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  "name": "",
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook creates monthly forcing files for the sea surface height (hourly frequency) at Tofino. We want sea surface height forcing separated from the tides. We will take the observed sea surface height and remove the tidal predictions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I will begin by looking at Jan 1, 2006 through to to Dec. 31, 2006. There was another great storm surge in Dec. 2006. Since data NEMO in UTC but the observations come in PST I will download data rom Dec 31, 2005 to Jan 2, 2007.\n",
      "\n",
      "Storm surge dates: Nov 15, 2006, Dec 14, 2006\n",
      "\n",
      "Observations: DFO\n",
      "\n",
      "Tidal predictions: t_tide"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "%matplotlib inline\n",
      "from matplotlib import pylab\n",
      "import matplotlib.pyplot as plt\n",
      "import netCDF4 as NC\n",
      "import numpy as np\n",
      "import datetime\n",
      "import requests\n",
      "\n",
      "from salishsea_tools import nc_tools\n",
      "from salishsea_tools import tidetools\n",
      "import os\n",
      "import csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "DFO observed water level\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Begin by collecting water level data from the DFO.\n",
      "\n",
      "In order to generate forcing files for a different set of dates, modify \"start\" and \"end\".  The time period needs to be at least one month long. Due to local time vs UTC, \"start\" needs to be one day before the month of interest and \"end\" needs to be two days after the desired end date. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Tofino mmsl over 1982-2001 (in metres)\n",
      "msl = 2.103"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yr=2009\n",
      "\n",
      "statID = '8615'#Tofino station ID number\n",
      "start = '31-Dec-' + str(yr-1) #start date \n",
      "end = '02-Jan-' + str(yr+1) #end date\n",
      "\n",
      "#tidetools.get_dfo_wlev(statID,start,end)\n",
      "#observations and tidal prediction stored in storm-surge/Revisions/tides/forcing\n",
      "filename = '/data/nsoontie/MEOPAR/storm-surge/Revisions/tides/forcing/wlev_'+str(statID) + '_' + start +'_' +end +'.csv'\n",
      "\n",
      "import datetime\n",
      "import pandas as pd\n",
      "import pytz\n",
      "\n",
      "def dateParserMeasured(s):\n",
      "    #convert the string to a datetime object\n",
      "    unaware = datetime.datetime.strptime(s, \"%Y/%m/%d %H:%M\")\n",
      "    #add in the local time zone (Canada/Pacific)\n",
      "    aware = unaware.replace(tzinfo=pytz.timezone('Canada/Pacific'))\n",
      "    #convert to UTC\n",
      "    return aware.astimezone(pytz.timezone('UTC'))\n",
      "    \n",
      "wlev_meas = pd.read_csv(filename,skiprows=7,parse_dates=[0],date_parser=dateParserMeasured)\n",
      "wlev_meas = wlev_meas.rename(columns={'Obs_date': 'time', 'SLEV(metres)': 'slev'})\n",
      "\n",
      "print(wlev_meas.time[0].tzinfo)\n",
      "\n",
      "#the average sea surface height over this time. \n",
      "sshavg = wlev_meas.slev.mean()\n",
      "print sshavg\n",
      "\n",
      "plt.figure(figsize=(14,6))\n",
      "pylab.subplot(1,2,1)\n",
      "plt.plot(wlev_meas.time,wlev_meas.slev)\n",
      "plt.title('Tofino Observations' + start+' to ' + end)\n",
      "plt.plot(wlev_meas.time,sshavg*np.ones(wlev_meas.time.size),'--k')\n",
      "plt.xlabel('Time (UTC)')\n",
      "plt.ylabel('Water Level Height (m)')\n",
      "\n",
      "pylab.subplot(1,2,2)\n",
      "plt.plot(wlev_meas.time,wlev_meas.slev-sshavg)\n",
      "plt.title('Tofino Observations, mean removed: ' + start+' to ' + end)\n",
      "plt.xlabel('Time (UTC)')\n",
      "plt.ylabel('Water Level Anomoly (m)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "UTC\n",
        "2.07770095368\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x7f8620d4d350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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BB/Dee8Wk/cUX8cdsTp/uy7sklUyZu1t0ktde8+MH8vL++/mlJSLZSWsuwMGD\n4y3Xbo2YorRt4+mEE+CYY7qe33prPulqx5IizJoFr7ySX3rOwcsv55de0ZrtPpdGeXDTTfGWmzGj\n9bSkGAce2DV+IA/hfqn6SkTKbtAguO++5J974IH08xJX2zaeTj8dzjor/3SzqIyuuAL+/Of019tp\nZs5sLfpZEi+84CM95mXIEFh11fzSe+IJPwdST3XOOd2ff/FFMfnImk6eO5N+dwk5ByNHFp0LaWe/\n/GW262+m4QTwwx+mm48k2rbxVJQsKqVjjoGjj05/vZ3mzDNhkUWyT8c5uPnmruennpp9mpMn+/+z\nZmWfFvhIcHlzrusva7/7XfZpxKUTXUmb9ikJPf44fOtbRedCymK11fyF5iRuuCGbvLSzHtV4euaZ\nonOQ3Guv+blYpHXvvJNPOjfcAAMHdj0/8cRs05syBf79b/+4d+9s0yrSSiv5sKP77pt/2h9+6I/F\npNI4SdW4pM5x0UX5pBPulxdemE96Ul499a56p4jWD2lcPJ0woX5k2Fp12rXXxk/joIN8Pf7ll8ny\n1k7atvFUrU/3uusWk5dWbLddc5979FGddBUl6whplQYPhttvzzfN0KOP5pfWW2/5/8OG5ZdmVN5B\nZ1qhOwvt4Y03YOjQrt/rt78tNDsi0sZWXz2d9TTTCNtjj/jLXnghXH55V53eSDvWZ23ZeDrxxOS3\nHXuajTaCxx4rOhedqR0P9GZttFHROchPURcj4uxP06fDvPO2tu4ydVXsFHvtBZttln+6WZRRn37q\nx3pK+5g1Cx5+OL/0xo3Lt34MI5D2VGPGwKuvdj1PK2hUvbouze+zJ1/gb8vGUx5jTJLYaiuYODH/\ndDu9ARk1c2bXuCBJ1+efNzcBrMQTp4JppuvNl192H7tWGSRDJIkDD4Q11yw6F+1v5Ejo2zeftIYO\n7ZpcNXoSnpVPPsk+jaieHoF0222zOc/Lq/HUk7Vl46lMXnjBRwp5443W15U03HoerfpTT80vgl0r\nzjwTrrmm6Fz0TPPPD4ccUnQuOlszFdoOOzRX8fbkq4VlMGlS9mmkfQI0c6bG5qblqafyGZ/70Uew\n5ZZdz7/5zezTDCdY3XDD7NMqyuWX+yEi552XfVpTpsR7Lam8yvie3BBr+8ZT3ncbKne68EpcGjtj\n3DEXee6QJ57oZ7guuzffLDoH2cm7AJoxA267rftrnTTnU1xjx8LXv150Lmb32ms+GuTddxedE6lm\nwQWLzkGqy7/DAAAgAElEQVRyp57q72JI+3jmmfyiswK8/Tb84Af+cVHjVvNw6KH+uz388OzTqlb3\nf+c7ra83r/3iyCPjBY1ox0ZW2zee2l1Zdxqz7KMXPvtsetFY8voe77oLTj45n7RqeeqpbNf/4INw\n7rnZplFGb77pr9bGNWFCdnmplGT/3mAD2Hnn5tPSnad0jB7tw0RDecv5uOIO/K5klt8E9lKsIrvN\n53l8FX0sv/56sek3cuONXY/vvBPefz/Z5/Mco9cKNZ4SqnXgJDnhWG659ugC8fzzra9jyBDYddfq\n762zDlxySetp5OmRR4rOAay3XjHpfvhhPmFvX33VB0e4/PLs04qaNs33MY+rWlkwenRq2fnKl19W\nv1vx9tvppwVqPKVll13yvfLfyFNPFTN2Ma1B7j1F0SffIlnaZZfuz5PWJ5tskl5esqTGU0K1doRt\nt41/Yvn+++3RzSycb6fZk6mZM/28JtdfX3uZNE7Gn3oKLr206/mRR7a+TunuoYdg6aV9l4UsVE7K\nO3UqXHBB+ulMm1b/BLLVwC8rrlg73VoaHV+ff179JLxfv9jZ+kpPnndDanvmGX/R5aqrWl/XBx+0\nvo60vfNOewS1GTkS9t47n7Q6rZH2hz/AaacVnYt0ZfUblukC2ZAh8MADReciOTWeEnjyydqhWj/6\nyF+Zb2TQoHTzVGbHHuuDadSTxkF88cXdn//lL62vU6rL6sSp2nqzKOBXW80HUmjVzJnxo37+738w\n99y130/7pK9ehRuOSagnjM4l6cl73GjlsRPOgVivEV9PdJ9adtl4nxkwoLm0mtG3L/z5z/ml16w8\npxfphMZT9GLXaafBn/6UfZrt+L0WMV1CIzNm+N/v738vOifNUeMpgd13r/9+nJO9E06Iv2w1d92V\nLL00NJtOnMlknWu9X3zv3s1/VpLJap+rtt7hw9MfX/bWW92PoWaNGwdXXx1v2a23bj29tMQZx9jT\nrt4WpR1PstJ05ZXZp3HppV1jQPIOk92MvPaJSZN6/nE8dSostVT31/I4J2rH47qMwV6OOQYWWgju\nuafonDSn7RpPv/lN/ff33DOffGSlcnzQ3//evQ9okjEZaRk+PFnf/auv7rrSGdeLLyZbPqpX2+3F\n8c2YAc89V3QuuuR9uz/vwaNZbF+jdTZ6P2ll3Wj5Aw5Q19aeLo0TvHvvTScscpYOOADOOqv19Uye\nXPvu4HzzpZNGnoYOzb/srNznsm5kFDXHU3S7br013kXitG28cWufz6oeT3IRvN3HQrbVaeeECbN3\n0apU5Fw/SXfIzz+ffQ6lyvFBN99cuxBMEsVk6tTmr8ydfjrcckv85f/zn/iR+tI4iJMW0mPG+O+u\nHSbYu+46+Otf802z3veZ5Pf6+ON85hYpQpqVT94N0ksvhQsvzDfNTlTkFepa+9TTT8ef+2vrrbuP\nJS2rMLBRK8fRBRd0nxMpasoUeOKJ5tcdmjEDRozoep51NNtONG2a76J27LHZpRE9Z9txR1h99ezS\nqlWGtBq4atFFsxn/Wm26nZVW6pr/K6od7+BFtVXjaY01ikv7jjsah4hsVHhHB4pecIEfezF2bP3P\nRHewyuAKu+8eP2zlr34Fiy8eb9lqmukrH+fgCAMFtFLxJf3sMsv4iIdJJ7kbNSr/MWuVgRTayauv\nxpvVfurU2SP0hNJsWBTRWJ4xo5wD2csUBU7S9eWXteuVyy+ffQ63at591/8v08DyWsJw8M3m9emn\n4fjj08tPLdddB+ef3/U8ae8MiWfo0Pwjtabtoovg6KPTWVetCZkrG09Tp/o7rOHxlKakF+5//evG\ny+Qx0XQ9bdV4Cgv0Ihx8cOvriEY6uvbaZHNnDBkC88wz++txu1UUsaPdcUfjZYqcM6lRw7XSFVdk\nko2m/OtfxURszOJk6p13/JxfaaT3xRe+AqjWMJ40KXneWrX55vk2VJ54wo/HaiTu3QdpPyecUL+B\nFOdC2AortJaHaL1U9gZYnDGQzvlgBK10hSvjRZSeKu997oYb0l3f6af7AChpdAns27f665X10gEH\n+HFIZRBnCpta25WXtmo8FeU3v4k3f0sWB2x492anndJZT9rLZvH5NMSZ4fyss/xv+/nn2eenWbW+\ny1/8wodmzVvcfXzmTD9mAnwo+Vrb8eGHsOqq6eQNfDSrY45pfvb3tI/hONG1GjWukhxPW20Vb7la\n23n33fHTktqee67+FdzoRJJpixP1NanKfbCye/mYMXDZZV3P55sv/Tw0kvTYHTeu8TCA6Lr/+Ec4\n88zk+QopsFF+8m487bdfuuvL4xyq8jvK8uZEM7/H8OF+CEhZ9djGU5IJXs3qn0DH/QFXWWX2MUyh\nWiHOGylDQwTKk49Kd95ZPdTlhhvG+/zFF+c/pigtWVQQb75Zu+9/EsOGwSmn+MfhpL7VgoKkHSGr\n3nFW1ivgjRpPScqOuMdpreV+9KP4aUlt4Rx5tdTqppqHNI6DysBGF18M++/f+npbce658SNggl+2\nUQCqUBFjc9vJ++/Dt75VdC66JP294vSSSTO9RopoPOWt0TbuvTdsv30+eWlGj2w8fetb8J3v+AHr\ncbU6OWaoVpeZNdec/bVG3a5uuaVxl4K4B0C4ox56qL/FfNRR8T5XhM02iz+Wq9E8UnHE7cLUaoG2\n++7pRkDKojtYowg4cfe3annLesziSy/BYYe1to60K5Q46/v889pzXTiXLLJSTz5Baxd//nPtbqhZ\nmzy59bG5L73U9fiVV2Dw4MZdluvtd8cdFz84wl13+YuQzcrqe0+jXEi6ji239N/r3/6W7HOzZiW7\neJyGOF2Fy2rmTNhuu3jLRgN+ZClOT6dWFd14aqTsd2p7ZONp5Ej/v9FJcdxoI0lOSNLYIcOB7dHB\npa0Kt+H8831/2rPPTm/dcSRJb+hQP7FoXv74R3jjjdbWEWdfuu46H3a+2buQlaZPL+ZkP460TuKT\n5Kfy7nHlGIO08jR9Ouy1VzrrCtXqa57094i7jQoYkZ0kdz/SdtRRrQ/4/va3ux5ffHG8nhfhfvfh\nh/DjH8/+ftzG0wMPxL9w1qowz3HGvoYhmPM84QxDpycdT3PPPcV06a5m/Pjswt2nFRU2ibXWSm9d\nY8b4v6JE64B//Qseeii/tKdNa9zrpFYvrrLokY2nuOaaq6t/eFoHWxrrSRLeOU56b77Z/cBo5urJ\nP/4xe7S/JBrd6brmGh+KPmu1KqI4Ie7rFdZzzRU/DxdcEH/ZemkOGRJvRvXbb4e3346fZpkMHQqv\nvdZ4uTfegPXX7/5anz7dn8c9Vl5+uf4yH30U/45n3MZMs3e+k16VTiNNqa+ou39PPVX7DmZUlg2A\npZf2XambleS7Gziwtc+HWjmGWhUneMfDD/vyJu4Fj1bq6bQts4yPKpy3JPt4+BtMm+Z7h9Tyi1+0\nlqfKNJdZxv+VQd5DFw4+uPF44Hp335qJ/py2Ht14inMAxTmByPvOU9pR1FrtygT+KljcWaqbqcBe\necX3Pw8Piqwq+F/+srnPTZ0Kjz6aTh4uuQRuuqnxcv/7n4+AU8/VV1efQyHqJz/xXWfScN99vltS\nI5tumk56EG8sRaPvIIk99qj//oMPppdWI42Og8oooEmOvcqTLHX5S0dW3+Pf/15/bG7cYAaXXpos\n8mSc7Wm0TNLu5XEUGak1qcmTqzds55473ue32qo8d5OqqfW7TZ2azZ3EWbP8GLda4u5v06d3BTc5\n6ijfO6SWf/2r9fRCZbjzn2d5n3ZwirjHTZZ6dONpmWVqVzZZ7Th59yPNM704aU2Z0vxdjsGDk03K\nOG1aeg3NRtv2t7+1Nl6psltgnGiAcbr3vfYabLtt9auN77xT/S7KrFmtze4ddidJQxlP2Ot1wZwy\nBfbcs/7nw236+OPW55bKqtteq+lIbXF/g6FD/bLVAqlUc+CBXREsW0n3oYe6uqHFWUejCWInT45X\nniUxbJjviZDG5LRZefLJ+F2L7rij9e6UcccxtVqmvvhiut3JsihbJkzw3e1bTTPaiAl7hDTz/SXd\nxnnnTZ5GGYRDYqrJM6JzGfToxhPAZ5/Vfz/NPqxQjisKUdOnp9cdLk4Bcfjh8cIz1xKebMZJ609/\nijfhYxzjxtVPs9U5OlZeufvzjz5qPCYvbgHzxBPVK5K+fWG11fzj8eO7tuGGG9IND96KOL9zmU7s\n4x7fU6YkL1ta3c6pU7MbXyDp22wz/7/s87U1unNw1ll+nE0awjJvww3hpJNggw3SWW+9tKIaddmN\nWn99+P3v08tPI6NH5zNX3Rpr+AipScac5D3+qGxjc8sq7oWZuNKKqNgTvvfSN55uuy29E+RqwpON\negdjkh/64IPT6Y/pXDonlocfnl6Etzj5aTX0dNiP/YQTGs9XknRAYb0T37/9Lf2J7uq59trqE7lG\nJakgGl0kuPvuroq+XtefMjVUQmk1sIYMaT0vcW28MXzwQbLPVG6Dc/CrX8X/fP/+vgHVjDL+7u0q\n6YlBo+9+iy26JlQv09jcsEtxnLurcdKbNat71LZG5WM148a1dgEzvNhUy6efdt/erCa+rVa3vfQS\n/PrXra33e9+Lt9x778HOO8dfb73f95134k0wP3Fi8+VX0b74Iv58kZUXUldfvbk0J09uvEy9CLfN\nlgHNXIz/6KPuz5OUkUnr0byUvvG0ww6tDTistoNcdVX8z48cmawb2oMPphNm8uij0xlbkeZVzTgH\nW6tXFMID86OPfKCDNNNqFPoyTtSlNL33XnrrivPbhCdgWV/1STtSYqNtGzt29mAR1SRpiNTKx5VX\nxlv2hRdaP4meMSN+elC/S0XStKV5aR9f998PjzyS7jrT+L2TdLGO4/LLfWCiVlx9tR9TmpXHHvNj\nj7I+XhZZpPrrjS6SQf3G7NNPx8/DAw/E6445ZUrj+bL+8IfGjdrll08vuMTEifECl9TqvtqMuFNK\nVHbhHzWqufTWXbf++0m6jCfZn8PziCQ22aS5z8HskTp/+9vm1pO20jeeWlVtp9h77/if/+EP08tL\nEmmFu02zIm/nE6xalVFU2carJfntRoxoPXraqFHx57uop16wiEcf9ZXoFVf47YuGRW5WXlHjJk6c\nPUBDPa02npJ+vlePL817pquvrt3dMk4U0GaUsSxv1NMg7/XUcsYZ8H//5x+Xrc4YORJ+/vP00qs1\nhULUqFGNG1nvv994YuiJE+HVV+PnrZFGgX8g3Qh6rVy8akajcctzzln//UsvbW7/bXafj96Za+W8\n9KKLmv9smkpf3bZ68t+3b7zbm5U++8wP+C6qb2blbc4yyOPOU5L0GqUVvQPY6pwBrVSSzUSWmTkz\n2dxTzz7bvaLbccfZlwm3odb3FickeHQ91TS6xb7RRv4O4D77xEurUXpZqPX9JI26mfRYaLa70Sef\n+L9WGk9lPJluV0l/93//u/ZV8jA4SZy7uXlHhW0m3TzE2bb//td3aW9Wkklon3mm+SivSbXae2LD\nDbs/jzsXZhw33VR9jNhPflJ9Hxo7tnz7Vj3tVoYef7zv6fOnPyWLJJzGdhZVVqWpbRpP//xn8+uI\njsOJexK9ySbwta8VO4lZHJtvXv/Ke72d9M474cQT46dV1p24lhVXTLZ8Vtu3wgrJ07v6ajjnnGTp\nRCPu1euOEB4DzTYo77239gDxZZdtbp31tPK7bLtt8s88/zzccsvsryetyFu98xR3u9de2/dtb+VE\no92O7bKZNKnrwkEW0bouu6zxOpKk+8or+QY3arR9jzySXijuOPvy8OGtpfHOO/7/6NGN51VKEoSi\nkXvuiTfNRbMq7yA99VS63TOrjV+Pds9/++2ukPtlvIBcT1plaJ7z7+25Z2vzsoXiRO8Nv58rr4w3\n8XbZtU3jqVGf2rjidN+C1uPQ52XChPrRkOpVqD/+MZx6avfXwu4ISddVhLTzc8YZte+cxEnr4ovT\nqyibGZQZp/AeOhR+9zv/eJFF4PTTk6cD8eZ6SksrdyDvuqu5NE85JVk61aQdOKCWd97xF3nySk9m\nt/vu6V84SDJ32YwZyU66zjgDbr45eZ4qPfssnHZa6+u5//7W1xHKY78OI8ref3/jRl/S47LRBc0s\nG0/VXH55vumFE7ZmHcEv7eAUcfK0zjqNP/uzn7Wel7hlx3/+k069kSR6b1pDUopW+sZTGM456xP3\nLAvcIvN+xx3J1tVq6NWyNbCSGDu29iR5cfaP3/zGn5REPfRQ7eWz2ufqDS7/+OPuz48/vuvKU08+\nmU56lb3a8kn27Rkzknd5qUwz6e/R7ETBzkGfPs19VrzoVfK07jwttli85QCOPTb5hYK40cHqiRuh\nNM3xna2mlbY4QRziuvLK2S9oJlFEGZ60C1arEybPmAGHHBI/zVrWW6/2e2EeZ87041wPOqjx+uJs\n17PPNl4mjcBV1cqOtDS7j4Wf6yljc9tmM9II/52Xxx8vx4lonPCgeUp7QGXcQnvEiNbSue8+Hzo9\njsrfvX//+Mu26sUX/dXnWlF/RozwEZQq/fjH6eajjJJOClzE8dtst71mhXfzy1BWtbtoWdRMQ+Df\n/27c/aueIuaKgq4uVq3Ku/FUprG5UQMGtJSVTKRZPowcCfvv3/W8MpJaVK3v7bPPYPDgxmk1yne9\nOZB69fLdFeeYw09jcuGFjdNLS94XoPPusaDGUxtaYol80hkwIN1+zo3U2pnTmrgwLUknWEur0G51\nIuRWZ4av5fzzqzdmoLkC9NlnYddda7//5pu+a2E1ffpUDzJRS1lOuCdMgFVWSX+91bav2VCrcYV3\nnvr0yWc8SjjvVVl+y3YWnhB88klzZf+NNzYXNvn88313waQXB/L2xBPNz4k0ZUqyMUp5N55alebJ\nayvbtc02ydN7773aXdFqifaMqBduu9a2xC2vxo+vPR1IGIilnnAes7haKUebnYogyTQ6teTVeDrj\nDPj+98t17LWiIxpP4azc0Qn46vnyy9bvdIXdDfNQa2e+/vp002kmamEtcW6DA+y2W/VxSLNm1Q7v\n24pWTySvvLIrHHcctU6Yms1Hs4M/s5rsMWtZTaBX+f1PmFB/wsE0hN38pk/3+08ejZq80unpwhOC\nPfdsbrxipbgXmu680x8Dzc4Vk5errqo/Z1m9E6oDD/RBUaLefz+dfOUh7ZPFDz6oPV4nzrH80Udd\n50RRzVxsbeZ3aJRH53yX0nDy2AUWSBZ5NqrW/H9ZTAHQSjm6ySbNfe6b32w+zVBejadbb/UXonvK\n2NyOaDytsUa8qHnDhvkxQltt1VoXirzltXPtvru/gljLG2/Av/4Vb11xboOb+dvzQ4fO/t4552Q7\nGWLo/feT37LfaCNYc0344x+zyVMZlOXqUZx8DBrUejp5XAyJzs9ywgn5HNft2mgumzDUbysXdKK/\nd60uzlnuE0nmP2xGvTGA9crKagPMl1uu9fy0q0ce6Qr604wll5y9l0G9/Tarfa5ewIbo+M3Jk2Hl\nlbsuSiTJT5oXfPOS5OJLGsNZHn442fLN7g/hPtZsECdoLnpuVjqi8QSNK7XbboOddvKThNbrh1tG\nebbM6929S+MkNSrcrmrbF/dK1IMPtpaHSy5pbtLFl17y8yc04/PP/TwkZVbtN2k1/G8trTbUTjih\n9XXk0Vh84omu7/XMM2HeebNPs6fceTKzhc3sR2b2GzM70My2MbOFis5XO7nqqmzXX2s/S2OAfJll\nUXZUBv4B31CoFiW0mso79llMMVFLuB+svHL99ystvHDzabWT6PyUZdRqwIikohfkW2l4pa1jGk+1\nDtTQQQd1FeJpFHZ5HrTV8luvi0Qr8tyuepFp4v5Gm2+eLM3K7SviDssll7TnPAiVXWvSMmwYnHtu\nNuvudHnO9ZMFM9vYzG4D/gf8ElgB6AfsCjxsZreZ2UZ55adedM1GwrImjXlX4hgwoPpd/azk1b08\nTpldb5mi7qovvXT8Zat9l4891vz+V2/Ov6eeSnfqltdeg1/9qnaX6zFjas+NePPNXWM1200aUwOU\nQbjv3XprPj0yrr02+zSaUVjjyczmNrMnzGyEmY00szozDElStQIDZCntSiePbnllM3w4HHlk0blo\n7P77u4cpjk5EnYVac5vE3edWWy1ZekWcQCUJ45uWHnDn6afA75xzazrn9nbOHe+cOy54vCZwFLBT\nKwmY2T/NbKyZvZBKjmsIf4ck0S9b/e2aHUvSjFp5ff75/PKQ1KuvxlvuxRdr926JW5Y008OhWa+/\nHn9qA+fSmXso6h//aO5zO++c3pyfaWv0O++8czny0arwON5xx675zrJWxjqqsMaTc+4LYDPn3HeB\nNYHN8rxCWM+MGUXnoHVZRYhLwxFHJFu+2oGTVQFx7LHdTyjynqPr3nuzTS9N0Qb64otnm1bRhWde\njam8t3P77X1X5XblnDvSOfdanfdfdc61ejnicqBGLLL07LqrvyrfyBdf+BPtG29sPaJq0ccVwD//\nme76Bg2qf1LnnJ9YOo44A/Kd8+Oqq3WZe/VV2GWXeGklUW1agySR12bM8HMBnX12axHbyjL2tSdI\nI3JeHo4/vutxXl0M82qkJVFotz3nXHitpg/QG2hymsd0pT3zdNbOOSedgYOtHghxCtLrr0/eBSvP\nxhPkP4N7lCqj+GbOhC22yGbdlb9DT/1dHnrIz2PW7sxsETM7zMz+YmbnB39/TWPdzrmHgRSnQq3t\nySfrv//55/7O9NJL+3lo2kmeXUTrTQ5/001w3nnpp1ntzlPck76k5xyVdeKjj3afPymuo46Cfv2S\nfy6UxkTLeStrkJxWfoc8RQNM7LknLL989mkmnXA+D4U2nsysl5mNAMYCDzrnUp5GtXh5nHTdcAM8\n91zr66k3oWta6s1DFNe//uUnrstD1r/fww/7vsNmfpzTscdmm14Wvv3tYtIdN672PB5py+M4LqLb\nXg9yJ9AXeB54Gngm+GsrjS4sDRjQ1V22JzToqwU+yFq98T1pi/sbtRocplro8ayNGpV8THEZbLll\nNuudNau1MY/tKo86uIxjc+eo96aZrY0feLsJfhCuA97GD869zjnXUnwt59ws4LtBVKR7zKy/c25o\nK+ssi7xPglpN7/PP4902LuLkrjLNeoEk0pb1CcqIEbDPPv5xmfv+1/PSS9mnkffdxyKo8dSSuVLo\nnle4p59uvEzcsSplU+14zSrCX54BIa64wv8v8tjNuyycObO8A/mjJk3y47uiAcOeeiq79H74w9aG\nfbRjoKg8tFXjyczuxHdVuA24EBgDGLA0sB5wlJkt7JxLMLy1OufcBDO7A1gXGNr93YGRx/2Dv/Iz\nK+aK/JgxsNRSyT/X6h2P+++Hyy5rbR21FNWFauZMP6Fg1j4LOgT1tMZAmopuVOSR/rRp8eajy8dQ\nZiuKy+06MzsAuB34qhOzcy6npsbAyOP+5FFPtVtU2GqOOiqb9aaxXa+/3nqaWZXpr70GEyfCggtm\nm04tw4bBaaflm2az1l7bf1fgx1u3Mhdb1rbfvugclNMzz8Cii1Z7ZyhF1VP17jzt45yrNgPDm8Hf\n9Wb2tWYTNrPFgRnOufFmNg+wJXDy7EsObDaJQn35ZT5X5EPhmKdlloHbb0/++bgTs9Xqe5r1HCEh\n5/Lr/3rhhXDBBfmkBfD73+eXVhqmTy/2ilCWJwxFNWT79i0m3dn1p3sDoErRXC5fAGcBJwDhXumA\nr+eT/MB8kolII7hMno2nDz7w6bV6bOWR5w8+gFVWSfaZPL/LF1+EQw7JbkqSRopudCcRDaeddML7\npGp9L3GjNkp1xx3n/2bXn6LqqZpjniobTma2oJktGv4Fy7RyXX5p4IFgzNMTwO3OuftbWF+prLtu\nvultuim88op/3MzVi7gV2s9+Bi+/3PznW/WPf/gIQXno6ZM3turRR+Hww/NJq+jb9u10stChfges\n5Jzr65xbMfhLpeFkZoOBYcA3zOxdM9snjfWWyWGHZZ/GGWfAdde1vp401tFIGpPGjhvn5zPKSng3\nBbKvf6dN8xdY99jDjxkbMCDb9LJwww3ZX3itVU/Eidoo7aXumCcAM/s1vjk3jRSv6DnnXgAymlaz\nM914Y/OfTVL4jhvXfDqtihPKNy29OmYK6eZdfXVxaaubo0S8BmQSJ9U5l0KYm3IKT/b+mkpcwsbS\nmA9ujz1a+3xW5Ua1CHhljBLWjOee81MaPPII7LsvvPVW0TlKbrfdis6B9CQNG0/A0cC3nXMFnjJL\nHCeemE86eQ/ej647jxPm447zkQfznECyXeUVuara777vvvmkDbrz1AamACPM7EG6xjw559yhBeap\n9Mxg6ND80mv1OGo1r9OmZbe9RV7MySPtF17IL60s5BUxVTpDnMbTm2R0RU/Ko10KxLzyufvuajyV\nycSJ/sRnrrm6XmtmbF9czz3nA6DssQfMPXd26Uhqbgn+wtMXizyWOjbbLP8011gDzjor+efijiOu\ndRJ75ZVwzTXJ0y2zN96ArbbKPp1wXPSLL2afVhba5RynHei7jNd4Og54zMweA8Kb0Lqi14M4131Q\nZZnl1ZVODadyGTUK9tvPn/hceWU+Ewruv78PHXvLLbqiWHbOuSuKzkM7+vWv803v8MP9xNYvvgg/\n+lHyz8c9aRs0yM8pWBnxNq967t13Yccd80lr1Kh80gkd2mZnflOmwPnnF50L6WninIpeAvwXeJw2\nnnxQajvjjGRX4/I+kbz++q6ZzHXFo3OFDdoBA+C3v80nzTBKkhpP5WRmd5jZz81stmlGzWxeM9sl\nmHZDSiKvuWzuvnv21/LqXp7lXEKVNDa3sSOPLD7okPQsce489e4Jkw9Kbc89l2z5vMc83XEH3Hwz\n7LlndmlI+UX3sZEj80lTjabS2wc4GDjZzGbSNR/hUvj67QZg7+KyJ5WqhxyOp10unuWRz9tvh/fe\na5/vpEitTFzbqqTnV9Ie4jSe7goi7t1GIZMPStauv771dagAl6wVuY+pEVVOwXQZJwInmtlSQDhT\n1veWAZMAACAASURBVNvOuQ+Ly5lkodUyIK8yJI90Zs6EnXbK9y6XJLfFFtmu/403YKWVsk1DZhfn\nhu9u+HFPw+jqsvd0lpmS9vL00/DPfxadC5H0qdHUPpxzHzrnngj+1HDqcEUeu3k10tRwKp/wzuon\nn8BHH0Hv3tmmt/LK+c23KF0aNp6cc/0ikw6mOvmgtKfKSqnIuX6kcxR158k5OOSQYtIWEe+VV+A3\nv4m/fN7dy8eP7wpIoZ4YneuMM/z/DTaAb3wjn+k8zjsv+zSku5qNJzPr3+jDZlZAkFMp2mabwcsv\n55/up5/Cf/+bf7pSDkWckLzyiv+fRtdWEWletQAQZXLttV3h19V46lzhbz9mjA/vPm1a/eWlPdUb\n87SdmZ2Jj7T3NH4gbi/8QNx1gS2AB4M/6TBjx8Kqq+ab5p/+BE88kW+aIorSVH5m9hPgP845/Vo9\n1B13tL6OrBs1776bTzpSftoHeraajSfn3FFmtgCwA7AlkYG4wCPAac65ydlnUcqoiP7kWfcdlvJ7\n//3805w+Pf80JbFdgHPN7Cbgn865Au6NS5buu6+1z8+YAY89lk5eGtFYyc4VNprUeOrZ6kbbc85N\nAq4J/kQKNUec2JDSY5nBcsvln67uPJWfc253M1sI2BW4wswccDkwOKjHpMNUNmBuuQUuv7yYvEjn\nKLLx9Prr+afZqTS9mrQNXcmRIugqcntwzk0AbsLP7bQM8FNguJkdWmjGpBDHHdd9bG6ed5AnTMgv\nLSmnvM9XJk6EVVbJN81OpsaTtIW99oL//a/oXEiRimo8hxG0pLzMbAczGwIMBeYEvuec+xGwJqBJ\n3jvUrbfmn+bQoZrQvZMVdedJd53y1bDxZGZzx3lNOsvTT+d/Rf7RR/NNT8qlqMaTuu21hZ2Bvzjn\nvu2cOzOYPBfn3BTgV8VmTTrJuHFF50CKFNZTeYQoj5oxI9/0Ol2cO0/DYr4mHeToo+Ghh/xjdaeT\nnmyRRYrOgTTinNvLOVf13rRzThMcdCgFNpIiHHxw/mnqIl++ag7BN7Ol8f3G5zWztQEDHLAgMG8+\n2ZMymzYNRozQBG2SDzXSpZKZTcbXS9U459yCeeZHyiuv8qOXBkN0vCuuKDoHkrV68cu2AgYAywJn\nR16fBPw+wzxJm5g1Cy67rOhcSKcYOrToHEjZOOfmLzoP0h7yuAt14YWw2GLZpyPlZabIwJ2g3jxP\nVwJXmtnPnHM35ZgnaRPOqYuCiJSDmX0H2AR/J+ph59xzBWdJOtCppxadAymSmSK0doI47eP/mNnu\nQD+gN0H3PefcKVlmTMrPTI0nESmemR0G7A/cjK+jrjGzS51zfy02Z1Kk44+HVVeFHXdUt1/p2W6/\nvegcdJY4jadbgfHAM8AX2WZH2o36d4tICfwKWN859zmAmZ0OPA6o8dThnn8ett8ehg8vOifSCYq6\n8zRoUP5pdrI4jadlnXNbZ54TERGR5s2q8Vg62MyZMGQInHlm0TmRTvDFF/5PerY4jadhZramc+75\nzHMjIiKS3OXAE2YWdtvbEfhnsVmSMpg1C778suhciEhPUi9U+QvBw97APmb2FjAteM0559bMOnNS\nfmef3XgZEZEsOefOMbOHgI3wASMGOOfUUUs0NldEUlfvztP2ueVC2pIGKIpIibwJzMDXa2Zmazvn\nni04T1IwNZ5EJG31QpWPBjCzRau8PSmrDEn7uOiionMgIgJmdip+XsI36T7eabNCMiQiIj1WnDFP\nzwIrAJ8FzxcBPjSzD4H9nXPPZJU5ERGRGHYBVnLOaXSLdKMQ5SKStjiBpu8DfuScW8w5txiwDfAf\n4CBA9x5ERKRoL+Ev7InM5k9/KjoHItKTxLnz9H3n3P7hE+fcvWZ2tnPuADPrk2HeRERE4hgEDDez\nF+ke2OgnBeZJSmDSJHjuuaJzISI9SZzG0xgzOxa4Hh8C9hfAWDPrjebSEBGR4l0FnA68SFe9VMBU\nlVI255xTdA5EpKcx12AqZDNbAjgJ2DB46VHgZGACsIJz7vXMMmfmVP+JiBTNcM6VdvSImT3lnPte\nQWmrnhIRKVx+9VTDxlORVCmJiJRB6RtP5+C7691GV7c98ghVrnpKRKQMStB4MrPznHOHmVm12Xxy\n6UuuSklEpAxK33gaSpXKwjmXeahy1VMiImVQjsbTOs65Z8ysf5W3nXPuoUxzhiolEZFyKHfjqUiq\np0REyqAEjaduC5nNCyzvnHsl+yx1S1eVkohI4crdeDKzhfFjczcJXhoKnOKcm5BD2qqnREQKl189\n1XCeJzP7CTAcuCd4vpaZ3ZZ1xkRERGL6JzAR+Dk+Iuwk4PJCcyQiIj1SnGh7zwKbAw8659YKXnvR\nOfftzDOnK3oiIiVQ+jtPzznnvtPotYzSVj0lIlK4Et15AqY758ZXvKb5nUREpCymmtnG4RMz2wiY\nUmB+RESkh4ozSe5LZrY7MIeZrQIcCgzLNlsiIiKxHQhcZWYLBc8/A/YuMD8iItJDxem2Nx9wArBV\n8NI9wKnOuS8yzpu6Q4iIlEK5u+2FzGxBAOfcxBzTVD0lIlK4kkXbK4oqJRGRMih348nM5gZ2BvoB\nvQHDT6lxSg5pq54SESlcfvVUzW57FZPjOnxl9NXzPCbJFRERieFWYDzwDJB5rwgREelc9cY8nU1X\no+lS4Fd0NaB0mU1ERMpiWefc1kVnQkREer6ajSfn3NDwsZlNds49lEuOREREkhlmZms6554vOiMi\nItKzxYm2JyIiUmYbA/uY2VvAtOA155xbs8A8iYhID1RvzNOi4UOgd+Q5AM65T7PMmIiISEw/qniu\nruUiIpKJmtH2zGw0XRWQ0b0ycs65r2ebtTCK0V8qXp0XOKDK0p/jh2ZV0vJaXstreS3f2vLljrYX\nFUyvsRPwS+fcj3NIT9H2REQKV4Joe865flkmbGbLA1cBX8PXPJc45/46+5KjK54vWGONM6osq+W1\nvJbX8lo+/eXLxczmAn4M7ApsDdwMXJzSurcBzsWHQL/MOXdGGusVEZH2VNg8T2a2FLCUc26Emc2P\nDzG7o3NuVGQZXdETESlcOe88mdnW+AbT5sBQ4F/A+Wld/DOz3sArwBbA+8BTwK6qp0REyia/eqpX\nHolU45z70Dk3Ing8GRgFLFNUfkREpO3cBSwKbOCc28s5dzvptmTWA153zo12zk0Hrgd2SHH9IiLS\nZgprPEWZWT9gLeCJYnMiIiJtZG38hbeHzOxuM9sP370uLcsC70aevxe8JiIiHSpOtL2q0oq2F3TZ\nuwk4LLgDVWFg5HH/4E9ERLIzNPgrt6D3wggzOw74Ab4L35xmdhcwxDl3SatJxFtsYORxf1RPiYhk\nbShF1VNxo+3Nxjm3YsuJm80J/Ae4yzl3bpX31ZdcRKRw5RzzVE0wTumH+Gh7+7a4rg2Agc65bYLn\nxwOzokEjVE+JiJRBfvVUkQEjDLgS+MQ5d0SNZVQpiYgUrn0aT2kysznwASN+CHwAPIkCRoiIlFCJ\nAkaYWS8z29PMTgyer2Bm66WQ9obAHsBmZjY8+NsmhfWKiIi0zDk3AzgYuAcYCdwQbTiJiEjnaXjn\nycwuBmYBmzvnVg3GQt3rnFs388zpip6ISAl05p2nOFRPiYiUQQkmyY1Y3zm3lpkNBx8oIhirJCIi\nUpi8AhuJiIiE4jSevgwG4AJgZkvg70SJiIgU6Vnq3/ZpObCRiIhIVJzG0/nAEOBrZjYI+Bnwh0xz\nJSIi0oBzrl/ReRARkc4SK9qema2GjzYEcH9eA2bVl1xEpAzKPebJzHoBuwMrOudOMbMVgKWcc0/m\nkLbqKRGRwpUoVLmZnQ8Mds4NyyNDFWmrUhIRKVzpG08KbCQi0tFKFKoceAb4g5m9aWZ/NrPMKyMR\nEZEE1nfO/RaYCl8FilBgIxERSV3DxpNz7grn3LbA9/CTBZ5pZq9nnjMREZF4FNhIRERyEefOU2hl\nYFWgL6BJAkVEpCwqAxs9CvxfsVkSEZGeKM6YpzOBnwJvAtcDQ5xz43PIm/qSi4iUQrnHPIECG4mI\ndLZyTZL7JvB959y4rDMjIiKSVCSw0QVF50VERHq2ON32LgF+ZGYnApjZCma2XrbZEhERiU2BjURE\nJBdxuu0pBKyISEcrf7c9ADNbDNgJ2BVYwTm3cg5pqp4SESlcubrtre+cW8vMhoMPAWtmCgErIiJl\nEw1sNLLgvIiISA8Up9ueQsCKiEhpmdmZZvYacArwIrCOc277grMlIiI9UJw7T5UhYH8G/CHTXImI\niMSnwEYiIpKLhmOeYPYQsMBE59z7WWYsSFd9yUVEClfuMU9m1gvYHVjROXeKma0ALOWcezKHtFVP\niYgULr96KlbjabYPmb3jnFshg/xUpqNKSUSkcKVvPCmwkYhIRytXwIhqSluJiohIx1FgIxERyUWc\ngBEiIiJlpsBGIiKSi5p3noIZ22tZOIO8NPT1r8ObbxaRsoiIlJgCG4mISC7qddt7huoduQ14Opvs\n1NfE8CwREenhnHPXmNkzdAU22gGYWGCWRESkh2oqYEReKgfirrgivPVWgRkSEcnBmWfCMccUnYuo\ncgeMqEaBjUREOkl+9ZTGPImIlMzRR+eb3mef5ZteTtqqsSciIu2hrRpPJb5JJiLSthYuZBRrz7Tc\nckXnQEREslQ3VHkQvehQ59xfcsqPiIhILGUMbNSnTxGpiohIXuo2npxzM81sN0CNJxGRHAwdWnQO\n2ooCG4mISK7iTJL7iJldANwAfB6+6Jx7NrNciYh0qE03LToH7cM5d0XReRAR6UTOgeU4svT00+G4\n4/JLr56G0fbMbChVruw55zbLKE/RtLtFMerXD0aPzjpVEZHihEVynpVS40qw/aLt5UX1lIh0orwb\nT2WqpxreeXLO9c8hH7GoO4SIiJRZnicTIiKSv4bR9sxsKTP7h5ndHTxf3cz2yz5rIiIi9ZlZbzM7\nouh8hHSRT0R6ujXXLDoHxYoTqvwK4F5gmeD5a0AhFZUqJRERiXLOzQR2KzofIiKd4rnnis5BseI0\nnhZ3zt0AzARwzk0HZmSaK5GSmHfeonMgZXHUUUXnQOp4xMwuMLONzWzt8K+IjOgin4hIzxan8TTZ\nzBYLn5jZBsCE7LJUW6+2mtJXegLtcxI666yic5Ctgw4qOgctWQv4FnAKcHbkT0REJFVxou2tA5yP\nr5heApYAfuacy/ymnaIYSdEWWAAmTSo6F1IGeUUWKiraXv00FW2vlsp6qm9fePvtAjMkIpKxTo8K\nG2eep5eATYFv4icefIV4d6xSpyhGIiJSycyWAk4DlnXObWNmqwPfd879o+CsiYhIDxOnETTMOTfd\nOfeic+4F59yXwLCsMyZSBr16wdVXF50L6ek6PXJRCq5AgY1ERCQHNRtPZrZ00GVv3mDw7TrB//5A\nIcPodeeptnXXLToH+ckziIMZ7LFHfulJuRxySD7pbLml/z9rVj7p9UClCWykekpEitLTL94su2zR\nOfDq3XnaCvgzsCx+4O2fg/9HAr/PPmuz++lPi0g1ufBEKE+dFNigLAeP9HytHleLLdZ4GSim/3gP\nU5rARiIiko333is6B17NUwPn3JXOuc2AfZxzm0X+fuKcuznHPAIwYACcfjpccUXeKSc311z5p9lJ\njaf7788vrU76XjvBwQfnm147XAV87bWic5CK3wG3A183s2HA1cChRWREDWARkZ6tYcAI59xNZrYd\nsDowd+T1U7LMWKWVV4Y55ij/yciSS8Lll8MSS+SbrhlstRWsuir89a/5pt2sCRNgoYWSfWbuuWGR\nRbLJTzU6EepZVlml6BxUV2S5tvLKxaWdIgU2ko62+OIwblzRuRDpDA0rFzP7O/AL/FU8Cx73zThf\nbWuNNXwhlrd11oF77oHzzss/7bzleXKy4Yb5pSXlk1ejpuwXhdpAaQIbqfEkRfj446JzIEXJa2zu\nTTf5/488kk96ZRbnytwPnHN7AZ86504GNsBf3ZMqiqg4v/e99mw0tUOXuCFDis6BVNp///zSqgzg\ncOyx2aSjxlNzyhjYKM87462Yf/5i8rrMMo2XEZH4fvCD1j6/6KLxlgvrKV1Ujtd4mhr8n2Jmy+Ij\nGC2VXZbStfnm+aZXRONpjjnaoyFSaY44s4xVMMvvO+7duz2/155u4YXzS6uy8XT66fmkI7GVLrDR\nJZfAiy8WkXIyZmq0Z+m664rOgUi6iiwvjihk4ona4pwa/sfMFgHOAp4BRgODs8xUNc2exO63X7r5\naKSIxlO7dhMxyzfsuPQMJ58MW2/d3GeTliPbbNNcOklVq5RWXz2ftNtZ2QIbbb89rL22v/BSdmef\nXczJ0JJL+v9zz11/ubK4557mPrfrrunmQzpHWfedIhtP55xTXNrV1Jvn6QgzWw8Y5Jz7zDn3b6Af\nsKpz7o95ZbArP819brfdYIUV0s1LPWE+X3gh/zQ7QZ53nqSc5pkHVlqpuc8m3Xe23x6OPrq5tMCP\ngYyjWqX00kvNp9tpwsBGZnaMmZ0Y/hWdr7Kaf37f/TXvk6F55oH//hc++MDnoR0UMYb5/9u773At\nivtt4PeXQzvSRIooiCgqoqgIiCIih2DBDjY0qKhYEsvP2KJEjWiIXZPYjTWWWGJ7jaIBFWJiVwQR\nRcHE3rAkmliwzPvH7ObZ85yts33P/bmuc52n7O7MPju7szM7haiI+KS6xq8eth+A3wJYLiKPichZ\nALYJWCc1do2xycF7881k4+LHvjkbMsRs/eefTy4uRWdSCBo1Kruw8vD003nHoNpM0sG335qF9dvf\nAuPHh1s2iUypLH1t0sCBjaLJ63r40ku6f8Vqq+UTflW98kreMaB6efbNtZ/uJo2Fpxq/eZ6OV0pt\nCd2/aTqATwEcDGCxiCRyqorIdSLyoYgEPqexL/ZhO7aVlUniLEvBwE2UuHfoADz0ULn3N8jIkWbr\nXXpp+Bv11swk7RxyCHCawbP2nj3Dn88/+YlZGPQ/iQ9sJCJ7ichiEfleRIaFWacsNxf2eZBlfEWA\ntdduGQeq6dLFbL311082HhSfaUWvifrC0wcfpBNOWa5vWQjzFKkRQFcA3ay/9wA8lVD41wMI1aug\nUyf9f9ddi9f20enww83Xfffd8Mv26wccfLB+XdZMKGqH5XbtzAaZAMyaXpTpdz3ySKB9+7xjUXwm\nx3TDDYEzI85qd9JJuslw2PS90UbRw0jC++9nH2ZK0hjYaBGASQAei7mdQE1NaYfQXF6Fp7LKauAp\n3pxWx4QJ5k0+o54rcVsdhE13TJ81fn2erhaRxwHcBmAU9JwZeyqlhiulDkoicKXU3wB8FrTcwQfX\nHoGKhH/k39gYI3IGJkwAdtut9n7PPaOtH2UI1x49gGuvBbbdFth772jhlJ1JJvzccxycokpML+JZ\n3cD17avDGjPGf7nTT08uTJPfpE9pxk0NlPjARkqpJUqp16KsY9q8/IEHzCuG4khr9Egyw5vT6lht\nNeA3vzFbN2o+teOOwOWXm4UFAN26hVvOLX1++ql5uGXm9+SpP4AOAD4A8K71968sIlVv8mT91CGq\nL79MPi5+6hP8n/4UfRtRL56zZwNHHRU9nCKIeoGIc+Pbp0/129lnnfFeeGG24cW1++7A1Knhl4/z\nJM9Oqz/6EXD++d7LJXnMBkVopFaVm+aiDWxkOirsSivl0zf3yCPNmsI/+6x5mFR+e+yRdwzKIatK\nPhFdoW5i5MjwhTy3/WmtfW39+jxtD2Ak9JwZCnrejOdEZLaIZNbAZPp0YLvtsgotniQyB9Y8edts\nM/N1TY7NI4+Yh1d0s2bF38Zxx+mKjbLYZptowyN/8415WGHTW5Ln+5gxwA03hFt2zeoMpRBrYCMR\nmSMii1z+djGJTJw8IMvJY53xNEmDm2wSL8yqW7jQbL2y5P933mm+7jbbJBePqjI5V5z9CaMYOLDW\nLSbIuuuaheFUlcKWb0MBpdQPABaJyL8A/BvA5wB2BrA5gEyGgf3b32Zgxgz9uqmpCU1NTUYXmK++\nit6Mr1cvYPny8Muz8BRNmzbh97ddOz1YhP16662BxyL0RDDpX7X11rX3//lP8YfWjXLztcMO8fbp\nq6+Cl2nNos7YnrXJk/Uw7PV+/nPgvPMAYJ71V2xKqeMBQEQ6ABgB3cT8YABXi8i/lFKDA9bfNpmY\nzAAALFkCzJvXhNGjm3DkkcBllyWz9aSZDkwD2OmD/Gy8sdl6Vc//ldJ9Ox9+OO+YFJvJveSIEdHT\njwgwcyawbFm45TffPJ80evHFXt/MQ175lGfhSUSOAbAldGb0HXSfp8cBXAsgs/nTt966VniKw2RC\nvg8/1I8zjz8+3PKtrfDUpQvwxRfm60f5vdq3rzXdbNMGePDB8LUlUcNyEyWsuN580+zJwGWXAS+/\nDDwVcjiXTp2iVxDY7PMpy98lrqxqvk88Edhnn3DL1o+SFFfY64eI+7E791z75rjJ+rOdETdqaXMb\n2OjFBLcfkHpmAACuuw7YYgv9yQknFLPw1KkTcP/9tfcDB+o+oWGdeCLw3Xfhl1+xAlhrLaBr1/Dr\ntFYzZujBZqgait43d5999BOrDz/0X+7uu3Wz9ySY/CZHH+31TRPyyqf8mjYMAHAHgC2UUmsrpfZT\nSl2hlFqolPo+icBF5FboQtl6IvK2iLQYiMItEWVVwBCJNsGuaXv3776rtSEvS+Fp0CDg88+B/fc3\n30aWzTiy7F8VV9RJne1azo4dgd69o6370UfRlq/3m9/oGq8sxGm2Wc/0XA1jjTXyabbXGqU5sJGI\nTBKRt6GHPX9ARB70W/6QQ2oFpyjeftssfqbat28+OMWjj6YbXrt2esLnsJU6rVlTEzBlSt6xSFfW\n17xTTsk2vCT8+MfZhGPnU6NGAbf6DK9j8vDBS5TxC26+Oblwk+bX5+lYpdRdSqn30gpcKbWvUmp1\npVQHpdQaSqnr0worqsFWY4+wJ3qPHsBVV5mF1dBQuwEty82UffN5443xthOlptzvfZj1y/LbRvXn\nP9deZ72PXbt6zzHiN0yrSdMf54AIcedDS7NwHCWt5vXkqUJSG9hIKXWPlS81KqX6KKV28Fq2b1/z\nKTT69TONoZn69NilS/SRSKOeP9268clTGCbXpR08U2X5DQs1u5q/00+vTetSBgccEK2f/wsvmIfl\nrET0uw4lPbDRLbeEW3aNNZILN2kp1r+mJ4sbBDvxhg1ryy2TGfZ36NDkHo+mKethdePe7JbpyVNU\nzr5OJudG3PPJa/3ly+Ha5Pbhh3XTn6hOPRW45hr92u4cG6WW1nlM+/aNHr5JOEGOOAK45JLkwq76\niJL1ijKw0aBB5hOcOr2UQYP4PFtzlFHUvCDOzSwQ/Vg4B/954IF4YRfN88/HG9zokUf0k46NNkou\nTkUzdKj5unm1kBg+PNxyo0c3b2JsizKybFpKWXgyFWVOFTuxhE00STUDamx0v+GsF5To067NnDmz\n9vo3v4n+1M2e/8b0yZOJrG4QTAuWpje+zvBWXdVsG2lxO+dMj+WgQcC0afr18cfrZqNHHBF+fTvc\nf/87+hxsaenfP9mpBiZMAK6+OrntlYFS6gel1CIAD1p/jwNYB8AxuUbMwIYbRl9nxQrgsMPCL5/E\ntbRMlUtJiJJ3OG9mTTrwx7HjjvHWj8J04JBDD432RCHOkzV7cmNWDrg7LeRkDnm1kGhoAHbaqeXn\nS5YkGx8ThS88JXmRNhl4ImwJuUgd53/6U+AXv0hv+4MHA7vuWnv/s58Be+0Vfv3XXos2Uh4Qv9ke\nEK1mOE66M20fnESt8yWXAEOGxN9OFHlkTG3a6OO55ZbR97dr1+Dj63bBDivvG8sknoCUhYgcIyK3\ni8hbAP4KYBcArwCYBMBgBiPTeLT8LKvzol278DdBgHn6XL5cF87L5osvgK22Ml8/zf6Rbspyo2/S\negDQLSV++tNo68QZmApofr+SJZNj6Uxvaaa9KVPCDz1eljSZpVIWnrI8kOus4/7YsJ7XqEpLlgCP\nP55snIJcfnn0i1OWBg6Mvk6cG9I5c/T/2bPNtxHF3nubrWcyWWW9xsbsm25FnbfD5Fh69asCgEWL\noocbdA0Jc85nYf788MuOHq3/J11LWHADkPLARmUQJU+cPt0sjJ499SinQLmeXnXurOMdtV+XLcv+\nsnlXvKTpd7+rvY76e8adJmTgwGSbR/u59NLktlWU9MC+uS0VvvBUBGEO9Moru38+aJCuHX/yyWTj\nVJSTyoRJ3MeNM9+GfVPZp0/4uZCiDN1b76yzgA02MF+/bA46CJg6Nd0wXnkl3e0nKclzc9NNwy13\n/PHAxInJhVsWWQxsFEZelXxR52vq3Rs49th04hJVljdQnTt759FBss5rq3Bj6Wb8+HjrP/NMMvGo\n98IL7i2H9tgD+Prr6NsbNqzlE6ODYo37mY4o6boIfYyKpvCFp7wyJWc/hCRK3fYQtoccEn9beUty\n8IYwx7JHD+Cuu+KFGUWbNmZ9D2xKtbra/0jyKPj3759f040sOM+jqt58lY3pcbD7g6YRllczIJO+\nOStWRFun3vcZPBd0Vro1NZlNq5BEi4CwylwpGsSZp5qcG0lOVeE0dKieML7eppsCHTpE396oUbW0\nPXmyHg037Lx/QC0NPPYY8NvfRg8/DYMHJz/inunopEVR+MKTm7Fj093+kUc2bwuaVKL5/HPdpC4J\nRbvIdu5sNkhFmHX69Uumz1NcUeZEippmwj4Rq+dWGM/j5jkoTOfcDvax++ST9OJTb8aM6HNglUnU\nCgkqrqj9QYHw/Sy9Ck9RJr21xb0GDxmim8VHmfclKuccVrfcUptPMYzddgO++SbdkTnzZNoU7qab\n4od94IHmzShNZT14B6DPy513jjb0uB3umDHpDgCV5z1kmzblGFXaTykLT/37p/skov4kS+pmpEsX\nnVG89Vbts1dfTWbbeWvXLlrGZAvzKH7u3OjbdUrqIhG2BlIkep+zhQubvw87moxb/4W0h5Hf8xCv\n8AAAIABJREFUbbeWnwVdCJ3H0D4eWdboRrHttvHWzyNT8is8/fKX2calNcq7b27v3sDrrwcvd8EF\n7p9feSWw+ebJxinI7NnAiy8Wt7B/zjl6QmEgmzh26aILk1FGEI3Dbx4+P/vtFz/svn11U+MsdesW\nbfmkr+Off558uJ99ZhaXuOHWizKStV0gLOp5H1YpC09pq6+dS/og28N0duoErLdestsumx49/E/a\nHj2A7t1bfm56osc5llHCPOaYaP2e6jOysG2M3eJ0443+taVxR8y6996Wn+22m26i4KWhIV6YcUU5\ndlkNLPLUU8lty6/wFHZEJSq3MJPQ7ruv++fTpun0eMYZycbJT4cOeoCbst9EJeXhh/UxHDMmfK18\nnL40Z58NbL+9+fpls99+5iMEhuVXgZHGKKim/fiSFnYk67XXBm64Qb8u+3lf+MJT0qX/MI+q68NM\n6yB7tW0PE16Y36Vnz/BDrUcRZ9hXN377W7TmiWHYcbb7uWWtVy//0ensEbOckkjjfv28nE/Dqtx8\nDgifZtOq6d999+b9u+qP7a23phMuNZfFzcEuuyQbXpSnlGW8NpsK+9u+9lq68ah33XXm63bsaN5c\nvIzatKlNrh6GSfqOsn0vTU3xtxFGHufvaqvV7sFZeCqZf/0reJn6RJVW86IBA9w/TypRffABcPHF\nyWzL1r8/cMUVyW4zbc7jmeVADldfDWyySbpheF0AvdJWmrz6Tfz0p7WncI895l+wS0v9k70000He\nzfY6dfJ+wgBE67xM5sI8CYpj+HDg1FNr75PKN+bPb9mMOE1lv4lySuopb5zf5LbbsgknriL2zXXj\nVtmYlsMOS6ZZZFFVqcKl8IWnpB91hmk+VH+Am5qA22/3Xt7kScxnnzWf98ApqRu7hobkE2tDQ7aT\nBnrF3/TC26NH8nHxWq5Nm/QvFl7bv/zydJ5s+LWxPvxw92aol1+uOwdvtlk+Q7hfeWXLSW+zLETn\nfXMYNfwDD0wlGpXmdh6uuirw5pvphZlW39xNNwU23hj4059qn/3kJy2XK+uNUNhR/py/53nnBS//\nxz+axSdpfs2nnUR0ZWgU9U+6Tj453Ho339zyM5NBSuIKGoDBeZzt9J3lxNBZnlN5n7/116s4E9Pn\nofCFp//7P/fPgzKKl182D9MtUfXq5b383/4WPYyVV651Rq0XJhMMe4MTtK277w63nbx4neBR+tA4\nt/HXv5qHaSKvG+f27aN3kA3Dr4319tvrAVCuvlqPMFTvmWfiFV5N9eqVXVNcoGXaLNuw9ddfn3cM\nqiPNiqa00/See9Zep9naIOtrZNhj4vx9N99cD0Htx+9pb1Gddpqezyis+j5WZ58dbj23/NqZvtLg\nNprrpEnAKad4r5N339woPv443vph73O23jpeOF5h1ueLXg8TiqrwhSfTYUwHDzYPM+8SedDN1tCh\nzeeh8hOUMUWdzyjr38av8PTVV9G317NncJ+bKVOib9fLDjskty03fscjr4LbIYfouS2S8vzzyW3L\nllaBZv/99Z9TFvPZeJk3L/8nX62B13loer0Ms17aAxvZvCpMksoLGhuT2U7SsqxwyYOI7ouaVx/U\n+man9d54o+VnUUYndutuIeLfDcN5v5nmEPpeopxTWVVEhqlwNlGf7urz5KIXZAtfePKS1k18587p\nj8gSZOONwxeOgowYARx6aDLbykMeBdk//MH9c5O4nH12OoN2tCbDhiW/zenTa6NeRmWPFuRm7NiW\nT5TXWsssnCi8bvTGji3fky8Cli4NXqb+mJf1OL/0Urj9jWrZsmS3l2XhKcuwzj47/WHqvfJOv4ql\nNdds+VkSoxN/+633d3Yf5T33zG7IeD9V65vr1K0b8M9/en+fR7POKEpbeErL5Ml6RJA8tW/vf+JG\nSfQdOiR7Ecj7hDORdQ1ifXhp3jwX8clTHKbp6+uvo22zb1/zZiNTp3qH4RbW5Ml6zhhTp51mvi5g\nVkP5zTfxwmxtkr4uDhwYPczVVgNmzkw2Hk880XyiWb/wTa21ltk1Mmjk3DC/oZ+s8zrn9drvBj9p\n3brpfm5p8votjz02euuXMO64w/s7r7SmVK2J2pQp4SeeTsq667a81pe1QsRE1PuVPn3SiUdYLDyV\nUNo3xUV6XOqXgZWlIHfjjXrywzT4/QZHHplOmEl6771kttOhQ/R1kjyPLrzQ+zuReE1AzjwzeBm/\nG8lddgHefTdaIcyrPya5izJJpNO0aeZh1p/7bdroQVu8mBSIR42Kd2Md9klB0LV80iTzOCQly8qo\nrCe2zquirVevdPo+7bWX/3crVnhfs1esACZOTD5OQSZObDl0fJbHJYvm5UlW9r79dry4xFXawlOc\nRGUyekoZa/HDsPfrkUdqn/mNEOU3KV9Rf6P6wmDa8ezUqfn7xsZ82pVPnNiyk3Oao3+ZWG215nNA\nlV1QZ/Svvw4/GlZYu+zSsqmxMx4iOlMOGmmKzJnO6XbNNeZhut2I+N2c5NGHI2x/xaBr8i23tPws\n65FMTfONDz6IHt7Ikc3nakuDM7y0h9UvUgsJuzLrmGOAjz5q+b3peeI2SFLUeNVL88nTAQdkF1YQ\nkyld8r5vKG3hKQ6/cfu9TuQiFQySyjQefLB20bQT7wMP6CZNbgYO9B+pJoyov+OOO3p/F/Z3qL+h\njXuR8/PJJ+4doJ3D/tYbP948vKDfoL42KerQtFnwa6Ly7LPpheuWFuN08J8zx3vELTusDh2Sn2dp\n8OCWT94mTQJmz042HCqWqAPb5PGkPsyk9EBwpUOW02N4Mb0HSKvS4q23ktvWGWcA222X3PaiyOve\nqqHBfxTlqJIcJMk2dKj5un5T6Eyblt1kvE5e16AFC4p1jx1GAS5JZvJsj5y3qHHxWn7CBF3zr1St\nmU4ShZWktG8fr2YWAP7zn5afXX+9d7+2efPihedl9dW9R4B8+GHz7QYdkyFDaq9nzfJftkhp3DZi\nRHrbdtvfOLVv22zj3XzQGdbEien/1u3bA9tu6x0HykZav/mGGxajWW5StdUi/pUoeRSe8h5tLyg8\n0wFvbM79a2xMr2l5fVj1ynhdMr0XeuKJaMsPGeI/x6gfvyl03OI/Zky4JuJeTOZwdB57k3SwaFH0\ndZJS2sJTGU+4vIT5rbp0Af77X/fv7I55QReMIh6TqBe5sWPTiQcQ/amP1/GI4ve/B+6/X7fvjjNs\n+imnAOeeGz8+efDqvxOnv0lUaZ4bYbddxPOTzHgdy7L0A42qqvvllPf5OX16sk9inPyOX9yBPbJw\n223JbCdorjA3SaaLvff2/q6hQVcAmlq82HxdQA+Ycc450SZpd1YOZ620haesFanWKWotXNi4r7SS\n//dRm4jFFRRenAw1j4zqrruijeQYdDyA4ME9Ghr0zN3HHRc+XDfjxgE//3m8beTFq+nDxhsDJ5yQ\nTBhlrFig9MW5RmUxxH1cSRZq/LZl53nOJs6XXOK9fM+e8eNTP9pa2udwVvN22eonUe/XL5+JfqdO\nbfkU1XQAlrTU91Mte2HeK/52mnvnHe/uG3GccYb3d23bAiedBBx9dPLhpqHVFp685jWob+piy/rm\nx685RNQTN6m4BxXaTJtwpPkEwOu3SqsZiN+x6dTJf4K+qK69tnqDACQ9bO1996WTCUSVZmYbdtss\nwJXLggV5x6AYOneupfF77619vs8+3k3X4s7xNHeuLkw4pX3+1FeWpTmIw+9/r5tp1Utj5DsgeNTc\n+s7/M2akE4843n/f+zuv+8YkJJ3u9twzuG9u377AzTcnG+6YMS1b9vTu3bJFTlnyqVZbeHrqqZaf\nTZ3q3Zm7SIWnqOLGXQSYP99/oA0gfOfgetdc416YSeLJk9cyjz0WvG4W4nTa3GyzxKJRCDfdBGy0\nUd6xSMcRR6Q3gAMLT9VkcvNc5hpxr7gvX66/e/75Wh7z2mv+0xPUP1WJKmqFS9xRTC+6qGWfkSuu\n0E/H3cS9WffavzFjgIMPdv8uzkTGUZ7MB4289uKL5vGIw29eoTQH53G7br/wgtm2RPTgVePGBYc1\ndqy+70tTp04tz52y5FOlLTxVfcAIv/Cy2Pf77mv+ftNNgQED/Nfp0SP8sKxhBA1wEMfAgS1rFufM\nCV4v7oSl9nEV0RnY3LnxtlclIvHOM5N143ZYtQWdk506pVs7Gca+++qbNMpOWW4Eisw+t4YN0///\n/GfvwQ26d08vHn7HMu4opsOGtbyGdO3qvZ9p3qx7Nb/3istDD8UPc8YM4NBD9eugJ65+FWxlrnzz\nGnrbrU9YnFH4/DjTuEiykydXrZKvtIWnsvzApvxq1bJotrfLLubhmagPo7HRu3YkznadnE0+Vl45\nXGfJOKPRAMDZZwPnn6/n+4nbtKRq4hae3BRhiOO0hT0/e/cGjj023bhQvsr85MlL/T7tvHNwn400\n5HHPkUeYp50WbdLx7bcPXiYoXXbvrpsSxt3fyZPLe2/oVTgdOVL3l85C1c6fNLWCW4vwijSc5vrr\ne88bEDWDDBpUIEhSneqjiNMkL+wyzhqzpAoyQXHadVf9e7Zv37JDcmuXxo2fyeR7REmJm6ZXXtn9\n87jDVBeRyW9Vny+nOdFnfUuFLORxwzlwILDffsluc8stk91e3nr0SHZ7xxzjP8pcUukgz4GN+OSp\nIIrUjC4tXs3komYyw4YBl15qFofVV49WW12WhA/oZhF2G/qkL4ZZqFotc5s2yaafO+4IvuHJKlNK\nU2t4ulZWcdPX66+3/KxnT+DOO+Ntt0z8zq36uaF++cv4E7l7hXn77cCgQfG3HcXUqelsN8vr1VVX\n6afeVTFxoh4VLkmdOhUjPx80CFhzzXS2zcJTQSTxA//9783fFyHxOnntY9RaHBFgiy3M4pDVyH71\n6yX15CnI+PHVqxUrK5HmNcf152dRbbONdyfrLBTtukXJcRuds0cP78F5qpgW/PbpgAOavz/+eGDm\nzOBtPv109Hh06uQ+BPqECdG3FdbEiS1HwEuiOXuQ+vx4yZL0wyyLfv28+yeFEXfAi2efNV836PrQ\npw/wxhvm209CHk94TZS28JSE0aObvy9Ssz1ATxq2887NPxs92qzTt+mwp3llxvfcE7xMEsfk3nuB\nxx+Pvx2K5/TT9YAKzs6x9ednVGHSbv38IiZ22knfWFVR0s13KF1lqbV1Y5LXnHee2WANI0cmF5eg\nUWiT9uijwcvETQf24BwffKArseI8catagT5u31y3QS2iNKcbMcI87DyFTQd9+5bjOlbawlMRhg7/\n9FP9l5aOHXXm4DR9utm21l0XeOCB6OvlMadU2JHJ2rTxHsrVVrULt1OV9m3GDN1peObMlhUGaVp7\nbT2MeFTOeTKyuBb5Pdny6heThCqlsTy0hublJ56Y7vbD3lhGmYDcVB6/r3Ni0T/8IZswjzxS7+uq\nq8avxKqaNK6JbiPqVU3V8hIWnlBrthX14Hbvnu7QqEDz/gxK6VpuU1Fvso44QndkjCKJ4xL2ONhz\nfxRJ2S8QXpP4xh10JKy2bdOdHNKNyb798Y+111kc82uvdf988uTo5yiVW9FaSNRX8MUxalTLz8IW\nntz6h5nK+jruF94GG9RGgq1vpkjZS3pU2GnT/AeLSFLZ70+KpLSFpyTZHeSKlikBwHrrJZs5RXHZ\nZboNeVL++tdwyyV5grMzfTSffNLyszPPBLbeOrs4JHWurbdeuOVmzow3GWCeaWzttYF27fILn9JX\n/2SniPmUG5NJ0594Ivo69j43NkZftyxWWinZ7WV5E121G/b6gY023zze9rp1y24UvL33TmY7JiqX\nDvKOQBruuCPa8vZwxkXMlESAn/0s+cf1ae2P33bTuAH3O2ZffZXdExNb0jeyeU+WDOhZ58tSCL3l\nFv3/2WeDm3TaunaNNxlg1TIFKpb6yrOypLfFi5PZTh7DK4f9jcPMDZiE668HXn45m7DIW8+ewFZb\nNU9zTz0Vb5th0tr668cLA9CtFHbbLf52iiiPa2JJbomi2Wuv8Mt+9x3w85/r135zZ2y9dXqzOgdp\n1648j+tNMzJnn7Ky3BzU++CD5GsIW6M4N0M//rE+p8vaqbZoynouFkUR+jxdd12tUiGrcLfZxmwg\nB0BPluqU59w0QebMySacVVYBBg/OJizytnw5sPvuwGGHZVdwBvQgEzfdFH0957mTxXni1zUkzbzE\na4LhNJW28JRUQmho0Af1k0/8B2Po3h248MJkwqyynj2z6bgbRtY3flk/5SJ3WR+Hqhcw3JpyUjhZ\nPxVxC2+//XSlQla++gqYPdt8/agjPG6wgR5iOUlJn9Pff5/s9sqkitfHLl30hPdZqp/TLAxnusvi\nOPgNoHbOOemHn6VWX3iyrbJK8Nj9RWpPXlSNjcCCBfG2EeUkr+KFOayyNKWLavfdgaamvGMRXp5p\nMO2w27XzHkSEsmVPml7E5uVArWVGu3bZnhP33w8sW5bc9nr10qPMJSnoWl3l/lpp8KrQybJZWlLn\nWtiBvJqa9LxfUTjPwyzOSa8wxo2rXmuQ0t5+he0MTjVlKfzVTwpoqsoFq0svzX62+6zstRcwd27e\nsaDnngPOPz/vWFRX1P6RYZoE53mNtwddiVup47xuL1wYvHzHjsnOs/bMM9kWZq6+Ovsby6Sfzr/6\nqvd3aeTDbhU6Z58NDBmSfFhpsPvYTplS6zYSZK21ws1/6aXK90N5KG3hafhw4Jprsg2z7InPba6q\nNJhk4E8+WXud1HEt+/HyM358tfevTKr65Gn48PSnYqg6v2vhihXht7NkSa1/jd8xdytEZJU+7SGc\nkwyvaP180shD118/22vICSfo/CNJrMyOZv584OmngauuAtq3zzs2ZCKgoVqxFaEzbplkVXgyscUW\nZuuxAJE+/sbujjsOeO+9fEcw4rFpHeynzPPm+Tcp69wZ+OIL3SejCvJI335hFjkPDWvs2Oo2+c5S\n3PvBkSOTiUdYVT/mc+fq5oFZKXXhKWtlv3Da8X///Xzj4aVnz2QzS95YJoM1Y+44gAwFSbrCbezY\n6OuU7TrojG+aN3zffuvedLKofcrKqmzpL6zevfOOQTR5Hoe0zxuR7PtJl7rwlPWFrOyFp4ED9f+k\nRyaqZ3pcFi2KfoJX9cIcJIv9vvJKYNQoPUwqFVNrTf9lUYQJjMucRtKMe9AAUW7SuAco8/EJI8sJ\n1rO0zz7JN3+sqjTT+GmnAeuum972vZT6QV5ehaeTT8423KT0759fzVmYcPv0SXaUo7RO2KrXPv7l\nL/r/JpvoiWarnrmXGY9NsfXrB9x2W7ZhVilNFK3Z3tSptdfvvJN+XNKQ5W96/fW1StuqESnX06eq\n9s0980xg//3T276XUheestarl/6/3Xb5xoPCqdJNRJa22w646KLaiEBEZG7DDbMNr75yh9fBaPx+\nr6OPrvVV6ds3m/gkreqVf9ScPejP6NH5xaGK16BSF56yvgiMGAF8+SUvPkGy/n2WL882PC9ptc/f\na6+Wn6V9MTr2WKBDh3TDoGiOP77lZ1XMlKom74GNypZG7BYeF12UbzyoXMqWzrPy6af6mnD44fnF\noYrHhoWniDiZXXRvv53u9otyTNIqPN1xRzrbpXK54IKWn+20U/bxoGIre+WefaN17LH5hB9UaXTO\nOcDFF2cTlzRkeSOb1cSse+zhXslIxcDCU8FU8YBUQdeuzd/365dueG43C08+mf3QnFUfCpSK5ZRT\ngM02yzsWFCSvvrllnaOrY0fg3XfzCfv552vN872MG6eb7yUlrfsYr9+w7IVr2w476P8XXQTceSew\n9tr5xoe8VfFePdfbPRGZICJLRGSpiJwUdf0DDsino1hVLj5p6dTJf8bxLAwfnn2YLDxRlpje0iUi\n54vIKyKyUETuFpFuJtvJq/CUVwEkCauv3rrCTUOV9sXNrFlAjx7mc0QSxZFb9isiDQAuBTABwAYA\n9hWRSPOJNzbmM3hDjx7Zh1k2eRcws67pmDYNWGml7MKrYk0ORcPCU+pmA9hQKbUJgNcATM85PqF0\n7w7cf3/+12BqbtasvGOgNTTkHYPkfPwxC09F49bSqIr3K3lmvyMBLFNKvaGU+hbAbQB2i7qRPDKI\noUOBzz/PPlxy55YGsj5Zzzgju5vZddct70hPlBwWntKllJqjlLJn9nkagFED5KzzKBH2hTNx5JHB\nTfbisJuZ1cs6r8pyCo8q3jSTP7c+7lmPOJqFPLPfvgCcP/M71meR5FW71qVLPuFSOFW+aM+ale1T\nLiqOceOAW27Rr5kGMnUwAKNnB3wCVA6nnJL9U5l+/YANNsg2TFa6UJZGjwbOOy/vWCTPYI7txCSS\npdTP+F2lR9JlluUNQ2u7OalywZD8Pfqo/j94cPY3XVUkInMA9HH56hdKqT9by5wCYIVS6o8mYay5\nZowIxtDarosm3nyzdnzyuK7ecQfQzagnnTkWnihLjY3VvC/Ps/D0LoA1HO/XgH761MyMGTP+97qp\nqQlNTU3Nvq8vPH33XWLxoxjyLjxlnRHyKQBlKe0JjOfNm4d58+alG0gBKKW29fteRA4EsCOA8X7L\n+eVTq6wCLF2qm9tSsfTvX3udR+EpjzAHDswuLFb0UZqF9TzzKVE5VU+JSFsAr0JnSu8BeAbAvkqp\nVxzLqKD4XXMNcOihtfesbSuGt9/WGVMWx+PLL/UIf05ph2tnClOmADfcALRNuRri5JOBc8/Vr19/\nncOyUrZEBEqpVnUrJCITAFwIYKxS6mOf5QLzqWXLWhae0r5GKQXcey8waVK64ZSdfS3/8EOgd+9s\nwrI9+WS6Ax7Uh3fnnXpOpKzC+/vfdbMtal2c6WCHHbIbLCXLfCq3B7hKqe8AHAXgLwBeBnC7s+AU\n1jbbJB0zSsIaawCffZZNWCutBLz/fjZh2UaN0v8vuCD9ghOgJ2YkokxdAqAzgDki8oKIXG66ofoW\nElkQYcGJ8nPTTSw4tWadO+v/VW0mmmezPSilHgTwYJxtDBgA/OEPwNSpycSJkrPyytmFVT8xb9qe\neELfnOTRLCHNEaGISFNKJdbQLo/CE0XTWprtZaXK+0b+/vtfoH17PQrxj36Ud2zSkWvhiSgNWTbd\nzDqDmD+fIz0SlU39Nen11/OJB3mrag25UxatJIjsPuC/+lW+8UhTJS4X7OdErcHNNwMbbZR3LIgo\nqvonT+yzWCyzZgE9euQdi3Rddx2w8855x4KoGipReCLKqwCd5ZOnKVNYc0hURlk2YaZobr0VmDAh\nm7Buv735+6zyj3vuAQ46KP0ho535MJvtUZWx8ESVsnx53jEgImqub19gyZK8Y0Fu9tknuxv9vfdu\n/j7tOZ7swkweBZm0Ry4kyhMLT1QJHTro/z175hsPIiKiILNnA4MG5R2LdMycyZGQqdoqUXhinydq\n2zafdMCmCUREFNUqq+Qdg3Sssw6w0055x4IoXZXqQXHDDXnHgFob+4kXERFRWFlWvGUZ1tKl2YVF\nlJdKFZ441xNlacUKoF27vGNBRERlcuyxwMYbZxceW0gQJYvN9ogMseBERERRHXUUR04lKjOevkRE\nRCljJR8BwEcfAb16ZRsmnzwRJasST57WXz/vGBARERH5y7rgBKQ/vxNRa1OJJ0+jRrFWj4iIio95\nFWXpkUeAsWPzjgVRtVSi8EREREREzf3oR3nHgKh6KtFsj4iIqMj4xImIqBpYeCIiIkoZR+ckIqoG\nFp6IiIhSts46wIIFeceCiIjiElXgtgQiooocPyKi1kBEoJTigMcumE8REeUvy3yKT56IiIiIiIhC\nYOGJiIiIiIgoBBaeiIiIiIiIQmDhiYiIiIiIKAQWnoiIiIiIiEJg4YmIiIiIiCgEFp6IiIiIiIhC\nYOGJiIiIiIgoBBaeiIiIiIiIQmDhiYiIiIiIKAQWnoiIiIiIiEJg4YmIiIiIiCgEFp6IiIiIiIhC\nYOGJiIiIiIgoBBaeiIiIiIiIQmDhiYiIiIiIKAQWnoiIiIiIiEJg4YmIiIiIiCgEFp6IiIiIiIhC\nYOGJiIiIiIgoBBaeiIiIiIiIQmDhiYiIiIiIKAQWnoiIiIiIiEJg4YmIiIiIiCgEFp6IiIiIiIhC\nYOGJiIiIiIgoBBaeiIiIiIiIQmDhiYiIiIiIKAQWnoiIiIiIiEJg4YmIiIiIiCiEXApPIrKXiCwW\nke9FZFgecSAiIvIiIr8SkYUiskBEHhGRNfKOExER5S+vJ0+LAEwC8FhO4QeaN29e3lFIXWvYRxv3\ntZq4r5Si85RSmyilhgK4F8DpeUeoXmtIE61hH23c12rivlZPLoUnpdQSpdRreYQdVmtIAK1hH23c\n12rivlJalFJfON52BvBxXnHx0hrSRGvYRxv3tZq4r9XTNu8IEBERFZGI/BrA/gC+BLBFztEhIqIC\nSO3Jk4jMEZFFLn+7pBUmERFRWEH5lFLqFKVUfwA3APhNrpElIqJCEKVUfoGLzAVwvFJqvsf3+UWO\niIj+RyklecchLyLSH8AspdQQl++YTxERFUBW+VQRmu157mhrzqyJiCg/IrKuUmqp9XY3AC+4Lcd8\nioiodcnlyZOITAJwMYCeAP4N4AWl1A6ZR4SIiMiFiNwJYBCA7wG8DuCnSqmP8o0VERHlLddme0RE\nRERERGWR1zxPibEm2n3B8dffZ9l5IjI8YHvbishzIvKi9X+c47vhVmfipSLyO8fnHUTkduvzp0Rk\nTcd35zo6Ie8dYz8nisgPIjLIdBuObZ0vIq9YE0DeLSLdHN9Nt/ZjiYhs5/jca9/XtCaQXCgic0Wk\nb9z4Wdv9TwLbKOSxdGwvsWPq2GYh91lE+onI/xOR10RkmYj8VkTaBazzMxFp9PjuFiuNLhKRa0Wk\nreO7i619WSgimzo+n2Cts1RETnJ8vomIPGn9ZveJSJcY+2lfj14SPbnqcSISu1mXtZ3F1j497LzO\nichU63d9TUQOcHy+log8be3vbfbvLSLdReQea1tPi8iGceNH/oT5lMm2mE8xn2I+Vfs1n4IeAAAM\nEElEQVSc+VSRKKVK/QfgiwjLzgUwPGCZoQD6WK83BPCO47tnAIy0Xs8CMMF6fQSAy63XkwHcZr3e\nCcBs6ELqStb6XQz383YA9wGYYbBum7r329qfATgHwDnW6w0ALADQDsAAAMtQezrpte9/ArC/9Xoc\ngBuzPq5lO5ZJHNMy7TN0v8ZnAEy10yOAa6AnIfVb758Aenh8t4Pj9R8B/MR6vSN0x34A2BzAU9br\nBis9D7DS9wIAg63vngUwxnp9EIAzk0i3AHoBmJPE8QXQBKCj9fonjmO0CnSTspWtv9cBdLO+uwPA\n3tbrKxy/0fkATrNeDwLwcFLpj3/B6SLEssynFPOpvI9lEse0TPsM5lOxjy9aUT6VewQSOFgtLl4A\nhgOYB+A5AA85TtK5AH4L3fF3EYDNArYtAD6xEvFqAF5xfLcPgCut1w8B2Nx63RbAcuv1CQBOdaxz\nDYC9DPaxM4A3APSvi0MTgMcA3A9giZXw7EzkPwAusE6+LX22PQnAzdbr6QBOcnz3EPTcJn77/hKA\nvo7f699JHVcAYwH82fHZpahd2N4AMAPA8wBeBDCoDMcy5DH12ucdAbxipeuLncsVeZ8BjAfw17rP\nukBPOtoROsO4APqcXAjgKABHA/jGOraPBGz/WAC/sl5fBWCy47slAPoAGAXgIcfnJwM42Xr9L8fn\nawBYHCfd1r1fC8DH1usG6AzhGWs/D3Msd5K1rwsAnB0QxqYA/m693hfAFY7vrrSOrQBYjtrN5xb2\n/kNfL7ZyrLMMQC/TfeZf9HRhfcZ8ivlUIY9lyGPKfIr5lF8Ylc6nSt9sD0Cj1JpC3GU9Fr0EwB5K\nqREArgfwa2tZAdColNoUuhbjuoBt7wHgeaXUtwD6AnjH8d271mew/r8NAEqp7wD8W0RWgU54E0Sk\nUUR6Qtd49TPYx92gE9RbAJaLyDDHd5tBn8QbABgIYHfr85WgazOGKqWe8Nn2wdC1OwCwet0+vmPt\nW/3nzn1fCP07ATqD6yIi3SPsWxTK+rNfL1dKDYfOjE8IWLcox9Lmd0ydFAAlIh2hLzgTrHTdE7Xf\nwktR9nlD6JuH2k4p9QWAtwCsC+Aw6Mx5E6XUJgBuUUpdAuA9AE1KqfFeG7Ye8e8HnbECOq2+7VjE\nmYbdPgeAxSKym/V6L+iMKRFKqX8CaBCR3gCmQWeAIwGMBHCoiAwQkR0A7Apd2zoUwHkBm52G4HN2\nFSusH6zP68/Z3QFAREYCWBPx0jIFYz7FfIr5lLui7DPzKeZToRVhqPK4vrIyGQCAiAyBPgketppw\nNkAnbkCfxLcCgFLqbyLSVUS6KqU+r9+o1b7yHOimA0aUUnNEZDMAT0CXrp8E8IP/Wq72RW2Cxj9Z\n7+25sZ5RSr1hxflWAFsBuAt6hKi7/DYqIqcAWKGU+qNBnGwnALhURA6Erl181wo7C3db/+ejlhm3\nULBjafM7pvUEwPoA/qGUetP67Fboi7n7CsXa56DMczx0rdQPVrifRdj25dC1hY87PovadvtgABeL\nyGnQzVNWRFw/rO0AbCQie1rvu0JnyuMBXKeU+hrw338R2Q/AMOhaTC9Bv/c5AH4nIvaTjReQ3Tnb\nWjGfYj7FfKp+hWLtM/MpjflUCFUoPNUT6MeZW4ZcvsUBFJF+0Be8/a0SOaAvts5Sbz/UStLvQtdI\nvGfVKHZTSn0KAEqpswCcZW33FgCvRtoZXbMyDsAQ0ZMxNlhxPtEl/oLaReNrZT3r9NjugdCP1521\nJe+ieW2GvY9u+/4uACil3odVoycinaFrUltk8oa+Q/NBTeo7ZX5j/f8eHmm5SMfSESe/Y1q/zx2t\n//XH0vPCW8B9fhnAns4PRKSrFeayoP3xIiKnQ7c1P9TxsVcablf3+RrW51BKvQpge2ub60G3oU+E\niKwN4Hul1EfWTfJRSqk5dctsjxD7LyLbAPgFgK2tWlpA72+TY7E1ADwK4FMAK4tIGyuzd56zX0Bn\nxPZ2/wngH0Y7SKaYT2nMp1CsY+mIE/Mp5lPOZZhPOZm29yvKH1q23WwPYCmALaz37QBsYL2eB6vd\nJXTN10KX7a0M/bhwost3T0N37hO07Mhob3cf1DrJtYHVkRDAxtCl5zYR9+8wONqKOvZjDHRi/BK6\nc2EbAH8BMMntd6lbfwKAxQB61n1ud8RtD90G9nXU2qZ77XsP1Nqr/hoJdSqFbkveD7ozZnvruPwD\nwAHW9/8EsIr1egSAuUU/liGPqes+Q2dObwFY01r+FgD3lWifn0Wtw3YDgKsBnG+9Pxy6VrPBet/d\n+v8igAEe2zsEwOOwOqc6Pnd2xN0CtY64ba30PMD6bZ0dcXs59vdGAAfGSbeO172gOzKfbr0/FMA9\nANpa79eDbra0vbUvjc79r9vuptAZ+MC6z7tbaWRl52vruztgtauHbkpjd8TtBqC9I043JHHO8i9c\nurDeM59y+V3q1mc+xXyK+RTzKTtONyRxzib1l3sEYu8A8LnLZ5sA+KuV8F4CMM36fC70I+j5VoIf\n4bLuqdCdWF9w/PW0vhtunZjLAFzsWKeDlQCWAnjKPpGgLySLrb8nAGxssH+PAtiu7rOjoR8Dj7X2\n0+6Ie7nf7+L4bimANx3751zvF9b+LQGwveNzr33fA8Br0DU9vwfQLoFj2ha1zovnWtv/C4A74Z4p\nDQfwaNGPZYhjelnAPu+MWkfcK2B1oC7JPveDbmrwmhX+7+y0Ap1JXWiFswDAEdbnR1npsEVHXADf\nWvG199HZYfhSK4yFAIY5Pt/BSqfLAEx3fP5/1uevAjgrZtr9zorPS9a+HIfajZ1A37i9aB2HR2CN\nCgXdEXexte5Ml+3OAfC+Y3/vdXx3kPVbLIXVadv6fC3oG5Gl0CNm2b/3Fta+LrHSV7e45yz/AtMF\n8ynmU8ynir/PzKeYT4X64yS5JSYiTQCOV0rtkndckiQimwC4Sim1Rd5xKRIR6aSU+q/1+jIAryml\nfhewGhFRbphPtS7Mp6g1qMJoe62ZQnCnu1IRkZ9Az4dwat5xKaBDrdG6FkN34rwq7wgREQVgPtW6\nMJ+iyuOTJyIiIiIiohD45ImIiIiIiCgEFp6IiIiIiIhCYOGJiIiIiIgoBBaeiIiIiIiIQmDhiVod\nEelhjQb0goi8LyLvWK+/EJFLUwrzKBE50Ho9T0SGO74bICKLRGQ7R7y+EJEl1usbRKSTiFwlIstE\n5DkRmSsiI0Wkg4g8JiI8l4mIKoL5FFFxtc07AkRZU0p9Aj0TNkTkdOjZti9KKzwREQDTAGxmRwEu\nQ/cqpWZDz/QNEZkLPTfKfOv9bQBeV0qtY70fAGADpdQ3IvI3ABMB3J3WPhARUXaYTxEVF2sBiPSM\n2hCRJhH5s/V6hoj8waote0NEdheRC0TkRRF5UETaWssNt2ronhORh0Skj8v2RwNYopT6zjBeAwGM\nhGNOEaXUG0qpWdbb+wDsG3HbRERUHsyniAqChScib2sBGAdgVwA3A5ijlNoYwFcAdhKRdgAuAbCH\nUmoEgOsB/NplO1sBeM4gfLvWb0MAC5T3pGwLAGxpsH0iIio35lNEGWOzPSJ3CsCDSqnvReQlAG2U\nUn+xvlsEYACA9aAzjId1iwc0AHjPZVv9Afy9bttu4fnFxftL3SSijYh0VEp97bcsERFVBvMpohyw\n8ETkbQUAKKV+EJFvHZ//AH3uCIDFSqkwtWnieP0JgFUc71cB8LHPui8D2ERE2iilfvDZvm/mRURE\nlcN8iihjbLZH5E6CF8GrAHqJyBYAICLtRGQDl+XeBOBsYz4PwH6O91MBPOoViFLqdejmFGf8L3J6\n5KMdrdcdAHyvlPomRJyJiKgamE8R5YCFJ6JaTZjyeA20rC1TSqlvAewJ4FwRWQDgBQCjXLb/dwAj\nHO9/D+ALEVlorbcSgAsC4ngIgFWtIWAXQbdb/9D6blMATwasT0RE5cV8iqggxLtvHxElwRoCdj6A\nzZVSK1LY/lkAnlVK3ZP0tomIqPqYTxGFxydPRCmzRh+6GsCUpLdtNYXYCsC9SW+biIhaB+ZTROHx\nyRMREREREVEIfPJEREREREQUAgtPREREREREIbDwREREREREFAILT0RERERERCGw8ERERERERBQC\nC09EREREREQh/H9K0uHOb39n9QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8620e32410>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Additionally, download measurements from the year prior. These measurements will be used to calculate harmonics and tidal predictions for the year of interest.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Predictions from t_tide"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Run t_tide on the time series to get the tidal predictions. \n",
      "\n",
      "This is a little bit clunky, but I have modified Kate's get_tidal_anomaly.m matlab script to use t_tide. This script saves the predcitions in location_t_tide_compare8_dates_snr2_filter.csv file. It also saves the harmoncis in location_harmonics_dates_filter.csv. Before this part of the notebook is executed, we must go to matlab to generate these files:\n",
      "\n",
      "eg.: get_ttide_8_filter('wlev_8615_31-Dec-2006_02-Jan-2007.csv', 'Tofino', '31-Dec-2005', '02-Jan-2007');\n",
      "\n",
      "The first string is a filename that stores the water level measurements. A harmonic analysis is performed on this time series. The second string indicates the location and the last two string indicate the beginning and end dates of the tidal predications. \n",
      "\n",
      "The matlab script get_ttide_8_filter.m is stored in the storm-surge/Revisions/tides/ repository. It is recommended to add this directory to your path and run the script in the directory where you would like the predictions to be stored. You also need t_tide added you your path. \n",
      "\n",
      "This has already been done for 2006, 2009 and 2012. The results are stored in storm-surge/Revisions/tides/forcing/\n",
      "\n",
      "For now, load the data produced by the matlab script. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = '/data/nsoontie/MEOPAR/storm-surge/Revisions/tides/forcing/Tofino_t_tide_compare8_' + start+'_'+end+'_snr2_filter.csv'\n",
      "\n",
      "\n",
      "import datetime\n",
      "import pandas as pd\n",
      "import pytz\n",
      "\n",
      "def dateParserMeasured2(s):\n",
      "    #convert the string to a datetime object\n",
      "    unaware = datetime.datetime.strptime(s, \"%d-%b-%Y %H:%M:%S \")\n",
      "    #add in the local time zone (Canada/Pacific)\n",
      "    aware = unaware.replace(tzinfo=pytz.timezone('Canada/Pacific'))\n",
      "    #convert to UTC\n",
      "    return aware.astimezone(pytz.timezone('UTC'))\n",
      "\n",
      "#read msl\n",
      "line_number = 1     \n",
      "with open(filename, 'rb') as f:\n",
      "    mycsv = csv.reader(f); mycsv = list(mycsv)\n",
      "    \n",
      "ttide = pd.read_csv(filename,skiprows=3,parse_dates=[0],date_parser=dateParserMeasured2)\n",
      "ttide = ttide.rename(columns={'Time_Local ': 'time', ' pred_8 ': 'pred_8', ' pred_all ': 'pred_all'})\n",
      "\n",
      "ssanomaly = np.zeros(len(wlev_meas.time))\n",
      "\n",
      "for i in np.arange(0,len(wlev_meas.time)):\n",
      "    #check that there is a corresponding time \n",
      "    #if any(wlev_pred.time == wlev_meas.time[i]):\n",
      "    ssanomaly[i] =(wlev_meas.slev[i] - (ttide.pred_all[ttide.time==wlev_meas.time[i]]+msl))\n",
      "    if not(ssanomaly[i]):\n",
      "        ssanomaly[i]=float('Nan')\n",
      "        \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "    print 'Comparison between ttide predictions and measurements'\n",
      "    \n",
      "    plt.figure(figsize=(13,6))\n",
      "    plt.subplot(2,1,1)\n",
      "    plt.plot(wlev_meas.time,wlev_meas.slev,'b',label='measured')\n",
      "    plt.plot(ttide.time,ttide.pred_all+msl,'m',label='predicted')\n",
      "    #plt.xlim(datetime.datetime(req_year,1,1,0,0,0),datetime.datetime(req_year,12,31,23,59,59))\n",
      "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
      "    plt.xlabel('Time (UTC)')\n",
      "    plt.ylabel('Elevation (m)')\n",
      "    \n",
      "    plt.subplot(2,1,2)\n",
      "    plt.plot(wlev_meas.time,ssanomaly,'g',label='anomaly (meas-pred)')\n",
      "    plt.ylim(-1,1)\n",
      "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
      "    plt.xlabel('Time (UTC)')\n",
      "    plt.ylabel('Elevation (m)')\n",
      "    plt.plot(wlev_meas.time,0*np.ones(wlev_meas.time.size),'--r')\n",
      "    \n",
      "    ssarray = np.array(ssanomaly)\n",
      "    mean_anom= ssarray.mean()\n",
      "    print 'Mean anomaly: ' + str(mean_anom)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Comparison between ttide predictions and measurements\n",
        "Mean anomaly: -0.0252720551771"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6d9xxBx599FFN4G1sbMQqWcPw5z//OVavXo1NmzbB7XbjiSeegD9C/MINN9yA\nb775BsuXL4fX60VzczN2y6yd48aNw4EDgXn8mWeeieHDh2PhwoVwOBzw+XwoLCzEDpkQ59prr8Xz\nzz+P1tZW1NTU6JK7EeZz2AjjKt/BIGnIztx+EI+hWLOEK9Q8TE1M1TIcnAMtX7ag5bv4J2c+pw+e\nZg8aPm5A2SNl2PjEIXx6bPy1bkePDmjQmw7JfZIThM7OviNIprkQTTi/4xA++EAkHYkWv18oQcrL\nxXtViuSaa/TrVD1fBY8UWJ0W8cD1c+BV7Ia3Ru8S25m7unWzFS1ftWgPuHCMbtZPiIZaxOSsvj7c\n2rFTcm8JHOVy4iePd8CioQ/G9cdYHqswgteCSuzjtAe2X/ZwJ1JInKje2+ShVa7c8T7gTyuuwt4v\n9bH+SsurCahMZPZt+DA0wU88WLdY4bP6YDFWuIlwqvzquowx1m8lNuPEb/bjxZYd+OW2XDw58yDm\nX1iP5RPy8PHk6JIFdRdlCXepe07OdbVkRfIMBgQ76XHR6IHPHpgYx6ow4DyQWCzROCtkcikeOtnu\nKpkbAFQvrEbe+X2/BnnI9doNlJLU7/Rj43DzCzffcgvwu9+JZ3FlJbD1pB3YfsJ23DyzFb88sRUV\nBR4Ub+l+gLHTqVecFpweOdtvj4U6qnAqKYBukAU7tuRIxeiqZuw8rXtJNYPrdF+Damz6SIyBxnvF\nYdA/cgC/QQVu9JRH0/OuUeOBElSV16BUQHrlUKDmVMYSZJG4807g9tuBa5vL8H8ICAtc/3jU7k+1\nvSefBIwDcWscU7HNn7th7wg8T3TLCHAAu87ahd0/2t3peu+/68fNF9qQmtuCBSgAHOJgcWV0ULHi\ndvne5se72AFPmxc/Qj18Po6LLw5UQ4g2sdvllwt3/hNgxWRPz1VYOZxhjOGGG27AnDlzMG3aNBx7\n7LF4/PHHw4aw3XfffbjyyisxZ84cjBgxArNmzdIs3DNmzMAbb7yBG264ARMmTMCoUaN07uOMMW17\nmZmZ+OKLL/DSSy9h9OjROPXUU5Evs1Xfeuut2LNnDzIyMnD11VcjKSkJq1evRl5eHqZOnYqxY8fi\nd7/7HaxWEQbz5JNP4phjjsGUKVNwySWX4Kabbup3oXf9icPGTd34QPCnMMPneouWck/3hnEpVhTt\nAVIBbN/ox8ll5Rh/23ikZ6bH1L/8X+5D28oGjDh7BKw5VpSOG43p9fEL+a9jF9yOmQACwp4xMVM4\npqNDvy70s9OXAAAgAElEQVSAe252Y8gwhquvjs4dePNm4KKLAvHh3OLGOmzGHFyAtcjGJTgfTqeQ\n0J3yYeiUSWH8QRaFdcgCmmYBGASvF9iyBTj9dISN/XM7uJgMRDh9Ga1iYmMcW8yOIzz414MYlCk0\nQJr1VH5XWAiMBeD1ik6U7edwH7cVGT/OwHFvdh255W5yo+TuEjhPGY80hO6LOnZeZfFs9aJ6YTWm\nvTAtzr0SVL1QhaT0UI2O3zCfUaiazj67D/b9dgyfOTzito25AkJc+yOUFMvbyWFmZJOKsy/I5wh2\n+NWEToNHjTrW3MfhKHNg8LTu+ZkX/XY/jvzZaAyHF5M6xGSJAbiqvAQd5ckYCh98AL66pxbOWjeu\n+u/kOPcsdF86rHoFZIiiUi6HNoqxof7f9fC7/EganITB0wajfkk9Rpw7AlOemQKWzDBoQvdCeBa/\ny3Hnb/3Yv9OLb2+pwK/zj49/pyKhzpdhst2ZlxAQsO6ZWU++ea8To0+I7XkRCUe5A+56N0r2A9MQ\n9HzrTrflOj5nYiTX998Xy0tQBydPhsfNkQbgdyW7kQqOrB8MwyS3Dd/rplup2+7HOmQD3XBXZobz\nzQC0NfsxYlSSKZ4ejSsbUXRNEY56UIw+OTnAhKB21XCh5WOIkjtRhqMhlLrqWhwcwZUqeHecThES\nYMZzTSWxVYnb/FoIn/i8tQ0YBGDhQuBB2ZNz0QTmHYXONP+McyQHX6DGB6Vaz/C7OTiEP6EYF8nz\nn50NXHihcOuXYa9R4b58Mz6+9nhMDtdmhGuEg8GWZ9OqtIR8zzm4m2NETj0WYx/s7ET1QwABL4KN\n2RzHA7jjNo4XAXiCjvHj2AtPYwaANO3QbNsGzJwZMC51xeefA2VlwN+xC3Z3oLMsmvGBiJozzjgD\nDz/8sO6z2bNnh7h8M8bwwAMP4IEHHgi7nZtuugk33XST9v7RRx/VXj8ptFIa5513HnJycmBk+vTp\nyM3N1X02fvx4LFu2LGybgwcPxvtq0Jb84Q9/CLsuET+HjWVckeb0YCpsQe6l+oFfCTBqQtrRiUui\n0k66nRyVz1aidkn0Vrn8+w5g6yWFKPxWqLeVcKMmh+5GN1q+Dl8b1b7fjt2X7AbnHPZ9oQUpOQdO\nghXuVmnxUhauMNmRIxH8MPoA2/B8R25nq4fFaMX2NYvZRJqcTHyJDVg8U7hKKjctp0P/W80rp0lm\nQWbAOecA77wT2G7Dxw1oyxFmg5AsoV2gtO5dJZSLBTWBV5mNlZCjXDPzpKHN7gAcJQ60ru+eEqZ9\nezsa/9OIZHlwnL/eJRsQCy3vgbyWlUXeLHfnA48cwIFHpCWDdXI9yeZUxYKqhVXYeWrn1iHOgXR4\nA4n7DB4qZdLAbzUcqjYLR9OnTXA3xF//FQCQbFDaqaVxTq1yTaiauz5g6/StXfaj7KEytHzTgsZ/\n1WL93cJ9JylCcrFkAM43yjFyZQW8Vi88rfGFH3i9wO/v8QUUj06DQkHto+HEHrmnUXvduLwR9R/U\no+6dOjhKHbB8bcG247dhx2k74LV5tcz+4fD7ReZy7xf1+BIbsOW1FkwpqMO2963IYlmwl9ixftD6\nuPYRAPIvzUdHkVAglJeHv/Yz/O5I82kAwA55uSqFkqvJg7rPozM/K0tny14x1hfMyEHhKhuyWJbW\nv3C/iWYoy5+Tj9xZ0Y/RQODZ53YqC6R5M/TbbwcWYjeeQBEexj486t8bcM2VyxFet4zr7p6HBZeu\nwd3p55DnpftQ0Kq5Y7Kx6qHG8D+Ikvbt7YAf2LhJvFehOinJskHper1ls/ze4UPdv6IraZkiBxkl\nlJ8IYcFKNwjl/iDtwumnA5dcElUzIbjdwBM32mCXBlWlrFHPFZXk9je3iGVBodznZOBZFGJoXhO2\nbQu15rrd4v+B1kIsRsCroasUFcpFezz0oQYqjPbCC6Pbv2CSrGJM1Q6hsUKIoXdd5UOp+1cdstOz\nA3H8ygVffq/mNY9L2Wr/XnlsZY4BNRb7DHOIe+8F3nqr6/350Y+Af/5TdlH2MUkneSvvNZLGCaI3\nGXDCeE1NwCU9GPW8PmNXGf6FHaGTak0Y11vGW54oidiWGrCVZnjr5ugHtJK/18PxVZM2EaqplhMh\nue3S+0uRP0e4mTR91iQmqaV2lD9ZjtasVli+sqA1qxXbvrcNPqcPpb8vhd/lF6VHVNIzl8EiLs/6\niOLwQr6OoF0aCS/Gcydqa4FYcjlwqwdPoRA7d4c+br9fIpLI+Q0J83wGYZy5/TgDLWDyA2tQYtk9\n1+3BvtvCJ6OIBJOTvu2ybnTtOivuR3Tb6AqV6OaoLyoABCVoVclhVdk89YMojSfJjxfqNuDZIYSE\nEMu4KtPnjM06Ew41EQ4XwhQSMy6XfnvX7fv9wBpshFcKnEbLuHLDVYkFtfI5Po7CnxXiwML4ym5x\nP4fP6cOuCHJNiHCuxg0ljCsvlAiW1JypOaj/qB7VL1aj9h0xKW+XlmmjMA4Weklk/yAPG46JT3NU\nXejGFX/bAL+U6bU8DWq8MAhEzXu69o3kHg6/ww9Pgwf5c/KxdfpWeNv0tbq5n8Nr9eL9p2y4e2g5\nBtv0263OEZPsxh128E7ChLpLy5ctaFkrxrqDWhitmhR3zyw6tUTEr2zbKvqz+sZq7Lu8c9dUI5cN\na0BdLUf+jBzs+q/QjFmqxc1fvy28MP7wsBIsXWCHvdSuCwmIRKTY1cw1Xef8UM8+5Z1kxvw876I8\nlP2xDB++7cEZsOAiqNrYoZtXwnnJPSXYcvSWrvurQphjGc7kaW8vNid23OjJE8kjQSVLbl3Xin2/\n3Rdxe3lBkRCvviqWSqA7BnrF+9EIlMo0smePsBTHQ/1+N3747x2awOZx6McJ9XxR1m3jpJL5OM46\nCzjpJL2y+9xzgdmzgemedkxC98+DCns13rspMfp5bt8eEFSNnjIhh7SbXhQNyxuQd1EeDuXKsBiV\nKNYo3BsEZHUMba3y2CqPHA/HsyjAg/8XUG52JzHed98BspqWZthIIsGbIPocpgvjjLF0xlivpRg/\n5RRRAiQ8PDDRNT481RrKLTqGSaDHxVE2vwK2CFaOYJ75nR1P/aYDXq4fnLUSSSomVgmlTp/Ijgug\n+dNmVD5dKcpJISDcOPY7UPNyDZxtYieUi71HarKT5cNKxX2M2Ne1ZceoMU2BH2edBXSzeoJGOrzw\nV9pxIZrwx0dE+//EjpD19nxvs+w7dPsQLOwtRD7Sc4UanHNg//5AHF1tlVgxGcAIf9dPq2n7D+ne\n/xANuAqh2eWjpT23HTnThatQ84sVuu+MspayonBuiIOOEeciYTb2SqusZsEIEsrbNpsbpOvnobOU\n4CSAwcvuzAXUMfK1GjUVAjURUZeFzDmi/S57S3xDW+VzldgweAMsyvIeMsnWf8ANSjx/BLlp88TN\nsGRZ4Cx3ovITsfHdBZ3P8MIdrtZSN5KsHnjbvHDVxVZizCfzM3hDLOPie6OC5YStFd3bcBIADjgO\nOOBp8qDoWlGru/rVamzJ3ILql6uxceRG+L84hJtQGZioSpSSs2ivtJy2ebFr1q6o96/ps6ZA0inD\nIY71DlO3ZrtTXF9ut15wCoeyCM7HHrTtFUJHm7yulGKsoEBUvGj+ohncz5E7Oxet61vxcxyE54t6\nbDt2GyrmV6Dm9RrY99vhsXjg9wRO0ME3DuLQB4cCSqkYdlDda+oa5mEyr3eXtk1tcFY50ZrVipL3\nmrAMerdJjiBPLdW+PPGWTe1wH3LD7xfH1+EQIT2HDgEvvCBcr594InCPOeIosRdv2IG7yY22LW2Q\nYZmQlVIxuNHgqWYY9JMGdz4+nXpqUBvynP4EnSc08RqSxQHApajDRTy2RCjt7eJeVLHhSsHoknKz\nMa+OEiSNuSWU4rykBAj2ct2xQ4SahRDhlBiFSDU3OQPNWIcsLRcNEJ03ybdn5mPVOxESf0bSKEcI\nb7EV2tD4SSOaP29Ga1YrdhUkqc6H3Z4aY43C+K9vEssd2+Sx93Cci2bNKyIanE7hlXJlS7muK2G6\nQwncTKS8vBw//OEPe7sbRD8hbmGcMZbEGLuaMbacMXYQQDmASsbYQcbYCsbYXNaDUf82m95ampsL\nfPQRcFF5OT4MmhAoi55Rg62sA7Zbu5Gkx7hXXo7qpyrw1Z2Rhbm6OmG5P+WdXZi9eLsmdGteUcYz\nIgfrDYM3oPbv+u0qYbxeJmbrkG7IdjkPUMKXUiwkJend1KMhWKBqtfCQ7OadwQ/YsAYbtYbVFPIo\nRJ5EBbTu4n2IXO3x4yg4wDjHP48vxoIrRKpZZf0FoivxoiWlkrdEXV3AIhELO0/bCWdZ+Af89N3h\nU6Fql6If+PrXlci6P3LK1MZPGlFwWUGnfWi4Vph1tSzZUthyOYHcc3PhbY8/Y7KWSC3os9Bs1NFP\neNUkJWKsuLxPNDd2JZxLpYDXC2w9ditavuqG90cYVKKvpMDMUrfUxg2lSFHCuEs/cVWhCBtGboAt\n3wb3QTfaNgpFyG45xKikQ1oTYe5P41xOjRuF8wqxZULXVkQjHXs7QuLcvYbJNTdYizqDB71gapxR\npfVkWErrula4ql0oyXaG/a0xQROTk2tHmQPWHH1t5c5Q1S0KryxE0yoxLlQbPXlifCIZSxkt+4cH\nj5/aufLulvQquFzqAaNv3mdQ2hT8tADVL1ajbX0b6j+RCkdZ/sLdwVF6Xymqnq/CplGbUPb7MuRf\nlo/KBZUoubsE+27dhwgVb7rFoSuFL74mZLk4sliWFr8aDbnn5SJrjijHk97kwDCDH28qOIYaPlOH\ntqxMvHruITdOHdSGp58WVtUVK4BHHgF2fOPG0GfyNaXB9imh8ZHdJV5hvPxP5cg9J1fz1Jm6WVxo\nY3dIJW+Ezdtt4T+PhzFcPE+DE5c+hH34E9+LdeuAr76KbnstLRznolFLSqmeH267fry4+mdGYVws\nD5SrLQX6E2zNPT+pEbMQSBFvkHNRZ/Dij3TLKk+BYAHceF91xtloAfJbdY0YcwyETppCn3vuRjc2\nXFeKorlFWrhhcmoEDxzDfDMZ4Ze/v0+OifpE9uG71AlnwIKzOiKHUfZYgkOCIMJihmU8C8DpABYB\nmMo5H885PwrAVPnZGQDiD/zrgl9e5wdjQTUpnX54bD68/YsGbP5FCSZZ24TwF2lSrT6OYt6RJOOX\nYXDVtViEdTNcLNt5J7hw9fnO0Ml1hDYaV0SOaVOCUGWleL87N6BFBQKCQcBNXS6jGMS9YR5qjzsL\n8TfkgvPw7smKffuA6dMBWPXCWXeaf+UlvWDgNSSfS/JzfIitGLezDpfhEKbvjy7+LhJqIvHBB0CE\nXBpdsvuS6NxXg9TsAIR3ROp75XC8Ft61tHlNM4rmdr/wu0depmoyFWvWdsWu83ah5F4RvqGu8VRw\nDIf+PI/2yvh++V4zJvu59lvXwVCFjN/l19WVB8JNrpQXge6t7mtHqSNmYVzbTKTEPVp4i9wXQ3x+\nyzdicpc3fQs6ijvgs/rQvlMGXRrGoBDPge70S/64fFf0ceOOcge2z9gecE93GsaLoCR0AIBulLlq\nkUJg8Jinkt+ppcqyr6oqKEK98uWkPin0iPgckWfZLV+3gHOOnGNyYCtUCRLEQqvoYpKFXDFsaz3+\nIMNagq9Rnw/4y18Av5/jdhyAtUHVKDKedPFi6MpK7bcqD8NW6dLL5Hi+UcYbFxeJbTXusqNlTQvq\nPxCWT85Dne5jiQVV44RHeli1NnT/odi8phkdxcIzrLkmuhJURgXFxCV78QZyNQVDusx15yu34yy0\naCFM8RCvEMJkObij88M/fwYf1F+HCpUVm/s4Glfqn/HPPx94fTvKcA4MNc0ioJTP4Xbppz+NIXa8\n3oVnUaQJjGqcUDkFVOy4MkgbLeOvvS6WLAlIlloolwu46y7gtdeAp/1FeAaFYRqO7qQo5UPwueys\n2kpYjMYFo1AuGSS1CZk2oRwc5wsoFjcfuRmD94hxv6JS/FAZV4z3oZaYVF7r6twlGZbq8+B51gRp\nHc/Pj3z9XnGFSNqWBK61HXx9qM/c8hmglMbDYMJNRRBE1JghjF/MOX+Mc76Vc67NrDnnLs55Duf8\nUQAXx7px6fa+lTGWxxjbwxh7Ptx6Ez8WE6IU+JECP5ZOLcSaI3Iwq64GVyOMhdEwITZauLrD4I8q\n9JsMMg+tT1uPivmBCdYjjwCLFgGvtG3HHypyAxNO44MnhsmBale5aSnhW2mw1eRKs8pEMesPZ70+\nydeK78OK227r3F19V44PR5UFTSS4etB0zWefinWXLFYCgtyE/F5N1NPa9QmyYp1cMcNEYpDLgx93\n4RYYCctXUdYVMkhjTvl8Dxfb5a53o+aV6OKh7TcKi5fXJGHcusnaqZKoK1Scd8N/GrBlYqhVNzs9\nG656cV6v/rn47MEH5ZeGYxVxT9Ql7/TjwIGAt0hXcB8XSceUzBThXkmpkxv8SAQhe/YJAWTvaSLD\nTtmvAsoSy9cWbdsAUCgdGkIUgWGsLcH4w3zXEUPIa1WpGAhUXgblTWIUylV/U57f2+U2Vc1j3T6p\nCa0UxvdE2ozBS0ktk5LEC5U0zb7Pjg1DAjXfG1c2iuSVpeJc5M/Jh7tWhma0y9JBUvFjPI/KsyA9\nVolO9TlNDLo7d3CckNIOtxt45RWgrcWPg4+XamUFVeiQOqaBBFFikVIZGtqkhWLI46FK0Fks4sel\nxeKLtiaZeTmMljWWO13zSpIWcaM3QGcUXFaAkruEou7oju6ZfyO5yqbI+J1xdRa8hDykl1vxMTZr\n63VWdjQSw78U9ysLUoaU/qEUtoLoTNV+jx/Oaieq8sVJGmQPrxQblRfeGjkoTfTdvs+Oonl6xWpQ\nomRcj2r8JazA2jXBRyeabOoulxCWk+R8Ql13Skln2yDGs5brRejINIhjl2wQJLX2GfANsnEeGpGb\nC7z5JvDSS5HbDwzxYgsXSGXEpTik+1ytp+TUYIE1XDz1mDGdlD7z6rdppEPex5OauxfeZVQ4KoyK\naeXdoQTlUAu5TFIYNC9diq2YADvef18ksA3nBbB6NbB4MbAaG3BxVblsO/R+Me7vyeihGpMEQeiI\nWxgPFsAZYxmMsVMYY6epf+M6MWzfCeAizvlMACcDuIgxdp5xve/BilT48Sry8DpyMbShAyN8nsiC\np2Hip4gU69l5J8N85gN2/qcdJfeWoH5ZPSa+sAuux4owAl6MhCcgVKpJfxRSpIr3LVDPaINiQXNH\nlmVU3DKmXO3byEXdt6oqwvVu06aAVT4cw3Y14VkUagdZJRJ5DHu6bG8phFno26/1goPfaAk1XME8\nzogI9esjt9fhMXQthBix7ui+O+3wptDyccHvk8DhqnNpbrcAUP1ytSbcRYvHpRe2nHYR2x4LKo6Q\nu7q+bgcbzBTK0uW1RBaEakrFxRoyUTcI48kthqEljEXjqmlteOyu7mVXr3iqAhuP2IiCfN3mQgTF\nkV/ITGDbhKTY+mjkZEyl95YCCISLNDXqtxVK+Gs42KIR+Eys27SqCQf+1HmSrsqtDuF2rLyHtFhx\nfUKmQMxwp5sLi9Ua8KZpl7dCg1FvY9zvCIZX1f5+mU9RZWbv2Cvum6J5ReAejm3HbtPuEWU512oh\nGy4x4/gwxhUhVjQCkcbp1q9b8DZ2omiHF00P7oG33YdrUQNHm5xMG8ItVDeS/9CJF00El1llqVRj\nYaucP/uCj2McJl/NQ0JlVe9GDovmL5q1c+BPMSkVjWx2UmUTTkMrBlW0YyzcQS6+0W8yrTpU6VHz\nUg0OvXcozNqRqXy6EjmZOUjZ2D2lZIpxnJK0u8zPoavNLYI+Gw8HJqB7GsnCQuD++wP3iseQNd3y\nvH6c+RNEmYw3IcKijErk7TvFdqbKcqlj4EKayxPSR/WYUJeu0dNKYUz2prbxj0Ue3IgKAMCIEaG/\na24GqquB448XGdnXTN8Fa6Vb16ZR2auU/jMbolPMpxhKM4TOM/VGBiV0G4XxVLm86kr1S9GfkfLY\n3H47sGRJ+D4wBgyGH5NlyZFwyl6KESeIvoFpTwLG2DMA8gG8DuCloP+44Zyrp0gahFdUiO/pFNjx\nD+zENNhwAsIIGEroDWxUv1Ta3BcjT6qjpbWV4eBfDyL7j7U4EVac5AkVophRiyw5vi7yQ75ppdAU\na88Hbd/0D00ttktNquJIyKMIvmCSvD4M7sStScW0K77+RixnhDs/EVAPJc0yzvVx7yGxXTFyHQzW\n5uToN5j/VhN2ndH9RFMZ9YbjwELf5l2Yh5xjctBR3IFD7x/SrIyxcPVVUtgKSuS287Sdnbr+RiQa\n7wq7YQKsrtdO9kW51qvrbZCMLx38jNBAjVgglkeuFTGaka6DQ/XAX5GLE74r7VZfbfnCytMWwUAw\n8ZPubSccefLSUBOxUMu4XEZyjQcLdWmXLyqfqUTVgs5LHJSuF8KScrH3Gd3UDTHjrv/rRt4MyWif\njFf1BfZLHUMt9tOgNNSIIOxpcZLy+11niQO4fcZ2bSzTQh6UAsFg7d+dF/+Y1ylagLs4EaktLlyM\nBs2F2iUt4j6DoNGd22d0ld6MZ/xN4BEW2aNialH04Tsew/PD7+H46NIKzT05HAU/LcD++0XiyK4S\n2hkZJAWRwLNZtwhYQA3jRUg+iViQmyyKUj/tro+udOLoT6SpVPNPFovmxsA+vPm6X2fdPRHdK3Fp\nRMuuHsQL1p2agrsrVO1qdVt6VRWObihegYA3nRq7t8qqZepZvhxb8HBzvm6dYNRZPiWClfZ8aSlX\nFnm1DfsGC34jhfFIeL1CuVdYCAwus2LTu+IZHGnPUtwxes6E3KxqqVekKSNDpJjxM+VU192JOasx\nzFRxIXZjarXwymCGMSc4mGUglDRjjNF/H/snosdMtex1AKZxzi/knF+k/s3YsEwSlwegHsA6znlY\n0+pUdCC9u3WhjBNh5cJdF52lpFPkNWmxhl6c2qTa0I/J1ijchAxJrIyWcWURN8s9WaG2cnttET5C\naPIcW7EdO2/cD65qasbRrHLfUhmftU1pQfdhJFgTULVa7faARb8rWu6IzZ0wkvsGB+CXZWQqnqxA\n8S3FcQnj6gGvLLQqMZTP2j1hvDW7VbOkm1Gr3BmUcqG0lMtyfGK7Kz4WSzVZeAGdJ6s76sPwJQhL\npezMutlflawoEZMUp0M/EeuqjW4akAEAtbWRt2UrsGHTUZsCwZ1cPx5o44MSxuMQcoKvTk1hFmlz\nhnHLuFpytphMpt4TquBan7JevIhw6ao69E6XElRVZ2RbMd5GJxTolXZGna5KWqcpvJRC1OD67/t1\naCUJI0ccatdvW7WpLFzKUh78uWHdpBjuU6My1+cGjvqyArUFoQJoZYkfbrmv+VLxMazdxGcogi4T\nreqEWH78j+gEYh1Mr9Rt6l5YNgDAXmJH4+r4clFooWXqGe7jmHFfNhqrfTgVFgAcf0WUWg1jG0Gv\nQ8oldoKv0YV1yNIUSHOvFL+tnbO9k18FeEJ6vgWLfMb+DPNFn+vCyHkQLlbKEt+dW1pZ39V5Nybw\nM44TR++OzmOiK1S76jmjxgk1zzlJKiAWQigrbofwQviJdNG/HkLhOhRenIhWMHB4vcBFrAEfv+XG\nf9lm/Pv6WpwBC46Vwni4kC7jsUqLtp5qH4Fzzui/b/739rXR3zBTGC8CkGHi9jQ4537ppj4RwAWM\nsdld/kYuQ2IFjdYZtTRBuAipk8b0Hwc/lIxxUbFgdKFV+6AGeLdxIhiHMB4cn6VeZ7psGBFkGXe7\nhfvXF482of3ftSGza+XGFg3qIVUjjX4qPkpp35jhCh7njq9urPF8/O9/wO03xD9x6AzjWdHK2gGB\nhFhyUfl8J3EBXfA2VOy4DFuIUE86EnkX5qHgp0Io9jZHbzGYXqNX4W/JEfu5fkkHao4VwpW6dpd8\nINa5uQtLRwhMP5lS5X66mmtYsiyoeqEqJK45pJRZHPgNwkQIhkmT5q4pfa0ZgBQpkaRzvRu/TYa8\nupvcmhu3wpZng6feE+LtoYRxpbzT3NPLuxlgH4Guj5RRE6p/m1In