsWXLFtTU1Dg0HsY9sATMcGr48OGYM2cORgwfbrE9JICHqrq2BJyUAd6M622e\nf/jwITynh5AVAAAgAElEQVTy8EPYuXOHo0OFt7c3gmYvwnKJL+ZU5+O9dY1QbbLsjli4cCEaGxux\nadMmh8fDuD6WgBlOeXl54YbFixEuEFhsj43yQXVd6/hbqr8EeqHE5vlNTU2IiBiE0NBQh8dq4jst\nFY0HXsPsmo1455d6/Lzrj9bu0KFDMXXqVGz/7TdcvXp1wGJiXBNLwIxT2L1rFz5dtQotLS0AgKgI\nL5RXGVHbYIRx5XK0vH6vzfMmT47D1m07MWHCxAGLNSQkBO9sPYSHRm7H9KDTeGNNJTYW/zE6IjUt\nDS0tLcjXaAYsJsY1sQTMOIXg4GAcOXIEhXv2AADGDGldpuTU5WYgfAigt13Ys6BgMwoKNg9UmGY8\nHg/j/vYRkhMrQKuP4eWv9NhzrHX69KBBgzBt2jTs2rULer31iAmGMWEJmHEK4ydMwJAhQ7B+/Xo0\nNTUhZpgPAODY+SaQwSOB2qug1Qar8775+ks89dRjAx2u2ZYCNcYVShFdfwLLV13GsfOtQ9BSxGIQ\nQqDesIGz2BjnxxIw4xQIIbj5lltQU1ODnTt2YHiEF0ICCA6faQKi2mbAnT9hdV5ERARGRY8a4Gj/\n8Oab70DxWzHeHvw/BDeU4tmPL+JyRQv4fD5mzpqFoqIiXLlyhbP4GOfGEjDjNMaOHYvY2Fjk5+ej\nsbER0YO9sftYA0j0tQAAevqw1Tnpi27A0888N9ChmhFC4OsfgIOihXj24rOorWnAc4qLqG0wYsGC\nBa1dK4et42YYgCVgxsmkL1qEmpoabNu2DUnXBeCyoQVl/sPBe/RdkLj5XIfXqeDIoZBqtuHF8heh\nuwK88o0egYFBuOuuuzC8wxA7hjFhCZhxKqNGjcL48eNRsHkzrhvZOgyt8EQTeKn3gAyxnul26OAB\nLF16P+dlgaZPn4Gf9xzBvGcewWOTz2DLwUZ8tqEKsWPHInrUKFY1g7FpwBIwIWTgBmoyLi190SLU\n1dXh3LFtiAjhYfuhetDKMhg3rQatq7Y4duTIaCQlLUBjYyNH0f4hJiYGWyuBIL0aC8cY8Jm6GlsP\n1GPHjh348osvcPHiRa5DZJyMwxMwIeQTQogRQAUhpJwQsszR92RcW1RUFOKmTEFRYSFmjvPBjiMN\naNAdhvHfj4Ee2G5x7OIbb4JS+ZnTLIg+ZowQf/7nGxh7+j1c23QYr3xTjpGxCXj44YcxbNgwrsNj\nnIxDEzAh5DkARQDCKaVeAG4HMJYQsocQEkoICSOE/IkQssCRcTCu58Ybb8Qzy5YhaUoQahsoCnlx\ngG8A6D7rKb4FBZudZh3e6OhoPPDAUoTGTccrhpfh1VCFf3xbhRGjWkdycN1VwjgXR7eADZTSlZTS\nSgCglGoopQ+jNRGvpJRWUkq/A5BGCHndwbEwLiQsLAxBQUFIiPVFqH8zNAdaQCbPAS3UWCRbvV6P\nu+7MgFLxMYfRWnrn3f8gZGQMop76J/526QUcv9iC/6ytRH5+PhSffOI0vywY7jk6AZfb2kgp1QF4\nnhCyoK1vuBxApoNjYVyM0WjEKuXHSAzbii0H6lE39Xrg8ingzBHzMeHh4bjp5lsQO/Ya7gK1YcKE\nibjv3VUQXhuK2w1f4Ifttbhg8IVOp2OlixgzRydgQTf70wC8ACACwBIHx8K4GB6Ph3HjxkF0XQzq\nGyk2B6YAPB7o4V3mYwgh+PjjlUhJEXMYqbXAwEAUFe7BxqgZkPqvw7jgCnyzdwzCBYOQm5vLuiIY\nAIB3VzsJIaMBZABIBdD+7yYDgNWU0u+7uT4hhIRSSm0tC8UHsIFSylavZjqVIhaDUoovCy/jf4U8\n3LCqGEQw1OKYgoLNqK2tRXr6IqcpCRQUFITf9x+Bt7c3aMN9+EelD+5/pxRlvrPgdXEtiouLMXXq\nVK7DZDjWaQuYEPIGAAkAFaU0jVK6sN3rdgCVbSMcbuvsGpTSlQBWEkJslTPQoTWxM0yXmpubkRi+\nExcvXUJJfYTV/tLSK7jt1sUoOWE9VZlL3t6t7ZuDx08gOtILjyWUYefFGASGDsGGvDzzym+M57JK\nwG0jEx6ilD5PKX2LUmqzbC2lNL/tgdrerpIwgOVtx7zeYSxwOAC2VBTTrcbGRlSf343RZDtU26rR\n8s7DaPn8H+b9U6fG454/34fAoCDuguxEXu56JMRPhnrdL7gp989IMB7A/poZKCsrQ1FhIdfhMRyz\nSsBtIxNW2XsBSunJrroi2h64JbS9TGOBywHkAFD0ImbGwwQFBSEpOQkCnMCWwpMwNPuB5n0B2lgP\noHUCxD333IeoqCiOI7WWvCAFb775LqbPmQevh17Dc2ezUNkSDeo/DGq1Gs3NzVyHyHCoxw/h2vqF\ne4RSaqCUpgGIRevDNjGlNLGTvmGGsTJv3jz4BwQiquU3/DhsKVBXDfrbT+b9c+bMxd69Wg4jtM3X\n1xePP/EUwsLCQKZfj6jJ4yGteB8H62bBYDBg5w7Hl1JinFdXfcBjCCGFbZMmktu2bQCga2vFTunu\n4h27Jtpay/mU0r19jpzxKP7+/khJWYBwchrrDtehbvhEGNd9Zt7/6SolZs6Id7p+YJONG/Pxzttv\ngvfga7il8n8Y7uONKjISR47ZLrXEeIauWsAytHYRvAFgOSHk2bb34Wh9eGbPuN3lpuTtKISQLEKI\nhBAiJYRIHXkvhluzZs1CQGAIIhq2Ye3EF4HjWtCjrf2oNyy+Cc8vfxGDhwzhOErb8nLX4b333kZD\nxHD4ZDyNZ6MKcJDehtOO/fFgnFxXCVjdNovtu7buA9L270pKqRaAPQWvCIA3CSEtbS3pFe1bzva0\noru8OCFyAFpKqYpSqgQQQwiR9OWajPPy9fVF+kIxwsh5rD0fiIY/PQcMHgmgtbpycnIKtNoijqO0\n7W8vvowS3Vn4+/uDd/syXJM8E7fNCcXGQz7Yse8M6urqur8I43a6SsCGDqMWVB32h9txfRmlNKFt\nHYjlAOIBqNoSch4Aec/CtSKllLb/RaAGm1Hn1qZNn46gkHAI6rfhx+GPgIT/0eKdMmUqzpw57ZR1\n2EJDQ+Hr6wtKKSilIBNm4IFANQb5VuC7r9/Hli1buA6R4UBXCVgP4KQpCZuGoxFCniOErIAdQ8go\npfnt/q0BkEMpjUXrDLmV6H6mXKcIIaJOYnauKVFMv/L29sbi69MQTK7gB80BXN1VAOOPHwIAdLoS\nLH3ofuTmruM4SttKSkowccJY5OdrgJYmBK3+F+5rWovjdCFoeALX4TEc6DQBU0r3UkojbIxUUAHQ\n2DELzlYXA227diWlVAVA2dOA2xHA+peAoe2+/D5cl3Fyovh4LLzlflxoiMbKX/UwfvUq6OXTmDJl\nKrbvKMSdd97NdYg2DR8+HKNGjQYAEF9/8JYsw/XH/w2/oDH4aH0TmprZIj2extZEjNFdnWAayWDn\nOV12B7TNlOstPqxb0KaEbNWybntIV0gIKSwtLe3DbRmu8Xg8iOdMQsJYP6ytS8Rln2EwrnkHhBCI\nRPHYsqWA6xBt8vf3x/pcDcTi1gmgZMEd8I6MwuN1CtRdvYTXs/+DyspKjqNkBpKtiRin2roZ7HpA\nRghZitZkaEtm+wdwAGI6nNuXKhnWNcr/SLxW3SOUUmVbf3RCZGRkH27LOIsbxh7DJPoNFNe+Bbpp\nNej5E63rRnz5Xyg++Yjr8DrV1NSE0tJSEB9f8G59DDOO/xfXDKKo0p9HXh4rY+9JbHZBUErfROuI\ngk8IIc+2LRs5uu01hRByGyHkDULIx2hdlGdfJ9fPRGtSlKM1+ZoS8mpCyENofTDXW3pYJ35+W/y2\nkjPjZqKjwjF4cCQ2V43C/uBEGL9+DYQQXL50Cb//Xsx1eDZRSjF1ykQsW/YkAICI7wIvbj4eSmjG\nJXod9uzZg/Jym6u4Mm6o09XQ2hZK/44QMhWtD7bS0JrgDAD2AFhhWmi9i2uYuhhUbS8QQoQAUtA6\nIy4FvUzClFItIaRjohXAvuFxjBsYP348soTjsFd+Be8GZWOl4BvwKMUPP/6C7dt/4zo8mwghuOnm\nWyEQtC4qRHz94fVPFaYCiDg6CMZTB7E+Nw/33H0Xt4EyA6LbqchtD+PebFuc5+G2/37XXfLt4nq6\ntvHFaQCe78012lF2GPebCra+hEcJ8OPhponnUV5Tjh/HPQ9CCHx8fAAAV68650z311+XIzFxmsU2\nWl2J+665ggt0Cor37sXlS5c4io4ZSL1ZC+KTtgkV/TGFpy+jIEAplQEQts2EywJQ0ja6gvEgVWe3\n4VqvPKxaX46La9fAuO1H1NXWYlT0UBw44Jzl4C9fvmSxdoXxy1cw+dMl4A+fBiN8kJubx2F0zEDp\ncQJuW4IyBkC+aUJFb2e09bYV3eEa2W0z4bLbZsMxHubW224Fz1iHyBYt3toSgpaVLyBx0njcc899\nCAnpy3Nex/now//gqScfNb/npd0LNNTiTsEBnKMiHDiwH+fOneMwQmYg9LYkkR5AKqXUq22BdvND\nONN6wv0THsN0Lzo6GhMmTkQ0rxB76DVQG6eDv+5jvP/Bxxg1ahTX4dl0+x1344EH/li6hMTGAcOE\nmFH8HohgGozEH99/9x2HETIDoTddECsAZHUcC2zS1qrNaRslwcrNMwMiPT0dxpZGTAnbi/eGvoRS\n9a+gRwtRUlKCb775iuvwrDzyyKO47/6/WGzjLV4K3sn9uG1iA84YE3Hxcimqq6s5ipAZCL1pAcd0\nt45v20y35wGEsyTMDIRhw4YhbsoUBNUVgfJaII96Hc0fPo3333sLTz/1GKqqqrgO0QKlFG+9KcdH\nH31g3kbm3gp4+yDdWAC9jwiVkfchODiYwygZR+tNArZ7kGLbULaYbg9kmH6QlpYGY0sL0kftwy6f\nRPw489944aV/Yl/xYYSE2CpLyB1CCN5//1389OMfM/pJaAS8Vu1D6G0PYUpMIA5dCsLVmiYYDGxY\nu7vqTQLu6QI6a9rWEmYYh4qMjERCYiJKTxdiZkw9Ptw3FFUt4RgWOQgA0NDQwHGElvLyNmFNzg8W\n2wh/MADg/tQQNDYD73+4El98/jkoZetEuKPeJOCKnpQlausTdo5a4YzbE4tbF8MT8QsRHMDDSx/p\nUL3sBtxx6w2YP3+WUyWya8ePx759lsVhqNGIluwHMGH3Bxg5yAuXeVORlpbGUYSMo/UmASvQ88kO\nvV52kmF6Ijw8HHfedRcW37AQL98VjtO1wXiv7ja8GFGJyEGRTlUK/syZM/jxh+9w5coV8zbC44FW\nloNuX4vUqQEoujQKkSPGgRDWhnFHvRkHvBdAJSHk9R6cJuzpfRimt+Li4hAWFobEa/xwf2oI1ofe\njBMNE/D6Lcnw9u509v2AKyk5gY8//sBqsghPtAA4dRDJwlpQCmzaV4V169ahuNg517dgeq+344CX\nAlhCCPlfdyuatXVXsF/fzIAqKyvDJx9/jEXXVSFxrA/eG/w3YOsWfP32q3jmmSe5Dg8AMHPmLJw5\nexlJSZaTSkncfADA6IvbER3phd8ONePIkSNYv26dU7Xgmb7rVQJu69dNA5CA1qoZr9vqF26bIacG\n0JPWMsP0WWBgIKqqqlB19Sr+eY8AglBvvBj+Kk5t34Z1v/7sFDXY/P39MXjwYPB4HX4Mx0wCAoKB\nwzsxe4I/tCWNSE5JQ3l5OQr37OEmWMYhetsCBqVUh9YabxvRuqhOSVu5+j1ty00eB1AEQNnFcpUM\n4xCBgYF49rnnMH78ePCDvfDGQ4Nx1X8ITs75HNt27EVAQADXIQIAnnziUbz//nsW24iXN0javSAj\nYjHjWn80G4Ea7xhEjxoFtVqNpqYmjqJl+luvEzBgnnCRgdaW8CoAFWhNyvEA9gJIaFtbmGEGHCEE\nx44eRb5Gg2uG++ClO8Nx+JwR7ysOomnV3/HBB//hOkR8/vmn2LVzp9V2r7/8E7zFUkwe4wtvL2Bv\nSSMWpaejsrISO3fs4CBSxhH65YlEW5l6Vo2YcTrlej1yc3MxavRoLIiLhTS9Ccrc0Rh2DPgl71nM\nn5+E666bzFl8q9f8YF4+syPa1AhfasSowd7YfrgBj980FrGxsdi4cSOmTZ8OPz+/AY6W6W99agEz\njLNLSEhAWFgYctevB6UU94lDcIPIG18KHsZDaVLUHud2ZEFAQIDNkRn0fAlabh8JunMdZozzw/ny\nZjQ2U6QvWoTq6mqs/eknDqJl+htLwIxb8/HxQYpYjNOnT+PI4cMghEB2RySmjzHiPYEM5Wvzse7b\n/6K5uXnAY6OU4qsvP8fBgwesdw6KAoxG4NJJjI/2RXMLcPJSE0aNGoVRo0Zh3759qK2tHfCYmf7F\nEjDj9qZNmwZBRARyc3NhNBrh7UXw2tIojApvhlzwMjasWo1XXnl5wOOqqqrCt99+hSOHD1ntI34B\nQNgg0PKLEA5tbSHrLrX+krjtT39CY2MjNm/aNKDxMv2PJWDG7Xl5eSEtLQ0XLlzA/t9/BwAE+vHw\n0TOjMGKQD4qmfITBvsMGPK7Q0FBUVTfizbfetX1AWCRQWYYRg7zhxQPOlLYm4KioKNxxxx0YN27c\nAEbLOAJLwIxHmDp1KoYMGYK8vDzzZIawIB7+/dgwDIsMwvdXb8Lavy7FhdOnBjSubdu2dv4wLSgU\nqKmEtxfBYL4XLpb/MQkjPiEBMbGxTjGemek9loAZj8Dj8bAwPR2lpaXQav+oxTYo1AvvPzwIAr8m\nvO/7LH7JvB9H9g/Mg7nvvsvB11990ekCQbyUO0Hm3gYACPAlOHXFcvxvUVERXnv1VbZcpQtjCZjx\nGJMmTcKIESOg3rDB4qHbkHAvfPjMaIQGEqwevxLVOWtAGxzfslyz+v9QULCp04V2eOK7wUv7MwAg\n0I+gtsEyUQvHjEFCYqJTrW/B9Az7P8d4DEIIbrzxRly9etVq+u+QcC98IovBk2+X4NmqB/H3rOUY\nfM/tmJQ402Hx/PXRJ9DY2Njpfmq4AvgFggQEw8+HoLHJMgGHCwS45ZZbHBYf43isBcx4FGFMDKZM\nnWq9/gKAyDAvfJQVg+Ehjfi71zO4/amVDqvJtnnzJhQX74Ovr2+nx7S8eAuM7z8BACAEMHaylLFO\np8PGjRsdESbjYCwBMx7HaDRi48aN2L59u9U+frAXlC9cg+tGeyFsxqt49s2tMJ493u8x/OtfL0Or\nLcT8+Uk291NKgStngYgoAEBTM+DtZbur4vChQ8hdvx6XLl7s9zgZx2IJmPE4PB4PupISnDl92ub+\nIH8e3n80CounBeL36ji88OpveGXJwi67C3pi48Z8hIWGITi4izp1V84CjfUgw2MBALUNRgT62U7A\nScnJ8PPzQ15eXr/Exwwc1gfMeKT77r+/0zUYgNbW5vIlYYgOrMbHm8SI9R6JdVmP4uZ3FCA2ui96\n4sCB/XjyqWWdtn4BgB4tBACQ2KkAgIpqI6IH2/5xDQoKwtx586DesAFnz57FyJEj+xQfM3BYC5jx\nSKbkqy0q6nRKLyEE99w4Atn3BuNCwGh8gqex7pksrHxnBerr63t8T6PRiAcfvA+RkZFdJl8AMOZ/\nCwSEAGMmoa7BiPIqI6YIO+8vnjdvHgIDA5G7fn2P42K4wxIw47Gqq6qQk5PT7QOs2VP4+EwWjcFh\nPLzm9QzeXVeHjxXKHt/v+++/w9qffsTWLQXdHut138vgPfIWiJcXDp5pAqXAhOjOE7C/vz+SFyzA\nsWPHoCsp6XFsDDdYAmY8VnBICCbHxeG3bdtQWVnZ5bEjI32w8sVxmDPeG/y4h6E+uwC5j9yL5//6\nEB588L5Oz6upqcGr//onNm/ehMjISHz08Up8+FH3NW1pzVXw5rVOwth2sB7eXuiyBQwAs2bNQmBg\nIH744Qenqv7MdI4lYMajpaWlwWg0Ij8/v9tj/XwIsh8aDPlfBDDygvB6wL8wvDYKw4/vQsGmfBQU\nbMYttyxG0vzZ+OyzVSgo2Ix333kLr776DxQXtxaFychY0mWFY3r5DJqfnAdaWQoAaGiiWLenFhOj\nfRHo1/WPq6+vLxYuXIhLly7h6NGjdn8NGO6wBMx4tIiICEybNg27d+2CvrzcrnPmTvLH//4eDdGI\nZvxfxMM4GvcZfHL+D4KSPaiqqsSVK5dx9Wprizo+IREb1JvxxBNPddvvS+uq0ZL9AHDpNHhjRQCA\nH7bXoLqeQrqoixET7UybPh0hISHYsmWLXccz3CKe+KdKQkICLSws5DoMxklUVlbijRUrEDdlCu64\n444enbvjcB2yv76Ay/UBSGwsxF//NBTjkqaBtjSDeNk/yIheOYsW+V+AkwfAe+FL8BLScK6sGfe+\nVYo4oS/eWSrosuXc3vnz5yEQCJym7p2nIYQUUUoT7DmWtYAZjxcWFoZZs2ZBW1SEK1eu9OjcmeMD\nsPqfQjx6QzCOhMTjLz+PwPJ3D+HgAzej5d+PwbhzHWh11/3LxuICtPx1BnDuGHjLW5OvvqoFme+X\ngccDsiRhdidfABg+fDgCAgJgNBphNBp79HmYgcVawAwDoLq6Gm+sWIFx48bhz/fe26trXK014n8F\n1VhdUIW6JoKJDftxU+UazK/JR2B4GCAYCgTzAR4PqKsGmTATJG4+aLUB9NdV8Hr83yBDolGsa0DW\nZ3rUNVL8OzMCU2N6XvutsrISK5VKJC9YgPj4+F59HqZ3etICZhMxGAZAcHAw5s6dC41Gg/Pnz2P4\n8OE9vkZoIA/SRaG4OzkYv+yuxbeb47DC7zq8RV7GVOMhzKzfievKCzG66RR8/X2AmkrwrpsNAKie\nsghFJxrwzepSHDjdhNBAAsXjgzB+ZNcjHzoTEhKCoUOHIigwsFfnMwODtYDdnE6ng0KhQHZ2NkQi\nEW6//XYAQHl5OXQ6HZYvXw6RSMRxlPZr/3mEQiEyMzMhkUggFAr7fO26ujr8+uuvSElJQXh4uNV+\nrVaL1atXIyIiAllZWd1ej1KK/acaodlXj03FdSiv+qM7YHAYDyGBPBAAZVeNMNS07gv2J5h/nT+e\nvjWs21EPjHPqSQuYJWAPERMTg8zMTIvEodPpEBMTg6Kioj4nYaVSCalU2tcw7Wbr8wwEpVKJnJwc\nqNXqHp1HKcVFfQsOnmnC6ctNuFjRguq61p+9kECCEYO8cd1oX8SN8e100Z3eqK+vx86dOzF79uwu\np14z/Yd1QTB2EQqFEIlEWLFiBXJycvp0raKion6KinsXLlxAcXEx0tPTrR5+9balTQhBVIQ3oiK8\nAQzc6ITz587h119+AY/Hw7x58wbsvox92N84Hs5gMEAgEPTp/MzMTOj1+n6Mils6nQ47tm/vdnac\nK4iJjcXYsWOxMT8fDQ0NXIfDdMASsAfTaDQAALlcDqC1j1OlUkGlUiEzM9OidppGo0F8fDwyMzOh\nUqmQkZEBpVIJjUYDvV4PrVaL7OxsKJV/rJFgMBiQnZ1tvmZ2drbV9TIyMqDRaKDRaCCTySzO7yuD\nwQCZTAaVSgWZTGb+vCqVCjExMYiJiTF/RpVKBUIIZDIZrr32Wpy/cAFff/WVxXmuKj09HTU1Ndi6\ndSvXoTAdUUo97hUfH097w88X1M8XFttuvWUx9fMF/eXnteZtK1cqqJ8v6CMPLzVvO3/+PPXzBR09\napjF+TOmi6ifL2hRUaF52yuvvEz9fEFfeeVl87b2+3tDKBRSiURCFQoFVSgUVC6XU4VCYXGMSCSi\nOTk5lFJKS0pKKJ/Pt9ivUCioUCikFRUVtKioiBYVFVFKKc3JyaESicTqniKRiFZUVJjfq9VqKpVK\nze9zcnKoUCikJSUllFJKKyoqaOu3pH2fRy6Xd3uM6dqm96Z41Go1FQqFVp+v/XnHjh6lFRUVVueJ\nxWK7YnQmn332GX3xb3+jNTU1XIfi9gAUUjtzEWsBe5DExERIpVJIpVJkZWVZPTTLz8+HRCIB8Edf\np06nM+8XCAQQCoXg8/kQiURdPrgztRr5fL55m1gshlKpNFfxNe0z3cv0vj+q/KpUKotrA4BIJMKa\nNWvMsQAwt4A1Gg2WLFlicd727dvx6aefYurUqebzXNXChQtRX1+Pgs2buQ6FaYc9hOuB+gbrESPf\n//Cz1baHHpLioYcsk1tUVJTN83fstH549dJL/8BLL/3DYptI5PjB9Hw+HxqNBjqdrtN+4fYJtSta\nrdbmNfh8PgoLC80J0N7r2UulUkEikUCn05k/j0liYqJFQs7MzIRCoYBCoYBOp4NYLLY4r66+Hrt2\n7sTw4cP7ZZgbl6KiojBlyhRs2bIFc+bORUiIfWtLMI7FEjBjlpqaiszMTHPLeOnSpT2+hk6ng8Fg\ngFAotPlgzrTPUUwtdtM9TIm+478BQCqVIjw8HDKZzHx8+/MWLFiA2poaNDU1ITk52WExD5S0hQtR\nXFyMH77/Hvfe1/kSmszAcYsuCEKIhBAi5zoOV6bValFYWGjuggD+6Aow/VneGaFQaE58Op0OIpEI\nEokEBoPBogvD1Dp1VALW6XTm8bmmz9H+/gaDweLBIp/Ph0QiQWZmpjk5tz+Px+NhYXo6zp07h6+/\n/tohMQ+kyMhIjBs3DgcPHkRVVRXX4TBw8RYwIUQMQAQgFYCum8M9kk6ng0qlMs8gA2Bz8oJIJMKS\nJUvMM8z4fD4UCgXkcjkyMjKg1WqhUChQWFiI7OxsSKVSc/eBSCRCQkIClEqlRbdDUVERVqxYgcTE\nROj1ehgMBvN4Y61WC7lcbh49IZVKsWLFCgCATCazaJV2/DymLoPVq1ebt+/ZswcajQYJCX+Mf8/P\nzzff36T9LxgAVqM9Op5HKUVNbS3OnjmD3bt3Qy6XQ6PRIDs7e8AngfSHP0kkKCstZV0QTsItZsK1\ntX75lNJMe473xJlwTO8dP34cSoUCN918M+bOnct1OP2GUtqjVdYY+7DlKBmmH40dOxaxsbFuNZkh\nd/16qPo4+5HpO5aAGcYOC9PTUV1djW3btnEdSr+gAEAIWy+YYy7dB8wwA2X06NGYMWOGzVXSXJGt\ndUYVoesAABs7SURBVC6YgecxCZgQIgUgBYDo6GiOo2Fc0Z86PMBzZabke/bsWfj4+GDo0KEcR+SZ\nXKILghCiJoSUtHuJuz/LEqVUSSlNoJQmREZGOiJMxgM0Nzfjt23bUO0Gw7iamprw6apV+HntWq5D\n8VgukYAppamU0ph2L9deHYVxWRUVFfjpp5+wr7iY61D6zMfHB8nJyTh27Bh0JSVch+ORXCIBM4yz\niIyMxLJlyzB0yBCuQ+kXs2bPRmhoKHJzc+EOQ1JdjUsnYEKIiBCSBUACYAkhJIsQ4jr1dRiXNGTo\nUMSOHYv6+nquQ+kzHx8fpIjFOHnyJI4ePcp1OB7HpRMwpVRLKc1u65YIb/u3tvszGaZvDhw4gFf/\n9S+UlZVxHUqfTZs2DeHh4chdv561ggeYSydghuFKdHQ0jEYjNuTlcR1Kn3l7eyMtLQ3nz5/H/v37\nuQ7Ho7AEzHi0jutA2Cs0NBRz5szBvn37cOnixT5dyx6Orsohio8Hn8/HD99/zyZnDCCWgN2cTqeD\nTCYDIQTx8fHIzs5GdnY2ZDIZUlNTQQixWDGsM1qtFjKZzFxWqOP77u4dExNjdWxmZiYIIcjMzDSv\nvJaZmYnMTLuW9OgzrVZr12fvTFJyMvz8/JDb1go2LXbkKI5MwjweD4tvvBHV1dXYu3evw+7DdGBv\n6Qx3evW2JJEr66yEj1wup2q12q5rKBQKi3I8Hd/35t7tSwZR2lryx1TqyJEqKiosyiP11oYNG+iz\ny5bRb775xlzOyVGysrIsSjz1t5aWFrpp0yZaXV3tsHt4ArCSRIy9pFKp3SWAOi4P2Zd1fZVKpc21\ngcVicZeljvqLUqnsl5b23LlzERQUhE8++cRqqcv+lpmZaV6y0xF4PB6SkpIQFBTksHswllgC9nDt\nSwJ1TMT9UZvNFqVSiSVLllglX4PBAI1G49C+VBO1Wt0vid7f3x8JiYmorKxEiYMnMwiFwgH52pw6\ndQr//ewzNDU1Ofxeno4lYA/Vvj+Wz+cjPj4eKSkpAFoTYUZGBsLDw/s9CctkMhgMBpu14PR6PRQK\nBWQyGQDHla431X1rT6VSITw83Fyq3tR3nZGRYbHfVqv57NmzmDVzJtZ3M4xLqVRCJpNBq9VCpVJB\npVKZP6u92lcfcZTm5mZcvHgR5eXlDr0PA9YH7CmEQiEVi8VULpdTiURiVZI9JyeHikQii20ArMrK\nt+/ztbdEu6kPWKFQULlcbnXd9jpesy+l6zujVqtpVlaW1XaFQkElEon5vUQisejXNZWt70gikdDt\n27fTLz7/nDbU19s8pqioiJaUlFC5XG7x+bKysmz2j3dGLpc7vK+ZUkqbm5sdfg93hR70AXvMamj9\nofmWzhfx4T3yNngL7wUAGPO+hPHjZZ0e6/1j6R/XfCYF0P1u8ziS+md4PfoOAICeKAaJjetN2Gap\nqanmMjodW3L9XZ24I7VajZycHPD5fKjVamRkZJjrt3Wlq9L1vY3ZYDAgIiLCavuSJUssvi4CgQAK\nhcJc366zEkmJiYmYMWMGZs6c2eXnEAqF2LNnj1WrtyddF3w+3+EtYADw8vJCU1MTLl68yFYPdCDW\nBeGh4uMdX+a+vdTUVHPCzMnJgUaj6bbYp0l//3KwVa3ZdB+xWAyVSmXuhigsLDT3TXesqgzAnKBN\nyztevnwZp06dsjrOlLw71q3TarUD/v/CXj98/z1WKpWora3lOhS3xVrAPdC+5doV3sJ7za3hbq/5\nTr5dx/W19duRqfQ8F0wFP5cuXQqxWOzw1ndHAoGg01ZnZmYmFAoFMjMzIZFIIJVKsWbNGotio+3p\ndDpzcqWU4rNPP4WXlxeybPTtarVac8FT4I+HjqZCpRkZGUhNTTX/vzHF0l5nLXFHmDN3Lvbs2YOC\nzZux6PrrB+Senoa1gBkArS209g/cHP20XSqVQigUYunSpX2+lqnys706ftb2JBIJNBqNuZV8++23\nQy6X20x6Wq0Wqamp5veEENx3//24vpNkpdFoLBL5ihUroFAozAlZIBCYW9lKpdLi2ibl5eUDloCj\noqIwZcoUbHOT9Y+dkr2dxe708qSHcCUlJTQrK4sCMD8M6zj5wSQrK4sqFAqak5ND1Wo15fP5VCqV\n0oqKClpUVETFYjEFQOVyudX7zu5teujWcTJGSUkJFYlEFACVSCS0qKjIfE0+n08VCoXVPSoqKsyf\nRSqVmj+HvRNC2uvqeNNnNmn/YK7jcZ0xGo20paXF6p6mh2imr3N7poegFRUVnd6zp5+zr65cuUKz\nnnuO/vTjjwN6X1eGHjyE4zwZcvHypATM2NYxyfb2GrbU1NT8f3t3H9xUme8B/PvQSml56Wm6CArD\nZU5gEUYXSXkRaJGXpOD15fqSVp25e3fuOk3+vO6oif1z7+wdN7m6ovvPps6s64zXWSh7V907Y+Gk\noCjC2iZV9A7KmuBLucoA7eFlEArhuX8k5+xJmtC0eXlyTn6fmYwmPUl+TXt+fXjO8/x+/KWdO/mh\nDz5Ie1ySpJzvqSXd0dFR/Y9NtmOKsXtvsnbt2sWf9ftLugvPSiaTgGkKglQlv99f0K6yPXv26GuE\nM9XX16O2thb9/f0YGxsDkJyusNlsOee7temJwcFB+Hy+rMcVa/feZLmcTnDO8d9/+lPZ39vqKAGT\nqiTLMux2+5SXdCmKknVVBJCcC95xzz04f/48Pjx0SF9RASDnXLXb7UYoFMr5mtrmkXJs087UZLNh\n+fLl+Pzzzy1R/7iSUAImVcvj8Uy5wthEKzdkWcaPly3DgQMHsGLFCiiKglgsNuV6EeFwWOjKlYce\nfhg1NTXYt2+fsBisiCWnLKrL6tWr+eDgoOgwiMUNDw/jpZ074XK50L59u+hwCvbhoUP44YcfsC3H\nKJ0kMcYinPPVEx9J64AJKZmFCxfi9jvuwMGDB7GxtdX0VcY2bNwoOgTLoSkIQkpo+/btGBsbw4ED\nB0SHUhSXL19GX18fvv32W9GhWAIlYEJKaP78+VjlcODQBx/g3LlzosMpiiOHD+PLL78UHYYl0BQE\nISXW3t6O2Jdf4vTp02hsbBQdTkFmzJgBn9+PhoYG0aFYAl2EI6QMEokEampqRIdRVGfOnEFzc7Ne\niIgkTeYiHE1BEFIGNTU1SCQSiJe4a0a5HD9+HMFAAF988YXoUEyNEjAhZfLugQP43e9+Z4nNDFpV\nt719fajGf0UXCyVgQspk3V134V9+9rOsxeDNpra2Fq72dgwPD+Ozzz4THY5pUQImVa0cTS41s2bN\nwu233573nOlUd+mVi8PhwNy5c7G3rw/Xr18XHY4pUQK2uHg8rjeYbGlpQTAYRDAYhN/vh8vlAmMs\nr3oIWj0DrZln5v2J3ttut4871uv1gjEGr9er1+f1er1lKzgTjUb1712LtampKWsdXk1PT4/erDMc\nDuf8fIPBoP79aU1Eo9EoGGO4//778dhjj6U1/tR+Jk1NTWmfUyUn4ZqaGmzfvh2nTp3C0NCQ6HDM\nKd+yaVa6VWM5ysx6vJpAIMAVRcnrNTLr7uZbh/dG751Zm1hRFB6JRPKKpxC5Sjtq9Ytz1UwOhUJZ\nm4rm+h4jkYj+uKIovLe3l+/v7+dPP/UUj8fjHEDa56+Vo9T4fL6KLgOZSCT4Cy+8wP/9l7+kRp4p\noHKUJF8ejyfv1vOZnRgK6czQ09MDt9s97jWcTmdZKn7lKu0oSRLcbnfWqmXRaDStn1s+HA5HWgsi\nt9uNjRs3Ytbs2eh7552s728s9OP1egsqm1lq06ZNw44dO3D+/HkMDAyIDsd0KAFXOePJnpmI803M\nk9XT04POzs5xyVfrkVaOeVlFUXIm+my92IBkM898/+ho0w4A9KStPXd6XR22bduWc+rHmORlWS7r\nPPVULF++HPPmzUPfO+/g6tWrosMxFdoJl6cNT/2f6BAAAB++cGtRXicYDOot6iVJ0jvzRiIRqKqK\nrq4u7NmzB6Ojo0Vtmun3+9Hc3Jz1NUdGRhAKhaCqKhRFQTgcht/vhyzL+mhVURTY7faCSjNqtXVz\ncTqdGBkZQTQanfJoPBKJ6P+vvYbxte666y689+67ADBuGVfme8qynNb8s9IwxvDgQw/h/YMHce3a\nNdx0002iQzINGgFXEUVREAwG0dHRkTbCczqd6O7u1u9LkqR36i2mnp4eNDc3w+/3Zx1dGxOtMS6t\nm7B2v9CLdPkkM4/Hk/YZ5WpLb6R9vl6vN20EnI22jAtA1jb2Rna7veJHwUuWLMG//vznqK+vFx2K\nqdAIOE/FGnmK5HK59FFvZhIrdWt4RVHQ29sLSZKgKAo6OjqgKMqEz9Pi0hKmcT51qjGrqjrhWlyv\n1wu73Z51KiIX4+drt9vT3i9brNq/OgYGBvDEE09g2rTs4yFJkqbcuaPcTpw4AVVVsWrVKtGhmAKN\ngKuUdvKXi8vl0pNQb28vwuFw3q3ki/3HQWs5fyOyLMPhcKCnpweqqqa1k8+HloiB3EvJtNoQ6ugo\nPv7440m9fqU6sH8/wopC64LzRAm4SolsbyNJEkKhELq6ukp2oe9GbDYbzp49O+Fx2sW4wcHBglZm\nTDR6tTU348SJEzm/rqpqxc7/ZnrE7ca/PflkztE8SUefEgGQHPEZk2Gp5xw9Hg9kWUZXV1fBrxWP\nx/MeTQPjv1cj4+MejwfRaLSgPxLaBcUbeeCBB/DII4/k/PrZs2dNk4AbGxsxffp0JBIJXLlyRXQ4\nlS/fBcNWulXTRoxYLMZ9Ph8HoG8WyLXJwOfz8VAoxHt7e7miKFySJO7xePjo6CiPRCLc6XRyADwQ\nCIy7n+u9tY0NmRsVYrEYdzgcHAB3u908EonorylJEg+FQuPeY3R0VP9ePB6P/n3kuyHEKPP4WCzG\nPR4PB8B9Pl/aZ6JthFAUhbvdbv39FUVJ+3wdDgcPBAL6TXu9zA0f2mYL7bUcDgf3PfMM/6/XX+dj\nY2MTxlrpxsbGeODXv+Zvvfmm6FCEwCQ2YlA9YFKVvF4vAoFAyS8+5uv06dN44fnnce+996Jt0yb9\ncVVV4ff7J3UxsBLs3rULQ0ND8D/7bMV8xuVC9YAJmYDf76+oHWZz587FP957L36ycmXa47l27FU6\np8sFznlF17KoBJSASVWSZRl2u72ilndt2rQprWWRtmGkHFuzi81ms2HdunUY+OgjS9Q/LhVKwKRq\neTyeihuhfffdd3j197/HpUuXEA6Hha5WKdQ2pxPTpk2Dsm+f6FAqFiVgUtUqLcFxznHs2DG89+67\nFRfbZM2ZMwcbW1sxNDSE77//XnQ4FYkSMCEV5NZbb8XKO+/E+++/jwsXLogOp2BbtmxBbW0t9u3d\nKzqUikQJmJAK097ejkQigf7+ftGhFGzmzJnYvHkzPv30UwwPD4sOp+KYPgEzxnypWy9jzDfxMwip\nbHPnzsXq1atx5PBhjI6Oig6nYG2bNmHevHmW6QhdTKYuxsMYC3DO/Yb7EcYYOOe5++QQYgJOlwuR\nSARhRUFHZ6focApSX1+Pp595RnQYFcm0I2DGmAQgc0N/CEB3lsMJMZWmpiasX78eg4ODOH36tOhw\nimLsyhUMDg6Oq39czUybgAHYAAQYY8ZN8iMAqmvbDbGsrdu2oaamxjIXsCKRCHb98Y84efKk6FAq\nhmmnIDjnccZYC+fcuJLeBaCyFnYSMkWzZ89GW1sbjhw5gkuXLqGhoUF0SAVZs3Ytbl2wAAsXLhQd\nSsWwTC2I1JTECQCZSVn7ugeABwAWLVrU8vXXX5c5QjHi8ThCoRCCwSAcDgceffRRANDLMXZ3d1fd\nXn0zuXz5MuKxGOrq6mBfskR0OEWTSCT0eshWM5laEMIrkxXrBkAB4Mjn2GqqhqbJ1jY9FApxWZYn\n3fY8FAoVMzSSh78dP84vXrwoOoyiUBSFv/ib3/BEIiE6lJKA1drSM8YUxljMcHNmfD0AIMA5r+zG\nWRWms7MT8Xgcu3fvntTzjA0nSXnsP3AAr7/+uugwiuJHzc04efIkPvnkE9GhCGeKBMw5d3HO7Yab\nPs/LGHMDULTHGGPmq1wiiDb1kG+7HVVV4fV682rpQ4qrpaUFa9esscQKgp+sXIlbbrkF+/buRSKR\nEB2OUKa9CAcAqZGwDUA4NQdsA/AogJKMhJ95+ulJHb9gwQI8+YtfjHv+fz7/vP7YzhdfzPuqsPF5\nxeD3++F2u+F2u/XHotGoXiFMURR4vV69Glc4HNbbtQeDQUiSpNcrUFUVzz33HNasWYOBgQG4XK4J\nuwiT/JW7h18pTZs2Ddt37MAfXn0Vg4ODWLduneiQhDFtAk4lXK3Xi7Fadf69aapMLBbDnj17MDIy\nglAopBclN+rq6kJ3dzfcbjccDgdaWlr03VjGRG1sOgkkE4SiKJBlGW63G3a7HZFIhC7wFdG1a9dw\n8OBBLFiwAMuWLRMdTkFWrFiBRYsWIawoaGlpQW2taVNRQUz7XXPOVQCsnO9Z6Ag02/ONI+RSs9vt\nehLt7OxES0sLZFlOG6n29/ePawUfj8dv2JNM68dmPMbhcGD37t2mr+hVSRhjGPjoI3x8001YunSp\nqRtfMsawfccOvNLTgyOHD6O1rU10SEKYNgGTwkiSBKfTiUAgkJaAJUlCOBxGPB7Pe25YKxxurK27\nZs0a0zSSNIuamhq0t7fjjTfewNFPPsGdq1aJDqkgS5cuhd1uR39/P9auXYvpdXWiQyo78/4JJQWT\nJAmZvfFcLhdUVYXH40mbcsgmHo8jGo3qidbpdOo3n89Hc8AlsPLOOzF//nzstcAFLMYYdtxzDy5e\nvIgPDh0SHY4QlICrmN1uh6qqetv1t99+G4ODg2mJV/uacZpBu0gXj8fhcDj0443tfVRVLXlr+2qk\nXcA6c+YMIhZoLLt48WIsXrwYw99+KzoUISyzE24yqqkrsqqq2L17t76aQbvApvF6vZAkCXa7HZ2d\nnfD7/bDb7ZBlGZIkIR6PIxKJoKOjQx/Rer1etLS0wGaz6a9lXAWhmWgETaaGc47fvvwyLly4AP+z\nz5r+Atb169dNPZ+daTI74SgBE2JCx48fxys9PfinBx9Ea2ur6HCKYnh4GE1NTZg5c6boUApCbekJ\nsTjjBayxK1dEh1Owc+fO4bcvv4yD770nOpSyogRMiAnpF7AuXMDRo0dFh1OwxsZG/PNPf4rNW7aI\nDqWszD15REgVW7x4MR5++GHLbHa54447ACTnuBkr6xJ/YWgETIiJrd+wAUuWLsXVq1dFh1IUw8PD\neGnnToyczWx2Y02UgAkxuaFoFP/xq1/hogXa2M+ePRunTp3CPkWZ+GALoARMiMktWLgQt912G65b\nYEVTY2MjNmzYgGgkglOnTokOp+QoARNicjfffDMee/xxzJkzR3QoRbFl61ZMnz7dMr3wboQSMCEW\ncfLkSXxogS29s2bNQmtbG44ePWr5Bp6UgAmxiI/++le89dZbOHPmjOhQCnb33Xejvr4ee/v6RIdS\nUpSACbGIbU5nso39vn2iQylYfX09Nm/ejGPHjuGrr74SHU7JUAImxCLmzJmD1tZWfDw0hO+/+050\nOAVrbW1FfX093vzzn0WHUjKUgAmxkM1btqCurg59FriANb2uDq72dpw7d84SS+yyoQRMiIU0NDRg\n0913438/+wzffPON6HAKtn79ejz++OOWXZJGCZgQi2lra8PMmTPRZ4ELWLW1tfjxsmVIJBI4a8Hd\ncZSACbGYGTNmYMvWrfjb8eOIxWKiwymK/fv347XXXsP169dFh1JUlIAJsaANGzagoaEB//OXv4gO\npSi2bN2K++67z3JFeqqyIDtj7DSArwWG8CMA5l+sWT3o52Uuon9e/8A5n5vPgVWZgEVjjA3mWzGf\niEc/L3Mx08+LpiAIIUQQSsCEECIIJWAxekQHQCaFfl7mYpqfF80BE0KIIDQCJoQQQagpZwkxxnwA\n4gBsAMA5n/CfRowxN4A1nHN/icMjxJSsdF5RAi4RxlgAgMI5D2v3GWNuzvmeHMc7ATgAuJD85SKC\nTeVEJ6VltfOK5oBLhDE2yjlvMtx3AvBzzl0TPC8AQOKce0sdI8kt24kOYCDXiU7Kw2rnFc0BlwBj\nzJHl4REAznLHQqbMoyXfFAVARZ281caK5xUl4NKwIfmLYaQCAGNMKn84ZDKseKJbhOXOK0rApSEh\nNW9ooP3iZD5OKo/lTnSLsNx5RQm4NNQsj2m/IJknNqk8ljvRLcJy5xWtgigQY0wBIBse8iL5y5A5\nUpIAgHOe7ZeIVBbLnegWYbnzihJwgXJdfWWMZf5C2ACEsx1LKo7lTnQr4JxHrXZe0RRE6fSkFn9r\nXABC2h3GmJzxdVIhOOdRjB8Fm/pEtxBLnVe0DriEDAv5ZQCqcSE/Y8wDoEMbQaeuvDuRnMKwAXgO\nQDiVDEiZZa77pXXAlcNK5xUlYEJyuNGJTkgxUAImhBBBaA6YEEIEoQRMCCGC0DI0UhVSF2O0CzEj\nqf/GtfKEjLFQpRVqIdZHCZhYWmrr8CtIruP1cs7jxq8xxkJIJWNBIZIqRlMQxLIYYzKACJIjXZcx\n+QL6popeAG4kq50RUla0CoJYUmrkGwEQ5Zx3THAs55yz8kRGyN/RCJhY1StITi105XEs7XAjQlAC\nJpaTuuDmBtCTZ+2GiuoTRqoHJWBiRdpqhtANj0qplG2ppPrQHDCxHMZYDIDN2DuMkEpEI2BiRTLy\nXFZGHS6ISJSAiRXFkUcCTiXfztKHQ0h2lICJFUUBZGusmambKpwRkSgBEyt6DoCco7sxACBVtDvr\n5gvGmIcxxhljIe01GGNOxlgkdXNru+hSx/kMzw2kHgto0xupIuGR1PE05UF0dBGOWFIqKXoBuDK3\nHyM57RDnnGdd/5s6ZjRzcwZjLALArz0vlZz7jRf7tOcCaDIugWOMuamYO8lEtSCIJXHOg4yxMIAA\nYwxIzgmf1b42wdOdSE5jZHJkJG0V43vHrU7914a/t7J3UvIl2VACJpaVWt97w23IOTyKjN1xjLFx\nSZlzHk8ld+0YbfWFnphTI2LqpEyyogRMyHhOJJs/egyPdSDHlmXGmJya5pA552HGmFbuEgBo9Ety\nojlgQgxSo9hYlvnfGJLNHqMZj48C2Ibkxg9tblhBchdeHMm5ZmplT7KiVRCEpHMCSBuxpqYR5Bxb\nlkeQnPc1TjOoSG4GsVHyJTdCCZiQdB0YvzzNidwV0+IAWjKS8wiSqy+oyhq5IUrAhEDvjuFDMtna\nDWt4PQC6U/+fbV1xFOOrqcWyPEbIODQHTAghgtAImBBCBKEETAghglACJoQQQSgBE0KIIJSACSFE\nEErAhBAiCCVgQggRhBIwIYQIQgmYEEIE+X+mHhQbp8krigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f12acaab4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_wq = 1.3\n",
    "y_wq = 2.5\n",
    "\n",
    "validation_plot_wq = plot_contours(\n",
    "    [ fi_truth_mean_wq, fi_sally_full_mean_wq, fi_sally_met_mean_wq, fi_rate_mean_wq ],\n",
    "    [ fi_truth_covariance_wq, fi_sally_full_covariance_wq, fi_sally_met_covariance_wq, fi_rate_covariance_wq ],\n",
    "    colors=[ col_parton, col_sally_full, col_sally_met, col_rate ],\n",
    "    linestyles=[ 'dotted', 'dashed', 'solid', '-.' ],\n",
    "    xrange=(-x_wq,x_wq),\n",
    "    yrange=(-y_wq,y_wq),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=10.,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=legend_elements_sig, loc='lower left', frameon=False)\n",
    "\n",
    "plt.text(-1*x_wq, 1.05*y_wq, r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(.30*x_wq,.8*y_wq,r'$WH$ Only',fontsize=16)\n",
    "plt.text(.45*x_wq,.6*y_wq,r'$\\tilde{C}_{HD}^{\\phantom{(3)}} = 0$',fontsize=16)\n",
    "plt.text(.28*x_wq,.45*y_wq,r'$L = 300\\,\\mathrm{fb}^{-1}$',fontsize=16)\n",
    "plt.savefig('plots/validation_plot_wq.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BtREvllIel1K+C0wXQjzWwVq6BAEBAYSGhnLw4EHDLAENCdD4ZXww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\ncAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1179d11550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_plot_wbkgs_wq = plot_contours(\n",
    "    [ fi_sally_wbkgs_full_mean_wq, fi_sally_wbkgs_met_mean_wq, fi_wbkgs_rate_mean_wq ],\n",
    "    [ fi_sally_wbkgs_full_covariance_wq, fi_sally_wbkgs_met_covariance_wq, fi_wbkgs_rate_covariance_wq ],\n",
    "    colors=[ col_sally_full, col_sally_met, col_rate ],\n",
    "    linestyles=[ 'dashed', 'solid', '-.' ],\n",
    "    xrange=(-x_wq,x_wq),\n",
    "    yrange=(-y_wq,y_wq),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=10.,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=legend_elements, loc='lower left', frameon=False)\n",
    "\n",
    "plt.text(-1*x_wq, 1.05*y_wq, r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(-.50*x_wq,.8*y_wq,r'$WH$ + QCD Backgrounds',fontsize=16)\n",
    "plt.text(.45*x_wq,.6*y_wq,r'$\\tilde{C}_{HD}^{\\phantom{(3)}} = 0$',fontsize=16)\n",
    "plt.text(.28*x_wq,.45*y_wq,r'$L = 300\\,\\mathrm{fb}^{-1}$',fontsize=16)\n",
    "plt.savefig('plots/validation_plot_wbkgs_wq.eps',format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information in Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_hist_stxs_outfile = './fisher_info/fi_hist_stxs.npz'\n",
    "fi_hist_stxs_cov_outfile = './fisher_info/fi_hist_stxs_cov.npz'\n",
    "\n",
    "fi_stxs_3bins_wph_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][0]\n",
    "fi_stxs_3bins_wph_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][1]\n",
    "fi_stxs_3bins_wmh_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][2]\n",
    "fi_stxs_3bins_wmh_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][3]\n",
    "fi_stxs_4bins_wph_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][4]\n",
    "fi_stxs_4bins_wph_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][5]\n",
    "fi_stxs_4bins_wmh_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][6]\n",
    "fi_stxs_4bins_wmh_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][7]\n",
    "fi_stxs_5bins_wph_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][8]\n",
    "fi_stxs_5bins_wph_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][9]\n",
    "fi_stxs_5bins_wmh_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][10]\n",
    "fi_stxs_5bins_wmh_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][11]\n",
    "fi_stxs_6bins_wph_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][12]\n",
    "fi_stxs_6bins_wph_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][13]\n",
    "fi_stxs_6bins_wmh_mu_mean = np.load(fi_hist_stxs_outfile)['arr_0'][14]\n",
    "fi_stxs_6bins_wmh_e_mean = np.load(fi_hist_stxs_outfile)['arr_0'][15]\n",
    "\n",
    "\n",
    "fi_stxs_3bins_wph_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][0]\n",
    "fi_stxs_3bins_wph_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][1]\n",
    "fi_stxs_3bins_wmh_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][2]\n",
    "fi_stxs_3bins_wmh_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][3]\n",
    "fi_stxs_4bins_wph_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][4]\n",
    "fi_stxs_4bins_wph_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][5]\n",
    "fi_stxs_4bins_wmh_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][6]\n",
    "fi_stxs_4bins_wmh_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][7]\n",
    "fi_stxs_5bins_wph_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][8]\n",
    "fi_stxs_5bins_wph_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][9]\n",
    "fi_stxs_5bins_wmh_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][10]\n",
    "fi_stxs_5bins_wmh_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][11]\n",
