CMSDAS_Postfit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MonoZ Analysis - Prefit Vs Postit\n",
    "Plotter for Higgs Combine FitDiagnostics and for MonoZ datacards\n",
    "## Import Pre-requisites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import uproot\n",
    "import matplotlib._color_data as mcd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract histograms \n",
    "We'll extract all histograms from the diagnostic file here and store them in a dictionary to be used down the line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------\n",
      "Uprooted file\n",
      "------------\n",
      "\n",
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      "Added TH1F: total;1\n",
      "Added TH1F: total_signal;1\n",
      "Added TH1F: total_background;1\n",
      "Added TGraphAsymmErrors: chBSM2016/data;1\n",
      "Added TH1F: chBSM2016/DY;1\n",
      "Added TH1F: chBSM2016/Signal;1\n",
      "Added TH1F: chBSM2016/TOP;1\n",
      "Added TH1F: chBSM2016/VVV;1\n",
      "Added TH1F: chBSM2016/WW;1\n",
      "Added TH1F: chBSM2016/WZ;1\n",
      "Added TH1F: chBSM2016/ZZ;1\n",
      "Added TH1F: chBSM2016/total;1\n",
      "Added TH1F: chBSM2016/total_signal;1\n",
      "Added TH1F: chBSM2016/total_background;1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added TH1D: total_bin_width;1\n",
      "Added TH1D: total_overall;1\n",
      "Added TH1D: total_signal;1\n",
      "Added TGraphAsymmErrors: total_data;1\n",
      "Added TH1D: total_background;1\n",
      "Added TH1D: overall_total_M1;1\n",
      "Added TH1D: overall_total_M3;1\n",
      "Added TGraphAsymmErrors: data;1\n",
      "Added TH1F: DY;1\n",
      "Added TH1F: Signal;1\n",
      "Added TH1F: TOP;1\n",
      "Added TH1F: VVV;1\n",
      "Added TH1F: WZ;1\n",
      "Added TH1F: ZZ;1\n",
      "Added TH1F: total;1\n",
      "Added TH1F: total_signal;1\n",
      "Added TH1F: total_background;1\n",
      "Added TGraphAsymmErrors: shapes_fit_s/cat3L2016/data;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/DY;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/Signal;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/TOP;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/VVV;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/WZ;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/ZZ;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/total;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/total_signal;1\n",
      "Added TH1F: shapes_fit_s/cat3L2016/total_background;1\n",
      "Added TGraphAsymmErrors: data;1\n",
      "Added TH1F: DY;1\n",
      "Added TH1F: TOP;1\n",
      "Added TH1F: VVV;1\n",
      "Added TH1F: WZ;1\n",
      "Added TH1F: ZZ;1\n",
      "Added TH1F: total;1\n",
      "Added TH1F: total_background;1\n",
      "Added TGraphAsymmErrors: shapes_fit_s/cat4L2016/data;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/DY;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/TOP;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/VVV;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/WZ;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/ZZ;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/total;1\n",
      "Added TH1F: shapes_fit_s/cat4L2016/total_background;1\n",
      "Added TGraphAsymmErrors: data;1\n",
      "Added TH1F: DY;1\n",
      "Added TH1F: TOP;1\n",
      "Added TH1F: VVV;1\n",
      "Added TH1F: WW;1\n",
      "Added TH1F: WZ;1\n",
      "Added TH1F: ZZ;1\n",
      "Added TH1F: total;1\n",
      "Added TH1F: total_background;1\n",
      "Added TGraphAsymmErrors: shapes_fit_s/catEM2016/data;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/DY;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/TOP;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/VVV;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/WW;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/WZ;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/ZZ;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/total;1\n",
      "Added TH1F: shapes_fit_s/catEM2016/total_background;1\n",
      "Added TGraphAsymmErrors: data;1\n",
      "Added TH1F: DY;1\n",
      "Added TH1F: Signal;1\n",
      "Added TH1F: TOP;1\n",
      "Added TH1F: VVV;1\n",
      "Added TH1F: WW;1\n",
      "Added TH1F: WZ;1\n",
      "Added TH1F: ZZ;1\n",
      "Added TH1F: total;1\n",
      "Added TH1F: total_signal;1\n",
      "Added TH1F: total_background;1\n",
      "Added TGraphAsymmErrors: shapes_fit_s/chBSM2016/data;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/DY;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/Signal;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/TOP;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/VVV;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/WW;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/WZ;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/ZZ;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/total;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/total_signal;1\n",
      "Added TH1F: shapes_fit_s/chBSM2016/total_background;1\n",
      "Added TH1D: shapes_fit_s/total_bin_width;1\n",
      "Added TH1D: shapes_fit_s/total_overall;1\n",
      "Added TH1D: shapes_fit_s/total_signal;1\n",
      "Added TGraphAsymmErrors: shapes_fit_s/total_data;1\n",
      "Added TH1D: shapes_fit_s/total_background;1\n",
      "Added TH1D: shapes_fit_s/overall_total_M1;1\n",
      "Added TH1D: shapes_fit_s/overall_total_M3;1\n",
      "\n",
      "----------\n",
      "Converted into histograms\n",
      "----------\n",
      "\n",
      "Error processing data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: DY;1\n",
      "Values: [2.2316185e+02 7.8466973e+00 2.2081811e+00 3.4824911e-01 1.3674780e-03\n",
      " 1.7417265e-03 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09\n",
      " 1.1744443e-09 1.1744443e-09 1.1744443e-09 6.9901239e+02 1.8186241e+01\n",
      " 7.5091463e-01 1.7628640e-01 1.9578895e-01 2.9432228e-02 1.5018205e-04\n",
      " 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: Signal;1\n",
      "Values: [2.6436081e-07 3.2617282e-07 3.4064286e-07 3.0857316e-07 2.7642594e-07\n",
      " 3.8097434e-07 2.6551200e-07 1.5948952e-07 1.1563403e-07 1.2201578e-07\n",
      " 6.0453900e-08 5.5549819e-08 2.8346619e-17 5.1050161e-08 7.7585071e-08\n",
      " 9.1956586e-08 9.3987190e-08 9.6176848e-08 1.5257537e-07 1.0746666e-07\n",
      " 8.2019540e-08 5.2108685e-08 7.0610156e-08 2.9798336e-08 3.7376509e-08]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: TOP;1\n",
      "Values: [ 1.1251571e+02  2.9270430e+01  7.6834183e+00  2.9286182e+00\n",
      "  5.2838129e-01  1.7933795e-01  1.0900758e-01  5.2419078e-04\n",
      "  1.4176429e-04  9.6121477e-10  9.6121477e-10  9.6121477e-10\n",
      "  9.6121477e-10  8.2983971e+01  2.3671926e+01  7.6007986e+00\n",
      "  1.7942533e+00  8.8819319e-01  6.7151666e-01  3.8200393e-02\n",
      " -3.9827655e-06  1.0673207e-04  2.0292781e-03  9.6121477e-10\n",
      "  9.6121477e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: VVV;1\n",
      "Values: [1.6645053e-01 2.2646634e-01 3.1858844e-01 1.7279060e-01 1.5249339e-01\n",
      " 8.6171679e-02 2.4176551e-02 2.2241779e-02 1.5842274e-02 3.8042635e-02\n",
      " 6.2973094e-03 6.7392872e-03 1.0496011e-09 2.1894901e-01 2.0319682e-01\n",
      " 2.3189948e-01 2.4582548e-01 1.2353863e-01 1.3502859e-01 1.9675380e-01\n",
      " 4.0049762e-02 5.6282967e-02 2.2369590e-02 4.6679345e-03 2.4587568e-03]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: WZ;1\n",
      "Values: [8.8520950e+01 6.3514008e+01 4.0511150e+01 2.1354185e+01 1.1920809e+01\n",
      " 8.9534788e+00 2.8853104e+00 1.0508723e+00 5.8012080e-01 3.1366783e-01\n",
      " 6.5765671e-02 5.5093333e-02 1.0265423e-09 4.5614410e+01 2.9448673e+01\n",
      " 1.9189224e+01 1.1527327e+01 5.9601111e+00 6.6037683e+00 2.2264721e+00\n",
      " 1.1524079e+00 3.8631734e-01 3.3878082e-01 5.5679966e-02 3.8097586e-02]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: ZZ;1\n",
      "Values: [1.24841393e+02 1.07276115e+02 7.77858124e+01 4.85376778e+01\n",
      " 2.92971859e+01 2.83812771e+01 1.19283228e+01 5.50179434e+00\n",
      " 2.80050611e+00 2.18347406e+00 6.80166781e-01 4.22578931e-01\n",
      " 1.17040821e-09 2.61271038e+01 2.12264805e+01 1.80964031e+01\n",
      " 1.33044977e+01 9.46548939e+00 1.11265888e+01 5.34012508e+00\n",
      " 2.85774565e+00 1.53027427e+00 1.35470581e+00 4.82107818e-01\n",
      " 2.93961972e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: total;1\n",
      "Values: [5.9305969e+02 2.1686900e+02 1.3055948e+02 7.4540894e+01 4.1922901e+01\n",
      " 3.7607693e+01 1.4959688e+01 6.7643123e+00 3.3966112e+00 2.5351846e+00\n",
      " 7.5222981e-01 4.8441160e-01 6.3614345e-09 8.6693347e+02 9.4393349e+01\n",
      " 4.7223038e+01 2.7572334e+01 1.6633121e+01 1.8894341e+01 7.8017015e+00\n",
      " 4.0501995e+00 1.9729813e+00 1.7178856e+00 5.4245573e-01 3.3451834e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: total_signal;1\n",
      "Values: [2.6436081e-07 3.2617282e-07 3.4064286e-07 3.0857316e-07 2.7642594e-07\n",
      " 3.8097434e-07 2.6551200e-07 1.5948952e-07 1.1563403e-07 1.2201578e-07\n",
      " 6.0453900e-08 5.5549819e-08 2.8346619e-17 5.1050161e-08 7.7585071e-08\n",
      " 9.1956586e-08 9.3987190e-08 9.6176848e-08 1.5257537e-07 1.0746666e-07\n",
      " 8.2019540e-08 5.2108685e-08 7.0610156e-08 2.9798336e-08 3.7376509e-08]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: total_background;1\n",
      "Values: [5.9305969e+02 2.1686900e+02 1.3055948e+02 7.4540894e+01 4.1922901e+01\n",
      " 3.7607693e+01 1.4959688e+01 6.7643123e+00 3.3966110e+00 2.5351846e+00\n",
      " 7.5222975e-01 4.8441154e-01 6.3614345e-09 8.6693347e+02 9.4393349e+01\n",
      " 4.7223038e+01 2.7572334e+01 1.6633121e+01 1.8894339e+01 7.8017015e+00\n",
      " 4.0501995e+00 1.9729813e+00 1.7178855e+00 5.4245573e-01 3.3451831e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing cat3L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: cat3L2016/DY;1\n",
      "Values: [4.29474020e+00 2.44581270e+00 1.04267085e+00 5.99176884e-01\n",
      " 4.42336380e-01 1.23055495e-01 1.47438809e-01 9.95883420e-02\n",
      " 8.39590468e-03 4.24000137e-02 1.31402351e-03 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/Signal;1\n",
      "Values: [2.2577233e-10 3.2755179e-10 1.8741217e-11 2.9822650e-10 5.7467694e-12\n",
      " 3.4627429e-10 2.7917213e-17 2.7917213e-17 2.7917213e-17 3.5499678e-10\n",
      " 2.7917213e-17 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/TOP;1\n",
      "Values: [10.619375    9.881928    6.253552    3.2689555   2.3911972   0.99558556\n",
      "  0.55625     0.1018003   0.06368483  0.05665475  0.03602773  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/VVV;1\n",
      "Values: [0.4170281  0.57293123 0.5351722  0.4974969  0.6451293  0.4264274\n",
      " 0.24955475 0.2523323  0.20507233 0.04060304 0.00120816 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/WZ;1\n",
      "Values: [97.60563   89.49726   65.63893   47.13961   49.378105  23.89633\n",
      " 12.843705   7.240059   5.9240174  1.7046576  1.2178665  0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/ZZ;1\n",
      "Values: [3.9423785  4.1862884  2.9724717  1.8544266  1.7945954  0.7284455\n",
      " 0.3309324  0.18096069 0.1306278  0.03994003 0.02380943 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/total;1\n",
      "Values: [116.87915   106.58422    76.4428     53.35967    54.651363   26.169844\n",
      "  14.127881    7.8747406   6.331798    1.8842554   1.2802259   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/total_signal;1\n",
      "Values: [2.2577233e-10 3.2755179e-10 1.8741217e-11 2.9822650e-10 5.7467694e-12\n",
      " 3.4627429e-10 2.7917213e-17 2.7917213e-17 2.7917213e-17 3.5499678e-10\n",
      " 2.7917213e-17 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat3L2016/total_background;1\n",
      "Values: [116.87915   106.58422    76.4428     53.35967    54.651363   26.169844\n",
      "  14.127881    7.8747406   6.331798    1.8842554   1.2802259   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing cat4L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: cat4L2016/DY;1\n",
      "Values: [4.0152434e-01 3.5239080e-01 2.7668118e-01 1.0125035e-09 2.5147470e-02\n",
      " 6.5727218e-04 1.0125035e-09 1.0125035e-09 1.0125035e-09 1.0125035e-09\n",
      " 1.0125035e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat4L2016/TOP;1\n",
      "Values: [0.9897093  0.5971781  0.6916932  0.20044087 0.31238544 0.08659876\n",
      " 0.13815397 0.05651288 0.01336283 0.00204357 0.0054929  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat4L2016/VVV;1\n",
      "Values: [0.32103845 0.19436786 0.19689284 0.2475703  0.18044214 0.04310834\n",
      " 0.12660599 0.03782709 0.02464591 0.01602303 0.02952506 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat4L2016/WZ;1\n",
      "Values: [1.56994358e-01 1.00442514e-01 6.39852658e-02 5.48382998e-02\n",
      " 3.14714946e-02 2.39481647e-02 2.10378002e-02 7.57192960e-03\n",
      " 9.91225324e-10 9.91225324e-10 9.91225324e-10 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat4L2016/ZZ;1\n",
      "Values: [36.83259    21.958372   13.018795    7.9346414   7.8629622   3.5095184\n",
      "  1.70886     0.86868227  0.7417786   0.26655486  0.1706338   0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat4L2016/total;1\n",
      "Values: [38.701855   23.202751   14.248048    8.43749     8.412409    3.663831\n",
      "  1.9946578   0.97059417  0.77978736  0.28462148  0.20565176  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: cat4L2016/total_background;1\n",
      "Values: [38.701855   23.202751   14.248048    8.43749     8.412409    3.663831\n",
      "  1.9946578   0.97059417  0.77978736  0.28462148  0.20565176  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: WW;1\n",
      "Values: [4.3853374e+01 8.7352848e+00 2.0523345e+00 1.1993690e+00 2.2664160e-02\n",
      " 5.6861830e-03 1.2870679e-02 1.8887953e-01 9.7922348e-10 9.7922348e-10\n",
      " 9.7922348e-10 9.7922348e-10 9.7922348e-10 1.2976664e+01 1.6568246e+00\n",
      " 1.3538010e+00 5.2414554e-01 9.7922348e-10 3.2800457e-01 9.7922348e-10\n",
      " 9.7922348e-10 9.7922348e-10 9.7922348e-10 9.7922348e-10 9.7922348e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing catEM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: catEM2016/DY;1\n",
      "Values: [4.461868   0.37070805 0.02046645 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/TOP;1\n",
      "Values: [4.9596964e+02 1.4659416e+01 3.7359092e-01 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/VVV;1\n",
      "Values: [0.4716881  0.12733345 0.02640671 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/WW;1\n",
      "Values: [136.54445      8.996124     0.72811437   0.           0.\n",
      "   0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/WZ;1\n",
      "Values: [6.61679    0.5625115  0.07246461 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/ZZ;1\n",
      "Values: [0.23319092 0.01601595 0.00145917 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/total;1\n",
      "Values: [644.2976     24.732111    1.2225021   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: catEM2016/total_background;1\n",
      "Values: [644.2976     24.732111    1.2225021   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing chBSM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: chBSM2016/DY;1\n",
      "Values: [2.2316185e+02 7.8466973e+00 2.2081811e+00 3.4824911e-01 1.3674780e-03\n",
      " 1.7417265e-03 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09\n",
      " 1.1744443e-09 1.1744443e-09 1.1744443e-09 6.9901239e+02 1.8186241e+01\n",
      " 7.5091463e-01 1.7628640e-01 1.9578895e-01 2.9432228e-02 1.5018205e-04\n",
      " 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/Signal;1\n",
      "Values: [2.6436081e-07 3.2617282e-07 3.4064286e-07 3.0857316e-07 2.7642594e-07\n",
      " 3.8097434e-07 2.6551200e-07 1.5948952e-07 1.1563403e-07 1.2201578e-07\n",
      " 6.0453900e-08 5.5549819e-08 2.8346619e-17 5.1050161e-08 7.7585071e-08\n",
      " 9.1956586e-08 9.3987190e-08 9.6176848e-08 1.5257537e-07 1.0746666e-07\n",
      " 8.2019540e-08 5.2108685e-08 7.0610156e-08 2.9798336e-08 3.7376509e-08]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/TOP;1\n",
      "Values: [ 1.1251571e+02  2.9270430e+01  7.6834183e+00  2.9286182e+00\n",
      "  5.2838129e-01  1.7933795e-01  1.0900758e-01  5.2419078e-04\n",
      "  1.4176429e-04  9.6121477e-10  9.6121477e-10  9.6121477e-10\n",
      "  9.6121477e-10  8.2983971e+01  2.3671926e+01  7.6007986e+00\n",
      "  1.7942533e+00  8.8819319e-01  6.7151666e-01  3.8200393e-02\n",
      " -3.9827655e-06  1.0673207e-04  2.0292781e-03  9.6121477e-10\n",
      "  9.6121477e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/VVV;1\n",
      "Values: [1.6645053e-01 2.2646634e-01 3.1858844e-01 1.7279060e-01 1.5249339e-01\n",
      " 8.6171679e-02 2.4176551e-02 2.2241779e-02 1.5842274e-02 3.8042635e-02\n",
      " 6.2973094e-03 6.7392872e-03 1.0496011e-09 2.1894901e-01 2.0319682e-01\n",
      " 2.3189948e-01 2.4582548e-01 1.2353863e-01 1.3502859e-01 1.9675380e-01\n",
      " 4.0049762e-02 5.6282967e-02 2.2369590e-02 4.6679345e-03 2.4587568e-03]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/WW;1\n",
      "Values: [4.3853374e+01 8.7352848e+00 2.0523345e+00 1.1993690e+00 2.2664160e-02\n",
      " 5.6861830e-03 1.2870679e-02 1.8887953e-01 9.7922348e-10 9.7922348e-10\n",
      " 9.7922348e-10 9.7922348e-10 9.7922348e-10 1.2976664e+01 1.6568246e+00\n",
      " 1.3538010e+00 5.2414554e-01 9.7922348e-10 3.2800457e-01 9.7922348e-10\n",
      " 9.7922348e-10 9.7922348e-10 9.7922348e-10 9.7922348e-10 9.7922348e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/WZ;1\n",
