xi0-pi-pi-pi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Xic+ -> Xi0 pi pi pi\n",
    "#Xi0 -> Lambda0 pi0\n",
    "#Lambda0 -> p pi-\n",
    "\n",
    "from root_pandas import read_root\n",
    "import ROOT\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "signalfile = '/belle2work/harsikad/ntuples_Signal_630K.root' \n",
    "ccbar = '/belle2work/harsikad/harshika_CCbarBkg1.root'\n",
    "uubar = '/belle2work/harsikad/harshika_UUbarBkg1.root'\n",
    "ddbar = '/belle2work/harsikad/harshika_DDbarBkg1.root'\n",
    "ssbar = '/belle2work/harsikad/harshika_SSbarBkg1.root'\n",
    "mixed = '/belle2work/harsikad/harshika_MixedBkg1.root'\n",
    "charged = '/belle2work/harsikad/harshika_ChargedBkg1.root'\n",
    "\n",
    "mycols = ['xic_isSignal','xic_M','xi_mcDecayVertexFromIPDistance','xi_significanceOfDistance','xi_ErrM']\n",
    "\n",
    "df = read_root(signalfile, key=\"xic_tree\", columns=mycols)\n",
    "df_ccbar = read_root(ccbar, key=\"xic_tree\", columns=mycols)\n",
    "df_uubar = read_root(uubar, key=\"xic_tree\", columns=mycols)\n",
    "df_ddbar = read_root(ddbar, key=\"xic_tree\", columns=mycols)\n",
    "df_ssbar = read_root(ssbar, key=\"xic_tree\", columns=mycols)\n",
    "df_mixed = read_root(mixed, key=\"xic_tree\", columns=mycols)\n",
    "df_charged = read_root(charged, key=\"xic_tree\", columns=mycols)      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate optimal value to apply cut (>)\n",
    "\n",
    "def optimalCut(var):\n",
    "    \n",
    "\n",
    "    signal = '(xic_isSignal==1)'\n",
    "    cuts = 'xic_M >2.3 & xic_M<2.7'\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    #run over new MC\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "    myrange=(min(nptrue),max(nptrue))\n",
    "\n",
    "    signal_without_cuts = len(nptrue)\n",
    "    bkg_without_cuts = len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed)\n",
    "\n",
    "    mycolor = 0\n",
    "    testvalue = min(nptrue)\n",
    "    mytestvalue=[]\n",
    "    mytotal=[]\n",
    "\n",
    "    for i in range(125):\n",
    "        cuts = f\"xic_M >2.3 & xic_M<2.7 & {var} > {testvalue}\"\n",
    "\n",
    "        nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "        np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "        signal_efficiency = (len(nptrue))/signal_without_cuts\n",
    "        background_rejection = 1-((len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed))/bkg_without_cuts)\n",
    "        total=signal_efficiency+background_rejection\n",
    "\n",
    "        mytestvalue.append(testvalue)\n",
    "        mytotal.append(total)\n",
    "\n",
    "        testvalue+=((max(nptrue)-min(nptrue))/125)\n",
    "        mycolor+=0.0072\n",
    "\n",
    "\n",
    "    print(var+\": \"+str(max(mytotal)))\n",
    "    print(mytestvalue[mytotal.index(max(mytotal))])\n",
    "#CALCULATE OPTIMAL CUT VALUE AND PRINT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate optimal value to apply cut (<)\n",
    "\n",
    "def optimalCutLess(var):\n",
    "    \n",
    "\n",
    "    signal = '(xic_isSignal==1)'\n",
    "    cuts = 'xic_M >2.3 & xic_M<2.7'\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    #run over new MC\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "    myrange=(min(nptrue),max(nptrue))\n",
    "\n",
    "    signal_without_cuts = len(nptrue)\n",
    "    bkg_without_cuts = len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed)\n",
    "\n",
    "    mycolor = 0\n",
    "    testvalue = max(nptrue)\n",
    "    mytestvalue=[]\n",
    "    mytotal=[]\n",
    "\n",
    "    for i in range(125):\n",
    "        cuts = f\"xic_M >2.3 & xic_M<2.7 & {var} < {testvalue}\"\n",
    "\n",
    "        nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "        np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "        np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "        signal_efficiency = (len(nptrue))/signal_without_cuts\n",
    "        background_rejection = 1-((len(np_dd)+len(np_uu)+len(np_ss)+len(np_cc)+len(np_charged)+len(np_mixed))/bkg_without_cuts)\n",
    "        total=signal_efficiency+background_rejection\n",
    "\n",
    "        mytestvalue.append(testvalue)\n",
    "        mytotal.append(total)\n",
    "\n",
    "        testvalue-=((max(nptrue)-min(nptrue))/125)\n",
    "        mycolor+=0.0072\n",
    "\n",
    "\n",
    "    print(var+\": \"+str(max(mytotal)))\n",
    "    print(mytestvalue[mytotal.index(max(mytotal))])\n",
    "#CALCULATE OPTIMAL CUT VALUE AND PRINT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotVar(var, var_range, mycuts):\n",
    "    \n",
    "    # Make nice looking plots\n",
    "    plt.rcParams.update({\n",
    "              'font.size': 20,\n",
    "              'figure.figsize': (10, 8),\n",
    "    })\n",
    "\n",
    "\n",
    "    signal = '(xic_isSignal==1)'\n",
    "    cuts = mycuts\n",
    "    nbins=100\n",
    "    myrange=var_range\n",
    "\n",