    "fi_stxs_6bins_wph_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][12]\n",
    "fi_stxs_6bins_wph_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][13]\n",
    "fi_stxs_6bins_wmh_mu_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][14]\n",
    "fi_stxs_6bins_wmh_e_covariance = np.load(fi_hist_stxs_cov_outfile)['arr_0'][15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_hist_2d_outfile = './fisher_info/fi_hist_2d.npz'\n",
    "fi_hist_2d_cov_outfile = './fisher_info/fi_hist_2d_cov.npz'\n",
    "\n",
    "fi_imp_stxs_wph_mu_mean = np.load(fi_hist_2d_outfile)['arr_0'][0]\n",
    "fi_imp_stxs_wph_e_mean = np.load(fi_hist_2d_outfile)['arr_0'][1] \n",
    "fi_imp_stxs_wmh_mu_mean = np.load(fi_hist_2d_outfile)['arr_0'][2] \n",
    "fi_imp_stxs_wmh_e_mean = np.load(fi_hist_2d_outfile)['arr_0'][3]\n",
    "\n",
    "fi_imp_stxs_wph_mu_covariance = np.load(fi_hist_2d_cov_outfile)['arr_0'][0]\n",
    "fi_imp_stxs_wph_e_covariance = np.load(fi_hist_2d_cov_outfile)['arr_0'][1]\n",
    "fi_imp_stxs_wmh_mu_covariance = np.load(fi_hist_2d_cov_outfile)['arr_0'][2]\n",
    "fi_imp_stxs_wmh_e_covariance = np.load(fi_hist_2d_cov_outfile)['arr_0'][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_stxs_3bins_mean = fi_stxs_3bins_wph_mu_mean + fi_stxs_3bins_wph_e_mean + fi_stxs_3bins_wmh_mu_mean + fi_stxs_3bins_wmh_e_mean\n",
    "fi_stxs_3bins_covariance = fi_stxs_3bins_wph_mu_covariance + fi_stxs_3bins_wph_e_covariance + fi_stxs_3bins_wmh_mu_covariance + fi_stxs_3bins_wmh_e_covariance\n",
    "\n",
    "fi_stxs_4bins_mean = fi_stxs_4bins_wph_mu_mean + fi_stxs_4bins_wph_e_mean + fi_stxs_4bins_wmh_mu_mean + fi_stxs_4bins_wmh_e_mean\n",
    "fi_stxs_4bins_covariance = fi_stxs_4bins_wph_mu_covariance + fi_stxs_4bins_wph_e_covariance + fi_stxs_4bins_wmh_mu_covariance + fi_stxs_4bins_wmh_e_covariance\n",
    "\n",
    "fi_stxs_5bins_mean = fi_stxs_5bins_wph_mu_mean + fi_stxs_5bins_wph_e_mean + fi_stxs_5bins_wmh_mu_mean + fi_stxs_5bins_wmh_e_mean\n",
    "fi_stxs_5bins_covariance = fi_stxs_5bins_wph_mu_covariance + fi_stxs_5bins_wph_e_covariance + fi_stxs_5bins_wmh_mu_covariance + fi_stxs_5bins_wmh_e_covariance\n",
    "\n",
    "fi_stxs_6bins_mean = fi_stxs_6bins_wph_mu_mean + fi_stxs_6bins_wph_e_mean + fi_stxs_6bins_wmh_mu_mean + fi_stxs_6bins_wmh_e_mean\n",
    "fi_stxs_6bins_covariance = fi_stxs_6bins_wph_mu_covariance + fi_stxs_6bins_wph_e_covariance + fi_stxs_6bins_wmh_mu_covariance + fi_stxs_6bins_wmh_e_covariance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_imp_stxs_mean = fi_imp_stxs_wph_mu_mean + fi_imp_stxs_wph_e_mean + fi_imp_stxs_wmh_mu_mean + fi_imp_stxs_wmh_e_mean\n",
    "fi_imp_stxs_covariance = fi_imp_stxs_wph_mu_covariance + fi_imp_stxs_wph_e_covariance + fi_imp_stxs_wmh_mu_covariance + fi_imp_stxs_wmh_e_covariance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots of Information in $p_{T,W}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rotate and Project the F.I. for Plotting:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project out systematics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_stxs_3bins_mean_nosyst, fi_stxs_3bins_covariance_nosyst = project_information(fi_stxs_3bins_mean, [0,1,2,3], covariance=fi_stxs_3bins_covariance)\n",
    "fi_stxs_5bins_mean_nosyst, fi_stxs_5bins_covariance_nosyst = project_information(fi_stxs_5bins_mean, [0,1,2,3], covariance=fi_stxs_5bins_covariance)\n",
    "fi_stxs_6bins_mean_nosyst, fi_stxs_6bins_covariance_nosyst = project_information(fi_stxs_6bins_mean, [0,1,2,3], covariance=fi_stxs_6bins_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_stxs_3bins_mean_rot, fi_stxs_3bins_covariance_rot = rotate(fi_stxs_3bins_mean_nosyst, covariance=fi_stxs_3bins_covariance_nosyst)\n",
    "fi_stxs_5bins_mean_rot, fi_stxs_5bins_covariance_rot = rotate(fi_stxs_5bins_mean_nosyst, covariance=fi_stxs_5bins_covariance_nosyst)\n",
    "fi_stxs_6bins_mean_rot, fi_stxs_6bins_covariance_rot = rotate(fi_stxs_6bins_mean_nosyst, covariance=fi_stxs_6bins_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Projected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_dw, fi_sally_wbkgs_ptw_covariance_dw = project_information(fi_sally_wbkgs_ptw_mean_rot, [0,1], covariance=fi_sally_wbkgs_ptw_covariance_rot)\n",
    "fi_stxs_3bins_mean_dw, fi_stxs_3bins_covariance_dw = project_information(fi_stxs_3bins_mean_rot, [0,1], covariance=fi_stxs_3bins_covariance_rot)\n",
    "fi_stxs_5bins_mean_dw, fi_stxs_5bins_covariance_dw = project_information(fi_stxs_5bins_mean_rot, [0,1], covariance=fi_stxs_5bins_covariance_rot)\n",
    "fi_stxs_6bins_mean_dw, fi_stxs_6bins_covariance_dw = project_information(fi_stxs_6bins_mean_rot, [0,1], covariance=fi_stxs_6bins_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_dq, fi_sally_wbkgs_ptw_covariance_dq = project_information(fi_sally_wbkgs_ptw_mean_rot, [0,2], covariance=fi_sally_wbkgs_ptw_covariance_rot)\n",
    "fi_stxs_3bins_mean_dq, fi_stxs_3bins_covariance_dq = project_information(fi_stxs_3bins_mean_rot, [0,2], covariance=fi_stxs_3bins_covariance_rot)\n",
    "fi_stxs_5bins_mean_dq, fi_stxs_5bins_covariance_dq = project_information(fi_stxs_5bins_mean_rot, [0,2], covariance=fi_stxs_5bins_covariance_rot)\n",
    "fi_stxs_6bins_mean_dq, fi_stxs_6bins_covariance_dq = project_information(fi_stxs_6bins_mean_rot, [0,2], covariance=fi_stxs_6bins_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_wq, fi_sally_wbkgs_ptw_covariance_wq = project_information(fi_sally_wbkgs_ptw_mean_rot, [1,2], covariance=fi_sally_wbkgs_ptw_covariance_rot)\n",
    "fi_stxs_3bins_mean_wq, fi_stxs_3bins_covariance_wq = project_information(fi_stxs_3bins_mean_rot, [1,2], covariance=fi_stxs_3bins_covariance_rot)\n",
    "fi_stxs_5bins_mean_wq, fi_stxs_5bins_covariance_wq = project_information(fi_stxs_5bins_mean_rot, [1,2], covariance=fi_stxs_5bins_covariance_rot)\n",
    "fi_stxs_6bins_mean_wq, fi_stxs_6bins_covariance_wq = project_information(fi_stxs_6bins_mean_rot, [1,2], covariance=fi_stxs_6bins_covariance_rot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_prof_dw, fi_sally_wbkgs_ptw_covariance_prof_dw = profile_information( fi_sally_wbkgs_ptw_mean_rot, [0,1], covariance = fi_sally_wbkgs_ptw_covariance_rot)\n",
    "fi_stxs_3bins_mean_prof_dw, fi_stxs_3bins_covariance_prof_dw = profile_information( fi_stxs_3bins_mean_rot, [0,1], covariance = fi_stxs_3bins_covariance_rot)\n",
    "fi_stxs_5bins_mean_prof_dw, fi_stxs_5bins_covariance_prof_dw = profile_information( fi_stxs_5bins_mean_rot, [0,1], covariance = fi_stxs_5bins_covariance_rot)\n",
    "fi_stxs_6bins_mean_prof_dw, fi_stxs_6bins_covariance_prof_dw = profile_information( fi_stxs_6bins_mean_rot, [0,1], covariance = fi_stxs_6bins_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_prof_dq, fi_sally_wbkgs_ptw_covariance_prof_dq = profile_information( fi_sally_wbkgs_ptw_mean_rot, [0,2], covariance = fi_sally_wbkgs_ptw_covariance_rot)\n",
    "fi_stxs_3bins_mean_prof_dq, fi_stxs_3bins_covariance_prof_dq = profile_information( fi_stxs_3bins_mean_rot, [0,2], covariance = fi_stxs_3bins_covariance_rot)\n",
    "fi_stxs_5bins_mean_prof_dq, fi_stxs_5bins_covariance_prof_dq = profile_information( fi_stxs_5bins_mean_rot, [0,2], covariance = fi_stxs_5bins_covariance_rot)\n",
    "fi_stxs_6bins_mean_prof_dq, fi_stxs_6bins_covariance_prof_dq = profile_information( fi_stxs_6bins_mean_rot, [0,2], covariance = fi_stxs_6bins_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_ptw_mean_prof_wq, fi_sally_wbkgs_ptw_covariance_prof_wq = profile_information( fi_sally_wbkgs_ptw_mean_rot, [1,2], covariance = fi_sally_wbkgs_ptw_covariance_rot)\n",
    "fi_stxs_3bins_mean_prof_wq, fi_stxs_3bins_covariance_prof_wq = profile_information( fi_stxs_3bins_mean_rot, [1,2], covariance = fi_stxs_3bins_covariance_rot)\n",
    "fi_stxs_5bins_mean_prof_wq, fi_stxs_5bins_covariance_prof_wq = profile_information( fi_stxs_5bins_mean_rot, [1,2], covariance = fi_stxs_5bins_covariance_rot)\n",
    "fi_stxs_6bins_mean_prof_wq, fi_stxs_6bins_covariance_prof_wq = profile_information( fi_stxs_6bins_mean_rot, [1,2], covariance = fi_stxs_6bins_covariance_rot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show the limits obtained from histograms of $p_{T,W}$ with various binnings:\n",
    "* The original STXS, with 3 bins (boundaries at 0, 150, 250 GeV)\n",
    "* STXS with an additional bin boundary at 75 GeV\n",
    "* STXS with additional bin boundaries at 75 and 400 GeV\n",
    "* A very differential histogram, with bin boundaires every 25 GeV up to 325 GeV, with extra boundaries at 375, 425, and 500 GeV\n",
    "* the infinite binning limit, obtained by training a neural network (with `SALLY`) on only the observable $p_{T,W}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ptw_legend = [\n",
    "    Line2D([0],[0], color=col_sally_ptw, ls='-', label=r'Full $p_{T,W}$ dist.'),\n",
    "    Line2D([0],[0], color=col_stxs_6bins, ls='-.', label='STXS, 6 bins'),\n",
    "    Line2D([0],[0], color=col_stxs_5bins, ls='--', label='STXS, stage 1.1'),\n",
    "    Line2D([0],[0], color=col_stxs_3bins, ls=':', label='STXS, stage 1'),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Projected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZH8P614q8eI/LlcuXMQc/i2NvDVY8uJgeA+9TqbWViwxiSaoEVu3fy6wvnyS1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\nEBMRmez/AVO8iVtS4EEtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1179b65a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_ptw_mean_dw,\n",
    "        fi_stxs_3bins_mean_dw,\n",
    "        fi_stxs_5bins_mean_dw,\n",
    "        fi_stxs_6bins_mean_dw,\n",
    "    ],\n",
    "    colors=[ col_sally_ptw, col_stxs_3bins, col_stxs_5bins, col_stxs_6bins ],\n",
    "    linestyles=[ '-', ':', '--', '-.' ],\n",
    "    xrange=(-2.75,2.75),\n",
    "    yrange=(-7.5,7.5),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=10.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=ptw_legend, loc='upper left',frameon=False)\n",
    "plt.text(0.42*ax.get_xlim()[1], -.75*ax.get_ylim()[1], r'$C_{HQ}^{(3)} = 0$', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], -.9*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/stxs_contours_dw_proj.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pS9+S9lTXOFcyMzNlcnKy1Ov1Mj4+/qyfrzgHXKksvRDCAGRKKbPqjyNlg9SEq+tKZelb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\nSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f118e9af0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_ptw_mean_dq,\n",
    "        fi_stxs_3bins_mean_dq,\n",
    "        fi_stxs_5bins_mean_dq,\n",
    "        fi_stxs_6bins_mean_dq,\n",
    "    ],\n",
    "    colors=[ col_sally_ptw, col_stxs_3bins, col_stxs_5bins, col_stxs_6bins ],\n",
    "    linestyles=[ '-', ':', '--', '-.' ],\n",
    "    xrange=(-.5,.5),\n",
    "    yrange=(-3.5,3.5),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=ptw_legend, loc='upper left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(0.42*ax.get_xlim()[1], -.75*ax.get_ylim()[1], r'$C_{HW}^{\\phantom{(3)}} = 0$', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], -.9*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/stxs_contours_dq_proj.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kqd2v0HqqN0O+iaHH7O/o/u5rtHpwNEKjwVUreLuzB/WdLOWJIrNEK0Cjuq4p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\nQmaHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f117a6368d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_ptw_mean_wq,\n",
    "        fi_stxs_3bins_mean_wq,\n",
    "        fi_stxs_5bins_mean_wq,\n",
    "        fi_stxs_6bins_mean_wq,\n",
    "    ],\n",
    "    colors=[ col_sally_ptw, col_stxs_3bins, col_stxs_5bins, col_stxs_6bins ],\n",
    "    linestyles=[ '-', ':', '--', '-.' ],\n",
    "    xrange=(-1.5,1.5),\n",
    "    yrange=(-3,3),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=10.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=ptw_legend, loc='lower left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(.42*ax.get_xlim()[1], 0.8*ax.get_ylim()[1],r'$\\tilde{C}_{HD}^{\\phantom{(3)}} = 0$',fontsize=16)\n",