      "Values: [8.8520950e+01 6.3514008e+01 4.0511150e+01 2.1354185e+01 1.1920809e+01\n",
      " 8.9534788e+00 2.8853104e+00 1.0508723e+00 5.8012080e-01 3.1366783e-01\n",
      " 6.5765671e-02 5.5093333e-02 1.0265423e-09 4.5614410e+01 2.9448673e+01\n",
      " 1.9189224e+01 1.1527327e+01 5.9601111e+00 6.6037683e+00 2.2264721e+00\n",
      " 1.1524079e+00 3.8631734e-01 3.3878082e-01 5.5679966e-02 3.8097586e-02]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/ZZ;1\n",
      "Values: [1.24841393e+02 1.07276115e+02 7.77858124e+01 4.85376778e+01\n",
      " 2.92971859e+01 2.83812771e+01 1.19283228e+01 5.50179434e+00\n",
      " 2.80050611e+00 2.18347406e+00 6.80166781e-01 4.22578931e-01\n",
      " 1.17040821e-09 2.61271038e+01 2.12264805e+01 1.80964031e+01\n",
      " 1.33044977e+01 9.46548939e+00 1.11265888e+01 5.34012508e+00\n",
      " 2.85774565e+00 1.53027427e+00 1.35470581e+00 4.82107818e-01\n",
      " 2.93961972e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/total;1\n",
      "Values: [5.9305969e+02 2.1686900e+02 1.3055948e+02 7.4540894e+01 4.1922901e+01\n",
      " 3.7607693e+01 1.4959688e+01 6.7643123e+00 3.3966112e+00 2.5351846e+00\n",
      " 7.5222981e-01 4.8441160e-01 6.3614345e-09 8.6693347e+02 9.4393349e+01\n",
      " 4.7223038e+01 2.7572334e+01 1.6633121e+01 1.8894341e+01 7.8017015e+00\n",
      " 4.0501995e+00 1.9729813e+00 1.7178856e+00 5.4245573e-01 3.3451834e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/total_signal;1\n",
      "Values: [2.6436081e-07 3.2617282e-07 3.4064286e-07 3.0857316e-07 2.7642594e-07\n",
      " 3.8097434e-07 2.6551200e-07 1.5948952e-07 1.1563403e-07 1.2201578e-07\n",
      " 6.0453900e-08 5.5549819e-08 2.8346619e-17 5.1050161e-08 7.7585071e-08\n",
      " 9.1956586e-08 9.3987190e-08 9.6176848e-08 1.5257537e-07 1.0746666e-07\n",
      " 8.2019540e-08 5.2108685e-08 7.0610156e-08 2.9798336e-08 3.7376509e-08]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: chBSM2016/total_background;1\n",
      "Values: [5.9305969e+02 2.1686900e+02 1.3055948e+02 7.4540894e+01 4.1922901e+01\n",
      " 3.7607693e+01 1.4959688e+01 6.7643123e+00 3.3966110e+00 2.5351846e+00\n",
      " 7.5222975e-01 4.8441154e-01 6.3614345e-09 8.6693347e+02 9.4393349e+01\n",
      " 4.7223038e+01 2.7572334e+01 1.6633121e+01 1.8894339e+01 7.8017015e+00\n",
      " 4.0501995e+00 1.9729813e+00 1.7178855e+00 5.4245573e-01 3.3451831e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: total_bin_width;1\n",
      "Values: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1.]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: total_overall;1\n",
      "Values: [1.16879150e+02 1.06584221e+02 7.64428024e+01 5.33596687e+01\n",
      " 5.46513634e+01 2.61698437e+01 1.41278811e+01 7.87474060e+00\n",
      " 6.33179808e+00 1.88425541e+00 1.28022587e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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      " 0.00000000e+00 3.87018547e+01 2.32027512e+01 1.42480478e+01\n",
      " 8.43749046e+00 8.41240883e+00 3.66383100e+00 1.99465775e+00\n",
      " 9.70594168e-01 7.79787362e-01 2.84621477e-01 2.05651760e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.44297607e+02 2.47321110e+01\n",
      " 1.22250211e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.93059692e+02\n",
      " 2.16869003e+02 1.30559479e+02 7.45408936e+01 4.19229012e+01\n",
      " 3.76076927e+01 1.49596882e+01 6.76431227e+00 3.39661121e+00\n",
      " 2.53518462e+00 7.52229810e-01 4.84411597e-01 6.36143449e-09\n",
      " 8.66933472e+02 9.43933487e+01 4.72230377e+01 2.75723343e+01\n",
      " 1.66331215e+01 1.88943405e+01 7.80170155e+00 4.05019951e+00\n",
      " 1.97298133e+00 1.71788561e+00 5.42455733e-01 3.34518343e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing total_data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: overall_total_M1;1\n",
      "Values: [1.17404514e+02 1.06490363e+02 7.69678893e+01 5.33756716e+01\n",
      " 5.45669277e+01 2.60124693e+01 1.40674842e+01 7.82151018e+00\n",
      " 6.31341129e+00 1.89742848e+00 1.28035877e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.86805770e+01 2.32514017e+01 1.40987764e+01\n",
      " 8.46945111e+00 8.35991720e+00 3.62534986e+00 1.96893821e+00\n",
      " 9.65006518e-01 7.69122887e-01 2.83783549e-01 2.04338097e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.42370311e+02 2.48879982e+01\n",
      " 1.13923214e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.86466382e+02\n",
      " 2.15567208e+02 1.30537015e+02 7.41415409e+01 4.20688822e+01\n",
      " 3.74652709e+01 1.50218678e+01 6.74054513e+00 3.31681870e+00\n",
      " 2.53872896e+00 7.46496872e-01 4.85323213e-01 6.36733185e-09\n",
      " 8.71993128e+02 9.28465941e+01 4.71456994e+01 2.77162172e+01\n",
      " 1.67720503e+01 1.87568223e+01 7.86321235e+00 4.06909261e+00\n",
      " 1.96593594e+00 1.73256700e+00 5.33552518e-01 3.31266538e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: overall_total_M3;1\n",
      "Values: [ 1.48777752e+01  2.08152776e+01  8.56936555e+00  9.18284729e-01\n",
      " -1.07050659e+01 -6.57473225e-01  1.45482790e-03  1.12037852e-01\n",
      "  2.12135598e-01 -1.24219264e-03  4.32390083e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.99124305e+00  6.55801176e-01  9.56541147e-02\n",
      "  5.39711727e-02  2.66843054e-02  7.02263828e-03  3.74922683e-03\n",
      "  5.73151684e-04  1.78479924e-05  2.82928878e-05  1.35315677e-06\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00 -5.51805741e+03 -2.21465315e+00\n",
      " -1.21914355e-02  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00 -1.97366029e+02\n",
      "  7.43893449e+01  8.35445758e+00  6.25583202e+00  2.23592020e+00\n",
      " -2.51770418e-01  5.44236358e-01  4.07506042e-02  2.39436049e-03\n",
      " -3.93867086e-03 -1.64486139e-04 -7.01884458e-05  1.43462531e-30\n",
      " -1.33904097e+03  2.26036625e+00  3.83996960e+00  1.72904805e+00\n",
      "  5.36370202e-01  8.77633107e-01  1.16327278e-01  1.46961926e-02\n",
      "  3.64349629e-03  2.98812639e-03  1.86864128e-04  6.82816692e-05]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing shapes_prefit/cat3L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_prefit/cat3L2016/DY;1\n",
      "Values: [3.9991508e+00 2.4621222e+00 9.7076225e-01 5.7145619e-01 4.4682673e-01\n",
      " 1.2874547e-01 1.4238153e-01 9.0538912e-02 5.0953776e-03 4.0533010e-02\n",
      " 1.0562623e-03 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/Signal;1\n",
      "Values: [7.12608080e-03 1.16690835e-02 9.99999972e-10 1.20594967e-02\n",
      " 9.99999972e-10 1.23309409e-02 9.99999972e-10 9.99999972e-10\n",
      " 9.99999972e-10 1.33139361e-02 9.99999972e-10 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/TOP;1\n",
      "Values: [10.139963    9.640158    6.041372    3.095616    2.3139107   0.96736795\n",
      "  0.5271489   0.09191187  0.05756902  0.04977909  0.03461195  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/VVV;1\n",
      "Values: [0.3883178  0.55827606 0.517584   0.46868965 0.6259905  0.41181412\n",
      " 0.24359849 0.23432612 0.19348371 0.03836792 0.00171609 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/WZ;1\n",
      "Values: [94.255936  86.64082   63.55738   45.637043  51.027977  24.86336\n",
      " 13.439676   7.5739527  6.3382173  1.8407842  1.3564565  0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/ZZ;1\n",
      "Values: [3.381045   3.5515277  2.4874198  1.5349156  1.5212678  0.5984606\n",
      " 0.2645664  0.1401832  0.10071287 0.02902054 0.01708666 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/total;1\n",
      "Values: [112.17154   102.86458    73.57452    51.319782   55.93597    26.982079\n",
      "  14.617372    8.130913    6.695078    2.0117986   1.4109274   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/total_signal;1\n",
      "Values: [7.12608080e-03 1.16690835e-02 9.99999972e-10 1.20594967e-02\n",
      " 9.99999972e-10 1.23309409e-02 9.99999972e-10 9.99999972e-10\n",
      " 9.99999972e-10 1.33139361e-02 9.99999972e-10 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat3L2016/total_background;1\n",
      "Values: [112.16441   102.852905   73.57452    51.30772    55.93597    26.969748\n",
      "  14.617372    8.130913    6.695078    1.9984847   1.4109274   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_prefit/cat4L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_prefit/cat4L2016/DY;1\n",
      "Values: [2.3037665e-01 3.4183109e-01 2.6097870e-01 9.9999997e-10 2.6261965e-02\n",
      " 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10\n",
      " 9.9999997e-10 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat4L2016/TOP;1\n",
      "Values: [0.9222576  0.58276725 0.6631518  0.1906431  0.302374   0.0820222\n",
      " 0.13418782 0.05216153 0.01279901 0.00186515 0.00476376 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat4L2016/VVV;1\n",
      "Values: [0.31187394 0.19064082 0.18860613 0.24128008 0.16994058 0.0434863\n",
      " 0.12204539 0.03361454 0.02480851 0.01542712 0.02909351 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat4L2016/WZ;1\n",
      "Values: [1.5476216e-01 9.5904537e-02 6.1765131e-02 5.5589736e-02 3.3167135e-02\n",
      " 2.7992163e-02 2.4902711e-02 9.1853328e-03 9.9999997e-10 9.9999997e-10\n",
      " 9.9999997e-10 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat4L2016/ZZ;1\n",
      "Values: [31.769913   18.751799   11.003929    6.6345854   6.7738194   2.9375021\n",
      "  1.3965567   0.69526327  0.5851219   0.20334339  0.1250581   0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat4L2016/total;1\n",
      "Values: [33.389183   19.962942   12.178431    7.1220984   7.305563    3.0910027\n",
      "  1.6776927   0.7902247   0.6227294   0.22063565  0.15891537  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/cat4L2016/total_background;1\n",
      "Values: [33.389183   19.962942   12.178431    7.1220984   7.305563    3.0910027\n",
      "  1.6776927   0.7902247   0.6227294   0.22063565  0.15891537  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_prefit/catEM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_prefit/catEM2016/DY;1\n",
      "Values: [4.31307    0.34555468 0.02360247 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/TOP;1\n",
      "Values: [5.1546851e+02 1.4799655e+01 4.1946873e-01 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/VVV;1\n",
      "Values: [0.4363693  0.12187744 0.02592376 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/WW;1\n",
      "Values: [140.31493     9.211699    0.9347852   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/WZ;1\n",
      "Values: [6.6105676  0.56711364 0.07737554 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/ZZ;1\n",
      "Values: [0.20215274 0.01299776 0.00100681 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/total;1\n",
      "Values: [667.3456     25.058899    1.4821625   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/catEM2016/total_background;1\n",
      "Values: [667.3456     25.058899    1.4821625   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_prefit/chBSM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_prefit/chBSM2016/DY;1\n",
      "Values: [1.8807906e+02 6.3892465e+00 1.8735304e+00 2.9623416e-01 9.9999997e-10\n",
      " 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10\n",
      " 9.9999997e-10 9.9999997e-10 9.9999997e-10 5.9870605e+02 1.5059024e+01\n",
      " 4.1625613e-01 1.5224023e-01 1.6427094e-01 2.2970947e-02 9.9999997e-10\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/Signal;1\n",
      "Values: [9.3733130e+00 1.1552597e+01 1.2056206e+01 1.0868348e+01 9.8170671e+00\n",
      " 1.3482400e+01 9.3699360e+00 5.6137342e+00 4.0616736e+00 4.3351712e+00\n",
      " 2.1322453e+00 1.9441316e+00 9.9999997e-10 1.7928951e+00 2.7392564e+00\n",
      " 3.2773824e+00 3.3163671e+00 3.3661513e+00 5.2383089e+00 3.8188372e+00\n",
      " 2.8709638e+00 1.8361264e+00 2.4566035e+00 1.0327951e+00 1.2999595e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/TOP;1\n",
      "Values: [1.17437859e+02 3.03819294e+01 7.91386795e+00 3.43574047e+00\n",
      " 4.75564182e-01 1.77638605e-01 1.20727845e-01 7.11734989e-04\n",
      " 1.87565238e-04 9.99999972e-10 9.99999972e-10 9.99999972e-10\n",
      " 9.99999972e-10 8.59687729e+01 2.41043873e+01 7.42983341e+00\n",
      " 1.82941282e+00 8.99545491e-01 6.44502938e-01 3.80809642e-02\n",
      " 5.46801777e-04 9.99999972e-10 2.43705278e-03 9.99999972e-10\n",
      " 9.99999972e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/VVV;1\n",
      "Values: [1.6384502e-01 2.0548077e-01 3.0800417e-01 1.6349363e-01 1.4649196e-01\n",
      " 8.0105834e-02 2.2409212e-02 2.2324096e-02 1.3976121e-02 4.0002305e-02\n",
      " 5.3288238e-03 6.9909352e-03 9.9999997e-10 2.1222158e-01 1.9275804e-01\n",
      " 2.1707591e-01 2.3192561e-01 1.1462985e-01 1.2603581e-01 1.9406953e-01\n",
      " 3.8561694e-02 5.3750843e-02 2.2193504e-02 4.4003269e-03 2.8145374e-03]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/WW;1\n",
      "Values: [4.4524235e+01 8.8661385e+00 2.0634601e+00 1.3034203e+00 9.9999997e-10\n",
      " 1.0470692e-02 9.9999997e-10 2.3219028e-01 9.9999997e-10 9.9999997e-10\n",
      " 9.9999997e-10 9.9999997e-10 9.9999997e-10 1.3371861e+01 1.6939201e+00\n",
      " 1.3173901e+00 4.9567685e-01 9.9999997e-10 3.3921111e-01 9.9999997e-10\n",
      " 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/WZ;1\n",
      "Values: [8.5219597e+01 6.1048092e+01 3.8946339e+01 2.0543882e+01 1.1493960e+01\n",
      " 9.1356030e+00 2.9737654e+00 1.0846716e+00 6.0688835e-01 3.3238468e-01\n",
      " 7.2020672e-02 5.7798102e-02 9.9999997e-10 4.5017822e+01 2.8989630e+01\n",
      " 1.8890711e+01 1.1422905e+01 5.9668784e+00 6.6232843e+00 2.2624128e+00\n",
      " 1.1772308e+00 4.1095182e-01 3.5100445e-01 5.6299794e-02 4.0562388e-02]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/ZZ;1\n",
      "Values: [1.0832677e+02 9.2200462e+01 6.6171059e+01 4.0858479e+01 2.4394745e+01\n",
      " 2.4327560e+01 9.9421988e+00 4.4750814e+00 2.2234585e+00 1.7117020e+00\n",
      " 5.1401591e-01 3.0545002e-01 9.9999997e-10 2.3019079e+01 1.8520657e+01\n",
      " 1.5645424e+01 1.1386917e+01 8.0150671e+00 9.2422104e+00 4.3282976e+00\n",
      " 2.2614093e+00 1.1863364e+00 1.0237176e+00 3.5273567e-01 2.0525321e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/total;1\n",
      "Values: [5.5312469e+02 2.1064395e+02 1.2933246e+02 7.7469604e+01 4.6327827e+01\n",
      " 4.7213779e+01 2.2429037e+01 1.1428713e+01 6.9061842e+00 6.4192600e+00\n",
      " 2.7236109e+00 2.3143706e+00 7.0000001e-09 7.6808875e+02 9.1299637e+01\n",
      " 4.7194073e+01 2.8835445e+01 1.8526543e+01 2.2236525e+01 1.0641698e+01\n",
      " 6.3487124e+00 3.4871655e+00 3.8559561e+00 1.4462309e+00 1.5485897e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/total_signal;1\n",
      "Values: [9.3733130e+00 1.1552597e+01 1.2056206e+01 1.0868348e+01 9.8170671e+00\n",
      " 1.3482400e+01 9.3699360e+00 5.6137342e+00 4.0616736e+00 4.3351712e+00\n",
      " 2.1322453e+00 1.9441316e+00 9.9999997e-10 1.7928951e+00 2.7392564e+00\n",
      " 3.2773824e+00 3.3163671e+00 3.3661513e+00 5.2383089e+00 3.8188372e+00\n",
      " 2.8709638e+00 1.8361264e+00 2.4566035e+00 1.0327951e+00 1.2999595e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/chBSM2016/total_background;1\n",
      "Values: [5.4375134e+02 1.9909134e+02 1.1727626e+02 6.6601250e+01 3.6510761e+01\n",
      " 3.3731377e+01 1.3059101e+01 5.8149791e+00 2.8445106e+00 2.0840890e+00\n",
      " 5.9136540e-01 3.7023908e-01 6.0000001e-09 7.6629584e+02 8.8560379e+01\n",
      " 4.3916691e+01 2.5519077e+01 1.5160392e+01 1.6998215e+01 6.8228607e+00\n",
      " 3.4777486e+00 1.6510391e+00 1.3993527e+00 4.1343579e-01 2.4863014e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_prefit/total_bin_width;1\n",
      "Values: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1.]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_prefit/total_overall;1\n",
      "Values: [1.12171539e+02 1.02864578e+02 7.35745163e+01 5.13197823e+01\n",
      " 5.59359703e+01 2.69820786e+01 1.46173716e+01 8.13091278e+00\n",
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      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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      " 2.10643951e+02 1.29332458e+02 7.74696045e+01 4.63278275e+01\n",
      " 4.72137794e+01 2.24290371e+01 1.14287128e+01 6.90618420e+00\n",
      " 6.41926003e+00 2.72361088e+00 2.31437063e+00 7.00000014e-09\n",
      " 7.68088745e+02 9.12996368e+01 4.71940727e+01 2.88354454e+01\n",
      " 1.85265427e+01 2.22365246e+01 1.06416979e+01 6.34871244e+00\n",
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      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_prefit/total_signal;1\n",
      "Values: [7.12608080e-03 1.16690835e-02 9.99999972e-10 1.20594967e-02\n",
      " 9.99999972e-10 1.23309409e-02 9.99999972e-10 9.99999972e-10\n",
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      " 1.34823999e+01 9.36993599e+00 5.61373425e+00 4.06167364e+00\n",
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      " 1.79289508e+00 2.73925638e+00 3.27738237e+00 3.31636715e+00\n",
      " 3.36615133e+00 5.23830891e+00 3.81883717e+00 2.87096381e+00\n",
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      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing shapes_prefit/total_data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_prefit/total_background;1\n",
      "Values: [1.12164413e+02 1.02852905e+02 7.35745163e+01 5.13077202e+01\n",
      " 5.59359703e+01 2.69697475e+01 1.46173716e+01 8.13091278e+00\n",
      " 6.69507790e+00 1.99848473e+00 1.41092741e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.33891830e+01 1.99629421e+01 1.21784306e+01\n",