7uHUdV27hrqkgkPdJxaVSE5+\nry7tgOeESa40hj5r2f9V4kEl0DqMIQDxNRtMJONbuP5Fg6dWaMmUJVxlUw83TpQfl40l19bp1j/S\nEdv1E5IsNORZrW//zH/tjKmdeCm5uwTeGMsJphwS17bf4NHhVUlO/RwvYzfmhstzEyMcTJdhvSv8\nFnGRKs+D5BgvpntRIn8vrp+bUam5kQc/Z42X1dFRppq/E2Vym13DOXAbysDsmtZVfqFfb3x2dVR9\nCCGSd5NsR3lHrVktluOlN8P98pgZKw38UgrhP4YQsF9EPv6KPHyH9Rh8sB1PYg9s71VjNNxwbo3g\nUdHJAZrczRAGgiASg5nC+HMAchljaxljn8n/VSZuH5zzNgCfA/iB8bv3gv7ygjTKRvk4aGv6z+NR\nDIZIU3KhhKqIfQh1IYqKCLMxZY1xd+gngvGWcYlE4VPVyJq8DW/f2Ii1E7fB6ZX7LZ+yWVmxb1tN\nBF5ZJCezhqzqZgc9qRtCnbNBBS1YhU3weoGVK/XrMgbU1wOfP9wIlz32C2ioQ07swmiwg8xeglhy\nGhgIJOySk2wvh3V792Pd42VIq1SYyIPs/TBgbVThCClyx3+I6BLFjS4WJi6jGzfjHM4qZ2DSa2D/\n45U48MgBLVGP8Wad9GV5VP0Ih88w/4skFiqBbjTXW/5SwTFYHpfBUhif6NBna9590W5sn7EdLWtb\n0LymGVksC6VV4qpWXhUhbuqyFrdWL/hv4b0MzEKNeUZFB5MT1JGruq9wUmObQ+ofHPpLKzTHhFxO\nPxh7AkIg6PIwCuUGBYfRMr73OROELM26z/TL+LcMALDdKzx8lGeVptT1cHx9Uh5sNW40/q9RK43I\nK+VNE2cHjDHCRllcG/tMUIyp8qfBWyp/shzWrZ2Pg7Xv1MKyNracHQAw8lN5bWv3oBqLZU/kPXAv\nYvI7e3oAACAASURBVA+HUQwOmtSESwYaEXk9GctuRYuKEX9MxpQD0NzIg8c8M+wgAq57zZgoh8eY\nfly4AdVI2ivOMzM8c9X7VHuM7umSpAghgWrsVc+5774W6/0p6BhFyxH7hZcG6yIMMeR+QuA85CFP\nN4cmCKJnMVMY/wDAAvlvWsw4Y2wMY+wI+XowRGb2EBPrLUF/MzEz8vYMPpFffSnfxvVE6ETaRkTl\nq64/mR3RJ9JKbdMnH1GDsUdaYzwdeks5b47drU9ZLZIQOqhvf6MFqLRjcHErpsCulfNQ/Vr1WczN\nYp6M5VYTAi32Un4fELrMlcqVpS3JKiSWLVuAefOE9f+HPwzsm8UCDF1YhC1Ds2NuK7NWuTzq9yHY\n7ZSbMAFVaIKCFMrb24BdZ+6Cs9Jc99JIHFUmzJfDC8Qy5ctAXKvlOzHRfRddu/GGY/hBcR+ld+gV\nHIwBOcfkoPjm8JOegmJjJsCYmu+U8StKw2/bcOm63NFfy+PbhVDeUSgmwPk/yUfHPjEDrW8wXFcq\nAZgcF5SHRCzZqY0EW7wief+EtCInqOOWRa8EUNbYRumRqVWOMLhyR2w7XtT9qbySPMoiLoUsNRbL\n5bjc2N1euxriQgubxYcaH5RS1+8GUgtbsfk/dhRdXYSCF+S+yOtphD2+8SNSwi7t+Wme1IbJK0Vu\nmGBhrPLpStS+E7lmfEdxByqeqjClfWM2baW0iSdEJGJbCBzDlrUtqPtX+DwCfq8fh5Yc0vp21RVi\n+RbM90AIpxwIF+seDb+S1uPjYcU6WU33mmvEd62twBloDjxHVckxgzBe1nkOzG4zdW+EYyzHunXf\nihZTTBiRtBAsJYxH3GSo0k5dFzMxUzeHJgiiZzFTGLdxzl/nnH/HOc+S/+tN2O54AN/JmPGtAD7j\nnH/b1Y8MeXVCEzIZLNJDX44+c7Zi2N4ImvIgQUDXWOgqMTFmuQyM5PrB2GvXTwBV4hXfM7FrX8ci\nsiCv7Z+MlwxkiJfLmFsFzpVxYcrVzad58+ndkc0i2ahYMQjBViuwbp0o/wIAJ5xgbvvBcAQmoLFk\nto6EVxMUxNKpJoL28I24G93ad+66OOI0DYwobA75bP/czmPDu8vkAjEZMsbXepr1fsKOCgdsu22A\nsaZ1hPNvChGyeCmF17GIzk0zEnkyUXdKqr45lWE8Uhb1eOlyKwZtXub62EMvWr6Q15BK6GawDvVU\nMhltCNZixdUYLN4/fGfslrZpu2r0jSiMYQ3oxrGPgoAwrr8+0gaJhvcU6l0cxlhDM5XHhX7zgfKj\nJoS3Jkf4vKEp9HrZt08k/Np36z6tdF68aMobee+5pMLDnSB9qBJ+t1xfgn2/3YdvvgGeeEK/Tsfu\nDhTfVKxZqm3tCRj7JMHzic7mFtGgLPGjw2zP7QYWogA+GYLDQqYP4tXsPWWm9EVhPIKqAsAX0j39\nRRkbHg/fqtmwsTEe/m2w0m50mNK1BEH0PGYK4xsYY88zxmYZS5vFA+e8gHN+Gud8Juf8ZM75i935\n3Vh/zw0yw4oiJHMJmd8HD/1iaBzpNyGIUI6ymoukqi/u0E+izEbbqsH0b8x0PAPxu0AHsqpzQ9vm\nCuWqJr1RmYODdqxDlla/1OcD1iELI2B+EKhuci0nntFm++2Mll+IhFhaybsuwhc2H7kZe38Vu7Kq\nN7EUSBdaeSKrDUnzC68qxI6ZOwK5B5j+Yh5VGVtG484wXqqqXNagNnPHLF+R8BJIMliBjK6xWq3b\np+O/yIZyH9IMs8DIt2b841LtvcLCaVRaKQtYiCxuknAe6movFsr132OwjD/WEF8yLqCTrgeNG+q5\nkuaNz80WCHa1l4pQj/6YqrCnROk7uPGFPMctoTq82DE8PvbtExZwd2NAoLv6pz5cfJID1s3mhfKk\nzxehAD6pEFOeFP/3O/Of1cHhLdYOsacvvww884xhRcP4kGGSkNzTqLrsF8gQp3XIgrNdPevEOsZc\nM4nQuQJh5GOfeRZxxaky675xLNLeGuZIwS0bx2qCIHoHM4Xx0wCcDRE7bmpps0TAQ570Zm5cvZCW\n4gQbZ7T4SJW4TcWKO/SxoOY3rF9qboRqYiwFydtQHndTmpu6R7UlluqhmuIz0XQMoEMaeTQFSpto\n2GX1YQ2yNaF8pIkTljE2ZVlS7mQM1lax30eVRJHut5to2ZKDStZUzK+AryP0WNr39s8EL2cbqiA2\ntwhLv8figd/jDym/pyEPyRF1sdVh74xkj5IY5QeyC2Yn0cnYXq/bviYwyhhxlZiruyWLTMMwbpiC\nSj7nUZnU5OcGRUSqyxzlWeDUyXYjWMa9nZQDi7nREEKv4YmW+AVHLdzJIIwrNK9xo1eJyWhhDrLB\nfzxnppCod91lALafsB1F1xTB3eSGt82Lm2v24kNsNbHNAFoZQZs4xg/v2JKQdjTUfZAa+IhzDkdZ\nIPGpEljjzebe28xHkXbtOFtliUFDAtjQJLuJ5aVFYjk+Tpf8YE6NUAIvUoJHHuY7giB6lxSzNsQ5\nn23WthJKAgfdkE13MuKZORhqViF3YPKUIpdpMFcY9yN0Lh2i19CEc9Oa1YTx1laOMQjNknxMfexJ\ndcKRtQ6YoeuAaMjd5kM6/LC3iZ37kcxuagbDXTIHgHzPYa57uhEtjEEu21o4Kp6qwIhzRmDUxaN0\n69qL+6cwrlCZ3JM5x+YjNwMAhs0cBmsH02kkE6mjU0zeqU/i9b3a+JKJdYnB2q+Eb6+TIw2AtYVj\nSCKbN34gZ77Td5uXMVopGLSyfX6D1VYuMwvMKVU0ZYNwreeGsU61ryzjnz/ciDmmtIhQTytD4jbT\nY8adHMkI9rAydEeOiZP2dZ31PhqMSQkVqz4FbgfwGBLvpeNr82HbcduQfkw6jvMkzsvOWyWEMqUY\nTTTJ8oId2u7ETDgwaFAGDq1pxe4f7caYlacDMK8Eal/g/2Sm9R+d78cKAB43RyrEPCYJgfEhyZeY\n0l6pHcp6ID+Q49KD2G96W0aFQqiCIdQzkyCIvkHclnHG2C2MsYhCPWMsjTH263jbMZtCWRLazOmL\nMVGbMaw5ePiLKrtpV+2qiaBLWcTlZFtOut99x7y2RAI3wwxX22HlKiqWVTvM0/4+KWuX/vMfYtul\nKtdTD6l4lVCs3Ak7ZDzdzYg93rUrkgCkJrD+p/VWEVSsXCUb5ZxaXUd+rx+e1sSWdetpMq0Bi6Et\nz4Yqg9u6ZjkKqbNkPhNr46tV3F00gVTVuFZKGGm1Xf12fOUA+wJaMjqleOwymZHJ7aua7S69V9Ic\nm3kKh+MKZAiN4XNdkjoT9zf5z+IhqfZFjQuaJlRaxNOc8bvEh8OoGEuEAJH5pcrYZVBs+Dm8Fi8s\n+fZARv4E4H1ehFk0/T72fC7RcKQs/Xlx/j68gt1wuwPhBnapb/V+l2DlYA9yjUwAq9zC3VKvoqI4\nlBJt7NLEVJCYuEW0b5dD7BEJdP2P6O2ppmlyOQyhoUQEQfQuZljGhwHYzhgrBrADQB3E7X8URAmy\n7wF4x4R2YiLZmIHV8PYumJeww5iXSauFrWKx5MCfbrKANeJVIaiqh6qyEikL2OQOY/a6+NBcM+V7\n7ZD69ctTtsXvnq5Qx0w9VNd8wXEWzCyLoieQdV9Osj0cSQDsVvHekcDkNke7hLv6YPh6RJjwO/Ru\nqCyZYfPRm01LVtSXSTPci+rezawxMzA1PEMcPXN8U4qEG6PPA83SyRBQwvzWhDCSzlD3kjq20/eb\nY50OpuVG4VKr9kkJ5YmqtqChPKhVVmyH8DbwSqtyQmH6F4kaKjx2rnlaAYHnjKlBbt2gJ/Su2rNb\nKhpS/X6MSqBCVOEtMTn5XReMksn21iELLPUUAEG5AJZU9WhfegJVKjNnC/AjAG4PkIbEzR+MjP2P\nqKTxIBJXNtIYM268X0xII0EQRIKI+3HKOf8bRLz4GwBSAZwH4FwIQf9vAE7jnL8ZbzuxMt4aPuZz\nILrpqAmolj1dLufBPOtMWEL81hN3bJW7urpwPZ7ETNGuQ7XuvaoL6pCJYC44J/ETtJ5CCTDqSDY1\n4bAQxAFggkM/CTaWwhoIpH8mrDNagseOBOeSkIz2Gd17Ez/mqhhtZcVliZ5tK2FcKULlvWTfnzhv\ng5BLNMHXrJZ135AQdFSueSE6YTE4NyR2N/Wx/4nXpPQuIz2B8X33dcIq73hmX291J+EopeuKD/UV\nJPyJd4ACAAxB4hsadTC80cXpEneOyWl1CIIwEVNixrlIXbtR/h+2jNkvrWlKw6xlaBYLoxXObIxC\nuLKQm80ww4NF8wjwGyY0CUCVOFPKlNWfA3cmoJ1RMku62hevU8Tfu21+pAIYGqEmbn+k+beinJgW\nXrDThdTOfjAAmVYv7t1ja81PlNdX0JR1UrhCD4cgHGFLvDu8NygpXTKguatPWJ4Yi1RVFTAOAcu4\nUgZkfJA4C5gRNdyOgBeJmPP7ghK5JUNcR8kAhlaYl108HJOtQrhQCcUS6cEx2CoUR0rg77AOPGV9\nRBrFvvuLzSmp2BeZJUukDpLzhwXPciwAkP6v0l7slbmMbgl//pSLvJ/SthFEn6WHHc16n6oqg/ui\niYxolla2gJ+67vuRCRbg1ITQ71Yxfj1jFVJlfpTkWro+cZPuE2WZNGUhv9PEMINwqHhBlcSowyI+\nWIptCW23N3CuFLW5U18zP7kM0fuwPUK4UcL46I8Te+8oJtrFJHFURw8I4xWiDc1NPUGJmRSFm4VC\nQ3nOqKzYieSIVvWcMbesYyS8h4Sw5jWEs/QUpSWJb+/oEhEnrRJ58R52GycSi6roMkhqq5TbenJD\nggq79ybydqkzRAONR//PDUIQA5XDRhgfA73L5M9Qm7jGVKy4/m3CSZWxoYFkRomdGGoZzbWUwmLx\nQ3/i3BdVHXAz63R2xr9k8jtVLu7Pjwwc93Qj7v/W9XYXiASS9rGIBS1eZn7t9D7DP8WkW7mp8wQL\njr9GBYBAkswRf098Iq4xygJmiBlPWHsrxTFVMeOpSysS2p6RK9Bz49LUUnMzwxN9i2Nk+cgLMXCS\n1BmpMSQlVSPg4B7IfUAQRGwcNsJ4ptQK9qijjmzM2tYzguPQHeIBoyzkE1cm1gWLGYL6eqpWJwA8\nhJ6Jb3tEtnNgr3iQJTrUgCASzQ+sCY717QPwKjHpTtmXWFdqxSMQQnhPZilWLU2yh8+LYjYqZjy5\nbOC6MxMDG+VJd7GJJUn7GjNl3fGBmBeJIAYqptUZZ4ylA5gHYHLQdjnn/Gmz2jCDi3pAI2orke5A\nUhifCXOzmXeFv4fdCJVlPOHJknqRb7/04ywALyG/t7tCEEQXpK8S5qGhmweupXN6feIz/gdzRA9Y\n/QmCiI8joM8FkkxCOUH0eUwTxgF8CqAVwE4AAzAQp/vMdIr6wb01BGauTWypIsVYWTOzXSa7mVY2\ncCe+j4ImogRBEARB9H1myPw6p/SwMYggiOgxUxg/mnP+ExO31+85rsT8erp9kQz0bFZmgiAIgiAI\nIjyjaF5GEP0GM2PGNzPGTjZxe0Q/YSJl6SQIgiAIgiAIgogKMy3j5wP4NWOsHNBSl3POOQnoBEEQ\nBEEQBEEQBBGEmcL4pXKpQqV7NHE5QRAEQRAEQRAEQfQXTHNT55xXADgCwJUArgAwUn5GEARBEARB\nEARBEEQQpgnjjLH7APwbwFgA4wD8mzF2rwnbncQYW8cYK2KMFZqxTYIgCIIgCIIgCILoTRjn5hTg\nYowVADibc94h3w8FkMM5PynO7R4F4CjOeR5jbBhE6bSfcc73Bq3D12FdPM0QBEEQBEEQxGHNRbgI\nnHMKNSWIHsLMbOoA4I/wOmY454c453nytQ3AXgATzNg2QRAEQRAEQRAEQfQGZiZwWwxgK2NsJUTy\ntp8BeNfE7YMxNhnAqQC2mrldgiAIgiAIgiAIguhJTBPGOecvM8bWAzgPIqP6LZzzXLO2L13UVwC4\nT1rICaJXaE9JxXCvp7e7QRAEQRAEQRBEPyZuYZwxNoJzbmWMjQJQDqBCfsUZY6M45y0mtJEK4L8A\n/s05/yTcOu/hPe31TPlH9CzFmePwvar63u5GQrCyFKRyjsHwwZo2aMAK4wcwFFPR0dvdIAiC6BUO\nIh1Hw9nb3SCIHiNP/hEE0TuYYRn/EMBPAexCoMZ4MFPi2ThjjAH4F4D/Z++84+sozr3/mz1VvRfL\nkizJtmTLli1b7rKxDKaYGAOhOEBouVwgCSFcAmlAKCGEBEhII8lLLgFSgJDkEkghgQAhIYTeMZiO\nsY0p7lWWzr5/zDyzu7O7p+6RLDHfz8denT17dmZ3Z2fmqfOiaZrX+h13Ck7JpZi8MxhiCA0Gkyxv\nX2P9pBqMeel9IMTzfexECIUYHOZaBYsJhoDSIOzTPIYKtGEHbkMjVuKd4a6ORqPZh3gBpZiCrfgj\nxmA51g93dfLC46jEWKzDjRiHU/DWcFcnL7zd24Tmh9YMdzU0+wiqAesm3DSMtdFoPnrknMDNNM2P\niW2LaZqt6r/cq4heAJ8EsIQx9pT4d0gA5x1S1s/iOefenV43zDUJnv7yOACAcm+uKSsb0vI/HF+Z\n9zJMxhMhOMotKsh7uUPFKpQAAAZEl7ALoeGsjkYzYhlw9RSjhydQAQDYO4qvka5tIPD8tsPPahQD\nAFiEX+OmlvLhrI5Go9FoEOw6439PZ1+mmKb5L9M0DdM0u03TnCH+3Z3reYcaFuW3OhEPMmdeal5v\nqs57GSwsJmaMb9kQzWF2NhQBALY3leStjI3RuO93mwtGjzD+Jvi9nNY9eieixL2oHe4qaEYxW0JR\nAMDbBcVDVuYtaAIA3JHnhUYqqnj/MGU67x9+h7F5LQ8AVk9rBAC8Vl+V13JuxjgAVt831AqHzYjk\nvYzEdC58szC/xi2tWhjXaDSa4SbnGTdjrIAxVgWghjFWafvXAgzBSL2P82O0AbA00bR9fW7TkJTP\nhGf8K5PG5K+MiDJpGaI5zPbpXNFgqOUHSH9oeC3E1H6GCkPoivpHoTD+ayGwkOJB89Hif9EyJOUw\nU3S6QxiVRNb4HYEukOKmazrfRgqFUD4v/wKk7IryPK6Q8K1axgfKovktWPBurDDvZUTi4maKMdM0\nRp+Hw7pC3r9/EIoNc000Go0mPYKYcZ8B4HEAHQCesP27E8APAzh/XukP5VfoWPFxfn5DWMYNYUVm\noaEZBOV8MJ+Drs+1rCnMn8UaAFjEcGzzAbnem2BDaid5ezbXYw3mWSh+Ek7LCLVPmtx/A5PzWv5Q\n8NpB4wHYJtfi2r6HicNWp48yZL19taN+SMr7u/CEGBTPXW3zQbA+4u8l80Zp/sJ2HhVu43Rtg/nu\npcQ4Ysi+N/+94lCNlZZFnG+XH8nL3bpiXB7LZHgfXNgvyKPsuGFpMwDb8xLPz2SjRxjfGPP3YtNw\ntk3m/cXGg4bGGKTRaNIjiJjxa0Vs+PlKvPg00zT3WWH8jeYaAMC7JfkVGEMxIXxHaRAUn4XQs70i\nP67Oa+JOF0kzD2Pujs9N4n/QZEkKrnB8zhdGlCaGfPsmgrcseBm3yPIlLWB5YKCUT9Bocn0r8jN4\n7hFdABNXSiEGA4qVaCRyjxDCqH109/BtU+vwuKF+1Pm5sEyrb8071fnNMfG68IRIiOdNcc/5Ziha\n18Pg3kG9+/E2nS9hfHUbz3VCgjFT+t68ohTxWp48W/qW8oLmL+L3MhQnRfrQ9hNrm4LPgSI984RR\ngLajyQFqsxTGnc9rGwuP6jwOmbB7Eu/7zBLu0fLBNB2ypdHsCwTWFZum+X3G2FTG2LGMsZPoX1Dn\nD5rBMGmG+ee1yI9WNRQTAzptFWF8Q3tNXsqVY4+Y+bI8WMalll1M0MwhGvC2TKt2lE/39PUJ+bC0\n0bX583pz8M+Qrokm1xvy1D7VJ0bCeEIK4/mbrT2APLV9wTpwRRe1k8ISvu2YMvLj4t/oawEA/BE8\n/OQGIej+EONzOm9/Ht9hWougq8u5f08kv67O1IZ7FzFRj3xco/ucpuuP4GnvdIY/zZ7L9/8SzYGW\nI/WtImrHiDuFun/OmBBoeYCtLSvW21jAXuNUDqMxukAI4+JzOJ6/d8KEa6hGfzz494HGEyPmbC9k\nGad+JF98cHzw7YN4XeQSSPaajc51bNJn18oWALY5k4hm2TtEIRgajSY5QSZwuwTAD8Bd05cA+DaA\nFUGdP2ioc863+BgSAzlZyMldXRVkg4b5jD5knQqCECkW6BKGSPm8Y1wpL44GFmG5aJ8cXAVWj+eC\nfToeBfmwkDPFXTzfC8VNFHMlao7LV+RPGP+VEBTIKk9umkHxG/CET6TICCn5GmhievxJfHt9bqsv\n5pW3D+J1e6OX37O1B3CXWWofE5dxD5ixbVxKomu+LkOh/C0RUvJWOXfhfisPXiZuIdj5+ZU6rmQL\nuq3TPSkuz58rt1c/oe56tTF45dOUac62XSQUTjvztBoCtTupZBZ976Spwd/TFYc7Pa7yNcBsF3H2\n8priZBkX1xoPvg+0K/8pUi4fV7d+hTNnjStcLg9l2lm71Nlf9R8XnMv/S83CWyPF/MNvLrQvMAhg\nm2h/2xnfrhe5A94q4fOc9UXcE2SjWL2F+pFXW9K3arMaPsYaylhIni5bO4bGW0ij0XgT5ChzNICl\nANabpnkqgOlAHoLzcmTN/mJwUPYHbdXddv4UADZXN2XyQoMTbV/ubQm0fBfiSQc5EZUduyvBjrOM\ntZFgJ/Y08aWMsOo9HSryMcZfhQ4A1qSJhPGgrXkviyVuapRk+/Qsw9H8uambynZPYbDCePE4p4u/\nayIac1q+XkJ+Q1WyYe2RTu0IU7Y0mS4pE9Z+YSE96mjn89qUYYZmylcY5D0hxcChh9Iep5BlvUf8\nr7VFwZRNOQGWHebsH1ra+PaHCN5al8CQ6SQB2McVZ/6M/Fj/rdQjUrlM2zzk7ZACY55vaHcPWfmd\nwnc47rSM32oE620wFFgKSOrzqB/h3+cr0mrtZCEo0sMjLW9T/pPUeTV9usz3ovvGCihbmX+ixb3K\ncjQ7hefQh8XO8Iw9Mb7/9WmpV1BQQy4MZf65q7U0nWprNJo8EeQIuss0zUEAA4yxMgDvAXkKdM0B\nM+JUQ5MQTp6Su2BIi10uhAudcWchxT1MdoYR56Q6X+Tj7AbN84c4Iyu51tFAYigWz7yQ5NRBJsEh\nAZIGzWM/IdwJA36C8mzKZIweZTjEvyBlALlB58JPfTLDB5WwnpYmap/k9CoIRZ33lN5Fsnzl0xU/\nUzbtzxP30WRZCuFhn63S9ul3Hz+Sb3+TogveaHgrQhq5cwHuCyCU4H3wzFQyVEZpyn6vzxs5WufJ\nwi4FVXHPOruC8zZ5tYnfH1XBlIxXmnOP0/y6SKxoeVw5n3/g1n8KdxKnNRQLOdUjSOha8q3dmDZD\nuRbRL4QVy/jhFwWf2yDfRltDHSNJeSMt48HW4N1afo/6C51KQGY465ELb8xwLtKT+gpMOX4mO/aF\ncdwbbu0RXHn4p2a+pXHl7oaWDGuaG6Qose6Yc4fab77U4RbKCw/n3gOyn3CNF86tRqMZHoKchT7O\nGKsAcD14dvWnAPw7wPOnZNvp7akPYj49WcBQ5xcucA70qpuQGkP+/RysNTu8XBOVSdTs2Vmf3gVZ\nT1U3MXViGvidpgKj3vc2SKxBnAU+cfFCFcZpG7Trbkxk7pXXJDaWMM63Z5zJdzwVgJPLoDIhCkrB\nQFb+D4W7O000D12uCCpKWANNsq+6dt8RxgfHcAFUKpqEAUUNa5HuwspxhjKp2m+hf5tN2P5mysSP\n4nLXIndLEllp3R40HDW2mpRb1EbfQ7ZpppUJqFzJwlmvIGCy7xuaSe1O6VrtPckO3DIuJAMpIIh7\nqCoDvolJORdFKzi4lDfqNiDonqlu6eEC2or9AYwvr4i+yt5O8jmuyH6E5iNxRfgSRY8LyOi/rTju\nOK9VEWd9siEh2sNANORZhOsuehQVSlJ+5VzukUN1JG+jxma+nSRyjfw/Eda0GsXqKVIy4OFTT88/\n13mTUe1WrJadxrWq5OURUi3jNGUchUvcaTQjiSATuH3aNM1Npmn+BMBBAE4W7upDR2kaa6wqAmO+\n5k5RYRmPSDd1b8FRFc5zsWh8iKhNaPMe4IszHz9cGFdPA2CbnBiWwDoUyMGKJhqKsBUoXqf0MYMF\nkQRnUBEgaPCcJOLhnwgo8sNvRT+aGBji+4ZGapfZdxV+dW5tyfqUDv4KbtGQQp+PxwTdS9rSJDta\nlPraHhRZq/8NnixolXDjpgnZHhgy+dkGITxm4up9l2g7fm6Eflu3oOlssEVJEk/bk0elYlsO61eT\n0J+u/pMpf9FrlnluAf7LkPiZqvjcf2lw/UU2fd/mDEMI7Fx4CfV9ivup+LxgYTDXttoQbVjp86Rl\nXFGIBmGRH1TeY5YnxfnbQtFkKNcQKnQK4RESygOMpomI18nZboRQJnat6c3esXDD8TxEQ+37LMs4\nP45KjwsZemu+1qkP5a7QkPcjle7CQzFGP2n0uKUyJIicJhVBdVKn+DrkVHRRAtIfZZCf4914LisB\nsKQfvUIOVC+wkKKQlsoRca3vLm/NoX4ajSZbgkzgdhdj7HjGWJFpmm+YpvlMUOdOl5CYiJhXdvke\no1pn3NrVYAZ+dcIfVmN2lIkhUzr6bGCGtRY2bV0K6gAuL0rXRK73dE8NpUylrA9CuS2k+mrHGMf5\n6Z6FlHt6pYi7zoYdylJz9vsn7y2NesoENZeJ6OXCGiSTjimxoLOER8PjyG3Zmw1+6yGrk21xKVEx\nAc3FMn8Pkme5J0voOsSzWoKG3tmVx3kLrKrAILdikh0t8i9znUi0lMvSOCTAJ6PzFP5cXZ4zLqEb\nji1Tjqd3UTZRsf0dLNfOrSEuBGbS1+WytJ5sWmrn5DO3zMTl2wtybz9uJf8cUpR2dE+ra/mW3FCz\nwqMrcFk6fa5388TskyZF5LiiCOWiXVSJ6ILfYqzrt5lQoC7iYDqVdRGpZObbMz+T+wBz9CeU3xcP\nKwAAIABJREFUccXnlLtgYFcOU5iiT7cAcIevREiRLoXyPHheuWVw11eJWPbxO65+I+qMf1c9aGhe\n8CKyix1+rUqMS34vrTi/XNniJ9PTPvdbB7TYT+F7bp+PYLBCoQyPW2ot66nMzRRjAwnnPbP5Zxqr\nNwsl4XeQhmdm6uonIUWPKL5u/aYlUFM7iBV5x4xLJa5PCJFGoxkagvTPvAbAIgAvMsZ+xxg7mjGW\nn/WYfJBa4Jj7srr+yAV01UvISzilztnT7TvDusgBXc2qHvHeLtqP/37Dyszd1cMR/wcq3QvF9VJc\nVDaobnuWNc5rGMwDJDCGnPc0FHUOktnwwQSn4ESCopcVUf2ci7Nhv3hy49ud7cFKuBPsvZRn86l0\nyOBfhCN0WOblbxFWFipi3jy+7e31HvgzLYGWb6Lzu96pqOKaR66aNOkmYVy8o5/CLFcZ98M7vjfo\nEIwxTT5uhKrSzhXmwn8fIvd11d1QVLSizuoZ7G3adaDSHhYt5NtslIRkOTr3XO83J9UZ6fuIYqxL\nlZTuaeGJIUMuxOF+7uqZJrnzrGs6tycdP9o0ka7USt8nrzHkFBSyx9ku1HEkrHib0HZVDgkAG5qc\nbT7XfsIPUiiElLAVKYwrCo8glgdN5qlCBGEQYIqwJecdKdzEMy3Zz1vFZeRgToVYRsvF+XS2mcw2\n4uJZmnv5ybr+2IXXZnEFo2zTimXc5Z0k3ilS4lGfSIrqTLwKMgrV8rlu1/xDnLSg1VK4kyIyVuyc\nG4ei3tem3dU1muEhSDf1B0zT/DSA8QB+AuBY8CRuQ4bs6MXAU3vzNI+DnBPBZNlEcxGuyEJBA3pI\njdnxcReqbxCfs+gTHR27j3lJduQ5dLrymnxjxvPcoUthnG9V16sDDw6+fLvFSy6VomRYmTo1+/PT\nwH7Iod4KhuAzxftE3JHlSxRHiQ0TyJwbRWxdvfDeVzO3q2SayG2jmAiqwrjMOK66p9NnxSIeEQ4b\n9nbrtzSYetcKPRwNrNfBdGwfh78lVI29lZYLJUZctWioMeNM7dEpJtTD+Ovl9eF3LcS1IkN5OpCy\nxJB18unbfApX77Wh9N07EPLM9q/eApcVSGknOfVX3roP8ZXPCKLsfiQDbxdqQxFKMibXxHa2eRZQ\nUkRXX6cILtTu1Pb7wszsvQ1CMv+BKMuvbkm+ywR6x8KFoj8ocgrldE1U2GXozLiM+7NIhJjNHGDn\ndN6WQi7LuLOfkCgToNo0q0keCRvkEm3O81C7Ub1h5L32MJiobJ/pHDBMa4LhUaI/zFZ+tI6PGVUf\nc3srMaWOLkFV7ucfafnPz36Ob0//NN9+GW7PzLVFWcQHpgyKV7/nB9QcW4Np94hQQqErIcu49IxQ\nrf86gZtGM6wEmrmIMVYA4CgAZwKYDeCmIM+fipDiMlfQYcvGS+OpdKV2DrBBpVDZ/mUukVGHrmrZ\nrczO5PrmzG4qJyCinpmsgUwDnyMxk9+V5fDkw6p2VXUnzLcsnhBCDg3sygDTOC74yTXz3BscVuZn\n5+RJJsmTgkN2bFe9PGhS7eNyL4VxMfFYmEEM6pUiiRN5KCxd6nzX/DX96V3dnXCGK4wRwr4qILiE\ncSV+jmLFSXn3u98BJ2JO0rLreXJalCihtPbrIAqVZODvwu0o9DnM4HVSrfrKNbgUDWp/wZxbiW2y\n3fBYr6POnkkJfQTlgw/h22NWpt8OSMFkqP1DuvjUJVU7sZISkvsp/+jnohlEsjOHpSuln71TiVfZ\nkX74zg1iPJDJxZQxT7aXoHMSmt4fqTw1DIuWk8uEPVd1A7CUcn7rR+caxqAiojbkNURIKA8wcZt6\nKUkVYTkUt2cOl6ZJYAwpBgpq875GiDTXOvvQL6mij6Asr188W/KuS8auGdXOH6eL15xO7Jt611TM\nXz9f7PK+Vqqj6kEj+1ofBSnday/F9Z5ybyfRbNqwXNpVfS8StJ+hZEYJypeUI0HJMIuddQypCkqa\nF+d7HUGNRuNJkDHjvwHwEoD9AfwQwHjTND8X1PnTwTXptlkTKXGJpf4UH5OcL6tuSQiK1HFHxcAu\nJ080aZGTbVGW6kIpTpdOZ/0MyhzH2ieXfp6RuSTFCbusMHRPnQKjqRYhPgeVGVxm9I06rUPUDnI6\nt+sPmeMkUF3DFUJw/a//dtZddT+la03HzdELn0dhocxwaU5GVqqTTuLbzwjhMRmDytZlrVXqYG+j\n6bT3XYpioZfLmLKuqveJtWQRKcaEZbyYEjOJ42uieEfEG1tKGO8akTLP6/tMLPw/uo5vXbG/ioVT\nteq63j2qrzoRtVWvfRbvbLaFbR4FKSQb5ny15bP8RhpZs6XA5iNcufoHOL/3w+7UQ2XYlU0dZLwX\nk1PpjuwTd79gYfLyvHh9TpOjfHulfS/LR6gk/oDU6wX/7Gd8a1nGlTauKO8OXJrylEkhZR1LqFKW\nU1CVfa9UgGU+tVAFBFc8q0JQHlh0r0j4Jst4TPQPUhgX196eXWiwJ4516ZUGkcjCHcmlvKM+Uc47\n6FqcHlCyzBTzAnrPIulGdqinM1IrON6kJQ2VJiSrpq75pdTZpQhggDnI90bKI4jVxxynkb9LWMcD\n/m7q0nKuKEbp3bvs6x7Xlk8Z1+OZRaoi6L6vG7X1zv7BGmeU8YO2WhbXaIaFIPXnNwBoM03zDNM0\n7zdNMxvP1pygzpAymJPGu+zoOpSIZStcHXAaQunLGSxhkRgQg5zooGVW9UJFM6layKlDVyeugmSJ\neNQJoWOAd3xjHew7EU4D6tDpAdfVO+vhN0mi/dtZZjGa60qV+EOy3hrOSVRIEWQygZZLcSMUDLYH\n4rqXyjx1QwZLMZFgKYVvim+lhDuUCVq8qXPnBTxa+nmrk1KJEjWJem3wsO4Sf1Gymsvs5slvbdqs\naXP6UKpzMdW9UFVsyK1oL7Ei67gl6INRFkGtEiI+q4dvm5qdiqZ0oPqR6+cYjzx2qgIhrAg1apyz\n6n7qp+jwY+dN8zH5T1byJF+ljArN1cSWnm3ydcidE7yUfU6aJk8vq+IZmIXnRfIppi7DRfNNxduA\n2gk920wSP7rHEb51eiV5wzJpROpvZeI0vpVLFineQaonWKa44l+Vvk7WR8nbQWMDteNMkGOiWmfl\nVLka7/6f4m1mCeNOIVx1U6drP+jA9B/gvSLnRD29+666M9DNVb8azEUY97HaqjO+dqG4MhlPUrmm\nogzlB/CcC2WLuJJ/FwzsFj/cKvIrUF1LKd+bTzgDlHeQhHEjSfugECEyGPg/b+8vmsc534FoFDD7\n03hmpHhSLd+qmzopSNXVOpJ5paQRseKei/q4o/jgtUKKVBioq/moiUBD3u1Do9EMDUG+ev8E8FXG\n2PUAwBibyBhbHuD5U2IlYrE60avQjkk3TUK4mPc6M8Xk2k8Iz9RK5/o9WWNEJyfjUgu8J01WYhV+\nvOwUlfr5uoUBqBbhT9aE0PbbFBeRyfI6rY/xDFxUd1MUOIEsBUzZyo+5ORTuJEmQJvXS5MY3YSUE\nIBu3wndmjXWWIfZ7CRB0PWWuxLP84P8YKYKjbfgtZSYntcoAn+2ddCtnlAmgMpkaKOftzSgR6xkL\nCSKUpAb3gPtwW4ltvNtyOk9nl5q1C0B/jBLC8TOQcEvCjcwhoGTPdgnl6qS7LITSUqCxEdiwAaIM\ncc50Bcg0KOVzW9xky95txVA66+pawowS+SkTP9UyTu3VVCbDtErAoSfFMGt//j5l5J6t3BD67V/S\nWM7PU/5w7E+vHqqyz/6rdSjAOejGMiy0CY6Km7rq4p/LuuOuPs76y0jRKPy+bcogYT09f2nNdQnj\nLGlZqaBrkIoDaRl3npHurdVvOd3nz0Z32mW6YsV9kkrl6p5+8EHOE0mFo7iHsRKnp1Uu1kJKzOkS\nszyVu84rG59BjtVdX54CwC0gunLTKOuLjxlj4mFU4s2JdTgQ++E/Lc3ovrcbfWYfZjzo9oKyap3Z\nU1DHbGov0x7psQ4S72NbW4ob7ufFI7YFBXSYeDfCJqbfOx3T73NmcDd8XPKl8k7pi1VLuSrQSmWS\naL+X2nILmM7b7lnvVLjHcOc527s8pvMyLEDxnFFjxaXjaA6NXaPRZE2QwvjPAfQDWCA+rwPwjQDP\nnxLS+sUKrc7yT2YDCgqtDqZULHksM2dm0Pek416tZkOOKvFnYUVDqR5vpKjXei/LpAwY5RvT9r8f\n9JOrM7AGhRRr3PgOcS0FTnO+HOOC6td9rLfkUmeo91Jsf5jB+p/y1KSNV4qOFjB0/aUL0/46TSYi\na2tzusvRdU+Zknqi8g11KTNVGCfBUVjGVVfNv6RYLswPK56W9jjrurOMt68d06txGHphjCvEcvQi\nVMUFOHuHQR4jFPdM10JCzYJFzvbispCqAo2tKuUN/r7eRcKLsVdxL7Ys5GKrhK3QhMTupn485iJS\nFMKWLUClLY/Wwcc4lVQsjVhKeVmqdUj5/q+lTfgNGvk+miwp7oMh1drit/64nyunwDSBFehF4VHe\nQrNLKZMCKk/1fjjHQ+i67XZVmPEpxW+W6VcHT+dwYBAGdiNsvYgJ5zGuOHzlXubi9uxp1E/VZpSH\nVVXJj6e2YedpEYokBciwMq4IIVjmllAuJdMlzlxCl7fB0+XSq8awX/O91FMM6qddSQuVfiIXby47\nquVSZoaPOYVxCl8pLw+mXDte7UVNetbSyrfvL02tpfFr2zIEx8eTxogY+Cqm4cOGcgDMc+KRVEBL\n5cWiKmIVQbewzZrPVNzOBfNTTvY5Wbq6EY8DyvcrR8USZwJN6ZKvWvHlmuj02bn1CxkiZZL9tDdl\nsGyiy71e2ar7VRZ9vxE9T/Y4j1U9V5TVWVxJ6jQazbAQpDA+3jTNb4EL5DBNc0cQJ2WM3cAY28AY\ney7VsdThSPdTDwupdH1KcuV+3dJLKdbgPA/TMO9Tpdg8xnKrJm275fLmdH1TtalqIqa4YhC/z2Op\nJUvr6j+pdM0LxSH/84Wkl+RATpKkYGhiwrUTUHUYN83vKI07zu2qgyw6S606fRZWGumFoAqyYruz\nJYM1U33HIoaaE+sx84pGVB1ShcqDKtG4shpF04r83WrTuLztwg1UCuN+LtWK62Ymd87uXqgiL1ex\niK+fMQaHoVfUkc9GdiCCSEMcS9CHDYjhPPBMrRdhquMaLAGNM7GDBvoMKk3VGbCu9G/C4k517aJF\nElRhVxXGI857KJc0EwqyeCHDehS4YrxPwyxsn1uXeaVTQO34lXVhTDqqTNTV+/n7ukCqHjWpYv1M\nE+u3R3Daf7sPcIazpNeyLDd1jpUfgH9xHcbLHBaWZVGpo7Ta5j4B9LJCM5/3UlrGFTd1K5Eb5x9I\n37PFKlQUyazyM746bx0OAOCF8UKYFo2IBIWosnSm36oLXV2Z1cYlCFDSTPWdk14HcNSD2nFBcepy\nl1/Gxw+5BB29j9R1udxTqP1YZBIa5ILuqShXZp4u4zsqmnhfXV3tfEDXJFlX+k6Xx4hTRc5se113\nSLp282/21KdOFCL7Edlf8P3u8AVLUizvK0fVcn7vSRHppT+yJ6IMp796lwNLJleUBXFrfHLnBjCV\n39r3JitM+ejj3vTy/BacgtnWeakDUJY4U2PDVfd18sQiLzYZaghrRZGEajCxVcnfS02tMd8hw7+U\nA0IFIZTMcIb00bXLfClx5VrU8UPL5BrNsBCkML5HZFMHADDGxgPYE8B5fw7gkHQOpEl3TExQ4oXu\nniVaT8mL1M7RvxeiwYg6v0fF8jIvibVUyZLxw4crUd0RwxHrelDX7Ez+QgKAaq0LKYOkzGop+tkD\nD/Su0y1owqcwC0+hHK801TquJVUSFjvxDFaCp2UyQhEGo9BAbGwMjZ9vRLg0jCcuWYhDfyQmIDKR\nmzoqOj+mn8hNDELiB+TOmRBJWWTcrbK8zle+msHIIg4NkyBnm1FPuXkSms6yLEsTvjMBs5+ZnWlI\nl+exNLCquQTkuvRy3WBRnYAGS5elV3obGNiOiGtSZl+e6imxjvOA4o5Oz7PvAOdkxfBzU1cmKMz+\nna1xtLb4XYRSR/GZJkP0LGV8rWgf0XL+RZwyzCrC+Gsotk6WRv8AJH/26i+LioBxreI7anfqes0k\nmMh1gp2xfqqXSrJC/ZP+Zd+YTj+DBFinIHs7mnCO8JSQrrHqRI+2rizr6fj02JWO1ikWCi+JH/8Y\neLJzHL6FDummTrhiPZXl4M4QSxPdnYbXibwEpd/gdUvhlZTm/t+KceUatOOwZfwOW2FQYlxRls5U\n82XI9pXlUurSWks7Bqx+0Y4abqEqE/E9f3f12fN8+joZq5u6nrm4rsvEXQP8j3ipgSVYjHCYYeG2\nhYjW8IGvaTz5WPN6VbtXx3Ix0Uded1rETcc2kYULgEu5r1rGFSWNaQLd93ejaEoRHn8cuPBCcR6v\nuGPbvpTL9fl4AxEUB09zs1CBgYWbs8igaC9DnWYo44pR6N1JXviNMO54yt050jvtt8SZr2U86vz9\nZ840rVwBXgHdmULvopJLIFmiv0hVBPPXznfNK9QEoLksdavRaHInSGH8EgB3A2hkjP0awH0AvpTr\nSU3T/CeATekcG5ZWaL4tqHBe3vy181H9cW71GCs8AbObNnE+FOsc/xwt+ATmYd4867v67gJHJxhT\nhHGykKtWQ/fE1b8Ob6AY56Ibq9oa8B5iWF9Y7L6mVJ6SGXTCdtf7/XbsJycpAPCFi8PS+uJX5Uwt\nYWvjRfRDAECDSDZM2WxNIbCp1sVcYseb+NLIKBNeoYkkg2jF0gpEqrOc5QpoDW6XmzrFXpJQJi3j\nqa+pP4P7TJPt15tq8ABq5MDuN8D39rpdlElups/Tup2TFVcvk6R6ZOmc9rdpcl+66zVb5+CEfZKj\nRcvCWII+FBQy3HEHUJ3EEOpKtuWjWPC6JNc+245d06vwJdt6tJbQrU7wlEm2GjOu9BNWxmG+SYT9\n2y+ztSbfR+LjfUChAgmbYGznm5iESIPQgtgUOUoFfD5618brcT/MqvAPVONvfwN27wbOPBPYVFyI\nuzHGmrySa7eaVd3XQp7++zOZR5ugpc2tIPBL1OYbxiP2U9LA+SLg64kxDRis4lpTc0/CcQ0UBhUt\ncF5LrjBFutpWyMvvj4ed10DHK0K/nPSL9kzWZS9CSh8nLYvyRvpfE30TzsL7hi5CrhxRwSsSi/Ez\nh0KQuWbar29HSY+wOorx5uNHuh+wXw4Wdz/iXy3Z94pjEknG8NJfzuRVUtcVV/qTkHL77euN9/Rw\ng8M11wBf/7p/WQAy1nqoMjr1tRQuCAMIl/HKxMi5wee98Ds35eJQ3Q7qaoGeJ3sw45/eK4CUlgLd\nNh0RedxJId6V8NG5X12dIayEDkWjwPr1/O+t5co6l0mux/VZuR8JJfxmbF3yhxJriLmUd2rMeDZh\nmxqNJjgCE8ZN0/wb+BrjpwL4NYAe0zTvD+r86UATkpJKiiF2Xl6sISa1plJjLWOE07dtqQmE/v1k\nGF+8xm1ijjXE5CBYWs//sLSs4swkDNPEwPnR1TmOa/aoHQOOw1zc0WpTw6cSwkl5TEJeaWr/s6iY\nYJXX+vfY25tK0PUp7jkQVL9O92IiLVkkNcT8DxoMI6prdxZycqOwuk+ZyrC3OIL24/wDBsddNA4L\n3ltg7VBmqC9CyQIPy7XxnM/zz2efQ8IV/2ytCOAUIFPlErDzQTKXTZflgu94rakWl2KKlQzXp/38\n8pf8V4eh12YR59sf/EidEIoyUllv5TWZMMCF++KuYpQuKHVWmd4R1zU5J9UE3TMSyklQiEat/Ycf\nnvyeysmkeozHb1R3Q7We9oRBRx5j4Kp/VLmz1iseEa7s6nL1Be962JU1p6EHa+e7448B4GJMwXXl\nk12Zx1OhhgJ891r5jeO4v6HeWloykqIBuExbymFJrG2XRLpwCaaioMCazPf3iy9VhZLhbJdqQjc1\nkRt5QHmREOeqaY+i4qAK1K+o9KpqUvzasSxDPOt164CtreVYgj4kxhZiLeKyLUXVJc4CWNYRcDsv\nLPjleDzxpfnoXzYWh2MB2F5+c6XXM7mxU2byuLM+XopRyl6vennIvs5w7iesdzKzayUPNvl76Z4k\nPKwqIliCPkuJZxPwG05rgCHa8WBlzFE/O3cJ9/TWVh+FkroMF2NWNyjqM1Z5ZZOtS2OtvuE9jqiW\ncbqXVZ3u+cq55wKzZjn3lc4rRfni3IPmBwb4lhTq1qtv3Y861SFFVVopHSt97VpqThxXUAiUzChB\n4cTUgjBgm3Mpyjs1l4GhKj6U0ABDqecyLML2FTRxcz7/ZIo/27Do2HZ2Oo/rmJFaE0V1iyhL3Enl\nHc0DtTCu0QwLOQvjjLEexthMxthMAM0A1ot/zWLfkHAjbsTPbrscN+JGPPjIP9Bn9iU9Xh1IPeOs\nld6wUOnTx9Tz/TNm8IHMi1iRgQHGEC1yurgZimVLXTpT1lPs+NihfDtWeEsP2o5kDNh/qYGPH82w\nwwhjTzTiPo9aMcWStveLneoRLgpKGWavmo1oddT3mOVv92DxFXxCkq4xwM9dnYzSPqFTSIgB3lCS\nlMgJYBpJSdYdIZK8eSwrcuC2Xiz6gYf2A/QTBsYYBsWozRLypwCAJzwm82Q5KSmhuvOty72TtjLL\nPhWa8pIccX1+h7vcTwWUuD6myPNqO92OiHxn1CR06mTFNWlVY4htu//FqnG/yItQtqAMkbqIq64u\nGV5FeZnUJfAqKvi66X5r5TIGzJ/vc2qliHSgQz+sKcF7QlESiwH77QfX6gsuC7iP+7pU6ill2Mu8\n8Z8l+J8veU/UTv9lDc68udozhtWr7lD6J2u5MP7HmWd6FgMASNSIib9fIj+fMtNZ8WLQo/OQk3Ph\nduzKtu8TCqCuH/wLtLjO/W0hQO4ti2H287PR9s02TP/rdLRe1goAiBqmLY7deTf9DOKuw8V1H3Zj\nI4xLeL+8//7Aaafxcj+JeYhUhrEWcamAlKt1qLHBhPj4LCWCS5PqA8rRXx7DnIUhfOHKGM74NMOb\nG6PY2VaG51HqsvhKBZhLMep+llc9yP281QSP6pJWUnBI+eInx+9n5l7nRXgJ48RB2A8DE0rxBUzD\n7sU8r8TP0YLlIs8GlUH9p2+Znh4l/JNUOst6uzubws/z9qZaZ13WWhK+bEnxOt9YiEVnptcOuh/s\nxtQ7prr2p2zDan0Vb/BYfQyLdi5y7CNjigsf7z1pzVWUOBm3j0HnAEOXYChWYzXOWl02zisUAADW\nbwrhwq95j3lmGtVV56bkXTCFJ9BHy2UtmPfWPCSD2kV5jfMaqE9+bu2/cSNuxG+f/w5uxI0paqTR\naIImCMv4Ncq/q8U/+jwknIJT8JUvXIxTcAqW9C1J/QNlYPdzCbKjxlN5rL7kIhRhWJpYbP2GLBdK\n4gyr/ORdMx03YKtoSwtwzz3A+ecDva/OwdKXLLcslsLUmc4Ep/1W3uuHCwwUTUqdTMavjAw93NAk\nLAQTJjj3UyxoJOy0jKuCrJFOrKEruRTVOf0R/V8zJ+J4zAVT/AntzUVN9qNONP0SuFlWIe/BPBX+\nHh98/x7hukGub+efD9x7L3DcccDdd1uWDJmg2maloT9vuU0RvhW3X7/noApbCTBcZE7FN8SSMOOv\nGo/ed3tdAqEvisLAVhIAINZWgK9iKmIx4Kab/F+1RAKOkBPHyelTGs9BWspFfV6ZPBYr4ZTy1Ymf\nusSZqmCSYS4+91TWK8SwcCFQXOx93AknAIcd5lZspEyuSPGt4qKoHr297kNDIWAJ+oAGnkrE/a4p\nn32lVX+8hPHLLwc+/BAYjDs7aDWcRbWIUz9y6ded1bkDZM4DVqEEx2Iexp/TgKIpRQgVWQ+i4+5u\nzLjXuXxSVoh7XD4uiv0u5oqppibg+uu5Aufuu4FEPMyFciH0ycRMSrtQ37G1KIAfG20u1vSrJX9s\nx9L35sr9hsGVWb3HF+HJU2ciIQRZqVSSeRqc97qolG87b7OUvlU13kKMmr2a3ueSEv+GkenYYmdw\nD/91Wxt/J0gI98qnshcGYjHgSVQiVBDCEvThZrRgh+Kebsmm3koR5y5nPyETlFJOFI+riy7knhgu\n66ziiSDHEVv1alvSz8JmRAxHpu10X001FGP2bPcv5SosAGqOrkG8hd/wlPMFnzgPK/mtuCex9Ka3\niQHFO0h2ymKj9hNSiSeuQ8l1QO/CgPCgKC+3hV54Pn8fkngEAZZQHoqHEG9OnvzH1R5kkkT+uaup\nF6fgFBw79VycglOSnkuj0QRPzsK4aZp9pmku8fsXRCXTxYqDSedg2iraTDCPzpE5fkJLW2WjoC+p\ndi5BY51E0f6nYKOIVz/tNOCKK6z95a1RlLdE5YCQQmHtyt6+hbnNhWOOrgaYNZlNl/XC/JtK4Pc7\nK7kT06AjXXqF+2a7ENJdyYMocZRo3WfBO2bMqw5yII+n/2rsNMJYjwLUTOIV3hPn93Cuh3VVbXZh\nxa3QZQGl56No/UlB8bZtcv0a/BUlqpBF9fi/9kk4AXPkPS4uBg44gN/7gw/mnhimaSmiGhqABcIz\nn84oFQl0LYrF0ZUNV1V8iI8Jg2H8eEvj74ds0zRpVb9XdsjcAgbDw6h2Wf2TIWOu/ZR3cH92Lx/H\n2bvXowByxVUsWS43dRn76RRgXPNSUvJE02u/mfZhLm8XKs/Wbbz+Ot9GlDZsprDmp66Vtf99RPFS\nWGRtV/r7cJhnh35vfgOOsSk/aALqihlXvJWoPyFl0wOoAQB8BV14E8V4em0cBx3iruOYg8tR11ea\nUqeQi+AYDvP3srERuOMOriDdjhBiZc5249fnprt2tRQQDSbds+00NwM33AAMCPclGcdKQ7Di6VEu\nnIQqD7XWDywSiiJViLH6PP5H71xelynd6lhtr28WiBMMTK/AhZiC4mLgzju5VXvRIv/kph0d/LiP\nfczad8YZfNu7wPs3yVDfB7LU04oSTR6RJmr2dFcCSDXXhKG0i8BxKv3VYuQqDLu9fe6klqzdAAAg\nAElEQVSn3D7F6rOUG6LmxVDPSTTbPMG7/tyFyb+enFbNSaHkl2OCqf1E2HnvZTiWbX50APZDf4fb\nvb+syj3fVPHvH3zcJ9OBFAvKEoiWd4E6H9VoNENJEG7qX7T9fYzy3RXuX2R8/lsA/BtAO2NsDWPs\nVL9jQ1IIS92jhApt60/YttxtKNlU27Y3i1lVdXNI1NFZvOpam0zAOArz8cFcbq2prfV2p6utSBJo\n5iiYCuCbMb+Y5j40xNCX6Et+PoXPtC5C7Bw+8yPrnOKF6cn3MQHvqNYbSiq2i//RcEYDeh7vkQOb\nahmXSbCUJYs8ofttq1P5knJUH5X+8kZnngl8+cvAit+Ow4INC7BqZjOOwny8NqcZj/nEnVIbk4oE\nnxhhea/EhJgpM/1XPOLS04HOsz0SxToU4jvfAd5/3//4adOAd94BCgqABx/k+6662llnOUmRS744\nqmorXJmQMIbIoXVoP7cBTz4JPPywcnia12IqYQJUzsBuviOZ+6kfe4po9YXUdUlVzzlzbDGTgrH7\nFeNcTJf3zHr+oq7qkmc+LpH2e3oParF7WiXSgdqhvxUq1e9FPYUVyjSB1la+T65CIQ7aM7bYucNP\nSZfGhPAkzMU3C6fihReA55/3PmYQhiN/guqNonpuqEL5T3/irshPfuJ+hiq1n25EdHmd79xZeiv5\nrNkUGkjRd4MLCocfzpUzh2ERYiX8ItS27SPHeGLXtT5mVHnmvFApmFuGT8CynNM10BAcFbefsr2H\ni8MyhCxW6nShjijeQGwggen3TUfr5xtQNK0INR9398nqWL0aPq4gACZ1OD/TY6isM/CQULgA/B5S\nH6dimlxZeNhhXGH5zjvAiScCn/kM/z6RbkJUj3F+g9BOkJWThPGJk/jBc16eA3yXe15YiiW+9bOM\nhxRPvHTii9PBlcBPVYD6uatPKpS5QPxPnqpwKlRsROOuqLD2Vy2rQvFU/7Zgp+pTY3EpOl2VVoVv\nlxCuLjtpu7UJGCj1uMymNueUmy8vmdx7zdczLIP5J3mrhctF7iKf5IhBLDep0WgyJwg39eNsf39V\n+W5Zric3TfM40zQbTNOMmabZZJrmz/2ODUV4xvRUwvisp2eh6TyeqUsmMEunE1IHnNS/cND7QS8i\nImOruj6wX4Zdr0nURsSwaDFDJMLj1b1oOakaVSuq0h/YFF48m4f7z35htvcBKXjx9RDOPY9XvnYM\nv8kU706x4Krbfz8Y/g+N2BPj94jc9BIig/C4C8eh42cdCBWFUNJTgqKp3BJsrVnqFFzUtU3tPOMX\nO8mA7vu6Ubk0PWEGAJYuBb75Te7SF62NIhEOYSNiGIiE8Vs04k1YyQbOP08pTo2xVCygLGbgNPQg\nMb0caxHHtno+waDl61ajGGeAp19W16RXLkvB6ZoXjyfPKg5Yz4+e29FC9SatudJtTxynJA2SdVEk\nBJMx9P5pMrqvbEZpqRVPL2uayr1DQY3MINmmvh6YmUEWi2MwH29NbxBFU119CrPh15N85jPA2rXO\nfU3jGJ40K2wxnk5Ll59lnCluj3b9w/y7OnHcOemtWRiSSkGa+PnUXsYpOHfT0eHmAjwM6515+22g\nTizVHo9zd/XdzfzBmtIKk/3EbzdCCMUNdHYCkyb5HLPbWXWX1VZtp3LSLU7gYagr8PfylvRc14YF\nd01OIxZU3cH3vDa1AT9FW+qCAHcG8rBy7jTGE1m87bsrolNwFlK/LIsWMTz2dgGgeGJIzx9q18XO\n7/vMPoQozl3kU6HM8Mxg+B9Mx+CcKlQsqUCoKITZz8xG7dHcZT/in7LEc0JDSoXCAqcFlInxZb/9\ngLfeSnmpnowdC9x8M1dWAra2LXClx1DeVxMMm1gU7yOKbWd24H8mLsS4i8dh4eaFGPuZsWi/vh11\nJ9ZhxsMzUNhujSNy/WhFIFSFcVLexWLA9L9Px5hF6QmoKqqrvF8z8mt3RLQuipkPJW9XrjaaodIu\n02W6lh8fwf1mrW8CUJnwUXFLV++1tRQfw9tvOz0nAGDyryZjwncn+Fbd17jjs7/pvCa0/8R/rXs7\njDH0mX3y3qhJiOVxQa6vpNFo0ib94KERgBFhiDWk9kEtnm4NSJMnAzvf8T7OzxpGk5ZBr0U5kxCp\nssXkUfZZdfBWyvbtn01b1mAPJlzNO/1Ha5/1/D7V+LZkCbDh+0BRZ+Yx4q6yaBmeKLALXNga3MRd\n58yECWYwXBTpwvY9DHV1QPziLjz08iBWvPEKBrYMoOpjXKlQ1FnkqE9BWwH6zD6seYYvZ09JjCKK\npdaL/8NYTMeWnK/NC7s78qOowqOownKscx5EE0ISxiM8kZ3MQkzXUBHBaygAGPBJzMNFzcAZmIm+\nWQU4e/UE9MNANr5l20MRVA/sycq7gzE+ASXBMCLW7SYhhywEcqsaAJXGlyqD6/g2AG/bK0AnUj5D\n2S9oPaAIJ3f04K4Y8MQTycuy8wFiHu+f35tjEwRcsX6pbzL1AzL21yeHgJpsTMVkDMuXpyxOEi9m\nSGy16uxXV992QsJGQQhfxTR8RexuarIOIfdRcvs1QzQhpHM4JRS1PXgVfdllNrdUH5YvB557zqq8\nYfB3TLbTMMMg3BZyNbv3F88HcBV3Dz/G4fuVnPdLixDfuDdtpS1NkN8rKsatKMYtafyGQgH8EoC6\n4pCSYF8zfuVxTIYbJIMx/qwNoxCHYh7u2c07P+m2LgTeSEUYk37h1JqEQgzvIYZJIu6+qYXhXQCo\ni+FpFMD0MOIWL6nAzP3Kse7S7Z73NV4APsjYeBXF6MQ29/UK92TGUreldOkXnjSZdMnnxWbA3J3A\nussNXHy58BYoMxAuC6PhNK4MLJvnVB5L7wE154h0T+ffU1svKQUqpvuvDpCKCIUtyK03vkJ6NgNN\nip8U+CRJJyV+zuXJECLRP0S8ty7LOHP2f0Td8XXWB9nlWXesuATAVtchvvUrGF+AgvFpaAc9CKtK\nOy2EazTDyqgSxn2zcSahsJhhJ5CNPIM19ZWIr3EP8ulQUZzALnjMkfysiDDxLMqwp60SeNg/E7SK\nr5I4e6NURnTe2ol4WxxPznnSlSWXMCLA05FKbN8DfPgiUFkZARBBon8KzEEToYIQmr+YZLYk3PlI\nkKEtTVh+9jOg/zRgO0IoVnO320yK4YowKpZmP2Ehurq44PHFLwL33Qc8+SRw4gkm8Cv3sfR8Qoqr\nfbiAYQn68IoSdsYYsBql6I0BSXQxvtCtv7Z+Gja+tRcnLeHJrjLlxBOBTZsMfBYz8HslztmVuM3P\n2JqmUmvqZ6uxjvUDitLMWppIPbHTNF5ZyXDXS9m588vM9P4yuC906Os1Vdj4gYm+dMpT8h6olnLf\n5eIYzx8Qm5jCBVRh1qM9GNw+iHULViU/0GlUdOEn69nn4JMnA3sA7GjgylA1D4LfyaTVnvHElZuN\nKC66KHl1Ae4FcfvtwJ2fdSo+QzZ3072wWb5s7Tdhq0a9WHJp/vJoWpZx4vhVnRjYmcBdXc/xHT7e\n53R9CaGkuOQSYMWK9MoIhYAbb7S8WmjJMXluVfElPv4HlZiHjY5jE2B4ESUYBMMNN6RXPjF2LPCu\nGcezj0fwU7ThCmovRWH0g8EwGOo/6Vy7ijFgJeZjZzlQPLMY8YYolqAPD4trqVeXugIw6z7upr3u\n0jcyq6AHu2uyE2T8WIl5OHNWDJ1/WZ3kfXF3JEZ1DJs3p1dGIiGmCHLcoLYsPquWcfF9QY469Q2I\nYQAMBX7rrCnWA+mVNCgUb3uyFJAB2YhtUyEAwNy5DBuehaP/mPC9CSjsTG8pMxUrzIn6C1G8od5T\nvl8Vyi2rc2qKShmwicIiOQUFwN6t7mPpfC0t4L/JVtlgI6ooawi9zrhGMzwEoQ+bxhjbxhjbBqCL\n/qbPAZw/bYwM3ZMAuCYpmVD0iQacYPisgZSEKb+fgomHUPyks3ym+p1CfoHPYwY2NXPpLF1hfDLl\nMMnwAuu7MshylYTalbUonc0FBJkIRc2eG2Z44AHgoYd44iXCiBqOjKt+7BVLnFECsbCScZpu6Vfg\njoenNyAeBxZuXIiK/XMXxs8+G9i1C6ipAY44QuykhHrKJM2yjAtFArkjCwE3lev4CScAa9YAOxDC\nuzEK0rW+d+t6eMlbjSjWoAiXXw6sSiGH+WEYwIsosyaCYXWrNm7aWhPSMzET987y8TMW1B5di+77\nul37XQZxZSKI/tSxt6nwEgiS1cFr3+PjGnEe3PW3E1K8CWjip647bsVJqoUynIy51jJiaVLYUYiS\nnhIPi6rPD1Srf5ojiGny5eKWoA/bpvJGneqndlde4ljMwyXlye+lysAYLnTJPA0ewjfgjgU1lIRM\n5vjMFDqVtQZqW8KY1EZJL5IH1m6q5/3kmDFuF9dknHwyfxd/gWaE2xU3ZHXZsBTnmvmvGZjzUGb3\n187Y1hCentSM2ikx/KCiEygK42As9s4izYCXX+ZCyKwnZiFUGMKkSTyruWkCs7OJkPIY56ho1Ti7\nbXwFz/YfEO8hbq2WksKsSYrInSyM228H/vzn9MqwvK5oLOWfXAncFKttpq7bKqdhlgyHAtyee34e\nNbTaQaw5/fmEmjxVTomUS1AFSQBoPLsxoxAzB6YzdEuWr/TJMmme3Ip6U7XTuNe0uoBdGHfXx/mx\nukrs7s9dGKf4ekvZrKVwjWY4CSKbesg0zRLxL2z7u8Q0zX3f8q70QV7dnF83de653kvrpKLmyBor\nvsg9qnmWrS5RkyrjNCHXRncJ986PalxlxfgYFtuWZMuVOavnoOvPXDcjs94DKJpWhLKFZejpsbJ0\nZ0pJWxyfwFxX7Hiy7O/9oulTjN/sOdmVnYo2Efoplz1Tk8SIKtKgGCky8C5iiBcwmCZfFsV+nNeY\n2dgIHIUF+EUj17zYhReXMO4x4ch2HKbJR0Rx0wv59CpM/YMBL6MU26KZKn7ERMZvTkKKjwkleNQn\ngV46rF4NfO5z4lxp9BN+36bjodnYHUPvloVSgRETRjvLPd15vCoEh8I8btVribGsUC17vvc6s4nh\nokXAMsokQsH8LsWCUgQ9bzBsQgw7jDQ1kVRMexmWoM/q46Sli382FDdUmXjQVo9EDkNl86m1qDm6\nxn2v6PLF/renjMlJOLwBbTAKM0/QdSZm4lFU4JGCGszrNTB3QfbXWlXFlXtFRQy/21iLceOAF1/0\nP75dCXldtYonJU3FBsTwRthSjlgxsNY9/nekxvU7AEiIMfu447jXVJCQkOPqrz3G91MxG5eWT8e8\nefy9SOv8wludXh3VVVr2FxGrjVcfVY1YU27K9dlLIug9KIP3TlzvfGGrqFhSIRP3paK0U3R+6lgp\ntn7riatK/qBQEztaMeOiPorSn41JrRCluppgCMvfO+vvWhVE7Ii3ZqZw9YJChuSKAW6HDY1GM4SM\nmkiRdDt6FUtj7O6N8p1Z8k6Mwd4m4T+muGLJsklmtwmWiUT6sYtVH6tC+QHuJTYsFG2A3aoaoLa0\ncGIh4o1xhEpDKOosQuetnZj868noebwHXX/MzYGipgZYnyiQAkuEEgH5WCdWoxjPQtyTEEPp/FJU\nL8vdIu7FCSdwhY1cIstZFalEDxt8RzjCcBzm+y6/pa4bTRbVPQihrDr160zCeCbLe/mhKhJCSsI2\nuTSMOjFVjEd79qRZoCr3+Vgy6POekhi+hOzXfZ440WY58EvgpuCwxXvrX3yJlIYtTw2RaToSd2ac\nthI9KhWpieGZZ4KLfVXxTaKXocvkgw8CPT3Av8aPQ+tZYzxPqfa7JgMeRwUeKuYxl5l2SwMDzs9u\nSxf/nCq8ItvusOmcJky5fQrKKYKA2oUQwj+oK8NaxF2JC7OB8oikSpJXIoad51GKl1GKL2E6bimf\nkHsFPJic3gpTGbHq/NkIXe7WSEuX3ktbUFdjOssXX1JC0PJy4L/+K8A6rbKWONtexgVKNZbf3tbf\nRBE2hjITrMY387pTTD55A1ox43x/VJzWCDNM/e1Uz+XpMuHvf+fr2/siLmy6cD4jI0Wmbbr6uT7M\nPoQPTtR0q4R3mFwKzyNEB0B6S9omYf4JzmVf1HHLL2bcvizYEvQh1pT8mbb/pB0TfzDRtsfZA1Li\nyxpvXRLavt2GhVsXJr+YFJD3wXjxyqdaflaj0eSXUSOMZ8vgTj5qJJ1k5+4V5EnjNR2YL7TNlLBL\nRQos5WK5EzOzSeHYz45F970ZuB3m7tmblPlr5mPqH6aidmUtao+phRExYKiCahYwZsuqXkjSIH1p\nHbcS83AGZuF/b+VS1t7SGGb+e6YrQU6QGAawtbcep2KWV/QBr7NYG4ZiucKKJbS5mVuYzj7b+t0B\nB3BhH+DZgO+4w122mnDnytpufBozccstfK3cXK/LXlcpzNDtlzNQ5+yGhK3QYAK33QZcfXV65Q2k\naieinIQIbUjl4p8JLsOWzzvodDvkv+rsBMrSbF6k0CBhXHp5+MT2mQbDEixGdFpmseIqfmujSxTF\nh5y7CYFy/HieVC1dLny1FQecxie/TVN5ozeYenK+SYDhfEzHXeUtmDo1cw+aXSKhl5xUUwZyJQsy\nCTIyu7qP91C29F3bgIazxmJdYTE2ISL72je7GvBJzMMFFwAvvZRbGbKd+SjACK/kV5ks+TfcXPLt\nMM76YhhRUqwoDbflay1yCTl12a3xC3K3LHoxaRLvC5dhEV6Z5lwc3FSex5aIU+BMF0rYNUheFdSW\nlYSPxWV8K93mc4Qx8S/FcUVCydPayre0PFu6TJ1q3RNqj52dfDtDTGOqKt2GA4Ar/HNhwnGV2G/v\nfq798h5TOFaSJc2eeQaYNSt5OQ1nNKBqGfc5L/SI5SchvEaMX6oi1AgbCJfk5nQaqeEXI1cTUhTk\nGo1maNn33cjzyLR7pqFgQgEe+dMjcp/30EGDulOwyZVzz7X+lsI4dY62uKNlWIhLpwT7qFzuc/Qx\ngOQgyQiX5q/JWa56TmsiXeMADLwHPhELhYAV6MXlnUPzCgwyA2+iGDB38irRbRbCTLiYj+jhsHvS\nv2ePJaQDwPXXA4sXc8stQRbRnQjhtXgp6vbuwqCtpcZCCWCQW2PeBV8Sz29ZvHQhoYas7EwVYnws\ni4wBv0EjyjtqccWx6Zf3aHcbLn2pCd+kzPQ+Vtrd3VU4BbPx5pzkKw6ky8/QitKJVeh8fb0rXCQZ\ntPzcNdcAV12V3m8KSoQwXqKsDODKQM434YownnqKyaWVssZXCE/xPWWBjyCtpGoqC95fgHBJGA/G\n16CkygA+AAzxTpDSZmMkBgzwdvP005kLMEccAWzcaFMkqG6nYYYB2GLHfVYBKMxtro/qQ6tQfWgV\njngWeOGFFtTu3oNff78O13ybjwXxONDRkfo8fvT38+ewHrawmBST7OIioH8TcNZZbpfxkUDD6WNg\nxAxs+f4GAMCbhWWI7OauEIaPm/N+36yHeVkd8sVuhKSAZq0cYL2/KzEPba1h4JnUOSlUCjsK0Wf2\nYe1/duIV2IQoUR4J4zGxXFxxnnTMUsnr40rd2gqsBZDYnb1231DzZIhrnDwZWHuP03Nv4eaFMApz\nV+obYcMjL4ZT+JaZ6qNOTzAYmffDhVETA7uSH0P3uv6UeoQrcp+v2L1ImTpY69hxjWZY+EgL49km\n+gglgjUfXx6fip8dV4E3v2ZzZwsxXIlJOHZRFXb/PgzT5DGhS5dmWYifSVaZbA/uza8wnk9CEZ6B\nfIcwfIQUq8CPfhHBASdan7chknTN2iAhV1mznBcoJzG09E9tFEuwGP0e84moUsfTTvMv5+NYgPYm\nhgUvbnAI46VzS2HEDPQUAq++mu1VOKEmRK7zIcpizywLecLjeJMBP8YEnJHhfPjgI8JYtykMttlp\nxZXZqGmpIoPhLXCTQ7qJDpPxK4zDcWl2FXYX61sbOrDt9T34h+HhWumDETXQZ/ZJF+awUHRY2ZL5\niQZ3JXAyZuO2qUXozj7flmSPoZhFfeZkaqImNr0Mp6MHq7MsN1rNG3fRzGJ0fK4GL5/6IYzBBN5C\nIV6qqsbZO7vQPNYAXgUaGrKz3lZVAeefDzx0HW+NhmLpoiRYIY/EbcRXv5plQjEPpk2jdaljWHEq\nf8BBuKjb2/qeGqE5kJYvH88rxn/305/mXv5w0H4d1yD8+0ebEEkkUPq1ifjDfyZiJeAa8xKG1T+x\ngCzGfpCFeGs5fw4Jm9DzHuJoCgOvv+4OO0qX8lKntxEpQuUqDFmsLJMOvy5pg7GlH8dgrfMLNTOp\noOGzDSiYkF3W+opqvqqBJSeq2l1rGy4Lfiori1O8D0LKvab+QvVeSodEf8IrTodvFM+O+hPrUX9i\nhtqbdNFu6hrNsPKRFsbt2Ccran+kflY17rly7y7uj7Q/ZuGa2XwU31sRw19RgaMpjMnkLlB5g+Lp\nMnQr25egR0hJSeyxn4djAf7U4pa8h8o1k4Txgc5yHIqF+F0/X0+M3Ph4PVjaQpsf198cwoQJwJ4F\nok2LxznjQW4Gv8O04g1zJRYDHn6Y130l5uGRMpJuxMZPoFPcENNl5Ur+7+Y5ZDkVkCJJmCiWLcvO\nSpuMsEdSL8BtGQKs/uKtwlI8n2V5ZI0pKhNeHnGKIWd4EuWYX1eAo8+N5G4RF0y8ZSo2vJMALhZi\nte8EUdltMryC3CXJ2U9w385jTwtjwcEFmHRIEY7qAt7+LtDdza3bFTmmdWBx3uDIMm53N+VLnJHb\nuvu33/hGbmUPJUvQh58IN+FkeT9+hPGINhbj5CGqVz65vLobG9ab2PJ5hrM/z/e9WVUBbNgtm/Kq\n2ePwrafrEJAu0pdnn+WJO2uuWohPdoYw69+vwmQM16MVFe0lwCNcIUSu3NlAHmz0PoYo1IKWRowb\n6P2gF5GKALSRNh4qrMf6LXAJ434JxGP1MdSfnJ0AOWkKw4tI0obF7khVsNfoU4yVoJRWPFEs46FI\nZpJs11+6YEQNPHPAM86ClPF5QpsJ8+kMK50hyRLEajSa/KOFcRUG34ko8WxDPV7aGAtwURTOGyhG\nggGHYBF+MMk5I8xVaDTV0dJHE5oIj6DAQYVIBPjXv/hE52aMw+V1UfwNdTi8tRBblabuF5udL+bO\n5VmCTRPYhTAGJ5XhX6jCsu4yXIipuE1OqnIr50Rh+b8Itdgci+PgCyqQGLTFxeZ2ehfz5vHte4hb\nCdv8sqnTgO9QQGTOpuZyhB770ObVwf/YOqsWX/5pIV4tzyx+OR3Srau9q8g5xli4E+78/SI0dITw\nCoBoTQTzHurGvPnA/INzO7+dBUdyDdbPL3bud12Cam0MOMfEPdurEQ5b7+WvfhXcuY3OUqxALx4P\n8Twh6lrMlKTd1X5zjM8cDtR+TZ1kJxjDb9GEeclye44gNg9EoC7R/FhLEy59sQnfaNiA0pc2gpVE\n8BryK7gBQJfIR7oLYYABf0AD+ltq8Ov1FTislmdwnzo1tzJkOBlZT0lQJAExwvJiLfZDtjeqVo7h\nbs0XNKNiaWrtW8+TPVlb3v0w5con9JlvXe7qlPiRQgQKMtOkVx0i1ipT5pxq+dUTo3g/T8I4U5JW\nBmxn0mg0afKRT+CmYvr8beepujG4Ap2Bl/3QQ8CKFTwzNrkc7t7Nt7kKac/PGIezbWsdqwpQcpcr\nm1OCT2BuboUNI7S008/RirIKhm9iMlihe1JSVASccw5w6KFDU6/584ENG6xlexI1cVyELrCwgYdQ\nbVs/NhguRyd+U9qG5RdWYMXF+ckUr+Jqo2peAtHb7C3JLTbg/Uk1+CTmYfu4UmxCBB/0jsWnMRPd\nMxnWxgPw9/Ug3XhhnsAt2BnNoUeGwBhw0yl9GNcVwYIFQ2DBUM/vM0vLZmnHZMTj+VOQ7d3LQ1MM\nRXCRk2nF7dQ0geXoBSbkp03lE+nJ4bP03Gibc3/qU8BJJzn3TRCZot8cX4eVmI+LLgKeempo68UY\ncC3asb6e98GmyTO4z81xiA2JJJXkDi+X1yLvjjy54RcIuVf1DpyoJOKnxLjZ0nZ5W0qrvjloomRG\nSc7JzFTCqoZRvEOy36CQoRh50jD8F2YhNja7JUom/2Iy2n/aLr3kSJERb+EK0kk3TsKCDVmu+5oC\n2V6EoiFXJZFGo8kOLYxnQdCCE7FggRX3Fw7zpD7Ll1ufc2FXPIrn4GEGEWNqu0gc1NUFvLQpWE3z\ncGCa1kSlVCSaLiwEHniAL9OyZAnw3e9mnkAnVxYvBjZtctYTCN7CeOyxwKmnBnvOVJAw7rtMisGw\nDAvx/gweLJ7rvd/WWo6PoxdmNISXUIqZM63M2UHy2GPAFVfwv90hKv5iTdBt6+c/D2ZJumxwLW0m\nJqhBt9t8Qv02NcuQEituZVcXB5gmdgyBJTUfTBcr+hmD3g+IwrJGYtI2L668ErjpJue+a64Btm2z\nPpeXI5D8CplASjwa14NSXhV2FGL+2vkyJIuEqWiBJSDmgwceAF5+GdhthNEPJpPaqg514746Dh03\n5JCN0I6aY4xitvPksbLkG3VgV0+TSlWZ+FFxRw/b3NRnryzOur+vO6EODac3ILFLvKuijUz+xWQs\neH8BQoUhRGvzk9yGRSzFIwCUjhJPGY1mpDHy/O/yxHYxWppgKTUUF18MPPlkfuvT1QV88pPW51wT\nUZFFlpAGE48M8eWjqEP+8EOgspKvy97RYQnow4n9/pIw09oK/O1vwZVx223BnStdQiHgUVRg/0Yf\nZU6IYTfCCIWBzZuzT16kTmjz7VpHS9V8EV3o7i7E+DXvJz1+jVGE5sEduP564IMP8lu3vEMTUSU/\nExNrNbe0DHmNsmbxYuDeewEmLE8kjMus6mQZp3CLPAk0+YbehxWYiEPn1qD+D6+7VzYwgK1bh0+5\nMxSEw7yPGS7X2/PPB04+ma/PfcghwAUXuMfhXIg1xLB3I9cwlRWLNh0zHNmyg6apiW+PGdONNW+b\n+L75HAAr2RhR2F6IwvYclx8AECoLoXSuc9lGFmaY/858RKrzoygrqgxh8RcqcSDr+mwAACAASURB\nVP93uNbcEsaVuHxK6BY3cOutuZfb/WA3EnsS2PLgFmx/anteV54BgMZzG1E8nQ/CpLJLlmNCo9Hk\nDy2MAzgcvRg/huE7W/6V/EDRKe+/P/+XL9TJw333cTfnXLj4YuC884A/HOW0aMkkqD7ujCOdSpEF\n+ze/Gd56+CGtdQw48MDhrUsuHHQQVzJ8CdPxuCpkU74CwxJ20l1324s9e5yfKZQj3zyGKkyghIo+\nk5YEGC4rmYYdmxPY0ppbkqbhQLpouhK1OT/vnV2N72Ii/tA2cuIMQyHggAOA1/8hBBcl9lNmVyc3\n9REqjBN3YSwOFnKMTDTIgPtQg+3NdYFkcB8JNDamPiYffPvbfLtqVf7KiFRG0PNUD6J1Ucx6JsUC\n1wGy1YhiE2xhEGITbw12DfdFmxcBAGJNMVQcWIE3LngD4fJw1i7hGaFcm0w8SpbxOOWaCKafKJnB\nX8iyeXlaj05hwjVWbIHpcmPTaDRDiRbGAXzt6gja2gB8XDEgiMQa5Ir1QawA5fnwg03BkiW5nyMS\n4cISXYuajbVqWRV6nujJvSBNWlB29SDWwd4X+Otfrb9lorY4BdkxvI4iGBO4ZJCra/OcOTyzNrXd\nww8H3nwzt3Omi9/qOnZ2IexKJjVSeGJMA57cUowp8PGnpflpURh3YuzQVSxAmEhsJZc4i/E/ZEI3\nco0KM/z5z8B++w11DYMjkQBeRRG2N/F3bzAawtcxCR8fmY8uKy64gK+jPlop6eZCXPG0LF2NskDm\nJBCfDfEOtV3ZhpZLWwIvb/7b3Box+8XZKGgbmjC6wX4TYbgNFbSEWVj0F9GikR/tOXcusArQS5tp\nNMPEyO9FAuALXwCOPJL/7Uzg5uyZftfcjtMxdNrnfPDWvCacgZkwxVxbumCFGEpmfkRMJfsAO3fy\n7dSp3Ko82vg7asGa+aTJDDH8F2ZjoIHHCJAiIls+8Qlg40a+fjQAdHYO/zrJVo46c8RYir14pLwe\n16I95aRsJHszJkqiWII+mUXdtVQRWcSLI1i2bOiWP8wHe/YA/43Z2NhejfMwDduObAFguRt/FAiH\nrb5CEwx/+hNfwm32/01G4Q+mYcx/j0H1kdUwIgbCxfmz8RRNLpKCf74Z7PfuyKUSLwJchk5E4yO4\nMxSEjRE8aGk0o4ARYRlnjB0C4FoAIQA/M03zW/kqK1mXtDMcwfoRmtCH2D4YxmqUorNjG7Zg5LiY\njjaOPpp7KlRXO63Ko4XL0YllMeD3GIslE7lVjjF+rblmEiZOPZXHYg4lFFawU0nikBDSa3SE56m+\n5Rbg/feBZz/h7adOVqKR3G/QM6RJtUGxn2QZDwH7YzEerxnZk+xjjwWWLeOxy2PGAHetq0RNDXDy\n53ILE9FoZOK/LssaX3/iEGdDzTN7W0vwAsoxIyGcJD1ix+9Hbc75fPYFEv20ftvw1kOj+aiyzwvj\njLEQgB8CWApgLYDHGGN3mqYZeCTWIIBN4RgqfXyHR/IElPjSl3i8+5j3Q9gCYGB8CV5AaeBrpmuS\nE49bmfJHG3ar6Q8wEUvEfK2jA1i4MLhyDAMYO4Tutg8+yGPAF98+Cx0dMUx5/30MitlLAgzXYTzi\nYXNEW1JbWvi/52W8pDNucsfkSvwT1Zg4gsMrxo4FZsywsiPHCpyx44xxr6iRPsmmJI5vvcXjpkn5\nEGQSMY1mtDLzwBi+vagb3aHNXBgXfSITKxRE4vyFytdSjENFdEwURVP2gcy2Gs1HmJHgpj4HwKum\nab5pmuZeALcCODwfBV08aT5Wn+ReaHE0KQsrK4GDDwbqjq/DvDXzMFgZx1mYOdzV0owSpk51Jy0L\nh7lMF6QgPhwsWsSFmtdRjF1hLqkNMksYvx1N+G2kGQ89BPznP8NZ09wxaOKp5HPrryvE1zDVlURv\nJFFSwlfDIDd1ivkMi8+UpXmkC+NEc7MtDl6j0aRFQwNXwIbitLyCsIhXRPAQqhAOA6+9NvLfrQXr\nFqBwosh8P5omuxrNCGIkdCNjAayxfX5H7Aucf62K4VvXW1k6GZxWodEEMxjijXHMnAnM1LK4JiCe\ne85auo0s5CPdcpCMTeE4BgFsZHwdWMaASZOCc8UfLh6ZMR4XYKpM+MgifKggQ/lRRwG//OVw1S4Y\nQnEDS9CHaJGwjBsmjyWP8mullRg0Gs1Hl3hXMb6OyWCNBbgAUzFoGLgQXQiHwRP/jiJMnzh5jUaT\nX0bCNHlYeodBACEx81xVXIGtGw0sXmwl3hotNDcDTzwx3LXQjEZGszAeCgFHYT7Gjglh7auDiBcZ\nwPaRbyUhPowV4N8owOkJvtZuooSbickFv7gYOOGE4apdMJBiIRRiOAsz8HA5b6iMjY6QJI1Gkzsz\nZxs4+ro6TOkCbnipGqWlQP3oCo8HAIy/ejyqD6/G6jNWD3dVNJqPHCNhmrwWgD33axO4ddzBJZdc\nIv/u6+tDX19f1gW+bJRgVzyK9l2bAQC/rZ+IFzcCie8A11yT9Wk1mo8UTU3A97438t3TVSIRYPFi\n4O9/j6EmBGxEGEKOG9Hu23aKRZz/+y2V2PP4FvRcNAaXrSnHL44fPe7bsRhQWMiVRS+gTCqPRotC\nRaPR5E4kAnz60/zvjg6+Xb9++OqTDx544AE8sO0B4MfAuvp1wLvDXSON5qMFM/dxEwBjLAzgZQAH\nAFgH4FEAx9kTuDHGzCCvY91aE5EI8Jexj6NkoB8XTO7FqlXaWqLRpMvg4MheEioZiQS3nhoGj5F/\n/nm+dNLZZ/PrvvTS4a5h7mzfzrOqX3cdcPXVo7vvSyR4W926Faip4dc+Gr05NBqNJh0YYzBNU0eQ\nazRDxD4/5TBNc4AxdhaAv4Ivbfa/+cikbqdhLO+DLqvqxnsbgMZ8FqbRjEJGqyAOOC2nJLRVVwNf\n+9rw1CcfFBfzf4ODw12T/GMYwIUXAkVFwO7dw10bjUaj0Wg0HyX2eWEcAEzT/AuAvwx1udsRwTaM\nbquQRqPJjrIyYPZs4M47ucvzaCSRGO4aDA1f//pw10Cj0Wg0Gs1HkREhjA8Xe/fy7Wmn8aVwNBqN\nhnjvPe4BMJq9AEZbtmCNRqPRaDSafYl9PmY8HYKOGSfKyngc4Si4RRqNRpMxiQRPSldQMNw10Wg0\nGs1QoGPGNZqhRVvGk3D66cDmzcNdC41GoxkeDEML4hqNRqPRaDT5QlvGNRqNRqPRaDQajbaMazRD\njF5RVaPRaDQajUaj0Wg0miFGC+MajUaj0Wg0Go1Go9EMMVoY12g0Go1Go9FoNBqNZojRwrhGo9Fo\nNBqNRqPRaDRDjBbGNRqNRqPRaDQajUajGWK0MK7RaDQajUaj0Wg0Gs0Qo4VxjUaj0Wg0Go1Go9Fo\nhhgtjGs0Go1Go9FoNBqNRjPEaGFco9FoNBqNRqPRaDSaIUYL4xqNRqPRaDQajUaj0QwxWhjXaDQa\njUaj0Wg0Go1miNHCuEaj0Wg0Go1Go9FoNEPMPi2MM8aOYYy9wBgbZIzNHO76aDQajUaj0Wg0Go1G\nEwT7tDAO4DkARwJ4cLgrsi/ywAMPDHcVNAGjn+noRj/f0Y9+xqMf/YxHN/r5ajSaoWSfFsZN03zJ\nNM3Vw12PfRU9YIw+9DMd3ejnO/rRz3j0o5/x6EY/X41GM5Ts08K4RqPRaDQajUaj0Wg0o5HwcFeA\nMXYPgHqPr75qmuZdQ10fjUaj0Wg0Go1Go9Fo8g0zTXO465ASxtj9AL5gmuaTPt/v+xeh0Wg0Go1G\no9Hs45imyYa7DhrNR4Vht4xngG/HoDsNjUaj0Wg0Go1Go9GMJPbpmHHG2JGMsTUA5gH4E2PsL8Nd\nJ41Go9FoNBqNRqPRaHJlRLipazQajUaj0Wg0Go1GM5rYpy3jownG2CBj7Cnbv+Ykxz7AGOtJcb4D\nGWOPM8aeFdsltu96GGPPMcZeYYx9z7Y/xhi7Tez/D2NsnO27b4nfPMcYOzbX6/0owBg7gjGWYIx1\nBHCuqxhjqxhjzzDGfs8YK7N99xXxzF5ijB1k2+/3nMcxxv4uznU/Y2xsrvX7qMIY2x7AOfS7ug8S\n5PtrO6d+1vsQjLFGxtgfGGOrGWOvMsauZYxFUvzmHMZYgc93vxL98HOMsf9ljIVt331fPMNnGGMz\nbPsPEb95hTH2Jdv+6Yyxh0VbuZMxVhLENX+UsM2rnmeMPc0YO5cxlnPYojjPC+JZ3mufrzHGThbt\naTVj7CTb/lbG2CPiOd9K7YwxVsEY+z9xrkcYY1NyrZ9GoxllmKap/w3BPwDbMjj2fgA9KY7pBlAv\n/p4C4B3bd48CmCP+/jOAQ8TfnwFwnfh7JYBbxd8fA/A3cOVMofh9yXDfs339H4DbANwJ4JIsfmso\nnw+kfQCuBHCl+LsTwNMAIgBaALwKy6PF7znfDuBE8fcSADcP970aqf8yeW+TnEO/q/vgv1zeX/2s\n9/1/4HlmHgVwsvhsAPgZgG+n+N0bAKp8vltm+/vXAM4Ufx8K4M/i77kA/iP+Dok+u0X04U8DmCy+\newzAIvH3qQAuG+57NtL+2ftnADUA7gnifQbQByAu/j7T9k5WAngNQLn49xqAMvHdbwAcK/7+sa1t\nXAXgIvF3B4B7h/u+6X/6n/63b/3TlvFhRFhKHhAWlLsZY/Yl3k4UGt/nGGOz1d+apvm0aZrvio8v\nAihgjEUYY2PAJ2yPiu9uBnCE+HsFgJvE378DcID4ezKAB03TTJimuRPAswAOCe5KRx+MsWLwSddZ\n4BNo2t/H2P9n777Do6jWB45/T3ohhRAIhNBBEBRBkKIo2BDUKxZQQaxg52e5XAvlShEVuwgiRSkq\nYr8UqYp0qdJBuvQWIKRn087vj9mZ7G520yu8n+fZJ7szZ2fP7mR35sz7nnPUCqXUr/ZoyOfmlXql\nVJJS6gOl1BaMcRAsWuvftNbZ9ofrgBj7/R7ATK11htb6EMaJXft89vPlwB/2+8vs2xBFpJTqrJSa\n6/B4nFLqUfv9Q0qp4Uqpv+wRrlxRVvmuVjz5fH897evblZG9stEeBc019abs6wrlJiBVaz0dwP77\n+jLwhFIqQCnlbf893m6PWg5QSv0fEA0sVUotcd2g1tpx3JoNgJl11AP7PtRarwPC7cfzdsB+rfUh\nrXUG8B05v8dNtNYr7fd/B+4rwfd+ydFaxwJPYXynse/f95VS6+379ymzrFLqNfvv9Ral1DtutrVM\na51mf+h4PL4NWKy1vqC1voDR+O9uP8bfCPxkLzcd5+PxUvt29wD1lVLVS/CtCyEqOWmMl51AlZOi\n/rM9vW0scJ/Wui0wFXjLXlYBgVrr1hhRkyn5bPs+4C/7wb42cMxh3XFyThhqA0cBtNaZQLxSKgLY\nCnRTSgUqpSIxDioxiLz0ABZqrY8AsUqpqx3WXYNxQtAcaATca18ehBExaaW1/jOPbT+BETkD48TQ\ncX8ew9iPrssd9/NWck7s7gFClFJVC/HeRN60/Wbej9Vat8GIhvwnn+fKd7ViyOv760gDWikVAEzA\niGa3BSLJ+R/wRPZ1+WoB/OW4QGudCBwBmmA03OoCV2mtrwJmaK3HAieALlrrm/HAnoLcF1hoXxSN\nfR/aOf5Ou1sOsFMpZTbMewF1CvsGhTOt9T+At1KqBtAPuKC1bodxUeRJpVR9pVR3jAtg7bTWrYD3\n8tlsP/I/HkfYX8u8oO56PL4XQCnVDqiHfI+FEA4q09RmlV2qvXENgFLqCoyThd/tgVNvjJMAME7y\nZgJorVcqpUKVUqFa6wTXjdr7H43GSHMuEq31b/bo+59ALLAGyM77WZe83sDH9vs/2h9vsj9eb49i\no5SaCXTCiHhl2f96pJQaAqRrrb8tRt3+A4xTSj0GrMA4McgqxvZE3n6x/91EzoWXXOS7WqHk9f11\npYBmwEGt9WH7spkYjTn3T5B9XRHkd7HkZuBzswGltY4rxLbHA8u11qsdlhW2r/ITwKdKqf9idJdI\nL+TzRd66AlcqpXraH4diXIS5GZhiRr7z2u9Kqb7A1RgZFZ7k9382GhijlNoMbAc2I8djIYQDaYyX\nHwXs1FpfW8DyuX7wlVIxGA2Bh+1XhMFoeDledY0h50rucYxIwAl7ZD5Ma30eQGv9NvC2fbszgD2F\nezuXDnvU6kbgCqWUxriQooFX7EUc95Ui5wQ6TWvt8cBtbzzfTk6aKhj7zDFiYu5Pd/v5OIDW+iT2\nyLg9Hfc+dxdyRIFl4pxF5Dq4k83+NwsPv6nyXa048vn+uu7rAPtf1++tx4aX7OsKYxfQ03GBUioU\n47Peby4q7EaVUsMw+pQ/6bDY0++0r8vyOvblZsrybfZtXoYxRoAoBqVUQyBLa33GHuQYoLX+zaXM\nbRRgvyulbgEGAzfYs1vA2M9dHIrVwegSdh6ja4KX/eKO4/E4EePCi7ndf4CDRXqDQoiLkqSpl589\nQHWlVAcw0t6UUs3t6xT2foxKqU4Y6U+Jjk9WSoUD84DXtNZrzOX2hliCUqq9vR/Tw8Bs++o5wKP2\n+z2BJfZteSmlqtnvtwRaYgwcJNzriTEoWn2tdQOtdV3gH6XU9fb17ezpcF4Y+3FVfhtUSnXDaAz0\ncOirBsY+e1Ap5aeUaoBxZX+9vV+q636eZd9WNftrAwwCviz+W76kHQaa2/dBOEZf1AKT72qFk9f3\n9xDO+/pmjIb4HqChyhnp/AHcXyCVfV1BaK2XAEFKqYfB6EMMfAhM1VqnYvT3fdq+HIeuPIkYUdRc\nlFL9MSKufVxWzQEesZfpgHHMPg1sBJrYjwd+GP83c+zlqtv/egFDMbq5iCKyf54TMLr/ASwCnrNf\n4EIpdZlSKghjvz+u7CPmu+vCpYzR8CcA/9Jan3VYtQjoqpQKtz/vVmCR/SL7UozuBmB8n83jcZh9\n36OUehIjo6LYs3QIIS4eEhkvO04nblrrdHv61KfKmMbKByNtcpe9bJpSapN9+ROuG8Pok9wIGGa/\nUg9wq/3A8RwwDSOCN19rbfZr+xL4Wim1DzgHPGhf7gessF9Jjgcecuj7JHJ7ECP1zNHPGKmu32MM\n7DMOaAz8obX+n71MXulsYzH2w2/2/bBGa/2c1nqXUuoHjP+LTOA5h+i6p/3cBXjHHvVbDjxfxPd5\nSbOfxNm01sfs+2AHxkjLntKZHfuSO5LvasXi6fv7oNb6eXf7WmudppR6DliolErG+I7Lvq747gHG\n21PBvTAulAy2r/sCuAzYppTKACZhpJ9PwtjPx930G/8c44LNGvt++VlrPUprPV8ZA/ztB5IxRkdH\na52plBqA0YjzBr7UWv9t31ZvpZT52/yz1npaCb/3S0GgPf3bF+P4+BU53U++wBjFfpP9AtgZ4G6t\n9SKlVCtgo1IqHeN/YqjLdt8DgoGf7Pv5sNb6bq11nFLqTYzvP8AI+0BuAK8B3ymlRmH8bpgXwS8H\nptuPxzsw+qALIYRFac9Zs0KIQlJKdQEGaq3/Vd51EcWjlLoKmKi17pBvYXHRU0oFa62T7fc/A/Zq\nrcfk8zQhhBBCCI8kTV2IkuUpOioqEaXUMxjzCLtGTMSl60llzIaxEyONeWJ5V0gIIYQQlZtExoUQ\nQgghhBBCiDImkXEhhBBCCCGEEKKMSWNcCCGEEEIIIYQoY9IYF0IIIYQQQgghypg0xoUQQgghhBBC\niDImjXEhhLhEKaWq2UcI36yUOqmUOma/n6iUGldKrzlAKfWY/f4ypVQbh3X1lVLblVJdHeqVqJTa\nbb8/TSkVrJSaqJTar5TaqJRaqpRqp5TyV0qtUErJcU0IIYQQlYJPeVdACCFE+dBanwNaAyilhgGJ\nWuuPSuv1lFIK6AdcY1YBN1MBaq0XA4vtz1kKDNRab7I//g44oLVubH9cH2iutbYppVYCdwO/lNZ7\nEEIIIYQoKRJBEEIIYVIASqkuSqm59vvDlVLT7VHnQ0qpe5VSHyiltimlFiilfOzl2tgj3RuVUguV\nUjXdbP86YLfWOrOI9WoEtMNh/net9SGt9Xz7wzlA70JuWwghhBCiXEhjXAghRH4aADcCdwHfAL9p\nrVsCqcAdSilfYCxwn9a6LTAVeMvNdjoBG4vw+mb0vAWwRWudK5putwW4tgjbF0IIIYQoc5KmLoQQ\nIi8aWKC1zlJK7QC8tNaL7Ou2A/WByzAayr8bmeh4AyfcbKsusMpl2+5eL6+6eF5ppKp7KaUCtNZp\neZUVQgghhChv0hgXQgiRn3QArXW2UirDYXk2xnFEATu11gWJSiuH++eACIfHEcDZPJ67C7hKKeWl\ntc7OY/t5NtqFEEIIISoCSVMXQgiRF5V/EfYA1ZVSHQCUUr5KqeZuyh0GHPuSLwP6Ojx+FPjD04to\nrQ9gpLmPsCpnjMB+u/2+P5CltbYVoM5CCCGEEOWqXBvjSqkpSqnTSqnteZT5VCm1Tym1VSnVuizr\nJ4QQlxjt8NfdfcgdddZa6wygJ/CuUmoLsBno6Gb7q4C2Do8nAYn23/ctQBDwQT517A9E2ac2247R\nP/20fV1rYE0+zxdCCCGEqBCU53FwyuDFlboeSAK+0lpf6Wb97cAArfXtSqn2wBitdYeyrqcQQoji\ns09ttglor7VOL4Xtvw1s0Fr/r6S3LYQQQghR0so1Mq61XgnE5VHkLmC6vew6IFwpFVUWdRNCCFGy\n7KOgTwYeKult21PUOwGzSnrbQgghhBCloaIP4FYbOOrw+BgQQ05KohBCiEpEaz2+lLZrA24ojW0L\nIYQQQpSGyjCAm+vgQTJKrhBCCCGEEEKISq2iR8aPA3UcHsfYlzlRSkkDXQghhBBCiGLSWhdkFg0h\nRAmo6JHxOcAjAPYpcy5ord2mqGutL7nbsGHDyr0OcpN9KjfZv3KTfXwp3WQfX9y3S33/CiHKVrlG\nxpVSM4HOQKRS6igwDPAF0FpP1FrPV0rdrpTaDyQDj5dfbYUQQgghhBBCiJJRro1xrXXvApQZUBZ1\nEUIIIYQQQgghykpFT1MXeejSpUt5V0GUMNmnFzfZvxc/2ccXP9nHFzfZv0KIsqQuhv4hSil9MbwP\nIYQQQgghyotSCi0DuAlRZir6aOpCCCGEEEIIYZGZlERl5O5ClzTGhRBCCCGEEJWKZMWKykQp9wkn\n0mdcCCGEEEIIIYQoY9IYF0IIIYQQQgghypg0xoUQQgghhBBCiDImjXEhhBBCCCGEuIQsW7aMOnXq\nFPn5gwYNYsyYMSVYo8qnfv36/PHHHwCMGzeO119/vdDbkAHchBBCCCGEEEIUSGxsLF9//TUHDhwo\n76qUK8dB2Z588kkaN27MwIEDqV69eoG3IZFxIYQQQgghhBAFMm3aNO644w78/f3LuyolJjMzs1jP\n9/f3p3v37nz11VeFep40xoUQQgghhBCihIwePZrGjRsTGhpKixYtmDVrlrVu2rRpdOrUiVdeeYWI\niAgaNmzIwoULrfUnTpzgrrvuolq1ajRp0oQvvvjCWjd8+HB69erFww8/TGhoKC1btmTfvn288847\nREVFUa9ePX777Ter/NSpU2nevDmhoaE0atSISZMmua3v+++/T8+ePZ2WvfDCC7z00ktuyy9cuJDO\nnTtbj5ctW0ZMTAzvv/8+NWrUIDo6mlmzZjF//nwuu+wyqlWrxujRo63yWmvrM4qMjOSBBx4gLi7O\nWt+rVy9q1apFeHg4nTt3ZteuXda6+fPn06JFC0JDQ4mJieHDDz/0uB/q16/P6NGjadGiBRERETzx\nxBPYbDanOr/33nvUqlWLfv365Vuvr7/+mnr16hEZGcnbb7+d6/W6dOnCvHnzPNbHHWmMCyGEEEII\nIUQJady4MatWrSIhIYFhw4bRt29fTp8+ba1fv349zZo149y5c7z66qv069fPWvfggw9St25dTp48\nyU8//cTgwYNZunSptf7XX3/lkUceIS4ujtatW3PrrbcCRiP+v//9L08//bRVNioqinnz5pGQkMDU\nqVN5+eWX2bx5c6769u3bl4ULFxIfHw8YUeLvv/+eRx991O372759O02bNnVadvr0aWw2GydPnmTk\nyJH079+fGTNmsHnzZlauXMnIkSM5fPgwAJ9++ilz5sxhxYoVnDx5kqpVq/L8889b27rjjjvYv38/\nsbGxXH311Tz00EPWun79+jFp0iQSEhLYuXMnN910U5774ttvv2Xx4sUcOHCAvXv3MmrUKKc6x8XF\nceTIESZOnJhnvXbt2sVzzz3HjBkzOHHiBOfOnePYsWNOr9WsWTO2bt2aZ31y0VpX+pvxNoQQQggh\nhBBFZT+nLvdz+/xuBTn3ZzglcisJrVq10rNnz9Zaaz116lTduHFja11ycrJWSunTp0/rI0eOaG9v\nb52UlGStHzRokH7ssce01loPGzZMd+3a1Vo3Z84cXaVKFZ2dna211johIUErpXR8fLzbetx99916\nzJgxWmutly5dqmNiYqx13bp105MnT9Zaaz137lzdokULj+/H19dX79mzx3q8dOlSHRgYmKse69ev\nt8q0adPG+gyaNWumlyxZYq07ceKE9vX11VlZWbleKy4uTiuldEJCgtZa67p16+qJEyd6fI+O6tev\nrydOnGg9nj9/vm7UqJFVZz8/P22z2az1l19+udt6ZWZm6hEjRujevXtb65KTk7Wfn59T+b1792pv\nb2+3dfH03ZIB3IQQQgghhBAXFT1Ml9trf/XVV3z88cccOnQIgKSkJM6dO2etr1mzpnU/KCjIKhMb\nG0tERATBwcHW+rp167Jx40brcY0aNaz7gYGBREZGWgOJBQYGWtsKDQ1lwYIFjBgxgn379pGdnU1K\nSgotW7Z0W+dHH32UCRMm0L9/f7755hsefvhhj++vatWqJCYmOi2rVq1arnpERUU51TUpKQmAw4cP\nc8899+DllZOk7ePjw+nTp6lRowZDhgzhp59+IjY2Fi8vL5RSnD17lpCQEH7++WdGjRrF66+/TsuW\nLRk9ejQdOnSge/furFq1CoBJkybRu3dvAKcR4+vWrcuJEyesx9WrV8fPHx4NGwAAIABJREFUz896\nfOjQIY/1OnnyJDExMdbyoKAgqlWr5vQZJCYmEhYW5vFzc0fS1IUQQgghhBCiBBw+fJinnnqKzz77\njPPnzxMXF8cVV1xhRvTzFB0dzfnz561GK8CRI0ecGoEFZbPZuO+++3j11Vc5c+YMcXFx3H777R7r\n0aNHD7Zt28aOHTuYN2+eU2q4q5YtW7Jnz55C18lUt25dFi5cSFxcnHVLSUmhVq1afPvtt8yZM4cl\nS5YQHx/PP//845gRQdu2bZk1axaxsbHcfffd3H///QAsWLCAxMREEhMTrYY4GJ+f4/3o6GjrseNo\n6HnVKzo6mlq1anH06FGrbEpKitMFFoC///6bVq1aFeqzkMa4EEIIIYQQQpSA5ORklFJERkaSnZ3N\n1KlT2bFjR4GeW6dOHa699loGDRqEzWZj27ZtTJkyhb59+xa6Hunp6aSnpxMZGYmXlxcLFixg8eLF\nHssHBgZy33330adPH9q3b5/nBYDbb7+d5cuXF7pOpmeeeYbBgwdbDeXY2FjmzJkDGFF9f39/IiIi\nSE5OZvDgwdbzMjIymDFjBvHx8Xh7exMSEoK3t7fH19FaM378eI4fP8758+d56623ePDBB4tUr549\ne/Lrr7+yevVq0tPTeeONN8jOznZ6/vLly+nevXuhPgtpjAshhBBCCCFECWjevDkDBw6kY8eO1KxZ\nkx07dtCpUydrvVIqV0TW8fHMmTM5dOgQ0dHR3HvvvYwcOdIapCy/5zo+DgkJ4dNPP+X+++8nIiKC\nmTNn0qNHjzyf++ijj7Jjx448U9QBHnnkEebPn09aWlq+9XDnxRdf5K677qJr166EhobSsWNH1q9f\nb227Xr161K5dmyuuuIKOHTs6beubb76hQYMGhIWFMWnSJGbMmOHxdZRS9OnTh65du9KoUSOaNGnC\n0KFDPdYxr3o1b96czz77jD59+hAdHU1ERIRTCnxaWhoLFizwOOidxzoWJGWiolNK6YvhfQghhBBC\nCFFelFJorT23oioIOfcvHUePHqVZs2acPn2aKlWq5Fl2yJAh1KhRgxdffLGMald4DRo04Msvv8x3\nxPWSMG7cOI4dO+Y0hZsjT98taYwLIYQQQgghpDF+CcvOzubf//43SUlJTnObV2Zl2RjPj6fvloym\nLoQQQgghhBCXqOTkZKKiomjQoAELFy4s7+pcUiQyLoQQQgghhJDIuBClxNN3SwZwE0IIIYQQlcr+\n8/sLNFWUEEJUZNIYF0IIIYQQFda+c/tyLWsytglrjq0ph9oIIUTJkca4EEIIIYSokE4mnuSycZe5\nXXc+9XwZ10YIIUqWDOAmhBBCCCEqpCyd5fw4OwtvL28A0jLT3D1FXCLymsdaiMpCGuNCCCGEEKJC\ny9bZeCkvfN704dTAU4A0xi9llWGQOSEKQtLUhRBCCCFEhZSZnQlAela6tSzeFg9IY1wIUflJY1wI\nIYQQQlRIGVkZANgybdayrGwjdf1M8plyqZMQQpQUaYwLIYQQQogKKSPb3hjPymmMZ+tsAIb8MYQL\naRfKpV5CCFESyrUxrpTqppTarZTap5R6zc36LkqpeKXUZvttaHnUUwghhBBClD3HyLgZEXdMWZcR\n1YUQlVm5NcaVUt7AOKAb0BzorZS63E3R5Vrr1vbbqDKtpBBCCCGEKDdmZDwjO8O6n5KRYq03U9Wn\nbp5KrQ9rlX0FhRCiGMozMt4O2K+1PqS1zgC+A3q4KSejJQohhBBCXILMyHhGVoZ1PzUz1Vo/ZfMU\nAJ6Y8wSnkk6VfQWFEKIYyrMxXhs46vD4mH2ZIw1cq5TaqpSar5RqXma1E0IIIYQQ5Sq/yPixhGPl\nUi8hhCgJ5TnPuC5AmU1AHa11ilKqOzALuKx0qyUuZWdTzhIZFFne1RBCCCEEOZHxzOzMnMh4Rio1\nq9TEW3mzYP8CAPy8/UjPSkdrjVKSVCmEqBzKszF+HKjj8LgORnTcorVOdLi/QCk1XikVobXONVrH\n8OHDrftdunShS5cuJV1fcZHbemorrSa2Qg8ryHUiIYQQQpQ2c57xjCznyLi/tz+BvoFgP1M0B3VL\nz0rH38e/XOpaHOdSzgFQLahamb7usmXLWLZsGZnZmdZnLYQoO+XZGN8INFFK1QdOAA8AvR0LKKWi\ngDNaa62Uagcodw1xcG6MC1EUMiKrEEIIUbE4pqmbjcXUzFT8vP0I8AmwyrWNbsvGExuNhnola4xn\nZWfR/ov2JKYncvo/p8v0tc0AVq8fe/HTrp/K9LWFEOXYZ1xrnQkMABYBu4DvtdZ/K6WeVko9bS/W\nE9iulNoCfAI8WD61FZeCLJ1V3lUQQgghKqVnf32Wn3f9XOLbdTuAW0Yqvt6+BPoEAqC1xpZpzEN+\nJP5IidehtN05804OxB2wRoYvD9IQF6J8lGdkHK31AmCBy7KJDvc/Az4r63qJS5M5f6kQQgghCmfC\nXxPYd34f9zW/r0S362kAN18vX6bdPY2m45qSkZ1BelY6NYJrEJcWV6KvXxbWHVtX3lUQQpST8hxN\nXYgKxYyMZ+vscq6JEEKIS9mG4xtIy0wr72oU2ppja0p8m+4i459v/Bw/bz8uq3YZof6hpGSkYMuy\nEeIXYkXIKxPHdHshxKWlwI1xpVSAUqpydcIRohBSM4x5S81BYIQQQojy0O6LdoxdN7a8q1FoV9S4\nosS36S4y3rFOR3y9fQEI9AkkNSMVW6aNUP9QbFmVrzEe6Bto3ddaBpEV4lLisTGulPJSSt2rlPpR\nKXUc+Ac4rJQ6rpT6SSl1j5K5I8RFZMupLQCV8qq6EEKIi0tljIx3rte5xLdpfg6OkfE/j/6Jj5fR\n0zLQN5DUzFTSs9IJ8Q+pdBfU95/fz8G4g9ZjxznUXaVnpfP8vOdLPINPRlEXovzkFRlfBrQBPgAa\naq1raa1rAg3ty64Blpd6DYUoI6NWjgKolFfVhRBCXFzeXf1ueVeh0P658E+JbzMu1egD7hgZP5V0\nipWHVwIQ5BtkpamH+odWugvqTcY2cXqcYEvwWLb+J/UZv3F8iQ9SN2rFqBLdnhCi4PIawO1WrXWu\nXzT7srXA2sqStj59y3RWHVnF5Lsml3dVRCVQGaMRQgghLg6rjqwCIDkjGa01lSkJsTRG5DanHXWM\njANojHRuxzT1EL+QSnVB3TEibkpMT6QWtdyWP5l0Eij5SPaI5SNKdHtCiILzGBl3bIgrpaoqpa5S\nSl1t3lzLVGRfbv6SLzZ/Ud7VEBXcAy0eACRNXQghRPlxjIwu2L8gj5KXBqsx7hAZdxToG0hKRgoZ\n2RlU8atSqY7hO8/sdHrspbxItCV6LN+8enMAktOTS6U+ft5+pbJdIYRn+Q7gppR6E9gGfAp86HCr\nNFYeWVneVRCVgNn/rDJdVRdCCHFx0VoTHhAOwJqjJT86eWn4ceeP1n1z/JXiWnVkFWqEYsqWKVQL\nrJYrMm4K8g0i3haPr5cvAT4BlarPeJBvkNPjOqF1SEz33Bi/ssaVACSlJxXrdc8knyE+LR6AYwnH\nrOWV6bMT4mJRkNHUHwAaaa07a61vNG+lXbHScDzheHlXQVRgZiNc0tQrhmFLh3Ey8WR5V0MIIcrU\nI7Me4ULaBV659hWq+FUp7+oUyP0/3W/d/2rrVyWyTcdGfVSVKM+RcZ9ALqRdwN/HH39v/0p1Qd11\nsLbqwdWJTY71WP77nd8D5NlgL4iGYxrSfUZ3wLkxLoQoewVpjO8EqpZ2RcrCudRz5V0FUYGZjfDK\nlOJ2MRu5YiQ/7vox/4JCCHEReaLVE3Sq24lQ/9BiN7rKg7fyzrXs1d9eLXRAxDEKXiO4Br/u/ZWz\nKWfpGNPRqVygr9EY9/P2w9/Hv1Idwx0b41HBUWw8sdHpwoajuXvmAkYqeXEj48kZydac8IOXDAYg\n/vX4Ym1TCFE0BWmMvw1sVkotVkrNtd/mlHbFSlL72u0B9wcIIUxWY7wSXVW/2L248MXyroIQQpSp\nLzd/ScOqDQn1D81zZO2Kytsr97nW+3++T4/vehRqO0OXDrXu+3n7sejAIt5b/R71w+vzwa0f8EK7\nFwAI8gkyIuPelS8ynpqZat0/9Z9TeZa967u7AOjVvFee/coLa2es0W891D+U6+pcV2LbFUIUTF6j\nqZu+AkYDOwBzYkNdajUqBeuOrwNkHsXKaMSyEXSI6cBtjW8r9ddKy0wj2De4Ul1VF0IIcXGJS4vj\nq61f0aVeF+JtlS9a6Rr40No4Zfzr5F8F3saxhGNOUePFBxYb20Lj6+3LwGsHWuscI+N+3n4kpifS\nYnwLel/Rm6E3DM217YrENU19Wo9p/LDrhzyfExUcxdmUs8V+7dohtQEIDwjnTPIZwP2FFCFE6SpI\nZDxJa/2p1voPrfUy+61Szi8ufYErn+HLh9NtRrcyaSCnZaYR6h8q/ydCCCHKXaBvYKU7Hr13y3u5\nItPbTm/L8znnU8/TZlIbp2VmRsCmpzahh2mr4Xgw7iC+Xr5OZZ36jNvT1HfF7mLh/oXFfTulzrUx\n3qBqA+LT4snKznJ73hMZFElUlSir8Vwc2dqIrz119VPWbDIrDq8o9naFEIVTkMb4SqXUO0qpjq5T\nm1U2rge1lIwU9p7bW061EYXRf25/Xv/99VJ9DVumjfCA8EqV4naxcx1pVgghLgVf/OsL/Lz93I4e\nXhFdFXUVr1/3ep6p9X2u7ON2+dH4o2w6uclpmS3TxlVRV9G6VmsAa3R5IFdjPMjXfZq6u8HeKprU\njFSGXj+UtCHG+WmN4BrEpsQyeMlgAt8KtMqZDedn2z5LiF9IscYSMM+Fzb+Z2ZnUDatb5O0JIYqn\nII3xq4EOGH3HK+XUZgDVAqsRm2KMUPnlpi85m3KW4cuG03Rc03KumSiIb7Z9w7ur37Uen0g8wYHz\nB0r0NdIy0wgLCJM09QokOiS6vKsghBBlql5YPW5scCN+3n6VZqopL+VFrxa9CPUPZfKmyagRitGr\nRgPQ5xejEe7pwoLZ0DT/gjHAWLBfsPV4/kPzrfu+3i6RcdcB3OyNcceuib8d+I3TSaeL8xZLRUpG\nCoG+gfj7+ANQPag6Z5LP8N6f76EdeoSa84oPuX4IwX7BJGcUfZ7xiRsnAkZ3iJOJJ7Fl2fD3Nl6/\na6OuRd6uEKJo8u0zrrXuUgb1KFVBvkHc3+J+9p3bBxhR1tiUWOvq7dH4o9QJq1OeVRQFNGXzFGbv\nmc0f//xBUnoSeljJDV+QlplGmH9YpUsLvJg5npwJIcTFTmvN4fjDhPmHVZrG+JnkM5xLPUeAT4BT\nA3rQkkG83ul1dsXuAmDzqc1un282nm2ZNgJ9jWhwcnoywb4523KM3HpKUw/0DXT6zBwb/12/6UpM\naAxHXz5anLda4pIzkqlZpab1uGpgVbcXLeJt8dQOqY2/jz9V/KpYjfOieGnRS9b9uXvnOn3ui/ou\nQj2sirxtIUTheYyMK6UeU0p5bKwrpfyUUo+XTrVKTkZWBrZMG80im3Ew7qC1fNCSQUz8y7g6WPcT\nSc+pLD5Z+wlz9swp9rQerjKyMjiacNSIjEuaernLys4CPEdShBDiYnQqyRhRO9Q/FF8v30rRGI/6\nIIoj8UcI8AlwO7DYXU2NUcD3n9/vNjptZqM5XghPsCUQFhDmVK5n855A7si4mabu5+1npKlnuk9T\nb12zdWHfWqlLSk9ymkveS3m5jXon2BII9Q8FINi3eJFxR9UCqzlFxoUQZS+vNPUqwAal1Eyl1ECl\nVB+l1EP2+zOBdUBgHs8vU/Fp8agRyhq102T+gEUERnj88aqIP9DCve1ntpf8Nk9v57HZjwFQNaCq\npKlXAOYFEcdpX4QQ4mKXkZ1BeEA4vt6+TlHeuNQ4XlxgTPU4e/fsXH2sPfl2+7d0mtKp1OrrKMAn\ngH/i/nFaNnfPXGqH1GZ45+EA1PywZq7nufZhBiMSHOoX6lSuX+t+gJvIuG8g/1z4h1NJp5zS1M2L\nuc0/aw4Y6f8VjWs6vivzPTg1xv2CCxyQOJ5w3Om82PyMqwZUpX3t9qRnpWPLtFlp8kKIsuexMa61\nHofRX/wzwBfoBFyHkdo+Drhaaz2+LCpZEGbK+YnEE07LM7Iz8PX2Jdg3mJSMFLcNrS71u5RFFYWD\ne76/h5OJJ/Mso0aUTarU9zu/59vt3wLGSKUSGS9/SelJ+Hv7k5ohjXEhxKUjLTONyKBIwJhb24zu\nrjqyik/XfwrA3d/fnWv0cdP4DeOdjp0P/fIQq4+uLpMsowCfAKoHV3da9uLCF/l84+f8dvA3j89z\n1xh/cu6TudLaG0c0BnDqSw052QS7z+7G39vf2s6BOGNcmb/P/g1QIY/trpFxV2YQ6ZO1n1jT9Ib5\nh3HowqECBQ5iPo5x+l95bt5zAKx+YjXrjq9j1p5ZEhkXopzlOYCbNqzSWo/WWj9nv72rtV6tXUPQ\n5cz8wfr575+dlmdkZeDn7UewXzAJtgRWH13ttH545+G5ppYQpWf6lun88c8fzNo9i+iPosuswZ0X\nxzlRfb18eWPpG+VYGwHGxbWaVWqSkpGSK9tFCCEuVmmZaQT4BAA4RcbN8TPym1/6+fnPu10+6a9J\nha7LqaRT7Dizo8DlqwZU5ak2Tzn17zYDJC2qt/D4PHeNccjdxzwiMAKAjSc2Oi1/vFVOj8kQ/xAS\nbTkjjf9+8Pdcr1ORuPaNd1Q7pLb1Xrae3motrxFcgxOJJ3h23rN5btvMCNhyaou1zPxMo6pEMbLL\nSP6O/ZvJmyZLZFyIclSQ0dQrBXMwC8cfYYD0rHR8vXwJ9Alk6aGluabHqhder8T7H1dGLca3YNqW\naaWy7a5fd6XtpLZk62wem/0YN391s9P6/E4urO04jPL5TJtnSuxKrrdXTmM8wZaARvPn0T9LZNui\naBJsCUQERuDj5VMp+kwKIURJ8NQYNyPkR+KPFGp7915+L1C0aSIHzB/AlZ9fmW+5ro26Mr/PfJRS\nBPgEsP3Z7dze5HbAiEZfV+c6+rbs6/H5ZsTajGCvPLwSgFevfdWpnNkYX3xgsdPyakHVrPXhAeHE\n2+LxUsbp7Tur3rHKXUi7kO97KWvuIuOvXPsKYGTqOc4nbn6G5jRvU7dM9bjdhfsXWp+nRluZEWbD\nvGpAVRYeWGh1/ZPIuBDl5+JpjNsj40OXDmXnmZ3W8oxsIzJu2nBig9PzwgPCpTEO7IrdxSdrPylw\n+azsrFwXPjz57eBv/HXyL77Y9IXb9WuPrc21zF00dFHfRehhGj1MM/LGkdiybMSnxRe4zp44NvbG\nbzR6XgyYP6DY2xVFF58WT6h/KIG+gdJvXAhxyfh84+dW5NfX25fdZ3cDRuMKnKfrKkia8tmUszSt\n1rRIKdpxaXEFKufa5zjUP5R5feYx4JoBXBN9DamZqdZo3e6YEWtz4E4zvXzUTaPcln/tutdyLVvX\nfx1r+60lzD+MC2kXqBpQFchJYb+5wc3M3TuXpf8sLdB7KiuJ6Ym5GuODrx/MkkeWsPX0VtpObgsY\nKfjm/0V+UeyMrAy6z+jutGzB/gVOj5VS3NLgFuuxnAcLUX4unsa4wzQPV3x+hXU/PSsdX29f2kTn\n7l815PohBPsWfCCMi51jGlR+fN70IXR0qNVXvyA8RZvNA7Ajx64Dh148xJp+a5zWm/3SvtvxXYFf\n35NAn5yTBPOkwNMULADnUs6RaEtk44mNFSLN/mKUYEsgxD+EQJ9A6TcuxCUuPi2ex2dX+MlbSoRj\nhprCOL7YMm18uflLAP679L/WescUbFfmcTUuNY5aIbWcUrTTs9ILdOwsSBehQxcOsfzwcqeghyky\nKJINJzaw6eQm/Lz9+KHnDwC0/6K9UzmzbuZxv3pQdWJCY3KNmg6QNCjJbSO9Xe12NKnWhPCAcE4l\nneJc6jmGdx5uTav20W0fAZTr/9GB8wdQIxTtJrezlh2NP+o0tRkYQaKbGtzktOyupncx+ubRBXqd\nVUdW5Vr214m/ACMibu6HF9q/YK0vSuaEEKJk5NsYV0oF2EdRH6KUGma/VbhOtZ5GSjf7jLv7oRl1\n0yiCfIOkz3gxjFg2Is/1xxOOW/enb52ea33fln2Jt+WObh9PNJ73fc/vqRdejw4xHdxuv1FEo8JU\n160awTWs+8+1fS7f8pHvR/Kvmf+i87TOxX5t4V5ieiKh/qHy/RTiEqe15s0Vb5ZaN6qKyGycNYpo\nRKh/KKeTc6YDc0zRNtOz3TFTslMyUnLNEjJz+0x6/9yb9cfX51kP14HS3GkwpgEAV9bInc4+6PpB\n1v0awTXo1aIXAOuPr+fHnT9a68zGeGK6kW2XkZ3B1bWudvt6wX7BTl3LXDme6zlegDAj5YfjD+f9\nhkqROWbRhhMbWHtsLZnZmdiybFbauauOMR2t+3P2zOFkUs6gtxPvNKbmdcyUMM3YPsO6bxtq7PeR\nK0by9daviUuL44oaRsDKMSL/UMuHivq2hBDFVJDI+GzgLiADSLLfSmaCwxLkGBl3ZPYZB7i2zrUA\n1Amtgx5mHGR8vX1zzUV5qYoKjnK7PCMrw+MV8vz6YL298m3AiD6b/b3Mq/2fdvuUMP8wt6nmyw8t\nB+D+Fvd73Ha3xt1KpD+xLcuGj5cPewfs5Y3ObxDkG5TvFCjLDy+3GonurkKL4kmwJRDqJ2nqQlxM\n1h1bhxqh+GnXT7nWeRo7JMGWwIdrPgTcZ1FdTMx+vcdePmYta1qtqdPMI7Wq1KJRVeMidMcvO/Lu\nqnetdYcuHLLuN/y0IQBHE44SHhDu1DA1z3nyy2wrzOCZ7gIejtFy1+jv/T/dz7Qt0zifet7KTjT/\nZmZn5pq+rKCUyslWG9Aup7uZ45zlv+79tUjbLi7H86WOX3bkYNxBsnW2U50dmen451PPA87768mr\nnwTcZ0eYWRRg7IPqQUYm4SOzHgGMQe4gJ919bu+5RXtDQogSUZDGeG2t9QNa6/e01h+at1KvWSF5\njIzbpzaDnLkpHfsu+Xr5lsmUH+Vp66mCpZ976lPmN8qPH3flXMU+cP6AdX/Klil59lszr7x+cdcX\n1gEle1g2epjm/9r/H2H+YVYamaOnfn0q3/r+efRPZu+enW+5/JxJPsOQ64fQpFoToqpEsbbfWg7H\nH2byX5NzlXU3Gmtscmyx6yCcJdoSndLULxt7GcsOLSvvagkhiiAjKwM1QvHkXKMB0evHXk7rt57a\nSvX3q7t7qtPFcp83fUqvkhVAYnoi4QHhTpHfmlVqWlNagRFhNvtUA7y+5HVajDdGKl9ycIm1PMGW\nwIrDK0jPSqdmlZpO/b/Ni9ifb/w8z/qYWWOuU8a64+NV+H3z+OzHqfZeNWvATnMcmoysjCJtz1Xt\n0NoAJLyeQIhfCP1b9wfgXzP/VextF4Vr8KLpuKZ5ljc/g2rvGRkQjhcXzAa8GdxwVTWgqhV0ahnV\n0mmdOXUeQPYb2dx52Z0Fqb4QopQUpDH+p1KqZf7FypcZGfdW3k7TRJhp6mBEM8HoW2Qqy8i4LdPG\numPr8i9Ygo7EH6HVxFa5lh+NP5orypBXlNlsgP9n8X9oPLax07q8Dih/HPqDJhFNuKHeDe63G3eA\nCX9NsKZtKYwEWwKTNhV+uhZ32wnzz7lqbkZoHC8INBvXDDVC8dGaj3I9XzIrSt7rS15n7PqxVpr6\nvvP7KtzAO0KIgjHTj82Rm105pmG7Ohp/1OlxfhfPtdaVdjrERFsiIX4hTstqVanFiwtfBOD6utdb\nY7s4Rp3NC9pP//o0YAxmdnnk5cSlGg3wmlVqOmUPmheVf/n7l1x1OJtylvZftGfxgcXWqO4FGazV\nU3T3zyf+5OALB52WXV/3eqfHY9aNITok2jky7qa/eEF9dfdXrOtvnGslDUoixD8EpRS3Nb6tyNss\nruT0ZIYtG1ao57gOOOw4vo2p35x+To/N4Itjmv+3933rVMbcr+B5vwkhyk5BGuPXA38ppfYqpbbb\nb9tKu2KFlZyRzBs3vMGWZ7ZQLzwnxdgxTd30coeXrftlGRn/fOPndPjSfd/n0lL/k/qAc3qf1pq6\nn9TNFWXIqzFuThNipgsCvHXTW0DefbA2ntjIvvP7iAmNcZsGb/ZZcz3Y92vdz+oT5cmYbmPyXF9Q\nZhTWdF3d6wDnFPk95/YAMOSPIU7PbRDewGnqkYoqPSudFYdXlHc1CuX+Fvc7pakX5YKNEKVt+aHl\nJTKrw8Usv8ZcXuNCuE7l5W6MEUfvrHqnxCLoaZlpZZo5l5jufCwC537hK48YU34NvX4otza81anc\ngfMHCPUPBYyoqC3Lxt3f3w0YGWqZOqdv8cDFAz3WYf6++aw/vp7bvrnN+u31lHlYEB3rdKRB1QZO\ny8yUdsfpSrXWTn3GixMZf/iqh62gS7BfTnCmU91O1n13GXmlyezv/WHXgieWumYdums4m+PrmMzg\ni2MDvEZwDYZ1Ni4EJA+ucL1MhbjkFaQx3h1oAtwK3An8C6MPeYWSnJ5MsF8wAT4BufpGuY7y2Syy\nmXW/LCPj5lXqsmLLtFkDsJgHOcBj49HdQCAmszHuaPD1gxnbfSy9mvdy8wzDPc3uYVqPaQDM6T2H\nRX0XOa1f+bhxcvH1tq+dlidnJOea7sNVt8bdgML1a3MnKcN5nk8/bz+iQ6L5Yacx4qinPuEf3/Yx\nj171aKVojA+YP4DO0zpXioiRWceWNVo6jaaepS/u/qImc6R+UbEt/WcpqRmpdJnehQELZCrEvOQ3\nY4m53vW3NMGWYDUoTfkdRzed3FRiF+7qfFynQF2mSkpcapxTlhY4R8DfuMEYO9fHy4feV/R2Ktfu\ni3b0ubIPH3b9kGC/YA7G5USjvZW30wV5x8i063H/0VmPWvfNvv2OI7gXV6/mveh/tZEufiT+CE2r\nNbXqYf4fnEs5VyqzaDj2Wy/rVHWzP/+VNa4k/vV4a4aYbc94jm259rN3VSe0DoDTvjY5DkwLMKjT\nIHY9t0tGTReiAsq3Ma61PgSEYzTA/wWE2ZdVKMkZyQT7BuOtvDnv1eUvAAAgAElEQVQYd9A6od99\ndrfVv2pwp8GAc9+msoyMm1dGCzMdWHG8vCgnA2DHmR3Wfde5Qx2nHDP7ho1bP46fd/3Mvxf9GzAO\nlI0+zT1yef3w+vy460ePB87M7Exr4JR2tds5XQmHnD5d51LOOS3/bsd3eV4cALis2mV4KS+n/nNF\nkZSelKvhb079seXUFq6fer27p7H77G6iQ6I5lnDM7fqKZO+5vQCVYjA0s46PtnqUIN+gSy4y3n9u\nf66ZfA1qhOLh/z1cqq81f998p9+G8jB189Ryr0NR3PTVTUS8ZwxK+c22bwo05/OlauJfeWc5mf/n\n5rgipr6/9M1VtvfPvXMtc+QaKSyOsylnWXF4RZkNHHcw7iANqzZ0WjZiec6MJWaU19/Hn1sa3sJd\nTY24SIeYDjzY4kFOJZ2ielB1K5356lpXM/rm0fh4+ThdzKxZpSYf3/YxYIzSnZ/5++Z7XBceEM6p\ngacK+A7hh14/0LN5T8A439g9wJhHvWlkU6sx/urvr/L9zu8LvM3COPMf44KPawN2z9k9TuPhlLTh\ny4YTExrDrY1uJdQ/1ApwXBmVexR6k3nRArDS7h2Z2RHuzs1c+fv4c3n1ywtbbSFEGSjI1GYvAt8A\n1YEo4Bul1At5P6tglFLdlFK7lVL7lFKveSjzqX39VqVUa0/bSs4wIuPm1cDdZ40f+Nd+f81KRzIj\n4k6NcW9fjiceL/UT/WWHljF5kzEg2LbTOVdCPb3u6iOr822M5uV4wnGnwVkcG5SuI8+b/YcaRzTm\nVJJxUP2/Bf9Hzx978vFa44A9+I/B1sGr9xW92fikEbkzP0uzEX8w7qAVSd59djdz987Nd2RycO7H\nbzqd5LkfoSlbZ9NkbJN8y+XFXWPcnEqt9cTc/3Jf3mWMVOrv7U9kUKR1Avnxmo+ZsHFCsepSWszx\nEvKLUFUE8WnxRAVHERkUSaBPoHXxqiiRktjkWF5Z/EpJV7FUmRkZYDTyStMd395By8/Ld0iQJ+Y8\nwZWfez4hrcgcs7AC3gpg++mcPtFj1o4hW2czfNlw1IhLu1/m2PVjrftmX1bzgrljCrvjiOp7zu5h\n7l5jlGfHqS3/OvmX9fxu33Tj8w3Og5CtPbY2z7pkZmcS+V5knmUcHYw7iM+bPk5TcZWW44nHqR1S\n22nZkOuH5CoXGRRJVJUoZj84Gz1M83KHlxm/cTw///0z0SHRVqr7ppObiKoSxcYTG/l2e07ackZ2\nhnVcduw/XFhaay6kXchzijVPlj+23GoYr+23lhfaveCUwVdaqgdX5+t7vua+y+9zWt7ss2Y0HtuY\nZ399tlRed/XR1U4X7ltGteTNG9/M8znmxZdRN45ye470+Z15D8AnhKgcCpKm3h9or7V+Q2v9X6AD\n8GRxX1gp5Q2MA7oBzYHeSqnLXcrcDjTWWjcBngI8/vJ8tfUrgn2DrR+v5uOb50rJNee4dEy5NlPA\nPE2NVhL2nN1D169zIsLXT72em6bfRGZ2Jt4jvXO9dkZWBp2mdipWI+KNpc5TwTsO/OHY/ysjK4Os\n7CzaRrflsmqXFWjU1Ck9ptAmug2Qc0JlPu/x2Y9bDX8zLSu/ucBva3SbU18o80LKrY1u9fQUy431\nbwQKNsCMJ+4GzXE3j+kjVz3C/v/bb0Uu3uj8BqH+oVZj8d+L/82z84p/IN9xZge+bxZ98Jq8VNTG\n+K7YXVbDJsGWYPV9DPQNtD7TY4mFz0D47eBvfLDmAz5d92nJVbaUXEi74LbRVtp9GzW6QmQdlEZa\namnxdKFw7t65/LDzBxbsW8BLi14i0ZboFNmsbLac2mJNM1lS3r/1fSDn+OA4zZnjzBSO/chD/UM5\n8EJO1HLHmR1cSLvAogOLeG7+c4V6/aT0JM6lniv0//y+8/sKVb4oTiSesLLFTKNuGsXvD//O022e\nti7Qd6nfxamMmeoNRh9zx4BDmH+YdTHWlJqRSoBPAA9e8WCuEb47xHRg1ePGBfU7L7uTZ9o849TX\n2tG8ffOAoo2kfkO9G6yptdrHtCciMMLp+PRAiwcKvc2Cig6J5lzqObfrJvw1gUl/FX9gWEeL9i/K\ntSzAJ4ChNwzN97l6mGbIDbkvyIBx/tq9cXfAGBfGPHc6OfCk2/JCiIqpII1xgGwP94ujHbBfa31I\na50BfAf0cClzFzAdQGu9DghXSrmfDBvngToAvEZ6OfW3CvAJ4NCLh5zKRARGEB4QTlJ6EttObyt2\nuuT8ffMZtjRnxMy1x9bS7LNmufqlLz201Dr5jLfF4z/K32qMmGm5m09tLlIdbJk20rJyIjYvd3jZ\nKbLg2Pg/nXyazOxMfLx8qFWlltvGeN+WzqmCjlfSb2t8G22j21onVmYj6kzyGWsuT3OZJ4sOLKL/\nnJx0LLPbQKuauUeBd2VeWf5x149FGqn+TPIZtpzakm//dIDpd0+nUUQjutTvQvYb2VQLqubUGC8p\nB+MOkpmdWSJzqLuqiI