Correlation energy scale

correlation.ipynb

{
  "cells": [
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport ROOT\nROOT.gROOT.ProcessLine(\".x Hfitter/RootCore/scripts/load_packages.C\")\nfrom ROOT import RooFit\nimport rootnotes\ncolors = [ROOT.kRed, ROOT.kBlue, ROOT.kOrange, ROOT.kPink]",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Use workspaces made by Nicolas (run1 and run2 separately), provided by Bruno. Both contains all the systematics, including the energy scale. Using narrow width, since it should be the one more sensitive to energy scale."
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "f_13TeV = ROOT.TFile.Open(\"bruno_models/ws_750_run2_NW_EScale.root\")\nf_8TeV = ROOT.TFile.Open(\"bruno_models/ws_750_run1_NW_SignalfromRun1Paper.root\")\nws_13TeV = f_13TeV.Get(\"modelWS\")\nws_8TeV = f_8TeV.Get(\"modelWS\")\nws_13TeV.Print()\n\nall_ws = {'13 TeV': ws_13TeV, '8 TeV': ws_8TeV}",
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Just define some conventient functions"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def safe_factory(func):\n    def wrapper(self, *args):\n        result = func(self, *args)\n        if not result:\n            raise ValueError('invalid factory input \"%s\"' % args)\n        return result\n    return wrapper\n\nROOT.RooWorkspace.factory = safe_factory(ROOT.RooWorkspace.factory)\n\ndef safe_decorator(func):\n    def wrapper(self, *args):\n        result = func(self, *args)\n        if not result:\n            raise ValueError('cannot find %s' % args[0])\n        return result\n    return wrapper\n\nROOT.RooWorkspace.data = safe_decorator(ROOT.RooWorkspace.data)\nROOT.RooWorkspace.obj = safe_decorator(ROOT.RooWorkspace.obj)\nROOT.RooWorkspace.var = safe_decorator(ROOT.RooWorkspace.var)\nROOT.RooWorkspace.pdf = safe_decorator(ROOT.RooWorkspace.pdf)",
      "execution_count": 56,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def print_fit_result(ws, variables):\n    for var in variables:\n        try:\n            v = ws.var(var)\n            print \"{:<20s} = {:f} +/- {:f}\".format(var, v.getVal(), v.getError())\n        except ValueError:\n            v = ws.function(var)\n            print \"{:<20s} = {:f}\".format(var, v.getVal())",
      "execution_count": 57,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def get_discovery_result(data, model_sb, model_b=None):\n    if model_b is None:\n      model_b = sbModel.Clone(\"model_config_b\")\n      var = model_b.GetParametersOfInterest().first()\n      oldval = var.getVal()\n      var.setVal(0)\n      aset = ROOT.RooArgSet(var)  \n      model_b.SetSnapshot(aset)\n      var.setVal(oldval)\n    profll = ROOT.RooStats.ProfileLikelihoodTestStat(model_b.GetPdf())\n    profll.SetOneSidedDiscovery(1)\n    hypoCalc = ROOT.RooStats.AsymptoticCalculator(data, model_sb, model_b)\n    hypoCalc.SetOneSidedDiscovery(True)\n    htr = hypoCalc.GetHypoTest()\n    htr.SetPValueIsRightTail(True);\n    htr.SetBackgroundAsAlt(False);\n    htr.Print()\n    return htr\n\ndef get_discovery_result2(data, model_sb):\n    pdf = model_sb.GetPdf()\n    model_sb.LoadSnapshot()\n    poi.SetConstant(False)\n    fit_result = pdf.fitTo(data, ROOT.RooFit.Save())\n    minnll_sb = fit_result.minNll()\n    \n    model_sb.LoadSnapshot()\n    poi = model_sb.GetParametersOfInterests()\n    poi.setVal(0)\n    poi.setConstant(True)\n    fit_result = pdf.fitTo(data, ROOT.RooFit.Save())\n    poi.setConstant(False)\n    minnll_b = fit_result.minNll()\n    \n    q0 = 2 * (minnll_b - minnll_sb)\n    print \"q0 =\", q0\n    print \"z =\", np.sqrt(q0)",
      "execution_count": 58,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def iter_collection(rooAbsCollection):\n    iterator = rooAbsCollection.createIterator()\n    object = iterator.Next()\n    while object:\n        yield object\n        object = iterator.Next()",
      "execution_count": 59,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "#ROOT.RooAbsReal.defaultIntegratorConfig().method1D().setLabel(\"RooAdaptiveGaussKronrodIntegrator1D\")",
      "execution_count": 60,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plot(ws, data, pdf):\n    frame_run1 = ws.var('mgg').frame(RooFit.Bins(100))\n    frame_run2 = ws.var('mgg').frame(RooFit.Bins(100))\n    cat = ws.cat('cat')\n    if data:\n        data.plotOn(frame_run1, RooFit.Cut(\"cat==cat::run1\"), RooFit.MarkerColor(ROOT.kRed))\n        data.plotOn(frame_run2, RooFit.Cut(\"cat==cat::run2\"), RooFit.MarkerColor(ROOT.kBlue))\n    if pdf:\n        cat_set = ROOT.RooArgSet(cat)\n        pdf.plotOn(frame_run1, RooFit.Slice(cat, \"run1\"),\n                   RooFit.Precision(1E-5),\n                   #RooFit.ProjWData(ws.data('data_run1')),\n                   RooFit.ProjWData(cat_set, data),\n                   RooFit.LineColor(ROOT.kOrange))\n        pdf.plotOn(frame_run2, RooFit.Slice(cat, \"run2\"), RooFit.Precision(1E-5),\n                   #RooFit.ProjWData(ws.data('data_run2')),\n                   RooFit.ProjWData(cat_set, data),\n                   RooFit.LineColor(ROOT.kGreen))\n\n    #canvas = rootnotes.default_canvas()\n    canvas = ROOT.TCanvas()\n    canvas.Divide(1, 2)\n    canvas.cd(1)\n    frame_run1.Draw()\n    frame_run1.SetAxisRange(1E-5, 1E2, \"Y\")\n    canvas.cd(1).SetLogy()\n    canvas.cd(2)\n    frame_run2.Draw()\n    frame_run2.SetAxisRange(1E-5, 1E2, \"Y\")\n    canvas.cd(2).SetLogy()\n    return canvas",
      "execution_count": 61,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Show that energy scale works. Plot separately the central, -1 sigma, +1 sigma signal model, varying the mass scale pull."
    },
    {
      "metadata": {
        "collapsed": false,
        "code_folding": [],
        "trusted": true
      },
      "cell_type": "code",
      "source": "mgg = ws_13TeV.obj('mconfig').GetObservables().first()\nframe = mgg.frame(700, 800)\n\n\nlegend = ROOT.TLegend(0.7, 0.7, 0.9, 0.9)\npeaks = {}\nstd = {}\n\nitercolor = iter(colors)\nfor s, ws in all_ws.iteritems():\n    color = itercolor.next()\n    ws.var(\"mX\").setVal(750.)\n    signal = ws.pdf(\"Signal\")\n    theta_ESS = ws.var(\"dEScale\")\n    for theta_value in (-1, 0, 1):\n        theta_ESS.setVal(theta_value)\n        name = str(hash((ws, theta_value)))\n        signal.plotOn(frame, RooFit.LineColor(color), RooFit.Name(name))\n        peaks.setdefault(s, []).append(ws.function('cbPeakSignal').getVal())\n        std.setdefault(s, []).append(signal.sigma(mgg).getVal())\n        if theta_value == 0:\n            legend.AddEntry(frame.findObject(name), s, \"L\")\nframe.addObject(legend)\nc1 = rootnotes.default_canvas()\nframe.Draw()\nc1",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAI8CAIAAAD0vjrdAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE\nQVR4nO3dz6/vRmH///H3y7/QHayS3BsUBSkBqW241H5XqvgREFmBRHdNpVZAU3KjRDcb7Hc2uVXV\n0Ba6qEi7rAKbRCKXtgt42wWiLiiRmlIggR39O/xZTM7cOeNfY3vGnvE8H4qic97HHo/fv/y6M+OZ\nrG1bAQAAgHX+v70rAAAAcASEKgAAAAcIVQAAAA4QqgAAABz4wN4VAA7ipZde+u53vyt//sIXvvD1\nr399WTk3btyw2eDNN99cVr5DH/7wh0f++vDDDz/88MMvv/xy719/53d+R5bwH//xH14q58dTTz31\ny1/+Uv5w9+7dvasDICwZd/8BTmRZpn5+5JFH/ud//md9OeMeeuihd999d9lR1nvxxRctU8VTTz31\n+uuv64/8wR/8wY9+9CMhxIc//OH//d//9VI/D/SX5vOf//wbb7xhs7H8jjV+DcrHP/7xt956Swjx\nxBNP/OQnPzF+3bt2QEzo/gMceOmll/Rff/7zn29w0Pfee88+ge3ojTfeGG/TWuO55567efPmzZs3\nn3vuOU+HkB5++GH1882bN/VfAUAiVAEOqI4/xYhZC7TDnnzySbXZZHehbw8//HBvJe/cuaOSxy9/\n+csXX3zRUwXefffdDVrsfvWrX8kf2rb95S9/Sd8fgC5CFeCAapo6n8/yh27McujNN9/82te+Jn9+\n7733/B1ojZdffvkXv/iFylXjnWWxuHnz5t5VABAuQhWwlmqUeuSRR9T4dN89gN/4xjfUz88++6zX\nY63xi1/8Qv4gx3en44Mf/ODIr0H50Ic+pP9g/ArAHnf/AWvpN/0JIR555BGZqL74xS9+5zvf2bFi\nt2/f1rvGbty4YXPP4Kc+9alf//rX8ucHH3zwgQce+Id/+AePtezz7LPPvvfee5M1f+6559Q27777\nrhxW9Td/8zf2B/rc5z6nH+XGjRvd3e/cuaN+/tWvfiV/nez++9CHPvTb3/5WZSnjV+kv//IvhRB/\n93d/J3/9zGc+o9odP/OZz6jHjT899NBD3//+9+3PcdJrr70m36ivvfZa91cAM4yM2wBgw/g0qR7A\nRx55ZH1pNlt+7Wtf6/71oYce6v3IP/nkk0MFPvDAA727PPDAA92NVdQYGlM1ckaf+MQn5CMf/vCH\nF9f8s5/97JrvtKHduyX0bvP5z39+vHyZsH//93+/99e2bZ955hlZ1DPPPPPpT3+6e4gHH3xQ32zZ\naVoyyvRxCCAFdP8Bq+h9f/KHzXoAFb0rUMqyTB9rpceUe/fu9Y5tf/DBB3/zm9+oX/WA9Zvf/Gbx\nbYZzx6cvqPkCn/vc5/SmL9lGpddhsoTJu/++853vfPCDH1Q9aMavuu9///v/+q//KoR48MEHH3zw\nQfX4r3/964ceeujv//7v5a/GX93e+PnEE0888cQTQ78CsLV3qgPiprLU+XzuPviFL3xhboGWn02V\nNrotT+pPDz30kP64PvTq2Wef1f/0yU9+Uv3py1/+snr8y1/+snr8k5/8pL6LZUuV2l3fbKilaqjm\nalS+6DTL3b59Wz5++/btkWr07tLdS49WvSdy8+ZNy6NMMpqg9D8ZDVfPPPNM716uagLAFT6WwCq9\nVzjVA7jgyqenh176fApG+GivJ6du4UN/Ha9t71/HQ9WdO3eeeuopPRncuXNH/bU3VOnJqVvg0F8X\nhCqVnHp3GfqrfNBTqOr+Vf3p05/+tPEnFbn0sAUgBIQqYDk5UEb0tUitD1WTekdHqcYeoy1qZAPV\nTGW0RSm9G+jDtyc99dRTeoG9oUpVrHeI2NAGC0LV+EujCrxx40Z3Lx+hSo6dsq8koQoIFmOqgOXU\nfX/du/xUD+AXv/hFT0fvnfFSDUh65ZVXevdSA7TV7upev6GB6v/2b/9mbDnLnTt3jGVqeqmad4eI\nSaqJbs3UXGri9aGB6uruv82WABoamO9qewCbYUoFwIHu/OlqYoXvfve7yyZW0Du8dO+99969e/fE\n1TI1bV/j1sh195VXXhlKLZNTJ+gj2XVGT58ysqDyAt/4xjf+9m//VqyLO/oECkPb3LhxY8dFFQHE\ni1AFLKQ3QZVlObLlSy+9pG4JtDcUfaQbN27IBpvPfvazNrNPdancMBSVLD388MM2DVGWfDfDqLOe\nNZ0VANig+w9YyH4hGh9zK6i+MNpUACAQtFQBS+j9ffq9frqf//znMngt7gEcofrC1q/998ADD6xs\nrHLI91KGqmvvueeeo7EKgFuEKmAJ1fj0hS98YaRrT7VmLesBXGwkmuh3txl/+spXvjI+rGpoJPs2\n1HwQa6YAVfuOtPDR+AdgGbr/gCVG7vvTqXsA7fsKV1JjkvQpLnVqAJaKF2qe7qH2qk996lPGlj7o\ncz30biCH54t1464mb+5Ttwe6mr0dQDoIVcBs3Xv9hqiJrDZbskbNFNA7zv327dvdORfUjAn//u//\n3lumelxt6YMaJSa7NQ1yiWX58/gQfktqAWaDCp0jiwMCQC9CFTCbanZSmWmI3uVnH8XWeOWVV1RD\njtHWcvv2bRVHjNYgNb1nlmVf+cpX1ONf+cpX1Bpz+lI2PnzjG98Yqvmzzz6rktbQTBP2VBveK6+8\nYuSqmzdvRnd74OOPP/74449/61vf0h98+umnn376acsNADiz9+yjQHxmfXxU8HrkkUfcFq627E5B\nbnzMjf6y7uI2bdsa46W6w6eM7S3X/us1tPbfgprrvZxyXWSbChitUMaCyr1Pvnzcx4zq3YVo2tG3\ngdrxmWee+ZM/+ZPulvLBxx57TP08sgEAh2ipAuZRDU5qvNQ4tZnzHkAVOLr9Za22qIu4Pm79ySef\n7B1O9Otf/1oPUvr4qgceeKCds37OGnNrrrcnvfvuu5ZjzL/3ve/pucrYcbOTXe/tt9+WX+VCCNXy\n9PbbbwshZHKa3ACAQ/3TMQM4gNu3b+tx4caNGzbThH7qU59Sy9E8+OCDDzzwwORM687JEVT2Nf/c\n5z4nN75x48b3vvc9+wOpHcVVe1UsvX7S008//U//9E/yh7fffvtnP/uZECLLsscee0z+PLkBAIcI\nVQBwBHLNoqeffvqf//mfv/nNb371q1+duwGAlQhVAHAEjz/++M9+9rPHH39cdvkt2ADASoypAoAj\neOyxx2Qf3ze/+c1lGwBYiZYqADiCb33rW3/xF38hhgfaT24AYCVaqgDgOCbv6eOmP8AfWqoA4CDk\nUPQ1GwBYg5YqADiCyRnSmUId8I1QBQBHIKf0XLMBgJUIVQBwBDIzGWv8zdoAwEqEKgCI3mRUIksB\nGyBUAcBxTM6TzkTqgD8f2LsCAIC1vvrVr7799tuPPfbY4g0ArMfttQAAAA7Q/QcAAOAAoQoAAMAB\nQhUAAIADhCoAAAAHCFUAAAAOEKoAAAAcIFQBAAA4QKgCAABwgFAFAADgAKEKAADAAUIVAACAA4Qq\nAAAABwhVAAAADhCqAAAAHCBUAQAAOPAB3weo67qua/VrURRFUfg+6JAsy/Y6NAAACWrbdu8qbCfz\ndLZ1XVdV1TRN71/zPC+KoqoqH4cekWW+zhcAsAzfzAeW2ovrvvuvrussy06nkxCiLMvL5dJqLpdL\nWZZCiPP5nGXZ9rkKAADAB8cRsiiKpmnKsrRJS1VVnc/nPM/1/kGvUovMABA+vpkPLLUX1/HZ1nU9\nd8jUgl0WS+3VBYDw8c18YKm9uImdbWKvLgCEj2/mA0vtxWVKBQAAAAcIVQAAAA4QqgAAABwgVAEA\nADhAqAIAAHDAcajaZZ70WbJRe9cOAADEyvG9jiqX5HleVdWOy/z1Su3eTgAIH9/MB5bai+u4papt\nW7kKTdM0p9Mpy7KiKDabMB0AAGAv7sdUVVUll/kjXQEAgHR4HKhOugIAAOnYtLNTrqAsf96lkzW1\nzl0ACB/fzAeW2ou73ZQKdV3TQAUAAI7Ke6iq67ooiizLTqdT0zR5npdlmVRuBQAAKfiAp3Lruq6q\nqmka9UhZloFPYQUAALCY41Al+/jUwCkhRFmWRVGENmEVgPXUdLn+mp6PcQgAiWDyTwCzdVcfcP7B\n6l3gYIOj8A2xvai/mWdd5hZcFuX2k6uVyN4h+cOs8n2L+sVdwPHZyhc+tCylTC5Ek9RrDywz8jFy\n9QHa4BAjR+FrYGMhXHf1m9MlyyErs9Y3WzAMRpU//hQVRSHH2+z+TBpCeHG3lNjZJvbqAs5NXkHW\nf8LGD7FBbnN4FNjY/Zt5KBjleT7Z8NMNSSqcyTkadQsGw6i0d7lcRvaVp2BT4Y3t/uJurfWsLMs8\nz4UQ8qY/+f+9bHC+wLEJcf+/8QcTPwTs7fvNLK9Q6iIlqTy04Jql9nVVw8maqCNeLhdXB3Ultcuu\nxwhZ1/XpdFK/ygQt0/R44vYnucgMOKX/e974JI38KcFDYJZ9v5nlVanbMbe4Q021Lbk6qcmaBNv3\nJ/Z+cbfncZ4qmahkcFb/FJBPrh62AESn+yWpPzJnkMk145lmg29mJ2eBiKjOsm4v3jZzAMkB5tJQ\nz51qgxjaQCaqbm8jtudrnipJzqdgPHi5XE6nk5wU1OvRATgUVMjIsoUZi7aoOHh6twX2khtjuYbG\nTqmmr97gpR7hkhoCXy1V8mXufY3lg6ENpgNgaejC5PCCtcEhbA4dVI6ED+oitX1LlT4DkRp8LISQ\njQ7GxvKv+nzaiqonoSoEvkLVSHIayVsAwjQ3XiyIIxsfIrA2C+xG9pqdz2c9RalRSpfLxcdB1RXw\ncrnIHsC6rtXYo+4IGVW37lWVvr+geBxBpo9J1zOWfHyXkWupjZgDXLGPI4snKLfccU0w2uAssMDu\n38zdSaqkZfdUTQ5UV3dx9ZY/dDtX76QJqqhgL227v7gb8zhQXQb80+kkI3/TNHJlZUGmBqKyLMfM\nakmy39hJ95yns0CkhoajeBqmMj4KSvb0dTsfe3sAWVE3NB5DVVEU8r4/9SZomibP88vlwvsAwMaI\nR+iVZZnqQVOzDak+QR8jVWSoUoOoenWHT/X2ANL3F5q02uVSa4cE1lvQTLWg72zuLmsOYbkXPYCb\n2fGbeWS+8vFOOpsyh07KcmWb7u5GD2D4fX8ivcuu3ykVAjT+bk7qtQd8s5z4YE0b0oK5FRaMxOKL\n4ahk+snzvBub9BsDPfUDjjdWdZVleT6fVSMWfT4B8hWq5LLKRVEY86ore8UXYhOwTNQfHfr+MGKo\nIUoOX+mdyGC9Bev0FUUhU6Cc6JG+vwC5H1Ml16LpvZNCknNyOD8uAOeWZZFZ8WvZKPiNJ6wCHBpv\noKqvdP/U23hGe1VQ3Icq2S5lzKWuVnkUQjRNw5sASERoTUTr52HHkch8M9QKIJuC5nbSTZJXwKZp\nepPT6XTqnf9Tkk0S6jLqvG5YyXGokkFq5P4+mauY+ROIS8gtQ5OJh0iEISPTkY9Mtr6SKrk7Nmby\noOpxGfi4mIbGcaiSkyaMv8xlWXrqogYQiLk38YWJHsDDK4pCzf+UZVlRFHJAsD7Pgo/goiZqVwet\nqko/6Mi+eusU3T6hcd/9N/n+kxvwVgACd4zE42ppmsCfDSxW17UKMU3T6LfXlWXp6VJVFIXKVfKg\nqgty8qDqIkvfX4AcTyCRZZnNu9ByM+dSmzADWGNlHLHZfeVcULMOsewoLBe4gUC+mdXo7+LKBgfV\nh6XLJqsNDrqlQF7czbgPVZO3icpJFpatqbRSaq8usMaWiSfYQzgpAeP4Zj6w1F5cx2crZ84YL9Nm\nG09Se3WBNXzHESeNQJOVdHgWawrBCL6ZDyy1F9fxmCrZdDkya3lVVXIwu9vjAnDrGEOIjnEWAGLh\nfkoFOeIvyzKjb1ifFNTTlP82slF71Qo4tvHPlpN/x/r7+Kb0z2wAq3hpl1PLSXYtmJjfodTaIYHF\nnAwkGu84i+UQDstBL76ZDyy1F9fj2co7KdSMtFveTzEktVcXWMx3HHE4UGmkqs7PYn1R6OKb+cBS\ne3ETO9vEXl1gmQ0Sj49DGEVtcAg4wTfzgaX24n5g5f5yBFWe53/4h384ufG9e/d+/OMfCyFu3br1\n5JNPdjdQ03X0ztWhT+ZhtHj1Lj+5e8MYACXL+uNIFN+3bcuYdwDTlkfIsixfeukl9evkYKkXX3zx\n7t276tdbt2796Ec/ulaV619axkRW3VHkes17R3F1q5RaZAaWcTiEaLIZye0o9Y0PASf4Zj6w1F7c\nhXf//fCHP5SJ6gc/+EHbtl//+tebphlpFrp3797du3dv3brVtm3btrdu3frxj3/84osvqg1kZirL\nsm1bOXm/vtKkLFn+tW1bdYOh2kCGp8t1O46IByCl9HUKIHULI6ScwPMHP/iB6vUbn9JTBiD9r/oj\nco51vWFJPqKWsulO1G4crlv+UDWSiszAAs7HD3UL9HeIDVqqXBUIhW/mA0vtxV3YUiXv6dPHUcn5\nPH/4wx8O7XLr1i391zt37ggh7t27J67amfRxVLJpykhR3TL1DZhQFICKPgyBArC95ZN/GiFG/io7\nAY1h5jI5GaFK/1UNPzcOoZYKb9vWKFP+ydilKAo5hyfj04H1XP3zcqSciP4FG1FVAezF2YzqstWq\naRo5PZUMN5MLbsubAXv1tjxVVSWTkxBCDr0SV5msaRq5AE6e503TZFnGmCogEcfIbQBityRUyT6+\noe62oijqupbjzWW6mixQtUhNUrOJdslh7HJuhe5QdwCBOEDH3AFOAYAPS0KVzZRUQktX6hHZMddt\nQLIfDiULbNs2z/PT6aT6DY3+waIohsocX/uPZQGRON/vdD5JAA5sefef0WI03nwlyamnTqeTyijG\nQKuR8g0yQo108HWHukvtUiOVAbCAp09VlvmKbnwNABjnLFSpNf66W8rJ03/84x/LMewyo+ijqYYC\nkCxN9iEaf9WHovdOpw5gDbcBgjgCIAULQ1U3PHUnWTAYY9Ll7Ooyb3VDVfd+QCM26Z19VVWprkBF\nTrDObYBAIuJdAwfAcSzrC/vBD36g7/71r39dCJHnudpApq7L5SJ/ffPNN4UQakZ1OUnVnTt3erdX\nt/UZXW/GjOpqA7W92l2WprbXy1l2vkAihHj/P38lH+MQcIhv5gNL7cVdfrYySCl6omqvYpAKVe1V\nkFJUwDJ2UfR9u381NlAxS+kmqja9VxeY5TCJZ5tDwBW+mQ8stRd37fzxMs3ked7t+MuyzFgUWQgh\n1/u7deuW7PgzyNFRRVEMzZ+uugXHNxiaHyu1+fKBWbyuxGIMHvfxQeyOT/d6FnyXuMI384Gl9uJ6\nPNsAn8oAqwSEw+1ieSPlezrEBqFK+H+WEsQ384Gl9uI6m1FdUk1ERVGooU6WuwDAGsZXd0rf5ACC\n4DhUCSHk6jSyF298y6qqWEwGAAAcg/t2ubqu1fowZVnKaKUCVn1FTsFQluWWLVWptUMC9jYYLcQh\n0Itv5gNL7cX1dbZ1XVdVNTQrep7ncmlkH4ceMbnaTFKvPaA7Rhw5xiFSk9p1Nympvbjez1b27qk+\nvqEb97aR2qsL2Ntg/PVhEg9j1d3im/nAUntxEzvbxF5dwB6hasFR+Dpxgm/mA0vtxXU/UB0AACBB\nhCoAW5ga0BilQ54UgMUIVQB2CAfEEQDHQ6gCcBBGUPOX21IaIgJgBkIVgPuICwCwGKEKAADAAUIV\nkDqGNwGAEx/YuwIAju/AuS3L6DOFrVlzXy9Yd0StCze+/ptc8mTZITAurVm5UpuFDLCx8bSfOrdH\n7B4l6olM0xHUN3NVVefzeXxd2qIojEXYLNexnVwqbUGZveWPP5+q/hs87UG9uBtIrqVq/D2d1GsP\nbK9tfbVa9RbrrxnJ34lgX+fzeeSvdV2fTqfeveq6VguyDSnLslugzDfdPy1oQyrLUta/ruuR3eUR\n8zyfWz6mtU5dLpfenwPh/HyBAxDi/f82O4SPI6oyjf/82eAQiQjnm1klm7IsezdQl079AqfSydBe\nNkdcWOOBGuZ5PrTB5XLpnoI/4by423A8UP10OqnmytPpNBnbAcCttqU/DvPIoUVZlk02U8kfLpeL\n3g5U17XMVeO7b0PWxOid1KlrNKOpfHDf/SdbQeXPI/3B5C0gBPRhIXF1XVuGoZE40h1l5YPew1gU\nRW8qUjUZ6gEc6m2EE45D1eVyOZ1O6r21wZsMQOAOn9u4ATBqRVHoCWMyYPUORSqKwnczlZHb5OGM\nNjNxNdBe/tBtvNAzmb+qpsxxqCqKor36dsmyrPt6AwjTLrGAOIJenoL40JtN71QZyUYjHSy++17U\nLVZ5nhdFoYa3n06n7nU2z/OmaXobNej7883j3X8kKgC67adsENwACP9U45CnPjV1JTWuqjJpnU6n\n9vpbvKoqeYtitweQvj/fPIYq9Vrq/wgY6gYGkA7iCA7AmKBnwbRSNlSjVLedom1bWQcjPOkXX70J\njb6/Dfidp6o7du98Pud5zih1IBEbhyf1L3ZyW9ToFFbGk5Ds6esOn+rtAaTvbwMe1/6TiSrPczUZ\nxuVyka80rygQAmIHsNjlippPwcelTaal8Yk6u8OnVH7SwxZ9f1vwNwWWGJh/TL6iu0wN6vV8gehs\nMEnmUPluj9tbGvN/xiKob2Z5ZZw7jadKKnMvbZOTfy6+lMvH1VVYzfk5q3rrBfXibsBX959Mx70d\nzHJM3/gk+v6wTA3QteMb/xg3AB7jLLDY+EQGTsxdVUYuWaMasXyM90JXcmv/EZuAg5nsxCTxwAnZ\nECCnX9/40AvGIus5r6oq+v624WtMlXrzdf8kH2RYFZCyY6ScY5wFLMmh3xvfaDW+7Ex9ZaQEfQPa\nq3zzOFC9LMumabIsk82hkpx2Vk5f5u/QABJkRBwSD3wYTzDOL229Q86V0+k0ssyubJeStweK+R2I\nWMLriK3elsaR1bN9832+QFw2G6XeewhXR9/gEDZH59tlsaC+meV1qnegurqidf+6+JI6OVB9pHAV\nkmz2HTop34J6cTfgsaVKCFFVVdu2l8ulLMuyLOVtEUxSBYTgGPMpHOMsEAXVaHQ+n2UnjBxfpe5/\n8jRiSd24l2VZURTyuFmW2QyT0lun6PvbwBYD1ZlFHUjcgXvimGU0KW3bqkmtjVUC/a3MVhTF5XKR\nK88YU3pOTuOulqyh728bWXvgb7uOLEvrfIERKgpssCRf7yH0LLK4DpaH8Pq53+YoBxbdN7M+PLy4\nsv1xo2h5iu7FXSmxs03s1QVGJBWq1hxiZR0wiW/mA0vtxfU7pgoAhqT0TQsgCYQqAF6EMNKI3AZg\nS4QqIEUhJJ7j4VkFEpfcMjWs/QdsbIPBTAAQAo8tVXVdZ8P8HXfc+LRde9UK2Avv+vV4DgFIHluq\n5NwYLN8IYNLKNY+JNQBC4CtUyYk0yrJ87733hBCPPvronTt3bPbSZ/4Y2aB3fo7J3QEka2VuA4BJ\nviaQqOv629/+9r/8y7+oRx599NH//u//Htmlqip9gto8z40FbYxOQ2P62m6XYvfUUpswAxiy+yRV\nTmpisy9TVYWPb+YDS+3F9TWm6j//8z9lonr55Zfbtv3Sl770zjvvfOQjHxnavq7r8/ms1lrO81wt\nrC3JzCTXg5QLIcnuRUmmK7VapOxz3HHkFpC4cD58G3+fh3PiALbnK0J+5CMfeeedd15++eXf+73f\nk4lHPjJ0OBmA9L/qj9R1fTqd9LYr+Yha9ijLMqNlSy7PZBwutcgM9Nqg8cb+EL5bqlYeYm5NvB7l\nqPhmPrDUXlxfLVXvvPOOEOLFF188nU7ydr9HH31UCHH37t2hu/+M5R5la5PMSd1xVDKoGSmqW6bR\ngQhgY0HNp+CvGSmlqwaAQR6nVJApyvhVhi2DGl2uP6j/2ruBEEIt1t22rTF0Xf6J4erAiKCiAB1n\nAGLnd0Z1fQooefef7AG0bwwcaWoyWrakqqqKopAtYXLoFYBjm/w6CSo7AjgwL6Hq7t27otNStYZq\nkZpU17X9xgB2R+IBcBheQpWakqqua9lupI+jWjDOqbdRqldd1+r+wdPp1D3WyCTv4+bWGQBgafE3\nMwK39ztrax67/955553T6dQ0TZ7neZ6//PLLQohHH330dDr1Tt3Za2RQ1HiLlDxEN1SNL1PDCjbA\neul9kV6T+OkvsPhrGeHb+821Nb+hSghxuVzkROfyV9knqE/yKfpu5RN9d/Z1E5JswZKLDBp/ZYg6\n0CvkS37IdQOASb5ClQxPZVmqcCND1Z07d+T48W5CMlqeZPCSu3dDVfd+QKNA+8YwAJ4ENZ/Cyh0n\npfdvcgAdnlr8ZGefKv9LX/qSEOLRRx9V7YG3bt0SQty7d08+IpOWmlFdTlKlZkhv21Y2Sl0uF7Wx\nXnn5qzGjevfs/J0vEAsh3v8vqEPM3UVtb7nL3O2X2eAQQFxSu+x6nOr0j//4j3vX/pOTocsH7927\n95nPfEb+7HztP2MDkd7UrkBXOKv+rdllg0MswAqAgCG1y+4H/BX9f//3f0II1UYlbwlUiaptWyMG\nVVeEEEVRdAdFtW0rh2eN/3VodwAAAH/8Rsih2yllG9L2ATa1yAx0hdlgQ0sVcEipXXa9z6helqU+\ny5Qc9lQUxSc+8Yl79+55PToA7ILbGIE0pRUhU4vMgEG/2Hv6KCw7xNy9Am+p8noUICKpXXb9tlQB\ngHMr24GYVQGAJ44HqstBVDKWjs9Pv1d0DbNWwMY2eKfPOkTb0mUGIHqOQ1VZlmoeBPsF+7ZEbAIO\ng9wGIChpdXam1rkLGEK+A85+xw0OsRg3AAK61C67HsdUFUXRXYtm8k8AwpVl7//no1gf/JUMAB0e\nQ1XTNL3Jqa7roT8BCFE3SzlJV0OFrI9BRsmesiAAXOe+Xa4oCmNp5F7dNWQ2kFo7JGCY3TllE0S0\nsmbMKdApORPv79CKiVqOncVohe8fglkVgE2kdtl1f7ZqrZjz+ZzneW9y2msZmdReXUA3+3pv37Rz\nVZztIfpKVolHTOWqwVA1VWFCFbCx1C67Hs82y7JdmqNGpPbqArp513sjoH3UetYAACAASURBVHR3\n6NvAqiVsoORr1RODdR08C4sKX8ttjFUH/EvtsutxTJVcjsZ4kKFUwO4cJCr5oP64ZbOWkYlai5Rj\nU7JlhecWCwBz+J1RXa6aXFWV/LWqqtPpND79JoCd2QSU3r9OfrTt28osAtD9TawrPLe7EABm8Tul\nQtM0+rCqqqrKshRT05oDCIVNu71l2/7c0UYLirXfa2h3AFjB75iqsixVM5VS1/XpdOLuP2Bjswc8\nzfqwZJkYGQluV2xPDTs7mtvMrPD93YeHba3EmCpASe2y66ulSo6dGrr1T+w3uCobtUuVgFCsuXWt\nbfVh4M6Mdy+Gfa8d3yhAahyv/Wdj37HqSUVmQNnzAj8/+mSZtqG+aF+WCRXdViYqo1i+GQCs5rf7\nT/SFGDnWapdwk1o7JKBMJ5DVrT5mz5osZE6xk9Ml9M9lZVHy4CH89NXRAwhIqV12PbZUXS4Xea+f\nGqsuF6gRQsjh6gC25ylR9ZtZrN54ZPcH25Kn0VgFYDW/EbKu66qqjFVrdpwRNLXIDCgTbScuQlX/\nGPCZxY7Plm6/lM2MQ3gIlLRUAVJql93EzjaxVxdQbNfLW/EBGQtV87vneuvpJVRNHHUJQhUgpXbZ\n9Tv5p1RVVVVVcnw6M6oD4Yruu895hblhD8AKfiOkHJMuf87zvK7rodHr20gtMgOK1SLE6z4d1w6x\ntNjxNp7+sfAz9R/CaeMSLVWAlNpld4sZ1S+Xi3qQGdWBQDlKVK5MF+jpm9rpmfBVByTFY6iSiaqu\n66Io8jyXD6qVaugHBDYzeGmP+pq/uvLXCkjpH9MAPPE7o3p3jRr1IKEKCIi7SGH2/Yl56WekItsl\nwNVHIqEBadphRvV9jfc8JtX1iwT1jyKK16LJpcYnvQKAxXy1VMmZqEZaqvaaqqodtUuVgEPpDSxO\nU0z/PFgOyh1dZxAApngcU1WWZdM0WZap+T/l+Krz+azmWAewj7CXIu4RXYUBpMfvvY5VVZ3PZ+NB\nOXrd30FHpHZvJyCG0ojrjNIz86ex9t/K+T/1mT9XTNkwdojpvzk6BJCM1C67W5xtXdcyRRVFsW8D\nVWqvLiAmQ5XTRCVGQpX1saxC1eB2VracX35dMUDcUrvsJna2ib26gBhflUV4C1WqWGNwksXhempn\nLPy3es0+25UQCVXAOqlddh3f/SeXo1HtUpPbF0XRO5gdQIz6V/1zMehbJSq3Jm4fXHR3ocQ9hkCC\nHEdIfRUa+2nTN4uxqUVmQHSbXTw0oQwOqBqshHWBrdlMJXqb3Ja2VPXv6nyFab51kKrULruO7/7T\nJyYYn7xAkrOrAzgUnzN4TnTYrSnE9s8A0G//CFkUxWY3A6YWmQEx0lLl7rMwvc7xzLafbtOXOaBq\nUbH9h5hMaUufKFqqgNQuux7nqZLk3FRFUWRZ1juCivVqgO14GOZjFtn7BerpW5XpOgGExG+EHBpW\ndblcdplbIbXIDJgNLj4HVAnZsDRU7JxD27ZUzSy2/xBDO61uaKKlCkjtsuuxpUrGprIsu4OoTqeT\nv+OOy0btVStgax6+5nytHjNxVP+NVauL5asFSITHCJllWVmWvf19p9Npl8aq1CIz0L2NTvvd9SFG\nmql6amO1objKaoMtVebW6+ZtX12s0wKA6KV22fXVUjUyVZV8kKFUQLpmNt1MTFLlaD0Zt8W6KABA\nZNZO/ikbovI8t+nRe/XVV3/6058KIb797W//9re/7W6gJg7tnRF0ZLkb9Sfd7qviAIHaYNj46pIW\ndpmtmK5zh2IBHIzNbFI2U0zleW5sYJT/Z3/2Z/r2H/3oR3u3Vy6Xi/7XPM9HNuid76q3SovPF4iR\nEO//d/8nt58CIeaVar21XvPpnRadndUe6540H085EJHULrsLu/8ul8v5fBZC/PCHP2zbtizLpmmM\nZqHL5SKEkDMpvPrqq//4j//40Y9+VCWq//qv//rzP/9ztbEcJC5Htcsd9aavqqqaplEhSW2gWqfk\nD5fr6GEEfFu+eox9S5RNExHNSABCsCyLyXYjmaj0R4zNZPoxyOSkH11upjcsyUfUnYPdqk5u0Gvx\n+QIxutZ846fNZHYLkeUOQuhbzWtSsj5HT8XOPgRwXKlddhe2VDVNI643JslmKiNFFUUhDyO0/j45\nXkr2Br766qviqp1JH0fVHcxudP9NbgDAO621ybapiCYlAMe1/O4/I8TIX2XYktT6MzI5fexjH9O3\n138dulVQldbty+vdRc7bLjscZ54NcGT7zCA1ye30TWFP2s5UVUAKnE2pIFut9EFOTdNMjmqSNwP2\n0kNbNyTJw8nGLXmUpmnkuKs8z5umybKMMVWAKYqGousBhDgCIBZLQpXs4xtvDSqKIs9zOZh9kt6+\nNamqKjmq3ehqlOOr5NwK3aHuABxbHHY2CHZz58Ea33xFhaMIsQBcWTJPlWVYqarqdDplWfbtb39b\nPaL++sEPflD9LNuWJguUU7HL7auqUqlOjtzSt5SRrrfMxWvRtHw7InYBvoeH539qRbb81kJ/mLAK\nwLDlk38