      " 7.12209845e+00 7.30556297e+00 3.09100270e+00 1.67769265e+00\n",
      " 7.90224671e-01 6.22729421e-01 2.20635653e-01 1.58915371e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.67345581e+02 2.50588989e+01\n",
      " 1.48216248e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.43751343e+02\n",
      " 1.99091339e+02 1.17276260e+02 6.66012497e+01 3.65107613e+01\n",
      " 3.37313766e+01 1.30591011e+01 5.81497908e+00 2.84451056e+00\n",
      " 2.08408904e+00 5.91365397e-01 3.70239079e-01 6.00000005e-09\n",
      " 7.66295837e+02 8.85603790e+01 4.39166908e+01 2.55190773e+01\n",
      " 1.51603918e+01 1.69982147e+01 6.82286072e+00 3.47774863e+00\n",
      " 1.65103912e+00 1.39935267e+00 4.13435787e-01 2.48630136e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_prefit/overall_total_M1;1\n",
      "Values: [1.12742951e+02 1.02165586e+02 7.40463797e+01 5.05596489e+01\n",
      " 5.71392822e+01 2.73612470e+01 1.47573879e+01 8.20713368e+00\n",
      " 6.51709496e+00 2.01369967e+00 1.37833735e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.32230305e+01 2.00461049e+01 1.17367348e+01\n",
      " 7.22276824e+00 7.39078598e+00 3.10018490e+00 1.66962139e+00\n",
      " 8.34459781e-01 6.10052104e-01 2.12589521e-01 1.54407823e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.79205902e+02 2.64292484e+01\n",
      " 1.35730349e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.39462137e+02\n",
      " 2.07930080e+02 1.29074565e+02 7.60232738e+01 4.61055977e+01\n",
      " 4.84362731e+01 2.21723056e+01 1.15978541e+01 6.50131331e+00\n",
      " 6.49132499e+00 2.58275005e+00 2.22513877e+00 7.02396117e-09\n",
      " 8.01182594e+02 8.74817902e+01 4.71867627e+01 2.91270294e+01\n",
      " 1.87573139e+01 2.13316381e+01 1.06609376e+01 6.17783925e+00\n",
      " 3.38035158e+00 3.99219509e+00 1.45385787e+00 1.67305898e+00]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_prefit/overall_total_M3;1\n",
      "Values: [ 1.36629948e+03  8.03512261e+02  4.94679534e+02  1.57860044e+02\n",
      "  1.30323625e+03  1.94297467e+02  2.99495293e+01  5.09266130e+00\n",
      "  3.46273517e+00  1.13113716e-01  5.46513182e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  3.05062946e+01  7.10544229e+00  1.83397315e+00\n",
      "  3.30980570e-01  1.58422786e+00  1.97414885e-01  1.01730663e-02\n",
      "  2.48925964e-03  5.97667832e-03  2.03967706e-04  1.41324625e-04\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  1.45906184e+05  4.47411401e+01\n",
      "  3.16347360e-02  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  1.75064890e+03\n",
      "  5.33523952e+03  1.40593497e+03  2.69543723e+02  5.29821966e+01\n",
      "  1.35339999e+02  1.16404605e+01  5.82071485e-01  1.04946839e-01\n",
      "  2.41301596e-01  1.36886564e-02 -2.40898599e-03  2.17313442e-29\n",
      "  4.36403221e+05  1.03990589e+02  2.01109376e+01  5.90968358e+00\n",
      "  1.54696551e+00  3.55455542e+00  4.70953773e-02  9.08274360e-02\n",
      "  5.24983539e-04  2.28289225e-02  4.81880506e-03  1.84052672e-03]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing shapes_fit_b/cat3L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_b/cat3L2016/DY;1\n",
      "Values: [4.2909083e+00 2.4460320e+00 1.0422063e+00 5.9898967e-01 4.4268763e-01\n",
      " 1.2318398e-01 1.4745231e-01 9.9513777e-02 8.3728256e-03 4.2408023e-02\n",
      " 1.3112213e-03 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/Signal;1\n",
      "Values: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0.]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/TOP;1\n",
      "Values: [10.616637    9.877206    6.252614    3.2680814   2.3910263   0.9953436\n",
      "  0.55610347  0.10178573  0.06364396  0.0565763   0.03602922  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/VVV;1\n",
      "Values: [0.41671517 0.5727909  0.5350134  0.4973998  0.64507544 0.42634666\n",
      " 0.24956357 0.25207976 0.20504262 0.04058797 0.00120936 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/WZ;1\n",
      "Values: [97.61656   89.510345  65.64916   47.144268  49.38316   23.899652\n",
      " 12.844766   7.241739   5.9235497  1.7049571  1.2177567  0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/ZZ;1\n",
      "Values: [3.9430637  4.187153   2.9731097  1.8548793  1.7949532  0.72861093\n",
      " 0.3310389  0.18100838 0.13064775 0.03995294 0.02382086 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/total;1\n",
      "Values: [116.88389   106.59352    76.4521     53.363617   54.656902   26.173138\n",
      "  14.128923    7.8761263   6.331257    1.8844824   1.2801274   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/total_signal;1\n",
      "Values: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0.]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat3L2016/total_background;1\n",
      "Values: [116.88389   106.59352    76.4521     53.363617   54.656902   26.173138\n",
      "  14.128923    7.8761263   6.331257    1.8844824   1.2801274   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_fit_b/cat4L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_b/cat4L2016/DY;1\n",
      "Values: [4.0208900e-01 3.5253736e-01 2.7669212e-01 1.0131672e-09 2.5174689e-02\n",
      " 6.5690541e-04 1.0131672e-09 1.0131672e-09 1.0131672e-09 1.0131672e-09\n",
      " 1.0131672e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat4L2016/TOP;1\n",
      "Values: [0.9896457  0.5972384  0.69144    0.200428   0.31237733 0.08657879\n",
      " 0.1381985  0.05644861 0.0133694  0.0020426  0.0054917  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat4L2016/VVV;1\n",
      "Values: [0.32097548 0.19434884 0.19695029 0.24742962 0.18037918 0.04312781\n",
      " 0.12660691 0.0377945  0.02464557 0.01601211 0.02951957 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat4L2016/WZ;1\n",
      "Values: [1.5705065e-01 1.0043178e-01 6.3977614e-02 5.4850910e-02 3.1530395e-02\n",
      " 2.3947427e-02 2.1070262e-02 7.5939246e-03 9.9157715e-10 9.9157715e-10\n",
      " 9.9157715e-10 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat4L2016/ZZ;1\n",
      "Values: [36.836525   21.961542   13.021039    7.936334    7.864195    3.5101275\n",
      "  1.7092867   0.8689132   0.7418717   0.26661626  0.17067653  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat4L2016/total;1\n",
      "Values: [38.706287   23.206099   14.250099    8.439043    8.413656    3.6644385\n",
      "  1.9951624   0.9707502   0.77988666  0.28467098  0.2056878   0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/cat4L2016/total_background;1\n",
      "Values: [38.706287   23.206099   14.250099    8.439043    8.413656    3.6644385\n",
      "  1.9951624   0.9707502   0.77988666  0.28467098  0.2056878   0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_fit_b/catEM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_b/catEM2016/DY;1\n",
      "Values: [4.461145   0.37075034 0.02054407 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/TOP;1\n",
      "Values: [4.9597989e+02 1.4657741e+01 3.7354183e-01 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/VVV;1\n",
      "Values: [0.47118083 0.12734729 0.02639352 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/WW;1\n",
      "Values: [136.53093     8.997271    0.7277211   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/WZ;1\n",
      "Values: [6.6163797  0.56234586 0.07247409 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/ZZ;1\n",
      "Values: [0.2331733  0.01601254 0.00145976 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/total;1\n",
      "Values: [644.2927     24.731466    1.2221344   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/catEM2016/total_background;1\n",
      "Values: [644.2927     24.731466    1.2221344   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_fit_b/chBSM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_b/chBSM2016/DY;1\n",
      "Values: [2.2317961e+02 7.8499284e+00 2.2070210e+00 3.4791863e-01 1.3665289e-03\n",
      " 1.7406773e-03 1.1745245e-09 1.1745245e-09 1.1745245e-09 1.1745245e-09\n",
      " 1.1745245e-09 1.1745245e-09 1.1745245e-09 6.9908728e+02 1.8198063e+01\n",
      " 7.5093579e-01 1.7637333e-01 1.9563106e-01 2.9390190e-02 1.4980299e-04\n",
      " 1.1745245e-09 1.1745245e-09 1.1745245e-09 1.1745245e-09 1.1745245e-09]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/Signal;1\n",
      "Values: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0.]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/TOP;1\n",
      "Values: [ 1.1252726e+02  2.9271008e+01  7.6821156e+00  2.9305074e+00\n",
      "  5.2784115e-01  1.7932452e-01  1.0907932e-01  5.2612054e-04\n",
      "  1.4254062e-04  9.6125430e-10  9.6125430e-10  9.6125430e-10\n",
      "  9.6125430e-10  8.2985855e+01  2.3671589e+01  7.6008673e+00\n",
      "  1.7943933e+00  8.8763827e-01  6.7100102e-01  3.8201999e-02\n",
      " -3.9810266e-06  1.0665374e-04  2.0340860e-03  9.6125430e-10\n",
      "  9.6125430e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/VVV;1\n",
      "Values: [1.6656490e-01 2.2632231e-01 3.1852514e-01 1.7263517e-01 1.5246892e-01\n",
      " 8.6097084e-02 2.4174450e-02 2.2245789e-02 1.5840584e-02 3.8100809e-02\n",
      " 6.2855631e-03 6.7509646e-03 1.0493091e-09 2.1883687e-01 2.0313226e-01\n",
      " 2.3180945e-01 2.4566314e-01 1.2358786e-01 1.3501120e-01 1.9674009e-01\n",
      " 4.0015943e-02 5.6262881e-02 2.2372723e-02 4.6642376e-03 2.4611377e-03]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/WW;1\n",
      "Values: [4.3840248e+01 8.7321806e+00 2.0518029e+00 1.1997532e+00 2.2456774e-02\n",
      " 5.8130831e-03 1.2857642e-02 1.8896663e-01 9.7897790e-10 9.7897790e-10\n",
      " 9.7897790e-10 9.7897790e-10 9.7897790e-10 1.2975629e+01 1.6566644e+00\n",
      " 1.3542457e+00 5.2362174e-01 9.7897790e-10 3.2796720e-01 9.7897790e-10\n",
      " 9.7897790e-10 9.7897790e-10 9.7897790e-10 9.7897790e-10 9.7897790e-10]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/WZ;1\n",
      "Values: [8.8528633e+01 6.3519203e+01 4.0513496e+01 2.1355791e+01 1.1921281e+01\n",
      " 8.9536047e+00 2.8853962e+00 1.0509443e+00 5.8035934e-01 3.1368831e-01\n",
      " 6.5782160e-02 5.5012688e-02 1.0265353e-09 4.5606369e+01 2.9443035e+01\n",
      " 1.9185745e+01 1.1525623e+01 5.9597788e+00 6.6031575e+00 2.2264032e+00\n",
      " 1.1522955e+00 3.8641286e-01 3.3873415e-01 5.5665277e-02 3.8074519e-02]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/ZZ;1\n",
      "Values: [1.24851730e+02 1.07283424e+02 7.77921295e+01 4.85434380e+01\n",
      " 2.93015347e+01 2.83837719e+01 1.19299841e+01 5.50276375e+00\n",
      " 2.80103350e+00 2.18369460e+00 6.80250466e-01 4.22661364e-01\n",
      " 1.17045984e-09 2.61219940e+01 2.12223644e+01 1.80933933e+01\n",
      " 1.33026543e+01 9.46441746e+00 1.11257353e+01 5.34003448e+00\n",
      " 2.85774040e+00 1.53033912e+00 1.35483491e+00 4.82164949e-01\n",
      " 2.94028610e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/total;1\n",
      "Values: [5.9309406e+02 2.1688208e+02 1.3056509e+02 7.4550041e+01 4.1926949e+01\n",
      " 3.7610352e+01 1.4961492e+01 6.7654467e+00 3.3973761e+00 2.5354838e+00\n",
      " 7.5231820e-01 4.8442501e-01 6.3610606e-09 8.6699597e+02 9.4394852e+01\n",
      " 4.7216995e+01 2.7568329e+01 1.6631054e+01 1.8892262e+01 7.8015299e+00\n",
      " 4.0500479e+00 1.9731215e+00 1.7179759e+00 5.4249448e-01 3.3456427e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/total_signal;1\n",
      "Values: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0.]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/chBSM2016/total_background;1\n",
      "Values: [5.9309406e+02 2.1688208e+02 1.3056509e+02 7.4550041e+01 4.1926949e+01\n",
      " 3.7610352e+01 1.4961492e+01 6.7654467e+00 3.3973761e+00 2.5354838e+00\n",
      " 7.5231820e-01 4.8442501e-01 6.3610606e-09 8.6699597e+02 9.4394852e+01\n",
      " 4.7216995e+01 2.7568329e+01 1.6631054e+01 1.8892262e+01 7.8015299e+00\n",
      " 4.0500479e+00 1.9731215e+00 1.7179759e+00 5.4249448e-01 3.3456427e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_b/total_bin_width;1\n",
      "Values: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
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      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_b/total_overall;1\n",
      "Values: [1.16883888e+02 1.06593521e+02 7.64521027e+01 5.33636169e+01\n",
      " 5.46569023e+01 2.61731377e+01 1.41289234e+01 7.87612629e+00\n",
      " 6.33125687e+00 1.88448238e+00 1.28012741e+00 0.00000000e+00\n",
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      " 9.70750213e-01 7.79886663e-01 2.84670979e-01 2.05687806e-01\n",
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      " 1.22213435e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.93094055e+02\n",
      " 2.16882080e+02 1.30565094e+02 7.45500412e+01 4.19269485e+01\n",
      " 3.76103516e+01 1.49614916e+01 6.76544666e+00 3.39737606e+00\n",
      " 2.53548384e+00 7.52318203e-01 4.84425008e-01 6.36106057e-09\n",
      " 8.66995972e+02 9.43948517e+01 4.72169952e+01 2.75683289e+01\n",
      " 1.66310539e+01 1.88922615e+01 7.80152988e+00 4.05004787e+00\n",
      " 1.97312152e+00 1.71797585e+00 5.42494476e-01 3.34564269e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_b/total_signal;1\n",
      "Values: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing shapes_fit_b/total_data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_b/total_background;1\n",
      "Values: [1.16883888e+02 1.06593521e+02 7.64521027e+01 5.33636169e+01\n",
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      " 0.00000000e+00 3.87062874e+01 2.32060986e+01 1.42500992e+01\n",
      " 8.43904305e+00 8.41365623e+00 3.66443849e+00 1.99516237e+00\n",
      " 9.70750213e-01 7.79886663e-01 2.84670979e-01 2.05687806e-01\n",
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      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
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      " 2.16882080e+02 1.30565094e+02 7.45500412e+01 4.19269485e+01\n",
      " 3.76103516e+01 1.49614916e+01 6.76544666e+00 3.39737606e+00\n",
      " 2.53548384e+00 7.52318203e-01 4.84425008e-01 6.36106057e-09\n",
      " 8.66995972e+02 9.43948517e+01 4.72169952e+01 2.75683289e+01\n",
      " 1.66310539e+01 1.88922615e+01 7.80152988e+00 4.05004787e+00\n",
      " 1.97312152e+00 1.71797585e+00 5.42494476e-01 3.34564269e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_b/overall_total_M1;1\n",
      "Values: [1.17526443e+02 1.06582718e+02 7.69649765e+01 5.32572326e+01\n",
      " 5.50203546e+01 2.63616928e+01 1.42758356e+01 7.95995926e+00\n",
      " 6.47228744e+00 1.90640399e+00 1.30336348e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.88062002e+01 2.32933660e+01 1.41724676e+01\n",
      " 8.49606299e+00 8.43442194e+00 3.66809329e+00 2.01079633e+00\n",
      " 9.86667161e-01 7.88313458e-01 2.86023533e-01 2.07242401e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.40959424e+02 2.47446338e+01\n",
      " 1.14026811e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.86393553e+02\n",
      " 2.16826868e+02 1.30869379e+02 7.45872239e+01 4.22787475e+01\n",
      " 3.79081985e+01 1.52322026e+01 6.82485664e+00 3.37171617e+00\n",
      " 2.59722831e+00 7.68695803e-01 4.98406643e-01 6.37265542e-09\n",
      " 8.69835334e+02 9.26911970e+01 4.71596319e+01 2.78284033e+01\n",
      " 1.69011557e+01 1.88239909e+01 7.87882035e+00 4.09060045e+00\n",
      " 1.98328714e+00 1.74561132e+00 5.40162245e-01 3.36138946e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_b/overall_total_M3;1\n",
      "Values: [-3.48540149e+01 -3.40500645e+01 -3.58416584e+00 -1.82121117e+00\n",
      " -8.06664882e+00 -7.30872407e-01 -2.10205654e-01 -3.22157728e-02\n",
      "  1.13952872e-01 -8.60133054e-03 -3.24974025e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  2.88024318e+00  7.46236732e-01  1.48620572e-01\n",
      "  6.08748383e-02 -6.40697928e-02 -1.98641415e-03  1.86100229e-03\n",
      " -3.06260802e-04  4.98616741e-05  2.50416518e-06 -2.69655405e-06\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.83198257e+03 -5.08725841e-01\n",
      "  4.20587312e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  1.21860847e+03\n",
      "  1.28949428e+02  5.94896001e+01  1.75642379e+01  5.48771267e+00\n",
      " -6.60911995e+00 -1.70407281e-01 -8.89350894e-02 -2.22711290e-03\n",
      " -1.50158008e-02 -1.73314883e-04  8.91352275e-05  5.77212149e-31\n",
      "  3.61882051e+03 -4.92272876e+00 -1.48635237e+00  1.39051066e-01\n",
      "  5.73512420e-02  7.20739097e-01  3.88117789e-02  6.69084893e-04\n",
      "  2.20989177e-03  7.83562202e-04  4.97097939e-05  2.45533903e-05]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing shapes_fit_s/cat3L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_s/cat3L2016/DY;1\n",
      "Values: [4.29474020e+00 2.44581270e+00 1.04267085e+00 5.99176884e-01\n",
      " 4.42336380e-01 1.23055495e-01 1.47438809e-01 9.95883420e-02\n",
      " 8.39590468e-03 4.24000137e-02 1.31402351e-03 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/Signal;1\n",
      "Values: [2.2577233e-10 3.2755179e-10 1.8741217e-11 2.9822650e-10 5.7467694e-12\n",
      " 3.4627429e-10 2.7917213e-17 2.7917213e-17 2.7917213e-17 3.5499678e-10\n",
      " 2.7917213e-17 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/TOP;1\n",
      "Values: [10.619375    9.881928    6.253552    3.2689555   2.3911972   0.99558556\n",
      "  0.55625     0.1018003   0.06368483  0.05665475  0.03602773  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/VVV;1\n",
      "Values: [0.4170281  0.57293123 0.5351722  0.4974969  0.6451293  0.4264274\n",
      " 0.24955475 0.2523323  0.20507233 0.04060304 0.00120816 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/WZ;1\n",
      "Values: [97.60563   89.49726   65.63893   47.13961   49.378105  23.89633\n",
