Two-State-and-One-Control-Bellman-Equation-Model.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two State - One Control Bellman Equation Model Computation\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### **Suraj Kumar**  \n",
    "\n",
    "**Post Graduation**  \n",
    "*MA Economics(Final Year)*  \n",
    "*Department of Economics*  \n",
    "**Delhi School of Economics**  \n",
    "*India*\n",
    "\n",
    "**Under Graduation**    \n",
    "*B.Tech in Civil Engineering from **Indian Institute of Technology, Delhi (IIT Delhi)***\n",
    "\n",
    "surajdhunna@gmail.com     \n",
    "[Github Link of Notebook](https://github.com/iamsurajkumar/Dynamic_Programming_Squared_Model/blob/master/Two%20State%20and%20One%20Control%20Bellman%20Equation%20Model.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ACKNOWLEDGEMENT / CREDITS**\n",
    "\n",
    "This notebook primarly deals with Lucas Asset Pricing Model. I have refered mainly to [QuantEcon Lectures](https://lectures.quantecon.org/). These are fantastic set of lectures for general introduction to quantitative programming. The readers are requested to refer to it to learn more about quantitative programming in python for Economics. For this notebook, I have mainly referred to following lectures of it\n",
    "\n",
    "1. [Introduction to Asset Pricing](https://lectures.quantecon.org/py/markov_asset.html)\n",
    "2. [The Lucas Asset Pricing Model](https://lectures.quantecon.org/py/lucas_model.html)\n",
    "3. [A Stochastic Optimal Growth Model](https://lectures.quantecon.org/py/optgrowth.html)\n",
    "\n",
    "Therefore, I would like to thank the Authors [QuantEcon Lectures](https://lectures.quantecon.org/) for creating it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally the bellman equation examples in python solves for only one state variable. In this notebook, I have shown a method by which we can solve bellman equation with two state variable and one control variable. This kind of model can be applied to Lucas Asset Pricing Models. This kind of computation program for bellman equation really helps us in understanding the elegance of bellman equation. It is because under some *nice properties(continous and bounded)* of utility and state equation, *every initial guess that is surface iin $3D$ leads to final converged surface as output*.\n",
    "\n",
    "### 1.1. Lucas Asset Pricing Model\n",
    "In Lucas economy an asset generates a claim to sequence of such consumption goods (states) or endowment stream $\\{y_{t}\\}^\\infty_{t=0}$. For example the value function and budget constraint for a representative agent in the lucas economy is given below\n",
    "\n",
    "\\begin{align}\n",
    "&v(\\pi,y)  = \\max\\limits_{\\pi'} \\bigg \\{ u \\big[ \\pi \\big( y + p(y) \\big) - \\pi' p(y) \\big] + \\beta \\int v(\\pi',G(y,z)) \\phi(dz) \\bigg \\}\\\\\n",
    "&c(\\pi,y) =\\pi \\big( y + p(y) \\big) - \\pi' p(y) \\\\\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here $\\pi$ and $\\pi'$ denotes the share of an agent of the asset in the current and future period, $G(y,z)$ denotes the state equation which is Markovian as $ y_{t+1} = G(y_t, z_{t+1})$. The $z_t \\geq 0$ is an iid shock sequence with a known distribution $\\phi$ "
   ]
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   "cell_type": "markdown",
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   "source": [
    "Next step is solve for $v(\\pi,y)$ but for this we need to have $p(y)$ function. The $p(y)$ can be evaluated by observing the first order conditions of $v(\\pi,y)$ as follows\n",
    "\n",
    "\\begin{align}\n",
    "&u'(c)p(y) = \\beta \\int v'_1(\\pi',G(y,z))\\phi(dz)\\\\\n",
    "&\\text{where $v'_1$ can be obtained with envelope theorem as shown below}\\\\\n",
    "&p(y) = \\beta \\int \\frac{u'[G(y,z]}{u'(y)}[G(y,z) + p(G(y,z))]\\phi(dz)\n",
    "\\end{align}\n",
    "\n",
    "Now applying the transformation $f(y) = u'(y)p(y)$, the above equation becomes\n",
    "$$f(y) = h(y) + \\beta \\int f[G(y,z)]\\phi(dz)$$\n",
    "\n",
    "where $h(y) = \\beta \\int u'[G(y,z)]G(y,z)\\phi(dz)$\n",
    "\n",
    "Now the $f(y)$ can be solved with bellman operator and from derived solution $p(y)$ function can be obtained. For more information, the reader may refer to [QuantEcon's The Lucas Asset Pricing Model](https://lectures.quantecon.org/py/lucas_model.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Assumptions\n",
    "\n",
    "In this notebook, I have focussed on solving, directly, the $v(\\pi,y)$ equation which has two states variable $(\\pi,y)$ and one control variable $\\pi'$. I have assumed that \n",
    "\n",
    "$$p(y) = \\frac {y}{1-\\beta}$$\n",
    "\n",
    "As without this assumption, We have to first solve for $p(y)$ with bellman operator and then use these derived value to solve for $v(\\pi,y)$ which will make code longer and difficult to compute. This assumption keeps the code simple to comprehend and easy to compute and keep the prime focus of program on computation of $v(\\pi,y)$. To ensure the continous and bounded nature of state and utility function, I have made following assumptions:\n",
    "\n",
    "\\begin{align}\n",
    "&u(c) = (c)^\\gamma \\text{  where   } 0 < \\gamma < 1 \\\\\n",
    "&ln(y_{t+1}) = \\alpha ln(y_t) + \\sigma \\epsilon_{t+1} \\text{ here } \\epsilon_t \\text{follows iid standard normal}\n",
    "\\end{align}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Limitations of the Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code has following limitations\n",
    "\n",
    "1. As first solution, no speed enhancing computation packages/way such as jit, cython or parallel computing has been used in bellman operator. I have planned include it the next version of this code. So stay tuned to Github repository.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Importing relevent packages\n",
    "\n",
    "# To showcase the outputs of all cells\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "# Numerican Packages\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import lognorm\n",
    "from scipy.integrate import fixed_quad\n",
    "from scipy import interpolate\n",
    "from scipy import optimize\n",
    "\n",
    "# Plotting Packages\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "# 3D Plotting Packages\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "# Impornting \n",
    "from numba import jit,njit,vectorize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
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    "### Creating a Class to hold the model paramters\n",
    "class LucasAssetModel:\n",
    "    \"\"\"\n",
    "    Lucas Asset Price Tree model\n",
    "    This class creates all model parameters needed for the computation \n",
    "    \"\"\"\n",
    "    \n",
    "    # Creating the class Paramters\n",
    "    def __init__(self,\n",
    "                γ = 0.5,\n",
    "                β = 0.95,\n",
    "                α = 0.9,\n",
    "                μ = 0,\n",
    "                s = 0.1,\n",
    "                rent_min = 1e-5,\n",
    "                rent_max = 4,\n",
    "                rent_grid_size = 100,\n",
    "                shock_size = 100,\n",
    "                seed = 100,\n",
    "                π_min = 1e-5,\n",
    "                π_max = 1,\n",
    "                π_grid_size = 100):\n",
    "        \"\"\"\n",
    "        Creates Instance of Lucas Model\n",
    "        --------------------------------\n",
    "        Parameters are defined as below\n",
    "        --------------------------------\n",
    "        #Model Parameters\n",
    "        α: relates the y(t) with y(t-1)\n",
    "        β: discount factor of agent utility\n",
    "        γ: Utiltiy parameter of the agent and 0 < γ < 1\n",
    "        \n",
    "        # Shock Parameters\n",
    "        s,μ : Shock Parametes as z_t = e^(μ + s*ϵ_t), ϵ_t is iid shock sequence with N(0,1)\n",
    "        shock_size: No of shocks to consider\n",
    "        \n",
    "        ### State Paramters\n",
    "        # Rent\n",
    "        rent_min: minimum y (state) value to consider \n",
    "        rent_max: maximum y (state) value to consider \n",
    "        rent_grid_size: No of points to select between rent_min and rent_max\n",
    "        # Share\n",
    "        π_min: minimum π (share) value to consider\n",
    "        π_max: maximum π (share) value to consider\n",
    "        π_grid_size: No. of points to select between π_min and π_max\n",
    "        \n",
    "        # Randomness\n",
    "        seed : For getting the same set of random numbers again and again so as to reproduce the results\n",
    "        \n",
    "        \"\"\"\n",
    "        ### Assinging Parameters\n",
    "        \n",
    "        # Model Parameters\n",
    "        self.γ, self.β, self.α = γ, β, α\n",
    "        \n",
    "        # Shocks Parameters\n",
    "        self.s, self.μ, self.shock_size, self.seed  = s, μ, shock_size, seed\n",
    "        \n",
    "        #Rental Grid State Allocation\n",
    "        self.rent_min, self.rent_max, self.rent_grid_size  = rent_min, rent_max, rent_grid_size\n",
    "        self.rent_grid = np.linspace( self.rent_min, self.rent_max, self.rent_grid_size)\n",
    "        \n",
    "        #Setting up shocks in the models\n",
    "        np.random.seed(self.seed) # setting seed to generate same random numbers, \n",
    "        #useful for repeating the steps of computation again to see check the result,\n",
    "        self.shocks = np.exp( self.μ + self.s * np.random.randn(self.shock_size)  )\n",
    "        \n",
    "        #Setting up grid for share π\n",
    "        self.π_min, self.π_max, self.π_grid_size = π_min, π_max, π_grid_size\n",
    "        self.π_grid = np.linspace(self.π_min, self.π_max, self.π_grid_size)\n",
    "    \n",
    "    # Initial Surface to input into bellman operator\n",
    "\n",
    "    # First Fuction\n",
    "    def initial_w(self): #Intialzing the grid with some continous, concave function\n",
    "        pi_s,rent_s = np.meshgrid(self.π_grid, self.rent_grid)\n",
    "        return 5*np.sqrt(pi_s*rent_s)\n",
    "\n",
    "    # Second Function\n",
    "    def initial_w2(self): #Intialzing the grid with some continous, concave function\n",
    "        pi_s,rent_s = np.meshgrid(self.π_grid, self.rent_grid)\n",
    "        return np.power(pi_s*rent_s,1/3)\n",
    "    \n",
    "    # Solving the Model\n",
    "    def solve_optvalue(self,\n",
    "                       tol = 1e-6, \n",
    "                       max_iter = 100):\n",
    "        \"\"\"\n",
    "        This function is used to solving the model. Its first extract the parameters to be passed on to bellman operator.\n",
    "        Then given the modelling preferences i.e tol and max_iter, it solves the model.\n",
    "\n",
    "        \"\"\"\n",
    "        ### Extracting The Parameters from the class object self to be passed on to bellman operator\n",
    "        # Parameters Extractions\n",
    "        model_parameters = np.array([self.α, self.β, self.γ])\n",
    "        self.model_parameters = model_parameters\n",
    "        \n",
    "        #Share Grid Extraction\n",
    "        π_max, π_min,π_grid = self.π_max, self.π_min, self.π_grid\n",
    "\n",
    "        #Rent Grid Extraction\n",
    "        rent_grid = self.rent_grid\n",
    "\n",
    "        #Shocks Grid Extract\n",
    "        shocks_sequence = self.shocks\n",
    "        \n",
    "        #### Initial Function\n",
    "        w = self.initial_w()\n",
    "        # w = self.initial_w2() # as second choice for the surface\n",
    "        \n",
    "        # Creating Dictionary to see the convergence of surface later on \n",
    "        d={}\n",
    "        \n",
    "        # Loop paramters\n",
    "        error = tol + 1\n",
    "        i = 0\n",
    "        \n",
    "        # Storing the first intial 3D surface in dictionary object\n",
    "        d[0] = w\n",
    "\n",
    "        #Create Storage for bellman Operator\n",
    "        Tw = np.empty_like(w)\n",
    "\n",
    "        #Iterate to find Solution\n",
    "        while error > tol and i < max_iter:\n",
    "            w_new = bellman_operator(w,\n",
    "                                     π_grid,\n",
    "                                     rent_grid,\n",
    "                                     model_vars = model_parameters,\n",
    "                                     shocks = shocks_sequence,\n",
    "                                     Tw = None, \n",
    "                                     compute_policy = 0) #computes the bellman\n",
    "            error = np.max(np.abs(w_new - w)) # Find the maximum absolute distance between the \n",
    "            ################################### two surfaces(w and w_new) over the state space\n",
    "            w = w_new\n",
    "            i += 1\n",
    "            d[i] = w_new # Stores the computed surface in dictionary object, \n",
    "            #it can be used later on to see the convergence of the surfaces   \n",
    "\n",
    "        return w,i,d # Returns converges surface, iterations it took and Surface history"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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    "collapsed": true
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   "source": [
    "#####NOTE####\n",
    "#Computation function have been written separtely from class object so as to make code faster \n",
    "#later on with the use of numba. At presently numba support for classes i.e @jitclass, is still at\n",
    "#early stages. \n",
    "\n",
    "\n",
    "# Writingn the Utility Function\n",
    "@njit\n",
    "def u(π_prime, π, y, p, γ):\n",
    "    cons = (π*(y + p)) - (π_prime*p)\n",
    "    return cons**γ\n",
    "\n",
    "# State Function\n",
    "@njit\n",
    "def g(y,z,α):\n",
    "    return (y**α)*z\n",
    "\n",
    "# Price Function\n",
    "@njit\n",
    "def p(y,β):\n",
    "    return y/(1-β)\n",
    "    \n",
    "\n",
    "# Writing my own Minimum Function to compare against fminbound\n",
    "\n",
    "def min_fun(objective,π_grid, ans = None):\n",
    "    if ans is None:\n",
    "        ans = np.empty_like(π_grid)\n",
    "    for i, π in enumerate(π_grid):\n",
    "        ans[i] = objective(π)\n",
    "    return π_grid[ans == min(ans)]\n",
    "\n",
    "\n",
    "# Bellman Operator\n",
    "def bellman_operator(w,\n",
    "                     π_grid,\n",
    "                     rent_grid,\n",
    "                     model_vars,\n",
    "                     shocks,\n",
    "                     Tw = None, \n",
    "                     compute_policy = 0):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ------------  \n",
    "    w = is mesh of different π values and rent(y) values\n",
    "    π_grid = share grid\n",
    "    rent_grid = rent grids or y grid\n",
    "    model_vars = [α, β, γ]\n",
    "    shocks = shocks value to be used\n",
    "    Tw = array to output values, Tw is just for computation optimization\n",
    "    compute_policy = to decide whether to ouput policy function or not\n",
    "\n",
    "    \"\"\" \n",
    "    α, β, γ = model_vars\n",
    "    \n",
    "    # === Appyling Linear interpolation to a two dimensional surface === #\n",
    "    interpolation_func = interpolate.interp2d(π_grid, rent_grid, w, kind='linear')\n",
    "    \n",
    "   \n",
    "    # === Intitiate Tw if required === #\n",
    "    if Tw is None:\n",
    "        Tw = np.empty_like(w)\n",
    "\n",
    "    # === Intitate compute policy if asked === #\n",
    "\n",
    "    if compute_policy:\n",
    "        π_opt_policy = np.empty_like(w)\n",
    "\n",
    "    for i, y in enumerate(rent_grid):\n",
    "        for j, π in enumerate(π_grid):\n",
    "            def objective(π_prime):\n",
    "                return - u(π_prime, π, y, p(y,β), γ) - β * np.mean( interpolation_func( π_prime,g(y,shocks,α) ) )\n",
    "            #π_star = optimize.fminbound(objective, π_grid[0], π_grid[-1])  \n",
    "            π_star = min_fun(objective,π_grid)\n",
    "            if compute_policy:\n",
    "                π_opt_policy[i,j] = π_star\n",
    "            Tw[i,j] = - objective(π_star)\n",
    "\n",
    "    if compute_policy:\n",
    "        return Tw, π_opt_policy\n",
    "    else:\n",
    "        return Tw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Creating an Instance of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mylam =LucasAssetModel(rent_grid_size=50,\n",
