{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This block does the setup\n",
      "from IPython.core.display import HTML\n",
      "from IPython.display import display_html\n",
      "from CoolProp.Plots.Plots import Ph,Ps\n",
      "from CoolProp.CoolProp import Props, get_fluid_param_string\n",
      "import CoolProp.CoolProp as CP\n",
      "from math import sqrt\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import scipy.optimize\n",
      "%matplotlib inline\n",
      "labels = {'P' : '$p$', \n",
      "          'D' : r'$\\rho$', \n",
      "          'C' : '$c_p$',\n",
      "          'O' : '$c_v$', \n",
      "          'H' : '$h$',\n",
      "          'S' : '$s$', \n",
      "          'V' : '$\\mu$',\n",
      "          'L' : '$\\lambda$'}\n",
      "sat_labels = labels.copy()\n",
      "sat_labels['I'] = '$\\sigma$'\n",
      "\n",
      "Fluid = 'Water'\n",
      "RPFluid = 'REFPROP-'+get_fluid_param_string(Fluid,'REFPROPName')\n",
      "Tt = Props(Fluid,'Tmin')\n",
      "Tc = Props(Fluid,'Tcrit')\n",
      "rhoc = Props(Fluid,'Tcrit')\n",
      "T = np.linspace(Tt+0.01,0.99*Tc,20)\n",
      "h0 = Props('H','T',0.95*Tc,'Q',1,Fluid)\n",
      "s0 = Props('S','T',0.95*Tc,'Q',1,Fluid)\n",
      "h0_RP = Props('H','T',0.95*Tc,'Q',1,RPFluid)\n",
      "s0_RP = Props('S','T',0.95*Tc,'Q',1,RPFluid)"
     ],
     "language": "python",
     "metadata": {
      "slideshow": {
       "slide_type": "slide"
      }
     },
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Literature References"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from parse_bib import BibTeXerClass\n",
      "BTC = BibTeXerClass()\n",
      "    \n",
      "EOSkey = CP.get_BibTeXKey(Fluid, \"EOS\")\n",
      "CP0key = CP.get_BibTeXKey(Fluid, \"CP0\")\n",
      "SURFACE_TENSIONkey = CP.get_BibTeXKey(Fluid, \"SURFACE_TENSION\")\n",
      "VISCOSITYkey = CP.get_BibTeXKey(Fluid, \"VISCOSITY\")\n",
      "CONDUCTIVITYkey = CP.get_BibTeXKey(Fluid, \"CONDUCTIVITY\")\n",
      "ECS_LENNARD_JONESkey = CP.get_BibTeXKey(Fluid, \"ECS_LENNARD_JONES\")\n",
      "ECS_FITSkey = CP.get_BibTeXKey(Fluid, \"ECS_FITS\")\n",
      "\n",
      "BibInfo = ''\n",
      "if EOSkey:\n",
      "    BibInfo += '<p><b>Equation of State</b>: ' + BTC.entry2HTML(EOSkey)\n",
      "if CP0key:\n",
      "    BibInfo += '<p><b>Ideal-Gas Specific Heat</b>: ' + BTC.entry2HTML(CP0key)\n",
      "if SURFACE_TENSIONkey:\n",
      "    BibInfo += '<p><b>Surface Tension</b>: ' + BTC.entry2HTML(SURFACE_TENSIONkey)\n",
      "if VISCOSITYkey:\n",
      "    BibInfo += '<p><b>Viscosity</b>: ' + BTC.entry2HTML(VISCOSITYkey)\n",
      "if CONDUCTIVITYkey:\n",
      "    BibInfo += '<p><b>Conductivity</b>: ' + BTC.entry2HTML(CONDUCTIVITYkey)\n",
      "if ECS_LENNARD_JONESkey:\n",
      "    BibInfo += '<p><b>Lennard-Jones Parameters for ECS</b>: ' + BTC.entry2HTML(ECS_LENNARD_JONESkey)\n",
      "if ECS_FITSkey:\n",
      "    BibInfo += '<p><b>ECS Correction Fit</b>: ' + BTC.entry2HTML(ECS_FITSkey)\n",
      "\n",
      "display_html(HTML(BibInfo))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<p><b>Equation of State</b>: Wagner, W.; Pruss, A., 2002, The IAPWS Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use, <i>J. Phys. Chem. Ref. Data</i>, 31:387-535<p><b>Surface Tension</b>: Mulero, A.; Cachadi\u00f1a, I.; Parra, M. I., 2012, Recommended Correlations for the Surface Tension of Common Fluids, <i>J. Phys. Chem. Ref. Data</i>, 41:043105-1:13<p><b>Viscosity</b>: Huber, M.L.; Perkins, R.A.; Laesecke, A.; Friend, D.G.; Sengers, J.V.; Assael, M.J; Metaxa, I.M.; Vogel, E.; Mare\u00161, R.; Miyagawa, K., 2009, New International Formulation for the Viscosity of H2O, <i>J. Phys. Chem. Ref. Data</i>, 38:101-125<p><b>Conductivity</b>: Huber, M. L.; Perkins, R. A.; Friend, D. G.; Sengers, J. V.; Assael, M. J.; Metaxa, I. N.; Miyagawa, K.; Hellmann, R.; Vogel, E., 2012, New International Formulation for the Thermal Conductivity of H2O, <i>J. Phys. Chem Ref. Data</i>, 41:033102-1:23<p><b>Lennard-Jones Parameters for ECS</b>: Poling, Bruce E.; Prausnitz, John M.; O'Connell, John P., 2001, <i>The Properties of Gases and Liquids, 5th edition</i>, McGraw Hill"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Aliases"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aliases = get_fluid_param_string(Fluid,'aliases')\n",
