Comparison of Assumptions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.integrate import odeint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Shared Variables\n",
    "P = 10 # atm\n",
    "\n",
    "end_time = 1000\n",
    "steps = 100000\n",
    "\n",
    "times = np.linspace(0, end_time, steps)\n",
    "\n",
    "A1 = 8.26e4\n",
    "A2 = 1.4e5 \n",
    "Ea = 88000 # J/mol\n",
    "R = 8.314 # J/mol*K\n",
    "T0 = 1000 # K\n",
    "\n",
    "Ctot = (P)/(R*T0*9.86923e-6)\n",
    "TiCl40 = Ctot/2\n",
    "O20 = Ctot/2\n",
    "print(TiCl40, O20)\n",
    "\n",
    "initial_conditions = (TiCl40, O20, T0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Thermodynamic Data:\n",
    "\n",
    "A_TiCl4 = 143.0480\n",
    "B_TiCl4 = 7.600362\n",
    "C_TiCl4 = 1.530575\n",
    "D_TiCl4 = -0.538376\n",
    "E_TiCl4 = -0.020638\n",
    "\n",
    "A_O2 = 30.03235\n",
    "B_O2 = 8.772972\n",
    "C_O2 = -3.988133\n",
    "D_O2 = 0.788313\n",
    "E_O2 = -0.741599\n",
    "\n",
    "A_TiO2 = 67.29830\n",
    "B_TiO2 = 18.70940\n",
    "C_TiO2 = -11.57900\n",
    "D_TiO2 = 2.449561\n",
    "E_TiO2 = -1.485471\n",
    "\n",
    "A_Cl2 = 42.67730\n",
    "B_Cl2 = -5.009570\n",
    "C_Cl2 = 1.904621\n",
    "D_Cl2 = -0.165641\n",
    "E_Cl2 = -2.098480"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Simple first order, constant T, constant P\n",
    "\n",
    "def first_order_rates(variables, time):\n",
    "    \"\"\"\n",
    "    Return the right hand side of the ODE\n",
    "    \"\"\"\n",
    "    \n",
    "    TiCl4, O2, T = variables\n",
    "    \n",
    "    k = A1*np.exp(-Ea/(R*T)) * 1e-3\n",
    "    \n",
    "    rate_TiCl4 = -k*TiCl4*O2\n",
    "    rate_O2 = -k*TiCl4*O2\n",
    "    \n",
    "    return (rate_TiCl4, rate_O2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Actual rate equation, constant T, constant P\n",
    "\n",
    "def isothermal_rates(variables, time):\n",
    "    \"\"\"\n",
    "    Return the right hand side of the ODE\n",
    "    \"\"\"\n",
    "    \n",
    "    TiCl4, O2, T = variables\n",
    "    \n",
    "    k1 = A1*np.exp(-Ea/(R*T)) * 1e-3\n",
    "\n",
    "    k2 = A2*np.exp(-Ea/(R*T)) * 1e-3\n",
    "    \n",
    "    if O2 - (k1+k2*np.sqrt(O2))*TiCl4*end_time/steps <= 0 or np.isclose(0,O2) :\n",
    "        return (0, 0, 0)\n",
    "    \n",
    "    else:\n",
    "        rate_TiCl4 = -(k1+k2*np.sqrt(O2))*TiCl4\n",
    "        rate_O2 = -(k1+k2*np.sqrt(O2))*TiCl4\n",
    "        \n",
    "        return (rate_TiCl4, rate_O2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Actual rate equation, non-constant T, pressure not accounted for\n",
    "\n",
    "def isobaric_rates(variables, time):\n",
    "    \"\"\"\n",
    "    Return the right hand side of the ODE\n",
    "    \"\"\"\n",
    "    TiCl4, O2, T = variables\n",
    "    \n",
    "    k1 = A1*np.exp(-Ea/(R*T)) * 1e-3\n",
    "\n",
    "    k2 = A2*np.exp(-Ea/(R*T)) * 1e-3\n",
    "    \n",
    "    delHr = (T-726.85)*(12/125) - 102\n",
    "    \n",
    "    if O2 - (k1+k2*np.sqrt(O2))*TiCl4*end_time/steps <= 0 or np.isclose(0,O2) :\n",
    "        return (0, 0, 0)\n",
    "    \n",
    "    else:\n",
    "        rate_TiCl4 = -(k1+k2*np.sqrt(O2))*TiCl4\n",
    "        rate_O2 = -(k1+k2*np.sqrt(O2))*TiCl4\n",
    "        \n",
    "        if initial_conditions[1] < initial_conditions[0]:\n",
    "            X = -rate_O2/initial_conditions[1]\n",
    "        else:\n",
    "            X = -rate_TiCl4/initial_conditions[0]\n",
    "        \n",
    "        \n",
    "        Cp_TiCl4 = A_TiCl4 + B_TiCl4*(T/1000) + C_TiCl4*((T/1000)**2) + D_TiCl4*((T/1000)**3) + E_TiCl4/((T/1000)**2)\n",
    "        \n",
    "        Cp_O2 = A_O2 + B_O2*(T/1000) + C_O2*((T/1000)**2) + D_O2*((T/1000)**3) + E_O2/((T/1000)**2)\n",
    "        \n",
    "        Cp_TiO2 = A_TiO2 + B_TiO2*(T/1000) + C_TiO2*((T/1000)**2) + D_TiO2*((T/1000)**3) + E_TiO2/((T/1000)**2)\n",
    "        \n",
    "        Cp_Cl2 = A_Cl2 + B_Cl2*(T/1000) + C_Cl2*((T/1000)**2) + D_Cl2*((T/1000)**3) + E_Cl2/((T/1000)**2)\n",
    "        \n",
    "        \n",
    "        rate_T = -delHr * np.min(initial_conditions) * X / (Cp_TiCl4 + (O20/TiCl40)*Cp_O2 - Cp_TiO2 - 2*Cp_Cl2)\n",
    "        \n",
    "        #print(X, rate_T)\n",
    "        \n",
    "        return (rate_TiCl4, rate_O2, rate_T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "first_order_result = odeint(first_order_rates, initial_conditions, times)\n",
    "TiCl4_first_order = first_order_result[:,0]             \n",
    "O2_first_order = first_order_result[:,1]\n",
    "\n",
    "isothermal_result = odeint(isothermal_rates, initial_conditions, times)\n",
    "TiCl4_isothermal = isothermal_result[:,0]             \n",
    "O2_isothermal = isothermal_result[:,1]\n",
    "\n",
    "isobaric_result = odeint(isobaric_rates, initial_conditions, times)\n",
    "TiCl4_isobaric = isobaric_result[:,0]             \n",
    "O2_isobaric = isobaric_result[:,1]\n",
    "Ts_isobaric = isobaric_result[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for T0 in [800]:\n",
    "    initial_conditions = (TiCl40, O20, T0)\n",
    "    \n",
    "    first_order_result = odeint(first_order_rates, initial_conditions, times)\n",
    "    TiCl4_first_order = first_order_result[:,0]             \n",
    "    O2_first_order = first_order_result[:,1]\n",
    "\n",
    "    isothermal_result = odeint(isothermal_rates, initial_conditions, times)\n",
    "    TiCl4_isothermal = isothermal_result[:,0]             \n",
    "    O2_isothermal = isothermal_result[:,1]\n",
    "\n",
    "    isobaric_result = odeint(isobaric_rates, initial_conditions, times)\n",
    "    TiCl4_isobaric = isobaric_result[:,0]             \n",
    "    O2_isobaric = isobaric_result[:,1]\n",
    "    Ts_isobaric = isobaric_result[:,2]\n",
    "    \n",
    "    fig, ax1 = plt.subplots()\n",
    "    plt.title('T = ' + str(T0) + 'K, P = ' + str(P) + 'atm')\n",
    "    ax1.plot(times, TiCl4_isobaric, 'b-', label='Non-isothermal')\n",
    "    #ax1.plot(times, O2_isobaric, 'b-', label='O2 non-isothermal')\n",
    "\n",
    "    ax1.plot(times, TiCl4_isothermal, 'b--', label='Isothermal')\n",
    "    #ax1.plot(times, O2_isothermal, 'b--', label='O2 isothermal')\n",
    "\n",
    "    ax1.plot(times, TiCl4_first_order, 'b-.', label='Elementary')\n",
    "    #ax1.plot(times, O2_first_order, 'b-.', label='O2 1st Order')\n",
    "\n",
    "    ax1.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "    # Make the y-axis label and tick labels match the line color.\n",
    "    ax1.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "    for tl in ax1.get_yticklabels():\n",
    "        tl.set_color('b')\n",
    "\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.25, .5))\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(times, Ts_isobaric, 'r.', label='Temperature')\n",
    "    # plt.ylim((1780,1790))\n",
    "    ax2.set_ylabel('Temperature (K)', color='r')\n",
    "    for tl in ax2.get_yticklabels():\n",
    "        tl.set_color('r')\n",
    "\n",
    "    ax1.set_ylim([0, TiCl40])\n",
    "    #ax2.set_ylim([800,1500])\n",
    "    plt.savefig('destination_path.tiff', format='tiff', dpi=250)\n",
    "    plt.show()\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib import animation\n",
    "import matplotlib\n",
    "matplotlib.rc('animation', html='html5')\n",
    "\n",
    "#fig = plt.figure()\n",
    "#ax = fig.add_subplot(111, autoscale_on=False, xlim=(0,20), ylim=(800,1500))\n",
    "#ax.grid()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "plt.title('Initial Pressure = ' + str(P) + ' atm')\n",
    "\n",
    "# Make the y-axis label and tick labels match the line color.\n",