    "                       shock_size=1000,\n",
    "                       π_grid_size=50,\n",
    "                       seed= 200,\n",
    "                      rent_min = 1,\n",
    "                      rent_max = 500,\n",
    "                      π_min = 1e-2,\n",
    "                      π_max = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Solving the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running the Model and Saving the Results\n",
    "w,i,d = mylam.solve_optvalue(max_iter = 100,tol = 1e-1)\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running the Model and Saving the Results\n",
    "w,i,d = mylam.solve_optvalue(max_iter = 1000,tol = 1e-1)\n",
    "i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Visualizing the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.1 Absolute Distance between the subsequent surface after iterations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First distance is  18.0386865678\n",
      "Last distance is  0.0961541522701\n"
     ]
    }
   ],
   "source": [
    "# Seeing the Results\n",
    "distance = [] # setting the distance list which the store the maximum distance between the two subsequence surface\n",
    "for j in range(len(d)-1):\n",
    "    distance.append(np.max(d[j+1]-d[j]))\n",
    "\n",
    "print('First distance is ',distance[0])\n",
    "print('Last distance is ',distance[i-1])\n",
    "dis = distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()  # a new figure window\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.plot(dis);\n",
    "ax.set_ylim(0,max(distance)+1);\n",
    "ax.set_xlim(0,i);\n",
    "ax.set_ylabel('Maximum Distance Between two subsequent surface');\n",
    "ax.set_xlabel('No. of Iterations');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3.2 Matplotlib 3D Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I plot 5 surfaces on using Matplotlib 3D plot, They are as follows\n",
    "1. Initial Surface\n",
    "2. 5 Iterations Surface\n",
    "3. 10 Iterations Surface\n",
    "4. 20 Iterations Surface\n",
    "5. 25 Iterations Surface\n",
    "6. 30 Iterations Surface\n",
    "7. 34 Iteations Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Generating Value Function\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "X,Y = np.meshgrid(mylam.π_grid,mylam.rent_grid)\n",
    "ax = plt.axes(projection='3d');\n",
    "\n",
    "#Choosen following Surface to plot on Fiure\n",
    "choosen_iterations = [0,5,10,20,25,30,34]\n",
    "colors_iterations  = ['black','brown','green', 'blue', 'yellow', 'orange','red']\n",
    "\n",
    "for iter,c in zip(choosen_iterations,colors_iterations):\n",
    "     ax.plot_wireframe(X, Y, d[iter], rstride=5, cstride=5,color = c ); #Mesh Plot\n",
    "\n",
    "ax.set_title('Two State and One Control Bellman Equation (Convergence)');\n",
    "ax.set_ylabel('Rent Grid');\n",
    "ax.set_xlabel('Share π Grid');\n",
    "ax.set_zlabel('Value Function');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### Generating the Policy Function\n",
    "\n",
    "# Last Policy Iteration\n",
    "w_new, π_opt_policy = bellman_operator(w = d[i],\n",
    "                     π_grid = mylam.π_grid,\n",
    "                     rent_grid = mylam.rent_grid,\n",
    "                     model_vars = mylam.model_parameters,\n",
    "                     shocks = mylam.shocks,\n",
    "                     Tw = None, \n",
    "                     compute_policy = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### Generating the Policy Function\n",
    "\n",
    "#First Policy Iterations\n",
    "w_new2, π_opt_policy2 = bellman_operator(w = d[0],\n",
    "                     π_grid = mylam.π_grid,\n",
    "                     rent_grid = mylam.rent_grid,\n",
    "                     model_vars = mylam.model_parameters,\n",
    "                     shocks = mylam.shocks,\n",
    "                     Tw = None, \n",
    "                     compute_policy = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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+y7UHsTtbdZB/h99Nvo7egD63d3FDkdx0HfGCaCjcKhAdgLo6LVgekP8zOIHkJkcEBnL7MW9SYJHd/F7ufD888/LoosuKtNNN13qk8zIlwC+/fbbgVixG0i14csvv5Rvv/02ZCibhZB+jm5VB5mO6ikjgjYegL4wyyAHv/dx3eZwdAt9R5wAOgqHE0BHb4URP8gW09xIBf//6quvgrvXtm7DwlYs2MN34YUX7vZavY0AkiyD6xvQtySQQATZro44SopP036IMUQbq18UfIexiooiswrawbg5IXQ4poQTQEdRUKHrBNDRqwCZgIRAPiAdUeIHWSHBAwKGqxfLVVIy9IUXXpAFF1xQZphhhtQnmQEp5ciHAGJRY8eRakM6AUwH/Rzdqo44y6i7mEzjaD8wRtEDMD5GBN1d7HD8Bn0PnAA6CocKWSeAjl4BprKRq++//z5Y+NiZAxkJGYT4sRuGZfbGJWBx8fe//z2QSjJ2cyFfAvjOO+8Ey1k1EsAvvvgiEDtiH+Ng4sSJXWSQA6S7i6MwIujuYofj93AC6CgKTgAdPR1MYSxNkCqIAjIR9+N7770XslMhgsSpzTnnnCGzN90FmRT+8Y9/BHJJzb5cKIQAUloG0lptyJcARsG4Qcj5PuNF+R0KSmO1hQxCeiF4URgZNELIWKe7i5Mm9g5HtcIJoKMoOAF09FREiZ9NY+QhB4QP1ymf4+adb775CsrszQcvvviizD333CGTOBdoM3GAcYnKu+++G9ym1UgAP//88+DiLYQApoNxjLqLsRZCfKPu4qi+Y2yjB+DvUTIIOezjOtLRi6Fz2wmgo3Co4HQC6OhRYMpiATKLH0AOckCuPv3005DZy3nszdvd1mxJ4aWXXgpWxu727C2EAPIMXLvaAAGkJMxiiy2W+iQ5EC9oZBBiyPhG3cVYC6NgvG1u8JPz7SeFq806yO8OR2+AzuU+PZnd1u9w9BGYcqduHwf/R/4ZkcIdSSkWrH8kY6Dwy0X+AG2hjUmjr8p4LLbUPoRcrrLKKsHKOGDAgJDBjbUVws1WfbiPIdU2Fxh3qy8IOX311VdDsWpczLic2QMZAm6LB4fD0TPhBNDh6ANAWaO0KTocVfaAYsQkYHz00Uey0EILBasflqJyK/i4BJBz8iWKpSCWSYB2lYOgMta4g3GxL7PMMoEQEs/JGJPcA/Fnz2TqOkLyrF1RUshP5g6uZbLEIYT85PdoGIHD4egZcALocPRioJSx9qUTP35i3XnllVdCkgQZsn/84x+7yrpwTrUSwHzBdR1TAgsf2dZG+JdffvlQfods79GjR4eSPP/85z+D+9jIoBFBsw7yGYsKLIKQRgghlkKba04IHY7qhhNAh6MXAuWLVQZlzE+AwuZAYb/55pvBtYelb9iwYSE+DgVv6E0EEFQrGamGdtHvxPhRKBs3MQsB3Ma4kHEPM19efvll+c9//hMIYTZ3Mc/CfIu6i4lDdHexw1GdcALocPQiRIkflj9+R1Fz8Pu//vWv4O5FcaPo2cWD/6cDBQ/KSVBKRQDtWaoV1dY+5golZHAR4zImDnSuueYKRO7f//532LGFBQR7P+MCZsx4BkggZDDqLoYAurvY4ahOOAF0OHoBUKgoXIgfiprfjfjxOdYb4rxQwCuttJIMGTLkd1mgUfA9UE7LDSSiVPerVsJR7USI9kHsqM3INn3MnWWXXTbUGiSz+LXXXgsLChYW33zzTVhkMI7MnzjuYs53d7HDURk4AXQ4ejBQnChQFKlZ/EwB83/KjGCxYWcPijovtdRSMnDgwNS3s6NSBLAvEgGeu1ph88nA/8kkJmaU7ev+9Kc/yaKLLhr2hMYiSOwgcaUkFEEQmT82HyGEHFF3sVkH3V3scJQfTgAdjh4KFCUK06wopmgBpVywzHzyySfBcrPCCiuE2m9xUSkCWApw3WolltVOeBl/mwuZwN+II6VQ+HLLLReyiym4DbkjiQSr81tvvRUWIlj8AOPh7mKHo/JwAuhw9DCglCF9KFkUpClUfmJ1IWAf5UtiBwqZnTXyJVd2fjmVL0QgDuEs9Fkc+YPxz6f/sAQy3xZZZJGwjzQlZyCIJJOQdMR2f8QRskBh8cK1Gfeouxiku4v5v7uLHY5k4QTQ4eghQPGZxc8ye1GeKFEsJtRxIyaL+Cxcc1hi+Hsh4Joc5bYA9kXlznNXKxiPYuYQ4QbMw6FDh4akI6zRkD12myE0gfnKrjOUJGKu8R1IYLq72GoPQgjdXexwJAMngA5HlQMFaJm9KD1TyhyQQax9WFawvkD85p9//qA8iwXX7w0EsJqJZbW2y2CkLAlA5ghDYH5SdxALITuVQOaoRQkhZP9pdqTB4geMEJq7mN8zuYvNGl7t/elwVBOcADocVQqUGcqO2mvsZ8vvRvz4/MMPPwwxVpBAsjMJxm9qakp9u3hUIwHExU2SAduY4UpkFxNIcU9GUgSrFGA8StU+5iqFx5m3hCqQoMRuJSQsEcbAdnXvvfde1xjTDuYkRDDqLsaCSFkadxc7HPnBCaDDUWUw4ocSs4MSGyg//ka2JcSPuCpirJZccslYmb35opoIIErdXNzTTz99qF8ICSDJhczT119/PfwfApDpGtVKBKq1XQbaVw6Cyj2oN0jcKvMZSzZjzByE4EXHmN1KzDJpLuLo/91d7HDEQ/UuPXsIVOD4EtORCJhKHLiyzHqBAkSJYfWidh+b96PsUI5s5VVK5fzss8+GUh8E8ZcDWPR4HrYnM6C4KSkC6Z111llDtimubkgxfQNQ+FhJIcRYCLkGrkYO4iHZ3xbgeqw28GyM94ILLpj6pLpA/UjmYqXbZ2NsB2BeMsaQfsgf74TB3iVIHz8B8yVqPeQo5fvjqH7o+PfpCeCzv0iocHEC6CgaKKp04mey6auvvgqxUbjMIEDETRn5KSWwMuKeg0SVA7j7eHYSBegPSB8Eaeqppw6kEAsR4G9RAhgFf4MQGCGEPKPwKXoNAeRa5ei7uKh2AkiYAWMSJVeVBu1hXI0MkkACsaNYNYQQYpgeA8t3jAxy8G4xDyy2EDIYfeccfQNOAB1FQYWJE0BHwWD6QAA4gMkjfuLGwuIHkYEYrrHGGkFhlQu43SAmM8wwQ+qT0uL9998P/YCLl/+jkDPdPxcBTAfnYVmMugGj1sFcu6GUA9ViYcsG5h/9zMKjWkESFO8R7waEEGshsYQ2ziwconqec+1gPgCeERIIGYwSQkfvhs4LJ4COwqFCxAmgI28wbVD8EB6UEHLIDhQYxADLHxv0U0YDMrbWWmsFxVQukFk877zzBstKOYCVE7JL32Cty2bpzIcAAiNZWLHMcsR9sBT279+/iwxCGsrZv4C28TzVZGGLAqssxIp5UK2AABIDS/wggOybdZCQAGBkkCOd9DPfOBgHE+fMLYggz86c4OjjXKFXQsfUCaCjcKjAcALoiA2mixE/mzpG/PiMgHdi1rB6QYLYdovPn3zySVl99dVD/Fu5QBbm3HPPHQr7lhIobCxNJLqgyCkRku7CiyJfApjNzYpFEIIAGYQs8LvFlUEI+/XrV3KlX40u1iiwnhJ6MM8886Q+qT6wcGDc2J4uHcyVqLsY0s+4Ghnke+mkn/EwMsjBHGCuubu490HH0Amgo3CogHAC6OgWplTM4geQPRz8zlZZWIMgQBCVQYMGhXMA333sscdk1VVXLavLklIcKFWSL0oB+gJyBuGFZKJg+WyxxRZLnZEZ9Fe+BBByF00uSQd9zFZlkAQIIXFlEB+IIAfjkYuUFgoIIKjGBBXwr3/9KxAmFgLVCkrAsGDCYtwdjPQbIaR+IGNrhJD3L8oJmBd22HvLvIMERq2Dceeio7rgBNBRFFQwOAF0ZIUpD4s/A0b8+ByrFyQABYIVCEWWSSZBANlJAZdlucDWXdRpww2dJOgHiv1CeLFwQsxwv0LUIGGLL7546szMyJcAYlXl/FwEMB1YaY0oQAhxyxtRgBDS7iR0R7UTQOpPWnmWasXo0aPDPOXIB7x/6e5i5pSRQY70upr2PjMH+WnvsruLeyZ0nJwAOgqHCgEngI6MQElg0YJMME1QLiZvIBW4PSEWKH+sbLkIDS5gXKOWCVsOUHcNQkoMYhKgDyjyS4IH/8fSGS1lA1HDXUfpmVwohABi6SG7uFBQh9DIIEQBhW/WQdyIKP9CYGV9qpUAskMH5DypOVAKUBuS9jGXigHzyjLIOfg/RN/IIAsAyF0UzGMjgxyMJfPS3cU9AzouTgAdhUNfeieAjikQJX7AZAw/ITgofUgEbrW55prrd0olE55++mlZeumlgzIuFyi8DLmhjcUCZQrx4ycZpbiW0wmcFfll39hcyJcAcl2IdjEEMAruj4vYCCHkkPIykAQIYXrWaS5Ue5Yt8XU8V9JW4CRBqAJJKmSPJwms9kYGOfg96i5OtwIbCeRgjgDGlvfbrIOQwj7OOaoKOhZ9ejB8JhYJJ4AOA1PBiB8KANliBwQEd9/XX38dyA8KK5+Ejueeey7ExqF4ygViqyCcxcR/RZ8bNyLJBNmsZZ9++mkgxuwEkQuFEEBcfYssskjqk2TBMxoZpP2Mt5EECGGucYYUQwyqlQDmE19XKZCshDW5lO8G77ZZgTnMCmzjzJE+znyHg/nKT0IeeJcI4zDrIEcf5yAVhfZ9n+78eBLU4XBkhRE/3IxYCfgdcsLB5yh5iipDDNnzFEtUvtm8XMssiuUC9zRLRr7guSF+bPBvz42SzuUqRRYXer9cKLWMJzEHFz4EnefkJ4kTxDlSvodYSuIbsRpmer5q1kHM5WrXkfQpRKqUoA+w+LF4I0SBreps720SuBhnduuB5EEQaRPf4R0yssciiM9ZvBDrijcAi7jtXczf6G+Ho1xwAuhwFAiENeQmE/Hj/1i0IH64NYnfw7VZaBIHCiQTeSglCiGAPDfEB+KHRWzZZZeN/dwozFIpwFJdNx30GW5CLJ08O4QQ0oCVkHg6+oWfX375Zfis2kG/9QQCSL+XE9yP8Ags+TbOxCFC5Mic5r1/6623AjmE7LEgAiz8IIQcJieQH5wDGaT4O/PC5InDUUr0afNnEtCX1N/SPgaGHKWDUDeChJI0AsNKH+sXpA2rF7FJxSrRl156KSiYUpVkyQQUmT1DHPz3v/8N1k76he9QQDqf54Y40nco1Fygz/NxAUPEUbBYbCoJ5kZ6IWr6F2shRKIShai7AwkWENhyFQMvBOxZzZzBQlcNYJyZb+Yutq3qmLPMQdzF6ZZwvmNyhZ+8N8xvI4vMC45i5YhjSmh/9ukO9dlUJPRldQLYh2DED8sfQ4+QNhmCUocAIegtszcp+VKqkiy5QBFg0F3yBFYLnpu4KIgMsX5xyVkUWMU4lltuudQnmZEvAaTOIG2sNAFMB1YerIHMJ/7PgVUJgkDsYDkKUXcH5h3jWWyGbanAO/jMM8/IiiuuGPqrGoGs+Pbbb8OuKpBUyCGJQjbOmfan5rmMDBo4x7KLOZgbfZy/FA3tvz7dgeW1mzscPRRGOiBFWHGQG7Yix5KDpYSsWQoaU6+PoPkkZQvCnzaUE9wz1/oG1xXbcLFlHC5e4qIIco9LzNJBf+W6X6Eo1XWLBcoc0oKFeKWVVpJlllkmEEAsqWS2ktwAseZ3cyGWG/RboeNZDti4VpvlNAraRoFp3L+Egqy88sphIRcNCyDbGgs48YA8k8kXs/7Zu+juYkeScALocOQAghUBC/lDCWOhQggjoMksRXDjnkXAQ4CI/SqFMuKa1ZIEQjtIaiDOib5AoRWS2JKOaiVqpQbPzcEcws1PFjRzicLgfG7JNCwwzJpZrn5i/GlDtcLmZzWTVIDsMLlA4gjWfCzSxA4utdRSwf1PjUwj/lgLv/vuuyB76H+eL0oI+YxrQhiZDxBCiCHvo3knHI7u4ATQ4cgABCgCFoFqK2yEMAKYzxDQKGWAEGeXiWIJUC5kI2OlRPo96YOvvvoqPDcuLeoSorySir1CqZVCcZXqukkgW7tQ8lgGIYG4N7EcUY6FeDKszX//+9+DNdpIQqlA+6qZANqiqNoJIO00AhgFfWs7rRjxJ36WcylgTnYx4201Mo2QmyzCisy5jBOLVAghZBAvhWcXO7qDE0CHIwIEJcI6nfgZGeIz3J4I2BVWWCFsW1aO7dkQ8qbsygUUjRFAgtmxTFC42EgJMUxJopqJWikRh2Axx3AbWgkSahqi/CEGkAR2beH/KP8k+5BrxWlfpRAlRNWMbAQwHZxDXCAxxMgX3jNiibHukVXMWFsWOR4IwPPzvai7mH5Bhpm7mJ/uLnako3rf7B4CfZn8beoFYBgRmlj9jPQgWDn4G5YvXHEIULIi89lXNgnETchIEmTP4pZCqZDggns77s4lhQBrFn2MSzkXGB8sG3GVPqU4SFDpbo/hSoDFBBbUYnZbQbFbZnG+hai7A4SDuobl3IEmH0BssJANGzYs9Ul1AsJGLGd32xzmAnLIssg5sAhSg9LGmvhRSGAUfMdkm4H3hvPs4HfmTF+EPnef5kBuAXT0aZhwhNhBKlipIxOMXECASHKAmGD5QtAidMsNSFdUiJca9AWEDFJBzBJWJzJ8S0X+AP3OeCSN3i7jiy1EnQuMRzX3H+9r3IVAJRHXApgLjAMZwywWCL0g2cz2kKYANaEZ6ZZgk2VG9mgDn0etg+nuYkffgRNAR5+FET+EIZY/hCUCkp+srrEskOSBckXY8hO3W7ldsQAhXo770icoEBI8UAhYfmzHg1KDfi8FAQSlum4S4LmTAvMkyULU9Fs1Eyzma08ggNEkkKQAoSNOlJhBXMW4jKlCQFIIW/gx1u+++27wXthYm4yzcjL0HX0YJYQsFHAv0+Zqfm8cxcMJoKPPAaFmxC9q8eMnK2FibcjGY7WN5YvSJia8+VkJAsh9S7k6p0+++eaboDRQGOzeUUxJl0JQKgKYJMFKGqVWsLh/IQWWcUqiAUkHjDUxncxzrNu4FDPNa+ZcNfcf7UuaWJUC9G2p24nVl/JTZgnG3Ux4AcXVGWuqFRDDiys6Kvdol1kH+cyyzbEMWvygyUonhL0LTgAdfQYIr2hmL0DgcfA7cXa4zPgdix8ra1bKUSAkK0EAEdSlui8rfogAz4+bl5p0WBa4ZzkFPv0e9375nAuqWXHxLOUA9zEXIhnckARIPu8EYw/5t+3LWAjRZxzlal8h6CkWwHIQwCjoE6z3jK+NNe824wkJxMI/evToEOcL0bNx5nvMB8IKaC+fR62D5i5GXpZyQeooD5wAOno9EGJG/HBr8juCjgMhRrkFBCJCDlcKyQLZdhVAKHKtcoP7Ji1wEeS4iogRI1kA0kumqSl8659ygfsyNnEQ9zxQzQQmn+dIGixu2OGDxKJshahpH4kllZjzcQCxYp5WO8pNANPBWFNGyNzFlBVi7CF1EEEWviQkYS2E3GER5L2hzVF3Mc+BO9lqD1oxauZHJeeyozA4AXT0WiCQEFiQPoQavxvxAwTJY/XAHYZrDAWIeywXEIRcs9xIkozRF+wwgdBHwFswOc8WRZL3jIN8CGC+qGblVA0ElTakF6K2BAPcgbwnlShE3R2Yn/Y+VzMgSOnvV6XAWFtZIRa7vP+WOERsKPKBfcAJDSAJDHnHd+hnngFCiNzgM85lIWnuYizJd9xxR+pOjmqHE0BHr0OU+HGgJEyAATJ7KaRLZqStiLGAxQGCrxIEMIn70g8ocKydCGuCxhH82bKaK0EAS3E/ruvID8w3Mt4BC6NKFaLuDswX2lrt4N2t1nbynlviENnFgMLUkFYreA/5/+yzz7rIfyZCyOdUTDjrrLPCNRzVDyeAjl4FFAIKCeIXXbnyk8xe3J1k9rL6ZeXLlkz5EIQkiFghKIaMIZjZuQOLH0Ic0odSJx4sF+iXclp6eMZSoZzPkQ+qtV3A2sa4VKoQdXfgnSjlvEkK1UwAozBXP4lDFhpANjkxwYQCGPnHQojnBDkLkBU8HwsEwggcPQNOAB29AigChBFxfgixKPGzWLdXXnklrHRRYATCF6I4EHImJMuJQomnkV7ie3hmiiwT+xOH9NI/hZLOQkCbSnG/OM9aSVRr+2ws0tvHvMA6ON988wXLIBZ0FlJWfiQaT2YEoVTgnXACmBxYPNOf1qeMPZnElBIy8m9loUgWYqyRqw8//LA8+OCDwWVslmNH9cMJoKNHA2uDWfyMmCG8EFx8zkoVIYWrAosfxZyxXBSKQolYseCZ8iFH1PHC0kkwP9mACG7iu/JRlvneMwnEsR4VQpiq1dJWre0C1rbu+ruUhai7A9fLZ05XCsimaokBzIXu2klfY+Ejo9jqTCJXqDd4wAEHyLnnnhuShy688MLgPi5mfhOqstFGG4W5xRy89957U3/Jjueeey5kPTMncWlfdtllqb84MsEJoKNHAsGCsLKSLvyOcOKAoKFwEAYQISwUQ4YMyRrrlg8QjpUggHGJJ31CmQcUL30C6SXOsRDlU24CyP3iKox8SGAhhLGcqNb22Vjk0z7GMMlC1N2B+dkTLGs9xQKYL1G1OpNHHHFEkLkQNizDWARJJiKWsNCkEKoyYHW8+OKLU5/kBiEI66+/fpB5xCyOGDFCDjzwQLnrrrtSZzjS4QTQ0aNgxA/hkE78+D9uCZQMwemsBDm6y+zNB5W0AOa6L4qQZ2fVTKzOcsstF4RntnI2cYDiLycB5H5xCCCknmfNJ94s7nmO38DYMybFENRiC1F3B9rIu1HNYO71JAJYqIeEcSDcZr311pNHH300yKFrrrmm4D24uc7JJ58sm222WeqT3MDahzXy/PPPD/GLw4cPl912203OPvvs1BmOdDgBdPQImBDF1WvKA8Vkwh/CR3Ayq0BefjJcSxGLghBHSJYb3Jc+yERkbL9inh1FC/lLYvN+I9WZ7lkKGNHIdj/6HbLAOPPMFC3G0ombnyQXFgSZUAyBKTXK1beFgLYl2XdcK99C1N0BmVDtBNAWUT2BAPIOFeOqxs1vchePy5prrhk8EOUAOmHttddO/daJddZZJySuZJMNfR1OAB1VDwQoLzDkD4FvblgUCgKHIGRiUFj9oVSwOJRK6XNvFFM5LWPAlFz0vtTeimY18+wzzTRTYs9u9ywXSbF2p9+P39meDoJAkWIymClXQUIL8WYEpFPeBjJINmp0d4OegFLN1WJB/5WSXGFp6q4QNfUq+T3boqsnWACRVaAYYlUu0M/FtBOrX6WygDEMIP+i4HeeiTnk+D2cADqqFgh3SF80s5dVNIqDz4nzgACx4iTJgXiTUisDW8WbUC8XovelPyC8KEjcacVkNeeCXa9cZDcTAYTg2x6mWBKw7BJjBmgf/ycgHasnJIJsVMgfuxtYrTpcxeUm7HFRzSSVPisXOeU+6YWoSdjic6y+VosuvRA1bax2y5rJrnL1ZTEohgAyJhDAuDVVS4H0PrZ50hP6vhJwAuioOvDSmsUPgQRQ9rzEkB+2buMciCABv5l2sSgVKkUAjYzZtnX0Dxa/hRZaqKis5lwwoVkJAkicH+5ACD4FiBlnywbMBiyBlo3K+bjD6Rvcw5AGSGG17WQBqlU50UeVahvvGbXnIIG2dVmmQtTMk2oay0xAVvA81TrOURRDAEHUBVxu4PnBChgFoUE8TyVJaTXDCaCjaoAgRwBly+z9z3/+00V+AMohiczefIAQpz1GTMsB+gEXKKDOFq4yrCQU5y0ljHSWS8GagiSbEHcugOSSVZivlYe244riu1gO6SvcQdRFhEAQM0mZCmIJyzmW6ahm8kLbqoW0ZCtETawgcqFShajjwAhgTwDvQqELSsaCzO5KkS0WCk888UTqt048/vjjQV6WapHc0+EE0FFxILDNtZlO/Pg/u1dA/KLxXyimSiluVpTlsgBC+HD14gajP7BulSvGxpR/OSyAjLOt3nlmSogUm8UcBX0322yzde19StwZShmyae5F5hnZ5dVGICoFxp1+qzbQJqxMkHvcxhD8ShWijgNkRbk8FMUC+VtoW7H+gaTkk40nB4Dg83/eU3D00UfLTjvtFP4P9t5772DhP/TQQ4N1mAzkq6++Wg4//PDUGY50OAF0VAxG/BDSHPxuFjZg25cR1I87DzcQwoVzEFKVIoAQh1ITQIQfVg2ICa4NSAur2HK5Y4GNRanvaXF+kFwAwbc4v6QQJXWMHwSC0AFiCs29SPwSLudo8kGpx9nmfDWimttmYG5GXf9YjMtZiDoOkFM9yQJYKAHk/SEmOSlrG5Z6PB0cAGLH/48//vjwOwTfyCAgo5z6g88++6wMHTpUTjrppFCQevPNN0+d4UiHE0BH2YFiQRCz2oT4oWSNbPATQUIWoG1flim7FYHaGwkg/UFZE1yUKDHcXRTV5Z70T6kJSTro81IpTdxFZDCjoIn3YpxLge5ITNS9CNG25AMST7AOYnWw0iR9CbynthirVvA+RNvI/8tZiDoOaGNfIYD0fXfvW1ysuuqqYQ6mH9ddd134Oz8he1EMGzYsxPriTcJiiFXQkR1OAB1lhRE/XlCEDcIC4chPC9Rn5QchgPxk276MVWYlCWDS90ZJWIIHge1ktBLnRCFdA/cttwWDvkfoJgmelbgtlDHXRkljjTPLQdL3A3GvSR9Hkw8gEVgLo6VJIIa4qZMi40kpzKTBXKvWthloYy6SWupC1HHQ0whgoRY8LKzmoXH0DDgBdJQFKGAjfghEhASCm5+sxClrgtWLpA6IH/E9uVailXQBc++klAX9YjXuUEooKIrkEtuUDvqrEgQwqXtGn5XkC+I50+P8mA9xyFo+SqZQhcT32AjfSpNgHWRe0h8kkPAcZCrjaoS0F4K4xLQSoG2F9l25wFjEJVc8S9KFqOOgpxBAnpe+KNQCCIkuV3yyIxk4AXSUFCZULMEDIIg5EIxW44u/UdgXqxcxPd2hkgQQYZ4EAcRlQuwb8WZYwLA45cqgS+q++SApAkj2LdYWe1Zi7zIpC+ZFKUhuEsqcOUesIKV3rHAxLi9KTTCOFseIIsznGaqVZPUUApjLApgLWLqKLUQdB8WQqnIC2cKYF9pW5JkTwJ4FJ4COkiBK/CzBA0HNwf/J1sLdibLEzYaFBWtLXPRkAoh1geQOXN0QCiyeZKh2p2yTtMbFRbGEzOL82K0FcosVLdez8jnzI0mUgsRwTay0FB8naYXnIu6MOUkMJ4sanru7WLOknzVJFEOuygH6jvcwiTbaeOZbiDoOaGNPsACaPC2UALLIq1QNQEdhcALoSBQmlCF9WPX43YgfwM1JZh7ZW0OGDAk7OBSS8VlpAljIvekTXE08P9YHcynGVQ6VsgAWQlJop5VYgUhYnF93yqXQ+3WHUlwzCp7LrElYsiGFuBst1gwCTNwjVpJ0Ql0KgpoE6LNqbRuwMU2CAKaDdy1OIWqsv+bZyIaeRABpZ6Fj7i7gngcngI5EgDA24seBkkOQIJz5iXDARYbQJO4GJYnCLFTYVJIAcu98iBh9QSkbyBB15lAokN98i1jTl+nkodTI957MA8oz8KyU8cGtRkmGuPX8mA9Jk7VC51ih4H4kGkRjzbAU8l4Q68oCgJ/EQ5Z7PPMB41AKcpUUrO/KQa6yFaIm05TxzFWIuicRwO4WaLlgSSCOngMngI6igSBmFYyCQ9ihAI34sS8rwpEDwofVi7IMxSqWShJAhHkcAogiMIsniQIUIYYQQQ4KAX2WD/FMAvkQQIvzI0HCYhrzVQilIICgFNeMC4gCZYwgDZBBCDHhDhBAFgS4GMkAp/8q2c50MO7lJs/5wOZluUkq97NC1FgGmee5ClEXS6zKBdpZaAYwwLrtLuCeBSeAjoKBAEbAZSrpYnW3cH9h/aE+E5v2JyUIK00Au7s3yhy3H/FgWDwJMseFVAziEs8kEYcA2lhD/lAA3cX55UIpCGA1kRjagmuYOcFiACLIvLC9j7GcQh5YOPBuVRKMQzX1XTrsXah0G7Hk5ypEjSwgkaRShajjgkV8sRZA33O3Z8EJoCNvoBhIZMDdYUQIooAgRohYAV0ENAIxvZ5dEqhWC6ApcsgfZAh3URIWTxCHjCUNxjTbPekDi/Pj/xA/YqaKUSLcrxRWsGqyrKUDKyl16ug/K4tDKRLIA/FmldrjttoJIPPS5E61gPakF6LGqoasqlQh6rigjYW+u8wVdwH3PDgBdMQGLzlCAosfCoo4JgQeB38jzo3MXgQByR24uoidKQUqSQC5dzoBhPhSMgIBj0JCmcdJesgHlbIAphMPfi8mzi8XSkEAq5nEAGsfP6eZZpou8kCcLJZU3MTmWsSiTJ93l3iQBIxgVStoH+9ENYOFL+OKxRcyWIlC1HFRDAEEXgam58EJoKNboJARTlbLj9+p1WcEzMiAxbmhvFBkpUQlCWCUiKGEyGjm+XH1EBNEH+Sb4BEH3DebNa5UgABE72mubeL8iIEqJM4vF0pBAEEprllq8I4RW0bCkLkW+YxSJJBB4mpZdBFnW6o+q2byXO0