DBF.ipynb

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    "# DMR Practical Assignment: Recover a broken robot\n",
    "\n",
    "## Recursive Bayesian estimation\n",
    "\n",
    "Localisation is a key problem in robotics, and a number of approaches have been proposed to address this. Recursive Bayesian estimation using the discrete Bayes filter is one such approach. \n",
    "\n",
    "We start by assuming that a robot has state $\\mathbf{x}_k$ at time step $k$, and can be commanded to take actions $\\mathbf{a}_k$. Let us also assume that the robot is able to sense the environment it finds itself in and obtains measurement $\\mathbf{z}_k$.\n",
    "\n",
    "Recursive Bayesian estimation is a sequential technique for maintaining a belief over a state, conditioned on a series of measurements, $p(\\mathbf{x}_k|\\mathbf{z}_{1:k})$. We briefly derive the equations for recursive Bayesian estimation below. First, we will use Bayes rule to rewrite the desired posterior belief,\n",
    "\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k}) = \\frac{p(\\mathbf{z}_{1:k}|\\mathbf{x}_k)p(\\mathbf{x}_k)}{p(\\mathbf{z}_{1:k})}$$\n",
    "\n",
    "We factorise the trajectory likelihood $p(\\mathbf{z}_{1:k}|\\mathbf{x}_k)$ in the numerator, to produce\n",
    "\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k}) = \\frac{p(\\mathbf{z}_{k}|\\mathbf{x}_k,\\mathbf{z}_{1:k-1})p(\\mathbf{z}_{1:k-1}|\\mathbf{x}_k)p(\\mathbf{x}_k)}{p(\\mathbf{z}_{k}|\\mathbf{z}_{1:k-1})p(\\mathbf{z}_{1:k-1})}$$\n",
    "\n",
    "The second half of this equation can be simplified using Bayes rule, providing:\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k}) = \\frac{p(\\mathbf{z}_{k}|\\mathbf{x}_k,\\mathbf{z}_{1:k-1})p(\\mathbf{x}_k|\\mathbf{z}_{1:k-1})}{p(\\mathbf{z}_{k}|\\mathbf{z}_{1:k-1})}$$\n",
    "\n",
    "Let us assume that our robot is Markovian, that is, measurements $\\mathbf{z}_k$ are only dependent on state $\\mathbf{x}_k$, and state $\\mathbf{x}_k$ is only dependent on previous state $\\mathbf{x}_{k-1}$. Then we can simplify the equation above even further,\n",
    "\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k}) = \\frac{p(\\mathbf{z}_{k}|\\mathbf{x}_k)p(\\mathbf{x}_k|\\mathbf{z}_{1:k-1})}{p(\\mathbf{z}_{k}|\\mathbf{z}_{1:k-1})}$$\n",
    "\n",
    "This equation is pretty interesting, the first part $p(\\mathbf{z}_{k}|\\mathbf{x}_k)$ is a likelihood - how likely are you to make the measurement $\\mathbf{z}_k$ given you are in state $\\mathbf{x}_k$. The denominator bit is just a normalising term, so we'll ignore this.\n",
    "\n",
    "The term $p(\\mathbf{x}_k|\\mathbf{z}_{1:k-1})$ is a prediction of the state given the history of measurements. Lets assume we have a transition model of robot motion $p(\\mathbf{x}_k|\\mathbf{x}_{k-1},\\mathbf{a}_k)$. Then we can make a prediction about where our robot will be for a given state if we apply an action.\n",
    "\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k-1}) = \\int{p(\\mathbf{x}_k|\\mathbf{x}_{k-1},\\mathbf{a}_k)p(\\mathbf{x}_{k-1}|\\mathbf{z}_{1:k-1})\\text{d}\\mathbf{x}_{k-1}}$$\n",
    "\n",
    "This equation is extremely powerful - it computes the probability of occupying a state for a given action, by taking into account all the possible states we could have come from, along with the prior belief that we had in these states, $p(\\mathbf{x}_{k-1}|\\mathbf{z}_{1:k-1})$. This is called the total probability rule.\n",
    "\n",
    "What's key here is that we have a recursive means of maintaining a belief over the world as we take actions, given a model of how we gain information from the world, and a model of how we interact with the world. This allows us to continue to update our beliefs as we act in the world and receive new information.\n",
    "\n",
    "To summarise, first we make a prediction about the likely state of the world if we were to take an action:\n",
    "\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k-1}) = \\int{p(\\mathbf{x}_k|\\mathbf{x}_{k-1},\\mathbf{a}_k)p(\\mathbf{x}_{k-1}|\\mathbf{z}_{1:k-1})\\text{d}\\mathbf{x}_{k-1}}$$\n",
    "\n",
    "Then we take the action, sense the world and update our belief in the states.\n",
    "\n",
    "$$p(\\mathbf{x}_k|\\mathbf{z}_{1:k}) \\propto p(\\mathbf{z}_{k}|\\mathbf{x}_k)p(\\mathbf{x}_k|\\mathbf{z}_{1:k-1})$$\n",
    "\n",
    "We repeat this as new information is gathered and actions are taken, continually refining our beliefs.\n",
    "\n",
    "\n",
    "## The discrete Bayes filter (DBF)\n",
    "\n",
    "The discrete Bayes filter is a discrete form of recursive Bayesian estimation. Here, we discretise the world into a number of possible states $\\mathbf{x}^j$, and maintain beliefs using discrete random variables for each possible state.\n",
    "\n",
    "Since our belief is over a finite set of $j$ states, the integral in the equation above changes to a sum for the discrete bayes filter prediction step,\n",
    "\n",
    "$$p(\\mathbf{x}^j_k|\\mathbf{z}_{1:k-1}) = \\sum_{x_i}{p(\\mathbf{x}^j_k|\\mathbf{x}^i_{k-1},\\mathbf{a}_k)p(\\mathbf{x}^i_{k-1}|\\mathbf{z}_{1:k-1})}$$\n",
    "\n",
    "Then we sense the world and update our belief in the states.\n",
    "\n",
    "$$p(\\mathbf{x}^j_k|\\mathbf{z}_{1:k}) \\propto p(\\mathbf{z}_{k}|\\mathbf{x}^j_k)p(\\mathbf{x}^j_k|\\mathbf{z}_{1:k-1})$$\n",
    "\n",
    "References:\n",
    "http://www.probabilistic-robotics.org/ \n",
    "\n"
   ]
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   "cell_type": "code",
   "execution_count": 1,
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "import copy\n",
    "\n",
    "from IPython import display\n",
    "\n",
    "from helper_funcs import Robot, make_world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment\n",
    "\n",
    "Let us assume you have a faulty robot equipped with a metal detector. The robot can sense if there is metal near it (p_sense_succeed = 0.8), but occasionally it's sensors fail (p_sense_fail = 0.2) and it returns the wrong measurement.\n",
    "\n",
    "The robot can take random actions to move a step to the left, right, up, down, or diagonally, but this is also unreliable. Sometimes it behaves (p_move_succeed = 0.9), but occasionally it just stays where it was (p_move_fail = 0.1)\n",
    "\n",
    "Unfortunately, your robot is lost somewhere in a grid. Luckily, it managed to make a map of locations of the metal in the world before getting lost. Your goal is to use this map, together with sensor measurements, to figure out where your robot is so that you can recover it.\n",
    "\n",
    "Complete the program below to implement the prediction and likelihood calculations for a discrete Bayes filter, in order to find out where your robot is. One you have completed this, compile a report including images of the distributions at convergence, and briefly discussing your solutions.\n",
    "\n",
    "Try to answer at least three of the following questions in the report:\n",
    "\n",
    "* How quickly does the DBF converge to a good belief? (re-run multiple times)\n",
    "* How do the sensing and move failure probabilities affect inference?\n",
    "* How does the environment affect the quality of the belief in position?\n",
    "* How does the predictive distribution change the posterior from one time step before?\n",
    "* When is a MAP estimator a good one? Are there better estimators for this problem?\n",
    "* When would the best time to attempt to recover your robot be?\n",
    "* Assume you can choose actions to take instead of executing random ones. What actions could you take to reduce the uncertainty in the robot position?\n",
    "\n",
    "This task is inspired by the search for the wreckage of Air France Flight AF 4471\n",
    "https://arxiv.org/pdf/1405.4720.pdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialise world and agent in it\n",
    "p_move = 0.9\n",
    "p_sense = 0.8\n",
    "\n",
    "world = make_world()\n",
    "N = world.shape[0]\n",
    "\n",
    "agent = Robot(p_move,p_sense,world)\n",
    "\n",
    "# Create list of possible states in world (x,y coordinates)\n",
    "possible_states = np.array(np.meshgrid(np.linspace(0,N-1,N),np.linspace(0,N-1,N))).reshape(2,-1).T\n",
    "\n",
    "# Initialise belief in position - uniform distribution over world\n",
    "state_belief = np.ones((N,N))/(N*N)\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.imshow(world)\n",
    "plt.title('Map of the world (1-yellow/metal, 0-blue/no metal)')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def run(p_move=0.9, p_sense=0.8, plots=True, times=200, actions='random'):\n",