    "ax1.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "for tl in ax1.get_yticklabels():\n",
    "    tl.set_color('b')\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('Temperature (K)', color='r')\n",
    "for tl in ax2.get_yticklabels():\n",
    "    tl.set_color('r')\n",
    "    \n",
    "ax1.set_ylim([0, TiCl40])\n",
    "ax2.set_ylim([700,1400])\n",
    "\n",
    "ax1.set_xlim([0, end_time])\n",
    "#ax2.set_xlim([0, end_time])\n",
    "\n",
    "line1, = ax1.plot([], [], 'b-', label='Non-isothermal')\n",
    "line2, = ax1.plot([], [], 'b--', label='Isothermal')\n",
    "line3, = ax1.plot([], [], 'b-.', label='First Order')\n",
    "line4, = ax2.plot([], [], 'r-', linewidth=2)\n",
    "temperature_template = 'Initial Temperature = %.0f K'\n",
    "temperature_text = ax2.text(0.52, 0.92, '', transform=ax2.transAxes)\n",
    "\n",
    "def init():\n",
    "    line1.set_data([], [])\n",
    "    line2.set_data([], [])\n",
    "    line3.set_data([], [])\n",
    "    line4.set_data([], [])\n",
    "    temperature_text.set_text('')\n",
    "    return line1, line2, line3, line4, temperature_text\n",
    "\n",
    "def animate(i):\n",
    "    if i<=250:\n",
    "        T0 = 700 + i*2\n",
    "    else:\n",
    "        T0 = 1200 - (i-250)*2\n",
    "    initial_conditions = (TiCl40, O20, T0)\n",
    "    \n",
    "    isobaric_result = odeint(isobaric_rates, initial_conditions, times)\n",
    "    TiCl4_isobaric = isobaric_result[:,0]             \n",
    "    Ts_isobaric = isobaric_result[:,2]\n",
    "    \n",
    "    isothermal_result = odeint(isothermal_rates, initial_conditions, times)\n",
    "    TiCl4_isothermal = isothermal_result[:,0]\n",
    "    \n",
    "    first_order_result = odeint(first_order_rates, initial_conditions, times)\n",
    "    TiCl4_first_order = first_order_result[:,0]             \n",
    "\n",
    "    line1.set_data(times, TiCl4_isobaric)\n",
    "    line2.set_data(times, TiCl4_isothermal)\n",
    "    line3.set_data(times, TiCl4_first_order)\n",
    "    line4.set_data(times, Ts_isobaric)\n",
    "\n",
    "    temperature_text.set_text(temperature_template%T0)\n",
    "    return line1, line2, line3, line4, temperature_text\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, frames=501, blit=True, init_func=init, interval=20)\n",
    "anim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "FFMpegWriter = animation.writers['ffmpeg']\n",
    "metadata = dict(title='Movie Test', artist='Matplotlib',\n",
    "                comment='Movie support!')\n",
    "\n",
    "# Change the video bitrate as you like and add some metadata.\n",
    "mywriter = FFMpegWriter(fps=30, bitrate=5000, metadata=metadata)\n",
    "\n",
    "anim.save(\"NewestP15atm.mp4\", writer=mywriter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "side by side subplots to compare lab and industrial pressures\n",
    "side by side subplots to compare low, mid, high temperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for steps in range(2,20000,1000):\n",
    "    times = np.linspace(0, end_time, steps)\n",
    "    \n",
    "    first_order_result = odeint(first_order_rates, initial_conditions, times)\n",
    "    TiCl4_first_order = first_order_result[:,0]             \n",
    "    O2_first_order = first_order_result[:,1]\n",
    "\n",
    "    isothermal_result = odeint(isothermal_rates, initial_conditions, times)\n",
    "    TiCl4_isothermal = isothermal_result[:,0]             \n",
    "    O2_isothermal = isothermal_result[:,1]\n",
    "\n",
    "    isobaric_result = odeint(isobaric_rates, initial_conditions, times)\n",
    "    TiCl4_isobaric = isobaric_result[:,0]             \n",
    "    O2_isobaric = isobaric_result[:,1]\n",
    "    Ts_isobaric = isobaric_result[:,2]\n",
    "    \n",
    "    print(isobaric_result[int(steps/2),:],isothermal_result[int(steps/2),:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for P in [5,10,15]:\n",
    "    T0=1000\n",
    "    Ctot = (P)/(R*T0*9.86923e-6)\n",
    "    TiCl40 = Ctot/2\n",
    "    O20 = Ctot/2\n",
    "\n",
    "    initial_conditions = (TiCl40, O20, T0)\n",
    "    \n",
    "    first_order_result = odeint(first_order_rates, initial_conditions, times)\n",
    "    TiCl4_first_order = first_order_result[:,0]             \n",
    "    O2_first_order = first_order_result[:,1]\n",
    "\n",
    "    isothermal_result = odeint(isothermal_rates, initial_conditions, times)\n",
    "    TiCl4_isothermal = isothermal_result[:,0]             \n",
    "    O2_isothermal = isothermal_result[:,1]\n",
    "\n",
    "    isobaric_result = odeint(isobaric_rates, initial_conditions, times)\n",
    "    TiCl4_isobaric = isobaric_result[:,0]             \n",
    "    O2_isobaric = isobaric_result[:,1]\n",
    "    Ts_isobaric = isobaric_result[:,2]\n",
    "    \n",
    "    fig, ax1 = plt.subplots()\n",
    "    plt.title('Initial Pressure = ' + str(P) + 'atm')\n",
    "    ax1.plot(times, TiCl4_isobaric, 'b-', label='Non-isothermal')\n",
    "    #ax1.plot(times, O2_isobaric, 'b-', label='O2 non-isothermal')\n",
    "\n",
    "    ax1.plot(times, TiCl4_isothermal, 'b--', label='Isothermal')\n",
    "    #ax1.plot(times, O2_isothermal, 'b--', label='O2 isothermal')\n",
    "\n",
    "    ax1.plot(times, TiCl4_first_order, 'b-.', label='Elementary')\n",
    "    #ax1.plot(times, O2_first_order, 'b-.', label='O2 1st Order')\n",
    "\n",
    "    ax1.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "    # Make the y-axis label and tick labels match the line color.\n",
    "    ax1.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "    for tl in ax1.get_yticklabels():\n",
    "        tl.set_color('b')\n",
    "\n",
    "    plt.legend(loc='center left', bbox_to_anchor=(1.25, .5))\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(times, Ts_isobaric, 'r.', label='Temperature')\n",
    "    # plt.ylim((1780,1790))\n",
    "    ax2.set_ylabel('Temperature (K)', color='r')\n",
    "    for tl in ax2.get_yticklabels():\n",
    "        tl.set_color('r')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.08524303249363 38.08524303249363\n"
     ]
    }
   ],
   "source": [
    "# Shared Variables\n",
    "P1 = 5 # atm\n",
    "\n",
    "end_time = 500\n",
    "steps = 100000\n",
    "\n",
    "times = np.linspace(0, end_time, steps)\n",
    "\n",
    "A1 = 8.26e4\n",
    "A2 = 1.4e5 \n",
    "Ea = 88000 # J/mol\n",
    "R = 8.314 # J/mol*K\n",
    "T01 = 800 # K\n",
    "\n",
    "Ctot = (P1)/(R*T01*9.86923e-6)\n",
    "TiCl401 = Ctot/2\n",
    "O201 = Ctot/2\n",
    "print(TiCl401, O201)\n",
    "\n",
    "initial_conditions = (TiCl401, O201, T01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "first_order_result1 = odeint(first_order_rates, initial_conditions, times)\n",
    "TiCl4_first_order1 = first_order_result1[:,0]             \n",
    "O2_first_order1 = first_order_result1[:,1]\n",
    "\n",
    "isothermal_result1 = odeint(isothermal_rates, initial_conditions, times)\n",
    "TiCl4_isothermal1 = isothermal_result1[:,0]             \n",
    "O2_isothermal1 = isothermal_result1[:,1]\n",
    "\n",
    "isobaric_result1 = odeint(isobaric_rates, initial_conditions, times)\n",
    "TiCl4_isobaric1 = isobaric_result1[:,0]             \n",
    "O2_isobaric1 = isobaric_result1[:,1]\n",
    "Ts_isobaric1 = isobaric_result1[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114.2557290974809 114.2557290974809\n"
     ]
    }
   ],
   "source": [
    "# Shared Variables\n",
    "P2 = 15 # atm\n",
    "\n",
    "end_time = 500\n",
    "steps = 100000\n",
    "\n",
    "times = np.linspace(0, end_time, steps)\n",
    "\n",
    "A1 = 8.26e4\n",
    "A2 = 1.4e5 \n",
    "Ea = 88000 # J/mol\n",
    "R = 8.314 # J/mol*K\n",