E1YCsQF7Rl5UoRB0XtKPQGECeESu1u4B7FpwAOrICAcSLTSwShykEhC6CAhcGq1jIAPF9WCyIZyqH0qg0AeTelICgHARKGCVdauKbTsbKAbtnNM4PwoeFk6zIpMea68VVfHHPK8d8LBRxn4FxwLXIe4Z13RIPIH9snxgtS5LUe0HbqplgIZuquX0G2pnJUokMLUch6rgoJgbQEpicAPYsOAF0ZARKH4EA8UOAGfHjJ0qHmmZYBNlnkzi3UpCBXKgkAeS5ifWDEFE4lpV8MSVt4iJqeSwnUEBYJ7g3z1psnF8u0IdxSVFvQSHzJpp4ABmPliVhrCCFhRQtjgIZUM53Ol/QvkzEqppAG+O0k34uVSHquECeFvpe4/5l/uVTzN9ReTgBdEwBhBWkL1NmL6SH3Q1YmdqLjgujEkIYQWXCtVwwKxhxWAAhTX23clkhuE+5nhcFQ5wSFiUUDi6rUsZ0GphnSSu3UlwzKSTRLuZFtCwJ2+yxIMEqQxIJe2xjpWf/5XwWTbSNvqtW8C5UuwXQFmz5ykjOT6oQdVwUQwCtCHS1j4djSvhoObqAsMpk8UPAWIIDSgErEPuh2ncqARNU5bACcg9bhfO8kCFQaLxMoUAplKO/ieWxEja4tLE0lau+VzWTtVIhaZJFMg4WeWJRsQ7yrvIuY7U3SxJxqySX5OrraieAvAu9lQCmo5hC1HFRDAH0DOCeCSeAji6Y8uUnBytshAqZvQgWVqEIIISRCYqkVp/5wshpKQkgfUFiC0rzhx9+CDF+VsS33NZHwDOX8p5YfbFwsmML8WYoGoLWC1UohcDmYNIo5zPkg1K3C+JBXFbUkoRlCYvNq6++OkWcWfriototbNXePkCfMgbM66TAM0ctvt0Voo4LZGmhi1pcwMiMJJ/TUXo4AXR0AcFiApVdCiA+/LQdLKIJDrzokMBKEcBS3x+FiOuMOncEaSNobc9iI7/lsMZFwdiU4p5ck50oIPooASy8Cy64YHhO+rmcRJf7JU2KXCn9BhZv1KVkIYN1EGIIrHYnBCKahVrNfce8LNayVmrwPpW6jd0VooboI8ew0uV6l4uxAHoJmJ4JJ4COKWDEB4VAHTvbwSKTImC1WCkCCBBWSVsASXAhxgZFiFBFSZLoEn1+E+jlJoDcN8l7ouBtqzqsBUsttVQIQI/G+UE6y2k9o59LQTjL+Qz5olIki/mENRCyjxUJCzeWJWQAWaj8xPLNUe65Hge0yRas1QraWE6SSn+kF6KG8Fv8MiSfn+mFqHk/iiWA5QoTcSQHJ4COLqB4iXUz4tPddl7VQACTUky4P3GbWIIL7k8SXDIpGPqkVNa4XECRJEWOonF+lBaB6Gcq4cBzloKQZUMcMpQvYaoUwYqDaiGm9BHz3rJQef9ZCNA+EkggDiSAYVUiGawawLx0ApgbFKJmActOM5DBbIWoSRAChRJAjwHsmXAC6OgCwhRLQNzMXghg0ha4fICwKpaAIqBxj+D+JF6GWoa4fLvbuo7+KTcBTIKMQXTZwSUa55erhE+5CWDc++VL6twCmB94t4w8WI06QkDIOmXuQBxIKsHyU875EYUTwPzAPMtWiBqPD0A2FFKIGgLoFsCeByeAjimQj0DtyS5ghJvFObK1FgKR1bGVt+kOCPVyk99iSCfK0uL8GLNonF8uoDTKreCTJmvVSLAM1UxMaRt9x0GNOkoeESaAdRDiwDzCam5uRd4nFhjlAvOyWshVNhTjVi01kN9WiJo4b9oZDQHIpxC1xwD2TDgBdBQMBEZPJIBkQCLcWPVadmS+q1fuXQkLIEo5H9LAuRBclHS2OL9csHuWC6W6XzmfIV9UK0HNZmFj7htxwGLOnMKtyPwifpjQgjhJB8WC9y+fBWslUE0WwFxAjkIICy1E7RbAngkngI4pkI8y6mkWQOqesUMCNbNmmmmmYMkg3rEQBVyMNa5QmCKJe1/i/MgAxEpDUHi2OL9cQMGW0wLIWCRN1qqVYFU7GIfu+o6/Q/6ibkUshVZSiAQj3IqQw6Stg9kIajWhJxFA5KmBNscpRP3EE0+E+EFPAumZcALoKBg9hQAS24ewQmixmT6rWxIfihHMfLcQ62MxMGXXHSGLxvkRtwXRJROwECJUjQSQJAQSE/K1MlWjFbAa22SgbfkSLGQCiysKFUMGrW4mWae8fyxICEWwvWOLgRPA5JBOANOBxyBTIeqTTjopVItgsXn//feH8U1CXlx66aUhzIASNywssEDmwvnnnx9CWih/g6w75JBDpshydmSGE0DHFMiHJFQ7AUQQWSFrgpqxgJENBwksFgj1clsATdllu689L8LSEloQioxToagmAshzk3iAVYn/84xmZcLKSWZjpvlYCPEtJ6q1fYxDMW3juyQdoMhJIoEQopwh8G+99dYU48ZY5gvmZbWTq+6IVbWAdsaVE8gErH0Uoka2YhHk++wus9Zaa4UFwPbbbx9KahWC2267TQ4++GA55phjgtuZBex6660Xrp8JN910kxx11FEycuTIsNC/+uqrwzWOPvro1BmObHAC6CgYCIxcBKzUyEYAUVy4nCBCBKZjhWAVSSB7UuDe5SaAKFSEb/p9eV7i/FCoWFqI4SEuK25CSy5UAwHkd0gC40kWKmQCIk8MGqQCqwSWAhQEfYCLP1OsUvp1qwHV2CYD454kOY2WJEGpk3iAxYasU6yDEIl8tjPjPbBFUbWCNvYGC2AuMKa4gW+44YaQMHLvvfeGMS5U3p577rmy++67y/Dhw8M7jnWPhcNf//rX1BlTgnhuFrvbbbddCEVYe+21Zdtttw3zyZEbTgAdUyAfgV9pC2AmAkosChlsuAitvh2xLEmjEhZAwH2jhMwKV+PyRfghCPON88uFTISslEi/nz0f9QqxOBCPFM025Hzc3Iz1csstF/6OQsLFyPdQDrgcQSXGKw6SJFlJgnEoVdts3KxgMe8pdUeJ07XtzBhzFjbZZAzvgRPAZEAfF0oAkbmA95JrIINGjBhR0NzBEswCDhIXBb+TYJQJLAL5DslHgPf94Ycflg022CD87sgOJ4COglFpAhi1AOLiRXGg9CF8WBhy1bcrFgj1Slg/zSKHoLTC1QThE5dDBl/Sz1spCyDzCncOz4clIe54YgmESGBd4ju4wA1YmShmnL4LQiVRTnKdL8pJsAjLYD/bIUOGBIXOTyyGWHLNqvvpp5+GBYH1mRPA5FCMBRACiKu/0O9HgQWRPsONHAW/4wXIhG222SbEIjJv0EksBldbbbXgFnbkhhNAxxTIh0DwwqOoK6XEEKzcH2sfSoLfUfpYipIQRrnA9SthUULhQWCIvSHZg9X2QgstFARfKVBuAghwAfJ8uHCxDOEGKuT5mA9YQ5kPALc4VifbBcHKlSSRkNAbQZ+UagGVC8w5rEmMW9Sqy7wgi98yUAuJGyw3kBGllkVJoFgCmHQNwPR5l2suPvvss3LKKaeExBHmx9133y0PPvhgIIWO3HAC6CgYppQrYQmDlLBaxJKDVWCFFVYIm6BjASoHIBflJIAIQOLfeF7ibYhrTCrOLxfKSQBRJLj8yOzFAkSsX5Jxm2QyUqLEypVgMaU/LSGhO5djqVAJkhUHzLlqsLClW3XJQLXFJ0lAKP1s9ekqjb5gAeR9hQAmMY/x3tBf6dY+ZF+6VdBw3HHHyY477hhiBtEBf/7zn+XUU0+V0047reyL154GJ4COKZDPS4zA4PxyKkwEvCU88BOg0HFBlBPlJIAWB4eyw1WGW7MUcY2ZwPiWWqlCwnDN8oyQNFyBCPukiFGm67B4sYQEyCCKg77FzcjcglSQVEJMWimfv9oISxS0LakxSAoQUjJQKT2C/GHcKEpt9emIE8MjwCKpEgvTdNCGnkIAC/UiUFg/KQsgbn/kOfUFo+B3vAGZQPhP+kKFPmf+VvP7VQ1wAugoGCgHW4mXA7jqcNsR+0bCA+4hUImXvBwEMFOcH1mT5XzeUloAuS4B22T38n+IWFxLQpLEhGdkX2Rihyh4izUZUoFFkrpm1FNk1xgUXSn6otpIloFnrda2AdoXrU+HdZBwCMaTckHMK8qIlIPIZ0NPsQAiwwu1ACbtAj700EPlqquukmuuuSa4+qnpxxjuvffe4e877bTTFCVeNtpoo5AhfOutt4YscsgiVsGNN964R/R9JeEE0PE75CP0WTWWeqVN3TAsRGT3Imgs4YHVIqjESh9hWar7otiwRBEHh3UsGueHciun67lUBBALDZY2yvSw4selDbll7iWtqG0+x70u7YiSCqxN9AHKCFKR5L63lSAlcUHbqp0AMj8NKHtiPqO7V2Aph7hD5FlIQeQt0aDUoP96CgFElhVKAFmYJ7kLyNZbbx1Kv5x44olBLiAHyeolfANABinzZTj22GPlsMMOCz8JD6CEzDrrrCOXX3556gxHNjgBdBQFSEmpLIAIJQQ2RAFhijJGuJugQvhzVIIAlsICyDMS68LzfvHFF131C6NxfqW4by7Qv7SLIwlgiSGjE0KPFRe3TlR5lIIAFgP6GxIBAaettu8tBBB3o+1sEbd2XSZUK8nieaIEq5oA+euufVgHqR/He2Syg+8gUyDyxH7ynrHALAXsPS2UWJUL9CVHoS7gUiSB7LvvvmERzCILecGi30DSx3XXXZf6rbN/KQKN1ZexhCBecsklwarvyA0ngI7fIR+FVAoCiDCiOCwrP4QLrl6sMVhm0lFKS1wuJE3EonF+Rowyxfmh8OifciFf61k2MEbvv/9+yOAkqB+FjBU3XYFzvzjPVwhpKvYZuKfte0uCCpZZLIVWgsiyUyHxlZiTSYK+4iikn8sBmyNxCaoReeJnsQ5SexDSgiUaFz/Hhx9+mKib3+QD965m2FwttJ2lIICO8sAJoKMoQMCSJIAIZCwrxHIQoA/5o3RHNlSSACZxX+L8yD61enfm3s6m2JImnt3B2lGoUoREYC3D4kKgPvF1jKu579MB4SiWqKWjVCSGZ4jWruO5WBBZXCPxZyxkIIjZkPSzJgVrV1yCVW7YfCyEtDAfsKrznrFrDosR4j95r8zNb/Uii7EOcj3uVa19aEB+08ZiCGCSLmBH+eAE0PE75KMwk7IAYgHDnYbgxaqCQo2TCdpTLYAoMIvzQ8nErXfHfZOyUMRBMQSQ2CDiNrH8YXmBzHeXrc39SkWKSkm2aDdWEGrXQXI5sDj98MMPoQ9yWZhKRVCLgfVVNbYN2LuXRPuQITPMMEOXm5+wCxadVBlg3Bg/3IsQnXzeA9pYKKkqJ5Cf9EGhSDoG0FE+OAF0FIViCSBJDmxjFs10xcUWd9VcKQLIfVEG+ZIKzsfKibuQ+CNc2yicuPXu6JdqJ4BYNRlTFCeKAQvLrLPOGktZc07SRK0SJIZwhWj8GRYm5inW3mgiSbXCxrtaCSDtY24m3T6ux7tIwgHxnixE55577iDjyMiPjl13SUDFEqtyoZh28q66BbDnwgmg43fIR6gWSgAROlaqgf8jaAvZ0aJSBNBW9vlYASlUawkQuJ+wNmB5yAfFWh7zBXOBIw4B5ByzakICGdNo0k5cxCGAnJMvUUyaWMYFz884Y+ElbhBiAcnAxQiYD4Q8RLc5qzSsHUkTrKRgBLDUQB5REig6dixUyUIlVMV2kyG8If0d6SsWQI8B7LlwAugoCgjIfAgYigXLF8QPSxjB9FhJyNgrBJUmgHHubXF+KAxij7AIYWEoRIGV2wII4hBAXJ08HzFvWDVRlIWMKc+XNAmqJhJDWyAQWJWw/AIKUrM4sG3OrJBxOYl+OmwMykGyCgHzsdzkysYODwVjxwKH9xhLIFZBsvexfEMOeed7EgHMd+Ft4LssXCi/4+h5cALo+B3yUZj5WACNJLBixtpHnFSxK8dKEUAUI0cuJY2SwiIG2Y3G+WVLgIiD7u5ZCuQiZSQ4kOzAwXZdWEnytWpGwdxLmgAaSnXdYgEBZEcLFgbUMYM0mHWc7OJSlirJBiP81USeo+AdqDQ5RfYRp8yYQQZZyLLAw7ILGUTOQQSrfa9p5HehFkAsn8AJYM+EE0BHUUBwdEcAzfUJSSAWDGFJ9mQSyqVSBBCgqLORMYvzwyLGHqb5xPnlAvcstwUQRZt+T56bpAaekTGAvGDVKlYpl4oAViuRiYK+I5aKwtOUKiFpht+ZS8UkIxQCxqDSBCsXeP5qah/ziwQn3gG8Gsg4EknoR9trmhhC9riFFFYTkJ+FEkDmIlsoFurBcVQWTgAdv0M+yhJrVjYCGHV9EhRPggcCMkm3SBwCWipkIoCQXer5WZxfsRaxdKD0KmEBNMKBQkOJYZ1iRwXqqdk+ukmgVAQQlOq6haK79qBUo6VKeHd4p3AzmruRsSjF/Kdt1Uyaq40ApgO5yPjh4YAMsgik/iVFihk7ZARxn8UUEE8KxRBA2we4JyywHL+HE0BHUTALXFSIQVCohWZbmeH6xE1SjOszG7h/uQmRAQJo1kcUMzXEILsI/mLi/HIhl9WxVEC4o3CtVA+k3sqdJF1tv1QEsJoVVJy2Mc9JRoi6G5lnWJghFFjYCTdgAZJE/zHe1dxntC/JhWQpwHtKG5EBWAPJBMeqy6IQTwhjhVeE8eOdouxMJRazyLBCYwBxAXsCSM+FE0DH75CP4DfBgeBC8VjRXywTWC4s47FUqKQF0MjvmDFjwjOzzVkpyS5AoZg1rlxgPvCMlOrBzQW5pVZjKQgC1yyVRaRU1y0UhbaHPmIc5plnnmCBZc4RUoE1CSLIIoTai8XseUvbSjG+SYHnqmYLIDACmA6s5RBAi/ukkDjyAgIfJfPlygpHfiLLCoFlAFfzXHFkhxNAR0bEfaERcJxLggdxSigei2EqR2CwkbBKAAFPxiZuHVw8xP6UkuyCqDu21ED5YGEiAcGSWAop1ZMPmEtxlF6+CqeaFVSxbTNCwRzEOsgYcc30PW+xxscFY1DNBIt3oKcSwCh4BggUFvXll18+yE2SgiDzZIVD5ku9vSDXLYYA+p67PRdOAB1Fwba5ogwCLiri/MgGLZfCrQQBtKQWLH4Iv6Tj/HIBhVKoVScfINix+OHKJ34Ta1OpyS1g3pTK6lEOa0qlwfxg4UX9RdvzljkKgWA8SSSxunW5+qMnuIB7AgHMl1gRJ4j8hMxjHaRqANew7QUhhSw4kT1JzediCaAXge65cALoKAgW80YWKEoHdwYxLt2teJNGOQkgrhKL84MUQfpQruVURNyrlBZArERYjAhSp8QFFiXcU+UiT6UigNVIZkrdpzwzZUmiu1pQw44xtrp1ZKZmij2jbdVMsHgHyi1r8gVyqZg20v+WFW7bC7LIhnQRi5uEqx8UQwA9BrBnwwmgIyOyKUwEr9W2YxWKlQElUykYASylMuWZiYEjqQXrH89scX7lIp8GFEopLIA8o1kZANYHI/QoomojgNVI6ApFuZ7F6tYtuuiiU2SmMrchg1iX+D/vNfOhmvuYd6CaCSqgjUmSVBadxN5SaJ3301z9lGNCNpFQQsgGXpm47yvnIcMKDetwC2DPhhNARywgKEjsQFHYHrbEvFEZH+FRyUQMUApSBFhds9JGMWLltGcGpSJjuZC0BZBxxT3IuLKDAfUKGVuIgSHpe+YCCq0U9+K6cZViuVDJ9tAf0cxUFjWQQyw6WJewdGPlJ7a33HM8DpgjfY0ARsF1o65+4genn376MF5sT0c8NjGg3Y0f/cg8LMYC6ASw58IJoCMjoqt/XnKECkqBWmTEvCFsDNVAAJO2xFmcHzsxsOrGYoL7JdovlSKACOwkSJI9I+5AXIMkeWQS5uUmgHGRz7nVjGp4Dos9M+sSSSWAJCeswtS1ZIcLtj2rBvR1ApgOSgLNPvvsoTwQ44fbGDmBiziaCJS+o4zJ7UIJIBZAdwH3XDgBdGQFwgLBgUUAYoBgQcikK6xKEkCUAEI2KQLIc0Tj/EhqgRxlUjZJ3jcuTKEUQ8hoM4qBZ7SahRQczkZE+LxcBLCUZLPaLIDVCuYYYR3MDRYFWL0pO4MHgDnDYpBwgUpucVZOclUoeM8KJVbFgH5hgb7gggt2JQJB0mxHGQ7cxhRxxsrL+YUsQhh7twD2bDgBdGSE1fNDMNiKMpswgwCWmwhFQbuKvT+kg+y6THF+2YDgLLcF0JReIfdFYGPFYVwR3HGeEUDKyqnoS3GvQhRcqVEp8hQHtI0+4yD7m0UQ4QFYwlks2OKQ0AGKGJeyTEkmuAUwHhg/yHx0Rxnc/rSNhS5xnyYX8ikTZHAC2LPhBNCREawYyTqzQPFcqKQFECBki1E+FudHckt6nF8uQDzLTQCNyORrJcNaYyt/gseJ+4rzjKCUVrl0lJJsVivhqlZymqldvOvUqbNEEt4XFhBsa8bCgkQEFlJWHqpUqHYCSPvow0oTwHQgs6hegAzAugsZ5DMywa1MUNz9pnk+TwLp2XAC6MgIVo24feIAAVJJAlioBZJsR4vzI/4pU5xfLlTCAkjb8rkvMVvsGYtgxy3EM7JrRD6ko5wEkHZ1R9T4OxZq6tnFUVQgn+ctF7p7zkqCtnVHsPg7ZZCsiDELRsgFrkXcxBAKczUmPX+4XrWRqyjs/azmNvJOILtx9VuZIGK8keWUB4LQEx/Mu5Yp9hNPCXK3HAX/HaWBE0BH0cACUEkCiBDLhwDSVoLbqWGIdZM4PwRfvhYFhHs53V6GOISMv1u5Hp4X4Z7LjZ8LKIqkFXg2dEcA2SEBMguxIH4JRYUbEpJLjFqueVithKsaySnjnW+7rEyJJSJADCFCuIijZIJxKxZcN9/3tZwwuVDNBBDQTpMJLKRZAFN8mkQ/28aT6gAW+2lFxHnPWHwxR3wnkJ4LJ4COjMhH+FfaAhiXAKLULM6PfTaJgcOV1V0MXDZw33JbAEF3FkBc2pBbaoKhjIn9YZVfKFC05SJP2Qgg8wsigRub8AQUlCkqnhHywdhaPbt0N2Q1kqxqJaSAthXTZ8xRczVGyQQEkLlJYhluYwh9If3Au1zNBJD3kz6oxnkXRZQARkG7CRFhYRyN/SRO8KmnngrFxffbb79A+Ck1kwQuvfTScD8W5dyTRUMuQERpAx4NvoM8ePjhh1N/dcSBE0BH0ah0DGAcAoiQKiTOLxe6I2KlQjYLIIQH8oNLm2zt9HI9hSKOxTEppBNA/k/5CpQBLnvilshuNKXF+YQqWD072y0BtyOWQggjMU20v1zPkA+qlSAkSbCiZIL3jnkJcWA8iRmEEJKQQJZqnIUcSLJ9pYARwGoH/Y387g7R2M9NNtlEbrnllkC8sAJSMohM45EjRwb5Uwhuu+02Ofjgg+WYY44JcwIL8nrrrRcWcpmAFXmttdYK8vzOO+8MVQ2uvPLKEMrjiA8ngI6MyEcxIRwQeJVSsLkIIEoGoYRQKSTOLxcqSQCj9+X/uESxfmHNRHhmK11TCCpFAM3dC4EjWxny0N1+xOluSPYwRllw4ILEZVzujNVsKMTyVS7QtlKRU+Yo5GHIkCFhjCAVvMO4FyH60R0tsoH5WM0EqycRwEwWwFzg/GHDhsn6668fClHjIj7wwAND4enrr78+dVZ+OPfcc2X33XeX4cOHB0ve+eefHxaxf/3rX1NnTIlrrrkmLPLuvffesKDAIolsp46lIz6cADqyIq4CsBVkpZRqJgKIRdLi/JqamoKiKSTOLxeMAJZbkXNfFCD3RfiiNHH7EoiPUuV5k0QlCKC5e8kwRLBjfciXkDAvIPuQR1xEjD99E81YhWikF8ctJ0pFsopFKQlgFMwtXPq2363taMF8Ngsui5v0ZB/euyTf5aRB+/IlVpVAIQTQAAEj/o93bMcddwxWQYhbvmBxRjLe2muvnfqkE/yO1yYT7r///hDCgwuYHWyQe6eeemrod0d8OAF0FA0ICcK4Um7gKAFEcaHUUfBYkCzOL2lSBExwllvo0Ndk4BGUDck15cnWXqUARKAcBJCxI5EDYLllZY+FIQlFSp9hPbSM1SjRgGRANrBAVbK4cTWBPqgEwbIdLaxmHRZc3u1osg+LnmongLS5rBbASZOk9s03pV5JWOOoUVKj8zoOkNmFvl9J1QDkHWQ8IXJR8LvJg3RQiBzXL98j7u/YY4+Vc845R0455ZTUGY44cALoSASVjAM0Amhxflh4IH3EpRQb55cLJuDLSQBZLWOxgqyw+kZJ4toupbUGRVtqUgTxgoQR0wNIGqAUUZKIPkOUaGBhxGVOgHu0uHE+MWmFoJqJJoS/0tZJ3utoVipufcaNmFDaxxgxX0joqra+RCYkRgD12er+/nepe/55BEDqwxR0odRwySUyYMgQGfCnP0m/vfaSJiVCAxZbTBouvTR8NxeY34USQCyASdYATJ9vjGm2Ocj4MzeuuOKKkDCyzTbbhPjBbC5jR2Y4AXRkRT4KACFSKQKIsIVA4NIjIBmFzuqx1AoMYsQ9ykEAEYYERGPZRPhBWKLJEKUEz1kqCyBzBkUO+UOZYMksBXLNBRYvzBcWDcwd3El8BsmGDJJUA+koZKeE7lDqOVoocinfSoC2kOyDRRCFDwgLgPwR48vCD2u4WZMqjXwIYO3bbwcSB8lTRpb6VKHXqL/rLumv5Lf/eutJ/w03lIH63jdvv700XH+9NJ51lgzQOdt89NFS++23qS91okaJYZMSIvnll9QnmVEMAUTmJrEPMJZ4+ird2kesbrpV0EAMKd6BaB+zUOAaLJId8eAE0JEIKmEBRHiR/cWBwipFnF93QHiW0koEWGlHM5ghShZ3WQ6UggAyXpAqSvIQ7J/u7uXvSSPONXnWaEwamcX0N8qIwsa2D26h5UuiKMUzJgXaVs73KB/YXIQE8D7w3qP8aS+JCCySsOQyvyoV3xmHANbqoqffllvKAF10QOIgeQOV4DbvtJM0nn22DFh2Wem3665S9+670jFwoLTPMIPUjB0rDQ88IM377y9NJ50ktSobDG1KjMffeqtMPOEEaZtzTmndaCORbsJCkF2FyhLiMpMggCQFQeqfeOKJ1Ced4Hey/jMBeWHZ/QbGnjlRaFmvvggngI6syMcCUE4CiHIizg/ywCrUsghLEefXHRDypbI4mEuSAGkEG9Yp3B48azmtHMyDJAkgY2blWbC2IfzN3WtzLmlyVKg1C5cj9c9spwT+D2HF2kyCUbFWp0LbVWow3tXcNmAElZ+QdBYQxPwS+gExgbRbfCdzjZi1JOdxLjAfMlrWdF7XPfec9FNyNmCttaT+scekQ9vfOmyYtOsz1GgbG+69V5pOPFFqtc0d+hyTRoyQsUoCx334oYx79lmZdNRR0q7zkO+FSzY3y8RTT5XxTz4pbeuvLy2HHirj33lHJnaTkcs7VowFEAKYlAv4UG3zVVddFbJ7KQl0yCGHBI/H3nvvHf6+k5Lio5UkG/bZZ58Q8nPQQQcF4vfQQw+FJBCSQhLCCXoghDIdz+rRK+AE0JEIIICltoQBs4ZhhbE4P5IfynHvTCgFAeR6VhIDWDB8VOGVS5EB7pcEIcM1QzA/Chm3D8+V7qovFQEExV6TOY7b0cqXkF1M30StTmyqn2nbrJ4G+qqnEMAoaDOLiShpJ1zCygDh0mcOdrdrTLfgnc8wn2pULjWeeaYMuvtuaVKC1AU9t+6RR6T/mmtKfyV/9UoCO3Q+TVZiM04XeBPuukta9tpLOswCrotZLHkQv8lK+JRp8cAh2aNO51qtkqMa7YfWlVaScSoPW/bfH2EUvhsXJjMLJYAs5JIigFtvvXXIID5RiS+xnizuSe6gvAuADJL8YyB+9/HHHw8FxdmvnjI0kMGj6KtkcLYeV+rxoh6zpI5z9PinHpvp0StQnW94D4IKyuQ1VZUAIhKXWLFqA7hiSgGsLrh6sbZQ9Dda5w43z3MqUNdZZ52yKy3IKO3JFquSD5hKJB7Ql5AN+jKTiwWrE0oQAlIOYEmhFAdul0LAc0GMGD/IOs+VLcGD50Kwr7rqqqF0SzZwHko9EwnIBBQFc4bdKZIGz8f8ZG5y4B4m4xiSy8H/M81L4tewJLIVYbUBkkS7TQFXE+hrXPHMkXzAODE2jBHWIzLNiStkjNjPljmZU36QcHHNNVL/wANSp/3Tod+bqKSlbbXVpFbfycZzzpH6O+4IxMzQtuSS0qrtrH/ySalTAgqw2LUo8Zt88MHSMXiw1OocaN5333BN0LL++jLpvPOkYxY4RydqlSQ2H3CA1CohpIUd2tZJShBb9tijkxgWAOQmFlL6MV+5SV+yAL/hhhvyHodqgj53rgenps1QPewBsQpuqgef9QoUNnMcjjSUygLINSEOrNy5B8oyag0DtoJN2hIXB1gAk3huyrrg6sVKwfPhysoWX8M9y20BLPR+udy9mWDyGAXTU0CbeSbIEs8HUaYYNWNqCQrMYUhH+hzNV/GWC4x3tbaNPoxL/KPgeViA2K4xvGNYdJmjr+kC4f1LLpG29daT+rXXlvqzz5baf/2Licgk7ozJ04VL8zHHSL3O5xol77WffCL9N9lEBuhCrP/yy0vDbbd1WuWUEP264ILhnnVK7pqUzEH+iOObpKRvnJK4SXq9DiWdjSNHSv/VVw/kr11/n3D11TLxllu6yF/diy9KP23PACWZxAIyIm3a/nH6ORbDQskfQG4hOwsdZ1zqScQAOioHJ4COrMhHMCBIkowBhABYkgCChoB8CESmOD8jgKUgoN2BexdDPOkzLHqQBEgEBBf3Rq6+R/mVk+zSlnwJYBx3bybwd46kCWAprpkNttOFJSiwHy5gnFnIvP3222FP3GrOVqSvuhurSoG5mC8BrFFZ0o8kCyV8/ZddVpqOO04GvPaazDbjjLLkl1/KeiedJMuOGCGD/v536acEr9+JJ8oAlTnNWKsXXbQzJk/lECD2rk0Jvs2mWr12jfZXy0YbybjnnpMJ998voy+/XD7WsZ6gpLJlm21kkhLH4M7V63ToPev0HgOUhEIOa/Rdbtl8cxmvJLR1yy3DNeueeCIQv/7rrBMIJ8BlPOnoo2W8Lio65porfFYMjAAWAuQWC5ykXMCOysAJoCMRYJ1LigAS50fGJXFwuDlZreOqyQYUFVaxJAloXBQaA2gEN1qwGtco/dgdKmEBjEueOM8KcZPEgjWMjFraHBflJGulBn2He5GSPWQ0YiGkNiUEkJhBlDDZ3SjTanpm2lKIla0cyIcA1uhcbN5550Di6pXc1YwfL3Xvvy+NF1wg/ddfXwYqUe+/9dZSr2QQ1+zkffaRCWefLRPXXFPadbHZoONUGymlMmnJJYP1bfy//iVjddyI05t05JEyTknaxJtuknb9O6j95huZRolgjZL8SccfL5P1nBDHN26cNO+ySyB3tWPGhHNBLWEfF10k9TfeKP1XXVX6KyE04gda9T0ap/NlMokQCRFz5GWhBJAEENDHCGDvEEoROAF0ZEU+FoAkCKBlV+IKxTVDAHfcLcAQZJWwABZCALFomlsUgptvwWqUXzktgNwvDuE0dy8JOli/Ci3mXCoCWGmCxXMRV0epIvY1Zt9S+pYFwGtKQFj0kFDCAqicBD8TuH8+7385wdzvbkFBMkbTvvt2FkS+555goevQ70zae2+ZcM01wSoXsm6VoIV4OnPNnnGGtO65p7TcfrtMPuKIYHUD7f36yaeHHCJPKuF7/r//DbUrv1V5N/6AA2TyMcdIeyoet0ZJXZOet/KOO8p0Z54pzfr/gbiIlfw37bqrDNSxb7j77s44Pj3adNGHRbFOr4c1sB+xgCoDbaZ2TDONTLj8cpnw8MMhXjBJFGMBhACy7zZHH8KvesysxwLht14AJ4CORFAMAUQQofhwjyGQLOs1H6tRJQlg3PuSHUqMHwkJJCPwnHEJbhTcs9wWwFz3S3f3WrmaQglEXAKYz/XjXrOcYM4ylmQxMhcoY0I/Qy6woDJXyHyshGWbvip0/EoN+iibBbBW5UizErgBuvhovPHGroSMkC2r/TpZSVnrFlvIxCuukHEffRSseWP//e8u16xePOygMXDOOaX5pJOkRvu+dfnlZbwubKYbNUpWGTYsLG4IRcFyi8wizvObZ5+V+t13lwFDh0rj1VdLrX5v0tJLS5t+t0P7kfi9xrvukpqJEwO5I04Ql+94fWfGKlkl9q91zTVD9i+g54NLWRcGrdtuywQOnyeJYgggixTi/6p1jpQIj6V+jk797PFwAujIinxebghgvgQMJWNuUFaU7M+KcM2V/ZkNlSKA3Lc7axwKi+3peE7aCEFiT9p8CG4UfK+cFkDmQSYCyPhF3b08V77u3kzIh6xVG6nLF/aO0WeQZ+IFcZtjPcVayPsBycAqPmbMmJC5Wo5n5h7ZSFbFwJzX589EAEnYwLXaf5llpOHWW7uIX8cf/iATlAhOePTRKbJqA7TP2xddVGTQoHDtev3eAH0vm486KiR5GOqVpPXfdFNpOvJIaXjuORnUv39IJEFerTJggCx56qky3yabSD8ygPU6P6+wgvxdP/vh/vtl0qGHSkekQkDbkCGBdBIn2J5KFBG9Htm9dU8/LTW6SAzJINddJxO13dHvJg1kUZyQk0zAi8FWlL2cAB6sRzTFmTRtLIADw2+9AE4AHTkR9wVHkEBKMhGFTLA4P9ygxL4R50eGXqGopAUwFxmj5AQFgymDwr6zHMW6TbqzyCUN7pdOOtLdvcS2UTQ5CcQlgPkQoWpUVNnaT1sJCcBVTHgAsYNYilG6WI/pd8rysGgq1TzguuXus5pPP5X622+XJrY2U8LbhQkTpOGyy8J+twNnnVUGb7mlzKXkiC3Uat98M2yNRsKGuVYN1Ngb+8Yb0rrxxqlPMqClRepvuilcu9+ee0qtvq+gTRcyE0eMkNbVVgtuYDJ+G//615D1ixu3eccdpd8WW8gf1lxTpn788eBinrzBBvLlPffIBxdeKL8MHizt220X4guJB2xXct86dKi0qZyjhp+MHx/uw24gbPUWkkG0z1v0miEZZLPNmAjhnFIBeVmoBTDJItCOyqH6pGIPgwrx0i/HKwjce3EeEYVB/bbVVGDm2pGDOD/cvdS7w81LbbZiLUaA2EFWpCjNcgI3EMIQYhcFz0nWJ0QXax+FaZOyqEAqcROWq34c1r1nn3021FnEHcn4kcRAX+frqo+DZ555JvQn45kNzDfmJiQlDlHBTUdmLke1AELHOGbb7ioTWGwwp6yWHf2AIrZadoVadNJBnT3GtxR1E9OB9avxtNNCfb3oSLI7BskPwaWattdtOpBQ9t12lSkTL7pI2vT7tfocTZR0UaKIO7Z13XWlbe21pUMJdoMSv8Zzz5Xazz9PfVOvM2CATNTzW5VUduHXX6Ve52TdY4+FnTtqv/su9Qc9X+deu77fk046KezCocJSau66S+oPOUSadZHE39tUHtbrOxQFrl6seySpQB7b9f/U/mvdcMPUGaUHYRvE6CKD88XFF18cKhc8oGPWk6Gyo09zILcAOhIB5AYikC1eidWmxflxHjFPuFGSIg+VdAFH7xt9TsqBQNKiRauTAH1WKstPJljbqcaPu5dYxqTcvZmATE56XVWNcr6QZ6S/IWVYzXEVs2sCFmXGxuLR+D8LkGJA2/KZszVKJpqGD5emY44JW51hWesOwXq33XYyQAkw+9umjxC7ZTSfemogf+2zzy4TlayNfftt+XLkSPmfzr9AovQ8I3/8hDBOYAs07ad+1Ohbc02pf/TRYIVruO8+6bfPPsHNO3CBBUKCRpT8tWy9tYxVQj4F+QNKFrEiTrrkEhmn7/Y4JYMkjbQtskggb3Uffij9dtxRGkeNCpbBgbvt1kn+tA38HfLXNuus8v1hh8k3m28uE4md03co7Oahf5+07bYyTolqOckfcAugwwmgIyfyUZwIk3QCiCLB/QlxwHJRTJxfLlSKAKKQscrwnATsR5+TuoWl2JgcxVzOGEB2rADEMSbt7s2EUhBAUIprVhL0E+WRrLAxtTJJvmH+kZBjmeZYGvNdMHB+17uv/Rbq0v35z9JPSUrDhRdKjZIegNu2GaK14orSePvt0njRRWGrs+Am3WWXEFcnP/wQzjXUvvqq9NtySxmgi6OGBx9Mfar31LaPv+kmGa+fURevfdCgQLImXnyxjHvjDWlRgkn9u5918dhKAWKVNbTQJBQ/KfXCdSnxgtWOrdUmKykbf++9YQ9dduaAdLHnrgFyyd8nXnmlSHeFjZXMNVx9tTRqm8jcDRa+RRcNySJN55wjDfffH4goIB6wfZ55QvvHK3FtPvRQ+YM+U7OSJ9CiBGr0KafIY9tsI29oPxJPWyxxzwfFEEDmlBPAno/42t2REapUepdWSQOELq7ywAJBJiNKCLBKZFszXHXUQSsk4zUuiInCVQlBKSfYJg1XL25vhDfPiZuxVM8JqBlH/ORaa62V+qQ0YNzM3cscwJpZSuJnoPg320zh0swG2pOPC/jNN98M83LWWWdNfVJ5QNTYIYQakEkD5R51FSOm6E+shyju7hQ/7r2FF1pIpseFesYZUheNyUuhffBgqdHFHYQKdPTrF7Jb65TgWSwdCIWTdUHUpvO1TmVE/dNPd36uByMHiWJLM+rlyYABYWu0DiVl6QkQFHMO261dd53UphZAfLd1661l0hFHhOti7avT+QNalPhNPuQQ6ZhjjvA7RLb+vvukSYkY7Qv33XtvmXTccSIDu4nrZzeQ888PBJfSMelow8o/ZkxXX/yivzfo87QqacYaWffUU9J84IFdFsfJu+8uk0aNEmXwYUs2GydkJhZdxoqDMIgkvQdRsEggPCXXe5YNu+22W8heP46+68FQ2dGnOVCffvgk0NsJIIokrrUJgcIuFpQHQLElHeeXC1inWJWmx+KVEhAQSnXwnMRLYYkpdEWdDyCakKRS7X3MlMYaAalGAZGZimWzu/15kwL3wsVJXFs20EZc0fkQQMjPbLPNlvqk8iiKACJ2Yo49fUWtQeYpJIP5w7hOr4p/en1X+6XXoNTzPzj3XFnsnnuk8e23Oz9SUtKipKVdyVT9ww8HktWVaavtoFQJ++LqBAkZtSRxQMYCIXv33XCewYgfaNO5Rbxe+1JLdSaAEK/30Uedf1t66c6YPe2f+jvvlAbKukSs/FgHJ9xwg3TMP3/qkxTMihZZrNR88400HXZYcDWDNl2oYZlrV2KaCzXaXw2XXCKNetQoUQO0n/1/aV+9Ejvq9hmI5WtRYvzqnnvKEpRvUZmEW7xR2xn+rrLQ4hMzAXkLCTRCiOyNxngm6VFgwc6CuZDku80220w22WQT2X///VOf9Ew4AXQUBSeAv4FitnQHRIxtv7AGloMwAMpl4IIla7LUwPoEQcK9RqkOLHJrrLFG6q+lB8SHRIm12U0gYeuAJSZg+YWEmTX3scceC3Gb5bAAopiwpOZKQGCeQcBBHBnOrhso0WojgFhYcd3Ghn4nuB91sTXxggukQxdYOglSf4yHiUoEGw48UKZ6/PFA3v63zDJh54v6jTeWQUpIm848U+q0v0CHjjfWucl6foeORyi3csABwcoHcH9OvOYaade5kg01n30mDdddJw1XXik1P//cpXTa9dptG20USrE06DVqP/00fE4iRs24ceH/mdCmMuUrJXOD/vKX7p9d39X6K66Q5lNOCfcOLuFDD5XJfFflWsPf/hZcusqspFUXVBDOdu2Pmu++k8YLLwx/o3afAXcxpBPCaoBcNvz1ryGpxBJE2vU+rTvvLPUPPRTiD7usjSkrZxwwx5EtRgYJxSA7HCLIXEb2FMNfSOxCXhZSrJ1kv0O1H7fbbrvUJz0TTgAdRaG3E0DIHyQwF+gC3IRklbFCxQpXTEmXQvCNClmsgKVwp0WBIMatDQmEIOGuKYc7NgrG48knnwykM6msT3P3QqKxZnJErbZPPPFE6FuUTqlB2RwSTIx8ZkIhBBDFOTjh3RSKAXMJK2ssAvjjj8EK1XjZZVIT2ZpsrC5CQgHjOFAC0XjWWZ3WrLRY3XS0Kskav+uuUqtEqQNLrJKgRiWGuEGxwpFFO+mEE4JVMBcJgzA2Hnus1Ot8tVHqSM2hGiU3UbQrwYRock3+RsZt/S23BLJLPB1oWX99eUeJVP2cc4Y5WvPRR1KjfRO2YIt6GfT9hFQ2KeGy+1CKZdKIEdL25z9Lw/XXS4MS6Volw+kIO4RoX0X7KBDHo4+WyQcfTM2r1Kf6fM89F3bvMNduu7ZrwlxzyQASYVIgS3jipZdKWz5EPwOY78wZCCGLB95PswxiJczHy4L8ggCSSJSrakMm8O4RB3yhEuT11lsv9WnPRF8ngKUJLnD0GeCusLpkCCGUdrnJH0D4dUdUiwFxOrgRybLEikQWLBYq7gtJLuc6wKx+CPFiQbstuxcFg0LIVKSaeyZxvzhAJid9r2qV8922C+J3yikycLHFgmUO8kcx4Y7U3thYqgyUFKlT8gxZmwL6HcjbQB3XJgicEhtmK2RqnBL7ScccE5IjQPuAAfKNEr8nrrhCnl13XXnjiy/kf/fcI/2U/OOehfy1bLBByFpt2WmnYNlj6zPct7TVUKuEu3mbbTrr86XIHyRqkp47Vq85dsyYkOwxeb/9QtzgxNNOk3HvvCMtBx0U4vGwAta+9prU6eIqJFMo0QnFkZUQTtL/D3jvvVD/b6C2e8Dqq8uAeeeV5j32kPq775b6m2+WAXgfDj1UoiSTuL9++hkJKk0jRwbyh0ub7OIJV10VavB1EHOn5CpK/tqWWUbG/+MfnVZDyJ++M3X6vlC/b8BGGwXyR39OVmJKkecvLr9c/qljRs2/SYcdJuN0TIolf4DFNfHFuG2xxrONJO8pngjeX+QTnhBkVXcwWVloyIpnAfcO9Gn2mwRUgfZqCyCKOFNpF4QMFiOSIMxihAWO+CKCg8sNXJfUAsQ1kSQgdzwXRya3NqTp6aefDhbAfFbgxSIJl2w2d28m8Iys+stB7rGoMp9IGsoGXrt8LIBvv/12iE0lRrVagCXno48+ChnjvwPE79JLQ/Fhs/hRSqR9pZVCckPTUUdJ3b//LePvuy9YnSBnWMsgS5AnChi3KsGr/fpraaTIcIQItSlZmogbU4lk5wdtndazH37ojOHT7xNisIy++9MpIWvSBQKYrKTzKyWLDZttJtMp0Wq84AKp/eqr8DdA2ZM2ahrqNeqVWEbRqgsmtl+Ls59t/R13SPPBB0+xGwfFmNvI/l1zTRl///3yB110hs8Z+6mmmsIqGkVw2Soh4+/1jzzSWRLm229DoWdcwa1bbfWbRU/7Aatg08knhzItxD2SINKyzz6d/QPx0+83nX76FHF/WEMhkSSjABZUFEovZ0IactdcxbzXyAUsgxy8s+mhIpxPrUfievMFOoHr4gnBUt+TobKjT3MgJ4BFoq8RQFaOkCEKIKcTIraqQghBFMqNpDNjGVbL8MXNCkGCQKSD/olTADtpFOOShTjlcvdmAu6iJZZYImMfJA0syhTOzpWxmy8BJFmHxIeqJYBKPurvuUcabr45kD/IXU0qmaFN+wICh2XKEGrM6XdadQ7UqSI3Fynuy+h5UQRCc+qp0rLrrnSg1N91lzSSdPHhh4G4ta63XjggPxN32UX+oHM/vWdblAS2K5loUpIBWmeZJRRApvxKnZ5vQCjyXdoTChxvuikDFf6WDdQS7LfTTqGuniE8uz4rO3FEQWZx65ZbyuTDDgsuVrKLG/XZ6nTucBdqBAYXNeQtSn70fSVmL2QYR+Z8rfY3Nfzq9L0AuKkpH0NyC0S5/t57QwZynbbREOL6tJ8mKSEU7VsD8pEFMha6SgAZnZ4Bbokk/MSSSFIQiyI8GfkCgsn7ybV7uhXQCaCjKPR2AmiKlp8QBrIWiXsjMzR9pwbiAFn95hXUnhCiu1UU+05DJlndEnQNwcXlm+2a9AsEsFwJEoannnpKlllmmbwscrTVsnshcoxh3DbHKc2SFMgmJ1YvV8KGzUsQlwDSVyiuagEK+uP//EdW+vLLTvISIT4AkkP8XXTnCSxauHjTY9dadeFDnTsSGNgirSFVk64rU1fJDtazUJZE+46adbV67+6ANXEyW6RpG+vZ8syIn47Nl0rW3lcCOkl/Zz7N+/TTMhPXTVnj2IqNHTL0j6w6wh69uIYD2dS26ICE80ItwQMPlDp9f20kqQk48ayzpA3iqKhVYob1rV7P+Vbb1KLnT7fccp1/e/11ad5vv1CXD5DIMVFJZ0echB8lu81KEiHDVsIlHR26wI0mgoA2lQsTL79c2jMsdnHJ4jkgkanS4D1BjhkZRLZROxIZDpFjEZmvvITgslc1yWjl9HqUAvrsxSmLHo4+/fBJQF+wXk8AsYRBiHjhIUTZ6txRZgKCWMiqslgkkRiBpRPhDUnCUkQsXJxrcV+sOGTolQv5WuTM3Us/Yc3MlWGbCXEyc5MC+90yx3IlbORLAN99992g+CpCACFhaS44Pht//fUy8OyzZUDKxWpoV2JErF00Eza4GfVcLFJ8t3m33cLet8QDkg3cnsp+T7dkAaxhWPXS0aFzZ/IBBwRXcd0zz4SMV0ghvYl1a5ISuBa9p5hlW9uEhY1kkjay3hsbwzhMVALX9Je/yDRKEMH4WWeVr084QfptsIEMrK8PJVBIHqn94ovwd0A8YNvKKweCiWu2q5agLkiC5W6vvRjY8Fk6urap0+82nXJKKNMC0W3XxcmkM8+U1i226PyuXpMYyZDEkk5U9Fkazj1XmpRkWt9AuFt22CHcGxdvIJy60KL8C/1BG0MyyOGHh4PnzwRkIMQI+VFtQIZDBCnODzHEa2GJJMiSOISOOGjKwEAqezp/6usE0JNAHDkBKXpdV9gofqxcuOWyvTOQpUzxguWACS4Lbs4HKDGCpwmkZoXM3qyQpLhEknsXct9iQExPnPI8kCSsX5AqXPYkeRRC4hjzcq114tyLv9PnHPRD0kkjsfHzzyFRAVI0BVTRNh18sAyceWYZqO9MsxKL+ptukhpVmrh6+y+3nMx04IFTkD9q05HoMHnEiC7CCCmhaPDYDz/sJH9AP2tPxZe1Dx0ayB9uTbY+668LESN/YWcNJZljv/22c/uyI46QNv0eMYOQrLFKigOR0b5ruPNOqUuRv9allpKnL7xQxitB6iJ/ANKmi7s2Mj8hPzoG1OabYdiwQP6wMk7QZ/pSCd3388wjPx5/vDQutJA06z0gf+3aF5OVuGI9g+Cy1VvDww93Eisdc1yuY7U/KJeikyB109+Dse7/8sshwSQUZtbfW7baSsa/9lpwC9Muyq/0X311GTj//GHrt2YldfQ7/d9w+eWdO5VYrJ/eKyTEaB9Pojag9hEJLpDJENOooI1t2tfjtc1hfLKQP8CcLEc90EIA4UOGc+C+xQvA+0ZICPKPbHlkIR6VbCABpByhII7So0+z3ySgiqhXWwAB8Sxx6s1BnthBgPp0lUAhlrhoIgTCkESIfBeFcQoXJ43uSqUwLbFkIthN0Bfjoo6TmJEUbMGRyVrHc1nWNWPGT3sFGTfmqf2MghJFxEvOqeQnEfzySyjJQk0+3KIdSghCksIGG4QYvmBZyqFEo4AQQSrayWRV0mi7brQpsZuoRIw6eemgjEnz/vtLq5Kv9llmkYbbb//N3Uus38iRnfFvueayvteNZ5wR6t1ByMgsZneK8UpW/0/n1zC9dh39mOEaoWjzkUdK7Q+d27y16fwnyYOtzxqvvLKzxIqSLTBJFx7/2WILGaOEbBr9/6xKmmc/+mipTxWZDlbMa6+V9jguU+3rn5TMza4kE7TPNlsoQN22zjp6oc44SuIazR3cHUIxaSXmHdr3XdD5FLKbjz02JKJgQZ181FEymQzlGMSuGhOO0kEM97hx40JYB+AdiiaSkMSCvDDrIOETJhfvuusuueSSS8KisqdDn6lPcyC3ADq6Rdw4D4KLWZ3HsUyVAqy641riWOEiqBFikCjc1ljICpEH9E+5nzmXBZAVOoQNIY+bmHidYuMTuV+5rGyMgZE6A7/b3LJ2MN/ssDnK35kDWD75GW1z+jULghICkgFCWRYsSJA/slD1frgLKT3SfNJJgfxxN0qcjHv4YZmkxI5dIqKYrGTp3eOPl/HPPCN1o0dLf7ZRU/IHEZuo9xhPJq0uqigDU8+euVGXcOp56557ThpvvTWQv2At3HnnUGalZd99c5I/4u2woDWde25naZeNN5Zx+i5Qg69DCcDCN94oUyvhH6ALByyZuEMhjHWPPy79lbD1Gz68i/wBklaa995bBiqhaDrhhED+bNeLyUq+Zz/1VFly6aVlXm3rnJtsEshfuz7DT2utJT8oYYQE5wSk7NJLZYBew8gfltFxL78cduWAEPO3frvt1rlHr47JJB2Lse+9J+MfeiiUaOmIxMvy9wnXXCPjX3ppCvJX88kn0k/7olnJHuSPUi7jX3ghJJvEIX+AORhXZlYK6VZK3jkKQrNAQl4gD9nBCTmJB4EQECoPXHXVVSEW3C2AvQNOAB2JwQRKpdzAcQgghIAgZqx2/B9Bl6nuXT6oBAHknlFyA4jvQVizIwtklmdLKmavkgQwSv74P23h4Dx+0hfpZJC/8R0jg3wvFgH86qtAeBpPPLEz3s3GFSJ23nkyAOI3alQoPkwpEerH4bacoIQJa5SB+nLjn3467BpRT4bqtdeG8iOGSYcfLp8rqZtaidNAJSDBCqf3atlww0DEcEP2+/OfZYASJPbi7bfddjJQFXK/LbeUpv33D9Y/YBSvTZX2uDfekElKuCRX6IKStmYlh/2V5JBZ2z7rrDLh5ptlorZfO06ajjtO/qDXWuDOO6VWyW0oI6NEqf9WW8lAfb7+W2whdSm3Nd+FZLUSy8c4KNGDEId+ufxyGaektkUJqQ5KILYzrrOOzIxlMPWOsp/vICW5M5KIoAuVsUq6xj75pLRH32EdQ1znA5SYNB91VGftPn3Px2+6aYj1I2t6wNChoT9qP/44xDVS1zC4t5WIdmgbGUPKv7ATCGjRvuTvIVYwhRod934bbSQDll02uKaxok5U0jpeSU8sy2QExRBACBcWuFIjnQCmg/AXZAgWQuQIpb1YWGL5O/bYY4Or+MwzzwyW9VjvVQ5cqsQe7wLVJKgggWyOg1tZ+Oh7vmkqUciRP/q0+TMJ6ORPwKxQ3YDQxVX+lCchC7icCREGyoewgiWBIBNIUiGZBdKAyzapjFYIF1bEciYYRN2kTEGyr8nu5Zlw95LllyTK+YwUtMXlhAWCecfBMxrpiwsjgCQxkdwT5oYqvf6qxBqUnLWpUmtTwtW6+upM8s69YpX4RLNBKUDMvq1YzMzihauWeLpQQ27CBGk67bRgnQo1+HCjksSw2WYhPi3supFyBZPcQaHhujFjZPLmm0vdI49IXarUiwHiEcqaKJEPv7OoUeIEubKt0qKgpROVhIbYt1x9o33RcP75gWhamZjJw4eHtpJsQk2/BiWptt/tT/qMTUqCyYCtv/9+aSBLVkkw6CqxErUyat/UP/NMSOpoIwTECJBeu2nEiOBSjfYr8Y6tm2wSCleH0i3ad4ZJ2kc/rbSSjFciOccdd0hdqtZg+LbeL3odQxgnJQ8TKcliFj0lo7hxG6+/PvzKt0KsIVUKlOyRLUw7Ifsk1Nh1IbQTddzYZo95l2nOEV6x3377BcKGvGNHjOWWWy4QP7LYWVSmyxcWIiRvYT2D6DCfDZC+v/zlL3LPPfcEAkkNQSoarKtt5NxMhJK2cb1bbrklvJucSwhMnNjlYpKiDtP3hPJYvKOE3RAWAokrZFeQ2267TXbcccfwfeKTL9eFA1ZGwnJytY2SY5zPXvOEuNx7772pv+QHHds+zYH69MMnAX0Jfy+NehnyIYAIJFaLlagPZZav9NgbYl0QWMT7IZj5e1T4FgsjLKxiywWKXqNIuC/CEqVRSHZvXJD5x5hCykoNrAsWr8e8Q0bbkQ+IaWLcqXm2oBKE2W69VZquuOJ3+8xCaLAS4QoNvyvpItmBTFCzGoF2Hd9JRx7ZSfz0HAhcSHBQMgAgfZOOPjqUO4kSv1xAeLSp0ibOj+LJdi3aQMFnihV3aD9A4GqUvPWjCHEaafxVvxNc0PfdJx26+MHt3LXfLMSPeLbjjusqrAyp+3nTTeVePWf5Tz6RhZSEWSYsu178fPDB8nddwK262mpSp0SZuEQjn7iKqevHvsAB+r32q6+WH7RdP2j/fazPMfUOO8gyW28tA7U9YTeOSIIMewezpdq3K64oL/zjHzJkyBCZV+cxzx4KNSuhyFTUebzOh58uvlgG6LN9oD8X03Y3adsg3O3a1jolNMwO+nOsjlOjXr/hscemcFN3h5AFvNtu8pMSwutvuilYuyBmq+sCAXIFIWMujRw5Uu5XUpwu+nk/qEMKKdl+++27CAzy5zodg4u13WTfAt5TrrfmmmvKc889J9crSc3mRSAOj7hqCBb1RnHV3qd9e74SemRPFMgDiNEVOs8heNnA9yCNuWptZsO+SvyRoaeeemqID6doOLKnEPkHYcXl/Ne//jX1iYRrYdU7TRdWmUA/EZ+66667Bmshct0JYGFwAlgk+gIBxIqSTTilI84+rqVCOhGj3RTaZbVITTnahYswaeB2xeJWzrIPEDKELyQHhcMzJ0lq01EuksvrRGwmShMFivKLY9FoUwL0y2efyTQQVO0Hxvzjjz+WaZQATT7jDFn+pZdkqtSrOnbBBaXuwANDTbqGO+6Q2h87tzDjrxNU2Z+pC5gmnb/rqLJd6Pvvpe7556Vd506wsikxq9W+JwGi/uWXw/daVRlOPuUUqdN2h6SQCPH7Rs+/TPts7hVWkA2++06mg+Ck3qUWXay8c+yxMj9uUsV3334ro//2N1lMF1yzKYlSBhw+n/TBB/Lz+uvLvKl6gF/V1cnt+vt+eq0GnQOT9dxgwUoRQwjeT0svLU/q39bUxcG0qfbwfNkE/ts6tu/p8y1CfT3t87dVqa/+0EOdhakV3+v83lWv8/6888r6eu8NtZ+WgSCffbYMSiPUAAoXpR+4hSdpH32hfXuR9tE1xN+l2ktpKcgN7895qvQP+uor2Vs/500lmOR0PU7Uo0bngcW0DlfitBgJH0q2a1Pjmuv5ntfjdNX145QgL6nX30Z/p4qgvTFjdCw+VhL7vM4biB/egkyAL5jIxyq3lS4GkDN4PiAiBtyrEDHIDFYu3KcA4keoBkQyEyCF55xzTkime/TRR0O9TxIyDLwLvBPE4QHkznbbbRf68hEl0LSBe/P3XIXpWSzzfhUip7fddttAwLBYFgMsosQm36Hv4J+pT5nCQQcdFOQNxDgTIODICKylu+yyixPAIuAEsEg4AZwS1OiCbOUq4lsq4NZA6KFIrGg1AgYhnGs1XCywwKEMylH4FYsY7icsWzwbxaCTdvdmAgIXy8O85l5LGLxGHDwfyoyi4ihh/o9lBUWF8kxXah16/iv77SfzKlH5dqqpZE5VBqN17v2sCmrQJ5/ICqpILfQfW8lIPe7XY30lApfonJ4rRapa9bnOXnJJOU4VycI637fXz8bo8Y4SyqU23lg23HBDWU7bMGDvvX/bbUKPb1XZvqHnr6p6pF9EFHyt8+FQvf7t+n+IzLF6HKGHUdmxgwfL56qwv/vhh/CuYM2BFEHqAZaZ9ddeW3ZVkrW8EtV0ByB2LXoiug/M5EUXlfb//U+aU+QgCqL2Ri2yiDyp78S62q6N9Heqdf5LyeIIJXZRVXuY3vt4bdfUWPf094v0oP04gLnfPnocpoeltFDd72w9mP8bKYmdTcfAFAv2TM7/ftllZR7tY5Q2BAhg4cUihnwBpIFcpccfw2/6jPrevn3AAXKnvteQG2J3ecOO1kPpcVef0Ov3aH+/uM02svYLL8jqSuIgdlSJfFaPk/VoVQJ+ww03yEzTTitjjzlGZrzqKqnTfoBaHaTH3/SIgrYdqGR46NChodD73UqwCbEwUCrqb0rW8TgAngHXL+3kXEqpRMHC6eCDDw7kiYUa1zzxxBPDewwYb8af60aB94WELsgg12ZBC7D+77XXXuGgID8WNK6HSxp5wPlLpvZ3zgSuyXgV4qmBrGN923333VOfFAbeceY+RoPoc2NZpG+R3+ng3K233joQRIiwE8Di4ASwSPQFAgj5MyHdHcw1WQ5XYToQpghADhQpAi5b0eokgaCij0q99RNWBHP3QsY4iPcrB4xcJ733Z5T4ZXL3YgmECHJgCSG21Mjgt8TxqQIcmrKmGEFJx4fa7i+GD5f3lFA8ef/9Muypp2Q/7UMIBATgZFWYf9U2zKXzBoK4JV+K4DU9oInr6mEWIyxTmeySqH2I3m16QJ62nm46uUSfa7qUBejveqysBwVQhqniZnHCO8OcBczZL5Tg76DE9yz93SJpudazqvCWUkXZT5+7KRWPB1CTkJinlAQ16HNdrP/Hpsjztehnp08zjYxUYhgVVBRMh3DwrhIfCWF47a675KjXX5fOktIitOgWPS7TYyElwCO1z2ej/EvK4veJHjjp/qELrhuVwC566aWhrh+YpOc+qIuTk5WMoqyjIGYO6xHuT7YsO/bII0Os3/E6BmwqOamxUdpOOimMLdZcUKPXIKljkD571M79sc6T69ZdVxbYbrvglmRRdOuZZ8prd94Zxq1Jnw+LHgS+VvuZBBjbzo3af9/pNR97553w/LgyiWeD+G2++ebB2gZZPeOMM+S8884L7x39xf+xWGWSK8xhwmAgdFjv8A7gvt2EeEcdC0A2LVYuCBDXwKV63HHHxcrUh4RCAqnJyvvP//k+hA7gIsbV3F0JGlynWFMLWRize8hJOj5Rq10hMAKItZNrGk455ZRA1o0cGyhcTXhRNN7QCWBxcAJYJJwATgkEHtlc5d4kHHcCbg2EBKtt3KK5stySBEIYooJwKgVQQpDMb7/9tsvdiyIoB+k0kO1HfyZp5eTV4RmMBHaX5MEYQwS/1Gf/36GHykbaJ5Aw6MhDSjZmVGVeR1mQu++WefRv47W93+22myx12mlSq/+ve+ABaTjssLCtGXhpjjlkpx9+kHodOyN+kAsSBdo22EDalbw0KCGKtoi3AFVLr1uIf5vO91+UmJ6gz3Ax5U/0s9mVdD6h78CCSpwBGbM/qdL8u7Z/o6OOkh/1Hn+IiI5ll11WjtbP11Yi2KDkoDFVQ48zXoAQXXWVLG3Zjvou8pyhPIs+5x2q9I/VZ5r9gw/kcv2z2Whb9PxP9JwD9fkhHQCFC/GLKtyQAHPIIaGos9USTIfthAEgnKfqcb8SkFEnnyx76OfNJJMoEe9Q0kTJlFA2JWWtxcoHwUKhQ4RwjTLOLGYu3HVXGfHvf4tVOvxF/1Z7+eXSkYqfo8h241lnhdjAKIiR/IA4Q+3TqZUYs+B7QMf3ISWWZynZnkfP+Vn7baz26VRKJJvPPjskunTtGMKuKpttBgPovGAGIE8gV0ZE2P3ibP0elqdswGIHuQo1FFOEzwBROfroo+Wmm24Kv2NNh8xMMRYxAdEkzg93KM9OzOzJOhZY5rrjNLxrkFTi7+KQzij4LosWEk/+9Kc/pT4tDPm6gPkMq2a0X+kHgOxARubroXAC6CgK+kL0egLISxa3tAvCkvPLRUy4l+1vC/HESoUyLSeot4d1LpfLpRDwbGT3YqFJz+7lebEakS1YDpA9jaxMwuLIK8PB83F0R/yiePOUU2T2M86Q2ZU4guemnVa+OuIImXrIEJldifK0SroGKUGOKolaJQZNBxwgNUq+AAkMXxxzjJyoimd1XEqcE/6i3EoVUYsq6Y5FF5W6Bx+UBhIgUi7Vcbj7dJwHqOICk/U+ZAPfpm0fcdxxXXFjtw0dKlsquSExA1B/rmXbbcO127T/pkrF9t2qpO6pl16SDZVsrgUJGzEi7MZh+FCv+9See8pWp56aMQ4ylKtRMkeBYp7N4vUoRzP+zDPl0i++CG7B6ZXgzqWE6Y9Kmg9SgtTlRtc+bLjoopDFbNm/JEKM23JLeXPttWVpHZuQmPHEEyGR4wMlVMfrM92h562nbb5o//1ldiUd9dqHgASSiRdfLO2Rd79G2wCxJLuZHUSIpZysz3rB6afLPOecIzvpPLC+R5Cyqwlb05FpzY4ddm3+ZjPkR12EkNnbrkQE/KRjcvPuu8taShKXCp9MiTZ93rqU23myjsFkvW/YHi4LIFS4IS/SvmF+YnHG6rfRRjjOc4PvYo3DEhed03feeacco3OO0BQ+z8fqlw5c4WQgU5sPQDax+uG2jgMWXZArrIhx4muj4L3Fq0LFhSRkDySULGeIsAHdwUIhPQkEeYcsjIKSNCz6L1ByTyxpvjHeOhZOAB2FwwnglMAaxo4guBdKDTL0ICYMAcQESxluBcoxlBMQUKxzxOMlhai7F4GYbnUgwQHBV45+Bkm5uRkrs/oB5G8cGfzt66/LN1tvLSumyNiXSlSe2WwzWUwVwLxKkhuUiDS98kooLvyDksGxq68ujUriZlayWKdkOR24N1EVRj4mb7ihtOm1OlSpBdJy2GGdxZcVlG7B6mZlUCAxZAO/p31/mBJAXIdgNyVelyrZavrpt2QACjZHS5xQroTM4hp9nz65+24ZpwumhZW4kcVqwKF9kc7nDW65JadFgwLR1AjEGlaj7Qtbmu2xh7y11Vay71FHybjXXpNj9DwSHqDDZMy2rrlmIFgyfrw0jxwZ6hkCRqN1rbVk4mWXyQ/atywwcNWSrHCSEpUnr71WPtZzZlAydK7ec3OycPVn2EpNz29bcUWZrOSS3VCUVUiNygH2/21g+7uI9+CXmWeW53Xerqr9FHXXUzQ6Y5kbiLzOlVDoWknkz3qPF5UkrqKkp0378J5ddpEldZyGRsRwu543SQkcyThNSjTp7xbt938pYR2jizRIFy5a3qlo/Cwy7nQ9HzIFkQMQEeIzc5WMYrcdiAn9BQGhRBOkjDlOtvDhhx8e5APAUoeFa++99w5JVZlAqANZwchSavDhrsf1jBzGegtxxOPAc+CKJRYvnwQw5CSxdKuuumpe3wOQMAgxVt1CMojTYWVgLtN5hyUUq+aVV14ZPA4Q2p122ilYrbNlBLsLuDg4ASwS+pL3egLII2KujwMsVlhCWNWVCghnLI1sW0TCB4ICQfbNN98EYpQeSF1qQDohgaxmi0XU3YvyJ5Yyk5DG6kj8FBmR5QCEgLZRtqMQMIfycfcaWlXhvKpKYLmHHgrxcFCJh3Sl308VwvQ61+a57joZ9NZb4dx0shVFqyrw97bZRl5VUvVH7TfL1/5ptdWkSYlUB0RaiUq9KqKGE08MpWIgNhQx7qrjpwoPC9U4VVhnvPSSnKdEAYW4pBKwh1VBz6Tz3p6odcEFZfxFF0mHErn6p5+WxocflvrHHw+Fkg0IjmgP4My6vqlJ2pRYbdmNK69G539/4tpSSSyGL/X7d+g4UQnTXNoAEhu9dxST5p5bWlQR01bAe4UCxjWHu5JxA8SyXbPffjKzkkt2/siESc3N8t/ZZ5dZlRCZyxjS+8NMM8nUes1oCg9//RFL6+WXS+3ii0uNkgoKNtdrX9W+8460K1HEPW+YNHiwTNpgA3lX2/u/0aNlPm3fQql7GF7Q9+XLESPkT9o3kC32/q176ilpUXL7rM6Ts846K1jOiJFDRmEF473FRQ7RY1GVDt4xYs7Ifmb+27iQ9HHuueeG5AyA1ZlFIOfghSB2EGtdJliWMOVluDahHcgvkjmuvvrqKbKEuS5tICGKcQF8F6tZIVn5LNCNpOYLLJiEgdCWpJLPeA6KSnNt+g5rq7mXIanIQMroZIITwOLQpx8+CagycwIYAS8x5KSQuJbugCJCoHIQrI3JP5oViuLCalZsbEq+oNAwBAmhXChY3Zu7F8sEQjaXgC2F1TEXaBdCP984RyN8PB8H8taO7vAe5O7gg2WelPX5jf795X39fXod82lVSSydIjQ49/5v4YVl8iGHyMpKmgcefbTUvvJKEG7s0fvullvKBs89J5+nMjMHDhgQ3Jdbbred1KRK91DapfGAA6Q2lbDQru2z8iIQP1Fiw04TBkopP6TH3NqWlZVw2dO0zzCDjL/wQmlZZ53w3FioUV7M0/WVhKysfTAVJWjSskQhtk8p4fxAFy9z7rWXrKTEIDq3AYTjYiWr+774oqwVuSdE9Q2dM4voPCSJIor/0/Z8uv32sszw4TKHjmHz8ceHcjWAGMm9lPTeqP9nxweICGVIHlJi9cI118hrEcsdM5GkFDJ6o8sRXNo/bbWVfHzPPTLXu+/KDKk+SwdjhK3RdpKGxpDJS29j3eK+EKw1lIzVq1KfQZ/Rno/vcs9czsoxOl47670tagxLHHKAa3L9C3VMSLaJA/oCsoublHJLUQxWEkpbGVeSFwBzGZKSjeyRpcv9sZxBFjmwGkbBYo932uQs7z/kh1g9FoQGZMIJJ5wQsn/ztd4ZIEwQyULkFfIViyQEtdD7VxN07LoXRL0Yffrhk4AK+V5PAAHWnziAhCEcWVknBboYsoPVD6VIEDJCNR0INgQ2hVvLCVzRCNRCiSeWPAQrz8mz5QoyN+CCodREElbHOCjE5czzFOLu/UkV6QdbbCF/0vE2FQMVeZ2Cv0palkm5LVGVNzQ0yAlKEKFT2+pxvh5W2ew7Vf676nx8WMlgOsgmpu82GjZMdlRlPAhXpZIzWhpt4Q/a3ulS7adq3RN6YF9OL7eNjfD+pZaSOiWfFFDGfYc7EctEVERQhuQFVfLzRSxb6QiEtq5OPhoyRAayBZySgcvPPlvWeeEF2Vf/ZkSI86CIOLgpoULp5/X0e8N1cfSzjtVJqqTNsUz6yOXadzOmSN0VehypBxQaC1OTjtMe+v+j9GD2kT71Lz2uGjpUNlp2WVleSWz/FBEHELdT9Hrfah+OVtKMSxJyd7cSlBVT7lMozig9dtFjLT5QjNd+b9l772DFfeCpp4LljXAHCCbnks1sUVyM76N63nubbSZPPv20/EHHkSi8TfTg/ODW1vFhR5KxRx4pLyqxhVw9/PDDGckY5AmX4m677RZkFO7Zhx56qEu2kRELucIKhWUXGcPYkQzCjheQMXMNA+LnqMFH1jBJbyx8iR286667ukJmcM/yWfpijvlBW0mOgUhakh1z8hBdyGAZxLOwvy5UntZnB1gBsQ7mm+iQDmQ073MhoTJYT/fYY49AVnsDd3IC6CgKKiD6BAFkZRrnUYlfwb2QFAmDdCCscVtg8SMeJNs7yzkIU1bv5QTEE+sCgd/5IK67NxNwF6HkSmFpzYR8El2YJ1j77Gdcdy81/V49+GBZ9JpruixJbzU3ywBVpvNFXLuoyr+rwp3t0ktl+mWWkdG33SaznHSSLKykGOCcpJAwxX9RuxyTBgwIcVf0G0r3VyXtF+jnu+mRHjYOmZxa2zt1qg0QP0LUsYDhcD1uzjlDnbz6VEzgfUqEdlYFbuV6UfZRokAcGfFa45RwXJ4iSobvGxulftQo6VDi//2118o0lCKJuP8ANijSHYxCQHpeWWghOULnzXO6eOC+LIeY98RPYfECWKWf175ZQj9bLUWaAT1J+sCniy8uSylxGvzWW6GA9YDxU+4wkgljZ59dbtA5cM6778pHEYvoCP38eG0LJWJwxY/Wd2H9l1+W7/T9pd33zzefrP7dd1KbejaKVbetuqpMWmcd+UoJ1pzsnpIaY/59eZ55ZOD118vc2kbm0c033yxn/+Uvcpr2uZXpaVV5MOmSS6Q9bRHE+VjYjAxCpCjYTOKFLa6I2zzggAOC1R3svPPOofyIlUXB2g1R4kCmUXIFlzLvK/FznDd8+PCuODjejaOOOipkxwJCU3DnxlmgIT+4JhZCXMe0H9c718PSRnLb8ccfL/vss88UyU2FgneAPikkfIRMa+LxqPbQG+AE0FEU9GV1AhgBlgBWiSijYt4tVtC4HVlpUrEegtRdxhqrdlbp3Luc7glIKllxbAMVB+nuXhJYEPL5ALczSo4g8XIgTmwn84OjEHfvmMcflwm77CJLqbIFH+pYv6xKeUklhMR4fqEK+0syBVVZzj5ypAzG2qoEuv7cc6XhrLM6kxG0Dz/ebjvZ47335C1dCByi1zlQD1R6q7a7faONpG3ddaWGHS7OOEPqMoQ1UJ5l2sg8/7SpSZbXa0P8lvvDH+QBJTozpmIOyUKdTBygKlLm/IMPPhgOrLM8M6UtjlSCNUQJWbv+f2AqfsvwXyUPPz/5pEw700xd7xbztlbH9b9XXy112s45VFFHZ3KrEt6P9XkPPvNMeVEJjtG6x++7T1aMLkC0/8MWcEcf3ZXhC7BL5XqLiEN8V8nLjNrnM+GGTZFCMosnXnCBtDHHU+PJ/Bt9992ysbZzplSfTNI+OVqf5zx9D+n346ebTg5QctyYGtf2mZX+ap/WjqHM9u8xUd/z73QBMG1qocG8O+jAA2Xmp58OhJ1UjHbto5ZDD5XJRxwRXPP5ADJH5iiFhgFzC/dsrsUbsggrP2QQaz1jxHvLAdlmFxASOyzDl0LFZKXmCuHIBogZFkUKRQMIIQkSSZbVQqZCWAspW0V9PhI3su3S0dOg4+UE0FE4VHA7AYyA83BZUOS1kDp83APXJsqFVTYuUVbecYAbBVcN1sdSbPmWDVh7EIjEBnUnTwpx92ZCsW7nfIHSwHKQrcQOz1OIu3eC9sebqjBXUcKGOxOq8qASqhlOO02G6M9sZTJqtb8blRxClkDbGmvIC0r+/qLf21CJNa7EzDmWv0fLVFPJrzpfptW2kLgAkfynEp4hH30kOI9X03l8ixLIDd98U2pTRLPlqKOkVZV+SBL5979D5nD7SitJh7aXcWHOzqXkoHb//aVRSZKROOxfb+i4D9PvtC62mEzWhQNkmb7jJ/3HQfLCgOOPl6bb2UtESY8SKWILL1OCMGrUqGDtXrquTl7R7xCrOPbdd7tq55Eg0m+HHaQu1TeAdk3ed9/gLiUDmESLSTfeKFO9/XZX2z7Sc1qVbHToGEP2hy6xhNQ9+qjeXNu3wQaps1LAMqXPVq/kj4xm9lN+Rcn1hkr8OpRkHaJjf6j2TT/tLwCBJEu4RccoEMBXX5Vmkkleo1yztk8/I5u5FgsibmIdz6e0PSPvuUeO1/drw3CWzg9dLL2vxG/eLbZIfRIfkCqIGgQd7LnnnsHlG1e+AMYIix3vH25UEhgsC5xFKtfD+kccYT5gzLEeYvXj+oS6QFRxASdh9YsCzwEyq5CMfsgyuz2xF3FvQF8ngOUzkzh6NOK+J2ali1s2JgpWpdTQQrASh0OCQz7CGUFJO+MWrU4KJqBRDtmAdfKtt94K7nHc2ARgF0r+AFYIy84sB7ifkbso+CxKYOh/zo0zX94888ywcf8aKfL3j2mmkQdPP12WufNOWVb7JxP5q1HC0rTKKtK8/vqB/HXMOKP896KLZB8lW8/svrs8ruTveD0P8temc2jSzTfLBP1s8oEHBpIRBaRk0hVXSIuSt+bPPgvnvXPqqbLaXHPJjkr+wEI6tj9ouzZ++eVA/n5QUvRvJWXfDx8uNUrkG5W89tN52rzpptJPCVjTVlvJEjrG8+l1m2efXZpT5A9b4/V6vwf/9jdZ6AJsWTpvUiVt6C/eG5R+o/7sr88/SMkk5A937wQlKq8oOVj9/PPDDhoLKfl7btAgeU373BJV+q+6qjQpuWlWYjRg+eW7yB9klZI1Y5VwTlZCSf27CUomcYOTDELboGgvKcGb/tNPZVaKTetzzvzAA9JP+7hB2xKyoM0tDTlRAjZQ+6jh3nsD+QO/alv+raTgOCV/n+vzHKPtgvy1zTprIHQU3yYbF6LMz3677vob+dNnxoIbyB+E+tdfpVGvvZ6O3Ysp8teu50w67jj5iPFMZSvHBXIF9/8W2jeQPzJncQ1T1Dkf+QIYK7ZPw3qPCxnyx1zH6odrlHIxuI/xCmR6XzKBhdU222wT2gj5wzVLMWnIatLkDyAfCy2ST18Wsn2cozrRp9lvEtCXPN5b3sMBoctFcKLACkfsC4H2cQA5wuJHLBzlEIiFK1Twsf0SVqpCtjgqFJAf3ECZLI/0WbHu3kwgNujVV18NGXnlAO6tMWPGhFpnBqZ+IVa/7958U75UhbmykgHwtSrVpzbeWBY59lhZQPurWYlOzVdfBXdj20YbSce880rN559LgxKhuuef7xJaZL9+Mf/88qASl22VQFhaUIv2cbuShTa9pk4qadphB6l9qXP/XkBZkpaTT5a2bbclIl4azjuP/bnkDX2Oc3ScKOqxoM7dV1WJp6Ndx/dnnduT9X4zpZJLIGkds8witdrmdNAzj+h3Xt5/f9l8550D+ajTses/eHD4+/gffuhyY9bowqdRiWpdyqIEgf1FScqp+j5RomRZVdyj9L1YS/sccF9lyaFkTTrCjhz77husbvoypD4Veeumm2RWvYdlVpMJPHHHHaVR29c688zSfOONUodbXfstCq7XttxygbRB1sJnev9vlOA1KalKpwSt880nk5QIDUjrk2ipHr5vpWLaFllEJhx8sFyk8+wNHZsztH1W4AS396/6/Le/+26Ia2RhSHZvnLlGkgdJFZAszieOjng63kFIMC5Nkj3IgOb97Y4QEgt72GGHhfcdRGP9WLiyGwfX5Tp8RjgK3hDi+9JlGu8NpXaoEwjxYxHATiHsGVzKXYyIqeb5CykhQ19Sv/Ccc85JfdKzoXOi+0nUi9GnHz4J9BUCyKoxrsUJdyhV4rtbKUKOEKhkxCEgKX1QLDkiBpDYlnKuUpkCZDPijo1araLuXtwtuYrJ5otyJ7xAzhknaizyPEb8OOJa/NomT5ZXd91Vlr733k4LnR4PE9t52mmytBKoaS+6SOpUIRopMLTPOafUKIm2z3FnTlIl1JyyoBnG6cKhZtQo+f6Pf5QZBgwIu3jU33Zb1/ZmHaqUJ48cKW1KJI341V91VVedP0OrPkud3sueCOsTMXyUlokWK4aA/khJkM02k//p/J1fie1gVYwDU9YyindcNGyYrKsKnlgzdoOABGyg5GUp/Zz4ugnvvBNct/WnnioN+t0afc8gWy0HHSSPrbiiHHbkkaGmHlZNS6uCRL2l5PBUbeNx338vi2k/0DO0Fzdqi7Z1ElbGCJkZo/PwJyXEK5GIkfosHbjfLWptnJKiOu0/agfi5k0vpv2DtnkDJYIv67yAquy16KJy3BJLyCD9/VclatPpT+u/77RNn+jfFpkwQabifdDPuvp22mllkpLUS/WcU5Q87ah9xzZzZDS3qyz4VfvuEiXQF116aSBxBsgLJJCDRKgoYWJOYuGDSDFvAe8eWdls5wbpglDbFm+GaOkYCCGWegNhKRA1Eku4PsDlS7we7t6rdB4xvpm8D8gErHoQTK4NaYTwQaZIqgBDhw4NRDJf13EhIIED0tvdfsGZsKu+v7QV93RvgBNAR1FQYTCltuqlyIcAQkwQjjOxdVYWkMSAAEYhEguXFGEjm46AaQhlORG1emLRJFuQZ4wWqk4S+cQdJgFLOoEAQtzN3WtHd/jg5pul+YADZKEU2Xpblfs/9ffBOvYdJ50kK3/ySSjrASaq8q1dbbWQBFGLxS/1ikG4ft12W9lBifUjTzwh8+qc3FLJ0tZKrufYf3+5Ua990Zlnyg6ffy5/0fPNFhtI0X77SeuJJ4ooYQrE7+qru4gfe/terMcGOnZbKUmtNwuXHjwZLsjPdVGx0YYbypba3oV1bHFTtu6yi3QssIB0KBn/cPvtZYjOAWgIpVVOUYI6w1FHyZJLLy333HWX/Kj3O1zbS5Qe4f1r6ryfSd+nD4YPl/mUENcqIU3Hy3pAJ6xaG+3BavY3JSqHahsp4nGXHkbzXtL37V9KKlbdcstAeJhz33/5pbymxG8TJXDmAMf298oyy8gPShTfv+IKWXzMGCEFgv76RI/T9aDs7kxKEDZeay05SPt7vvvvD0S6pV8/+YuS6wsi7eUdHq7XO0DnyCI6jjYb6N1sy7nWpqaufoaeU4uQ4ib2rNhAL1Wi8aQSbsgSgJBBWgijiIaYQGZYCEGuIILsj8tiJROiGdp4CSA0XAvCyGI0CkoeUY4F6/3dd9/dRfyY79nEPkSP7GCsZJBFFoa2RSBgTMgc5plYxNFeEoUOPfTQbpPckgKlstjOjSNfbLrppoFEsxVdb4COZZ/mQH364ZNAXyGAkL+4sXW4JhEuFE1NB1nCED8EIEQNgZ7kO0ixXK6ZxDZF+YBYIBQGWYYojBlmmCExd28mUEKGe5J5XKi7PB+QAYnlgCxglChjFmfcflEy9u8ttpBh774bLE+hhIsq7B+VPA164AFZRRW12W7u1+MEPf6l5OAvQ4bIX1QhT42LVDF5hRXkiJlnlouUiFgoAsoTBQ3xvkmJzEZ6LyxlRoh4MQlVP0wXA39ec03ZR0nbPOxrGyF+I/WAbJA0cqI+T399nYnXY79blhDkde+hn18Vec3JSt9QySB7w07z0ksy58kny6yRxRE2J/YleFYJRp0q+aO1vdnsOlzVevEXJW23KEFYQgnDb472Tktp+giP0zYNyCB6yDN+QP/2sfYfBYI21zEzBzC9dpselMiJFpoh9nCvbbaR3f70J3lp/Hi5TckOsbgraj9dqX+3qnMv6VzeQj/rTKGQMMdX1nsNV+IXTRGhvW+tvLIMvv12Gafk8pOLLpL+Tz0l8+p55ALjeOR56Cd2+t1MD8NEJUFnaD+M+uab0DeARRTWMuLscLOSaAZZg7RBsEjIyASIIXF5vCucy4KJ/7M4hMBQD9C2Y0OMs2jjPIgbiQ6ZRDslfcjK5VzO48ClusEGG4SYPeYkoR5mPWSuUkcQtzDWPr5n4LmII8TzwXc4aHPSi8V08GxYUBm/fEFxauJQiVnsDXAC6CgKTgB/D2riIciiMSZ8l1U5cWQQQ4RfKTJ1sQ4g2FDS5QRkDMHNkbS7NxPoT6yOxACW0nLA9EaJYTXBnU0QODXRUKIcxDplkqHU9Hv9yCNlQVWWM6UIW5TsRPGyKr6fVHk+qor85XvukUN0jlitN6q0nT/PPHLJF18ExQ8gvLjVCLTHZcnuFMfpES0N/rOSxVFKgh5QIrOfLjr20s/Mvcn+DRBNorjWVJL2V23ffErUwLN6cO5USnRv1zGc5/HH5ecDD5RbdUwp8UKMKX0BOYQcbaxHFJO1bVbPLopJSpo/VfLw85JLytxKMqZXMmJ9gX0Iewqk07CAPt+Diy4q87z5ptS1tASr3Rl6fKvvzok6JtN++eUU/Yl1lF6uS/V1Oj5QojOVPsv/dOwgIxAdFipYdMg0ZWcdw6d6z+nPPFNmSe2FHAW99KTea6AS4FX/9z+pf4GKgr/hO33vGu64Q+oXpnLhb6j7v/+TRiVd9Skr283aTwdoP7GjyipKQG+Yd16ZTWXCJCVsHbqAI1OVdwovAiTfFjnMQRYgJluQSxAaXLpk+fI785FYP7Klo7upYHGDgOFm7W5hRqwh1+DagEUl+/Cy2EgH74i9A5bkFe1PAPnDJY01EBmBO/mII44Iz8U7xQILIst7jTfECGEp3m08NHhdrF5kXPCc1AHFVV2u0JNSwwmgoyj0FQIICYib2fvuu+8GckfhZrqH2la4D1HakKO4ySGFgExbrk8ySTlg7l6SJBD8KJdSr+AB44HCY0VeCisj48bBfTiQkxwoKJQVLmEUFuMM4YYMQvo553NV3L/utJMsowQBfFRfLy/usIPMraRhopKDqZRELajX+AA35YknysK77IIml/orrpAGVdq4V9mK7UZVgPuqwqQSHUU/sIqNXXxx2eOuu2SgjvHXI0fKbKqUKRRt4GVsX3FFaVVSU/v3v0u9/t0sfi/puByvzwLx+4O26cGhQ2UFVdi4mIlFe23bbeUOJRcrrbyyrL366tK0++5Sr/dqW3NNmXT33TBPGa9kcowSg8X08+jy5d+q8GtPOklmUZLXqApW7rtPOvQgllAOOEBa995bOmjHIYfIgEgJjZ+WWEIO0me65dZbw/tF/12qpH4PnVN1n38ezmlTMkrB46+1jbPpMzUqOatNxa+R5cvzETc48emn5UedEzVnnCGzpMjy//SZvzzlFBm8115hHA22WEmfq/VKVBr0+eot61fBsg+39WJ6pEeNGQkNcZmnniotjGXkmoxBs16vLkX8JuqYHqhz5spUcggElP10ITtxgGzBdWsLPOICcZ9CzgGxx5RmyWfHmnRAjrHmET9I/2ClGzFiRKy6funWNd4RiB5xhwD5hwWROLp08L5BUnm/OPg/z2pkEPmZBF95/vnng6s63wxo2keCHguIQnYRqUZofzoBdBQOfSmcAKYBQgRRwNKHewSShJuDVXGp3zdqsLFqhnyWEvQH1kysKJAfSj8Q61dO1zPuL7bciyaeJAGmNJYUm9qMWaZx4xwSXSCDWDbaJ0yQ9tNOk7WVVEFJoV0PKbmZ9vTTZbFllgmWw0yoeeONsA9vXWqv1rZllw3xgXtceKHM8dprwU07JPylE8T0aYO6SpAY+JwkinT8V+fCAaqIb9UDnKyE6sgvvpD6VIJAqxK/ydpu1dqYVqVOyVgDJCsSR0b5lDYlhq1Kbpsj5OidaaaR/6qCXxCXthLgjNB+rLvxRmlUElGj/RWsdTpudc89J8+ogl89db0dVbleqsRtYCRzltI1bUqc25dcUuqvu05qdb6Fz/V7rUrqWpToNu2xh9Qp8fta516DfhcqhQ3y1ZVWkkXuvFPqU27Odh0vUZLYcNNN0qHke9K660rrCitIjb4vdTqGA3beWepTFi9AMswr2vaQzKJz/Cgdjw24x9VXd5aHiWQfk5jStsoq0rr++uFgr+Mm6vylCkSD5xdeWDZSecDTIguw2hG3lw/efvvtYCFjsRWtm8c7j2sSMlioZ4G5DFm7PVV7ERkCmcyH7OA6R9bRxnQiSduI94taJXMBd7WRQayEfM/IIK7rdPIeB7zTWFaJ5c134cj7znNhHSXGuzfACaCjKPQVAshjmguuO2DtwyKGAGM1zBHN0islIJ8IqkKKnMYFq3qILcCVgruXOB/cVYVk1hUKSlFQliUpiypjzAG55Wc24pcJb513nsw0alRXeZEXBw6UT1WZLpDKeszoylLi03DiiVJ/+eWd+/CqUht7zDFy/GefhV0/jtPPsDoBCE+7Xqf2zTe7snoNVk5kIpY1nWd1Dz4odU8+KRNVWZFResrLpFOIrKRjcyfB7ymS065KbPIFF0g7u0Bou+uUULCrSO3HnVuccc8abSOELf2eH+h9/k9J3woHd+5Ukk0Z1yiJDORWyR5oX2wxGat9de9ll8lOSqKohHdsv35yjbZ11i8tuk5ksvYFfd+QSoAwdOh5kD7InzLO8NnHV10liyjRsBZA/iYo4W4YPlza1luPFFipe+QRqWf3k1TtPUO7XqNN+7V+9OiuZ2zTZ3lB772EvkdGaRFy9H+HzvG6f/xjikxt/pdrlrQq0Rihi5SzUhbhHXfcMWT8DsrTBQkIL2Fusr+u7ZaRRAYt5WIgayxmGEuye7H65UuS2BEG6yTbytm2cBBC2kf5mkJhiy3IIPKHdxS5Y0dcVzGLeGoMku2cr0zm/lgAIaNZFzs9DE4AHUVBhZETwBQQStS8gwAiXCAnSVunugNxhiSaFLLNUXfAkkkCC9au9OxeFBMxNQjIcoEdV0jKsED2QsHYcjB+HEb84sjG7999Vz7baiv545jOrb2+1e88oaRvnqOPlqlU2bQo4fhmttlkaiVfWEpxjTWpMms49FCpv/nmrppwbaqQHt1mG7n/uONkP1VwNnptSm47ll8+WJJqtN+BEQ6sTq177CG1ZBA/9phMPv/8sCNIq5KUy667Tk5UcokbrVHPe3DZZWVNbUsos6JjRo1AyB818uq0HYH4pdyUWPsow8LuF/2WWmoKgoNT9uZVVpGljj8+EI9sVk0IZb1ev0GJStimjntq217dYgvZ9dxzpZ8uVLB3YmWrj4gQXLoTtA++1PO+VzLS9uKLMhtxrdq2dnbXUTJZkyL8E3WeP7/11rLOM890xTemg/uGGoUpcsn1W3faKZSggShT5sXAuf/UuTS9KniLYAst08/TCTAIlliFWV07lMhCUGv0XmF89NmeV5K93n/+E0rMQIzYIm2N1VMFbWLMryiYoyeddFJwoYZxbWwMdfMs+aIQZLL6Qday7XhjoC033nhj2FKOou4kqUCKKC59xRVXBNd0MUQyF7g38a9mHUTeIQOiruJsIH6VbSsJHcmX+5CAA4lFDpYj8awc0D7IrxN6Gfr0wycBfRn7BAEEWPSyAUEEOaI7sISxSqU+V7mBW5Z7F7LReTZAiqLu3kz1CnFNIXghhuVCEjUPGS9z93KgtOLIxHZV+q8MHy5L33FHqOkHPXhEyW+NEp6lllhCplMCVq+KtEYVNfFp45ZbTr7So/2rr2T+u+8OiQ3pYHlhzjvKjdSqYq2NEj+9jrl9IYwQOMqwNBx1lDRcdFEgbmyhNl6f4SEdMzJx59bxGKnfb0jtRRsFZEg1p9SkXMEdSk5bDj44kEoI3Nh995UZU/F60KTTZ51VZlfCsc6664Ys92z9VKtEsxFiqguhTMClSrLIYinya2j98587Sel0vyUQteu8+5++d99qe6ycCCT6Bx37BY45psviCoyo/qiEefL888v0OifZ7SP8TYmquYyVJQSra8MJJwQ3LkT6R53XE7/+WqIBDG3ajl8uvVQeU5K0sJLkxZWMQgappyhNTVKbqsPYquOEa7suZRUHv2hfUZz7YX0XwR7aH2yTBmEmhozwBaz0uIDjWM1593ChMucBRAT3LJn2uCQvvvjiEB8IGSMjl7/nIinMdb5D/CHyIkrWIJZYBHFRk/mOHLPagIR43HXXXaHUDLHNBogXC16LRUQOQCQzJY0kDUgdz2CuYmIVjQwSQ2iLVABxfPPNN0PoSL7Ay7GVLvawkvYW3uQE0FEUVJD0GQKIBTD9cYl9w+2KAELoscpnVU22Hm6GcoOtnjiSClLmuXgW5IS5ezOBc1A4kMNyAVdOofsJM44cZvWLS/zAh0r6avfbTxZLxYD9U8nAW0qW5idb88ILZfFnnpF6nReAWLOaFAmIgln0zdJLy71KZhqffFI2UEKJ1YkyIBA/1VLSmLJOtSuxro1cj3i9NvaUVVArsOGww0LMWXf4Usne57vtJksoMWl6+OHfLH66YGlRctGqf1PtKRPYDQOLjirWKNr0b9/pwuI6VbK3/PKLLLXGGqEUzGqrrda5IFDl2qjXwaIY7cmP9J24RsnbKno9bF/pEWDsN7It5HaddUJ5GYjGjEoeG3DZat9A0Nq1T1qV2LyhRI2s5LX0Xl0uXx27lt13l/cnTpQlbrghfMbIPKZE7U/6t+m1rW26UGg55ZRgDWzUsapLucHH6vUmq0KPLiGwBE484AD529xzy9lKrK2mHk6/02aeWYbrs9SzaJhqKplw0knSRuKHzqX6W26RCUqorvvmGzlaiT+EnsXgpZdcEvroTiVO5513Xlf4hIFFDM8McSN5IzoPyQbeV9tLfU/A38gKhrhhZYOk3X///b+TS7wTnMc1sXaZpZbYZCx37AZi9QVxn1LehZqAWLmwUkI4M4FwC0iUAULIws/Op31kD0MQ4ySNJA2eL+oqpl+QWSwaWCjSdhbqhSzOCTkh5hJ5H1dWVDv0Ofo0B+rTD58E9AXrkwQQqxHCknpcBHTjOrHgZmrhsVos1zZlUUR3rCgGUXcv9QohttGVdDrKEXuYjjgFtzOBMTSrH0AGxpGDY7/+Wt7dfHNZ9a23AvlADT6k/Tz9QQdJhxK/5VRJmzP63fp6eXbYMBmsRHEtVUj9RoyQWiUagFiyq1Q5H3rttcF9BRaefXa5UcnsQi+/LP1T1jpKDaO2TY2+vcwy0qzKebCSBNyX7CCChdBAhBn0++jBg+VAJWn1qWtjpxqux8PhN72mkoG111pLdhw6VP4011zST0lXIH463l9uuqks/nln9m0UlHFpihBC7G7YorAyPqF/O0zn/+5KbOojYRKf6XPu9tVX8lSKvNLDZ+g8Olj7saGlZQrXMs/KtaA5O+uxqh4gul1aOmjDbUpInldy9eQ77wSyREHns/Q7S2f5jl2vTcdnspKFdIpChOA/tN3j9T06W8kC1H2GaaeVE5Tgbqnttspx7F9xqvbzUO07rGN4B9gdgvcgHexv/AclIbaTBySKfXk5F3ckCxADyR2QQRaPt956a0ikiAPIHu0gwxWiggwyQM4hgbjsKeWCBcvAOx29vwF3LgWdIZCQTyyCtNXeGUgjJI8MYUgXO4EQkkG7qTGYS1aUC7SVLSPNVcxinblPHCAeknwJKpnMl19+eai12lvgBNBRFPQlyyxpeyEggAhLSBbkCMIH4UmPQUPQYJ2iVlS53y9WvWQCF2p95Pm6c/dmAufjiqEMRbmAQiIOMW5Ff6ZqIe5eavqNVuU+30UXySwRZYkD811VKHPqeA9KvQbvNzbKyUoubuIz/Z3adXuEvyipVsX/ylZbyWb33CM/KCkE9Cx/P0oPcz/iWCOiK30vFwqfPKTHBjoetqMIJPRaPQ7Ug0g0HLab6wGw7+2vx9Oq6Cj6y9i+pPc+XYkIPXapHjfV1cnyOlf20Ott9OKLUxAiCBbXmF8P2kf5mM21z3bW+T6bEsxs+Kc+52FKEHCdGpYcOFAuGzs27N4RxTi9/wAdk3RAI3ku+m8+Hd9jlXzhtGO0GIEntQ3bo9wj4gdL1BlKjvccPVoa08I1OIsDWkI/pUfM8TeuTZ0/Kw7yq86Nb3T+z6sktlbvBSDC5+uCaKS+/5OyxARDnsjIJREM4gQxBVjlsOZBrCyJAGKCOxiiR41FZEc6mKe777677LLLLoGAEG6BGxOL+5Zbbhnq60UXXhAcFkfcm0LNvM/pwCp25ZVXBhctxI37k1QCQdp7771l55137kquYpELqUOmAdzMuHfT431ZzFAEH7JZjUA+sTjGQogcYL5EXcXdyQJiGyHXuPB7C5wAOopCXyKA5g4lABtihPsj0/uDAEaYswl6ubJ/Daz8KcbKvpv5IuruRaHkE1uHksOlRKHUcoGaY4xBph1XojDCBwGy6cozxpF9X77wgvxvhx1k+VT82RhVuv+eYQYZrM86JEXCwH+U+H25556y9CmnSKsql49PPFHmu+wymTp1zlV6HKmHUSJsxcfNNJMcpErJ9s6l6DMEhBLC1jL2/P2ewrpKOOoj5BPid6Ee5+qBI2+iPktD6tkgOHx+lpKV7bVNJAnMpO1uPOIIqbv99iksapzLtaL5qBC/L9ZaSz7bfntp0WdYU4n2VUroRitxIdgfK1XNhx+GmoV1bJGWInA/K1F5TdvIbhvsakJk4RxK3v5vmWVkdiUj0USKF3VxUTtypCw+dKg0r7JKp1VO/1+jZGPcUkvJzSusIDdr30/1/POBqFpZ8/9Twr+oXqt57rmDIoc8ERe3oirzPZ58UhpSGczASF0m5PpbNrQNGSKTbr6ZTXCDDKAQ+UW6KMAixLyCqO21115y3HHHdZViYY4RowcJGzZsWM4EBazuZM6S4Wt7+GK1u+6667rqeloNO8gM98sWkmGgXZA1kkcgaLQHMocLuLuFHe8LFkOeh+/SdhKLILCZLHxY26hDWkh8XblAkh4yktAR5J25iukXI4Mks2WS22eccUYgkJbd3Bugz53va9Cr0KcfPgmogPlNm/Ry4NZFsON2zEXs6BIUE1a4XAK/FEBQ47LJp1K9uXsRhhbHmEnA58Lnn38elFYxpR7yRZxdTxiLQty9k1XBv77ddrKiEnlGEFvPQ4suKlOddposvvzyoYjs16r4x5BMoIRoqCrJOp0bNe+/HxIg6rRtoG2hheSZrbaSPa6/XhqV3FDTbyM9+uv8qWvtzB5t1++3Komtf/VVqU2RJAoLt6iSbtlnH/l1/Hj5QQl2hxKftkcekSc++EBO0/ZBJtdVAnzboEEytRJ3QEmTyTfcIN8ouWTuTaVEgZp+9Vde+Vu2qt67Y+aZfxc3CPF7W4nXNEpAZlluudBP9UryKLzcps8+iTg0JYKQv8YDD5Q6JSOAzFdK2tj1AU/xvZK8GbXtJMIARqBtnXWkRa/ZEbEUN+62m9TfBm38DeN0IfGytm/1FPH+Vedjhy5KmnfZJdQFZLcMUKN9QZmZWiWLNqqZyF0uwkcsnzK6QELblVi265jVKemt+fHHzkxfHevJSvTaNvtt0zbmOzuIQAIBpAyiNURJIsSJOWcLjujc472yn1FwPt+HYPE+Yonj/1j+oudCdonvjSNXIDbRIsz5lGOBsPJ8bB8HsPqRdBLd3SgdJGAQ31hs+EkpQdgO7noIoIFxghQaIaT/sdAaITSiTPwfY0k/9BboXOzTHKhPP3wS0BeiU7r1AWDZQ1jEARZASingWignEG4UOoUAdkfieBbcO6xqiaMjjjGOuzcTyAhEKZYj68+Qacs9A9PSFDA/TfHGwT9VSU47YoTMl3LxvaoE4OPDD5elt98+e/arEq1GVZh1SmTMwtauBOjvSiZOVzKxvV6L3UPTRyScqSSji5zV1UmrKv2Ws84Knxsg9liGsDhBFqZXAnDD/PPLOu+8EyxrRnBCJu255zK40nD++Z01BiOuSnb1GHvVVXKRksTHTzpJjtO/zaDP85W2c5ASs4XWWKNzDuj9Gk4+Wer1fl3Po/0Mgau/9trO0i7aVu3gLssem6JBh7bVOb9gyqoJ+Ha7EsvJqjg7oklC+sxcn/vYbiXAngXY25beb+1LLCHt000ndU8/3XVu9HsGPntHF2yr6xzHHY4VFjc3MYC/6MLhm113ldrNN5eZlbw0K8lu20jpOf2ubauBdLO4UNKrE0i/Rbe2h3g3rGJYAemrY445JmTQZloUcr4tQGw+AiOB/CR8AtewbbtGwsiFF14Ywhui4LtxixiTjYtr2Or6YQWmZEx33+MeWBx5Jp6PODli/XALdydPIE/IknLKgHxBiS6eI1e1AtzwPAsHxBC3Nv2IxRkZSY3DYgGJPEvfccIEqN9IMk82yymu+ut1AUmWN6D0FbIgiUQ/nX8ZhFnfQZ9++CSgAqNTovUBEOyMMI8DXDW82N25aJIG7SNOBRdwrh0BEG6s1nn/83X3ZkJSySf5gFgoLCXRqvxMRw6ULQfPZ0d3+OG99+STLbeUP6XciN/rdx5XIj2/EqMFlLhk7E+9V70K7yiJCbXn9PPaHK9G67BhIQu3NhKf1aafTfrb3yTsyBEBMUe4XnFfgRO1j49WRVavY5gJ3D+Qygjxax88WCZfc428qgQAFyDlPQAue8gHCUvEfNJPtY8/Lo1KHqJtS0/G4H/Wo0/rcZIe/5x+erl5l11kdbKkU9/FbRqInyqtKGrefFOa9L6WxNK24oryg5L5Qfqsjan7RO9B37QpMSIrmO3VsrUlivEzzyzbaV/clypXsvHGG8v5FOxmLuiCB0VPkhPKHUVPzBsWZfqBeZU+ZyBq9B3FjgFzHUVOklQcGAHkHeX/LCgvueQSOfPMM8PCjcUi5GIn6hSm3RvwPSxyq6yyStZ3GysW8Yd33nln+J0yMVj9IA3d4YsvvgjPB8kEZMrSvrilnUhyoQJBnHtVCsRHM7Zx65UyRpBpCBhxlZD8rbfeOswl3plCPDy36SKRYuDMHSyrxHWyqCD8JpM3Y3tdeHKeEX/my9133x2ehXCMYqDzLNOr02fQpx8+CahA+00S93IggCGBcYCwIG4nfVP0UoPhIJgbJYGgS0cS7t5MQPEUk3xSCKJ7LgNTrjYlkW1x5Bs1/V7dZx9Z4pZb5A+p7z6i/dKmpG5JfZ7plZRAPNoXX1zaNtlEOlCIel7tvfd2EqUUEeObE5V4H6Mk4hYV8iQwbKWH2VywCIb6cZ9/Hlyqddpf4fM555TJqgTYHi1ASQnu1W/1/EOuuCIIe7DkLLPI47imImVEOvT5W5VItel1m884Q1pVIVkJGoALs+Woo+THPfeUUaedFooI0z9YTgn032GHHQKBCTspKIlv1HPrU0WB22efXSbrNZtTJWfadKFQS4xo+E3kGb3ncUpcyNzdb6ut5AxVlgPuuSf8jdIyky66SNo32CD8bqhRwtc0fLjUpK7ToQptgvbhSCWr/6dEepQSo3U6T+1C+6yzSssxx0ibKsIa7bPmjTYKdfeygWe+T8dqq9Gjg1sbUodFDaWdDSR4YY3B2oNSh4xZ4W7c/RAh4uii7tk9qXXIu5NjjrFIgRihwKPvIwSccilYsQHxwuecc05Q6FzTjiggIyRsEGsLeV1rrbWmiH994IEHgtUPUst3WTSw9Vp3Vj8sfbg379Gxo0wK5xMjSPvyKXgM+ePemfb5rRYQo8iivLu44Uygv7HSMQ70NV4PFtH5Fr/HQmohAwZc0uwLfRpbMnYDZBxxitRxZLFQDJwAOoqCKhMngBmAIoH8lXNrNAPuZ+J8otnJWByi7l7igeLuyRkHKCWUGe6rcsEsmDwL09Csfii/uHLtIyVx7XvvLUukapv9WwnVaFXsQ/WY9+WXQ/xbdE9cQIyYKPFhBw7AC8AWZ3dvs40cet55srESwqP1M1MxIcbvL3+RVv17E7UC33wzfB7cvSrAW84+m1odgfg1XHCB1Cvps31myfwls3eYKojlqT+mzwcgOa3a7pbDDw8Ww1qdb82rrho+N3fyL6pk3tBznv7iC7lclc1Cen1sglOtsYbsoZ8TokAyweB+/aRZSSTbpWFZw9rXpu2crKSnRklRs45p1Jr4rirQ3ZTwv6r/n1cXOXf9+c+y+HXXhXqHQRjoc5EYQsHktrXXlsnrrSdPaB/OTKkcYgI5Jw3Y6CwLmmtkOscETfrfouf/T8nsZj/+KM+lSDmWFpQqhJeFEdmzWFKwbpnLlj5gRwvccIQx4PZE0VPYGNJ29dVXB9chYH5DBnHPvqPk7m/XXx/OseLLzD3mIlY0CJ0VboZU8V1KvBCHxjUgErSL5IJtt902kLqbb75Z7rvvvrB45FzawXchZrSDEABckQbIFsklLIZ470HU6sdCj+xVLIKQHkrFcLDwg6yxcwcuRpNruBX5blyrZhSWYFHOSgD5grhhSHYhC3P65vTTTw81ExljFtL0dT4cioUGCwziMv+s740BFz1z02Iuc4G5wOKEa1A3sxg4AXQUhb5EAM1tEwe8zFgRLHuvnECIEIxu7uek3b2ZgFAiGxJLRrlAHTUUlxFAwDPGkWnjvvtO3tEV95/eeiuUBCFN4WFdmc9wwgnSduutssDtt8scSgxAu/ZX2847S60q/FpV6Jb1Cth9Y/TBB8uBd98tQ0ePnoL4tc0yizysCvqwf/5TVlOScIaSqUGp4rtRYC3DBVqnBIUtygCUk6VDuqOPO9/c0CDPK+FYbcstg1VigM5LihvXpyyFJDVgufv7AgvIIapYFtb7H6ufDwl/Ve6qpONf884rF48ZI8vq3KBG4O+j1/Q6ukAg1s8wUYldc+rZH9A+/k4V2I5KRhpTJAdqajar8drG/lneFXr1ZD1+1muso+O2lv4fG1Um4pfpMwNXJ9KQWQ5dHqHHxXrQDhY5uNUgR1i2IDrm9ga8GxAsFmicBxkCWEIzveMslnDFofyx/ODGM3Jn4J7U48PqZ5Y9LGjEjeJeTQfkgaLLvKuQMFyC0Rp9AOsjhJWSR2TZAq4HgSNmMF38QgiJT8QqhbWXos8Q3HRwb1zaRvx4Z7AGYynNx+oXBZUAuFe0JE21ARmF5yPf0Bz6GTkD6cK7UiiYOxBQEvWi4TLE9LEIyVRHMh246UkyhPR3Z93tDjruTgAdhcMJYGbgDsXCgNAoNxAuCDkIKKtU3LNJunszAetFOWsfMu1QYLjtUHa46nLFPEYxetQomeucc2Rwisx8pOP08hZbyB/0+/Mr+Zs3Ze2iCPBZelyhpGCVP/5RRmnfDX3mmU7rlp779S67yJ+1r5fWsYZ8GPEbr4TxKSWTw//xD2n5+We5Xj9LX6dP0mv+svrqMj3ZpnqOAavaKD1IprhAidoeqvTJDIbUYN/ZXo9OqtKJg7Xtp+nfm/WI4nu9/n06V1FVC3V+JGNVsdfq+f0jpM7At2/U4/b+/WXvwYNl2I8/ylRKirAGsl9vQ+r6RDk26BGlCFGS9rze4wTtH6gRkWAkXWyiB98BE6adVl7Rts2iRIfUHdzHONDSnWjc7S691s1K0Db49FPZRX/nHlAgahO+qX1Tf+SRsuT668uDhx4qlykZGxMh5gBFC3HD4gaw1FGjjvAMMlajwKKH+xSLIRY4XKJGCqOAfDHnAPPcMv2Z+7hSDVgRqdtHcgjWmsMPPzwoeGRIOpATRsSw0lFmBWsadfysHAzg/d18883DtbgXW8NxDqAt2UQxcZ64c3kezn/xxRenOJdFKpbHYokb7yPPZyEZ1QhkI4Q73/3D6S8IPpUgiO0uFEYAmYPR3UiI/bzhhhuCvM4F4v+wQrL4SGK/dyeAjqKgL0afIYA8Kib8OMBlBFksRlgUCqwFKD4sf6Vw92YC/UJ8Upzs42LAGHCgaAicJ+4IqwlZslg2UbYcmcggZVu+33ZbWSGlVKELmWwdP6pMfF2JwnMqYG9/5BFZQMcSy5LlZD473XTyV1Waf3j11UD8ok5+qAY1/4iig7RAgIz8cD8qiO0QfuusvTeDEo81tL9m+eQTYX8B9n1YX1f1502cKKZG2XWCDNapdC6RycmY/vvii2WLBx+UWSKkByc2pXuxIUZD06E61AW8SA8sZXvrMUqf0eIdv1VSc+S888r9SmSNGFEomk3VcOinjyb2JA7abwL0DW1T00knyeCttw7K7TklFKvdeaesnbI+RfcxzgVa9LISv6f23FN2VBJGGQ7CFp66/XZ5+Kmnwj7H1NojXgpyRKkSrCGARQ57tRJMj7uXRQkgXopEF76H5QeyRRuJ44IcsisHB59jicEVzPzi3vQ3oR8kAkAe+D9zC2sgpIpFFsSKtlDvzwLzyZplHo4ePVr21GfBAg9oN3F5WKK4JtZ65AQxYBA6CjuHeEwFbcBlSZFm3l+svZzH74cddlh4v7HWQTLpByyCFH7moE0kKUBqIalGEKlhR2kYrIncJ1cGc77AesV1oklZ1Qb6G1d9NB4zDpAvRv6LiesuxgWMFfvkk08OpYeSKrflBNBRFFSoOAHMANwhxMOUOyAapYD7CUHMvVF+5QCKMU72cTEw4scBkF0mv1D2EEEOlBsrfIgSlsF6/d5rO+4oKzz6aNhaDRrykCrSelW4zaqI++nnS3zxRbB0vT5smAy54gqZSpV4jZLLhsMPl/r7KWusq3d9rn11/CFH6cQvF3hBqF1HrN9/VfgPWnJJmeqHH2QtVcBPRkjRYCV+jw0ZIou8xoZkSuimmkrO0HY8r0RkHyUw7LtL/GDYyzbizvxeie+Beu7aY8bIrvo7tQZfU6K0z9xzy5B115Wp9Rne0b89poRsufvuk030WUOsn1538umnS9s224RdLUhWeO2uu2TWK6+UVT/7rIscYy/8WJ99ISVI0WLO4CudX2P1uQbrNQKUeDStv35XsgjPbkI2G+E2/Kpk6kEdp1WVlOSy0DAPKFA8YsSIYAljrpP5CrGxuYcrEisJsan0G67UXIAQQhIhcQAiRpkO5o+B2DssQMS4QQaigMRhaYd48Q7yPpC8g1XHyCTEMqr0ATKCRQwu2fSFE7IGixD3ZRHHc2L9swxfyCBuY/YPpk9MFPMTwkJ8oV2TsYVkQA4BVkGyT7lvUsg3w7bcYByYEyTk5LsgZozobxadxco3kkCIz2TsDFhfCS/IlgTCXIT8sdghNjUpOAF0FAUVNn2GAAIEQBwQTI7QJdC+HEDhmbuXFSbWBywi5QLTAOFEzFXSm8BzbSN+/B+llktukamJIoYMjrnlFlnm6qtloRRxH60K6v2DD5ald9opWGrsOq36HdyjHKJEp/6MM6RBiQ3JDyGTdp995GUdz7l05Z5O/EKSh5IPSpU0HH201Gk/WG2+dh3/yXr/johVpFHJRf3DD8uRSvjO1PvSgr+qUN/j/fel9scfO92