      "if aliases:\n",
      "    display_html(HTML(', '.join(['\"'+alias+'\"' for alias in aliases.split(', ')])))\n",
      "else:\n",
      "    print '\"'+Fluid+'\"'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\"water\", \"WATER\", \"H2O\", \"h2o\""
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Fluid Constants"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "params = dict(mm = CP.Props(Fluid,'molemass'),\n",
      "              Tt = CP.Props(Fluid,'Ttriple'),\n",
      "              pt = CP.Props(Fluid,'ptriple'),\n",
      "              Tc = CP.Props(Fluid,'Tcrit'),\n",
      "              pc = CP.Props(Fluid,'pcrit'),\n",
      "              rhoc = CP.Props(Fluid,'rhocrit'),\n",
      "              Tmin = CP.Props(Fluid,'Tmin'),\n",
      "              CAS = get_fluid_param_string(Fluid,'CAS'),\n",
      "              ASHRAE = get_fluid_param_string(Fluid,'ASHRAE34')\n",
      "              )\n",
      "\n",
      "s = \"\"\"<table border = \"1\">\n",
      "<tr> <th>Parameter</th> <th>Value</th> </tr>\n",
      "<tr > <td colspan=\"2\"><center>Triple point</center></td> </tr>\n",
      "<tr> <td>Triple Point Temp. [K]</td> <td>{Tt:0.3f}</td> </tr>\n",
      "<tr> <td>Triple Point Press. [kPa]</td> <td>{pt:0.10g}</td> </tr>\n",
      "<tr > <td colspan=\"2\" ><center>Critical point</center></td> </tr>\n",
      "<tr> <td>Critical Point Temp. [K]</td> <td>{Tc:0.3f}</td> </tr>\n",
      "<tr> <td>Critical Point Press. [kPa]</td> <td>{pc:0.10g}</td> </tr>\n",
      "<tr> <td>Critical Point Density. [kPa]</td> <td>{rhoc:0.10g}</td> </tr>\n",
      "<tr> <td colspan=\"2\"><center>Other Values</center></td> </tr>\n",
      "<tr> <td>Mole Mass [kg/kmol]</td> <td>{mm:0.5f}</td> </tr>\n",
      "<tr> <td>Minimum temperature [K]</td> <td>{Tmin:0.3f}</td> </tr>\n",
      "<tr> <td>CAS number</td> <td>{CAS:s}</td> </tr>\n",
      "<tr> <td>ASHRAE classification</td> <td>{ASHRAE:s}</td> </tr>\n",
      "</table>\"\"\".format(**params)\n",
      "HTML(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border = \"1\">\n",
        "<tr> <th>Parameter</th> <th>Value</th> </tr>\n",
        "<tr > <td colspan=\"2\"><center>Triple point</center></td> </tr>\n",
        "<tr> <td>Triple Point Temp. [K]</td> <td>273.160</td> </tr>\n",
        "<tr> <td>Triple Point Press. [kPa]</td> <td>0.611654771</td> </tr>\n",
        "<tr > <td colspan=\"2\" ><center>Critical point</center></td> </tr>\n",
        "<tr> <td>Critical Point Temp. [K]</td> <td>647.096</td> </tr>\n",
        "<tr> <td>Critical Point Press. [kPa]</td> <td>22064</td> </tr>\n",
        "<tr> <td>Critical Point Density. [kPa]</td> <td>322</td> </tr>\n",
        "<tr> <td colspan=\"2\"><center>Other Values</center></td> </tr>\n",
        "<tr> <td>Mole Mass [kg/kmol]</td> <td>18.01527</td> </tr>\n",
        "<tr> <td>Minimum temperature [K]</td> <td>273.160</td> </tr>\n",
        "<tr> <td>CAS number</td> <td>7732-18-5</td> </tr>\n",
        "<tr> <td>ASHRAE classification</td> <td>A1</td> </tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<IPython.core.display.HTML at 0x5becc70>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Saturation Property Deviations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(13,6))\n",
      "ax1 = fig.add_subplot(121)\n",
      "ax2 = fig.add_subplot(122)\n",
      "\n",
      "for ax, Q in zip([ax1,ax2],[0,1]):\n",
      "    for key, label in sat_labels.iteritems():\n",
      "        CPval = Props(key, 'T', T, 'Q', Q, Fluid)\n",
      "        RPval = Props(key, 'T', T, 'Q', Q, RPFluid)\n",
      "        if key =='H': \n",
      "            CPval -= h0\n",
      "            RPval -= h0_RP\n",
      "        if key =='S': \n",