    "    \n",
    "    values = []\n",
    "    \n",
    "    # Initialise world and agent in it\n",
    "    world = make_world()\n",
    "    N = world.shape[0]\n",
    "\n",
    "    agent = Robot(p_move,p_sense,world)\n",
    "\n",
    "    # Create list of possible states in world (x,y coordinates)\n",
    "    possible_states = np.array(np.meshgrid(np.linspace(0,N-1,N),np.linspace(0,N-1,N))).reshape(2,-1).T\n",
    "\n",
    "    # Initialise belief in position - uniform distribution over world\n",
    "    state_belief = np.ones((N,N))/(N*N)\n",
    "\n",
    "    if plots:\n",
    "        plt.figure(figsize=(15,5))\n",
    "        plt.imshow(world)\n",
    "        plt.title('Map of the world (1-yellow/metal, 0-blue/no metal)')\n",
    "        plt.colorbar()\n",
    "        plt.show()\n",
    "\n",
    "    # Run the discrete Bayes filter\n",
    "    plt.figure(figsize=(15,9))\n",
    "\n",
    "    for j in range(times):\n",
    "\n",
    "        # Sample a random action\n",
    "        if actions == 'corner':\n",
    "            a = np.array([-1,-1])\n",
    "        elif actions == 'nomove':\n",
    "            a = np.array([0,0])\n",
    "        elif actions == 'global_min':\n",
    "            global_min = np.unravel_index(state_belief.argmin(), state_belief.shape)\n",
    "            global_max = np.unravel_index(state_belief.argmax(), state_belief.shape)\n",
    "            a = np.sign(np.array(global_min)-np.array( global_max))\n",
    "            print(\"min:\", global_min)\n",
    "            print(\"max:\", global_max)\n",
    "            print(\"act:\", a)\n",
    "        elif actions=='information_gain_proxy':\n",
    "            agent.state[0] = 8\n",
    "            agent.state[1] = 14\n",
    "            a = np.array([0,0])\n",
    "            actions='goto8,14'\n",
    "        elif actions=='norepeat':\n",
    "            while 1:\n",
    "                a_tmp = np.sign((np.random.rand(2,)-0.5))\n",
    "                if 'a' not in locals() or (a != -a_tmp).any(): break\n",
    "            a = a_tmp\n",
    "            a = np.array([5,5])\n",
    "        elif actions==\"goto8,14\":\n",
    "            global_max = np.unravel_index(state_belief.argmax(), state_belief.shape)\n",
    "            if (global_max==np.array([8,14])).all():\n",
    "                a = np.sign((np.random.rand(2,)-0.5))\n",
    "                a = np.array([-1,-1])\n",
    "            else:\n",
    "                a = np.sign([8,14]-np.array( global_max))\n",
    "        else:\n",
    "            a = np.sign((np.random.rand(2,)-0.5))\n",
    "\n",
    "        # Apply action and sense\n",
    "        measurement = agent.move_and_sense(a)\n",
    "\n",
    "        # Make prediction about state belief given action\n",
    "        predicted_state_belief = np.zeros((N,N))\n",
    "        for state in possible_states.astype(int):\n",
    "\n",
    "            new_state = agent.move_perfect(state,a)\n",
    "\n",
    "    ############################ Complete this section ##########################################\n",
    "\n",
    "            if 'likelihood' not in locals():\n",
    "                likelihood = np.zeros((N,N))+1/N**2\n",
    "\n",
    "            predicted_state_belief[new_state[0],new_state[1]] = \\\n",
    "                (1-p_move)*likelihood[new_state[0],new_state[1]] + p_move*likelihood[state[0],state[1]]\n",
    "\n",
    "    #############################################################################################\n",
    "\n",
    "        # Evaluate sensor belief for each possible state\n",
    "        likelihood = np.zeros((N,N))\n",
    "        for state in possible_states.astype(int):\n",
    "\n",
    "    ############################ Complete this section ##########################################\n",
    "\n",
    "            #Hint the expected measurement in a given state is world[state[0],state[1]]\n",
    "\n",
    "            likelihood[state[0],state[1]] = \\\n",
    "                (p_sense if measurement == world[state[0],state[1]] else 1.-p_sense) * \\\n",
    "                predicted_state_belief[state[0],state[1]]\n",
    "\n",
    "        likelihood = likelihood / likelihood.sum()     \n",
    "\n",
    "    #############################################################################################\n",
    "\n",
    "\n",
    "        # Compute posterior belief over possible states\n",
    "        state_belief = likelihood*predicted_state_belief\n",
    "\n",
    "        # Tip: never become overconfident, so add some probability mass to all possible states before normalising\n",
    "        state_belief = (state_belief+1e-15)\n",
    "        state_belief = state_belief/np.sum(state_belief) \n",
    "\n",
    "        # Compute Maximum a-posteriori position estimate\n",
    "        map_estimate = np.sum(possible_states*state_belief.T.reshape(-1,1),axis=0)\n",
    "        \n",
    "        # mse\n",
    "        mse = agent.get_mse(map_estimate)\n",
    "\n",
    "        if plots:\n",
    "            # Compute most probable positions\n",
    "            true_position_val = tuple([int(el) for el in agent.state])\n",
    "            most_probable_position_val = np.unravel_index(state_belief.argmax(), state_belief.shape)\n",
    "            most_probable_position_without_measurement_val = np.unravel_index(\n",
    "                predicted_state_belief.argmax(),\n",
    "                predicted_state_belief.shape)\n",
    "            most_probable_measurement_val = np.unravel_index(likelihood.argmax(), likelihood.shape)\n",
    "\n",
    "            # Compute messages\n",
    "            true_position = 'True position:\\t\\t%s' \\\n",
    "                    % str(true_position_val)\n",
    "            most_probable_position = 'Most Probable pos: %s' \\\n",
    "                    % str(most_probable_position_val)\n",
    "            most_probable_position_without_measurement = 'Most Probable pos: %s' \\\n",
    "                    % str(most_probable_position_without_measurement_val)\n",
    "            most_probable_measurement = 'Most probable measurement: %s' \\\n",
    "                    % str(most_probable_measurement_val)\n",
    "\n",
    "            # Compute matrix for all values\n",
    "            all_values = np.zeros((N,N))\n",
    "            all_values[most_probable_measurement_val[0],most_probable_measurement_val[1]] = 0.25\n",
    "            all_values[most_probable_position_without_measurement_val[0],\\\n",
    "                       most_probable_position_without_measurement_val[1]] = 0.5\n",
    "            all_values[most_probable_position_val[0],most_probable_position_val[1]] = 0.9\n",
    "            all_values[true_position_val[0], true_position_val[1]] = 1    \n",
    "\n",
    "            plt.clf()\n",
    "            plt.subplot(2,4,1)\n",
    "            plt.cla()\n",
    "            plt.imshow(agent.visualise_true_position())\n",
    "            plt.title('True position in world')\n",
    "\n",
    "            plt.subplot(2,4,2)\n",
    "            plt.cla()\n",
    "            plt.imshow(predicted_state_belief)\n",
    "            plt.title('Predicted belief given action')\n",
    "\n",
    "            plt.subplot(2,4,3)\n",
    "            plt.cla()\n",
    "            plt.imshow(likelihood)\n",
    "            plt.title('Likelihood')\n",
    "\n",
    "            plt.subplot(2,4,4)\n",
    "            plt.cla()\n",
    "            plt.imshow(state_belief)\n",
    "            plt.title('Posterior belief given measurement')\n",
    "\n",
    "            # Ploting all values in one\n",
    "            plt.subplot2grid((2,4), (1,0)) # Using subplot2grid just for fun\n",
    "            plt.cla()\n",
    "            plt.imshow(all_values)\n",
    "            plt.title(\"\\nAll values together\\n%s\\n%s\\n%s\" % \\\n",
    "                     (true_position.expandtabs(),\n",
    "                      most_probable_position.expandtabs(),\n",
    "                      most_probable_position_without_measurement.expandtabs()\n",
    "                     ))\n",
    "\n",
    "            #plt.subplot(2,2,2) # Original code\n",
    "            plt.subplot2grid((2,4), (1,1), colspan=3)\n",
    "            plt.plot(mse)\n",
    "            plt.grid()\n",
    "            plt.title('MAP position error')\n",
    "\n",
    "            #plt.tight_layout()\n",
    "            display.clear_output(wait=True)\n",
    "            display.display(plt.gcf())\n",