    "    ax = plt.subplot()\n",
    "\n",
    "    # define a numpy array from the \"M\" column in the dataframe\n",
    "    #run over new MC\n",
    "    nptrue = df.query(cuts+'and xic_isSignal==1')[var].to_numpy()\n",
    "    np_dd= df_ddbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_uu= df_uubar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_ss= df_ssbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_cc= df_ccbar.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_charged = df_charged.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "    np_mixed = df_mixed.query(cuts+' and not xic_isSignal==1')[var].to_numpy()\n",
    "\n",
    "    scale = 1 # scale factor by eye\n",
    "    wnp_dd = [scale] * len(np_dd)\n",
    "    wnp_uu = [scale] * len(np_uu)\n",
    "    wnp_ss = [scale] * len(np_ss)\n",
    "    wnp_cc = [scale] * len(np_cc)\n",
    "    wnp_charged = [scale]*len(np_charged)\n",
    "    wnp_mixed = [scale]*len(np_mixed)\n",
    "    wnptrue = [scale] * len(nptrue)\n",
    "\n",
    "\n",
    "    # use the numpy data to create a matplotlib histogram\n",
    "    ax.hist([np_dd, np_uu, np_ss, np_cc, np_charged, np_mixed, nptrue], bins=nbins, range=myrange,\n",
    "            label=[\"ddbar\", \"uubar\", \"ssbar\", \"ccbar\",\"charged\",\"mixed\", \"signal\"], weights= [wnp_dd, wnp_uu, wnp_ss, wnp_cc, wnp_charged, wnp_mixed, wnptrue], stacked=True)\n",
    "\n",
    "\n",
    "    plt.xlim(myrange)\n",
    "\n",
    "    plt.ylabel('Number of Events')\n",
    "    plt.xlabel(var)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "#PLOT A VARIABLE - STANDARD GRAPH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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taRdgzexKgvD5LcHd/ysKUs/dN5vZMwQBtj3bAmx2z2fNmBW3bV8Vsa2k6oiUSemw6la8r/o1B6uISPpLqwBrZn2Ahwjmcu0U9rYm4o/wuWr2Bnf/08wWAg3NbJcY42D3CJ8jx67OJVjooLmZZcUYBxurzpzwOd4Y11h1RCRUXL2qiY5J1RhWEZH0Vy7VDchmZjcShNcvgKMKEV4B2obP86K2Z0/V1TlGneOjyhBOmzUVqEIwc8B26xCE3gXAnma2awHriIiIiEiC0iLAmtktBDdtzSToeV2WT9lWZpan3WbWCbgm/HFY1O4nwud+ZlY7ok4zgqVlN7JtOqxsg8PnO8NptbLrHEywGtcfRMxNG64Uln2e+yLbaGZdCILwt8CEeK9NRERERLYv5UMIzKwncDvBV/aTgKuDNQFyme/uQ8M/PwjsYWZTCZaFhWAWguy5Xm9x96mRld19qpk9SLBS1ldmNopgKdluBMvDXhW1ChcEy8ieTrBYwSwzG0Mw52w3gim2LnH3NVF1HgROCut8amYfEcwNeybB7Am9tAqXiIiISNGkPMAC2V+3lwf6xCkzARga/vlF4DTgYIKv5SsAS4BXgIHuHmvhAdy9r5l9TdDjeimwFfgcuN/dx8Yo72Z2NsFQgl7AVcAGYCJwZ3RIDutsNLNjgJuAswl6hNcQTPl1m7t/G+8iiEjiKlUz6u1XmTpNK5FVMc9/fAHYdObAEm6VZDQH1v+JfT6Lch9+SLllcb8QFJEUsuCbb0knbdq08RkzZqS6GSIFFm+6rEQkehNXpWrGvsfXpH7DndihYlXKlStPjG9vqL52QYzaIrG5O5uBdVu2sPyXXyh3/wCFWJEI+3z/XZ5tZjbT3duUZDvSYgysiEii6u1XmfoNd6LaDjUpXz4rZngVSZSZUcGM2llZ1GnalK1HH53qJolIDAqwIpKR6jStxA4Vq26/oEghVStfHm/VMtXNEJEY0mEMrIiUUvGGBSRjKdisika5cuVzbdNwAUmmLIAqVVLdDJG0kowhY8mgACsiJS5ZixZo2IAUJzMDvcdE0pICrIiktVgrY2m5VxGRsk0BVkQyTsfxV7DpzIFUX1sh1U0REZEUUIAVkYQU1/inWD2tUnR7H3ccAN+/9952y/6ycCH7dO7MeaecwlN33ZWz/dJ+/Rj25pt89+67NG3YsNjaKiJSUAqwIhLTbzfFXBMkY+wz7JdUNyFf353XNNVNEBHJWAqwIhLTyJ/vTXUTREREYtI8sCIiIiKSUdQDKyJFlqxpsaRw3J0nXn6ZZ155hXm//sqOtWpxSseO9L/66pjl1/75J3c+/jivvfcey1etommDBvTq2pWTO3bM9zxbt27l0eef57lRo/hl0SLq1KrF6ccey81XXEGNatVylZ3w2We88vbbTJs1i4VLlrBp82aaN27MacceS99evahcqVKu8ncOGsTdgwfz7nPP8fvSpTw+fDjf/fQTdWrXLtD4XREpWxRgRUQy3PX33sug4cOpv9NO9OralQpZWYz9+GOmf/01f2/aRMUK22Zr2Pj335xw8cXMnD2bFnvtRfcTT2TV2rXc8+STTJoxI9/z3HDffUyZOZPTjzuOk6pX54OpUxk4bBhTPv+cj154IVcofeC55/jh559pe+CBdG7fng0bNzLtiy+4a9AgJk2fzltPP0358uXznOOR559n3LRpnHDkkRx58MGsXrcueRdKREoNBVgRkQz2yRdfMGj4cJo3bszEl19mx5o1Aeh/9dV07tWLxX/8QZMGDXLKP/L888ycPZsuRx/N8AceoFy5YCTZdRddxGHdum33XJ+MGpVzvNv79OHcvn1548MPeWjIEP7du/e28/TrR7NGjfIsNvHfxx7j3qee4vUPPqBr5855zjHhs8/4eNgwDtpnn8JdEBEpEzQGVkQkg70wejQAN1xySU54BahcqRK39+mTp/yLo0dTrlw57rr22pzwCtCsUSMuP/fcfM/1z3PPzRWGy5Urx93hcbLbkW3Xxo1jrpR21fnnA/DBlCkxz9HrjDMUXkVkuwocYM2smZmdYGZVI7Zlmdl/zexLM5tqZqcVTzNFRCSWL779FoAj2rTJs69dy5a5vqZf++efzF2wgAY770zzxo3zlG9/8MH5nivWOXZt3JhG9evzy8KFrFqzJmf7n+vXc9/TT3N49+7UP/RQqh5wAFVatKDR4YcD8PvSpTHP0aZFi3zbICICiQ0huA04BagXse1m4JaIn18xsyPc/ZNkNE5ESkZJL06gpWCTZ004RnTnOnXy7MvKyqJOrVo5P69euzZuWYB6cbZni1uvbl0WLFrEmnXrqFWjBps2beL4iy9mxtdfs9/uu3PGccdRd8cdqZAV/Mq5e/BgNv79d9xjiUj6in3T7lsl3o5EAuyhwEfuvhnAzMoB/wS+B44F6gMfAtcA+Q+kEpGMlYwZB7TqVvJk3/2/dPlydq1SJde+zZs3s3zVKhrWC/odalavnlM2liVxtmdbunw5e+66a956y5blasvYjz9mxtdfc16XLjx15525yv7+xx/cPXhw3HPEGnYgIhItkTGw9YDIpW0OAuoCj7v7b+4+A3gDyP87KBERSZqD9t0XIOYMAlNnzWLLli05P1evWpXdmjRh0dKlzPv11zzlJ06fnu+5Yp3j519/5bfFi2nasCG1atQAYO6CBQB0OfroPOUnb2emAxGRgkikB7YC4BE/Hxb+PC5i22/ALklol4gUg0SGCmhu18zQo0sXhr72Gvc9/TQndeyYcyPXho0bufXhh/OWP/VU+j/6KDc/9BDDBgzIuZFr/m+/MXj48HzPNWj4cM495ZScG7m2bt3Kfx58kK1bt9KjS5ecck0bNgRg0vTpnNihQ872n3/9lZsfeqgoL1dEBEgswP4GHBDx8wnAMnf/LmLbzsAaRESkRBzasiWXn3MOg196iTanncZpxxyTMw9srRo1qL/TTrnK/6tnT8aMG8foDz7g0LPO4ph27Vi1di3/9957HNa6NW+NHx/3XG0POoi2XbtyRufO1KxWjQ+mTuXrOXNoue++XNurV065E448kt2aNOHRF17gmx9/5MC99+bXxYt5Z8IEOrdvz6+//15cl0NEyohEAuxY4BozGwBsAI4BhkSV2ZPcwwxEJEWK68YsST8DbrqJ3Zs25akRI3j21VdzVuL677/+xT+6ds1VtlLFirz19NPcNWgQo957j8eHD6dpgwbceNllnNKxY74B9r4bbuDNceMYEq7EtWOtWlxx3nnccsUVuRYxqFqlCu88+yy3PPQQE2fMYMrnn7Nro0bcdNllXH3++Yx6993iuhQiUkaYu2+/FGBmOwNTgebhpoXAP9x9UcT+34BH3f26YmhrmdGmTRufoXFiUkRFDbDpfrPWpscHske9etsvKFIEPy5ZQoUrrkx1M0TSRqxZZK58stNMd887z14xKnAPrLsvNbMWQKdw0wR3XxtRpC5wPaBFq0VERESk2CS0lKy7/0UwlCDWvm+Bb5PRKBHJLJoWS0RESlKBA6yZbQH6u/sd+ZTpB/zX3RMKxiJSeBrrKiIiZU0i88Ba+ChIORERERGRYpHsntLaBDMUiEgG0ZyvIiKSSfINsGbWPmpTsxjbAMoDTYBzgTlJapuIRNBQARERkcD2emDHs231LQd6ho9YDNgK9E1Ky0Qk6dTTKiIipcH2AuztBMHVgFsJAu2EGOW2AMuBj939+2Q2UETSi2YcEBGRVMs3wLp7/+w/m1lPYLS7P1rcjRKRoitqb6uCqoiIpKtEFjLYtTgbIiIiIiJSEJqvVSSFdGOWiIhkknT5di6hAGtmewD/Ag4hmDKrfIxi7u67JaFtIqVKcYVV3ZglIiJlTSIrcR0KfAjsAGwGloTPeYomp2kiIiIiInklshLX/4BKQG+girs3dvddYz2Kp6kiIpIKl/brR5UWLfhl4cJUN0VEBEhsCMHBwCh3f6q4GiMikiw7jDos1U3I119dp6S6CSIiGSuRAPs3sKC4GiIi+dNYVxERkUAiAXYq0LK4GiKSaeLdlNV35NgSbknRpctdpSIiIgWRyBjY/wDtzKxHcTVGREQSM3H6dKq0aMGdgwbF3L/3ccex93HH5fx856BBVGnRgonTp+cp+8vChVRp0YJL+/WLeaytW7fy6PPPc9DJJ1O7dWt279SJG+69lzXr1uUpO+Gzz7iif39adelCvbZt2bFNG9qcdhp3DR7Mho0b85SPbNfIt96i/TnnsNMhh+Rqu4hItkR6YLsA44ChZnYxMBNYFaOcu/sdSWibiCSZelqlKG647z6mzJzJ6ccdx0nVq/PB1KkMHDaMKZ9/zkcvvEDlSpVyyj7w3HP88PPPtD3wQDq3b8+GjRuZ9sUX3DVoEJOmT+etp5+mfPm8MzE+8vzzjJs2jROOPJIjDz6Y1THCsYhIIgG2f8SfjwgfsTigACsZKRnDApIx36vGu0o6+uSLL/hk1CiaNGgAwO19+nBu37688eGHPDRkCP/u3Tun7CP9+tGsUSPMcs+s+N/HHuPep57i9Q8+oGvnznnOMeGzz/h42DAO2mef4n0xIpLREgmwRxVbK0TSnBYhEIF/nntuTngFKFeuHHdfey1jxo3jhdGjcwXYXRs3jnmMq84/n3ufeooPpkyJGWB7nXGGwquIbFeBA6y7TyjOhoiUdiUdVjVcQJLtiDZt8mzbtXFjGtWvzy8LF7JqzRpq1agBwJ/r1/P48OG8+dFH/PTLL6z980/cPafe70uXxjxHmxYtiqfxIlKqJLSUrEhpUly9qiKl1c516sTcXq9uXRYsWsSadeuoVaMGmzZt4viLL2bG11+z3+67c8Zxx1F3xx2pkBX8yrl78GA2/v133GOJiGxPwgHWzA4AzgH2Aaq6+9Hh9mbAIcAH7r4ymY0UEZHYyoVjTDdvjrWyN6xeu5aa1asXqPyqtWvzPdfS5cvZc9e8iy0uWbYMgBrVqgEw9uOPmfH115zXpQtP3XlnrrK///EHdw8eHPcc0WNmRURiSSjAmtntBNNpZU+/5RG7ywEvA32Ax5LROBHZPg0VKNuyv7JfuGRJnn1zFyzIE2Brh+V/W7w4T/nPv/km33NNmjGDw6OGEfz866/8tngxTRs2zGnL3AXBmjddjj46zzEmz5iR7zlERAqiwPPAmll34GbgA+Ag4H+R+919HjADOCWJ7RMRkXzsteuu1KhWjbEff8zS5ctztv+1YQN9//e/POVbh2NMXxw9Olcv7G+LF/O/J57I91yDhg9nwaJFOT9v3bqV/zz4IFu3bqVHly4525s2bAjApKi5Zn/+9VdufuihBF6diEhsifTAXg38BHRx97/N7LQYZb4DOiSjYSLpLt5NWRtWPlgs51NPq8RSoUIF/nnuudzz5JMcetZZnNKxI5u3bGHctGnssvPO7LLzzrnKH3LAARzeujWTZ87kiHPOocMhh7B0+XLenjCBo9u1i9kzm63tQQfRtmtXzujcmZrVqvHB1Kl8PWcOLffdl2t79copd8KRR7JbkyY8+sILfPPjjxy49978ungx70yYQOf27fn199+L7XqISNmQyEpcLYD33D32yPvAIqBe0ZokIiKJuOWKK7j9X/+icsWKPDdqFO9NmsSpxxzDm088kXPjVKRXHn2UC844g0VLljD4pZf48vvvufOaa7jzmmvyPc99N9zAjZddxqTp03l8+HCWrVzJFeedxzvPPptrEYOqVarwzrPP0u2EE/h27lwGvfQSs3/4gZsuu4znYvQKi4gkyiKnNcm3oNk64Fl3/1f4823Are5ePqLMs8Dp7l67OBpbVrRp08ZnaJxY0iQy20AivaqaFiu1Nj0+kD3q6f/LUrx+XLKECldcmepmiKS1fed8P9Pd886zV4wSGULwI9Au3k4zKwccDuR/F0DeenWA04ATCXp5GwJ/A18DQ4Ah7r41Rr12BGNy2wI7hO17DnjM3bfEOddJwHVAS6B82NZB7v58Pu3rCVwB7AtsAWYBA9w95tJMZlaeYLjFhcAewF/AJ8Cd7j41v2shRacFB0REREq/RIYQvAK0MrO+cfb/B9gdeCnBNpwJPA38A/gUeBh4DdgfeAZ4xaLmVTGzLsBEoD3wOjAQqAg8BIyIdRIzuxIYEx53WHjOBsBQMxsQp84AYCiwS1h+GEHIHhMeL7q8hed/MGzPwLB97YGJYbtFREREpAgS6YF9mCBs3mdmZxFOoRWGvCOANgQ9jU8l2IYfCGYueCuyp9XM/gN8BpwBnE4QajGzGgRhcgvQwd1nhNtvAcYBXc2su7uPiDhWM2AAsAJo4+7zw+23A9OBvmb2mrtPi6jTDugLzAUOzp7b1szuB2YCA8xsbPaxQt2BrsBUoJO7bwjrPAFMBp42s3Hunv9ki1Ii0qFXVcMCREREElfgHlh3/ws4CngRaEWwaIEB1wKtCXonO7t77Nm04x93nLuPiR4m4O6Lgew5XTpE7OoK7ASMyA6vYfkNBEMKAC6POk0voBIwMDJwhqH07vDH3lF1sn++K3JhhrD+4+HxLoyqk33em7PDa1hnOjAybHdXRERERKTQElrIwN1XAxeY2bXAwUAdYDXwmbv/UQzt2xQ+R4bijuHzuzHKTwTWA+3MrJK7byxAnXeiyhTkPO8At4RlbgMws8oEY4TXA5Pi1OkR1hkSY7+UcuptFRERSY6El5IFcPcVwHtJbksuZpYFnB/+GBki9wqff4jRrs1m9jOwH9CcYF7a7dX53cz+BBqZWRV3X29mVQluJlvn7rEmLPwxfN4zYttuBDeGzYvTCx2rjoiIiIgkKJGVuF4xs+PD2QZKwj0EN1y97e6RYblm+Lw6Tr3s7bUKUadm1HNxnKNWrJ1mdqmZzTCzGX/8URyd2SIiIiKlQyJhtCswFlhoZveb2f7F1CbM7GqCG6i+J/javdRz96fcvY27t9lpp51S3RwRERGRtJXIEIK2wAVAN4Jwea2ZzQKeB15292XJaFA4PdUjwLcEd/KviCoS3VsaLXv7qqg6dcN9y6MrkLf3tLDnSLSORIg1h2vfkTGn201Icc02EG9M67gOjxfL+URERCRQ4ADr7p8Bn5lZH4Jpr3oCxxGEzQFm9hbwAjA20ZkIsoXHfgiYTRBel8YoNodgyq49CaaziqyfBexKcNPXvKg6dcM606Lq7AJUBX5z9/Xha/3TzBYCDc1slxjjYPcInyPH1M4lmNqruZllxbgGserIdiS6MIGmxhIRESn9Er6Jy93/BkYBo8xsJ+A8gjB7KtCFoIdz50SPa2Y3Eox7/QI4Jp8e3XHAuUBn4OWofe2BKsDEiBkIsuscFtaZFlXn+Igy0efpEdaJnjUgTx1332BmUwnmxD0C+LiA55E0ol5VERGR9FeoWQiyhVNnPWRmDwPXAP8jmForIeEiBLcT9KgeG2PYQKRRwL1AdzN7LGIhg8rAnWGZwVF1hgA3AFea2ZCIhQxqE6wgBtvmnCXi5x5APzMbHbGQQTOCpWU3kjfYDiYIr3eaWeRCBgcTDL34g3BBBsks6lUVERFJH0UKsGa2F0Hv63kE004Z26aLKugxehKE1y0E86deHbVyLMB8dx8K4O5rzOwSgiA73sxGEKywdQrBdFmjCBYNyOHuP5vZ9cCjwAwzGwn8TXBjWiPggchVuMI6U83sQYKFGr4ys1EEy8N2A3YEropahQuCZWRPD487y8zGEAT6bgRTbF3i7msSuT4iIiIiklvCAdbMagFnEwTXgwlC6xrgWeB5d5+S4CF3DZ/LA33ilJkADM3+wd1Hm9mRQD+CpWYrAz8RhM1H3d2jD+Duj5nZfOA6gvllyxHcKHazuz8f66Tu3tfMvibocb0U2Ap8Dtzv7nnuLnJ3N7OzCZaS7QVcBWwgWGDhTnefGvcqSIlTr6pIXi+OHs1lt9zCk3fcQY9TT011c0REYipwgDWzkwmC30kEPZEOfEgwC8H/RS6dmgh37w/0L0S9KcAJCdYZA4xJsM5QIsJzAcpvJrgR7aFEzlOWJHpjlkhhHDLz7FQ3IV+ftY4ewi8iIgWVSA/sG+HzDwSh9QV3X5j8JklporAqIiIiyZZIgH2SYIjAJ8XVGCnbYk2BtWHlgyloiYiIiKSzROaBvbw4GyIiIoUz9uOPGTR8ON/NncvK1avZsVYtdm/ShDM6d+ay7t0B+PnXXxnw7LNM+OwzFi1dyg6VKtFg551p27Il/a++mjq1auU57jsTJ3LfU0/x9Q8/UDEriw5t23L7v/7F7k2b5ir34/z5vPD664z75BN+/f131qxbR726dTm6XTv+3bs3jerXz1V+4vTpdO7Vi/9cfjnHHXEEdw8ezGdffsnKNWv47t13adqwYbFdKxEpHfINsGbWnmAGgAUFOZiZHQAc5O4vJKNxIslYmEA3a0lp9uyrr3LV7bdTr25dTjjySOrUrs0fK1Yw+4cfghuyunfn9z/+4Iizz2bNn39y3OGHc+rRR7Ph77+Zv3AhL48dS++zz84TYN/46CPenzyZUzp25IiDD+ar779n9AcfMPGzzxj34ovsueuu28p++CHPvPoq7Q8+mLYHHUTFChX49qefGPp//8fbEyYwecQIGtarl6ftn335JQOeeYZ2rVpx/mmnsXzlSipUqFDcl0xESoHt9cB+DPyXYJorIGfBgRvcPdZ8r6cBtxKsyCVS4hRWpax59tVXqVihAp+OGsXOdXJ/LC9buRKA0e+/z4rVq7n/xhu54rzzcpX5c/16ypUrl+e4b48fz6iBAznhyCNztj0+bBjX33svfe66i7efeSZn+9knn8xV559PpYoVcx3jw6lTOfXyy7n3qad49JZb8pzjw6lTefSWW7j4rLMSf+EiUqbl/dTKLc+ErARTVtVKflNERKQwsrKyqJCVtz+ibu3auX6uXKlSnjJVq1Rhh8qV82zv8I9/5AqvAL3PPpvmjRsz/tNPWbBoUc72hvXq5QmvAEe3a8e+u+3Gh1Niz654wN57K7yKSKFsL8CKiEga637iiaz/6y9anXoqN9x7L29+9BF/rMi9mOGJRx1FtSpVuObuuzn7mmt49tVX+fann4gxZXaOw1u3zrOtfPnytGvZEoAvvvsuZ7u78/KYMZx48cU0ad+e6gcdRJUWLajSogWzf/yRRUuXxjxHm/33L8xLFhEp2kpcItniTZeVjDGsIhLf1T17Uqd2bZ4aOZJBL73EwGHDMDOOaNOGu/r2pfV++9GkQQMmvvwydw0axAdTpvDGhx8C0Kh+ffpccAH/PPfcPMeNHo6QrV7dugCsWbcuZ9uN993HwGHDqL/TThzdrh0Ndt6ZymGv7rA33sjVWxvrWCIiiVKAFRHJcOeecgrnnnIKq9as4ZMvvuDNceN44fXX6XLZZcx680122nFH9m7enBcHDGDz5s18NWcOH3/yCYNfeonr7rmHKjvswAWnn57rmEuXL495riXLlgFQo1q1nHKDXnqJ/XbfnXHDhlG9atVc5V9955247Y6xbLiISIEowErCtDiBSHqqVaMGndu3p3P79mzdupUXXn+dKTNncuoxx+SUycrKotV++9Fqv/1oe9BBHHPBBYwZNy5PgJ08c2ae42/ZsoWps2YBcNA++wAw/7ff2Lp1K53atcsTXn9bvJiff/st2S9TRKRAATb+ICkp1RRURdLfhM8+o/3BB+fpzcweB7tD5cp8/s037NakCTWrV89VJruXtUqMm7jGf/opb0+YkOtGridefpl5v/7KkYccQpMGDQBoEs7ZOnXWLLZs2UL58uUBWLd+PVf078/mzZuT9EpFRLYpSIDtb2b9ozea2ZbkN0fKslhTYI3r8HgKWiKSObr36UPVHXbgkAMPpGmDBrg7Uz7/nJmzZ9Ny333p2LYt/3nwQZ599VXatWzJro0bU7tGDeb9+itvT5hApYoVuTJqai2AEzp0oHufPpzSsSPNmzThq++/5/3Jk9mxZk0e7tcvp1z9unU58/jjefWdd2jbtSud2rVj9bp1jJs2jcqVKnHA3nvz1fffl+QlEZEyoCABNtFBSuqxFREpIbf36cOHU6bwxbff8t6kSVSuWJHGDRpw5zXXcEm3blSoUIEzjz+ejX//zadffMGsb7/lr40babDzznTt3Jl/9ezJfnvskee4XTp1olfXrtz31FO8O2kSFbKy6HL00dz+r3+xR7NmucoO/u9/adaoEa+9+y5PjhhB3dq1ObFDB2658krOueaaEroSIlKWWH7TqEhqtGnTxmfMmJHqZiRlCEEisxBoEQJJxKbHB7JHjNWdRJLpxyVLqHDFlaluhkha23fO9zPdvU1JnlPzwIqIiIhIRtEsBFLiPa0iIiIiRaEeWBERERHJKOqBlYQVtbdVY11FRESkKBRgJa5kDAtQWBUREZFk0xACEREREcko6oEtpeLdmNV35NgSbomIiIhIcsUNsGa2ArjH3e8Lf74VGO/uE0uqcZJ8v900KdVNEBERESmS/HpgawGRC2T3Dx8KsBls5M/3proJIiIiIkWSX4BdAjQqqYZI6mgOVxEREckk+QXYT4AeZrYF+D3c1sHMtndMd/c7ktE4yRyabUBERERKSn4B9npgT+CyiG0dwkd+HFCALUHJWElLREREJFPEDbDu/pOZtQB2BRoC44GhwPMl0jIRERERkRjynUbL3bcCc4G54dCB+e4+oSQaJkUTb1zrhpUPFvgYGhYgUjb9snAh+3TuzHmnnMJTd92V6uaIiORR4Hlg3V2LHohIxpjf9cxUNyFfzUa9muomiIhkrEItZGBmjYCWBFNtrQY+d/ffktguKSaacUBEREQyXUIB1syaAk8Cx8TY9wHQ293nJ6dpIiIiIiJ5FXhYgJnVByYDxwK/AC8C94XP88Ptk8NyIiJSgqZ//TU9rruO3Tp1olarVux61FGcfOmlvPbuu4Uql23OvHmcdfXVNDzsMOoecgidzj+fD6dOzVNu9dq1PDRkCMdfdBG7d+pEzZYtadK+PV2vuopPv/gi5rGrtGjBcRdeyOJly7j8ttvYrVMnqh14IC+OHl3UyyEipVwiPbC3EMxGcCPwoLtvyd5hZuWBawgC7c3AlclspBQv3awlktmeGzWKf915J+XLlePEDh3YrWlT/lixgs+/+YanRo7kjM6dEyqXbf7ChRx13nnst8ceXHTmmSz+4w9Gvfcep15+OUPvvZeuEeXnzJtH/0cf5fDWrencvj21a9Tg199/563x43l/8mRGPfYYxx5+eJ62r1y9mg7nnku1KlXo0qkT5cqVY+c6dYr3golIxkskwJ4IvO/u90fvCMPsADM7GjgJBdgSpXGtImXXd3Pn0ueuu6hRtSofPP88++6+e679vy1enFC5SJNnzqTPBRdwd9++OdsuO+ccjjrvPK6+/XaOPfxwalSrBsBezZszd9w46taunee47c85hxvvuy9mgJ3944+cc/LJPHH77WRlFeq2DBEpgxKZWaA+MHM7ZWaG5UREpAQ8PXIkmzdv5qbLLssTSgEa1a+fULlINatX59+9e+fa1nq//eh+wgmsWruWNz/6KFfZ6PCafdzTjjmGOT//zK+//55nf8UKFbi7b1+FVxFJSCKfGKuBptsp0yQsJyIiJeCzr74CiNm7WZhykQ7aZx+qV62aZ/sRBx/MsDff5Mvvv+e8Ll1ytk+bNYvHhw3j0y+/5I8VK/h706Zc9RYtWULjXXbJta1pw4YaMiAiCUskwE4GuprZIHfPM4LfzP4BnAm8lazGSW7xloyNNYQg3rjWcR0eT2qbRCS1Vq9dC0CDevWSUi5SvGBZr27dXMcEeOOjjzj32mupXKkSHdu2pXnjxlTZYQfKlSvHpOnTmTRjBhujAi1APYVXESmERALsXQTjYCeY2QjgY+B3giEDHYCzga3A3UluoySRbtgSKV1qVq8OBL2bezVvXuRykZYuXx5z+5Jly3IdE+COgQOpWKECk0eMYO+o41/53/8yacaMmMcKV3kUEUlIgcfAuvvnQFdgDXAu8DQwFngG6BFuP8vdtzdOVkREkuSQAw4A4P3Jk5NSLtIX333H2j//zLN90vTpABy499452+YuWMDezZvnCa9bt25l2qxZBT6niEhBJLQ8rLuPJRjneh7wEPBc+NwDaOrubya9hSIiEtcl3bqRlZXFPU8+yXdz5+bZnz27QEHLRVq9di3/e+KJXNtmfvMNI95+m5rVq3NKp04525s2aMDcBQtYtHRpzjZ3585Bg2KeT0SkKBK+7dPd/wReCh8iIpJC++y2Gw/368fVd9zBoWeeyUlHHcVuTZuyYtUqZs6eTY1q1Xj3uecKXC7S4a1bM/S115j+1Vcc2rJlzjywW7du5bFbb82ZQgvgyh49uPqOO2h35pl0OeYYKmRlMW3WLL6fN48TOnTg7fHjS/jKiEhppnlLSgGNaxUp23p17cp+u+/Ow88/z6QZMxgzbhx1atdm/z335MLTT0+4XLZmDRvy6K23cstDD/HMK6+wcdMmDtpnH/7duzfHHHZYrrIXn3UWlSpWZOCwYQx/8012qFSJdq1a8eQddzD6ww8VYEUkqczdU90GidKmTRufEeOGh3izEJzwpb6ek7Jn0+MD2SOBO+pFCuPHJUuocIXW5hHJz75zvp/p7m1K8pzqgc0g8VfcUg+siIiIlB0J3cQlIiIiIpJqCrAiIiIiklEUYEVEREQkoxQ4wJrZODO7ozgbIyIiIiKyPYn0wLYFyhdXQ0RERERECiKRAPsj0Li4GiIiIiIiUhCJBNhngBPNrEmyG2FmXc3sMTObZGZrzMzNbFicss3C/fEeI/I5T08z+8zM1pnZajMbb2axJ1cNypc3s2vM7Csz+8vMVpjZ22bWLp86O5jZf81sjpltMLOlZvaKme2T2FURERERkVgSmQd2DHAMMMXM7gWmA4uBPCshuPuCBNtxM3AgsA74Ddi7AHW+BEbH2D47VmEzGwD0DY//NFAR6A6MMbOr3H1gVHkDRgBdgTnAQGBHoBsw0czOcPc3oupUAj4ADgNmAI8Q9FqfSRD+O7r7pwV4bTFpxS0RERGRxALsPIKwagTBLB5P8LgA1xAEy5+AI4GPC1DnC3fvX5CDhz2mfYG5wMHuvjLcfj8wExhgZmPdfX5Ete4E4XUq0MndN4R1ngAmA0+b2Th3XxtR51qC8DoK6ObuW8M6IwnC9nNm1iJ7u4iIiIgkLpGg+QIxeluTwd1zAmvQ8Zl0vcPnu7LDa3je+Wb2OHALcCFwW0Sdy8Pnm7PDa1hnehhIexAE3CFhuy3iPDdEhlR3f8PMJgFHUPCALiIiIiIxFDjAuvsFxdiOwmhgZpcBdYDlwDR3/