    "T02 = 800 # K\n",
    "\n",
    "Ctot = (P2)/(R*T02*9.86923e-6)\n",
    "TiCl402 = Ctot/2\n",
    "O202 = Ctot/2\n",
    "print(TiCl402, O202)\n",
    "\n",
    "initial_conditions = (TiCl402, O202, T02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "first_order_result2 = odeint(first_order_rates, initial_conditions, times)\n",
    "TiCl4_first_order2 = first_order_result2[:,0]             \n",
    "O2_first_order2 = first_order_result2[:,1]\n",
    "\n",
    "isothermal_result2 = odeint(isothermal_rates, initial_conditions, times)\n",
    "TiCl4_isothermal2 = isothermal_result2[:,0]             \n",
    "O2_isothermal2 = isothermal_result2[:,1]\n",
    "\n",
    "isobaric_result2 = odeint(isobaric_rates, initial_conditions, times)\n",
    "TiCl4_isobaric2 = isobaric_result2[:,0]             \n",
    "O2_isobaric2 = isobaric_result2[:,1]\n",
    "Ts_isobaric2 = isobaric_result2[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oQrilsk0Ju5sQfgkujNu+MNonIiIiybkJaADMwmw14YvkK3B/qpT2ZwCLIMxMjTQBVuC+\nKK6trs95xozahJ/5k+78EW1uAqyIvoyPpZ+fiIhIbiTSt2oC/Fhsr/tqzH5F128RyYCcJ47NaEqo\n43OwOyvTc07bHrgQmAFrB0giIiKFYj1gV+AOd59Twv5TgFOBLsBnhC9v78RsAe6Pl9C+B/AE7isy\nFbBkhhk1gKcJX8KfV7FzqX8lIiIFrbz+lYhIlZPzxDHhdoxNgQ/MsGhbdaCDGb0Ii/YY0Jjis44b\nA7G3Z8S6EPhXZsIVERGpVHqVsO0W4Ebcn46ef4rZ1sBlQPHEsdl+QHPg5Lhz/ADUwqxB3MyYxtE+\nybGYpHEz4KCY2cYQ/fzMaBA367isn5/6VyIiIkFJ/auKSqRv9QPQqNhRZtWBjVD/S0QyIB8Sx5OA\nVnHbHiEUgL/JnS/N+IGwYugMWLs4XjtCXeSSzADo2bMn7du3z0TMEhk8eDB9+/bNdRhVnj7n7NDn\nnB36nDPvnXfe4Z577oHoeliCesDquG1rKHntg38A03D/JG77NGAV4focu4DLlsCUlAKXtIlJGm8L\nHOjOb3FN1vn5RYvjlfXzU/8qS/R7Mjv0OWeHPufs0OeceQn0r5LhJWxLpG81BWiI2R4xdY47Eibb\nvZ+GuEREisl54tidJYTbZNcyYwnwizszo013AAPMmAN8BQwCvgXGlXLaPwDat29Pt27dMhG2REaN\nGqXPOAv0OWeHPufs0OecHdHAprRyAi8AAzD7lrAyd2ugD8UXvyta3fukaF9x7oswexAYgtlvwGJg\nKPAO7lPT8y6kNGbUB7aHtXdrbWvGbsCvwPfAM4QSJEcDNc1oHLX71Z2V7iwy40FgiBnFfn7ulPbz\nU/8qS/R7Mjv0OWeHPufs0OecHWX2r8zWuTZHCxD/ivs3mG1ISAJvEbVpiZkBP+C+MKG+lfsszCYC\n92PWE6gFDANG4q4ZxyKSdjlPHJei2Ldv7txiRj3gXsKq4G8BR7ijWosiIiLJ60X4EnY44XbHBcA9\n0bZYp0R/lrZoXh/CzOUxQG3gZVTKIFvaAK8R+kwODI62PwpcCxwTbf8w2m7R8wOBN6Nt+vmJiIik\nT1nX5jOBTsDDMftHRvuvBQZGf0/k2nwqcBfh7u01UdsL0v5uRETI08SxOweVsO0a4JqsByMiIlLV\nuC8BLooeZbW7H7i/jP3Lgd7RQ7LInTcoubRIkbL2FZ1DPz8REZF0cS/72uz+KCGJXNY5yr82u/8P\n6J5KiCIiySp3UCEiIiIiIiIiIiIihUWJY6mQrl275jqEgqDPOTv0OWeHPmcRkbLp92R26HPODn3O\n2aHPWUREMkGJY6kQdVCyQ59zduhzzg59ziIiZdPvyezQ55wd+pyzQ5+ziIhkghLHIiIiIiIiIiIi\nIlKMEsciIiIiIiIiIiIiUowSxyIiIiIiIiIiIiJSTI1cByAiIiJpYLb2ry1zGIaIiIiIiIhUDUoc\ni4iIVFYxyeJim7MchoiIiIiIiFQ9ShyLiIhUJqUki0Wy5Ztvch2BiIiIiIhkg2oci4iIVAZmShpL\nXrj8cpg8OddRiIiIiIhIplXpxPG0abmOQEREpAKKksVKGEueOfhgGDEi11GIiIiIiEgmVenE8e23\nw777wk8/5ToSERGRBDVoUOFksacxHJF4hx0GdetC9+5w8825jkZERERERDKlSieOa9WCKVNgs83g\nyivBNZIWEZF8VZQsXry4YudxZ9YTT6QnJpESrFoFc+bAxhvDpZfCv/6V64hERERERCQTqnTieMUK\nuOoqaNgQrrsONt8c3nkn11GJiIhE0lWKwv2vh0iGnXFG+FJ+3jzYemu4+2444YRcRyUiIiIiIulW\npRPH++4LzZuHUhV9+sCPP8Lf/gbdusHq1bmOTkREClY6ksVNmypZLDm1/vowaxbsths8/zyMHZvr\niEREREREJJ2qdOL4vPNCktgMhgyBr7+GPfaAJ58MSeXp03MdoYiIFIx0zy7+5pv0xCVSAbVrw//9\nH5x4InTuDGPG5DoiERERERFJlyqdOI7XtCl88EEoV/Hnn9CmTZiJXNFykiIiIqVSKQqp4mrWhBEj\n4OSToUsXGDUq1xGJiIiIiEg6FFTiuMi++8K0aXDTTXDffbDjjvDssxqPi4hImqR7drFInqtRAx5/\nHE49NTxGjMh1RCIiIiIiUlEFmTiGMDvmkkvgs8+gdetwi2WnTvDllxqji4hIijS7WApY9erw8MNw\n+unw97/DY4/lOiIREREREamIgk0cF9lqKxg3Lizo8uGH0LJlmIH8ySe5jkxERCoFzS6WArN8Obz6\naij7Fa96dXjgAfjHP6BHj/D3W2+FNWuyH6eIiIiIiFRMwSSO3eHll2HlynX3mcFxx4XZx0ceCbNn\nw667hoX1Fi3KfqwiIlIJaHaxFKgFC+Dgg0tfZLhaNbj33lCy4pxzoH//sHDe0qXZjVNERERERCqm\nYBLHn38ORxwBL75Yepv114fnnoN334XNN4cnn4QmTeDuuzVTRkREgH79Kp4wrl5dyWKp1LbcEmbN\ngnbtSm9TrVooW3H88eGf/PjxsP/+8P332YtTREREREQqpmASxy1awEcfwbHHlt92n33g66/huutg\n1Sr417+geXOYOjXzcYqISB4qShbfemvq5yhKFq9alb64RHKgevXQr6pevex2NWqEL+EPOyw8//pr\naNs29MdERERERCT/FUziGEL5iUQniVWvDldcAfPnQ8eOMHcuHHQQzJyZ2RhFRCSPqHaxSIXUqgVj\nxsDf/gZLlkD9+tC+fbjDS0RERERE8ltBJY5T0aQJTJoEEybAJpvA7rvDVVeVvCCMiIhUAXXrVjxh\nrNrFImvVqRMWIt5jD1i4MCSOjz8+zEYWEREREZH8pcRxgo44Isw27t8fbroJWrWCV17JdVQiIpI2\nRcniZctSP4eSxSIlql8/rDOx5Zbw6adwzTVw+OG5jkpERERERMqixHES6taFgQNhxgxo1gwOPTSs\nGP7DD7mOTEREUqbZxSJZscEG8NJLofbx6NG5jkZERERERMpT0InjFStSO65lS5g8GR57LJSxaNkS\nhg2DK6+EH39Mb4wiIpIBTZumL2EsUqBWr4ZvvknumM03h4kTQ8mKTp1U+ktEREREJJ8VbOJ46tRw\nu+Tnn6d2vBn8/e8waxZ07gznnw833gjbbQe33ALLl6c3XhERSYOiZPF336V+DiWMRQAYNAjatEn+\nv0OLFqFsxfTp0KULrFqVmfhERERERKRiCjZx3KoV1K4dahZXxEYbwX33wdtvww47wB9/wKWXhlnI\nY8YotyAikhdUjkIk7fbcM9xp9fXXyR/brl3oJ02YAD17rvtfS8lkEREREZHcK9jEcd26YZG7556D\nN96o+Pnatw+1j2++GWrVCnWPTz4ZOnSA//634ucXEZEUqByFSMa0axf+fP/91I4/4gh48EF44IHQ\nfypy772w//7w/fcVj1FERERERFJXsIljCLdHtmsX6hWnQ82a0K9fKF9x8MFh20cfwV57wQsvpOc1\nRESkHLVrK2EskgWNGsHWW8Nnn6V+jtNOg6uugssuCzOQAXbbDb76Clq3Dnd0iYiIiIhIbuQ8cWzG\nuWZ8ZMbv0eNdMw6P2f+wGWviHhPS9Nq89hpce206zvaXrbeG55+HZ5+F9dcPM5A//hhWrkzv64iI\nSIyiZHGqK58WUjkKs2qYDcLsS8yWYjYHswEltNsRs3GY/Q+zPzB7H7OmMftrYzYcs58xW4zZGMwa\nZfOtSG599FHF+1HXXANdu4a1I6ZOhb33hmnToHlzOPBAGDq0MP5bioiIiIjkm5wnjoFvgP5Aa2BP\nYDIwzowdY9q8BDQGmkSPrul68bp103Wm4szg+OPD7ON//QuuvDLUAnz33cy8nohIwdLs4lRcCpwD\nnAe0BPoB/TDrtbaF2XbAW8BnQAegFTAIWBZznjuAo4ATozabA89kPnzJFw0aVPwcZvDQQ7DHHtCp\nE8yfD02awKRJYfHhCy6A7t1hyZKKv5aIiIiIiCQu54ljd15052V35rozx50BwB/A3jHNlrvzkzs/\nRo/fcxRu0tZfH4YMCXWO69QJtZDPPht+/TXXkYmIVHJKGFfEPsA43F/GfT7uzwL/AdrGtLkOeBH3\ny3Cfgfs83Mfj/jMAZg2AM4E+uL+B+3SgB9Aes7aIJKFOHRg3DurVg6OPhkWLQgmwwYPhqafCvr33\nhi++yHWkIiIiIiKFI+eJ41hmVDOjC1APiJ2be4AZC82YZcbdZmyUoxBTtsceMGUKDB8Oo0ZBy5bw\n+OMhZ/HBB2GfiIgkoCIJ4+bNCz1hXORdoCNmOwBgthvQHqJSUGZGmEn8BWYvY7YQs/cwOzbmHHsC\nNYBX125xnw3MJySmRZKy6aYwfnyYcdylC6xeHbafckpYgG/VKpg7N7cxioiIiIgUkrxIHJuxixmL\ngeXA3cDx7syOdr8EnAYcRLiVdn9gghkVmGaWG9Wrw3nnhfIVHTuGBWE6doTrr4d994WTToLPP891\nlCIieWi99SqWMC5KFs+eXX7bwnATMAqYhdkKYBpwB+5PRfsbAesRSklNAA4BxgLPYrZf1KYJsAL3\nRXHnXhjtE0naTjvB00/DxIkwIKbq9s47w4wZcPjhpR8rIiJZUNQfK8BHy+7dc/3pi4hkXY1cBxCZ\nBewGbACcBDxmRgd3ZrkzOqbdp2Z8DMwFDgBeK+ukgwcPZtSoUcW2de3ala5dSy+R/O23sHgx7Lhj\nqU0qbLPNYORIOOOMUP/47bfh2GPDbJqddw6lLK66Cho3zlwMIiKVQkVKUUBBzCweOXIkI0eOLLbt\n22+/Le+wU4BTgS6EGsa7A3ditgD3x/nri+XncB8a/X0GZvsC5xJqH4tkxCGHwC23wMUXhzu2OncO\n22vWzG1cIlJgevSARx7JdRSSRyrdzDURkTRIKXFsRk3CbKJ6wE/uVKhirzurgC+jp9PNaAtcAPQs\noe08M34GtqecxHHfvn3p1q1bUrF07QobbBBulcy0ww6Djz+GG26Am2+GZs1CMnnECHjsMejXDy66\nCOrXz3wsIiJ5RQnjhJX0heiIESPoXvasmFuAG3F/Onr+KWZbA5cBjwM/A6uAmXHHzSSUtAD4AaiF\nWYO4WceNo30SJ939p3yxciW8914ow7Xppuk550UXhVJePXqE8+66a3rOKyKVQEX7ACIiIpI2CZeq\nMGN9M3qa8QawCPiKMID8yYyvzbjfjL3SGFftUuJoCmwMfJ+m1yrmwQdD4jZb6taFQYPC7ZdbbgkP\nPBDKV3TvDtddFwZKK1ZkLx4RkZzSgnfZUg9YHbdtDUX9AveVwP8BLeLaNAe+jv4+jZBc7rh2r1kL\nYEtAlfsjmeo/mbGfGc+b8Z0Za8zoVEKbgWYsMGOpGa+YsX3c/tpmDDfjZzMWmzHGjEbJxrJkCey/\nP7z4YrJHls4M7r8/lCU/7jj45Zf0nVtE0iRTJQFEKiuz/TB7HrPvMFuD2TrXZswGYrYAs6WYvYLZ\n9nH7X4+OLXqsxuzuuDYbYjYCs98x+w2zBzDTdDMRyYiEEsdmXEQY6PQAJgHHEW5rbU5YAOdawuzl\n/5jxshk7JBqAGTdEg5+tLNQ6vpFQx/gJM+qbcYsZ7aL9HYHngM+BiYm/zcQ1bx5mHGdby5YweXKY\nafzWW6GUxYABof5xrVrZj0dEJKsqMlg8/3wljJP3AjAAsyMx2wqz44E+wLMxbW4FTsHsLMy2w6wX\ncDQwHCCaZfwgMASzAzDbE3gIeAf3qdl8M/kqk/0noD7wIXAesM4/fjP6A72As4G2wBJgohmxvYo7\nCIsgngh0ADYHnkkiBgAaNoRPPglfeqdTvXowdiwsWhQWy1u1qvS2X34Z2vz8c3pjEKnSlOAVSUoC\nPc0yr82YlXhtxiz22uzAfYQ7uJoAmxHWeor1JLAj4cv7owjX8HuTeCsiIglLtFTFXkAHdz4tZf9U\n4CEzziUMjvYDvkjw3I2ARwm/EH8HZgCHujPZjDrAroTF8RoCCwgJ46vcWZng+SsNM/j73+Goo+DS\nS0Od47ZtoUWLUONPRKTKqejsYklVL2AQIQnciHB9vSfaFrg/h9m5wOXAncBs4ATcY2cT9yHMXB5D\nuFPoZeC/7antAAAgAElEQVRfWYi/sshY/8mdlwmfN1bygsEXAIPcGR+1OY2wcOFxwGgzGgBnAl3c\neSNq0wOYaUZbd5JK/u+0UzKtE7f11jB6NBx6KFx2Gdx6a8ntvvsOXn019Jeeegraty+5nUiVpmSu\n5EqB9MlmjRhR9rek7muvzViJ/yEvAAbhPj5qU+zaHNNuKe4/lfgaZi2Bw4A9cZ8ebesNvIjZxbir\nXJiIpFVCM47d6VrGoCe23XJ3/u3OQ4kG4M5Z7mzrTl13mriHpHG0b5k7h0fb60TterpT8i/RKmKj\njeC+++Cdd+DPP6FNG+jTJyzaJyJSJVRktpJmF1ec+xLcL8J9G9zr474D7lfjviqu3SO4N4/atF47\n0Plr/3Lce+O+Ce7r434y7j9m863ks0z2n8pixjaEWUqvxrzGIuB9wkxngDaECQSxbWYD82Pa5IWD\nDgoJ49tug3HjSm6z334wfXpINO+/f1hcb82arIYpkjmaAZw/ivogehR/SPnM1rk2R3dvxV6bi3TD\n7CfMPsbsBszqxuzbB/htbdI4mESYqdwuI7GLSEFLuMaxZN+++8K0aXDTTSGRvOOO8Oyzxa/Nw4aF\n+sgiIpWCEsYi2dCEMIBcGLd9YbQPwi2wK6KEcmlt8saFF8Lxx8Ppp4eyFCVp2hReew0uuQT694dO\nnVQbWSoJJYTLl+vEqPogUnGJXJsBRgDdgQOAG4C/ExYujj1P8S/p3VcDv5KH128RqfySShybcYgZ\n15pxUPS8gxkvmTE5ur2xynGHr78uv12m1KwZBkCffQatW8OJJ8Ixx8C8eWE28l13we67Q7duMHdu\n7uIUESmTEsZSwAqx/5RuZvDQQ7DxxtC5MyxfXnK7GjXgxhvDQn3vvRdKV7z9dnZjFSlVISSGlbAV\nqRj3B3B/BfdPcR9JSByfEM1YFhHJuoQTx2Z0ByYQFsYZZ8YZwDjgW2Ae8G8zTspEkLl03XXQrh38\n9ltu49hqK3j+eXjuuTDDeOed4c474YMP4J574PXXwwJ7PXvCggW5jVVEBICBA5UwloKXo/7TD4AR\nZhXHahztK2pTK6p1XFqbEg0ePJhOnToVe4wcOTIdcZepYUN4+mn4+GPo27fstkceGUpXbLUVPPFE\nxkMTKa4yJ4iV4BUBYOTIketc6wYPHlyRUyZybS5J0ZoD28ecp1GxFmbVgY3KOY+ISEoSXRwPoC/Q\n152hZnQkrMh+hTu3A5jxGXAhYYGcKuPMM0Ndvcsug3//O9fRwLHHQseOcM01MGBAGAzdcw/MmRNm\nH994Izz6KPTuHW7T3GijXEcsIgVnr73gv/9N/XgNPKVqyXr/yZ15ZvxAWG19RvQ6DQi1D4dHzaYB\nq6I2Y6M2LYAtgSnx5yz2hvr2pVu3biW9Ll98EUpG1KuXpjcTp3Xr8MV5z57QoUOYfVyaZs1C6YpV\nq0pvI1Jh+Z4Q1jVVJCVdu3ala9euxbaNGDGC7mUtjlcW93mYFbs2YxZ/bS7JHoQSF99Hz6cADTHb\nI6bOcUdCUvr91IITESldMqUqdiAMdnDnVeIWVAFeBFqmL7T8sMUWcMMNcO+98O67uY4mWG+9sEDM\ntGmw/vph4NSrF/ToEUpY9O0Lw4eHbSIiWdO0aRhAp5I0rl1bs5WkqspI/8mM+mbsZsbu0aZto+fN\noud3AAPMOMaMVsBjhFnO46JYFgEPAkPMOMCMPYGHgHfc185uSsq8edCiBbz6avltK+Kcc6BLFzjr\nLPj887Lb1qgBdepkNh4pIPk0i1gzgEXyj1l9zHbDbO21OXpe7NqM2TGYrXNtxmxbzAZg1hqzrTDr\nBDwKvIH7JwC4zwImAvdjthdm7YFhwEjcNeNYRNIumcTxSqBWzPPlwB9xz+tSBfXsGWoL//lnriMp\nbrfd4J13wkzoZ58NpSrGjg13h8+dGxLeIiIZt9FGYfD83XfJH7vrrmFgu2xZ+uMSyQ+Z6j+1AaYT\nZg47MBj4ALgWwJ1bCAPJewkzkOoCR7izIuYcfYDxhNnOrwMLgBNTiAWAbbb5a5ZvJpmFRYM32wy6\ndoUVK8o/RiQluUwSKyEsUhmVeW3GvcRrM+5FV7IVwMGExPBM4FbgaaBT3OucCswCJhGu428C52To\nPYlIgUsmcTyH4jNitiDU5iuyHeHbsiqnenUYMyaUiMg31aqFmTezZsHhh4dZxwccEFYR33rrXEcn\nIlVaw4ZhIJ1KEfg33ggD348+Sn9cIvklI/0nd95wp5o71eMeZ8a0ucadzd2p585h7syJO8dyd3q7\ns4k767tzsnvcSu1JMIODDsp84hjCHVcjR4Z6xwMGpH4e5d+kmGwnii+7TIlhkarE/Q3cq+FePe5x\nZkyba3DfHPd6uB+G+5yYfd/ifgDum0b7W+B+Ge5/xL3O/3DvjvsGuG+I+z9xX5q19ykiBSWZxPEN\nwNrsgDuL3Int1bQBRqcrMElO48ah3vGkSfD992E28uWXw9IyLh/qk4pISjbZJAyof/89+WOLBsQd\nOqQ/LpH8VFD9pwMPhB9/zM5dWq1bw/XXh7UoUimPsWYNHHMM3HGH+kQFLRuJ4urVS04O6/ZAERER\nyXMJJ47dGevOm2Xsv8mdK9MTlqSqY0eYMQOuvBKGDIFddoGXXlq33W+/wU47wbBhukNcRBK01VZh\nYP3LL8kfqxlUUqAKrf/UrRt8+y3UzVLxsr59Q9/ntNOS/9W0ejU0bw59+sARR8CCBZmJUfJQJpPF\n11+/boJYKzSKiIhIJZXMjGOpJOrUgauuCrdvbrcdHHkknHxy8fKjK1ZAu3Zw4YWwww6hTrJqBIpI\niQ48MAyu589P/lgljEUKSo0a2S0FW60aPPpo+BL8rLOS+3VTs2b4kv3ll8OX7rvsAqOrzNxvWUem\nksXxSeLLL0/v+UVERERyKKXEsRn7pjsQSb8ddoD//AeefBLeegt23BGGDg0zbBo3hkcegZkzwx3j\n550X2j/wAKxcmevIRSQv3H57GGC//nryxyphLLIO9Z8yY4st4MEH4bnn4P77kz/+sMPCl+0HHwyn\nnALdu8P//pf+OCUHMpEsVi1iERERKSBJJ47NOBIYm4FYKpXRo+Gf/8x1FOUzCyuOz5oVBkIXXght\n28J//xv2N28OI0bAJ5/A3nuH99S8eRh8iUgBM4OLLkr+OA2kRUqk/lNmHXccnH126OfMnp388Rtv\nDKNGhfUixo+HVq20dmells5ksRLFIiIiUsCSShyb0R14CuiWmXAql+XLK095h4YN4e67YcqUsBhM\n27bQu/dfa1vttFMYMM2YEfaJSIFKdbCtAbVIqdR/yo4hQ6BpUzjjjHB3VbLMQo3mjz+GAw4IZd2l\nEknn7GIlikVERESAJBLHZlwIPAB0d2dS5kKqHDp3hsceg1q1ch1Jctq1g//7Pxg8GB5+GFq2DAnj\non5xq1bh+XHH5TZOEckyJYxFMkL9p+ypXz+U4Zo6FW67LfXzNGsGjz8evnSXSiAdyeJatZQsFhER\nESlBMjOOhwAXu/N8poKR7KhRI6wgPnMm7LsvdOkSVhOfOzfXkYlI1ilhLJJpBdl/+uYbuOuu7P+a\n2Hdf6Ns3LBL86afZfW3JsnQkjIuuZcuXpycmERERkSommcTxO8B5ZmycqWAku5o1g2eegRdeCDWQ\nd9kFrruu/L7zqlVhMLh4cXbiFJEMUMJYJFsKsv80cyb06wfz52f/tQcOhO22g9NP14K/VVJFE8aa\nWSwiIiKSsGQSx4cA84BXzGiQoXgkB44+OszKueACuPZa2H13eP310ttPmxbWzNp665BoLqqTLCKV\nQKoD7qFDNcgWSU1B9p8OPBB+/TU3dYLr1IFHH4UPP4Sbbkr/+d1h5MjwRbpkUUUSxptsomSxiIiI\nSAoSThy7swzoBHwGvJyxiCqxytwXrV8/DK6mTw8rix94YJip8+OP67Zt1y6UtTj11JA43moruPrq\nMEAUkTyV6oB7p53CL7fevdMfk0gBKNT+U82aIYGbK3vtBZdeGmYff/hhes/9wQfQvTvsvTd88kl6\nzy0lOPzw1BPGRcnin35Kb0wiIiIiBSKZGce4s9qd7sDUDMVTaS1fDscfH2agVGa77AJvvgkPPADj\nx4fF8+6/H9asKd6uWTMYNgy+/BLOPBNuvTUkkC+5BL7/Pjexi0gJUk0Y164dBtsqEipSYeo/5cZV\nV4XvvtJdsmLPPWHKFFi6NPz9hhs0+zhjzGDixOSP0+xiERERkbRIKnFcxJ0L0x1IZVe7NtSrB+ed\nFxaEqcyqVYN//CPUPT72WDj7bNhvP/j443Xbbr45DBkC8+aFCYn33Vf5k+ciVUJFbul1h2XL0huP\niKj/lGW1asEjj4Tvv267Lb3nbts2zDy+6CK48krNPk471eEXERERyQspJY6lZMOHw3rrQY8e687Q\nrYw23RQefjjUO/7tN9hjj7DQzZIl67Zt3DjMuJk/H849N+uhikiR3Xev+C29IiJVxB57hOTutdfC\nF1+k99x16sCNN2r2cdopYSwiIiKSN1JKHJtRx4y2ZhxtRqfYR7oDrEw23DDMbPnkkzADt6rYf/9Q\nH3DgwFCeYqed4IUXSm67wQZh5rWI5IAZfPRR8sdpwC2SFeo/5cY118AWW4Q7qDLxqy529vHdd8Oi\nRel/jYKQyixjXb9EREREMirpxLEZhwPzgfeA54HnYh5j0xpdJdSxY6j7u912uY4kvWrVgssvD7d7\n7rQTdOoUajqnUpbj3XfVxxdJK93SK5L3CrX/tHQpPPEELFiQuxjq1YN//zvcQfXQQ5l5jaLZx7Nn\nw0YbZeY1qjQljEVERETyUiozjocBTwObuVMt7lE9zfFVSlV5xu2228KECTB6NLz/Puy4Y6hxnOht\nmdOmQfv24XbOp5+G1aszG69IlaaEsUhlUpD9p5UrQwmvsTlOjR9ySFgk7+KL4YcfMvc69etn7txV\nUq1aqSWNRURERCQrUkkcNwaGuLMw3cFI5WAGJ58cFs8780y45BJo0wbee6/8Y1u3hkmTwmyczp3D\n7OUHH4TlyzMft0iVoYSxSGVUkP2nDTYIJa9KK3GVTYMHQ82acMEFuY5EgHAdW7ky8fa6homIiIhk\nXSqJ4zHAAWmOQyqhBg1g6NAw87hGDdh337Aw3m+/lX6MWSjnMWlSOG7nneGss2CbbeCmm8o+VqTg\nKWEsUpkVbP+pUyd47TVYvDi3cWy8MdxxR7hravz47L++eyiXoV/HJHct22wzfWgiIiIiOZJK4rgX\ncIIZj5jR14zzYx/pDlDyX5s2IQk8dCg8+SS0bAkjRpTfx2/bFp59FmbOhKOOgquvhkGDshOzSKVy\n4IGpJYz33luDbZH8UbD9p2OOgcMPh19+yXUk0LUrHHYY9OoV6i9n09tvh1/nxx6b2hoRVUYy1zP3\n3BbIFhERESlwqSSOuwKHAicCvYE+MY8L0xda1fHii6G0Q1Wu51u9ehiEzZoFBxwA3buHeoKzZ5d/\nbMuWcP/98PXX0L9/xkMVqVzMwhS1ZLnDlClpD0dEUlaw/adttoFx42DrrXMdSfiVOmwYfP99WMwu\nm/bbL3xh/t//hlJdd91VtfuG66hePfmksYiIiIjkVCqJ4+uBq4EN3NnanW1iHtumOb4qoU6d0E9e\ntizXkWTe5pvDqFHw0kswbx60agVXXJHYrJ4mTaBx48zHKFIpqCyFSFWj/lOe2GGH8EX1LbfAF19k\n97WPPx4++wy6dYPevaFdu5BIrvLMYM2axNrqOiYiIiKSN1JJHNcCRrmTYO9POnYM9fQKaaXtww+H\nTz6Byy+H224LM2vSsTDO228nt46KSKWjhLFIVaX+Ux659NLwZXfv3tn/1dmwIfz73/Duu7BqVSjd\n1asX/P57duPIGs0yFhEREam0UkkcPwqcku5ApOqpWxeuuSYkkFu2DIvjHHssfPVVauf78cdQG3C7\n7WDIkNwvsiOSVkoYi1R16j/lkXr14M47YeJEGDs2NzHss0+YbTxkCEyYEJLIVY6SxiIiIiKVWiqJ\n4+pAPzPeMGOYGUNiH+kOUCq/HXYIpSuefhqmTQuzj2+4AZYvT+48jRrBBx+E5HH//tCsGfTrV+AL\nzEjVoISxSCFQ/ynPdOoERx8NF14IS5bkJoYaNcLrz54NG2+cmxgyJtFr23PP6XomIiIikqdSSRy3\nAqYDa4BdgD1iHrsnezIzzjXjIzN+jx7vmnF4XJuBZiwwY6kZr5ixfQpxSw6ZwUknwcyZcN55cNVV\nsNtu8OqryZ2nVSt49NFQP/nss+G++8KiO126wPvvZyZ2kYxJZZbxeutpgC0VZ1YNs0GYfYnZUszm\nYDYgrs3DmK2Je0yIa1Mbs+GY/YzZYszGYNYom2+lEklr/0nS4847wx1N112X2zhq1szt66ddotc2\n93A7moiIiIjkpaQTx+4cWMbjoBRi+AboD7QG9gQmA+PM2BHAjP5AL+BsoC2wBJhoRq0UXitvuMN3\n3+U6iuxbf/1Q83j6dNh0Uzj4YDj11LC6eTKaNg2L2nz7LdxxR5jJnO3V0UVSVpGyFKrRIulxKXAO\ncB7QEugH9MOsV1y7l4DGQJPo0TVu/x3AUcCJQAdgc+CZzIVdeWWg/1Qp/fOfoTRDvth2W7jsMhg8\nGD7/PNfRVBHJJI1FREREJK8lnDg2Y74Zd5lxiBk10hWAOy+687I7c92Z484A4A9g76jJBcAgd8a7\n8wlwGmFgely6YsiFwYOhdevCTB5DmDn85pvwyCMwaRK0aBFm/SRb32+99cKCMrNnw4MPZiRUkfRR\nHWPJH/sA43B/Gff5uD8L/IfwBW2s5bj/hPuP0eOv5bvMGgBnAn1wfwP36UAPoD1m8ecpWJnqP1VW\nTZrAhhvmOori+vcPC+VdfHGuIyndjTfCnDm5jiIBShqLiIiIVCnJzDj+O7AcuBv42YxRZnQzo2G6\ngjGjmhldgHrAu2ZsQ5jhtLaggTuLgPcJg95K67TTwm2JnTvDypW5jiY3zOD000PSt3t36NMH2rSB\nKVOSP1e1alWwNqBULUoYS355F+iI2Q4AmO0GtAcmxLU7ALOFmM3C7G7MNorZtydQg5hrNO6zgflU\n8mt0mmW8/1SZDBoEPXrkOori6tSBW2+FF16AV17JdTTr+uknuOce2HlnuOKK3NVjLpeSxiIiIiJV\nTsKJY3fecKevOzsQBpcfAr2BH8yYbMaFZmybShBm7GLGYv4aWB3vzmxC0tiBhXGHLIz2VVqNGoXF\n4qZOhUsuyXU0ubXhhnD33aFGcY0asO++4VbWX35J7+uMGAGvvabximRZKrOM33hD/1Al024CRgGz\nMFsBTAPuwP2pmDYvEe7yOYhQymJ/YAK29h90E2AF7ovizl3pr9HplMn+k6TPSSfB3/4WvsRO9u6n\nTNt007BGRP/+4Y61Fi3gqafy7DKhpLGIiIhIlZTSLZPufAp8CtxoRhPgGKATcIMZXwL93XkxiVPO\nAnYDNgBOAh4zo0MqscUaPHgwo0aNKrata9eudO0aX6IxN/bZB4YPD0lkgb32Csnje++Fyy+HsWPh\nppvgzDPDjOKKcA8L6b35Juy+e1jBvEsXqF07PbGLrCOVGcagQbUkbeTIkYwcObLYtm+//ba8w04B\nTgW6AJ8RFme7E7MFuD8OgPvomPafYvYxMBc4AHgtHbEXmgz0nyRNzMKaCXvtBfffDz175jqi4urX\nh4ED4YwzoG9f6No1zEIeOjQsNpxTShqLiIiIVFnmaezEmVEfOBRY7M6kCpznFWAOcAthkLq7OzNi\n9r8OTHenT8nH26nAiCeeeIJu3bqlGobk0MKF0K8fPPYY7L13GBztXsE1591DPeU77oAJE8IMnrPP\nhnPPDYvtiaRFjRqwenXyx2lALWk0YsQIunfvDtDN3Z9cp4HZfOBG3O+J2XYF0A33nUo9sdmPwBW4\n34/ZgcAkYMNis47NvgJux/3OtLyZApCu/lOmFUL/qkePULJizhxomMfFRP7zHzj/fPjiC3j00VDy\nKyeUNBaRAlJu/0pEpApKaB6nGQ0SeQDV3RmbhkFPNaC2O/OAH4COsbEA7Qj