3x+LR4WoxvQeBbxgUjp8a4w0UkdyP/5seMVL608KUSqGnpajimodvlLca3\nKJGGalpmmlN3EMeLla0m5P9dK2lH448yetVo63F+0yxVJDU/zOnLWT+8vnV/yB9DeOCnB7j929sB\n6PNLH2udGqEKHSFPzUgt13EeWk9sTZfpXTyudx3pPD93N7ubzvU6A7DmmNFn1hyULNAnkNiUnMa4\nuRzglWtfcUrfvvLzK932k3Xk6XMzfwPzGjTO3fNL+2JRSkaK28g4wM0Nb2bCnRN4pu0zfHffdzSO\ncJ7RxHHqKvN++9rtAeP/85NunwBGn/QlB5ew6MAiwgLCqBpQNVcf/ERbIqH+oTzQ4gEeuvIhJvw1\nweO4KeEB4UV/wy6q+FUh0ZZoXXDIL2pcHJFBkZxNOUu2zsZ/lD/vr37fab05Kj3A7N2z+XjNx0V+\nLa013WYY49v82KvksyteaG8kqvqP8ufyz4x4Vn59zYUQFUtBGuNTgXVKqeFKqRHAWmBKCbx2bcDx\nSH7Mviy/MjGeNug4pZkpPSud167LyYB3HGndFBkUSWJ6Im0mteHKz69k5vaZjFs/riDvIZe3V77N\nyBUj+WjNR0S8G8HPu352Wu/YKA4dbTQ4rptyHelZ6dbJgfn3ujrXFfr1E2wJPDvvWSslbca9M3i4\n5cNOffGS0pO47/L7aBLRhOT0ZGvk0uiQaE4m5r6i+uhVj+ZaZvJSXlxX5zrOpZ5j8YHFVgN84OKB\nfLbhswLX27GfX4ItgY4xHQv0PHPk835z+hVppPpWE1qh0QVqjDsyI/mh/qHsP7+/0K+bF7OrQEH6\n8uVn2pZpXDXhKqoFViPQJ7BCNsbHbxjv9HjK5ilWKqrrYDOFrf/o1UZjzxzIsCJzl41hCno7iBHL\nRnDVhKs4l3KOzSc3F7qRFvhWIJHvR6JGKM4kn7HGEQDPU045+t/f/3N6TnHV/aQug5YMsh5Xxgjy\n7Adn88+L/7DjWfcXcd31tT2ddJqUjBR+P/h7ng3KYwnHCHo7yCm9uLT9uPNHtwNbdf26K7HJsU4X\nCM1ZOQoz5eD/Hvgf3l7e3NHkDuuC29QtUwGjm5Rjmvo7q94BYFHfRdzS8BYApyky205um2v7jinq\nrv3PTXU+Nga9CnknxKmbgStblg0/bz8SXk9g/O3j85x+rbi01gS/Hcys3bNyRcYdRQRG8MAVuSPG\njpllZuafObBq61qtiQ6JBuDd1e9yy9fGZxnoE2g0xl3GkEmwJRDiH8J3Pb/jwSseZGz3sVYdXWVk\nZXBN9DWFeasehfiHkJSeZGVHFCd9Pj+RQZHsOLODfnP6kZ6Vzqu/v5qrTGZ2JhlZGbz6+6v8e/G/\nmb5lOt9s+6bQF/3Nbkc1q9S0+sqXpJsb3Fzi2xRClK18c4u01h8ppZYDnQANPKa1LlrI1mXTBSzn\nGkpw+7x1k+Dy/z0LflXYctqfZbVsvGRk7/DRmo8YfYs9AvPnn/CSc5Tsf2dOELZ1GD4NfMjMzuTZ\nec8Sb4tnQLsBbssD0LEjjHGeWutC2gWyV69m3UJg0kAWAvABvYA1deCl7sbAYM3HN7caFR2PwCeT\nDgEQMKsz+AZSNcvGJ75wqL2bK/f51OfOb+90iijceiqY0MceY9rZ3TDF6HPUJeUsYfX9efVOYw7n\n00mnCfMPw8/bj/nT/8vjz0xgnUPW9zXzBrOt4Y20vHwpepjLx//nn7z4wrfWAdQ8TG2s9z3fuJvS\n00391x03Ph/sCQUvLnyRv8/+XaD3W9jPx1X9XSeZtRBq/toDHEeM79gRIowD6LGXj2HLsrndfoOs\nDBafiifh0ONQq2DvN7/6t+r1NOuAhj++BkHvFfn9Pn5TAtO2TAOM95GamkrSssUwtvQ+z6KUf/jJ\ncdxs/3/T89oxPPk0t9Y2rrkF+hqRcTMD4cIfC6jyxvu5t+Nh+x2PwCcL7Q/mtcu3fFm8X7N8ls7G\n2/4/l9L2Ks5G5TRExt8+nmydzdofPuL/ZtobbJOG0x048GYka+pA+k9rSUpP4uaGN7vdfq76ROQ8\njPogilY1Wzl9PvG/NM0Zydml/ocvHObeH+7l5ez2fLTATXJUIT+fuNaXQ7TzsgRbAt2fC+PDBVkk\nZ6RwTbRDY6sC7C9Hn3gZv+fmAFotarQwNuP4/+bA/P0H56j6tUdg9XY3jZmOHakTYXStsH4Li1n/\nxPREvJQ3wb5BHstv/Hks/524Ej5uR3JGCuvOmPX/jRoHa/Cfjv/h/a7G92/lkZV0PAJX/qs/uPQZ\nTmpzJW/3jOLtm9+20sFn3DvDqs87Z3YQGbQVqoy0fv8X/l9tpwZ0ZnYmHY9A195DASOdd6nWbDzh\n/Hk6Un+uYZ09w7jKnJvA2z/Pz+fw/Jk0HeUmI6NjRy68NYiqAVUJ8Q/BS3kxedNkxlfti8+//+O2\nfHH+3xzPB1r3eAZcZoApyPbXHbdPgzavHXTsyMuffGwNAKbsp1EZK5ez7gvjqS1/7U/N5FgystKh\nW6y1/cR0h25bf/7Jff3fpV08ZP96jfV7Zdbnpq9uKtL7dVe+hs5m7skzhHxzC1NCQ6gzrE6e5Qu7\nfUdRHdpBNazjo7UZh+/vP1/XIDk9iRnZmayOgcd4zCpnnQcVoD5mtmGbWm1K5ffKcT52q/7z2nks\nX6DtCyHKlMfGuFIqVGudoJSKAP4BDtlXaaVUhNba/WXngjsOOP7a1sGIfOdVJsa+LJd7ouCupo2J\nqhJF+5a9mbz/VcBId3Kalqt5cxjnHPUeO/dp+nW5new1Rhqs04jTbsoDEBaGGqHo2bynlXrU47se\n7KoOA27PXTwp0Isx3T6mTlgdEgclWumKjuVHdulFtybdOH7+IOPn9ebqZDcjlOZRH3BO7ZvfZz7V\nq3ck/uMPGPjDvSx5xHjee4tfYU/mKbae3sr8ffNJyUhBo0lOT2ZXdXjilmQSbPDqda/w3ur3Wf/k\nOK4IDeV8PTc9BJo354HOsbkWxwd4GKHeTf0HTG5PfAA8lZHK8/Oft6KiBXm/YKTlWYPiFaC8I/Pz\nX//k+Nzlv/uUQJ9AvL28CfIKcrv9zIwUBky7kel9e1Dtz7mcSz3HqaRTOWlihawPzZs7/P8cZP2T\nM/Mt77r95PRkAiNrMu375tYyM9r+aeLvdC1kfQpb/8KWv/f6nK4Rix8exZJ98zmkjWiNOX/zfZff\nx67YXRyPCSUmn+3vObsHW5aNllEtnb5f658c57Z8cetflPJ7z+2l7y8PM6/Pr/Sf05/7r+0GDl3D\nn73G6Cf/bJPedIjPPThSfAC8ZM8EcbpA5lKfJFsSwX7BJAf5wC/O/ea3nNrCO/e8xqY2Z5iyeSqw\nl8dbP8azbZ/NVf9NJzcBsLO6LpHPp8PX7a3725/dbg3gtiY0gceMwB1LHnknZ87lct5fjg5dOMT4\nubmjk1lvZLHvwAYe9sqdoRPvIci3szqe6/Odsb/eWvkWo24aVez63zzZ+MzXP7nOY/lDtQIZcDvM\n7fNfJmyYwLx9O53q/8GaDziRdIIZ987geMJxdlWHPwbew7bT2xh6w1Armrnw2G+8s2oob930ljVC\ndZ8r+8CFCzBuHP0mtweOsf7Jn63f/+7VmvLhmg/5oOsHgDGYW4bL5+NFzvHC0Z6ze2ga2ZQ1IfHY\newjQpX5V3rv1PafPx3VQ1D9DLtDUw+d5PvU8EYHGFSxz/JLjMWHUK4X/N7P/8q7q4D1uPHj75Fne\n3fYbJp81vi8+/hAWhpfysmYyqRViXCn+0raWldbv4ees3z2bbae38d+7jH73WmtjDBXze9e8Od6f\nfc6Ab//FgofezBmorRS+X17Af6Z2ZniXR1hxeDaPl/D2HXmHhcF3uTP3dlWHrUP7M3nTF4QHZHMh\nzTiP8fT9dd1+VnYWqRmpVKmRk91gDkrXpX6XUv+9evHxCbTsdzm4ZhXks/1lf/3Fsk2bcpZv2OC+\nvBCiVOQVGZ8J3AFswn00ukExX3sj0EQpVR84ATwAuM5ZMgcYAHynlOoAXNBau80VO9EDBjz7jhWh\n6Jr9Aj5vGm9vTm+HdN/wcGjnfNXw2P5oztSpSvoqIwXPqa+um/KOftr1E7HJsYT4h7Di8AoIhA1u\nEun1MPdTo8Q7lD94WSRc047zJ7zYuw5auhtNPY/6uE7RViesDoSHE9zpRpYvS0Vfcw1KKT5YsMJY\nlwBj1o2x+uk9dfVTjA6EJYHGwePeR0dzfc+BUCUKBVR196Lh4QwYMN1pblJHfz//d67yrvWf0/wk\ntT40+qubKYtWOn8+nz/ApDsn5aSoF6C8SWud8/m7ec7ChxZSPbh6nvUPAvasDOVc3UjCNoVxLvUc\n205vy2mMF6I+Znnz/+GxVo/l/1yX7SelJxHyTghJg3Knc9/f4n6jD3Yh61Oa5bNCQ5y+L39UT2LK\noR1Wf0fzuzilxxTu/u5ujnsnQzt3KRdGGrW/jz8P/fIQF9IuoIdpWjXrTKuarRizbkzB6lXK75fw\ncFJaX0HTt9tDDNy6Zwhbw08xZ5fRP3L63dP53+7/WcW9qkawIcYYcOhE4gnrryM1QpH9RraRqhoe\nzrIaKXSI6cB3O77j8QXGKa1rH1NTzZhm3Hz98zwba3zvNsRO45qY52ldszGOSfNm+vDuzFMl8vns\nXWD8PTnwJDWr1GRx38V0/aar0+9hr8Pv88sDv+Q9L24Z7C/X8g1GtIdI2Pd/zgN6eSkvmjZuz5Jx\nCSw6sIhePxppwjuf28nZlLM0qtqImI+dDw7xgfB7ZIKVhm1y7c+cnG7MFuJYnzPJZ3LNJeyp/knp\nSdbnmnZ1S7cpwLHJsfxwYjHEwBSfHQxPnQ8xOf1rTd9u/5YdZ3YYF0AD4YEjHxrv39aBkdeOZOCi\ngXyx1wi/zt83n/5zc8YDMetT+5+7mbV7FmqB8T1oGdXS7Xgtd7V/ONfnv2FB7rd5LvUcyenJvLx+\nhNWRbUPmch6pH8QVNa6wyr276l0AmkQ0Yd/5fTyx4t887prtZW7z8EqrMd6wakPa1GrDGd906pXC\n/1u3b4w+xfGB4NPx2iJtP6/x4YN8g/6/vfsOj6LqwgD+nvRQQ++9SZEiAqKAlI8O0usnWBDhU+wi\nShVBFAS7oqgINgRFFFGB0LGBSJEmvSk11EAgCcn9/piyM9uTbDaF9/c8eTI7OzuZzWyZe++55yBf\nRD5chPY6uPTsJSAyP67nO4bN23cBNbQEcNeuX0NYSJgj105MDIq36oILO6rhTJ1KKKJXpDH0qtnL\nnmgtg++vo7+VxNoSV3E5xEOofia/3y9GA417jMCDpz8EcNHv/c/fMR8Ldy/EV7u0gZlDjx1CRX0T\nY+rfw40eBsIz9/u3/a39EJWWefz6/ls2boyWltUTP/zQ/30QUYZ5nDOulOqs/66olKrk/JPRP6yU\nug6tob0M2pjQfKXUbhEZJiLD9G1+BHBQRPYDeB+Ax7Spp54+ZTbEAW3+pTGXybm2tbP8EfldsnGn\nZb5S8enFzeRTAPBeZ3t5q6OPH3V5jEu4N4CHf3wYgCNRjL+lza5dv4Yf9v6AiMn20DZjTl5YSBii\nwqLMULheNXthRrsZ6HFTD1vCnG432fPnhUgISuTzmC/PNLjeYAAwQ9t+HKjNkxx6y1CznJw3JfOV\nRNOyTXHowiFz3cfdPvb5OIM1sY+R5Xj9kfV4fOnjXuvDGgny3J0LQEtOZ5Th8SY+MR4b/tlgXkC3\ndxufn3bOIXTeKKWw+tBqvLRem2dpJEey+mb3N5i7bW5Aji1QjP9Z/zr9AWjZqFceWmlmTh/VbBQ+\n7fEpAKBcgXJeE0b1XNATnb/obMsOnJCcgD61+iBEQrxmT15xcAWeXfFshp+PP0avHG0ubzu1zXbf\n4HqDsajfItu6DQ9swJp71qBq4ao49sQxnB913pbYDrBH87Sa2wrvb3of933nGFsy8hocePQAnrjt\nCXN9XEKcOWpmaPRBI7MMo+HBJQ8CAI5edP0sSyvr3FOjoe3ufbbswDLknZLXTA6Z3Xjq4MgfmR+9\na/XGiadOIHlcMmoVq4UWFVqgTIEyWHuva2bytp+2tZ0rwDVbe+lXS6Pca+Vw8PxBKKVQ4fUKKDHd\n92czoL2287/kqBbx2V+fuZ3/22N+D3PZWoLp5FMn8dCt9q9ea3lOw6R1WofSq7+/aia17DKvixmV\nY/VFT/s8+LIFyuJioqPxY7yH3eUr+bbfty7rYg/EIt9LWt6PaoUdpS5f+dUxpeXCtQsYu1oLd/+s\np6Nk4Lmr59zmorCOjANasjLnzOOBsufsHgDAmx0yr+rD9v858kIYI9+Fou0J3OKTLKPiFiXylbC9\nJo1paVeSr3jvLEujwxcOY+Laidj478aA7dOTz3porwFrUrVnbn8GRfP4X/bO8M4f75gNccCRB+X0\nldN478/3sLj/YnPKVWZIHpeMC6MuBDShHhEFjz91xlf6sy49lFI/KaVqKKWqKqVe0te9r5R637LN\nCP3+ekqpzZ725W6EYFbXWW5rMzrLH5HflvUWSH/ykI0PbMSwW4fhh4E/YM+IPUgZn6KNQruxd8Re\nLBmwxGW90UBxHun2JPrFaHSZ18W27tbSt9oyahaILICLiReRkpqChbsXIiI0wiyD4Y5RfsZf1YtU\nx2NNHsOJp06gfVWtMepz1MaidP7SmLDGkYXeNrXAB+vo9YCFAzBu1Ti0mNMCb2x4w2ud5k+2feL3\n3/CmU7VOuJx02Taa5S0xkC83Fb0Jr7d/PU2PWXFwBVp/0hpTfp4CQLvAB+CYAwzfnTunr5zOUG37\n9LiSfAVFooto80nh6ICoHKN1sBTPW9zM5l8yX0msOrwqTfu/nHQZMVExyBOex2PyN6UU2n7aFlN/\nmeo2gWEgXbx2URuld+PX+391u75xmcaoVqQa9j2yDyESgpioGBx5/Ihtm7xT8uJ4/HGzUsITy55w\ntytULlQZr7Z/1bx9Oemy27JEziX6+tbua5YRzKhirzjer8YIXJE8RXBltKPKw6vtHMfoLglaTlAy\nX0mX/601AZmVteNNKWV2zL7dUQtRvZR4Cf9c+gdV3qyCoq8UNTtF/CnPteqQ/T0z9Puh9nnoOmsD\n/N1Njmk7oSGhGNVslMv27ny42f2I2qONH7Xdjg6PRu1ijs7zV9q+gkF1BwEAvv37WxSaqsVhOXcU\nAVqncer4VAxvOBwL+2oJUp9f+7x5/5jmY/DPE9pz+XLHl+Z663u7cZnGZsLXItOKoNIbruMLJy6f\nsH2HFYwqGPDGeHJKspmpfP8j+/FIk0cCun8rI+t/eIhjjnGhqEJYe2St+bn/z6V/UCqf6/88LiEO\nRy5qnznvbHwHxacXx5WkK0hITnCpYhMIgej088W4buhavSvUBAU1QWFq26k+G83WMm8Hzh3AH//+\nYZseCDjm6G87qXW2li3gMe9wQISFhJlTEogo5/HY4hGRaBEpAqCYiBS2/FSEa9bzbKl+yfrY8IDv\nzJfuehPT+6VrNMg6VeuE6kWqe21UVitSDZ2rd8aWYVswpfUUc2R5yOIhABw1PH0Rpxx33/b7Fhsf\n2Ggr61YwqiAuXrtohhsmpiTi1yGOi3/nMiL/uzVt9bL3jNiDSa0noWS+kgiREKy9d60ti70vC3cv\nxK/HHMdjHd1Ii79O/YXJ6yebt71l325dqbXH+9KiadmmSExJREJygtmgcldX1DB7y2zsPuN6MWxI\nSklC8wrNXWqfezN+zXiXdaESigvPXkDX6lp25OfvfB6A5wudEtNLYPK6yW7vyyxXkq6gQGQBl/fJ\nM3e4ZrfNG5HX7wzzJfJqo4ZXkq8gX0Q+XE66jF+O/uJ2W+sFe+lXS7vdJlB2ntlpLq8YtMJcTh6X\njKbl/KsgAGgXkmefOWvrzCvzahlzdNBd9njraLrRuH76di0ZVcLoBNvIGQD8e8mRnqNgZEGzukNa\ny1lZTf15qjk/Vk1Qtk7PPOF5sO+RfYgbGYfHbnvMXG80TK1SUlOypDJA4vVElCtQDoceO+R7Yy8e\nvOVBxA6KdXvf3Yvuxk/7tVjshxq5BoNZk5yFvhCKam95/6w0spJbOX9nADAbW1bXxmidiuULlne7\n74kt7RnwPZULu5Do+n06s7MjcVqtYrXw35v/C8A+Ql+rWC2XxwFa9vCZXWaiZ82eLvcNrjfYzEhu\nbVg6l4pcf5+jARWXEIfTV07btjl68aitbF1MZIw5er9o9yK3ZUB9UUrZaqkvP7AczT9uDgCoUrhK\nmveXVk/e9iR2PexITmGM/Bu5Oc5fPe92ZPjvuL8x9RctxH/ETyMAaB1E646sc/tayqjpbacHfJ/O\njO8co9a5wdqBbVh+93KMazEOsYNiIRAkpyQjITkB3ed3R+MPXQd8jISvxgBDvZL1An34RJSLeBt+\nHAZtXncNAH9afhYDSF/dr1zuP5X/g5tL3Jzmx9UvWR+dq3dGfFI8zlw541dotJXzhXeTsk1spU4A\n7cu0zsw6OH3ltHms3kKaMtrb3aJCC7fhbp5YL8p3P7zb5VepQgYAACAASURBVPh9SRitXQQaF/oG\nX+VojIZqRhTNUxQnLmujLk3LNUWJvCXQfX53FJ5aGLvO7LJtm6pSMWTxEDPs15lSCgfPH0SxPMVw\n7fo1JF5P9Pn3k1OSbSV9DClKC9HvXas3WlRogQkttQuDhrMa4sgF1wtvwPPIVmb5+ejPtukJBneR\nKXeUuwO3WjNsexEaEoqU1BQcvXjUfC3P2jzL7bbOUTGZ6Y7ZWoP2+rjraFO5DVLHpyJ1fKrb0Wlf\nCkcXRufqnX1ut2WYNh3j8mhH43V+7/lYdc8qs6Sf80gloNWYNnyw+QOzEegcwu6vq8lX8exKbSrA\nsIbD3G5TtXBVFMlTBCESgriRjnnKzq/vF9e/aAu9TqtUlYqTl09i5+mdvje2iHoxCscuHXNbRtNf\nzcs3R7eburmUrnx/0/vYeXqnrZSZP5+D+8/tx+wts/HEUvfREO7UercWklOSMXDhQLzyyyu4lHgJ\nNYrUcAkBtzZUqhSyNxZPP30a4+8cj5G3jzQ7eh5bqnWifNf/O3zX/ztz2y7V7JFbANC8gtYINTow\n01t+cODN9vev8T/rU6sPut/U3VxvJNLqVkObjtWgVAPb40pML4HIyY7nG5cQh2J5HFEcCdcT8Prv\nWsRSzwU9UebVMpi9ZbbPEoMyUcwG+Pyd81F8umO0Pa3fcxk1o/0M2/QKo9Sr0WFzKfGSOb3N6oEG\nD5hRN0YHhxFKbpRNCwRj+kKX6q6vl0CrU7wOmpdv7rI+NCQUGx/QntuU1lqk2YVrF/BCqxcQExWD\n9UfXI2JyBPJOyeuS5+DJ254EACzZuwRJKUn47Z/f8Fr719IU6UdENx5vc8Zf1+eGj3SaL15XKZWr\nGuNFLGVZ+tXuZ44gevLVzq/MbOhGEp/GZRojdlCsbY5ZWhhfgMWnF0f9kvUxupk2r9T5i/781fO2\nOV7Wxxqc55NanUk4gzsr3OlynGmtWRxo1rmU/swzd+YptGztEdc5mobE64kuveLpUTRPUaw8uNKc\nO2d0AJy/dh6137U3cIypB55q4BqjfQoKRfMUdelccMc6LxIAJtw5wXZ7cL3B5v+3V81eiEuIQ4fP\nO7jdl7XeeyCkpKbghbUvYN/ZfW7vv/e7e92ud3eRGhUWhU3HN3n8W/kj8uOHgT/gm77foFBUIXy+\nXQt9zxeRD5NbTca3f3+LpfuXYtj3w/DN7m9QcnpJ9PvakXyoauGqHkfiMmro4qHmZwbgqCkuIhm+\nID/1tPsOp87VOiN5XDLql6yP5HHJPi8IjeMwRrrOJJxBqko1j/tysvbatIYzp8W6I+vM5Tc6uA/V\ntyqSp4g5f77pR/aoAeuUFl/OXDljG41MTklG/pfyo9SMUqgzs47bx2w5sQUf/GnvdFiy1xGFUCja\nbTpLv6y7bx06Vetk/r+NnCLDfxju9nhWDV6Fj+76yGV9jSI1zOUhi4fg9Q2uU1ve2ahljH6k8SNQ\nE5Ttc3/J3iWYt2MenlnxDAq+XBB7zu5BhZgK2P+Ill/AaIhYj3vnQ47OCyPMd1rbabiz4p22be+q\ncRfuqnGXGf7bp3Yft/8LNUGZESFGx1Bazb5rtrl8YZRjBP6rXV/ZpoQY0W7ze8/3a79xCXG2UeKD\n5w9i55mdKPOqIzBwyOIheHPDm5CJgufXPO9xX13mdcGsP2eZ0SZGdNKAhVrOWiOSJ6sYiVOPXTrm\ntiO0Z82e5vfy7eW0BHOrDq2CQDzmTkiPATcPQPxz8ahRtIbvjTOoZL6SWHffOrf3NSrTCM3LN8ej\nTbTpFcZ3u6/58cb/5sD5A2bHTt0SdQN1yESUS/nsrlNKvSkidUSkr4gMNn6CcXDBYs22Wji6MIbf\nOhwAcOi8+1DEvl/3NZeN5GH+fsF7UqFgBXN50rpJiImKQXRYtL3MGoCOn3dElTftIxTOc8u9zXl6\nctmTtgZqoSjtorJXrV4AgDMjz+DfJwPbIPOHMbffeW5hWlhHbqLCorCg9wKsO7IOz8S6hjwDWthi\nhHM913SIDIvEsUvHzKRF3hgNRCNJnzNj9KZE3hIuWYw9GbNqjO12/zr9Mab5GOwZscdl24W7tTmW\n/oy4B8KFaxcwYc0EVH+7utv7H7zlQTzeRKtz2rSs9zBt4yLR3TzZOVvnID4pHu2rtEeLCi1wPP64\nmeE/MjTSDJ/t+HlHzNo8C70W9MKpK6ewYOcCcx/9avfDrjO70P3L7i7798afjqwPtzgiDj7pHphc\nBYbieYvbLhLvr38/1ASFJQOXmCPu/o68t63c1pyHCwBztzoS/r3fRUvlYVy4p5W1A8jfTrAy+R0N\nH6Ojylq5Yfup7S6PMVxNvopjF4+h+PTiKD69OM5dPYdLiZfw5oY3beHU1vMXnxiPPXF7MGbVGJfo\nla7zHFE06YlkcGZ0jljrBFsZiUBbVWqF+xvcb2Zcb1a+GdbeuxZ/j3DN+THixxFmqPWKgyvMkGJj\nyov1e6bnAtcQ70oxlVClcBWce+Ycnmv+nO2+0vlLo1axWlg5eKVLArY+tdw3ttMiKizK9tq7t/69\nfj3O+lqyvg9aVmxp227R7kW4pdQttu2X373cZX9G0k/nxnj9EvUBwCU8/fFl2ufX2xvfxpkrZxCf\nGO8Srr/x340YtmQYno7VpoUcuXAEhy8cNr8zsjrxlhGO/9jSxzB/p+u1TOHowlh2YBmmrJ9ifoe8\nufFNKKiAj+6nt1Mm0Nbdtw55wvOgXol66FRNqwfnrTFeu1htRxUYi0BNhyOi3MufBG7PA3gLWmh6\nKwDTANyVuYcVXNZGd/86/c0vAyMJljchEoJdD+2yzS1LD+cvtLwRec25rlZxCXE4f80xMn4p8ZJL\ng92aoMXc7lntS985e7OxL2NEsGieogENO0sLNUHhjY6+R8w8sY78XR1zFdWKaHMpI0PdX/iPWTUG\n87bPS/ffMzhHIjhfRFov3ox8AM4Xa13ndcXmE5sRnxiPaoWrITIsEkXzFLWN6LnjnChu50M7cVPR\nmzC59WRUL+LaADbWHbpwCEcvHsXZBPvIe6B78d/b5KgsYMxLtPp8++fm++3XIb/iwKMHPF68GOfT\n3X6MbNShIaFahmDLe0RE8HzL570e5yfdPzEjCr7b853Xba2UUgh5IcSlUXg1+Sp+2Os+50N6prL4\ncmX0FSSNTcLmBzfjo26uo6j+Wj5ouS1BkXWaR3qTWgLApLWOnBTWubq+WEeg671XDzJRbIkX677n\neL0qpTBn6xzIREFSShIGLRqE8q875joXmVYEBV8uaDaIDL0W9MLqQ6vxw94fUODlArjpnZtsIdMH\nzh3wK1FaWpmN8ZBwWyMUAFLHp2LYrfZQ/thBsUgdn4p1964zE8EZSc8M7/zxDkrNKIWklCRbFIkx\nZWh2t9nwxkgC5W3kv3Wl1hhws70K6f0NtA6gjCaq6nGTY764ER3mj0caa4nPrB0bA+rYj3H21tnY\nfMKeA7ZtlbZQExQW9HZ0yhlTuZwb4546TQxnr55F8enFUeDlAsg7JS+UUh4TYraY08JMGlcibwmM\nvH2kr6eYaWbfNdtnRJARPejc8ZvbiQi2Dt9qXp85N8atUXw7Htph5iogIkoLfyay9AbwHwAnlFL3\nAagHIFfVT7B+ydYsWtMcWXaX0Mbqrhpan0TNYjUDchy/3O9IMBUdFo28EXldSq45N9rv/+5+2+0N\nD2xw21Ntnb/dplIbczm79EIH0lsd3wLgCP3zlGX0yMUj6Z6naGX0hhvzz4xSTMYF3uwtrhfA1gYP\noIWMzt8x3zZnr0BkAXOk3BNruZlTT5/yeVH1YVfHCG2F1yuYo5XG6GCohAa04bH68Gpz+enlT7vc\nfyX5iq0jq3Khylg52Huxhl4Lenm9P0RCUL9kfds6bxfSMVExGHjzQJ8X227/1gvaR+gfx/+wrZ+3\nYx66zOti/i/bV9EqDHSo2sHl2AIlPDTcZR5selirEzy38jkvW/rPmmDQ+bXvjTUB18HzB71u22JO\nC7NTJnJypDmC58uivxeh9SetbRUplu5fCkDL7F31rapYuMu/faWF0RgPCwlDz5o9zXmqgOe5xM7T\nGuZ2dy1VeO7qOTT/uLnt3Bnhs+6SU2UnIoLfh/yOpLFJZuebP2a0m+ES0WXMO7ZGPhjzxZ31qd0H\naoJCtcLVzM/cMwlnbO8F52iIpLFJLvP+rSasmYDVh1Z7vN9w+PHDGHLLEJ/bZZayBcriRPwJM4z/\n2TtcSzw6h6IPazgMYSFheKrpU0E5xuzC+G4uEq11TribPmJNfrvjfztc7icicuZPY/yqUioFwHUR\nKQjgNAD3tbpyKOvIaXhouHmR5KvMk3M94IwyLpgAreRP3vC8LiOoRhIVg/OcYn9CZq29txefvWgr\nKZQbjGishWYaZXFGxo50iTBQSqFdlXaY9p9pGf57RoIw4yLZKIdjhLaNWz3OlsQHsIdaG2Gl036d\nZqvzmic8j8fsxAYja+u0/0zzq5Rc8wrNbXVVNx3fhBPxJ8w6t1tObkHdmYEbHV95yNGw/nDLh7bz\nYHRa+JuUzWBt4ANayH2lmEq2zOIHzh0A4Dp/3sqYnjKz80xzDvdLbeyZp+MT420lAL/Z/Q32n9uP\nGb/OsL3XhiweYntuxvxiI/Lg0IVDGNt8rFnCLadZ3F/LYl+vRNqzAlujN4xEi/7KG5EXKeNTvG5z\n8vJJnL963iwRFUhGsjrr52ygSrwZc/ONTqBGZRrh8GOH0xQ5YHzm7Htkn62xaK3THCqh5kh6pUKV\n8ONA96XietfqnbYn4MaSAUuw5p41GdpHk7JN0twxFh4a7hLRZXwefr3ra6w9rE3N+qLXFy6PtSoQ\nWQCXEi/hStIVxCXE2ebY31zcEdHyaY9PER4ajp/v9/yam7RuEgYtGuSy3phuYMhIxEkglMpfCsfj\nj5tVEvwpB1u5UGVcT73uNYdHbmScqy97axU4IkIjkDQ2CRefdURrWZMG1i5uzxlDROSOP43xTSJS\nCMAH0LKrbwHgviBuDmWdQ2YN8S6apyguXrvoUuasXIFyOPL4kUzJkHnyqZMonb80OlbriLCQMJd5\nyEbjy2BcZBj8CRO0djKESIjPpCQ5hbtRbiPMsNgrxTB21Vhz/bt/vIvlB5ajUiHX+rLp8XKblzG+\nhTb6N6PdDCy7e5lLRnprFl3riHeJ6Y7kPdaR8eiwaJfG+K4zu/DsCsfIhZG9eOQd/oc5Ol90n716\nFjXfcUR3WMtvZYRzVAcA27QAI0wzLfMlaxerjYalGiL2QKw5n77j5x1x6MIh28W48f+1jkL/MNAR\nNr76ntX488E/AdhHfYyOnFl/zoJMFBR4uYDtf9NrQS9Ue6sano592uVz4XLSZSilUHdmXSz6W+uo\n+zvubxR/pTj2nt2L/nX6pzvBY7A5j7h2raHNl05PLdvoF7VIo241uvms4etOiITg7DP2TsdJrSaZ\nkSelZpTC2xu95xT9oKvWqDZyZACuc4rdMWqcbz25FQDweJPHseqetNW698RoSIdKqLmuQkyFNEUO\nANoUpKqFq9qSq1kZVRUMHat1NLM+96vdDyXzlcTr7V+3ddKlV72S9VySuWUVo3Oi79d90XJuSwC+\nE3AVz1scx+OPo9579Wz7ALRQ/KtjrkJNULi77t0ujzU+/x9u5CjFZ0zzKJanGBqU1KJWYgfFmhnk\nrdtmlVL5SmF33G7zWD11hIxrMc7sKDKm8pQrmKvGZfzy+5DfzejConmKIjw03JZE984Kd2Jer3m5\nbpCDiDKPPwnc/qeUOq+Ueg9AOwD36OHquYa1AW58Ef025DdUKVQFd865EzfPtM/xTEhOyLQGbIl8\nJfDvk/+icHRhbDu1Dc0+tl+YOSdrMxqgE1tOhJqgvM5Z+mv4XwAcCWpymwW9F2Dpf5fa1hkXmNeu\nX8P3e7831y8/qCXuCdR5HNVsFNpU1r6gyxYoi3ZV2nnd3tpQtTbq4hPjzWRLecLzuCR6e+P3N8x6\nr4AjnDatXmv/mrns/Pq2qvh6RTz6k++keqkqFXO3zkWHzxxJuqzZp41RaHf10NOSmXrgzQPx54k/\n0e6zdhizUpu/aIyUu5vvbr2YNiIVSuUrhZYVWyIiNAIp41NsI/NGyaphS+zzdXec3uFSOqrwNHvD\n+sK1C0hOTcb204754y3mtMCZBC0iwt0c/uxqcL3BbkMw3+6oNXrT8xmSkUgioxOjZtGaiH8uHmNb\njLVl6PbUyB9UdxC+6PkFHrjlAaSOT8Xuh3cDAEbePhKL+y/2mIne2ft/asnrxrQI7JzZVYNXZbii\ngxFJU71Iddxf3z5taU63ObYM44YpbaZgVpdZeKvjWzjx1AlbXfcbWaHoQriUeAkHzh9wuU9E3I5i\nVyushdOPaTEGs7rMwtud3naJDvhtyG/YPGwz1ATt+7ppuaZQExTe7pT1hWmM99aaw2sAuI7cG15o\n9QKalW+G86PO4/ayWhTfnG5zgnGI2YpROjZxbKLbXEGRYZHoX6d/rhnkIKLM508Ct+9FZKCI5FVK\nHVJKbfP1mJymU7VOGNtcGzU1GuYFIgtgw78bsO3UNvxz6R+8s/EdTF43GTJRcPbq2Sz7oHWeQ9y2\nclt80v0TjL/TtZHjzEgcld3nDaZXg1IN0L5qe9s6a4/1X6f+wrd/a3V0F+/Rwm4zmnDIF3fZ2qPC\norDh3w0uUwoqxlS0j4yHu46MG7Wyz109Z5bJSY/Hb3vcvDA0dKneBSNvH2m7GDty8Qh+OfaL88Nt\n5mydg9AXQnHvd/di2YFl5nrjeXzT9xts/5/WQD15+SQA+3QKd3VtPbGW9VtzZA2KTnMkWDJCzQGt\nhBsAM/TSMPuu2TjyuCMXhHN0i6e5uuNWj3NbOgoA3uzwJgAtVN25s8wwqO6gdM1Jz0pG2P24FuPM\ndUbDz0iMlqpSMXTxUFuSQuu5fe03R6dPRrMuH3rsEHY9vMttnouRsY7IkE7VOmHfI/ugJih80uMT\nM9mYiKBEvhIY32I8JrWahPyR+f2a2mFldJQFSqtKgQl5N3zU7SNb7pG+tfu6jWaIDIvE0IZDbXOi\nCfhi+xduQ8u9WTl4JbYN34aI0AgMbTgUAGzRAQmjE1ClcBVPD89yIoIHGjyAF9e/CMB32HxMVAwm\ntNSm/1g/c280gajEQkQE+BemPgNAcwC7RGShiPQWkayd5BRgeSPy4unbn0Z0WLR5wejcYB3x0wiM\nW+24KA3GPC8jEYgRVu6u8RV7MBblC5Z3We/JtuHb8Eq7V3xvmEs4X7j3mN/DVi7MmiAqMxhzkK1/\nZ2bnmQAcGdXbVWmHztU64/CFwxjx0wjM3KTdnyc8j5kp/+ejP9vqVN/z7T1mfXFrwpi0eq6ZI8lT\n49KN0bRsU3N02F9T1k9xu96YC++cVCxVpZrPvUNV9/XOPbEmddp7dq85l/ehWx+ybfd1368BaCG5\nVvc1uC9djWKjE8dgjBADjjmCvx77Fflecm0o9q7V22Uuek5gZKi2dlgYI0FG4/tswll8uOVDlHm1\nDLaf2g6lFMInheOL7V9AJgqeXK6FQzt3/KSHu1Goa2Ps1QS2Dd+Gr/t87bX28cRWE92ORhthxIaS\n+Uq6bJPRUexgMMptvtj6xXRNC8hN9o7Yay47T3Vwx1tpNE/KFSznNirHyOaeE86B0SFqnTLhTb6I\nfAF5TxMRkX9h6muUUv8DUAXAewD6QkvilqsUjCqIhDGOUUhfo3WZMV/c2Tud3gEA7Du7D0cuHEHZ\n19yP4hqNHn/ULVE3TSOROZ0RQmhlnTtulGzJLE82fRJqgsKOhxxZVQfXGwwA2B2nhcwmpyTbyvB0\nrtYZgHZhaCTse+ePd2z7XbJ3Cd7YoJWBc/cc/WVNcDao3iDki8iH7/Z8h/jEeKw7sg6Aaymxfl/3\nw6LdWsjxycsnse/cPrf7NuoYmzVsm2ihsKEvhJqN1oalGqbpeAfePNDtemPagZWaoNJVcvCfJ/4B\noIX9Dm843O021mSL5QqWc1uG6dHGWnj/i61fzJElb4yOSXe1cwGtSoA1D0Ld9+pi26ltSFEp+O83\n/w3KMRolAAGtk6tuibppbvwYr6nNwxxlr77q8xUOPXYId9e92+w8yylK5iuJSa0mYUiDrMvQnV1Y\nP9/9ydcwotEIc/mzHp9l6G/fXu72NHdsZpX95/cD0BIBEhFRcPnVohSRaAC9AAwH0AiAaz2VXMZb\nya/YQbFBOQbjYrjJh03MElSG4/HHzWzURqZcciUiuDrmKmbf5SgvVqNIDQBaKbBgdKoA2oXg132+\nRtOyTc2/2eiDRvj12K84k3DGHM0CtAY8oI2cz946G8sPLMeXO770uG8jNDI9IsMi0aZSG0z9z1RU\njKlohiEXeLkA7pyjhVpeTLwIpRQuXruIgQsHYsHOBfjkL63Wc6kZrpEFk9ZOwv5z+13WuxvBn9x6\ncpqO19P7skX5wL0HjIbza+1fw8wuM10a2sMbDjdH0IwkYg83tidiKhhZEK910EK0jRJ7OZGaoDxG\nLwxZ7NrY23fW9WI+s0fQzow8A4G4HZ30xwddPzDnju9/ZD9OPX0KvWv1RlRYFD7t8Snub6DNwz79\ndM7pgx7bYixK5Mu5r7tASWtjeNydjui3jP7/RjQegcujL/veMBswpm05Z6QnIqLMF+ZrAxFZAKAJ\ngKUA3gawVqkAFiLOprzNbzSSUQXDsIbDIBB8sUMryfJK21fw/d7vMWfrHEz7RSvLlRNCJ7NSVFiU\nbR60ETqb1vmiGdWrVi/0qmWvkX3H7DsQKqG2qQZGQrF6Jerh3NVzPmvVZrRW/IrBK8xld50TcQlx\nZj1tg3MZqQF1BmDeDi1T+vg1491mqc/MBGbvdXkvoPuzNiAntpqIdze9iwdveRC1i9fG4HqDzXJn\nRhIx54vYH//7I0IkBJefu+ySVT836/t1X9vty88FpzGSOiH9X0l5wvOYnSvu5vZGhEYwJDeHigyL\nxO3lbsfRi0f92t76WepcVzw3q1m0JnbH7ea1BBFRFvDn22Y2gAF6rfEbSsNSDfFxt49R9z37iIu1\nPE5mu73c7bjn23vM2/VL1sfcbXMxZlVgs/rmdukpyRQsKSoFxfIWQ+VClXHw/EFzGoHRgfDyLy8D\n0MKFw0PCsf6oow5xoENoG5Vu5Nd2cQlxaPC+Y47tk02fNBvjANwmQRIRzL5rNjb8uwExUTHYe3av\nyzb+uD7uOv44/geaftQUJ586mekjgGEhYTg/6rxtXY0iNbBy8ErbuvX3rUfzj5sjaWySOS89NzbE\nYwfFou2nbX1ut/zu5bny+VPOEjsoNk3Z/1tWbGlmFr9RTP3PVI/TjYiIKHP50xhfD2C0iJRXSg0V\nkWoAaiillvh6YE636cFN5nLp/KVxPP44+tbuG9TeY+fszFULV8WO0zs8bE2e5IvIh8H1BuOTbVp4\ntVEnNLuICI0wM40bjJBxQDteYwT78IXDZo3uPrX6IJBEBA/e8qCZtd0bo/ZykzJNcGvpW5E4NhHT\nfplmS3To7L4G9+G+BhmrjBgaEorbyt6WpaOVIoLWlVrb1jUr3wynnj6V47Kmp5Vz6aNyBcrh2CV7\n1vofB/6ItlV8N9iJMltaK5+4S9yX23Wt0TWrD4GI6Iblz4TZjwEkATAyFh0H8GKmHVE2tKjfIhx4\n9AAmtpyIGe1mBPVv967V21yefdfsdCWkIi38em73ubgyWkuI9nG3j7P4iFx5SzA0q6ujcVwxpiIq\nF6qMl9q8lCkJ6IwEcwBw+LHDtvu+6PkFNg3dhK7VHRdv3/bXMo1HhEZgTHNHxEbDUg1t2dpvBMGe\n+pCVQiUUyeOSse+RfSiT356gzjmLPVFOMavLLBx67FBWHwYREd0g/GmMV1FKTYXWIIdS6krmHlL2\n0/2m7ogKi8L4O8dnel1qZ9bwaqOE0tp715rrOlblRW9aGKMkWT16ubj/Ypc5iZGhniMunBvqBx49\ngGebPZspx3ZH+TtwYdQFJI5NRIWYCjj51EmzVFT/Ov3RsHRDvN1JK+11d927bSNJIoLGZRoD0CJL\nprRxX/aMcrbRzUZjYsuJCAsJQ2RYJO6tf29WHxJRQOSPzM9ObyIiChp/GuOJejZ1AICIVAGQmHmH\nRM4qxWghyYWitbnq1kZcuQLlsuSYcrqMJj3LqK41uiJ5nH0KQo2iNVy261VTS/jmXPc+sxWMKoiI\n0AgAWlbhfY/sQ9zIODOxoZEh/MOuH7o8NrNrt1PWe7HNixjTwhEFMbn1ZLOWOpOdEREREfnHn8b4\n89AyqZcVkS8ArAIwKjMPiuxeaPWC7ba1Md6jZo9gH06OpyaoLG+MG3b8bwf2P6KVAfuqz1eIGxln\nu9+ow+0tu3+wWEPiI0Ij8HCjh93mTzBG0enG8myzZ9kQJyIiIkoDUcr3xZOIFAVwm37zd6VUnLft\ng01ElD/PI6dKVak4Hn/cDJG/lHgJ/b7uh6X7l+L8qPOIiYrJ4iOkzJKqUpF4PRHR4dG+N84mklOS\ncSX5Cl+XREREOYyIQCmV9SMARDcIj41xEWkIwHqn8cZUAKCU2py5h+a/3N4Y9+ToxaO2+tRERERE\nROnFxjhRcHlrjK+BvTFuo5RqlUnHlGY3amOciIiIiChQ2BgnCi6/wtSzOzbGiYiIiIgyho1xouDy\nmMBNRJ6xLPdxuo/1ioiIiIiIiIjSyVs29QGW5dFO97G4NREREREREVE6+VPajIiIiIiIiIgCiI1x\nIiIiIiIioiDzlk09BUCCfjMawFXL3dFKqbBMPja/MYEbEREREVHGMIEbUXB5bFArpUIz64+KSGEA\n8wFUAHAYQF+l1AU32x0GcAlACoBkpVTjzDomIiIiIiIiomDJqjD1ZwHEKqWqA1ip33ZHAWiplGrA\nhjgRERERERHlFlnVGL8LwFx9eS6A7l62ZagMERERERER5SpZ1RgvoZQ6pS+fAlDCw3YKwAoR2SQi\nQ4NzaERERERERESZK9OSsIlILICSbu4aY72hlFIi9ycyEQAAEPJJREFU4in72h1KqRMiUgxArIj8\nrZRaH+hjJSIiIiIiIgqmTGuMK6XaerpPRE6JSEml1EkRKQXgtId9nNB/nxGRRQAaA3DbGH/++efN\n5ZYtW6Jly5bpP3giIiIiolxuzZo1WLNmTVYfBtENy2Nps0z9oyLTAJxVSk0VkWcBxCilnnXaJg+A\nUKVUvIjkBbAcwESl1HI3+2NpMyIiIiKiDGBpM6LgyqrGeGEACwCUh6W0mYiUBvCBUqqziFQG8I3+\nkDAAnyulXvKwPzbGiYiIiIgygI1xouDKksZ4oLExTkRERESUMWyMEwVXVmVTJyIiIiIiIrphsTFO\nREREREREFGRsjBMREREREREFGRvjREREREREREHGxjgRERERERFRkLExTkRERERERBRkbIwTERER\nERERBRkb40RERERERERBxsY4ERERERERUZCxMU5EREREREQUZGyMExEREREREQUZG+NERERERERE\nQcbGOBEREREREVGQsTFOREREREREFGRsjBMREREREREFGRvjREREREREREHGxjgRERERERFRkLEx\nTkRERERERBRkbIwTERERERERBRkb40RERERERERBxsY4ERERERERUZCxMU5EREREREQUZGyMExER\nEREREQUZG+NEREREREREQcbGOBEREREREVGQsTFOREREREREFGRsjBMREREREREFGRvjRERERERE\nREHGxjgRERERERFRkGVJY1xE+ojIThFJEZFbvGzXQUT+FpF9IjIqmMdIRERERERElFmyamR8O4Ae\nANZ52kBEQgG8DaADgFoABohIzeAcXs6wZs2arD4ECjCe09yN5zf34znO/XiOczeeXyIKpixpjCul\n/lZK7fWxWWMA+5VSh5VSyQC+BNAt848u5+AXRu7Dc5q78fzmfjzHuR/Pce7G80tEwZSd54yXAXDM\ncvsffR0RERERERFRjhaWWTsWkVgAJd3cNVop9b0fu1ABPiQiIiIiIiKibEGUyro2r4isBvCUUmqz\nm/tuA/C8UqqDfvs5AKlKqalutmXDnYiIiIgog5RSktXHQHSjyLSR8TTw9IbfBKCaiFQEcBxAPwAD\n3G3IDw0iIiIiIiLKSbKqtFkPETkG4DYAP4jIT/r60iLyAwAopa4DGAFgGYBdAOYrpXZnxfESERER\nERERBVKWhqkTERERERER3Yiyczb1XEVEUkRki+WnvJdt14hIQx/7aysim0TkL/13K8t9DUVku4js\nE5E3LOsjRWS+vv53EalguW+q/pjtItI3o8/3RiAi3UUkVURqBGBfr4jIbhHZJiLfiEhBy33P6efs\nbxFpZ1nv6TxXEJGV+r5WiwirEKSTiFwOwD74Xs2GAvn+teyT5zobEZGyIvKdiOwVkf0i8rqIhPt4\nzOMiEu3hvs/1z+HtIvKRiIRZ7ntTP4fbRKSBZX0H/TH7RGSUZX09EflNf60sFpH8gXjONxLLddUO\nEdkqIk+KSIanLer72amfyxXW6zURuUd/Pe0VkcGW9ZVEZIN+nr80XmciUkhEFun72iAitTN6fESU\nyyil+BOEHwDxadh2NYCGPrapD6CkvlwbwD+W+zYCaKwv/wigg778EIB39eV+AL7UlzsDWA6tcyaP\n/vj8Wf0/y+4/AOYDWAwt0WBaHxvidLutsQ7AywBe1pdrAdgKIBxARQD74Yho8XSevwIwSF9uBeCT\nrP5f5dSftLxvveyD79Vs+JOR9y/Pdfb/gZaPZiOAe/TbIQA+BDDNx+MOASji4b6OluUvAAzXlzsB\n+FFfbgLgd305VP/Mrqh/hm8FUFO/7w8AzfXl+wC8kNX/s5z2Y/18BlAMQGwg3s8AWgKI0peHW96T\nhQEcABCj/xwAUFC/bwGAvvryTMtr4xUA4/TlGgBWZPX/jT/84U/2+uHIeBbSR0rW6CMoS0XEWgpu\nkN7ju11EGjk/Vim1VSl1Ur+5C0C0iISLSCloF2wb9fs+AdBdX74LwFx9eSGANvpyTQDrlFKpSqkE\nAH8B6BC4Z5r7iEg+aBddI6BdQBvrW4rIOhFZoo+GzDR66kXksohMF5Gt0PIlmJRSsUqpVP3mBgBl\n9eVuAOYppZKVUoehXdg18XGeawJYpS+v0fdB6SQid4rI95bbb4vIPfryYRF5XkT+1Ee4XEZZ+V7N\nfny8fz2d606iRa9s0kdBXUp08lxnK60BXFVKzQUA/fP1CQD3i0iUiITqn8fb9VHLESLyCIDSAFaL\nyErnHSqlfrLc/AOAEXXUDfo5VEptABCjf583BrBfKXVYKZUM4Es4Po+rKaXW68srAPQK4HO/4Sil\nzgB4ENp7Gvr5fUVENurn90FjWxEZpX9ebxWRl9zsa41S6pp+0/p93B7AcqXUBaXUBWiN/476d3wr\nAF/r282F/ft4tb7fPQAqikixAD51Isrh2BgPnmhxhKgv1MPb3gLQSyl1K4CPAbyobysAopVSDaCN\nmsz2se9eAP7Uv+zLAPjHct+/cFwwlAFwDDAT5F0UkcIAtgHoICLRIlIU2pdKWZA33QAsVUodBXBG\nRG6x3NcI2gVBLQBVAPTU1+eBNmJSXyn1q5d93w9t5AzQLgyt5/MfaOfReb31PG+D48KuB4D8IlIo\nDc+NvFP6j7F8RinVENpoyNM+Hsv3avbg7f1rpQAoEYkC8B600exbARSF4zXgCc911qoN4E/rCqVU\nPICjAKpBa7iVB1BPKVUPwOdKqbegVW9pqZRqAw/0EOS7ASzVV5WGfg511s9pd+sBYKeIGA3zPgDK\npfUJkp1S6hCAUBEpDmAIgAtKqcbQOkWGikhFEekIrQOssVKqPoBpPnY7BL6/jwvrf8voUHf+Pu4J\nACLSGEAF8H1MRBbZobTZjeKq3rgGAIhIHWgXCyv0gdNQaBcBgHaRNw8AlFLrRaSAiBRQSl1y3qk+\n/+hlaGHO6aKUitVH338FcAbAbwBSvT/qhjcAwGv68lf67c367Y36KDZEZB6AZtBGvFL03x6JyBgA\nSUqpLzJwbE8DeFtE7gWwDtqFQUoG9kfefaP/3gxHx4sLvlezFW/vX2cC4CYAB5VSR/R186A15tw/\ngOc6O/DVWdIGwEyjAaWUOp+Gfb8LYK1S6hfLurTOVb4fwJsiMg7adImkND6evGsH4GYR6a3fLgCt\nE6YNgNnGyLe38y4idwO4BVpEhSe+XmcvA3hDRLYA2A5gC/h9TEQWbIxnHQGwUyl1u5/bu3zgi0hZ\naA2BQXqPMKA1vKy9rmXh6Mn9F9pIwHF9ZL6gUuocACilpgCYou/3cwB70vZ0bhz6qFUrAHVEREHr\nSFEARuqbWM+VwHEBfU0p5fGLW288d4IjTBXQzpl1xMQ4n+7O878AoJQ6AX1kXA/H7eWuI4f8dh32\nKCLn5E6J+u8UePhM5Xs1+/Dx/nU+11H6b+f3rceGF891trELQG/rChEpAO1/vd9YldadisgEaHPK\nh1pWe/qcDndaX05fb4Qst9f3WR1ajgDKABGpDCBFKXVaH+QYoZSKddqmPfw47yLyHwCjAbTQo1sA\n7Ty3tGxWDtqUsHPQpiaE6J071u/jeGgdL8Z+DwE4mK4nSES5EsPUs84eAMVE5DZAC3sTkVr6fQJ9\nHqOINIMW/hRvfbCIxAD4AcAopdRvxnq9IXZJRJro85gGAfhOv3sxgHv05d4AVur7ChGRIvpyXQB1\noSUOIvd6Q0uKVlEpVUkpVR7AIRFprt/fWA+HC4F2Hn/2tUMR6QCtMdDNMlcN0M5ZfxGJEJFK0Hr2\nN+rzUp3P87f6vorofxsAngPwUcaf8g3tCIBa+jmIgTYX1W98r2Y73t6/h2E/122gNcT3AKgsjkzn\n/eC+g5TnOptQSq0EkEdEBgHaHGIAMwB8rJS6Cm2+7zB9PSxTeeKhjaK6EJEHoI24DnS6azGAwfo2\nt0H7zj4FYBOAavr3QQS0181ifbti+u8QAGOhTXOhdNL/n+9Bm/4HAMsAPKR3cEFEqotIHmjn/T7R\nM+a7m8IlWjb89wB0VUrFWe5aBqCdiMToj2sLYJneyb4a2nQDQHs/G9/HBfVzDxEZCi2iIsNVOogo\n9+DIePDYLtyUUkl6+NSbopWxCoMWNrlL3/aaiGzW19/vvDNoc5KrAJig99QDQFv9i+MhAHOgjeD9\nqJQy5rV9BOBTEdkH4CyA/vr6CADr9J7kiwD+a5n7RK76Qws9s1oILdR1PrTEPm8DqApglVJqkb6N\nt3C2t6Cdh1j9PPymlHpIKbVLRBZAe11cB/CQZXTd03luCeAlfdRvLYCH0/k8b2j6RVyiUuof/Rzs\ngJZp2VM4s3UuuRXfq9mLp/dvf6XUw+7OtVLqmog8BGCpiFyB9h7nuc7+egB4Vw8FD4HWUTJav+9D\nANUB/CUiyQBmQQs/nwXtPP/rZt74TGgdNr/p52WhUmqyUupH0RL87QdwBVp2dCilrovICGiNuFAA\nHymlduv7GiAixmfzQqXUnAA/9xtBtB7+HQ7t+/ETOKaffAgti/1mvQPsNIDuSqllIlIfwCYRSYL2\nmhjrtN9pAPIC+Fo/z0eUUt2VUudFZBK09z8ATNQTuQHAKABfishkaJ8bRid4TQBz9e/jHdDmoBMR\nmUR5jpolojQSkZYAnlJKdc3qY6GMEZF6AN5XSt3mc2PK9UQkr1Lqir78DoC9Sqk3fDyMiIiIyCOG\nqRMFlqfRUcpBRGQ4tDrCziMmdOMaKlo1jJ3Qwpjfz+oDIiIiopyNI+NEREREREREQcaRcSIiIiIi\nIqIgY2OciIiIiIiIKMjYGCciIiIiIiIKMjbGiYiIiIiIiIKMjXEiohuUiBTRM4RvEZETIvKPvhwv\nIm9n0t8cISL36strRKSh5b6KIrJdRNpZjiteRP7Wl+eISF4ReV9E9ovIJhFZLSKNRSRSRNaJCL/X\niIiIKEcIy+oDICKirKGUOgugAQCIyAQA8UqpVzPr74mIABgCoJFxCHBTClAptRzAcv0xqwE8pZTa\nrN/+EsABpVRV/XZFALWUUokish5AdwDfZNZzICIiIgoUjiAQEZFBAEBEWorI9/ry8yIyVx91Piwi\nPUVkuoj8JSI/iUiYvl1DfaR7k4gsFZGSbvZ/B4C/lVLX03lcVQA0hqX+u1LqsFLqR/3mYgAD0rhv\nIiIioizBxjgREflSCUArAHcB+AxArFKqLoCrADqLSDiAtwD0UkrdCuBjAC+62U8zAJvS8feN0fPa\nALYqpVxG03VbAdyejv0TERERBR3D1ImIyBsF4CelVIqI7AAQopRapt+3HUBFANWhNZRXaJHoCAVw\n3M2+ygP42Wnf7v6et2PxfKcWqh4iIlFKqWvetiUiIiLKamyMExGRL0kAoJRKFZFky/pUaN8jAmCn\nUsqfUWmxLJ8FUNhyuzCAOC+P3QWgnoiEKKVSvezfa6OdiIiIKDtgmDoREXkjvjfBHgDFROQ2ABCR\ncBGp5Wa7IwCsc8nXALjbcvseAKs8/RGl1AFoYe4TzYPTMrB30pcjAaQopRL9OGYiIiKiLMXGOBER\nGZTlt7tlwHXUWSmlkgH0BjBVRLYC2AKgqZv9/wzgVsvtWQDiRWSb/rg8AKb7OMYHAJTQS5tthzY/\n/ZR+XwMAv/l4PBEREVG2IJ7z4BAREQWOXtpsM4AmSqmkTNj/FAB/KKUWBXrfRERERIHGkXEiIgoK\nPQv6BwD+G+h96yHqzQB8G+h9ExEREWUGjowTERERERERBRlHxomIiIiIiIiCjI1xIiIiIiIioiBj\nY5yIiIiIiIgoyNgYJyIiIiIiIgoyNsaJiIiIiIiIgoyNcSIiIiIiIqIg+z/aN5XotU5utgAAAABJ\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8620cbb390>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Save as a NetCDF"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Follow Susan's SSH notebook for this. Make a new netcdf file for each month. I will save the ssanonmaly at each point along the western boundary."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get some preliminary data first: bathymetry, indices of the edges. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "startj = 384\n",
      "endj = 471\n",
      "lengthj = endj-startj\n",
      "r = 1\n",
      "\n",
      "fB = NC.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc','r')\n",
      "lat = fB.variables['nav_lat'][:]\n",
      "lon = fB.variables['nav_lon'][:]\n",
      "fB.close()\n",
      "print lat.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(898, 398)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A function for saving the netcdf file."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def prepare_netcdf(ssh_file, count, ssh,month,year):\n",
      "    # dataset attributes\n",
      "    nc_tools.init_dataset_attrs(\n",
      "    ssh_file, \n",
      "    title='Juan de Fuca SSH hourly values', \n",
      "    notebook_name='SSH_Tofino', \n",
      "    nc_filepath='/data/nsoontie/MEOPAR/NEMO-forcing/open_boundaries/west/ssh/' + filename,\n",
      "    comment='SSH from Tofino: measured - tides (from t_tide)')\n",
      "\n",
      "    \n",
      "    #dimensions\n",
      "    ssh_file.createDimension('xbT', lengthj*r)\n",
      "    ssh_file.createDimension('yb', 1)\n",
      "    ssh_file.createDimension('time_counter', None)\n",
      "    # variables\n",
      "    # time_counter\n",
      "    time_counter = ssh_file.createVariable('time_counter', 'float32', ('time_counter'))\n",
      "    time_counter.long_name = 'Time axis'\n",
      "    time_counter.axis = 'T'\n",
      "    time_counter.units = 'hour since 00:00:00 on 01/' + str(month) +'/'+ str(year)\n",
      "    # nav_lat and nav_lon\n",
      "    nav_lat = ssh_file.createVariable('nav_lat','float32',('yb','xbT'))\n",
      "    nav_lat.long_name = 'Latitude'\n",
      "    nav_lat.units = 'degrees_north'\n",
      "    nav_lon = ssh_file.createVariable('nav_lon','float32',('yb','xbT'))\n",
      "    nav_lon.long_name = 'Longitude'\n",
      "    nav_lon.units = 'degrees_east'\n",
      "    # ssh\n",
      "    sossheig = ssh_file.createVariable('sossheig', 'float32', \n",
      "                               ('time_counter','yb','xbT'), zlib=True)\n",
      "    sossheig.units = 'm'\n",
      "    sossheig.long_name = 'Sea surface height'   \n",
      "    sossheig.coordinates = 'nav_lon nav_lat time_counter'\n",
      "    sossheig.grid = 'SalishSea2'\n",
      "    # vobtcrtx, vobtcrty\n",
      "    vobtcrtx = ssh_file.createVariable('vobtcrtx', 'float32',\n",
      "                                   ('time_counter','yb','xbT'), zlib=True)\n",
      "    vobtcrtx.units = 'm/s'\n",
      "    vobtcrtx.long_name = 'Barotropic U Velocity- ZEROD'   \n",
      "    vobtcrtx.grid = 'SalishSea2'\n",
      "    vobtcrty = ssh_file.createVariable('vobtcrty', 'float32',\n",
      "                                   ('time_counter','yb','xbT'), zlib=True)\n",
      "    vobtcrty.units = 'm/s'\n",
      "    vobtcrty.long_name = 'Barotropic V Velocity- ZEROD'   \n",
      "    vobtcrty.grid = 'SalishSea2'\n",
      "    # nbidta, ndjdta, ndrdta\n",
      "    nbidta = ssh_file.createVariable('nbidta', 'int32' , ('yb','xbT'), zlib=True)\n",
      "    nbidta.long_name = 'i grid position'\n",
      "    nbidta.units = 1\n",
      "    nbjdta = ssh_file.createVariable('nbjdta', 'int32' , ('yb','xbT'), zlib=True)\n",
      "    nbjdta.long_name = 'j grid position'\n",
      "    nbjdta.units = 1\n",
      "    nbrdta = ssh_file.createVariable('nbrdta', 'int32' , ('yb','xbT'), zlib=True)\n",
      "    nbrdta.long_name = 'position from boundary'\n",
      "    nbrdta.units = 1\n",
      "    \n",
      "    for ir in range(0,r):\n",
      "        nav_lat[0,ir*lengthj:(ir+1)*lengthj] = lat[startj:endj,ir]\n",
      "        nav_lon[0,ir*lengthj:(ir+1)*lengthj] = lon[startj:endj,ir]\n",
      "        nbidta[0,ir*lengthj:(ir+1)*lengthj] = ir\n",
      "        nbjdta[0,ir*lengthj:(ir+1)*lengthj] = range(startj,endj)\n",
      "        nbrdta[0,ir*lengthj:(ir+1)*lengthj] = ir\n",
      "        \n",
      "    for ib in range(0,lengthj*r):\n",
      "        sossheig[0:count,0,ib] = ssh[0:count]\n",
      "        time_counter[0:count] = range(1,count+1)\n",
      "        vobtcrtx[0:count,0,ib] = 0*np.ones(count)\n",
      "        vobtcrty[0:count,0,ib] = 0*np.ones(count)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Loop through the ssanomaly and time arrays to create the netcdf files.  Save them month by month.\n",
      "\n",
      "This is really clunky for now. I need the netcdf files in UTC, but all of the data files are in local time. So I will just ignore/delete the first and last month's files since these are incomplete. There is definitely room for improvement with this part of the code."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#setting up the loop through data\n",
      "\n",
      "#length of data:\n",
      "l=len(ssanomaly)\n",
      "hour_start = wlev_meas.time[0].hour;  yr_start= wlev_meas.time[0].year; month_start =wlev_meas.time[0].month; \n",
      "yr_end= wlev_meas.time[l-1].year;  month_end =wlev_meas.time[l-1].month; \n",
      "print 'Date range: ' +str(wlev_meas.time[0]) + ' to ' + str(wlev_meas.time[l-1]) + ' UTC'\n",
      "\n",
      "#files that will be deleted if hour does not begin at zero. \n",
      "if month_start<10:\n",
      "    filename = 'ssh_y' +str(yr_start)+'m0'+str(month_start)+'.nc'\n",
      "else:\n",
      "    filename = 'ssh_y' +str(yr_start)+'m'+str(month_start)+'.nc'\n",
      "file1=filename\n",
      "if month_end<10:\n",
      "    file2 = 'ssh_y' +str(yr_end)+'m0'+str(month_end)+'.nc'\n",
      "else:\n",
      "    file2 = 'ssh_y' +str(yr_end)+'m'+str(month_end)+'.nc'\n",
      "\n",
      "m=month_start; yr=yr_start; count =0; ssh=[];\n",
      "\n",
      "for ii in range(0, l):\n",
      "    yr=wlev_meas.time[ii].year\n",
      "    month = wlev_meas.time[ii].month\n",
      "    #If we entered a new month, save all the data.\n",
      "    if month != m:\n",
      "        #save the month's data:\n",
      "        ssh_file = NC.Dataset(filename, 'w', zlib=True)\n",
      "        print filename\n",
      "        print count       \n",
      "        #save data in netcdf    \n",
      "        prepare_netcdf(ssh_file,count,ssh, m, yr)\n",
      "        #Set up the next month's file\n",
      "        m=month;\n",
      "        if m<10:\n",
      "            filename = 'ssh_y' +str(yr)+'m0'+str(m)+'.nc'\n",
      "        else:\n",
      "            filename = 'ssh_y' +str(yr)+'m'+str(m)+'.nc'\n",
      "        #clear the ssh and count\n",
      "        ssh = []; count=0\n",
      "        ssh_file.close()\n",
      " \n",
      "    ssh.append(ssanomaly[ii])\n",
      "    count=count+1\n",
      "    \n",
      "    #If we are at the last item, save all the data.\n",
      "    if ii == l-1:\n",
      "        ssh_file = NC.Dataset(filename, 'w', zlib=True)\n",
      "        print filename\n",
      "        print count       \n",
      "        prepare_netcdf(ssh_file,count,ssh,month,yr)\n",
      "        #clear the ssh and count\n",
      "        ssh = []; count=0\n",
      "        ssh_file.close()\n",
      "    \n",
      "#If the hour is not zero then we have incomplete monthly data. Delete the incomplete files. \n",
      "#Note: this is not a proper check for incomplete data. For example, if the day is not the first day of the month...\n",
      "if hour_start != 0:\n",
      "    print '**** Removing ' + file1 + ' and ' + file2\n",
      "    os.remove(file1); os.remove(file2)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Date range: 2008-12-31 08:12:00+00:00 to 2010-01-02 08:12:00+00:00 UTC\n",
        "ssh_y2008m12.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "16\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:52] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m01.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "743\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:53] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m02.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "672\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:53] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m03.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:54] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m04.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:55] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m05.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:56] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m06.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:56] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m07.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:57] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m08.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:58] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m09.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:59] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m10.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:45:59] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m11.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "720\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:46:00] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2009m12.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "744\n",
        "file format: NETCDF4\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:46:01] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "ssh_y2010m01.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "33\n",
        "file format: NETCDF4"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Conventions: CF-1.6\n",
        "title: Juan de Fuca SSH hourly values\n",
        "institution: Dept of Earth, Ocean & Atmospheric Sciences, University of British Columbia\n",
        "source: https://github.com/SalishSeaCast/tools/blob/master/I_ForcingFiles/OBC/SSH_Tofino.ipynb\n",
        "references: REQUIRED\n",
        "history: [2015-04-23 14:46:02] Created netCDF4 zlib=True dataset.\n",
        "comment: SSH from Tofino: measured - tides (from t_tide)\n",
        "**** Removing ssh_y2008m12.nc and ssh_y2010m01.nc"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Check the NetCDF files\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A quick check that the data is saved in the netcdf file as expected. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = 'ssh_y2009m12.nc'\n",
      "\n",
      "f=  NC.Dataset(filename,'r');\n",
      "\n",
      "for dim in f.dimensions.values():\n",
      "    print dim\n",
      "    \n",
      "ssh=f.variables['sossheig'];\n",
      "us=f.variables['vobtcrtx'];\n",
      "vs=f.variables['vobtcrty'];\n",
      "print us.shape\n",
      "print us[1,0,:]\n",
      "print vs[:,0,:]\n",
      "    \n",
      "mn = ssh[:].min(); print mn\n",
      "mx = ssh[:].max(); print mx\n",
      "\n",
      "fig, ((ax_net,ax_data)) = plt.subplots(1, 2, figsize=(14,4))\n",
      "ax_net.plot(ssh[:,0,0])\n",
      "ax_net.set_xlabel(filename)\n",
      "ax_net.axis([10*24, 17*24, mn,mx])\n",
      "\n",
      "ax_data.plot(wlev_meas.time,ssanomaly)\n",
      "ax_data.set_xlim(['Dec 11 2009', 'Dec 18 2009'])\n",
      "ax_data.set_ylim([mn,mx])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'netCDF4.Dimension'>: name = 'xbT', size = 87\n",
        "\n",
        "<type 'netCDF4.Dimension'>: name = 'yb', size = 1\n",
        "\n",
        "<type 'netCDF4.Dimension'> (unlimited): name = 'time_counter', size = 744\n",
        "\n",
        "(744, 1, 87)\n",
        "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
        "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.]\n",
        "[[ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " ..., \n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]\n",
        " [ 0.  0.  0. ...,  0.  0.  0.]]\n",
        "-0.156304\n",
        "0.479705\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(-0.156304, 0.47970501)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HpIrIYY4ckSF5LqceHiIjgaFDZY7chx8C169b3SIiIuMOH5ZYReGhWzdZZOnO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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8620670c10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This looks good so let's move the files out of my working directory and into the forcing directory. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "srcdir='.'\n",
      "dstdir='/data/nsoontie/MEOPAR/NEMO-forcing/open_boundaries/west/ssh/'\n",
      "\n",
      "import shutil\n",
      "\n",
      "for basename in os.listdir(srcdir):\n",
      "    if basename.endswith('.nc'):\n",
      "        pathname = os.path.join(srcdir, basename)\n",
      "        if os.path.isfile(pathname):\n",
      "            shutil.copy2(pathname, dstdir)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Improvements to this code"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are a few ways to improve the functionality of this code.\n",
      "\n",
      "1. Include the matlab call to t_xtide within the notebook somwehere. At the moment, the call to matlab is performed outside of this script.\n",
      "\n",
      "2. Improve the method of handling local time and UTC. Right now, I have to download  extra data and then delete the incomplete month files. \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}