    "plt.text(.30*ax.get_xlim()[1], .65*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/stxs_contours_wq_proj.eps',format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bilVKXITgsX7e/PtTMvEXjkXMvIPzMnsQ+PVgHnvzQXoHVv4Uo9ViZfmEp+m6\n/0ce7TmSWz9/p8wyNqWOObIhRXP9ammb/lRXzj4+Pr5CdQ3KVejIyMiQMTEx9uOYmJgyFS6kLK5y\nERUVVeP714WaVPuoL/Hx8TImJkbqdLpGq7jxzYl8ecePZpmWb5HHvv5ORg/pKN/t3FvOOH+yfGjw\nE/JY6vEq21qtVjl74QOyR+ylcsLc5fLwmZZbYaO2UBU6lNIcKWdvNpvPur2jrU3pa9jKwNu2lATs\n20zW9P51ISkpqU6vdy5sm8c35l4VrVwEXq6C9C++Ys2Dt7H+rkJevbA77S/uz7yPo+ncq2OVbT9+\ncBmHv/yZnh5tWfpUJD19PBqw5y2TCsTn4N6kM3x8NB8oXhJ0b9IZPjtWfJxrKT7+6njxcWaRlXuT\nzrD1RHEV3IyC4uNvTxYfn8ovPt5eUiX33zwL9yadYWd6IQB/5xYfJ2YUH/+RbTmnPjtSzt7RzXbK\n70wWFxdn30/XaDRWutG8I/evDbPZTGRkJOnp6bW+VlNmK1d/RTt3lrfLIDF8Ev1OnkfIG4MY3vkW\nHn//YXzb+1TZ/uPYtZjXvMDoP3PYOmsbnb08G6rrLZrKEbcg1ZWzd3QEV/48rVZr32hdr9dXGdCr\nu7+jYmNjCQwMxGw2Ex8fT2RkpL1uXnJyMrGxsWg0Gnv5puTkZHuFDdv5pT9Damoqer2ekJAQEhIS\n7DlsKK4ybctt29639d3ZJGYUMufnTJZe4sNFvoU8fGgBPcNHccAkGB18DVNjJ+HmXvVfeeNGPY+4\nzuXmKzWMXLIJVzfXBux9C+dI/qK5frW0HLGUxTnUwMBACUig2hwmDlZxlrK4OnJlVZRrc//K2pfO\nKcfFxdk3cK+qinPpPHT5Ks22923XiI+Plzqdrsz7Wq22TNVnrVZboTJ0ZRq6KvOZAot8dl+W/GHp\nizL0tcGy/Sv+8tobrpfvLfrAvpF7Vb757GOpjfGT2hg/+b/tDbcxf3OHgzniRg+GjfnVEgNxaY6U\ns3c0EMfFxcn4+HgJODz55sj9y4uPj5darVbq9Xp7cLQFxaoCcfmgqdFoygRWSk02lg/UcXFxUqvV\nlmkfFhbmUJ8bKhDvP1Moi6xWabVa5XfPPS0ve0IjO73sJ6/T3Si3bKi+Ysa+gwdk75jzZLdlfjL+\nkzfrvb8tiaOBWOWIW4i6KmdfGVtaQKfTERMTw7Rp0ypU2air+9uWzcXHxxMQEEBQUFC1bTQaDSaT\nCYPBUGmzncrgAAAgAElEQVTRzrCwMHudPZPJxLhx48p8Nlt721dISEiDbl5/NkdzLdyVeAZ9Sg5b\nnniQ6daV/NvZhcBNQ3n+/vmE3j3srO3/PvYPo42jON3Wwhi/59HdPLGBeq6U5rSBWAgRJYQIE0JE\nCCEiqjk3rOR8rRBCY/u+ofraFNRVOfvK6PV6ez42KioKrVbLtGnT6uX+RqMRnU5HXFwcUkp0Oh0G\ng6HCeaUnBkNDQzGbzfZKzeWNHz+e9PR0jEYj/v7+6PV6+3u2gKvT6exfUVFRTpMjPs/Llag+XnRY\neT/3t9nIaY0rV8RdR+xzL3D5qLPvR5524gSjVw0mQ3OKF7QxLLtzegP1WinPKQOxECIGSJZSGqWU\nBiBACHG2dVX+QAyQAhwGUqWUqWc5v0VypJx9TcXGxlZoHxcXZ19BUdP7p6amVhqwq3p//Pjx9u+1\nWq39+qmpqQQGBpKcnExiYmKZAGwbrdv6l5CQYA/S5QO17bh0vysb3Te0xIxCjuQUr6Dp55nC470/\nJ9fLjaGbb2LpysVcNHTAWdtnZ2Zyy5LL+bNHFjPOjGVq6F3qgY1G5KyrJiKklNGljuOBaKDivyv/\n4wf4qwBcOUfL2UNxoDEYDCQkJADFT8qNHz++TIl3o9HIpk2bMBqNpKWllVk3bAuU4eHhREVFERkZ\n6fD9jUYj0dHRxRMYlbAtg7MF0dTUVKKiogDsRT9t9edsr40bN47Y2Fi0Wi0ajca+ztm29jkgIAA/\nPz97G61WW+aJwK1bt9pXTdicbb217TMajUbS09MJCAgo82dXW4VWydP7stG6W4jqeIxRppsoLHQh\n9MsxLNwwn87aTmdtbymyMG5uIAcvOc1VR4bz5Hz9Wc9XGoAjieSG/AICgYxKXpNnaRNxLvdq6ZN1\nzsrRVRp1ISkpSUZFRdkn6zIyMmRSUlKZlRTO6ODJTLnlxjD5Xpf+8trbhslZNz8o049Vv5LDYrHI\nDYPGyKcG+MppD1xZ7WoKpXZwcLLOGUfE/kD5VflmACGERkpZaa31kjyyrZ1WShl7lvMiALp3714n\nHVbqVvmJvvpkm3yz5ak1Go19ZO1sdpsLScm2cEvbIj6aM4LC3/aQmRPMtUXX88gbs2mtaV3tNZ5Y\nsoaeKXvodsEInl32rkpHOAmnq+JckgteK6X0K/WaBsgAAmQlqQchhLb060IIPZAki/PLVVJVnJ1P\namoqZrO5QR8Ptk32+fv7k56ejtlsRqvVVvu4d0N7Ym8WB4+mM+XZqURdn4Tb6bZMPPYg96+bjmcl\nm7mXN1+/gJdYSrDlWd6aOh1/T/dq2yi105SrOFc2HPIv+W+lz69WEpzjKZ68O2sgVpxPYywLs634\ncHbPDGjN9sWPcvR/SQw9OpDeN45mzmszcfOo/q/x5jkxyNfeZ/h9obzxdCQe7ioIOxNnXDWRDpRf\nz6QBqCwtUbJcTZaMmm3MgFq+pjR5+84U8fAvmeRaJBv3riO2Xwo7NVdx+bh7eGjlbIeC8IsLH+HU\n+hj823mx8ZF1eLirTXycjdONiKWUyUKI8gHXH6h6TRPElgvSWkCtnlCavL9yLfx1+B8eN97Hm12T\n6HCyC5MeeYYJj4U5lN/Vv/QUS9qu5+JJbVj66Ad4tq16wx+l8TjjiBjAUG7dcChgX2NT8uBGGNhH\nyWnl2odTvNxNUZqkImvx3M2VecdpvfkG3uyaRLefO/FC38XcMTfcoSC84dXlzJeraXfKlZfv/4yL\nqlnWpjQepxsRA0gpo21P1lE8uk2RUpZeQ6yjONjaXjMIIaIoTkkEAPpy5ytKk5GSZeHBXzJ50vsE\ny1cMJ/7KHHoldeaFa9Yw/I5rHLqG6adtPJG7GJ8cN94Y8T69B1xSz71WasMpAzFAVcvPSt4zUGoi\nrmRUXOX5itKU+LgJehxN4YUvR/H94Dx67+zGstvWccWokOobAzuSv+fu7ZNxc3Hjcd3HDAyufj8O\npXE5bSBWlJYmvcCKn7ugnYckK38R3w/Ko//2Xqy6ayOXDr/IoWv874dt3Lk9HDzceOfq97hqoArC\nTYEKxIriBNLyrUxIOE2Y+xkOnV7Np9nb6L/9YtbOWEv/K/o6dI29u3/i7u/Gk99a8ozfM1w18Mp6\n7rVSV1QgVhQn4O8huO3Ub/jeNR6XLn0IvHQwrzzxMr0u6eFQe/OfR3li8a1kXlFEpOtc7rlzRj33\nWKlLKhArSiM6lmfBQwhyk3eQvH4Ml+e5UuTSlw3PzeP8vl0cukbmKTNvD7mZ0cdzCQt8iEkPR9Vz\nr5W6pgKxojQSq5Q88HMW5+1JwP21O/hoUhH5lmtZ8soCOnZv79A1TmeaGWmYTL+RoQR31jLz4XPf\n0lRpPCoQK0ojcRGCh9vlkvLAZLILPBjb5iaef3Up/l38qm8MFGTnMn92NCkhO7lSdyMzxjWNR7WV\nilQgVpQGlpZv5bfMIi7yyeHp7ZM5P6AP3TSDiXnzybOWui+tIC+fVwaP5qJ9u3j58uWMGa9KHDVl\nKhArSgN7KSWHXz/bzGGvGP5yOU6XQbcR9eI8Wrf1dqh9QUE+N8+9kH6+J2k36X6emamCcFPnrI84\nK0qzNXL/pxzMmsNRjjH6l3G8suIlh4NwkaWI26IvZdeAdDKDLmHu+vn13FulIagRsaI0gDOFVt44\nksegXe8zdd8DpLeH8F8nsGT5cjwc3BfYarUy6plhJA44wQ2/9OLVl75TG7s3E2pErCgN4PtThWx9\nawN37r+fDA3ccfAulq5cUaMgPGnlZBK77aXviUHol/6ognAzogKxojSAi1r/y97WC8n0Edz1RwQx\ny5Y4tJewzdQnbyDe+wuuOH0Vnz/6Ka1r0FZxfioQK0o9ybVIovdksfvYv4S+eR1n3Czce/Q+FixZ\niKubq8PXueuxEXzWM4nAg1344KHNtG2lgnBzowKxotSTk/lWTm/axO6Lh9L1i848cOZR5r3wDC4u\njv+1mzP3Vj7X/kTflA6seXwnbq4qCDdH6v+qotQxq5S4CMHv7y6n6zvzOZPZgYig2Yx/cnyN8rqf\nznoO//ivCQzVsOn5nWg0qrpGc6UCsaLUoUKr5NE9WQz8ahMvn1yA+W53FrgtJPyxcTW6zrq34sjf\nvImO7v34cIEJL03beuqx4gxUakJR6lj7bZ/j9tjD3LzRn4d9nq9xEDa8t5bHM6fzzuMTCfrmc7z8\nVBBu7s55RCyE8JVSnqnLzihKU2WRkgIr/HZ8N9+lr8Xq3YfL5y/ghukjanSdLc+v5WTsGgZFXMnr\nT0bi39a/nnqsOJPapCZiALXpqaIAC/bncPiLNWz1WAa+EPnyam64s2ZB+KXlT+L6oh6/1l14e85a\nfFUQbjFqk5oIEUKMqbOeKEoT5vvx82wTz+OeVcSyrqsYe+foGrV/edXTLHR/mc3j3ej20Yf4nq8q\nLrcktRkRh0spDwshrgPSpJS766pTitIUSCn5K9fKT4anWNZqDR65rizptYbRt99Wo+vo9QtZwCo6\nnHDhlRmf0fsSbT31WHFW5xyIpZSHS/67VQjRVggxFohXeWOlpXj9SB6frHmKH7usxzPbjWX9NnDz\n2FE1usam+PeYZ12B/2l3Nt7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UFYSdR8apE4xZM4R/O+czK3My08KnV9umqLCIZQ+sZrFY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1179bd6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_ptw_mean_prof_dw,\n",
    "        fi_stxs_3bins_mean_prof_dw,\n",
    "        fi_stxs_5bins_mean_prof_dw,\n",
    "        fi_stxs_6bins_mean_prof_dw,\n",
    "    ],\n",
    "    colors=[ col_sally_ptw, col_stxs_3bins, col_stxs_5bins, col_stxs_6bins ],\n",
    "    linestyles=[ '-', ':', '--', '-.' ],\n",
    "    xrange=(-5.5,5.5),\n",
    "    yrange=(-16,16),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=10.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=ptw_legend, loc='upper left',frameon=False)\n",
    "plt.text(0.24*ax.get_xlim()[1], -.75*ax.get_ylim()[1], r'$C_{HQ}^{(3)}$ Profiled', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], -.9*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/stxs_contours_dw_prof.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wK7I1v5wurpoGBWNLeTkIYU9BXDp/DFJaWHHTn2hE7efb+sd2Ppv2DdmHchgw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\n5nQHVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f117a444190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_ptw_mean_prof_dq,\n",
    "        fi_stxs_3bins_mean_prof_dq,\n",
    "        fi_stxs_5bins_mean_prof_dq,\n",
    "        fi_stxs_6bins_mean_prof_dq,\n",
    "    ],\n",
    "    colors=[ col_sally_ptw, col_stxs_3bins, col_stxs_5bins, col_stxs_6bins ],\n",
    "    linestyles=[ '-', ':', '--', '-.' ],\n",
    "    xrange=(-6,6),\n",
    "    yrange=(-6,6),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=ptw_legend, loc='lower left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(0.22*ax.get_xlim()[1], 0.8*ax.get_ylim()[1], r'$C_{HW}^{\\phantom{(3)}}$ Profiled', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], 0.65*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/stxs_contours_dq_prof.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IIq4cAUB5aQV/b85k1+9/sXPNbhbNWYqUEt9AH+689D68L9GyNSiDbcZtjJ75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\nUiYBoUKI6LO5plI/MV09iO/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SlJilIz3UVrXtd6+0GRUW8NBYf+rS4cKxXJP2O9Fel/L0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\n9YO5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f117a6b3cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_ptw_mean_prof_wq,\n",
    "        fi_stxs_3bins_mean_prof_wq,\n",
    "        fi_stxs_5bins_mean_prof_wq,\n",
    "        fi_stxs_6bins_mean_prof_wq,\n",
    "    ],\n",
    "    colors=[ col_sally_ptw, col_stxs_3bins, col_stxs_5bins, col_stxs_6bins ],\n",
    "    linestyles=[ '-', ':', '--', '-.' ],\n",
    "    xrange=(-18,18),\n",
    "    yrange=(-6,6),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=10.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=ptw_legend, loc='lower left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(0.24*ax.get_xlim()[1], 0.8*ax.get_ylim()[1],r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$ Profiled',fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], 0.65*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/stxs_contours_wq_prof.eps',format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 1D vs. 2D Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine & Rotate for Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_imp_stxs_mean_nosyst, fi_imp_stxs_covariance_nosyst = project_information(fi_imp_stxs_mean, [0,1,2,3], covariance=fi_imp_stxs_covariance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_imp_stxs_mean_rot, fi_imp_stxs_covariance_rot = rotate(fi_imp_stxs_mean_nosyst, covariance=fi_imp_stxs_covariance_nosyst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Projected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_dw, fi_sally_wbkgs_met_covariance_dw = project_information(fi_sally_wbkgs_met_mean_rot, [0,1], covariance=fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_2d_mean_dw, fi_sally_wbkgs_2d_covariance_dw = project_information(fi_sally_wbkgs_2d_mean_rot, [0,1], covariance=fi_sally_wbkgs_2d_covariance_rot)\n",
    "fi_imp_stxs_mean_dw, fi_imp_stxs_covariance_dw = project_information(fi_imp_stxs_mean_rot, [0,1], covariance=fi_imp_stxs_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_dq, fi_sally_wbkgs_met_covariance_dq = project_information(fi_sally_wbkgs_met_mean_rot, [0,2], covariance=fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_2d_mean_dq, fi_sally_wbkgs_2d_covariance_dq = project_information(fi_sally_wbkgs_2d_mean_rot, [0,2], covariance=fi_sally_wbkgs_2d_covariance_rot)\n",
    "fi_imp_stxs_mean_dq, fi_imp_stxs_covariance_dq = project_information(fi_imp_stxs_mean_rot, [0,2], covariance=fi_imp_stxs_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_wq, fi_sally_wbkgs_met_covariance_wq = project_information(fi_sally_wbkgs_met_mean_rot, [1,2], covariance=fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_2d_mean_wq, fi_sally_wbkgs_2d_covariance_wq = project_information(fi_sally_wbkgs_2d_mean_rot, [1,2], covariance=fi_sally_wbkgs_2d_covariance_rot)\n",
    "fi_imp_stxs_mean_wq, fi_imp_stxs_covariance_wq = project_information(fi_imp_stxs_mean_rot, [1,2], covariance=fi_imp_stxs_covariance_rot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_prof_dw, fi_sally_wbkgs_met_covariance_prof_dw = profile_information(fi_sally_wbkgs_met_mean_rot, [0,1], covariance=fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_2d_mean_prof_dw, fi_sally_wbkgs_2d_covariance_prof_dw = profile_information(fi_sally_wbkgs_2d_mean_rot, [0,1], covariance=fi_sally_wbkgs_2d_covariance_rot)\n",
    "fi_imp_stxs_mean_prof_dw, fi_imp_stxs_covariance_prof_dw = profile_information(fi_imp_stxs_mean_rot, [0,1], covariance=fi_imp_stxs_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_prof_dq, fi_sally_wbkgs_met_covariance_prof_dq = profile_information(fi_sally_wbkgs_met_mean_rot, [0,2], covariance=fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_2d_mean_prof_dq, fi_sally_wbkgs_2d_covariance_prof_dq = profile_information(fi_sally_wbkgs_2d_mean_rot, [0,2], covariance=fi_sally_wbkgs_2d_covariance_rot)\n",
    "fi_imp_stxs_mean_prof_dq, fi_imp_stxs_covariance_prof_dq = profile_information(fi_imp_stxs_mean_rot, [0,2], covariance=fi_imp_stxs_covariance_rot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fi_sally_wbkgs_met_mean_prof_wq, fi_sally_wbkgs_met_covariance_prof_wq = profile_information(fi_sally_wbkgs_met_mean_rot, [1,2], covariance=fi_sally_wbkgs_met_covariance_rot)\n",
    "fi_sally_wbkgs_2d_mean_prof_wq, fi_sally_wbkgs_2d_covariance_prof_wq = profile_information(fi_sally_wbkgs_2d_mean_rot, [1,2], covariance=fi_sally_wbkgs_2d_covariance_rot)\n",
    "fi_imp_stxs_mean_prof_wq, fi_imp_stxs_covariance_prof_wq = profile_information(fi_imp_stxs_mean_rot, [1,2], covariance=fi_imp_stxs_covariance_rot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Comparison of 1D and 2D STXS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparison of the STXS, a 2D histogram, ML with 2 observable, and \"full\" information from `SALLY`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "imp_legend = [\n",
    "    Line2D([0],[0], color=col_sally_met, ls='-', label=r'Full Kin.'),\n",
    "    Line2D([0],[0], color=col_sally_2d, ls=':', label=r'Full 2D dist.'),\n",
    "    Line2D([0],[0], color=col_stxs_5bins, ls='--', label='STXS, stage 1.1'),\n",