    "\n",
    "            plt.gcf().savefig(\"img/%03d.png\" % j)\n",
    "            \n",
    "        values.append(tuple([likelihood, agent, map_estimate, state_belief, predicted_state_belief]))\n",
    "\n",
    "    errors.append(mse)\n",
    "    del likelihood, agent, map_estimate, state_belief, predicted_state_belief\n",
    "    \n",
    "    return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = []"
   ]
  },
  {
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    "scrolled": true
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     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors = []\n",
    "for i in range(1000):\n",
    "    run(plots=0, p_sense=.8, p_move=.9, times=200, actions='goto8,14');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for error in errors:\n",
    "    plt.plot(error[:200])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "mean = np.mean(errors,axis=0)\n",
    "std  = np.std(errors,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200,) (200,)\n"
     ]
    }
   ],
   "source": [
    "print(mean.shape,std.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the result\n",
    "plt.style.context('ggplot')\n",
    "plt.plot(range(len(mean)), mean, '-', color='#421b9b')\n",
    "\n",
    "plt.fill_between(range(len(mean)), mean - std, mean + std,\n",
    "                 alpha=0.2, color='#a06ee1')\n",
    "\n",
    "plt.title(\"MSE (for %i repetitions)\" % len(errors))\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"error\")\n",
    "plt.ylim(bottom=0)\n",
    "plt.savefig(\"imgs/mse_p99999_%i_repetitions.png\" % len(errors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#errors_corner = copy.deepcopy(errors)\n",
    "#mean_corner = np.mean(errors_corner,axis=0)\n",
    "#std_corner  = np.std(errors_corner,axis=0)\n",
    "#del errors_corner, errors\n",
    "\n",
    "#errors_p51 = copy.deepcopy(errors)\n",
    "#mean_p51 = np.mean(errors_p51,axis=0)\n",
    "#std_p51  = np.std(errors_p51,axis=0)\n",
    "#del errors_p51, errors\n",
    "\n",
    "errors_pm51 = copy.deepcopy(errors)\n",
    "mean_pm51 = np.mean(errors_pm51,axis=0)\n",
    "std_pm51  = np.std(errors_pm51,axis=0)\n",
    "del errors_pm51, errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ymxl+eQsnZVzD7aTbD9N/o23hRbT5j1JGG62KxeRVr6yKsDALljCwWEpOklQWhbKAxWL4pbXx2mJThfaKwhgVWKwKq9W8nkujXWa5RRWxVcrovyLR2rgv3rrTYpUJz4JwOrJp0yaaNGnCiRMnSE9PJyYmBoCHH36Ypk2b0qRJkyr2MPRMmjSJ8PBwDh8+zKZNm7j++uvp0qULHTt29Gk/ceJE6tevz8GDB0lLS6NPnz688cYbTJkypZI9L51A43I4HAwcOJA777yTr776inXr1tG/f39+//134uLiqsj7EIozpdS7QD8gVWvdySx7AegP5AO7gFu11mlm3UPAeMAJTNFafxEq34IhP8fFkb/ysNpUoXZRYFEKZTW+mMNrKlmR5AeXS6OdUJCrcQs+b7RXcaF4NMSlBpRZUUQQeqlArXQR4Vikb7NPZS0Ubu4Kt3B2H+7rocHl0B4RaCpqXC4NLtM3lMcvNCgLWMMUEXYrkVEWwiIshdf0iscj6jSerKLnc6MKy7UG7TTvncvLV7NPixWsNhW08NRay+dUELzYtGkT5513HkePHmXr1q1cdNFF/Pbbb/z444/07t2bWrVqeWznzp3LSy+9xP79++nRowfz58+nfv36pKamMmHCBH755RdycnK49NJLWbRokUfovfXWW6xatYqWLVuybNkywsPDmT9/Pn369PHr16JFi3j99ddp1qwZ69atIywsjDlz5tC3b98KjT8rK4uPPvqIxMRE7HY7vXr1YsCAASxYsIDnnnvOZ5u//vqLyZMnExkZScOGDbn22mtJSkoq81qVFRMEF9f27ds5cOAA06ZNQynFFVdcQc+ePVmwYAFPPvlkhfsWKKHMnM0DXgPe9yr7CnhIa+1QSs0CHgIeUErFA8OBjkBj4D9KqTittTOE/gWM1aaIjLZWtRunJRaLAgtY/QioUOOd6XJnu7z1iXeZOxtpC1OeOrcwspoZSl/ixsimQk6ak8xjBZ4LuLWTIcTcJ14NvbKqniJvleorm2kKU402hJpNFckUFsEscDk1LofG5dLYwi2ERSpsYV4zGsx2VhuERVqxhSusNhFxwpnP77//zrnnnktqaiqJiYlcdNFF3HvvvTz77LO8+OKLTJo0CYBnnnmGjz76iE8//ZTmzZszceJEpk+fzpw5c0hPT+fuu+9m+fLlZGVlcf311/P2229z3333AbBlyxZ++uknpkyZwuzZs3nyySeZNWtWqeIsISGB33//nalTp7Jw4UJeeeUV7rzzTvbu3VtmTP369WP9+vU+63r16sVnn33mOf/zzz+xWq1FMkRdunRh3bp1fvu/5557WLp0KZdddhknTpxgzZo1AYmYyoop2Lh8Tb/RWpOYmFimX6EkZOJMa/2dUqplsbIvvU5/Bm40Xw8Elmqt84C/lFI7gQuBn0Lln/D3QJmqqzxSI9A2SimsNkxBUyh6tMvMjlXw0Kqnf12YWfNvZPhlCzeyuy6npiBXk59d8u8eY56gMXdEKSMb6B7WxhxutoYpLFZlZBILR809QtFqA4vV4unDYvUaovYhIr2HhN1COlT3S6h6js2dQf5fZWdZToXwVh2pc9sTAdlu2rSJgQMHcujQIZKSkvj000/Jzc1lyJAhjBkzhvPOO4/U1FSeeuopNm3aRJs2bQAYP348EydOBKBNmzae8oiICPr06cOJEyc819iyZQsPPvgg11xzDQDx8fF8//33pfqVkJDAtGnTGDp0KAC33HIL9913H7m5uURGRpbatrhQKY3MzExiY2OLlMXGxpKRkeG3zaWXXsrcuXOJiYnB6XQyZswYBg0aVOa1KismCC6u9u3bU79+fV544QWmTZvGN998w7p167j88suDumZFU5VzzsYBy8zXTTDEmpsUs0wQTltCLTKUObQeDBarCmiOnLfwK8wAgiPfWMTiPbdPm+pNu/CItsJkoTEfUaviWUE8iUCLTXkWx2htzil0D9t6DXF77M2hXa0BF1hsEF7TGE424it570sbzZU5g39P8vLy2LZtG+eeey4HDhzgww8/5IsvvuDdd9/ljz/+wOVy0aFDB5YvX05+fj4XXnihp63WmvPOOw+A5cuX8/LLL7Njxw7y8/PJzs5mzpw5HtuEhATmzp3rOU9MTCQ+Pr5U3xISEopko1JTU7Hb7WWKmGCx2+2kp6cXKUtPTyc6Otqnvcvl4pprruGOO+7gxx9/JDMzk3HjxvHAAw/w/PPPl3qtyooJgosrLCyMlStXcvfddzNr1iy6devG0KFDiYiIqHC/gqFKxJlS6hHAASxyF/kw85kPUErdDtwO0Lx585D4Jwh/d/wLv4pfYGEsXimcQ6ddGpcLU4wZ2TtvgYgGZ0HhnL2CPE1elgOXy72AxWseoveIcrH/UdxzDZUCa7hyjzIDEBausEVYPEO8RQScKRa11uZQuNdCFEWhQJT5fSUINKNVGSQmJlKjRg3OPvts6tSpw7fffsuwYcO46KKLWLRoEZ06dcJms3lW/S1fvrxEH19//TUPPPAAy5Yt84i1li1bcu655wLG/CyHw0G7du08bX7//fdSM01paWkkJydTr149T9mHH37omZtVUFDAuHHj2LVrF06nk5UrV9KoUSOPbd++ff1m5nr37s2aNWs853FxcTgcDnbs2EHbtm0B2Lx5s9/FAMePHyc5OZnJkycTERFBREQEt956K9OnTy9VnJUV06OPPsr333/PoUOHeOedd+jVq1eR9sHEVJ64OnfuXGTIs0ePHowZM8ZvPJVBpYszpdQYjIUCV+rCwd4UoJmXWVPggK/2Wus5wByAbt26hXyvhgJXJnmuDCKpH+pLCcLfDuVjtbGyKKxlbvJT2MiKglP4I9c9ZxAMEejOEOZnOz0rnw1fC8Wc9lrF4haC7tXW7nNbuCLSbiW8hgVlAZcTXE4XLqex4MPp1J5MY0RNC+E1LMZQssWc32gRgRdKfv/9d7p06YJSirPOOotvvvmGTp06AcZwp1tgnX/++cycOZPffvuN888/n/T0dL755hsGDBjA5s2badasGe3bt+fEiRP885//JDU11ZMZ27JlC+eccw4Wi6XIdWfOnAnA2LFjAZg3b56nPiEhAavVyuLFi/nnP//JF198wRtvvMG3334LwIYNG4iJieHHH3/0ucinuFApjaioKIYMGcKMGTN455132LRpE5988gk//vijT/u6devSqlUr3nzzTe69914yMzOZP38+Xbp08diUJ6YHH3yQJ598kg0bNrB48eIS4iyYmMoT15YtW4iLi8PlcvHGG29w8OBBTxxVRaXuc6aUuhZ4ABigtc72qvoUGK6UilBKtQLaAv+rTN/84dJ5ZLMTly6oalcEQQgBxpzBwgyZMUfPQnhNC5F2KzWijcP9OtJupYbdRo1oGzXsNiLt7jKr5zwiyoKyKLJPOjmWks/R5HyO78/nZKqDzONOstOd5Oe4cOYbW/FkHHVwZG8eh3blcujPXA7+kcv+bTns35bDge05HNqZS+pfeRzamcuB7UZZ6l95nDiQ79nXMC/bRX62sQdhvte+iu69El1O2XfQG28BBnDZZZdRt25doHChAMDFF1/MjBkzuOGGG7Db7cTHx7N27VqUUowcOZKCggIaNmxIv379aNu2LfHx8YSHhwPGl773NY4dO8ahQ4c8IjA5OZmePXsW8SshIYGRI0fy008/UatWLWbOnMnKlSs9gq9r165ER0czfPhwVq9efcr34Y033iAnJ4f69eszYsQI3nzzzSIZpr59+/LMM894zlesWMHatWupV68ebdq0wWaz8dJLL3nqg40pNTWVO++8k8svv5zbbruNZs2aURGUFlfxmBYsWECjRo2oX78+//3vf/nqq6+qfFhThWqjUKXUEuAyoC5wGJiJsTozAjhmmv2stb7TtH8EYx6aA5iqtS5TKnfr1k1v3Lix4p33Ii0tlT0pP1E7Kp4oW4uyGwiCIFQQxVcbuzNqAC6HmX1zuoeGi61ILrJI2Fj5awtXhEUqc96eMrNzRp8Wq8UcjvXausaiPGVQdGi4eFLPs+2LxZgXqCzwx5/b6dChQ8juz+lMfn4+Xbp0YcuWLYSFhXnK77rrLuLi4pg2bZrPdtnZ2dSsWZO0tDQGDhxY6srKyqY8Md19991cf/31XHvttYwbN44RI0aUupL1dGHbtm0+P/tKqV+11t3Kah/K1ZojfBT/uxT7p4GnQ+XPqWClJjmuFCJ1fayqRlW7IwjC34TSVhtbw4zVs8HgXq3rXmjh2XpQm/P8ANBewkt5hmuNM+W1o4tZ5rURtTaMcO9x7X7iCJbCPSILYys6bOvZtNvdh5+ta84UwsPD2bZtW4nyhIQEBg4c6LfduHHjSE5OpqCggCeeqD5z+KB8MfXs2ZN7772XZcuWkZSUROfOnUPt5mmBPCEgICworGQ7k4m2Vd2OwYIgCKdCoKt1K4w0DEHmAlfxOs8SXD/LeAGU9mwUbZwXirYzdU5eYmIi7du391u/dOnSSvSmYigtpuHDhzN8+PBK9qj6I+IsQGwqmlxXKjV0E2wqqqrdEQRBOC1wr2L1L6NK7J7seeUeRtWFT9AzXxjPbFNKF9k/r8iwrvuaxRedqGJZPF2sb+UxM8Vg5QrAtLS0Sr1eZXAmxhRqRJwFiFIKi7aR6zyM3XZ2VbsjCIJwxlMkY1a0BvCaA6eLzofTRQSXl1F5toLxyt65RZ/hW+EWKmdiBk+oWkScBYFN2cnRB6mpm2JR4VXtjiAIwt8ab/FWoi6AkkDwzt65oHgKz/RDGws1is2pM7J7pVzeR6ausL0Ivr8zIs6CQCkLyqXIcx2hhlUeYCAIgnCm4z97V4jWxYZewWtOXQnrwg79oj2ZuVJ9KuaDTzvveXqecxF+1R0RZ2XgcrpwFS5jwqbsZLuSibQ0RAX77BxBEAThjKPseXVFrMu00LrkEy4KK8t4nq4qaleoEV2eSvdcPc9WKn7EmvYpMAttS9V4VTx373RHxFkZFGRmkJeWBtHGYycsykaBdpDnOkGktW4VeycIgiCcaZQ6FBqoxlFFfnheeU/BczkpluELLKtXPnSROXvF8afdjDZ/v6dmiDgLAEdWJi6nE4vVyJTZiCLHlUyEpc7f5oMiCIIgnP6UPkwbuu+zItk+H7un+MoGFl3U4W6kPT8A/y67980rvkuLG7MP7yKLFWxhlfrgJL+IOAsAR34+BdnZRJhPtLeqCPJcx3DoDMJUTBV7JwiCIAjVm+LbmRSt9NPGx5kOJLnnr97HdT1F5rzB6kL1kIjVHKWs5J04UaTMqiLIdR2sIo8EQRAE4e+H97YmFXmEMGlYLkScBYDFZsWRm4szP99TZiWKPNcRnDq3Cj0TBEEQBOFMQ8RZgFisFvIyMjznxlwzC7mu1KpzShAEQSg3beLOpqY9kqNHjxYp73rB+YRFWNmzZ0/VOFYFHD9+nBtvGkJsrWhat23FkqWL/dpe2ecK7DE1Oat2DGfVjqFjp+r5cPtgYjp+/Dg3Dh1CVFQULVq0YPHioravvfYa3bp1IyIigrFjx4bYcxFnAWOJiCQ/LQ3tNSgdpqLJde3HpR1V6JkgCIJQXlq1bMXSZYXPq0xITCA3J6cKPaoaptwzmfDwcPYnH2T+vAVMvnsSSVuT/Nq/8vKrpB1PJ+14OkmJJR92Xh0IJia37eHDh1m0aBF33XUXSUmFto0bN2b69OmMGzeuUnwXcRYgymLB5XTizCsc2lTKiks7yXedKKWlIAiCUF0ZOXIUCxct8JwvWPA+o0aNLmJz4MABhg67kUZNGtA2rjWzX5vtqXv+hVm0a9+WWnVi6dylEys/+bhI2zZxZ/Ovf/0f53U9lzr1anHzyOHk5gY2HWbxkkX0vrQXN48cTtPmjWnVugVr1645hWh9k5WVxYqPV/DYzCew2+306tmLfv36s2jRwgq9TmXFA8HFlJWVxccrvWx79WLAgAEsWFD4uRgyZAiDBg2iTp06IfG3OCLOgsBisVBQ7C8qm4oix5WCLnVXQEEQBKE60v3C7mSkp7Nt2zacTifLP/yAm0eM9NS7XC4GDRlI585d2PtXMl+s/YrZs1/hyy+/AODss8/mm6/XcezICaZPf5QxY2/h4MGii8WWf7Scz1fNRspcAAAgAElEQVStZscfu0hISGD++/MC8i0xMZFNm35n8OAh/LVrL3dPmsKkuycG1HbgoP7UrV/b5zFwUP8itn/u+BOr1UpcXJynrMs5XdhaSuZs+qOP0LBxfS65rDfr1n0b8nhCGZPHtq2XbZcuRTJnlY1spREEKjycgswMatSq5SkzttU4LttqCIIglMGbT/zM7m3HQ3qNszvU5q4ZFwXVxp09u6T3JbSLa0eTJoWP59uwcQNHjx5h+iOPGv2ffTbjx09g2fJlXH31Ndx4w00e26E3DWPW87PYsOF/DBgw0FM+edJkGjduDMD11/dj8+bNAfmVmJjAPVOmctONQwEYNWo0Dzx0P7m5uURGRpba9pOVqwILHsjKzCQ2NrZIWUxsDBmZmT7tn3n6WeI7xBMeHs6yD5YyaMhANv7vN1q3bh2yeCB0MfmyjY2NJcNrnnllI5mzILDYbDhz83A6nUXLVRi5rkNV5JUgCIJwKoy8eRRLly3h/QXzSwxp7tu3lwMHDhTJ0jw361lSDx8GYMHC9+l6wfmeuqSkRI4eK7rAoGGDhp7XNWvWJDPLt+gpTmJSIkOG3OA5Tz2Sit1uD0jIBEOU3U56enqRsoz0DKLtdp/23S/sTnR0NBEREdwyegw9Lu7BmgCGJysrHgguJl+26enpRJt7m1YFkjkrB868PKw1a3rObUSR60qlpm6BVUVUoWeCIAjVl2AzWpVFixYtaNmyFWvWrmHO2+8UqWvatBmtWrZi29Y/SrTbu3cvd951B1+s/YqLL7oYq9VK1wvOr5BpLmlpaSQnJ1Ovbj1P2YoVH3HtNddSUFDAhNvHs3v3bpxOJx8tX0GjRo2KtO/X/zrW/7DeZ9+9evbis1WrPedxbeNwOBzs2LGDtm3bArA5YTPx8R0D8lUpVWbMpcXjZuZjM1j/w3oOHzrEW2/NoVfPXpUSk8d25w7iO7YzbDdvpmPHwOIPBZI5CxJlteLIyipapiwoLOS7jvppJQiCIFRn5rw1ly+/+A9RUVFFyi+84EKiY2J44cXnycnJwel0kpiUyIaNG8jKzkIp5REc8+a/R1JSYsDXHDfhVsZNuNVnXWJiAlarlaXLluBwOFi9+nPeevtNHn10Jhs2biAmOobv163nh+9/LCHMAD5btdqzmrL44S1iAKKiohg8aDCPPzGTrKwsfvjxB1at+pSRI0eV6DctLY0vv/yC3NxcHA4Hi5cs4vv133N1n6tLjau0eNzcf98D/Perr3nvvfl8/PGKSospKiqKQYMG8/jjpu0PP/DJJ58wenRhFtXhcJCbm4vT6cTpdHriDxUizoLEEhZGvo8xa5uyk+1KRmunj1aCIAhCdaZ169Z069qtRLnVamXlik/YvHkTbdu1pmHj+txx5+2knzxJfId4pk39B70v7UmTZo1ITEykR4+eAV8zJTmFHhf38FmXkJjIiBE38/PPP1GvQR0ef/JxPly+gvgO8XQ9vyvR0dGMHDWCNWtW+2wfLLNffZ2cnBwaN23I6FtG8trs1+nolWXq1/86npv1LAUFBcx4bAaNmjSgYeP6vP7G63y0fAXt2rUrNa7S4gFITU1l4uS7uOrqK7nzrjto2rRZpcUEMPuV18nJzaF+/fqMGDGCN998s0jm7KmnnqJGjRo899xzLFy4kBo1avDUU0+dso/+UKfzKsNu3brpjRs3hvQah/fuYGfSeqJjG3jKCrKyiG3ZAmtYeBHbPNdxoq3tiLTWK96NIAjC346DaTtp3659VbtRLcnPz6drt/P47ddNhIWFlaifNHkibdvGMfWeqSXqsrOzqVmzJmlpaQy5cTBf/+ebynA5IPzFVVo8APdMncJ1fa/jmmuuZcLt4xk+dDhXXdWnstz2PLMzLKJiclbbtm2jQ4eSm/MqpX7VWpf8K6AYMuesnDhy80qIszBlJ9u1lwhLHZSSpKQgCILgm/DwcBK2+N+qITEpkQH9B/isu+328SSnpFBQUMBjMx4LkYflw19cpcUD0KNHDx548H4++PADtm7dyjnndA6lm9UeEWflwGKz4cjOJqLYSg6LCqdAZ1Cg0whXtavIO0EQBOF0JykpkXZ+so6LFi6pZG9OndLiARg2dDjDhg6vRI+qNyLOyoH7Qei+sBFFlnMfYaqW+fxNQRAEQQiOo6mh3Q+usjnT4gk1MvZWDpTVhis/H5fLVaLOqiJx6AwcOt1HS0EQBEEQhNIRcVZONKD9LKO1qSgynbtk5aYgCIIgCEEj4qycKMBZUOCzzsieZZPrSq1cpwRBEARBOO0RcVZeLBaceXl+q8NUDFmuv3Bq/zaCIAiCIAjFEXFWTixW/4sCACzKhtKKbGdyJXolCIIgCMLpjoizcqJsNpyliDMAm4ohz3UIpy7dThAEQRAEwY2Is3KiLBZc5jO2/NooBVjIk2duCoIgCIIQICLOThHtZ1GAG5uyk+NKkZWbgiAIgiAEhIizU8Tfik03FmXDhYM814lK8kgQBEEQhNOZkIkzpdS7SqlUpVSiV1ltpdRXSqkd5s9aZrlSSr2qlNqplNqilDo/VH5VJBartcx5ZwA2aprZs9P3IfOCIAhnGm3izqamPZKjR4tOPel6wfmERVjZs2dP1ThWBRw/fpwbbxpCbK1oWrdtxZKli8tt+/obr9P94guJiq7BuAm3htr1cuMdR5syYr7sssuIjIzEbrdjt9tp165dSH0LZeZsHnBtsbIHgf9qrdsC/zXPAfoCbc3jduDNEPpVYQSyKADc+55l4iCzErwSBEEQAqVVy1YsXbbUc56QmEBuTk4VelQ1TLlnMuHh4exPPsj8eQuYfPckkrb6fjB7WbaNGzfi4QcfZuyY6ivMoGQcd0+ZRFKS/4fRv/baa2RmZpKZmckff/wRUt9CJs601t8BxR+mNRCYb76eDwzyKn9fG/wMnKWUahQq3yoKi9WKMz8voIyYBRu5zsOV4JUgCIIQKCNHjmLhogWe8wUL3mfUqNFFbA4cOMDQYTfSqEkD2sa1ZvZrsz11z78wi3bt21KrTiydu3Ri5ScfF2nbJu5s/vWv/+O8rudSp14tbh45nNwA/qgHWLxkEb0v7cXNI4fTtHljWrVuwdq1a04hWt9kZWWx4uMVPDbzCex2O7169qJfv/4sWrSwXLaDBw1h4MBB1KlTJyg/KiteKBlHz5696Hd9fxYsWFB240qgsuecNdBaHwQwf9Y3y5sA3huCpZhl1RulcLk0Lkfp887AWBiQp1Nx6bJtBUEQhMqh+4XdyUhPZ9u2bTidTpZ/+AE3jxjpqXe5XAwaMpDOnbuw969kvlj7FbNnv8KXX34BwNlnn803X6/j2JETTJ/+KGPG3sLBgweLXGP5R8v5fNVqdvyxi4SEBOa/Py8g3xITE9m06XcGDx7CX7v2cvekKUy6e2JAbQcO6k/d+rV9HgMH9S9i++eOP7FarcTFxXnKupzTha0+MmfB2AZLZcXrL47OnbuUmjl76KGHqFu3Lj179uTbb78NOr5gsIW098BRPsp8pqOUUrdjDH3SvHnzUPoUGFrjLHBgDQsv1UwpC7g0+a4TRFrrl2orCIJwJrLs01dIPrAjpNdo1rgtwwbcE1Qbd/bskt6X0C6uHU2aFOYGNmzcwNGjR5j+yKOAIcbGj5/AsuXLuPrqa7jxhps8tkNvGsas52exYcP/GDBgoKd88qTJNG7cGIDrr+/H5s2bA/IrMTGBe6ZM5aYbhwIwatRoHnjofnJzc4mMjCy17ScrVwUWPJCVmUlsbGyRspjYGDIyS07FCcY2WCorXvAdR2xMDBkZGT7tZ82aRXx8POHh4SxdupT+/fuzadMmWrduHdR1A6WyM2eH3cOV5k/3wydTgGZedk2BA7460FrP0Vp301p3q1evXkidDQQV4KIAAKuShQGCIAjVjZE3j2LpsiW8v2B+iSHNffv2cuDAgSKZmOdmPUvqYWOayoKF79P1gvM9dUlJiRw9VnSBQcMGDT2va9asSWZWYEImMSmRIUNu8JynHknFbreXKVSCJcpuJz09vUhZRnoG0Xb7KdkGS2XFC77jSM/IIDo62qd99+7diY6OJiIigjFjxtCzZ09Wr15d4X65qezM2afAGOA58+cnXuWTlVJLge7ASffwZ3XHYrPhyM6G2rXLtLWqCPJcx3GQSRi+PwCCIAhnKsFmtCqLFi1a0LJlK9asXcOct98pUte0aTNatWzFtq0lJ4Dv3buXO++6gy/WfsXFF12M1Wql6wXnV8gf4GlpaSQnJ1OvbmESYsWKj7j2mmspKChgwu3j2b17N06nk4+Wr6BRo6LTtPv1v471P6z32Xevnr34bFWhsIhrG4fD4WDHjh20bdsWgM0Jm4mP71iibTC2FRUvwMzHZrD+h/UcPnSIt96aQ6+evcodr784tmzZTMeOgcWhlAppoiVk4kwptQS4DKirlEoBZmKIsg+UUuOBfYA7H7wauA7YCWQD1XuJhxcWmw1nbg5aa/OJAGXYYyPPeYQwm4gzQRCE6sKct+ZyIu0EUVFROBwOT/mFF1xIdEwML7z4PJMn3U14eDjbtm8jJyeHqKgolFIeQTFv/nskJSX6u0QJ3NtMvPvOeyXqEhMTsFqtLF22hGlT/8GXX37BW2+/yX+++poNGzcQEx3D9+vW+/3uKS5GSiMqKorBgwbz+BMzefutuWzavIlVqz7lu3UlxU4gtg6HA4fDgdN8ik5ubi42mw2bzeY37tLiBbj/vgd4/LEn2LBxA0uXLikhzoKJ12ccmzax6rNP+fHHH0vYpqWl8csvv3DppZdis9lYtmwZ3333HS+//HJQ1wyGUK7WHKG1bqS1DtNaN9Va/1trfUxrfaXWuq3587hpq7XWk7TWrbXW52itN4bKrwpHKbTThauMzWjd2JSdXH0Il84PsWOCIAhCoLRu3ZpuXbuVKLdaraxc8QmbN2+ibbvWNGxcnzvuvJ30kyeJ7xDPtKn/oPelPWnSrBGJiYn06NEz4GumJKfQ4+IePusSEhMZMeJmfv75J+o1qMPjTz7Oh8tXEN8hnq7ndyU6OpqRo0awZk3FDK3NfvV1cnJyaNy0IaNvGclrs1+no1c2rF//63hu1rMB2T7z7NNEx0bx/AuzWLx4EdGxUTzz7NOlxl1avKmpqUycfBdXXX0ld951B02bNqMiKB7H7Fdf92TO+vbtyzPPPANAQUEB06dPp169etStW5fZs2ezcuXKkO51pk7n+U/dunXTGzeGVscd3ruDnUnriY5t4NfGkZVFVOPGhEdFBdRnviuNGpYmRNlaVJSbgiAI1Y6DaTtp3659VbtRLcnPz6drt/P47ddNhIWFlaifNHkibdvGMfWeqSXqsrOzqVmzJmlpaQy5cTBf/+ebynC5QvAXd2nx3jN1Ctf1vY5rrrmWCbePZ/jQ4Vx1VZ8K9csthcIiKiZntW3bNjp06FCiXCn1q9a65F8BxaguqzVPa5TFgiM3N2BxFqZiyNH7qaEbYVGlr/IUBEEQzjzCw8NJ2OJ/24bEpEQG9B/gs+6228eTnJJCQUEBj814LEQehgZ/cZcWb48ePXjgwfv54MMP2Lp1K+ec0znUblY5Is4qABUWhjM7GwLccM/YVgNyXIeJslZMelYQBEE4c0hKSqSdn6zjooVLKtmb0FNavMOGDmfY0OGV7FHVIuKsArDYbDiystDaZQivAAhT0eS4kqlhaSDZM0EQBKEIR1OLP2DnzObvFm9ZVPY+Z2csGnAVOMq0c6OUFTTkOH1u5yYIgiAIwt8UEWcVhAIceXlBtQlTMWS7UnDorNA4JQiCIAjCaYeIswoimCcFeNooC1YVQZZzlzw1QBAEQRAEQMRZhaFsNgpycoJuZ1NR5OuT5LmOhMArQRAEQRBON0ScVRAWmw1XXh4ulyvotmHEkuXajUsHtpGtIAiCIAhnLiLOKhgd4JMCvLEoG1q7ZHGAIAiCIAgizioSDTjyy/dYpjAVQ44rBacOfmhUEARBEIQzBxFnFYjFasWRk12utkpZUNjIdqZUsFeCIAhCRTP//XlcevklVe2GcIYim9BWIJYwG46c4FZsemNTdnJdh6ihG2FT9gr0TBAEoXpwNCmRgsyMkPUfZo+mbsdOZdqt/2E9Dz38IFu3JmG1WmnfvgP/9+K/uKDbBcx/fx7vvvcu6775LmR+VhZLli5m+qOPcPToUa668irmzvk3tWvX9mn7zTdfc/+D97Nr107q1qnLfffdz20Tbq9kj4NHa83DjzzEu+/9G4Bbx47j2WeeQynl0/a5Wc8y9505pKWl0ffavrz5xttER8dUttulIpmzCkRZbbjy83E5neVrrxRWFUmWc49srSEIwhlJQWYGEWfVCtkRiPBLT09n0OABTJo4idRDR9n7VzKPPvIoERERlXAHKo+krUlMnHQX896dz/7kg9SsWZO7p0zyaVtQUMCNQ2/gtgm3cezICRYtWsJ999/L5i2bK9nr4Jn7zhw+/fQTft3wO79t3MTq1Z8zZ+7bPm0XLHyfRYsWsu6b79m3J4WcnBzumTalkj0uGxFnFYzxpIDyr7q0qSjyXSfId8mjLARBEELBnzv+BGD4sBFYrVZq1KhBnz5X0/mczmzbto1Jkyfy888/cVbtGOrWN7JMx44dY/CQgdSuexYX97yI3bt3B3y9+e/P45LLenPP1CnUqVeLTufE8/XX/w2oXVy7NtSqE0vbuNYsXrIoqDiXLFlMv+v70bv3Jdjtdh6b+QQfr/yYjIySAvb48eOkp6czauRolFJc0O0C2rfvwLZtWwO61pV9ruCR6Q9zcc+LqFOvFkNuGMTx42V/jw0fMZSmzRtTp14tLr/yMpK2+n8YvD8WLFzA1KnTaNq0KU2aNGHq1Gm8v+B9n7aff/4Zt44dR7NmzbDb7dx77/0sX/4B2dnlm5IUKkScVTAKcJZzUYCbMBVtbq0R+OOgBEEQhMCIaxuH1Wrl1vFjWbt2DSdOnPDUdejQgddfe4OLLrqYtOPpnmc+TrlnMpGRkSTv3c/cOe8wb/57QV3zf//7hVatzubQgVRmzJjJTcNuLFW8ZGVlMe0fU1n16eecOHaS79atp0uXcwFjSLZu/dp+j/U/rAdg69YkOnfu4umzdevWhIeHe8SpNw0aNGD4sOHMmz8Pp9PJTz//xL59e+nZo1fAMS5ctIC5c95h354UbDYbU/9xT5ltrrnmWrYl/cGBlEOcd+553DJmtKfu+RdmlRqnm+Jxdu7cha1+RJ7WusjIlNaavLw8duzcEXCclYGIswpG2Ww4yrEZrTcWFY5L55PjOlhBXgmCIAhuYmJi+Pbr71BKcefEO2jUpAGDhwzk8OHDPu2dTicrPl7BzBmPExUVRaeOnRg96pagrlm/fn3umXIPYWFhDL1pGHFx7Vi95vNS21gsFpK2JpKTk0OjRo3oGN8RgF49e3E09bjfo1dPQ1BlZmYRE1N0LlVsbCyZPjJnAMOGDufpZ54kKroGl19xKU88/iTNmjULOMaRN4+iU8dOREVF8dhjT/Dhh8txljHN59ax44iOjiYiIoIZj85ky5bNnDx5EoD773ug1DjdZGZmEhsTWxhjTCyZmZk+pwddc821vPvev9mzZw8nT57kxRefByBHMmdnNhabjYIKeJPDVCw5zr0UuNIrwCtBEATBmw4dOvDuO++xZ/c+Nv22hQMHD/LPe6f5tD1y5AgOh6OIUGnevHlQ12vcuEmRCeotmjfn4EH/f4BHRUWxeOES5sx5m2YtmjBgYD+2b98e1DXt9igy0osKsfT0dOzR0SVst2/fzs2jRvDev+eRnZnL5t8T+L//e5HVq0sXkN40bVp4f1o0b0FBQQFHjx71a+90Onn4kYdo174tteueRZu4swFKbeMLu91Oekbhd2V6Rjp2u93ngoBbx45j2LDhXHX1FXQ57xwuvfRyAJo0aRrUNUONiLMKRlmtuBwOnI5TG5I0nrsZxUlnIg6dWUHeCYIgCMVp3749t4weQ2KSMRRW/Eu9Xr162Gw2kpOTPWXerwPhwIH9RTI5+5KTadSoUaltrr76Gtau+ZLkvftp1649d068A4D167/nrNoxfo/1678HID6+I5sTCif07969m7y8POLaxpW4VtLWROLi2nH11ddgsVho164dfftex9ov1gYcY0pK4T3Zt28fYWFh1K1b16/9kqWLWbXqU9au+ZJjR06w809jHp/7Pj0369lS43QTH9+RLV4LF7Zs2Uy8mWUsjsViYeaMx9j552727N5Hx/h4mjRpQpMmTQKOszIQcRYiXAWnNu8MwKoisBJJmiMBh65eKVdBEITTle3bt/PSS/8iJcXYVzI5OZllHyyle/fuADSo34D9+1PIN+cPW61WBg8azJNPPU52djZbt21lwcKiE86v7HMFTzz5uN9rpqamMvu12RQUFPDhR8vZvn0bfa+9zq/94cOHWbXqU7KysoiIiMBut2O1WgHo1as3acfT/R69evUGYMSIm/n8889Yv/57srKyeOyJmQweNJhoH5mzc7ucx86dO/jmm6/RWrNr1y5Wr/6czp07A7Bnzx7CIqzs2bPHr8+Llyxi67atZGdn8/gTM7lhyA0en32RkZFJeEQEderUITs7m+mPPlKk/sEHHio1TjejRo7ilVdeZv/+/Rw4cICXX36JW0b7HnY+fvw4u3btQmvN1m1bue/+e3nk4elYLNVLDlUvb84QLFYrBZlZFdKXVUViIYwMx5+yvYYgCEIFEB0dzf82/ELP3hcTWyuaXpf0oGN8R16Y9SIAl19+BfEd4mnavDENG9cH4JWXZ5OZmUnT5o0ZP2EcY24ZW6TPlORkelzcw+81L7ywOzt37qBh4/rMmPEoy5Z8QJ06dfzau1wuXnrlJZq3bEr9hnX57vvvmP3qa0HF2TG+I6+/9ga3jB1N46YNyczIYParr3vq+/W/judmPQsYiwXmznmHaf+YSu26Z3Fln8sZPHgI424db8SXkkyLFi1KzTCNvHkU4yeMo2nzxuTm5vLSv14p1b/Ro0bTonlzWrRqRudzO3nEcbDcftsdXH99P87r2oVzz+9M377Xcfttd3jqvbOJR48eZcDAfsTWiqb/gOsZM+bWarmXmzqdv/C7deumN27cGNJrHN67g51J64mObRBwG+104iooILZVK59j3uUh33WcWNs5hFliyzYWBEGoBhxM20n7du2LlFWXTWgrkpSUFIbfPIz13/3gs/5M2NT2mWefpm7dukVEjzdX9rmCm0fczPhxEyrZs4rBLYXCIiomZ7Vt2zY6dOhQolwp9avWultZ7eUJASFAWa24cnJw5edjraBNDS0qkmxXCrEizgRBOI2pbOFUGTRt2tSvMDtTePihR8o2EioMGdYMEcpiOeUtNbyxqZrku47L4gBBEIQziLIm9Z/uLF6yyGd8Xc49p6pdq9bIsGYZlGdYE8DlcKAUxDQLbrl1aRToDMLVWUTb2lVYn4IgCKHC17CmIFRHqtuwpmTOQoTFZjwE/VS31PDGhp0811GcuuIycoIgCIIgVC9EnIUQpVSFDm0qpVBYyHWmVlifgiAIgiBUL0SchRBls+HIrNg5YjYVTY7ej0uX/+HqgiAIgiBUX0SchRBreDj5WVlo7aqwPpWygNbku/w/MFcQBEEQhNMXEWehRCnQGmfeqT8twBubiiLblSyb0gqCIAjCGYiIsxCjlKKgAuedAVhUOE6di0PLQ9EFQRAE4UxDxFmIUeHhIdkN26oiyHbtr/B+BUEQBEGoWkSchRiLzYYzNw+X01mh/Vpxb0orD0QXBEGoCsZNuJUZMx+tajeEM5AqEWdKqWlKqSSlVKJSaolSKlIp1Uop9YtSaodSaplSKrwqfAsVzvy8Cu1PKYUFK3nOwxXaryAIQig5sieP/dtzQ3Yc2RP4/7Xrf1hP70t7UadeLeo3rMsll/Vmw8YNALSJO5v//vc/oboNIef48ePceNMQYmtF07ptK5YsXVxu29ffeJ3uF19IVHQNxk24NdSul4tg4r2yzxXYY2p6nlbQsVPJzWKrmkp/tqZSqgkwBYjXWucopT4AhgPXAS9prZcqpd4CxgNvVrZ/oUBZrRRkZRNWo2aF9mtsq3GQGropFhVWoX0LgiCEgvxcTaQ9dHmB3MzAVsenp6czaPAAXpv9OjfdOJT8/HzWr/+eiAp6HnJVM+WeyYSHh7M/+SCbNm9i4KD+dO7chY7xHYO2bdy4EQ8/+DBffvUlObnVcxP0YOIFeOXlV4s8pL26ra+rqmFNG1BDKWUDagIHgSuAD836+cCgKvKtwrGE2cgPwbwz97Yaea4jFd63IAjCmcyfO/4EYPiwEVitVmrUqEGfPlfT+ZzOjLn1Fvbt28egIQM5q3YML774AgC/b/qdC7p3o1adWG4eOZy83NyAr7d4ySJ6X9qLm0cOp2nzxrRq3YK1a9eEJLasrCxWfLyCx2Y+gd1up1fPXvTr159FixaWy3bwoCEMHDiIOnXqBOVHZcUcTLynC5UuzrTW+4EXgX0Youwk8CuQprV2P+soBWhS2b6FCmW14Spw4Cyo2C01wMyeufajdcXOaRMEQTiTiWsbh9Vq5dbxY1m7dg0nTpzw1M1/732aN2/OyhWfkHY8nXvvvY/8/HxuvGkII28eReqho9xww42s+HhFwNdLTExk06bfGTx4CH/t2svdk6Yw6e6JAbcfOKg/devX9nkMHNS/iO2fO/7EarUSFxfnKetyThe2bk0q0W8wtsFS3piDibW8MUx/9BEaNq7PJZf1Zt26b8sVXyipdHGmlKoFDARaAY2BKKCvD1OfSUal1O1KqY1KqY1Hjpw+GSMFFb7fGYBF2XCRT4E+WeF9C4IgnKnExMTw7dffoZTizol30KhJAwYPGcjhw77n8f7yy88UFBRwz5R7CAsL44YhN9Kt2wUBXy8xMYF7pkzlphuHEhYWxqhRo9m3bx+5AWbfPlm5iqOpx4WwLWcAACAASURBVH0en6xcVcQ2KzOT2NjYovHGxpDh44k1wdgGS3ljDibW8sTwzNPP8uf2nez9K5kJ4ycwaMhAdu3aVf5AQ0BVDGteBfyltT6itS4AVgA9gLPMYU6ApsABX4211nO01t201t3q1atXOR5XBBYLzvyKF2cAFiLIkW01BEEQgqJDhw68+8577Nm9j02/beHAwYP8895pPm0PHDxA48ZNUEp5ylo0bx7wtRKTEhky5AbPeeqRVOx2O5GRkeUPwA9Rdjvp6UX3wcxIzyDabj8l22CprJiDjaH7hd2Jjo4mIiKCW0aPocfFPVj7RWiGmMtLVYizfcBFSqmayviUXwlsBb4BbjRtxgCfVIFvIcNiteIIYn5CMNhUTfJdJ3Hq6jlRUxAEobrTvn17bhk9hsQkYyhMoYrUN2rYiAMH9hd5Msu+5OSA+k5LSyM5OZl6dQsTCitWfMS111zrOZ/52Ayu7HMFnc6JZ/0P60v00a//dZ7VhcWPfv2vK2Ib1zYOh8PBjh07PGWbEzYT72NyfDC2wVBazAUFBYy59RZ6X9qLHr0u5uDBg+WOtSJiUEpVuyfuVMWcs18wJv7/BiSYPswBHgD+oZTaCdQB/l3ZvoUSZbXizKvY7TSK9I+VXFkYIAiCEBDbt2/npZf+RUpKCgDJycks+2Ap3bt3B6B+gwbs/mu3x/6iiy7GZrMx+7XZOBwOPl65gg0b/uepHzfhVr/bTCQmJmC1Wlm6bAkOh4PVqz/nrbff5NFHZ3ps7r/vAf771de89958PvYxl+2zVatJO57u8/hs1eoitlFRUQweNJjHn5hJVlYWP/z4A6tWfcrIkaNK9BuIrcPhIDc3F6fTidPpJDc3F4fD4an3FXtpMW/YuIGY6Bi+X7eeH77/kUaNGpU71mDjTUtL48svv/DEsHjJIr5f/z19+lxdwrYqqZLVmlrrmVrr9lrrTlrr0VrrPK31bq31hVrrNlrrm7TWoVMyVYCyWnE5HBW+Ga0bm4oi13VAFgYIgiAEQHR0NP/b8As9e19MbK1oel3Sg47xHXlh1osAPHDfAzz73DPUrV+bf/3r/wgPD+eDZR/y/oL51GtQh+XLP2DwoMGe/lKSU+hxcQ+f10pITGTEiJv5+eefqNegDo8/+TgfLl9BfId4AFJTU5k4+S6uuvpK7rzrDpo2bXbK8c1+9XVycnJo3LQho28ZyWuzXy+yrUS//tfx3KxnA7J95tmniY6N4vkXZrF48SKiY6N45tmnS429tJi7nt+V6OhoRo4awZo1JcVWRcfrHWtBQQEzHptBoyYN+H/27jtOrrO8//7nOmVmdravdtWbi5orsgW2ZRtwLxhjjCvgEEpMAgQ7CYEkTwglhZQnEOKYJATi0LGxTTE2YDA27rLVrN4sy5Ksru1lZk65f3+cM7Ozq22SdmZX0vV+vVa7M3PKfUa7M9+56+SpE7n3a/fy0I8eZt7ceaNSjtEi460q73AsWrTILF26tKTn2Pv6ZrasfZbq2klHfSy/u5uaGTOwSzSPTs60UGMvIGE1lOT4Sil1OHa3bmH+vPl97tu/LUsuU7r3nURKaJpd3rnKcrkc5y5ayPJlK3HdQ+ec/PgnPsacOXO5+667B9z/rrs/ybXXXMtVV13NR+78MLfdchuXX35FqYs9Kga79qGuubu7m3Q6TWtrKzfe9G5++5sny1nkAeWjkJscnTqr9evXs2DBoZPbisgyY8yi4fYv+yS0JzRjCHy/ZOEsGhiwS8OZUmrcKndwKodEIsHqVYNP27Bm7Rquf+f1gz6+ePFiPvMXn+aBBx9g3bp1nHnmWaUoZkkMdu1DXfMf3Plhduzcied5fP5vPl/iEh6bNJyVkyXRiM3KypIc3qaCXNhCaHJYx9fqV0opdcxau3YN8/rVIBa79ZbbuPWW28pYotIb6pq/990flLk0xx4NZ2Vk2Q5BTw/U15fk+CKCGME3nSREa8+UUmo8OLCveayLUHYn4jWPprFavumEJLY96gug92dJQpdzUkoppY5hGs7KSGyb0PMJw5EtzHskbCrImoM6alMppZQ6Rmmz5igIQ0NPlyEMDAjkJ5AWkeg28X0CfndIoj2LnUgUtkMAA0FgCIPoe+BHx4vuy9+mz+3Cz0WPIxBandQkXiXpVmHZFo5jkUjauInoK5GwcZMWrhvdtp3ejB4GIZkenzAwiCVYlpBM2Vi25nillFKqHDScDWHPjg7u/fx62ppDbLuZMOwNUD3dId0dId2dIT3dhsObkeToF5Qd3sDrww3EsgU3YQOQ7fEH3MZxLZIph0TSxnFtvFxALutjWUIq7ZJMOogVD0cuei7cRLxfyiGZskkkHRzHwo6/HEewHQtjomAYhpBI2oeEScsWLFtIphwqqxJUVLqFfcSKyp9IxsEz/kLAEkGsaLZvsYp/jh5DBMuKgnQQGLxcgO8FhWNEwdujp9sj8EPCwBCGvV+pCod0VYLq2mThORxPwtBgQoMxBsu2sCwZfiellFJjSsPZEDo7u3i9+5dk95yOa9nRG7ot2BZUpC0aJzmkqyzSVUK6ysK2ozc+YygsBZEPK4boe5DL4aYrcSoq+mwHYDuCHYcQ2xZsp/dnyx78ccumcD9Eszn7QZY08wkDg++FcZiKgkcuF+DlArxsQC4XPeblAsLQkK5yqUi72LaFMYbAD8llA7JZn2wmIJfx8bwQN2GRSDqY0JDp8fuGurj20BgK581lfDrbsmQzPr4f4nshgR/i+yFBECJE1yMi5OJ9THjszMEnAo2TKpkys4a6CRVU1SRJVTh4uSB63rI+mR4fL9d7XabwT+/vge9FNZfZjE+m2yOT8fG9MPr/tuNQa0uhRtSyhVw2oKfbI9PtYUxUmxoaDnn+RCBdlSBdlcC2o7BqxbW7liXUN1YwcWo1DU1pUhVREAcIgqgZvrI6SVVNAssSfC8kDA2OY+G4Fo5r4yas+LaN68b3J+zCNq4bhezitQmVUkodSsPZEKTiAMlpq5m2YDc3XfL7uM6hkwseriCbxU2nqZx09JPaDiUbtlDvVuFIaabtKAffD/GyUXAMgpAgMOSyAd0dObq7clhxs2sYh0AvG4WgXC7A90KMMZgQDPF3E9VwhqEB0xtg8vc5juC4UZjw/Si0iiWkK10qKqPAmj+nxLVtmR6fni6P1uYedu9oZ/f2dnZtb6ezPUu2xyeRdEikbJIph2TSwU1YUQ1eHFCKc4pIFFArqxM0TIwCUqrCxXEswjDE9w1h/DwEcagNfEMiaVNR6ZJMOYWA21vO+LsIXi6gqyNLd6cX1fyZuFYtBD8IadnfzZInX6ejrYTLjAmFMJcPbCJEHwAyfqEW13EsgsDge0EhyEc1lS7pKhcn3k8EqmqSVNelqKlLUlOfoqYuheP2/l8d8lwU7iM+XlQTG8bPZyrt0NCUpqLS1SCplBoTGs6GcMrsM7j1mj/k+4/ey2MvPsR1i2/Gto6u6cpySrcAejHBIhe24NjHbjhznKgmpqLy6EPxWDDGHJNv7r4fkstEtXcicY2sga7OHJ3tWUwY1eJalhD4Bt8P8HJhXCPa92ffi2pJvQF/Dgo1cPkm8zDMNy2HUYhzbOw4NItApsenuzOH70W1eWFo6GzP0ryvi22bmmlryRD4ozPgJh968wE5/wEhan6PQr1lFdVuW1JoOs7Xbva5v2g7ESEbP8duwqa2IUVtfSpq9netqFm9qEbSdW2c+D43YRe2SSYdquuSVNclSVW42myt1HFCw9kwzpz7FhZvXcnz65/j8Zd/xlVvfheWdeSd48V2CDJdGBMiUrpO9o6kyYS7qbCmHZMB4XhwrD7vjmPhxM2fxWrqU2NUopEzxvTpHxj1S4xqCfP97/Lfg8CQ7fHp7MiS6fELTcfdXR4tB7ppOdATNS33xE3LTvS4ZUthEE2+D2IQB7bQ73c7CIsG7YTksr3lSqZsqmqSeLmAnVvbWNuyBy8XhdcwOLImfcftHfyTSDokEkX9N4v6cub7ZxZvW7i/33Zuwh5x6HNcm1SFE9UUVzgY1xCGFGo5lVIjo+FsCN6u18j+2x9zxpsvgzMu4fk1TxIEAVefdwOOfXRPXej72G7pZvG3xMUPO/HpxKW6ZOdRajwREdKVx/7qGGGQr2WMaxpzQfRzLohqJeMQl+n26WjL0N6aJdPjFfp4Rk38QaH/Zr5faVuXVxjMk8v2PpbLlmbqnc/edxavcRDIf1gxGOIR7Pk+j/SOISqudexbK1lU8xjfzv8sIhr81HFHw9kQJJEk3LWVht+28JYP/DWu4/K7lY/zs+ce4J2Lb8J1jvxNoNThDOKmzeAgrqPhTKljiWVbJGyLRJkqK43pO3AoFwe4fMgzIxiObkw0oCWb8QuDWiprfBqa0n0GP+VDWhgWDVqJw1UY5PtVhni5+OfQ9BkB3l++6T2qnTv0e37AS35ATX50tojE/VBNoXx9mqqLmqnz9+X7LObD4Ic+8kGmT5vOF7/wt0fx7Ct1KA1nQ3Aap+Le+mfwf18gfP5RFl50PQknwW+WPsovXvwx1114M9YRNk2G/sBTVowmWyrJmD2kzcySNqEqpY5tIlKYPiZdNXrH3d26hfrGij73Hex+BS/sGLws9H1jMlDUHB1NtxN9RbeDMBrcQzwAyJZKaqwze0NXCJ4fkAn8aORxUdBbtuJF/uUrX2TL1o3Yls3JJ8/hrz71d5x5xkIuveZc/u5zX2bx+W87pID5wNbd4dHRlmX3jo5DBptI/wEp+VrC4pH80Cf4WUV9Eo+mNrC5uZk7P/oRfv2bX9PY2Mjf/e3fc/tt7z2i7e/92r18+zvfYs2a1dx662387zfuO/KCldDhXPNlV1zKkiUv4jjRb9q0qdNYs3p9OYs7LA1nw3DOvZS2535CxfM/Jzj5DE4/6U34gc+TK37Js6ue4K1nX3HYxxTbJshkKXVroyUOXujhmw5cqS3tyZRSagS8sIOkU5r1hQGyfguTqwZ+cS3UkIXQ1tbGx+6+g3u+ei8333wLuVyOZ599hkmTmpg1px7HtWicXMmUGTW9cxsGhjAMCz/nP/Pma/qM6Z0DcajavuEU1wbmb/d9vHiScynUPOb98Z98lDC0WPrcRtZtWM0H/+A2Zk6dx+mnn16Y6zE/5ZGI8Ecf+xiWOGxev53Va1/hllvfzbw5Z7BgwWk0TpjEp/70MzzxxK/pyWTI9PhxuYT4W6GMIvSZoqeczc2fvOsTJBIJ3tixm5WvrORdN7yTs846m9NPO33A7b/6b//Ohz/0kcLtw5urtPQ0nI1A60XvJLVnB6nH7qPrQ1/g7FMX0dJxkOWbllBX1cBZp5x7WMcTxyHIlnaNzTxLEmTCA7iWhjOl1ImtEGos2LptCwC33347AI5TwZVXXgnABz74e+zYsZ1bbrsR27b567/6LJ/61J+zYuUK7vzoH7Bly2auufoaLFuork0yfXbf19d8CCwehPLDH/6Ar3/zv5g+bTrPPvcMrutyz1e/xhWXXVUIe0FR8MvvWzhm0Q9959E0fXJgd3cXv3z8ER5/9HkqK6tYdM75XHbJVXzvB9/hzz752UOek+6eLh555Cf87KGn6WgJmT31TN7+1iv5xv/8L39212dZdNYlADz91Asc7GznjW1tI3quH3nsQb5//31MmTyVl5a+gOM6fOmLX+GSt11ZNAl47/PVpzm6qPZQpHfqIilMLN63+bq7u5uHf/wwS15YjmOnWHTO+Vx79XV8+1vf5otf+Pu4BrV3svgwiObv7GzPUegESTRpupscH31Whw1nImIDnzTGfKUM5RmXTLKCzKW3kP7pf+FsXIa/4M289U1X0NrZwlMrfsXUxhk01k4c8fEs2yYow3QaAA6VZM0+qsxsov9KpZRSc+fMxbZtPvjh3+fWm2/lvPPOp74+qtH71n3f5rnnnuW///PrXHbZ5QDkcjluuvlG/vgTd/Hxj32cnz3yU95/x/v48099+pBj54ND8SjXjZvXsWrVSu6+6y6++53vcs89/87df/oJXt382rBlfdcN7+S5558b8LELF1/IT3/ySOH2ipWvYts2F73tnMJ9Fyx+M7975nfMntsQhbnCxOiGFStfw3ZsLn77OYVJ09/ylnN59vlnmDqzBqK7qKpNkO50mTKj5pCm2cIxDYX5E1/fuZn1G1bz0Ts/ztfu+SbfuO8/+evP/xnP/251NLeiCXunG4qXMAzj1Uw+8ke3s2zFEgonL3LOwvP473u+1+e+dRtWY1k2aWdSITzOmDaHl5e9wM7XDg2T2UzA577wWf7m83/NSbNO4e5P/BXnvflCaupTVNYcI+HMGBOIyLuAEzacAfhzzyFomEzyxcfw5y/CEour3nI93/7Vf/Hrl3/OrZf9/sj7n4kQhiFBEGDbpQ1MIhbGBPimC1dqSnoupZQ6VtTU1PDUb5/mX/71n/nDj32UPXv2cM3V1/Bf//l1Jg0wSfiSJS/ieR53ffIuRIT33HgT//bVfxvx+dasWc1dn7ybm2+6BYD3v/8OPvOXnyaTyZBKDT3yozh8Daers5Pa2r41eTW1NXR2dsar2PRta8zleqitrSWZ6o0DE5rq6e7p6jPHpOva2K5Fumpk806+unUDd911Nx/+yB0A/NHHPsLffemzNExMDHu9v/71Lws/50Nfn8EbYdEgjtDw2huG2tpapszofY+bPmMizy/pYfL06rj2LW4mNvBP//hPzJ+/gFQyyY8eup+P/8kdPP/MS9Q1zBnRtZXDSHuJPyci/yEiF4vIOfmvkpZsvLEschdci71vB86rqwCoSKZ5+8Kr2Nuyi5WbXzqswwlAGQYFAFjGIRc2l+VcSil1rFiwYAH/+4372LZ1OyuXr2LX7t382af+ZMBtd+3exdSpfeeNnDVz5ojPtWbtGm688T2F2/v276OqqmrYoHK4KquqaG9v73NfR3sH1VUDj/Q43O1HarSuVwQsK1qi0HEs3Hguv2QqmlOvotJlQmMdHR3tpKvcwlc2101dXQ2V1Yl4WUInWnUl7XDxWxfTNLGe6to0H/rQB1l8wWKeePLXOO74GTg30pIsBk4Hvgj8a/z1/5eqUOOVt+AthLWNJJ5/tFCfO3f6aZw8dS7Pr3mK1s7DC0CB55WimIewJU3W7B/RcHillDoRzZ8/n9+74wOsWbsWKHR3L5gyeQq7dr3R53V0+44dIzp2a2srO3bsoKmxqXDfww8/xNVXXQ2A53l84IO/x8Vvu4jFF13A7t27++x/3Tuvpa6hZsCv6955bZ9t586Zi+/7bN68uXDfK6tf4bRBOsYf7vajcb0An/v833DZFZdyxpmn8exzzx7x9Y7GNeSnVRlPRhTOjDGXDPB1aakLN+7YDtnzr8HZvRV7+0Yg+k+99JyrsSybX7z4E/xghLVhlkWQy5WwsEWnEofAZAnoLsv5lFJqvNuwYQNf+cqX2blzJwA7duzg/gd+yHnnnQfAxEmT2Pra1sL2559/AY7jcM9/3IPv+/z4Jw/z8su9LSYf+sgH+dBHPjjgudasWY1t2/zw/h/g+z6PPfYo//Xf/8lnP/s5AF5e+jI11TU887tnee6Z55kyZUqf/X/+yGO0NrcP+PXzRx7rs21lZSXvvuHdfOGLn6Orq4vnnn+ORx75Ge973/sHLNtw2/u+TyaTIQgCgiAgk8ngF7X6DHTdw10vwKf//DM88evfct993+LHP374iK/3cK+5tbWVxx//VeE6vv+D7/HMs89wxRVXDvj8jJURhTMRqRWRL4vI0vjrX0VOzLkZvDMWY5IVuGtfLNxXVVHDlW++nr0tu3j6lV+P6DiWbROWKZxB9CnQC0c2ykYppY531dXVvPTyEi68+AJq66u56K2LOf200/mXf4oahT7z55/hS//4DzRObODLX/5XEokED9z/IN/+zrdomjSBH/3oAd59w7sLx9u5YyeLL1g84LlWr1nD7be/lxdffIGmSRP4wt9+gQd/9DCnLTgNgHPPOZfq6mre9/7b+cUvDg0fh+uef7+Xnp4epk6fzB2/9z7+4557+0wpcd07r+Uf/+lLI9r+H77091TXVvLP//JPfP/736O6tpJ/+NLfD3ndw13vvn37+Ngn/ojLr7yMP/yjjzJ9+oySXnPx9Xqex998/m+YMm0Sk6dO5N6v3ctDP3qYeXPnHXUZRpOMpCpPRB4C1gDfiu+6AzjbGHNjCcs2rEWLFpmlS5eW9Bx7X9/MlrXPUl3b20E09cj/4Ly2ls5P/CsULYT+zKonWLbxBa56y/UsmHXWkMc1gY8JDbWzZpWs7MUCkwWEevdNZTmfUkrtbt3C/Hnz+9w33CS0R8u1qpmQPrtkxx9ILpfj3EULWb5sJa57aIf5j3/iY8yZM5e777p7wP27u7tJp9O0trZy403v5re/ebLURR4Vg133cNd7192f5NprruWqq67mI3d+mNtuuY3LLz/8OUNHUz4KucnR6Xe2fv16FixYcMj9IrLMGLNouP1HOs/ZKcaY9xTd/oKIrBzhvscdf+45JNYtwd6xmWBW7wvPhWdcwp7mN3hi2WM01k6iqe7QET955VoAPc+WJLmwmcBksSVZ8vMppdRAyh2cyiGRSLB61dpBH1+zdg3Xv/P6QR//gzs/zI6dO/E8j8//zedLUMLSGOy6h7vexYsX85m/+DQPPPgA69at48wzh67MOBGNNJz1iMhFxphnAUTkQqCndMUa3/yTzsA4CdxNy/qEM8uyuPb8G/n+r7/Boy88yO2Xf5ikO/TIlHKssdlL8MJ2bLtp+E2VUkqNirVr1zCvXw1ise999wdlLE3pDXe9t95yG7feclsZS3TsGWmVzR8C94rINhHZBvwH8NGSlWq8SyTxTz4DZ9OKaOG2IpWpKq694Ebaulp5/KVHhh0BUo41NvNsSZExu4ffUCml1Kg5sK+Z2bNnj3UxyuZEu95SGDacSdTmNs8YczZwFnCWMWahMWZVyUs3jnlzz8HqbMXedejsztMaZ3LxWZfz6q6NLN+0ZMjjhF45w1kFXthOYE7YSk+llFJq3Bs2nBljQuAT8c/txpj2YXY5IfinnIWxbJxNywd8fOGct3DKtHk8v+ZJ9rfuHXAbse2yrbFZOCeiE9IqpZRS49hImzV/LSKfEpEZItKQ/yppyca7VJpg1gLcTcsHXM5eRLjs3GtJuil+9dJPB5z/rJwLoOc5UkVPuGvcTbinlFJKqchIw9mHgI8DTwPL4q/SzmFxDPDmnYvVuh9rz7YBH08nK7l80XUcaNvHi2ufPuRxawxqzixxCU0W35RuKLtSSkG8nrV+EFQnmNH4nR9pn7P3G2NO6vd18lGf/RjnzTsXYzu46wbvV3by1DmccdKbWLrxeXYf3Nn3waIF0MtJxCUT7i/rOZVSJx7LsvH98ixTp9R44XkejjPSyTAGNtI+ZyfcOpojkkrjn3Im7vqXIQwH3ezis6+gqqKaJ5Y9RhD2DWLlXAA9z6GSrNlLaPRFUylVOim3hn379hEO8fqo1PEkDEP27t1Lbe3RLaI00mj3uIi8B3jYaB11H96C83A3rcDevpFg9qGzAQMk3SRvX3g1P3/+R6zYtIRF8/sudRH4PnayfBPDilgQhuTCFlL2xLKdVyl1YqlO1dPStYfNWzZr86Y6JtiODL/RMCorK2lsbDyqY4w0nP0pkAYCEclQ6Epgao7kpCJSB3wDOAMwRH3aNgL3A7OBbcAtxpiWIzl+OfmnnIVJpHDXLRk0nAGcOm0eJ0+dy4vrnmbOjAXUVtZHD1gSLYBeWVmmEkeigQE7SVpNiBz9L6NSSvUnYtFQNXWsi6HUsAI/+vAw8aTxsYLOSAcE1AK/D/xdHMhOB45mIayvAr80xswHzgbWA38BPGGMmQM8Ed8e/9wE3txzcDcug2H6Vlyy8CpELH6z9NHCp0ixbcIyDwoAsCRBYLp1YIBSSik1zow0nN0LnA/cHt/uIFol4LCJSA3wVuCbAMaYnDGmFXgXvQurfwu44UiOPxa8085Dcj04rw49L291upa3nX0FO/Zt4+UNzwPRiE1/DMIZRAEtE+qKAUoppdR4MtJwdp4x5uNABiBubjzSBSFPBvYD94nIChH5hohUApOMidYWir8P2BlKRO4UkaUisnT//vEx4jCYNZ8wXY277qVhtz39pDcxZ/ppvLD2KXYd3InYDmEuhzHl7zBrU0k2PEBgxiYcKqWUUupQIw1nnojYRP3DEJEm4EjThAOcA/ynMWYh0MVhNGEaY75ujFlkjFnU1DROFvC2bLwFb8Z59RXIdg+5qYhw+aJrqa6o4Rcv/phMrgdDedfYLC4LCDmdVkMppZQaN0Yazv4d+DEwUUT+HngW+IcjPOdOYKcxJj852INEYW2viEwBiL/vO8Ljjwn/tPORwMfdtGLYbZNuimvOv5Gung4efeEhQhOOSTiDaGBAd7hDp9VQSimlxokRhTNjzPeATwNfAnYDNxhjfnQkJzTG7AF2iMi8+K7LgHXAz4APxPd9APjpkRx/rARTTiKsaxpyQtpiUyZM4/JF17Fj3zZ+t+YJgjIugF7MEgdjQrJae6aUUkqNCyOewtYYswHYMErn/WPgeyKSALYCHyQKig+IyIeB7cDNo3Su8hDBW3AeiRcfRTrbMFXDT0B32uyzaOk4yMsbnqPplad468XXl6Ggh3Kkmu5wO0lrIpYc3azGSimllDo6I23WHFXGmJVxv7GzjDE3GGNajDEHjTGXGWPmxN+bx6JsR8M77S2IMbgbXh7xPovPeDunTJ3L75b/kk1bhx7tWSpR7ZlPLjw4JudXSimlVK8xCWfHq7BxKsHEGSNu2oSoU/5Vb34XE2sn8fAv72PP/p3D71QC+dozY8q7zqdSSiml+tJwNsq8087D3v0a1sE9I94nkUhy7bnXk0pWcP/PvkZnV1sJSzgwS1xCk6VH5z1TSimlxpSGs1HmnX4+RgR3zfOHtV9VRTW3mYQ2EwAAIABJREFUXP0RujNd/PiX/zcmCwW7Ukt38DqB6Sn7uZVSSikV0XA2ykxVHf5JZ+CueQEOJ2AZQ2PdJK55+21s27mRZ1/+ZekKOQgRGxGHrmCbLlKslFJKjRENZyXgnXkhVmcL9rZ1I98pXgD97NPO58z5b+HpJY+ybeem0hVyEK5Ukw0PkAuPufEYSiml1HFBw1kJ+KeeTZiqJLH62RHvk18AXUS45pLbqK9t4uHHvsn+5vL3AXOlmq5wK6EZm7nXlFJKqROZhrNScFy808/D2bwSejpHtEvxAujJRIpb3/mHIMJ3Hvpq2QOaJQkdHKCUUkqNEQ1nJeKdeVG0nNP64RdDBw5ZAL2xYTJ3vOcuAL7z0Fc52LK3ZGUdiCt19ATbdXCAUkopVWYazkoknDiDoHEa7vqRT0gLfRdAb2qYwh3vuQtjQh74+dfJ5TKjXcxBiVgINl3B62U7p1JKKaU0nJWOCN68c7F3bkE6Rz5vWf8F0JsapnDj1R/iYMseHv3tD8o6itKRarLhfnJhS9nOqZRSSp3oNJyVkD/vHASDs3nFiPcJB1gA/aSZ83nb+dexZuPLLFv19GgWcUgigivVtAcbCEz5au2UUkqpE5mGsxIKG6cR1E/C3bhsRNuLbRNkBg5BF735Kk6dfTqPP/MQe8u4xJMlCQSLzmBzoT+cUkoppUpHw1kpieDPOwd7+8YRjdoUxyGIR2weeiiL66/4PVLJND95/Fv4vjfapR2UK9Xkwja6gx1lO6dSSil1otJwVmLe3HMRE+JueWXYbS3bJsjlBu1XVpmu5rrL3su+A2/w9JLHRruoQ0pIHd3hdrLBwbKeVymllDrRaDgrsXDyLMKaBpyRNG2KYMKQMAgG3WTuyWfxptMX8/yyx9mx69VRLOlwRbNwpYaOcCO+6S7beZVSSqkTjYazUhPBm3sOzrZ1kB1+zjBjDMYfemb+K996EzVV9fz8ie8TDBHkRpslLhYJOvwNhKZ8zapKKaXUiUTDWRn4p74JCXycHRuH31iEwBs6+CQTKa5++60caN7NkhVPjFIpR8aRNKHxaPGXkQn26wLpSiml1CjTcFYGwbRTMI6L/fqGYbfN9zsbztyTz2TuyWfx9JLHaG0vbz8w16rGpoLOcCPtwRqtRVNKKaVGkYazcnBcgmmn4ry+fthNxbYHHbHZ31VvuxmAx59+8KiKdyQscUlIA37YSXuwXgOaUkopNUo0nJWJP2sB9v43kK72IbcT2yboGdl6lnU1E7j4vGvZ+OorbNq6ejSKedhcqxY/7KYj0H5oSiml1GjQcFYmwaz5ANGcZ0MQyyIMwxF39D9/4aU0NkzhV797AM8bvjm0FBJWDV7YQWewRfugKaWUUkdJw1mZBJNnYRIVONuH73cmAMOM2MyzbYdrL7mV1vaDPPPyL46ukEchYdWRNQfIhLvGrAxKKaXU8UDDWblYNv7Mudgj6HdmAH8EgwLyZk2fy1kLzuOFZb9hf/Puoyjk0UlQT2ewFS8cuulWKaWUUoPTcFZGwcz52C37kGFGV1qWRTjMdBr9XX7RjSTcBE888+OjKeJREbFwpIq2YA1e2DZm5VBKKaWOZRrOysiP+505w0ypIY6DP8gC6IOpTFdz3sLL2LxtTVkXRu/PliQ2FbT6q+jyX8eY8k2Sq5RSSh0PNJyVUdg0jTBdPex8Z2LbBIcZzgDefPbbSbhJnlv6+JEWcVTYkiQh9fSEO2jxV5AJDmBMOKZlUkoppY4VGs7KSSyCmfOimrMhRjWKZREGwWEvzVSRSnPuWRezbvMymlv3HW1pj4qIRcJqQLDpDDfS4i/XkKaUUkqNgIazMvNnLcDqbMFq2TvkdoczYrPYeQsvw7JsXlj2myMr4CjL16JZOP1CmjZ3KqWUUgPRcFZm+X5nw43aPNwRm3nVlbWcveB8Xln/Iu2drUdSxJKwJFEIaR3hBpr9ZfQEuwhMj86NppRSShXRcFZmpm4iYXXDsIMCjmTEZt7iRVdijOGZJY8d0f6lZEmCpDRgk6I72EaLt4wW/2W6/NfwTddYF08ppZQacxrOyk0Ef9b8aKWAIfpfHcmIzbz62kbOPfMiVqx9noPDNJ+OFUscXKuOhNWARYpMuJdWbwVt/moywV68sF2Xg1JKKXVC0nA2BoJZC7B6OrH2DT7lxZGO2My76M3X4DguT73wyBEfo1yioFZDwmogNDk6gy20+qs46L9Eq7+KnmAPvuke62IqpZRSZaHhbAz4M+cBQ893JpZF6PuEhzliM6+qsobzF17Kus3L2b13+xEdYyzYUkHCqidpNZCgDmN8uoJXafWW0+ItL/RTU0oppY5XYxbORMQWkRUi8vP49kkiskRENovI/SKSGKuylZqpaSBomIQ93DqbIkfc7wzggnMupyJVeUzUng1ERLAlRcKqj6flsOjK91PzltMVbCcbHsQL2/FNJ77pIjA9hMbTQQZKKaWOWc4YnvsuYD1QE9/+J+Arxpgfish/AR8G/nOsCldqwawFuGtfgMAHe+D/BgECL4eTSh3ROZLJCs5feBlPvvAz9h54g0mN046ixGMvGvEZZfbQ5MgEuzAE0RPVP4uJ4FCJLZW4Uo1jVWNTgYgAYIwp/KyUUkqNJ2NScyYi04F3AN+IbwtwKfBgvMm3gBvGomzl4s+cj+Sy2HteH3QbcRz8nqNrwjv3rItx3SQvLh8f856NFksScT+1ehJSH9eu9X651AAGzzTTmW8W9V+m2XuZA94LHPRfoNlbTru/gS7/dbLhfrywncBktdZNKaXUmBqrmrN/Az4NVMe3JwCtxpj8rKs7gWO7mmcYQdzvzN6+gWDaKQNuYzk2fs+RDwoAqEhVsvD0C1i66mkuueB6aqrrj+p4xwoRCyGBRSKe0ReMCTAYbCoAweATmC4804oJQxADBkRsHKnGlVocqcKVakTsMb0epZRSJ46y15yJyHXAPmPMsuK7B9h0wOoLEblTRJaKyNL9+/eXpIzlYNLVBE3Thx4UYDsEudwRDwrIO2/hZRhjeGnlk0d1nGOdiI0lThTcRLDExZaKuAaurlAD51BFaLL0BDtoD9bQ7L9MV7CDwBxdUFZKKaVGYiyaNS8ErheRbcAPiZoz/w2oE5F8Td50YNdAOxtjvm6MWWSMWdTU1FSO8paMP3M+9htbwB+8078A5giWcSpWVzOBBXPOYdmaZ8lkdaTjcEQsbEnhWrUkpAGbND3BDlq8ZbT7G/DCNkJzdP8nSiml1GDKHs6MMX9pjJlujJkN3Ab81hjzPuBJ4KZ4sw8APy132cotmDUP8T3s3a8Nus2RLuPU3+JzryCXy7BkxRNHfawTjSUOCasOV+rwTQetwWqa/SW0eCvo8DfR5b9GT7ATz3RofzWllFJHbTzNc/YZ4E9FZAtRH7RvjnF5Ss6fMQ8jgjPEOpuWbRMc5aAAgCkTZzL/1IW8sPwJOrvajvp4JyIRwZFKktJAQuoRBM+0kTH76A520Oq9Qqu/kkywT5tAlVJKHbGxnEoDY8xTwFPxz1uBt4xlecoulSacNDNaymkQ4jh4R7FSQLFLF1/Pxldf4eklj3HtpbePyjFPZJbEAw6g0GsyMBk6g00QSDyhbiOuVGNLGluSY1dYpZRSx4wxDWcq6neWWPYEeFlwD33zthwHv6uLMAyxrKOr6JxQP4lzzriQ5Wue47yFlzKhftJRHU8dypYUtkTz0uXnYusWHzGCJRUkZQK2VRE3f4YYAgwhFjaWpLCIBilY4o7thSillBozGs7GWDBzPvLSr7DfeJVg9mkDbySC8TxIHn3Ny1vPewerNrzEb5/7KTdfd+dRH08NzpIElvTWrIUmRybcFU3bEd8pCPlZdA0BRqJ7HKpwpQZL0tjiYjAY42MIAQtLLGypxJH0mFybUkqp0tFwNsb86XMwYuFs3zBoODPG4Ody2KMQzqoqa7hw0ZU89cIjvPr6Ok6ZNUggVKOuT1gbgjGGkBwZsw8TBkQ1bPkYZ2EwRGHOkLBqqbCm4Ui11rYppdRxYjwNCDgxJVMEU2Zjvz54vzPLtglGqd8ZRGtuNtRN5JdP3o8/xDQeamxEa4omcaU6mn/NaiCZX/nAqo3vixaHD0yWtmAdzf5LtPnryAQHdJoPpZQ6xmk4GweCWfOj6TSyAwcwcRy8URixmec4LtdcchvNbft5bumvRu24qvwcSZOUBlzqCEw3HcEGmv2X6PA30em/Rpf/Ol3BdrqDHfQEu/FN91gXWSml1DA0nI0D/sz5iAlxdm4e8HHLcQizWYwJB3z8SJw8cz6nz13Ec0sf52DLvlE7rhob0TQfaZJWAy41eKadnNlPJtxNJthFT/AGXcFrtHrLafVXkQn26lqiSik1Tmmfs3EgmHYKxrKxt2/AP+XMAbcxxhB6PnZi+D5LI3XFW9/Dlm1r+fkT3+X33nM3IprVjwciFg7xQIEBFkYLTIbOcEt+HAKI4FCJiENgMoTkSEgdKWsyjlRpXzallCozfTceD9wkwbRTcLYPsc6mCIE3uv3DqitrufKt72H7G1tYuuqZUT22Gr9sSZGw6gtribrUAIbQZLFwcanGN520x33ZDngv0OwtJxMcu2vZKqXUsUTD2TgRzJyHtXc7ZAbuEySWRZDNjvp5zz7tAk6ZdRpPPPcTWtoOjPrx1fgnYmFJAltShYXhHamMwpvU41KNhUVHsJ4OfzOh0UEkSilVShrOxomo35nB2bFpwMfFdfG7R78zt4jwjkvfi4jw8998lzAcvX5t6viQD28JmUA2PECLv4JMsH9U+0AqpZTqpeFsnAimnoxxXOxBmjYt2ybIZkrSebu2poErL34P23Zu4pmXHhv146vjg4iQsGqxSdIRbqTFX0EubNYBBUopNco0nI0Xjksw7VSc1wfpdyaCCQ1hieYle9PpizlrwXk8veQXbNm2tiTnUMcHS1yS0oAgtAVrafVXkA0P4psubfJUSqlRoOFsHPFnzsPevxPp7hjwcQMEXmkmGBURrr3kdiZOmMJPfvV/tLU3l+Q86vhhS4qkTACgw99Aq7eSZu8lmr1ldAc78U0noclhTDDGJVVKqWOLhrNxJJi1AAB7sH5nljWqKwX057oJbnrHnYRhwIOPfUNXD1AjUhj9GX9Z2HQHO2j1XqHZe5mD/osc9F6k1VtFh7+F7mAn2XA/XtiuTaJKKTUADWfjSDB5FsZNDtq0aTkOfgnDGcCE+om884o72LV3G79+5uGSnksdnyxJkLBqewOb1ONQhSHAM830BDvo8DfR6q+ixV8ar1zQqctOKaVUTCehHU9sh2D6nMEHBTgOQU8PxhhEBphddJQsOHUh559zGS8uf4LpU07mzPlvLtm51IlBxMImCST7TIwbmhxdwWsQGBCwSGKRwBKHhEwgYTXoJLhKqROO1pyNM/6s+dgHdyOdbYc+KEIYhiUbFFDs0sU3MGPqKTz6xPd4Y8+2kp9PnZiiWra6Qg2bRQIICUwPncGWeJ3QLXhm4H6YSil1PNJwNs74M+cDDFp7hjElGxRQzLZtbrr2I1Smq/nhz76m62+qsrDEiSfErYhXL6glFx6g1XuFFm8lPcFuvLBN1wRVSh3XNJyNM+GkmZhkBc72jQM+btl2SQcFFKuqrOW9N/wxAN//yT10dA1Qm6dUCYlYuFYNSasBCOkKXqMtWE2Lt5SD/vM0e8vp8l/DMx0a1pRSxw0NZyMQhgFBLgvlePG3LPwZ8watORPXxevqLH05YhPqJ3L79R+jq6eT7z78VTo6W8t2bqWKRaNC60hIQ1yrVoeFTSbcG9Ws+Uvp8LeQCfbGYU1XMFBKHZs0nA3DrayicuJEnEQSv6cHr7OT0C9ts2Iwcx52yz5kgLnGLMchyGTLuszS1Mmzuf1dH6O9o4VvP/gVnQNNjQsigiVuoWbNIoFnmukMttDmreKg/yLt/jp6gj1xWNP51pRSxwYNZ8OwHBsnnSA9pZHak0+mYkoTvmmjp3s3ubAFT9oIGd0Fyf1ZUb8zZ7B+Z0CYy43qOYcza9oc3vfuT9LV08m3Hvwy29/YUtbzKzUcS1wcqSpM4eFSg2+66QpeLYS1Vu8VuvzXyYUtupqBUmrc0nA2DNeqpZJ5GEI8WnGrEkyctZjJk95ORfcs3PZJGOPjSQeG0Wn2DJumEVZUYQ+2lBMQlDmcAUyfchJ3vOcuRIRvPfgVfvnUA+Ry5en/ptThErFwJF0U1uoAQybcTXuwjoP+S7R6r9Ad7MQL23WeNaXUuKHznA3DEoeUNZmkM5WQDBZJRCyoh3T1FLr376drbxK/tgPPbUaMg20qEOwjP6lYBDPmRZPRGgP95jQT18Hv7iZZU3OUV3f4pkycyUff9//x2+d/xsuvPMXW7eu57fo/oqFuYtnLotThEBGEBJYkCvcFJktPsIMuCRHAoQrXqseRNBYuliR6/+aVUqpMNJyNQBAYvB4DJAkAJAQDxghOzUSSvou3axeJykZ8t52ssx8DuFT3yVXFP+fHFvT/nmfNPJ2qTcsId+0kaJrR5zETuvit3Ti1AdGMngaM9JncM9owf+LoZ5G4DFb83cSbmGibPrtL0TeJHszvn0ikuPrttzDvlLN56LFv8M0f/jM3veMPmD19buFajImPW3xh/Y7TfyLd/qPtSjnR7vHAGEMYgAnj74UHijaK/5/7/Oeaoe8f6FnPbyrCIRsU7jMcWnccQmh6txOr8GtQOG7hdyW+o/A30b8ARdckxd/p/T0WK/7qX8D87z59j2MVhTVjDCE5eoI3MISFvxFEcKjEkiQ2SSypwJZkHNxcBKcQ3owxhesxJv6/CfMXGp/XAivOeoVrDfP7DF77bgDLEiw7+tswxkT75f9WBcSS3r8va4C/r6L/o4F+JRjg73LQ8pR4MmylTmQazobhJoXaSQPPUG7FbwT1UyaRnZGk7dVXsRINSKqJjnArXtCBbaoxYfxCXbSvHb9RIWAJh77pnXkW/AYq31hFcPKMwptP9NptkevySVeFuBVJ8h/q8+fJvwkWCwMIg+iNIgwMJjSFF+/8C31/JowKHRow+X19ouZbY5jaOIf3v+tTPPz4f/O9H9/DFRfezMIzLo6eF+I3CluKjhcdMzBgAoqagaO3hv5vFhC/8/QLmX1+lqKEkb99SPiQ+Inpc3AK7+6F4+S3LTpHIfj2+85Q+xD/R0lv+QZ7zz30ovvuM0SoEsBOCG6FheMItts3hBSHhMLp+v+e9StXaHr/34vLKPHxwpDCf0t+/3CIUG3ZvYEs+h2EMAj7PF+WbRXKVQhuVhRE+v8uR4En+h0+JGjE5wj8sE/gy//e9R9Ck/976XucBIZ8WIuOaUJDFg9DO0ZCjPEwYqLf2HhHhyrS1mxcq6ZQdssWnET03bJ7k2Tom6j8+edW8tvQ53noE1rjv6nAAz8XEvhgu4Id/32FoYmfFwh8Q+ib+EOSiX8fpOiDmRSCWpT4ev8vC6Eyvl34EFf8IZPiYGaIn4min+IgKICR3j/B+PH+f7aFc/f/wNB/m8Ldvb9vxWfu8wG036Gj4F70HBc/3v/vg/i1q99r6GDh9ZDQWwjLGlzVkdNwNgwnYVE7cfgmjXTtBKomVNC8cSNBrpummlPoMK+RM60kqD78ZpF0LWb6bJxXX8G56tpDHrZMQCLpk6hMH95xR4kx0UtkI9O4e+5f8v0HvsHjz95Pp7eXd113K7Y9/K9WIRBK/2gWPx6/4vV57y/6Ob9/oaau8J7fL1nk7xvgDSFf8yBxtZAU7VIoXlzTEb2xmENezAu3TVFZis4nURXNgDVSfYpYdEzpX4U1wM+WvviXTKFWqiBZ9HtCnzdjDPimmyDcQEWiiUp3Gq59BH/zJWBCc1i1YcX7DVarX1wzl68pNIUPfflQaeJtpVBT3ye0SO+xis9ROG+/zwfRg/H9hT/M3iCYr3kMg5DigezFZfVzBj9nCHxTFLQoPD/54xYCrtd7DcYY8uk+DOMPkxKd27KkEHoh+iCbfw2IAr70+ZxXHESj/FoUaqOYGf/b76Wh6CUh/xwWh/ni53HQD2hSdNuYwV4pD/n9LpynaJtC2aS3trZ/DXWfD0lxuSwrLntxC078NVitb3Ss+Perz2t2b2geTJ/nwPR/rPeOMIg+8IwXGs5GkZtO03TmmbRu2UK2vZ3q6pPpZhcZ9iPGwSF9eC+S88+CJx6Bznao6tu/zLJscl3dJKrL3+8MiPvvRCoq0nzwjk/w2K8e5qlnfsXuvW9w+80foqG+cdhjDPk4csgfe/8tym/8/PGq0hERZKhuo8XZWSBBJcZKk/Pb6fEOYolDhTsR16rCtpLRl5S/79pAb3Ij3W8ke4r01sjZzmj8bRztMY6ir+8IFQfXwYIE9PvwmG/alt4Akg8k+eMVWliKA6op+jCYD8L5bYMoKIahGeDk8fc4DFlWVGNpwihMih3VasebFHYxxC05lnVIyDJheEhQz5czalWJy1IUtPLnzwtDQ+hHXYWMF394KKolDw29tb5xoeIKWCwByxXs+ENBEPQ+X4UAONAHiXwtaFyWQmms6AOuFNUcJyrG/gNVnhzLs2ovWrTILF26dKyLcQg/k2Hv8uUk6+oQyyIwGbrMbrI045rKkS/k/MZ25J6/xdz0+7Dowj4PBb4HYUjD3HmjfwFHYfnKF3nop98D4Pp33MJbzr1Iq/fVCSc0Pn7YQ2j8uL6VaLUDSePYVbh2FY6VwhIXEQdjAozxsSwXxxqb2nClxpN80Mt/EDrSDxrjjYgsM8YsGm47rTkrASeVomrqVLr37iVRW4stKWrkJLJhPZ2yHd/0xIMFhvllmzoDU1sP61cdEs5sxyXb0U6Qy2InkiW8msNzzpvOZ/asOdz/4H386OFv88KS33HFpddx2vyzNaSpE4YlDgm7us99xhhCkyPrN9Pj7c1Htii8iZBvR03YtVS6U3GdKmxJ6d+NOiFZx0kYO1IazkqkasoUunbvxoQhEg/NSlp1uKaSTnbGtWhVWDLEf4FI1LS54kXwPXAOrXHzurvHVTgDaKifwEc//KcsXfECTzz5KPd9516mTZ3JFZe+k9MXaEhTJyYRwY5HfA7FD3tozWzorW2zq0lY1bh2Fa5djWOlylNgpdSY0WbNEmrfvp3O3btJ1tb2ud8YQ9Y008Hr2MbFkSGaMTasQv7vHsyH7oK5Z/R5KMhmsZMJamefVIrij4og8Fm+cgm/efJRDjbvZ+qUGVy8+DLOOuNckkl9k1FqKMaEBCZHaLx4RQND0qkn5TRhiY2IjWtVYlvj6wOaUmpgI23W1HBWQkEux95ly0jU1hZqz/o8bjJ0mu3kTEc8onOAzqxeDr74J1Gz5rve2+chgyHX0UHjaadj2aXvCHs0giBgxStLeOKpX7D/wB6SiSRnnnEuZ56+kLmnnobrJoY/iFInOGMMgckQhBmKxyYnnQYqE5OxJYVtJUber1UpVVbjts+ZiMwAvg1MJhqc/HVjzFdFpAG4H5gNbANuMca0lLt8o8lOJKhoaiLT2kqiqurQxyVFDaeSYT9dvIE1UC2am4BTF0T9zq6/vc/QxWieJYPf002iqprxzLZtFp2zmHMXXsC217fw0rLnWL12OUuXP4/rJpg35zROX/AmFsw7i6pxfi1KjRURwZEKHKuicJ8xBj/oorl7bTQ/gTFY4uBYaRyrAttK4VgViNgIFpY4iDhY4mINOSRVKTVWyl5zJiJTgCnGmOUiUg0sA24Afh9oNsb8o4j8BVBvjPnMUMca7zVnALmODvavXk2qoWHI7XzTQ5fZSY72Q0d0vvQ08vB3MHd/DiZP77tfTw9udRU102dwrPF9n62vbWLN+pWsXb+StrYoi0+aOJWTZp/KSbPmcNLsOdTXNWg/NaUOgzEBofEJjY/BJzQBxR/r8rVutlVByplAwqmN+sNZyaH7wSqljsox06wpIj8F/iP+ersxZncc4J4yxgw5T8SxEM6MMexbuRKxbezE0E13xhhypo12tuKYCuz8GoDtrcg//Dnmqhvgknf02yfE7+5hwmkLxsWkl0fKGMMbu7azYdMaXnt9C6+//iqZbA8AtbX1nDQrCmunnDyPSROnaFhTahSExiua8iOe58py49q5dFzrlgSEIMzihz0k7FqSTh22pV0RlDpc47ZZs5iIzAYWAkuAScaY3QBxQDsuVtIWEaqmTqV169Zhw5mIkJQ66sxcWtkEhiig1dRhps+Kmjb7hTMRizAMohq0dGUpL6WkRITp02YxfdosAMIwZM/eN9i6bTPbtm1h67bNrFz1MgB1dQ3Mn3MGM2ecxNQpM2hqnEQikdTAptRhssQlYfftnxbGc65l/WYMAaEJ40WXbCyx6fb2AEQBTZJY4sYT7aawJRE3l7rH9IdFpcbamIUzEakCHgLuNsa0j/SNVUTuBO4EmDlzZukKOIpSDQ3Ia6/1mVZjKK5UUcc82ooD2vyzB10tQCwbr7PzmA5n/VmWxdQpM5g6ZQYXXXApxhiaWw6wect6NmxazYpVL/Hiy08Xtncch8p0FfV1E5gwoYkJDRNpnNDEhAkTaZowifRx9NwoVUqW2CD2oFN+uMQDE8IMvunBEGKMT2HxITGIEcRysIj6tjlWmoRdEzebuljixPuFiNg6PYhS/YxJs6aIuMDPgV8ZY74c37eR47BZM6/11VfpPniQZM3Il1vyTBdtZhO2SWLv2oPc83cDrhYQ+j7G92mYP3+USz1+hWHIweb97Nq9g4PN++nu7qKzq4PmlgMcbN5f6L+WV5muor5+AqlkBclkimQySSKRJJWsoKamjtqaempr66itqaOmunZEa4MqpQZm4sVu8wEsNB6B8YrWlIzXdYzXMUo69VQlpuFYaUQcBIswnkJExOkT3vLvWVpTro5F47ZZU6K/qG8C6/PBLPYz4APAP8bff1ruspVS1bRp9Bw4QOh5WO7Ihrm7Ukkd82hlM2ZKE05N3YCrBViOQ7ZEZVtOAAAgAElEQVSnO5737MSY78iyLJoaJ9HUOGnAxz3Po7nlAAcO7mP/gb0cOLCXltaDZHNZuloOkMtlyWQzZDI9BIHfZ18RoaqqhtqaOurrJtA4YSJNjZOYPm02kydNxRpB7adSJ7LoZd5GsEHAJslgr3rRaNNuDnavjtdLyB+EwkrhjlVBwq7BD7rwwi5sK0GFO4WUU48VN6VqWFPHk7GoHrgQuANYLSIr4/v+iiiUPSAiHwa2AzePQdlKxkmlqD31VFo2bCDZMPLRh46kqWMundZ2ggXzsVcsx+SyyACrAuS6u6g4QcLZcFzXZdLEKUyaOGXI7YwxdHd30tbeSmtbC+3trbS1t9DW3kpbWwt79u5i3YZXCIIAgGQiyZQpM2icMLHP14SGJioqdE1EpQ6XiODalbgM3vUgNB5ZvyXuI1eHwaczu52O7DbiSYVw7WrS7hQdrKCOC2M+WvNoHEvNmnktmzeTOXiQRF3dYe+b2fw83v/8Nbn3vh/rzPOxpTeIBbkstutSe9LJo1lcRTSBbnPLfrbv2Mbr219l9943OHhwH+0dbX22S6eraJzQRFPjZCZNnMLEpigcNtQ3Yo/zSYKVOpblJ+f1wx4EsK0UCbuOlFOPa9f0CWvGGK1lU2Nm3DZrnuhqZs8m29Z2RE2QyVPOw69tJLViI7mzzidr2khQg4hgJRJk29sJg2DcrxZwrLFtm6bGyTQ1TubchecX7s/msjQ37+dg834OHNzHgYP7OHhwP1teXc+yFS8U7e/Q1DiJiU2TaWqcREVFmmQi6veWTKZIJKLvqUSKquoa0hU6eEGpw9F/ct6opq2Zbm83EC0mjwnxTDcAaWcSKbcRx6rQkaVqXNJwVma261I/dy4HVq/Gct0Rjd7ME8vGedNleM88SG3XZ+iu7KLH7CVhagsvLsfCagHHi2QiyZTJ05nSb2JggJ5MN/v272Hfvt3s3b+bvft288au7axeu5zhaqurq2uZ2DSZ2pp6qqtqSKfT2LaDYzvYtoPtOCQTKZoaoybVxDhb+F6psWaJi2W7uFTFtWpZBCFh1QCGjH+gENwM4FgpLCuJI6k4rNlY4hSmBsmvqBDdrx9+VelpOBsDyZoaambNon379mFXDujPPedyvN/dj7/6aSoveBeC0G32kjA1WK4bLxWl4WysVaTSzJpxMrNm9G1mDsOQnJcjm82Qy2XIZqOBCdHPGdraW9m7bzf79u/htW2b6ehsx/e9Ic9Vma6iurqW2po6Jsb97Koqa7BtOw50NrZtU1VVQ33dBB3QoE4oUa1a36k6XLv3NdIYE62iEHpkTQZDCPEo03hMaTw2QRBjAAvLcrAlGS+PlSbp1OFaVdpcqkaNhrMxUjV1Ktm2NnKdnQOuuzkYe8opWFNOxlv+GxKLbyDNNMCim904yTTZ1jbCKVO1aXOcsiyLVDJFKjmyeZ2MMfi+TxAGBIFP4Pv4QUBPT1dhFGpbeysdnW20tDaz9aXNeF5u0OO5boKmxknUVNdSWVlNVWU1lZVVVKajGgbP97AtmwkNjUyYMJGKVAX2/2vv3IMku+r7/vmd++rHTE/PzD60Wi3Srh4gCRuhqHgaWxiTCMq8TCoWScVUmTKVsklMUq4yDlWOk6okdii7bFcSU5hQMSliVDFQwRRgEjuAQxUEgbRIKwn0QALtrmZ2nj3Tr/s6+ePe7ul59PTsama6Z+b3qbrTt889997fmXMf3z6/c87P9fA9X0WdcigREQRvx8HiMzGXYG1CO14mtXOstJ/FMT4l7yS+U8Vzyhp8XnlRqDgbEmIM1Ztv5sr58yRhODB6QC/eK3+O9hc/Rjp3EXPsNGW5HjctUTPPkKRtwtVVChMTe2i