      " 12.843705   7.240059   5.9240174  1.7046576  1.2178665  0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/ZZ;1\n",
      "Values: [3.9423785  4.1862884  2.9724717  1.8544266  1.7945954  0.7284455\n",
      " 0.3309324  0.18096069 0.1306278  0.03994003 0.02380943 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/total;1\n",
      "Values: [116.87915   106.58422    76.4428     53.35967    54.651363   26.169844\n",
      "  14.127881    7.8747406   6.331798    1.8842554   1.2802259   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/total_signal;1\n",
      "Values: [2.2577233e-10 3.2755179e-10 1.8741217e-11 2.9822650e-10 5.7467694e-12\n",
      " 3.4627429e-10 2.7917213e-17 2.7917213e-17 2.7917213e-17 3.5499678e-10\n",
      " 2.7917213e-17 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat3L2016/total_background;1\n",
      "Values: [116.87915   106.58422    76.4428     53.35967    54.651363   26.169844\n",
      "  14.127881    7.8747406   6.331798    1.8842554   1.2802259   0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.       ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_fit_s/cat4L2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_s/cat4L2016/DY;1\n",
      "Values: [4.0152434e-01 3.5239080e-01 2.7668118e-01 1.0125035e-09 2.5147470e-02\n",
      " 6.5727218e-04 1.0125035e-09 1.0125035e-09 1.0125035e-09 1.0125035e-09\n",
      " 1.0125035e-09 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat4L2016/TOP;1\n",
      "Values: [0.9897093  0.5971781  0.6916932  0.20044087 0.31238544 0.08659876\n",
      " 0.13815397 0.05651288 0.01336283 0.00204357 0.0054929  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat4L2016/VVV;1\n",
      "Values: [0.32103845 0.19436786 0.19689284 0.2475703  0.18044214 0.04310834\n",
      " 0.12660599 0.03782709 0.02464591 0.01602303 0.02952506 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat4L2016/WZ;1\n",
      "Values: [1.56994358e-01 1.00442514e-01 6.39852658e-02 5.48382998e-02\n",
      " 3.14714946e-02 2.39481647e-02 2.10378002e-02 7.57192960e-03\n",
      " 9.91225324e-10 9.91225324e-10 9.91225324e-10 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat4L2016/ZZ;1\n",
      "Values: [36.83259    21.958372   13.018795    7.9346414   7.8629622   3.5095184\n",
      "  1.70886     0.86868227  0.7417786   0.26655486  0.1706338   0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat4L2016/total;1\n",
      "Values: [38.701855   23.202751   14.248048    8.43749     8.412409    3.663831\n",
      "  1.9946578   0.97059417  0.77978736  0.28462148  0.20565176  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/cat4L2016/total_background;1\n",
      "Values: [38.701855   23.202751   14.248048    8.43749     8.412409    3.663831\n",
      "  1.9946578   0.97059417  0.77978736  0.28462148  0.20565176  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_fit_s/catEM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_s/catEM2016/DY;1\n",
      "Values: [4.461868   0.37070805 0.02046645 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/TOP;1\n",
      "Values: [4.9596964e+02 1.4659416e+01 3.7359092e-01 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/VVV;1\n",
      "Values: [0.4716881  0.12733345 0.02640671 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/WW;1\n",
      "Values: [136.54445      8.996124     0.72811437   0.           0.\n",
      "   0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.\n",
      "   0.           0.           0.           0.           0.        ]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/WZ;1\n",
      "Values: [6.61679    0.5625115  0.07246461 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/ZZ;1\n",
      "Values: [0.23319092 0.01601595 0.00145917 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/total;1\n",
      "Values: [644.2976     24.732111    1.2225021   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/catEM2016/total_background;1\n",
      "Values: [644.2976     24.732111    1.2225021   0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Error processing shapes_fit_s/chBSM2016/data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n",
      "Histogram: shapes_fit_s/chBSM2016/DY;1\n",
      "Values: [2.2316185e+02 7.8466973e+00 2.2081811e+00 3.4824911e-01 1.3674780e-03\n",
      " 1.7417265e-03 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09\n",
      " 1.1744443e-09 1.1744443e-09 1.1744443e-09 6.9901239e+02 1.8186241e+01\n",
      " 7.5091463e-01 1.7628640e-01 1.9578895e-01 2.9432228e-02 1.5018205e-04\n",
      " 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09 1.1744443e-09]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/Signal;1\n",
      "Values: [2.6436081e-07 3.2617282e-07 3.4064286e-07 3.0857316e-07 2.7642594e-07\n",
      " 3.8097434e-07 2.6551200e-07 1.5948952e-07 1.1563403e-07 1.2201578e-07\n",
      " 6.0453900e-08 5.5549819e-08 2.8346619e-17 5.1050161e-08 7.7585071e-08\n",
      " 9.1956586e-08 9.3987190e-08 9.6176848e-08 1.5257537e-07 1.0746666e-07\n",
      " 8.2019540e-08 5.2108685e-08 7.0610156e-08 2.9798336e-08 3.7376509e-08]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/TOP;1\n",
      "Values: [ 1.1251571e+02  2.9270430e+01  7.6834183e+00  2.9286182e+00\n",
      "  5.2838129e-01  1.7933795e-01  1.0900758e-01  5.2419078e-04\n",
      "  1.4176429e-04  9.6121477e-10  9.6121477e-10  9.6121477e-10\n",
      "  9.6121477e-10  8.2983971e+01  2.3671926e+01  7.6007986e+00\n",
      "  1.7942533e+00  8.8819319e-01  6.7151666e-01  3.8200393e-02\n",
      " -3.9827655e-06  1.0673207e-04  2.0292781e-03  9.6121477e-10\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/VVV;1\n",
      "Values: [1.6645053e-01 2.2646634e-01 3.1858844e-01 1.7279060e-01 1.5249339e-01\n",
      " 8.6171679e-02 2.4176551e-02 2.2241779e-02 1.5842274e-02 3.8042635e-02\n",
      " 6.2973094e-03 6.7392872e-03 1.0496011e-09 2.1894901e-01 2.0319682e-01\n",
      " 2.3189948e-01 2.4582548e-01 1.2353863e-01 1.3502859e-01 1.9675380e-01\n",
      " 4.0049762e-02 5.6282967e-02 2.2369590e-02 4.6679345e-03 2.4587568e-03]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/WW;1\n",
      "Values: [4.3853374e+01 8.7352848e+00 2.0523345e+00 1.1993690e+00 2.2664160e-02\n",
      " 5.6861830e-03 1.2870679e-02 1.8887953e-01 9.7922348e-10 9.7922348e-10\n",
      " 9.7922348e-10 9.7922348e-10 9.7922348e-10 1.2976664e+01 1.6568246e+00\n",
      " 1.3538010e+00 5.2414554e-01 9.7922348e-10 3.2800457e-01 9.7922348e-10\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/WZ;1\n",
      "Values: [8.8520950e+01 6.3514008e+01 4.0511150e+01 2.1354185e+01 1.1920809e+01\n",
      " 8.9534788e+00 2.8853104e+00 1.0508723e+00 5.8012080e-01 3.1366783e-01\n",
      " 6.5765671e-02 5.5093333e-02 1.0265423e-09 4.5614410e+01 2.9448673e+01\n",
      " 1.9189224e+01 1.1527327e+01 5.9601111e+00 6.6037683e+00 2.2264721e+00\n",
      " 1.1524079e+00 3.8631734e-01 3.3878082e-01 5.5679966e-02 3.8097586e-02]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/ZZ;1\n",
      "Values: [1.24841393e+02 1.07276115e+02 7.77858124e+01 4.85376778e+01\n",
      " 2.92971859e+01 2.83812771e+01 1.19283228e+01 5.50179434e+00\n",
      " 2.80050611e+00 2.18347406e+00 6.80166781e-01 4.22578931e-01\n",
      " 1.17040821e-09 2.61271038e+01 2.12264805e+01 1.80964031e+01\n",
      " 1.33044977e+01 9.46548939e+00 1.11265888e+01 5.34012508e+00\n",
      " 2.85774565e+00 1.53027427e+00 1.35470581e+00 4.82107818e-01\n",
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      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/total;1\n",
      "Values: [5.9305969e+02 2.1686900e+02 1.3055948e+02 7.4540894e+01 4.1922901e+01\n",
      " 3.7607693e+01 1.4959688e+01 6.7643123e+00 3.3966112e+00 2.5351846e+00\n",
      " 7.5222981e-01 4.8441160e-01 6.3614345e-09 8.6693347e+02 9.4393349e+01\n",
      " 4.7223038e+01 2.7572334e+01 1.6633121e+01 1.8894341e+01 7.8017015e+00\n",
      " 4.0501995e+00 1.9729813e+00 1.7178856e+00 5.4245573e-01 3.3451834e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/total_signal;1\n",
      "Values: [2.6436081e-07 3.2617282e-07 3.4064286e-07 3.0857316e-07 2.7642594e-07\n",
      " 3.8097434e-07 2.6551200e-07 1.5948952e-07 1.1563403e-07 1.2201578e-07\n",
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      " 9.1956586e-08 9.3987190e-08 9.6176848e-08 1.5257537e-07 1.0746666e-07\n",
      " 8.2019540e-08 5.2108685e-08 7.0610156e-08 2.9798336e-08 3.7376509e-08]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/chBSM2016/total_background;1\n",
      "Values: [5.9305969e+02 2.1686900e+02 1.3055948e+02 7.4540894e+01 4.1922901e+01\n",
      " 3.7607693e+01 1.4959688e+01 6.7643123e+00 3.3966110e+00 2.5351846e+00\n",
      " 7.5222975e-01 4.8441154e-01 6.3614345e-09 8.6693347e+02 9.4393349e+01\n",
      " 4.7223038e+01 2.7572334e+01 1.6633121e+01 1.8894339e+01 7.8017015e+00\n",
      " 4.0501995e+00 1.9729813e+00 1.7178855e+00 5.4245573e-01 3.3451831e-01]\n",
      "Edges: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.\n",
      " 18. 19. 20. 21. 22. 23. 24. 25.]\n",
      "Histogram: shapes_fit_s/total_bin_width;1\n",
      "Values: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
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      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_s/total_overall;1\n",
      "Values: [1.16879150e+02 1.06584221e+02 7.64428024e+01 5.33596687e+01\n",
      " 5.46513634e+01 2.61698437e+01 1.41278811e+01 7.87474060e+00\n",
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      " 0.00000000e+00 0.00000000e+00 6.44297607e+02 2.47321110e+01\n",
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      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.93059692e+02\n",
      " 2.16869003e+02 1.30559479e+02 7.45408936e+01 4.19229012e+01\n",
      " 3.76076927e+01 1.49596882e+01 6.76431227e+00 3.39661121e+00\n",
      " 2.53518462e+00 7.52229810e-01 4.84411597e-01 6.36143449e-09\n",
      " 8.66933472e+02 9.43933487e+01 4.72230377e+01 2.75723343e+01\n",
      " 1.66331215e+01 1.88943405e+01 7.80170155e+00 4.05019951e+00\n",
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      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_s/total_signal;1\n",
      "Values: [2.25772331e-10 3.27551791e-10 1.87412169e-11 2.98226499e-10\n",
      " 5.74676938e-12 3.46274287e-10 2.79172133e-17 2.79172133e-17\n",
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      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 2.64360807e-07\n",
      " 3.26172824e-07 3.40642856e-07 3.08573163e-07 2.76425936e-07\n",
      " 3.80974342e-07 2.65512000e-07 1.59489517e-07 1.15634030e-07\n",
      " 1.22015777e-07 6.04538997e-08 5.55498190e-08 2.83466193e-17\n",
      " 5.10501614e-08 7.75850708e-08 9.19565863e-08 9.39871896e-08\n",
      " 9.61768478e-08 1.52575367e-07 1.07466661e-07 8.20195396e-08\n",
      " 5.21086854e-08 7.06101559e-08 2.97983362e-08 3.73765090e-08]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Error processing shapes_fit_s/total_data;1: 'Model_TGraphAsymmErrors_v3' object has no attribute 'axis'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histogram: shapes_fit_s/total_background;1\n",
      "Values: [1.16879150e+02 1.06584221e+02 7.64428024e+01 5.33596687e+01\n",
      " 5.46513634e+01 2.61698437e+01 1.41278811e+01 7.87474060e+00\n",
      " 6.33179808e+00 1.88425541e+00 1.28022587e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.87018547e+01 2.32027512e+01 1.42480478e+01\n",
      " 8.43749046e+00 8.41240883e+00 3.66383100e+00 1.99465775e+00\n",
      " 9.70594168e-01 7.79787362e-01 2.84621477e-01 2.05651760e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.44297607e+02 2.47321110e+01\n",
      " 1.22250211e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.93059692e+02\n",
      " 2.16869003e+02 1.30559479e+02 7.45408936e+01 4.19229012e+01\n",
      " 3.76076927e+01 1.49596882e+01 6.76431227e+00 3.39661098e+00\n",
      " 2.53518462e+00 7.52229750e-01 4.84411538e-01 6.36143449e-09\n",
      " 8.66933472e+02 9.43933487e+01 4.72230377e+01 2.75723343e+01\n",
      " 1.66331215e+01 1.88943386e+01 7.80170155e+00 4.05019951e+00\n",
      " 1.97298133e+00 1.71788549e+00 5.42455733e-01 3.34518313e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_s/overall_total_M1;1\n",
      "Values: [1.17404514e+02 1.06490363e+02 7.69678893e+01 5.33756716e+01\n",
      " 5.45669277e+01 2.60124693e+01 1.40674842e+01 7.82151018e+00\n",
      " 6.31341129e+00 1.89742848e+00 1.28035877e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 3.86805770e+01 2.32514017e+01 1.40987764e+01\n",
      " 8.46945111e+00 8.35991720e+00 3.62534986e+00 1.96893821e+00\n",
      " 9.65006518e-01 7.69122887e-01 2.83783549e-01 2.04338097e-01\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 6.42370311e+02 2.48879982e+01\n",
      " 1.13923214e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.86466382e+02\n",
      " 2.15567208e+02 1.30537015e+02 7.41415409e+01 4.20688822e+01\n",
      " 3.74652709e+01 1.50218678e+01 6.74054513e+00 3.31681870e+00\n",
      " 2.53872896e+00 7.46496872e-01 4.85323213e-01 6.36733185e-09\n",
      " 8.71993128e+02 9.28465941e+01 4.71456994e+01 2.77162172e+01\n",
      " 1.67720503e+01 1.87568223e+01 7.86321235e+00 4.06909261e+00\n",
      " 1.96593594e+00 1.73256700e+00 5.33552518e-01 3.31266538e-01]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n",
      "Histogram: shapes_fit_s/overall_total_M3;1\n",
      "Values: [ 1.48777752e+01  2.08152776e+01  8.56936555e+00  9.18284729e-01\n",
      " -1.07050659e+01 -6.57473225e-01  1.45482790e-03  1.12037852e-01\n",
      "  2.12135598e-01 -1.24219264e-03  4.32390083e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.99124305e+00  6.55801176e-01  9.56541147e-02\n",
      "  5.39711727e-02  2.66843054e-02  7.02263828e-03  3.74922683e-03\n",
      "  5.73151684e-04  1.78479924e-05  2.82928878e-05  1.35315677e-06\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00 -5.51805741e+03 -2.21465315e+00\n",
      " -1.21914355e-02  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00 -1.97366029e+02\n",
      "  7.43893449e+01  8.35445758e+00  6.25583202e+00  2.23592020e+00\n",
      " -2.51770418e-01  5.44236358e-01  4.07506042e-02  2.39436049e-03\n",
      " -3.93867086e-03 -1.64486139e-04 -7.01884458e-05  1.43462531e-30\n",
      " -1.33904097e+03  2.26036625e+00  3.83996960e+00  1.72904805e+00\n",
      "  5.36370202e-01  8.77633107e-01  1.16327278e-01  1.46961926e-02\n",
      "  3.64349629e-03  2.98812639e-03  1.86864128e-04  6.82816692e-05]\n",
      "Edges: [  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.\n",
      "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
      "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
      "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
      "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
      "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
      "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
      "  98.  99. 100.]\n"
     ]
    }
   ],
   "source": [
    "_verbose = True\n",
    "#open the file and grab the histograms\n",
    "fn = '/eos/user/r/rhashmi/work/CMSDAS2024/Combine/CMSSW_11_3_4/src/fitDiagnosticsMonoZ.root'\n",
    "f = uproot.open(fn)\n",
    "if (_verbose):\n",
    "    print(\"\\n-----------\\nUprooted file\\n------------\\n\")\n",
    "\n",
    "# Function to recursively extract histograms from directories\n",
    "def extract_histograms(directory, histograms):\n",
    "    for key, classname in directory.classnames().items():\n",
    "        if classname.startswith(\"TDirectory\"):\n",
    "            subdir = directory[key]\n",
    "            extract_histograms(subdir, histograms)\n",
    "        elif classname in [\"TH1F\", \"TH1D\", \"TGraphAsymmErrors\"]:\n",
    "            try:\n",
    "                obj = directory[key]\n",
    "                histograms[key] = obj\n",
    "                if (_verbose): \n",
    "                    print(f\"Added {classname}: {key}\")\n",
    "            except Exception as e:\n",
    "                print(f\"Skipped {key} due to error: {e}\")\n",
    "\n",
    "# Dictionary to store histograms\n",
    "histograms = {}\n",
    "\n",
    "# Extract histograms from the root file\n",
    "extract_histograms(f, histograms)\n",
    "\n",
    "if (_verbose):\n",
    "    print(\"\\n----------\\nConverted into histograms\\n----------\\n\")\n",
    "\n",
    "    # Histogram data\n",
    "    for name, roothist in histograms.items():\n",
    "        try:\n",
    "            values = roothist.values()\n",
    "            edges = roothist.axis().edges()\n",
    "            print(f\"Histogram: {name}\")\n",
    "            print(f\"Values: {values}\")\n",
    "            print(f\"Edges: {edges}\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error processing {name}: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting distributions\n",
    "### Channel 4L - Prefit\n",
    "#### Extract data from relevent histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZZ bg:  [31.769913   18.751799   11.003929    6.6345854   6.7738194   2.9375021\n",
      "  1.3965567   0.69526327  0.5851219   0.20334339  0.1250581   0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "WZ bg:  [1.5476216e-01 9.5904537e-02 6.1765131e-02 5.5589736e-02 3.3167135e-02\n",
      " 2.7992163e-02 2.4902711e-02 9.1853328e-03 9.9999997e-10 9.9999997e-10\n",
      " 9.9999997e-10 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "WW bg:  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0.]\n",
      "TOP bg:  [0.9222576  0.58276725 0.6631518  0.1906431  0.302374   0.0820222\n",
      " 0.13418782 0.05216153 0.01279901 0.00186515 0.00476376 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "DY bg:  [2.3037665e-01 3.4183109e-01 2.6097870e-01 9.9999997e-10 2.6261965e-02\n",