    "    Line2D([0],[0], color=col_imp_stxs, ls='-.', label=r'Imp. STXS'),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Projected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l+t3V+tUQpzhrNJoSm386ioawsWldSE36S0Tc314MmNtWTF75sMgryKlyWca8\nAnFxTbIwFRSKf6PeFRMHNhNfPff4Vbfhpz0hpzhL9UVNbmwaEBBgXdwH6nZj09p24bc/2DzgPpwU\nZ/oc70J4y7dwc/GocnlZO45x4vOt7N2cwP6o9xna7F6GvL9EjhW2A9lHLNW5q31j07pwKmEz3z05\nitYtWjHlva8QJwrx6le9qeNe/f1Zd3oX+44u55NtG/DtdI1cSc1OZBA3cAkJCURERFiXfgSsrUOt\nVktycjJ6vZ74+HjrThrFrwkKCkKtVls33bQsEF9ZV8vGpo5i+fEVbAlzI2bwYrw6doCOVS8rPeEg\nntc25/zfv3P7QT/uengmzTpfb7e6SnKtiSqtNTF+dUiF59ysGciIPk9bzx/ULYS7uw8nPVvP1G8r\nXoi99PkhvUPp6x/EcX0K7dX+FV5fHn9/f8LCwqxr+MJ/awDHx8ej0WgwGAz4+voSHx9vDaKgoKAS\na/RarklJSbGWExISQp8+fUqUXRkxMTFER0dbF10PDQ0tsVtGaYqilKjjlfj6+lp39rDX5zuiSyk6\nvPw1HNu9i4RPFjJ89CS8bulU5fJMOQUcHruCHa134PrhWnx7dEO79QdcPCs/5K0hkmtNSDaz9I1a\nWouljy3f6/X6EteVbl2GhYUxe/bsKtcjNDSUpKQkhBDExsZaRzBUV1xcXLm7htTG59eWXUsWMSPs\nZk58u4EON/TgiTnvViuEARQPF9YN3MpnrTZw8Y7WDPhhrQzhGiC7Jqrg3RGV22yz+Pk+jdSVur70\n+dVpDV9JWfvGWXassJWlNV1ZBoPBurWRRfGNRava3QH/bWwaHBxs3djU0u1QG59fWxI/nM87urmk\nB7lz3lRAO8C5sXuVy8s/nU72X6dYmfcZmwwJBPztwdilG3Fv2tR+lZasZItYshvLjseVdbVsbFpX\ntkdNZ+aJuVxq6corppfo3uv2apeZ9vVuVv34HnFHVnH9Xjemvp1Ao9at7FBbqSwyiKUqK70RaOmh\nYZXZJLM+bGzqiHb99BWRGdHkqt2YM+JLBk+ZiHt732qXm3jzcWI7/ED/zvcw/4NdNOnYwQ61lcoj\nuyYaCJ1OR3R0NDqdjtWrVwPmFmJycrJ1U8+oqChCQ0Ot/bwRERFERESQkJDAmjVrSExMRKPRWB+O\naTQa4uLiUKlUJCcnExQUVOKf8aU3ySxPfdnY1NHoLuxj2r6pCE8PpvpOo0eHW6pVnig0cnF5En91\n2sf8P19TsAbUAAAgAElEQVSne5OuvHrPAtxcqt7FIdlGjpqQO3RUSVxcHNHR0cTHx9d1VRocYTLx\nzevPsLjFL3h4NGaa/xyu63crTp6u1So3Z+9ZNrw9m4W91uF3WjBn+Cra9r3NTrVumGwdNSFbxJJU\nj5iMRta8MIpP22zDO8ebBU+so5VPe/sU3tKFL6/5Bt/zJmbet0SGcC2SfcRSpSUnJxMdHU1iYmK9\nGt5V35kKCvj90afIXP4DnS6oebvpHLuEsGHDHjJ2pPDrkIe5e+Elpt/+Phrt3XaosWQr2TUhuyak\nesCYl8eqcQ/Cil8JmD2dLqFhOPt4VHudB1FgZMeET/i9zT/cdq4pLfrdQvuHhtqp1pLsmpCkq8ix\nCwf59IZdDB5/B9eHv2y3ck3CyIG7DGw/kcjoZ7/Hz0sOUasLMoglyYEVZGZizM6hU+vuPOP0NLcO\nGGyXcnMPnsew+QCHNsfgs2MnC5N+kCFchxw2iBVFCQd0gBpACHHFzsii8wH6ADuFEI618rkkVVJB\nRgZRzw6g5WkYs+kPgie8ZreyT/+xm+kHnyXwt3NoJ87Ar0X1pkJL1eOQQawoSiQQL4RIsBwrihIs\nhChzdoCiKJFCiIhix0mKoiDDWKqvclNTmTlxANt7pXFzi84odlxu8lJuOm8df5mzLbIY+vxjdH3l\nebuVLVWNo46aCLWEcJF4oMwpToqiqIDS29BGA/V7VW+pwcq5cJGpk/qzvXsafenFlCeW2WXxdeOl\nXFKmfc34BYM445LGmOxBDJvyvh1qLFWXw7WIFUUpa+FZPVDeFCc1EKkoSpwQQlfsfMdfJECSSjEJ\nE1Nn3M3Obhlo1Xcy+YlPcVLs017KOp3KfNO7HHU+w1jXYEaGvyt313AQDhfEmINVX+o1A5hbv0KI\nEst7CSF0iqIEFgthgCCgfm6tIDVYRpOReZvC2dn+HHccu4HJLyyxSwgLITCaCpkaP5Z93imED5rH\n3d2H26HGkr04YhCrKHpAV4wlmNUUhXJxQohky/dFXRXDgcCyClcUJRQIBWjf3k4zkiSpmtKP6Ji2\n/HH+bnSU0QEvMOqRMJzcqv/rKYwmTs3dzPzst/nb+wghhQNkCDsgR+wjLmtBW0swl24plyUWGFiq\nhWwlhIgRQvQWQvT28/Orah0lyW4yUlKY8M6d/N3oKI9dO44xA8Jxa+Vjl7JNBUYWnJ/O395HCDrm\nT9iEpXYpV7IvR2wRl9W/qwIo3S1RWtFoi8jiLWRJcmQZBw+x+c4htOxaSC/3AYx+OqLii2wkjCaO\nfbkG/t5P//xrCJ+3CScXR/yVlxzu/4oQIllRlNKBq6aCPl9FUYIpOeQtQAay5MjO7vqLr0YH45cv\nmDBvIz7XXWeX7giA1Li/Of5nEkdXRHF3t77cPmc1zm5udilbsj9H7JoAiCkKVosgzEPSAFAURVP8\nfUVRtJjDOlFRFJWiKBpgRK3VVpKq4KP4t/k6JJ8+az7Dt0d3u4UwwA953/NG8yi6bVhM/69W4ezh\nYbeyJftzuBYxgBAiQlGU8KKw1QAppSZzaIEQIK7o4ZxlUdziW+7atjWEJNWy3IupeDRrStjdM7jp\nuw207dv3snN0ZwuI25bF7/vyMJoEzw/x5q6ARhWWbcot4MxPP9F6TypD7x7DNT372W34m1Rz5Opr\ncvU1qRbptm3m47lPEDZkBp3HPn7Z+xfSjXyyIYONSTm4uyr0u96d3UfzUXs58+n4Kz9czko+yYaZ\nb2Dc8C2+3buh3fo9Lo0b19StSDaQq69JkoM5tHUjkzc8xaX/KRh8S25JbzIJ1v+ezScbMig0Ch4d\n0IRRA5rg09iJJ9+7gLtrxRMvvtkdzaK+Cdzp1IpJC9fJEK5HZBBLUi3Yv/k7piQ8TbZaIdw3gt7D\nQqzvnUszMuPLNJIO59OnizuTHvKhbTPzr6bubAH7TxTw7L1e5ZZdqM/mm18/YtH5T+lw2p2XFsht\n7+sbGcSSVMOOHkxm8k9Pk+vtxMzBMfTu+d/uF1t35zBrtYFCI0SE+HD//xqVmHa8LCETDzeF+24q\nu3+44EIm69+YRsw1q2l3zp35r2zGq1WbGr8nyb5kEEtSDTptOEZEfBj5jV15o81b1hAuKBR89F0G\na37N4rp2rkx/1NfaCrbYdyKfTX/l8NjAJqialL362o5zP7OocyzXNetJ5LPLadzIPhNBpNolg1iS\nakjS118w+9hcCp1NRN29jO4B5s04L6Qbee1zPf8eKyDktsY8f583ri4l+4CNJsHctemovZwYfWeT\ny8ouTM1i6x+rmL17Gh0ad+CdkSto7O5dK/cl2Z8MYkmqAX+uWcq0PW+guLny3rjvuab59QDsOpLH\na5+nkZ0neHu0LwNv9Czz+rhtWew7UcDUUSoae5QcfiZMgu+mvMXHnb5EbXDi7fs/pIkM4XpNDjCU\nJDs7+fUG9oRNolmqK293mGMN4e/+yOb5T1LxcFOIebFZuSF8/EIhC7+/RN+u7gT1uvyc9MOHWKla\ng1e6wrxRa2nd9cYavR+p5skWsSTZ0R8rYjj85GRa39iT6Ndj8WjZDKNJ8PF3GazamkWfLu68PdoX\n70Zlt4EKjYJpK9Jwc4GIEFWJB3emnALS/zzI1keDudfFif5rV9CuR5/aujWpBskgliQ7STOcZ9qx\nWXQd2Zx3PvgaV29vsvNMTF2exra9eQT3a8yLQ71xcS5/TPDiHy+x70QBMx7zxc+n5AO6P5d+yY8p\n67h/1Eg6PvIQvj261/QtSbVEBrEk2YGpoACVjx+PNnqCgAf64urtzcUMIxMX6zl8uoAJD/jw0K1X\nnmDx54FcvvgpkyE3NeLOniW7JLJPnuJAmyPsFSd4YUw0vo2b1+TtSLVMBrEkVdOPC2dwdNUaHluT\nwCMvTwXgyNkCXlmsJyPLRNRYNX27XnnRnQvpRqauNNCxhQvjH/jvwZswmjj9+c8kvzMRNbDor814\nNy69b4JU31X5YZ2iKPIxrdTgbVw8g3n6hWy5KdO6wtnfujye/vAihYWCj59rWmEIFxQK3liWRl6+\nYMZoXzzc/vu13P31d7x4YAzHxFlu/nyhDOGrVHVGTUTarRaSVA9tiJnG/AsL8clzZ+agL3Dz8ebn\nXTm8HJ2KbxNnYl5sxrVtK14D+INvMth1NJ8pw1V0aulqfV13OInX/32B3EYm+n30Pn63/K8mb0eq\nQ9XpmuijKMqDQoh1dquNJNUTG2Le5r3URajzPfnwxZ/wa9qO9b9lMXddOt3auzJnbFN8Glfczvkh\nMZu47VmM6N8YbdFQNSEE+5Z9z2unX6YQI9NviqLXXXKfuatZdYI4RAhxRFGUgUCqEOJve1VKkhzZ\nzqNbWJDxKepLnnzwzCaaqdvyafwlFm00j/2d8VjJ7oXy7DmWT2SsgcBr3Hjuvv96+k4fP8RbpyZT\n6OHM9Jvm03ugDOGrXZWDWAhxpOi/mxVF8VEU5SHMWxVl2K12kuRgflgzj/dOf0z7Zp2ZcdcCmrXp\nyIJvMlj9SxZ3B3ry6gjVFYenWVxINzLlMz3NvJ2Z8Zjaes2pk/t4adUD5DVRmDt8Nde27FnTtyQ5\ngCoHsaIo3pbQFUKkA2sVRemlKIqvEOInu9VQkhxE7JzxRJtiaePcgnkjvqSxu4p3YtP57s9sQm5r\nzEv3e+PkVHEI5+abiFiqJztP8G7of10Y+5Z+w2tHXyTLvZA3/WfIEG5AqvOwboqiKF6KonRUFOVG\nRVHuBDoBgYqi/Kgoipx3KV01DnwYze61y2h+wZ05g1bi6arireVpfPdnNk8GNeHlobaFsMkkmL7S\nwIFTBUx71Bf/VuaHcxkXz/J6yotkeRQy2T+Cfvc+UcN3JDmS6vQRRwDhRd8bgDRAV/T9Ecz7ysl+\nY6neS35vPvvHT0U77D4ixr+HS3M/Xv1cz/a9eTw/xJtH7rh8dbTyfPL9JbbszuXF+73pd715WFu+\nIZ0dQ0dznUs2A0InM2DkCzV1K5KDqk4QRwGzgd6AztJnLElXky1bVvFO5nweHd6Xfl98Rr5wIXxJ\nKjsP5TPpIR8e6Gv7dkRf78hixc+ZPNC3ESP6m6879t3vHNy4iZxTZwibG0P74GE1dSuSA6vOw7rJ\nRd9uVhSlk6Iod8q+Yelq07V1H3pd6sagKe+TJ1yYtETPP7p8Xh+p4p4+Fe+qbPH7vlzmrk3n5uvc\nGT/MB0VRMObmsvDc++zvsJtlyVvxUjerwTuRHJldd3Gub0PZ5C7OUnm+iZyExrk13SeORxhNZBfA\nhEV69hzP581HfMtcnrI8+0/k89zHqbTzc+GjZ5vS2MOJvPNpbBs5Gnp2QD1+FL3a963Bu5Hqiq27\nOFdnivPEUscdgVSgt6Ion8gp0FJ9tWxWKO8rq4jbtwphMpGVDy/HpLLneD7TR1cuhE+lFjJxiR6f\nxk7MfUpNYw8nTm1OZHrYLZz5ZRua3v1lCEvVGjURpShKqqIoRkVRjJgf1P0ETAb8gSn2qKAk1abl\n857jc9fvaXuxMS+8uIKsPBi/KJX9RUtTDuhhewjrLxkZH5NKoVEwf5yaZt7OGAznmLTlEf64OZPW\nH06m06gRNXg3Un1RnYd1MUA0YJAP6qSrweczx7HM7QfaZ/ry4Vu/grM3L8f8F8K332B7CGfnmZi4\nRM+FdBMLwprSsYUrFw8e4eUV93C+STbP+47hztDJFRckNQjVGr5WNJFDkuq9hH/Xscx9I+3ONubD\naeYQfmVR1UI4v1Aw5VM9h04V8M4YNTd0ckN/6BjhK0ZyziubZ5s8xrDQt2vwbqT6pjpBPBBwmAV/\nFEUJx9w9ogYQQsTUbY2k+uL77UuZ/8c0unnfyLTBH+Dk4s0ri/XsPV7A9NGVC2GjybzV0c5D+bw2\nQkW/6z3IyLhIRMLjnPQ+z5t3fcxtN9xbg3cj1UfVCeIpiqKkCSF+tlttqkhRlEjM61wkWI4VRQkW\nQsTVcdUkB/f5zKdY5raR65v2JHL0apzwZOLiVHYfyWfqqMr1CQshiIpL5+ddubxwvzf33tSIc3/u\nZeK3D3LaK5MpfWfJEJbKVJ2HdQowp+hh3U5FUWYXn9Zcy1OcQy0hXCQeCKvFz5fqoX/nLeC7i9/T\n9rQnsx5choviyauf6UlOyee1kSrrspS2EELw4bcZfPtHNo9rm/Dw7U0wXDjLhNVDOO2VydONH0F7\n62M1eDdSfVadII4QQvQWQjhjHiERCMQVBfOP1NLC8YqiBJTxsh7zFGtJKtPeeR+wa+LrjD1xK++O\n2Yinl5q3lqfx+/48woN9GNzb9skaAJ/GZ7JqaxbB/RoTercXxvx8tj/9DDnk8JRzMCHPRdXQnUhX\ng+rMrNtc7PsERVE6CSEWKYriAwRhXouiNqgxB29xBgBFUVRCCEPxNxRFCQVCAdq3b18rFZQcy5pP\n3+SH3TE8FjKE21d8Bs4uzPjSwJbdubw01JuhN9s+bRngy62ZLP7xEvf09uTlYd7o/9FxZu13pK/b\nzMyP5nLds6E1ch/S1aM6y2DeWGoGnQDrkphxiqL4VrdyNlJR9ICuGEswqykKZYuih3gxYJ5ZV+O1\nkxxOodENxa85vZ5+B8XFhfnr09mYlMO4u70Y0d/2BXwA1v+WxYJvMhjQw4PJw1WYRCGv/jgWtZM3\nE7Yn0KzvTTV0F9LVpDpdE1fsgxVCLKpG2ZVhKOM1SzCXbilLDdiupYvIPnmKh8e+xkcvb0Xl346Y\njZdYuz2bR+5ozBPayoXwd39mM2dtOv2ud2fqKF8UYwGJ416ib+c7uGvoGBnCks2qFcTFH9Rhnk1n\nVYtTnPWYW8XFqQBKd0tIDdfyOc8x6fQ0foh6FUVR8Gzjy6qtmXyekMn9Nzfiufu8UZSK1xO22JiU\nzew1Bm7q4s6Mx9QY/t7PCu2d6D5dTj99JwYEPFCDdyNdbarbIlZjfijnz3/BvFpRlKeopSnOQohk\nLm8Vq4GEMk6XGqBVH4znU/E1zfUe9B9tns32Q2I2HxR1KUx6yKdSIRz/Vw4zVhkI8Hcj8kk1eXl6\nJqwdxirtEa6Lmso1Tz1eU7ciXaWqHMRCiEVCiHQhRJwQYrgQQg10xhyAw/lv0fjaEKMoSnCx4yDM\n06+lBu7LBa+wOCeWtpnefPjqdvz6dOW3fbnMWm2gd2c33hrli7MNO2tYJPyVw/SVafTUuBH1pJrc\n7Is8P+c2Tvlm8ZhXMAGTXqnBu5GuVtWZ0HEZIYQO8+y2RYqiTLJn2RV8boSiKOFFYawBUuRkDmnL\nX+tZnBtLa0NjPnx1G15N1Ow5ls/ry9Lwb+XK7CfUuLlULoSnrUyje0c35oxVk3XgCJO/Gs3pJpcI\ndQ9hxIvv1uDdSFezEkGsKMpCzFsebbLDjLlanWIshJADNSWr7Yd/ZOaWV+hY2J637/oIryZqTlwo\nZNISPWovJ+aPMy9JaatNydlMX2nghk5uzHtKTWF+Gm/8+SzHGp3itTveZ0DvB2vwbqSrXYkgFkI8\nrSjKGsy7bgjM3QwRVVnoXS4IJNWVdR9O4eOcFXRp1YPI4BV4efhYl6QEeHdcU9RezjaXtzEpmxmr\nDPTUmFvCOWeO8/KngznTOJM3h3xC/6731dStSA1EWU0CPRAkhHAWQgwqHsKKovgUPYiTJIeUtORj\nPsn8guYXXZl13xK8PHzIyTMxaYme1Esm5oxV087P9h657/7I5u1VBnr5uzF3rJrMQ4d54cM7OdPo\nEk97jpQhLNlFiSAuGoYWXnzWXHFFrdxYRVHeURTlztqooCTZ6siK1RwYN4X7t7cm8vblqFQtMJoE\nU1cYOHCygOmP+tKtg5vN5a3dnsWsoiFqc59qipvIJ3ZeGOf98gl1DiH4hTk1eDdSQ1K6RewvhMi4\n0gVFIyUmA74yjCVHsWHpTGLffpYWd9zGM6u30O6eWwD46LsMft1jnrp8W3cPm8tb+XMm89alc2s3\nD94Zo8aYco6dz07E97MkZni/wvCJ8sGcZD+lgzjV1guFEGspNYlDkuqCEIJ1577jn/tU9F35GS6N\nzAv2fPV7Fl9uzSL41saE3GbbrDkhBIs3ZvDhdxkM7OnBrMd9yU4/x8urHsLQtjW3fBHDzc/IIWqS\nfZXuLCu9ZkNF1iiKMlEIMddeFZKkysg4cAjvazsTOWIV2fvP4tnSvCV94sE85q1L55au7rw01LZJ\nniaTYME3Gaz5NYv7bmpERIgPIi+XQyu+wNjKHVW/QDrdMKgmb0dqoEoHcZqiKB2FEEdtuVgIka5U\nZkqSJNnRT6veY9WGSCbcOZvrnnwCNB0BOHmxkNeW6Wnf3IXpj9o2YaPQKHhnjYHvE3MY0b8xLwzx\n5tQvSex59TX0O3Ywc+v3tLyhX83ekNRglQ7i6KKvyvy1X9lWtCRV29bYj4jUzaVxezc8O19vfT0r\n10T4Uj2KAlFP2jZWOK9A8OYXafy6J5enBnkxJqgJ5y8c45VNITRtl82kcR/R8jYZwlLNKfFTKoT4\nC0hXFGVWJcrQ2LdKknRl29cvYvaB2TQudCXylqV0uM28ypnJJJi20sCJC4XMfFxNm6YVD1PLyDbx\nckwq2/bmMuEBH568y4vzF47w/Cda0r0KGNL/GfzHPFrTtyQ1cGX9pI4DkhRF0WDegqjcURSKonTE\nvGWSJNWKf/duZca/0/AwubDgyQ20bfdfa/jzzZls25PLy8O8CbzGvcKyzhuMvLIoleMXCpk2yhdt\nL0+ObvmTyX+MI8M1lwktn2PQmFpZu0pq4C4L4qJ+37uATcARRVGigZjS/cZFe9LFAiG1UVFJOnJx\nP69vfh4P0YiprWeUCOEd+3NZ/OMlBgV6EnJrxTtspJwpYMKiVDJzBfOfakrvLu6cSTvOlL+eJcMp\ni1lBiwkMuLsmb0eSrMr8t5sQQqcoSiCwGJgMRCiKYsC8oI8OCMDcJTG5KtOfJamy/tq0hmn7puHq\n4Unk/Z/TqUcv63vn0oxMW2FA09KFiOCKl7RMPJTHq5/p8XBT+OT5ZnRu7crxo//y0rL7yW/izJzh\nq+jWoU9N35IkWZXbiVY0iy6kaHPOMGAg5g1CfYFkYHhRn7Ik1ajdP33FG9tfARdnFoz6ivbNOlvf\nKzQK3lqeRoFRMPNxNR5uV34490OieUH3ds1cmDdOTUtfF/as3cCru54l38XI5BbhMoSlWlfh04yi\nhdfl1vRSnUjdmcTfDz+H3wMuPNX0lRIhDLB00yV2Hc1n6igV7a+whoTJJIjZeIllmzMJvMaNWU+o\n8fJ0It9gYP62l8n3NfJqx3BuG/ViTd+SJF3GrusRS5I9Hd7xEzvvexxv76YseGk9jf07lHj/H10e\nyzZncm8fT+4KaFRuOTl5Jt5eZd6lecj/GjHpIR9cnBVyDp9l68gR3KpLo2tMJDcHj63pW5KkMskg\nlhxSfmEuUzY/i+9QJ+a//i1NOpUM4eyicG2pdublYT7llnNGX8jkT/WknCnkhfu9Gdm/MYqisHtT\nPKt/fJ9hfW/jhumv0uYeOWNOqjsyiCWHI4TAzcWDEc3G0ORMLo1atr7snIUbLnEmzcjHzzYtd9JG\n4qE83liWhtEkmDNWzS1dzYv+5J6/wN6cJPa3PUnbxz6iZdMOZV4vSbVFBrHkUE7uTuTHV19kxIKV\nBIdFYMorxMm95I/pnmP5rP3NvJhPT83l44VNJsHKLZks/P4SHZq7MHuMmvZ+LgghOBm7lX+mhuOW\nkcHSvb/i7e1XW7cmSeWSQSw5jLP7dzHhiwdJ/5+JoCPHadKp42UhbDIJ5q9Pp6mXE6F3e11WRnqW\nibdXpfHbvjwG9vRg8nCVtcW844dYZu2ZgLYwn5HL42QISw5DBrHkEC4c3sf4JfeT2kwQevgBWj11\nY5nn/bwrl30nCnjjYdVlXRKJB/OYviqN9CwTrzzgw0P9GlnHFP+R/C1Td02gUb4g6KNFtLjjthq/\nJ0mylQxiqc7pj6Xwyif3cKGFkYk9X2PQpDCUMlZME0KwbPMlOrZw4a4AT+vr2Xkmon+4ROyvWbT3\nc2HOWDXXtjXvxCGMJn6KjiEqazaNDYJ3Bi+my+1yxpzkWGQQS3UqvzCXGb+Ec7plIWGuj3P3oKfL\nPffQ6UIOnS5k0kM+ODspCCH45d9c3v86g7NpRoL7NebZ+7xKTOr4fc+PRGZF0tqtLVOHz6FjQN/a\nuC1JqhQZxFKdyTx/lpm/TOLv838SWjCG++555ornHzxZAMB17VzZsiuHVVsz2X20gE4tXfjkuaYl\nHtwJo4n47cuI+uNNOqm7MPeRWHw8fWv0fiSpqmQQS3UiJ/UCE2f052CHHF7WzuL+nqMrvKZ1U2cA\nxr53EYAWvs6EB/tw302NcHH+ryvDlG/ky9cmsKT5WpqfgalBs2UISw5NBrFU6woyMlj+2H0cuiOb\noSf6MaSHbev99vJ348NnmvLvsXyubetK4DXuJQLY4kDyzyz1W0vLkzDnyfW07tnb3rcgSXblsEGs\nKEo45pXe1ABCiBgbzgfoA+wUQkTVbA2lqijIzGTLPcE0+eMQb/5vAjcOfqjC1dIsFEUh4Bp3AspZ\na9iYmUfaP7vZO/QpBnd1Y/TC9TTv1sOe1ZekGuGQQawoSiQQL4RIsBwrihIshIgr73whRESx4yRF\nUZBh7FiEEEx/axCNDft5ZOUS2oc8YNeyP3sngmaXGuHToxuhiz7Ay19uHiPVDxVv6FU3Qi0hXCSe\nclaAUxRFBaSWejkakFsrOJjcwhxOtRJk972BVv21di374p5/2dbmL3b1voD2pw0yhKV6xeGCuGj9\n49L0QHm/uWogsmhrp+Lnq+xdN6lqTAUF6FatwcPFk/dHruOF/nNwad7ELmXnnUknJSaWn2++i3Gn\n+vHqqE/sUq4k1SZH7JpQYw7S4gxgbv0KIQzF37DsJiKE0BV7OQgo3qKW6ojJaOTdiYP5O+dfZrdp\nQdv+t+P1aEu7lC2EIPK9RzlyIZEH2rem13Mv4uzkiD/SknRlDtcixtySVZd6zRLMpV8HrIvXA9au\niuGU35URqihKoqIoiRcuXLBDdaXyCJOJj8OHsaH9AbzcWuCZ3dRuZZuEiZlLR7Ol+d/4Cm+Cfv4B\nz1b2CXhJqm2OGMSGMl6zBHDplnJZYoGBpVrIVkKIGCFEbyFEbz8/uehLTRFCsGTKCNa1/ItrC9ry\n1h1L8L3jWruUfWnXCabOG8lPhq38b68Ps+Zsx7NFc7uULUl1oU7/HacoSjzmTUgtwii7f1cFULpb\noozyIoHI4i1kqW7Eb1rEqqa/0ym/OfMnbcbTrfwdNCqjwJjP7G0T2aH8TkjLYJ588k3cfeVkDal+\nq9MgFkIElfV60Y7RxampoM9XUZRgSg55C5CBXDeSj29n3r5I2mY1J8I4xW4hnJOVyZQlD7CrYD9P\n932V4bdceUq0JNUXjvpkI6bUuOEgzEPSACgaIRFgeV9RFC1FYV3UR6wGRmDebVqqRT/Mn8ICYmmj\n7sisWz9E3aKVXco9u+kfXt/8OLpmFxm8vwMh4+V+ttLVwyGDWAgRoShKeFErVwOklJrMoQVCgLii\n4I0vej262DllTv6Qas6WD2fyftYXNMaTOaEradqkhd3K/njnFI40u8iwff489/GPKE6O+HhDkqrG\nIYMYuOKsuKLpzjFF3xsA2+bISjVG99kKjk6KxP/Jtjxy/nF8Cr2rXaYQgtx959B9vYKOM7fT8ZF+\nPL7wK5zd3OxQY0lyHA4bxFL98c+qT9n19Hja9x/AI9OXUXA0AxeVZ8UXVkD37Ta++Gku97sFct2D\nIdy8cCFOLvJHVrr6yJ9qqVoKjPnMPjYX77AWfPT2cly9vXBrWv729rYSQnCgyT4SO+zn0VGz6OV3\nvc2LA0lSfSODWKoyk9GIq7MbT/SaRGHSPrJ3nMLnruuqV2ZuAWe++J1TievIXBVLzM7vad28m51q\nLEmOSQaxVCUnt//Cd689zyMxa7l70BNk+Z2g0Y1tq13u7u0/8+bJMG7bbGDgMy/QqotcxlK6+slH\nzz9lZscAAB3eSURBVFKlnU9O4tWVo1g7OA293rxbRuOAdmVu+GkrYRLsOvY7kxNDEcZ8ej0exo3v\nTJPdEVKDIFvEUqXo9+7jtYUPcPJaePTQYJzTU+Hm6pWZf8rAj/M+4MPWS2mkz2eS37P0nfi6fSos\nSfWADGLJZplHj/HmnHtI6S54stuzDB/5DE6e1R9K9tu5n/mg9RLaeWuY0Plprh863A61laT6Qwax\nZBMhBJ8fXMze7oUMdbuHR++u/rr7eUf1JFz4jvlbX+Navx5EjliBl4dcRlpqeGQQSxXK0+tZs+cz\n1u7+jEHug7l7UyAFIy7h2tyrymXm6i6yZNZ41nbdQpvDRiZcFyZDWGqwZBBLV1SQkcH8ZwcS3+ci\nA697gImD5lMwKK1aIQxwLP8w667dQsc9Rt4ctpCO991vpxpLUv0jg1gqV2F2Nt+GPMjmOy/QvbAL\nE2+bhbOLC86dq7aOsxCCtLh/cLmuMQcfeoYhroWEvL+cNtqBdq65JNUvcviaVCZjfj6/Bo8mN34n\nr3g8xbhtD5H546FqlZl7Ss+7f7zJr79+g5val1GLvpIhLEnIFrFUjvURT3Hg5M+MiH6fa8Y9Qe49\nF3DXVG2rI2ESKE4KGZlnONc1l7Rubox6ZrMcIyxJRWSLWCrTnwE5/Du6HX43DQDAo7MfinPlf1wK\n03M4MGEVhxev5dd+9zLmn+48evMLMoQlqRgZxJKVEILTP2xCCMH0kcuISHuF1AW/IYymKpd5SVwi\n0nc+M/98ERdvL254+QU71liSrg6ya0Ky2jFrOsv+/piX0t/lupGPcMOUkRjTc6rUEs7VpZKuymHi\nyoc463aW+475cte2TTRq07oGai5J9ZsMYgmA3R9/xLsXFmK4yYNLbh4IowlnL3ecvdwrXVZhahY7\nX13MB31WkZ6XSvAvLXl8+UY8/JrVQM0lqf6TXRMSh1asJPKfGaS2dSG8y3S8Y86TlXiiyuXpCnXM\nu2U5BR6Ct2+ey5gvE2QIS9IVyBZxA5d19ixR8RM5fYMLE+98B22vUWT7n6FR98pt+ilMgtTliRy5\nLpW3fn0OTzx4b/TXtFNraqjmknT1kC3iBkwIwdK9H5NygzOPdwojqLV5dltlQxjAmJHLz7/H8drW\nMDzO5jAm8XraqjraucaSdHWSQdxA6f/6h08+fZ71f33KQ73Gctu3Gs7M/anS5Ziy8xFC4OTtzo9t\nEvA9kc9zqYO454tVcqdlSbKR7JpogDIOHuLjCUP56V4jd3YewjMD3qSwcwY4V25sb2FaNsdeWU+j\nwddwPGEFt35xiC6hY7ll8TwZwpJUCTKIG5jsU6f56a5hHL7XRM9mgbzQLhwnxQm3dpVf+czZx5Nv\ne24nJXM1T/ccTEDLcG6Y9pqcrCFJlSSDuAHJS03l57uGka83EPnEesQFV85O2Yjr3KGV6hfO2HqY\nRje2AZf/t3fv8U3X9x7HX9+2tEAF0kC5eIWgztvUpUVkygaSiBsc3bQF9Xg5Hkfqzo46dTar7njb\nHLYHN486twbZ2bwNSLxvgiY6mY4ppBFU9Ig2XgDl2gaoXFrS7/kjSZekaRuhzS9tPs/How+bX36/\n9FvbvPvl8/te2rEcdzwFQ0dz4rQq8pT0goU4GBLEOeTNx+txf3Mjt85dyJhJZ6APhBlSWMyQk8em\n/Rpt21r45NcvsnlaGOX5M/vfeY95wbUSwkIcgqwNYqVUNRAEzABaa9dXuLZea13VV23rrwbNOoO9\nBeMonDCe9n1t5A0exAjb8WldG1u4Z09xK78572k+3rmei9/7EtuiRRSNPLjFgIQQEVnZjVFK1QIB\nrbUnGsATlVIVX+Ha8j5tYD/SHg6z+tqbCL2zjikTbTxy9evoBe+x6fZlaK3Teo0DO77k0+ufIvi3\nN/nPR2cT3PkBM5a28Z3FSzm64nt9/B0IMfBlZRADDq21L+6xF+ixh6uUktkDcbTWrLruJh7c8zhP\n+x4AoKhoCKVXn4n5YmvaN9XyigvZNGwr1WuvoWnXFmY/Dv/+wDOMO1fWEhaiN2RdaUIpZU1xuAmw\npXG5jUhop3PugPf2nb/kf3f8iQ2TChljm0b4y1byiwspLj8qretb3viE4rKjeGebn3vGPcCQwmLu\nr3yO0RcOYdhE+ZsnRG/Jxh6xmUjwxgsBKKW6HGOllLIBS/uwXf3Kh799mEf+cR8fTirkijN/zDnF\nNoJXPkbL6s/Sun7fh9vYdMdyli1xcbP7Uoq27qXuHBcTx54sISxEL8vGIDYRvUEXJxbMyccTrtNa\nh3p6caWUQynlV0r5t23bdrBtzGq6vZ3nV/2Bt84dzHknzeHKb95IQclQDps8nsE97Den2yN148HH\nlbL2mn3cu2U+Iz/ezyXLRjPmsK8+9VkI0bNsDOJUYRoL4OSeMgBKqQqttSedF9dau7TW5Vrr8tLS\ng9sEM9v5P3uNZaduouyos7nx3HtQSlFQMpRxN59DgWlIl9ft+2g7n/xwKa0bIj+Cgs1bGf92K5e9\ndQL/4l3OkHHpD3MTQqTP0BqxUsoLxP87t4pI2CaXIEwAqXq80Rt0PfaEc0FTYA0v/uJmHpu2gWNG\nHscdF7jYt2oTO19ez9gbppFfXNjt9fnDi2gvgoZNf2fsit3s++GDXHaejakvPkpBcXGGvgshco+h\nQay1tqc6rpRKDlYz4Et1LmAFLHE3+SYBpug4ZI/WOtgrjc1yuxuD/LnyQtxXHGBowSjmf/+PFBcN\no3nbpxzY1kJeYX7K69r3trHr1Q8xfeckBo0exmtzN/LYygdYeOEznPTTG/n6nbeQX9h9gAshDk3W\njZqIciWVG+xAfezJaC/YGh1nnFCSUEo5AIvWui5zzTXW3i1b+evM7xMaHmbwqJHcU/EYpcMi9dyS\n80/BNOukLrc7Ci1/n20L/8Hgr42h8OgRnP4GTJz1GyaMPx3mn57Jb0OInJWVQay1diqlqqOTOCxA\nY1Lg2oBKIFUIVxLpIVcDrnRu4PVnbbt3s2JWJXs/38ylrzzPNeWnUlgwmL3vbwGtGXLS2E4hrLUm\nHNpLQclQSs4/hS1H7eaut25k2k1fsv2Zlzj7mEfgBIO+ISFyUFYGMUB3PdrobLtOU567Oj6QtTY1\n4zvlC068/nJGnXlGx/Htj66m7YtdTHj44k5BvOX+v7Fn7SbGP1TJmi1vctvKeeTt2seRK0PYH/qV\nzJYTIsOyNohF93R7OyhF0ZHjGPq9qbSahic8f8RtM2nbvLsjhLXWoEHlKYZPP46i8WZe+uhp7n2p\nmpImxbn1u7H/ZhHHzLnQiG9HiJwmQdxPram5g71btnDmooe444KFndaNyBs8iKLxkVF/7fva2HTn\ncorLj8Z80WkM+fo4lrYs5pEXf82pI7/BlN9+xLf/9HvG2qYb8a0IkfMkiPuhD+7/LSv/eD+B/5yA\npeULxo44EqLLRrTvaWXjHcsZdcUkCscNp2BkMXmDB1FQMpS8oYW0hVu59yUnL73nYebJldxov4e8\nS5GREUIYSIK4n/nsyWf5+y01vPLTUhhJ54V7CvJo/bSJJvca9qzZxMQnLie/uIhx1TPYtbcZ55OX\nsWbDP5j0SpgLCo9n0HmFkHpkmxAiQySI+5Gtr/+D1y//ASuuG03LYWHu+97vGTP8iIRz8goLmPDw\nJbR+HuKwM45OCOrFq3/LuxtWMW3xPsrbjucYuSkn+lggEADAarXi8XgwmUwEAgFsNhtWq5VQKITP\n56OiIq1VbgesbJziLLoQ3rsX/xWlbCjdy80zF3DiuG+kPC9/WBFDvjYG06yTyRtaSLg9DMCZq4cw\n676dnDVsMrYVL8iUZdHn/H4/VquVYDBIfX19RwDPnz8fAJPJ1BHOuUyCuB9ob2sDYFXpRtYct5uL\nJ/0HthO/n9a1r37wPNc89l02vvUm66rvpGzK+Uxb9iSFI0b0ZZOFwOPxYLNFVqS1WCy43W4g0kue\nO3dux3k2m40lS5YY0sZsIaWJLNfW0sLL58wmfPlZPHjgCSZPOIerz65O+/qSoaWUDB3F8BOOZ8aK\nZYyacgZ5+VIUFn1vyZIlCSUHk8mEx+OhsbGR6urE3+FQaEDPu+qR9IizWPuBA/z94qvY8NFaFoaf\nZtyIo7l11gPk53UfpHvb9rBi/V9o3bmTpnm1XDf4MoYPKWH02VMkhIWhKioqsNvtVFZWJhw3mUw5\nHcYSxFlKa43/2pv5/C8v0nLHbNry2vn5BQ9zWNHwbq/buutzrl98ET//849wz7az+eUVtIV2ZqjV\nQqQWCATw+SLrdsVu3MUbOXIkTU0pV7nNCVKayFLvL7ifj363iJOcN3DJtXdQGfqEI0smdHvNu5v8\n3P7cPPa17mH2M4UMWrORqX9xy95yok/U1dWxY8cORo4ciclkwmw2U1FRgdPppLa2NqGH6/f7CYVC\n2Gw2QqEQFkviLi+NjY2Yzd3t+9A36urqsFgsHX8EHA5HxtsAEsRZS+Xns+eabzHy5itQSvUYwsve\nWcyvfbdQOnQMMx/czqjdRUxb8QJmq6ygJnpXIBDoCFur9Z9bTAaDQcrKyhJu0MU4HA5cLhcejwev\n19tx4y6mqakJk6nLndD6hNPpxG63d7TX6XTi8XiMGUqntc7Zj7KyMp1twm1tWmut97ft03Prz9C3\nPn1Vt+e3HWjV/+P7mZ6+4Ej9k6WX6NCXTfrtu+7Ru4Mf931jRc5xu93aYrHo5ubmlM9XVFRor9er\ntdba6/XqhoaGtF63urq619qYLpPJlPDY6/Vqm83Wq18D8Os0ssjwMDTyI9uCeHfwY/3ssafpza+s\n0FprvXnnRr2jZUuX5+9o2aqv+9OFevqCI/UvfnWR3vHuugy1VOSixsZGDXQbrsmBWl9f3+Prut3u\nLoO9rzQ0NHQK4oaGBh3pm/aedINYbtZlidZQiBWz57B/exP/V7CRdt3OmOFHYC4enfJ8rTW3PfsD\n1m95m0u2T2HCjS8RdP0hs40WOcXpdHZMyOhKTU1NwmObzdbtZI1QKNQxqSOTmpqaOtWkY20wYvSG\n1IizQPuBA7w+50p2rf+IYUt/xp2rnNw0QjHr1Es6nau1pl23k5+XzzVTalj3X78k/PhyvnbDj7Au\nuNuA1otc4fF4qK+v7/ac5EBNvimX6vxYjfarqKqqSuu82tralCEfCoU6jdKIBbMR9WoJ4izQ8GMn\nm71/5cSFtdz1xe84dvQpnHvyRZ3OC7eH+eUL11I67HCuOuWHbLrkFsKrGrDedw8nXP8fBrRc5Ipg\nMLL1Y3l5ucEtiejpD0JPUgVtLJiNGL0hpQmDtYfDHNjdwok/uY7nx7zNntYWbvnu/zAov/OylPl5\n+ZQMLWX44BIKDjuMQtMIpj71uISw6HOxcOqup+hyJW6OEwgEKCkpwel0EgwGCYVC2O127HZ7p3KF\ny+XC5/Ph8/moq6vrCP6+YjabO5UgYo8z3RsG6REbSmtNXn4+Z/7hd7wZfIVXn/k3rjrrJ4wfeXzH\nOe26HbffxWlHnckJY09nzqCZjDjuBPKLipi27KnOy2AK0QdMJhMWi4VAIJCy3BAKhTr1JGO15Nra\n2oTXqaqqSqgzV1ZWUltb2/G65eXlzJgxg4aGhi7bc6ilCavV2ul4U1PTQZVJekU6d/QG6oeRoyZ2\nfrBeL588Xe/8YL0+EG7Tly/6lr580bd064H9Hed8EfpM37Bkjp6+4Ej94Cu368b/fUw/UVCi35h3\nrWHtFrnL6/WmHLrW2NiYcnREQ0NDp+FgJpMp4XqHw6Hdbnena7sbItdbqqurE7528uPeQJqjJqRH\nbIDWnTv52wWXsH/7DvIGDeLl959hY3OQn1/wMIPyI8tWPrvmERa9XotSipvstRy+eD1vzP8hY23T\n+UbdXUZ/CyIH2Ww23G438+bNY9KkSR09yvLy8pQz0vx+f8dCPxCpM5vN5oTRCUuXLk1Z783EdOfa\n2lrq6urweDwEg0EmTpxo2LrIEsQZ1h4Os/LSq9n9UZBzfM9x2ITxLF9azdHmY/nmxHNp+PQ16lfc\nzUfb1jFp/Le57uzbCf7oLt5/8lmOdVxF+YMLyBs0yOhvQ+Qoq9XaaVZcV7xeLzU1NR1lCJfLlfBP\nf5/Pl7IUkBzYfSl5FTijSBBn2Du3383nL7zEpId+xZhvnw3AhuYgO1q24Hj0PBq3vceY4Udy2+yH\n+Pbxs9m3dRv+hjV8495fcsINP5KasOg3AoFAQi3Y6/UmrEOcaiwvREZExNeVc4EEcQaFW1vZ/PIK\nJl59Bcdec3XHccfUW3A3LGRQ/iBusM1n5smV7F3/MTocZsiY0cxa9yYFQ4ca2HIhvppgMNipRxsI\nBBICNlbqgEjv2Gw2d5Qkcm3rJBWpJ+em8vJy7ff7M/o1w/v3A5BfVNTlOZ8ufYo3rryGE6uv59Q7\nb81U04ToFcFgkNraWvx+P263G7PZzNKlS6mqqsLr9XYqT3i9Xux2e8d/DRu50AeUUg1a6x4HX0sQ\nZyCI21paWHvLnZx65y0UlpR0eZ5ub+ft2+9m3S/+m9KzzmTqU48zeHRpn7dPiGxQVVVFVVUVS5Ys\nGTCliXSDOGtLE0qpaiAImAG01q4ezjcBNcDq6DV+rbXhOxJqrVlVdT2fLX6Soy48nzHTpqY8r23X\nLlZe7mDTcy9g+ffLmfTQr7rtNQsx0JSVlTFjxoy0bwYOJFk5s04pVQsEtNaeaABPVEp1WTSKhvDL\nWmun1toDxELZcI0P/5FPn3Dz9btu7TKEAVo++Yytr75O2f11TH74QQlhkXMcDgfNzc0DqjSRrqws\nTSilmrXWJXGPbYBTa23v4vx6oCG+16yUMmmtu11Gqa9LE6F33+PFSdMonTqF6cufRuV1/rvXvPYd\nSk77OgD7m5ooMmCeuxCib6Rbmsi6HrFSKtUae01Ad38mHYAv/kBPIZwJDT/+KYNGDGfKows7hXB7\nOMzan93FstPPYsNTzwFICAuRRVwuV7dLePambKwRm4kEb7wQpO7lKqViE98t0RA3AyatdV2ft7QH\nZz2xiJaPP2HImMQ1hfdu2crKS69myysrmPiDKzn8u+ca1EIhRCoul4v6+voel/HsLVnXIyZS303u\nGsaCOVWXseP/VFxNOVZn7kQp5VBK+ZVS/m3btvVGezvZs+lz2sNhBo8uZdTkSQnPbXn1NZadfhbb\nV77J5N8/xOSFD5A/eHCftEMIcXAcDkdGl/zMxiBOVVKIBXCqCeixY/HFXh+Qcu6i1tqltS7XWpeX\nlvb+0LD2tjb+OvP7rLz06pTP79/RRKFpBDNX/ZWJV13W619fCGHMLhuHwtDShFLKS1yPFqgiEqzJ\nk8xN0GXdN5TiuS5LGX3tw/rfs3Pd+5x2920dx3a+9380vbWWCf86l6MvuoAj/uU75Bd2Xm9YiN7m\n8/mora3F5/PhcDgoKyvLyJbxoVAIl8uFyWSisbERoNPY4J62sk93q/vYoj2rV69m+vTp3HvvvV9p\nTYxsYGgQdzMKIjk8zSTdjIt7jaBSKqSUsmitY6tJdxfcfSa8fz/r7l7A6GlTOeL87xJubeX9uvt4\n9+d1FJWO4qgLz6dgyBAJYZExsT3jmpqaDnlXi69i/vz5CcFbVlaGy+XqCNOetrJPd6v7UCiE0+mk\nsbERj8eD1Wpl9OjRLFmypMu2uVyulD1mq9Uq6xHHfwC1QEU3jy1Jj6sBR9L51T19nd5ej3jj8y/o\nxxmmNz7/gv5ksUc/d9zp+nGG6dfmXKn3bO56N2Yh+pLNZsv4dvUWiyVhjeKKigpdUVHR8binrezT\n3eo+1XG3253wtQ6Ww+HQXq/3kF6D/rwesdbaqZSqjk7isACNOjJRI8YGVAKe6Pl10fNjdeEd2oBR\nE/u2bid/6FCKSkex4nwbI078GtOWPcnh56Xs+AuRET6fD6fTmdGv6fV6E0YcBIPBjpXXUg0JM5vN\n+Hy+tJ7PBI/HQ2yOgdls7nbn6t6QlUEMkXDt5jkX4Eo6ZvhwtWPmXsiQI8YxavIk7K+/xMjJ5eTl\n5xvdLJHDYqGW6X9yx4dwrA2xtX972sq+p+djnwcCAdxuN8FgkLq6uoTSQigU6liQvqcacyoVFRUZ\nXQEua4O4PyooLubwmZFfhNJvTja4NUJEesNftTd3qPvBxcR24HC73SxcuDDheHdb2ae71b3VaqWy\nspJgMNhpgfdgMJgQpE6nM6FGnW0kiIUYwJKXnUxHb93UM5lMOByOjtEaVVVVOByOHrey742t7pMn\nYtjtdiorK7M2iLNxHLEQopf4/X7s9s73KFyubhczPGTJoxJiS1xCz1vZ98VW9xaLJavHFkuPWIgB\nKhAIEAqFUs4Q6y6UDrU04fP5sNvtNDc3d3o+FAr1uJV9X2x1H19bzkYSxEIMULH6cHIAVVZWUlPT\n9Sqxh1qaiO3qHP91vV4vFRUVHcccDkfCuGCv15vwB6Cn53sSDAYTHmf7PnhZuQxmphixVZIQfS02\nq23+/PmYzeaOAGtsbOwYAhab7dZXAoFAx9fasWMH0PXMutj+dl3NrOvq+UAggNPpxO/3U1NT0xH+\nHo8Hk8nU0evv6vpMkK2S0iBBLIToS/12PWIhhMg1EsRCCGEwCWIhhDCYBLEQQhhMglgIIQwmQSyE\nEAaTIBZCCINJEAshhMEkiIUQwmASxEIIYTAJYiGEMJgEsRBCGEyCWAghDCZBLIQQBsvpZTCVUtuA\nT41uRwqjgO1GN0J0Ij+X7JTNP5djtNalPZ2U00GcrZRS/nTWMBWZJT+X7DQQfi5SmhBCCINJEAsh\nhMEkiLNT3+51Lg6W/FyyU7//uUiNWAghDCY9YiGEMJgEsRBCGKzA6AaI1JRS1dFPJwGrtdZ1RrYn\nl0V/FkHADKC17vc1yYFgIL1HJIizkFKqVmvtjHvcoJSiP/+i9VdKqVrAq7X2xR4rpSq01h6Dm5bT\nBtp7REoTWUYpZQJ2JB2uB2oMaI4ARyyEo7xAlVGNEQPzPSJBnH3MQK1SyhJ3rAkwGdSenKWUsqY4\n3ATYMt0WkWDAvUckiLOM1joIlEX/G2MHfF1cIvqOmcgbPF4IOnplwgAD8T0iNeIspLUOxD6PvuHn\nAGXGtShnmYjeoIsTC2Yz0VAWmTfQ3iPSI85+bmBG0l9/kRmpgjYWzMk9ZWGcfv8ekSA2kFLKq5Rq\njPuwJT1fC9TG//UXGZWq7mgC0FpLbzgLDJT3iExxzlJKqQogFDdsytrff9n6I6VUs9a6JO6xDXBq\nre0GNkswsN4j0iPOQtE3uxnwK6VM0bvDcw1uVq5yRd/wMXYiQ6WEgQbae0R6xFkmeuOhOcVTHq11\nZabbIxJm1lmI9MBkZp2BBuJ7RIJYCCEMJqUJIYQwmASxyElKqeroqJXa6GNL8qgVITJFShMi50Sn\nLoe01sHYKAjALbVfYRQJYiGEMJiUJoQQwmASxEIIYTBZ9EfknGiNuIp/rq5mBoKxhcaVUvVaa1lz\nWGSMBLHIGdGJAAuJrBdRFb9ITHR2Vj3RUDaoiSJHSWlC5IToFNgGIj1fe/JKXdFFfNxABZFdOITI\nGBk1IQa8aE+4AQj0NAVWKaW11iozLRMiQnrEIhcsJFJymJfGuf12lwfRf0kQiwEtemOuAnCluYaw\ns+dThOhdEsRioIuNfkhr6cr+up6t6N+kRiwGNKVUI2COX9xdiGwjPWIx0FlIczia7MwsjCJBLAa6\nIGkEcdxOwEJknASxGOgCgDWN82pk9TVhFAliMdDNByzR0RMpRfekSzmJQynlUEpppVR97DWUUjal\nVEP0o6KL82ujnzuin1f35jclBha5WScGvGgIVgH25GnNRMoRwdhOwCmuNQHNyZM8lFINRHZz9qV5\nfjUwMraehRDxJIhFToj2ZmuiD4PADgCtdV0P11UQKVuUJR1POQMven6V1tqedNwCNMjoDZGKLPoj\nckJ0fPDB7PA7l6TZdtFdPboab2wndZnDFP0QohMJYiG6ZwNcSilH3LFKup4KPQcoS3Hc0s01IsdJ\naUKILkTLCY0p6r2NQGXyLLzuyg9KKTewuqdSiMhN0iMWoms2wBN/IHozztLFVGgbKXq90fq0paeV\n30TukuFrQnStks713pRhG9WpPhztJddwcPVpkSOkRyxEkmiv10EkdANKKZPWOhStE1cBTUopa6xX\nHHd+BRBMqicjPWHRE6kRCyGEwaQ0IYQQBpMgFkIIg0kQCyGEwSSIhRDCYBLEQghhMAliIYQwmASx\nEEIYTIJYCCEMJkEshBAG+3/UwCxJuNP/tgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1179c00750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_met_mean_dw,\n",
    "        fi_sally_wbkgs_2d_mean_dw,\n",
    "        fi_stxs_5bins_mean_dw,\n",
    "        fi_imp_stxs_mean_dw,\n",
    "    ],\n",
    "    colors=[ col_sally_met, col_sally_2d, col_stxs_5bins, col_imp_stxs ],\n",
    "    linestyles=['-',':','--','-.'],\n",
    "    xrange=(-2.75,2.75),\n",
    "    yrange=(-7.5,7.5),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=10.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=imp_legend, loc='upper left',frameon=False)\n",
    "plt.text(0.45*ax.get_xlim()[1], -.75*ax.get_ylim()[1], r'$C_{HQ}^{(3)} = 0$', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], -.9*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/imp_contours_dw_proj.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8v/ErJiam1fl4RTkRKjCf5IxGY7OK0I2io6NP+Ffy9PR0Z/oiOTkZvV7P7NmzO6X9rKws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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f118e9a1990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_met_mean_dq,\n",
    "        fi_sally_wbkgs_2d_mean_dq,\n",
    "        fi_stxs_5bins_mean_dq,\n",
    "        fi_imp_stxs_mean_dq,\n",
    "    ],\n",
    "    colors=[ col_sally_met, col_sally_2d, col_stxs_5bins, col_imp_stxs ],\n",
    "    linestyles=['-',':','--','-.'],\n",
    "    xrange=(-0.4,0.4),\n",
    "    yrange=(-2.8,2.8),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=imp_legend, loc='upper left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(0.45*ax.get_xlim()[1], -.75*ax.get_ylim()[1], r'$C_{HW}^{\\phantom{(3)}} = 0$', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], -.9*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/imp_contours_dq_proj.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DgrHQa/G+wn5Ks5SSsrR8Av28eWjcC4zrPZnEP2eyZNc3rD+5hl45S4nK60za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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f11799872d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_met_mean_wq,\n",
    "        fi_sally_wbkgs_2d_mean_wq,\n",
    "        fi_stxs_5bins_mean_wq,\n",
    "        fi_imp_stxs_mean_wq,\n",
    "    ],\n",
    "    colors=[ col_sally_met, col_sally_2d, col_stxs_5bins, col_imp_stxs ],\n",
    "    linestyles=['-',':','--','-.'],\n",
    "    xrange=(-1.1,1.1),\n",