anWvd5qtu9tIbrmTSMgZa6YxIVFWV3N1yfZuiKGT/o7Ex2QgI2vA/e6Y/uzPnDF0+xSgA\n9FkeqozQI3/VB0IZ3XM//elP9Udee+019fNQAFKlyUSV53nvMKmRJXEA+LO8JWmDOOO2SclFhWnk\nAg5v4UqHMgbp+3YfuXaYLBPXW4n0R2S/nr76svFId/duZYwWrN5dUlvZESkz1yR2+87Pri1yLBZ0\nLQ6tNqwtdGzEGKtDzFzE2HqJ5+XLI7OsMlKW2mV34d1/clkYNT5JzXguf63r+ubNm1mWPffcc/IR\no7NPzqWuFq5Ro9plm5MaO6Xf3Ceu1vLTyQ3k/9UE66rFq3ftGgDOBfSdGVBV3hdejQB4s3ja0KG1\n//QewNu3b6tDLF77r3daduOg3fykJls3DrH4fIG4mHOpeyh97XTh3f2vT1+u/zbjKHP2WTgd/EzM\nq45kpXbZXdsuJ1uJ8jxXbUtqOPnpdLp9+/Yrr7yij4WSbVQf+9jH/vRP/7RbmpwQQbZCLaiM3F1c\nv81Ql1o7JFJ2v2NLzvzp8J1/VbTq/ltYdrfv7XpX2cKOszm7zTvEjM5CJ/sB0Uvtsuv4bPWRVfKp\nHB9rtbHUXl2k7Fqo8jCgSjgMVaqITvpYGEesd1sYqmZWiFCFZKV22XU2o7qkRlbdvHlT9v1xUx4A\nK3tMb5DStz0A75ZPqTDuV7/6laeSARxEkhMrADgwxy1VAILjue/PbbFCRNJ8tChdEcmAYyNUAQeU\n+sU7ilgG4HDcd/81TaPffDd0O97QDXq+ja/9l9R4OsCJVR+a0Q41N9HQugfQasNFPYB0GwKJcDws\n33654l3iS2q3ISBZvm790z7ga2/96ytTXC9xyYzqvcWO7jm743HRvXzcAIg0pXbZddxSdblcepfn\nA7CP6L7OXFXYunVoeTMSi/kBuC6tCJlaZEaissxZM1KnZHFVrrOml+H2qFUtVWJG69C8c1k0pp6W\nKqQptcsuA9UB7GqDL1y3A5pSukIAmIVQBcAOY6275j8nPIvAgRGqAMzmMhlYlzX7oDQpAdgWoQo4\nFm1AlS/HDSu2uW3+M3Dc5wzAfYQqABYG4ob7rKBmbHfbTUavGwD/CFXAYdE6YmnVE0VcA3CFUAVg\nDj8rCfo6ILkSwIbcL1MTOJapwZHRarIZlp4B0JFcqCI2AbNtkB70jOJppnImQAfgGd1/AHZymJae\nmSdymPMGYCBUAUexTXuSv+NYNCMtObReLHEGgE+EKuBQ3E9StVVWC8SM051Z7ZDOEoAXhCoAC/lL\nCWuXUjYQZwBsglAFHJP7ILH5ZApbWntygZ0OgF0QqoBDOMZFvRNt/M3YDgDOEaoADEtsQNVsUVce\ngGuEKgAJ2CD90AYGJI9QBcRvw/YkN4dKPn8k/wQAx0SoAo7D/XwK25tqUnIQR9wmGnoAAVxJbpka\n1v4DbE18WFwcoq+U3aOhv/VsWDAQOLbkQhWxCSk43tvcQRzZINGwvCCQNrr/gMhFd4OedYVbsWmr\nDnEIwEqEKgDpYVgVAA8IVcBRRDfjOVkEwLEQqgAELLomJQaiAwkjVAEx27Y9KbjpDGLGMwEcD6EK\nOIh0LtIhTkBKVyYAQhVwENsOqHJwtIEiAomG/qpB+gIOjFAFHM3RLttXAcffdKOOBRIMAWyOUAVE\nK7ob9OZUeONJqt4/6MHyKIBtJTejOsvUAHGIbgJ01qABkpdcqCI24YCYoQoAAkD3H4DgRXenHk1W\nQJK8t1RVVSWEKIqiKIrJjeu6rut6ZHu1gSx27u7AcWzenrT2gOSMDtZfBg4m89cdVlXV+XxWv+Z5\nLhPP4u2N4VCXy0VPTkVRNE0zsoEsge4/HIT+cWjb6785Knk4VC05hN3+5sGv/+7sNIcLGjj7+SVb\nnOPyowCRSO2y66v7r65rmZAul0vbtmVZNk0z0nokt8/zvG3btm3zPG+aRm+OkomqLMu2bS+XixDi\ndDqpv1ZV1TSN2l1tMB7jgCPYfEDV2gPapY3dMVUVgLl8RUjZbqS3FclHhg4nM5P+V/2Ruq5Pp5Pe\ndiUfKctSBq/u7sYGqsykIjOObKAJx1kzVaesfZpwOg+5P9MQGuSAg0rtsuurpUr2xBndc0KIkaaj\nPM/1X8uyVNt3x1F1SzN2nzwcELGj36C348FT+v4H4JjHu//sU44aXd7dfmQDcRXdhBCXy8UoeWgX\nADtYnAKjyzjRVRiAO9tNqbCs6ciyZasbnuSIq96bBIHj6AzfXiWoMU1dnqoX+FkDiIeXUOW8lci4\nrW9cVVVyiJUcrg4gIEk15FjENRIdcCReQpXzTjejJ3FIXddZlsm7CLvzKUjZUm7PCPDBWWLpFLTn\nJ2Dw7hYvxQLAYh4n/3QyyGlke6P5Sk1zNRSnpKRuQ8AxRRfxo6vwlYWTc1osAsg6gcAheRxTZYSe\nkVDVO9xK/3VoPJZqwZKJSs5TxeB0wB9//yqxChlZ5qUCBBwALvgKVd0Ou8mWKiOEyWYnuX03VBml\nyY2ZQAFpYR1lP1xWk7gGpMRXqNLn5BTajOdqg6IosixTMUgOKlchSe4up6qSj+d5fj6f1bRV+s19\nqpCig7v/gJ3NTxU9mcZTGosk5AGIha8xVUVRlGV5Pp9VrjLW8jPapXq31yORHISuL03Tvblv1k2C\nQJRoT4oCY6aAJHmfP14GI9luZB47y7qDyke2F0LUdV3X9dBfJ6U2Xz4OaHjZFrEyDo2unLJ8WRXr\nPSc21P7sco2XgafPwSGmimClGqQgtcvunme7/XOd2quLA+pcit2EqtFSVh3CeagSIhPtwsqMFrtX\nqFp1FCBsqV12t5tR3VAUBZNzAg6F+MXluQvMQfEbPGv0AwLJSCtCphaZcTR9jRtuepF89/1N7Wy1\n4dVGqqVqSZXsju2gGcmiCHoAcXipXXZ3a6kCEIrYBr/H8RUdRy0BuESoAiJ03Au21Zm5TYF9hzzu\nEwzAI4/L1IRpfBW/pFopAdNo399sjCVSFq53AyAyyYUqYhOOJK3cssHkT6QfACvQ/QdEwlOesC52\nedhINqYke+JAqghVwBFw+V5l76cvrRZH4LgIVUBsWAhPE00ciaaiAJYjVAHwYE6GmLHtHsnPXxyK\nM8cCGESoAmKw94Cq0LTCW839z9cA4KgIVQB8mpMqgkog7isTbYQFYIlQBURl2wFVCcWAvQNdQk81\ncFyEKiB4A9fbcC/DXmu2d/qZLboKA1iKUAVgmr9gEGI03LBOJC7gSJKbUZ1lahCxgffnwrdtbOso\nD2EWdACBSC5UEZuAMO3/0RxeBsdZbiMAAodG9x8Qtujak7bqO/M4qwIALEKoAhCbDRp7sszlcWid\nAtJAqAIisXl70tomJ5LETCGO2QcwB6EKiBIXYC+2agMDcEiEKiBgYQyoSms+hc3RogccBqEKiFtY\nl+TNUlJYp20hugoDmI9QBcQgzhv07K08Py8nFN6zBCBwhCoAHhyuYcZlxCKuAQdFqAISFnn08TJV\nVeTPCYAdEaqAUEXXnrFxhTdJPy4PQlwDji65ZWpY+w+wEV2i82LzVWVYxgaIWnKhitiE+HTetKsS\nTxjTNADA8dD9B2BMipNUDZyzv7HqpFDgGAhVQJDsLuCrLsbHmqZh7+MDAKEKwN6Wp7tNVpVhrDoA\nS4QqIGzRtSdtmxu8zKoAAIsQqgCgg5WVAcxHqALCs3d7Epf7HfHkA/EiVAGRCfGiu2OdtHQY4jPT\nxbAq4LgIVUDA9h5QleJ8Cl1aXf1Vm6wFHAChCohViJfhPeoU8Vj1mKIlgGnJzajOMjXA7g1gOgd1\naf3EqrZVp6b9CACDkgtVxCaEjqv34ZHRgIOi+w+AIyH8i8VTWCEDAbBAqAJCtVMnHflhd7wEQKTW\ndv9VVSWEKIqiKIrJjeu6rut6ZHu1gSx2qBBZQu+OOstaAREJ7nIbWIUyt1F0g366zHGVAewoWzzG\nqKqq8/msfs3zvBtrZm1vDCG/XC69kSjLsu6+RuEjh2BMFYKmfwr63qtTf7coeWo36w2XV2j5WWxV\nYLfoTNwv18EhBmo878kHgpfaZXdh919d1zLEXC6Xtm3LsmyaZqRZSG6f53nbtm3b5nneNI3eHCUT\nVVmWbdteLhchxOl06pYzdAgZni7XjYc8IGrBfU3tWqHgno1J8dUYwLSFEbIoiqZp9MYk+chQaTIz\ntdf+QXb/kbquT6eT3rAkHynLUgUvvR1rqJVr8lxSi8yIz1RLxcKWjDktOQtbquZUyEd7zP0y3fYB\n6jN/XjVWuSm+71mgpQoHk9pld2FLVdM04nq7kfx5pHEoz3P917Is1fbdcVTd0sorluUD8C6wAVVe\nsLIyAGvL7/4zQsxIqOodWq7/2ruBuIpuUnVlpEpFUWRZlmUZ49MRpb3XUQYArOFsSoXJlqpe9i1b\nk4U0TdM0TZ7ncsBWlmWMqQKEmJfVFuY6stoCo08arVdAjJaEqqGGpcX0FqnF5CB3ObfCyFB3IAJT\nA6owIhOu50EgMgKws2SeKueda7JtafHuRVEY4+CKohgqc3ztvxFJDbXDMc15D09vu/SjdIBo6H5u\nqasSWcAGiNry7j+jc21Z89XI9iubr4a6I9ul1lQGcIv3YxfPCYDdLQ9VRugZCVW9+Ub/dSgAWQ6r\n6p1OHYhMvIvWLY0z0cWgVrh+MqN7CgCMWhiqunFnsqXKCGFy7lC5fTdUzWr3qqrqdDoZuUovH0By\nohtWRbcfEL+FoUpObaDGJ1VVJe+8UxvI2Q1U0JEjx1XEkburSafkEKjz+aymrZJjzMcnUDAqo3JV\nXdfyQCOTWgFh8bjMiudiAQDK4jFGRmRRS9DoI5DkIjY223cHLen7Gpt19+3mJ3kzYHffeScJbEOI\n+/9ZbLWkZGe1mLvpmhotLdnHAYScT91D2dcLXfHUAsFJ7bK7dv542UpUFEW3oy3Lsu6iyCPbi6vR\nUUN/naQGVw01caU2Xz6iYbE6yZLGrJn7zNh8RdOav5VYrlXK7WI1V6U7XqlGK1kVukGrJbCZ1C67\nHs82wKcywCoBQgQXqmbMp7A0Uc3fdWbhclC560UAVahyWXbnSWEFQBxGapddZzOqG4qikOOoALi1\n5AsqjS81v2eZxnMIYI20ImRqkRlxsGu9WdJ6MXMf282D7Pszy3feUiV26AHk6wqxS+2y66ulCgAA\nIClLlqmJ2vgyNUkFagTHQ7OKX3xenHO/Ag6A7SQXqohNQK9jzj3pNqNsvjIfEQuIC91/wK7sLtKr\nLuXRNYC5oN+mFwGiE3AIhCoA1/i7vkeSx2z5Ox0iFhApQhUQBrsL6ZIZqjwJ8sofZKUApIJQBWBr\nm0Yf1ysrvz9Zgz8Ha9ADUkKoAvbDgCoAOBBCFQDCUgDouQTiR6gCAsCAKg8iuwFwAHkXiAihCsB9\nx7z1z/WwKk8F30eSAuJEqAJ2El17UiRX+hga0aYd4yyA1BCqAGzqAHHB1w2AB3hqgLQlt0wNa/8h\nOHu3J83eg48JAPRJLlQRmxCXSPrcgud+EUBnhfXLMnGIgfZAUuj+A/YQ5ICqsT0Id/vhuQdiQagC\n4kAb6wJbzKrg7dZCANEhVAG72ntA1WyLKnzgthZ/uY18BUSHUAXgaPrjSHTJLroKA8kjVAGbC6k9\nyaou7ip8mNaXw5wIAIcIVUC4aKpwYIP4w7AqAEIIQhWwp5AGVFnVJfbrfbQpNdqKA2khVAER2DPM\nRH49j7z6AGJCqAK2FdKAqm3sH2s8PyEebwD0PscoAJeSm1GdZWqAhaL6dLTttmHO/YztxCkgPsmF\nKmITQhHAgKqNL9xBfPjcpR+SDwAD3X9AoIK4YAdRCQjBSwHEgFAFbCjUAVX+2pBCiQLRTawAIEKE\nKiB003kg1KwWiCjTztUTzlh1ICKEKmAPMWeUZQI6Yw8ha4uVmwEEj1AFHEgAg9/DEVCMA5AGQhWw\nFU8ZZWmxkUem+TyELLPI5J5TANcQqoAQhXV1Pl6bT1jP74DO0x5FrYGUEaqAzXnKKEuLPf6tf5tg\nWBUAQhUQtN0aiY4ViN4/mzib3LgBEIhFcjOqs0wN9hHYgKqNHfiDZc6r7na9GgBRSS5UEZuAeWL+\nyIytJBNF+mEpHCAqdP8B/unXxTAGVHGlBgDnCFVAcPZPPPvXwJvo1qu5XuEDvzLAAWzX/VdVlRCi\nKIqiKCY3ruu6ruuR7dUGstihQmQJs+sKeBLe5Jzc+udQJloGlQMpyzYYY1RV1fl8Vr/meS7jzuLt\njcHml8ulNzllWWbsm2VbnC9gUu9Yu7ef7eYrehUnDjGzwuOHWFfMWj3VcF2za+XJUOUhPaspG/gO\nQ0RSu+x67/6r61ompMvl0rZtWZZN04y0Hsnt8zxv27Zt2zzPm6bRm6NkoirLsm3by+UihDidTt1y\naKBCKKJb7dh1hQ//jXr4EwRgyXuELIqiaRq9MUk+MnRcmZn0v+qP1HV9Op309if5SFmWKnjp7Vi0\nVGF/89tFZrdUzX9Xj+3qqCHHRWuXGz01cV25++UJP6edZbRUIUapXXa9t1Q1TSOutxvJn0d6APM8\n138ty1Jt3x1H1S2tvLKy5oBjwbQn2e6a0ldhRBIcrAbEYou7/4yQNBKqeoeW678OjT2X0U2qriyt\nL7CnY1wywzyLnlqFWVEAcdphSoXJlqpe9i1bQEDCHlA10feH+XytANhyXyEQAb+hyvmkBnqLFHBs\nY2EpnugTQhdiTx2im60KQAz8hirnt+Ctb5TKlnJSf6QrhHAxS3QVXizs6b4ARGSLyT+NnrtlzVcj\n289qvkrqNgQc34r7/gAAbm0xpsoIPSOhqne4lf7r0HgshlUhRPPzy86JJ6nAFV0P4LWJZlwWDMAV\n76GqG3cmW6qMECbnDpXbd0MVa9EgOY6uqGkuULNB3XyNVQcQPO+hSk5toMYkVVXVNI2etIqiyLJM\n5SQ5SboKSXJ3NelUURR5np/PZzVtlZxOnQkUELT5+WXnburUeslDjoEabgAEArf/2n8yb+lTrrP2\nH45g0bzkVhN9r5sNfHBvdyvihTOXum5sXnXhpq7Mqw4YUrvsbne2sjGpKIpuAMqyrBuMRrYXQtR1\nXdf10F+HpPbqYk+EqsAu/P21clrXnlDlqOSeQ4T03AJDUrvsBnG2mz3pqb262NP8q59VqlkXfcb2\ndne5DvPCPxGqzD+sO4T/UOW6YMCL1C67O8yobiiKQo6jAnAA4Q9P2qKGKV1FAChpRcjUIjN2Q9+f\ng5IcG6yYhyY6QQ8gIIRI77K7f0sVAFvhtwJpQvsina5PVE8vgABtMaN6UMYXnEkqUGMLUbyjgm1c\nik3bas/ltV/8yDJeLyAoyYUqYhO8W3QpnbfTur4/XLNB+vGDSAWEhu4/AFecDioK2WA9PZ1ALM8L\ngHUIVYA3izKK1WQK6wzOKQB3/D2vvHxAsAhVgFMb9P3FIMx+qcFaeapumM8CAG8IVUBsPA2oIgFI\n6xLuBg2NAIJFqAL8CLXvz58Y6ng0mYh1lD1wSIQqIC2MyJHMU9+gBzDhZxtIBKEKcGeDe8di6KSL\noY5b2GisOoBgEKqAnQXRfpHMVdrqRN2+JMk8twAIVYAHgd1N1h8SgkhzB7RDiOKlBMJAqAIcWX1h\n678YR3K9jKSaozZIQx6epkzQEgaEIrllalj7D6mZHpGV6tt+bOm86JbVi67CwBElF6qITfBi6Vjy\nGS0Xbt+63lqW+ITp3o86HpYXjHbFQuDI6P4DAub0sknckcaehzh7AAEEglAFOLX0qrxb4nFx4GPm\nhBVntc+recyXAYgJoQpYbYOLWSR9fxjkM2cxVh0IBKEK2M0G2Wbj+BRdD2PP8+P6HHoOQagFDopQ\nBbgTXXsSfX/jwj63nlcv7AoDh0eoAtZxcRnboIHn/iG47m78hG9yVHoAgRAQqoDguZ1I3Zvo+v4G\ncQ8ggEUIVYAjM6/EE1dV+v62sv8LsQI9gEBQCFXACpFcwOj729f9Z/04rXkAeiQ3ozrL1CA0E2+6\nSN6TkVTzvokZyTeYsNz1wjKZaFtBaAb2lFyoIjbBi/D6/ibK4INgb2n62W0lGdYBBHZC9x+wVHRd\naa4rHN0TMGTTE3Edd4hPQDgIVcCeJgYau7hgctFdyG3fXG9uO0wsBSCEIFQBDoQXWzbu+wvvCbAy\no9rRpZ/oKgwcAqEKWGTFRct217DnZ+eqbaP/NfQWQpkCFNgXoQoIieuoEmkbUijoAQQwB6EKWGfF\ndXefxEPfX5/pbOM2/Th91voLI64BmyNUAfOF3ffXcwiur/uZfiV5dYCjIFQBwYjn4hpPTSfoiaf/\npKa3CMu1YVUxVBg4EkIVsELYfX/vH8L1HA3JcfekXQs5vBbA4RCqgJmi6/vz6QDBYN4peHp+Vxd7\n7SwO8KoAcUpumRrW/oMzYU950FOsowrTpzTXbuvVCJasATaVXKgiM7y3xQAAFoJJREFUNmEVR9dG\n823o4ZLLO32BwQTiKRbpxbpLPxnTVQE7ofsP2MjOfX8eQlaiuW1dutqoySrR1wbY2dqWqqqqhBBF\nURRFMblxXdd1XY9srzaQxdofTu2os6wVsISni1bwXYqH7Pvbs3vOXWPV4FnQAwhspl2qLEu9nDzP\nV25vVOxyudjvbvx15BALzhR4nxD3/1uxt9tixw7hruTBQxyF1XnF8waQ8fywrxbikdpld2H3X13X\n5/NZRZ+yLJumGWkWkturlJPnedM0enOUHD9elmXbtpfLRQhxOp3sDyebqS7XdduugL1s0BCyQd/f\nIZupDDu8Uhu0eqbwygEByNpFn+eiKJqmuVwuKtnIR4ZKk5lJ/6v+SF3Xp9Mpz3MVg+QjZVmq/j6j\ncOORbvlD1Vh2vsDK2+jG9lZ/W/fmNIvxfN/fwT5Jtqe24sWyeg8sKrm3pLY99AuGSKR22V3YUtU0\njRBCbyuSP480DuV5rv8qO+zk9t1xVEZp8nC67uGM8oFw2F5NHR3CxBB1C7PPaP4Lt8OTRmMVsK3l\nd/8ZIWYkVKnB6d3tRzYQ17OUzeGKosiyLMsyxqfDsQ3+xc8Q9WDscKYe0k86rxcQDmdTKky2VPWy\nadmyzGRN0zRNk+e5HLCVZRljqhCCjZupjteGFBZH6We7xENjFbChJaFqqGFpsW7v3qyaSHKQu5xb\noTvUHVguuszCaKpFdj4vR4c/6qsDRGHJPFXOO9dk29Li3YuiMMbBFUUxVOb4MjUjkhpqBx88jU1O\nIe6EZcWsVra7OppZ6v1i9pyGC0jL8u4/o3NtWfPVyPYqEvV2LE4ebqg7cvHkE7POC8e07oavHTBE\nfSnbF85tD+AGTy7pCvBpeagy2oFGUs5IKhrZQFwfnD5yuN7p1AE3NrgIue33YYj6BsJOPz21SyQL\nA3tbGKq68xdMNh0ZqUhO5im374Yqo7Txw1VVdTqdjFyllw844G9eohV2nlP0cJZkj5lP0P7xJqlX\nFNjYsr4wORJc7S4nndKXhZExSC01I7dXG8jt5dDy7vZG4ZO7q+3V7rI0vXzVhbfsfJEoR8uSjK1M\n4raCPtelSeTTY3u+K56Xif1cPOM9ZaT2QiIMqV12fa39p6ccm+3bzj/fVq79101UbXqvLtZyceEc\nS1Tr3pDbXDhTuxDPeAqXPtsT+3kKVY5KBmZJ7bK7dv54tYxMt6MtyzJ9HZvJ7cXV6Kihv1ruLq5P\nzm5UaeX5IiEbrEuzqOTBYphJwRHbpWiWPjvT+7l43nvOIs2XE7tK7bLr8WwDfCoDrBLCteIKtEOi\ncldy71GS+tzMeCJX56oNQpUgV2E/qV12nc2obiiKQg11AuIT3bXHczMVPOl/kqN4ywHoSCtCphaZ\nsRzNVKk2U0kzzn3RM79bD6CjkgFLqV12fbVUARGL7qpDM5U308/Doid8eienbzxeTWAbhCrApX2a\nqVwUOySKVBmKReFleie3mYgllgFvlqz9F7Xxtf+SaqVEv6ibqcItMjLzlsvztLje6mL1AhwtJwhg\nTHKhitgEf47RTBVdqvRtXhyx3npe4nGbiUhbgB90/wEaT4HCX8sPbUqB8JRLVnfVkZeALRGqADds\n8xizfUZi+elbp5+NRzddOwQjqwAPCFXAleimUcBWrFJHqI1VVshVgAuEKiBONFOFbFFj1bqNrPYe\nbKwC4AKhChBC0ExFouoxu5FoXauS1R5MrwAEjFAFeEtUuuDnT0fQ1qUf3iPANghVSJ6/gOLin/4T\ntaPjz7MNGqv2HK6+/eGBQyNUActt0PE3cVTX5ZGowkX6AYJHqELaNuj4W2GDjj8S1aTZT8u6xipP\nxipFXAMcSW5GdZapwX3+EpWLqELcCZDt9OMrpiwf29zfTOjMsQ64kFyoIjbBu206/mim2pCnxf2W\nWJF+CE6Ab3T/IVUbzKGwAh1/wbJ9hWf2qe3fBbd/DYDoEaqQpG2GUjns+CNR7W3Js+QppqwodsY7\nllwFzEeoQnpWXC32GUrF7X6BWfiCTO02I9K4SD+kJsA5QhXSNnME8dh+2ySq1SGIS+liaxur/Jnz\nok7kMRqrgBUIVUjM0ugzcX2JM1HRTLXYjLwxJ6YsbKyaV6Ep5CpgKUIVUuIoUfmY53O6DBJVABbm\njaW5akaxc8w7C3IVYI1QhWREl6icDn0iUe1sURyb3nCDsfBuSwYOjVCFNLhIVG1LooKjGDMxC/Gc\nQyyq0PRO5CpgPkIVEuAoUbkqVi+ARBW7hZ2Abm2TqwBMyZKaYXx8jRrBfOvHszRQTO/nIlH1H8Jd\nouq+33mDu7L8VbLec/YhFtVpeidm4MAKWZZYzEjrbBN7dVMXXaJy2qZEA5VvweWqRS+51SHIVVgq\ntcsu3X84qFATldHlJ0hU0Vo+uMrYc3jn5YOrrOtkdQjrCgOJI1ThiJb+k336Rr/VicrQtjYHnncI\nEtVm3OSq0Z1DzFXWJQOpSatdLrV2yBQtGkY0vdPq0UmDBTgd90Sc2oWbfsDRnTcYXzW7H9C6ZKQs\ntcsuLVU4kO43fgCJqre3ZLDLb8W8DFzv9uKyvWpg/9mHmN9hR3sVsF5aETK1yJyQzRqo7EqePoS7\nBqrVdYQbq3qG7RLxqvYqu92WtFfZ1gYpSu2ym9jZJvbqJmFRprDaaXUDVf/exKnjcpmrBorYOFeN\nbU60goXULruJnW1ir+7BxROnhOtERZwK1qpO2Pm5yvYQM9945Cq4ktplN7GzTezVPaz5mWJo7IfD\n/r6xQ7gLQcSp8K0d3GaRVJaEmZlvHdtD8I7EqNQuu4mdbeSvbtT1d1N523A0cw+LjUbqPztOmYef\ntr6YqN88Irb6d16vmZW3ekOO/HFmydNv7EyIdmzbsKNVXG8eQ9SVF/HXf64P7F0BwM7MWDFyW9Lc\nODW3RuL+7FMLS7Y9BEIlXx395ZM/275q3f07pbSt+XerQ3R3G96ztxZZ1neIoU31vwIJSC5UjS//\nl1SgjsPMWLE2Sw0UO+MQ8s/r4tT4jeq8SWOxMPfo+4uxaDWSZMYOMV5sZ+ehs+g5ikUWBI4tuVBF\nbIrDnFgxOVfO/c2XppX3k9JInBJrs9SMs0A8HLTgTGUg+Z02+xBDxQ7sPKOFa2ZoA44kuVCFcM2J\nFTaTDq7MUlaH6I9REyXPOwQXoEMYCSXC5lVWWwyU8v7dpcIsaOIQI8V2HpwXlmxC22C1gFhFH6pe\neOEFIcQTTzzx1FNP7V0XLGKXpbJsoB2of/PhIU09m9pW5P5+K7KU5VG41hzPzLah+aVcvTO70Wr6\nEKPF6n/qbRgzDnH/KOOhrX8fIGIRh6oXXnjhr//6r9WvTzzxxE9+8hMnJVverWCzmcOi7G1fsXn1\nH40VWfff3P2b378dSRj32WUDG13bWq9I/1Y9+1lkqSzL7MZu9RzUeAp588y1fcUWVH40Zlx7+wwV\nfO2gfaVcNYrZNMFer/9kBrr60/1W4GtHuf+uNsPS/Q9dNvh8GXcb8uaZI/bL1mHEuvbfG2+8IRPV\n66+/3rbt888//9Zbb3384x/fu17oyLL3/1M/dzcRrfpP/TpZcCsy9d9k+49+CL1GMw4xuFEr/zPO\n0vIQWgFWG+MwJl90/Y00+HYafvdYvXs7RzGLnXpfzj3E/cInTZ88EJxYk+bHP/7xt9566/XXX1e9\nfvKR8dMJ9t8iB6nYVBOUXYPQYLuR/sXdu1FfFLNphbre6DXaMmaR9qaP2La8eQ5SMedHXPAZ6Sl4\nuKDOG9j2iP23GvaVNfUZGWg+vv65m/FEhPpSBnjEkCt2GLG2VL311ltCCH0clWymeuONN3arUwq6\n/3a++kdkll1vDer8t+Boxj+1exu01h9CHej9Uxyu/8JDtOZ/wJAFb5KeT6Te4nu9LMvmq8GjWHy0\nlx2i9+Nm9dEe+lKifQt7iDVUCSGeeOKJ7q8ybGGeqy+g3m8k48vaVWCyqtcmh1AHWl+avH4RobCe\nqyy+wcd25J86zg8xnr16jtv/RTYQvwhhWC3igeoG2Wrlaqx6CCzucZ637/A2XPnnISphe/ogcou+\nuKR1ezltOu6zqXtR1HaG/t2GXh6+Pg4tylAl+/iWDUt3GEosN3N7RPg2fMsVX4YIy8gbku+TLfWO\n5hyMcdm1jazK57IVjyhD1bIpqbT75602d7fZ9ke03CyFI1pupg2BH7uX3OqIDjfb/oiWm1GxHY9o\nvVkEH7fANkvhiJabuTxiUv8ijTJUSUZP32TzVduKkRlS4FU6nygANmjkSEdS3/8RhypjTLr81Ri9\nbkjqpQWAYPFtnIK6roui2LsWm4p1AonurFQ281QBAAB4EuuUCs8//7zQBha88MILb731lt5M9ZGP\nfCTLsr/6q7/ap34AACAxsbZUib61//7oj/5I/lxVlcxbd+/e/d3f/d26ruWDveXIx4ui2LiVcqg+\nvX+qqiqo+ltWvq5r+eQPVS/wJz/2+usP9lYy2PqrZ17XrWfIb34xVb0wn/zeZ14yqhpm/dXP6kSi\n/uYMqvL2FQi//h610ZL1f/75559//vnXX3+9e1JCiLt37+qPl2Wpl3C5XHZ8NuxfFFnPbgk71n+y\n8t26GdUL/MnvVi/P84jqryvLUsT25pd1HnkJQn7zd+sf0Zsnz/Ohv6q3UMj1761eOM//gsoH9clt\n+94hl8tlZIPQ6u9bxOdz6aO/hMYr2n359Ufkl6Dx2du3/mqzoXfejvWf++R3qxf4k2+8W+TXhP7S\nBF5/fcver7bA6y+fcGODQOo/WXmjPt1QG/iT3/2rfDliqX/IH95ZlVfP/CWYy5ZxxEvn8mQ826Fd\ndjcQcajq0j/5jz766N27d41Xq/tNp7/Y3bfvxoxvLuMfu8bGodVfr/yl7wKvbxBa5dup6rWxvXkU\n9f7RX47w6z90OlJo9e/NHPoG+iOhVb6derblx1lVL/D6y9p2v/mDff7j+ubpvlWMKvU++eHUfwPH\nCVXy1VK/9l7X9Y9W94uvd5fNGPWX9SnLsizL3q+8oOpvVL73q0F/MKjKtwP1N7aJ680jqS8s0fcv\ndX3L0Oo//u/XoOpv882jC6ry7fCbRxF9bcz6BkHVP65vfqPyvV/1+oO7V75bAf2LPa4n35PjhCox\n9W/xdurd2U59lXs18saavMbrD+5S/27lu//yiPTJl3qb6LuFBFV/9RGwCVVtYPWXlVGDM0YaHoxd\n/Fa0z8g3j+rfMbYPp/Lt1Ju/N+8GXn+jhkb3a1D1Nyq/IFS121Z+qC9b/2tEl10fDhKquu/F3nen\n/gXR+0Lu9eradHYYD4ZT//HKS0bfeTiVb6fq33tdj6L+6ruvN1SFXH81UMOIViNdDEMP+jb0zWP0\n3Uf35lGG8m53s3Dqr79/Qn7+J7vSJP3NH0Ll1edRMYavGdsHe9n1JNZ5qgxN0xjfYiOGbhje0az6\nh2ay8kVRnM9nEeQzL0brX9d10zQb12eu3vrLKUVG7t8Ox9DzL6/l8t5seZk8nU6b127CUOXP53NZ\nlrKZKs/zpmnCvHV8/MMr3z8hv4t666++Z1QiD/NT3K28fKpPp5Oa28JyLcgtySdT/9eOzRd7mF/+\nXuyd6hyYbDJVwozMk/9YDLmlyqaNTQT8j12bZra2c5NL4PWfHGUSeP1Htgzn3+vt6DeP8YY3ekBC\nqHxr10xlWdVw6t8dqB7mh3foyTea2VR2CeSdL2s1NNI8osuuP0cIVb0vSUSdu5MHDXlM1cgR1ZfC\n0J/si/LH/qARvXn0r2O9rT7P86EbBYaK8s3+oOM3OswqyhX7b54AKz950N4TaYOv/8h1Pajnf/yI\n+mi8cL55hgaVqwpEdNn1J/ruv6EGatnYbjQ5jjcCq7mznVXOgsMG9u3rP1J52Wp9uVwsW32DevKL\nopjb6h5U/VWXh6QeH3n/B1X/kUm9hwT15p8rqCdfkV32NrUKs/72AnzzFFdEkJetIbFcdv3aO9Wt\nNdKCLWKYMMOm+6O3pSqE+o+3YE/eUhTsk9/7uP55Cbz+hu7LEXj9eysT2vM/95snqMq3dn1/vR/h\nwOs/2VgSQv3tvzmNR3avfPe9bfS3RnHZ9Sr6UNV9CRVjcIP8XtNfPP2brtsTv82LPVJ/pTdUtQHU\nf6jy8tB5n3Aq3w7XXw1rMO5qGbqROLT6GyYnjwmt/sbzf7maVzqo59/+m0d9HPR9g33ypaG+P7Vv\nyPXv/fAG9fxbVt4YDaZvIH/evvLGk9mtYRSXXa/iDlWTLSL59Zs/jZfq0rnztruv11d3sv7SUKja\nt/4jlTee9t6XIPAnv3tLUe+/z4Kt/+SWgde/W72g6j/3mye6N894O1bg9e9WL6jnf27lQ7tsdb/e\ne9sFg62/b3GHKhuXq3nJxzfofRXzq4G9IYu6/oFXnjfP1hW6LurnP+rK2wi8/ur5H6pJyPUfqdvk\nBhtU3vK5PfCbf0TWXjXHoSvL4n5+oq5/1JUX1H9vUdc/6soL6r+rqCsv4q9/9Hf/+VNVVbcPKCJR\n1z/qygvqv7eo6x915QX131XUlRfx118IEXckBAAAht5ZG4qi0Cc4yPO8qir76Qzquj6dTmVZ9k4G\nIQtv27auazkj/LKax45QBQDAoRidaDIPCSHyPFezScmANRSShooVQnRjgyw/z3OZpbIsu1wuh5p9\nytoH9q4AAABwpigK/RY8lai6QSfLsvP5rCYanZTnedM0dV0b28sspcJZWZan0ynNJhvGVAEAsLXu\nygGuusyaptEbn+TPvU1HcoKD3paq3oUN5Jbdx43594c2SwHdfwAAbEEOPLpcLrLpSGrbVh/tpDrR\nlqmq6nw+61f2LMtGyuwuFGMMvTLSWLcH0Oj7U4WIJHMVLVUAAGzndDpdLpfL1VIBWZbJpCXbjWT/\n2uLCz+ez0fcnRhfXM/r+ZGXkJFLyRrzT6aTXRxauP2L0/alix1f9OypCFQAA25FtP0VRqGgiG6uK\nopC5amUDjx6SekNV3aFvLIeuF0VRVZVskep2Juo17F17O9mWKgaqAwCwHSN/6A1LK++Ym2yXkqqq\nMpqR9PBktDnJwelGDc/ns75Zd+0aFapSuweQUAUAwJEZ4UZvndIDlvzBiEHdXryyLM/nsyyzN4el\njO4/AACOaagbrtCMl5DnudEQJXeRQaq37y9ltFQBAHBMMu6MjBmXqUhS01DNKjP2hWXcoqUKAIAj\n6G2XkqGntzGpe8ted/csy+Q0CkNl9pZsObrreAhVAAAch5GKqqqSTVBZlqlV+eSIKGP+BZmx9DkU\nZCrqtkXJLXvHYOl1IFQBAICdFUWRZZmKJpLxSO+Oxs16Ul3XMhidz+fT6ZRl2el0kpNjGQlMzukg\nt1FzVvWOQ5dpbKjvr67r7i2BKWBGdQAAwlJVlZwsqq5ruTpy95HeHeX85iN/nYxl+maLb+vLsmzW\nUs2HQagCACBEahJOfQYE45Gu3QNNd6mcdND9BwBAoGQD1fgjhsvlot/Tt73z+ZzsLYGEKgAAQtS9\nvW7khjt9mzzP92qpkqOpEuz4k5inCgCAQHXzk80tdTsuuqevaZggWqoAAAhRt70n2RagWBCqAAAI\nUXdyhJG50RGC/5/YCwBAmGisigtTKgAAADhA9x8AAIADhCoAAAAHCFUAAAAOEKoAAAAcIFQBAAA4\nQKgCAABwgFAFAADgAKEKAADAAUIVAACAA4QqAAAABwhVAAAADhCqAAAAHCBUAQAAOPD/AMTPZXtY\nc32JAAAAAElFTkSuQmCC\n",
            "text/plain": "<ROOT.TCanvas object (\"icanvas\") at 0x6f48930>"
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Quantify the peak position (sigma cb)"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "for s, p in peaks.iteritems():\n    print \"{:>6}\\t-1sigma: {:.1f} GeV ({:.1f} GeV {:.3%})\\tcentral: {:.1f} GeV\\t+1sigma: {:.1f} GeV ({:.1f} GeV {:.3%})\".format(s, p[0], p[0] - p[1], p[0]/p[1] - 1, p[1], p[2], p[2] - p[1], p[2]/p[1] - 1)",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": " 8 TeV\t-1sigma: 743.8 GeV (-4.6 GeV -0.616%)\tcentral: 748.4 GeV\t+1sigma: 753.0 GeV (4.6 GeV 0.620%)\n13 TeV\t-1sigma: 744.6 GeV (-5.2 GeV -0.689%)\tcentral: 749.7 GeV\t+1sigma: 755.0 GeV (5.2 GeV 0.698%)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The mass systematics is 0.62% on run1, 0.69% in run2 at 750 GeV."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Here the hard part. Combine the two workspaces (run1, run2) in two different ways: mass scale fully correlated, mass scale fully uncorrelated. This is done simply defining 1 common pull (dEScale) or 2 separate pulls (dEScale_run1, dEScale_run2). Other systematics uncorrelated.\n\nAlso merge the data, create model configs. Adjust nuisance parameter list (energy systematics missing in the list in the original workspace). Define xs_run1 = xs_run2 * scale_xsec_run1. Create snapshots for gg-only initiated process and qq. "
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def combine_ws(correlated):\n\n    # merge pdf\n\n    ws = ROOT.RooWorkspace(\"combined\")\n    if correlated:\n        getattr(ws, 'import')(ws_8TeV.pdf(\"model_constrained\"), RooFit.RenameAllNodes(\"run1\"), RooFit.RenameAllVariablesExcept(\"run1\", \"dEScaleAux,dEScale,mgg,mX\"))\n        getattr(ws, 'import')(ws_13TeV.pdf(\"model_constrained\"), RooFit.RenameAllNodes(\"run2\"), RooFit.RenameAllVariablesExcept(\"run2\", \"dEScaleAux,dEScale,mgg,mX\"))\n    else:\n        getattr(ws, 'import')(ws_8TeV.pdf(\"model_constrained\"), RooFit.RenameAllNodes(\"run1\"), RooFit.RenameAllVariablesExcept(\"run1\", \"mgg,mX\"))\n        getattr(ws, 'import')(ws_13TeV.pdf(\"model_constrained\"), RooFit.RenameAllNodes(\"run2\"), RooFit.RenameAllVariablesExcept(\"run2\", \"mgg,mX\"))\n        \n    ws.factory('scale_xsec_run1[0, 1]')\n\n    cat = ws.factory(\"cat[run1=1,run2=2]\")\n    combined_pdf_independet = ws.factory(\"SIMUL::combined_model_independent(cat, run1=model_constrained_run1, run2=model_constrained_run2)\")\n\n    ws.factory('expr:xs_run1_scaled(\"@0 * @1\", {xs_run2, scale_xsec_run1})')\n    ws.factory(\"EDIT:combined_model(combined_model_independent, xs_run1=xs_run1_scaled)\")\n    ws.var('mX').setConstant(False)\n    if correlated:\n        ws.var('dEScale').setConstant(False)\n    else:\n        ws.var('dEScale_run1').setConstant(False)\n        ws.var('dEScale_run2').setConstant(False)\n    ws.saveSnapshot('original', ws.allVars())\n\n    # merge data\n    data_13TeV = ws_13TeV.data(\"obsData\")\n    data_8TeV = ws_8TeV.data(\"obsData\")\n    cat.setIndex(2)\n    data_13TeV.addColumn(cat)\n    cat.setIndex(1)\n    data_8TeV.addColumn(cat)\n    data = data_13TeV.Clone()\n    data.append(data_8TeV)\n    data.SetName(\"data\")\n    getattr(ws, 'import')(data)\n    getattr(ws, 'import')(data_13TeV, ROOT.RooFit.Rename(\"data_run2\"))\n    getattr(ws, 'import')(data_8TeV, ROOT.RooFit.Rename(\"data_run1\"))\n\n    # merge model config\n    model_config_8TeV = ws_8TeV.obj('mconfig')\n    model_config_13TeV = ws_13TeV.obj('mconfig')\n    model_config = ROOT.RooStats.ModelConfig(\"mConfig_basic\", ws)\n    model_config_8TeV_new = ROOT.RooStats.ModelConfig(\"mConfig_8TeV_basic\", ws)\n    model_config_13TeV_new = ROOT.RooStats.ModelConfig(\"mConfig_13TeV_basic\", ws)\n\n    if correlated:\n        nui_names_8TeV = [(x.GetName() + \"_run1\") if x.GetName() != \"dEScale\" else x.GetName() for x in iter_collection(model_config_8TeV.GetNuisanceParameters())]\n        nui_names_13TeV = [(x.GetName() + \"_run2\") if x.GetName() != \"dEScale\" else x.GetName() for x in iter_collection(model_config_13TeV.GetNuisanceParameters())]\n        nui_names_8TeV += ['dEScale']\n        nui_names_13TeV += ['dEScale']\n    else:\n        nui_names_8TeV = [(x.GetName() + \"_run1\") for x in iter_collection(model_config_8TeV.GetNuisanceParameters())]\n        nui_names_13TeV = [(x.GetName() + \"_run2\") for x in iter_collection(model_config_13TeV.GetNuisanceParameters())]\n        nui_names_8TeV += ['dEScale_run1']\n        nui_names_13TeV += ['dEScale_run2']\n        \n    nui_names_13TeV.remove('xs_run2')\n    nui_names = set(nui_names_13TeV + nui_names_8TeV)\n    model_config.SetNuisanceParameters(','.join(nui_names))\n    model_config_8TeV_new.SetNuisanceParameters(','.join(nui_names_8TeV))\n    model_config_13TeV_new.SetNuisanceParameters(','.join(nui_names_13TeV))\n\n    if correlated:\n        aux_names_8TeV = [((x.GetName() + \"_run1\") if x.GetName() != \"dEScaleAux\" else x.GetName()) for x in iter_collection(model_config_8TeV.GetGlobalObservables())]\n        aux_names_13TeV = [(x.GetName() + \"_run2\") if x.GetName() != \"dEScaleAux\" else x.GetName() for x in iter_collection(model_config_13TeV.GetGlobalObservables())]\n    else:\n        aux_names_8TeV = [(x.GetName() + \"_run1\") for x in iter_collection(model_config_8TeV.GetGlobalObservables())]\n        aux_names_13TeV = [(x.GetName() + \"_run2\") for x in iter_collection(model_config_13TeV.GetGlobalObservables())]\n        \n    aux_names = aux_names_13TeV + aux_names_8TeV\n    model_config.SetGlobalObservables(','.join(aux_names))\n    model_config_8TeV_new.SetNuisanceParameters(','.join(aux_names_8TeV))\n    model_config_13TeV_new.SetNuisanceParameters(','.join(aux_names_13TeV))\n    \n    model_config.SetPdf('combined_model')\n    model_config_8TeV_new.SetPdf('model_constrained_run1')\n    model_config_13TeV_new.SetPdf('model_constrained_run2')\n\n    model_config.SetObservables('mgg,cat')\n    model_config_8TeV_new.SetObservables('mgg')\n    model_config_13TeV_new.SetObservables('mgg')\n    \n    REDUCTION_QQ = 2.7  # at 750 GeV\n    REDUCTION_GG = 4.7  # at 750 GeV\n    ws.var('scale_xsec_run1').setVal(1. / REDUCTION_GG)  # fix ratio to gg\n    ws.var('scale_xsec_run1').setConstant(True)\n    ws.saveSnapshot('gg', 'scale_xsec_run1')\n    ws.var('scale_xsec_run1').setVal(1. / REDUCTION_QQ)  # fix ratio to qq\n    ws.var('scale_xsec_run1').setConstant(True)\n    ws.saveSnapshot('qq', 'scale_xsec_run1')\n    ws.var('scale_xsec_run1').setConstant(False)    \n\n    getattr(ws, 'import')(model_config)\n    getattr(ws, 'import')(model_config_8TeV_new)\n    getattr(ws, 'import')(model_config_13TeV_new)\n\n    #ws.Print()\n    return ws\n\nws_correlated = combine_ws(correlated=True)\nws_uncorrelated = combine_ws(correlated=False)",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Redo Bruno's profile (page 79: https://cds.cern.ch/record/2056957/files/ATL-COM-PHYS-2015-1246.pdf). Result is a bit different. Do it with fully correlated or fully uncorrelated."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Bruno's profile\ndef do_profile(ws):\n    #ws.loadSnapshot('original')\n    ws.var('scale_xsec_run1').setConstant(False)\n    nll = ws.pdf('combined_model').createNLL(ws.data('data'))\n    prof = nll.createProfile(ROOT.RooArgSet(ws.var('scale_xsec_run1')))\n    prof2 = ROOT.RooFormulaVar(\"prof2\", \"2 * @0\", ROOT.RooArgList(prof))\n    frame = ws.var('scale_xsec_run1').frame(RooFit.Range(0, 0.15), RooFit.Bins(10))\n    prof2.plotOn(frame, RooFit.Precision(1))\n    canvas = rootnotes.default_canvas()\n    #canvas = ROOT.TCanvas()\n    \n    frame.Draw()\n    return canvas",
      "execution_count": 65,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "-2NLL profile of xs_run1 / xs_run2 for the mass-scale fully uncorrelated model"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "canvas_uncorrelated = do_profile(ws_uncorrelated)\ncanvas_uncorrelated.SaveAs(\"profile_uncorrelated.png\")\ncanvas_uncorrelated.SaveAs(\"profile_uncorrelated.root\")\ncanvas_uncorrelated",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAI8CAIAAAD0vjrdAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nvWkAAAAASUVORK5CYII=\n",
            "text/plain": "<ROOT.TCanvas object (\"icanvas\") at 0x6f48930>"
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "canvas_correlated = do_profile(ws_correlated)\ncanvas_correlated.SaveAs(\"profile_uncorrelated.png\")\ncanvas_correlated.SaveAs(\"profile_uncorrelated.root\")\ncanvas_correlated",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAI8CAIAAAD0vjrdAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\n0Cx681IAAAAASUVORK5CYII=\n",
            "text/plain": "<ROOT.TCanvas object (\"icanvas\") at 0x6f48930>"
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The two profiles are pretty identical (fully correlated/uncorrelated), but quite different from the Bruno's ones."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now compare the results of combined fit (run1/run2) in the scenario (scorrelated cross section / gg / qq) and fully correlated/uncorrelated mass scale"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# correlated, free xs\nws_correlated.var(\"scale_xsec_run1\").setConstant(False)\n#ws_correlated.loadSnapshot('original')\nws_correlated.pdf('combined_model').fitTo(ws_correlated.data('data'))\nprint_fit_result(ws_correlated, ['mX', 'dEScale', 'xs_run1_scaled', 'xs_run2'])\nplot(ws_correlated, ws_correlated.data('data'), ws_correlated.pdf(\"combined_model\"))",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 742.746718 +/- 5.620728\ndEScale              = 0.010857 +/- 0.703769\nxs_run1_scaled       = 0.269080\nxs_run2              = 6.579080 +/- 2.567646\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nABCJQAEAAEQiUAAAAJEIFAAAQKT/HxtrAVm+XFvVAAAAAElFTkSuQmCC\n",
            "text/plain": "<ROOT.TCanvas object (\"c1\") at 0xc237d90>"
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# uncorrelated, free xs\nws_uncorrelated.var(\"scale_xsec_run1\").setConstant(False)\n#ws_uncorrelated.loadSnapshot('original')\nws_uncorrelated.pdf('combined_model').fitTo(ws_uncorrelated.data('data'))\nprint_fit_result(ws_uncorrelated, ['mX', 'dEScale_run1', 'dEScale_run2', 'xs_run1_scaled', 'xs_run2'])\nplot(ws_uncorrelated, ws_uncorrelated.data('data'), ws_uncorrelated.pdf(\"combined_model\"))",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 743.089241 +/- 5.322264\ndEScale_run1         = -0.171172 +/- 0.869664\ndEScale_run2         = 0.187853 +/- 0.888825\nxs_run1_scaled       = 0.271773\nxs_run2              = 6.516795 +/- 2.542922\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nAAAAIhEoAACASP8/EhYI+ami5ewAAAAASUVORK5CYII=\n",
            "text/plain": "<ROOT.TCanvas object (\"c1_n4\") at 0xc631510>"
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "# correlated, gg\n#ws_correlated.loadSnapshot('original')\nws_correlated.loadSnapshot('gg')\nws_correlated.pdf('combined_model').fitTo(ws_correlated.data('data'))\nprint_fit_result(ws_correlated, ['mX', 'dEScale', 'xs_run1_scaled', 'xs_run2'])\nplot(ws_correlated, ws_correlated.data('data'), ws_correlated.pdf(\"combined_model\"))",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "mX                   = 742.400934 +/- 5.384341\ndEScale              = 0.016124 +/- 0.724065\nxs_run1_scaled       = 0.601850\nxs_run2              = 2.828696 +/- 1.163763\n"
        },
        {
          "execution_count": 69,
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nAACASAQKAAAgEoECAACI9P8Dq146r8YyQA8AAAAASUVORK5CYII=\n",
            "text/plain": "<ROOT.TCanvas object (\"c1_n7\") at 0xd246cb0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# uncorrelated, gg\n#ws_uncorrelated.loadSnapshot('original')\nws_uncorrelated.loadSnapshot('gg')\nws_uncorrelated.pdf('combined_model').fitTo(ws_uncorrelated.data('data'))\nprint_fit_result(ws_uncorrelated, ['mX', 'dEScale_run1', 'dEScale_run2', 'xs_run1_scaled', 'xs_run2'])\nplot(ws_uncorrelated, ws_uncorrelated.data('data'), ws_uncorrelated.pdf(\"combined_model\"))",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 743.100277 +/- 5.595346\ndEScale_run1         = -0.216124 +/- 0.941707\ndEScale_run2         = 0.240745 +/- 0.910869\nxs_run1_scaled       = 0.612726\nxs_run2              = 2.879813 +/- 1.136714\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nAACIRKAAAAAiESgAAIBIBAoAACDS/w+vqteNmd4oUgAAAABJRU5ErkJggg==\n",
            "text/plain": "<ROOT.TCanvas object (\"c1_n5\") at 0xc84b380>"
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# correlated, qq\n#ws_correlated.loadSnapshot('original')\nws_correlated.loadSnapshot('qq')\nws_correlated.pdf('combined_model').fitTo(ws_correlated.data('data'))\nprint_fit_result(ws_correlated, ['mX', 'dEScale', 'xs_run1_scaled', 'xs_run2'])\nplot(ws_correlated, ws_correlated.data('data'), ws_correlated.pdf(\"combined_model\"))",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 741.562305 +/- 5.296256\ndEScale              = 0.004088 +/- 0.701805\nxs_run1_scaled       = 0.569084\nxs_run2              = 1.536526 +/- 