      "            CPval -= s0\n",
      "            RPval -= s0_RP\n",
      "        ax.semilogy(T/Tc, np.abs(CPval/RPval-1)*100, 'o', label=label)\n",
      "    if Q == 0:\n",
      "        ax.set_title('Saturated Liquid')\n",
      "    else:\n",
      "        ax.set_title('Saturated Vapor')\n",
      "    ax.set_ylim(1e-18,1000)\n",
      "    ax.set_xlabel('Reduced temperature T/Tc')\n",
      "    ax.set_ylabel('Absolute deviation [%]')\n",
      "    ax.legend(numpoints=1,loc='lower center',ncol = 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6fkyud5X7eZ1nEcl9pvHCxrnaD/Ei12MFKF6IZEtBJhd+WE7O6/pgJ8nymlbeEJFMMo0X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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5ca2510>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Along the critical isotherm where $T=T_c$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rho = np.linspace(1e-12,1.5*rhoc)\n",
      "\n",
      "fig = plt.figure(figsize=(6,6))\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "for key, label in labels.iteritems():\n",
      "    CPval = Props(key, 'T', Tc, 'D', rho, Fluid)\n",
      "    RPval = Props(key, 'T', Tc, 'D', rho, RPFluid)\n",
      "    if key =='H': \n",
      "        CPval -= h0\n",
      "        RPval -= h0_RP\n",
      "    if key =='S': \n",
      "        CPval -= s0\n",
      "        RPval -= s0_RP\n",
      "    ax.semilogy(rho/rhoc, np.abs(CPval/RPval-1)*100, 'o', label=label)\n",
      "\n",
      "ax.set_ylim(1e-18,100)\n",
      "ax.set_title('Critical isotherm Deviations from REFPROP 9.1')\n",
      "ax.set_xlabel(r'Reduced density $\\rho/\\rho_c$')\n",
      "ax.set_ylabel('Absolute deviation [%]')\n",
      "ax.legend(numpoints=1,loc='lower center',ncol=4)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/4G88A3vixcXhe+KJZvas2SxeujgktMjsWbNjfi3dQsQi3/qRaLbalei+gpA4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\niIiIiIiIiIiIiIiIiIiIiIiIiIjY9/9cQYKX9/SkuAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7574930>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Check of p,h and p,s as inputs (X: Failure .: Success)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,5))\n",
      "ax1 = fig.add_subplot(121)\n",
      "ax2 = fig.add_subplot(122)\n",
      "\n",
      "Tmin = Props(Fluid,'Tmin')+3\n",
      "pmin = Props('P','T',Tmin,'Q',0,Fluid)\n",
      "pmax = Props(Fluid,'pcrit')*2\n",
      "hmin = Props('H','T',Tmin,'Q',0,Fluid)\n",
      "hmax = 2*Props('H','T',Tc-1,'Q',1,Fluid)-hmin\n",
      "smin = Props('S','T',Tmin,'Q',0,Fluid)\n",
      "smax = 2*Props('S','T',Tc-1,'Q',1,Fluid)-smin\n",
      "\n",
      "Ph(Fluid, axis = ax1, Tmin = Tmin, Tmax = Tc-0.01)\n",
      "Ps(Fluid, axis = ax2, Tmin = Tmin, Tmax = Tc-0.01)\n",
      "\n",
      "for p in np.linspace(pmin,pmax,10):\n",
      "    for h in np.linspace(hmin,hmax):\n",
      "        _bad = False\n",
      "        try:\n",
      "            T = Props('T','H',h,'P',p,Fluid)\n",
      "            rho = Props('D','H',h,'P',p,Fluid)\n",
      "            hEOS = Props('H','T',T,'D',rho,Fluid)\n",
      "        except ValueError:\n",
      "            _bad = True\n",
      "        if _bad or abs(hEOS/h-1)>1e-6:\n",
      "            ax1.plot(h,p,'x',ms = 10)\n",
      "        else:\n",
      "            ax1.plot(h,p,'k.', ms = 1)\n",
      "\n",
      "for p in np.linspace(pmin,pmax,10):\n",
      "    for s in np.linspace(smin,smax):\n",
      "        _bad = False\n",
      "        try:\n",
      "            T = Props('T','S',s,'P',p,Fluid)\n",
      "            rho = Props('D','S',s,'P',p,Fluid)\n",
      "            sEOS = Props('S','T',T,'D',rho,Fluid)\n",
      "        except ValueError:\n",
      "            _bad = True\n",
      "        if _bad or abs(sEOS/s-1)>1e-6:\n",
      "            ax2.plot(s,p,'x',ms = 10)\n",
      "        else:\n",
      "            ax2.plot(s,p,'k.', ms = 1)\n",
      "\n",