    "    yrange=(-2.5,2.5),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=10.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=imp_legend, loc='lower left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(.48*ax.get_xlim()[1], .8*ax.get_ylim()[1],r'$\\tilde{C}_{HD}^{\\phantom{(3)}} = 0$',fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1],.65*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/imp_contours_wq_proj.eps',format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Profiled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3l0ooqrARFRVFVFQUkZGR5qW/YFoGrCgKoaGhREZGmu8lKCjI/N7d3d18Xmio\naXcwd3f3Evs6FJ0XEBBQ4vuVXb+xE0Jw/Km+bLof4lueACD36GVy/rmIMbvAojZsXZ1w7OqBXWsX\ndh/fyi/HtjBrxEs8uy6eXk803ymAVmHJ8rv6eAFhQODV/4aU+iwE04KPoveqouOACCDQkms0xyXO\narW6RPHPhqI5FDatD8lxCeLXh0LE21vDxchlncQbm+aI7DNXhBBCGA1GYcir2sbuQpiWLyfMfUGs\nfGySMBQWWrvLTQqNfYmzEKLCIZswLXeOKvZeDzSsIZ5ksdosbLpmzRqCgoLQarVA/RY2rWtJv+1l\n+/hJ7JzswLG/C5gy6HHG/diPC+u30V0TjGJni+JQeQi4/Oke7Dxa4DFpAFv+Xo3yXTwpb2roOetR\nuYualTTYQCw1H029sGl9uLhjF7H3Tmb79Bac6lxgXi2X0+YSxux8FDtbi9oRBiMFlzIQhQZyCrL5\nfNsSPP5JYvoDU/D7v2VyT2ErkYG4mYuNjSU8PNy89SNgHh36+/uTkJBASkoKWq3WXEmj+DkBAQF4\neHiYi25Wd7lwUyls2hCc37KNbfdPRRvaikttDTzR7llGnbsZBoHzdW2r1JZia0OHhQFgFOi+XMXY\nxWdQB9zOkC/lnsLW1CA3/akr1d3055k1QZUeM0Q92rxe/5k1QYy9IYhxfYNJy07h5e8r34i99PFB\nfiEM8w7gdEoiXTyqv2LJ29ub0NBQ8x6+8N8ewFqtFrVajV6vx93dHa1Waw5EAQEBJfboLTonMTHR\n3E5QUBCDBg0q0XZVREVFodFozJuuh4SElKiWUZqiKCX6eC3u7u7myh7Wun5DdWy3lhe0s0h1M7Bo\n/IeoV9sgCg10fPn2CvcMLs2QkcflT3/D6+EhHM0+zE9Hv+ce43COf7ycYV9/iq2jY+WNSBZv+iP/\nSZPMudGi0WLp90Vfp6SUXE9TenQZGhrK0qVLq92PkJAQ4uPjEUIQHR1tnsFQUzExMRVWDamL69cV\n/UHTSjf3Af1o1a0br9++nFt73U778NF0fGmcxUEYIPfYZTJ/1XH86J/MW/cAvyVqaTF0ALdGfymD\ncG2w5IleU33JWRMmWq1W+Pj4lPgepWYbhISElKhcHB0dXabsfGJiojD9SFVN0WbspWk0mjLXKN3H\nyjaRT0xMNPc7IiJCqFSqMlWdq3v9huT4ZyvER63dxPFVq4XRYBS62WvF6XnfVbnScnFnzx0X977b\nT4x/saMz3pTXAAAgAElEQVTY97Xllbal/yCrOEt1rajicVU1lcKm9eXIBxp2PjOb759ToW17BMVG\nwfPegXgE3ljlh2npO4+RFX+GlKwk5q6fQnZ6CkE729Hv9uaxN3N9kYFYqrbShUBLTw2rSpHMxlDY\ntCE6vOx94mfPpdeoO5iqfoyxTrcD4DqiBy19O1epLWEUpG78mzPr9/DcV/dyJeMCd29zJzh6M44e\nFe23JVmDnDXRTOh0OjQaDTqdjjVr1gCmEWJCQoK5qGdkZCQhISHmPG94eDjh4eHExsaydu1a4uLi\nUKvV5odjarWamJgYVCoVCQkJBAQElJg1UbpIZkUaS2HThib1r4Os/+oVeszw5+aPvuDMc99h63YJ\nMVRUa1qZYqPQ+tXRhK+/nzOXT3LXFhce+OZHnNu2qYXeS8XJWROyVFK1xMTEoNFozAslpLq3+e/V\nvLUtjCHdR/Pavf+jMDkLm5YO2DjZV6kdQ1Y++m//ptWk61m0aSbxp35hpmMQ429/Ahd191rqffPQ\n2EslSZJUDiEEB+a/TMKAXL4+v4p+hdcTcu4+0zJZz5bVajNzzwmurIwn2SOFMxeP8mxABHf0m2Ll\nnkvXIgOxVGUJCQloNBri4uKIiopq1jXf6pIwGtk3+znWHP+aP72cuK3nHYRceQBnD7carXBzHd0L\no6uBX+6bwr2ikLEz77ViryVLyEAsVZmPj49MSdQxo8HA7yGzWX0lhoNjnAjofjdhE97B1qb6v8Jp\n2/7FqU9bPj34Joe/jebWK9n4a7/F1sHBij2XLCEDsSQ1cMbCQnZPD2FlwQ8cvc0R/5RbmHJ2BDb3\nWLZfRHkMWfkk/W8vDje4c/nf77DJy2Lkd2tpfVOl6UypFshALEkNnaIQrT7IUTcHHhwyh2CPqdi1\ncqpROsK2pQMeb4zi9EefcX30RW77fo0scVSPZCCWpAbKkJdH3pVkWnTsgG+vO/EDHrz5uRq1mX8h\nnez9Z/m980E+2fUqbz+7hoDAu/Ac5GudTkvVIgOxJDVAhTk5xAbfx9nLR3loZxyjj5gCpRDVmyNc\nJHXjX+xIiOGz3usY0P4mOnl649BW7h1R32QglqQGpjA7m58n3se6Nnu5OMKN+2wNdFgYgGJrU+P9\nfxNHZPJ55nq8ThUyq9sUHOxkEG4IZCCWpAakICODXROCSfp1D/dNmU52ejta2LW8ZlHPygiDkeTV\nf3LKJ42Xvw/F/UIhC/q9SI/AyrdzleqGDMSS1IDsXPA8m10TeOrrKDw9+2PIzKtREAbIPZZE3Pcb\neffsl7gkFxDe7Vn6hzxupR5L1iADsSQ1EOf0J1l+fQIZ3VyxG+uLWw0KABR33jOFj4auwzlF4fnW\ns/B7+nmrtCtZjwzEklTP8pKT2f7GCyxX/0F+ZhbPHX2ETi7datxuWuwRctwKmRs3DSenlrz1+AY6\nt+td8w5LVicDsSTVo9ykK3wTPI7okRdxKnDnrTtX0YmO2DhUf7EGmPLCKesOcPrP9dzq58m9L31I\nZy8ZhBsqGYglqZ7kXLzE11PGss7/Ci0d3Hh32rd09rCsWnVlkrIucaDVdrL3bua2obPo0rqXVdqV\naocMxJJUD7LPX2DF1DFsGJOKu60Hz+sep4Njpxq3m3cyhfTtR1mc9BwnHU8y//Hp+Ly9VJa9b+Bk\nIJakevDnsZ/YMFZP21adePuBDXjYeGDbsuab7WT+fpJ/P/iIAbqD3DbtdoZ98K4Mwo2ADMSSVIfy\n9XocVCo66rtyY05/wmdE4ena3iptZ+VlsLnbL4x6YwaOGz3xe38Zio2shtYYyEAsSXUk88RJVkwd\nx20TH8V73H3MOTQHd/e2NW7XkJXPqfe2sczjPY7lHMdvynpuGvuOFXos1RUZiCWpDmQk6oi55w7W\nP5CFvXcy/QZ0pMWAjlZpO/PcZV5PfYET9leY1W4mfdoPtEq7Ut2Rf7dIUi1LP3qM2OF34HIyi2lx\nI5jc/0mrtZ1fmMeirdPRtbnCXYe9uTt4ntXaluqOHBFLUi0qzMrinWfG4ell4L5132N7xR5X7w41\nblcYBefe3c6y3Nc46HicOw515cmPt2LrKDfxaYxkIJakWrT2wHJ2jcpnZIcAPAffaLV2C/PzeT/9\nLf5qdZyAv9sz5yMttk5OVmtfqlsyEEtSLUj96yCr9mtYl7QRv4vX8/Sol6zWtqGwkGU7w4lrdZDJ\nqrt5+IOl2LVoYbX2pbonA7EkWVnK/r94/dUJJAwTBFx3L08HLqJF19YljskvFOw5nMtfJ/Kxt1OY\ndHNLvNwqX9acuvFvDkev5dDQPcy4+XkeGPJ0bd2GVIdkIJYkK0pO2M+SN+7kwDAI8BpL+B3vYKP8\n90w8r0Cw7tcsVu3KJCXDiIMdFBhgf2I+n8xufY2WTdU5kg7HcWrtR0w578MdTzxV27cj1REZiCXJ\nSq7EJ7B42UQODobhFwbylP8rJYLwLwdzeWdjGhdTDQzq6cDCyS4M6uVIZEwavx7KvWbbxtwC3l77\nBP8e3cTdfXvjv26NXDHXhMhALElWIITg7U3Pc9AP7u45hSefWoqNvenXKzXTwLJ1aez8K5fu7ex4\n/zFP/Hr+N7vh8Jl8urWp+FcxK+EMR+ZGkap8j1tXN0Zv+w5HD49avyep7shALEk1JIxGCpOyUCdf\nR7u2NzD7zkjzaPW3w7m8tlpPZo6R0NtbMXWkC3a2/41kD57KJ/FCIc/d61Zh+2mqHNLS/2FIegdG\nf70ZZ69rpzCkxqfagVhRFFchRLo1OyNJjc2lPXvYEv4kkz6PZkLPKbjf3hdFUSgoFHy8OZ3Vu7Lo\n0d40CvZub1/m/P9ty8C1hcI4X+cynxUmZ7H62Oes3vcxH2zeSHub1jh6etbFbUl1rCYj4ghAFr6S\nmq2kPXuJiAzk4B02jDTq6Tr7NgAu6w0s/DKFf04VMOnmFjx5pxuO9mXzuXuP5LLn3zyemOBKS6eS\ni1zzL6bzwQvT2dRnHyO7jqOzZw9sbWq2WbzUcNUkEA9SFOVeIcR6q/VGkhqJpN2/s3PcPQxs4cTA\ngZPo2tsHgD8T83jhy1TyCgRLHnRn1ICyI10wzZ54e30anVrbEnRryzKfr/79bTb12UevI3Y8PWmB\nDMJNXE0CcZAQ4oSiKKOBZCHEfmt1SpIasot/7OX9xZO4qWM7Rr6pwX3YDQBs+C2Ltzek0dHTlg9n\nedKtbdlURJHl2zI4c8XAe6GeONj9N1rOv5jO2t3v8sXJz/E+bkfEvFhcu3Wv9XuS6le1A7EQ4sTV\n/25XFMVNUZRJgFbmjaWmLK8ghzf3v8GfY2wYP/IlvHxuotAgeHtDGjG/ZjG0jyOvTHXHxbni/bT+\nPpHPyp2Z3HlTCwb1+m/2hDAY+d/ip1jjvR11oi2Rz23FTW2dSs5Sw2aVh3VCiDRgnaIoAxVFcRdC\n7LBaDyWpgbjwx++8pXuH/enxzEi/n2EDJ5KVa+TFr1LZ828ek29ryZN3umJrU/H83qxcI698k0pb\nd1uemuha4rMth9ayxns7PS658sacdbj3kHXmmouapCbmK4ryOuAJqACPq//1VhQlHAiX6QqpqTi1\nI5aF6x7iQncbnrt1KWNvCOJKupHnP0tGd7GQsEA37h5aNtdbnBCCN6L1XEo18NGs1uYHdIX6HA7u\n2MLbJ8O5qdsIFj/9GQ52che15qQmgTgcCLv6tR5IBXRXvz4B+AMyEEuN3qntWhZsmM7FbjY8e/Ni\n7hg8jZOXCnjm0yukZxl58xEPhlxX+c5nG/Zks31/Lo/d0Yp+3f+rT3fq9RiOvT+f+2cOYursKBmE\nm6GaBOJIYCngB+iKcsaS1JScjN3Kgm8f5lJXGx5Jmsztvg/wz6l8nvssGVsbhY+e8KR3p8qLfh48\nlc+7G9MYep0j00a6mL+/aaeGkz9F0q6lHZMeX4qjffmzLKSmrSYP64pKAWxXFKW7oiijZG5YakrS\nM5NZuCWUS11sCB/+Bv43TmHfiQLmf5GKZysb3gnxpFPryn+FrqQbWPBFCm3cbHlpqjs2Noqpztwn\nP7Diymu4+MI7y3/E7frr6uCupIbIKkucr46G5VQ2qUlJ23kcO1dPwgc9xZibprLzrxxe+jqV7m3t\neHumJ56ulc/tzSsQLPgihcxcgWa2J64tTHnhjN2JHHhlHne2goBN61H1vb62b0dqwGoya+J5IcSy\nYu+7AcmAn6IooZge1smpbFKjo9NuQf/rPq4LnslrB5fSYdhotsRl89pqPTd0tWfZo560usb0tCJC\nCCKi9Rw8VcCSB93p2cE0r/jno5v5uWAz01d9RMvObXAf0K+2b0lq4GqUI1YUZT6mmRIACqYHdSmY\nHtrNv/qSpEbj3PYdLPjhUVSGFvxf12fpNH8MG/dkERmTxqCeDrwxwwNnR8tq7q7YnsmP8TnMHNeK\nUQOcMeYVsmHpS3zs8jV9OvrgOWkIzg7XnmkhNQ81CcRRgAbQywd1UlNwcccufhk/heuHujDk6XDs\nXVyI+dW0Wm5YH0dee8ij3D0jyqP9M4eoLRmM8XFmur/p4Zx2+//4uMWXeJ0VvDx5mQzCklmNpq9d\nXcghSY3e8W0/sPnJR/Du0Z1Hg1+n/YQRRP+SyTsb07n1BidefdC9xFLka0k4nseSb1K5Ue3Agskq\nEBC7dyVvHlxM6wuCyCmr8ewsV8xJ/6lJIB4NNJgNfxRFCcOUEvEAEEJE1W+PpMYiPSeVV/e/RNJ0\nR75+aA0eHbsR82sW72xMZ3g/JxZPc8fewiCceKGAef9LoWNrO96Y4YGd0cj6RYv42P0rUxAOWknn\nIbfU8h1JjY1lya7yzVcUZaTVelIDiqJEAAlCiJirAdhbUZTA+u6X1PBduXiS51YGc4ErPNNpIR4d\nu7FxjykdcesNVQvCF1IKeSYqGScHhbdneuDawobfdFo+8VhpDsJdhg2v5TuSGqOaBGIFeFNRFIOi\nKPsURVmqKMqN5g+LfV0HQoQQscXea4HQOry+1Agdj93MrLdu5XT6cV64MRL/qY+yeV82kTGmnPCS\nBy0PwikZBuZokskrELwT4klbF4U/jmxn8ZZZ9Gjfjw/n/iKDsFShmgTicCGEnxDCFtPsCF8g5mpg\n3opp4/hapyiKTznfTsG0xFqSynU8djPzt8wktTW8OOpdbhsTzM6Deby+Rs+gng689pCHxUE4I8fI\nM1HJXE4zsuwRT9Tt7DixMBrdmNkMcx3Mm4Er8ewgt7KUKlbtQCyE2F7s61ggWgjRA1OO9tOr/60L\nHpgCb3F6AEVRVKUPVhQlRFGUOEVR4pKSkuqif1IDc3z7Fub/GIK+jQ1zjLO4+caJ/P5vLi+vTKVv\nV9MUNUtnR2TnmTb+OXGpkKXT3enX3YGEv7exf80SlMspPNFnDi5OFdejkySoQSAuJ/UgwLQlphAi\nBtP0trpQtPNbcUWBucw/BkKIqKsjeT8vL69a75zUsJz9J575W2ai91J4YfAy7ggL5++T+cz/wrRi\n7s1HLZ8nnFcgCF9uKon0yjR3Bnvbc/TnXcz78VF2D0xlVOxGvIYNruU7kpqCmqQmrpmDFUJ8WoO2\nq0JfzveKAnDpkbLUzB1I2Et6WzteCvg/ho+YzIkkA89/loyXm2nvCEtWzIEpCM/7XwoJifm8cJ+K\nkf2dOffuVg6Of5iR6w3MfuZLvG4eUst3IzUVNZm+FqooSgiQAMSW/rAOqzyn8N/qviIqACFEeUFa\naoYu/7oHR6/WDDzfm7cLIrn+xolc1ht4NioZBzuFd0M88WhlWV24/ELBghUp7D2Sx4JgFeN8W7D7\n+FYcxznSdvdIbnn+EdrcMrSW70hqSmoUiIG1QAAQDPhfncsbg2nWgjd1sMRZCJGgKErpgOtBOf84\nSM3TiV2xLFw/nZEXvHn0m520F4LMPMGznyaTmSv46InWdPC07Fchr8AUhPccziMs0I07Bjqyafk7\nvKd/jwGdh/LmhlUoimX5ZUkqUpOHdZ8W5YOFEMFCCA+gJ6YAGMx/m8bXhahS84YDMC2/lpq5pD17\n+XnSNIy2NvR6+nEUWxsMig0vrEjh1OVC3pjuQa+OFRf5LK4oHbHncB7hQaaKHJu+epd3r7xNm4s2\nvDThYxmEpWqxyjaYRYQQOkyr2z5VFGWuNduu5LrhiqKEXQ3GaiDx6gNDqRk7vfsndo+fiqpVa5bY\nzEM9KAghBMvWp7HvWD4LJ6vw62VZNYzsPCPhy0054QXBKiYMboF270reS3mXNueMvHZPFK2cy0zS\nkSSLlAjEiqJ8gqnk0TYhxM4atl2nS4yFEJF1eT2pYcvMS2fB1sdoeb8j7yzYSotOHQH4Zlcm3/2e\nzYOjXRh/UwuL2srIMU1R++dUAYvuUzGmrwMxS1/kY7v/0faMYOmkr+g6fFRt3o7UxJVITQghHsOU\n291etDCjuivk5IZAUn3JzEvn+S+CuNQqh0lj5pmD8J7DuXz4fToj+jsRMq6VRW2lZBh48qMrHD5j\n2lN4nG8LtHtWm4LwaSNvyCAsWUF5qYkUIKD4go0iiqK4AUFCiM9qvWeSVA1n//iNBT88ygW3HOY4\nPI3/uBmm718p5KWvU/Fub8eiKSpsrlHyvsiFlEKe1iRzJd1I5MMeDO7tSOzhDbyZsJDr2wwk/Ja5\ndBp6a23fktQMlE5NLAXCKpp2JoRIUxQlWlGUNzClL2SNOqnBOB+3l7CVk7ncEeYNXYr/0GkA5OQZ\nmfe/FGxssHhj9+PnC3j2U9PeEe+FeHJDW0h84ksOGVfRf8RgXpv0Bc72lqU2JKkypX8ivSub+3t1\npsQ8wF1RFPk3mdQgnE/Yx9yvArnUEWY5hpiDsBCCyHVpnLhUyOJp7rT3qPz5dPzxPB7/8AqKAh8/\n2Zp+3R04pzvIX6tfo+2XB1jY5wUZhCWrKh2Iky09UQixDlM+WZLq1aUDCYR/MYmLneBxlxAmPvzf\nzMlNf2SzNT6HR8e24qbeTpW2tTU+m2eikmnjZkvUbC+6uhjZunsFj2yayHmvTEbFfovngP61eTtS\nM1R6eFDVjXrWli4iKkl1bUX8J5zvLJjjt5A7Rz5m/v7JSwW8vSEd3x4OPDja5ZptCCFYvi2Tz7dl\n4OPtwOvTPXCxExyb+QXJO96izxDBPZ+vkXtHSLWidCBOVRSlmxDipCUnX80ZyxnsUr3IuXQZR3cP\nxv0yiN5dupUIwoUGweJVepzsFV6a6o7tNR7O5RUIXlutJ3Z/Dnf4ORMepMLeTmH38W20u9GdFrF2\nLHpuPa2H3FQXtyU1Q6UDsebqa2wV2qir7S4lySz530MsWzCewP4zuGHB4/RROZf4/Ksdmfx7toDX\nHnKntWvFe0hcSjUwf0UKR84W8Pj4Vkwb6ULhlSy+3vshy498QOhtCwl68gA29patvpOk6igRiIUQ\nfyqKkqYoyutCiAUWtqGuhX5JUoUyEnV8NeNO/ri3kNG+nXDqWXI701OXC/lCm8HoG50Y2d+5glZM\nRT5f+CqV/ALBG9M9uLWvE0IIPln2JBvabOdGY08m+TyCja0MwlLtKu8R8kwgXlEUNaYSRBXOolAU\npRumkkmSVCcyT55i+8gJdEzOZV6HqYx6YlqJz4UQvLMhDUd7hWfuLn9DdqNR8PXOTKK2ZNDZy46l\n093p1tYUbD//cTEb2mynx9+CebPewE4GYakOlAnEV/O+Y4BtwAlFUTRAVOm88dUVd9FAUF10VJLy\nc7OZ/9po1F55PBjzPa06qrFp4VDimD+O5vHH0Tyenuha7raWKRkGXv1Gz94jeYwe4MS8YBUtnWzI\nPZGMZtMLfJu/id5/weLZG/Hy86urW5OauXInVQohdIqi+AKfAfOA8KtbTRZt6uODKSUxTwixv646\nKzVfBqOBN3+cyz/X5eE7djqeN5VXqhC+iM2krcqWe4a1LPPZT3/lELkujexcI3MnuXH30BYoioIQ\ngg/XzecHZQt99sMrc76ltW/57UtSbahwdvvVvSKCrhbnDAVGYyoQ6o5pM/hgIcSfddJLqVnLPHeO\nZVue4+e03dx1ciRT7nqq3ONOXirggC6fJye44lCs8GdSmoH3v01j+4FceneyZ9F9nqjbmVIORqOR\nj3ct5gdlCyPsBvPEsy/gObAuC5BLkgXbYAohEpCl6aV6kn3xEoteHMmB63OYOvAxHnr4Gezcy1/V\nFncsH4BRN5oWbmTlGln7SxYrd2ZSaBCE3G6aFWFnawrSWX+e5bhmI/F9tdzr8whPjHhJ7ics1Qur\n7kcsSdaUczmJFxfcxoEbchjfciwPj1xwzUCZm28EYP3ubDJyjMTuzyErV3BbXyeevNOVTq3/+3E3\nGA0kHTrI0a8iGNemJfceCpdBWKo3MhBLDVJucjIvhw8noW82t526nkfumltpoBw5wJldf+fy9c5M\nnB0Ubu3rxOTbWtKnc8kHegX6bN748UkSf4tlgqsDAZs2YOdc8TQ3SaptMhBLDdKHX85mX98M/D1H\nEf7EZ9g6VT6NrKOnHZrZrbmSbsSjlY05BVFcxq86dC+vArGdDq1sCdixGbc+vWvjFiTJYjIQSw2O\nEIKutv0YXlhI+IPLsbWxrLoygI2NQhtV+cfnF+Zxpm0SGYqOvoccGb3zB1x797RWtyWp2mQglhqM\ngsxMtobM4Jb5r3CLYSi3uQ7HRql2fVszIQQpu48SefFV/j73B1+u24VvloJz+3ZW6LUk1ZwMxFKD\nUJidzcchY/m+/ylcDsYyfM5sEFjlAdqVvUdY+P0DHPe6yJM+YbR2bQ+uVui0JFlJzYcbklRDhtxc\nfr77Plp9e5Bbz/Rh8JiHUBQFxYJyRpXJysvgxb9nk9j6AmO1zoxR32WFHkuSdckRsVSvDPn5fPXI\nXdj8tJch818lgJ44tiq7Kq6qjPmFnIjSslR5g5NZidwe68Ljn26jZdcuVui1JFmXDMRSvfr16Ba+\nuvEwY4eMp8/s8lfMVUfSiRO8mDSPSy2SmRDrSsj/tOZqzpLU0MjUhFQvjIWF7Dn8I69pn6Fbdmem\n+y2ySrui0EBSxgXCfwshxT2LB08OIvTLHTIISw2aHBFLdc5oMLBq9mS+7L6Prl69mZs1C/fuNQ+U\nhclZnFmwiYS+h0jmEhGBK+nfSZY2kho+GYilOiWMRtbNeZCvOu3FU/HgzcmrUbXwtErbhc6QmXWC\nguc/ZO5j98kgLDUaMhBLdUYIwXfPP8rnbXbhUuDMIseXrBKE804kc8H5CnPXBHPT/gv0vaEPg198\nueYdlqQ6IgOxVGd2vD4fjdtWWji68Frn9+nYtVeN2yzU53D6uW9JaXOWloVJdGjbg9Ex3+Hg7m6F\nHktS3ZCBWKoz27ufxv5cS957ZAudPaxT6vBEXiKtHunHwUff4P7u1zNiyzoc3MovkSRJDZUMxFKt\nu7BtO+0CRvG4YRanDu2lvWhb4zZTN/3DYXcdS+KeY2zfYKbv3ESLju2xb9XKCj2WpLolA7FUq/a9\nHcH/fnuL2eciUI+ciJPBAVuP8jd2t5QxO58dP67kk64raWNUMW3wU7i5tLFSjyWp7sl5xFKtOfbx\nZ8Quj0A3qAWMGYBjNw+8pt9U7f0jhMGIEIKdpzbzcbev8Tibz/0/d8bdUeaDpcZNjoilWnF8+Vfs\nm/Usg0eNImDvbXTwaQk1mCpszC/k/JJt/Np5P5+mf0xbXSEPnR+G/7qV2NjLkvdS4yZHxJLVpZ07\nzZI980i/fyC3bVxJlzuG0nJQzfZ4UOxt2araRVT6x3Q8XMAjSSPwX/sNtk5OVuq1JNUfOSKWrKrQ\nUMCyfa9w+jobgm99BLtWLWnz6NBqt5d/IR3F0ZaV/2pYKVYxyPlGxue34ZZvNNjYyR9fqWmQP8mS\n1ZzdspWPT3zMnpx4pqUH0TfaDTFIVD8nXGjg7IJNGNs5sbfHVsbdEMxzYyKxnWV5xQ5JagxkIJas\n4uLOn4lYPoN/htgyY+hz3K1MhAJjjTZ2N9qAa6gvJ5ZrGPZ/cdzxS0SVyiZJUmMhA7FUY0l79rLs\nnfv4Z7gt96jvY9rQp2sUgPVbDmHj6sT7qW9xav/vDP/8GH1mheBx4wAr9lqSGg4ZiKUaSfnzAO++\nEkhCgA2jWo1mzBc9ybshCade1ZvXKwxG0rb+i627E63TzpIVf4obnn2KGyNftUrZJElqiGQglmpk\nvfY99gTALZ1GM9f//0gTf+HYveob+RjzDQBkGzNJeswDz7hLuD6wk/sWhdHvlYUyCEtNmgzEUrUI\nYXoI1/PuSQyOz+OFSRoc7BxpE3pz1dsyGDm7cBNZrY28017DOf1JVj76GyO81tNhrH8t9F6SGhY5\nj1iqsqzTZ/j25ltJSdjPrV3GMHPnnaRq/qh2e4qtDTk+zrzWIoJTl/4lzOdFVC08ZBCWmg05Ipaq\nJOfiJb6Zcgcx4/W0uPIHAY4DcBvVC6frqr6RT/bBC9i0cOCyWyqL0haSkZXEWE0andsIuKUWOi9J\nDZQMxJLF8pKT2REwEafzVxj6WAC+fhNQFAXPKT5VbksUGrj41k7OdkzlnfYaCvUZ3PFRGncs+T/U\nD02thd5LUsMlUxOSRfLT0vju7rtIPZmIf/Rqnun6Ivo5O8i/kF6ldoRRmPLLdrYkz/Ikwuv/EMkZ\njH8/jTvf+hTvGdNq6Q4kqeGSI2LJImmF6awZe5lu0wYzbdRwco8l4dynHfatW1rchjHfwMU3t+PY\ny4t/ByaxePcTdHDtzF1bHfDTzKbz3RNq8Q4kqeGSgVi6JkNeHunpSSz8MYTsVgrTx78MgFNPLzrM\nr9rDNMXeBmxtUGxsyEtNpWfr63lt0gpcZ6jk9DSpWZOBWKqQsbCQn+57iC97xHO+fSFLJi5H9WkS\nKf324xF4o8Xt5By5hH1bV+xUzuQ/2gW3bGcyRy9lUu8euE2TewlLkgzEUrmE0cjuGaF87foTZ9va\ns2Dc+wzqdCsXnX/Cxtny/X8NWfmcXfgDLjd15cAdySzbNpegNc54JGUx9EtNLd6BJDUeMhBLZQgh\n+GzHcdQAAB5cSURBVOOJZ1mZt4lTgx14cuRiRve5B4D24aMtbkNRFGxbOtBhQQBOPby48eQhhuyy\nwUOXw+gd3+PhY/moWpKaMjlrQirjUMQ7rD65kqODHZg25Cnu6jaZ869tozA5C0VRKs3nGjJyObtg\nE5l7T5FfmMuanNXkORby50NP4fOHHQE//SiDsCQV02BHxIqihAE6wANACBF1jWMDATUQA6QAIUCM\nEEJXB11tcs7c6s4BeyfG97uPGcOeJzv+LNl/nceQkYedZ+WzJBR7W4y5hWRkpPDm+rnsP7OHnm36\nMuyb5Si2Nrj27FEHdyFJjUeDDMSKokQAWiFEbNF7RVEChRAxFZziAURcfemBmTIIV92lnT/TZvgt\njLhpCqn2OQT7haAoCi39OqNeMRUbp4pzw0IIMnefwGVwV2yc7HFZfDPzNz7EicuHmZo+nFt6jJMz\nIySpAg01NRFSFISv0gKhlZzjDngLIdyvEbClCui+XMXqhyZy4P/ex8nemftumkXh6XQy/zgFcM0g\nDJB3LInzS7ah33KYc/qTPL32Xs4kHWXMihzaxRylIL1qCz8kqTlpcIFYUZTy1sumANectCqE0MtR\ncPWcWf8dvzw2i22Pu/GD+pj5+8lr/uTi2z9hzC2o8Nyiz5x6taHTkvEkDTLy1Df3kpaexLgP0+lr\n14vRu7bg4OZW6/chSY1VQ0xNeGAKvMXpARRFUQkh9OWdpChKSLHz1EKIyNrrYtNxYdt2dk+ZQXs/\nPxbdMxfvjv3Nn7V7ZgQF5/QVjoYz957i4js/0WXZRBw6qTjS5jQvxjyKU6Edt0cm0UPty4gfonFQ\nqerqdiSpUWqIgVjF1Qd0xRQFWA+uBuVSYouPhhVF0SiKElLeA76rATsEoEuXmpV4b+wKMjPZ/sjD\npAZ05d6vo3FwNy2uMGTmYeNkh42D7TU3eXdUe9KiX3tsWjjw19m9zFv3AJ3cuzOn1QySB6zjlpiv\nsHdxqavbkaRGq8GlJig/0BYF5tIjZQDKSUlogfAKjo0SQvgJIfy8vLyq38smwOBoyy8veLM5IIMs\nR4P5+5c/2c2pORsQBmOZczL3nuJy1G8A2Hu50GHhGOw8WnBduwHc3vlO3puyjv6TpjFiy3oZhCXJ\nQvUaiBVF0SqKkljs5Y8p2Jb+W1YFpjxwOW2oFEURiqIUP0ePaTqbVI70o8dI/HIlS354guOZx5k/\n/n3cW7Q