      " 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10 9.9999997e-10\n",
      " 9.9999997e-10 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]\n",
      "VVV bg:  [0.31187394 0.19064082 0.18860613 0.24128008 0.16994058 0.0434863\n",
      " 0.12204539 0.03361454 0.02480851 0.01542712 0.02909351 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "\n",
      "\n",
      "Tot bg:  [33.389183   19.962942   12.178431    7.1220984   7.305563    3.0910027\n",
      "  1.6776927   0.7902247   0.6227294   0.22063565  0.15891537  0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.\n",
      "  0.        ]\n",
      "Tot bg Low:  [4.12725258 2.5338462  1.65289293 1.01479778 1.64226685 0.77203993\n",
      " 0.40954759 0.21923555 0.24359397 0.09058549 0.0664791  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "Tot bg Up:  [4.12725258 2.5338462  1.65289293 1.01479778 1.64226685 0.77203993\n",
      " 0.40954759 0.21923555 0.24359397 0.09058549 0.0664791  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "\n",
      "\n",
      "data:  [59. 27. 15.  4. 10.  3.  3.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.]\n",
      "data Low  [7.65931222 5.16377124 3.8293801  1.91433919 3.10869444 1.63270469\n",
      " 1.63270469 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.        ]\n",
      "data Up [8.72456484 6.26024384 4.95873843 3.16275317 4.26694976 2.91818583\n",
      " 2.91818583 1.84102164 1.84102164 1.84102164 1.84102164 1.84102164\n",
      " 1.84102164 1.84102164 1.84102164 1.84102164 1.84102164 1.84102164\n",
      " 1.84102164 1.84102164 1.84102164 1.84102164 1.84102164 1.84102164\n",
      " 1.84102164]\n"
     ]
    }
   ],
   "source": [
    "_verbose = True\n",
    "for name, roothist in histograms.items():\n",
    "    name = name.replace(\";1\", \"\")\n",
    "    era = \"2016\"\n",
    "    channel = \"cat4L\"\n",
    "    #channel = 'cat3L'\n",
    "    fit_type = \"prefit\"\n",
    "    values = roothist.values()\n",
    "    \n",
    "    # ren ame freq to bin_cont\n",
    "    \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/ZZ\" in name:\n",
    "            ZZ_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/WZ\" in name:\n",
    "            WZ_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/WW\" in name:\n",
    "            WW_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/TOP\" in name:\n",
    "            TOP_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/DY\" in name:\n",
    "            DY_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/VVV\" in name:\n",
    "            VVV_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/total_background\" in name:\n",
    "            bg_freq = np.abs(values)\n",
    "            MCsyslow = roothist.errors()\n",
    "            MCsysup = roothist.errors()\n",
    "\n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/data\" in name:\n",
    "            # TGraphAsymmErrors values has two arrays [[x pos], [y pos]]\n",
    "            x_values, data_set = values\n",
    "            #Calculate Poisson errors\n",
    "            #alpha = 1. - 0.6827\n",
    "            #L = np.where(data_set == 0, 0, stats.gamma.ppf(alpha / 2, data_set, scale=1.))\n",
    "            #U = stats.gamma.ppf(1 - alpha / 2, data_set + 1, scale=1.)\n",
    "            Ylow = roothist.errors('low')[1]\n",
    "            #Ylow = data_set - L\n",
    "            #Yhigh = U - data_set\n",
    "            Yhigh = roothist.errors('high')[1]\n",
    "            \n",
    "# No WW case so no entries. Setting to 0\n",
    "WW_freq = ZZ_freq * 0\n",
    "\n",
    "if (_verbose):\n",
    "    print(\"ZZ bg: \", ZZ_freq)\n",
    "    print(\"WZ bg: \", WZ_freq)\n",
    "    print(\"WW bg: \", WW_freq)\n",
    "    print(\"TOP bg: \", TOP_freq)\n",
    "    print(\"DY bg: \", DY_freq)\n",
    "    print(\"VVV bg: \", VVV_freq)\n",
    "    print(\"\\n\")\n",
    "    print(\"Tot bg: \", bg_freq)\n",
    "    print(\"Tot bg Low: \", MCsyslow)\n",
    "    print(\"Tot bg Up: \", MCsysup)\n",
    "    print(\"\\n\")\n",
    "    print(\"data: \", data_set)\n",
    "    print(\"data Low \", Ylow)\n",
    "    print(\"data Up\", Yhigh)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trimming arrays before plotting\n",
    "Check relevant shapes ROOT file for number of bins and bin edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZZ_freq = ZZ_freq[:11]\n",
    "WZ_freq = WZ_freq[:11]\n",
    "WW_freq = WW_freq[:11]\n",
    "DY_freq = DY_freq[:11]\n",
    "TOP_freq = TOP_freq[:11]\n",
    "VVV_freq = VVV_freq[:11]\n",
    "\n",
    "bg_freq = bg_freq[:11]\n",
    "MCsyslow = MCsyslow[:11]\n",
    "MCsysup = MCsysup[:11]\n",
    "\n",
    "data_set = data_set[:11]\n",
    "Yhigh = Yhigh[:11]\n",
    "Ylow = Ylow[:11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set-up the bins and make the plots!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Data/$\\\\Sigma$MC')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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upkKZj0fE/u2JqRIR8bmIuCQiLgEOKew+rrgvIj7Xwar3KHne1vDTqqvS57JLFRpgijdzTkop7Z5S+nlK6caSz8CeQHE+s29HxPqldRQa9IoNodtExG5lrrUxcEDh5e0ppbbmOijmDsGa32XbrS+NcR9RZn+L63F3gZ+STdBRnESruDTcAc3KzUsp/b2D1ziabIxv8/HyrSlONHJgRLw/pXRvB69dqjTR/e+I+O82yieyO3WntHCs9AP+k5Zazpv5CllXc4D/SpXNRVC6NMqQFo5XsrRdaZz/qKDM/7SRdI4oeX5TSummcgUL4/kHF14u7kR840uen5XKrFVfYvPC49KU0mutFSxMaLMN2ZeJDcjGvZfeONy75Hm51vn2fhZgzRb3cuPcv8zqngrnppQeqqBeSZK6y6PA59v4fvgm2dC62WTztDSQ/b3dg6xn50iyeX/+FhF7VvA3fi0ppaUR8TeyoZfvjYgjUgsTO0fEwWST7BaVm6C2K1ybUlqrBTSltCIippH1suxHNsFua5MSd8ReZN/FS+3Nmt9xOjJR3+Ylz8t9z6uFSj6X3eW/Co/XppTOaalASmlVoaX9o2SfycPIWulL/YLVv8NiK31zR7A6h65k/frSXtdb0Zme3p3ta99TNrKxti2NdXi9SvWPKlP/JSVlftXs2NIWyh9XUn5RC8cXVRDLFLLJut4uE1O57UlgnU7+HNZvx/WWkw0j2K2V+p4oKV92LHMbP7P2bDu0UOfckuMtjtUuifMtYEAF76XVZdTIur8Xy57QRtkdSsr+uRPxPVtSz8g2rrlhSdmyY3bI/oP6KWuONW9r268an4XCOUPJ5oxIwPMtHN+UbBWDRLYM34hq/H/g5ubm5ubW3q3wN2tsYduJLMG4hOz73DO0vvzYiFaODSOboLf4N/R/OhHjzqxeAmwF2WRbW5AlMpuTNaAsK8Rc/Pt7byeuV4x5VitlZpWUa3G525KyxZ9D2fl58raRLRlcfH9rzdnUzp/jTd35uezEe15E5XnPZiXvr81YgHsLZS9q4ViUXPslWvjODNxTOL6sku+NwG4l8X2tMz+XvtRV/sUy+4cVZkHvDs2XXBvc7PUrtLHEQyVSSrNTSgeSLbX1UbJuwDfT9kyP7yS7+9QZpa3tz5BNTlLcTiFbr/uTZK2fw1JKn0gptXQ3i4jYgNXdq55MrY9lLs4KvkXnwufxZnUOZPWs94tSC2O1m8U5L6W0VrfxZmUeSSm1dce09Od4axtlW+0GX2F8m7B6yMZjqczybpVes1Dnl4B/kd3pb8/yKg+2UFe7PgtFKWtRKM4uX9/CkJDvsLol4JupjZ4DkiR1lZRSQ1rdFfy+lNLvU0rHkK3ksiFwcUTMKHPua63Uu4Rs7p/id5j/KHy/6UiM95J1SV5B1nv0dLJEZyXZRLPfJZtT6kusHr7XXb1boe0x4MXjQ8gmgu4JSvOU17r74p35XHaT0qGQFxe79ZfbWN2Tdq15vlKWaRdX/tqQrOG3SURsR3bzCuCaCr83luYO5YaDVqQvdZVfQPafSvP1ngEmA7/v6gBSSvdExJ3A7mWK/Dy1PWa3Pdd7BbiqsBXXNd+NbMKFz5F1FWpuTzp386C0W/mtqXNrSZYmh5UsfVE6iclfyJZNa49lae2uY+NY/ZmpZHz7PyooU8n4pGLivpws+W1NW0l0V8bX4jUj4v+xeoK9RrLue1cXyj4FvJFSWlEoux7ZEnX9gKdTSi3dZGvvZ6HUnaz+bOxauD4RUZysBGAe2QoDkiTlSkppdkT8mKw1e2ZE/Da1c1hXSunViLicbJjmELLvax1aViyldFlE/BP4OvABVg9RTMAcspWMHiebSwng1Y5cp4OaTwbdXGnDyYZ071rsHZVKnq9D994IKasan8sq2aSD5zVvQC36Bavny/o02fdXSl6XlqvEuiXPOzUnVJ9J3FNKDRFxK6vX9S71H3RD4l5wNi0n7quAc7vywikb730bcFtEPMLqCblKdfTDX1SauLfYEtsO40ueV5JMlt45+33KZj7vrErGt48veV7uPZeWafW9FGb6L76XB1Pb4/TbStyrGl9B2cS9MPvpdwovlwEHp9bHP+3D6ptIlfyM2zsxyx1kM4tCdle2OPHIj1ndGvDl1Pp6tJIk1dI1ZAlSHVlvyrbmD2rJ/JLnm5ctVYGU0oPAJwpz2LyDLDl5NmUTfBERB5a5bldLbRwvN4lvnr1W8nwDyvciroVqfC47q7Qh8mgq/564tKWdKaVHI+J2svkhDoqIDVJKr0REAJ8qFHue1ZNbt6W01+lrFZ7Tor7UVR7KtyTvHxFHtKeiQut1R1xJNpa4uWtSSk92sE4iYvPCB6pS5Za7avFD3A6ttsS2U3tbgUeUPK/WZ7srZpRv672UXrOtJD/IxrgDvJZSeryFYpXEN74d8ZXW2cjqJVaKPg4MKjz/cRtJO6w5kUslk/91JHEv2hUgIj7F6llar0kp3dDOOiVJ6k6lyVpHhwW2ldS2v8KUVqWUnk4pPVJM2gtKJ2RrcUhkF2lr1vzSBqouXVWqip4oeb5+2VK1UY3PZWeVLqscac3VB1rb/t1KncXW9IFk32sha/wtDrm8rB0NPqW/sw7netD3EvdfkHWZb8lFEXF4WxVExIYR8UPgqx0JoDC++LwWDjUf/95e/w+YX1iKopI1Altc4oBOfKAiYjirl4aA7k/cXyt5Pq5coXZqz4zyibWT2OZlWqunI2W3YnUXtX+0UV9r8Y2v9JoRMYTVy+Y93OwPNcCYkuetjjWLiF3I7tAWVePGR3MLWd1Nb6dC/MUeAStoeTUDSZLyZGTJ87bmLCqn9O9zS41IVVFohS+ucPQK1Z+9vTXlVpApKo6HXsraS+/mVWmPhW1qFkXLqvG57KzS747NV+vqqMvJhqvC6u7xHekmDzC65Hmnep/0qcS9cGfkOLKZtZtbB7g8Iv4aEZ+IiC0iYnBEjIiI0RHxqYj4JfA0WZLc0lj5Sv2MrFt8cfvvlNKcTtRXtB3ZsgYvRMTVEfHliNiz0Bo/uHDTYcfC8mzluuV35j/XHVndBemZMmOVKxIR67L6g/5ES5PCtaA0oftURGzUjuut9W+h0KuieAPgqZbeT7M4H00prfWfVgfeS8Ut7rSR0FYY32BWJ+JPppReal6mhWsWf88txVc6ZmhkC8eL192UbFWB0i5Obb2HSj8LTQoTjRTv9g8DLmJ1F8Efp7bX35QkqdY+VvJ8rUlc2xIRI1idTL9JNrN2VzmObLUlyOZvaul7d1c5ptyBiBjF6iGzf+9BQ+Tmks3gDmtOxJYHnfpcVkNK6TGyuYoADo2I91ShztfJhgEA7B4RO7C6oen+wlCRShUbS18BHulMXH0qcQdIKd1KdseksUyRqcCvyWbIXErWUvcQWTf7I8kS/M7G8FJK6Qsl29c7W2czg4APAz8gm5H8KbL38hLZP/6vseZECUX3kE0q0lHVHN8+jrbHPTd3K6vvnq4P/CUiys4YGhHDIuLIiLiLlm/EjGV1l+9yMZTGWe49t/e9FBP3lbT9n2Bb3eArie+97YyvrWuWTkxycks3UCJiH7JJ47Yq2f1iSunpFurryGehudLu8sU/Mi8A3+pgfZIkdVpEHFXoCdZamcOBzxdevg78odnxD7Y2hLPQE/MKVo+1LZtMl8y+vaiV+sp2iY6I/cm+f0LWi/OMcmW7yMGF4XBrKMyi/3NWf5/o0nmlqqkwcfKdhZdt9Sioimp8LrvZ6YXHAcBVEbFZuYIR0a/QINvWPA+lreq/YvUqRO1pbYfVv7MbCo1JHdZnJqcrlVL6bUS8RDbdf9lfbB/zEvCZTn6gqpm4t7trdErp7Yj4Atl/HP3Ilmt4OCL+VohnCdkyDPVkyeoEsrErD5T5A9be8e3/qKBMW93QR7D6LvW/KrhLXY0Z5ceXPO/0jPJk/6F9jexnuyXwSERcQTZGawTZKg67kN09/h7ZpCatXbsz3eSL7mxh39cLS+RIklQr04GfRMRVZI0nj7D6+8q2ZMv0fqBQNgFfaqHn2TnAwIj4PdmN6n+TtaqvD+xFllwVe8A9RLbueWc8WFgl6Qqyrr9vkS3ZeijZkr9ReA8fa6mnXxe7B7g0IiYBvyUbRrkt2bC48YUyV6aU/trNcXXW78l+lzsWJ0srVzAixrPmd7tSm0bEMc32XZdSer7Zvmp8LrtNSumKiDifrLfHWLLhw+eTrTC1mKzBchQwkSz2d5A1DLXUYFR0PfBcoWxxaei3gf+rNK7C8nHFXPOqSs8rp08m7gAppb8Xuj38J9kveUQ7Tv8n2broefJ3stnqJ9L+nhQ3A59PKS3sZAzVnJhufMnzipO1lNK1EfFxsiED65N9xj/A6v9cWlJueEC3zyhP+7rJw+qkdhlrtnS3dO0un1EeIKX074g4DriQ7AbKCFbP6l70RGHfu0r2dcWM8kV3kfWyKf7b+AdZl3lJkmptGNlErUe3UuYV4IsppXJJwzuAEwtbOX8HjkopdXZ5tjpg/8LWkoWF69zTyet0xMeBG8i+2x/XwvG/0/rPOa9+TdbYMYCs5+DPWil7CDCzzLHRwMXN9u1LNkt6c9X4XHan48mS9K+Rfff8Cqsbh5pbweox7C1KKa2KiMtYcy6ka9s5FPiThcclrO5632F9NnGHrMs68NWIOB04kGwGzN3IluLagOzuzBtkXWofIvvyf21KqbNJadWllK4Fro2IDcjWYt+NbBKSrcj+Mx9C1u17Kdndx4VkrchXppQ6Pc6p2YRlUIMW96KU0u8i4kaycU7vJ7ujtgHZH5oGsqED/yJbGu/PKaVFZaqqJImu9hrp7Wmdr2f1snH/LDNWq6ot7hExiGwuBWhlvHlK6RcR8RDZJI57A8PJ/h0tIFuO7ZcppaUR8YOS07qyxX0JWa+S4myy/y+lVG64jCRJ3eXDwBRgP7LvbfXARmSJxUtkjUXXAf+XUnqtTB1Hk/Vm241skuCNyP7uLgWeIet19n8ppdlVinka2dDSXcm+Yw4txPog2epJv+zmce1NCo0HOwEnk41JHkV2434ecAnZMIEe9/c/pfR8oUfF4WRLkrWWuFdDNT6X3arwe50REReT9TKZQtbzczhZkv4MWdw3kC0b3dacTpD1Ii1N3C9tZ1jFxP2ilNKydp67luhkV3tJyr2I+ATZ3WrIblZ9rLXykiRJeVK4IXEvWdf07arQU1ZdKCL2JevhsRLYppWGwor1ucnpJPUthQl7Tiu8XE75blOSJEm5lFK6j2wepwC+WeNw1Lbi7+jCaiTtYIt7bkTEUFbPVngnWdf2nrK+pJRn9ayelOe5wiZJktTTDAK2J0veixMD9iWjgCUppbKrRuVBROxNNqnfErLW9sVVqdfEPR8iYhYlE0n079+f9773vbULSOoFGhoaePTRR0kpMWjQILbbbjvq6uxoJEmSeqZXXnmF5cuXM3ToUIYOHdr2Cb3IggULWLZs2SsppQ1rHUtrIuJDZHNl3ZtS+lPV6jVxz4dmLe7XjR8/ftz99+duDjwp1x5++GGuv/56li9fzn333ccVV1zBqlWrGDhwILfccgu77toty59KkiSpynbaaSfmzp07N6W0U9ule58+Pat8nqSUGshmPCciVtoqKLXfr3/9a2bNmrXGvn79+vHzn//cpF2SJEk9lom7pF7jvvvuA7KhJiNHjmS33XbjlFNOYZdddqlxZJIkSVLHmbhL6jX+8Ic/1DoESZIkqersjy1JkiRJUo7Z4p4TzSanG9DY2FjLcCRJkiRJOWGLe35MB54pbOMWL67Kcn+SJEmSpB7OxD0/zgJGFrYH6+vraxyOJEmSJCkP7CqfEy4HJ0mSJElqidmhJEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjjnGXJEmS+rCUEm+++SZLlizhjTfeYNWqVaSUah2WeomIYODAgQwfPpzhw4fTv78paEf4U5MkSZL6qMbGRp555hneeOONWoeiXiqlxFtvvcULL7zAyy+/zKhRoxg4cGCtw+pxTNwlSZKkPiil1JS09+vXj/XXX5+hQ4cycOBAXOFI1dLY2MjSpUt5+eWXWbZsGU899RSjRo2iX79+tQ6tRzFxz4mIGAoMLbwc0NjYWMtwJEmS1Mu9+eabTUn7u971LtZZZ51ah6ReqK6ujqFDhzJ48GAWLVrEihUraGhoYMSIEbUOrUfxVlp+TAeeKWzjFi9eXONwJEmS1JstWbIEgPXXX9+kXV2u2KsDYOnSpTWOpucxcc+Ps4CRhe3B+vr6GocjSZKk3qw4rn3o0KFtlJSqY7311gOy3h5qH7vK50RKqQFoAIiIlY4rkiRJUldatWoVgBOFqdsMGDAAWP3ZU+XMDiVJkqQ+qLjkmw1G6i4RAeBygx3gv1JJkiRJUpcrJu5qPxN3SZIkSZJyzMRdkiRJkqQcM3GXJEmSJCnHTNwlSZIkScoxE3dJkiRJknLMxF2SJEmSutDnP/95IoKI4Nprr23XuY899ljTuf369Wt6ft5557WrnoaGBtZbbz0igq233ppf//rXTXV95StfaVddKSW22GILIoJhw4axbNmydp2v9utf6wAkSZIk5d9pvXwpr5lduLb4Mcccw/nnnw/ApZdeyoEHHljxuZdeemnT80suuYRjjjmGxsZGLr30Uk444YSK67nyyit58803Afj0pz/NRz7yEYYPH87rr7/OZZddxne+8x3q6ipr173pppt48sknAfjYxz7GuuuuW3Ec6hhb3HMiIoZGxGYRsRkwoLGxsdYhSZIkSaqCiRMnss022wBwzTXXsGTJkorP/dWvfgXAkCFD+OhHP8qUKVMAuOuuu3j44YcrrueXv/wlkK2lftRRR7HOOutw+OGHA/Dss88ye/bsdtcFcPTRR1d8njrOxD0/pgPPFLZxixcvrnE4kiRJkqrl05/+NADLly/niiuuqOicW2+9lccffxyAww47jPXWW2+NRLk0gW7NU089xU033QTA5MmTGTVqFLBm0l3ast+aZcuWceWVVwKw5ZZbsvfee1d0njrHxD0/zgJGFrYH6+vraxyOJEmSpGo56qijiMJwg0qT5NJyxST7Ix/5CEOHDgWy1vhUQRf/X/7yl03lSpP1Pffck/e85z0AXHXVVbzxxhtt1nXVVVfR0NAAZDcjopcPocgLE/ecSCk1pJSeTSk9C6ysdHyJJEmSpPx717vexb777gvALbfcwhNPPNFq+bfeequpZX6LLbZg8uTJAAwePJiPfexjACxatIhbbrmlzWsXW+YHDx7MYYcdtsaxYk+ApUuX8vvf/77iukrPVdczO5QkSZKkblBs7U4ptdnN/Q9/+AOvvfYasHbLdnu6uN9zzz089NBDABx66KEMGTJkjeOldbdV1/PPP8/f/vY3APbee2+22mqrVsurekzcJUmSJKkblCbObSXupUl085btvffemy233BKAK664guXLl3eoHlizNf/GG2/k6aefLlvXZZddxqpVq8rWpa5j4i5JkiRJ3WC99dbj0EMPBeDhhx/mrrvuarHciy++yHXXXQfAHnvs0TQOvSgimhLnJUuWcM0117RYz8qVK/nNb34DwOabb85+++3XYrliC35jYyOXXXZZ2fiLNxvWXXfdphnp1T1M3CVJkiSpm1TSzf3Xv/41b7/99lrlS1XSxf0vf/kLL730EpBNjlduHq3ijPVQvifAgw8+yAMPPADAIYccwrBhw1osp65h4i5JkiRJ3WSfffZhiy22AODyyy9n5cqVa5UpJs+la603t9VWW7HXXnsBcP311/PCCy+UrQda79peXCMeYP78+cydO3etMi3NcK/uY+IuSZIkSd0kIjjqqKMAePnll/nzn/+8xvEFCxZw7733AvDhD3+YESNGlK2rmEC//fbb/N///d8ax1577TX++Mc/ArDbbrux7bbbthpXaz0BVq1a1VT/Zpttxvve975W61L1mbhLkiRJUjdqLUluT8v2xz72MdZdd90W67n88st56623KqoHYN999+Wd73wnsGZXfYAbbriBZ599FoAjjzySfv36tVmfqsvEXZIkSZK60Xve8x722GMPAP785z/zyiuvANkyccXJ4TbddFOmTp3aaj3Dhg3jIx/5CAD3338/8+fPbzpW7CY/aNAgPv7xj7cZU11dXVNPgBdeeIG//vWva9UFziZfKybukiRJktTNiq3gK1as4PLLLwey5dieeuopAD71qU9V1LLdUuv9448/zm233QbAwQcfzAYbbNCumErreuONN7jqqqsA2GmnnRgzZkxFdam6TNwlSZIkqZt9/OMfZ5111gFWJ8kdmQDufe97HyNHjgSyddYbGxs7PJHcNttsw+677w7AH/7wB15//XWuvPJK3nzzzXbXpeoycZckSZKkbjZ8+HA+/OEPA3DnnXfywAMP8Pvf/x6AHXfckXHjxlVUT11dHUceeSQAzzzzDLNnz+ZXv/oVAJtssgkf+MAH2hVXMTlfvnw5V1xxRVM3+QEDBnDEEUe0qy5Vj4l7J0XEiRHxz4hYUtjuiIiDah2XJEmSpHwrbcE+8sgjaWhoWGt/e+s5+eSTeeyxxwD45Cc/Sf/+/dtV1yc+8QkGDRoEwA9/+ENuuukmAA466CA22mijdtWl6jFx77ynga8CE4Cdgb8DV0fEe2salSRJkqRcmzp1Ku94xzsAmDdvHgD9+/fnk5/8ZLvq2W677dhll13WqAc61rV9xIgRfOhDHwLgX//6F42NjR2uS9Vj4t5JKaVrUkp/SSk9mlJ6OKX0daABmFjr2CRJkiTlV79+/fjUpz61xr4DDzyQjTfeuN11NU+s3/ve9zJ+/PgOxdW8rg033JCDDrJTcS21r99EDxQRhwGTgfHADsBQ4LKU0pGtnLM5cDpwALAh8BxwNXBaSunVVs7rB3wMGALcXp13IEmSJNXezJRqHUKvdPTRR/P973+/6XVHl1s74ogjOPnkk1mxYkWn6gE44IAD2HTTTXn++eeb6h4wYECH61Pn9frEHfgGWcL+Blm39m1bKxwR7yZLujcBrgEeAnYFvgQcEBF7ppRebnbOOOAOYJ3CdT6SUnqwyu9DkiRJUi8zduxYUhVuimywwQa89dZbVYgo6wnw3HPPVaUuVUdf6Cr/ZWAbYBhwfAXlzyNL2k9KKR2SUjo1pbQf8ENgNHBmC+csJGvR3x34KfCLiBhbhdglSZIkSX1cr0/cU0o3ppQeSRXcxoqIrYCpwCLg3GaHZwJLgaMiYr1m11hRGON+b0rpa8A/yG4YSJIkSZLUKX2hq3x77Fd4vD6l1Fh6IKXUEBG3kSX2uwOzW6mnDhjU1sUi4r4yh1rtzi9JkiRJ6jt6fYt7O40uPD5c5vgjhcdtijsi4jsRsXdEjIqIcRHxbWAf4LKuC1OSJEmS1FfY4r6m4YXH18scL+4fUbJvU+BXhcfXgX8CH0gp/bWti6WUdmppf6ElfkIF8UqSJEmSejkT9/aJwmPTePmU0jG1CUWSJEmS1BeYuK+p2KI+vMzxYc3KVU1EDCVbYx5gQGNjY2vFJUmSJEl9hGPc17Sw8LhNmeNbFx7LjYHvjOnAM4Vt3OLFi7vgEpIkSZKknsbEfU03Fh6nRsQaP5tCi/iewDLgzi649lnAyML2YH19fRdcQpIkSZLU05i4l0gpPQZcD4wCTmx2+DRgPeDSlNLSLrh2Q0rp2ZTSs8DKujp/NZIkSZKkPjDGPSIOAQ4pvNy08DgxIi4pPH8ppXRKySknALcDZ0fEFGABsBuwL1kX+a93UZyOcZckSZIkraXXJ+7AeODoZvu2KmwATwBNiXtK6bGI2Bk4HTgAOBB4DjgbOC2l9EoXxTkdmFl84Rh3SZIkSRL0gcQ9pTQLmNXOc54CPtMV8bTiLOD8wvPr6uvrx3Xz9SVJkiRJOdTrE/eeIqXUADQARIRj3CVJkiRJgIl7bjjGXZIkSZLUEpt188N13CVJkiRJazFxzw/XcZckSZIkrcWu8jnhGHdJkiRJUkvMDiVJkiRJyjFb3HPCyekkSZIkSS2xxT0/nJxOkiRJ6qUuueQSIqLD2zHHHAPATTfd1Kl69tlnn5r+HNQxtrjnx1nA+YXn19XX14+rZTCSJEnSGh6KWkfQtbZNtY6gVRHV+flXqx51LxP3nHByOkmSJKn3OuSQQ9h5550rLv/vf/+bQw89lJUrV9K/f3+mTZsGwC677MKDDz5YcT2vvvoqH/rQh3jttdcA+PznP9+uuJUPJu6SJEmS1MVGjBjBiBEjKir71ltvcfTRR7Ny5UoAvv3tb7PXXnsBsN566zF27NiK6kkprZG0n3jiiXziE59od+yqPZt1JUmSJClHvvjFLzJ37lwAPvzhD3PKKad0qJ5vf/vb/OlPfwKylvof/OAHVYtR3csW95xwVnlJkiRJl156KRdccAEAW221FZdcckmH6rnxxhuZMWMGABtssAFXXHEFAwcOrFaY6ma2uOeHs8pLkiRJfdi8efM4/vjjARg0aBBXXnllxd3rSz333HMcccQRrFq1iojg0ksvZYsttqhytOpOJu75cRYwsrA9WF9fX+NwJEmSJHWXhoYGDj30UN58800Azj77bHbcccd21/P222/z8Y9/nGJD4KmnnspBBx1U1VjV/ewqnxPOKi9JkiT1XdOmTePhhx8G4Mgjj+S4447rUD1f+9rXuOWWWwDYZ599OOOMM6oWo2rH7FCSJEmSauhHP/oRV1xxBQBjxozhZz/7WYfqufrqq/n+978PwKabbsqvf/1r+vXrV7U4VTsm7pIkSZJUI3fccQdf+cpXABgyZAhXXnklgwcPbnc9jz32GMcccwwA/fr14ze/+Q2bbrppNUNVDZm4S5IkSVINvPTSSxx++OFN67Wff/75bLvttu2uZ/ny5Rx22GG8/vrrAHzrW99i8uTJVY1VteUY95xwOThJkiSp72hsbOSTn/wkTz/9NAAnnngiRxxxRIfqOvHEE/nHP/4BwMEHH8xXv/rVaoWpnLDFPT9cDk6SJEnqI2bNmsXf/vY3AHbZZRd+8IMfdKieiy++mIsuugiAUaNGcemllxIRVYtT+WDinh8uBydJkiT1Addddx1nnnkmAOuvvz5XXHEFAwcObHc9DzzwACeeeCIAAwcO5IorrmD99devaqzKB7vK54TLwUmSJEm935NPPsmRRx5JY2MjEcEvf/lLtthii3bXs2TJEg477DCWLVsGwA9/+EN23nnnaoernDA7lCRJkqRusGLFCg4//HBefvllAE499VQOOuigDtV1zDHH8OijjwJwxBFHcMIJJ1QtTuWPibskSZIkdYPp06dz1113AbDPPvtwxhlndKies846i6uuugqAbbfdlvPPP79qMSqf7CovSZIkSV3s8ssv5yc/+QkAgwcP5utf/zoLFiyo+PyBAweyzTbbcOutt3LqqacCUFdXx6xZs1i0aFG7Yhk7dmy7yqv2TNwlSZIkqYv97//+b9PzN998k/33379d52+xxRYsWrSICy+8kLfffhvIlpT7xCc+0e5YUkrtPke1ZeIuSZIkqW3bmux1RrWSZZPuvskx7pIkSZLUxW666SZSSh3eit3hL7nkkk7VY+LfM9ninhMRMRQYWng5oLGxsZbhSJIkSZJywhb3/JgOPFPYxi1evLjG4UiSJEmS8sDEPT/OAkYWtgfr6+trHI4kSZIkKQ/sKp8TKaUGoAEgIlbW1XlPRZIkSZJki7skSZIkSblm4i5JkiRJUo6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIm7JEmSJEk51r/WASgTEUOBoYWXAxobG2sZjiRJkiQpJ2xxz4/pwDOFbdzixYtrHI4kSZIkKQ9M3PPjLGBkYXuwvr6+xuFIkiRJkvLArvI5kVJqABoAImJlXZ33VCRJkiRJtrhLkiRJkpRrJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJXayxsZENNtiAiGD77bdvs/y4ceOICCKCGTNmtFp24cKFTWWPPPJIrrrqqqbX3/ve91o997777msqGxE8/vjjrZb/2te+1lT2hhtuaPN9qDqcnE6SJElSmza5ZmStQ+hSL3z4mS6tv66ujj333JM//elPLFiwgBdffJGNN964xbKvvPIK8+fPb3o9Z86cVusuPT5p0iT23ntvIoKUEnPmzOErX/lKRecWX2+11VZtlu/fvz8TJ05sNS5Vjy3ukiRJktQNJk2a1PT8lltuKVvulltuIaVEv379ALjrrrtYsWJF2fKlyffkyZPZaKON2G677QC49dZbaWxsbPPc4rVau0mwbNky7r33XgB23nln1ltvvbJlVV0m7pIkSZLUDSZPntz0vLUEuZjUH3jggQwZMoTly5dz9913t1m+vr6e0aNHr3Gt119/nX/+859lz7311lsBOPzww9uMq/QGQulNCHU9E3dJkiRJ6gYTJkxgyJAhQOsJcvHYvvvuy+67795q+aeeeoonnngCWDOZLn1e7tx//etfvPTSS0QEp556KgCPPfYYzz77bKtxwZo3IdT1TNwlSZIkqRuUjgt/4IEHeP3119cq88Ybb3D//fcDsPfee7PXXnsB5ZPvm2++uel5abJeSet+cf/222/Pe9/7XkaNGlVR+eJ4fXUfE3dJkiRJ6ibF5LqxsZHbbrttreO33347b7/9NkOGDGH8+PHsvffeTftXrVq1VvlyreDveMc7ePe73w2UH09fPLd4jeJjS4n722+/zZ133gnA+PHjGT58eBvvVNVk4i5JkiRJ3aStlvBikj1x4kT69+/P7rvvTv/+/WloaGhqiW+p/AYbbMDYsWNbvNYLL7zAQw89VPbcYsLeWuv+fffdx9KlSwHHt9eCiXsnRcTXIuKeiFgSES9GxB8jYmzbZ0qSJEnqa3bddVfWWWcdoOUEuXkr+ODBg5kwYUKL5V988cWmhLy4BFyp1sa5//vf/+bpp58GVifsxWv+61//4uWXX24xLnB8ey2YuHfePsB5wB7AfsDbwA0RsUEtg5IkSZKUP4MGDWLXXXcF4N577+XNN99sOvbWW281zR5fTKJLn7e05npRS63grSXuxddbbLEF73rXuwDYbrvt2GijjUgprdW9vlg+ItaITd3DxL2TUkrvTyldnFKal1J6EDgK2BhwtgZJkiRJaym2WK9cubJp3DjA3XffzfLlyxkwYAC77bZb0/5ii/itt95KSqlpf1ut4FtuuSXvfOc71ypb+rp5El6cdK60fEqpaTz+mDFj2HDDDSt9q6qSXp+4R8RhEXFORNxS6M6eIuJXbZyzeURcFBHPRsRbEbEoIn4UEetXcMmhZD/XV6vyBiRJkiT1KuVawout3DvvvDPrrrtu0/5i4v7yyy8zf/78tcoPHTqU8ePHt3qtp556ikWLFq11bvPEvaXW/QcffJBXX311rdjVfXp94g58A/gCMB54pq3CEfFu4D7gM8DdwA+Bx4EvAXdERFu3l34M/AO4o8MRS5IkSeq19thjDwYMGACsmSCXawXfaKON2Hbbbdcos2TJEh544AEgS+z79evX4rVauknw/PPP88gjj7R4reJNgn/84x80NDSsFaPj22ujLyTuXwa2AYYBx1dQ/jxgE+CklNIhKaVTU0r7kSXwo4Ezy50YET8A9gIOTSmtvVaDJEmSpD6vdMK5O++8kxUrVrBq1Spuv/12YO1kunRfMYm+9dZbaWxsBFpvBW8pcS8+lt4QKJowYQKDBw9m1apVTd3j2xpLr67X6xP3lNKNKaVHUulgkDIiYitgKrAIOLfZ4ZnAUuCoiFivhXN/CBwB7JdSerzTgUuSJEnqtYot18uWLePee+/l/vvvp6GhgYhoGmdeqvlSbZW2gm+77bbU19e3eO5ee+211kz0pePri+WK3eq32WYbNt1003a+U1VDr0/c22m/wuP1KaXG0gMppQbgNmAwsHvpsYj4MfBJsqR97QUSy4iI+1ragG3bPFmSJElSj9W8JbyYHI8dO5b11197aq1ii/tzzz3Ho48+2lR+8ODB7Lzzzq1eq3juI488wvPPP192fHvz8nPmzGk6p3nM6l4m7msaXXh8uMzxRwqP2xR3RMS5ZOPhjwBejYhNC9uQrgtTkiRJUk+21157UVeXpWNz5swpO769aMstt2TkyJEA/PWvf+Xee+8FYOLEiU3j5cspTbj/8Ic/MG/evFavVWzdv+eee7j++uub9ju+vXZM3Nc0vPD4epnjxf0jSvadQDaT/GzguZLtlLYullLaqaUNqLjVXpIkSVLPM3z4cHbYYQcAbrvtNm699VagfDINqxPqH/zgB6xYsQKorBW8NOH+7ne/S2NjI+uttx477rhji+UnTpxI//79WbFiBT/4wQ+a9tviXjsm7u1THADSNF4+pRRltlm1CVGSJElST1BMhJcsWcJLL70ErE7OW1I89vjjq6fUqqQVfNy4cU3d74vnFpPzlgwZMqTppkKx/KhRo3jXu97V5rXUNUzc11RsUR9e5viwZuWqJiKGRsRmEbEZMKA4Q6QkSZKk3ql5C/aWW27J5ptvXrZ889b4QYMGNU0k15qIWOuGQGst+y0dt7W9tlq+xdJ3LSw8blPm+NaFx3Jj4DtjOtnM9QAsXry4Cy4hSZIkdcwLH36m1iH0OpMmTSIiKC6A1VprO2Qt58OHD+f117N2xF122YV11lmnomtNnjyZP/7xj02v27rW3nvvzY9+9KM1YlXtmLiv6cbC49SIqCudWT4ihgJ7AsuAO7vg2mcB5xeeX1dfXz+uC64hSZIkKSc22mgj2tPTtq6ujtdee61D15o+fTrTp0+vuPxHP/pRKlhRW93ErvIlUkqPAdcDo4ATmx0+DVgPuDSltLQLrt2QUno2pfQssLI4w6QkSZIkqW/r9S3uEXEIcEjh5aaFx4kRcUnh+UsppdIZ4E8AbgfOjogpwAJgN2Bfsi7yX++iOIeSzU4PjnGXJEmSJBX0+sQdGA8c3WzfVoUN4AlKlm5LKT0WETsDpwMHAAeSLe92NnBaSumVLorTMe6SJEmSpLX0+sS9sCzbrHae8xTwma6IpxWOcZckSZIkraXXJ+49RUqpAWgAiAjHuEuSJEmSABP33OhNY9znz5/P7NmzWbJkCcOGDWPKlCmMGTOm1mFJkiRJUo9k4p4fPX6M++zZszn99NOZM2fOWscmTZrEjBkzmDJlSg0ikyRJkqSey/7Y+XEWMLKwPVhfX1/jcNrnwgsvZOrUqS0m7QBz5sxh6tSpXHTRRd0cmSRJkiT1bCbuOdGT13GfPXs2xx13HG11729sbOTYY49l9uzZ3RSZJEmSJPV8PSc7VG6dfvrpbSbtRY2NjZxxxhldHJEkSZIk9R4m7jkREUMjYrOI2IweNDnd/Pnzy3aPL+fmm29m/vz5XRSRJEmSJPUuJu75MR14prCNy/PkdLNmzSIiiAjGjh3boTrGjh3bVEdEMGvWrOoGKUmSJEm9hIl7fvToyekkSZIkSV3D5eByIqXUADQARESPmpxOkiRJktR1zA7VbrNmzSKlREqJefPmdaiOefPmNdWRUrKrvCRJkiSVYeKuThkzZgyTJk1q1zmTJ09mzJgxXRSRJEmSJPUuJu450VNnlQeYMWMGlXbtr6ur45vf/GYXRyRJkiRJvYeJe370mFnlm5syZQrnn39+m8l7XV0dF1xwAVOmTOmmyCRJkiSp5zNxz48ePav8tGnTuP7665k8eXKLxydPnsz111/PZz/72W6OTJIkSZJ6NmeVz4neMKv8lClTmDJlCvPnz2f27NksWbKEYcOGMWXKFMe0S5IkSVIH9bzsULk3ZswYTjrpJL7xjW9w0kknmbRLkiSpz2tsbGSDDTYgIth+++3bLD9u3DgigohgxowZrZZduHBhU9kjjzwSgH322adpX0e2m266qRpvW1Vii7skSZKkNg264I1ah9Cl3jp2SJfWX1dXx5577smf/vQnFixYwIsvvsjGG2/cYtlXXnmF+fPnN72eM2dOq3WXHm/vik/lRERV6lF1mLhLkiRJUjeYNGkSf/rTnwC45ZZb+OhHP9piuVtuuYWUEv369WPVqlXcddddrFixgoEDB7ZYvjRxL845dfHFF7N06dKKYzv33HP53//9XwDGjx/PbrvtVvG56nom7jkREUOBoYWXPWo5OEmSJEltK53Iec6cOa0m7gAHHnggN954I2+88QZ33303e+21V6vl6+vrGT16NABbbrllxXHdfvvtXHjhhQAMHz6cK6+8knXWWafi89X1HOOeHz12OThJkiRJbZswYQJDhmRd8lvr/l48tu+++7L77ru3Wv6pp57iiSeeADrWTf7FF1/k8MMPZ+XKlUDWUv/ud7+73fWoa5m450ePXg5OkiRJUuv69+/PxIkTAXjggQd4/fXX1yrzxhtvcP/99wOw9957N7Wyl0vcb7755qbn7U3cGxsb+eQnP8kzzzwDwMknn8xHPvKRdtWh7mFX+ZzoScvBLVy4sOp1Frv0SJIkSb3ZpEmT+Nvf/kZjYyO33XYbBx544BrHb7/9dt5++22GDBnC+PHjWbJkSdP+VatW0a9fvzXKtzS+vVIzZ87khhtuAGCPPfbgu9/9bkfekrpBfrNDSZIkSeplmo9zb644Xn3ixIn079+f3Xffnf79+9PQ0NDUEt9S+Q022ICxY8dWHMd1113HmWeeCcDGG2/Mb3/7W/r3t103r0zcJUmSJKmb7Lrrrk0Tv7WUuBf37b333gAMHjyYCRMmtFj+xRdf5KGHHmoqX+kSbk8++SRHHnkkKSXq6uq47LLLGDlyZMfekLqFibskSZIkdZNBgwax6667AnDvvffy5ptvNh176623uPvuu4HViXvp8+aJe0fWb1+xYgUf+9jHePnllwGYMWMG+++/fwfeibqTfSHUbocc+pOq17lg3jltlpk/fz6nnHIKb731FoMGDeL73/8+Y8aMqXoskiRJUleaPHkyc+bMYeXKldx5553st99+ANx9990sX76cAQMGrLGO+l577cVZZ53FrbfeSkqpqWW9I+PbTz755KabA1OnTuWb3/xmtd6WupCJu9rt6h9f363Xmz17Nqeffvpadxivu+46Jk2axIwZM5gyZUq3xiRJkiR1VGnr+Jw5c5oS9+J49Z133pl11123qUxxZvmXX36Z+fPnN41lL5YfOnQo48ePb/O6v/nNbzj33HMB2HzzzbnsssvI86TYWs3fknLtwgsvZOrUqWWXv5gzZw5Tp07loosu6ubIJEmSpI7ZY489GDBgALBmq3nz8e1FG220Edtuu+0aZZYsWcIDDzwAZIl989nmm1uwYAHHHnssAAMGDODyyy9no402qsK7UXcwcc+JiBgaEZtFxGbAgMbGxlqHVHOzZ8/muOOOo62fRWNjI8ceeyyzZ8/upsgkSZKkjiudcO7OO+9kxYoVrFq1ittvvx1YO3Ev3VdM3G+99dam78ltjW9funQphx56KG+88QYA3/3ud9ljjz2q82bULewqnx/TgZnFF4sXL65hKK0bPfLhbrnO6aef3mbSXtTY2MgZZ5xhl3lJkiT1CJMnT+auu+5i2bJl3HvvvQwcOJCGhgYigj333HOt8nvttRcXXHBBU+LenvHtxx13HAsWLADgox/9KF/+8per+E7UHWxxz4+zgJGF7cH6+voah1Nb8+fPL9s9vpybb76Z+fPnd1FEkiRJUvU0H+deHK8+duxY1l9//bXKF1vcn3vuOR599NGm8oMHD2bnnXcue51zzz2X//u//wPgPe95DxdffHHV3oO6j4l7TqSUGlJKz6aUngVW9rVJImbNmkVENG3FCTfaa+zYsU11zJo1q7pBSpIkSVWy1157NU0MN2fOnLLj24u23HLLprXW//rXv3LvvfcCMHHixKbx8s3dc889nHzyyQCss846XHnllQwbNqyq70Pdo29lh5IkSZKUA8OHD2eHHXYA4LbbbuPWW28FyifusHp2+R/84AesWLECKD++/ZVXXuFjH/tYU7mf/OQnTddTz+MYd0mSJEmqgUmTJnH//fezZMmSpn3F5Lwle+21F5dffjmPP/54076WxrenlDjqqKN44oknANh3333ZbbfdmDdvXsWxbbLJJmyyySYVl1fXMnFXLsyaNWuNru2l61O2x7x58xgzZkwVI5MkSZK6xqRJk/jxj3/c9HrLLbdk8803L1u+eWv8oEGD2G233dYq9+STT3Lttdc2vb7xxhsZN25cu2KbOXOmQ09zxMRduTRmzBgmTZrUrgnqJk+ebNIuSZLURd46dkitQ+h1Jk2aRESQUgJab20HGDduHMOHD+f1118HYJdddmGdddZZq1yxPvUeJu7KrRkzZjB16tSKloSrq6vjm9/8ZjdEJUmSJFXHRhttVPHyx5B9533ttdfaLDdq1CiT917GyemUW1OmTOH888+nrRn26+rquOCCC1zDXZIkSVKvZIu7cmHhwoUt7t9rr7248MILOe+887jnnnvWOr7LLrtwwgknMHHixLXqGD16dJfEKkmSJEndycRduTdx4kQmTpzII488wne/+11WrlzJgAED+OpXv8rWW29d6/AkSZIkqUuZuKvH2Hrrrfn5z39e6zAkSZIkqVuZuOdERAwFhhZeDmjPJBWSJEmSpN7LxD0/pgMziy8WL15cw1Bq4Dfzql/nzMrGuJeuT+lalZIkSZLyxsQ9P84Czi88v66+vn5cLYPpbh/d4D1Vr3N+heVOO+20pucm7pIkSZLyxsQ9J1JKDUADQESsbGsJtN7m99sf3gW1tjxTfan58+ev9XrMmDFdEIskSZIkdUzfyg6lgtmzZzN58mTGjh27xv6xY8cyefJkZs+eXaPIJEmSJGlNtrgrF0aPfLjbrnXhhRdy3HHHUW4CwDlz5jB16lQuuOACPvvZz3ZbXJIkSZLUElvc1afMnj271aS9qLGxkWOPPdaWd0mSJEk1Z+KuPuX0009vM2kvamxs5IwzzujiiCRJkiSpdSbu6jPmz5/PnDlz2nXOzTffvNYEdpIkSZLUnUzc1WvNmjWLiGjamk9EV6mxY8c21eFycZIkSZK6m4m7JEmSJEk5ZuIuSZIkSVKOuRyceq1Zs2at0bV9/vz5HeouP2/ePMaMGVPFyLpe6fu2e78kSZLUs5m4q9dauHDhGq/79+/PzjvvzL333ltxHbvssgv9+/dvqmv06NFVjbGrnHbaaU3PTdwlSZKkns2u8upTTjzxROrqKvvY19XVccIJJ3RxRJIkSZLUOhP3ToqISRHxh4h4JiJSRBxT65hU3sSJEzn99NPbTN7r6uo444wzmDhxYjdFJkmSJEktM3HvvCHAPOBLwLIax6IKHHbYYVx44YXssssuLR7fZZdduPDCCzn00EO7ObLqaL7uvOvQS5Ik1daiRYvWWKa4o9uiRYvWqnvFihX88pe/5OMf/zjvec97GDZsGIMHD2bLLbfkgx/8IP/7v/9LQ0NDp2IcNGgQ73jHO9h///354Q9/yOuvv94FPyW1xjHunZRSuha4FiAiLqltNFrDb+aVPTSRoUw86Ks8sstTfPC8Lzft/+MJP2TrTd4JTwBPtHD+zPyOcZ89ezann346c+bMWWP/2LFjmTRpEjNmzGDKlCk1ik6SJPV0/dc5ttYhdKm3l19Q6xDa7S9/+Qtf+MIXePzxx9c6tmjRIhYtWsSf/vQnZs6cyf/8z//w6U9/ukPXWbFiBc8//zzPP/88N9xwA9///ve58sor7Z3ajXp94h4RhwGTgfHADsBQ4LKU0pGtnLM5cDpwALAh8BxwNXBaSunVLg5ZVXLIFTe1+5z/vPkx4LGyxxfMzGcr/IUXXshxxx1HY2Nji8fnzJnD1KlTueCCC/jsZz/bzdFJkiT1bSNHjuTBBx8se/z9738/zz77LJttthl//etfW62n6Gc/+xknnngiq1atAuADH/gAhx9+OFtvvTX9+/dn0aJFXHPNNfz2t7/lhRde4Oijj+bhhx/mW9/6VpvxfvjDH16j3PLly3nooYc499xzufPOO3n22Wc56KCDmD9/Pu94xzsq+RGok3p94g58gyxhfwN4Gti2tcIR8W7gdmAT4BrgIWBXsq7wB0TEnimll7s0YqkdZs+e3WrSXtTY2Mixxx7LFltsYcu7JElSNxowYECryxIPGDCgonJFf/vb3zj++ONJKTFkyBAuv/xyDjzwwDXK7Lbbbnz84x9n+vTpfOhDH+LZZ5/lzDPPZMstt2TatGmt1j9ixIi14th555054ogjOOyww7j66qt59dVXOeuss/j+97/fZrzqvL6QuH+ZLGF/lKzl/cY2yp9HlrSflFI6p7gzIn5QqOtM4D+6JlRV09U/vr6icuf8YoOm5188urJz8uT0009vM2kvamxs5IwzzjBxlyRJ6qHefPNNPv3pT5NSIiK46qqreN/73le2/E477cQNN9zAzjvvzJtvvslJJ53EAQccsEbrfaX69evH//zP/3D11VcDWVd9E/fu0esnp0sp3ZhSeiSllNoqGxFbAVOBRcC5zQ7PBJYCR0XEelUPVDXzxaM3atp6mvnz5681pr0tN998sxPWSZIk9VAXX3wxzz//PADTpk1rNWkv2m677fj6178OZIn/j3/84w5f/z3veQ8bbrghAE888USH61H79IUW9/bYr/B4fUppjSbMlFJDRNxGltjvDszu7MUi4r4yh1rtzq/KjB75cK1DqNisWbM47bTTuu16lXTBasnMmTOZNWtWdYORJElSxS666KKm51/+8pdbKbmm448/njPOOIPly5dz8cUX893vfpeI6FAMxa79xfH16nq9vsW9nYpThpfL+B4pPG5T3BERQyJifESMJ/t5vqvw+l1dF6YkSZKkvmbJkiU88MADAGy99dZsv/32FZ+7/vrrs/feewPw0ksv8dBDD3UohhdeeIHFixcDsNlmm3WoDrWfifuahhceyy1MWNw/omTfzsD9hW1d4LTC89PbulhKaaeWNrIJ8SRJkiSpybx585pauXfaaad2nz9hwoSm5//4xz86FMN///d/UxyFvO+++3aoDrWfiXv7FPuSNI2XTyndlFKKFrZjahOieqJZs2aRUmr3Nm9e+bXqWzNv3rwOXc9u8pIkSbXz0ksvNT3fdNNN231+6TmldbVlxYoVPPDAA0ybNq1pfHz//v3b1VVfneMY9zUVW9SHlzk+rFm5qomIoWRrzAMMqHSWcPVtY8aMYdKkSe2aoG7y5MmMGTOmC6OSJElSV2hoaGh6PmTIkHafX3rOkiVLypb7xS9+wS9+8YuyxwcOHMiFF17od8puZOK+poWFx23KHN+68NgVs55NJ5u5HqBp3Ij6hoULF7ZdqIzPfOYz3HrrrRUtCVdXV8cxxxzT4euNHj267UKSJEnqEkOHDm16/sYbb7T7/NJzhg0b1krJltXX1/OBD3yAU045xaS9m5m4r6m4xvvUiKgrnVm+0CK+J7AMuLMLrn0WcH7h+XX19fXjuuAa6oUmTpzI6aefzowZM1pN3uvq6jjjjDOYOHFiN0YnSZKkatloo9XLFxeXhGuP0nNK62ruwx/+MN/61reaXg8cOJD111+fjTfeuN3XVHWYuJdIKT0WEdeTLfl2InBOyeHTgPWAn6WUlnbBtRuABoCIWFlX5/QDqtxhhx3GyJEjOe+887jnnnvWOr7LLrtwwgknmLRLkiT1YGPHjqVfv36sWrWK++4rt7J0eXPnzm16Pn78+LLlRowY0eHlg9U1en3iHhGHAIcUXhZnY5gYEZcUnr+UUjql5JQTgNuBsyNiCrAA2A3Yl6yL/Ne7KE7HuPcAnenS3tUmTpzIxIkTeeSRR/jgBz/YtP+Pf/wjW2+9dStnSpIkqScYNmwYO+ywA3PnzuWRRx5hwYIFbLfddhWd++qrr3LLLbcAsPHGG7Ptttt2Zaiqsr7QrDseOLqwvb+wb6uSfYeVFk4pPUa2xNslZAn7dODdwNnAxJTSy10U53TgmcI2zjHu6qjmSbpJuyRJUu/xmc98pun5j370o4rP+9nPfsby5cub6oiINs5QnvT6xD2lNKvMcm3FbVQL5zyVUvpMSukdKaWBKaUtUkpfSim90oWhngWMLGwP1tfXd+GlJEmSJPVEn/nMZyjmCj//+c+58cYb2zgj6zV6xhlnADB48GBOOumkLo1R1dfrE/eeIqXUkFJ6NqX0LOAYd0mSJElrWW+99bjkkkuICBobG/nwhz/MddddV7b8/fffz5QpU3jzzTcBOPvssxk5cmR3hasq6fVj3HsKx7j3Luecs3pewy9+8Yttlj/k0J90WSzVrHvBvHPaLiRJkqQudcABB3DOOedw0kkn0dDQwAc+8AEOOuggDj/8cLbeemv69evHE088wTXXXMNvfvMbVq1aBcB//dd/MW3atBpHr44wcc8P13HvAT56/fKKyv3r3HObnt84+tg2y1/94+s7HFNLtn1f19UtSZKk2jvxxBPZYost+OIXv8iiRYv485//zJ///OcWy26yySZ873vf4+ijj+7mKFUtJu754TruPcDvtz+8onKlc3RWek41nXjUBt1+TUmS1Lu9vfyCWoegZg4++GD2339/Lr/8cv74xz8yd+5cFi9eTGNjIxtvvDFjx47l4IMP5sgjj2To0KFtV6jcMnHPCddxVzV98eiNah2CJEmSKrRo0aIOnzto0CA+/elP8+lPf7pTMYwaNYqUUqfqUNcxO5QkSZIkKcdscc8JJ6eTJEmSJLXExD0/nJyuBxg98uEuOWfhM9t0JBxJkiRJfUBVEveIOBN4PzA1pfRKyf4BQH1K6elqXKeXc3I69TmzZs1q8Xke9aRYJUmS1LtENSYgiIi7gIEppR1L9u0I/BXYEHgC+GxK6aZOX6wPiIj7JkyYMOG+++6rdSgteyhqHUHuxXarn6cFtYuj6rat7oQlEas/S3mfDKUnxSpJUiUWLMi+pGy33XZtlJSqp6Ofu5122om5c+fOTSnt1BVx5V21JqcbBdzbbN+ZwEbAbcAI4E8R8e4qXU+SJEmSpD6hWon7MKBpUHZEjAD2B36bUpoE7AoE8JUqXU+SJEmSpD6hWpPTPUPWul40leymwPkAKaVHI+IPZMm8WuCs8pIkSZKkllSrxf2fwPsjol/h9aeAN4E5JWX+DWxWpev1RtPJboA8A4xzVvmea/4jrb+WJEmSpPaoVuL+Q2ALYHZEXAAcDFybUnq7pMwmwPIqXa83OgsYWdgerK+vr3E4aq/Zd8Dko2Dsh9bcP/ZD2f7Zd9QmLkmSJEk9W1US95TSLcA3gD2BacAS4LRmxcYAz1Xjer1RSqkhpfRsSulZYGVdXbXuqag7XHglTP0czGk+RWPBnHuz4xf9rnvjkiRJktTzVSU7jIhPAr8gay3eB3hPSulfJce3Ipug7u5qXE/Kk9l3wHEzoa1pCRob4dgZtrxLkiRJap9qNev+imyd9hdSSnNSSi83Oz6MLLG3vVG9zunntZ20FzU2whk/7dp4JEmSJPUu1eyPHeUOpJT+AVwL7FLF60k1N/+R8t3jy7n5HieskyRJklS5DifuEfGJiHhPRJRN2JsZA/xXR6/X20XE0IjYLCI2w+XgcmvWTyC2W701n4iuUmM/tGY9s35S3TglSZIk9R6dWcf9/4AEvFF43D8ingXmAg+mlFY2K78e8FYnrtfbTQdmFl+4HJwkSZIkCTqXuJ8C7AjsBGxLNqP8HoVjKyPiX2RJ/P1ks8wfATzRiev1dmcB5xeeX1dfXz+ulsFILVm4cGGPqXv06NFVrU+SJEmqlQ4n7imlHxSfR0QjcAlwBzChsI0Dxjc77QsdvV5vl1JqABoAIsLl4HJq1heyrWj+Ix3rLj/vDzBm6+rFJUmSJKn36kyLe6nvAfemlK4s7oiIfsD2ZMn7hsB9hfXepV5jzNYwaef2TVA3eReTdoBHHnlkrddbb53PH8z8+fPXej1mzJgaRSNJkqS+pirNuimlU0uT9sK+VSmlB1NKv0wp/cikXb3VjBOg0g4SdXXwzeO7Np68u+OOOzjyyCP54Ac/uMb+D37wgxx55JHccUd+FrqfPXs2kydPZuzYsWvsHzt2LJMnT2b27Nk1ikySJEl9if2xpU6aMhHOP63t5L2uDi44PSvfV1155ZVMmzaNe+9tuYvCvffey7Rp0/jd737XzZGt7cILL2Tq1KnMmTOnxeNz5sxh6tSpXHTRRd0cmSRJ6sluuukmImKtrV+/fqy//vqMGjWKPffck5NOOolf//rXLFu2bI3zU0pMmjSJiKB///7cfffdFV337rvvpn///kQEkydPJqXUFW9PXaRaXeWJiPXIusWvAzyWUlpUrbqlvJt2GIwaCWf8NFunvbnJu2Qt7X05ab/jjjuYMWMGbS112NjYyDe/+U0222wzJk6szQ9s9uzZHHfccRXFeuyxx7LFFlswZcqUbopOkqQaOf3mWkfQtWZMrunlGxsbee2113jttdd44oknuP322znnnHMYMWIEn//855k5cybrrrsuEcGFF17IDjvswLJly/jsZz/L3LlzGThwYNm6V65cybRp01i1ahXrrrsuP//5z6l8VW/lQVVa3CNiV+BRYA5wPfBYRLwSEbMj4nuFNd/zOXhVqpIpE+GmS7OJ50rN+0O2vy8n7QDnnntum4lwUWNjI+edd14XR1Te6aef3q5YzzjjjC6OSJIk9UbHH388Dz74YNN25513ct111/H973+fD37wg/Tr14/XXnuN7373u+y88878+9//BmDrrbfmtNNOA7K5d84888xWr3PmmWcyb948AM4444zcziuk8qrVVf6HQD1wFfDfwG+AxcBksmXjLgMeiojXqnQ9KbeaTzznRHTZxHPluseXc88996w1gV13mD9/ftnu8eXcfPPNa01gJ0mS1JZNNtmEsWPHNm277bYb73//+5k+fTp/+MMfeOSRR9h3330B+Ne//sXBBx/MkiVLADj55JPZddddAfj2t7/NP//5zxavMX/+fL797W8DsNtuu/HlL3+5G96Zqq1aXeXfC/w5pXRY6c6IGMLqtd53JlsmTi2IiKHA0MLLAZW29kndac+9j+LlF1sYC9BFmk9g1x6TJ0/m5pu7r0tf8wnsKjVz5kxmzZpV3WAkSVKvsOWWW/K3v/2Ngw8+mOuuu45//etfnHbaaZx11ln069ePiy66iAkTJrBixQqmTZvGnXfeSb9+/ZrOb2xsZNq0aaxYsYKBAwdy0UUX4bLTPVO1fmtLgbWam1JKb6SUbinMKn9kSmn7Kl2vN5oOPFPYxi1evLjG4Uhr+8QBj9U6BEmSpD6lX79+/OIXv2Dw4MEA/OxnP+Pll18GYMyYMXzjG98Askl+zzrrrDXO/dGPfsRdd90FwIwZM9h+e9OxnqpaifvfgW2rVFdfdRYwsrA9WF9fX+NwJEmSJOXBJptswic/+UkAli5dyvXXX9907Gtf+xrjx48Hsp58Dz/8MACPP/443/zmNwHYcccd+epXv9q9QauqqtVV/kzgnojYIaX0QJXq7FNSSg1AA0BErLQLi/Loi0dvxBeP3qjd5z2y6C0++Lkn2n3eH3++BVuPGtTu8wBGT7mpQ+fNnz+/Q93e582bx5gxYzp0TUmSpLZMnTqVn//85wDccsstHHHEEQD079+fiy66iF133ZXly5czbdo0br75Zo499ljefPPNpuP9+1dtQTHVQLWyw08C1wJ/jYgDqlSnpF5i61GD2Pm967brnF3eu26Hk/bOGDNmDJMmTWrXOZMnTzZplyRJXWrChNXThRVb1Yt23HFHvvKVrwBw66238v73v5+///3vAJx66qlNLfLquaqVuH8N+AiwCfDniPh3RPwsIo6LiAkR4e0dqY878cgNqatwudC6gBOO3LBrA2rFjBkzKp64pa6urqkbmiRJUlfZcMPV341eeeWVtY6XjmG/4YYbgKxBwu8pvUO1EvepwKnA5cAjwDuBY4GfAvcADRFxT0TUbmFmSTU1ccJgTj+5vs3kvS7gjJPrmThhcPcE1oIpU6Zw/vnnt5m819XVccEFFzBlypRuikySJPVVQ4YMaXre0NCw1vFBgwatMWt8XV0dF110EQMHDuy2GNV1qpK4p5RuSCn9T0rpiJTStsBwYG/g/wG/ABaSLRn3+WpcT1LPdNgHhnPhdzdnlzLd5nd577pc+N3NOfQDw7s5srVNmzaN66+/nsmTJ7d4fPLkyVx//fV89rOf7ebIJElSX1SarA8bNqzFMrvtthvvfOc7AXjnO9/ZtM67er4u6cKeUloK3FbYAIiIgUDHFjqW1GtMnDCYiRMGrzVhXWcmousqU6ZMYcqUKWtNWOdEdJIkqbu99NJLTc832GCDGkaiWqhq4h4R2wK7AQE8mVL6e/FYSmkFMLea15PUvUaPfLjtQhXXtebrg/ds/6zz3aV5km7SLkmSutvcuatTqdGjR9cwEtVCVRL3iKgDLgQ+XdwFNBbrj4hIKaVqXEuSJEmS+prStdv33nvvGkaiWqjW5HRfBY4G7gD+A/gdWfJetEdELIqIfat0PUmSJEnqE1544QV+/etfA9kkdfvvv3+NI1J3q1bi/hmyCegmp5QuAOaVHkwp3Qa8DRxepetJkiRJUq+3atUqjj76aJYtWwbA5z//ece490HVGuO+BXBuSmlVK2XuA/ao0vV6nYgYCgwtvBzQ2NhYy3DUSTNPrHUEWrhwYY+o2zFqkiSpnEWLFvHZz36WG2+8EYDtt9+eGTNm1Dgq1UK1EvclQFvTQT8D2FW+vOnAzOKLxYsX1zAUddasL9Q6AkmSJOXdCy+8wLx5qzsrv/nmm7z66qvMnz+fm266iWuvvZZVq7K20e23354//elPZZeCU+9WrcT9HuB9EVGXUirXVNxItr67WnYWcH7h+XX19fXjahmMJEmSpK7105/+lJ/+9KetlhkxYgTHHXccs2bNYt111+2myJQ31UrcLwSuAE4HvlGmzHuBl6t0vV4npdQANABExMq6umpNPyBJkiRVwYzJtY6gV6urq2Po0KEMGzaMzTffnJ122ok99tiDQw45xIRd1UncU0q/i4jLga9FxHbA8tLjEfER4H3AldW4niSpZbNmzWrxeV/R199/V/BnKlWP/54EsM8++9CVK2UvWrSoy+pW7US1PjSFtdzPBo4v2T0H2AjYnmxW+T1SSvdV5YK9WETcN2HChAn33ZfTH9VD0XYZqQKx3ernaUF1614YD1W1vm233bbp+UMPVa/uak9OF7H632dXfinIq77+/ruCP1OpevL272nBguyP73bbbddGSal6Ovq522mnnZg7d+7clNJOXRFX3lWtP3ZKqTGl9AWymeN/DbwETAbGAA8CB5u0S5IkSZLUPtUa494kpXQXcBdARAwia9Vf3vpZklRdhxz6kx5R94J551StLkmSJPVOVWlxj4g7I2L35vtTSm+ZtEuSJEmS1HHVanHfEbitMEHdqSmlJ6tUryR1SN3nP1fdCk9a3cpe9bolSZKkVlQrcd8W+B7wCeCQiPgh8O2U0htVql+S2uX32x9e1fq2LXle3boXVrEuSZIk9UZV6SqfUvp3SuljwN5kE9F9DXgkIqZF6fSZkiRJkiSpXao2qzxASum2lNJuwJHAW8D5wP0RsV81ryNJkiRJUl9R9VnlAVJK/xcRvwO+DJwK/C0i/gT8Z0rp4a64pqSeZ+aJtY6gcicetUGtQ5AkSVIf1SWJO2QzygPfiYgLgdOAY4H3A+t01TUl9SyzvlDrCCr3xaM3qnUIkiRJPVpKqdYh9FhV7SpfFBGbRcT+EfFl4DvABOBtYEBXXK/WIuKEiPh3RCyPiPsiYu9axyRJkiS1pjgVVWNjY40jUV9RTNydBq39qtLiHhEnAmOBMYVtRPFQ4XEV8DDwQDWulycR8XHgx8AJwK2Fx79ExPYuiyfVzuiRjsqRJKk1AwcO5K233mLp0qUMHTq01uGoD1i+fDkAAwb0yvbcLlWtrvLnlDx/BbiZLEn/Z+FxfqHrfG90MnBJSumCwusvRsQBwPFks+tLkiRJuTN8+HBeeOEFXn75ZQYPHky/fv1qHZJ6sZQSr776KoA3ijqgWon7f1FI1FNKz1SpzqqIiMOAycB4YAdgKHBZSunIVs7ZHDgdOADYEHgOuBo4LaX0akm5gcBOwPebVXE9sEfV3oQkSZJUZcOHD+fll19m2bJlLFq0iPXXX5/11luPAQMGEBF2Z1anpZRIKbF8+XJeffVVlixZQkQwfPjwWofW41QlcU8pfaca9XSRb5Al7G8ATwPbtlY4It4N3A5sAlwDPATsCnwJOCAi9kwpvVwovhHQD1jcrJrFwPuq9QYkSZKkauvfvz+jRo3iqaeeYsWKFSxe3PwrrVRdEcHmm2/OoEGDah1Kj1PVWeUjYltgN7Kx7U+mlP5ezfo76MtkCfujZC3vN7ZR/jyypP2klFLTEICI+EGhrjOB/2h2TvPpEaOFfZIkSVKuDBw4kFGjRtHQ0MDSpUt58803WbVqlbN/q2oiggEDBjB06FCGDx9u0t5B1Zqcrg64EPh0cRfQWKw/IiLV6F9/SqkpUW+ru09EbAVMBRYB5zY7PBM4DjgqIqanlJYCL5FNvLdps7KbsHYrvCRJkpQ7/fr1Y8SIEYwYMaLWoUgqo1rLwX0VOBq4g6w1+nesnlEeYI+IWBQR+1bpel1lv8Lj9SmlNdbFSCk1ALcBg4HdC/tWAPcB+zerZ3+y7vaSJEmSJHVKtbrKfwZYCExOKa2KiM2AjxYPppRui4i3gcNpu6t6LY0uPJZbR+oRshb5bYDZhX0/AH4ZEXeTJfb/AWwG/G9bF4uI+8ocanUcvqTeY+HChT2m7tGjR7ddSJIkSVVXrcR9C+DclNKqVsrcR/5nWi9Ob/h6mePF/SOKO1JKl0fEhmST4L0DmAccmFJ6oquClCRJkiT1HdVK3JcAbc0y8AyQ967ybSl2/19jvH5K6TyySe3aJaW0U4sXyVriJ7Q7OkmSJElSr1OtxP0e4H0RUdd8bHiJRla3aOdVsUW9XJzDmpWrmogYSrbGPMCAxsZyP0ZJkiRJUl9SrcnpLgS2Bk5vpcx7gZdbOZ4HxQGh25Q5vnXhsdwY+M6YTtYr4RlgnOtoSpIkSZKgSi3uKaXfRcTlwNciYjtgeenxiPgI8D7gympcrwsVJ86b2rz3QKFFfE9gGXBnF1z7LOD8wvPr6uvrx3XBNSSpw7pyIr2ukPd4nexPkiRVqlpd5QE+RdaifnxxR0TcCGwEbA+sBL5bxetVXUrpsYi4nmzm+BOBc0oOnwasB/yssIZ7ta/dADQARMTKurpqdYaQJEmSJPVkVUvcC63TX4iIXwJfJFvLfHLh8D+B/0wplVv+rMtExCHAIYWXmxYeJ0bEJYXnL6WUTik55QSyNdjPjogpwAJgN7KJ9R4Gvt5FcTrGXZIkSZK0lmq2uAOQUroLuAsgIgYBkVJa3vpZXWo8cHSzfVsVNoAngKbEvdDqvjPZeP0DgAOB54CzgdNSSq90UZzTgZnFF45xl/qGQw79SY+p++rffaGq9UmSJKkyVU/cS6WU3urK+iuMYRYwq53nPAV8piviaYVj3CVJkiRJa+nSxF2Vc4y71DfVff5z1a3wpNWt7FWvW5IkSTVh4p4TjnGX+qbfb394Vevbtgvrhj9UuT5JkiRVwmbd/HAdd0mSJEnSWkzc8+MsYGRhe7C+vr7G4UiSJEmS8sCu8jnhGHdJkiRJUkvMDiVJkiRJyjFb3HPCyekk5V1XrjnfFfIe74J559Q6BEmS1EOYuOfHdGBm8YWT00nKm6t/fH2tQ2jTtu9b/bwnxCtJklQJu8rnh5PTSZIkSZLWYot7Tjg5ndQ3jR75cI+pe+Ez21S1PkmSJFXG7FCSJEmSpBwzcZckSZIkKcfsKp8TziovSZIkSWqJLe75MR14prCNc1Z5SZIkSRKYuOeJs8pLkiRJktZiV/mccFZ5SZIkSVJLzA4lSZIkScoxE3dJkiRJknLMxF2SJEmSpBxzjHtOuBycJEmSJKkltrjnh8vBSZIkSZLWYuKeHy4HJ0mSJElai13lc8Ll4CTl3eiRD9c6hHbpafFKkiSVY3YoSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo6ZuEuSJEmSlGMuB5cTETEUGFp4OaCxsbGW4bTqtO2qX+fMBdWvU5LybOHChbUOod3yHvPo0aNrHYIkSV3CFvf8mA48U9jGLV68uMbhSJIkSZLywMQ9P84CRha2B+vr62scjiRJkiQpD+wqnxMppQagASAiVtbVeU9FkiRJkmSLuyRJkiRJuWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmJPTSe3QFWvYg+vYS5IkSSrPFndJkiRJknLMFndJktQrLFy4sNYhSBXz86o8Gz16dK1DUDMm7pIk1cLTH6p1BO2X95g3/0OtI5AkqUvYVV6SJEmSpByzxT0nImIoMLTwckBjY2Mtw5EkSZIk5YQt7vkxHXimsI1bvHhxjcORJEmSJOWBiXt+nAWMLGwP1tfX1zgcSZIkSVIe2FU+J1JKDUADQESsrKvrW/dUump9dEmSJEnq6fpWdihJkiRJUg9j4i5JkiRJUo7ZVV7KgZ4yVGDmglpHIEmSJPU9trhLkiRJkpRjtrhLkqRe4ZBDf1LrEKSK+XlVni2Yd06tQ1AzJu6SJKlXuPrH19c6BKlV275v9XM/r5Law67ykiRJkiTlmIm7