1GaWKatw4DIRefx0W\nLYI99wyzhBfF3xCdBDM45BB48UWYNQtOOSUsyLf11nDyybB4cbqil4JllnzSWHWMJTfqAfH/WNdQ\nVr/ArCmwMfB9tGUasIqYazRmLYAtgRSq1Vc9Oeg/SQXdcAMsWxZqMeezQw+FGTPgttvg4INzFESt\nWom10zVOREREpNJKtADA/4DfynkUtUmKGTeYsZ8ZW0W1jm8k1FF8ImpyBzDAjGPMaAU8BnwLjEv2\ntaTy2X9/+PDDULLi/vuhZcv01PVr0QKGDYPvvgszkNesgfXWS0/MUoBSqWP8yScaTEsuvQAMwOxI\nzLbC7HigD/AsAGb1MbsFs3bR/o7Ac8DnwESAaJbxg8AQzA7AbE/gIeAd3Kdm/y3lpYz1n6qCfPwV\nuNlmoVzW0KHw+ee5jqZstWqFmsxNclFR/JNPElvdOR9/yCIiIiKSsERrHB+YwRgaAY8CmwG/AzOA\nQ92ZDODOLWbUA+4FGgJvAUe4syKDMUkeqVkzLCDYpUuYddy1a0giDx8eEskV0aAB9OoVHiJJUx1j\nqbx6AYOA4YTr8ALgnmgbhNnIuxIWx2sY7Z8IXIV7bLaoT9R2DFAbeBn4Vxbirywy2X+q1D7+GDp1\nCiWkttsu19EU16dPWBfhkktgnKYplKxVq/Lb6FonIiIiUukllDh2541MBeDOWQm0uQa4JlMx5JsF\nC2DqVDjuuFxHkl+aNYNnnoGXXgqJ3l13DYO6K66AevUy+9qrVkH16qnnCaUKSuUfgwbRki/clwAX\nRY+S9i8DDk/gPMuB3tFD4mSy/1TZbbcd/PwzjBgBV12V62iKq1s33OnUtSu89Rbst1+uI0rdwoWh\n9P7GG6fxpIlc/3S9ExEREakSEi1VUYwZDc3oa8YD0aOPGRukO7hCNWxYWDF7+fJcR5Kfjjgi3CF5\n+eWhtt9OO4WFbDLpwQdhxx3hrrtUC7ngpVKWQnWMRQT1n2LVqwcnnBASx/n467Fz57C+Qr9++Rlf\noi6/HLbfHm6/HVak4149JY1FRERECkrSiWMz2gBzCbenbhQ9LgLmmtE6veEVpoEDYcoUqF0715Hk\nr7p14ZprQgK5Zctwu2unTjBvXmZeb889wwznCy+ELbYIM54//TQzryV5KpWEccOGGkCLCKD+U0m6\ndQt1hD/6KNeRrKtaNbjlFnjvPRg7NtfRpO7GG0Opr4svhp13Du8l5cuSksYiIiIiBSeVGce3A88D\nW7tzgjsnANsA4wkL2UkF1awJjRrlOorKYYcdQumKp5+G6dPD7ONrr4U//0zv67RpA6NHw1dfwfnn\nw5gxsMsu0KEDjByp2eFVXqplKX4ryPWuRKRk6j/FOegg+OAD2G23XEdSsoMOgsMOg8suS2wduHzU\nqBHccw/MmBHKg5xwAhx4YPjck/KvBEqXK2ksIiIiUuWkkjhuA9zszqqiDdHfb4n2iWSVGZx0Esyc\nGWYEX399mFWTifIVTZvCddfB/PkwalSoG3jqqeHvUgWpLIVI5pith1lXzO7G7EXMXsNsNGaXYbZn\nrsPLgKz2n8yoZsYgM740Y6kZc8wYUEK7gWYsiNq8Ysb26Y6lNDVqwB575Pf6ATffDF98EUpWVWY7\n7wwvvxy+bP/pp3AnVbduYQ2HhNx9d9n7dZuciAiY7YfZ85h9h9kazDqV0GYgZgswW4rZK5htH7e/\nNmbDMfsZs8WYjcGsUVybDTEbgdnvmP2G2QOY1U8izkLrg4lIBaSSOF4EbFnC9maAqr9Kzqy3Xrgl\n8+OPw0zkTp3g6KNh7tz0v1atWqH+4eTJIWF90knpfw3JoVQSxpddpoSxSCLMNsXsLuB74DZgM+Ar\nYBrwB3A88C5m0zE7Nmdxpl+2+0+XAucA5wEtgX5APzN6FTUwoz/QCzgbaAssASaaUSsD8VRKu+0G\n3buH8lh//JHraCru8MNDaZD77gvVlGokskx2ItfDZcsqHJuISBVQH/iQcO1dd2BgVuJ1F7PY6+4d\nwFHAiUAHYHPgmbgzPQnsCHSM2nYA7i03usLtg4lIBSTSXYw3CnjQjIuBd6Nt7YFbgZHpCkzW9dNP\nsOmmuY4i/7VoEWbVjB0LffqEWTb9+sGll4bFeNKtZcv0n1NyKNWyFCKSqE8IfYn9cS/5hnmz9YGT\ngKsx2xb327MYX6Zku/+0DzDOnZej5/PNOJUwUC1yATDInfEAZpwGLASOA0ZnIKZKaeDAcGfR7bfD\nlVfmOpqKq1ED/vnPBBurrrGISOLcX4boumsl/gK9ABiE+/ioTfHrrlkD4EygC+5vRG16ADMxa4v7\nVMx2BA4D9sR9etSmN/AiZhfj/kMZERZqH0xEKiCVGccXA88CjxG+nfoKeAQYA/RPU1wS57PPYNtt\n4Zn47xqlRGahjt/MmWFBmJtvDvWPn3su++Mbd+jdG159VWOrvKayFCLZshvu55c6YAFwX4z7w7i3\n/n/27jzOpvp/4Pjrc8dYhrHvhDE0SJSxtFlHkfCzpJpM1qS02CpC1oqUPYpSZKyVLJEsSd8KhZJs\nEZE9u+yZz++PzzXNnfXemXvvucv7+XicB3POuee+Zz2f8znv835jsmoCgbfHTz8AMUpREUApqmMm\nqpfbP44AigNrbr5Aa84DGzGTzsKuXDnTFHf0aDhxwupovOjDDzPeR86BQgjhHKVSnHfROvl5tyYm\nuS/pPruBg0n2uQs4kzhpbKzGZDjXySCKYB2DCSGywOWJY625pjU9gQLAHfaloNb01hppEeYhlSqZ\nsgtxcbBxo9XR+I+wMFOT+LffzMRx69bQrJmpV+gtR4+aSePGjc33cexYOHXKe+8vMpCZCePjx+Vi\nWYjM0voYSlXMcD+TPQNaH/d0SN5gwfhpFCaraJdSXMM8hjpea+bZtxfHXGQm//oet28TSQwYACEh\nZtlR4L8AACAASURBVEwRLHTXrhnsIOdBIYRwgTPn3WLANfuEclr7FAccb2NqfQM4TUbn7yAdgwkh\nsiYzGccAaM0lrdlmXy65MyiRks0GH30ENWpAixamOZtwXsWKsGyZyTjetQuqVoWBA+HiRc+/d8mS\nsH07rFtnvn/9+5t1sbGmRnJCgudjEGnIbFmKokUz3k8IkZ5VKFUyza3mgmWM98LxHi+Onx4FHgce\nA+4EOgIvKcUTHnzPTDtwAN591+oo0laoELz0EkydCn/9ZXU0nnfRlzsWCiGEyIqgHYMJITLH5RrH\nSpETeB5oCBQl2eSz1tRwT2giuZw5YfFik7FaooTV0fgfpeD//g8eeABGjTLlK2bNMjUL27TxbFd3\npaBePbOcOAEffwwffAAxMXDfffC//3nuvUUqpI6xEFbbAqxGqfvQ+rTDFqWeBcYBGaQ7+hcLxk+j\ngZFa84n94+1KUQ54BZgFHAMUJrspaUZRMSDp468pjBkzhvnz5zusi42NJTY2NtPB/vij6TH68MO+\n28/hhRdg/HiTdTw14xZE/issjNwZ7SPnRCFEgJs7dy5z5zq2IDh06FBWDunMefcYkB2l8ibLOi5m\n33ZzH8csFqVCgIJJ9klP0I3BhBBZk5nmeNOBBzA1+X4ktW6hwmMKF4Y33rA6Cv+WKxcMGwYdOkCv\nXuYi9f77YdIk01jP04oWNXWX+/aF776DY86c3oV7hIXB5cuuvWbiRFOkWgjhTo9hau2uQKlGaP0P\nAEr1wHQT74bWMy2MzxO8PX4KA24kW5eAfcJaa/YrxTFMR/ZfAZQiL6Y+4uT0Dty3b1/at2/v1mBb\ntYKHHvJME1t3CQ+Hfv3MBHe/fqb3REBK4zypgUvAH1s11bwakBBCeF9qN0Rnz55NXFxc5g6o9X6U\ncjjv2pvhJT3vbgb+te/zuX2fKKAMsN6+z3ogP0rdmaTOcQxmUtqZopbBOAYTQmRBZiaOmwPNtOZ7\ndwcjhDdFRsLSpfDFF9CzJ9x+O/TpA4MGQZ48nn9/paBuXc+/j7CTLGMhfIfW11Dq/4C1wGKUehCT\n3TIR6I7WH1kan2d4e/y0FBikFIeA7UANoDfwQZJ9xtv32Ytp1jcCOAQs9lKMiUJDzeLrevSAMWNg\n+HCYMcPqaDwgg3NltfKa/XeYsdLw4V6KSQgh/IVSuYEKmElcgPIoVR04jdZ/YT/volTq512tz6PU\ndGAsSp0BLmDGRt+j9Y/2fXah1FfA+yj1DJAdmATMReuMU5KCcwwmhMiCzNQ4Poz5AyZEQGje3NQg\nHjQIJkwwDewWLPCdOcP16+Hzz+H6dasj8VOZaX6nte/8AAgRqLS+CDTFNHLZiLlgeRqtp1sal+d4\ne/z0HCa7eTKwA1O64l1g8M0dtGY05mJzKuZ7kAt4UGuueTFOvxIWZnokzJpleiYElJCQdDcrzOc8\ndarc+BZCiDTUxJSd2Ix5UGMMpjTEMAC0TvW8i9ZJz7u9gS8w5/BvgCNA22Tv8ziwC1ht3/dboLvT\nUQbfGEwIkQWZmTjuC7ypFGXdHYzIGpnnyrycOWHwYNixA2rWhEcfhcaNYedOqyODzz4zNZhvucU0\n1tu71+qI/IirE8YlSsgvkhDeoNQDKPUA5gJrDFAZ80jmwcRtZnsg8er4SWsuak0frYnQmtxaU1Fr\nhmjNv8n2G6o1JbUmTGuaaI2cZTLQrRuUKgVDh1odiZtl1K1Xa0JDzed///3eCUkIIfyK1uvQ2obW\nIcmWLkn2GYrWJdE6DK2boPXeZMe4itbPo3VhtA5H63ZofSLZPmfROg6t86F1AbTuhtbONdwNzjGY\nECILMjNxvAnICexTigtKcTrp4ub4hJMGDDB1e0XWRETAokWwfDkcPAjVqpku6hcszLF/+23YuhUe\necRk+VSsCPXrm0dk//nHurh8WmazjI8c8Uw8QojkViRZPsA8ZvlwsvVfWhadZ8j4KUDkyGFuNs+f\nD7/+anU0bpLROVNuqgohRKAIxjGYECILMjNxPBcoBQzAdAfvnWwRFsib1yzCPR58ELZtM9lEkyeb\npnmzZ1t33VStmunRduQIxMebOpCdO0Px4rBsmTUx+aTQUClLIYR/yOXE4sNt0jJFxk8BpGNH0xxv\nyBCrI3GDjAaQGZSwSO76dZPMIM1/hRDCJwXjGEwIkQWZaY53D3C31mx1dzAi8/r3tzqCwJMzp6lj\nGBcHffuaf999FyZNgjvvtCamXLmgfXuzHDgAM2daF4vPkeZ3QvgPra9aHYIFZPzkpC1bzI3bBQvM\nudgXhYaaGDt0gE2bTJkrv5XRY1X//pv+9mS2b4dx42DUKOjeHfr1M5WghBBC+IDgHIO5RClVBihs\ndRxCeMlJrfXB9HbIzMTxLsxdKCGCQtmy8OmnsGYN9OwJ0dGmvt9rr0GRItbGNXhwxvsFPJkwFsK/\nKHUnWv/s5L45gHJovduzQXmFjJ+clCcPLF0Kn3wCTzxhdTRpe/xxeOMNk127dKnV0WSSB0pU3HEH\n/PmnaTg8fjy89x489ZSZQC5VKnNhCiGEcIPgHYM5TSlVxmaz7U5ISPDRW9dCuJfNZruilIpKb/I4\nMxPH/YExSjEQ2AZcT7pRa85n4phC+LyYGPj5Z5N1PHiwyYQaPhyeeQayZeY3yUveegsaNDDZUJmZ\nY/Vprn5CEyfC8897JhYhhLM+R6mtmLp6XyXrJG4oVR6IA7oBQ4BAuGiR8ZOTbr3VNF+bPNm3J45D\nQsyTSU88YcYHfvcE0MKF6W93sURFUvnzmzIevXqZU++4cTBtGjz5pHlKrnTpTB9aCCFE5gXrGMwV\nhRMSEnLGx8dTuXJlq2MRwqN27txJXFxcTkyGvVsnjlfY/12TbL0CNJD5UaYQPi40FF54AWJjzcVi\nz57mQmjCBGjUyOroUjp3zsT28stw222mLnJcHBQrZnVkWSRZxkL4s0rAC8AkoDhKbQeOAFeAApju\n3kWAZUBrtN5kVaBuJuMnF/ToAa1bw+bN5kkfX/XYYybjeMSIjOdhfU7btulvd7FERWry5YNXXzXj\npUmTYOxYiIyE3lLVWwghrBCsYzCXVa5cmRo1algdhhA+ITPN8Rral0bJlpvrhI+4fNlcE6xaZXUk\ngadIETNh/NNPpqdMTAy0a2fqDvuSfPlMTF9+aSaOBwwwWT4tWphHgK9csTpCFzVtKs3vhPB3Wl9B\n69FABNAY+AQ4BFwEfgFeBsqgdaBdsMj4yQXNm5vz1O23Wx1J+rJlM+fWzz83TXX9hgdKVKQnb15z\nw/3PP+Hpp916aCGEEM4K3jGYECILXM441pp1nghEuJ/NZiaPW7aEJUvMY5/CvaKj4bvvYM4ceOkl\nqFTJ1PB7+WUI85FetCEhZr61aVM4fRrmzoVZs+CRR6BkSTOx7MulNhJJlrEQgUVrDXxnXwKejJ9c\nky0bPPyw1VE4Jy7OlK567TWYP9/qaHxbeLjVEQghhAi2MZgQImucyjhWijKuHFQppPWFD8iRwzw2\n2aiRyTBdudLqiAKTUtC+PezebWr5jRwJlSubhnq+Nm9ZsCA8+yxs2AC7dpmagz4/aayUZBkLIfyS\njJ+CQ2govPKKyZDeudPqaJzg5WxjV/3sXNsmIYQQQgjhBc6WqvhJKaYqRa20dlCKfErRTSl+AzIo\nmia8JWdOM3nctq0pWyA8JzzcTBr/9htUq2ZKV8TEmI99UVSUyTr2aa5OGFepYvkFrxBCJCHjpyDR\nsaMpBfX661ZHkoGXX05/u8Xn0A0boEYNqFvXlNmSU7oQQgghhLWcnTiugql7s0opjinFMqV4Xykm\nKUW8UmwBTgBdgJe1ZqKnAhauy5EDZs+GOnWsjiQ4VKwIS5fCsmVw6BDccYdpqHfmjNWRuW7jRtOc\naOFCuHrVi2+c2Szj7ds9E48QQmSOjJ+CRI4cplTV3LmwZ4/V0aTjrbesjiBdtWvD4sVw/To0a2Ym\nkefPhxs3rI5MCCGEEJ4yY8YMbDYbBw8e9Or7Dh06FJvNxunTp736vp7m7q+nUxPHWnNKa/oAJYDn\ngD1AYaCifZfZQLTW3K01y90SmRB+rlkzk208ciR89JGZUJ42zb8ufi5dgr/+MhnrJUrAM8/ADz94\nOANIylIIIQKEjJ+CS9euUKwYvPGG1ZGkwcdLVIDpz9GyJaxfD19/DYULw2OPmRJgH3zg5ZvYQggh\nRICZOXMmNpuNsLAwjh49mmJ7gwYNqFatmtfjUkqhMtPTyEkjR45k8eLFXn9fq7j783I24xgArbms\nNZ9qTS+taa01TbUmTmvGaI2PPpAvhHWyZzdN837/3XSI794datWC77+3OjLnNGwImzaZRN7u3eGL\nL+Dee80k+ODBpq6z22Q2y1gIERiUcmlM4k9k/JQ1CQnw4YewzsfbC+bMaSpBzJoF+/dbHY2Lsme3\nOgIHSpkxyKpV8OOPULUqdOsGw4ZZHZkQQgSgAB6DidRdvXqVUaNGpVhv1SRqhw4duHz5MmXKuNQe\nxGlvvPFGqhPHwjnyByLIXbxodQTBoUQJmDHDZNCEhMB995mGeocOWR2Zc6pUMZnTBw6YDKB69WDi\nRHjvPTe9gWQZCxGczO3wl1DqD+AKSpW3rx+CUh2sDU74CqVg+nRz/vF1Tz0FhQpBKtdi1sroPOvD\nqby1apmSWTt3wvPPWx2NEEIECBmDBbU77riD999/n2PHjlkdCmB+HLP72E1sd7p8+bLVIWSJTBwH\nsWPHzKN/8+dbHUnwuOsuUzd4+nRYvdo0qBs+3JSE8Ac2m8kA+vBD8/Pz6qtZPKBkGQsR7PoDzwJv\nAP8mWf878LQlEQmfo5SZNPaHbNOwMOjTx9wsTuUJUN/kJ+fVSpXMjXghhBBuIWOwIKWUYsCAAfz7\n77+pZh0ndePGDUaMGEGFChXImTMnERERDBw4kGvXrjnsV65cOVq2bMn3339PnTp1yJUrF5GRkcya\nNcupmFKrybtp0yaaNGlCkSJFCAsLo3z58nTt2tXhdZcuXaJv376UKVOGnDlzUqlSJcaMGeOwj81m\n49KlS4nvYbPZ6NKli8M+Z86coVOnThQoUID8+fPTpUsXrly5kiLO+Ph4atasSVhYGIUKFSI2NpZD\nybIBb5b72LJlC/Xq1SN37twMHDjQ4eu0bt06atWqRVhYGNWqVWOd/bG6hQsXUq1aNXLlykXNmjX5\n5ZdfHI69bds2OnfuTGRkJLly5aJEiRJ07drV4zWaZeI4iBUtajJj6te3OpLgYrNBly6mfMVzz5kO\n7FFRMGeO31y7AeaR3IIF09/n0KF0stoly1gIAZ2Bp9B6OpC0AvwvQCVrQhK+KEcOqyNw3tNPm3Pk\n+PFWR2IXgLX70rJ5syltIoQQIkMyBgtiERERdOjQIcOs465duzJkyBBq1qzJ+PHjadCgASNHjiQ2\nNtZhP6UUe/bsoV27djzwwAOMHTuWggUL0rlzZ3bu3JlhPMlr8v799980adKEgwcP8sorr/DOO+8Q\nFxfHxo0bHV7XokULJkyYQLNmzRg3bhyVKlXipZdeom/fvon7xMfHkz17durVq0d8fDzx8fF07949\ncbvWmkceeYSLFy8yatQoHn30UWbOnMmwZBkLr7/+Oh07diQqKopx48bRu3dv1qxZQ/369Tl//rzD\n53Ly5EmaNWtGjRo1mDBhAg0bNnT4OrVv356WLVsyatQozpw5Q8uWLZkzZw59+/alQ4cODB8+nD/+\n+INHH33UIYZVq1axf/9+unTpwjvvvENsbCzz5s3joYceyvBrnCVa64BbgMcBHR8fr4XwdXv3at26\ntZkVvfturTdutDoi92nXTuuwMK1jY7VeulTrq1f1zelf5xebzepPQwi/Ex8frwENPK594Lyc5gKX\nNZSz//+ChvL2/1fWcNHy+GRxWGR85bz+/bUOD9f6zBmLA+nUSad7jg0ghw6ZIUOlSlq//77Wly9b\nHZEQItD4zfjKmUXGYGmNdWoAevPmzSl/AFJx8aLWmzd7drl40alQnDJjxgxts9n05s2b9b59+3Ro\naKju1atX4vYGDRro22+/XWut9S+//KKVUrp79+4Ox3jppZe0zWbT33zzTeK6cuXKaZvNpr///vvE\ndX///bfOmTOnfumll5yO68CBA1prrRctWqRtNpvesmVLmq9ZtGiRVkrpkSNHOqxv166dDgkJ0fv2\n7UtclydPHt25c+cUxxg6dKhWSulu3bo5rG/Tpo0uUqRI4scHDhzQ2bJl06NGjXLYb/v27To0NNQh\nhgYNGmibzabff//9FO938+u0Mcmkz8qVK7VSSufOnVsfOnQocf20adO0zWbT69atS1x35cqVFMec\nN2+ettls+rvvvktcl/zrmZbNmzff/JtWQ6fzeyEZx0JYLDLS1O5bs8Zk59apAx06wOHDVkeWdW++\nCYMGwa+/QosWcD2HwqWcYa3hxo2M9xNC+KvdwN2prG8N/OrlWIRwm5494do1ePddiwOZMcPiALyn\nVCn43/9MGbannoKyZWHoUFNaSwghRAoyBnODXbsgOtqzy65dnok9IiKCJ554gmnTpnH8+PEU25cv\nX45Sit69ezus79u3L1prli1b5rC+SpUq3HPPPYkfFy5cmKioKPbt2+dybPnz50drzZIlS/j3339T\n3efLL78kW7ZsPJ+sAULfvn1JSEjgyy+/dOq9lFIOGcgAdevW5dSpU/zzzz8AfPbZZ2itadeuHadO\nnUpcihYtSsWKFVm7dq3D63PkyEGnTp1Sfb8qVapQu3btxI/r1KkDQExMDKVKlXJYr7V2+PrlSPII\n3tWrVzl16lTiflu2bHHq882MLE8cK8XXSlE2C69/RSl+VIrzSnFcKT5XiluT7fORUiQkW5ZnNXYh\nfEmjRrBlC0ydCitWwK23wogR4M911CMi4JVX4LftigQUYYBTD8yWLi1lKYTwJKVsKDUCpfah1CWU\n2otSg9LZ/z2USkCpF5Ktz4FSk1HqJEpdQKlPUaqoC5G8BryDUj0xY5JmKDUJGAwMd/0T8x9ZHT8J\n31a8OHTubMpVWHYez6i+RwCeZ++5x9yM370b2raFt96CMmXMDfnNm62OTgghfErQjsHcqVIlc37x\n5FLJg4VDBg0axPXr11OtdXzw4EFsNhsVKlRwWF+sWDHy58/PgQMHHNaXKVMmxTEKFCjAmTNnAEhI\nSOD48eMOy/Xr11ONq379+jz88MMMHz6cwoUL06pVK2bMmOFQW/nAgQOULFmS3LlzO7y2cuXKidud\nlTz2AgUKACTGvnfvXhISEqhQoQJFihRJXIoWLcquXbs4ceKEw+tLlSpFtmzZnHqvvHnzAlC6dGmH\n9fny5XOI4eb/e/bsSfHixcmVKxdFihShfPnyKKU4d+6c05+vq1L/TFKhFC3T2FQPaK4UfwFozRIX\nY6gLTAI22eMZCaxUispak3So/SXQif/mnXy3/XMA+PZbqFED8uSxOpLgEhJismQefRRee81MHL//\nPowebdb5XZnC336D228HnJwwhoC8kBXCB/UHugMdgB1ATWAGSp1F63cc9lSqNVAHSO05iPHAg0Bb\n4DwwGfgMc27PmNafotRZYAimMct4TG29dmjtXJqAj/Pg+CmoXbgA4eFWR5G+F1+EadPgo4+gRw8L\nAkjWuMZBtWrei8MCFSvClCmmj8T06fDOO/DPP2ZSWQghBEExBvOGsDAzb+KvIiIiiIuLY9q0afTr\n1y/VfZSTkxAhISGprtf26/u//vqLiIgIlFJorVFKsXbtWurVq5fq6xYsWMCPP/7I0qVL+eqrr+jS\npQtjx45lw4YNhIWFORWTszKKPSEhAZvNxooVK7DZUubf5kk2cZYrVy6X3yujGADatWvHhg0bePnl\nl6levTp58uQhISGBJk2akODBJg9OTxwDizC1L1L7qZlk/1cDqX+2adCaZkk/VopOwAkgGvguyaar\nWvO3K8cWmXPpErRrB7fcAsuWQbFiVkcUfPLlM1ky3bubC8/YWJg0yWQu1apldXROcnWW+8knzSy5\nEMIb7gYWo/UK+8cHUepxoLbDXkqVAiYATSDZkz5K5QW6AI+h9Tr7us7ATpSqjdY/phuBUiGYc/2P\naF3Xvk45jI4Cg0fGT8Hs22+hWTP4+WczQeirIiPhkUfM+fyppyCNxBPPyOgcvHWrd+KwWIECZhzV\nqxecPWt1NEII4SOCZwwmnDBo0CDi4+N58803HdaXLVuWhIQE9uzZQ1RUVOL6EydOcPbsWcqWde3B\nueLFi7N69WqHddWrV0/3NbVr16Z27dqMGDGCuXPn0r59e+bNm0eXLl0oW7Ysa9as4eLFiw5Zxzeb\n8SWNz9nJ77RERkaitaZcuXIpMrC95ezZs3z99deMGDGCgQMHJq7fu3evx9/blVIVX2Gyfotrje3m\ngunAWdX+sTsuevJjLqBOJ1vfwF7KYpdSTFGKgm54L5GKsDBYuRKOHDGP/O3ZY3VEwatCBVi0CFav\nNtlVtWtDx47me+OzFi50fdJY61QnjTt0gIEDzfWtDGOEcKsfgBiUMtNuSlUH7iXp5LAZYX0MjEbr\n1NohR2NuQK9JXKP1buAgqdfMc6T1DeB/QOEk6wLxN91b46egUbu2ucE63A8epO3XD/78ExYssDqS\nJALy1yx92bJB4cIZ7yeEEEEheMZgwgnly5cnLi6OqVOncixJY4BmzZqhtWb8+PEO+48ZMwalFA89\n9JBL75MjRw4aNWrksNwsx5Dc2VTu9t6cZL569WpifP/++y/vvOP4sOS4ceOw2Ww8+OCDiety586d\n6jGd1aZNG2w2G8OGDUt1++nTyacv3e9mRnLyzOJx48ZleWI8I07nPmjNg0rRG9ikFD205gt3B6MU\nCvOIxHdasyPJpi8xj77uByIx5SyWK8XdpvGncLfq1eGHH6BpU1Nz9+23rY4ouMXEmMyqDz4wzeY+\n+8zUDu7TB9J5CsL7XP2D9dtvcNttqW66ccOUZ3z3XXjjDYiKMplbjzwCVau6IVYhgtsoIC+wC6Vu\nYG4kD0TreUn26Q9cS1G64j/F7dvPJ1t/3L7NGTuAWwDXu2b4CW+Mn4JNzpzmXPjss2Zi1pfPCXfc\nYcZSo0aZp4e8UnLK7+pa+YYPP4QiRUw2expPiwohRCAJ+DGYSF1q9wgGDhzIrFmz2L17N1XtA6tq\n1arRsWNHpk2bxpkzZ6hfvz4bN27k448/pk2bNtSvX99jMc6cOZMpU6bQunVrIiMjuXDhAu+//z75\n8uWjWTNTtKBFixY0bNiQgQMHsn//fqpXr85XX33F0qVL6d27NxEREYnHi46OZvXq1YwbN46SJUsS\nERHh0KAuI+XLl+e1115jwIAB7N+/n1atWhEeHs6+fftYtGgR3bt3p0+fPm7/OiQVHh5OvXr1GD16\nNNeuXaNUqVKsXLmSP//80+P3fVx6aE5rxinFWmC2UrQAemf0GhdNAapgsp6Svm/SPI3tSrEN+ANo\nADi2L0xizJgxzJ8/32FdbGwssbGx7oo3oJUrB+vX+34NwWAREmJKV9ysfzxs2H/1j9u1s/g68fhx\n0wnIFRn8cQsJMZ/f5MmwZo3J1po40dR9rlLFTCC/8IJ5DFWIYDZ37lzmzp3rsO7QoUMZvexR4HHg\nMcyFwx3ABJQ6gtazUCoaeAG40/0RO3gZeBulXgE2AxcdtmqdTpFW/+GF8VPQ6drVnBM2bPDtiWOA\n/v2hQQP48kszKWkpSShLldYwf7554q5sWXj6afMzVqSI1ZEJIYTHBMUYTKSUWnZqZGQkTzzxBDNn\nznTYPn36dCIjI5kxYwaLFi2iePHiDBw4kMGDB6c4ZlpZr5nJhq1fvz4//fQT8+fP5/jx4+TLl486\ndeowZ86cxBIUSimWLl3K4MGDmT9/PjNmzKBcuXK8/fbb9O7tONQeO3Ys3bt359VXX+Xy5ct07NjR\npYljgH79+hEVFcW4ceMYbn/s7ZZbbqFp06a0bOnY0iS9r0Vq25xdP3fuXJ5//nmmTJmC1pomTZrw\n5ZdfUrJkSY9mHavMzEwrRS5gHNAIKA9US5YhnJljvgO0AOpqzUEn9j8BDNSaFM+3K1OncXZ8fDzt\n27fPSlhC+Kw9e0zdviVL4N57Tf3jmjUtCCQzZSky6epVWLXKTCKvWAF794K9CakQIonZs2cTFxcH\n0F5rPSfFDkodBEai9btJ1g0E2qN1FXuH7THg8FRPCJAAHETr8ijVEFgNFHDIOlbqT2AcWk/IMFCl\nbj5rlfofBq0DKu/PE+Mnb/K18dW1a5A9u9VRZExrU/orNNTUZ/aojM7JMnGcrp9+Mk86zZ0LCQnm\nxvyzz8Jdd0kitxDCifGVPwmyMZizlFI1gM2bN2+mhj93vRPCCVu2bCE6OhogWmu9Ja39MtWmQ2su\nA0/bO4U3BE5mKko7+6Tx/wH1nZw0Lg0UAo5m5X2F8GcVK8Lixab+ce/epmle+/amrEOZMl4KwouT\nxmBKVzRvbpYbN+RRUiGyIAxTYzepBP7rffAxsCrZ9pX29R/ZP96M6cIdA3wOgFJRQBlgvZNxPJjx\nLoHD3eOnYOcPk8ZgTpX9+0OrVqYM2D33WBSITBpnqFYts7z9Nnz0kZlEnj3blBxZtUrqJAshAkpQ\njcGEEJmXpf7OWrMEWJKVYyjFFCAWaAlcVIpi9k3ntOaKUuQGhmBqHB8DKgBvAr9jGs4IC9y4AZcv\nQ548VkciGjeGX34xtflefdXUP+7d21ykeiwb18sTxqnJaNJYa1POo3Fjc5Fuc6UVqBCBbykwCKUO\nAduBGpjyCR8AoPUZ4IzDK5S6DhxD6z32fc6j1HRgLEqdAS4AE4Hv0fpHp6LQOijP4+4YPwn/0qKF\nqdU/ZowHJ44lJdZtChaEvn3NeGrVKli2DAoVsjoqIYRwoyAdgwkhXOfUxLFSvODsAbVmoosxPI15\nPOKbZOs7YzKbbgDVgA5AfuAIZsJ4sNZcd/G9hJsMGQLLl8PGjebRS2GtkBDo1g0eewzeestk5SV2\nHQAAIABJREFUynzwgek4/+STppu42/jApLEzjh6FadPM5HHx4tC6NbRtC/Xru/nrIYR/eg4YAUwG\nimLOre/a16UltV/m3pjz9KdADmAF8KzTUSiVfnExZyegfZSHx0/Cj9hspqHt00/DH39AZKSXA5Bs\n40yx2aBJE7MIIURACfAxmBDCfZydPnG2iYsG1y58tCbdPECtuQI0deWYwvNiY6FCBZk09jXh4Way\n+KmnTMf5Hj1M86C33jINebKUjOQnE8Y3lSwJhw6ZBo+ffQYLF5pHTgsVgv/7PzOJ3KSJlLsQQUrr\ni0Af++Lsa8qnsu4q8Lx9yYwNmLFD0j8wyesq+zOPjZ+E/3niCXNuHjcO3nnHzQdP7xydK5eb30wk\n988/pk+w128ICCFE5gX6GEwI4SZOPbytNRFOLikvKkVAuu026NTJ6ihEWkqXhhkzYNMmk23bvDnc\nfz9s3ZrJA/rZpPFNNptpHDh2LOzfb5redOsG//uf/PwK4SNKACXt/5bA1EduBfxCANTek/GTd/32\nG1y6ZHUUacuVyzRa++gjOHXKjQf+7bf0t/vyFyVAzJljEioaN4b5800zXyGE8HEBPQYTQriPS1U/\nlSKvUilfoxQ2pfBUNVUhRCbVqAFr1sCSJSb79s47oUsXOHLEyQMo5dqk8cSJPjNpnJxSULMmjBwJ\nu3fDr79KtrEQltP6eLLlEFovBV4CXrM6PHeR8ZPnnTkDtWvD1KlWR5K+Hj0gIQHee8+NB7399rS3\nVavmxjcSaXniCfj4Y7h2zZQNK10aXnzRjDeEEMInBckYTAiRdU5PHCtFa2ATkDOVzbmATUrxmLsC\nE0K4h1KmKc+2bTBpEixdChUrwtChcPFiBi90hdbwfGafVvcupUwmdnquXoXx42HfPu/EJIRwcBi4\nzeog3EHGT95RoAB89ZXJ6PVlRYpAx47mfOyWrNThw9PfnulHjYQrcuUyk8fffgs7dkCHDubJr0qV\noF49WLnS6giFEMJpATMGE0K4hysZx88Ao7UmxfNuWnMRGAU86a7AhH/65x9o0wa2b7c6EpFcaKi5\noN67F557zmTeVqwIH34IN24k2dHVLON69Xw2yzgrtm2Dfv1MvcLq1WHwYNiyJSA/VSGso9StyZYo\nlGoATAJ+tTg6d5Hxk5fUrQvZs1sdRcZ69zb1cGfPdsPBhgxJe9vrr7vhDYSrKleGMWPg8GGYO9c0\n5D12zOqohBAimeAYgwkh3MCVieOqwDfpbP8WqJKlaITfO3sW9