0.716787\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE\nQVR4nO3de4wcx53g+V+2DHHmbqmq1o1vMWO+Rh6SO7RnBUm8wc0AoypJNiCaI9EGJAEyVuYsZMj9\nsP6wYZndtMUuQSexbcDCHZbNNnhen87etQBbA1uCZM3C9nW1jLvB2RS1a8u0PA8tLfb8sbeQWSUt\nYNNAV9wf0ZWdle/KyorKzPh+QBD1iIqIfFTlryMiIxyllAAAAISZmnQFAABAcREoAACASAQKAAAg\nEoECAACIRKAAAAAivWvSFQCs8+STT548eTImwac//Wnv06eeeipzWQsLC96ny8vL7uOPfOQj3/72\ntzPnHOqpp57yVR5A6SkABt1///3xX739+/cHv6dHjx7NVlwwq2PHjrlvffjDH4757COPPPLII48M\nW9aXvvQl74t79+5tNBqNRmPv3r3DV9/vxIkTeteJyIkTJ0bPEEAiuh4Ao376058mptm/f7/3W3r0\n6NEXX3wxc4luZKCUOnbs2HPPPedrZojy/PPPP//880OVpZTytSj88pe/HCoHAEVDoAAY9dprrz3x\nxBMi8tGPfjTlR1544QUZ7I+477777rvvvlar5U22tLR077333nvvvUtLS1FZfec73xGR119/PfiW\n77Of/exnfQ9Cy3JfOX78uPu/1969e/ft2+c+faTv7rvvvvvuu/WL3sfxvF0n3scAxsh0EwZgMd3v\n4H0QtH//fl+Lwqc+9Sk3sb6QHzp06NChQ/qBfv2ee+7xvn7PPffo12WwRcH7ivS7Hk6dOhXM03sV\n99XQTaYfnDp1Sil14403isiNN97o63o4fvy498GBAwdE5MCBA3fddZf7K+Q+TrMPdY8D/Q6AMQQK\ngDkicv/99yuldKPCE088EUyjxygc7XOf6nf15dmb4dLSkn7gBgf6wu8mOHjw4LE+7/XYDRSCeepr\n/8GDBw8ePOirns5cJ1D9AMX9YOIe0IGCfqzjA2+5iR8HYB5dD4AhTz75pIi8//3vFxF918Nrr70W\nlfjv+nyvX7x40dvgf+jQoYsXL+pmhm9961v6xccee0xE3E6EX/ziF6/3uW0Jvjz19d6bZ1TF9Fu6\nCJ04bpvD6FhBRA4ePDjsZwGYR6AAGKLDgs997nOO4ziOIyLPPPNMaMr9+/f/nYdvMKNvaEKiY8eO\nuYGCHqOQKCZQkMHgwI0YAFQVgQJgyDPPPKP7HTTd+6CbGeK5f4Jr3kAh/oqezcWLF+PbCbyFxgyc\nBFANBAqACTog+MY3vuG+onsfohoVohw6dMjtYtARwze/+U39Z73bJaEfpP9b/9ChQ88++6x+7A6W\njEqsS3fjg2effTZD7wOAMpnc8AjAInpogu/F0Hsfgnc9KKXEM57R+/2999579Yt6jKHLHWwogbse\nvHm6Ey55P+sOitQ3PgTvevAOaJAhRyMeOHDgwIED+vFnPvOZ4McffPDBM2fOKKX0/76nAMxzVNjc\nbQCKTLclBAcr6D/0s40bCP2snkThi1/8Ysr0o/v4xz9+4cKFCxcuOI6jlPI9zbcsAGnw3QNQLCsr\nK6+++qqIfOUrXwk+BWAYgQKAwvG1H9CcAEwQgxkBFM6DDz4Y8xSASQQKAIplZWXlwoULUU8BGEag\nAKBwbr755pinAEyi5w8AAESqSItCu90edl5bAACQqCKBgoi0Wi1iBQAA8lW+QMFtPGi3281ms9ls\nikiz2SRQkFecrX8AAOTE0BiFVqvVbrdFpNmXOSv98Var1Ww22+22zlY/Dc1ZL9NnA3Xe/8qth+WH\nk6gJANij8kP93mWgDN/f+vrqnjlWcGMOX4YZsqra0Q20Jbx8XtQH6jIzI/Pzzq5dIqLW151GYxKV\nA4AKsuFvURNdD74egRFbFILyza3EbgmJe5zvd5x7lp3/srv38MNqelqaTTU9rRYX1caG+QoCAEqn\nfGMUXK2+xEAhakUsI9U06xal/npRfWUm+M7UX51xvt9xRJxOR7pdeeghYgUAQCITYxTcIQXjLihU\nTLtQJWMFNT3tdDoiIoEhCwPJ/u2s1Gpy5530RABAZjYsRGJ0MOPoIxkzsOEoulS3K/W6PzKKjhjU\nYZH6wCAGAEB6NlxiDHU9uCMQb7vtNjMlejkRzNdk3JxaTep17ytKRwOHI9KfF+f7Az0Ran09vojE\nBACAKjERCk2wOUHsCPe81OKidLvO6ur2KyIDMVFEA0Pv4T9zfv5z6XRC2xjUxoacPSurq1EJAMBC\nNlxiTGzhKDdDjs66MQobG/LQQ7Jvnxsr9G6++c0LF/a5CXTcENUfcVhERM3OyqVLcu6c/OM/Oo2G\n2tiQT3xC9uxxvvzlrUz6CYgVANiMQCE3ejpF7xRJxthwFH3UxoasrMjqqnS7Uqupj35UXn/d+Rf/\nwlld1TtiO3SKHr7Qe+hm+dnPnKtXpV5Xf/zHX//bvz3uK2WW4ZAAbGfDJcbQFrohgvnVm2xrUfBS\n7bbTbMpg6KCCI1Ni7o84LCLSu/baqd/+diDn/v+OMBwSgL0IFHLjrs7g/m+MDUcxPdVuy3/4D2eX\nl+fdV0QkvjNC8wyHdPemG4L5uipyqy4AFJsNlxijWziRwQo2HMWhhAxi2LFj6urV7RRJEYO//0JE\nibwicvOOHbqrggYGAJaw4RJjaApnHSJMatole26PTMPZtUvOnZNaTdVqSkTVanLTTU/3342/nVJE\n5Lw458XxBBP6K3KLyNTVq8z8CAAVY2geBR0fTOr2B4umcE7H2bXLOX3a6XRkbc3pdJxvfev4kSNq\ndla87QSH+/+inB9oe/CGXc7q6tmXXpKVldxrDgAwzNxaD4aHJiANPdTR38awY8dA58NhkcPy1aiI\nIdDAoM2LyOqqMEETAJSciUDBvSXS/L2RSGmgjeEf/uGWI0e8rQFnRP71nXeq//FNuUWpd9bCszg/\n0MagRFS326vXWa8SAErN9FoPhu+NFDtGmuTONxPD1uDE3bu3333oIefxl+JyOCxXRX6n/3RFZO7I\nESZoAlAxNlxiLNhCi+dRGJ07E4P/dU8kEex3GNDvs1gRmVtYcE6fzr2SADApBApVYMNRnCDVbssf\n/ZE89NDZl16aT7yvslaT557LfaIFtb7O5A0AJsKGS4yhMQoiou+NNN/1gLFymk09FnJuYUHdUVPx\n91X+oOv8s9yGLKiNDXXypJqeZhgEAIyPuXkUhBsfqss7FlJuUeoD9Zj7Kp3vd5x7lkecaEGvU7V6\n+rTT6ejJG84uj5onACDIRKCwvr7uhgjECtW2NaBhZmZFEmZicB5/yfkvu+UV/wiStLdTnj27+t3v\nznlemBeRffvCJ294xdn+BwAYhrnVI6W/NFSOcy65q0y5a00FAxEbOpCKRt8Wcfall/SKEsqdjil2\nEIP660VZXZVOJ80k0Gp62ul0Ql6v1fyv+4KDWzgZAOTGhkuM6S3UIxXyihV0Vr4FrH2Z23AUC8i9\nLUJ1u87gvI0Ja0mIiMjK4bjbKVW3K/V6aOOAEpErV5x6ffulYCsCsQKAnNhwiTE3M6OW73IPwaGR\ndG0UhDtqwVlbE+9lW1JMDi0yf367byLYGeHUav48XbWa/Kf/NFRVmTsSAGIYGszo/t0/7rUeQvOP\nWhSKxaIMcJpNmZnRC0l4fVXk/z4svzosvdiIQUScf9aUV5wnHWdgoKIeBuGhRH4s8utuVzWbv3L6\n6aMHJaiNjScc51eOM5AeADDI3BTO2pgChVbfUPmzWJQh8/Ny6ZI3Vuh97GN/9e53/9m/+lfXizju\nepWxzQwnz8tAA8P8/Fx/ISvRM0bv3XvL7t3/nciUyPUii7Oz8tBD4Xm94uibJk7OzFw/mJ5YAQB8\nTHSu6IYEPd7QvVXSGGZmLAK1sXF69+5ZkZpIV+SiyD+IHHff9Q1i6C9dHT/nY6/3o+U//VOd529E\nfi5yiy+F5+O/OizXe5fG/utFuXLF+fKXvcmZOxLAsGwYo2Durofcb3lIyYajWCJ6TmjfPQtbYYE3\nmfdpisGP6gN1380OyhdnHBY1O+s8uOp9JSSfwZsmdJT5FyI/1O9yIgEYZMMlxoItpEWhYIL3LLiH\nwfE8DSZIWFTCdXjrI9vpg69IRKAg2zdNqI0NOXtWVldVp+OkuGMTgIVsCBTeZaAMXyuC+RsTKn8U\ny8Wp1VS9Lt4/3PuhQM9N8JvfOFevDnxKPJf2+IjhfD+9h85fHU6KNmq17SjhE5+QPXv0zI/S6ahu\nVx56SOW0AGbi+hQsYAGgIAwNZnTnOZjI7Yvc3VA4gXsWHJEVkS+I3CYy1e0uX73qJgiJ8vojH+OW\nlggTP53DGRGZmdl6cvas7NnjHcTgrK6efeml8JkfU0u81YJ7MQAUTfXbTGxoFyodPXWj7NvnrG4N\nGlBzc/LGG3LunLN7dzBBcLTj1qf6Qwp0eufxl0ISuf0O+ql3SOPh7Wz9FUg/82Nq260U/fhDzc7K\npUvuvFKJCQAUjQ2XGNMTLk0ELQpFoxeclFpN1Wo9EVWryc6d7kU6JMGOHcEeA28DgE6vnl1Qd9R6\nh0XdUet98n9++vB2lLDN0wjhnJeeiLcCU3v2OI5zp+OoqGig2418S+fpOI7j3Bp6jp09u/rd7/pa\nKQbWp0hMEJ8/AIxB9UMhG8K9ktLXuVtFXhaRsKEkboJ/EDkn8qHZ2agWiGCGfyCyceRIeJtEIOhw\nDouI9C5fdkcviuNMhZ028S0K8eMfE1spkhMwvhIoGBsuMSYGM+qJlvWQRj1GITj18lhF/eFV+aNb\ncIn735tALx6hajXV7TqBFojQDL0fkR07Xrl6das14bA/VlDnRUSUZ/SiUqr3vvdN/exn3mRnRObd\nQQzB2saOf1TdrsS3UjhOQoL/9t/GOr4SAEKZC4VCV2wywIZwzxKJLRAxHwm2SSROz6AOy09F/qX7\nNKwNYyD9yZOrp0/P+V6cnZVaTU/iNGKLgjp5kkmigKKx4RJjwRZacBSRxsCClrXaVrv9/7cn1Wfv\n6KcfjBK8jVVviVwf+lk3DlhcPLu8PO97d25Odu50lpcTE4xjfCWAEdlwibFgC5lwCYMajrPuO/TR\na0cNiFif+lbHWe90Ele+HvZeD28Cue66IVbWBmCKDYGCiTEK7vQJBlaPDFX5o4iU3KhRP9g+MW5R\nA3+vR/VKDMYT6p9vDX5si8i+fWrHDt8kUVv6kzg5u3apc+diRlrEJ/DNUhXMHwDGwWgopCMG8yMZ\nCRSQKLTZ/4zIJ1PPG9276aZfvvrqH/qy9fQseIW0aiQlUIuL0u1uj7Ho13D+xIlg/gDMsOESY8EW\nWnAUMbqEfoF0fROXDsuem26aevXVkBwGJZ6WwQSJPRcAzLPhEmPFhEtAooQ5oG5RA/8i7DsvU//7\nq3Je5HzILFLjrSEAjIehUKjVajWbTd3pYHi5BwYzYlgx/QL6dNop0nGj7BR9E05/OkillO+EjJpm\nKiaBr4bx8zOO4yRPU8PRP1J2Fm6ynWxoUTAxmFH660JNZCSj8BXFkF6OfkufS47jOO7QQh0ExIYL\nyrvAxDDTTKWpoVsl7wfH+uMVWmLuHyk7CzcZVWXoJNZjGN1lJA2U6OKLimGlGUDQW1iIHFp47xdS\nFRPdhZGhhiYDhcxFWPhltHCTbWPDITY0RsF7h6SZEoHx+QsRmZ+XS5fU7Kz7opqbm7/zTpmfl1uU\nc1jiRzOIiLzi+P+NWKXop+OQoQgDtSoaCzcZ1WMiUHDvijR8YySQL7WxoU6eVNPTbRH5kz9Rf/iH\namoqZmih2thQf72oPlDvHY7Mc8DwQYO3Smp6ujc/33v4YfepWlxUGxvDbeSQJaYpIsNHys7CTUaF\nmQgU3GGMY2V+hgZUj7t2c3AR5601n65ccTqdKRGn03GUct54Q1577Tb9dHnZ2b3b/eB7HOfF3bvd\n9HJY1L+dVY8eUf/8cqqqRIQL3hq+x3G8VZJOR154QZ57zq2h6FWj8rtEBXdCYhEZPlJ2Fm4yKk4Z\n0Wg0DBSxtLQUfN3YNqLaeouLKyJq8N8Zkd7CQug5FpM+JPfzkupfbBE9kV76EvPeCUN9pDc7m2Ot\niqa3uNibmRnrgUBx2HCJMXp75FgHKERN+2jDSBMYELMm01S3GzzHsq/hlL7f4QN1b1ZKJHwxiPxW\njcqwURauZWXhJtvMhkuModsj9c0Oejyj+Q6C+BvNgyp/1DEs1e2Gr7MgIt3udUOmV51O3OoMeghk\ninDB+b4nSogZBpFYYjoZNmqk/VBOFm4yKs/czIz63khjxaUR1cwy6XqhcJxaTaJ+32u1t4dMn+pS\nkW4uyO0Sz4tzPmI6h5xWjcqwUTnsh7KxcJNReYYCBT2JwliLMDNkEvaamVkJvHZGRGZm8kkfL33c\ncN7zb5QSQ2XYqLCPqLm5PGtVNDMz3vtmtZwPBGCQ0TEK7T4DJbps6ECCATFrMk3t2RMyRmH8azjp\nIpzHXxriMyPM8iSZNsrCtaws3GSb2XCJMd2iMJE/+p0I5muC8hp2TSYDazjpItSzC+qOWpapGoaf\n4inDRlm4lpWFm4xqMxQKnTp1an19/dSpU3fccYeB4rxYFAq5860alfgnRcwqU2OqUpZ5HodsbMiw\nUQb2Q9FYuMm2saFFwegWnjp16vHHHze8T204ijDMPalSrhBofuWFkBLThw7pIgbWekjDwk22jQ2H\n2FDXww9+8INGoyET+iOergeMSZnul0k/HDKn5ScAVIPpUMj8TZI2hHswbNiTqhAtCjEyNTbQopCG\nhZtsGxsOsYkWBT2AUccH3MEIFE7q2Rq8jQ3qfKaREADKxtDMjJMV1ctQ+TAQGI43VkgZBPiSjXb7\nJYACMhEouHMnGJ5BwUVAAAztlkxjIYPJCB2AkjPRueKLDxijgLKr2hiFbDJMw3DYuqid35/Ks+EQ\nW7CFzKOAvBEohBSR9+2X1WDDVcRyNhxiC7bQgqMIwwgUUhWRMnSodNzA70/l2XCILdhCC44iDCNQ\nGLoIW9sb+P2pPBsOsQVbaMFRhGEEChmLyHY7ZZlDB35/Ks+GQ2zBFlpwFGEYgUK2IrIPa/AqVdzA\n70/l2XCILdhCBjMibwQK2YpI+Ejm6ZsKHDrYcBWxnA2H2IIttOAowjAChWxFDP2R8jc58PtTeTYc\nYgu20IKjCMMIFLIVMWqtShg38PtTeTYcYgu20IKjCMMIFLIVkX+tCh868PtTeTYcYgu20IKjCMMI\nFLIVMfZaFS9u4Pen8mw4xJXawtA1rG04ijCMQCFbEaa/jAUYHcnvT+XZcIirs4XtdrvVagXXnbLh\nKMIwAoVsRUz+y2i8yWHym4wxs+EQl2mZab0KpY4GWq2WeNalFJFms2l4uSkAJcOSmMDwyhQotFot\nHQroWEFLExzETKUQqvLhIQCRsOt9ttCBuAGVVrJAwdez4AsUdDNDegQEAAbQ5AAElClQCKKvAcAY\njdzkoM6LvOIQN6DUShkotFott/chTXpaDgDkgyYH2Kf6wzVZ6wG5466HbEXYMD68gHM5YKxsOKst\n2EICBeSNQCFbETb8pPoRN1SdDWe1BVtowVGEYQQK2Yqw8MsYssmEDtViw1ltwRbSooC8EShkK8KG\nn1Sf5E0mbig5G85qC7bQgqMIwwgUshVh4ZfR0MraQugwMTac1RZsoQVHEYYRKGQrwsIvYw6bTJND\nsdlwVluwhXQ9IG8ECtmKsOEn1acoK2sLocO42HBWW7CFFhxFGEagkK0IC7+MJjaZJoeJsuGstmAL\nLTiKMIxAIVsRFn4ZJ7DJNDmYZcNZbcEW0vWAvBEoZCvChp9Un0JsMk0O41SIQzxmFmyhBUcRhhEo\nZCvCwi9jETeZJodcFfEQ582CLbTgKMIwAoVsRVj4ZSzHJtPkMIJyHOLRWLCFFhxFGEagkK0IC7+M\npdxk4oZhlPIQD8mCLbTgKMIwAoVsRVj4ZazIJmfurfCqaCRRkUMcy4ItZDAj8kagkK0IG35Sfaq5\nybnEDVKR0KGah3iQBVtowVGEYQQK2Yqw8MtoxSbbHTfYcIgt2EILjiIMI1DIVoSFX0YLNzlEpe+z\nsOEQW7CFFhxFGEagkK0IC7+MFm5yKhUaL2nDIa7IFrbb7Xa73Wq1gm/ZcBRhGIFCtiIs/DJauMlZ\nlLnJwYZDXJEtbLfbzWaz1WoFYwUbjiIMI1DIVoSFX0YLNzkf5Rn3YMMhLs0Wum0GbsuBfkW/GxUl\niB1HEYYRKGQrwsIvo4WbPBYFjhtsOMSl2cJms6mjgWaz6YYIzWbT+67m+2DM7ZFRyrJPMCkECtmK\nsOEn1cfCTTakMKGDDYf4XZOuQFq6LcH7iu5ucB/HfLbyRxEA7JJ4gU8ZSfiSFWDQQwGVJlAICjYe\nRIlqVCCAAIBq8l3yiRtGUL5AQY9F8A5QAAAgTvCSnyZ0SExjRyRR/c4VpnBG7hijkK0IG3pzfSzc\n5LIacdBDpSOG8rUoZMAXFQAQJ1tXhR2sCBQYowAAGEJegyUrofrNYjT9IXd0PWQrwsIvo4WbbBsb\nDjEtCgAAIJIVgQIBAQAA2UxNugIAAKC4rGhRoOsBAIBsrAgUCAgAAMiGrgcAABDJihYFuh4AAMjG\nikCBgAAAgGzoegAAAJEIFAAAQCQCBQAAEMmKMQoMZgQAIBsrAgUCAgAAsqHrAQAARKpIoNBut1ut\n1qRrAQBA1VQkUBCRVqtFrAAAQL7KPUah3W7rB81ms9VqNZvNSdYGAIDKKU2Lgtu50G63m82mjgma\nfUQJAACMg1OWOwK8AUG73dZtCWmCg6h7I2OUZZ9gUhxnuC/OsOkz8BVhvsQxfaTsLNxk29hwiEvT\nohAcf+D2OyRSQ8q35gAAlFeJxyik72tgwiUAALIpX6Cg725wex8AAMD4VL9zJWaMQuW3HWPCGIVs\nRdjQm+tj4SbbxoZDXL4WhQwqfxQBABgTKwIFxigAAJCNFYECAQEAANlYESjQogAAQDZWBAoEBAAA\nZFOaCZcAAIB5VrQo0PUAAEA2VgQKBAQAAGRD1wMAAIhkRYsCXQ8AAGRjRaBAQAAAQDZ0PQAAgEgE\nCgAAIJIVXQ+MUQAAIBsrAgUCAgAAsqHrAQAARCJQAGyk1tcnXYUc6jDxHCZeAXIoSAWqjUABmLzE\nH6m8fgfVxoY6eVJNT0uzqaan1eKi2tgILSJY4rCVjEofVYfEEt2nE88h/W4M2fycDkTmHIqzG9Pn\nkPtOyCUHi6iqWFtbC329StuIghj2pIpK37t8ube42KvXeyK9er23sNC7fHmoBFFFuE+9OWxed93m\ne9/7tIjq/zsjsnnbbZuf/KRbxObcnPdpb2Fh80c/Cq1DaBGhObh1FpHe5cu9D31oxVOHr4ps3nDD\nZq0WVaIvw825uc3bb59gDml2Y/xeGv1AZMihaLsxTQ6574RccvB9B224xFRnCxuNRujrNhxFGJZL\noBC8ZJ4R6R054v4MJSaIKUI/9eXQE/mJJzf9yhsiPx58+p99P9zvfvfTYXWIKsKXg7fOItJbXFwZ\nrICvVr4Sgxn+ePCp+RwSd2OavTTigRg2hwLuxsQcct8JueTgPZ977XaGX4MyKs0Wrq2tLS0t6QeN\nRkOHBWt9S0tLS0tLoY0KNhxFGJZPoDB4ydz+GVpYSJkgpoit61PgqqwCP4u96Kehv6RKpDc721tY\niCoiWIpbZxHp1esxFQiWGJpgsjkk7kbvVo/pQAybQwF3Y2IOue+EXHJQIl8V+aHIWyKbIm+JPCES\n1c5XGaW5iDYaDR0oeEMEbwL9bpDN3THIXbZTJTxQGLxkbv821WopE8QUsXV98uQQ+pMX/8Md+or+\n96v+HngrXXpd553Dl5jmamQyhzS70bvV4zgQGXIo2m5Mk0PuOyGvHHoim9de674S085XGaUZzNhq\ntXyvtNvt+ASJonZK9lqi6oY9VRzH0fN9uQ+28ul2pdMJ/0y3qzqdxARRRbhPr3OcgWTBbYl9GvqK\nqy7Su3Kl1+lMp0uvut2a47wjcmWYEoMJnKQE484hcTcOvNXtXjeGAzFsDgXcjYk55L4TcsnBNfXb\n37qP50Vk3z5ZWYlOXnqlCRSCms1mypQEBJiUqFPOqdWkXg//TK3m1OuJCaKKcL2tlBPIwXvS+34o\ng7+b4ROaRlcyJr1Tq3WVUkpNLyysRKdPrJKmohOYySFmNw58tlbrju1ApM+hsLsxJofcd0IuOUTm\nvLoqq6sRyaugfIFCqy99oOBEGGc1gSQzM8G/Qc6IyMxM2gSZiviJ57G6+eZLg+8Gw+efBF5Rc3Mx\nlQzmMFDn+fm5I0fU7Kz7bu9974svMZjhJZHeTTflmENwGxNziN+NkuJQjn4gvBuemEMxd2N8Dml2\nwui7MX0OcX9cDrbzVU2K7olys3nbUWS9y5d7R470Zme3uz/n5np33tl7882UCTIUsfmxj23ecMPm\ndddtivRqtc3Z2c3bbnMT9EQ29+zZ3L17IP3v/d7mAw+krGQwh2Cde5cv9xYWerXapsjmzp2bN9zg\nzd9XYmiGm83m5tzcBHOI341pDuXoB2KoHIq5G+NzyH0n5JPDjh3uY++/0JFDlVG+FoUMojZ+0vWC\n1Zxdu+TcOanVVK3WE1G1muzcKefOObt3p0yQoQjn939/zxtvXPP227eJTHW716yuOl/7mptAajU5\nelTuusubXl580XnPe1JWMphDsM7Orl1Ty8tT3e5tIte8887uN97w5u8rMTRD52tfu+bs2QnmEL8b\n0xzK0Q/EUDkUczfG55D7TsglB7nppqcDX7SBNrZKMhmVTITN247i06firdHnZGKC3IsIph89h2Fr\nOGyGxcxhxASJVRo2h4nshBFzSMzQcA5/oO9x8DQO/RuRodr5yojVI4FJSjw5Rz97Ry9i3JUMfnzY\nDIuZQwETDPtxdmPIuxsbsrKiajXV7Tq12jvd7lDtfGXkVP4iGjNosfLbDgDInb6s3CrysohYcCmh\nRQEAgCF4ryk23EBnxWBGAACQjRUtClERHy0NAADEsyJQICAAACAbuh4AAGv0rs0AACAASURBVEAk\nK1oU6HoAACAbKwIFAgIAALKh6wEAAEQiUAAAAJGs6HpgjAIAANlYESgQEAAAkA1dDwAAIBKBwrbS\nTdlNhceNCo8bFR43KozRVSRQaLfbrVar3W5PuiIAAFRKdQKFZrPZbDaNlThU2Du+xEWoAxUedx2o\n8LjrQIXHXQcqXGrlDhTafTpKaLVak64RAACVUppAQXcuyGDjQbPPDRcmWkcAACpHlUSj0VhaWtIP\nlFJra2tra2tpPjjpHYyyOn78+KSrAKAExnjlK4bSzKMQHKuYsglBESsgq6effnrSVQCACStN10MQ\nHQ0AAIxb+QKFVt8ogYI70ME7FlImcffEUIKVLHKFQytZ2ApHVbKYFQ4dsuN9/OijjzYajUcffXSS\ntfQob4W1ElW4dHu4FBVOc8koVIVzNum+j8nQwx30A++gB/14aWkp5QAIk9yqlqLC7h5Wparw2tpa\nKU6J0CE73qreeuutSqnPf/7zE6ykl1vh4H4ueIX1Y+9Xr+AV1pV0X1QlqbCuZGErnOaSUagK56t8\nLQq50JGs7y9FdwxE0f58lIgBGQWvsG718Y4sKXKF9cmgG6vcFwtb4eCdwFFV/d73vmeiQkncCusH\n3v1c/Ar79nbBK6wfe796Ba+wW1u3ngWs8FCXjCJUOF+WBgruTI7eA+w+LuAMj/rXan19PdgcKoWs\nsPTHn5alwrq2JaqwT1RVP/jBD5qvTIx2YBLVUlR4fX3drWfBKyz9n4uyVNj9oXC/egWs8FCXjCJU\nOF+OsvKmAPcAu51k3ouE+7hoMzi5PWTFr7Bbt7LsYbdipaiwW5+ok0F3lL788ss6uJx4td0aer96\n+ue14BXWT/VXrxR72Hsml2IPl6LCaS4Zhapwzibd91Fc3l72UqDC41a6CmulqzYVHjcqPG6lq3A8\nS1sUAABAGpaOUQAAwMsdsag7DoJDmL3DLXV3g8nqTRCBAgCgmtyRMaEXfl9K9+aLVp/7um/AQaPR\n8N3SVW0ECgCAanIDBX2xj7m0ewMF8dw54h1ieerUqaWlJT2VgqENKAYCBQCALdy2gdC7Etx2Am8L\nhH6l2Wzefvvt6+vr6+vrt99+u4GqFgeBAgDAFm6fgu8ORrc5wb1p040PvMMR9DSdxms9YaVZPRIA\ngKG0PLO5xHPHMfi6J3wjGx577LH8alcatCgAADD0TO2VmlIpFvMoAACASLQoAACASAQKAAAgEoEC\nAACIRKAAAAAiESgAAIBIBAoAACASgQIAAIhEoAAAACIRKAAAgEgECgAAIBKBAgAAiESgAAAAIhEo\nAACASAQKAAAgEoECAACIRKAAAAAivWvSFQCs8+STT548eTImwac//Wnv06eeeipzWQsLC96ny8vL\n7uOPfOQj3/72tzPnHOqpp57yVR5A6SkABt1///3xX739+/cHv6dHjx7NVlwwq2PHjrlvffjDH475\n7COPPPLII48MW9aXvvQl74t79+5tNBqNRmPv3r3DV9/vxIkTeteJyIkTJ0bPEEAiuh4Ao376058m\nptm/f7/3W3r06NEXX3wxc4luZKCUOnbs2HPPPedrZojy/PPPP//880OVpZTytSj88pe/HCoHAEVD\noAAY9dprrz3xxBMi8tGPfjTlR1544QUZ7I+477777rvvvlar5U22tLR077333nvvvUtLS1FZfec7\n3xGR119/PfiW77Of/exnfQ9Cy3JfOX78uPu/1969e/ft2+c+faTv7rvvvvvuu/WL3sfxvF0n3scA\nxsh0EwZgMd3v4H0QtH//fl+Lwqc+9Sk3sb6QHzp06NChQ/qBfv2ee+7xvn7PPffo12WwRcH7ivS7\nHk6dOhXM03sV99XQTaYfnDp1Sil14403isiNN97o63o4fvy498GBAwdE5MCBA3fddZf7K+Q+TrMP\ndY8D/Q6AMQQKgDkicv/99yuldKPCE088EUyjxygc7XOf6nf15dmb4dLSkn7gBgf6wu8mOHjw4LE+\n7/XYDRSCeepr/8GDBw8ePOirns5cJ1D9AMX9YOIe0IGCfqzjA2+5iR8HYB5dD4AhTz75pIi8//3v\nFxF918Nrr70Wlfjv+nyvX7x40dvgf+jQoYsXL+pmhm9961v6xccee0xE3E6EX/ziF6/3uW0Jvjz1\n9d6bZ1TF9Fu6CJ04bpvD6FhBRA4ePDjsZwGYR6AAGKLDgs997nOO4ziOIyLPPPNMaMr9+/f/nYdv\nMKNvaEKiY8eOuYGCHqOQKCZQkMHgwI0YAFQVgQJgyDPPPKP7HTTd+6CbGeK5f4Jr3kAh/oqezcWL\nF+PbCbyFxgycBFANBAqACTog+MY3vuG+onsfohoVohw6dMjtYtARwze/+U39Z73bJaEfpP9b/9Ch\nQ88++6x+7A6WjEqsS3fjg2effTZD7wOAMpnc8AjAInpogu/F0Hsfgnc9KKXEM57R+/2999579Yt6\njKHLHWwogbsevHm6Ey55P+sOitQ3PgTvevAOaJAhRyMeOHDgwIED+vFnPvOZ4McffPDBM2fOKKX0\n/76nAMxzVNjcbQCKTLclBAcr6D/0s40bCP2snkThi1/8Ysr0o/v4xz9+4cKFCxcuOI6jlPI9zbcs\nAGnw3QNQLCsrK6+++qqIfOUrXwk+BWAYgQKAwvG1H9CcAEwQgxkBFM6DDz4Y8xSASQQKAIplZWXl\nwoULUU8BGEagAKBwbr755pinAEyi569A1tdVo+FMuhYAAGyrSItCu90edl7b4tjYUCdPqulp1WzK\n9LRaXFQbG0RvAIBCqEigICKtVquMscLGhvrEJ+TKFel0HBGn03G6XXnoISFWAAAUQfm6Htrttm4/\ncFsR2u22lDZQcMQREXEGjsLsrKrV5PRpuiEAABNmKFDQ13URafZlzkp/vNVqNZtNHTTo/KNy1sv0\nFZRv3w+EC1dErjdaGQDA8Er39/aw3mWgDN/f+vrqnjlWcGMOX4YZspr40e12Vb3m6f1R3taF+gsv\nbB49Wp2+IQConkL/LZoTE9chX4/AiC0KQfnmZlKt5tSne/5XlSPKEVF/+ZcOYxsBAJNV4j9YW32J\ngULUilhGqplgZkbEOeMboyAioq4RNdW5MuUd27i+Xog6AwDsYWKMgjukYNwFhYppFypCrLCxoR56\nSPbtk9VVR6Tf+xDwZ3+++fOfO52O1OsyMyPz87JrV/XbuwCg4GxYiMREi4I7KCE4vMCMIrco7Nrl\nnDsntZrUakpEibMZ0rog8rf/zzWdK1Oiprz3T9LAAAAYN6OhUKvVeuyxxwxfoQveouD1wgu9u+5y\nRPoVjmhdEKcn8sqOHTdfverQwAAAE0SLQj50v4P+fyI7tMgtCl5/+ZdT9brnuaNCWxdETYn6n67+\n5homaAIAjJuJUGiUmyFHV6IWBRFZXFTdbn+8gkh/poW44QsiIs6ZhYW506cdVosAAJNsaFEwtIV6\nFkXvFEnGlOso+sc2iuzY0bt6td/wExMriNSne77RjsQNADBW5brEZGPo9kg9jDH3GRRSciKYr0ki\n39jGWk3ddJOIPL31ttMTpxfeHyHijnb8p39SjYaq13usMgUAGJG5KZylvygDLQoptduq2XQCbQxq\ne7SjxPdHbG317Ky6dEnOnQsZ8EiTAwCMoryXmPSMbuFEBitU4ChubKiVFVldlW5XduxQhw7Jq696\nm4KUqOiWoa1wYcU7iGFjQ509K6urwsQMADCKClxiEpnYQveuB/3UfItC1FulO7rttvqjP5JAA4Mk\nj3YU2fE7m7/7u06nI9ddp979bvnzP5evf30rvIhpcgAAxLAhUDA0RkE3JEzq9oey3B6ZqNl0AoMY\nZMeO/oY4amsQQ5irv9masuntt51//MefuVGCiKyuOi+9dHZlxcAWAABKxtxaDxOZk7GSdu1yTp92\nOh1nbU06HedTn3JE3It8f/3JiAGPInoahn8ZeHV+dVUksJxEcPJHpoMEAKuYm8JZRlgPGqGaTUdE\n5uflyJG52dnt6/f73tcT6YcLURHD1hqV28+73e0bJebnew8/3JueVu59Ez/+ce/kSeV9hTspAMAG\nRtd6IFAYB19nxM6d6te/lgcecDsg1NYdleGTPDpuxHDttdLtTok4nY688IKcOfNmp+PoyR+Xl58+\nelQ2NpT7CtNBAoAlqj8Ko0qDGRO5d1S6d0nUavKb36jtKZskYcyjOGpggGT/MyKvifyJN+HsrKrV\n5PRpxj8CsJcNgxkt2EILjmIUHTcsLqrl5bMi8/2XlYjE3VGpDbRADE7e0Fer6TYGALCUDZcYQ10P\nErhJEgZ4BzEMDnj8P2547+Z1tc2oWyREvIMYIr8D3a50OhX/hgCA5QytHjmRORmh6UEMCwtz7rTQ\nJ07863bb6Xan1tZEiapPR04LLcoRNRXV/FCrSb1OiwIAVJmJQGF9fd0NEYgVJsJ3R+XysrN7tyP9\nJoeZGRFZSXWXhGd8w9ycmpnxp+LmSQCoGBOBglLKXQ4q364HvSil9KdymuBi1mWhIwMf/w2WTm/P\n3s3ILPoRw9mVqS8sb50/GxuqRDdPEs0AQHqmR2HokQp5XdF1Vr4FrH2Z2zDSZHQbG2r37tMisyI1\nka7Iv5ubm//3/97pdlMMe9ScFZG5/pOVI0fmijYnNCtcAMidDZcYczMzavqinmNuvlfo2shm1y5H\nqZNKTa+tOUpNK/XwysqU7qpQovS/hCzUvKdvYt6dEzrxz3cDf9+vr6uNDfWJT8jp06ueySHOMhUE\nACQyNJjR/bt/3L0Dofk7QxprDQvO1zfhfepGDErU5Y3E2yXmlpd/7Ti/ajaV4/zKcZ70XZI3NpTj\nPBGTYHTeInbv/vV3v/uKp81DvNEMACCKuZkZ3fkZx1FEq2+o/CuzWJRh+q/zmdne1jJUodSUqP9e\n1P8gMiVy/ezsovfP960cZk6KXB+awCtx+Ym4Sm4X8bsihwOptla4AABEirpY5qjRaCwtLTUajbW1\ntUajYaBEr8lueyUtLvZEVkRU/19v64GSuH9yZmGhF5GD/redQCl1+XJP5H8ReUtkU+Qtkf9N5H/1\nPH3i8uVeRAWDRfS2K+n/17tyJS4fAIhhw6XE0CgMd60H8zcm2DDSxLDpad+EjINTPsdPEa05b4lc\nH3zZnepRtwfs2SNf/vLWpE979iil5PLlrTaw2Vl16ZLEjJcMq2Royisix0R+KFWc0hvAuNlwibFg\nC21a68GAblfV6xJYCULzzuTopIoYxD9X9JUrUq87J0+q06dXPUMKgstPiMjKwsJc6GIT0ZX0vXJ+\nx45brl6Vet3hJggAGRAo5MPXimD4xgQbjqJhgT/WpX8ZViJSqznDLUPlcpTbopCyPUCnX19Xjcb2\nu/ppRLOHuNHM3r2q1xuiiQIAgmy4xBjaQj3S0EBBQbQo5C6wypR2RuQdkb8ReVnkCZGafxkqfYVO\nGzT0PJFBaHOC9AcZdEVqIh2RfyeiRB4QqYt0RH4u8vcif9VPu/XxHb+zefWq7Nghf/zH8h//o28w\nb2QTBQCEIlCoAhuOomEbG+qhh2TfPlld3bqmzs2pN96Qc+dEzwwdSBBsD0i3gqW2tfJ1SA7XXqt+\n+1udiX8QwwMP9P7mb+TOO+XrX58KiU6SBkkAQBo2XGJMT7g0EcyXkC+9ylStJrWaEunVamrnzu0o\nIZAgdDZoR0TE0StYRk8XremFqUJmfPppP0oQEXnzTceNEkTk61+f+q//9f/8+7+PasOYDi2K9TAB\nwKf6oZAN4d5E9COtW0VelrB+HE+Cb4f++S5yReT6fg5/sLi4sboqnSupgtcdv7M5MAwi6qaGmJ6O\nsOWvaFEAMBQbLjHvMlCGHp2ghzTqkYyGxytENR5U/uiOVeLecxMsLqpuV7n9FH1nTpyYX15W0r8Z\n8soV6XQcff2enVWvv67W/q9rojK/+pvBt0LnfRqMEpQoxxNMzM6GVGlmxjfwAgBsZy4UCl2xyQAb\nwr2CSxzTcPKkunLFnTLBtSLSFflc4ioTfo4KNiS4a1UMxApzvagqAUAaNlxiTLQoaKwBba1du5xz\n59TKitRqqttVtZrjG9OwuiphDf7ztZrqdE76XnXC503yiI4SfPQoCm+V9uyJ6/hQSvlap0b/gcg9\nQwDIV/VDIW6PLBTHaSi17n0lbHIk19b8S8E3tlspziaMaQhGCd5QQ4kKVslxblXq5ainoa+MKPcM\nAZhhQ4uCoUWh9DwKk1oDOmr+6olUxlr9O01e9t1yUqs59Xr4R2q18ChBvDdW1HvibNbqvdk5/90T\nb17uJa6O7ch2lTY21MmTanpaibSnp9X8fO/hh3vu08VF9eMf97wJFhfViCte+kocPUMAyJ2JQEGv\nMa0HME5q2iVMXEyUNjMjs7PBC+SZmZm4DHftck6fdjodR+S2Tsc5e3ZKL4Etzq36QdRoA3/0oEQp\ndflyb3tApUx1OvLCC/Lcc+5T55/+SR09Khsbyn2l25WoFS/TGBjCmUeGADAO1W8zsaFdqOwSRzvG\n8x3ilEc8ZKyDsxK7uoQSeU3kTwY/k30yx8B6FqNmCMA8Gy4xFmyhBUexAjY21MqKrK6KHlqol2hK\neQNCtkBBEsdF+idaiFtvIk1xPmFLZoyUIQDzbLjEmFvrQXdAyCQWhYp6q/JHt4yCQwtjE8ddUDO2\nK0Qm7UWMuOyJTIu8HVpi1E0N6Ydw5rCNI99YMWwONtzKUcxtHP1swbBsCBQM3R7Zbrebzeak7pCs\n/FGsliEG/49+ZPV4hVThgndlCk9jQ63mdLtvR9VEvx78KanVnHpddTohH/EN4QzNYajfpqg6pDds\nDqOXWHzF3MbRzxYgyNBaD62+Sd34AMRQohYWe+KcEUeFTu0c+ICz9S9pxGWMmRkRWQm8nD1DABgH\nQ4FCu92e1MyMQBrz83LkyNzWzReOEqe3Z+/m7j2bCXGDevgLy1OiEtsk/iKhRBERmZtTd945Px8+\ni/RfxD5NI8NHRsxh9BKLr5jbOPrZAmwzNI+CO5WCgeKADAJLYsrRo3LXXVKrKT1Pw4mF3v/7o7iF\nLh1xvP8kaZqExEU4gzkEp3ZIvJdy9Kkahs3BhskhirmNo58tQChz8yiMuxQdi4y7FIyPOxHTpBYB\nD07MsLIy5T5dXnb+9E+3p2pIzM0RZ/euqdNPTnWuTIm6pnNlKjhNgq/E5WXHFyXET+2QOO/C6FM1\nDJuDDZNDFHMbRz9bgEhRsxbmq9FoGChiaWkp+LqxbURl+M6Z4CkkIouLvZmZnojaGhA5/L/4IpRS\ni4s9kRXPZ3oivUBGZxYWelEbEsgh+SNpcpid7UXlMHqJxTdw6AuzjaOfLcjGhkuM0dsjxzpAwe3g\n8L3OiF8MK3HQuOM49XovahaEbidjQ51vysjARAtDT+Qw+lQNw+Zgw+QQxdzG0c8WZGPDJcbQ7ZHN\nZtMdz2i+g2DYduzKH3WMbGfonY0i0u3KlU7PcWRgmoTAgpah/CMiOz3Pk8hzstuVTkcFF8XodsNv\nv4z5yIg5jF5i8RVzGwO1GvpsAWIYuutB+lMpGCsujahmlknXC8X3TvxCVv6VrvRdl44SR9XqvfBP\nBqmp7fsw1dTARA6BEsNez7La1ig5jF5i8RVzGwO1iqxGZQ4ETDI3j8K4Z1AwM2QS0BIXsoqZJkGP\niPT+S1vqdtzg9Fsp4uZdGH2qhtAc5uZUVA42TA6RbQ2zcQvb84WrJErK6BiFdp+BEl02dCAhX2nG\nKFy+3ItfyGqola78JaafWHqQL+YYcbWtDDmMXmLxFXMbA7VSe/YopeTy5aniVLKSbLjEmG5RmMgf\n/U4E8zVBZSTOgpBmmoQobkvD5Y3ewmIvfW+FbyKHUeqQbStGL7H4irmNcROBFKaSKClDodCpU6fW\n19dPnTp1xx13GCjOi0WhMKw0LQqDCRrxC1mlSJDwTfTlkL7JwW1jGGq1rTR1yD19GRVzG/1nSyEr\nWRk2tCgY3cJTp049/vjjhvepDUcReUlcfC90zcAUl/nIBCkXIUyOXdKFDkOMh0iqQ+7py6iY28ii\nUCbZsHsNdT384Ac/aDQaMqE/4ul6QErxs44EE+ReYvZ80g2N9M0zHfyXuQIAqsp0KGT+Jkkbwj1M\n1igtCtmKSJNhLld9b9hBi0JQMbeRFgWTbNi9JloU9ABGHR9wByNgRsY7MAcNNDZU/McQQDhDMzNO\nVlQvQ+XDQGCbk3DCp2mBCKYZZdADgFIwESi4cycYnkHBRUAAJPJd8lP2XPiSETcA1WOic8UXHzBG\nARVTzDEK+dYhrzmgKqaYPy+MUTDJht1rwRYyjwLGzIZAIaQOmUKHisUNxbxIECiYZMPutWKMQuWP\nImBeLl0VwXwAFI0VgQKAcQte7xnlAFQDgQKA8Ri8z4ImB6CkCBQAmJBXk0OanAHkyIpAgXkUgALK\nNsohiM4LYKysCBQICIDiy9zkEP8p4gZgRFYECgDKKPEaz2ySgAEECgDKitkkAQMIFABURF5TO8Rn\nC9jGxOqRxkxqLQkABeRbPzOXJTS3FtIEbFKdQKHdbrOGNYAYucQN4gkdRGW/WQMoizJ1PehVKFut\nlhsTuOtSikiz2TS83BSAUstlsGSaZHReoNTKFCi0Wi0dCuhYQUsTHMSsCxWK2ykByNju2AzNGSis\nMnU9BHsWfIMShu16UBFGqiWA6sq984JBDyi+MrUoBNHXAGCSnIQGSDovUAGlDBRarZbb+5AmPY0E\nACaC6SZRAU7lL6IxAxQqv+0ww3ESvkeJCYYtIkOGudfBQInFl8NezanfwRs6jH62ID0bdm8pWxQA\noBrGcueFCnlKIwQysyJQqHy4B6CqWGMTE2dFoMAy0wCqYXx3bCYWBGtZESgQEACoqpDQwTdGgbkf\nMBorAgUAsNfgPZx5dV74EEZUmBWBAl0PAKDlNXwy8VOEDpVhRaBAQAAAKTEMAj5WBAoAgMzir+jc\niFF5VgQKdD0AwJjQAlF5VgQKBAQAYIyxYRDDlotsrAgUAADFkdcsUj4MqBwTAgUAwCSNqQUizQeJ\nJNIgUAAAFNqkIgnCCM2KQIHBjABQYXkNqEzMxM7QwYpAgYAAAKxibkClBZcXKwIFAAC8cpwcovKr\neBMoAAAwYKi+jGpHCSIyNekK5KPdbrdarUnXAgBQTUpU6L+cbu0stIoECiLSarWIFQAAyFdpAgW3\nzaDdbjebzWazqR9rzWaTQAEAgNw5ZbkjQAcHrVar2Wzq4EC/6H3XDSC8ou6NjFGWfYKCcJyE71Fi\ngmGLyJBh7nUwUGLxFXMbRz9bkJ4Nu7c0gxlbrZYODly6IcF9HPPZyh9FAADGpDSBQlCw8SAKEy4B\nAJBN+QIFPRbB7X0AAADjU/3OlZgxCpXfdpjBGIUxlVh8xdxGxiiYZMPuLV+LQgaVP4oAAIyJFYEC\nYxQAAMjGikCBgAAAgGysCBRoUQAAIBsrAgUCAgAAsinNFM4AAMA8K1oU6HoAACAbKwIFAgIAALKh\n6wEAAESyokWBrgcAALKxIlAgIAAAIBu6HgAAQCQCBQAAEIlAAQAARLJijAKDGQEAyMaKQIGAAACA\nbOh6AAAAkSoSKLTb7VarNelaAABQNRUJFESk1WoRKwAAkK9yj1Fot9v6QbPZbLVazWZzkrUBAKBy\nStOi4HYutNvtZrOpY4JmH1ECAADj4JTljgBvQNBut3VbQprgIOreyBhl2ScoCMdJ+B4lJhi2iAwZ\n5l4HAyUWXzG3cfSzBenZsHtL06IQHH/g9jskUkPKt+YAAJRXiccopO9rYMIlAACyKV+goO9ucHsf\nAADA+FS/cyVmjELltx1mMEZhTCUWXzG3kTEKJtmwe8vXopBB5Y8iAABjYkWgwBgFAACysSJQICAA\nACAbKwIFWhQAAMjGikCBgAAAgGxKM+ESAAAwz4oWBboeAADIxopAgYAAAIBs6HoAAACRrGhRoOsB\nAIBsrAgUCAgAAMiGrgcAABCJQAEAAESyouuBMQoAAGRjRaBAQAAAQDZ0PQAAgEgECsAErK9PuJVr\n9AqQQy6KsAkVyGHiFag2AgUgf1E/Ohsb6uRJNT2tmk2ZnlaLi2pjIzzlsD9bvvQZKpB7DlGVjMoh\n+PGoKhUhh/htjEkfvxNyySFlDYuwG1PmMJHdOOyhrzJVFWtra6GvV2kbUUzuOXb5cm9xsVev90R6\n9XpvYaF3+XLPTXD5cu9DH+qJrIio/r8zR45spUmTQ2KJc3Obn/zkZvDj+iOhFbjttoGPjJ5DsM7e\nSl533eZ737sp8rQnh6/ecMNmrbbpfvxHP9qM2ai5uc3bb98crIPpHOK3MTS9N8OwnZB8IBJzcM+l\n0LMlWMOJ78bEHCayG9N/I3xfxgqrzhY2Go3Q1204ipisxDhAJ1hc9L2rRNTsbG9hYet3JzGHpBJ7\nIm+I/Oeoi8fiYm9mpjdYAf2RH6fPIbAJvhz8dQ6r5E8GP+575avvfrf3tztYpR8PPp1IDonb6Esf\nzNBbXJoDkZiDEjmjz6Xg2RJ2ahVhN8bnMJHdOMQ3ot0O2duVVJotXFtbW1pa0g8ajYYOC9b6lpaW\nlpaWQhsVbDiKmKzEOEAnqNd9F+mtf7XaVqCQmENSifp3NvLiEVYB30dGz8Ff57BKxn88NJJQBcth\na7ekPhDBDIc9EIk5bJ9LwbMlIkac+G6Mz2EiuzHNN+KrIj8UeUtkU+QtkSd8bQzVU5qLaKPR0IGC\nN0TwJtDvBtncHQMzEuMAEel0Qn9xtn6MrlzpJebgluXxVuzPru/isTPiZzHfHPx1HtyoxEtFYpWK\nkIP771epD0TitWeoKiWcS8FAIXBqFWE3pokbzO/GNDn0rr120/OKvwOxekozmLHVavleabfb8QkS\nRe2U7LWEZRzH0dN5Oc51nU74mdPtKpHr6vUpkSuhCWo1qdedbld1OuGl6Bx0We5Z2un0RKY9qSLP\n225XOU5N5J1ABVTs0ww5BOvs2y2+qc+CH0+sUhFycNWvXOmlOBC+EhKe3gAABvxJREFUV4LzvyWW\nmJiDqzM9PSX9M7N/fu4MnJxF2I3xOUxkN6Y99L/9rffSOb9vn6ysRKWtgtIECkHNZjNlSgICjInn\nbHq7Xg//+a7VHKW6SqmFhWkR/8/J3JyamdlKVq+Hl+Lm4D1pA+kjLx7eCszOxly2U+UwuAmJHwnd\nLW4dgm+lrFIRctiK8FIciNAchjoQiTloZ06cqIf91EWdnEXYjVE5TGQ3pqyz//XVVWd1NSJtJZQv\nUGj1pQ8UnAjjrCasMzMjMXGAiMzPy5Ejc95L9dyceuMNmZ9Pm0OKEkMuHt4KXLok3grcfLMSuTRU\nDr5NCMthoM6hG/W+9/VCH/f9JKlKl266Kc8cAunT5HAmdhuD6eOKSHcg/Dl4N3xuTt1557x7LvnM\nzMhgjKgVYTfG52BiNw6TQ0wTmkS1KVbBUB0VZWTztsOky5d7R470Zme3OzXn5np33tl7882Bux8X\nFnq1Wk9ks1brnTjhfzcxh9gSe3v2bO7evZm+ArOzm7fdtplrDv6PBDfqYx/bvOGGzeuu2xTZ3Llz\n84YbNh94YNP77u/9nveVkCo1m5tzc5u6DpPKIXYb/ekDGQ7shDQHIjEH37mU5uQswm6MzWECuzEx\nhx07vKMTtv+5Q5IrqXwtChlEbfyk64VK2bXLOXdOajWp1ZRIr1ZTO3fKuXOye7fjTbO8PNXtTonc\n1u1OfeELU753E3OILVGOHpW77hqiAqur13zta06uOfg/Etyo3/9954039rz99jUit73zzjVvvLH7\nPe9xvO+++KJ4Xgmp0te+5pw9e42uw6RyiN1Gf/pAhgM7Ic2BSMzBdy6lOTmLsBtjc5jAbkzM4aab\nRORp3+6NafarCJNRyUTYvO0wr39y3Zr5HBs2B1/6DBXIPYdhEwTfHbZKBcwhMcNx5BDP/E4o6W6M\nzeEPfG0zIv8mptmvGlg9EsjT6CfbsDmYLzFDDvEJgu+OvhOKkIP5BLnnb+dujE+wsaFWVqRWU92u\nqtWcbvedmGa/anAqfxGNGbRY+W0HAOSuf1m5VeRlseBSQosCAABD8F5TbLiBzorBjAAAIBsrWhSi\nIj5aGgAAiGdFoEBAAABANnQ9AACASFa0KND1AABANlYECgQEAABkQ9cDAACIRKAAAAAiWdH1wBgF\nAACysSJQICAAACAbuh4AAEAkAoVtpZuymwqPGxUeNyo8blQYo6tIoNBut1utVrvdnnRFAAColOoE\nCs1ms9lsGitxqLB3fImLUAcqPO46UOFx14EKj7sOVLjUyh0otPt0lNBqtSZdIwAAKqU0gYLuXJDB\nxoNmnxsuTLSOAABUjiqJRqOxtLSkHyil1tbW1tbW0nxw0jsYZXX8+PFJVwFACYzxylcMpZlHIThW\nMWUTgiJWQFZPP/30pKsAABNWmq6HIDoaAAAYt/IFCq2+UQIFd6CDdyykTOLuiaEEK1nkCodWsrAV\njqpkMSscOmTH+/jRRx9tNBqPPvroJGvpUd4KayWqcOn2cCkqnOaSUagK52zSfR+ToYc76AfeQQ/6\n8dLSUsoBECa5VS1Fhd09rEpV4bW1tVKcEqFDdrxVvfXWW5VSn//85ydYSS+3wsH9XPAK68fer17B\nK6wr6b6oSlJhXcnCVjjNJaNQFc5X+VoUcqEjWd9fiu4YiKL9+SgRAzIKXmHd6uMdWVLkCuuTQTdW\nuS8WtsLBO4Gjqvq9733PRIWSuBXWD7z7ufgV9u3tgldYP/Z+9QpeYbe2bj0LWOGhLhlFqHC+LA0U\n3JkcvQfYfVzAGR71r9X6+nqwOVQKWWHpjz8tS4V1bUtUYZ+oqn7wgx80X5kY7cAkqqWo8Pr6ulvP\ngldY+j8XZamw+0PhfvUKWOGhLhlFqHC+HGXlTQHuAXY7ybwXCfdx0WZwcnvIil9ht25l2cNuxUpR\nYbc+USeD7ih9+eWXdXA58Wq7NfR+9fTPa8ErrJ/qr14p9rD3TC7FHi5FhdNcMgpV4ZxNuu+juLy9\n7KVAhcetdBXWSldtKjxuVHjcSlfheJa2KAAAgDQsHaMAAICXO2JRdxwEhzB7h1vq7gaT1ZsgAgUA\nQDW5I2NCL/y+lO7NF60+93XfgINGo+G7pavaCBQAANXkBgr6Yh9zafcGCuK5c8Q7xPLUqVNLS0t6\nKgVDG1AMBAoAAFu4bQOhdyW47QTeFgj9SrPZvP3229fX19fX12+//XYDVS0OAgUAgC3cPgXfHYxu\nc4J706YbH3iHI+hpOo3XesJKs3okAABDaXlmc4nnjmPwdU/4RjY89thj+dWuNGhRAABg6JnaKzWl\nUizmUQAAAJFoUQAAAJEIFAAAQCQCBQAAEIlAAQAARCJQAAAAkQgUAABAJAIFAAAQiUABAABEIlAA\nAACRCBQAAECk/x+uV2jhSKDoRgAAAABJRU5ErkJggg==\n",
            "text/plain": "<ROOT.TCanvas object (\"c1_n3\") at 0xc18e840>"
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# uncorrelated, qq\n#ws_uncorrelated.loadSnapshot('original')\nws_uncorrelated.loadSnapshot('qq')\nws_uncorrelated.pdf('combined_model').fitTo(ws_uncorrelated.data('data'))\nprint_fit_result(ws_uncorrelated, ['mX', 'dEScale_run1', 'dEScale_run2', 'xs_run1_scaled', 'xs_run2'])\nplot(ws_uncorrelated, ws_uncorrelated.data('data'), ws_uncorrelated.pdf(\"combined_model\"))",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 741.810689 +/- 5.999553\ndEScale_run1         = -0.065744 +/- 0.975210\ndEScale_run2         = 0.074001 +/- 0.943947\nxs_run1_scaled       = 0.574294\nxs_run2              = 1.550594 +/- 0.732770\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nRU5ErkJggg==\n",
            "text/plain": "<ROOT.TCanvas object (\"c1_n6\") at 0xc0d0f90>"
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "When comparing correlated / uncorrelated the difference on the xsections are pretty negligible. As expected (?) the cross section in the uncorrelated cases are a bit larger, since there is more degree of freedom"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Probably the best test to do is not with data, but with Asimov or toys. Here I generate what I think could be the wrost case scenario, where the correlation should play the maximum role: no signal in run1, signal in run2 (as the observed fitting run2 alone)."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Do a preliminary fit with independend cross sections to fix the observed values"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# ratio is free\nws.var('mX').setVal(750)\nws_uncorrelated.var('scale_xsec_run1').setConstant(False)\nws_uncorrelated.pdf('combined_model').fitTo(ws_uncorrelated.data('data'))\nprint_fit_result(ws_uncorrelated, ['mX', 'dEScale_run1', 'dEScale_run2', 'xs_run1_scaled', 'xs_run2'])",
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": " mX                   = 743.097587 +/- 5.307110\ndEScale_run1         = -0.175294 +/- 0.868417\ndEScale_run2         = 0.189916 +/- 0.888135\nxs_run1_scaled       = 0.273461\nxs_run2              = 6.521381 +/- 2.544478\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Generate Asimov with M=750 GeV, no signal in run1"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "ws_uncorrelated.var('mX').setVal(750)\nws_uncorrelated.var('dEScale_run1').setVal(0)\nws_uncorrelated.var('dEScale_run2').setVal(0)\nws_uncorrelated.var('xs_run1').setVal(0)  # no signal for run1\nmodel_config = ws_correlated.obj('mConfig_basic')\n\nasimov_data = ws_correlated.pdf('combined_model').generateBinned(model_config.GetObservables(), RooFit.ExpectedData())",
      "execution_count": 78,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "plot(ws_correlated, asimov_data, pdf=None)",
      "execution_count": 75,
      "outputs": [
        {
          "execution_count": 75,
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAHYCAIAAAApvgy/AAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nTkSuQmCC\n",
            "text/plain": "<ROOT.TCanvas object (\"c1_n8\") at 0xce5d190>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Fit the Asimov datase with correlated / uncorrelated mass scale, qq / gg"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# uncorrelated qq\n#ws_uncorrelated.loadSnapshot('original')\nws_uncorrelated.loadSnapshot('qq')\nws_uncorrelated.pdf('combined_model').fitTo(asimov_data)\nprint_fit_result(ws_uncorrelated, ['mX', 'dEScale_run1', 'dEScale_run2', 'xs_run1_scaled', 'xs_run2'])",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 741.651119 +/- 5.406032\ndEScale_run1         = -0.008876 +/- 0.921581\ndEScale_run2         = 0.004753 +/- 0.908524\nxs_run1_scaled       = 0.552644\nxs_run2              = 1.492140 +/- 0.666421\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# correlated qq\n#ws_correlated.loadSnapshot('original')\nws_correlated.loadSnapshot('qq')\nws_correlated.pdf('combined_model').fitTo(asimov_data)\nprint_fit_result(ws_correlated, ['mX', 'dEScale', 'xs_run1_scaled', 'xs_run2'])",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 741.596451 +/- 5.115906\ndEScale              = 0.001087 +/- 0.708563\nxs_run1_scaled       = 0.552888\nxs_run2              = 1.492797 +/- 0.670825\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# uncorrelated gg\n#ws_uncorrelated.loadSnapshot('original')\nws_uncorrelated.loadSnapshot('gg')\nws_uncorrelated.pdf('combined_model').fitTo(asimov_data)\nprint_fit_result(ws_uncorrelated, ['mX', 'dEScale_run1', 'dEScale_run2', 'xs_run1_scaled', 'xs_run2'])",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 741.601375 +/- 5.404695\ndEScale_run1         = 0.001880 +/- 0.912776\ndEScale_run2         = -0.001262 +/- 0.906769\nxs_run1_scaled       = 0.453079\nxs_run2              = 2.129471 +/- 0.971637\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# correlated gg\n#ws_correlated.loadSnapshot('original')\nws_correlated.loadSnapshot('gg')\nws_correlated.pdf('combined_model').fitTo(asimov_data)\nprint_fit_result(ws_correlated, ['mX', 'dEScale', 'xs_run1_scaled', 'xs_run2'])",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": "mX                   = 741.608675 +/- 5.175063\ndEScale              = -0.000251 +/- 0.704142\nxs_run1_scaled       = 0.453152\nxs_run2              = 2.129815 +/- 0.971699\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Comparing uncorrelated vs correlated the difference is very small"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Stop here."
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "model_config_sb = ws.obj('mConfig_basic').Clone(\"model_config_sb\")\nmodel_config_sb.SetParametersOfInterest('xs_run2')\nmodel_config_sb.SetPdf('combined_model')\nmodel_config_sb.SetSnapshot(ws.allVars())\n\nmodel_config_b = model_config_sb.Clone(\"model_config_b\")\nws.var('xs_run2').setVal(0)\nws.var('xs_run2').setConstant(True)\nmodel_config_b.SetSnapshot(ws.allVars())\nws.var('xs_run2').setConstant(False)",
      "execution_count": 62,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": false
      },
      "cell_type": "code",
      "source": "get_discovery_result(asimov_data, model_config_sb, model_config_b)",
      "execution_count": 70,
      "outputs": [
        {
          "execution_count": 70,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<ROOT.RooStats::HypoTestResult object at 0xc3a21f0>"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2",
      "language": "python"
    },
    "latex_envs": {
      "current_citInitial": 1,
      "eqLabelWithNumbers": true,
      "cite_by": "apalike",
      "bibliofile": "biblio.bib",
      "eqNumInitial": 0
    },
    "hide_input": false,
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.5",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 2,
        "name": "ipython"
      }
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Correlation energy scale",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}