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYs8p3UkRMAk4BdgI2Az6TUrqkpkFJknJv9MiHax1Cu+U95oXPbFPrECRJ6hK2uHfeEGAe8CVgWY1jkSRJkiT1Mra4d1JK6VrgWoCIuKS20UgqOm27WkdQmZkLah2BJEmS8q7HtbhHxGERcU5E3BIRSyIiRcSv2jhn84i4KCKejYi3ImJRRPwoItbvrrglSZIkSeqIntji/g1gB+AN4Glg29YKR8S7gduBTYBrgIeAXcm6th8QEXumlF7u0oglSZIkSeqgHtfiDnwZ2AYYBhxfQfnzyJL2k1JKh6SUTk0p7Qf8EBgNnFlaOCK+VWjFb23bp6rvSJIkSZKkMnpci3tK6cbi84hotWxEbAVMBRYB5zY7PBM4DjgqIqanlJYW9v8IaLXrPfBk5RFLkiRJktRxPS5xb6f9Co/Xp5QaSw+klBoi4jayxH53YHZh/0vAS90apSRJkiRJZfT2xH104bHcwrOPkCXu21BI3NsrIoYA7ym8rAPeFRHjgVdSSq22zEfEfWUOtTpuX5IkSZLUd/TEMe7tMbzw+HqZ48X9IzpxjZ2B+wvbusBpheend6JOSZIkSZKA3t/i3pbiIPnU0QpSSjeV1NPec3dqaX+hJX5CR2OSJEmSJPUevT1xL7aoDy9zfFizcjUTEUOBoYWXAxobG1srLkmSmhk9stzIOCl//LxKao/e3lV+YeFxmzLHty485uF/zunAM4Vt3OLFi2scjiRJkiQpD3p74l5cOm5qRKzxXgst3HsCy4A7uzuwFpwFjCxsD9bX19c4HEmSJElSHvTqxD2l9BhwPTAKOLHZ4dOA9YBLS9Zwr5mUUkNK6dmU0rPAyrq6Xv2rkSRJkiRVqMeNcY+IQ4BDCi83LTxOjIhLCs9fSimdUnLKCcDtwNkRMQVYAOwG7EvWRf7rXRxyRRzjLkmSJElqSY9L3IHxwNHN9m1V2ACeAJoS95TSYxGxM9nybAcABwLPAWcDp6WUXunqgCs0HZhZfOEYd0mSJEkS9MDEPaU0C5jVznOeAj7TFfFU0VnA+YXn19XX14+rZTCSJEmSpHzocYl7b5VSagAaACLCMe6SJEmSJMDEPTcc4y5JkiRJaonNuvnhOu6SJEmSpLWYuOeH67hLkiRJktZiV/mccIy7JEmSJKklZoeSJEmSJOWYLe454eR0kiRJkqSW2OKeH05OJ0mSJElai4l7fjg5nSRJkiRpLXaVzwknp5MkSZIktcTsUJIkSZKkHDNxlyRJkiQpx+wqnxPOKi9JkiRJaokt7vnhrPKSJEmSpLWYuOeHs8pLkiRJktZiV/mccFZ5SZIkSVJLzA4lSZIkScoxE3dJkiRJknLMrvKSKnbadrWOQJIkSep7TNxzwuXgJEmSJEktsat8frgcnCRJkiRpLSbu+eFycJIkSZKktdhVPidcDk6SJEmS1BKzQ0mSJEmScszEXZIkSZKkHDNxlyRJkiQpxxzjLkk1dNp2PafumQuqW58kSZIqY4u7JEmSJEk5ZuIuSZIkSVKO2VU+JyJiKDC08HJAY2NjLcORJEmSJOWELe75MR14prCNW7x4cY3DkSRJkiTlgS3u+XEWcH7h+XX19fXjahmMJEk9TVdO9ihVm59X5dnMVOsI1JyJe06klBqABoCIWFlXZ2cISZIkSZJd5SVJkiRJyjUTd0mSJEmScszEXZIkSZKkHHOMuySpIj1tIqW8xztzQa0jkCRJPYUt7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmLPK50REDAWGFl4OaGxsrGU4kiRJkqScsMU9P6YDzxS2cYsXL65xOJIkSZKkPDBxz4+zgJGF7cH6+voahyNJkiRJygO7yudESqkBaACIiJV1dd5TkSRJkiTZ4i5JkiRJUq6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjjmrvCRJNXDadrWOoP16YsySJPUGtrhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo6ZuEuSJEmSlGMm7lUQEV+LiHsiYklEvBgRf4yIsbWOS5IkSZLU85m4V8c+wHnAHsB+wNvADRGxQS2DkiRJkiT1fK7jXgUppfeXvo6Io4DXgT2BP9YkKEmSJElSr9AjW9wj4rCIOCcibil0T08R8as2ztk8Ii6KiGcj4q2IWBQRP4qI9bsgxKFkP9tXu6BuSZIkSVIf0lNb3L8B7AC8ATwNbNta4Yh4N3A7sAlwDfAQsCvwJeCAiNgzpfRyFeP7MfAP4I4q1ilJkiRJ6oN6ZIs78GVgG2AYcHwF5c8jS9pPSikdklI6NaW0H/BDYDRwZmnhiPhWoRW/tW2fli4UET8A9gIOTSmt6vA7lCRJkiSJHtrinlK6sfg8IlotGxFbAVOBRcC5zQ7PBI4DjoqI6SmlpYX9PwJa7XoPPNnCtX4IfALYN6X0eBvnS5IkSZLUph6ZuLfTfoXH61NKjaUHUkoNEXEbWWK/OzC7sP8l4KX2XCQifkyWtO+TUnqo01FLkiRJkkTfSNxHFx4fLnP8EbLEfRsKiXt7RcS5wFHAIcCrEbFp4dAbKaU3WjnvvjKHWh2zL0mSJEnqO3rqGPf2GF54fL3M8eL+EZ24xglkM8nPBp4r2U7pRJ2SJEmSJPWJFve2FAfJp45WkFJqfaB9+fN2ajGgrCV+QkfjkSRJkiT1Hn0hcS+2qA8vc3xYs3I1ERFDyVrtAQY0Nja2VlySJEmS1Ef0ha7yCwuP25Q5vnXhsdwY+O4yHXimsI1bvHhxjcORJEmSJOVBX0jci0vHTY2INd5voZV7T2AZcGd3B9bMWcDIwvZgfX19jcORJEmSJOVBr0/cU0qPAdcDo4ATmx0+DVgPuLRkDfeaSCk1pJSeTSk9C6ysq+v1vxpJkiRJUgV65Bj3iDiEbOk1gOLSaxMj4pLC85dSSqUzup8A3A6cHRFTgAXAbsC+ZF3kv97FIbfJMe6SJEmSpJb0yMQdGA8c3WzfVoUN4AlKlmJLKT0WETsDpwMHAAeSLdd2NnBaSumVrg64AtOBmcUXjnGXJEmSJEEPTdxTSrOAWe085yngM10RT5WcBZxfeH5dfX39uFoGI0mSJEnKhx6ZuPdGKaUGoAEgIhzjLkmSJEkCTNxzwzHukiRJkqSW2KybH67jLkmSJElai4l7friOuyRJkiRpLXaVzwnHuEuSJEmSWmJ2KEmSJElSjtninhNOTidJkiRJaokt7vnh5HSSJEmSpLWYuOeHk9NJkiRJktZiV/mccHI6SZIkSVJLzA4lSZIkScoxE3dJkiRJknLMrvI54azykiRJkqSW2OKeH84qL0mSJElai4l7fjirvCRJkiRpLXaVzwlnlZckSZIktcTsUJIkSZKkHDNxlyRJkiQpx0zcJUmSJEnKMce454TLwUmSJEmSWmKLe364HJwkSZIkaS0m7vnhcnCSJEmSpLXYVT4nXA5OkiRJktQSs0NJkiRJknLMxF2SJEmSpBwzcZckSZIkKcdM3CVJkiRJyjETd0mSJEmScszEXZIkSZKkHHM5uJyIiKHA0MLLAY2NjbUMR5IkSZKUE7a458d04JnCNm7x4sU1DkeSJEmSlAcm7vlxFjCysD1YX19f43AkSZIkSXlgV/mcSCk1AA0AEbGyrs57KpIkSZIkW9wlSZIkSco1E3dJkiRJknLMxF2SJEmSpBwzcZckSZIkKcdM3CVJkiRJyjETd0mSJEmScszEXZIkSZKkHDNxlyRJkiQpx0zcJUmSJEnKsf61DkCZiBgKDC28HNDY2FjLcCRJkiRJOWGLe35MB54pbOMWL15c43AkSZIkSXlg4p4fZwEjC9uD9fX1NQ5HkiRJkpQHdpXPiZRSA9AAEBEr6+q8pyJJkiRJssVdkiRJkqRcM3GXJEmSJCnHTNwlSZIkScoxE3dJkiRJknLMxF2SJEmSpBwzcZckSZIkKcdM3CVJkiRJyjETd0mSJEmScszEXZIkSZKkHDNxlyRJkiQpx0zcJUmSJEnKMRP3ToqIEyPinxGxpLDdEREH1TouSZIkSVLvYOLeeU8DXwUmADsDfweujoj31jQqSZIkSVKv0L/WAfR0KaVrmu36ekQcD0wE/lmDkCRJkiRJvUiPa3GPiMMi4pyIuKXQNT1FxK/aOGfziLgoIp6NiLciYlFE/Cgi1q9ybP0i4hPAEOD2atYtSZIkSeqbemKL+zeAHYA3yLqpb9ta4Yh4N1kSvQlwDfAQsCvwJeCAiNgzpfRyZwKKiHHAHcA6hbg+klJ6sDN1SpIkSZIEPbDFHfgysA0wDDi+gvLnkSXtJ6WUDkkpnZpS2g/4ITAaOLO0cER8q9CK39q2T7NrLATGA7sDPwV+ERFjO/EeJUmSJEkCemCLe0rpxuLziGi1bERsBUwFFgHnNjs8EzgOOCoipqeUlhb2/whotes98GSzmFYAjxZe3hsRu5DdYJjWRj2SJEmSJLWqxyXu7bRf4fH6lFJj6YGUUkNE3EaW2O8OzC7sfwl4qZPXrQMGdbIOSZIkSZJ6feI+uvD4cJnjj5Al7ttQSNzbKyK+A/wZeAoYCnwS2Adocy33iLivzKEdFixYwE477dSRkLrcc7UOQFJFflbrAGqsr7//ruDPVKoe/z0pz/6QwzxkwYIFAKNqHEbN9PbEfXjh8fUyx4v7R3TiGpuSda3ftFDfP4EPpJT+2ok6Vy1btuz1uXPnLupEHeo5ihMsPlTTKNTrVPkmW4/7nHqTsfpy/jPtcZ9R9UlNn9Oc/3tS37btc3PnQv7+Px0FLKl1ELXS2xP3thQHyaeOVpBSOqYT5+bvVpa6XbHnhZ8H5ZmfU+Wdn1H1BH5O1RP4Oc2nnjirfHsUW9SHlzk+rFk5SZIkSZJypbcn7gsLj9uUOb514bHcGHhJkiRJkmqqtyfuxaXjpkbEGu81IoYCewLLgDu7OzBJkiRJkirRqxP3lNJjwPVkExmc2OzwacB6wKUla7hLkiRJkpQrPW5yuog4BDik8HLTwuPEiLik8PyllNIpJaecANwOnB0RU4AFwG7AvmRd5L/exSFLkiRJktRhkVKHJ1SviYiYBcxspcgTKaVRzc55J3A6cACwIdmKNlcDp6WUXumSQCVJkiRJqoIel7hLkiRJktSX9Oox7pIkSZIk9XQm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIm7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIm7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIl7FUXEURGRCtvnah2PJEmSJKnnM3Gvkoh4J3AO8EatY5EkSZIk9R4m7lUQEQFcDLwM/G+Nw5EkSZIk9SIm7tVxErAf8BlgaY1jkSRJkiT1IibunRQR2wHfAX6cUppT63gkSZIkSb1L/1oH0JNFRH/gl8CTwH914Pz7yhwaSzZWflGHg5MkSZKk3mMUsCSltGWtA6kFE/fOmQHsCOyVUlpWxXr7rbvuuhtst912G1SxTkmS2m3u3LlNzydMmFDDSKSez39PUsctWLCAZcuqmXL1LCbuHRQRu5K1sp+VUrqjI3WklHYqU/d922233YT77ivXIC9JUvfI5l/N+HdJ6hz/PUkdt9NOOzF37txFtY6jVhzj3gElXeQfBr5Z43AkSZIkSb2YiXvHDAG2AbYDlkdEKm7AzEKZCwr7flSrICVJkiRJPZ9d5TvmLeDCMscmkI17vxVYCHSoG70kSZIkSWDi3iGFieg+19KxiJhFlrj/IqX08+6MS5IkSZLU+9hVXpIkSZKkHDNxlyRJkiQpx0zcqyylNCulFHaTlyRJkiRVg4m7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIm7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo71r3UAktQTzJo1q8XnkiRJUlczcZekCpx22mlNz03cJUmS1J3sKi9JkiRJUo6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIm7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu6SJEmSJOWYibskSZIkSTlm4i5JkiRJUo6ZuEuSJEmSlGMm7pIkSZIk5ZiJuyRJkiRJOWbiLkmSJElSjpm4S5IkSZKUYybukiRJkiTlmIm7JEmSJEk5ZuIuSZIkSVKOmbhLkiRJkpRjJu4dFBHfjYjZEfFURCyLiFci4v6ImBkRG9Y6PkmSJElS72Di3nFfBtYD/gb8GLgMeBuYBfwzIt5Zu9AkSZIkSb1F/1oH0IMNSyktb74zIs4E/gv4GnBCt0clSZIkSepVbHHvoJaS9oLfFh637q5YJEmSJEm9l4l79X2w8PjPmkYhSZIkSeoV7CrfSRFxCjAEGA7sDOxFlrR/p5ZxSZIkSZJ6BxP3zjsFqC95fR1wTErpxbZOjIj7yhzathqBSZIkSZJ6PrvKd1JKadOUUgCbAh+F/9/evcfZUdaHH/98F4QKCCpoidzihYvNKpiAGKwssBqxiqKiVStaiVAloFatl2qSTdJarTdUoC1IoFVbK1Bb8EcldcHEC4IkYM0qNxEFDYJgUTBc9/v7Y2b15OTs/dx29vN+veY1e2aeZ+Y7J8/Z7PfMM8/DU4BrImJ+ZyOTJEmSJFWBd9ybJDN/AXw5IjYANwD/AvSOU2dBo+3lnXgTf0mSJEmSiXuzZeZPIuIHwEERsVtm/rLTMUnSbDEwMNDwZ0mSpJnMxL01nlSuH+loFJI0y6xYseJ3P5u4S5KkqvAZ9ymIiAMiYvcG23si4m+BJwLfzsxftT86SZIkSVKVeMd9ao4GPhoR64AfAXdRjCzfRzE43e3AiZ0LT5IkSZJUFSbuU/M14CzgucCBwGOB+ygGpfsc8OnMvLtj0UmSJEmSKsPEfQoycyOwpNNxSJIkSZKqz2fcJUmSJEnqYibukiRJkiR1sUok7hGxfURcFRGDEfGoMcptV5b5zljlJEmSJEnqFpVI3IE/AxYAH8/Mh0YrlJkPAh8Fnl3WkSRJkiSpq1UlcX8FcHNmXjJewcz8KnAj8KqWRyVJkiRJ0jRVJXF/FvD1SZRfBxzUkkgkSZIkSWqiqiTuuwG/mET5XwC7tigWSZIkSZKapiqJ+2Zgp0mU3wm4v0WxSJIkSZLUNFVJ3G8FDplE+YOBn7YoFkmSJEmSmqYqifvXgedExMHjFYyIBcBhwOWtDkqSJEmSpOmqSuJ+OpDA+RHx9NEKRcQBwPnAI8CZbYpNkiRJkqQp27bTATRDZl4fESuBAeCaiLgAuAy4jSKh3xPoB14JbA8sy8zrOxSuJEmSJEkTVonEHSAzV0bEw8By4HXAa+uKBPAQ8IHM/Lt2xydJkiRJ0lRUJnEHyMwPRcQXgBOA5wJzKBL2nwPfBM7NzJ90MERJkiRJkialUok7QJmYL+90HJIkSZIkNUNVBqeTJEmSJKmSKnHHPSLeMJV6mfkvzY5FkiRJkqRmqkTiDpxHMXr8REVZ3sRdkiRJktTVqpK4AzwMfAX4QacDkSRJkiSpWaqSuK8FDgeOBZ4InA18KTPv72RQkiRJkiRNVyUGp8vMI4H9gY8BTwPOBTZFxGci4pkdDU6SJEmSpGmoROIOkJk3ZeZ7gb2AVwNXAm8FromIqyJicUTs2NEgJUmSJEmapMok7iMy8+HMvDAzjwaeCnwImAOcBfw8IhZ2NEBJkiRJkiahcol7rcz8SWYuBU4CfgbsBDyhs1FJkiRJkjRxVRmcbisR8STghHLZB7gf+DywoZNxSZIkSZI0GZVK3COiB3gJ8GbgaIrr+z7wduBzmXlPB8OTJEmSJGnSKpG4R8STgcXAmyieZ78P+Gfg7My8qpOxSZIkSZI0HZVI3IGbyvXVwHLg3zLzvg7GI0mSJElSU1QlcQ/gIYq77cuAZRExXp3MzH1aHZgkSZIkSdNRlcQd4FHAnp0OQpIkSZKkZqpE4p6ZlZ7WTpImamBgoOHPkiRJmrkqkbhLkgorVqz43c8m7pIkSdXgnWpJkiRJkrpYJRL3iDg8IvaeRPlnRsQbWhmTJEmSJEnNUInEHbgc+PNJlH85cG5rQpEkSZIkqXmqkriPO/ebJEmSJEkzUVUS961ExD4RcVlEHNLpWCRJkiRJmqpKJu4RsT/wDeAIYF1EnNTZiCRJkiRJmprKJe4RsQBYB+wOfATYDPxDRKyOiO07GpwkSZIkSZNUqcQ9Ip4HXAY8BnhFZr4fWAD8L8Xgdd+KiH06F6EkSZIkSZNTpcT9SOCrQAJHZ+ZXADLzx8BC4AvAfGA98MedClKSJEmSpMmoUuJ+OHAvcFRmrqvdkZn3Z+bxwNsp7sYf1YH4JEmSJEmatCol7rcBz8vMDaMVyMzPAP3AHW2LSpIkSZKkadi20wE0Q2ZO+AuIzPwmMGc654uIXYGXAy8GngHsATwIfB84Fzg3M4encw5JkiRJkqAiiXsHvAr4B2ATcDnwU+APgVcAnwVeFBGvyszsXIiSJEmSpCqoRFf5iDgtIg6PiGjTKW8AXgrsmZl/lpnvz8wTgAOAW4FXUiTxkiRJkiRNSyUSd+AUijvfv4iIz0bEn0TEdq06WWZelpkX13eHz8zbgX8sXx7RqvNLkiRJkmaPqnSVfxJwLMVz568H3gTcFxGXAF8GLsnM37QplofK9cNtOl9XGhgYaPizJEmSJGlyomqPYUfEzsBLKJL4FwI7AQ8AgxRJ/EWZeWeLzr0tcA3QSzGX/KXjlF8/yq4D5s+fv8P69aPt7n61Ty1UrY1pdpopbXqmxNkqs/36W8H3VGoeP0/S1C1YsIANGzZsyMwFnY6lE6rSVf53MvPXmfmvmfkq4AkUd+K/CBwKnA38PCLWRsTbI2KfJp/+wxRJ+yXjJe2SJEmSJE1EVbrKN5SZDwAXARdFRA/QRzFo3MuATwKfiIhrm/GtTUS8DXgXcB1w/ATja3je8k78/OnGJEmSJEma+SqduNcqB5K7vFxOjYhDKLrTHzvdY0fEEuBTwA+A/sy8e7rHlKbDMQYkSZKk6qjcM+7tFhHvoLh7v5Eiab+jCcdcP3/+/Pk+466p8v1vvpnyns6UOFtltl9/K/ieSs3j50mautn+jHtl77hHxBygH9gD2L5BkczMVdM8x3spnmu/FnhBZv5yOseTJEmSJKleJRP3iFgBvI8try+ArPt5yol7RCwFVgLrgUV2j5cmb6Z06R8aGtrq9bx58zoUjSRJkmabyo0qHxF/BiwFvgEcR5Gk/zPwOopR5YcpRpk/ahrneCNF0v5IeZ63RcRA3fLn07qQGaxRkiM1smLFit8t3WhwcJC+vj56e3u32N7b20tfXx+Dg4MdikySJEmzSeUSd+CtwG0U86h/udx2S2Z+MTPfQjHH+6uBnadxjieX622AdwDLGyx/Po3jz0gmOaqSc845h0WLFrFu3bqG+9etW8eiRYtYvXp1myOTJEnSbFPFxP0ZFPOoP1yzbZuRH8r51S8F/mqqJ8jMgcyMcZYjpnwFM5BJjqpkcHCQk046ieHh4THLDQ8Pc+KJJ/qllCRJklqqion7o4C7al5vBnapK7MROLBtEVWcSY6qZuXKleO25xHDw8OsWjWtcS4lSZKkMVUxcd8EzKl5/VPgmXVl9gAeRk1hkqMqGRoaGrXnyGjWrl3rWA6SJElqmSom7tdQdJcfcRnwvIg4PiJ2jIgXA68sy2maTHLUrQYGBoiISS/1YzRMVG9v75TO182j6UuSJKk7VDFx/wowLyJGBpD7MHAPcB7wa+AiipHmP9iR6CqgNiFqVpJj8iJJkiRJjVVuHvfMPI8iSR95fWtEHAK8C3gqcAtwZmZ+vxPxSZIkSZI0GZVL3BvJzB8Dp3Q6DkntMzAwMKWeHENDQ1PqSbJx40bmzZs36XqSJEnSeCrXVT4i3hAR9YPR1ZfpjYg3tCumqhkYGCAzyUw2btw4pWNs3Ljxd8fITLvKq2vMmzePww8/fFJ1+vr6TNolSZLUMpVL3Cm6yR87TpmXAee2PJJZwCRHVbRs2TJ6eib267Gnp4elS5e2OCJJkiTNZlVM3CdiGyA7HURVmOSoavr7+znrrLPGbdc9PT2cffbZ9Pf3tykySZIkzUazNXHfD/hVp4OoCpMcVdHixYtZs2YNfX19Dff39fWxZs0aTjjhhDZHJkmSpNmmEoPTRcTquk3HRsTcBkW3AfYGngf8v1bHNZssXryYuXPnsmrVKtauXbvV/r6+PpYuXTrtpL32WXifi1er9ff309/fv9WAdQ5EJ0mSpHaKzJnfYzwihmteJsU87aNJ4Erg9Zl5c0sDm6KIWD9//vz569ev73QoU9LKJCfi9/+0zWq7VfwyoBXvUyvMlDhh5sQ6U+Jsldl+/a3geyo1j58naeoWLFjAhg0bNmTmgk7H0glVSdz3GfkRuBk4DfhUg6KPAL/KzPvaFNqUzPTEHVr3H1MrjlvF/0RnyjXNlDhh5sQ6U+Jsldl+/a3geyo1j58naepme+Jeia7ymfmTkZ8jYgVwee02SZIkSZJmqkok7rUyc0WnY5AkSZIkqVkql7iPiIg5QD+wB7B9gyKZmavaG5UkSZIkSZNTycS97C7/Pra8vuD3c7eP/GziLkmSJEnqapVL3CPiz4ClwGXAGcCFwHnAGuAIYDFwPvBPnYlwdli+fHmnQ5AkSZKkSqhc4g68FbgNODozHy5H77wlM78IfDEivkwxh/u/dTDGyqvKtGqSJEmS1Gk9nQ6gBZ4BXJKZD9ds22bkh8y8FLgU+Kt2ByZJkiRJ0mRVMXF/FHBXzevNwC51ZTYCB7YtIqmNhoaGxnwtSZIkaWapYuK+CZhT8/qnwDPryuwBPIxmFBPSsQ0ODtLX10dvb+8W23t7e+nr62NwcLBDkUmSJEmajiom7tdQdJcfcRnwvIg4PiJ2jIgXA68sy2kGMCEd3znnnMOiRYtYt25dw/3r1q1j0aJFrF69us2RSZIkSZquKibuXwHmRcSTy9cfBu6hGFn+18BFFNPBfbAj0WlSTEjHNzg4yEknncTw8PCY5YaHhznxxBP9okOSJEmaYSo3qnxmnkeRpI+8vjUiDgHeBTwVuAU4MzO/34n4NHGTTUj32Wcf+vv72xRd91i5cuW479GI4eFhVq1aNSvfJ6nbXH/99Z0OYdJmYsxSt/LzpG62//77dzoE1alc4t5IZv4YOKXTcWhyTEjHNzQ0NGpvhNGsXbuWoaEh5s2b16KoVFUz7Y/MmRavJEnSaKrYVR6AiNgnIg6OiAURsXen49HkTCchnYkGBgaIiEkv9c/9T1Rvb++UzjcwMNDcC5ckSZI0rkol7hGxW0R8IiI2ATcDVwJXAT+OiJ9HxEcj4vGdjVKN1CeuzUhITTIlSZIkVUFlEveI2Be4Gng78IfAI8AdwJ3lz7sD7wSujoindCpOdRenmJMkSZLU7SqRuEdED/AFYG9gLfB8YKfMnJOZuwOPARYB64C5wOc7FKq6RLdNMTcwMEBmTnrZuHHjlM63cePGKZ3PXgySJElS+1UicadIyg8GvgT0Z+ZlmfngyM7MfCAzvwYcBVwAHBoRL+hMqGqkPnFtRkI6WpJZpSnm5s2bx+GHHz6pOn19fQ5MJ0mSJM0gVUncXwk8AJyamTlaoXLfKcBDwHFtik1T0KqEtIpzni9btoyenol9lHt6eli6dGmLI5IkSZLUTFWZDm4+8K3MvHO8gpl5R0R8s6yjLrZs2TIWLVo0oSnhJpqQVnGKuf7+fs4666xxv5Do6enh7LPP7vrrmW1aOWWZ06FJkiRVQ1XuuO8FTGZUsSFgnxbFoiYZSUjHu5s80YS0ylPMLV68mDVr1tDX19dwf19fH2vWrOGEE05oc2SSJEmSpqsqd9x3Bv5vEuX/j2LAOnW5xYsXM3fuXFatWsXatWu32t/X18fSpUsbJu0DAwOsWLFi2jHUDmC3fPnyrh2grb+/n/7+foaGhraIeePGjU17pt27w4WZFKskSZJmvqok7ttRTPk2UcNlHc0A7UhIO62ZieC222671WsTTUmSJGnmqkpXeYBRB6VTNdQn6VVJ2iVJkiRpLFVK3Aci4pGJLMCyTger1mvnFHOSJEmS1CpVStxjkotmGec8lyRJkjQTVSJxz8yeKSzbdDputZ9znkuSJEmaaaoyOJ20lUYDsu25556sXLmSZcuWjTvn+apVq9hzzz0d2E2SJElSR5m4a9K6JZGdahzHHXcce+yxB2eeeSbf/e53t9p/yCGHcPLJJ7Nw4cLphihJkiRJ02biPkURcRzQBxwEHEgxL/wXMvP1nYyr6pYsWdKU4yxcuJCFCxdy4403cswxx/xu+8UXX8y+++7blHNobDfeeONWr33vJUmSpK2ZuE/dBykS9nuB24ADOhvO7HDqqac29Xj1iaKJY+tdccUVnHHGGVx99dVbbD/mmGM4+OCDWbJkib0dJEmSpBqVGJyuQ/4S2A/YGXhrh2ORZoQLLriAxYsXb5W0j7j66qtZvHgxF154YZsjkyRJkrqXifsUZeblmXljZmanY5FmgiuuuGLcQQEBhoeHWbp0KVdccUWbIpMkSZK6m4m7pLY444wzxk3aRwwPD3PmmWe2OCJJkiRpZghvGE9fRBwBXM4kB6eLiPWj7Dpgz6fN22GbV35kQsd57bP34u9e8cwttr3/P/6Xf7vq1gnVf3v/vvzlC/bbYtvi877L4HV3TKz+YU/gT/bfZYttSy66lZvuemBC9Vf0z+E5e++4xbbXfvHH3L35kQnVP/2YPdl3tz/YYtsLz71pQnUBbjvjDTxy790AXHfdddz124d53b/fMuH6l77paVu8vvGX93PKxbdNqO7jH70N//aaJ2+x7Ts/vY/lg5smVP9pu27PGS/da4ttl1x/D5/69p0Tqn/oXjuw8vlP2mLb5665i89f+6sJ1X/Rfjvzjuc+cYttp33rDv77hl9vse3BO3/CptWTH1hwzglnsN0T9hl1/0xve//6p3PZdYffDzVi22t+2xvN6w96HMc/a9ctti372s+58tbfTqi+bc+2V8u2Z9ubiEP32oEvnXLU715fd911tj3b3oTqd/r3XqdzjQ+9/Bm87tC9WbBgARs2bNiQmQsmVLFiHJxO0pR96Utf4h8Xn77Ftse/8BQec9DRW2y7/yffm9Lx7//J98ZM3JctW8Y7v3fpFtt2f+NpbL/700apIUmSJM08Ju4dNNq3ReWd+PltDkdqmeEHJ/ZtfrPqSZIkSVViV/kmmGpX+TGOt37+/Pnz168frSd9Z11//fWdDqGpDjjg9zP5XXfddV17zG44/2c+8xnOOOOMph2vlZYsWdLU6QM7/W86UTMlzlaZ7dffCr6nUvP4edJMsf/++3c6hK3YVV5S5SxZMvnnySfi1FNPnVIyfOONN3LMMcdMut7FF1/MvvvuO+l6kiRJUpWYuGvWa1WS20nNvNPcDPvuuy8HH3zwqPO3N3LIIYeYtEuSJEmYuEtdl+RW1ZIlS1i8ePGEpoTr6enh5JNPbkNUkiRJUvdzHvcpiohjI+K8iDgPeF+5eeHItoj4WAfDk7rOwoULWblyJT09Y//a6enpYdWqVSxcuLBNkUmSJEndzTvuU3cQ8Ma6bU8pF4CfAO9uZ0Dt0o2DVTRStUH0quC4445jjz324Mwzz+S73/3uVvsPOeQQTj75ZJN2SZIkqYaJ+xRl5gAw0OEwNIZu+YJhInHMpi8ZFi5cyMKFC7casM6B6CRJkqTG7CovqSPqk3STdkmSJKkx77hL0gRUcfYBSZIkzQwm7pI0Ac4+IEmSpE6xq7wkSZIkSV3MO+5SF+iWgfTGM5sG0ZMkSZK6hYm7pAlr5RcMM+XLi2bzyxBJkiSNx67ykiRJkiR1Me+4S1IHzaReDPYOkCRJ6gzvuEuSJEmS1MVM3KUmGxoaGvO1JEmSJE2GibvUJIODg/T19dHb27vF9t7eXvr6+hgcHOxQZJIkSZJmMhN3qQnOOeccFi1axLp16xruX7duHYsWLWL16tVtjkySJEnSTGfiLk3T4OAgJ510EsPDw2OWGx4e5sQTT/TOuyRJkqRJcVR5aZpWrlw5btI+Ynh4mFWrVtHf39/iqKTma+UI+K3Q7fE6Sr8kSZooE3dpGoaGhkbtHj+atWvXMjQ0xLx581oUlaSZoNu/WGik22P2yxBJUlWZuEuTMDAwwIoVK6Z9nPoB7JYvX87AwMC0jytJs1m3f7Eg1bK9SpoMn3GXJEmSJKmLmbhLkiRJktTFTNylSRgYGCAzf7ds3LhxSsfZuHHjFsexm7wkSZKk0Zi4S9Mwb948Dj/88EnV6evrc2A6SZIkSRNm4i5N07Jly+jpmdhHqaenh6VLl7Y4IkmSJElVYuIuTVN/fz9nnXXWuMl7T08PZ599tnO4S5IkSZoUE3epCRYvXsyaNWvo6+truL+vr481a9ZwwgkntDkySZIkSTOd87hLTdLf309/fz9DQ0NbzNO+ceNGn2kfxfLlyzsdgiRJktT1TNylJqtP0k3aR+do+pIkSdL47CovSZIkSVIXM3GXJEmSJKmLmbhLkiRJktTFTNwlSZIkSepiJu6SJEmSJHUxE3dJkiRJkrqY08FJLeD85JIkSZKaxcRdagHnJ5ckSZLULHaVlyRJkiSpi5m4S5IkSZLUxUzcJUmSJEnqYibukiRJkiR1MRN3SZIkSZK6mIm7JEmSJEldzMRdkiRJkqQuZuIuSZIkSVIXM3GXpIoYGhoa83XVzfbrbwXfU6l5/DxJmg4T92mIiD0jYnVE/DwiHoiIWyLitIh4XKdjkzR7DA4O0tfXR29v7xbbe3t76evrY3BwsEORtcdsv/5W8D2VmsfPk6RmMHGfooh4KrAeeBNwFfBJ4Gbg7cAVEbFrB8OTNEucc845LFq0iHXr1jXcv27dOhYtWsTq1avbHFl7zPbrbwXfU6l5/DxJahYT96k7E3gi8LbMPDYz35eZR1Ek8PsDf9vR6CRV3uDgICeddBLDw8NjlhseHubEE0+s3F2d2X79reB7KjWPnydJzWTiPgUR8RRgEXALcEbd7uXAfcDxEbFjm0OTNIusXLly3D8IRwwPD7Nq1aoWR9Res/36W8H3VGoeP0+SmsnEfWqOKtdrMnOL38iZ+RvgW8AOwHPaHZik2WFoaGjUrpejWbt2bWUGQ5rt198KvqdS8/h5ktRskZmdjmHGiYiPAu8G3p2ZH2+w/3RgCXByZv7DGMdZP8quAx/96Edv8/SnP70p8UrqXps2bWLTpk2dDmNCdtppJ+69995Oh1EZvp/N53sqSc0xZ84c5syZ0+kwtvDDH/6QzZs3352Zs3IssW07HcAMtUu5vmeU/SPbHzvF4z+yefPmezZs2HDLFOtrZjmgXF/X0Sg00+wOPGkK9X4O3D6FegeUCVG3tNN2X3/TdWGCOdPf025ro5rdZvrnSbPbAeWNhW77fToX+HWng+gUE/fWiHI9ZneGzFzQhljU5UZ6Xtge1M1sp+p2tlHNBLZTzQS20+7kM+5TM3JHfZdR9u9cV06SJEmSpCkxcZ+a68v1fqPs37dc39CGWCRJkiRJFWbiPjWXl+tFEbHFexgRjwGeC2wGvtPuwCRJkiRJ1WLiPgWZ+SNgDcUACUvqdq8AdgT+JTPva3NokiRJkqSKcXC6qTsZ+Dbw6YjoB34IHAocSdFF/gMdjE2SJEmSVBHO4z4NEbEXsBI4GtgV2AT8J7AiM+/uYGiSJEmSpIowcZckSZIkqYv5jLskSZIkSV3MxF2SJEmSpC5m4i5JkiRJUhczcZckSZIkqYuZuEuSJEmS1MVM3CVJkiRJ6mIm7tI0RMSuEfHmiPhyRNwUEZsj4p6I+GZELI6Ihp+xiDgsIi6JiLsj4rcR8b8R8Y6I2GaMc70xIq6KiHvLc3w9Il7SuqtTlUXE8RGR5fLmUcrYTtUREfG8iLgwIjZFxAPlek1E/EmDsrZTtV1EvLhsk7eV//ffHBHnR8TCUcrbTtV0EXFcRHwmIr4REb8u/0///Dh1Wt4WI+LREbEiIq6PiPsj4o6I+FJEPH061zvbOY+7NA0R8RbgH4BNwOXAT4E/BF4B7AJcCLwqaz5oEfGycvv9wL8DdwPHAPsDF2Tmqxqc52PAu4DbgAuA7YDXAI8HTs3M01t0iaqgiNgL+D6wDbATcGJmfraujO1UHRERHwRWAb8EvkLx+3U34FnA5Zn5npqytlO1XUR8BHgPcBfwnxRt9WnAS4FtgTdk5udryttO1RIRcS1wIHAvRVs5APhCZr5+lPItb4sRsT0wCDwXuBq4DNgLeBXwIHBUZl45neuetTLTxcVligtwFMUvvJ667btTJPEJvLJm+87AHcADwME12/8A+HZZ/jV1xzqs3H4T8Lia7XMp/mi4H5jb6ffCZWYsQABfA34EfLRsW2+uK2M7denIQvGHXQL/Azymwf5H1fxsO3Vp+1L+//4IcDvwxLp9R5bt6+aabbZTl5YtZZvbt/y//Yiy3Xx+lLJtaYvA+8s651Pz9zHwsnL7EHV/N7tMbLGrvDQNmXlZZl6cmcN1228H/rF8eUTNruOAJwBfzMyra8rfD3ywfPnWutO8pVz/bWb+qqbOLcAZwPbAm6Z3JZpF3kbxhdObgPtGKWM7VduVjxZ9BPgt8LrM/E19mcx8qOal7VSdsA/Fo6ZXZuYdtTsy83LgNxTtcoTtVC2TmZdn5o1ZZsbjaHlbjIioqfOe2r+PM/O/gG8AfwT0TSBe1TFxl1pn5A/Mh2u2HVWuv9qg/DqKP1gPK7sZTaTOf9eVkUZVPlv2YeBTmblujKK2U3XCYcCTgUuAX5XPEL83It4+ynPDtlN1wo0U3X2fHRG71e6IiMOBx1D0ahphO1W3aEdbfCqwN3BDZv54gnU0QSbuUgtExLbAG8qXtb/s9i/XN9TXycyHgR9TPB/3lPI4OwJ7APdm5qYGp7qxXO/XhLBVYWWb/BzFIxx/PU5x26k64ZBy/QtgA8Xz7R8GTgO+HRFrI6L2TqbtVG2XmXcD76UYz+YHEXFWRPxdRHwJWEPxmMdf1FSxnapbtKMtjnqOMepogrbtdABSRX0Y6AUuycxLa7bvUq7vGaXeyPbHTrG8NJplFIN7/XFmbh6nrO1UnfDEcv0Wij8gnw9cSdE1+ePACymemTyiLGc7VUdk5mkRcQuwGjixZtdNwHl1Xehtp+oW7WiLtt8W8o671GQR8TaK0TevA46fbPVyPdnpHpweQqOKiGdT3GX/eGZe0YxDlmvbqZppZCqiAI7LzMHMvDczh4CXU4xo3DfadFsN2E7VEhHxHorRtc+j6Bq8I7AAuBn4QkT8/WQOV65tp+q0drTFqZ5DmLhLTRURS4BPAT8Ajiy71NUa+aZxFxrbua7ceOXH+2ZTs1xNF/kbgKUTrGY7VSeMDHx0c2Z+r3ZH2UtkpPfSs8u17VRtFxFHUAyieFFmvjMzb87M32bmBoovmH4GvCsinlJWsZ2qW7SjLU72HJoEE3epSSLiHcDpwEaKpP32BsWuL9dbPdtTJlhPphjM7maAzLyP4o+AnSJiToPj7VuuR3uWSNqJor09Hbg/InJkAZaXZc4ut51WvradqhNG2t3/jbJ/JLF/dF1526na6SXl+vL6HZn5W+Aqir+vn1Vutp2qW7SjLY56jjHqaIJM3KUmiIj3Ap8ErqVI2u8Ypehl5froBvsOB3YAvp2ZD0ywzovqykj1HgDOGWW5pizzzfL1SDd626k6YR3FH437RsR2Dfb3lutbyrXtVJ0wMuL2E0bZP7L9wXJtO1W3aEdb/BHFILj7RcSTJ1hHE9XpieRdXGb6QtH9OIGrgcePU3Zn4E6KZOrgmu1/AHy7PM5r6uocVm6/CXhczfa5wF3A/cDcTr8PLjNvAQbKtvXmuu22U5eOLMDny3b0N3XbXwAMU9yNf2y5zXbq0vYFeHXZhm4H9qjb96KynW4Gdi232U5d2rJQDNyZwOdH2d+Wtgi8v6xzPtBTs/1l5fah2u0uE1+ifCMlTUFEvJFicJpHgM/Q+JmdWzLzvJo6x1IManM/8EXgbuClFFNoXAC8Ous+mBHxceCdFIMzXQBsB/wpsCtwamae3sTL0iwREQMU3eVPzMzP1u07Ftup2iwingh8C3ga8A2Kbsf7UDw7nMDrMvP8mvLHYjtVG0VED8V4C88HfgN8mSKJfzpFN/oA3pGZn6qpcyy2U7VA2baOLV/uTjH7xs0Uvz8BfpmZ764r39K2WM4DfxlF0n81MEgxt/urKHqiHJWZV0732melTn9z4OIykxd+f8dyrOXrDeo9F7iE4pnNzcD3gb8EthnjXG8EvgvcR/HHwlrgJZ1+D1xm7sIod9xr9ttOXdq+AI8HPkExJdyDFHd1/gt4zijlbacubV2ARwHvAL4D/JriEY87gK8Ai0apYzt1afoygb9Db2lQp+VtkWIskhUU87Y/QHGn/3zgjzr9ns3kxTvukiRJkiR1MQenkyRJkiSpi5m4S5IkSZLUxUzcJUmSJEnqYibukiRJkiR1MRN3SZIkSZK6mIm7JEmSJEldzMRdkiRJkqQuZuIuSZIkSVIXM3GXJEmSJKmLmbhLkiRJktTFTNwlSZIkSepiJu6SJEmSJHUxE3dJkiRJkrqYibskqfIi4oyI+I9OxyFJkjQVkZmdjkGSpJaKiMcBD2XmvZ2ORZIkabJM3CVJkiRJ6mJ2lZckzQgRsWdEZET8aURcFhG/jYjvRcQBEXFwRKwrt10VEXs3qLd/+Toi4j0RcX1EbI6IOyLiwvH2TSLOfynr7djcd6C1ImJB+T4t7nQskiRpSybukqSZ4qBy/VZgBXAosD1wHvD3wF8DC4HdgHfW1fstcGP5+q+ANwEnAwcALwX+ZwL7xhURBwOvBz6cmfeNUma/iPhERGyIiLsj4qFyfWVEfCwiFkz0fKMc/1/LBPytEyj7P2XZYzNzPfCfwN9ExE7TiUGSJDWXXeUlSTNCRHyAIrHePzN/UW77DPAa4IDMvKvcdi6wQ2b+aU29l2TmwvL1ZcA1mfmuBucYdd8EY1wDPBuYk5mb6/YFsKxceoANwFXA3cBjgGdSfPGwHXBKZp4xxRiOAC4vr2P+GOXmAjcDtwN7Z+bDEfFs4ErgA5n5oamcX5IkNd+2nQ5AkqQJOgj4ykjSXtob+I+RpL1m23fr6l1b8/oi4OMRcSBwPnBhZv5yAvvGFBH7Ac8HPluftJeWAQPArcBrM/NbDY7xROAdwC4TOWcjmfn1iLgBeFZEzM/MDaMUXQwEcG5mPlzWvSoirgP+IiI+kpmPTDUOSZLUPHaVlyR1REQcVnbT/nREvDYivhER90TE/eVz6kfWVTkQ+E7dtmcBVzQod03d62tHXmTmacD+wFcpusT/KCKePt6+CTiBIhH+9wbX+hTgg8CDwIsaJe3l+e/IzL+m6Ppff4xDI+KCiLg9Ih6MiFsj4p8i4kkNDnV2uT6x0XkiYhuKRwIS+Gzd7i9SfPnx/EZ1JUlS+5m4S5I6ZaQb9wuAz1F0Gf8n4BvAIcAlI4PMlQO9PZWahDwiHg/sVbdtL2DXkW019a6tPXFm3pSZHwMOpki2nzmRfeN4PvAIW3+5AEWSvC1wQWYOjXegkTvgNdf1JuBbwIsousGfBlwNvBm4unYwvtI/U3xJ8LqI2KHBKV4E7AF8LTN/XLdv5EuFF4wXpyRJag+7ykuSOmUkcX8ScERmfnNkR0R8GjgVeBvwbn6fPH+vpv6zgAeAH9RsOwi4F7ipfD1S7/vlcd8L/ILi2fKHgTdSJLhfH2vfeBdSfkFwEPDDUQale265vmy8YzU49n4UX2jcAvRl5s9q9h1FMXjep4CXj2zPzDsj4j+BV5fLeXWHHbkTf1aDU448ZnD4ZGOVJEmtYeIuSeqUkcT9lNqkvfRZisT9j8rXBwI3Zua9NWWeBWzMzIdqth0IfC8zh2te35CZvy1fbw+8F9iHYqT57wD9mfmLiBh13wSuZQ9gG2DTKPt3L9c/q99RDhL353Wb/6/stg/FKPqPAt5em7QDZOZlEXERcExEPCYzf1Oz+yyKpP3N1CTuETEH+BOKLyn+qz6ezLwnIu6n6C4vSZK6gKPKS5LarkySf0MxovncmkR7ZP8ewG0UXbm7vst2RCwEvg18aWQ0+7r9P6SYXu7ozLy0bt8RFN3fa/0kM+eW+79DMfXdRym+UKj3AuAw4OBySreR4wbFFHhPBf4oM39Ybv9r4G+Bj2Tm+0a5np8Bf5iZfsEvSVIX8D9kSVInPIPiLvKl9Ul7aW65/mnbIpqekVHk/2CU/ZsoEvc96ndk5tcpnqUnIrYFHqorsmu5/qtxYthi7vXMzIj4LPB3FHfd31Um8yfQeFC6Wo/m99ckSZI6zMHpJEmdMNJN/pZR9r+4XK9pfShNcUe53nWU/SMDvvVP4dj3lOtdMjPGWNY2qHsuxRcBb4iI7YCjKO7AX56ZNzUoT0T0AI+tuSZJktRhJu6SpE4YSdwfW7+jHC3+JIqu8v8VEYvKaePGWo5vY+yNbALupJhKrpHzKAa8O24S08uNGBml/nmTDap8Pv8iYDfgWIo779B4ULoR+1P0ALh2sueTJEmtYeIuSeqEBeX6uHJEdgAiYifgXynuXL89M++nmB5uTs1yK/Dxum1bzZ3eTlkMGLMO2C0intZg/4+AvwG2A/47Ig4b5VCPbbDtdIq75p8sR5jfQkRsFxFjJfUjc7q/i2Lk+V8CXx6j/HPKdf1z95IkqUN8xl2S1Fblc9y9FFO77QL8bzky+vbAyyimh3t/Zv4HQGZupnzeOiJ2AfYEvpWZt3cg/LFcCLwSeCG/n46u1kqKO9lLgW9FxHqKqefupkjY51LMBQ/FlwAAZOZ1EXECsBoYioivAjdQjBGwN8Wd+DspnqFvZA3wY+DZ5evTM/PBMa5jEcV89FuNOC9JkjrDUeUlSW0VEQdSdMP+R+ATwKcp5jkP4Erg7zOz4bPtEXEkxVzo+2RmVw1cVz5D/lOKEeEPHaPc/sBbgCMpkvUdKUbY/xHwTeBzmbmhQb1nUNw1P5Jiern7gJ9TPD//75k56hzxEfEBijv+AAdk5vWjlNuFYqT/SzPz2DEuV5IktZGJuySprSLiTRR3j/8iM8d61rpR3XcD78vM3VoS3DRFxPuBDwHzM/OaTsczWRFxKsUXKYdn5jc6HY8kSSr4jLskqd1GBqabSmI7H9jqbnQX+STFXfeVnQ5ksiLi0cD7gQtN2iVJ6i4m7pKkdptP8Qz196dYd31zw2mecjC944GrawfdmyHmUow2/+4OxyFJkurYVV6S1DblHOG/Bm7JzN5J1t2JYk7z12Tm+a2IT5IkqRs5qrwkqW0ycxjYaYrVn0XRU6ybu8pLkiQ1nV3lJUkzxXyKO+43dzoQSZKkdrKrvCRJkiRJXcw77pIkSZIkdTETd0mSJEmSupiJuyRJkiRJXczEXZIkSZKkLmbiLkmSJElSFzNxlyRJkiSpi5m4S5IkSZLUxUzcJUmSJEnqYibukiRJkiR1MRN3SZIkSZK6mIm7JEmSJEldzMRdkiRJkqQuZuIuSZIkSVIXM3GXJEmSJKmLmbhLkiRJktTFTNwlSZIkSepi/x+JMWnFNrJuYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 452,
       "width": 503
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.array([100,125,150,175,200,250,300,350,400,500,600,1000])\n",
    "binc = np.array([ 0.5*(bins[i]+bins[i+1])for i in range(bins.size - 1)])\n",
    "xerr = np.diff(bins)*0.5\n",
    "\n",
    "#Set up variables for the stacked histogram\n",
    "#bg_freq = VVV_freq + ZZ_freq + WW_freq + WZ_freq + TOP_freq + DY_freq\n",
    "freqs = [VVV_freq, ZZ_freq, WW_freq, WZ_freq, TOP_freq, DY_freq]\n",
    "binsc = [binc, binc, binc, binc, binc, binc]\n",
    "proc_name = [\"VVV\", \"ZZ\", \"WW\", \"WZ\", \"TOP\", \"DY\"]\n",
    "colors = [\"maroon\", \"gold\", \n",
    "          mcd.XKCD_COLORS[\"xkcd:green\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:azure\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:darkblue\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:pink\"].upper()]\n",
    "\n",
    "#Set up variables for the stacked histogram\n",
    "plt.figure(figsize=(8,10))\n",
    "plt.gcf().subplots_adjust(bottom=0.15, left=0.17)\n",
    "ax1 = plt.subplot2grid((4,1), (0,0),rowspan=2)\n",
    "\n",
    "#the histograms (SM,Data)\n",
    "#print(bins,'\\n',freqs,'\\n',colors,'\\n',proc_name)\n",
    "ax1.hist(binsc,bins=bins,weights=freqs,stacked=True, color=colors, label=proc_name)\n",
    "ax1.errorbar(binc,data_set, xerr=xerr, yerr = [Ylow,Yhigh],color=\"black\", fmt='o')\n",
    "ax1.bar(binc,MCsyslow+MCsysup,bottom=bg_freq-MCsyslow,color='gray', hatch='',width=xerr*2,alpha=0.3)\n",
    "\n",
    "#Labels and limits\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_ylim(0.001,5000)\n",
    "ax1.legend(loc=\"upper right\", fontsize=14)\n",
    "ax1.set_ylabel(r\"$events$\")\n",
    "cms = ax1.text(\n",
    "        0.5, 5500., u\"CMS $\\it{Preliminary}$\",\n",
    "        fontsize=16, fontweight='bold',\n",
    "    )\n",
    "lumi = ax1.text(\n",
    "        800, 5500., r\"%.1f fb$^{-1}$ (13 TeV)\" % 35.9,\n",
    "        fontsize=14, \n",
    "    )\n",
    "\n",
    "\n",
    "#Adding in the ratio plot\n",
    "ax2 = plt.subplot2grid((4,1), (2,0),sharex=ax1)\n",
    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "#ax2.errorbar(binc,data_set/bg_freq, xerr=xerr,yerr=[data_set/bg_freq - (data_set-Ylow)/bg_freq,(data_set+Yhigh)/bg_freq - data_set/bg_freq], color=\"black\", fmt='o')\n",
    "ax2.errorbar(binc,data_set/bg_freq, xerr=xerr,yerr=[Ylow/bg_freq,Yhigh/bg_freq], color=\"black\", fmt='o')\n",
    "\n",
    "ax2.bar(binc,(MCsyslow+MCsysup)/bg_freq,bottom=1-MCsyslow/bg_freq,color='gray',width=xerr*2,alpha=0.3,hatch='')\n",
    "ax2.axhline(1, ls=\"--\")\n",
    "ax2.set_ylim(0,4)\n",
    "ax2.set_xlabel(\"$p_{T}^{miss}$ (GeV)\",ha=\"right\")\n",
    "#ax2.set_xlabel(\"$M_{T}$ (GeV)\",ha=\"right\")\n",
    "ax2.set_ylabel(r\"Data/$\\Sigma$MC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Channel 4L - Postfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "_verbose = False\n",
    "for name, roothist in histograms.items():\n",
    "    name = name.replace(\";1\", \"\")\n",
    "    era = \"2016\"\n",
    "    channel = \"cat4L\"\n",
    "    #channel = 'cat3L'\n",
    "    #fit_type = \"fit_b\"\n",
    "    fit_type = \"fit_s\"\n",
    "    values = roothist.values()\n",
    "    \n",
    "    # ren ame freq to bin_cont\n",
    "    \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/ZZ\" in name:\n",
    "            ZZ_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/WZ\" in name:\n",
    "            WZ_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/WW\" in name:\n",
    "            WW_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/TOP\" in name:\n",
    "            TOP_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/DY\" in name:\n",
    "            DY_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/VVV\" in name:\n",
    "            VVV_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/total_background\" in name:\n",
    "            bg_freq = np.abs(values)\n",
    "            MCsyslow = roothist.errors()\n",
    "            MCsysup = roothist.errors()\n",
    "\n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/data\" in name:\n",
    "            # TGraphAsymmErrors values has two arrays [[x pos], [y pos]]\n",
    "            x_values, data_set = values\n",
    "            Ylow = roothist.errors('low')[1]\n",
    "            Yhigh = roothist.errors('high')[1]\n",
    "\n",
    "            \n",
    "# No WW case so no entries. Setting to 0\n",
    "WW_freq = ZZ_freq * 0\n",
    "\n",
    "if (_verbose):\n",
    "    print(\"ZZ bg: \", ZZ_freq)\n",
    "    print(\"WZ bg: \", WZ_freq)\n",
    "    print(\"WW bg: \", WW_freq)\n",
    "    print(\"TOP bg: \", TOP_freq)\n",
    "    print(\"DY bg: \", DY_freq)\n",
    "    print(\"VVV bg: \", VVV_freq)\n",
    "    print(\"\\n\")\n",
    "    print(\"Tot bg: \", bg_freq)\n",
    "    print(\"Tot bg Low: \", MCsyslow)\n",
    "    print(\"Tot bg Up: \", MCsysup)\n",
    "    print(\"\\n\")\n",
    "    print(\"data: \", data_set)\n",
    "    print(\"data Low \", Ylow)\n",
    "    print(\"data Up\", Yhigh)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ZZ_freq = ZZ_freq[:11]\n",
    "WZ_freq = WZ_freq[:11]\n",
    "WW_freq = WW_freq[:11]\n",
    "DY_freq = DY_freq[:11]\n",
    "TOP_freq = TOP_freq[:11]\n",
    "VVV_freq = VVV_freq[:11]\n",
    "\n",
    "bg_freq = bg_freq[:11]\n",
    "MCsyslow = MCsyslow[:11]\n",
    "MCsysup = MCsysup[:11]\n",
    "\n",
    "data_set = data_set[:11]\n",
    "Yhigh = Yhigh[:11]\n",
    "Ylow = Ylow[:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Data/$\\\\Sigma$MC')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 452,
       "width": 503
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.array([100,125,150,175,200,250,300,350,400,500,600,1000])\n",
    "binc = np.array([ 0.5*(bins[i]+bins[i+1])for i in range(bins.size - 1)])\n",
    "xerr = np.diff(bins)*0.5\n",
    "\n",
    "#Set up variables for the stacked histogram\n",
    "#bg_freq = VVV_freq + ZZ_freq + WW_freq + WZ_freq + TOP_freq + DY_freq\n",
    "freqs = [VVV_freq, ZZ_freq, WW_freq, WZ_freq, TOP_freq, DY_freq]\n",
    "binsc = [binc, binc, binc, binc, binc, binc]\n",
    "proc_name = [\"VVV\", \"ZZ\", \"WW\", \"WZ\", \"TOP\", \"DY\"]\n",
    "colors = [\"maroon\", \"gold\", \n",
    "          mcd.XKCD_COLORS[\"xkcd:green\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:azure\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:darkblue\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:pink\"].upper()]\n",
    "\n",
    "#Set up variables for the stacked histogram\n",
    "plt.figure(figsize=(8,10))\n",
    "plt.gcf().subplots_adjust(bottom=0.15, left=0.17)\n",
    "ax1 = plt.subplot2grid((4,1), (0,0),rowspan=2)\n",
    "\n",
    "#the histograms (SM,Data)\n",
    "#print(bins,'\\n',freqs,'\\n',colors,'\\n',proc_name)\n",
    "ax1.hist(binsc,bins=bins,weights=freqs,stacked=True, color=colors, label=proc_name)\n",
    "ax1.errorbar(binc,data_set, xerr=xerr, yerr = [Ylow,Yhigh],color=\"black\", fmt='o')\n",
    "ax1.bar(binc,MCsyslow+MCsysup,bottom=bg_freq-MCsyslow,color='gray', hatch='',width=xerr*2,alpha=0.3)\n",
    "\n",
    "#Labels and limits\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_ylim(0.001,5000)\n",
    "ax1.legend(loc=\"upper right\", fontsize=14)\n",
    "ax1.set_ylabel(r\"$events$\")\n",
    "cms = ax1.text(\n",
    "        0.5, 5500., u\"CMS $\\it{Preliminary}$\",\n",
    "        fontsize=16, fontweight='bold',\n",
    "    )\n",
    "lumi = ax1.text(\n",
    "        800, 5500., r\"%.1f fb$^{-1}$ (13 TeV)\" % 35.9,\n",
    "        fontsize=14, \n",
    "    )\n",
    "\n",
    "\n",
    "#Adding in the ratio plot\n",
    "ax2 = plt.subplot2grid((4,1), (2,0),sharex=ax1)\n",
    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "#ax2.errorbar(binc,data_set/bg_freq, xerr=xerr,yerr=[data_set/bg_freq - (data_set-Ylow)/bg_freq,(data_set+Yhigh)/bg_freq - data_set/bg_freq], color=\"black\", fmt='o')\n",
    "ax2.errorbar(binc,data_set/bg_freq, xerr=xerr,yerr=[Ylow/bg_freq,Yhigh/bg_freq], color=\"black\", fmt='o')\n",
    "\n",
    "ax2.bar(binc,(MCsyslow+MCsysup)/bg_freq,bottom=1-MCsyslow/bg_freq,color='gray',width=xerr*2,alpha=0.3,hatch='')\n",
    "ax2.axhline(1, ls=\"--\")\n",
    "ax2.set_ylim(0,4)\n",
    "ax2.set_xlabel(\"$p_{T}^{miss}$ (GeV)\",ha=\"right\")\n",
    "#ax2.set_xlabel(\"$M_{T}$ (GeV)\",ha=\"right\")\n",
    "ax2.set_ylabel(r\"Data/$\\Sigma$MC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Channel BSM - Prefit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "_verbose = False\n",
    "for name, roothist in histograms.items():\n",
    "    name = name.replace(\";1\", \"\")\n",
    "    era = \"2016\"\n",
    "    channel = \"chBSM\"\n",
    "    fit_type = \"prefit\"\n",
    "    #fit_type = \"fit_b\"\n",
    "    values = roothist.values()\n",
    "    \n",
    "    # ren ame freq to bin_cont\n",
    "    \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/ZZ\" in name:\n",
    "            ZZ_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/WZ\" in name:\n",
    "            WZ_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/WW\" in name:\n",
    "            WW_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/TOP\" in name:\n",
    "            TOP_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/DY\" in name:\n",
    "            DY_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/VVV\" in name:\n",
    "            VVV_freq = np.abs(values)\n",
    "            \n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/total_background\" in name:\n",
    "            bg_freq = np.abs(values)\n",
    "            MCsyslow = roothist.errors()\n",
    "            MCsysup = roothist.errors()\n",
    "\n",
    "    if  \"shapes_\"+fit_type+\"/\"+channel+\"2016/data\" in name:\n",
    "            # TGraphAsymmErrors values has two arrays [[x pos], [y pos]]\n",
    "            x_values, data_set = values\n",
    "            Ylow = roothist.errors('low')[1]\n",
    "            Yhigh = roothist.errors('high')[1]\n",
    "            \n",
    "\n",
    "if (_verbose):\n",
    "    print(\"ZZ bg: \", ZZ_freq)\n",
    "    print(\"WZ bg: \", WZ_freq)\n",
    "    print(\"WW bg: \", WW_freq)\n",
    "    print(\"TOP bg: \", TOP_freq)\n",
    "    print(\"DY bg: \", DY_freq)\n",
    "    print(\"VVV bg: \", VVV_freq)\n",
    "    print(\"\\n\")\n",
    "    print(\"Tot bg: \", bg_freq)\n",
    "    print(\"Tot bg Low: \", MCsyslow)\n",
    "    print(\"Tot bg Up: \", MCsysup)\n",
    "    print(\"\\n\")\n",
    "    print(\"data: \", data_set)\n",
    "    print(\"data Low \", Ylow)\n",
    "    print(\"data Up\", Yhigh)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Data/$\\\\Sigma$MC')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 452,
       "width": 700
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.array([50,100,125,150,175,200,250,300,350,400,500,600,1000,1050,\n",
    "                1100,1125,1150,1175,1200,1250,1300,1350,1400,1500,1600,2000])\n",
    "binc = np.array([ 0.5*(bins[i]+bins[i+1])for i in range(bins.size - 1)])\n",
    "xerr = np.diff(bins)*0.5\n",
    "\n",
    "#Set up variables for the stacked histogram\n",
    "#bg_freq = VVV_freq + ZZ_freq + WW_freq + WZ_freq + TOP_freq + DY_freq\n",
    "freqs = [VVV_freq, ZZ_freq, WW_freq, WZ_freq, TOP_freq, DY_freq]\n",
    "binsc = [binc, binc, binc, binc, binc, binc]\n",
    "proc_name = [\"VVV\", \"ZZ\", \"WW\", \"WZ\", \"TOP\", \"DY\"]\n",
    "colors = [\"maroon\", \"gold\", \n",
    "          mcd.XKCD_COLORS[\"xkcd:green\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:azure\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:darkblue\"].upper(), \n",
    "          mcd.XKCD_COLORS[\"xkcd:pink\"].upper()]\n",
    "\n",
    "#Set up variables for the stacked histogram\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.gcf().subplots_adjust(bottom=0.15, left=0.17)\n",
    "ax1 = plt.subplot2grid((4,1), (0,0),rowspan=2)\n",
    "\n",
    "#the histograms (SM,Data)\n",
    "ax1.hist(binsc,bins=bins,weights=freqs,stacked=True, color=colors, label=proc_name)\n",
    "ax1.errorbar(binc,data_set, xerr=xerr, yerr = [Ylow,Yhigh],color=\"black\", fmt='o')\n",
    "ax1.bar(binc,MCsyslow+MCsysup,bottom=bg_freq-MCsyslow,color='gray', hatch='',width=xerr*2,alpha=0.3)\n",
    "\n",
    "\n",
    "\n",
    "#Labels and limits\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_ylim(0.001,5000)\n",
    "ax1.legend(loc=\"upper right\", fontsize=14)\n",
    "ax1.set_ylabel(r\"$events$\")\n",
    "cms = ax1.text(\n",
    "        0.5, 5500., u\"CMS $\\it{preliminary}$\",\n",
    "        fontsize=16, fontweight='bold',\n",
    "    )\n",
    "lumi = ax1.text(\n",
    "        1700, 5500., r\"%.1f fb$^{-1}$ (13 TeV)\" % 35.9,\n",
    "        fontsize=14, \n",
    "    )\n",
    "\n",
    "\n",
    "cms = ax1.text(\n",
    "        400, 2000., u\"0-jet category\",\n",
    "        fontsize=12, \n",
    "    )\n",
    "lumi = ax1.text(\n",
    "        1400, 2000., u\"1-jet category\",\n",
    "        fontsize=12, \n",
    "    )\n",
    "\n",
    "#Adding in the ratio plot\n",
    "ax2 = plt.subplot2grid((4,1), (2,0),sharex=ax1)\n",
    "plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "#ax2.errorbar(binc,data_set/bg_freq, xerr=xerr,yerr=[data_set/bg_freq - (data_set-Ylow)/bg_freq,(data_set+Yhigh)/bg_freq - data_set/bg_freq], color=\"black\", fmt='o')\n",
    "ax2.errorbar(binc,data_set/bg_freq, xerr=xerr,yerr=[Ylow/bg_freq,Yhigh/bg_freq], color=\"black\", fmt='o')\n",
    "\n",
    "ax2.bar(binc,(MCsyslow+MCsysup)/bg_freq,bottom=1-MCsyslow/bg_freq,color='gray',width=xerr*2,alpha=0.3,hatch='')\n",
    "ax2.axhline(1, ls=\"--\")\n",
    "ax2.set_ylim(0,4)\n",
    "ax2.set_xlabel(\"$p_{T}^{miss}$ (GeV)\",ha=\"right\")\n",
    "#ax2.set_xlabel(\"$M_{T}$ (GeV)\",ha=\"right\")\n",
    "ax2.set_ylabel(r\"Data/$\\Sigma$MC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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