uyBu+6Czz+3OhqRmnz54M03zeOT\n9etD166mpMXq1WQuy3jdOo/EabWaNeHkSfjkE/MU8KRJEB0NZcuaxOo1a2QSWQg32AXsTLZ8DRQG\nulkYlzvJ+Ek4iIqCli3N5GKWziOtWqW/fcCALBxcZFWOHKZsxddfm2xkIYTwMcEwBhNCuIErE8cF\ngGzpbA+17yOCWOnSsH49NG1qMo/dWsNPuFW5ciYTZsMG+O7XHMTcr3D6+jVv3qCYNQ0Ph4cfhvh4\nOHHCTK7/3//B4sUmY8zVeXYhRAqVMZOmN5cooCBa10DrDDp++Q0ZP4kU+vY1JQ1WrMjCQRYvTntb\nEJyj/UlG44UDB5I9/SWEEJ4XDGMwIYQbuDJx/CdQM53tNYEDWYpGBIQ8eWDBAnjrLdNdWvi2Oncp\n8nANBTg1D6o1nDvn4ah8T2goxMSYzOMDB2DVKqsjEiIgVAf2o/Vu+7IHrc+iVChKPWJ1cG7yJzJ+\nEsnUrQu1apms40ypVcut8QjraA33329u6A8cCL//bnVEQoggEQxjMCGEG7gycbwQeF0piiXfoBTF\nMZ03P3NXYMK/KWW6SVeoYHUkIk2dOyemwKQ3YeyQsyQZTID5shVL8ZfQ0YkTpgHhpk2QkOCVsITw\nR3OB/Kmsz2vfFghk/GSR69fh/Hmro0idUibreM0a+OWXTBxg06a0t8m52u/Mnm0aGU+ZYkqZ3Hcf\nTJ8OFy5YHZkQIoAFwxhM+IB169Zhs9lYuHCh1aG41YEDB7DZbHz88cdWh+JxrkwcjwIuAHuUYopS\n9LQv7wK/A//Y9xFC+DqlTLvvDGjgEjB+nObqFbkQdcWOHTB+vEkKK10annoKliyBSynaYwkR1BSk\nWiWnBOCjU34uk/GTBW5mcT7/vNWRpK1tW1M33+Ws49uk2X0gUcqMFaZMgaNHTRmx3LmhWzcoXhw6\ndIBTp6yOUggRgIJhDCaSmTlzJjabjS1btrj92HPnzmXChAmpblNS49GvOT1xrDUXgHuBeOBRYJx9\nedS+7j77PkJk6N9/rY4giLnwR/sS0Lubpm9fqFQJ5syR7FlnNWgAf/9tmuLExsI335j6yIUKwUMP\nwYcfWh2hEBZSaj1K/YC5YFmOUj8kWTZiGsattTZI95DxkzWUMg3JPv7Yd3u4ZssGvXrBvHlw6JAL\nL9yxI+1tkm3s13LmNA31vvrKlMUaOBD27TOtJYQQwi2CaAwmUuepSdw5c+akOXGsZXzi11zJOEZr\nzmlND0ynzWJAcaCQ1vTQmjOeCFAEnm++gdtvd/EiSWSdUq51c9Oa3FozbRps2wbVqkH79iYrZvVq\nz4UZSEJDoWFDk032+++weze89prJOg6wJ3WEcNU3wDpMtst6+/9vLiuAvkCcVcG5m1XjJ6UoqRSz\nlOKkUlxSiq1KUSPZPsOV4oh9+yqlCJgiU507w913w9NPw5UrVkeTuq5dTXbpxIlOvkCyjYPGLbfA\ngAHw3XdmPCGE8BNK5UGp8Sj1J0pdQqnvUKpmku0foVRCsmV5smPkQKnJKHUSpS6g1KcoVdRNEX5D\nEI3BRPC4JI/2eoxLE8c3aY3Wmr+15oTWqT7eIESaSpc2TfNKlLA6kiDi6l3FZHcEq1Qxzdv/9z/I\nnt08/tukSSbrMgaxW281NS3XrjVlK4QIWlq/gtavAM8ALyd+bJYhaP0RWgfc6M+b4yelyA98D1wF\nmmC6p/eF/yaqlaIf8BzwFFAbuAh8pRTZPRmbt9hsMG2aydY8ccLqaFIXHm5KGU2bBv/848QLJNtY\nJHP9unkizKmfHyGEN0wHYoD2QFVgFbAapZJe/X7JfzeSiwOxyY4xHngIaAvUA0rirn4IQToGE6k7\nfvw4nTt35pZbbiFnzpyULFmSVq1acfDgQYf9pkyZQtWqVcmZMyelSpXiueee49y5c4nbGzZsyLJl\nyxLr/tpsNsqXL5+4XSlFQkICr7/+Orfccgu5cuWicePG/PHHHyli2rhxI02bNiV//vzkzp2bBg0a\n8MMPPzjsM3ToUGw2Gzt37uTxxx+nYMGC1K1bF4BOnToRHh7OX3/9RfPmzQkPD6d06dJMmTIFgG3b\nthETE0OePHkoV64cc+c6lvQ+c+YML774ItWqVSM8PJx8+fLRrFkzfv3116x9sf1YNmd2UootQIzW\nnFGKn0m9Fg4AWjtmsgiRXIUKMGmS1VEEiSxOGCd3333www/w+efwyitQowbExcGIEaZOo3CeLYPb\ndr/9ZhpMPvggNGsGFSt6Jy4hvErrqVaH4EkWj5/6Awe15skk6w4k26cnMEJrvrDH2wE4DrQCFrg5\nHktUrQobNrh+OvSm556DsWNh5kx49tl0dqxVy2sxCf+xYYN5Iix3bmjd2ozLYmJMKRQhhJcplRNo\nA7RA6+/ta4ehVAvMRO1g+7qraP13GsfIC3QBHkPrdfZ1nYGdKFUbrX90S6wBPgYTzmnTpg07d+7k\nhRdeoGzZspw4cYJVq1Zx8OBBypQpA5hJ2uHDh/PAAw/Qo0cPdu/ezZQpU9i0aRPff/89ISEhDBo0\niHPnznH48GHGjx+P1po8efIkvo/WmpEjRxISEsJLL73EuXPnePPNN4mLi2P9+vWJ+3399dc0a9aM\nmjVrJk4Of/TRRzRq1IjvvvuOmjVN8v7Nchvt2rXj1ltvZeTIkYnlMG5OUj/44IPUr1+ft956i9mz\nZ/P888+TO3duBg4cSFxcHG3btuW9996jY8eO3HPPPZS1T2js27ePJUuW0K5dOyIiIjh+/DhTp06l\nQYMG7Nixg+LFi3vle+NLnB1SLMZkrAAs8lAsQgh3cvOkcdLDtmljun9/8AEMGwbz55sGRAMGQMGC\nmYhVpHDpkqkn/fLLpgZmZKSZQH7wQVM/OVcuqyMUwg2UsgE9gEeAMpAs01XrkhZE5U5Wjp9aACuU\nYgFQHzgMTNGaDwCUIgKT5bTm5gu05rxSbATuJkAmjsG3J40BypQxjfImTIBnnknnxuKmTWkfRLKN\ng1bduqYO8uzZMGsWxMebpnqxsWYS+c47ff93QIgAkg0I4b9z/02XgfuSfNwApY5jngL6GhiE1qft\n26Ltx0k8P6P1bpQ6iDk/u2fiOPDHYCID586dY/369bz99tv06dMncX2/fv0S/3/y5ElGjRpF06ZN\nWb78v4oqUVFRPP/888THx9OxY0diYmIoVaoUZ8+eJTY2eQK9cfXqVbZu3UpISAgA+fPnp1evXuzY\nsYMqVaoA8MwzzxATE8OyZcsSX9e9e3eqVKnCoEGDWLFihcMx77zzTmbNmpXiva5cuUKHDh14+eWX\nAYiNjaVkyZJ07dqVefPm8fDDDwPQuHFjKlWqxMyZMxk82NzXqVatGr///rvD8Z544gmioqKYPn06\nAwcOzOArG3icKlWhNcOAmkqRTWuGpbd4OF4R4K5elcZ5Wda0qWtXCNWqZeqCMzTUXODu3WsmjN97\nz0xujh4Nly+7fDiRTO3asHKl6aS+ZAk88ID5t1kzMznfqZPVEQrhFgOBV4GvMI9sTsdcQOUExloY\nl1tYPH4qj8lu2g08ALwLTFSKJ+zbi2MyoI8ne91x+zbhRb16wZ49sHx5Gjvce69X4xH+JSICBg2C\nXbvgp5/gkUfMBHJ0tGnMK4TwEq3/wdQNfhWlSqCUDaXiMBO+N0tVfAl0ABoBL2Nu7i5P0rGsOHAN\nrc8nO7q7z88BPQbzpqNHYcuWtJf0qkzdtGNH6q89etRzcefKlYvs2bPzzTffcPbs2VT3Wb16Ndev\nX6dXr14O67t160Z4eLjDBG9GunTpkjhpDFC3bl201uzbtw+AX375hT179hAbG8upU6cSlwsXLhAT\nE8O3337rcDylFN27d0/z/bp27Zr4/3z58hEVFUXu3LkTJ40Bbr31VvLnz58YA0BoksYCCQkJnD59\nmrCwMKKiotiyZYvTn28gceUhprWYP3Y+WiVOBIJ+/cyAd/ZsKFfO6mj8kIeyjNOTJw8MGWKaDw0f\nbiaRJ00y6zp1kkclsypPHpPd3aKF+Xbt2gVffmmykYUIAB2Bbmi9BKX6AzPQ+g+U6gtUtzg2d7Fq\n/GQDftSaV+0fb1WKqsDTQMrUDGGpu++GOnVg3Dho3jyVHZLV9nMg2cbCTimoWdMsb79tmhlfv251\nVEIEnTjgQ8yTPv8CW4A5mExi0DrpEz3bUWob8AfQADNm8JZgGIN5xdSp5inctFSpAtu3p3+Mdu1S\nn2AeMgSGDs1SeGnKnj07b775Ji+++CLFihXjrrvuonnz5nTo0IFixYoBcOCAqXJ26623Orw2NDSU\n8uXLJ253xi233OLwcYECBQBTUxhgz549AHTo0CHV19tsNs6dO0e+fPkS10VERKS6b86cOSlUqJDD\nunz58lG6dOkU++bLly8xBjBlNcaPH8+7777L/v37uXHjBmAmqgsXLpzu5xioXJnSkYechMc98ggs\nWgTVq8OUKfD44/J4ndMsmDROqlgxmDwZevaEwYOhWzdz0TJihHkEN6OaviJjSkHlymbJyNWrcOSI\nyUISwoeVBG622bwI5LX//3NInPD0d1adxY4CO5Ot24mpvQhwDBNbMRyzjosBP6d34DFjxjB//nyH\ndbGxsWk+miic06uXKS/w66/mYaBErVpZFpPwX6GhpryVECJz5s6dm6Jp1qFDhzJ+odb7gYYolQvI\ni9bHUWoesC/N/ZU6CVTATBwfA7KjVN5kWcfF7NvcJRjGYF7RvTu0bJn29pw5Mz7GJ5/AlSsp15co\nkXKdO/Xs2ZOWLVuyaNEivvrqKwYPHszIkSNZu3Yt1au79/5B0mzjpG7WJk6wZ0aNGTMmzfdOWjcZ\nTNa0K++VUQwAr7/+OoMHD+bJJ5/ktddeo2DBgthsNnr27JkYY7BxNRdQUhqER91zD/zyi2kO8+ST\nUL8+pHJTSCRl8YRxcrfeCvPmmdq8AweamwHR0fDGG3D//XIjwFu++cZULYmK+q82ct26zg1chPCi\nQ5gLoYOYC6pGmEnLO4BAypOzYvz0PRCVbF0U9gZ5WrNfKY5hOr//CqAUeYE6wOT0Dty3b1/at2/v\n9oC95c034exZGDnS6kgctW1rxjzjx8OHHybZsHhx2i+SbGORRR99ZBozxsaan8EgTaYSIoXUbojO\nnj2buLg45w6g9WXgMkoVAJoAL6a6n1KlgUKYG74AmzGZyjGYSVxQKgpTh3h9KkfIrGAZg3lciRJZ\nn+C1l/i1REREBL1796Z379788ccfVK9enTFjxvDxxx8nNozbvXs35ZI8En79+nX279/P/fffn7hO\nZfFCPzIyEoDw8HAaNWqUpWNlxWeffUajRo2YNm2aw/qzZ89SpEgRi6Kylqs5gDOUYmF6i0eiFEEl\nf35TqmL7dpk0zpCPTRonVaOGKanwzTeQPTs0aQKNGpnu38Lz7rkHFi40k8ULFpgayQUKmMnksWMz\nflxKCC/5Amhq//8UYJT9kc3ZBFY5BSvGT+OAu5TiFaWIVIrHgSeBd5LsMx4YpBQtlOJ24GPMhWQ6\nM5X+L3t2s/janGtoKDz3nBkDnbhZ2MTe1EUITylVytxUfvZZ01TvwQdhxgxzc0UIkQlKPYBSTVCq\nHErdj6kbvAOYgVK5UWo0StVBqbIoFYNpnvs7ptYw9izj6cBYlGqAUtGY0hffo7V7GuMZwTIGE2m4\nfPkyV6869nGMiIggPDw8cX3jxo0JDQ1l4sSJDvt98MEHnD9/nuZJ6mvlzp2bc+fOZTqe6OhoIiMj\nefvtt7l48WKK7SdPnsz0sV0REhLikIEM8Mknn3D48GGvvL8vcjXj+AKmI6gQHle+vNUR+LDChU3X\nNGdZeHVcvz58/z188YXJQL77btOo5bXXoGpVy8IKeOHh0Lq1WbSG334zzfZWrjTfh5kzYetWq6MU\nQU/rPkn+H49ShzENZPag9SeWxeV+Xh8/ac0mpWgNjMI8crof6Kk185LsM1opwoCpQH7gf8CDWnPN\nm7F6W+/eVkeQtm7dTL+Ad981dQ156620d/a1mW/hlx54wCx//w2ffQZz50KXLvDUU9C4sSlB1qSJ\n1VEK4VfyASOBUsBp4FNgEFrfQKkbQDVMc7z8wBHMhPFgtE6a5dsbuGF/bQ5gBfCsW6MMnjGYSObm\npOjvv/9OTEwMjzzyCFWqVCFbtmwsXLiQEydOJGbbFy5cmFdeeYXhw4fTtGlTWrZsya5du3j33Xep\nXbu2wxNo0dHRLFiwgL59+1KrVi3y5MnjMLGcEaUUH3zwAc2aNeO2226jc+fOlCpVisOHD7N27Vry\n5cvH4vSewnKT5s2bM2LECLp06cI999zDtm3bmD17dmJGdDBydeL4Ba2lOZ4QlvLhLOO0KGWauzVr\nZspYDB5s6jfGxZlGAlKH17OUgttvN0vfvnD5MjhTok0Ij1IqFJgAjEbrPwHQei3ebQzjLZaMn7Rm\nObA8g32GAkO9EY/IWMGC0LGj6fPQv9JCclgdkAgaRYqYRsdPPw2HD8Pnn8Onn8p4QQiXmUnX1Cde\ntb7Cf1m+6R3jKvC8fXG/4BqDiWRulpS45ZZbePzxx1mzZg3x8fFky5aNSpUq8cknn9AqSX+FIUOG\nULRoUd555x369OlDwYIFefrpp3n99dcdagb36NGDrVu3MmPGDMaPH0/ZsmUTJ47TKmORfH39+vVZ\nv349I0aMYPLkyfzzzz8UL16cOnXq0L17d5c/R2fWK6Uc1g8YMIBLly4xZ84cFixYQHR0NMuXL6d/\n//4pXp/V8hz+QiVPwU5zR8UNoIQ/TBwrpR4HZsfHx/t1DT6Rug0bzOTjqFFBWK/VlT9MRYvC8eMZ\n72eBa9fggw9M47xTp0xDgUGDTIM94RuOHjUXjzczkSpUkPrU/iRJDb72Wus5VseTJqXOA9XtjWQC\nkj+NnzIi4yvv2L0bKlWCBFTanRV94KawEEIEG78ZXzkjCMZgmaGUqgFs3rx5MzVq1LA6HCE8asuW\nLURHRwNEa623pLWfKzWOZcpA+ITffzcdx0NDrY7Ei5RybdZOa5+dNAZTW7JHD9i71zySGx9vSpMM\nGiQ19XzFqVNw7hz06mUaHkZGmonkzz8364Vwk6WA88+w+ScZPwmX3GxqKoQ/mDULJkyAv/6yOhIh\nhIuCYQwmhHADVyaOG2Jq9AhhqQ4dYM0aSPJURGDzw9IUzsqdG/r3h3374IUXTNO28uVh9Gi4dMnq\n6IJb1aqmseHp07BkCTz0EHz9NbRpA4UKQUyMX/2oCd/1KzAUpeJRqjdKPeWwBAYZP/mBq1fhn3+s\njuI/C5anc+6XP77Ch2zZYno4lilj+li8/bZJDBBC+LxgGIMJIdzA6YljrVmnNf96MhghnBUUj8yX\nLet6lrGfXkwWKAAjR8Iff0BsrGneFhkJEyfClStWRxfcwsNNfepJk0y2/759MHmyaXoYFL+HwtN6\nAdeAGOBlYFiSZah1YbmPjJ98n9bm5liXLr5zGg1DUtWFfxg3Dk6cME+PFS8Or74KFSuaG9CDBskk\nshA+LODHYEII93Al41gIn3flihm8+j2l4OBB5/f3lSvdLCpRwkxK7t4NTZtC797m4mPqVFMXWVgv\nIsLUpB48OP39EhLgww/NRLMQadK6RDpLSavDE8FBKXjmGfjkE3MOspyS2sbCv+TLB+3bm3JWJ0/C\nwoUQHW2aPO7aZXV0QohUyRhMCOEkmTgWAeXNN6FyZfjoIzNx5ZdcSeOsUiUgLyLLlzffwx07oG5d\nc0EfFWXW/St5e35h3z546imTOR4RAd26maaWAXFjR7ifUjaUKotSwVKESPiYtm2hZ0/o0wd++MHq\naITwX7lzQ+vWMHOmOec3bWp1REKIdMkYTAiRAZk4FgGlRw948EHzuGn9+iZz1W9kpgHe9u2ei8cH\nREXBnDmmGWJ0tPm+Vqli1t24YXV0Ij0VKpgGe4sXm1IXP/xgypAUKwbVq0PfvlLHWgBK5USpycBl\n4A+grH39OJTqY2VoIviMHm3+TuXLZ2EQaYwDNPh001shUpMtm1nSM3kyvP8+HDvmnZiEEHYyBhNC\nOClTE8dKYVOKW5XiPqWol3Rxd4BCuKJIEVNjbc0aOHMGrl+3OiInBXADPHeoWhU+/dQ0YKlUyTwO\nWa0afPaZH2eWB4F8+aBlS1Orevt2OHzYdF+/805Ytw5y5bI6QuEDXgPuBZoBSSuafwu0tyQiD5Lx\nk2/Lnt1kSd52m9WRpKFoUasjEMLtvv0Wnn4aSpY0zfXeeMMkDATZUFcIKwTVGEwIkXkuTxwrxV3A\nXmAn5o/KN0mWte4LTYjMa9QItm0zE44+7ZtvgqYBnjvceScsWQIbNkDp0vDwwyYTeenSoP6y+I2S\nJSEuDmbMgE2bMv7R/+03qW0dBB4GnkXrNdiTKu1+AypYE5JnyPhJZCidbONB+SdKqSYRkObPN8n0\nH35omuu98YZ5MqlsWfMk4e+/Wx2hEAEraMZgQoisyUzG8XvAJqAqUBAokGQp6L7QhMgaV5N4vU4p\naNjQ+f1lZjRRnTrw1VcmS+VmVutdd5l18mUKDFeuQM2aUKAAPPAAjBxpbhj4zVMEwllFgSOprM8F\nafcH81MyfhKZ9sbZ51m0yOoohPCMwoWhUyfTXO/UKTOea9UKVqyACxesjk6IgBVMYzAhRBZkZuK4\nIjBAa3ZqzVmtOZd0cfVgSvGKUvyoFOeV4rhSfK4Ut6ay33ClOKIUl5RilVJyF0xk3qpVZmLKMq7M\naufPL7OhaahbF9auhdWrISTENGCpV88kcgv/lj07fPcdDB0KoaFm4vjuu6FgQVPH/M03Ted2kUmm\nEcoIlNqHUpdQai9KDUq2zxCU2olS/6DUaZRahVK1k+2TA6Umo9RJlLqAUp+ilCvP0/8MpNY6qROw\n0cXPyte5dfwkAkw64wLVti316sH48V6MRwiL5MhhbhhPnAh//AE1algdkRABK5jGYEKILMjMxPFG\n3PvoQl1gElAHaAyEAiuVIrH6pVL0A54DngJqAxeBr5QiuxvjEEHi5EmToTp9ugVvnju366Upzpzx\nXDwBQCmIiYHvv4fly+HyZZPI3aiRqaMr/JPNZjKOX3oJli2D06dNxvHAgebXYsQIyT7Oov5Ad6AH\nUAl4GXgZpZ5Lss9u4FlMhuy9wJ/ASpQqlGSf8cBDQFugHlAS+MyFOAYBo1FqHBACdEeppcAz9m2B\nxN3jJ+El69ebvz+W+fRTevc257mffrIwDiG8zJm+0V26QNeuJltZspOFcEkwjcGEEFmQmYnjScAY\npeikFNFKUS3p4urBtKaZ1syyZ+Bsw9zhKgNEJ9mtJzBCa77Qmt+ADpiL01aZiF8EucKF4eef4ckn\nvfzGSsGlS87vL1nGLlHKZKL+9JO5eDhzBho0MItkIPu/bNlMiZL+/c2jq2fPQokS6b/m6FG4ccM7\n8fmhu4HFaL0CrQ+i9UJgJebmrKH1PLT+Gq3/ROudQB8gL9jP9UrlBboAvdF6HVr/DHQG7k2RmZwW\nrdfa37Mwpv5vO+AqcC9aB1q2i1vHT8I7tIYhQ+Cttzz4JunNjEWb4XCLFlC+vGQdC5Fc6dLm5k6b\nNuappIYNzVNJW7fKUFqIdAXXGEy4yGazMXz4cKvDED4iMxPHnwGVgQ+Bn4BfMI853Pw3q/JjirOf\nBlCKCKA4sObmDlpzHpO5c7cb3k8EoUqVzKNwXiMN8LxGKVMXb8sWWLwYzp83FxH168MShJQ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dqVNm3a8MMPP/DOO+9Qp04datSoEXDMkcbveidXb+TnraUscB5wnrWUtZYXpJe6UjHMGCkjzMKZ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\nUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2421b6f0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax3)  = plt.subplots(1,2)\n",
    "\n",
    "ax1.set_title('T = ' + str(T01) + 'K, P = ' + str(P1) + 'atm')\n",
    "ax1.plot(times, TiCl4_isobaric1, 'b-', label='Non-isothermal')\n",
    "#ax1.plot(times, O2_isobaric, 'b-', label='O2 non-isothermal')\n",
    "\n",
    "ax1.plot(times, TiCl4_isothermal1, 'b--', label='Isothermal')\n",
    "#ax1.plot(times, O2_isothermal, 'b--', label='O2 isothermal')\n",
    "\n",
    "ax1.plot(times, TiCl4_first_order1, 'b-.', label='Elementary')\n",
    "#ax1.plot(times, O2_first_order, 'b-.', label='O2 1st Order')\n",
    "\n",
    "ax1.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "# Make the y-axis label and tick labels match the line color.\n",
    "ax1.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "for tl in ax1.get_yticklabels():\n",
    "    tl.set_color('b')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(times, Ts_isobaric1, 'r.', label='Temperature')\n",
    "# plt.ylim((1780,1790))\n",
    "ax2.set_ylabel('Temperature (K)', color='r')\n",
    "for tl in ax2.get_yticklabels():\n",
    "    tl.set_color('r')\n",
    "\n",
    "    \n",
    "ax3.set_title('T = ' + str(T02) + 'K, P = ' + str(P2) + 'atm')\n",
    "ax3.plot(times, TiCl4_isobaric2, 'b-', label='Non-isothermal')\n",
    "#ax1.plot(times, O2_isobaric, 'b-', label='O2 non-isothermal')\n",
    "\n",
    "ax3.plot(times, TiCl4_isothermal2, 'b--', label='Isothermal')\n",
    "#ax1.plot(times, O2_isothermal, 'b--', label='O2 isothermal')\n",
    "\n",
    "ax3.plot(times, TiCl4_first_order2, 'b-.', label='Elementary')\n",
    "#ax1.plot(times, O2_first_order, 'b-.', label='O2 1st Order')\n",
    "\n",
    "ax3.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "# Make the y-axis label and tick labels match the line color.\n",
    "ax3.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "for tl in ax3.get_yticklabels():\n",
    "    tl.set_color('b')\n",
    "\n",
    "ax4 = ax3.twinx()\n",
    "ax4.plot(times, Ts_isobaric2, 'r.', label='Temperature')\n",
    "# plt.ylim((1780,1790))\n",
    "ax4.set_ylabel('Temperature (K)', color='r')\n",
    "for tl in ax4.get_yticklabels():\n",
    "    tl.set_color('r')\n",
    "\n",
    "ax1.set_position([0,0,.75,.75])\n",
    "ax2.set_position([0,0,.75,.75])\n",
    "ax3.set_position([1,0,.75,.75])\n",
    "ax4.set_position([1,0,.75,.75])\n",
    "\n",
    "ax3.legend(loc='center left', bbox_to_anchor=(1.25, .5))\n",
    "\n",
    "plt.savefig('Temperature_Comparison.tiff', format='tiff', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rzJhsRrtcxSsiSekDjFPSWCTPmO2H2UuY/RCZPx1R5Hgi86vpReZmmzF7oMiYBpiNxWwF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2421c4fba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax3)  = plt.subplots(1,2)\n",
    "\n",
    "ax1.set_title('T = ' + str(T01) + 'K, P = ' + str(P1) + 'atm')\n",
    "ax1.plot(times, TiCl4_isobaric1, 'b-', label='Non-isothermal')\n",
    "#ax1.plot(times, O2_isobaric, 'b-', label='O2 non-isothermal')\n",
    "\n",
    "ax1.plot(times, TiCl4_isothermal1, 'b--', label='Isothermal')\n",
    "#ax1.plot(times, O2_isothermal, 'b--', label='O2 isothermal')\n",
    "\n",
    "ax1.plot(times, TiCl4_first_order1, 'b-.', label='Elementary')\n",
    "#ax1.plot(times, O2_first_order, 'b-.', label='O2 1st Order')\n",
    "\n",
    "ax1.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "# Make the y-axis label and tick labels match the line color.\n",
    "ax1.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "for tl in ax1.get_yticklabels():\n",
    "    tl.set_color('b')\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(times, Ts_isobaric1, 'r.', label='Temperature')\n",
    "# plt.ylim((1780,1790))\n",
    "ax2.set_ylabel('Temperature (K)', color='r')\n",
    "for tl in ax2.get_yticklabels():\n",
    "    tl.set_color('r')\n",
    "\n",
    "    \n",
    "ax3.set_title('T = ' + str(T02) + 'K, P = ' + str(P2) + 'atm')\n",
    "ax3.plot(times, TiCl4_isobaric2, 'b-', label='Non-isothermal')\n",
    "#ax1.plot(times, O2_isobaric, 'b-', label='O2 non-isothermal')\n",
    "\n",
    "ax3.plot(times, TiCl4_isothermal2, 'b--', label='Isothermal')\n",
    "#ax1.plot(times, O2_isothermal, 'b--', label='O2 isothermal')\n",
    "\n",
    "ax3.plot(times, TiCl4_first_order2, 'b-.', label='Elementary')\n",
    "#ax1.plot(times, O2_first_order, 'b-.', label='O2 1st Order')\n",
    "\n",
    "ax3.set_xlabel('Residence Time (ms)')\n",
    "\n",
    "# Make the y-axis label and tick labels match the line color.\n",
    "ax3.set_ylabel('Concentration TiCl4 (mol/m^3)', color='b', )\n",
    "for tl in ax3.get_yticklabels():\n",
    "    tl.set_color('b')\n",
    "\n",
    "ax4 = ax3.twinx()\n",
    "ax4.plot(times, Ts_isobaric2, 'r.', label='Temperature')\n",
    "# plt.ylim((1780,1790))\n",
    "ax4.set_ylabel('Temperature (K)', color='r')\n",
    "for tl in ax4.get_yticklabels():\n",
    "    tl.set_color('r')\n",
    "\n",
    "ax1.set_position([0,0,.75,.75])\n",
    "ax2.set_position([0,0,.75,.75])\n",
    "ax3.set_position([1,0,.75,.75])\n",
    "ax4.set_position([1,0,.75,.75])\n",
    "\n",
    "ax3.legend(loc='center left', bbox_to_anchor=(1.25, .5))\n",
    "\n",
    "# plt.savefig('Pressure_Comparison.tiff', format='tiff', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}