ypO2Y7h87sx9r1DEGA7EgZYM6sMtAPWA5Pi1OkR1hkSbtsNaAL84O4/x6lzRFhHAVZERESkkBL9qj+dHBM+cpjZeKBn5BhcM6sKNATWufvvMY7zY/i8Z8S23QimDJvn7psLWGev8PmHOO2NVUdEREREElSolbjMbG8zO83MeiS7QQWwHrgDaA3UDh/ZX8t3AD4KQ2u2muHz6jjHy95eKwV1RERKxC8LF1KlRQsu7dcv1U1JqUv79aNKixb8snBhqpsiIkWQUA+smR1EMJ1Wy4jNL4b7jiT4mrybu49JVgOjuftS4NaozRPN7FiCm6v+AVxM/jeapR0zuxS4FKBJk6TPVCZS5jw3cGmqm5CvXlfunOomiIhkrESWkt0TGE/wVfkjBGE10kRgBcGNTSUu/Kr/mfDH9hG7sns+axJb9vZVKaiTw92fcvc27t5mp512inMIEREREUlkCMFtBHOn/sPdryWYBzaHuzswDTg4ec1L2B/hc84QAnf/E1gIVDOzXWLU2SN8jhy7OhfYAjQ3s1i91LHqzAmf441xjVVHRERERBKUSIDtBPyfu3+bT5lfgQZFa1KRtA2f50VtHxc+d45R5/ioMoTTZk0FqhDMHLDdOgShdwGwp5ntWsA6IiJJMf3rr+lx3XXs1qkTtVq1YtejjuLkSy/ltXfzTr7yy8KFnH/99TQ+4ghqt27NYd268faECXnKrV67loeGDOH4iy5i906dqNmyJU3at6frVVfx6RdfxGxHlRYtOO7CC1m8bBmX33Ybu3XqRLUDD+TF0aNzyowYO5ZDzzqLHdu0oemRR3LRv//NoqVLOe7CC6nSokXM434wZQqnXn45jY84glqtWrHf8cfznwceYNWaNTHLj5s2jaN79qTuIYfQ8LDDOOvqq5kzL/pXg4hkqkQCbG2CxQbyYwS9tMXGzFqZWZ52m1knggURAKKXoX0ifO5nZrUj6jQDrgA2sm06rGyDw+c7w2m1suscTLAa1x/Aa9nbwx7o7PPcF9lGM+tCEIS/BfL+lhARKYLnRo2iY48ejBk3jrYHHsjVPXvSuX17lq5YwVMjR+Yqu+D332l/zjn8smgRZ590El07d+bbn37irKuvZsJnn+UqO2fePPo/+ijlzOjcvj1Xn38+Hdu2ZcKnn3LMhRfy/uTJMduzcvVqOpx7LtO/+oounTrR++yz2blOHQAefO45ev373yxYuJBzTzmFHl268O3cuXQ6/3xWrV0b83h3DR5Ml969mfH113Ru355/nnsuuzVpwsNDh9Lp/PNZs25drvKvv/8+p/TuzefffMPpxxzDRWeeyYrVqznqvPOYr5u3REqFRG7iWgLsvp0y+xH0wibEzE4FTg1/rB8+H2pmQ8M/L3P368I/PwjsYWZT2RaoD2DbXK+3uPvUyOO7+1Qze5BgpayvzGwUQdDuRrA87FVRq3BBsIzs6QRjemeZ2RiCOWe7EUyxdYm7R//X/0HgpLDOp2b2EcHcsGcSzJ7QS6twiUgyfTd3Ln3uuosaVavywfPPs+/uuT+mf1u8ONfPE6dPp98//0m/yy/P2XbWCSfQpXdvHhoyhCMPOSRn+17NmzN33Djq1q6d6xi/LV5M+3PO4cb77uPYww/P06bZP/7IOSefzBO3305W1rZfMz//+iv9H3uMurVrM/WVV2hUP/i4v8OdC268kVffib61AiZ89hl3DRrEPw48kNcHDaJWjRo5+14cPZrLbrmFOx9/nPtuvBGAdevXc9Xtt1OuXDk+eP55Wu+3X075G+69l4HDovs3RCQTJdIDOw442cz2irUz7JnsBLxXiHYcBPQMH8eF25pHbIu8MexFYBbBWNtLgH8SjC99BWjv7nfGOoG79yVYbWsxwd3+5wPfACe7+8AY5R04myD0bgauIgi0E8PzvBGjzkaCuWnvIJgu65rw59EES9h+uv1LISJScE+PHMnmzZu56bLL8oRXICckZmvSoAE3XXpprm3HHHYYjXfZhZmzZ+faXrN69TzhNfuYpx1zDHN+/plff887vXbFChW4u2/fXOEVYOTbb7N582Z6n312rnaZGXf06UP58uXzHGvQ8OEAPN6/f67wCtDj1FM5YO+9Gfn22znbxo4bx4rVq+l2wgm5witAv3/+k5rVq+c5h4hknkR6YP9H0JM40cz6E451NbP9CO76vw1YCwxItBHu3h/oX8CyzwLPJnqOsO5QYGgC5TcDD4WPgtZZTzDNV/RUXyIiSffZV8EChLF6QmM5YK+9YgbFRvXr8+mXX+bZPm3WLB4fNoxPv/ySP1as4O9Nm3LtX7RkCY13yX1/bNOGDXOGDET68vvvAWjXqlWefU0aNKBR/fp55mf99MsvqZCVxf+9/z7/9/77eept2rSJP1asYPmqVdSpVYsvvvsOgMPbtMlTtmb16hyw115MmjEjzz4RySyJLCU7x8zOAF4GsnssDfgqfF4FnB65CpaIiBSv1eG40Qb16hWofK04PZBZ5cuzdWvuEU5vfPQR5157LZUrVaJj27Y0b9yYKjvsQLly5Zg0fTqTZsxgY1SgBagXI7wCrAnbGivcZm+PDrArVq9m8+bN3D14cMw62f5cv546tWqxOhwPG+8c9erWzfc4IpIZElrIwN3fDe+w70lwx38dgvlPPwGGuPuK5DdRRETiyf5KfNGSJezVvHlSj33HwIFUrFCBySNGsHfUsa/873/j9mSaWczt1atVA2Dp8uUxhzssXb48z7aa1aqxdetWFk6ZUqA214w4RyxLli0r0HFEJL0lvJSsu69y90fc/Wx3P9bdz3T3BxReRURK3iEHHAAQd0aAopi7YAF7N2+eJ7xu3bqVabNmJXy8A/feG4Cpn3+eZ9+CRYvy3HAGcPABB7ByzRq+/emnAp3joH32AWByjHC9eu1avpozJ892Eck8CQdYERFJH5d060ZWVhb3PPkk382dm2d/rFBYUE0bNGDuggUsWrptWV53585Bg2Kea3u6nXgiWVlZPPHyy7na5e7c+vDDbNmyJU+dq3r0AOCK/v1ztSPbn+vX81nE2N2TOnakdo0ajHz7bWZ+802usncNGpQz5EJEMltCQwgAzOxcoBfQEqgBrCGYFeA5dx+e3OaJiEh+9tltNx7u14+r77iDQ888k5OOOordmjZlxapVzJw9mxrVqvHuc88V6thX9ujB1XfcQbszz6TLMcdQISuLabNm8f28eZzQoQNvjx+f0PGaN27MLVdcwW2PPMI/zjiDMzp3pma1anw0bRor16yhxV57MfuH3IsVHtW2LXf06cOtjzzCASedxHGHH06zRo1Yt349CxYtYvKMGRzaqhVvPhFMw12tShUG3nYbPa6/nmN69qTrccdRf6edmDprFt/++COHt27N5JkzC3U9RCR9FDjAmlkFYBTBPKdGsNTqH0Bd4Cigg5mdBXR197yj+kVEpFj06tqV/XbfnYeff55JM2YwZtw46tSuzf577smFp59e6ONefNZZVKpYkYHDhjH8zTfZoVIl2rVqxZN33MHoDz9MOMACXH/xxTSsV49HX3iBF0ePpnrVqhzdrh13Xnstp1x2GTXCMayR+l50EYe2bMmg4cOZOmsWYz/+mJrVq7PLzjvTq2tXzjrxxFzlTzv2WN6oVo27n3iC195/n0oVKnBY69Z8PGwYDzz7rAKsSClgwXSnBShodivBVFefAP8GJrv7FjMrT7DK1P+AQ4Db4s3FKgXTpk0bnxFj/NZ3e++TgtaIpKdNjw9kjwLeeS/pb826dTTr0IED9tqL8cPT58u8H5csocIVV6a6GSJpbd85389097xz1xWjRMbAng/8BHRw9wnuvgXA3be4+3igAzAPuCDJbRQRkVLijxUr2BQ19dbmzZu5acAANmzcyCmdOqWoZSKSSRIZA9sIeMzd/4610903mtkbwBVJaZmIiJQ6oz/8kDsff5yj/vEPGtWvz4o1a5gycyY/zp/PAXvvzeXnnJPqJopIBkgkwC4CKmynTIWwnIiISB4Ht2jBoS1bMnnmTFasXg1As4YNueGSS+h70UXsULlyilsoIpkgkQD7EnCBmd3q7muid5pZLaArhVzmVURESr+D9tmHEQ8/nOpmiEiGS2QM7O3ADOAzMzvHzBqZWYXw+VyCm7s+A+4ojoaKiIiIiEA+PbBmthWINUWBAS/G2b4H8Fd+x5XtWzLvJx7odlKe7SekoC0iIiIi6Sa/oDmR2AFWRERERCRl4gZYd+9Qgu0QERERESmQRMbAioiIiIiknAKsiIiIiGSUhG+2MrOTgYMIFjaINS+su/tFRWxXmWbl61G59rUx9miNCBEREZECB1gzawqMAfYjmHEgHgcUYEVERESkWCTSA/sosD/wHPACsBDYXByNEhERERGJJ5ExsB2B99z9Ynef6O5z3f2XWI/iaqyIiBSfOwcNokqLFkycPj3VTQHgl4ULqdKiBZf265fqpohImkmkB3YT8HVxNUREJJkG3XFTqpuQr3/eck+qmyAikrESCbBTCIYQiIhIKdT77LM5s3NnGu+yS6qbIiKSr0SGENwKtDez7sXVGBERSZ26tWuzV/PmVNlhh1Q3RUQkXwUOsO4+C+gEDDSzj83sATO7NcbjluJrbtlQY+0COo6/Is9DRCRa5DjReb/+yjnXXkujww9n53/8g5MvvZRvfvwRgD9WrOCK/v3Z9aijqN26NYd3786Ezz7LdaxYY2Cvu+ceqrRowY333Zfn3EP/7/+o0qIFJ11yCVu3bs3ZvmL1am59+GFannIKO7ZpQ/1DD+WEiy/mw6lTY76GtX/+yY333cfunTpRu3VrDjr5ZB59/vlcxxQRiZTINFo1gbuBHYEjw0csDtxR9KaJiEhB/bJoEUeecw57NW/OeV268MuiRbz50Ud07tWLj4cNo0vv3lSvVo2unTuzcvVqXn3nHU69/HK+Gjs23yEDd/fty9TPP2fgsGF0aNuW49u3B+Dbn37iunvuoV7dujz7v/9RrlzQH7Jg0SKO69WLXxYu5LBWrTjmsMP486+/eGfiRLr07s1jt95Kr65dc46/8e+/OeHii5k5ezYt9tqL7ieeyKq1a7nnySeZNGNG8V40EclYiYyBfQg4CvgQeBFYhKbREhFJC5NmzOC2q67ixksvzdn2vyee4I7HH+fIc87h9OOO45Gbb84Jmh0PPZSL//MfHnvhBe678ca4x61YoQIvDhhAu7PO4rKbb2baq69Su0YNzr/+ejZs3Mirjz5Kvbp1c8pf0q8fCxYt4vn77uPM44/P2b5qzRqO69WL6+65hxM7dMip88jzzzNz9my6HH00wx94IKd91110EYd165bUayQipUciY2BPAqa6+7Hu/qK7f+TuE2I9iquxIiISW9OGDbnuotxryJzXpQsAGzdt4u6+fXPCIUC3E04gKyuLr+bM2e6xd2vShIG33caylSu58MYbuebuu4Me2Isu4qi2bXPKfTVnDpNmzODUo4/OFV4BatWowc3//CcbNm5k9Icf5mx/cfRoypUrx13XXpurfc0aNeLyc89N7CKISJmRSA/sDkDsAUwiIpJSB+y1F+XLl8+1bZeddgJg96ZNqV61aq595cuXZ+cdd2ThkiUFOv6Zxx/Px59+ytDXXmPyzJm0a9WKW67IPTb/0y+/BGDNunXcOWhQnmMsW7ECgDnz5gHB2Ne5CxbQqH59mjdunKd8+4MP5u7BgwvUPhEpWxIJsLOA5sXVEBERKbya1arl2ZaVlRV3X/b+TZsLPhLstGOOYehrrwFw+dln5wnMK1atAuCjadP4aNq0uMdZt349AKvXrgVg5zp1YparF2e7iEgiAfYOYKyZHe7uk4urQSIikn6WrVzJ5bfemjPF1g3330/7Qw5hpx13zClTIwzKA266iX8W4Ov/mtWrA7B0+fKY+5fE2S4iksgY2F2AscA4MxtqZleZ2fmxHsXUVhERSQF355J+/Vi0dCn333gj9994I78vXcrF//kP7p5T7pADDwRgysyZBTpu9apV2a1JExYtXcq8X3/Nsz9dlrQVkfSTSIAdCpxK0Gt7PvAIMCTqMTR8FhGRUuLRF17gvUmT6Nq5MxeecQYXnnEGXTt35oMpU3hoyLaP/Nb77cdhrVrxxkcf8fzrr8c81uwffsjV49rj1FPZunUrNz/0UK55X+f/9huDhw8vvhclIhktkSEEFxZbK0REJC3NmD2bWx9+mGYNG/LYrbfmbB94223MnD2b/o89xuGtW+f0vg65915OuPhiLr/1VgYNH87BLVpQq3p1Fi5ZwuwffuCbn35i/LBhOeNe/9WzJ2PGjWP0Bx9w6FlncUy7dqxau5b/e+89DmvdmrfGj0/FyxaRNFfgAOvuzxdnQ0REJL2sXruW86+7DoDn778/Z8wqBONdXxgwgE49etDzxhuZ9sor1KpRg0b16zNl5EgGv/QSoz/4gJFvvcWWrVupV7cuezdvTu9zzmG/PfbIOU6lihV56+mnuWvQIEa99x6PDx9O0wYNuPGyyzilY0cFWBGJySLHL0l62L/yDv5qs2apboZIWtv0+ED2qFcv1c2QUu7HJUuocMWVqW6GSFrbd873M929TUmeM5ExsCIiIiIiKVfgIQRmNq+ARd3ddytke0RERESkiM76d96I98r/Cj7vc7pL5CauckCs8Qa1gJrhnxcBm4rYJhERERGJECuQQukKpYlI5CauZvH2mdnuwKNAVeC4ojdLRERERJKpNIXgRHpg43L3n8zsdGA2cBvw72QcV0RERKSsiRc0S/J86R5qk3aF3H2DmX0AnI0CrIiIiJRBifRyJiOolnTYTRfJftWbgfpJPqaIiIhIWimrwTFdJO3qm1ld4DQg74LWIiIiImWYAm9yJTKN1q1xdmUBjYEuBLMRaPiAiIiIiBSbRP470H87+9cAd7r7fYVvjoiIiEjxy8Qbl2SbRALsUXG2bwVWAt+7u/7mRUREJCPpa/7Mkcg8sBOKsyEiIiIikh7Sfc5Y/VdDRERE0lIyQpR6VUuncvntNLNyhXmUVONFRKRgjrvwQqq0aJHqZhTIi6NHU6VFC14cPTrVTZEiOuvfWXkeIsmwvXfSpkIc0wtwXBGRYrV82MpUNyFfdc6rneomiJQ6Cshlx/b+pn8lCKQFUQ2ok2gDzKwrcCRwEHAgUB0Y7u7n5VOnHXAz0BbYAfgReA54zN23xKlzEnAd0BIoD3wDDHL35/M5T0/gCmBfYAswCxjg7mPjlC8PXA1cCOwB/AV8QjA7w9R45xERKW7P3H036//6K9XNEIkrkfCpoCr5vgPcvdn2DmBmFYCrgH7hpvkJtuFmguC6DvgN2Hs75+sCvAZsAEYCK4CTgYeAw4AzY9S5EngMWA4MA/4GugJDzayFu18Xo84AoG/YpqeBikB3YIyZXeXuA6PKGzAiPO4cYCCwI9ANmGhmZ7j7GwW4HiIiSdd4l11S3QQRQOFTkqNI7yIzOxP4H7ArsBq4AXg0wcNcQxASfyLoif04n/PVIAiTW4AO7j4j3H4LMA7oambd3X1ERJ1mwACCoNvG3eeH228HpgN9zew1d58WUacdQXidCxzs7ivD7fcDM4EBZjY2+1ih7gThdSrQyd03hHWeACYDT5vZOHdfm+D1ERHJ19iPP2bQ8OF8N3cuK1evZsdatdi9SRPO6NyZy7p3B4IxsJNmzGD911/nqrvx77+5/5lneOnNN1m0dCm77Lwz3U44gX/37k3t1q05ok0b3hsyJKf8nYMGcffgwbz73HMsX7mSB4cM4duffqJyxYp0ateO/113HQ3r1ct1js+/+YaXxoxh0vTp/LZ4Mes3bKBR/fqc2KEDN156KbVr1iz+iyTFSqFUSlqhbrgys3ZmNpWgx7ExQWjdzd0HuPvfiRzL3T929x/dvSBDFboCOwEjssNreIwNBD25AJdH1ekFVAIGRgbOMJTeHf7YO6pO9s93ZYfXsM584PHweBdG1ck+783Z4TWsM52gp3insP0iIknz7KuvctbVV/Pd3LmccOSRXN2zJ8cdcQR/bdy43Zug3J2zr7mGuwcPJisri95nn80JRx7JsDfe4Pzrr8+37lMjRtDr3/+maYMGXNa9O/vuvjuj3n2Xky65hI1/5/41MOS11xj1zjvs0awZPU49lUvOOov6devy6Asv0On881n7559FvQwiUsYk9F8mM9sNuBc4DTBgFPBvd59bDG2LpWP4/G6MfROB9UA7M6vk7hsLUOedqDIFOc87wC1hmdsAzKwy0C48/6Q4dXqEdYbE2C8iUijPvvoqFStU4NNRo9i5Tu7bEJatzP9GtpfHjuXdiRM5rFUr3nrmGSpWqADALVdcQYdzz8237gdTpjDp5ZfZf889c7ZdcMMNvPLOO4wdN44zOnfO2X79xRfzcL9+lC9fPtcxhv7f//HP227jqREj6HvRRQV6vZJ66m2VdFCgd6GZ7UgQ1i4jGAs6Dejr7p8UY9ti2St8/iF6h7tvNrOfgf2A5sB3Bajzu5n9CTQysyruvt7MqgINgXXu/nuMNvwYPu8ZsW03ghvD5sVZjSxWHRGRpMjKyqJCVt6P87q185/pYPgbwbD82666Kie8AtSqUYObLruMXv/+d9y6l59zTq7wCnBh16688s47zJg9O1eAbdKgQcxj9DztNG66/34+nDpVAbaEKHxKaZHvO9nMKgJ9gJuAWgRjQm9y99eKvWWxZQ+UWh1nf/b2WgnWqRqWW1+M54iuk4uZXQpcCrBLjF9EIiKxdD/xRG4aMIBWp57KmZ07c3ibNhzasiU77bjjdut++f33lCtXjrYHHZRnX7tWrfKt22q//fJsa1S/PgAr16zJtX3Tpk088+qrjHr3Xb6fO5fV69axdevWnP2Lli7dbltFRCJtLynNAZoQ3ADVB3g83jRVUjTu/hTwFMD+lXco6NRlIlLGXd2zJ3Vq1+apkSMZ9NJLDBw2DDPjiDZtuKtvX1rHCJrZVq9bx441a5IV4z/N0cMRotWqUSPPtqxwiMDWLbl/TfS4/nre/Ogjdm3UiBOPOor6detSsWJFAB4fNizPmFlJjHpVpSza3ru+KcE8sEYwh+p1wWxR+XJ3b5qEtsWS3ZMZ75bV7O2rourUDfctz6fO6qjnRM+RaB0RkaQ495RTOPeUU1i1Zg2ffPEFb44bxwuvv06Xyy5j1ptvxu2NrVG1KitWr2bz5s15QuzS5bE+LhM385tvePOjj+jYti2jw5vFsm3dupWHhui2gGjpvga9ZIavf16QZ1uLXZukoCXFoyD/bTOC+Uy3/31U8ZsDtCEYSzozcoeZZRFM57UZmBdVp25YZ1pUnV0Ihg/85u7rAdz9TzNbCDQ0s11ijIPdI3yOHFM7l2Bqr+ZmlhVjHGysOiIiSVWrRg06t29P5/bt2bp1Ky+8/jpTZs7k1GOOiVn+wH32Yfynn/LJF19weJs2ufZN/fzzpLRp3oLgl+iJHTrkCckzvv6avzZsiFWt1FEolaKKFUihdIXSROQ7jZa7lyvMoxjbOy587hxjX3ugCjA1YgaC7dU5PqpMoeqE02ZNDc9/RALnEREpkgmffUasWQj/WLECgB0qV45b95yTTwbgv489xt+btq0cvnrtWu558smktK9peAPXxBkzcm1funw5fe66KynnEJGC+frnBTEfmSjTBs6MIpjGq7uZPRaxkEFl4M6wzOCoOkMIFli40syGRCxkUBv4T1jmiag6TxBMe9XPzEZHLGTQjGBp2Y3knQ5rMEF4vdPMIhcyOJhgNa4/CFYQExFJmu59+lB1hx045MADadqgAe7OlM8/Z+bs2bTcd186tm0bt+65p5zCq++8wwdTptDmtNM4sUMHNm3ezOgPPqD1/vvzw/z5lCtXtD6J1vvvz6EtW/LGhx9y1Hnn0a5VK5YuX877kyezR7Nm7LLzzkU6flmisa6ZQT2lJSPl/xrM7FTg1PDH+uHzoWY2NPzzsuylXt19jZldQhBkx5vZCIIbzE4hmC5rFMGiATnc/Wczu55gsYUZZjaSbUvJNgIeiFyFK6wz1cweBK4FvjKzUQTTh3UjGEpxVdQqXBAs6nB6eNxZZjYGqBPWKQ9c4u5rEBFJotv79OHDKVP44ttveW/SJCpXrEjjBg2485pruKRbNypETI8VzcwY+cgj3Pf007w8diyDX3qJ+jvtxLldunBpt26MGTeO6lWrFql95cuX59XHHuO/jz3Ge5MmMWj4cBrsvDM9Tz+dmy69lFannlqk42c6hdLMlYyey3Tp/czE8bJWsAWwirEBZv0JFwSI4xd3bxZV5zCgH3AoUJlgGdrngEfjzZJgZicT3IjWimDoxLcEq3M9n0/bLiDocd0X2Ap8Dtzv7mPjlM8CriJY/Wt3YAPBuNs73X1qPq8xl/0r7+CvNmtW0OIiZdKmxweyR9SSpZI8H02dysmXXcZ1F13E7X36pLo5JWZe/dw3Ki/+eTF9vu0Ts2y88asKpWVDouEzViBMlwAbS7wAG+t9v++c72e6e5sYxYtNyv+VuXt/oH+CdaYAJyRYZwwwJsE6Q4GhCZTfDDwUPkRE0t6ipUtpEPU1/vJVq7jl4YcBOLlTpxS0KjMoqEoi0jmsZiL96xMRKcNuuv9+vp4zh38cdBA71a7NwiVLeH/yZFasXs1FZ57JwS1apLqJRRbdqwrQfLGm2y7rMvFrc9lGAVZEpAzr0qkTS5cv550JE1i1Zg2VK1Vin912o+fpp3PB6aenunkJiRVURRKhG7C2iXctviP20tAlTQFWRKQMO6NzZ87oHGvGwNJNYTczJCNQlqabrWQbBVgREUlb8YKmhgCIlG0KsCIiIlIqqKe0+MW8efGCEm+GAqyIiBRdMm6USuRrfQ0ByAyJ3Cil8CmJUIAVkczk4O6YKcikq0z/+j/V86SXVhqTKsmgACsimWn9n2wG4q8zJcWhTPV8boH1W9anuhUZQYFSSpoCrIhkJPt8FuuaNKF2lj7GpHhs/mszs9bMSnUz0o7CqqQDffKLSEYq9+GHLG95EDRtSrXy5ckCDSfIEOnci+vusCUIrwuWLODjZR+nuknFTnOfSiZSgBWRjFRu2TK4fwDLjz6aZa1aQpUqoACbVH/UiLHx5xJvRolbv2U9s9bM4uNlH7Ns07JUNyepEuk9VU+rpDMFWBHJWOWWLaPciBEwYkSqm1Iq9Yk1XY6kHQVNKYv06SQiUgrFmqvxlf9tLnBZEZF0pk8tEZEyQkE1dRIZZ6oxqSLbp08zEZEMoQAqIhLQp6GISAol8lW/lD66qUqkcBRgRUREQomERC2JKpI6CrAiIkkU72t+9aqWPgqqIqmjACsiUgI0frX4JaP3VEQygz5RRUS2o6R7VRV2i596T0Uymz4lRUQKSUFTRCQ19OkrIiJpS3OiiqSXWP8mU7GItwKsiJRquqmqZMT6pZZoyNSUUiJSUAqwIiIRNCxARCT96ZNaRMokBdXCSUYvqb7+F5Gi0ie4iIiUKH39LyJFpQArImlNS62KiEg0BVgRyTj6+r9kqKdURNKVfguISLFJZAYAhdLkSmRWAAVVEck0+o0hIiVOYTV5FD5FpCzSbxERkQxR1LCqsCsipYUCrIgUmXpUCy8ZCwCIiJQ1+q0jIjFpBavkUu+niEjyKMCKSELU25o/BVURkeKn30QiZYh6VQunpFeUUggWEcmfAqyIqFe1kBQ0RURSQ7+1REophdJASfeeiohI8dNvOBGRCOpVFRFJfwqwIhlC41eTS0FVRCRzKcCKZDgNFRARkbJGv/lE0pBC6Taa6F9ERKLpt6RICimoFo6+/hcRKdv021OkkBIdk1oWw2oiMwAolIqISEGVvd+oIsWsLAbVRCmsiohIUZRLdQNERERERBKhriKRCOo93UZf/4uISLrSb2spsxRWC0dhVUREUk2/wUXKEC2rKiIipYECrIioV1VERDKKAqyUemV1qIBCqYiIlFZl8ze7ZAzNtbqNvv4XEREJZOxvezObDzSNs3uJu9ePUacdcDPQFtgB+BF4DnjM3bfEOc9JwHVAS6A88A0wyN2fz6dtPYErgH2BLcAsYIC7jy3QiyujSnP4LE7qaRURkbIm0xPDauDhGNvXRW8wsy7Aa8AGYCSwAjgZeAg4DDgzRp0rgceA5cAw4G+gKzDUzFq4+3Ux6gwA+gK/AU8DFYHuwBgzu8rdByb8KkuZZATV0h52FUpFRETiM3dPdRsKJeyBxd2bFaBsDeAnoCZwmLvPCLdXBsYBhwJnu/uIiDrNgO+BP4HW7j4/3F4bmA7sBrRz92kRddoBU4C5wMHuvjLiWDOBqsDe2ceKZ//KO/irzbb7sjJCaQ6ayfhKX0FVREQynf13zUx3b1OS5yy96SK3rsBOwAvZ4RXA3TeY2c3AR8DlwIiIOr2ASsC9kYHT3Vea2d3As0BvYFpEnd7h813Z4TWsM9/MHgduAS4Ebkvia5M0o1AqIiJSvDI9wFYys/OAJgQ9pV8BE2OMZ+0YPr8b4xgTgfVAOzOr5O4bC1DnnagyBTnPOwQBtiOlMMCW5p5WERERSS+ZnjrqAy9GbfvZzC509wkR2/YKn3+IPoC7bzazn4H9gObAdwWo87uZ/Qk0MrMq7r7ezKoCDYF17v57jLb+GD7vWZAXls5Kc1hV76mIiEj6y+QkMgSYRDArwFqC8HklcCnwjpkd6u5fhmVrhs+r4xwre3utiG0FqVM1LLe+kOfIYWaXhm1nl6z0+GspzUEVFFZFREQyVcYmFHf/b9Sm2UBvM1tHMAtAf+C0km5XYbn7U8BTENzEVZLnLu1BVUREREqX0phcniAIsO0jtmX3ftbMWzzX9lVRdeqG+5bnU2d11HMi5yg2iS4AICIiIpIpSmOA/SN8rhqxbQ7QhmD86czIwmaWBewKbAbmRdWpG9aZFlVnl/D4v7n7egB3/9PMFgINzWyXGONg9wif84ypleTRsAAREZHSrzQG2Lbhc2QYHQecC3QGXo4q3x6oQjB7wcaoOoeFdaZF1Tk+okykcUCPsM6QAtaRUCLhU8unioiIlF0ZuZCBme0DLHD3P6O2NwM+AHYH+rn73eH2GgSLC9Sg4AsZ7EowI0FGLmSQzuNa1UsqIiJSemghg4LrBvQ1s4nALwSzEOwGnAhUBt4GBmQXdvc1ZnYJMAoYb2YjCJaSPYVguqxRBMvLElHnZzO7HngUmGFmI9m2lGwj4IHI8BrWmWpmDwLXAl+Z2SiCpWS7ATsCV20vvBaGwqqIiIiUJembfPL3MUHwbEnwNX9VgpujJhPMC/uiR3Utu/toMzsS6AecQRB0fyIIm49Glw/rPBYuWXsdcD5QDvgWuNndn4/VMHfva2ZfA1cQTIu1FfgcuN/dxxblRadzUBUREREpKRmZiMJFCiZst2DeelOAExKsMwYYk2CdocDQROpkEvWqioiISCqVS3UDREREREQSkZE9sFIy1NMqIiIi6UgBVhRURUREJKMowJZS8UKp5k8VERGRTKcAW8aot1VEREQynQJsKaBQKiIiImWJAmwGUVAVERER0TRaIiIiIpJhFGBFREREJKNoCEEamrtL7GVjv/45BY0RERERSTPqgRURERGRjKIAKyIiIiIZRQFWRERERDKKAqyIiIiIZBQFWBERERHJKAqwIiIiIpJRNI1WGtpv49/M0KpbIiIiIjGpB1ZEREREMooCrIiIiIhkFAVYEREREckoCrAiIiIiklEUYEVEREQkoyjAioiIiEhGUYAVERERkYyiACsiIiIiGUUBVkREREQyigKsiIiIiGQUBVgRERERySgKsCIiIiKSURRgRURERCSjKMCKiIiISEZRgBURERGRjKIAKyIiIiIZRQFWRERERDJKVqobICIiIlJaNNvwUszt8yufU6SykpsCrIiIiJQ5scJjIsExXvhMVvmC1o/X5qKeL91DtAKsiIiIFIviConFFa6KGvqSJZF2pEubS5oCrIiISBlW0j17ibajJNsg26T78AYFWBERkTSSiYEyHY4rZYsCrIiIpIVEgk1Rb4jJxBCViW0WKS4KsCIiZVwmBj993SySGrH/PZ1U4u1QgBWRUi3dx3GV9J3QiVDwE5F0pQArImmtqF8rJ+O4iZwvGYFZwVFEJH8KsCISU2n/Wrmkz5cu10hEpDRQgBXJEMUZgNQ7KCIimUQBVqSQkjHVTUlPixOPQqmIiGQSBVhJa4kGq3T4eltfK4uIiBQvBViJKxPDVSa2WURERBKjAFsKKLSJiIhIWaIAm0EUVEVEREQUYNPS196cZhseTnUzRERERNJSuVQ3oDQys0Zm9pyZLTKzjWY238weNrPaqW6biIiISKZTD2ySmdluwFRgZ+AN4HvgEOBfQGczO8zdl6ewiSIiIiIZTT2wyTeIILxe7e6nuvtN7t4ReAjYC7grpa0TERERyXAKsEkU9r4eC8wHHo/afRvwJ9DDzKqWcNNERERESg0F2OQ6Knx+3923Ru5w97XAFKAK0LakGyYiIiJSWijAJtde4fMPcfb/GD7vWQJtERERESmVdBNXctUMn1fH2Z+9vVb0DjO7FLg0/HHjL/eeNDu5TSsV6gLLUt2INKTrEpuuS166JrHpusSm6xKbrktee22/SHIpwKYJd38KeArAzGa4e5sUNynt6LrEpusSm65LXromsem6xKbrEpuuS15mNqOkz6khBMmV3cNaM87+7O2rir8pIiIiIqWTAmxyzQmf441x3SN8jjdGVkRERES2QwE2uT4On481s1zX1syqA4cB64FPtnOcp4qhbaWBrktsui6x6brkpWsSm65LbLousem65FXi18TcvaTPWaqZ2XsEc8Fe7e6PRWx/ELgGeNLde6eqfSIiIiKZTgE2yWIsJfsd8A+COWJ/ANppKVkRERGRwlOALQZm1hi4HegM1AF+B14H/uvuK1PZNhEREZFMpzGwSWBmdczsYjN73cx+IuhpPR2YC1wO7OrufQoSXs3sXjP7yMx+NbO/zGyFmc0ys9vMrE4+9dqZ2dth+b/M7Csz62Nm5ZP2QhMQfU3CNq02s8lmdlH0GOHtHCuha2JmzczM83mMSO