ue+4pLSOVyqWIUI2S+gYlPPWp4sugQ8nXpAsvlGemmy5Ycbb4z38E9fGAfv6zfo7lB+IB6vR+DXpObcp9SVmYFiXAbVtthYlMaj76SBpUydTddNNviSZ63F9XJ6/tv79sqsRsZlWa073wgjQoQa59990uix79E+rn6bvBfdJHhqulWxHJMG4jKxdrl/Z3uypAsnyV5aTOyAysgVjXIGzAiEyhlnYI5AknnNCVHY11hzg4kjoKBS5W2ohLFPBO4FLGNYoF0ix8uYBFkfcZIgzxgLhB4Mzqh7UQ66QRGd4NSKe9K/w03KWkHqsjZJh78z0sg0lY/aIg9pEFZyUS3+IAQk0JH8Yj3zhH4u3WWWed0IfFejisDAxzDjcwSTrEgEKgCUUgsxfZZGQQty9xnbjpIa8GFjXdLWy6gxNAR1FQQdOnCCBCJM4jQ/5wL0UDfUsBhD3WRu5l7l4UD8K93PGHWAARaMUKJQP9zGHkD1llR3f4UfvgP1tsIcNSFp0f9DuPrr66TH3AATJ4jjnCFnK0M/1atapom1R516iiNTDak/W8prRxpzxJqyrWVhXmta+8EnbHqE1li7atumqwrkV3vAC4At/edFPZ9Ysv5Gr9/f6FFpLrVYlPk6qHFzJwlQy2qRJoUxJCpjE1Buu0bw2BkB5yiHy7774yQhUDgf5g16mnlmt++UXGDRki7UqQgpJTwteoxKE+FSuWDq5FUeXa0aOnqK0HdblxvfVktxQxglD/8NlnMtc998h8SoLrYiyEuFr6SLXNM0/os7att+4kfzGBe5YMWEqxsAizAsVY7QpNWiBkAdep7ZWL4rWM4UKAFZryMJTnoM94H0nygIjTfxwWqoBlkSM9iD9q9cM6x7OlW/0gDJQRyQYjgLgro3GCxIyR4QtZLkWYBp4Hnpl3qxrB+OAmL2SvYuq64vJnriTBmbD+QewYI2ISzzvvvK4SWrQPKyoFuQH/z7SfM/OfxUsx0Gfp0xyoTz98ElBB0z0b6kWISwCxxLFqZLVZKqBQIBQoCVwI5u617Nhyxx9S+gKLTKEKNAr62CwaADkVR1Z1qPJ7RUneYn/7m0yf+u5jgwfLpFGjZIk11gjXxDKIJQVXjsUMDlLC3qQEoFZX4XYXdppoV0WMCzmKQPyOOCKUcJGxY6XxmGOk/gYi5vT+SubalOC1jBjRWS8wBSy0CHwE/UYtLXKXfjZ2qqlkgCqlaFZxFBR0Tne5timJnaSk7Nb/+79Oq44+BzR/fiVrB667rqymZK9D+3+CkrWwy4YSkmgmb7suClogqp9/HvbxxVUbBWdeoARlyDXXyGq4Hc89N9QF7FCiwtZoVg6GHpmk861RP0unEhOV2Jyq51+s9zhYf99GifaMK68s/bbfPvSNTtjOE2OC94g4Omq4ARQlCjTXtmS5gBUH9zFJGYDkCkgbMXOFgvhACAIWSkCMIOVd0kMwICFmnU4vPo01EvLHdyB/6VY/4v6wJJrVj88pDcMCENKAhYrv8s5g9YP8IYew+uEeP0Dfi6jVDxJoRxLAPU5f8izVCEJDsFIWksV7v74rvL82B3sLnAA6ioIKm+7ZUC9CXAKIsGG1WYqyKFF3L/W6cLlEhThKCGVCbFA5kcTuJ/Qth1kx4hI/8MlDD0nL8OEyVPsefKCK75Xdd5eh++0n82hfNZ12mtQqcWpfZhlp2Xhj+V5JCUp0Nl1Fz/D6613CgPp1k/SezanagKQSvDnTTDJE+7N5/fU7iZ+SR9yljUcfnbUoMW7N9oUXlm/0Hvv+5z/ywJdfhs/P1RX/IUpqomjXVX7LscdKu/ZfnT5HvSrw2lTiAOhQpTr5wgvlP/pdSMHzTz0lO+vnx6tCn13JftuAAdK+1lrScO+94XyKTtcqyTC040pWYgf5o6SNsgiKosnELbeUP0QyTaGbbUst1UkM3ngjIzkNO40ojJz+pM/533nnlbr+/eUuJTUjHn9cWvRvkBEyFiFDucYQUs6cTrccY+mjIDE185gLEHZIEOQtF2nhXGK2IFbpsV4ocogaJBBQZw/y1124BOcT1sFCK2px5D0j/vD668n17iwXQ4FtPuvOEoalHnKL1YzrAFz29APk3pJbuCZ1AskOJq4XaxDWUAgs/WaA3BFyQn/atnLEK2IxtIxR3iv+bu8XB6A/7V0rlBCSfIY8Kub9LyWQl8R4FhJDR19Dqm2XlN4CHW8ngI7CoQKkTxFAgr0Rnt0BwUxGF6vypN4x7mvuXmKViClKV3CAEhJYGcq9JRNWEBRAIRYAU0Y8I4cpozh9N14J1ltbbSV/evnlkHFLputDSvJmPPVUmXfcOJn5ssukIaVM0xHd4qwd16qSjKbxndmjlIFBlY1VIqGdHggAym0qJRdh399UHJqhXUkQxZ75vFbHPt16N0aJw9SzzSbT6vgY2lXht5xxhrQpEQuWMW1L3Q03BKsixDLEA+69t4xXonmRKqFzTjlFtps4MWQX26ZRFJTOZkWcTDymnm/bqwHOb1FyxlZyUVXfrp/XZrkOoC08UV3qWm9qv3y5664yVEklhc+JQ6SPAEWPcU9hySITljp4fBZNnuAdQbFaAWbICwkb7NjBAoesXaxk6aBsCQVwOZeAeiNkzBvcnWRLYo3Cusb7x3ksTLAi0pZM4N52zWjoBEQSsnXttdcGkgZBo34g5/GOQdSMvKWD94/zuC79YHMZVynPTJIIbcadDSkh7hCCQoJLtnJTvPdYD/kegBBiESUzlOxnA2QQqx9ENFvSAtfIRAZppxHCfMgg7z/9nIQHoBQgrpJ5VkgGLePF3KGmZG+CjrETQEfhUKHhBDADcMEilHErxQn67g7Z3L2ZQK0pBF25N2XHAkAQc3qGZHdgCqGITAmZ8omDN5QQzXv66TJzakxe0H75esQImVH7qVk/Xz5lBWvX6332xz/KtKqw+73+utRfeWVwgwImcEf//lKbIn7sSnGGnv/LuuvK1Y8+Gvb0XX+JJeTpt96Sk/W6h+i9jAQBiB/brYWECkWHEoa6kSND0WRaxZnpTk/UO7PiyFlnlbqddw5EYYnmZmnaZRepe/vtcA4WxEmXXiqXKLk6T59zy59+kiP0c7MrTVYC9pCes7eSnU2V6J6qn2WzvdAG7HyZClhM0PvS1miZFqrS0WYbhS/mmEO21/4ZraSRoAb6qLPUcSdhMDGANevcc88NMWooS4iTZdZyHrsp8KyMN5Y35nV3IAZq9913D8kV7HYRTcTCygdx4p3gXlZuJRcgXFhRSZaiFItZywwsrCBtWIcpm2JkDCKFByATIHgkV7D4smtGRSOlUdiBA4tf1IpEGRkr8JwOLPiUtaGPIB+QbLP4IVfY6g3XL5Yt3L1YqADtJ34sHw8AMi16RNuOzGHsuiODeAAIAYF4VyMg84xpIZ4RFjNYgXG59ybouDoBdBQOFRS/SYo+AIgdyqs70C3UIsPFhKIpFAj8qLuXGJvu3lksBCjdaMZYOZDv9nf0EYcpnXyI37eqEL/eemtZScmu4XP9/psq3KfXz1ZMuTUZqZv1OEkPaMjaSnauUkI+e8q9S0Fni2sjP/ZKPe7Q4xftZ4ru7nX55bKgEh/y8fbQozOnthPU/Ltt4YVlqj33lPU33lim+/RT+XW77WRw6t7/p8deevw4cKCcpgRhXVX+00ycGFzKOCDJCz5fD0jdFXqwq2dUxbbW18uT+kyva/vYrWOmzo/lZ/3sPn3Og5TsdOj1TtfPqO0HoBK8kOlFVFr1uf+r15s5Za2CmGZS5+xDAH2fOvzWeb2j9LhMD65LdiIZpBA54hmppZf+PvA3doYxMkaiA3PCiGAUzGfICzXuIIRYv4zgYUlmOzVKp9i8wNrGwgpXLi5Sc+UayJrF0oebF0sbVjCLy2MhxudkVEbfSQLxqa3Hs1AmJN36xjsM2aLEERnCxIKZ1Y/2YyFMD/UgCYxrQgYhfFHiCKHimrvttlsgrzw3rnIjd5BZMkSxIPJ+kDgCCWRRB7HknvQx/UPiFWSWv3NdLH5YJYspVWIEMGodNPCO2hEF59B3WDKTrgKQFHCdM26FxEZj3cb6SzmW3gQngI6ioC++E8AsyLQnb1xwD/bu7c7dmwlkELLtVSkTUDIBhYtlMk4dMFMwNn2QQ3FkEeVaXt15Z1lOFSt2BixVX6kymkUVVdTOCsH5hyrKQeeeK19PPbU8eeutstRtt8nWqkwBI2hWuW/0IIUDx9/GfKCAurCxGjm89pnhR1Vwo/S4WPuZ6xC5BsE8QA+uSSllSN3rStI+++IL6a/kHRK1ux7pI8jTh502Uv3A9ajUxp4M6ZsIRkkbUYe0eTslNTOkCEu7/r82Ql4ykTxym6/WebT+VFPJYv/tzHTmzv/UY3clEIvvuqscNny4zPPoo/LT22/LDUrcbnr++TDvcclupwSX+QVpg4QBxhuLFHMuak3DugUZY+sqyAoE5vbbbw+kCIKIK5UCzJAxCNCtOkaA2DksLsQO5gJEjR04uB73Zhs24uS4L/X3sMhBECF+tIGix90txrDGsXDDlQzJof1Y0rkOCRhcF/DMEGAKn3cHnhXSCiFkvkN2sdwx3+lHcytCTCB+uTJ8rcQR8YMQUaxuALc1W9rl+m6hgARGyaC9s1EyyN8JeSHBohjyWUqwEGAeF1IdgbhOZDnxp70JTgAdRUGFQac06CNA0CFE4gDhzGrearHFBQIe6wlxPJR9yOXuzQSUGNa4YrIaCwEZdhSCzbUTANPFiB8HyiOuDHpflW+/gw+WhVQJgreUhP1LV+ZL7rKLTKVj8qEq5MbHHpMWVfIznHaazKUEQ28yRUxdFJmIH3Fu2vEZd63oUCI/+eSTQxLFhMmTA9n59PzzZVMl+rYjx016XDb//HKREp0FlWjVqZKuv/FGqU3NmXGqROr1Geruu0/q78DW2Aleoq+VMI5QonTTM8+EOQb5PFRJ2XbapsbU91tnmUXGT5okU0eeJVO2sCHE7dEHqd+joATMzxtvLPcqyft00KCwF2yuxAUIADF7ECli85ifEBgsTlbOxKxptH/rrbfOGg/G2HMNapuxDRYEi3lAGyjFku+cN+CCpbQLiy8AIaL0CaU2CgXZuGTQ8my0kRp/o0aNir2bBM+KRZDsfErA4C6GtGJVtAxfSr1AMOOQJ75Le7D08/5QU44dRhgDKy/Dffhb0shGBhlvQkCwbCYR8lIK4EmhbYXsVEJcKu54LMi9CU4AHUVBBYATwCwgZglLQdyYOEpEIKSwsGDxw21WyPvJdcq5L68h1/7HpixMcQDaFqd9P48ZI+9vvrn8SUkxKg2n3yOrripznnSSLKQEu1nvqRf9XV25Gm1P0/bbS22a6/FHJWZXK4liq7Uo8SMRo0XJjIwdK0177im1kY3Z+fuEL74QZTTh9xr9f4cq4f6PPx5+x8l4uBLPeZQcbD50qCx8zz0y6IEHpCY1VygS3XL00dK+yipSd/fd0qgKvyYVn9i2/PIy8dJL5eoXXgjuSQj8H/Tzp3ThsIQShEw91K4Knj2Es5aQUVLxtM6fvT/9VEij2E2vdcicc8qMxBcqMWhV0tyqRLRjDkslyQ0sThAr5hXAPUl2LlmmhQAyhevSatSR2IHVDxczbjoIIFZDyEycDe+ZVxTTpf9wmfKdY4899nelT/IB7yEuVbNMkikMmcyntifubBZzkF1LBEm3+lHImti57pDeHhaHfJex4PnZxo/FI8QQOUXCEn0Yt/h0vuCeHFhisa5xb6ylZhXkWUtBQgsF8ZfEJxKnnC8gtlj/IIG9CTpGfZoD9emHTwJ9jQCawIsDMhERwMTs5ALCmuxeDty9EKhi3CjEG1HkFlcT1oVyAQsHz4LVMwojfhwAmRNH7lDT77XDDpOFr7pKZkx99wksYKNGyZLrrBPi8RouuEDqVQlCatrWXTfUmWtbccXO7dIeffR3LzjReaRYrBV+6yR2bVtsEZI4OpSssxtG/TnnBAsgiSEt1Pi77LKQMDJRCV37CitIgyrhmptukno9h5lwth5f7LqrjNx9dxmkpKjh9tu7iNkPSy0lPyoJGciWbKqgm1T50y6ABQ6C+ImSlu2USL708svBRXytksytlQTWpp6Z82TAgED40pFu/WPP3X9pG7d9801559dfw/hDgrrcn8Su0fcxrVfMdYoZs0cuZIZrUJql0ALMzIXrr78+WA1xjULOiCnEXWtEj1g4SAwHxAmXLkSQdymT1Y3YQly8ZKECCBp1AomZLRTEAxL3ZVY2yG967GAuMNdtNw88ALzT6Va/fOL1sELyjFyT9tBnjGkmckwf07e0HVLWXfHpYsD4QKwYR957wlR49qhaMEJYaTLIXrvMoXwLVfMsJI6wwChFWa9Kwgmgoyjoy+EEMAsoGsqKE+tGNkTdvQjQJEoo0EbimCi7ETduMAlgJUJ5m1WIqWHKgJ8ogLjyZsyTT8r4nXeWpVNB/h9p/7y4006y+EEHydxKCJovvjgQs5pUTF8utKmCHaP3HawExlRt1OLXsdBCUquEuVGVam0qYYBiz/ytbdttQ8kXiiGzQ0eNknpz5xLW/7S2ZTZIqo5hHcRPnxO0Lb64TDzzTPlugQXk2y+/lKmuu04WhDROnBjuHd11A0BK/6X9s5n+bExdA3JHUefaqLuXDEtV7um9SDLLf/V59tYxuFuJJKBECFa6QvejJqYToodrH5AEQcJDnBjPTCCmFfepWRGpTYcFy2rUYfnkXrhr7T1gMWNWLSxgJBiYZRAyRgIFe6QSGwc5ZCcO4vaMbEAOSaAifiuOJZB7RXfewGqH1Y+kCwNthFRROib9mjwbbkIWc1gxcWmzrzDxgmb14/mI9Yta/bCgEe9LEkU0ixbXOIksN+ncARBJ+iyfLSat+DQH1+P6UUJdCAfgfaa9JFYg35gT0evw90yu4kqSwUILVdN2vkdYAdbW3gQngI6ioC9HnyKAPG62UhDpoC4XK/1MLlFz9+K2sWLOSb6LEECygOPGKSUBlB4WAVbL9BPCn4PnsqM7TFQF9cbWW8sqL7wQrGHkRT6sBGbQKafI4vPOK4OuvlrqVSHXpLIw2/VvYdcNvQ9bntWmMnBBu5IismV3ePXVkHk7xzTTyJWbbCLDBg+W9s03D8RPmUUo5lyfcqulo0MJB0Wa65TgGaBnRCFmsgWxE4jtvBHqAu6+eyCPtW++GT4ztKkCbNU2Z6Ln4YUaOLDrGQHtmHzaadI2fLjUKKlhX+C6558PBLFFn+kiJUPHKjkzKx3uTyxGcUhPOpibWJeweDCOEAYSDog1K2SOQgSwIkLOWDzRJuLdsIDxf4gJFjsKFlvSBrXtiLsiS9ZCKAi9gMxBBlHmZGTaFlkQPOITjZwSfkEGMZYzgAWMJBGuSWxsuiWP56SMCtZSiCgEZddddw3PjcWMeUzWMUTOLI24Vq0mIM+BC5tklGzgmrvsskuoU2gLs5eVrFNjDosjbcAayMKNa3JfrI4We4hFkt+LybJFdmGBpM/5STuMDEK644wv1kWsfgAyGyWsmZCNDHIv+sR+lhrEKBJaw1zIB8w7vgPhLXT3mWqF9n2f5kB9+uGTgL7MTgCzAOsDCjkagB5196LYEEilyJrDBcxqtZAM5EJBAD7KGYtTVMjHlTFvqWKc5eSTZa6UhfVFbfvnRx4p8y+3nPS77DJZgJp8RvwWXzy4Z9uos5iy0Jn1DcvZW0qKNvvHP+STFCEkIYHkAsgFyRtLLLaYDH7iCWlUIlKjCs1Aq9s220w6Zputc6u0FMEwvKbHeSutJMdcconMqQrhv0paZn79dWkcN05qUmU82uecU9qVyNRnctnSH6m+4SlfVeKwhB4DUoktmdCibb9DCdEZl18e+pfYzk30+SAKWKMgesRfAsgNpUogQtSOw1KFVSkaA4aVFjcspAvLGfXpIEZkcGKlIjYPEhQFBMEKJZPBGp2zKMg77rgjEBmSHXCT0T5+0i4yh6lNGQXkBosi7wCJIFYCBRICCTQwd6x2IG0crOSd9rH/cbro4Vr0zQcffBAIIOD7XJOFlgECRdt4Hp6de2+zzTYZi0QTZ0efYv2xsjb0Jbt1RK8ZBeQSKyQuX6zi6SAGjfviIfhHpJg48Y/UqksHsYdY/QrZwSIXkEVYVc1VDMxNDLlNd/FD4CDcECGeActfvsSNa0SPqJwoNRnEOsvilJCCfABR5lktHKE3QfvbCaCjcOgLPKUU7gOwOmXdAZKHMjNXD4IWqx/WgqTcvdmQxLZs+YBpgJKH9KKwUCJxrU/fv/OOfL7llrLK552Fmb9RBfCkKufZd9lFWs46S5ZVZW409sMBA+SfW2whix5xhMypxKFBSaNZ3Cwe7nv9ORPKRT9DcGN9gpTiLoT0DFbCh0MtGnbPuSEWEJewjk2NPsv4PfaQ6Z59NvwdaolaeuCEE2SVAw8MBYIfOekkOVkJT3pZ2Z+wPOoiYR39vxVexmlsvQG9gRZE9yOw6wPagmD6Sfvw2Z12kr/cd19GIgERsXAExpm9ZyG6lBzBOmbkAlcklioIFK5GCCKxZOmA1EUXN4wji4j0enssKthhA5KHcsQqhns3HYx/NGGKdmym5Jq6eJCIKMjWxfUK0eNZsYhxkM0eRfo1IVIQFerhQWYN9A33wn3Kc2D9oVA0JCBKskz/mRjje7htITq8Q9F7Yemidh+Em4LX9C+uXIgeZArg7qV/jShwP4gelvhHdQHD2KS3k23eILV4Cmgj1lcIOG3DBY9lMW7sYaHg+SE4yCgOZBxzyggh8wwyz08WtEksLI0ARq2DBkigHUmAa1OnkCSVfPuS+Qj5xjJeSNxrNcMJoKMo6Iv121vbR4CSjPPYbH8FKUJgYj1AiZbC3ZsJKCfKHaCoSgnrB4Q4yhLSizUBJUfwO/fnZyYy2Kb9+Oruu8vSd989ReFi7Gb/nHFGWVQVkX3+T/3+sXr9B/X/1NOj+LHRZ1rwwx//KBt+/bU8psKa7xyuBzasZ5RkD1l11WDBaVHid5F+tpseplYgXrfpcdfCC8vi22wjmyoBme7/27sXeHmnen/gj3u3c06lOtFJLtUpRcmlJETpuJRLR4g4/aQSEX4VuSREVIToQlG/E6FDEaWkG07knlP/UklJVylJ5NL813v9ZukxZu89s2dm79kz38/r9fx+e8+eeeZ51lrP+n7W53tZZ51VPTYRqEe7p/S3o9Mhig6hOzn15Xnp/uYnAvXS9HuBinro1nrpqNP6B9J1S/RoZ8Z+m4wJ59mjmuSBDvmpRBQ+cNddFUfT5ekodAx5kogg9guJ5eJnNAuQN0k/xpvYvfIaIoKktcIYlJAgtgkxVsOt9KUgeWoecmWcMvrIkLp/SFk78qiPXR8Di8TY5qyQJ+RH4og6ggiP70EmnAtxlFlJDWz3TLgf5xLLSGEuoH4i9qWkB+Osj5FV5ASRBBnFjDb3suun3CGG2ubMM8/M7kwwPtUQ5DrX1kDhcz7EwXNrN5KyaPM5iSvuFah3yKD7mAwUWeq8/kOs3pgWGZRLhE+WaTmf+3K+bjKO+wX94zo9x8hgUYPdu5jIqVy+00EhgP4vP5fx2A8yaH4yxqdTpobHgIpN3R41vpTuJwhgYPpID+nUTGjE0CkBFLuD+Jl8BunubQeTFqPkGBS0gXvzv8MEbT7xM8PLeCALXGzUhBK8bwK+8YwzqiX22KN6TjLc8P9Su/wqkZWVb789F3UuuHGpparf7rprtVoiEHcmI/5PRxxRPabmslXgZZN0SN0QVSU6qx6mjVp5jY739nRtj2r2m39/8pznVIc+4hHVaddck6+ZkmdHjhLuj4C97ZGPrJ6y8cbVC5PR3veOO3LtwPp2ajJ079lii+qKRHQ/mvr78u98p5q3wgrV2xJpeuwNN/wjHjAd56bvv3755at10j2+OJ2rRGfS1i5MY2OFRLqeu956OXYUWeGOpcogYggP1yUio84eGFOUCQpTXVGjcFCqZIpqc+7Qc889N8fDOQeiRnVCDJFAChYgg0iIWL+JwDgbW66vlHCR2KFQLlCrJE14H/JJwXLO6SonFjLOUXYRYcDF9nW6mwPXNGWxHQkGxI9qKi6vU/ceAueei5KorWVJT5cYOZ97VCgbJJF0U2dwUPDcGovIoIWc/7mMja/yLGuzQXAI46euDDrAd5V5phtCSNEUuyl8oVsiaRFgYWAuHzW+lO4nCGBg+kgPZhDANkB+qBzeSxUZpLu3HcQzCVyebsbmZCgTssnZYQ4pRzswHIigNrn95purR73vfdV//OQnuTgxte/Lyag/5dBDq5VXWaVaKpHDH37609UdZ59dLbnaatVqBx1ULZF+XuK9760WrSlAdfwyHV9JxyvTMZXeqdf+vs461b0f/nDVaCbn/O5nP6v+mIzuKpdckt20rmn/dC/3JcP+/kQMHnHVVdUS73xntVhTWZsIjWQk7+eSFKuYyGB+LR2XpuPIlVeutkyEjEKk1M9t6V5+8KEPVXem/1dK5372JPFd2toepFQ0hJAB42pEbPQvUicoHyFDaJAxxnki6DNKE6NG3UHInUtSxnSzxqlkSFEhMa4Pqeq2CHqBBcTBBx+cY9/cvzbjWpUU0gmMNfdU9selXPk8d569iv3P5aoINZWtEyKjrah+9tkF8YiuD6mYDqiI3L1lf1kJBsgzkjub0N6UV7GUiJ+Fa1HwqbrIdEkiQeyNNYf5plty1QnKPFMO1wf6q1MyaDxdffXV09odSd1FWxSWuNJRQmq7IICB6SM9jGNHALnETETtwBVV3L0UFS4urrmZRie7ckwHurtMxGD+6GQOkaV79X77Vc9MBu7Jzc9elIzGHxMJWSkRshUTkXnEH/5QNZZZxmapLE212JlnVksceWS1aEu8GNzy3OdW3/3xj6tXpJV9q+5iQLqie847LydyLK6kR+qPxtJLV/el8yntki46v3fRNLkvrlhwU4lUcOX8pzylemG6zqctuWS1xBFH5Gzb6cBV75L6YNVddskupOkowIiKzNSy5Zcgdq5P6l8m1LffnvuZIsNY+3my/mDUne/SS9HSKruUuVensz8qGOf1LdJcF5eyjNtOwKi6Fu1T1GquXNdYMnx32mmnrLAV1+xkMD6VcCmqH4Ki8LJrLPXvjF2xuBZlsorL+4qq5XtaCQWVjgsbMQLuYuSRS3k6QJgpfcWt7dyHHXbYrKt+lDKqH7IrTnmqhUS9+DRyiPD7jP+7dbV2At+pj+vqYIE+K0crjFML8unsj84VL261KO+jhCCAgZ6QHsAggAkmJG44hK+4e73HRC8zcaoVar8xWQma6UA3u58y6bqfTueOX37rW9UdO+5YrZmMLfxs8cWry5LBf1Yy8o9caqmq8fnPV0899dTqX1Lb3f/4x+ckjMVSWy7aNLZ1/GX11av56ftPamZsvjmRhuPuvrtaKhEhuD8Rj0XSZL/Y5ZdX9ybS0Fh22WqJo46qFm1mcMLf/+3fcvZwYlXVEj/7WfPViVEIZUH99wfWWCMXn170yiurRS64IMf7qUy4dzpu3nDD6k3JsFOYui09YYwhejKXxVNS+ahFiFE9ntL7EBgG2P9IDiLjqKta3ieuz/kYeW487lqq3XTds/UtyUCMnJp8nbhSqV9Kmig1A4gxokcxX7BgQX7NAkrdwU6L7wq54NIWWwj1enueT4si52xNjDKmERn34fA7EqMNkRgu7HJNYiepfkVJQuBKm7cC2Rbjh3y4N23t2XHfXPkgm5bqN9P7dreDGDfEWPtQTLshcO5Ln2o/hJDqj0gXUl3Idz9R5qIyLzkKWsmgZ8P8XK/n2Ck8MxYjpRbjKCEIYKAnpAdw7AiglS6DUmDSo/qZMCkpJUPO+2T9cfvNVOxfAePDmNZL0EwHurc+yZovyjEV/vbnP1fXbLddtU4igKZ/0XAXrLJK9c+JmK2SiNNjEzmmsC06Se20gvtTu56cJu+9Tz89E5inJeL4xWc8o1qlWY8sF24++ujqgVe9qlr8xBOrJWXzJtJUSrPY1eOB9darFrvkkocVj5aTyfFZv6MyqB/yWrrnB7bdtrovkYxMKFN7KCb969/+NmebfvGcc3Ks3zLpWnZ505uqrbbaKrv1uiVYXFUyTUtNOfF/iFCpQUadQeDqRBCMyToZ9HfGl2G340Sp3YZQcYeWLbGMU/3Z6XU6v/hCcYXguhAtZWSmgu9CPBEgJAGUWvH81IEsiYPrRGGjurseyp+x4b7dLxXQmGW4kWkZ0O6RCiQjWhkYhK4OY52bHZkUa/iFL3zhwRI1Yv2QASqd5xqhtsDTdsI8SqkaZNK1UzLrQIYQ+aJsUhER5kEkVXQD8wTiR03WF/1IHNNmhQwiwPqxkEE/dzJ/dAt9XVcGi2nyXa5Dn06nkLNn27ihbI8aUtuMNQca65vvB9JDNrYEsO7upfiJCao/T5pGth/DONOuHatdRGG6u0CA63ef9Ym00/nihkQwnvjud1cr3rewTMkV//zP1c3JIK+2zTbVctdeWy2ZDOmi31u4w0SDOpAIzSJNFc+3lW+5e+mlqy9tvXX1jtSOMoy9fsyznlXtceut1WJ33plLv9yfyMJ9Bx2Uz7HY5z5XLXH44dWibZQ99fn+lvrhkT/4Qf4d8UPLizbrOhqJvNeLSYPv4DbOJWJqLnWGhvsUoaHSMRIyTBEFZU26dQ+KU+KqFGOmzSkoSrtwj2p3yRcKE0vm4GITD6cUixi01tg91yYLXdwgYuR8lDnXini4VmNECRc19fwNIUJg2p0PnAOZ4lJF3pxDcWLKJGJDZZR0UcrCqJ+HFElioQAhiQhUIX4FCJmFU0k4cI8l+9WzRSUUs2gRVa5R4WdETZyhPvDdBcix71QCSFsiIuAaC5kroA5qQ9dJ9fJ338e9qz8KXD+SZ5cbJWdsewgUJm09ESwKxUNKZimlcpBOmdxTZQzPBBAj7a7/y1Zu/QaCaUHiu/xfik87uOGLStdP6JNymK8tiP3sHn2f56nT7+WelwWO1I8aUjuMNQca65vvB5JRGDsCaEIzoSAk4pZMDhMpfGKHkIGZTgIpRZkZym6hSx0mTP+bIzqdJ25LxuTnibCt21Q5fps+97VEVFZ617uqxhe/WD3z9NOrpZtxT3bNyMSvWVDXPrb24IW/J0L9+7Ty3utrX3tw8/sN/+mfqlOToVqumX35R2QskYql1lprYbzgBz5QLdrMFi24Kxn9O7faqnrSV75SLdp0QSutQjt7kPila7x1vfWqRT/xieqxyy5bLXLlldWSabJf5JZbql8sv3zO/r06kdNSgBk5UZ9OsgWSVQcDw+BvueWWeVFQB3UK2UJKjAfnQmgkOBRi1Uo61NujAFKiqE3twHiLufOd1D2LjbKrBZJU4HVZw0gPNbC1lEwB4ur6HMgdcmSsy5ZFFgrK9SFkiBqVrV6qpYAix+jXiR8ixQDLYK5DjGMhtTKNJUiUUi11aDNKmzYtsNihdjrqsDBDVPUXAlLK2Yg/rE9f+stnLZwKxNBqQ5+rw70j4MgBIiNGTEFrO5QU2M0EcXWtCKq/yxyWhd2Jm3yQKKRIcpYwEWErM8EFLChL8Wltqv0tZqiDXM8WFf2E58lY9z3UTf1W73NjsBwTQZki45zKPGoIAhjoCelhGjsCyBhy4zD2UxVEVQiWssBIzCQEqzPG3eweoCsdZeVciF8nc8Tfk0G5MhnD1RNZY9rQiq8st1x1/2GHVY+8+upq2ZNPrlZuqi/+vS+Rgn9On4E8gNIErIizsioKML/h17+uzj7//GyolGe5IJGRZRPJcCU+dUo61PrbNr3/kES+l20mcTiX93DQMyUKnGyeDrttnJDOtXb6uT7V/yoZnS0SMbnqnnuyWoN4cN2K2aMu1Q16AYWJ+ltgDFCbkJk6oWJwCsmjAHEZliSCOih9yEwB0oEwtJIpREr5FoYIUeGCRWbqhIeBQ9zqr1mkOF9RwuqwOBG7xw2JYFLwWgmXvzGk5VG32OEmLBm/dRjnCI6YNuUzEFFEo8DnkKIS84ZAF0ImGaQdIfWcie1zb6WcTZ1ManuFuYU7+DwXunP6Xu2PkLeLZ0N+BPZ7LwJXVxH1v+tEqJGWUvpGjUXKJjJZ2pSy5bqQe+PVeOA+trvIMNpXBExChHGsbSmjswHjCbkubUhFrxef7iVsxrmNT0lUFFfPFJJnfPhb8WzUx9tEZFASn4SdUupolJDG51hzoLG++X4gPURjRwBNHib/Tp4dOxAIPFdcdyZhUmUAO4nLgvqk6DAJdjo3/CQZ+UWUUWlx7aE0ty21VPWMpkrD5Xp3Ou/jmpMubYrpaTfNX5GOc9Kh+PIG6Wh3Jc5SpupC+OCv6bp/t9Za1fJXOMvCOntIaX1av3uxxap3pPd9JBlsA5gK0aryAOOIIHFTUqMousVoMKDz5s3L5ISCwogpxVLUujqhKDAOqFGUK6UlSjwYUKrE5lEa4brrrsukwyJi1VVXzcTPWKrDtSAnvhMJqt8DVcWOINzS+tS+s4gKsmNR4rqp14gQRdN7xYKB+0Gy6moYAmqLM0kRSCbCW8ib8/ubGoLazO8UL7F4yK3xROFURgWRawftVwgZMuie58+fn9UXY9G9ih3k1nZtCAK3nHjJXpQjO3Qw8J4Z3+M+Os3IpYhK9NBXwE2sf6lq2r9dRvFswfONEFEh7Y6CGA2L/Tde9Kkx4DDuEGlEUDt61joFIlnCCSwK9MFEMKa0i/9dgwO0S5kDJY5YDJbi4qOEdH9jzYHG+ub7gfTAjB0BNFm0M+7tQI0wibUa7kHDKl/W41Q1ysqk554c3RC/uxJxuCGRi/WS8eNSpRt9Y8UVqyUTqVo9rb6f2BwaiN8DyeiX/W7v/6d/qn6fJvRlWmLt7EaKfijU0Goy6W0HpuPaZOgPSORl/UR0lkif/3v6fZE0gXfzIHMoc+b4dpM7UmKVL64LQUKmBIzbEowxR3y4Qhc0M0EZJlurITQMvb+3gksUmXE+CQPcgN7vnAyw8yJigPipyTdZAebJ4HzIYSlTwWCq6YfUTNWXwhnE7B155JH5Z4YWoTFuECyKF1Kr3qD3dZKwQHnhfhYrB9y6Ml39P11I4FAsuZSvsbAR+4fITBf6SKII4gzuEcHspFQIpU97KE+j3RBiJIG70HmRSYdnqihaFhn9dnF2CkobV2hJVJvpmORugcRZzGhDc5lxaVxrR+7zica1xYy4bG3t2Wyn/E4EfVVXBsvcSD20yOHSHzUEAQz0hPSAjB0BdMsm/U4wqHp8U8EKmjI0WQkN91EmPDAXdDIfqOl3zcEHVysdd1y1bPo8fDNNzLclkrT65pvnifrvaQL/XSIBT07k4dFNFamRjOQDyXAv9vWvV4vUXKhFvVM65fh0HPOv/1rtQTVKbSw+729bb12du/HG1U9/85vqP//zP6unJgK2RCI8i591VrVI89obj3lMde+7313dtOaa1fmJ+Kz8wx9Wqi9SF29fdtnqX5DE1A93Hn54dUG6fq5dsXUC8Se6Z+1yVvoOWYBFWaMCUATVz2P0O0Eh1hYNFD6kgYGjhJXSLt0YqgLnpUoqK8Jt6xyIH1LTSTC/YuFIYskOprSJ4/NZCorzOYwlxE+/OiYiD66HqonMuh7nQa6R0+ncH3A9ux/9QDX13YceemhW6XpR1riREcqi+vlZgkwnShOV1Pdb3IHFgCxtqmkdxg/iRdHyPcXFqQ0RlF5cnJ1Cn0j2oTRzlSO5c83mI9vqNWpDzyESXUh1KT7tPfpFW3Nr95rJrN0sAsuiw+LKfDFqSGMhCGBg+kiTXBDAScCQmqC43GYSYtQUDy7uszpc/3Tdvb/63/+t/rD99tUL00QLP0+T8SXbblutPH9+VmMWTwZ7sXPPrZZIRGLRZqZk43GPq+7fZptqsYsuekhR5wcSqTg9GaTrbrgh77d7SzrXo5Zbrlq6mcH7QCJZ9ybC1Fh11fx79ac/VYsfeWS1xMc+9mCyiJjB+3bfvbonkb8T0uuyN90747p/ev11r351dXsiIr9vGvonJcPAOCBvkxEIGZs26KfeAePJ6IvnUz6lW/JBSWNMLAiAwoYMdrIwsA0a4yf5pPQTtVKsndhC6Kags/ZRfgRpYeiQEu5f8XLCBiguEiJKYoCxXkgMQ4yE1cmgtuBadD3c1SD2VKbrZHUo3Zf3OC+3t6QKxAi4jWUMq7nHuAMS6Rp32GGHBxXKbuG8CGVJLFLCRtbv2muLDl0ICpJ4PqQX4UcotIPrQJCNMWTUGJKxXLK0J4PnjEvSvToQW58v7TiRW7wXIOGF3HOFdqLeDjuMV31YXMUWVRRB7WksSi7qtS31FdXb82oBbYx2W8dzriCN27HmQGN98/1AeljGjgBCPftwMjByVv4mppkEoy1ezQRWXJS6ymESLd3m+e9kDrg3TbBXv+511dqJxNFI0N8LkmF8VCISz0vG818SaVgyEYBFv/GNBx8qtffue+Mbq0WTEVrs4osffP3vaaK+LBnwbRJB/U0yUnSTXyQSsXi6LpAN/PdEmL3//vSd92++ebXEpz9dLX7ssdUiqS3ze9LxQCIM937yk9VVTTJU6uZxDyJXdfJRDEcxwO6f4UUG63FajLx6XwiSkiBIhmB+AeBi0rqJRQJGXzwZcuYaGBIKIFfwVO1u3HDNcjW6LkZc3J7kEORIHzN6lCvZqJ24F8UmaiuJTMBlqdSM8yN/rk+7tSqI1BCkyFhClsT5UWP8ztXLxet6tY+4vMmuh3JmOzau9jr0gb5Din1XPczC37RfgfhCmc+t16nP1N9DXiW41NtYbKHrKnGNSD23vOxpZIzSI1FHTGAdrodbWMxjyVq2sDJOphvb6zoLqeYyLnXyCqnuxS5rJ4of5c9ixX12u2CZC7CIRdaNTePA8+BZ1obUwekQQURSKaZzzjknk30FvHvpi2FHurfRvbkOMNY33w8kQzqWBNBk08mtU5IoKozRTIIRUOaDUmIidK1eK0bUc9/ps//9j32sety73lU9Pd0zXJWM1U+S4Vxt++2rp6ZJeKk99qgW5dbNf50YjUSmfvna11Y7JKJ2SXMnj31WWKE6MrXPErVyJa3QyvVz//3pT6/+lgjCHcm4cQeqL+f+kBfqjFi6ye7Nexndsj8xQ8JoMCTcqUWlUy6Fi5Trp6hh3QAxoiKWRA/ZqBQj3zUZXB/CKJPU9QEiaszVIW4R0e0kvpRChzxaFIDMYJ+VjYsU2sqNmogsUNkc3IVi75AihKz+/RY0Eh6ozCXLV4yfBBFKpSxk59eWSJ1SMciwc5Vt7epoJXiTobz302lRsPXWW+fXKJOUPES0uOvdo+91j9qdy7sdkFhEyb2Cfqb8IcUU4Pp9U5sQ5qnGWDdw/hLv5j48r4XEiDft5nu0sQxf5NmCwedHEYhaXd1EoCci1drRz1O1o7AQ4R0WhcbWTIftzAZSm/RnEM9RjPXN9wPJWAUBnAQC9JW6YCxnGggIVxxVxrUymoxnp8/87cmw35QM7HpNw3hb+txXX/GKaoV3v7t69qMfXf3T/PkLiV+zHbhj70+kkGK3ZCJSizV3d1BI+Z5Eft6dCNqHTjopE65npWv6+tJLV8vc8o9SInWiJ/avnLeAonjvkUdW97/+9dXZn/98JlcmelAehbI2FblqhXbRR7JVJWz4nZF/ffoO5+S6lyGLQCAzAsGRIwoQpaYdGCFxb0Xhkm1JTfAZ4A6uF0umQFEXfbeizeL4CokBbkpKHeN+5pln5vchIery6UsLDK5SBAf5cr6ya4ixp53qqhaDyJ3t3hg6BBDZaQU3MOJYiJlzMqQIRp2sGVPc/67ZtXgPA90JLIyUxqmXrSlAQCmxyukIwneUuLsCtf+4vT+fxkMpzSPhxve3uwbkSswl9dDzQRUstQ39jTLr7+4HsVU0u3wnwo2Ylz2LBwHPhnYsLs4S72ZcT5ZRbEyUsieuz/XPVsLJIFG/z3p5l1YUUq0N/U8h1I6O1vAPY99iSxgE9c8ipl1i1ygiCGCgJ6QHMgjgJLAa5QbuJLOwn3BtskwRi5I1V46pkGv67b579bzTTqse37zHC5dbrvrbe95TLZdI2xPSJPlvidw9SNbSZIr43fe+91WLK8isiPKdd2YSl74w1/f7UZpQP5vO+/n0/g+lc22QJvEHieMSS1T3v+UtUperxc8/v7ovEctrEjG4JBHNJ990U/WENMn/+yqrVP964IHVj//85xwDVow2mMwlc6jfJ0avxJEVUEO4DNWkU/YEQUKUGEjKl5iwOllgVCinlADB36XERx1coAiBc0kC0MbaXFwdl2wJEfA+KpUDwZApWrJj60CuXGe9sDRjxXjVxxkyRNVS2kXsnu+TCNJKdvQ7A+n6y+fdL4LRDgiGGDGki6vcPReS5zwIWnHJGkOdjH1geNuFSyDSBx98cF6gIK5c7FyW4Fq4iLVt63hl/LmwS2xmO7gPu7FQdC677LL8mvhBai6Ft9WNj0wUpdJ3ayPuXa5s165vueJn2h2o/UvYAiLjugqJqWcUU748DwgwMjyq8WpCDNynhdBU5V3q0G4WJtqxPE+8I54lKj9lXPsuWLCgq7qpo4A0nmduQA8hxvrm+4H0MHVmCUYMjGFdBZkIJnBxQwjFTEGXmPQYNRMeY4HM+H+qle1NX/xidf+b3lQ9vxkn9ZNkOC/fZZdquUSYHp+I38q33vqwEi33/cu/VI1kcBf71reqxZpuNoOiPFz1Gn11eP2UdOyfjmevu2615RZb5PizDyfjq4hwKxjiel06hl4Jlfo+soziuulcCBLFzXmQrtaiyowkt2pdeaIMyNpsHdLIA6UO0aN+MR5lWy9wHnFxshC5lQsmIj8IK3cw1yRlzjXW38ewCTxH9Bit8xMppiZ+K7VvXamrEzEuWfdMzZOIMdHYRBC0I7JA4aLWMX6tENuGDBlD5VyIR7v3AjW0PBNUPdeF2BmH7sf9GoPIKsVPLT/1+/QNsud9CC0lBlmezC5R7aizSIA2tLMKUuAziHQhkqCtKXqIHzI+FSzW7EOsfifod31hnM0mtKexbzw4kKEyho05qifFdlSVq17Ku9ShHT3jFh8SPbQlxVSmvzFqzI8TggAGekJ6oIIATgKZeIwJtWjQ0BUO1+Vg/BhGk6eDUjARGbwrTYQ3bLNNte6VV1atU+svk5FZ9t57H0b8OCnt6lHfTr9O/ED024fTwfmpII2/ec8F6Xh9Ov6eyAGS3ArXzsUpO5R6J7C/tDdiRbVzMICIDPetwG2EpR1M7BIAJD94X3EXAmJDBVDUWXtxNXIpOi9SxZ0rlkosnxg2/WlTeQRUPBsFqQ4kRPwbIurvrg1p4a7mYpR4INZOX3HBKpXCuLtnpIhxakdWtBOj5XzaA2mkWHAZl2xv1+g+SzZugb5u5+YF3+s+qajUSYS03j7uFVHmKgekiJKKkCKTFDlucedH5IrCJp4NYfb54i4Xo8lVL17Q34vi6fPathMDrA+RP9ddxgTVttQa/MQnPvFgwgqFVIZ0HT6jXA23ufGlJqPngXuX6odc6VuqH2VyGG2ksUAN80wbR/Xkh06I7lyBMdXP8i6g7TzT4mE9a9RzzyfF2JxjW8ZxQRDAQE9Ik89YEsBi7KYCY0KVYWgYrEGgdIHrKT97rlufbRNdOzJ4azKEyx99dE7ogMse+9jqN2lVvHoiNsvXlKlC7hqLLSy+/EAysO99wQuqF6eJ82UIZ37XQsjVFTrfuuPp6el4RzqWSiQJcVFCQ/IFhcvBqFG+rMhdmwxahp3RRiK8X/wbEtgO1J9yLrFblCyES0wf1Y6xL6VT3L/zcQ1OtSsCd7rP1tUlLl2qVSFGVJiy80YrSt8U0o00UqZKQgTVDAHhugXvbXc9xhNyw1h5L0KJhDm/zzNo9aQFZEDtROTOPZbdO2S8uhafZwzrAe/GhvM4H1W0xFlKhEFsN9100/z7VEAikSjZ2WLpKKi+1/8F2k87cMUh2UiMQ9u6p/oipYAiKjO7jHVG23Vx82lXfY6gtouBQ5D1N/Ja4LsQ8hJWoK2oftprGIGMI0XaThyndqJkIUnITUl+cPSaUTybcC8SPSwoKNf6qRcYLxZGJf6VclzP4qZMGx/1kkCjjjQ25ubg6BPG+ub7gfRQBQGcBN5DVZFJOuZ04DgAAFb0SURBVBFp6QWa33f434FkdvJMI4M/TNfVSKRm/WQ44Zfps99M5GXt1Vevll+w4B9JHOkoZ3zgRS+q7k3GcclEGhZL5KX+twLq3t7IxdveVu2bSNESn/xkft/18+ZVn0uTOrIx0f6sBYw84lbIlfcjft0oACUTUF8hZtyLlFsGRRanWMKpDAuVSlA40gUMhp0yJGtQtwAB4zYUV8gtV4xvu/5m1BBJKmO5Fu5QNceoj9Qr8WdImEQFcXDUY/dQ/obAF/gOu5kgpqWtwDWVLewQBeqJtihwHQiS9xk3rWPmrLPOyopoASWMcocQdgJEzz2VzFpwDVTL8hrSa+9hxNnrSIwYP7GaSKrfxeUh1GoEFsLaqm4Ccqr9/V3po9bFFuOuj8QEItGIMeKJ2EvyAe2jvI77Hka7aLxwgyK64tfaPQv9zCieLVjsGSPCNhD5qRZnncD4spik+lKhPYODWpDPJaR2Hf4BMUCM9c33A8l4jCUBRLoY5U4gZoxxqhvgXlEIn8nS4Tkux1S4769/ra7aaafqhV/+cuWKhPZf8MxnVk/+j/+oVk6G95+bZUvakTuvNZ7//GqRG27IKmAdv0rHnun4eyItRx9zzLTcNUgM1y4CAJISuAan60KnsJnsxXaBfqAASEJgGCdqL22LBJVdQLzP5yR4FNLEhURlRI7cK2LB8CJo4owYXK/7HmoYVY3qoL+AAaKmUSG5rpWzKWpbHUgisuycwCBS0biB6wQLXBtlVWycc5XPWIDUg+Zdvzbm+na9SBiVVLYxolx3g3FR292jQFu6Hn3TCsqkdlKoGqisCJzt1oRDtEIbuRdlV6ha9TIurdBepe30h3tQvoWCXFc93YvzUZJ9v4VDyfAGCg8iYCHiNQklCCvCPNNbNnYK6h6FUv8i0p0sJs1RSGBRB7VfWZwYC8NIgCxMW8u79ArjigJtvraQKyp7IAhgEMAekSbQIIBTgMqgXEXdAPcCTV6IH3iGO32O/98pp1SPSQTh3xNZgWsTubgrEb810qT7iCZJai3Bck+aiKuttqqWSER2sSuuaL5aVXc/+9nVUk2V8MR0fFLZjg9/OLvQuoX2tA+rGCxEgdqjfImyDCWmrBtQSZAWMXZAAWHguUOpCpOpj9xA3KKIO1BbuEOnKuXDNUdh0teSBhheio14M1uPdTJeBLjPnz8/kxNubEojglP+RhXl8pSlKyu1ntwibu2K1D9i5PydklZq32lPiUgURSQCGS3FoNvBePJ9jCfFU9kaREnCRnFbM9DOJ34P2aSUFjWtDqopl5vnoD5dIDH15Jc69BfFDnGWOFKIseuS6U255V4WX4hcIp7qANr+kJLaDgiPNuMubuceHkYYM2JRkXT9MZ16lGCuoPoigsal5w05RgYpxO1c7TMJ46KT8i7dwD1Tzc0DxjB1t1c38qghjaUggIHpY1wJoMmFS6YTUBdM3oxaL6gTPz+bIDt9fv/4059WP9l662r9ZEzg9vS57ycD/oJkLB/djGtrJX5/T9f788MOq256+tOzIWYolk3f/YRELs66/vrqraedVj3r7rurBxKZ2qyZlDGdCVZcGndcqbfGpSl4H8GYDNQCk7qaZww7o65duBUpd4wdIAzIn/NO5sJERhBQRML9SjChHlKMZFg6L9UNwVNyhlqEGCJq+kFCQyF5CA/1Qs0/xrYAmfQ5iRbl/hBHhNE1U65A+RZua8TGfcnypTQi1xS+Qk5bIQHFuCjKoP6gkhVFrhVc1ly1EmNKDFwrLFwY5ZJgUwyzcdgO3KsMLrerLGf9VECJ5M41VpxXXUNKJKJL+Wslydq1jHWKHmNO1TRWtJfrR3YRXWQUkEPPGjJenlFtTdU0VuYKLGKEGVgAUf36ldyhPUtGsWdEuEEJXdBuxv1MgnJu7CHu+qkfC2WEWUKVueXUU0/NHoQx5zptkdokCGBg+kiTSRDAKUCVYUDrAcfdQBM7CvnzzJZjKjTS+69829uq56RJ8InNrrouTfYrJGPyL82YsVbipy7fffvtV90/f74aKPm1kk3M7UkFopKBTFdFjhHcboEYMOgUMiSJOxABQ9bcm0B9BEs7I0aO4k61Awh3YSEMFD3xbs6JNIHAfq4fmbKUIsqV7F4kAIFj9KhLiIL7qxMGf+Mqoij5vl7BeItBUp9O+ylw3Io11lgjEzzxgaW0DWVmIuXQ35xPoL/7qJfI0ZZcsRIeuKERQLGGiKUxhFRSWJVHKQafa5fKxyB7X0mWKfA93KvIJbURMUYi6nUSJWcYD8V151yUP+RRIpRkD/ej9Ia+4prlFtcvgOhoH31IqXN+pJl73P8IkT7ifkay3cuCBQvyfZQxWQcCzG2P5M4V1c+zQAkTDmGs9iMGbjIY+9rZQX23SCpksF+kcyKYU/pR3qXAPElt19/mJq5+5w60RxpXQQAD08e4EkC3XY87mgzUCpMQJaZb+B4GoTSz57XTZ/bmCy+s/pYIwmrNOLBfJQP4yEQMHtdM+mi328adL3lJdUgy8hfdeOOD9fQoZ1xwCITVNFilU3mQLkYCuWWo6q4khp6yQ60qqpn3gFgcrs6iDnEHSvQQD0eJE7dVarEVUIGQOaoa1QK0RbshyM2rJAiXJGLQzt2IENTVuV6gb52rXsi5HRCzEpcHFA9tRqlodx9IFlKF5BSFVDtwAyOR3MOIn89S1tyTftI3vgsQNqVaiutWf8pyRagmA2USIdV+yLfEGTGC7UB5ZGw7LXhuLBgXpf9cN1XQjiwlvg15RAQRY9fiPiilZZcLSSSILWWQiuh+uYWVypF4RTV1TYjiXIGxIQYOEaJklvI5MwVtXtzEg8woNjcIL/BdSP904oVbYaEmDOGMM87IIQuSeYpaHWiP1J9BAAPTR5q8gwBOAeoHN0437ifnd1A4/O857fRZvTsRvOuSoV7/O9+p0DG05+5kVB/bJEAoT6sWcs/SS1cfTwZzfiIJD6TvrAPZYxgK6eJuRT64qGSGijVjsBhqmatcrt9J3y2ZoLVGHlLmc+J9Cny2nZqKCJjEkUtu1hLPVkDVUyNQG4lpkzmM5PWL1FHG9HFR4ZAfKhkCKokEOXLd3EtqzSF/FMeSpYpgcVFzbbp+pMT1FfUMAaJSIn/cpciPe/BdDJf+br0XbuBSq07bMszc09pQxqxrQ54RPp+VNUx51HfGoHaiCnaqhlFanU9xZt9H7XOP6g7qXwSNq42S202sJgPtM4CoUfgsElphgcA9qJ0RoroLX1tYYBRwmbs3n6GeGWfFvYlgWITNtHuzUxjDMrmRbWOCwjnbtlmbI2gOY9jzWMggsj3d6+t3eRcwLqnhxofdfSwAA1Mj9WEQwMD0kcjJWBJAaFWUJkJx502luECd+Dk8n+XoBNcdeWT1lEQ6npZIxGTQac7oXcel4+B0lNB5ZTQYfKqKWLU6CWFEqU8Mgpg419gK11qGhQmZqofkcLHWwQ3IzYdIcRNyAyJAyI3JXIaqoHcql7i34iZkiJzTah859F0C5bmLvYfhh3IdlIuyE4vvoRQgYSUr1TX6rAxh369uHTXNdVFkkBVkx+/cs9qjTqC0gWBzyptzImOul6JV1CzwN+3A6CEiAt29hqRpy1KbjpsUwWaAJbHoAwSGa9Q1Gk/aiHIiLrNA7KLkFa9zq3KDFXe4rGD3SKnrBlRDWboIqfuUQGJBU+JZEeTpkCqKHiJJZdbPrefwXRJcqH5UY23VSloRPu5dYPCpvdq+jlb3psVMITH1vplNuK565quxOWww5lszio0B7Yhgd6K06VP92c/yLq5LyIGFFo+CUIDWMRCYGKn9gwAGpo9kYMeWADJ+ndw+5QTpmKr8gHOZ0PzvKCpQJ/hdWgHfmojZOk0XJD2t3TRYiB/8LRGNczfeuDrpu9/N7lbkAqmi0iEb73//+/M9mlC50WSNFpdtAXIgU9d1SiRAwKgYiCKlEPHyORm+pW4eQiZzlLLEeBSIlaQoikND7Lj2EANkph7fVgd3pqQKalr9Pa7HtbgOagrDr02pA8qacGl7j10zqGKIzURwP0ibzyNwdSWXG4sCVtzVMoUROiRsKiBrSFsBVzpVjHpXXOmMpjiposT4nVuOAUX+6uPDPSFKyCGyZCy5L8aRWtbpWKrDIgdRL+ovYoqwDRLGmLFCCW1V/epAjEvRbSV7kPbJ4B4KGUTstQ0Cg1wPOtatHfSlsUX5E0JA+euESM02XLexVtrSOPOcacuJMor1ab/LuyCS4lypvRZKwg+mM8bHGam9xrrBYrT0iGQUgwBOAZM8xYzLsh2cw2FidXRD/O5Pxv7KnXeu1jz33LzrRp3g/T0RrQcSObklkZKfnXKKp7167jvfWT3ussuqRiJED2y/vUC45rsXggqFmFDUgBpIAWOcEAtZsCXrlbuNWlWALJiI1e1zH2rcMbTcdIw5w1DuEfyOIHInUtZKjNpkQAbExHF1Iox1uEaqGxes0iCIIWLGXWUXEEkPyCUgaIjaVKVdCpSPkdEqW1mcmb6nChaSzKBxzTJInRpxZBLpBgqd63EPraAQIUQMrRi4omrpg6JmIbi+l3Iqsxa0P3VkuslHBYi4fkekxSEOqpSGcVEIkb7TFpO5qpEK7nckEZHuxpYZqwi1dqQYDyrWbSLow5LQghCVeM25BvOWhVdpy+Jy144UQovHfpd38Z3mFPOUhaQxPlfbb7aRxvlYc6Cxvvl+ID2MQQCngPguk2A7suHzRfUDz2Onz+SPPvOZaikuv6Y6U/DAox5VPbDbbtX9e+5pr63mq5PDBE79E1MHVBHkBvGZ6npM+lQ6qqF7bQfnKPdIYURq6tuqgfdQFLl9nU+7FFAWkC8Ey+vcoAy3mDwxYsVtjVQySJJFkAlkkfJKuaLCcvtRAJHBblyXyJ+2QLSUE+FuKq59cY+MEHLWLbjZER+GrLWd3Sf11H20KkQ+Q4WhDjK82pbBRbRdi3bUHp2Opcng3hFr57UP8CBQVD/3jNDNpEEvsW7asb57hmeAO7YfbVignwohoqyWhJZRQVmcaE+Lv6IGCn+RQd4rnNOzZ0yKG5U41s/+GTekthvrxouR0yPShDa2BJDhKGrWZDAZylYUpF+g2Qrxc3Sj+t2RCMGPknFfLxGf+lr6/mS47n3LW6qKi65D4uf6uUW5HhEq1yA2S2xVcb1R1CiDEh3UcSvECaFSfoRCiIgAo4nAldIgzleGiMQJ7mL/e12sHTIl/k1MWCu49LgvETqJDr63FMWlJFAUJlITlDChLNb7x/eKOZxOgLi+otTUCy9ze1EBOyHJ3QK5Q4iQA4Rosrgw7VtcctrGtZb4LMR5mAmG/ulG9Rs0tB21vpAYClZRBj0PvfSzhZLFiudGn9bDH0YNxqF7leihP41PKmsZl37upi2Ncc+07HZjxALReAn0htQHY82Bxvrm+4H0YAYBnAKy3hAi5ElzOXyuNJ1nsJPnUE2/q9Lq93knn1w9ttbs9y21VHXXzjtXS+y7b7VImmBb4XvEyAmSl8mKTAEFixu37N5AHfN3ShsXKdKHgClkXUABk+XLrYj8MZZAzRCDIy7LKh0xk4QgGYK7FhFRtqMdXB/XrNIk/mdorfKt7hkLKgKCiPyJ2xKP2EnMFpcsFdM12/9TTGIv890HP/jBHC8I9hHWNu6rn6DgUYfENSG5jFw3LjNtiTAXZZBKjagi5v4fJjJY4sI8CzOt+nUC11VPfDB2EBhtWVzunUCfUMaNX5/1/BVlbNRg/LYr72KuLMTa/+aaQqz1+2TPpTHsWRMiYWFqN5xhXtTMJaR2DwIYmD7S5Da2BNBkRzGYCgyd1at4JUalkEbPXqfP3y8SMVo8kY6np3MV3JuMyO077lg9+qCDqsWaE20dukZWq8SKsosDcMVwySB45VpaYWI2WQPlTRFfRLEofQXI2rx583KGr6LJIC7QTh71+MBOUTJ4kUjfpe0YS9epUCzi2WmbuX9Zv8qMMNy9QtwYkorIitnrNxhGpBy5ZTy7KavSDu6fq10/aksxnHUyOFskRF+KyeTeHgbVrxO45tbEh05UVmOGEoaU69N+jMNhRaflXbQdYo0k1om1tmzNKBbioTA89ZSnYqJFZGB6SG0fBDAwfQQBnJoAMrzf/OY3c8kLLiUTXKfP3T1pUr0lka9Vau7e+9Lnb3n1q6tHJOL3mOWXf5ghZ6wkVVg1c7OCSdnrrqUVCBIXMLIlk1e9Oqtuq3TkzoqbwudeEVl1/7huubLq4NZRCsUerr0adNdayoA4r9/FFzEQCAxjMSrlHrQ1dYgx5J7W1v2elz2m+reQQQYVcdGOjO9MtWVJaNGfw6j6dQJtidCVtkTyEOrSluV5pFpTw7Qz1XpUxmsr9OV0y7v4bCHWxj9iKJGMl4FqSHGXwU7J73VBFHg4ggAGesI4E0CECAmcCJrG4X2KGJvouiEwdyRS8LjVVqvKOhrV/NF661V/mj+/+syFF+Yt1GQsytJUDgPBtIUX4ldKLogd4+ZVWsR3I3hcmSZsWZ0K/YqVq88D1Ddqnpg38XztgEAggrIv1ZoTrycJZDqJEK3w/ZQwxoFSyd0GCCeDW5TBuU4GjQ2GD0ng2navg8qwbQUyrR19f70tEZhukmM6hb4sqt9cKnkyFfRhIdYO7Wq8mheM17obdBTh3vtV3kVbGh/mJ4tQ9T8Vl5f4JUlqJtRTLmahI3XovxKj7Br9XVkriqfEPvU1LWYKvG4PcQleYH7mESkx1cOEIICBnpAeiCCAbaBZ/L00j+eM+sbomkxMnJSBYnQnIjB/SgTuycl4XrDYYtW5m25aLZYImRUy1aEVFLvyep349TtOrRUIWz+UHG2JmNpRAzlVtHgiksC4Mrjak6o018igsYD4WRRQh1z7bM3FrW2JwJRxaUz1iqL6eRYYyslqLs51GLvUXH3p+Wf0jUnHTJH7mYC+lM3MRcuN36/yLsoNqc0p4UzCmAQ09TwtMiWbiU8eJBBAJWZKuSjgzSjkU+1S8cSSUMRyqi6gfJUkv5KoJQPfOEASAYE1n/HKDBuCAAZ6Qnpox5YAUjW4KerQHA5/c0zk7i1qFjLo5zoZLC4k5OCQrbaqLr3++uoHLYTPVmLKmXgvJc6m+mLIGFfJF4oTI0VzBdw/CBEjSQnrRkmgRmrLuUAGjQ0JHgwnt6G4xkEobtOFtixkkJuzFwJj/FN0KH+jpPq1g8VLyVBHDMSqWowVZdCzXApPO+ayO9PiRVwjtROh78c8Y9wJH1mwYEHe4aW1nqYkGs+O8IhBAgFU57RUMajD9+tX9Qdt2wj62FyDGCKuPBdUX/VHS9kvP5uvzW+e92FCEMBAT0gPRRDAhNIMrapfJ89XccchgyZCE6pzSIiggknaEEPIfYssmhwVUK6fmwHiirHSHEZXw0QwgVo9i/3pR/zbRGQQsZ5toqXvGAj9jeQWVWFYwdC3EphCrKfKwtb2xTU46qqf55TCidQx/u3aRpxnaUvvF7pRyGC3JVFmE54rY7gsXvqxwBKnLNHDAuMzn/lMVsRnCwggAsqjQf1G4iTRWbwoVUTpFM5TT3Djnjbn8swIyxEz7Xmpw98pmPPmzWu+MhxI426sOdBY33w/MM4E0K2b2P1fiJ9jItVvKvismmjcoAVIAgJIIRyl0gfutShh7o0x6Ye7sY52ZLAY3Zkkg+61JLSIqUR0ZysDd7pA1Km02lKME3dXIYN1Nauu+k3lxp/r8MyXkj0KOnea/NBaEgXxKeMSUR5Gm2yBaaHmmi1e+lHUWfsp7cKNKmZObN1sL9K+/OUv54UaFddYd22UOwTf/St1pb/ru+tYkJu3lcBCFrmHqcF1OB/yx2szTAgCGOgJybiNLQE0kTs0AcPnWSpHtzDpmGgQFZMF40opogqaiBBNBsLrCNNcNqruiwuJwmS1PxNK2GyRQd/lXhlQ6lBJaJnLMBYLGaRmUbC0I1Wr7O4y6qqfuFcKJwXMvbr36QAJqtcatMgr49JYGQb73Gl5l27ApYs4GS8Ik7JKw8hFzFVUv3e+8515dyEE0LXXk+MUpxYPaXcSBJASiCzWYdFH5RTXOExIbR4EMDB9jDMBVDrlggsuyBm4DopHt88T4liyI60qKQmtbhVNjEgUMohMMBDIICIzV8hgXR2SLWxinQ0lrJBBRlecW0l66CcZrN/rXKl1Nx1YAGlH92kRY+zqW+05l1ybnUK/Ii0UH306nWd+Ijg3Ql3IoOe+kMHZeM5dz3TLu0wE9yTGTjKHZAkK4LAvitRvNS8rah8u4NHCWN98PzDOBNBkLQHjrLPOyjF6JgXbgiGDnRQs9nnxNIgBt0onmbSaG2kpZNAkXcigiXRYySAVwb26vk7vdSYwCDJo8qf6uVeq3ygrYdqMe8xYp+aWTPe6a1N7chnPdVtjEVYveVKyPgcBz7lxZFw6LPpKrUH/D3oxUcq7uA51Qnsp71Kg/ZAo2bCI3/bbbz/0Y0LoA9JHrTzooIPyvL733ntnRRCKZ6Y1CeSKK66o1lprrfweP1MPIwlk+BAEsEeMMwEs0AQmaWTwzDPPzMWSBQ8jgltttVWeIOrPmUlF7JvPmFwm29N2MvherqhCBv3O2BYyOAzPNoXIvbpGikm3W5vNJJBBfaItp0MGS0yYEhDuVebrsN5rr6grYeL8KGH1e9UWJc6NmqX9Chkc1ji3ieBey37Fs5HN7Lk2HrWlA8muk8F+Zrr7Lu5M49i8ZH7q9V6dU11RLlDnpJQZL8OIt7/97dWrXvWqPE9pazGAdkySqKLvET11Vk899dSsinL5Wvy3loHhJv74xz+ef0cefTbKwAwfggD2iPRwjz0BrENzePg/97nPZWXQ9mkveclLsjKour3JT1kAJQ+sBvsRTwO+l8qGvDg814UMcj/M9HPuelxHmRipfp3s3zss6JYMiuOi+ulPCsB0Y8LmAiw63KsxJSZsKiWsNc6N2187OmZjbHYDMWAUTgsZqt9sK9eeK9dUyGAp4l3as5cQhkGUd9Fusmq5P4XMUM6GORRiu+22y3X9LF7EJlPuDjvssPxMg/aXrILcmW9LIWhjo4Bnp7UQNMXTWB82pGcvCGBg+kgPRBDACaBpKAeIIOInJowrZYcddsiukEFNCBSLQgYZCSv4QgYZsEE/88gTdweigOTKGJzL80xxa7Yjg+6r1H+jCIh/G9U5dSrVrxM4BwOpvZBBbaUdy0JlJpW1yeDZLUqY0kRiwIaRuIi7LGTQ2PR8l7HZzeJSf/S7vIuxsssuu2RXtn18FXcODBfS8xcEMDB9BAGcHFboakt95CMfyXtampgFQVPGBBdzE9tGrR8xNu1QN7iMBPWlkMF+u+IMhVLuxPllM/fTPTUMqJNBBFf7lezIYYlrHATcKyUMQetE9esEZaFSCIzxU8ggkj1bZNAChhKGXPVLCZsJGJtItbbUrvpIezomUqQHUd5Fv6rnR+0T52drt0HNb4HeEAQw0BOCAE6Oj33sY9Xpp5+e/6+7Eay2zzjjjOwqpqjYj3frrbfO/9frqvUTJmauuEIGuYsYW5N+rxmb1Af3xKAwJHPFaE4HYjgpnIg1xQRhKMpgITC9uOKGCdy3lBzEfpBxjZ4JSlEZm763kJeZKnvkGoRvUHTLAmau1WssKKV6tKVn3pxSxmZ51pHEfpd38V3cn5dddlneJ5z7c8w5xlAjCGCgJwQBnBwMGeM10XOm+a6//vqcPIIMMoCbbrppjhl8+ctf3rcYwVaUuCzfx1Aowlwng53CeSh+3GXIAdfgKJY7AX1lg3qKCVIi67UQPeoLY1uUQe5M7cno9rvA9UyhqH76E0GYKRVHO/vuQgZLBqz2HFRBdKSe6ofIW6gN+y4t3UD71QtPG7MO98q17bntlQfoM8kQEh5WXXXVnCTRDzUxMFgEAQz0hPTgBwHsEyh0Nj1HBm1IzgjKSEMGbf02KFUJiWMYZOr6X7JGIYOTJTN4L9UPwWE0R9nNU9yCAuQRP8RuIsx1Mmg8zITq1wlML4hKaU/KVp0M9kOhM+4pus4n/m1U1Nt2MB5ltJYC9q0JOdPpZ+NdmIsaeDJk7UM+W+Ml0B2CAAZ6QhDAwQAZVE4GGVRehuHjTuEmllU8KNdUUQsYxeI6QgQZ3OKadi1UMO+jIIxy4oPhXZIBtINEj27iGucaGSw1DGda9esE+kJMbVEGkXJkUFtS7Lp9JpAgCxiufGEL+mVUUR/HpbwLtMZgakftKYSjE6XVWNl5553ze8X9GTOBuYMggIGekCaNIIADBkVGaQJk8Jxzzsmr6y233DIrg8oUDMIlBsgg93Ahg8gAddDPRS0ZlIt6GIBsMHAIL4Wz17jGYSaDdVf+XKlhWCeDlFljUnsiMVORdONa30qE0rdz1U3fCbi3ufInK+9iGjcmS3six5MprRao4popf295y1tyvbxRbsNRRRDAQE8IAjizMDF//etfz2RQNjEXLTJIGVx99dUHZrQZBwHjVBddznAWZXDUSCDjVrbnK2pJv0n2MJFBqh+CwMgPm+rXKUptPO05WW08i5pStkeSRyc79sxluM9uy7t4vu3aoT0dnnntqZizqgUWC7vuums+r/JWG2ywwUi34SgjCGCgJwQBnD1Y2X/1q1/NZFCVeZO0CRoZtH1TP8hgnQyVemheK8aWC6mQQcZ2rpNBZIwyBJShmSjt0koGfWdpz0GSwbrqh+QO8y4t3QBh0ZbatJ6djQgif0IZ9O1cKkzeLRDdfpV3QajFSL7xjW/M48X4VAOSy9e5ZwNiDffff//qbW97W3Xsscfm18yHdvL47Gc/m8fAy172slx+S4hKgbhWMYoW0fq/lKkZ5bjPyRAEMNATggAOB9Qs+/KXv5zJ4Je+9KWsbBQyaJKeznNe4sF8diIyxD3K2Dq8f6bIS79RJ0PTLXLcD7Qjg5RBRz/bs6h+FCGq36juXKI9hTAw/AgCo0/VHUXlumAQ5V2QwP3226/62te+Vq277rq5Pb/zne9Ua6+9drXHHntU22yzTfOdg4fdlXyfhSf1sRBArmgL4U996lPZbT1//vwc33n11VdnBd8z/vznPz+HCBx99NE5lEVtVvPkhz/84XyOcUMQwEBPCAI4fDBZmwjPOuus6sILL8zqjklOzCAFb6pnnptZsLiaaN3EgzGwhQwiL1NtnzYsYCQQXdeI6A6LC1R7FjJYyHVpz+kadUZQ39qv2LjoRwmQYYZxiAwhuhJ4PBvatCjX2lKbjoIaSJkvO7V4zim6/ehbBEqih7ZasGDBgwkkiPW5556byZaF5kxA/73gBS/Iyp64Q4QOAdTPiJ0dR7bddtv8XvMXsm9BrL6qBbLtOC3yLJBBLdbXv/71eUwYD+OGIICBnhAEcLhhYhQriAxavSM4iCBC2Gr8daWJkLsHCaIclszfbkF5KWSQG457upCXbrJoBwlE98c//nE2ZAwmYzGs82E/yCDSg+iOuuoHdTJkEUPRrfct5Vp7OiwAjPfSnnOxXRAjRNczLPyjH4sYbuRjjjmmev/735/VPy7XQVUf6BQUO3OJvYWVxioEkEuXy1dfWngWPO95z8sx0vbvtf86wqruaoFnwvl8npo4bggCGOgJQQDnBnSTyVEWMTKoaOtqq632IBlE2OxPvNNOO1VrrbVWtcwyy/SNDJWYLESLoeomW3NQKESXsZ9r8WDdksG66tdPZWhYIYEBGQKb9E+1bZ2FgKxg7cktaExoS2067Akxnmv9aiEj1k3/9iN0Qdyvos7ahOrH1TvboNZR/dRKNc7rBNBuS/PmzcvPRh2veMUrckjHxz/+8Xw/7kvcdB1CK7iNX/va1zZfGR+MOwGc+xHPgUAH8JwjXgK5TYCMBtfHBRdckAsbI30yKRmQfpI/QK4oMErWvPjFL84Zr5QZpW2uu+66vLsGtWEmQPn53ve+l+Pf3Ct30lxz/zFY1Mo11lgjx2OJuUQIL7300hwfJT4LoQcKh1gtKqz2H2WXL9VPspJsVYuLF77whR3tWWwRwiVoQbT++uvnsWqhcsUVV+RanMgzUjlsa11k59prr82kBhGS1dwr+XOPkig8p+aFa665ZijIH7ethI/TTjutq/AH91Mf7+3Gfut7AuOD6PUekR6eUADnKBAhq2LKnO3nkDEEQqFpyuAWW2yR3SODmhwRzqIMUgmVqkBm/N/vsiuGqe+RGclFxLjNpSSVTtCqDCI21C2ERtzWKBs5Ywmpd79UP6por7AooQhqT0XPxYgWpVW82Gy2p2vqtrzLVLBY2HvvvauLL764Oumkk7JnYFjGjDCWrbba6iHzAmXb9SG9X/nKV/LWmeEC7g6p/caaA431zfcDQQDnJigaYqMoggcddFBWwXQlZY6L2L7EJkqTIkNgSzrK3aBAcSlkkHpFwUEGqZa9kkHkkrF0z4gfIz7KYASRIXM7YuC+R6lUTx3GLHWISlfKFPV78QDIRiGD3MXatbiJkc2ZsqP9LO9SoA0vueSSvBj0fNjSrV46ZRhgDJub6pg3b16+3n333Tcr4uYMpWlKRjLPgvtoTQLh/eDlAFUTxBVqz0gCGT8EAewRQQDnLqhEE5E63cqomiCRQUZno402ymRws802G1hslO+tk0Eu2zoZ7MbF5VwlPoqh5iLrh1IyrEAO9JnsR0SoJLUUZdBB8WDotIdjLpNBxF5SixJIE+1wMQhwNSODpU0RTmTQQX0alE31vIpttFjrV3kXY0NcnV09/M/N2o8YwplAPQYQlIE5//zzczyfsaAmoH5qLQNj3H/gAx/ICyVhMBTCKAMznggC2COCAI4+dDEFTRA2dVCMmRW10g/+n26m8FTwvVb+iCBCiOAUMmiCn8xQcQkiB9RESR7I4yiDMXO/3NrIwUR9UrJftedcJYPGBZKrqLPrRuxnKzsVGdSO2lO7sqd1MtgPMjWo8i6SoHbZZZfsNhdbt+qqqzb/MjfQSgBLIpuEkHohaAuhAnPXbrvt9rBC0KMWDtIpggAGekIQwPECY8Q1TBn8n//5n2z4xA+KGaQQDmoiNcwkMhQy6DoKGawbWq8zlAxmyYochEtwWIAUUzi5u+qqXyeYi2SQYoXoGguIvTEwLDD2qHSFDBqzrk97TrVgmQgWMjfccEM+V7/Ku7jOT3ziE9WBBx6YCeCRRx45UmEBgc4RBDDQE4IAji8YEiUZKINnn312rjkoVpAyaHU+KHerIee7Chn0OyPLOHL5+h05GGTM4jCgxPpRMtxvL0rsRGSQkjUsWdL6m2pFzZX4MMzFxY1BZLC0KfcjMqg9O4lr9flBlHdxPRQwizixfsqkjDkHGGsEAQz0hDRRjTQB5EIQL0Jh4VrjblB6YyIgQpIqbCsm8/Lwww/P2WujDmRQyQzKoDZAKDbffPNMBmUVD8pFZ/iJ8ymlOhhWAd6UQQRwFOe3uupndwsEoZ/3OWxkkItSCALCK/HBtcwlGKMUy6IMal/Zu+6jXcY7lROxp/71K7bRNdgVCPlbZ511cpav7w6MN4IABnrCKBNAZGbHHXfMJNCkqZgo1wkXlDicVqi3hhwedthhmfR9/vOfz6UH1GdTk2xcQO1Q40/7KTxNuRBozU2sFl0/XbIIiv5AMJEDZKEYWt/LyDpmMlNzkEB23W8/VL9O0I4MIoLadCbIoIxb9+t73e9cj9UyXVqolDEqbq1OBvUvsksllOHaDxVdkswBBxyQ6/vZ2cO2bv1QEwNzH0EAAz1hlAkg0qZQ8Ec/+tHmK1UmGcjM+973vuYr/4A9KK30lRso2HjjjXOMmsl3HIGQCbhGBtXyssuC9qMMrr766tM2RHUVjNLaGhhPkaQYFUOLIBYyiEzMtXnP/Up64AYdhOrXCWaSDJb79V2SPBRqHjVbZeqU8V7alOLnHks5m36QP7U93/CGN+S+sk+utgwECtJ4G2sONNY33w+MKgFk7KgrSqDUXbjKJJhUv/WtbzVf+QeQEIVUHQX2rOQ2bq1hNY7g2rILCTL4xS9+Mbu2lJVBBgW4d0oGqUJUEv3TiQqGDFJWChksBX25icUNDvscWFf9uARnww3bikIGHYi2HTcKwe71+uqxjcNyv4NEKe9iXApbcP+IoedDe4od7DbekQp//PHHV0cccUS1zz77ZE/EKJdACkwPQQADPWFUCaAyE1bil112Wd4WqcCE+ulPfzrXxWuFSVoNKqUFCibao3LcwS1FKUUGFWql8BQySGVtNy8hHdrdrgxUMP3T7fzVWtCXS7FOBocJw6D6dYJ+kUF9I5bz1ltv7TqjeS7CwmSi8i6eD+1pnHIZ8yIUMjiVG1xhbEWd/W+uEoM7yu0YmD7SuBjrgRFPRY8YdQIosaG+F6akDq4U2YitQABNuPVNxdXX4oIpe7MGHg5qB0XwrLPOyoHqXLrIoJhBhtEQU+EfSeNyFBvVj7IVCAcyiWD5H1kpZJCrejZRVL+ics4VFQwZRKwRl27IoKxuKhiViuo32+0/aHRT3kVNu0IGhZhQCT0Hjvpz4FwSsHgpJGBRAPuxJV5gdDHuBDAiYQNtUbLzkIM6TMSMWTsgDt28P7AQjB/SLGlG+ynmqnr/WmutlQ+JNfvtt1+O47O3Z79qlulffeOc66+/ft4aj9py+eWX54Sen/3sZ1mJmUmImUT8lOlYYYUVcgzqXHKBWgRZOLnu9dZbL6uWiCAl/YorrqhuvvnmTGgKqGBUP+WEqMBrrLHGSJM/JI0ypy24eMUZT6U86/+nPe1p+Vmg5iF+5hXJZQcffHB16KGHVtdee23e1nGvvfbKu3rwRMwU+RMjrYi0OEOHBXM9Dpr3Y4899shzqr5FTpW4qUOBZiWk/N379txzz7yYCAQGiVAAe0Sa0EY6CUSigizgAmrMFltsMWESCALBpVmwySab5BX7uCaBTBfUueOOOy5nLyIGFDq136iC1MFBJgVwvVKxkFFKHAON3E+lYvUK94j8MYJzSfXrBIhtUbGKMui5cM9iP5/73Ofm10YZ/S7v4nwLFizIZO973/teHjdvfetbs8eBij5ToN5bTFHrgRdE6Syk1H3aos17XKfs5vnz5+cx0LpFG/f20UcfnZ85+/N6zsd1i7aZQriAAz1hlAlgKQNjRW1Vq3bWySefnCdxK/Kddtopqx2FDHIXUz24iZHEc889N1fbH7cyML0CAZM9LctX6R3bzSEP3Fv6RDtrTwZCgg5FZFDzGOJSyGAhLoUM9kuJ9B1i/dzjdGMb5xIQFyEU7hfqbuJBl7WZLbhX5L6f5V0oZOaeE044Ie97K4ZQ2aWLLrooEy8JV0jVbAC5RQLF9LoGYTMWyCC8RnynhbJnm1r4yle+MiujFnaguLx9erUbVTEwGAQBDPSEUSaAQP17//vfn8uNUClk9SJ5YLeL5ZdfPq9sC2yPhvQJ7i6FoBGVQHdA9uxS0KoKGW4MiOxsMYNXXnlldotRBpFuhmdQc1pJdqBilTIoJS5xumSwZDRTGSW/jJLq1w7ULwsopNfzhPCVNkWwtcMokUGLGYlL7lH/Gi/9gMWRbdy0J3LFU1Egqxj5e81rXjPjCwlqnmeTgkcBtHCyJ6++lchSIOxCOahDDjkkZyhbLAt7KPB8eZaVkNpggw2arwb6jSCAgZ4w6gQwMLww9GRQIoIObjDGAuEWT8TFOCggg0iLg8EVb1XIYCfFiuuq36jWuatDX1F4xPtROLkLWwuCF7VVmxbX+1wmg6W8C1JPkeuHYixmkotVTCwPhMXpMMRMSmjhJZHspt9UP7BH+ERVECzuxLhS+GUsiw1FWuvwHFlc15PquoXr6ZdSP4oIAhjoCUEAA8MAwxC54CKmQFBdNtpoo0wGN9tss2yUBgXGrZBB2aylZAcy2K5+W131E+s36gZK0gf3p4SaTmPf5jIZnKy8Sy8QLynG77vf/W71yU9+MhOsYbHfFkQSOZBe6r0dk9RKVTO1HQH0bPKQCK9BALXVV77yleZfF8KzI8Zxu+22a77SOcRiO6++MCcE2mPcCWBkAQcCIwDzmPg57ndG55prrqlWW2216qijjspu+te97nU5y3gQWb2UCkZ+zTXXzO5oWYzc1LbDcx3q2iE0DoqQg/FzfaNM/pBy9y6r2n1SiDpNfBAjRxXVRjK0tS9yIf7T+WYjQ7sTcMkiaMirOFWxwr3aWO34ta99LW+j6GeuUouaYbLdyBqyK4tbXCIXryQuqjhyyKVbB+UbmQfvaa2e4P2el/KebuGZtNgQbsBFHgi0w1iz334gTUihAAaGFhQABpMKID6TosR4UgapEJ24a6cLypfvY9zUOgTuuqnqvo0CKD5UP3XrqJz9SkaYSBmkts6mK9Q0qLSJ2Dylb5Chfuy3awyJkaOEcfe++c1vnhP7+Ir7k+iBBOp7dTy32Wab/Dfx1NqoNQlE+y2zzDL5PZ5XcYSIYrdJIOIulYwyTiwIldzhFbBADDwU464ABgHsEUEAZw8SVGTamVCtdm05p2ZeO8heZkSoTyBo3K4maouNC5BB9eZkGHJTcdeKFZSpKKFnEFtlISwlCUAGKNXK4WfEhXFkrEYJyBkXN7VPxms7N3g/MCxkENntZ3mXAnF1SrpQTxEobTmM2H///XO5K4SP69XzdeSRR+ai7hZZysCcf/75OZ5P28hY1l+tZWD0nflMwogMYEki3ZaB8YwXgsyt7HvNfRKOtGHgoQgCGOgJQQBnB1bIStQggeuss04OphZ3Q3XhLmvFDjvskN9nWzsGhZqgZATDJSh/3MBQcCdqR2SQm0qBWmSQG7cfpAzpQ4QoGDJAi7sXUSjKIIWHy5gbrBQfn6tAyNwvA46s9CvjtRO0kkEEEKFwDJIM6uN+l3dBiE488cTqsMMOywWUFXoeFInuB5DUiy++OC9EJUMpCr3vvvtm8gcSMRR3lxBivFMHzVsIY4H4wd122y1n/UqasZ3mBz/4wSkVeuYHhyn/FyCZaq9a2IlFNCZVZKjv0x4IAhgEsEcEAZwdiC+y24Iq/AVIhlVzuyLVrWBkJCuoISabcJyhLcTrIYNIMQVBOyotI+6qW1KGjKhzV4pXc2tNNM9yDRcyyFBSBBEnhGIukUHkCxFCdrl8B+lanwozQQYHVd5F7KjkhZ/+9Kc521f845jb6AmBNFrQnnfeeXkuLOBSRiZPPfXUHHfKtUylFAoiecZCOLAQQQADPSEI4MyDWiUTUlyLQsgF9gCVAGHFOxW4arjKnEP8TWAhkAcqBDL4hS98IRMGZJAyyG0+VfzVRKrfVPAY1cmgPq6TwWGN+0KElLNx3cNYzkZ/IuKuz//9IIODKO+i/9XCo/iJi6MA1uvmBR4OxE6tQWEcdVjUCm9RFaBkjIsDFA+onS+44IJZXaAME8adAA5/NG0g0AKGjGrFiNXh99ZsuomgjhjX78tf/vLmKwHgwmOATznllOzS4qqSkcg9LHnjoIMOykoCF3Id4sDEbHGpI0JimrohBuZhRa8lD1AoZFP6PCURoUc49Hvr984muNXsmSymkVI6jDuY6E8KrP6gpskIl5hS9ntWroVLvhNoe6RC7Br3JdWpH+TPYoxitfvuu+ekidNOOy3IXweQSIL8WSxptwLkThiMupMFPCbUQM/Se9/73uargXFHEMDAnEWrsbW67cQAi/8TH8Pd2Q8DNqpgSBgYwePIoJgkRkVsE+MvNovbU3IN44JIcElN5vLtBD5LQZS1KB7RdyEyvourGsnk2pwtMmjxwf1JbVbmhDI6F3Yw6YUMen0Q5V2c05hR1kbJIOWKej3vqEO7FXCZ22dYbB/3LhiPiLoklJJ9DwijRbJ4af0dCMST1iPSwxgu4BlGLy5gJMYKWF0xKlOgezAqjI76YtoRsdAPNrmn/g3KgHvUZC5Tebk0/c6Nz01MMZoJ4uD7qZHumfuzXzF1swlu7HrMoGdLuxY38SDKu3BNy3g95phjqgMOOCAnTYxaNvggYNFT2n/nnXfOv5v3ZB6L+ePqtcOImD8LM25fJZ8sUPbaa6+s3ArpsAgIhAs4CGCPCAI4O6BCWOlyURYIvrcf7kRJIAwO8qfiPpddYPqgnipvgUTb1so2VsigPpA8wuj0QyWaCB47rmmkxeF7EBaHLfD6/b0MbdndYsUVV8z3Nqxxib2gTga53N2jtkTs+5Utrx3f+MY3ZrJJXR6nUkzdwBhvN471i20f1UcUK6noNHWcG506fckll+T32Sv5sssuy5nH+pX6K0FEtn1gIVL7BgEMTB9BAGcHpQyMrZS4H0866aRc74p7kHGW2ctgFTLI7St+TSmGehac2mmjXpS43+Cu0+Yf+tCH8jZV5lCPgZg4xPCss86qvvnNb+ZdLAoZHGRyBHJWyKAkFKSlkEFlOXr9XjFqpX6kempiFUcd2tGzRAGkdupbKlJp1+k8M/pJfN873/nOvL8tNT6evYejTvwopX4u6qjMX657Cy21ApV7KZDcgVgrS6OEjthUCxbuXhn1CksHHorUtkEAA9NHEMDZA/UPsROfxjAjJOutt17+m/pXJkrFV8HPJsNWHHzwwdV73vOe5m+BTjHZJvMeCQTCziPIoHqDFFtEkKuYe3FQ8y6SgawUMshwFtIirrCb73UuY4ZiZVFB+RtF1a+Oicq7FGXQ6xSobsmgPtlzzz2rSy+9NC/UJBWNue1tC2OujDGLKXF82l6CDI8HHHLIIfmg/lHhC4RmeM2cKERDbGVgcgQBDPSEIICBwMTweNgPV7ym48orr8yJHcrKIAF2RhgkGeRmLGRQMWGEBalBWib7XkkPFDAKjMUFJXHU0Wl5F4QECSxu4snIoP4Xk6u2nyxy2eVckYGJwY37mte8Jpd4URbHGOTeLcqzGGhJPBY3VD8LmwILFu/1WQkigckRBDDQE4IABgKdwaPCQFEFHeKYNthgg6wMyjYWuzcoMKqFDFKyZDjXyWCBa5TpLItSCIGkh7m8O0knQJRLfKP7VUKkU7vYjgwqE7Tmmmtm4kypkp0qFIOKNZMKqu+koikl5LooYkcddVQuTl6gfFHZNaO+S4eElwJuV9feukvHIHYnsViSCQ3CWijP5Xs8L0gf96+SS2InxQG+613vyn8vkNUtbKHsRBKYGEEAAz0hCGAg0D08NkiWWE7KILcjg4UMbrbZZgONDUMGkRXZxHUFCwG9+eabc+xUP/e0HWZQOpEJ/dFrfGMhg1QrCUHOhbDYrUfm6Uzb2o033jjHqCKjrk22sXuVMFGyt7lQuUuFiig4LpOdu7p1n15FyY8++ui8iBBLZ5x2u09vJ7AwkqgmOcaWcgVvfvOb81aX3OgInzCK448/Pm8xh5jG7h7TQxDAQE8IAhhoByqCrGPxicjEscceW6277rrNv04M5RwEyMtmthPHOMAjZPcQ984AUlwYbwkkilKX3QwGAcSAIkj9opogLEplSFoZ5PfONrT5IMq7UBPVmbPvbNnGTRwbJWubbbbJMbezFUepnxEnLmmxwkr6IHbKGW277bb5Pdym+v9LX/pSHnuyZu0URBU2JsA4lYAhrKDufu0V+kRGvTaTVQ/Gp9dc64YbbpgVTW1ob3ME1TyhfiKXb2T3do9xJ4CjHdEcCMwCqFpqblEcTMyI3yabbJKJzWRAQrijOiGKowRzMLdWKSytdIWyIwydmmbqnZ1//vnZXddvUHgogc4tRu1Zz3pWJoISV7jSZDxzDY4S3KtxSe2kbmnrfpAy7Yi0S8ai6iIrDkSJK9j3zhb5AyQKirJL5RNfh2AVIHmUUP0PMmj9XsgfIIbuxef7CbF9xp6alog0QmhBIrHD9VhQcvtyDSuIDv6m/xDFQKBbhALYI9JDGgpg4CGQ8Wr3Cq6vAhmV3GAT1ShERCgm8+bNy3W8BOSPiwI4ERjBq666KisuZ599djbgYgUlkMjyVp6kF4hbozwiBIhfPaYLMaAYITVcgtyZ4gW5iufy7jHIGJLN3emee21DMAVKRhAnp1+UZnL+YYJrpJYpF1Tq5CkJ5XlrXVgghBYelEzJK4hyUeQKxJByG1Pr+wnJH2IWuXWpkwXmB4qfRaJ+Q6iV0wn0hlAAA4FA32AVTxmoqwrg96IqtAP1y4SvhldgIahFFA+7RVDiqIDi9MRtrbTSStVb3/rWXG+wW/UDuRNQj/wxpmKtWgP6ESOqDyLPXSghRHybMia2L6PmKoUzV6CNZDU73DO1sx/kT+kRMX/qzyk/Qv0eNvIHxoo+l+wxFZDFOi9oxxFa3zMV6jqBhU0rymu77rprdeONN+YQkvpnSiISAi+ZxSIzEOgVQQADgT4CSbBapxTV4XdqUjtwecqUVB8t0B7IoPIxAu/FY3ExIm1isbjA9tlnn9yO2n4yUPUQce9TzLrUuZsMvkecnDpsyKAyJgwxMqisjetpVZGGCdRk7myEtdN77gQWOvpEAo84NGrabLp4JwKCet5551Xf+MY3HpLdqx0s2KiCdejb8vx6T+tz6/0WEa3P+GQoZFEftGuj8pri6UigRBBJH2VvZolJ6mpysfMmRNJHoB8IAhgIDACt6sBEioGYH2UfkL8I4u4M1BDlY7galc349Kc/nQ25QH6xhPaVRczqSgujrTyJOncSHsS+ceN1C2RQkoAt8MRqFmLPrYgQSaxwLcMA9y/T2nW5ZmpmP9zX1ETxaGLhJCNIqqDIDhs8c5Q/cYhcqty6dSD0VNCLLrqo+UqVk7aMkVJEGWH2u9cLuIONnVKYuRMYI55zRBRs0ybRpBXCEfbbb7/sWpb5S53mPRD2IBZ27733rk444YQcGxgI9IrONexAW6RJJmIAAw+C8Zc9SqGy60WBDduvu+66bCzr8JpVf3HxQCEuVAHqyjAa12EEFY5x5oZU2oMxVa4D+TnyyCNz+Q5xU8q+9BuUHTGFjj//+c/5u5FDWaf9cLV2C8oR4mIs9VrepQ4xaNy97nPBggV5T+1uXKEzCQWRxfmde+65D6n9p6h3GQPCCYQWiOfTZ5KwlHpBmj2TlGKLBX2J9IoHpTqL5+2mDAwV1lj0TKu5qF/sSV6/rlZQ/KiWxo+yNUhjK4kN9IY0dseaAwUB7BFBAAOtEJ9DHRDHU0CZEoTemgSCOFBp6jjwwAOzMnjcccdl92ZrfFpganCZSaKxzR+DS7GTZCPIngttkPO+rOFCBvWjmDgEQoznoMmg6WgQ5V2cF7Hmai+Zvv0sgTIITNTHp556aiZx4PlTSw9RrBeCtmgoEO+JTLYWgu5UQaaYUuw810cccUQmlFRH46Ikd9TR7rXAYBAEMNATggDOfejCfs4DDOWOO+6YXZRcSMo2cPEKwFcPbaeddspJBRNlBDNOkQXcG6644oqs+CmpwaDLrFRjUE06iio1BpFBkAZpAxDRQgYlTHDzFzLYbzceBdQYo/71s5C1scj1qLizzFjtNuZ2c0pQ+FqJN8VQ/yjBY2wildDv+SfQOVK7j3XDx6jrEUEARwd112uvQDpkRYod4oKjmEggAKUyll9++ex2aocggL0DYUG0qDt1NUUpGe2KDFJhECWEBiFEzgdpDxAzRFA8GLWpTgZ7VXwGVd5FootSKJRoylk9iSLQHnVCJ2Ocyvi85z3vwf2kxapyM4v1e/nLXx6K3ywiCGCgJwQBnLugmMgIlRlqIu6XYhIYfnhsxXNJEEAGlZORJIEIOpSAGaRtoAYWMoggIIEyThG4bsgA96I4UQQQ8ZOh3A94NuzmoZblYYcdlmNYg6RMjbryxwtgJxHtpuyObeYUhJeMJPZQKIK9fC089KPPRrjHzCIIYKAnBAGcm5AVaosnipAAazXC7BlKOdp8882z8TeRc+GN8pZggYVkEBlTbBoZtCiQ3IAICvaXyDEoO+G762RQElGdDE6mRlOJJXrI7KUy96tANXe5hAOlTuxJS70KdA6Fo8WdIs9HHXVU7iNhIYifxA/hH8o+caeLB6RA27PYe6mtgZlDEMBATwgCODdB1eDS4oZF/BhfE7J6ZmpwKUAMj3nMY/JEroTDmM8VYwGPs9IysrgdysmodacMh4UBlXiQZFDSSCGDVKFCBn1vIYOUIgRDRq4YxuWWW64v1+S8iIktDBUklzk9iIzpUQbybK7Qh3ZG4eYFNSr9jNSX0A5z0MUXX5wTdnwGAQzMLIIABnpCEMC5CUHYP/3pT3OMU4G4HHtsKrVgZU4Jkjkq+2/c9ucNLCRkSBZV0EElVn+QMmhLOruSDAq+WzkZRBCZECdGifSdCk/7ez/Lu3AhIywSFE455ZRc4y8WPJOj7u4toMraueb444/PmcKUVNB/p512Wt7fes8998yeBq5/caHiQSO2cnYw7gSw92j3QGAOwq4FavCZoG0zBlbnavdJDACJHNRBdfqA0TXpz3W4LySXy1C5mrI36kRg1JAD8WU+I3apXRHbUQPbIFlH7UBKoAxOCwHtt+KKK+Zgfq49Ltx+w3dLGlAnzndakCAKEj2QBiVYuGh7XX/6vDg1pYvEn3FTbrzxxkH+pgBCV8ifRA/KH+UYQVew2UIBCUTeQRzgpptumgtn28fXHteeJXNOkL/AbCGe8h6RJtBQAOcoFA02GZvIxd4I2q5DCRG1uwRpqws2CvtvlhI1SIztpLi9bTmFWHAltkJMmvdRn/bff/9srChQlKdxjQ3zyNtH+IwzzsjKoDpxSJNYLspZv2NG6+Vd1JNEzhALh59lEjuQj26Im/hW7l774x599NHZ7TtZzGHgoUACt9lmm0wAuegtlLhxhQtYVJk7SvZ0gRATbW17OnGmgdlFel7GmgMFAewRQQDnNgRoq9GHGKnLpwSLOYH7V+wX0sfV9vnPf756xjOekd/XjiiJ17LKH/b5xP3IdhXXWEDRk+zQri6hWoZ2QKBwzMaOFsMOijDVzLiwcwNSttlmm2U38UYbbTSt7ebqmKy8i++WzOQ7vQ95K2SQejjZWKR+I3yUxP/+7//ORCXQGbQ79625QtiIRZRYTAsrpYUkzlAATzzxxPz8yP7V1gVR9mV4MO4EMNAjEMDA3MGvfvWrxqWXXtr87R94zWte03jpS1/a+M1vfpN/33777RtpUm+cf/75+fef/vSnjUQEG4cffnj+He69997Grbfe2vxt+PG3v/2tkQxP45xzzmm+shB77rlnY7311mv+9lBssskmjR122KHxxje+sfGkJz2p8ZznPCe3QSK8zXcECpJhb1xxxRWNvffeu5EWCY1Ewhqve93rGl/4whcaf/rTnxp33XVXx8cdd9zR+O53v5vH309+8pO276kfd955Z+PnP//5g5+58MILG9dee20e73/5y18efN+f//znxnvf+97GIx/5yMYBBxyQx3Dg4Ugkr/nTQ6GPIRHuxhprrNG44YYb8u8LFixoJDLdeOtb35rbGMwNfl9mmWUaaaGZXwsMF5pmfGwRen9grGAvXityCl8BFxvXGjWQegL2D7UZu7gdEPMlRioZ1Pw7WO1Tzihogrq5RlsxTDGDt912W1Yfyj0W+F2yQTvINqVs+Zy4P24tLiw14gIPBQVO9rgkAHGl9pjlllXzze4jiQzkeoPU4snAlXj55ZfnmD87yXRS2893KywtflXiErWQ+96etuJcuRztj0ydlOmrHIks1JlWdSVZSaApdRZbi52zybbv83cZyIqmc3/XQfWktlE5HX7WZv2CsV6EIZnglDweADGXxUUuBIDqSjm1vaA4UWqf3T6ERygF4x6EBTiixmggMIJYuI4IzBX8+Mc/buy8886NNDk3Ntpoo0aatBvz5s3Lv++22275PclQNh7xiEc0kvHMvwNF4HGPe1x+f0EikY2vfe1rjc9+9rONDTfcsLHxxhs3fvnLXzb/+nDMtmpGkTBkXXcdFKF///d/b/72UDzjGc9oPPWpT33ItScC2Hjyk5/c/C0wFbTd17/+9UZaUDSe+MQnNhLhzj9/9atfzWpRUeduv/32xne+853Geeed1/jBD37wEOVuuofzn3rqqY111lmnkcheI5GTxtvf/vYHlauZRlpEZOXx7LPPzmMxEavmXxbiyCOPzNfo765x2223zQpaUdXAc0aNN44dfn7lK1/Z/Gt/8Pvf/z63WSJ4jec973mNROAa//Vf/9X8a6Pxhz/8IT8ziRA2tthii0Yi/M2/NLL6qo2puIHhRjbigcB00RxHgTmGm266qfGud70rT/LbbLNN4xOf+MSD5O3Vr351Jod//OMf8+/AIDHcdfcxt9t9993X/K2RjfunPvWp5m+Nxv/8z/9kdytDPAyYjgvY6y972cuavy0EI27oO1+gO3C5IggWHRYU//Zv/5bdhEgasrHddtvlMIRWIjfdgwtYOIPvMjbPOuusHO7ABbzyyis3fvvb3zavbOZhDNUJoEWWhQUSWHDPPfdkV3pZeCHGPnf55Zfn3wFp9toPf/jD5iu9gct+ySWXzISPq1cb7rvvvo1FFlmkcdFFF+X3aNv3vOc9mZzqz4Jf/OIXmaC+9rWvzYQ+MNxI4yYQmD6a4ygwh/HXv/61+dNCmOg/9KEP5XifEgu05ZZbNjbddNO8qncceOCBOWZQnOBmm23WOOWUUzKR3H///fP7Yccdd2yssMIKjXe/+9358wcffHA2aCWOCPw8k8rgWmut1XjLW97S/G0hnv3sZzf222+/5m8PBZL8tKc97SHXfOyxx2bDF+gNCPS5557bWHPNNbOStNpqqzXe9ra3ZVXL4qKVzHVzUA+RFX23/vrrN26++ebmty6E81sITBTrNhMwfdYJoDhbr11zzTXNVxZi8803b+y00075509+8pOZELbCa57BXoFEPutZz8rxfXV89KMfbSy++OJZnS2gUCKJj3rUo7JSucceezSe8IQnZEUwYivnBrIRH2NEDGBg7CHWqMwFyXjmYq3KeYj3EQskZkslf/t4ypoUQ6Woq/2DxQetuuqqeU/PCy64IO/aAOIJZW+KLxRTtOGGG+ZCwrJCndf+reKW/DyTGYH77LNPLvui2K84JrGLypjsuuuu+e/J0FaJ9OWfQfyaTEd7wd544435Ho844ohcFzDQG+xFLG7s17/+dY7PU5JFjKlxJVP70EMPzWOoWzslVk18XyIiuf/sNqGUUR12uFHzssS6DQNKHOpkMar+V5KoFV6bKI61G6i7qI6fGn3i+UC8pHJR4nnF+XpeQHWAk046KfeTWE9zx3HHHZfjGiNjPhAYA+RlRGCkQaXhErrkkkuyYkJBo6TVIY5upZVWanz/+9/Pv1MMVlxxxRwfWIe/v+lNb8qqz9JLL9144Qtf2EikqvnXmUEirVkZck+JaDS+9a1vNf/SyGpRPdYJKFKuM5HXfE+RBdwfUJnbuQqpc6effnpWksSiyrw+6KCDGtddd92UcYHeQ72i6l511VXNMw4nTJ91BTAtsvJrXK517LLLLo20IMs/G3tc5a2gxL/vfe9r/tYb9Ifv9CzoA+5z6t4JJ5yQ3burrLJKjuGUnV1HPBNzD9mIBwLTRXMcBUYUxUXGPVWCurmhlPn4yle+kktvfPzjH8+/FyPFELzhDW/IxkKweAHjLaAcgVSuw2cFi4uxa3XR1THTbuLAzKAT96vyMWL3hB8g7M9//vMbhx12WF5I1Mkg0ojYP+Yxj8njy9+GHabPYXMBF4gxlNjlvF/+8pebry4k59/4xjeym1i8ouc3MHeRjXggMF00x1FgjCBDUKwWY/uKV7yi8fKXvzwnV3zkIx/Jf7/yyitzfKD4uQIETqC/WC+qTz2InaJx/PHHN39biInIgfMEGRw/GA+33XZb46STTsrjTTwaFVrChNqDr3rVq3KdRhnsnRDLYYDps10SyFFHHdV8ZWGcJBLWmgTings8S17rVxJIgef1RS96UY7dLSiJT5I9EE7PemDugg0PBKaN5jgKjCluvPHGxsUXX5yNcSn2yrUnu7hkDDJssoVlY3LNKTcjo5ixU2B59dVXbxxyyCFtDfehhx6aFZC6oSwIIjieME5+/etfZ5fkuuuum8kPoiJ7eNhBQVOg2uG6jznmmPwzNRwQWoRPgookC4uldmVgVl111Zz96+CS7XcZGJDIMX/+/Ny2MvqhngwVmPvIRjwQmC6a4ygQyEDKxPitvfbaDxqtQtQQvaImKDFDrWHg1DErqkcrCbzllltylqHMZKqPuKSyO0mnaEcsA6MBfYsEcQPPBXCfmjZbjxJ36n48IxZHYk6VIWqtWSiswu406gU6/Fwv2dRP/OhHP2psvfXW2QXfb4UxMPtIYy8QmD6a4ygQeAgog1AnX9Q+yRTUmzrEa5X4wvL+ojRIwKB2nHbaaTlGatddd81kECkUD9WO3E2kUni93fuHEeLZll9++UwCJKp8+9vfbv6lPaiuXOmSJtTW22uvvRp3331386+BwPTxuc99LifiSOwKjBaaZjwQmB6a4ygQmBIIHGXQXrHUBESQwtDOlVtI3D777JPjCUuskd0juJtlQ062v6h4MUWrxRbKYp5LOOOMM/KuFSeffHKO+RJv+ehHP/pBN2ErPvOZz2SiiCTbkUFyDrchEhgI9APDnlEdmB6aZjwQmB6a4ygQmBRFebN1nPhACSTKdbzjHe+YdFsu5WIQxuJOVn5CtnE7hbEASaSaUQklqShGzVVmCzxlQoYdCC6lsw5ZlxMVq959991zxmYdiPNLXvKS5m+BQCDwcDTN+NgiCkEHAjOAUnD3ZS97WXXppZfm4r8KTm+99da5+GwdidTl/7/+9a9Xd955Z7XaaqvlDeYVDk5kMW/4v+KKK+b3lPOWzySCWc2bN6963OMeV1144YXVZz7zmVyA2s+LL754LlR766235vcOI+69997q6quvrhJxbb6yEH7/3//93+ZvD0Uievkz3/3ud/PvN910U/WlL32p2myzzfLvgUAgEHg4ggAGArMAOzG88pWvrNZaa62H7RpQFqZ2FFh++eXzTiOA3CFIa6yxRt49pL6AtaMIfPzjH8/v+e///u9q3XXXrZ74xCfm73rxi19c7bvvvvl9X/3qV/N7Cwp5LJjNhfFtt92Wd06ZbDeIVmy33XZ55wtEUFuutNJK1QYbbFDtt99+zXcEAoFAoBVBAAOBIQNyh5RRAJ/61KdWT3/60/Pr3/jGN6rHP/7x1eqrr55/LyiE7Zprrqmuu+66atNNN62WWWaZ/Lrz3HXXXfnvVEPbwG2//fb5d+oiFPJYiOAll1ySiaktsGYLrVuUuZfW1wq++c1vVocffnj1kY98JLfBOeecU51//vmZFAYCgUCgPYIABgJDCKQM4Xv7299ePfrRj857CiNAXMdFHSuEqPzP9fnb3/622mijjfLvXvf7m970pmrllVeujj322OqOO+7I+xHff//9mSC95jWvyZ/jFi5EcL311qs+9alP5c/ATCqCT3jCEzIBblX7fve73z1MFSw46KCDqh133LHaZZddqlVWWSXvcWu/4ve9730PUzcDgUAgsBBBAAOBIQX37TOf+cz885JLLlltvvnmmQiefPLJ+bVWPOUpT6n+8pe/5BhB4EqlBH7gAx+oXvva11b77LNPJkXw85//vPrhD3+YYwOPOeaY/D0vetGL8t+4YZE/6iMgkoUEOif0Y+P9dnCfFM6LLrqo+cpC+J0bux3++te/PkheC4qLfCbJayAQCATGCMnABAIzilLfrpSKKfjxj3/cWHbZZRvvec97mq/8A9dff30ulaKuINhZRGkVRauvvvrqXD7Fe5SOWWmllRrnnXdefl8rSsmaFVdcsXHQQQc17rnnnvx7P1HKwKhzqAyMci6uteyXvOOOOz4kI1jhYFnOn/3sZxs33XRTzoJ2D9tss03zHYFAIPBwNM342CIUwEBgjuERj3hE/r+uenF1ihX8r//6r+q00057WKKHBJKll166WnvttbOKeOWVV1ZPfvKTqxNPPLF6wQteUC233HI52eTss8/OsYLURDBHXnXVVdXpp5+e3cmUtf/7v//LLuRnPOMZ2Z3cb2y77bbZXX3ooYdWz3/+86tvf/vbOav3aU97Wv77L37xi+wKLzjwwAOr+fPn5/8pl294wxuq//iP/8gJMYFAIBBoj/ZR1YGOEauIwDDhD3/4Q85+veCCC6qXvvSlOeHjT3/6U7XbbrtVO+ywQ/XRj360+v73v59jC7l4TzrppOq+++7L2bPcx5tsskl2wXIbe+3Nb35zJpPcwL/61a+qz33uc7mMDVLGFf3c5z63+c2BUYckG+MC+RZmgKTLNA8E5irSvDbWHCgUwEBghEDlQ8woeRTCd7zjHbkkzIYbbljtscce+T0I4C233FJtueWW+feyhpF1jASuueaamfw5z4IFC6p3v/vdWfW77LLLMmH87Gc/W62zzjoPKnKB0ceZZ55Z7bXXXtUBBxxQXXvttZn4WSxQYwOBwNxEKIA9IhTAwLBDwsaTnvSkTAjvvvvu7C5V6kXxZEkX3Mf+tvvuu1c/+clPsltYAopaei984QuzamiYWyzvvPPO1XnnnVedcMIJuf5eYDxgHAgVMBYKnv3sZ+dFREksCgTmGkIBDAQCIw2xfiVeEJFTKFmpGOSP+9ffuImpfFy6YgCVhbHryK677po/J24QfH7ZZZfNu5MExgPT2Z0lEAgEAoHAcGPx5v87pOOmdOyUf6uql6VDBelH59/+gZ+l44PpeFT+bXyxXjq+mI5fpYMXYKE/fXKsn46r03FPOrT1QnY9/Fg2He6xtQ7P/un40cIfA4HAXEMogIHAeOP+5v/Xp+MT6bg8/1ZVf07H79OxcNuQhXh1Op6cjsvS8VcvjDEQY2321vzb1FghHV9KxyXpIJ8ekY7j0/Gf6ZgraA134T6LEJhAIBAIBEYMJ6bjlnQclI7j0vGndHw7Hc9IR+Af6EQBPCod/2/hjw/iY+n4zsIfhxpLpsNCYav82z9gTHxr4Y+BQGCuIRTAQCAwEfZKxyHpoFhxXXL7XpmOW9MR6A5rp+OhxRmrymbLa6Rjifzb8OLedOj/hXsM/gN+jyDAQCAQCARGGIulg8r1kvxboI5OFMAb0yFmrg4xdT67TP5tuLFtOhDBndPx7HR8KB1/SUfUAgoEAoFAIDCW6JQAvmvhjw9inXT4rLjKuYDd0nFzOqSEUwQlwgQCgUAgEAiMJTohgGInxczVIabuvnQMuws4EAiMICIGMBAIBAYPyR6tMXQK612VDiQwEAgEAoFAIDDkeEw6nt88KIB7N39eLh1ge4wFC3/MUAZGXcVj0iGGTiydmLq5VAYmEAgEAoFAYKzx0nQgfq3Hp9IB/v/mwh8fhELQ16RDDJ2C2nOlEHQgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCcwNV9f8BMZT64iDmcicAAAAASUVORK5CYII=\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "X,Y = np.meshgrid(mylam.π_grid,mylam.rent_grid)\n",
    "ax = plt.axes(projection='3d');\n",
    "ax.plot_wireframe(X, Y, π_opt_policy, rstride=5, cstride=5,color = 'r' )\n",
    "ax.plot_wireframe(X, Y, π_opt_policy2, rstride=5, cstride=5,color = 'black' )\n",
    "ax.set_title('Policy Function');\n",
    "ax.set_ylabel('Rent Grid');\n",
    "ax.set_xlabel('Share π Grid');\n",
    "ax.set_zlabel('π');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}