2f+4ypBtuY65DsS3745F7PImshLMYsvIQQhAT/xkZOXoOv/Im7SavIG//Uf6/vTsPb6pK\n/wD+PV2g7JcUEAFBL26ACqStK4LYRGUAAUmoOi7jlooLjCM29qeig0tJRx0dHZ0EdFxGh5KIOigM\nJggiKNIkg4LglojsW5sLAmVpc35/NDcmadqkpc1Nk/fzPHkk956bnMbw9uW9ZwFAIyQIaQZFSxOc\nc22044yxyICrAuCI1jagPCJIi6gfg0wiHN62Hcu1k7Di8sPYONKP+698EqPPGhfWptuo336H8To/\npCWbkDO4FzoN7YvcopHInTYSLDsTP+//DpZVz2D7+4vR5+kVGHznrVAV5CX6RyKk3UvG0gQAWAKT\nNGRaAMGFCRhjonw+EICrIq7Xo5HSRDo7um8/Vlw1GeuGHcTGkX5cXzAdU0b+IbyNtwrHtlQHyxK8\n1o/qhetxYHkg083KhD+TAwAGCWfinu8vQe+nV+CcB+7FhZa/ISMzM6E/EyGpIBlv1oFzbmSMlYTM\nmPNEjA3WoD7YyscsgZl4EoDBAMw0ljjciYMHsXLcVKzvvh1fFWbjynMn4c7LH27QruqtShxauwUd\nBvbE6eZpyOiYhUEvTEGmqjMAoPrwXjzy/m3Q5xsw+Js6HDEvxvmPP4zzHy+lcgQhLZSUgRgAmhp+\nFhgNYQl5LgGg4WpN2P6fJdh08FusursbhvfPR8nVzyGDNfwHUZ/7RqHTsL7wHzkOfqwWLCc7OK15\na7UHD793M6Qj+9G5QzcM1I9Fzil9cMoY2mCOkJORtIGYtK5+RZOwRipD/y65mDNpHjpkdYzaLrtX\nV6j0DRfk2bjDiUc/uA0MGbjZeQ7OLewLxhgFYUJaQbLWiEkr4H4/nPfPwv6169AxuxP+PGU+yq57\nE91ymjfBYtWPSzHLdj26ZnfDNGsXYMEa/Pqjp416TUj6oYw4RXHO4XrgYWwyW+A57SiKLr4Q5/Uv\naPbr2Fzz8erKOTg7dxjG/m0v+OZtGPXBu+g//po26DUh6Yky4hS18aly/PC3f2BX6WjMy1qMrdXN\ny2Dr/HV4ecUTeGXln3Fx/9HQPLUN+Gk3rvjvIgrChLQyyohT0A+vzMOG2U/jjFtugO7RF3HljrUY\nqBrcrNfY4fsZH3/zDq5T3wHDRQ9h3aJ7MKRkJnLzo63JRAg5GYxzrnQfFJOfn8+dTqfS3WhVnHOs\nnnYrvuuyC7qX3kHPbn2adX3NiSPolF0/VG3z6qUYPOzi4BoUhJDmYYy5OOf5sdpRaSKFyPvEdXu2\nGNbhHrz2xV+adf3egztxxxsaLNmwALsdK7DhmttRed+sNuotIURGgThF7F+7Dp9cUgjP95V44qPp\n6CcMwt1jHm3Wa+R27YMLBlyELhv3YeV4PbqKp0P93DNt1GNCiIwCcQo4sOk7rPydDgcP7MeTn88C\nhx9PT34dXXPiW4zdsfl9VB/ei8yMLOh352HnTY9BlTcCms+WoFPfU9q494QQCsTt3OGt2/DpVZPB\nOnVE5aPnYsevW/HERAv69zwj5rV+7of5s6fxzJIZWOi0oLamBhufLMcpmitwpf1Dqg0TkiA0aqId\nO7q/CiuumozaQ4ex57UiOLdUYGbhUxg58NKY19YcP4xnls7Emp+W4drht+COSx9CVnZHaD5bgpw+\nvZHZMfrMO0JI66OMuB3zHz+OrO7dkP3GTCzaUoHx59+Ia4ffEvO6vQd3YuaCqfjSY8e9Yx7HZYuO\nwD39QXDO0eW0ARSECUkwCsTtkP/ECfjr6tC536kYunQ+LL9YcF6/AswofDLmCmibdrox/Z0J2Hng\nFzw5YR5OeXY1fnz1NXTIjdydihCSKFSaaGe434+1t01HXc1RjLK+hf49T0dRwXRMHP57ZGd2aPLa\nTza9h+c+MaJX11NQPuGf2HrXE9i1bDlGlD+JoQ/NTNBPQAiJRBlxO8I5h/vB/8OWdxZCyB+BX48d\nQGZGFm699AGoujQ9caNyy0rMXfpHDOuXh7/fuBhbbnsMu+0rcNFrf6cgTIjCKBC3I5v/8iK+f+EV\nnD3jbnxzZUfc+dZVqDq0p8lr5JmTeYNG40/auSif+i8InVUY8tAMjLK9jcG335yIrpMk4Ha74Xa7\nAQA2mw0OhwPl5eXBY5IkwWaj/RSUQIG4nfj57X9jvXE2BhZdh7y/zsVFZ1wJzZApTWbCP+//HjMW\nXIc9B3cgg2VgdM6F2PZu/V+0fldrcNqUiYnqPkkCTqcTarUaXq8XZrMZGo0GarUaZWVlAABBECAI\nQjAwk8ShGnE70fVMEQP1U1Dw+stgGRk4s88wnNlnWJPXZGZk4WCNDwdqqpD9416sGDcVLCMDAyaN\nR3b37gnqOUkGNpsNGo0GACCKIqxWK4D6LLmoqCjYTqPRwGg0Qq2mxZ0SiTLiJHesqn5f1N6XXITL\nKt7AnGX346VPZzfa/kTdcdg3LQLnHANVg/H6H5aj+7c+OK4Yj6zOnaFZtZSCcBqqqKiAKP62O7cg\nCLDZbPB4PNDpdGFtJSlyE3XS1igQJ7GDP/6Ej4YU4PuX/gEA+HjDu/jS68CpPQZGbb/34E48UDEN\nZUtn4psdXwEAdrz3H6wcNxVdTh8I7Rd2dD/7rIT1nyQ3nU4HrVYLvV4fdlwQBArGCUaBOEnV7NmL\nlddcB3COU68uxO4D2/DKyjlQDxyF69S3N2i/1rschrevwc/7v8PsCa9g+ICLAQCHtmxFboEa2lVL\n0bnfqYn+MUgScrvdcDgcAAC1Wt3gBl1ubi6qq6uV6JrilPoFRDXiJHTi0CF8NkGPml17ULjiI3Q7\n60w8s+gWMDA8dPWzYbsvH689hvmr58Lmmo/BvYdi9oRXMaDnGTi05Rd0PX0QhsyagXNmTkdmh6bH\nGJP2rby8HFVVVcjNzYUgCFCpVNDpdDAajTCZTGEBxul0QpIkaDQaSJIUVrIAAI/HA5Uq/gk+8s2/\n8vJyqNXqYM25qqoKXq8XpaWlrVZzdrvdqKioQEHBb9t+yaUV+Vxubi5KSkqa9bqSJOGuu+6CzWaD\nImu0c87T9pGXl8eTjd/v5yt+N5W/m9GDb1+8hHPO+aoflvCxzw7gNtf8sLY/7d3E73hTy8c+O4C/\n6HiUHztRw+tqa/lXxTP5wu79+a8/b1HiRyAJ5HK5uEaj4S6XK+y4x+PharWal5SUcM45NxgMYefN\nZjO3Wq3cYDA0uFan07WoL6IocpPJ1KAfABq8R0v4fD4uimLwucvl4oIghLUxm81co9G0+D3qQ2Lr\nAeDkccQiyoiTDGMMp+kmY8DkCeg/YRzq/HWY/7kJg3LPxuQRtwIAautO4N+Vr+DtL19EtxwBz0x5\nAxeLhag7ehSrb7wF299fjKEP/wldBkWvJZPUYLPZYDQa4XK5IAjhO3OLoghRFKHVagEAer0ebrc7\nmJkaDAYAaHCjTr62tYiiGBwiJ4/UaCmHwxGWWavVarhcrgbv1x5RIE4iR7bvQOcB/TH4tpuCxyq3\nrMQ2nwezJ7yCzIzf/net+mEJLj/rGtx/5ZMQOufiuM+HzybdgH2rv4T6hbk4d+Y9SvwIJEG8Xi/0\nen3UICwTRTE4ZE2j0cBiscQsEdhsNpSWlrZqXyVJalapoznaa+CNRIE4SXhefxvOex+EZtVS5Bbk\nBY+7fvkcANBPGIRHP7gdxmueR7ccAS9e/x46d+gabLf52ZdQtbYSl777Gk6/vmGWQ1KL0WgMTsho\nTGRA1Wg0YVlxJEmSgpM6Wot8U9BkMgWfG41G5OfnQ6vVoqKiAlqtFgaDAZIkwWKxBIOr1+sN1nod\nDgcqKirgdrtRXl4OoL6WbbFYmqzpSpKEsrIyFBQUoLKyElqtNvjLCaj/HOV6c2v+3M0WT/0iVR/J\nUiPeuczB380U+HLttbzu+PGwcx/8700+d+kD/Kc933Ldq2q+Yfu6sPN+v59zznntsWN8/zpnwvpM\nlAWAm81mpbsRRhRFrtPpuNls5mazmZtMpqh9NJvNXBRF7vP5uMvlCtaP1Wo19/l8wXZ2uz2stm21\nWhvUrxFR07Xb7WE1YlEUucfjCXsuv0dkbV2uZ7cmUI24ffB9vQGf625Bj2FDcLntbWRkZ4edv3b4\nzRh33jR0yMrBAsNXYeWJfWvWwj3rEYxZvBA5vXLDMmmSurxeLwAgPz/m5sAJV1BQEKw/N0alUkEU\nRQiCEMzO5cw5NCvVaDTQarUwmUwtylblYXmh5Qu1Wo2FCxciPz8/OOVbpmSZg8YRK6hmz16sHK9H\ndvduuOJja9QZb4wxdMjKAYCwILzt/cX4VHMtjlf7UHf4cML6TJQn11ubCk4WiyXsudvtRs+ePWE0\nGuH1eiFJErRaLbRabYO1JSwWCxwOR3BRIDnwt6bIvrvd7qh1ZEEQ4HQ6W/QeXq8XgiAEfxaHw4GC\nggKIogin05lU9WXKiBXUsVcuzripCINu0KHzgP5xX/fD3y1w3v8Qci/Mw5iPrMjplduGvSTJRhAE\niKIIt9sdNZhEuzkmZ35yrVZ+neLi4rCsUK/Xw2QyBV83Pz8fhYWFDUYntDZRFKNOIok2zrk5rwkg\nrCYs/9nhcLTJL5iWooxYAf7aWtTs3oOMzEyMmPtn9Bx+ftzX/vjqfDjvm4X+E8eh8NOPKAinKbPZ\nDKPR2GAmmNfrxcKFCxsMS3O73Q1KGQ6HI+xYcXExioqKGqxJIUlSm8840+l0kCQpLDjabDbodLoW\nB2L5Mwh9TUmS4Ha7odFogr/MZIquOhdPITlVH0rcrPP7/Xzd9D/yRf3P4Uerqpp9/ZHde/jXs5/i\ndbW1bdA70p64XC6u0+mCN8XMZnOjEyfMZjPX6XTcarVyq9XKTSZT2OQIn8/XYHKETBCEsJtokTwe\nDzeZTBxA1EkdkX3WaDRcEARuMpnCXtfn8/GSkhJutVqDN/uiXWc2m4NtAfCSkpLgjT+NRsMBBK8N\nfU35Ee397HY7t1qtHADX6XRN/rzNgThv1ikeDJV8KBGINz//En8H3bi75LG4rznm8/H1j85pMKKC\nkHjpdLqwIG02m2OOSOC8PsiGBmzSPPEGYipNJND2Dz+G+8FHcNrUSRhR9kRc1xzeug32UVdjs+kF\nVFXSgt2kZSLHD9vt9uCsOwCorq6OerPMbDaH1ZVJ26BAnCC+bzZizY13QJU/Epe8ZQbLiP3RV//v\na3xycSGObNuBK/67CL0vvSgBPSWpRh49ECoyMGs0mmAt1eFwhK3QFm0aNGldFIgTpMvAARion4wx\nHy5AVufOMdvv+mQ5HJdfA5aVBe2aT9D3yjEJ6CVJNV6vN5jRysPWLBYLvF5v2E0sURRhNBphNBoB\n1C8kD4Cy4QRh9WWM9JSfn89bOkYxXrU1NWCMITMnp1nXVbvXw/XHh3HZgn/SOsIk4YqLi1FcXIyK\nigoKxieBMebinMeceUMZcRvinOOr2+/B8sKJ8NfWxm7v92P74qUAAJV6BDSf0WLuRBl5eXkoLCwM\nqyOTtkOBuA1tKnsOvyx4D/0njkNGVtNzZ2qPHMHnupux6toi7F21BkD9rDpClGAwGODz+cImQ5C2\nQzPr2sj2/yzB14/MwaAb9RhqfKDJtjW7duOza4tQ7VoP9Qtz0Wf0ZQnqJSEkGVBG3AYObPoOX/z+\nTqjyR+Ki+S83mdn61n+DZReOxcHNP2D0hwtoHWFCkoTFYknYbDvKiNtCRgZUeSNw6b/mIatTpyab\n/vqjB8jIgHb1MvQccUGCOkgIaYrFYoHZbE7YwkAUiFuRPAKlx7lnQ7NySZPtpG82oufw8zFQPwX9\nxl8d15A2QkhiGAyGNl/oKBSVJlrRxqfKsfYPd8N/4kSjbWpravDlzXdhWcEVkL7dDAAUhAlpZW29\nSFFro4y4lex2rMCGx5/B6TfqwRoZIXFk5y58PuVGVK1z4YKnHkOPoecmuJck3TgcDphMJjgcDhgM\nBuTl5cVcuL01yBNHBEGAx+MB0HBySHl5edjyl5H9inVeZrPZ4PV6UVlZibFjx+K5556DWq0+6c1K\nE4kCcSs4smMn1txwO3oMOQcXml+MenNu3xdf4fOpN6H20GGM/uDfGDBpvAI9JelG3qeuuroaZrM5\nYe9bVlYWFnjz8vJgsViCwdRoNIbtH2c0GoPLXsZzXiZJEoxGIzweD2w2G9RqNfr06ROcGRiNxWKJ\nmjGr1WrlhuvFszJQqj5aY/W1utpabh8zjld06culzd832u7rx5/mHw6+gPs2fHvS70lIc2g0Gl5S\nUpLQ9xRFMWy/Op1OF7a6W+SSm5F7zcU639TxxlaSay6DwcDtdvtJvQZoz7rEOPjdD/Ct34D8l59F\nj3PPDjtXW1ODQ56fIZw3FOfPfhjnPnAvOvTooVBPSbqSd05OJLvdHjbiwOv1oqioCED0BdhVKlVw\nkaFY5xPBZrMFt2hSqVRN7pbdGigQnyRh2BBM/PF/6BixU8avHi9W629Fzc5dmPjTemR37UpBmCSc\nHNQS/U/u0CAs96GkpARA9CU35dXhJEmKeV7+s9vthtVqhdfrRXl5eVhpQZKk4OahsWrM0eh0uoSu\nOkeBuIXqjh7FLxXv4Yybb0BO715h57a+9yG+uuM+sAyGS962ILtrV4V6SdKdw+FodjZXXFwcV7tY\nuytLkoSFCxfCarVi3rx5Yccj96eTA291dXXM8/J7qtVq6PV6eL3eYJCXeb3esEBqNBrDatTJhgJx\nC20y/RUbnihDt7PODK4T7D9xAs4ZJfjpH69BVaDGqIo30PWM0xXtJ0lvdru92dlwa93UEwQBBoMh\nOFqjuLgYBoMhavCWA69KpYp5Ph6REzG0Wi30en3SBuKUGkfMGNMxxtp8zb7DW7fh27LnMej6qWGL\ntbOsLBzbuw9DZs2AdvUnFISJ4pxOZ9QV1CwWS5u+b+SoBHlZTaA+mEael58LghDzfEuIopjUY4tT\nIiNmjGkAqAFoAbT5Htmb5j4PcI4RpjmoPXwYG+aYMPjOW9D9rDNx2cI3kZGZ2dZdICQmt9sNSZIa\n7N4MND3h4WRLEw6HA1qtFj6fr8F5SZKgVqsbHK+urg5m7rHOt0RobTkZpUQg5pw7ADgYY7kA2vTT\n5n4/trxrQ1/tWOz8eBk2PvUX1OzchU79+qL7zDMpCJOkIdeHIwOQXq9HaWlpo9edbGkiPz+/QQnC\nbrdDp9MFjxkMhrBxwXa7PewXQKzzsYTuPgIk/957KbVDR6AsIXDO4/o/1pIdOrjfj/f7nY2je/YC\nAHpdciFGPvs07SdHkoY8q62srAwqlSoYwDweT3AImDzbra2E7nlXVVUFoPGZdfKeeo3NrGvsvNvt\nhtFohNPpRGlpaTD422w2CIIQzPobuz4R4t2hgwJxC7ZKOlZdDc9rb6P3ZRej1yUX0gLuhJCo4g3E\nKVGaSLSOKhWGPjRT6W4QQlJESo2aiAdjzMAYczLGnPv27VO6O4QQ0r4CMWPMzhjzhDyafRuVc27h\nnOdzzvN79+7dFt0khJBmaVelCc45bSlLCEk57SojJoSQVNSuMuLGMMbUADQAdABUjDEPAAfnPDE7\n/xFCyElIiUAcCLhuAOVK94UQQpqLShOEEKIwCsSEEKIwCsSEEKIwCsSEEKIwCsSEEKIwCsSEEKIw\nCsSEEKKwlFoGs7kYY/sA/KJ0PxTWC8B+pTvRTtBnFT/6rOoN4pzHXNQmrQMxARhjznjWSyX0WTUH\nfVbNQ6UJQghRGAViQghRGAVi0rb7qqcW+qziR59VM1CNmBBCFEYZMSGEKIwCMSGEKCwl1iMmJ4cx\nVhL4YwGASs45rescIvD5eAGogPp9D5XtUfKi71LLUCBOc4wxE+fcGPLcxRgD/QWqxxgzAbBzzh3y\nc8aYjnNuU7hrSYe+Sy1HpYk0xhgTAFRFHDYDKFWgO8nKIAfhADuAYqU6k6zou3RyKBCnNxUAE2NM\nDDlWDUBQqD9JJbAXYqRq1O+PSMLRd+kkUCBOY5xzL4C8wH9lWgCORi5JNyrUB5NQEhDMAEkAfZdO\nDtWI01zoTteB4DINQJ5yPUoqAgI36ELIgVmFQFAm9ei71HKUEZNQVgCFEVlNOosWaOXAHJkpk3D0\nXWoGCsRpgjFmZ4x5Qh6aiPMmAKbQrIZErXEKAMA5p2y4EfRdaj6a4kzAGNMBkEKGaKnpL1E9xpiP\nc94z5LkGgJFzrlWwW0mLvkstQxlxmgsEFhUAJ2NMCNz1LlK4W8nEEgguMi3qh2WRCPRdajnKiNNY\n4IaKL8opG+dcn+j+JKuQmXUi6rM9mlkXgb5LJ4cCMSGEKIxKE4QQojAKxCQtMcZKAiNJTIHnYuRI\nEkIShUoTJO0Epi5LnHOvPAoCgJVqv0QpFIgJIURhVJoghBCFUSAmhBCF0aI/JO0EasTF+G11NRUA\nr7yoOWPMzDmnNYdJwlAgJmkjMOlgHurXiygOXZAmMBPMjEBQVqiLJE1RaYKkhcB0WxfqM19t5Kpg\ngUV8rAB0qN+Fg5CEoVETJOUFMmEXAHes6baMMc45Z4npGSH1KCMm6WAe6ksOd8XRlnaUIAlHgZik\ntMCNOR0AS5xrCBtjNyGkdVEgJqlOHv0Q19KVtHYuUQLViElKY4x5AKhCF3cnJNlQRkxSnYg4h6PR\nzsxEKRSISarzIo5AHLLrMCEJR4GYpDo3AHUc7Upp9TWiFArEJNWVARADoyeiCuxJF3USB2PMwBjj\njDGz/BqMMQ1jzBV46Bppbwr82RD4c0lr/lAktdDNOpLyAkGwGIA2cloz6ssRXnnX4SjXCgB8kZM8\nGGMu1O/m7IizfQmAXHk9C0JCUSAmaSGQzZYGnnoBVAEA57w8xnU61Jct8iKOR52BF2hfzDnXRhwX\nAbho9AaJhhb9IWkhMD64JbsJFyFitl1gV4/GxhtrEb3MIQQehDRAgZiQpmkAWBhjhpBjejQ+FXoa\ngLwox8UmriFpjkoThDQiUE7wRKn3egDoI2fhNVV+YIxZAVTGKoWQ9EQZMSGN0wCwhR4I3IwTG5kK\nrUGUrDdQnxZjrfxG0hcNXyOkcXo0rPdGDbYBDerDgSy5FC2rT5M0QRkxIRECWa8B9UHXzRgTOOdS\noE5cDKCaMaaWs+KQ9joA3oh6MigTJrFQjZgQQhRGpQlCCFEYBWJCCFEYBWJCCFEYBWJCCFEYBWJC\nCFEYBWJCCFEYBWJCCFEYBWJCCFEYBWJCCFHY/wN8hdGKFTfaEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f118e549610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_met_mean_prof_dw,\n",
    "        fi_sally_wbkgs_2d_mean_prof_dw,\n",
    "        fi_stxs_5bins_mean_prof_dw,\n",
    "        fi_imp_stxs_mean_prof_dw,\n",
    "    ],\n",
    "    colors=[ col_sally_met, col_sally_2d, col_stxs_5bins, col_imp_stxs ],\n",
    "    linestyles=['-',':','--','-.'],\n",
    "    xrange=(-3.75,3.75),\n",
    "    yrange=(-11,11),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=10.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=imp_legend, loc='upper left',frameon=False)\n",
    "plt.text(0.24*ax.get_xlim()[1], -.75*ax.get_ylim()[1], r'$C_{HQ}^{(3)}$ Profiled', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], -.9*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/imp_contours_dw_prof.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yIxqtlmKDgd2vvUlpfr6dX4HC3jR6ARZCRAgh5tjbDgtO3t60u24MR378CVNJ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\n0SuKEvgCo1JYRUVHM/vXP+fSzTZihiXzlu5att5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89o2A54bdgxvvYueRrQC0tH4zVjx+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f118e67f310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_met_mean_prof_dq,\n",
    "        fi_sally_wbkgs_2d_mean_prof_dq,\n",
    "        fi_stxs_5bins_mean_prof_dq,\n",
    "        fi_imp_stxs_mean_prof_dq,\n",
    "    ],\n",
    "    colors=[ col_sally_met, col_sally_2d, col_stxs_5bins, col_imp_stxs ],\n",
    "    linestyles=['-',':','--','-.'],\n",
    "    xrange=(-4,4),\n",
    "    yrange=(-3.5,3.5),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=1.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=imp_legend, loc='lower left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(0.22*ax.get_xlim()[1], 0.8*ax.get_ylim()[1], r'$C_{HW}^{\\phantom{(3)}}$ Profiled', fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], .65*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/imp_contours_dq_prof.eps',format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kC83knU1m59z/o/WIYfR95klMBgNIicbOzir2VEVe8jlOrfyRlL+2cXHjXxSmmYtjh2yN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\nKEQylbyPTqdDp9P5l9MM9fHK0KPagIc439q3HRcp1+v1/qAYbEqfjqm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f117a7b8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plot_contours(\n",
    "    [  \n",
    "        fi_sally_wbkgs_met_mean_prof_wq,\n",
    "        fi_sally_wbkgs_2d_mean_prof_wq,\n",
    "        fi_stxs_5bins_mean_prof_wq,\n",
    "        fi_imp_stxs_mean_prof_wq,\n",
    "    ],\n",
    "    colors=[ col_sally_met, col_sally_2d, col_stxs_5bins, col_imp_stxs ],\n",
    "    linestyles=['-',':','--','-.'],\n",
    "    xrange=(-12,12),\n",
    "    yrange=(-3.5,3.5),\n",
    "    contour_distance=2.4476,\n",
    "    xlabel=r'$C_{HW}^{\\phantom{(3)}}$',\n",
    "    ylabel=r'$C_{HQ}^{(3)}$',\n",
    "    scale_x=10.0,\n",
    "    scale_y=1.,\n",
    ")\n",
    "\n",
    "plt.legend(handles=imp_legend, loc='lower left',frameon=False)\n",
    "plt.text(-1*ax.get_xlim()[1], 1.05*ax.get_ylim()[1], r'$\\times 10^{-2}$', fontsize=14)\n",
    "plt.text(0.24*ax.get_xlim()[1], 0.8*ax.get_ylim()[1],r'$\\tilde{C}_{HD}^{\\phantom{(3)}}$ Profiled',fontsize=16)\n",
    "plt.text(0.30*ax.get_xlim()[1], .65*ax.get_ylim()[1],  r'$L = 300\\,\\mathrm{fb}^{-1}$', fontsize=16)\n",
    "\n",
    "plt.savefig('plots/imp_contours_wq_prof.eps',format='eps')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}