      "plt.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CUl4WEXFROOMyzgMaAJuBXCADqAr8y72wok5j30Qkplwek6y8LCISoWiPSR4C\nbABeBr4F+gI5JFYiFhFJJsrLIiIxEKqa3gB4gP1AQ2AKcLXLMblBPRYiElMu9iQrL4uIFEO0e5JP\nYhIxwPdA+eKFJSIiUaK8LCISA6GK5DqYXXqvOLcLA56/HMby6wKLMT0f64GHnPaRwA5gjXPrGTDP\nU5hxdt8A3QLaWwHrnL+NDWgvD/zTaV8B1A8jLhGRRFWSvKycLCISplBnt3gc/1WcAFY5z9OC2ouS\nBzwCrAUqO/PPd+Z90bkFagbc4txfCCwAGjvTjwMGAyuBOUAPYK7TdsCZ7hbgeWBAGLGJiCSikuRl\n5WQRkTCFKpInOfcNMbv1Al0VxvL3ODeAo8C/MYkWCh8T0heYiknkW4EtQGvgB6AKJhmDuexqP0xC\n7gOMcNo/BP4eRlwiIolqknNfnLysnCwiEqZwLybyAWYXn09HYGKE62oAtMTsfgN4EHM09gT8V4mq\njdnl57MDk8CD23fiT+wXAtudx6eAw0CNkNHk5sInn0S4CSIicaOkebkB8ZSTRUTiTLhF8m+BmcAF\nQC/MuLeeZ52joMqYhP4wpvdiHOa8npdjLqn6vxEsq+Ryc2H4cGjbNqarFRGJopLk5fjKySIicSiS\nK+49hBm7dhy4BnMJ1HCUxexyeweT0Amadzww23m8k4KXVa2D6a3YScEeE1+7b556wC7M9lQDDgYH\nMXLkSPPgxAk833yDZ9IkSE8PnkxEpFgyMzPJzMyM5SqLm5fjIidDQF4GPB4PHo8njPBFRMJT0rwc\n6lxxs4OeN8X0MuRiDtzoE8byJ2MO4ngkoL2Wsxyc9t8At2EODpmCGVfnO0jkImdd2ZgvhJXAJ5he\nk7nAUKAFcD/m4JB+nHmQiDkfp68HedQoFcgi4ioXz5NckrwcLzkZdJ5kEYmxSPNyqJ7kwna5RXJ2\ni7bAHcDXmNMKATwN3IrZrefFXCnqt87fNgLvO/enMMnWt56hmANWKmKOpJ7rtE8A3sacbugARR1F\nvXUrjBnzS4GclZUFQPv27QtM5ma7lq1lx+s6tWz3lu2CkuTl+MnJmNesuO9FSd9L2/MnUqy250+k\nWLWt7k1rQ6giObOEy/+cwsc9f3qWeZ51bsFWYXongv0M3BwykowMyMlRD7KIJLrMEswbPzlZRCTO\nhepy/hi4NgrT2Ob15uQU6EkWEXGTi8Mtkicva7iFiMRQpHk51ISHgaUhprkEc1R0PNOYZBGJKReL\n5OTKyyIDG4F1AAAgAElEQVQiMRLtItkTxjJ+BpaHu0JL/MlYhbKIxICLRbInjGkSKy+LiMRAtIvk\nZFEwGatQFhGXuVgkJwsVySISUyqSC3dmMs7NhWXLoHdvOxGJSFJTkRySimQRiSkVyYVTMhaRmFKR\nHJLysojEVKR5OdzLUgNUAi6ONCAREXGN8rKIiEvCLZL7YE48P8953hKY5UpEIiISDuVlEREXhVsk\njwRaA4ec52uAhm4EJCIiYRmJ8rKIiGvCLZLzgNygtvwoxyIiIuFTXhYRcVG4RfIG4HbMZawbA68A\nX7gVlIiIhKS8LCLionCL5GFAc8wJ6qcCR4D/disoEREJSXlZRMRF4ZwGowwwH+jkcixu0qmGRCSm\nXD4FnPKyiEiE3DgF3CnMODddmk5EJD4oL4uIuKxMmNMdA9Zhei6OOW1e4CE3ghKJpvz8fDZs2EBm\nZiYdO3bk0ksvJTs7mxUrVtCyZUsuv/xyqlatajtMkUgpL0tCOHDgAO+//z4dOnTg2LFjrF69mmuu\nuYZGjRrZDk3krMItkqc7t0DaTyZxa/PmzcydO5fMzEyWLFlC9erV8Xg8dOjQ4ZdpvvnmG6ZOncq6\ndeu44IILaNmy5S+3Dh06ULlyZYtbIBKS8rLErdOnT/PRRx/x9ttvs3jxYnr06EH79u0BWL58Oc88\n8wyVKlXimmuuoVu3bvTq1YsKFSpYjlqkoFS5ZKrGvqWIvLw8/vrXvzJu3Dj69u2Lx+OhY8eO1KlT\np8h5Tp8+zaZNm1izZg1r167lq6++YuPGjTz++OMMHTqUihUrxnALJFnostQhKS8nqdOnT3P33Xez\nceNGHnjgAW688UaqVatWYBqv18v69euZP38+s2fPZv/+/UydOpUWLVpYilpSQaR5OdwJcwpp85I4\nJ65XMk4BBw8e5LrrriM9PZ3x48dTq1atYi9r3bp1jBgxgi+//JIJEybQrVu3KEYqqSAGRbLyssSl\nBx98kH//+9/MmjWLSpUqhZze6/Xy1ltv8dhjjzF+/Hj69u0bgyglFUWal8MdbvGbgMcVgP5AzfDD\nEnHX/v376dSpEz179uSFF17w/SMUW4sWLZg+fToLFy5k0KBB3HjjjYwZM4YyZcL9lxFxnfKyxJ2Z\nM2cyZ84cVq9eHVaBDKZwueuuu2jevDnXXnstgApliQslqSRWA1dEKxCXqcciiR0/fpxOnTrRqVMn\nnnvuuagv/9ChQ9x6660ATJs2jSpVqkR9HZJ8LA23UF4Wa06cOEHTpk2ZOHEinToV7+yEq1atomfP\nnsyZM4crr7wyyhFKqnNruEUr/AeElAKuBO4HLoskOIuUjJPY/fffz8GDB3nvvfdK3INclFOnTnHf\nffeRk5PDnDlzNE5ZQopBkay8LHHlhRde4IsvvmDmzJklWs706dP5/e9/z+rVq6levXqUohNxr0jO\nxJ+MTwFbgb8B34YfmlVKxklq+vTpPPbYY6xZs+aMA0Oi7fTp0wwcOJDc3FxmzJhBuXLlXF2fJLYY\nFMmZKC9LnDh69CgNGjRg2bJlXHzxxSVe3oMPPsiRI0eYPHlyFKITMdwqkhOdknES2rdvHy1atGDW\nrFm0bt06JuvMy8vjpptuomrVqkyePNm1nmtJfDq7RUjKy0nk5ZdfJisri2nTpkVleUePHqV58+ZM\nnDiRLl26RGWZIm5ccQ/gYaCqs+AJmHFv3SMNTiSannzySe64446YFcgAZcuWZcqUKaxdu5bXXnst\nZusVKYTyssSF/Px8xo4dy6OPPhq1ZVauXJl//OMf/O53v+PkyZNRW65IJMItkgcDR4BuQA1gIDA6\njPnqAouBDcB6/FeCqoG5StQm4DMKXlr1KWAz8I2zPp9WmKtLbQbGBrSXB/7ptK8A6oe5TZLAli9f\nzrx58xgxYkTM112pUiU+/PDDX04RJ2JJcfKycrJE3eLFi6lSpQpt2rSJ6nKvvfZaLrroIsaPHx/V\n5YqEK9wi2dc13Rt4G5Ncw5EHPAI0B64GHgCaAk9iEnITYKHzHKAZcItz3wN4NWDd4zBfCo2dWw+n\nfTBwwGl7CXg+zNgkQZ0+fZphw4bxwgsvWLucdOPGjXnllVcYOHAgJ06csBKDpLzi5GXlZIm6SZMm\nMWjQIFeW/de//pVRo0bx008/ubJ8kbMJt0heheld6AXMw+ziyw9jvj3AWufxUeDfwIVAH8A3Gn8y\n0M953BeYiknkW4EtQGugFlAFWOlM91bAPIHL+hAIa/BS7olcPtn0STiTSpx55513qFixIrfddpvV\nOG655RaaN2/OM888YzUOSVnFyctxm5MlMR0+fJjZs2dz++23u7L8Vq1acfXVV/Pqq6+6snyRswm3\nSL4Hs8vtSuAYUBaI9GdjA6AlkA2cD+x12vc6zwFqAzsC5tmBSeDB7Tuddpz77c7jU8BhzK7DIuWe\nyGX4wuG0rdc2wk0Q237++WdGjBjBc889FxcHzf3jH/9gwoQJrFu3znYoknpKmpcbECc5WRLXtGnT\n6Ny5M+eee65r6/jLX/7CmDFjOH78uGvrEClMuEVyG8xphXKBO4E/YhJfuCpjehQeBv4T9Dcv/tMY\nuc5XII/qMor0CumhZ5C48sYbb9CsWTPat29vOxQAzj//fP785z/zyCOPoCP1JcZKkpfjJidLYps8\neTJ33323q+to3rw5V111Fe+8846r6xEJFu41dl8DLsWcpP5RYDxm91rHMOYti0nGbwO+M4zvBS7A\n7PqrBexz2ndiDizxqYPprdjpPA5u981TD9jlbE814GBwECNHjuTEqRMs/H4hI+4eoQI5Af300088\n++yzfPJJfA2T+d3vfse4ceOYNWuWLqWawjIzM8nMzIzlKoubl+MiJ4PJyz4ejwePxxMidIknO3fu\nZMOGDfTo0SP0xCX0yCOPMGzYMAYPHkypUuH270mqi1VeXuPcjwCGOI9XhzFfGiZpvxTU/gLwhPP4\nSfxHZDfDjJcrB2QA3+E/SCQbMxYuDZiD/yCRoZgDSAAGAO8VEoc351COd+jHQ72Hjh/yer1e79Kl\nS71Lly71BnOzXcsu2bJffvllb7t27eIy7nnz5nkbNWrkXbhwYVy8Vlq2/WXjfm9scfJyvORkgBK9\nFyV9L23Pn0ixFjX/ww8/7O3evbvr6/d6vd4lS5Z4GzZs6J0zZ46r67I9fyLFGsttjRYizMvh/hz7\nD/A0cAfwMVAa0xsRSltnnk6YhL4Gk0hHA9dgTjfUGX9C3gi879x/ikm2vg0aiukp2Yw5eGSu0z4B\nqOm0/zf+o7ILyBibweNtH1cPcoI6efIkf/vb37jjjjtsh1Kobt260bBhQz799FPboUjqKE5ejpuc\nLIkvMzMzZr3/aWlp3Hjjjbz++usxWZ8IhH/VkVrArcCXQBZmV5oH0yORCLw5h3IYs2yMxiInqEmT\nJvHOO++wYMEC26EUafny5QwYMIDNmzfrktUSiyvuJXxe9mocf8LavXs3zZo1Y/fu3VSoUCEm6zx6\n9Ch169Zl48aN1KpVKybrlOTi1hX3dgPTMSeJB/gR/1i2hNAgvQGjuoxi+MLh5J7ItR2OROD06dOM\nHj2ap59+2nYoZ9WmTRuaNm3KpEmTbIciqSHh87IkrhkzZtC7d++YFchgrsLXv39/Jk+eHHpikSgI\nt0i+D5gG+PZz1AFmuBKRi9IrpKtQTkAzZ86kWrVqdOrUyXYoIY0YMYLRo0dz+vRp26FI8kuKvCyJ\nafr06dxwww0xX++QIUMYP368ziYkMRFukfwA0A5zCVQw49Z+5UpELlOhnFi8Xi/PP/88Tz31VFyc\nFzmUNm3acMEFF/DRRx/ZDkWSX9LkZUksR44cITs7m27duoWeOMquuuoqKlasyNKlS2O+bkk94RbJ\nPzs3nzIk8Hk0fYXysm3LbIciIaxYsYKDBw/Sp08f26GE7dFHH+XFF1+0HYYkv6TKy5I4PvvsM9q2\nbUvlypVjvu60tDTuuOMOpk6dGvN1S+oJt0heAgwHKmGOgJ4GzHYrqFhIr5BO7ya9bYchIYwdO5Zh\nw4Yl1Hkx+/Xrx44dO1i5cmXoiUWKL+nysiSGjz/+mGuvvdba+gcMGMAHH3zAyZMnrcUgqSHc/del\nMOfh9O1bmYc59U+i9FroKOoEtGPHDi699FJycnKoVq2a7XAi8tJLL7Fy5Ur1dqSwGJzdQnlZYu70\n6dPUqlWL7OxsMjIyrMXRrl07nnzySavFuiSeSPNyOBOWAdYDvy5mTPFAyTgBDR8+nCNHjvDKK6/Y\nDiViubm5NGjQgC1btnDuuefaDkcscLlIVl4WK1asWMGQIUNYv3691TheffVVPv/8c6ZMmWI1Dkks\nbpwC7hTwLVC/mDGJROz48eO88cYbPPjgg7ZDKZb09HT69OnD22+/bTsUSU7Ky2KF7aEWPjfddBNz\n5szh2LFjtkORJBbuQM8awAZgEWbM22xglltBiUydOpUrr7ySJk2a2A6l2O69917eeOMNnapI3KK8\nLDH38ccfc91119kOg/POO4+rrrqKuXPnhp5YpJjKhDndH537wC5qffOLa15//XX+/Oc/2w6jRNq1\na8fp06dZvnw5//Vf/2U7HEk+yssSU7t27WLbtm20bt3adigAXH/99cyYMYMbb7zRdiiSpEIVyRWB\n3wEXAV8DE4E8t4OS1LZ+/Xp27txJjx49bIdSImlpaQwZMoQ33nhDRbJEk/KyWLFgwQI6d+5MmTLh\n9q+5q2/fvgwfPpyTJ09Srlw52+FIEgo13GIy0AqTiHsBf3M9Ikl5EydO5K677qJ06dK2QymxgQMH\nMmPGDI2bk2hSXhYr5s+fzzXXXGM7jF/Url2biy++mMWLF9sORZJUqCP81gEtnMdlgC+Blq5G5A4d\nRZ0gTp48SZ06dVi+fDmNGjWyHU5UdO/encGDB3PzzTfbDkViyMWzWygvS8x5vV5q1arF8uXLrZ76\nLdgLL7zA999/z2uvvWY7FEkA0T67xakiHou4YtasWTRv3jxpCmSAW2+9VedLlmhSXpaYW7duHZUr\nV46rAhnMuOSPPvqI/Px826FIEgpVJF8K/Cfg1iLg8RF3Q5NUNGHCBO655x7bYURVv379WLRoEbm5\nubZDkeSgvCwx99lnn9GtW7fQE8ZY48aNqVmzJitWrLAdiiShUEVyaaBKwK1MwOOq7oYmqWb79u1k\nZ2cn3ZHK6enpdO7cmRkzZtgORZKD8rLEXLyNRw7Ur18/PvroI9thSBIK9zzJIq6bPHkyt9xyC5Uq\nVbIdStRpyIWIJKoTJ07wxRdf0KlTJ9uhFKp37958+umntsOQJKQiWeJCfn4+EydOZPDgwbZDccW1\n115LdnY2P/74o+1QREQi8vnnn9OiRQvS09Nth1Koq666il27drF9+3bboUiSUZEscSEzM5PKlSvT\nqlUr26G4olKlSnTp0oVPPvnEdigiIhGJ56EWAKVLl6Zbt266+p5EnYpkiQu+XmTn9CxJSePmRCQR\nxetBe4F69erFnDlzbIchSSZ5K5KCdD7OOJabm0uDBg347rvvqFmzpu1wXHPgwAEaNmzInj17qFix\nou1wxGUunic5WSgvJ4B9+/bRpEkT9u/fT9myZW2HU6T9+/dz0UUXsW/fPsqXL287HIlT0T5Psojr\npkyZQvfu3ZO6QAaoWbMmLVu2ZMGCBbZDEREJy4IFC/B4PHFdIAOcd955NG3alM8//9x2KJJEVCSL\ndcl4buSi9O3bV0MuRCRhxPt45EC9evXSWS4kqlQki1Vr165l//79dO3a1XYoMdG3b19mz57N6dOn\nbYciInJWXq+X+fPnx/14ZJ+ePXtqXLJEldtF8kRgL7AuoG0ksANY49x6BvztKWAz8A0Q+F/ZylnG\nZmBsQHt54J9O+wqgflSjF9dNnDiRQYMGUbp0aduhxETDhg0577zz+Oqrr2yHIqlLeVnC8u9//5sy\nZcpw0UUX2Q4lLK1ateLHH38kJyfHdiiSJNwukt8EegS1eYEXgZbOzbdvpBlwi3PfA3gV/+DqccBg\noLFz8y1zMHDAaXsJeN6NjRB3nDhxgilTpnD33XfbDiWmunfvzrx582yHIalLeVnC4htqkShnHSpV\nqhQ9e/bUkAuJGreL5CzgUCHthf3H9QWmAnnAVmAL0Bqohbnc6kpnureAfs7jPsBk5/GHQJdwA8vN\nBZ2y1q6ZM2dy+eWXk5GRYTuUmFKRLJbFbV6W+LJgwYKEGY/s06NHD+VXiRpbY5IfBP4FTAB8l/Cp\njdnd57MDuLCQ9p1OO8697xI7p4DDQI1QK8/NheHDoW3b4oYv0ZDMV9g7m/bt2/P111+Tm5trOxSR\nQFbzssSXvLw8li5dSufOnW2HEpEuXbqwZMkSTp06ZTsUSQI2iuRxQAZwObAb+N9YrtxXII8aBXF6\nhc2U8MMPP7B69Wquv/5626HEXMWKFWnbti0LFy60HYqIj9W8LPEnOzubiy66iHPPPdd2KBH51a9+\nRYMGDfjyyy9thyJJoIyFde4LeDwemO083gnUDfhbHUxPxU7ncXC7b556wC7MtlQDDha20pEjR3Li\nBCxcCCNGeEhP95RwM6Qk3nzzTQYMGECFChVsh2KFb8jFjTfeaDsUiZLMzEwyMzNth1Fc1vKyj8