6rLbhkXpcYxz4v4jVenE+5k8xsfHjedWb2qZn1LOx5kyGV18XMOmzn/XJP0V5d4SX539H8fF7j4nzqlebPloSuSVn7bDGzTuHxFpvZRjNbZGbvmdkJccqn1XslbFPKrktZeL+Y2QXbeY1uZlvi1C3S+0XzwCbHmcBggp7Wj4EFQD2CEPsMcLyZnekF6+6+Bvgc+ABYClQlWHq2P3CpmbV1918jK5hZF+A1YAMwElgBnAw8RHDj2JlFfH2FkdJrEvoSGB1jeyoXiUjmdclhQa//QGAdUC2fclcCjwHLgWHA30BXYKiZtXD36xJ+RcmR0usSmgCMj7F9ciLnTLJkX5fVwMMxtq+LVbgMfLZAgtckVOo/W8zsPuB64DfgTYKJ+3cCWgMdgLejyqfjewVSfF1Cpfn98gXw3zj7jgA6Au9E70jK+8Xd9SjiI/wLOhkoF7W9fvimcOCMAh6rcpztd4XHGRS1vQZBqNsItIk8DsFYXAe6l7Fr0izcPjTV743ivC4RdQ34kKDH//7wGBfHKNcs/LBYDjSL2F4b+Cmsd2gZvC4dwn39U/3+KM7rAswH5idw7rLw2ZLoNSkTny3AJdmvE6gYY3+FTHivpMF1KRPvl3zOMS08zinF8X7REIIkcPdx7j7G3bdGbV8MPBH+2KGAx9oQZ9cr4fMeUdu7Evzvb4S756yEER7n5vDHywty7mRK8TVJW8m8LhGuJvgwuhD4M59yvYBKwEB3nx9x7pXA3eGPKZkhI8XXJW0V03UpqFL/2VKaJOu6mFklgs6BBcCl7v53jHNtitqUlu+VsA2pvC5pq7j/HZlZC4JvShcCb0XtTsr7RUMIil/2G3pzEY9zcvj8VdT2juHzuzHqTCSYd7admVVy941FbEOyFPc1ydbAzC4juJFuOTDN3eOVTQcJXxcz2we4B3jE3SeaWcd8iuf3Xnknqkw6Ke7rkm33cIhFDWAxMMndf0y4tSWnMP+OKpnZeUATglD/FTDR3WONUSsrny2JXJNspfmz5RiCcPEwsNXMTgT2J/j25jN3nxajTia+V6D4r0u20vx+iefS8PnZGP+WkvN+SVX3dVl4EPwH4WuC7vDjEqx7HcEYz4eASeExvgR2iio3PdzXOs5xZof790n19SjBa9Is3Bfr8THQJNXXIRnXJawzg2AFuB3Cbf2J/1X5H+G+OnGOty7cXyXV16OEr0uHfN4vo4Daqb4OSbou8+O8xnnAkTHKl/rPlkJck1L/2UIwntGB/0XUi3xMiPGZm1HvlRK8LqX+/RLnGDsAKwkCcOMY+5Pyfkn5xSrND2BA+JfwViHqLo56s78D1ItR7odw/+5xjjOFFI5tTNE12ZlgGrNWQK3w0R4YF9b7Eaia6mtR1OsSvsYtkX+35B/U/g73ZcU53sJw/y6pvh4lfF32A24k6E2pBtQlmALv87DOZKLGiaX6UcjrchtBz0c9oEr4ep8AthL0eBwYVb7Uf7YU4pqU+s8Wght7nCB8fAUcHv67aAG8F+4bn8nvlRK8LqX+/RLnGD3DY4yNsz8p75eUX6zS+iAYf+cECxnsWITj1ANOI+hNWgS0Ko43Qmm6JvnUyyJYxteBf6X6ehTluhAsjrEZuC9qe39KSYAtqeuSz7FqEPTEOdAl1dejKNdlO8fL/oX1etT2MvfZsr1rkk/50vTZ8mRYZwMRN3uG+6oAv0b/vWfSe6Ukr0tZeL9s5+/75Dj7k/J+0U1cxSAcR/cI8C1wlLuvKOyx3H2Ju79OsDxtHeCFqCKrw+eacQ6RvX1VYduQDCV8TeLV20wwPQgE/wtOucJcFzPLInjNPwC3JHC6gr5XVsfZX2JK+LrE5O5rgJfCHzP2/VIA2TdsRL/GMvfZEiHeNYmptHy2hFaFz7M84mZPAHdfT9DbCHBIxK6MeK9AiV+XmErZ+yX6OPsB7QimGYs1pRgk6f2iAJtkZtaHYJ7N2QRvgrgThCfC3X8heGPtZ2Z1I3bNCZ/3jNGWLGBXgh6pecloR2Gk4Jrk54/wuWoy2lAURbgu1Qj+vvcBNkROGE3wlSjA0+G2hyPq5fde2YXgmvwWfhinTAquS35Kw/tle+K9xjL72ULh/t5Ly3sl++99VZz9K8PnHWLUSdv3StiWPpTsdclPaXm/RMvv5q1sSXm/aBaCJDKzGwnufP4COMbdlyX5FA3C58g3xTjgXIIxey9HlW9P8NXGRE/RnZ8puib5aRs+p/qDtCjXZSPwbJx9rYCWBOM25xDMw5dtHMEE0Z2jtgMcH1EmZVJ0XfJTGt4v2xPvNZblz5bC/L2XlvfKRwRf3+5rZuU8apolgnHCAD9HbEvr9wqk7Lrkp7S8XyKPVRnoQfD7ON5nMSTr/ZLqMRel5UHwlaUT3P2c79gRoAKwN7Bb1PY9gZoxypdj26T9U6L21SD4n1w6TiCdqmvSihg33QCdCMYvOdAuk98r+ZTvT/wxsLuSpgsZpPi6tIlT5zyCm3k2EjXmLdOuC0GvdJ6bRQjukv4xPP5/ovaV9s+WwlyTMvHZArwRHueaqO3Hhv8mVhLxuZzO75UUX5cy8X6JKNMjPN6Y7RwrKe8XCytJEViwjvxQgv91PEbsMYTz3X1oWL4Zwf/SfnH3ZhHH6UMwRcfkcP9yghuWjgSaE9yF38ndv406/6kE0/1sAEYQLMl2CrBXuP0sL+G/6FReEzMbT7C4wVSCcTgAB7Bt7rlb3P3Oor7GwkjWdcnn+P0Jvi6/xN2fibH/KuBRgus4km1LyTYCHvAULSWbyutiZvMJvq6aQfB+qQwcTDCWbXNYZ2iirykZkvjvqD/Ql2COxV+AtcBuwIkEr/dt4DSPmpy9lH+29CfBa1JWPlvMrBHBa2xM0PM4i+A/wKeyLVy8FlXnVNLsvRK2K2XXpay8XyKOOYlgdoZT3H3Mds5/KkV9v6Qq+ZemB9t6d/J7jI8o3yzcNj/qOPsTrNv+BcH6ypsJ3lTTw3PE/R8SwVfDbxP8D/AvgnncrgHKl7VrAlwEjCWY43Edwf/yFhAEtiNKw3ulAMePe7c9wQIQEwh+Yf8ZXsueZfW6EEyh9QHBXcR/EXygzgWGEDWNUqZeF4L/8L0MfE8whm8TQQ/IB8D5EHRmxGlDaf1sSfiaUIY+Wwgm7X+MINz/TfD5+zpwSKa8V1J9XcrY+2WfcP+vBf37Lur7RT2wIiIiIpJRNAuBiIiIiGQUBVgRERERySgKsCIiIiKSURRgRURERCSjKMCKiIiISEZRgBURERGRjKIAKyIiIiIZRQFWRCSDmFkHM/NwJSkRkTJJAVZERGIys/5hWHYzeyGfckdGlJtfgk0UkTJKAVZEJLN8RrBs48ASPOdmoKuZ1Yqz/5KwjIhIiVCAFRHJIO6+3t2/d/dlJXjascAOwLnRO8ysNnAGMKYE2yMiZZwCrIhIipnZ6PDr96tj7Lsj3Pds+HPcMbBmtqOZ3WVms81svZmtNrMvzeweM6tahCa+C/wGXBxjXw+gMvB0EY4vIpIQBVgRkdTrBSwA7jOzltkbzawT8B/gW+Cq/A5gZrsCn4flNwCDgecIguc1wE5FaN+W8FgHmVmbqH2XAD8DHxbh+CIiCVGAFRFJMXdfAZwNlAdGmlk1M6sHDAM2Ame5+/rtHGY40BT4j7u3cfe+7n6Nu58INAQWF7GZzwFbCQIrAGbWFtgfeBbwIh5fRKTAFGBFRNKAu08FbgH2AJ4EXgTqA1e7+zf51TWz1sChwBfAvTGOvczdNxSxfb8A7wNnRwxHuISgd3ZIUY4tIpIoBVgRkfRxL0FIPAc4BnjZ3Z8pQL224fN77r61uBpHMM61OtDdzGoA3YC33H1RMZ5TRCSPrFQ3QEREAu7uZvZ/wLHhpocLWLVW+Lww2W2KMgZYQnAzVwWgKrp5S0RSQD2wIiJpwsz2AAYAKwnGmz5jZpULUHVV+NywmJoGgLtvIhgu0BboR3CD2DvFeU4RkVgUYEVE0oCZVQJGEvRqdgP+B7SgYL2wn4TPx5lZcX+uP0Nww1Yj4Dl331LM5xMRyUMBVkQkPQwAWgL3ufsHwG3AFOAyMzszv4ruPhOYChwE3Bi938zqFLAnd7vcfS7QGTgNeDQZxxQRSZTGwIqIpJiZnQZcCXwK3Azg7lvM7GyCmQWeMbOZ7j4vn8OcB4wH7jazM8I/G8GsBscCewPzk9Fed38/GccRESks9cCKiKSQmTUhmEd1NdDd3Tdn73P3XwkWOagBjDCzivGO4+4/A62A+whmCrgSuAhoAjwALC2u1yAiUtLMXXNPi4iIiEjmUA+siIiIiGQUBVgRERERySi6iUtEpIwwswuAZgUo+oW7jy7WxoiIFIHGwIqIlBFmNh44sgBFn3f3C4q3NSIihacAKyIiIiIZRWNgRURERCSjKMCKiIiISEZRgBURERGRjKIAKyIiIiIZRQFWRERERDKKAqyIiIiIZJT/B/coPxiXy/8jAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Before selection criteria\n",
    "cuts = 'xic_M > 2.3 & xic_M < 2.7'\n",
    "plotVar('xic_M',(2.3, 2.7), cuts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#After selection criteria\n",
    "cuts = 'xic_M > 2.3 & xic_M < 2.7 & xi_mcDecayVertexFromIPDistance > 0.3617 & xi_significanceOfDistance > 3.46215456 & xi_ErrM < 0.00695869'\n",
    "plotVar('xic_M',(2.3, 2.7), cuts)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Belle2)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}