fj\nwePxFDN8ibb58+cn7FmHunbtyoIFC2jTpo3tUMSyRMjLDSh4FHWtgMePAFOcx82AtUA5TI/Gd/jH\nyGVjxsGlAXPwHyAyFNMDAjAAeK+IGLw5OV7v0KFe76FDXq/X6/UuXbrUu3TpUm8wN9u1bNN+6tQp\nb7169byrV69OqLijuYwNGzZ469Wr512yZElSbI+WfWY75mC4eNWAOMjLJXkvSvpe2p4/3mNt06aN\nd/78+XERf6TrGjNmjLdDhw5WYk32z0U054902mggwrzsdk/yVKAjcC5mjNoIwIPZpecFcoDfOtNu\nBN537k9hEq1vY4YCk4CKmGQ812mfALyNOdXQAUxCLlRGBuTkaIhFPFi0aNEvV5/LysqyHY4VTZs2\nJT8/n+3bt1OvXj3b4UhqiZu8LPHp6NGjrFu3jrYJeuDOpZdeyjPPPMPRo0epXLmy7XAkgSXGeV1K\nzpuT42XMGI1FjgcDBgygffv2PPDAA7ZDsWrgwIG0bduW3/72t6EnloTjnDYrVXJscTgdOxJvPvro\nI/7+978zf/5826EUW6dOnXj88cfp1auX7VAkjkSal1PminsNGpgCefhwc/Ce2HHgwAHmzp3Lbbfd\nZjsU6zp27MiSJUtshyEiUkAinvotmG9cskhJpEyRDKYHWYWyXe+++y69evWievXqtkOxzuPxsGTJ\nEtSbJiLxJJEP2vNRkSzRkFJFMqhQtsnr9TJhwoSUPDdyYRo2bEhaWhpbtmyxHYqICADbt2/nxx9/\n5PLLL7cdSom0atWK7du3s2fPHtuhSAJLuSIZ/IXysmW2I0ktq1ev5siRI3Tq1Ml2KHEhLS1NQy5E\nJK4sWLCALl26UKpUYpcHZcqUwePxsGjRItuhSAJL7P+CEkhPh969bUeRWiZMmMCgQYMSPvlGk8fj\niftzOIpI6kiG8cg+11xzTUIffCj2pcqR1zqK2rLjx49Tp04d1qxZo1OeBdi0aRNdunRh27ZtvqNu\nJUno7BYhKS/Hmfz8fGrVqkV2djYNGjSwHU6JKb9KMJ3dQuLS9OnT+c1vfqMCOUjjxo05ffo0OTk5\ntkMRkRS3fv16qlatmhQFMpj8mpaWxqZNm2yHIglKRbLExIQJE7jnnntshxF30tLS6NChA0uXLrUd\nioikuGQ4q0WgtLQ0neVCSkRFsrjuu+++Y926dfTt29d2KHGpTZs2rFixwnYYIpLikmk8ss8111yj\nIlmKTUWyuO7NN9/k9ttvp3z58rZDiUtXX321imQRsernn39m2bJlSXf2oc6dO5OZmcmpU6dshyIJ\nSEWyuOrUqVNMmjRJ50Y+i5YtW7J582aOHj1qOxQRSVGff/45zZs3T7oLPZ1//vnUq1ePr776ynYo\nkoBUJIur5s2bR506dWjRooXtUOJWuXLluOyyy/jyyy9thyIiKerTTz+lR48etsNwhcYlS3GpSBZX\njR8/niFDhtgOI+5pXLKI2DR37lx69uxpOwxXdO3aVedLlmJRkSyu2bNnD5mZmdxyyy22Q4l7Gpcs\nIrb4Lt/cqlUr26G4okOHDqxatYpjx47ZDkUSjIpkcc3kyZO54YYbqFKliu1Q4l6bNm1Yvnw5uriC\niMTavHnz6NatG6VLl7YdiivOOeccWrVqRVZWlu1QJMGoSBZXeL1eDbWIQJ06dShXrhzff/+97VBE\nJMV8+umnSTvUwqdr164sXLjQdhiSYFQkiyuWLl1KuXLluPrqq22HkjBat26tI7BFJKby8vJYuHAh\n3bp1sx2Kq7p06aKD9yRiKpLFFb5eZOc66RKGK664gtWrV9sOQ0RSyIoVK2jUqBHnn3++7VBc9Zvf\n/Ibvv/+e/fv32w5FEoiKZIm63NxcZs+ezZ133mk7lISiIllEYi2Zz2oRqGzZsnTo0IHFixfbDkUS\niIpkibopU6bQvXt3zj33XNuhJJQrrriCVatW6eA9EYmZuXPnJu35kYPpfMkSKRXJEnU6YK94zj//\nfCpWrMgPP/xgOxQRSQF79uzh+++/T5ljR1QkS6RUJEtUrV69moMHD9KlSxfboSSkVq1aaciFiMTE\n3Llz6dq1K2XKlLEdSkw0a9aM48eP6yxCEjYVyRJVr732GkOGDKFUKX20ikPjkkUkVmbNmkWfPn1s\nhxEzaWlpdOnSRaeCk7CpkpGoOXz4MNOmTdNQixJQkSwisXDixAkWLlxIr169bIcSUxpyIZFQkSxR\n89Zbb9GtWzcuuOAC26EkLB28JyKxsHjxYi677DJq1qxpO5SY6tKlC4sWLSI/P992KJIA3C6SJwJ7\ngXUBbTWA+cAm4DMgPeBvTwGbgW+AwDObt3KWsRkYG9BeHvin074CqB/d8CVcXq+XcePGcf/999sO\nJaFdeOGFeL1edu/ebTsUSV7Ky8Ls2bO57rrrbIcRc3Xr1qVGjRp8/fXXtkORBOB2kfwmEHxumScx\nybgJsNB5DtAMuMW57wG8CviuRDEOGAw0dm6+ZQ4GDjhtLwHPu7EREtrSpUsB6Nixo+VIEltaWhot\nWrRg/fr1tkOR5KW8nOK8Xm/KjUcOpKvvSbjcLpKzgENBbX2Ayc7jyUA/53FfYCqQB2wFtgCtgVpA\nFWClM91bAfMELutDQKdUsOTVV1/l/vvv1xX2ouCSSy5RkSxuUl5OcWvWrKFSpUpcfPHFtkOxomvX\nrjp4T8JiY0zy+ZhdfTj3vmth1gZ2BEy3A7iwkPadTjvO/Xbn8SngMGa3ocTQnj17+Oyzzxg4cKDt\nUJKCimSxQHk5hcyePTtle5EBOnXqxLJly/j5559thyJxzvbJEb3OzXWDBg2ifn0zNM7j8VC6dGkA\n2rdvX2C6rKws19qTddlLly6lf//+VKtWLaHijtdlX3LJJbz44otkZWUlxfakyrJffvll1qxZ80ue\nSWDW8rLH4wn7vSjpe2l7fpuxzpo165ccY2P9ttdVvXp1Lr74Yl5//XVatmyZ1Nsaz/NHOm1xZGZm\nkpmZWez5bRTJe4ELgD2YXXb7nPadQN2A6epgeip2Oo+D233z1AN2YbalGnCwsJXec889hb4JUjL5\n+fm8/vrrzJw503YoSaNZs2Zs3bpVR18nmJYtWxb4wn3mmWcsRxSRuMjL4r4dO3awdetW2rZty/Ll\ny22HY03Xrl356quvaNmype1QxEW+H98+8ZiXG1DwKOoXgCecx08Co53HzYC1QDkgA/gO/wEi2Zhx\ncGnAHPwHiAzFHDwCMAB4r4gYvOKOmTNnelu3bm07jKRTt25d73fffWc7DCkBYtQbW0wNUF5OSS+/\n/LJ34MCBtsOwLjMz03vllVfaDkNijAjzsttjkqcCXwAXY8aoDcIk32swpxrqjD8ZbwTed+4/xSRa\n38YMBcZjTim0BZjrtE8Aajrt/43/iGyJkbFjx/LQQw/ZDiPpaFyyuEh5OYV98MEH3HTTTbbDsK5N\nmzZs3ryZffv2hZ5YUlaqnIrA+QEh0fT111/Ts2dPcnJyKFeunO1wksof/vAH0tPTefrpp22HIsXk\nnOklVXJscSgvx9iePXto2rQpe/bsoXz58rbDsa5fv37cdNNN3H777bZDkRiJNC/rintSbGPHjuWB\nBx5QgewC9SSLSLTNmDGD3r17q0B29OjRg3nz5tkOQ+KYimQpln379jF9+nTuu+8+26EkpebNm7Nh\nwwbbYYhIEvnggw/o37+/7TDiRvfu3Zk3b54OkpYiqUiWYnn99dfp378/5557ru1QklLjxo3ZsmWL\nkreIRMX+/ftZtWoV3bt3tx1K3MjIyCA9PZ21a9faDkXilIpkidjJkycZN24cDz/8sO1QklbVqlWp\nWrUqu3btsh2KiCSBmTNn0qNHDypWrGg7lLiiIRdyNiqSJWLvv/8+zZs355JLLrEdSlJr0qQJmzZt\nsh2GiCSBadOmaahFIbp3787cuXNDTygpSUWyRMTr9fLiiy+qFzkGVCSLSDTs3buXlStX0rNnT9uh\nxJ2OHTuyevVqjhw5YjsUiUMqkiUiCxYs4Oeff6ZXr162Q0l6KpJFJBree+89+vTpwznnnGM7lLhz\nzjnn0KZNGxYtWmQ7FIlDKpIlIqNHj+aJJ56gVCl9dNymIllEouHdd9/ljjvusB1G3OrRoweffPKJ\n7TAkDqnSkbB9+eWXbN68mVtvvdV2KClBRbKIlNS3337L9u3b6dy5s+1Q4tZ1113Hxx9/rLMJyRlU\nJEvYnn/+eX7/+99TtmxZ26GkhIYNG7Jt2zby8vJshyIiCerdd99lwIABlClTxnYocatx48akp6ez\natUq26FInFGRLGHZtGkTS5cuZciQIbZDSRnly5endu3a5OTk2A5FRBKQ1+vVUIsw9enTh1mzZtkO\nQ987oAAAAB30SURBVOKMimQJy5gxYxg6dKgO/IixjIwMtm7dajsMEUlAK1asoFy5clxxxRW2Q4l7\n1113HbNnz7YdhsQZ7X+RkHbu3MmHH36o8bEWNGjQgB9++MF2GCKSgCZNmsSdd95JWlqa7VDiXps2\nbdi5cyc//PAD9evXtx2OxAn1JEtIzz33HIMHD9YlqC1o0KCBepJFJGJHjx5l2rRp3H333bZDSQil\nS5emV69e6k2WAlQky1lt376dKVOm8Pjjj9sOJSXVr19fRbKIROz999+nffv21K5d23YoCaNPnz4q\nkqUAFclyVqNHj2bIkCH86le/sh1KStJwCxEpjjfeeEMHWkeoW7duLF++XFffk1+oSJYibd++nffe\ne0+9yBZpuIWIRGr9+vVs27ZNl6GOUJUqVejQoYN6k+UXKpKlSM8++yz33nsv5513nu1QUlbt2rXZ\nv38/J0+etB2KiCSI8ePHM2jQIJ0buRhuvvlmpk2bZjsMiROpcsir1+v12o4hoWzdupVWrVrx7bff\n6oA9yxo2bMj8+fNp1KiR7VAkAs4ZBVIlxxaH8rILjh07Rv369fnyyy/JyMiwHU7Cyc3NpV69euzY\nsYOqVavaDkeiLNK8rJ5kKdSf/vQnhg0bpgI5DtStW5dt27bZDkNEEsDbb79Nu3btVCAXU3p6uoZc\nyC9UJMsZ1q5dy4IFC3jsscdshyJArVq12L17t+0wRCTOeb1eXn75ZR5++GHboSS0m266SUMuBFCR\nLIV44okn+OMf/0iVKlVshyKoSBaR8CxYsIDSpUvj8Xhsh5LQ+vbty6JFi3SWC1GRLAUtWLCA77//\nnvvuu892KOJQkSwi4Rg7diwPP/ywrrBXQr4hF7NmzbIdilimIll+kZ+fzxNPPMGzzz5L2bJlbYcj\nDhXJIhLKpk2byM7O5rbbbrMdSlK47bbbeOedd2yHIZbZLJK3Al8Da4CVTlsNYD6wCfgMSA+Y/ilg\nM/AN0C2gvRWwzvnbWFcjTnKTJk2iXLly9O/f33YoEkBFssTQVpSXE9Lo0aMZNmwYlSpVsh1KUujX\nrx/Z2dns2rXLdihikc0i2Qt4gJbAVU7bk5hk3ARY6DwHaAbc4tz3AF7FfwqPccBgoLFz6+F+6Mkn\nNzeXp59+mr///e/aVRdnVCRLDCkvJ6Bt27Yxc+ZMHnzwQduhJI1KlSpxww038O6779oORSyyPdwi\nuBrrA0x2Hk8G+jmP+wJTgTxMT8cWoDVQC6iCv8fjrYB5JAIjRoygT58+tGrVynYoEkRFssSY8nKC\n+dvf/saQIUOoUaOG7VCSyl133cXkyZPR+bxTl83L8XiBBcBp4HXgDeB8YK/z973Oc4DawIqAeXcA\nF2KS846A9p1O+xmysrJo3759gedAgTa32+N12evXr2fKlCls3LgxoeJO1mUHt1evXp1jx46xePFi\nypQpk3Dbk+rLTjBW87KvDUK/FyV9L23PH611HTp0iHfeeYcNGzbE7bbGy2sV6fzt2rXjp59+Ys2a\nNRw7diypt9XG/JFOa4PNIrktsBs4D7Mr75ugv3udW1RMnDiRhQsXAuDxeChdunS0Fp3QvF4vDz74\nICNHjuS8887jm2+C3waxLS0tjfT0dP7zn/9QvXp12+FIEdasWcOaNWt+yTMJympe1qnLIjd16lQG\nDBhArVq1bIeSdEqVKsXAgQN58803ufnmm22HI8WQmZlJZmZmseePl8GnI4CjwL2Y8XB7MLvsFgO/\nxj8GbrRzP9eZ5wdnmqZO+61AR+B3QcvX5U+LMHHiRP7xj3+QnZ1NmTI2fzPJ2TRp0oTZs2dz8cUX\n2w5FwpQEl6VWXo5zO3fu5NJLL+Xrr7/mwgsL7ayXEtq+fTuXXXYZ27Zto3LlyrbDkRJKlMtSV8KM\nWQM4B3NU9DpgFnCX034XMNN5PAsYAJQDMjAHgqzEJO0jmHFwacCdAfNICLt37+bJJ59k4sSJKpDj\nXPXq1Tl48KDtMCS5KS8nmGeeeYYhQ4aoQHZR3bp16dChA1OmTLEdilhgqzI6H5gREMO7mFMLfQW8\njzkqeivg27+x0WnfCJwChuLf5TcUmARUBOZgejMkDMOGDePee+/lsssusx2KhFC9enUOHTpkOwxJ\nbsrLCeTbb79lxowZfPvtt7ZDSXpDhw7liSee4N5779XZn1JMqrzb2q0XZPr06Tz99NOsXbuWChUq\n2A5HQrjtttvo3bs3t99+u+1QJExJMNzCbcrLJXDTTTfRqlUrnnzyydATS4nk5+dz8cUX89Zbb9Gm\nTRvb4UgJJMpwC7Fo7969PPDAA4wfP14FcoJQT7KI+CxevJiVK1fy0EMP2Q4lJZQqVYqhQ4fyf//3\nf7ZDkRhTkZxivF4v99xzD/fccw/t2rWzHY6EqVKlShw/ftx2GCJiWV5eHsOGDeOll17S1fViaMiQ\nISxcuJAtW7bYDkViSEVyinn11VfZt28fI0eOtB2KRKBChQqcOHHCdhgiYtkrr7xCnTp1uP76622H\nklKqVKny/9u79+imqnyB49/Q0lIotPJuKQwPldcCBAS8UnnJ3AE6DFfgjpZBpDCKV7n2IiKUpQM4\nckGUGVDW4OiAdYCZqSIvKbAGQaGICF4qcC0wdaTc0qJtgdDSCjRN7h/7hKYhKU2bnNOc/j5rdTU5\nOcn+7Tx+2dlnn715+umneeONN4wOReiooYyXk7FvwDfffMOIESM4fPgw99xzj9HhCB8sXbqUsrIy\nli5danQoooZkTPIdSV72UV5eHv369ePzzz+X6SANUFBQQPfu3Tl9+jTt27c3OhxRCzImWXhUUlLC\n5MmTWbFihTSQg1CTJk1kuIUQDZjD4eCpp55i9uzZ0kA2SNu2bZkyZQorV640OhShE2kkNwAOh4Pp\n06czbNgwkpKSjA5H1EJERIQMtxCiAUtNTSU/P5+FCxcaHUqDtnDhQtatW0deXp7RoQgdSCO5AVix\nYgUXLlzgzTffNDoUUUuhoaGUl5cbHYYQwgC5ubnMnz+f1NRUwsLCjA6nQevQoQMzZ87k1VdfNToU\noQNpJJvc7t27WbVqFZs3byY8PNzocEQt2e12QkJCjA5DCKEzm81GYmIic+bMkYWf6okFCxbw4Ycf\nkp2dbXQoIsCkkWxix48fZ9q0aWzZsoWOHTsaHY6og4qKCho1ko+rEA3NSy+9RGRkJPPnzzc6FKFp\n1aoV8+fPJzk5GTn51NzkW9ekcnJyGD9+PH/84x9lhSATqKiokJ5kIRqYnTt3smnTJjZs2CA/kuuZ\n5ORkcnJy2LFjh9GhiACST50JFRQUMHbsWF588UUmTpxodDjCD2w2mzSShWhATp06xYwZM0hLS6NN\nmzZGhyPchIWFsWbNGpKTk7l27ZrR4YgAkUayyRQWFvLwww/z6KOPkpycbHQ4wk9KSkpo0aKF0WEI\nIXTw/fffM378eFavXs2DDz5odDjCi1GjRjFy5EheeOEFo0MRASKNZBO5dOkSo0ePZsKECSxatMjo\ncIQfWa1WoqOjjQ5DCBFgVquVhIQEkpKSSExMNDoccQerVq1iz5497Nq1y+hQRABII9kkcnNzGTZs\nGOPGjeO3v/2tc1UZYRLSSBbC/EpKShg7dizx8fH85je/MTocUQNRUVG89957/PrXv5a5k01IGskm\nkJWVRXx8PDNmzGDZsmXSQDahwsJCWrVqZXQYQogAsVqtjBs3jr59+7Jq1SrJ40Fk5MiRzJ49m0mT\nJnHjxg2jwxF+JI3kILd3715GjhzJ0qVLmTt3rtHhiADJycmhc+fORochhAiA/Px8hg0bRv/+/Vm7\ndq00kINQSkoKcXFxzJo1S6aFMxFpJAcph8PB8uXLmTZtGmlpaUydOtXokESAOBwOcnJy6NKli9Gh\nCCH87NSpUwwdOpTExERWr14tU70FKYvFQmpqKmfPnmXu3LnSUDYJ+TQGocLCQh555BG2bt3KsWPH\nGDFihNEhiQDKzc0lMjJSZrcQwmQ2btzIqFGjWLp0KSkpKdKDHOQiIyNJT0/nk08+4aWXXpKGsglI\nIznI7Nixg379+nHvvfdy8OBB4uLijA5JBNjRo0cZPHiw0WEIIfykuLiYJ598kldeeYX9+/czZcoU\no0MSftKyZUv27t3L7t27mTVrFjabzeiQRB1IIzlI5OXlMWXKFObMmUNaWhorVqwgPDzc6LCEDg4f\nPsyQIUOMDkMI4Qd79uyhT58+WCwWvvrqK/r06WN0SMLP2rVrx4EDB8jJySEhIYHCwkKjQxK1JI3k\neq6srIxly5bRt29funbtysmTJ3nooYeMDkvoxOFwsH37dhISEowORQhRB2fPnmXChAk888wzrFu3\njnfeeUeGUJlY8+bNSU9Pp3///gwYMID9+/cbHZKoBWkk11OlpaWsXLmSbt26cezYMY4ePcqrr75K\ns2bNjA5N6OjIkSNYLBb69etndChCiFrIzs7mqaeeIj4+nvj4eLKyshg9erTRYQkdNG7cmOXLl/Pu\nu+8yffp0nnjiCQoKCowOS/hAGsn1TE5ODgsXLqRr164cPnyY3bt3s2XLFrp162Z0aMIAK1asIDk5\nWU7oESKI2O129u/fz+TJkxk6dCgxMTGcPn2aefPm0aRJE6PDEzobM2YMWVlZtGnThp49e5KSkkJR\nUZHRYYkakEZyPWC1WtmwYQMJCQkMHDiQsrIyDhw4wEcffcR9991ndHjCILt27eLEiRPMnDnT6FCE\nEHfgcDj4+uuvefnll+nSpQtz585l+PDhnDt3jiVLltC6dWujQxQGioyM5I033iAzMxOr1co999xD\nUlIShw8fllkw6jGzNJLHAGeAbGC+wbHc5rPPPqty3W63c+rUKVavXk1CQgKdOnVi8+bNJCYmkpub\ny6pVq+jRo0dAytablF+78k+cOEFSUhLr16+nadOmupfvD8H63Au/qdd52R9yc3P54IMPmDVrFh07\ndmTy5MmUlZWxfft2MjMz6dOnj+mHyDWEz5k/69ipUyfWrl3L2bNn6dWrF0lJSXTp0oXnnnuOvXv3\ncu3aNb+V5auG8Fr6ygyN5BBgDSoh9wISgZ6GRuTCbrezbds2tm3bxqJFi5gwYQLt27dn4sSJZGVl\nMW3aNC5cuMD27duZOnVqnRpEnhj9ppfyfSvfZrPx9ttvM3r0aNasWVPnObClkSwMUq/zsq+uX7/O\nyZMnSUtLY/HixUyePJm4uDgGDhzIpk2b6N69O/v27SM7O5uVK1feOgLYEN6DUsfaadu2LfPmzePM\nmTOkp6cTExPDkiVLaNeuHQMGDODZZ5/lnXfe4eDBgxQUFOjS29wQXktfhRodgB8MBr4FcrTrfwMm\nAKcDVaDNZuPHH3+ktLQUq9VKYWEhRUVFFBUVUVhYSF5eHufOneO7777j/PnzhIaGkp2dTf/+/Xn8\n8cd566236NSpU6DCE0HEZrNx8eJFMjMzycjIIC0tjbvvvpt9+/bRt29fo8MTorZ0z8s1YbfbuXHj\nBtevX+f69etYrdbb/i5fvkx+fj55eXlcuHCBvLw8Ll++TLdu3ejZsye9evVi0qRJvPbaa3Tt2lXO\nFxB1YrFY6N27N7179yYlJYUbN25w/PhxvvjiC44cOUJqaipnzpzB4XAQFxdHbGwsMTExxMbG0q5d\nO6KiomjRosWt/86/iIgIwsLCCAsLIyQkRN6ntWSGRnIHINfl+gXgtkllH3jgAaKiorDb7djtdi5f\nvozD4aB58+ZUVFTc2n716lXsdjvNmjW7tf3mzZtcuXKFGzducPPmTWw2G02bNiUiIoLw8HCio6Pp\n1q0brVu3pk2bNvTo0YNOnTrx2GOPMWnSJF5//XUWL15MRkYGwG0NZOd296ndfNnubd/z58+TkZER\nkMcOZNz+euzz58/jTq+4T548ycaNGzl06BBFRUXYbDYiIiIoLy+nvLycS5cuUVhYSFRUFIMHD2bI\nkCGkp6fTp08fMjIybnvdahN3TesfzK+xt33r+tr7K+4GqsZ5+a677sJut+NwOKrk5qioqFvbrFbr\nrXzt3Le4uBi73U5kZGSV+1+7dg2Hw0FoaOitBnFpaSk3b96koqKC8PBwmjRpgsVioXnz5sTGxhId\nHU10dDR33XUXJSUltG7dmmnTphEXF0eHDh3Izs4mJCTktvdHfn6+399LwXZ/989ZIMqvL3XV4/7h\n4eHYbDYGDRrE888/D6jx7jt37qSoqIiYmBjy8/O5ePEiBw8epLS0lIiICIqLi7l69SrFxcUUFhZS\nXl5+q/1it9sJCQmhcePGNG3a9FbjuaKigtDQUJo1a4bFYqGgoICtW7dSWlp66/NhsViwWCxcu3YN\ni8VCixYtbjW4S0pKsFgsREVF3drPYrFQXFyMxWIhOjr61jar1YrFYqFly5ZV9r1y5QoWi4VDhw4R\nEhJS7XNoBDP8tJiEOqT3pHZ9KioZ/6fLPhdQSVsIIfTyT+Buo4MwiORlIUR95FNeNkNPch7Q0eV6\nR1TydSVrNwshhH4kLwshRD0Qivpl0BkIA74miE8QEUIIE5C8LIQQ9cRY4CzqRJEUg2MRQggheVkI\nIYQQQgghhBDBRK/J7HOAk0AmcFTb1hLYC/wD+DsQ7bJ/ihbTGeBfa1HeeuAH4JTLttqUN1B7jGxg\ndR3KXowab5ip/Y0NUNmgxjZ+CnwD/C/wnLZdr/p7K38x+jwHTYAvUYevs4Bl2nY96u+t7MXo9/qD\nmoM3E/hYu67Xa++t/MXoW/9gZ/pFRvCeJ8zI/fNgNtHAZtT0hVnAA8aGExApqPfqKeAvQLix4fiN\nr22lBiUEdZivM9CYwI6JO4d64l2tAF7ULs8HlmuXe2mxNNZi+xbfF3V5COhP1Rfel/Kcs5ocRc1n\nCrAL9eVVm7IXAc972NffZQO0B5xrdUeiDuf2RL/6eytfz+fAueJMKHAEiEe/+nsqW8+6o5W1Cdih\nXder7t7K17v+wUzPvGwkb3nCjNw/D2bzPjBDuxwKRBkYSyB0Br6jsmGcBjxhWDT+5UtbySMzrLjn\njetk9uVUTmYfKO7T6f0C9eFC+/9v2uUJwF+1mHK0GAfjmwzgSh3KGwLEAM2p7Pn+s8t9fC0bPE8n\n6O+yAb5HfbECXEP9uu+AfvX3Vj7o9xyUaf/DUI2OK+hXf09lg351jwPGAX9yKVOvunsr34J+9Q92\neudlo3jKE7HGhRMwnj4PZhKFamit167bgKvGhRMQxajPYlPUj4CmqNlpzMCXtpJHZm4ke5rMPlBz\ncjqAT4CvqJwXtB2qmx/tfzvtcixVp0LyV1y+lue+Pa+Ocfw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       "text": [
        "<matplotlib.figure.Figure at 0x770d230>"
       ]
      }
     ],
     "prompt_number": 7
    }
   ],
   "metadata": {}
  }
 ]
}