9sl+ICJ7n4W2ciGBymtPXb56EOU1TFpfmaTYbJEkm5NIkIU5iaitrrXKrqzVemL1Evb66rZjrYIzDsenjnDxxPSeOX8fJE9fngx6uw/N0ZJxydMjEnAvirpuoN7UR9fASK/ZHgFD0TlD2rsNzMleqJUVwtHVN2REqzoaIWyhQve02Fh57DDM5ueP+Z+5db6L9pT8levDLBPe9D4DAVJm0t7NYfILa7A8pTNy1l6YrI4oxhump41e1Txi2qTdWMWLwPJ8ojpifn2V+4Qrtdosojmk268xeucwLM5e48PjDpGkKZC+qqcljTFQmCYKAQqHIZHWayeo0lUqVYqFIsViiUChSLJTw/UBb4JRDSTbNR/aj2FpLGC/SjGayiT5EsNZixMF3qvl0H1m4KxEHwSBiEFwVbwqg4mzoFCcnGT9zhtWLFwkmJ3e0j6kex739tUTf/Ev8n/2HiJ+NUHKlyJR/J3PLD9Goz1IqH4rwpMoe4/vBpkEFE5Uq587etmX+OI64MjfL7JXLzMxeYnb2BWqry9RWlpmZvcz3Hv1Od164jYhIV6hVq1Mcmz7B1OQxKnlkhkJQwHU9gqBAdWIKb4cTNivKKJHN3Ta+aeLd1CbEaZ12a4FuuKssZAJiM1EmxsUlwJggD3fViVfq5ELPYHDyQQouILmgMzpY4RCh4mwEGL/hBtrLy0SNBl5pZxOZevfeT/yf/xnRt7+M//p3ddMd8ZlwbiVcWiQsLOE7Vz+fmqJsh+t6nLruNKeuO73l9jRNqdWWWFmt0Ww1aLWaNFsNms0mrVaTVqtBo1lnYXGex5/4Hiurtb7nGh+fYKo6zeRk1ho3OTmdibnxKmPlMUqlMVxXH2PKwcCIg5ESrun/nM8CzyfEaYMoqXVDYK0NTsgEXWeAwtpABegOVsDHmABjfBzp9KcTQDKRJyZ3sa7/BEhsiyhZJbUJgTuRD3TQ1u79Rp9qI4AYQ/XcOWbPn8ctFHbk3nRvejnmxjsI//Yv8F7zdqRnAEBQrmLnU5yTZVrxAoEzqU3lyr5hjKFanaJa3dlI5CgKqa1kLW9h7kZttRosLS2wsDTP4uI8P37+WR658N0tW+QKhSLl0lg2sKE8Trk0lsVIrVSpTkxmcVMrk5TLY+pSVUaeTuD5ayEbrJAFoo/TBjZZyQcvpGt5On9Fsi8CWNuNewogZPfJSjtFxOBJCccp4kgncL2XC7rMZStkU4+siTzpEX767rkWVJyNCF65zPgNN7By6RKFHUYP8H/mflqf/G3iR76Od9cbu+liDGIdio0TuBNFmtELBM603iTKSOJ5PtNTxwf2lUvTlJWVZRaW5qnVlqk3VqjXV7MlX6/Vlrh06UesrNa6/eI6OI7D+PgE1cpkFux+YpKx8jjFYolSsUypWM7WS9lnISiqmFMOFJILpWsVd1vRCVwfJSu07VIu9NJep2z2btkwf2Pnm/T8XdtiMMbDwQPjYHBzl6zJXbXkrXUm3zvrk9cRgFn7YaclcC2tc8Ys/8F29ao4GyHGrr+exuzsjkdvune8Djl2A+HXPo37invXiS9/bIzVi5c5cfwVGBzq0WUK7tXNqaYoo4QxhomJSSYmBvfNTNOU1dVaFiO1tsjycidm6iJLy4tcfuF5Hv/+I9uOVO30j+uKtg0CrlAo4nk+vh9QGZ/IB0JM6aTAyqFCxKwLFbgbZG7a3F2bRqSEPa17aS7s0g16r+PalR6ptyYB181J182z5u7tLY8RN28dlG4/PoDArTIe3LibRb1mVJyNEMZ1qd5yC/MXLuBMTw/ML8bg/8wv0v7M75M89RDurXevHcvzCFdWCFdWqUycIyGkHS0QuDsbdKAoBxljTDbIoFLlDDf1zRdFIY1mg0ajTrNZp9nqrGf94hrNBs1GnUarQbNZZ2FxjmazQbPV2NQy16FcGmNycppSqUzgFygUiuvCdRWCIkFQyNcL3fXOd98PtJVbOdRkLk+zvkFtD1kflaUj8mzuBrZgE1Ib044WVJwpW1OoVikeO0a7VsMfHzzTv3f3mwn/6hOEX3tgnTgDcItFVi9epFCtUi3cxmL6OGGigwQUpYPn+Ux4PhOVq7snrLWEYZsoimiHLWq1JRaX5rNlcYHF5QWazTpLS4u08+gP7XZrYOguyFrsioUS4+MVxsayWKtBEKwTdcVCqduaVyyurRcK6opVlI2s/7HjrGnC3uR0tH4QqTgbQSo33sjsQw/tKIi5eD7eT72b8MsfJ7n0NM71N3e3ucUirYUFonodr1xmsvQyFhqP0Y6XCVydpFZRrhUR6QqlMcaZnjrOWW7ddh9rbTd0V2dprVtv0m63abdbNBqrrKzWWFlZ5srcTL4ty9evxa5DISh2Bds60SZZS4Xn+pRKHWFX7rpofc8nCAr4uatW56RTlOGh4mwEcQsFxm+8kZXnntvR3Gf+a95G+DefIvzaAxTf8y/XbTOuS31mhuq5cxjxmCzezkJTBZqi7DciQuAHBH4A49d271lriaKQZquZuWGbjcz1um69QbNV767PXrlMq9XMXDjWEkURrXZzR+dzXW/NzWotxnEoFcuMlccJggKe5+G6Hp7r5Xn9zH3rF/CDwjp3bpALviB33Xqup+JPUfqg4mxEKZ88SWNmhrjdxg2274wppXG8V/880Tc+Q3rfL2Mmr+tu88bGaMzMMH76NE4Q4BifqeIdmUBLFgkc7YOmKAcFEem2al2tK7aXJEm68801GnVarSZhGBJGbdphm7C7hIRhO3PHipAmCfXGKvX6Csu1RaIoJIoj4jgmikLCMCRNt56AeCscx6VcKjM+PkG5NIbn+XiehyCkNl03abHjOF238Jp7t0ihUKQQZJ9BLggLhWwyY0U5qKg4G1GM41A9d465Rx/F8f2BHYT9N7yb6BufJfzqAxTe9evddDEGRKjPzlI5cwYAx/hMl17OUusHtKI5AmdKOyAryhHCcZxsTrjy4H6tV0scx3k/u8xFG3bWO33vcuEXRSFhFNKoZy7cRqPOar1GGGYjaEUM1qaZm7fZIE2TfMoEO9C12ylj4BcICgU81+u2HPq+n7lzO6NvSyV8L9jQCuhm656H67i5aPS74tHzfPx83XH0NarsPnpVjTDBxATl666jOTeHPyCQuameyFrPvvmXeK96K87ptf4vfqXC6sWLlE+e7E7RYcRlsvAyavIs9fBH+Txo6mJQFOXFkQmbMcqlsT05vrWWOI5otjoRJ7Kl3W7RClu0W82uOGzl61EUYUw2aWo7bHdjxXZG5cZxfM32GOPgeR6+5+Pmn57n47kenu/juWtCbjuRt3HbuuN4Po7j4DgOxujErkcBFWcjzviZMzTn5kjjGDMgTE1w3/uIH/k6rc/+AaVf+4+IyQYTdCIO1Gdmuq1nkP0yrQRnMeJSaz9DwZlCDuiEfYqiHA1EpCtiKtfYd28jaZoSd9yzcUQcR0RR/hlHmfs26nxm62EUEuetf1EcEYUhUZy5djv7tFpNVqJad78w3zeOoxdlrzGZUHOMg3HW1jsCznHcTCQ77tq6m61ny9q+rpMfwzhr2/JjdI7Zzdf5bgzGZJ+d43TXO+lOlscxPfmd9XmMMSo0+6DibMRxfJ+Js2dZfPJJCgPmPpPiGMHbfpXWn/9bom99Af+17+hu8ysV6pcurWs9g+xBNx68BBBq7adVoCmKcuQwxnT78u0HHTG4pajbQuRFUUiSJKRJQpImJPlnmqytJ0lCmibEcUySJMRJnAnOJKbVbpIkMXGc50nitX2TuLv/TqZ62W06ws3pEXzGGCT/NGJ6hNzaemebbMyzYT8RwTFOni7ZPmIQI/mEtIIxBovl7I0388ZXv3Lf/wdboeLsAFA8fpzG3Bzh6ir+2PauAveun8X59pdof+njuHe8DjORhcQRY7DWUr98mcqNmyfZGw/OIBiW20+qQFMURdlD1onB/jHQ9500TdcJv66ISxOSOCZJU9I0yfLledI0XfueJqRJZ3192rrvado97lp6Jy3Fpimp7ZzL9nzPF5vn6fmeJDFRtCFfmkUhWLeftd19rbXdtKwfo+WNrx52LWSoODsAiEgWGP3hhwe6N0WEwrs+SP0P30/r0/+e4q98pOve9CsVVp5/nmBykqBS2bTvWHAaQAWaoijKEaTT+uRx9Ea6Jmm4XwELdoT2AD8guIUC1XPnCJeXB+Y1x2+g8M5/SvL0w4RffaCbLsbgjo2x9NRTpH06wI4Fp6kEN9NKFkjttXeSVRRFURTl2lBxdoAoHj9OcXqa9g4EmnvPfbiveCPhVz5B8txja+lBQNJuU/vxj/vuOx6cYbJwO2GyTJK2d8V2RVEURVF2hoqzA4SIUL3lFrxSibBWG5i38Av/HJk4QfNT/4Z0ZaG7za9WqV+8SH1mpu/+Jf8kx0qvILEtoqS+a2VQFEVRFGV7VJwdMIzrMvXSl2I8j6i+vWiS4hjFX/rX2PoyzU/+K2zcmdxR8CcnWXrqKRpXrvTd33cnOFa+CxEhTFZ2tRyKoiiKomyNirMDiOP7TN9+O1hL3Nw+Rp5z+lYKv/ibpM9doP25P+oOlTaOgz8xweIPfkBzfr7v/q4pMV16BY4p0E4WhjLUWlEURVGOEirODihuocD0HXeQRhFxq7VtXu8n78V/0z8m+vaXaH/hT7B56BPjuviVCgtPPMHKxYt9hVcn3FPRPUk7nidJw10vj6IoiqIoGSrODjBeqcSxO+8kabVI2tt33Pff/F6817+L6G//gtan/x02n6HaeB7B5CS1Z59l6emn+47iNOJSLd7GVOknSGyLdjx4UIKiKIqiKFePirMDjlcuc+zOO4kbjW0FmhhD8PYP4L/lV4gf/huaH/sN0tkfrW2bmqI5N8fs+fPbjgYteFMcL/8dAq9KK57T6TYURVEUZZeRg9yH6J577rEPPvjgsM0YCcKVFeYuXMApFnGD7UOQRA/9Na3P/SFEIf699+Pfez8SFAFI2m2i1VVKJ05QPnWqb0QCay3NaIbl1pMY8fCc8V0vk6IoylHG2oTsDW3zpZO+/nuWw3bzDX6vp+uO25vdMR5GAgQhsSGpDbt2SH6WDtKxIo+PKYPOK5IfB7CWQVbKhjNKz9+++0rHjo3W0jPJ7MbpZi0WS+BMMl3+iQFWvThE5DvW2nsG5lNxdngIV1eZv3AB8Ty80vYxQdKVBdpf+CjxQ/8biuP4r3kb3uveiZk4hrWWqF4nDUP8sTHKp09TmJjYMjJBnDZYbj1DO57HM+M4Zn9i0ymKoowqqY1J0haJzUfIQx9RA9hMgvSKiY4IETGIOAiGXkeX5NsyUdTJbbJFTJ6/P51jiph8f0EwWFKiZIUwrWHTBM8dxzfjOKaIEQdBukIst6KnRL3l2zzX/mahtP5YvTm3Ek89/5V+pdpQRmHzcbdHcHGMPzDfi0HF2RElbjaZf+KJTFhNTAzMnzz3GOHXHiC+8H8Bwbnllbg/eS/uy16FmThO3G4T1+uI41A+eZKgUsEtlXCCIL/4s19xrXielfZzxGkdwcV3xvMbX1EU5XCT2og4bZCSgrUY41NwJgncSRxTyFue0lxUubkwysVOR3RsFFpDfn5aa7vPeGX3OLDiTETuA/4IcICPW2t/t19eFWdbk8YxS08/TXN+HicIcEulgTdZOneR6MEvE53/Knb+IgAydQrnpjtxTt2CXHeWtDyFDcbB9bKpOCoV/EoFr1jE8X2M55GYkGY0Qz16HteUcU1xP4qsKIqyCWuTrmsuEzwuIFgbkdgYsGuusx7XXOetKAhWQGyn5ae3Dayzp+Aan4J7nMCdxDVF9SAofTmQ4kyySNs/AN4MPA98G3iPtfaxrfKrOOuPtZZwZYXVS5doLS5iRHDHxrYNmt7ZL738DMnTD5H88BGS5x7DrmyYB61UwYxPwdgUFMcgGINCGSmOQXEMpzyBLQjtoIYNDH5xCqdQAr+A8QMk8DF+ATwffC+zSWSb5mpFUY4q695Rm95Xa99TG5EkLRIb5RIqc/R5TgXfGcfalDitk5LgmTE8KWFMJtYEWef1kp4WrMy9KOudYrZXoBkc2RAofBs7N27f/A7un3fgsTdt2n7fTb2+dvi/frF2bSrz1ZzLdnrL2Z3ZsZ3G2bBNyhW86ev6598FdirOtn9T7z+vAp6y1j4DICKfBt4BbCnOlP6ICEGlQlCpEDebNOfnWb10iTRJMHmfNDGbm81FBOf6m3Guvxne8PcBSFeXSC8/g12aIa3NYWsL2NocaW0eu3gZ21yF1mr3Qk/yY3WOnvSkbYUVAccF1wXHgU5zfsc+kbUFAbPhU7KHJ1stbJW+sdwDfqBsejBs7EHSJ+/Vbt/RA6rn3HZQ3quw46rsGrR9m5fFwAf8Nvn3tExXY8duHmvDy3Lg/2vnx9rEVdl5FecddO4Bdg3sRP4i0ejAyk7xXvt2Tv/WR4dtBjB64uw00BuR+3ng1UOy5dDgFouM33AD5VOniFZXaVy5Qmt+Hpska6NnHAcxJltkTfCIMRCUkRtfjrzkzrW2LWuz0TZ5vwSbJtBqkDZqELYgDiGJsFFImrSJkkXCeB6JQyQxmNhCEkMcI0kMSYyNYySO10Yi5eeAzgO8Jy3tCJQ0H3CUrrOLjfk7LwBrIY3Z3OF0Q8fRTW7gno6rsjG/rF/d1AAo23zdblv263+77Vsk9Gy6yn23dX1f7bFexL6bNl9FGV/Uvuu/b5IMu22HbFq5tmP3CHabDYPrk892T9XN0c0vm0yxG9uyZWNWF7Gmm7DWR2qLztxi8taptRvESoolm4rHEGBwAdPd24rt9tOCTv8sSyoJWZuWg+CsnX+LMmyZ0Pv/HHANbH+sXby/+tnR7xrZdG9f+3W+nd1bdoe5mudEb/4dXcc9mG3s3nisjZe8bPj/2S22bTquxT1z6wAb949RE2db1d66f7uIvB94f/51VUS+v+dWwTFgbh/Os68IGCMieRuUSP50zeWHCEhi7aRjzGIubrp1sVXbTc+2fj/Rxbg4ni++mKxhLU1J+r5LRoAwSiu+Z7aPMn+IOcjl3+I6vCqiKK14Oy37oDNt0P4DjyKW3rdI7vUX2cb3b1NS2/X22I7naJ3nKv89ZTvnyr6v5ey5y22S2inHyELnd03nN5u12DQlHVDiA09q7bQR6R/bbvfY8XW6n92QLEwL7KT8+2LUfpwkf2ZY9vadf+NOMo2aOHseONPz/QbgUm8Ga+3HgI/tp1Ei8uBOfMSHERF5MIzjI1l2yMrfbmv5h23HMDjKZYes/EmcHunyx+nRLn96hN97w37nj9pcB98GbhWRsyLiA/cDnx+yTYqiKIqiKPvGSLWcWWtjEfkA8FdkU2l8wlp7YchmKYqiKIqi7BsjJc4ArLVfBL44bDs2sK9u1BHjKJcdtPxHufxHueyg5dfyH12GXvaRmudMURRFURTlqDNqfc4URVEURVGONCrOtkFE7hOR74vIUyLyoWHbs9eIyBkR+T8i8riIXBCRX8/Tf0dELorIw/ny1mHbuleIyLMi8khezgfztCkR+V8i8mT+OTlsO3cbEXlpT/0+LCI1EfngYa57EfmEiMyKyKM9aVvWtWT8cf4s+J6I3D08y3eHPuX/iIg8kZfxcyJSzdNvEpFmz3UwGjN1vgj6lL/v9S4iv5XX//dF5O8Nx+rdoU/ZH+gp97Mi8nCefhjrvt+7bnTuf9uZ+EaXdQvZgISngXOAD5wH7hi2XXtc5lPA3fn6OFkorTuA3wF+Y9j27dP/4Fng2Ia0/wB8KF//EPB7w7Zzj/8HDvAC2Xw8h7bugZ8G7gYeHVTXwFuBL5HNWPYa4FvDtn+Pyv93ATdf/72e8t/Um+8wLH3Kv+X1nj8HzwMBcDZ/NzjDLsNuln3D9t8HfvsQ132/d93I3P/actafbigpa20IdEJJHVqstZettd/N11eAx8miNhx13gH8Wb7+Z8A7h2jLfvAm4Glr7XPDNmQvsdZ+HVjYkNyvrt8BfNJmfBOoisip/bF0b9iq/Nbar1hr4/zrN8nmmjyU9Kn/frwD+LS1tm2t/SHwFNk74kCyXdmzecn5B8Cf76tR+8g277qRuf9VnPVnq1BSR0aoiMhNwCuBb+VJH8ibcz9xGN16PVjgKyLyHcmiUQCctNZehuymBk4Mzbr94X7WP5iPSt1D/7o+is+DXyZrLehwVkQeEpGvicgbhmXUPrDV9X6U6v8NwIy19smetENb9xvedSNz/6s468/AUFKHFREZAz4DfNBaWwP+BLgZuAu4TNbkfVh5vbX2buAtwK+JyE8P26D9RLLJn98O/I886SjV/XYcqeeBiHwYiIFP5UmXgZdYa18J/Avgv4tIZVj27SH9rvejVP/vYf2Ps0Nb91u86/pm3SJtT+tfxVl/BoaSOoyIiEd2sX7KWvtZAGvtjLU2sdamwJ9ygJvzB2GtvZR/zgKfIyvrTKcJO/+cHZ6Fe85bgO9aa2fgaNV9Tr+6PjLPAxF5L/DzwD+yeYeb3J03n69/h6zP1W3Ds3Jv2OZ6PxL1LyIu8AvAA520w1r3W73rGKH7X8VZf45cKKm8r8F/AR631v5BT3qvb/1dwKMb9z0MiEhZRMY762Sdox8lq/f35tneC/zP4Vi4L6z71XxU6r6HfnX9eeCX8lFbrwGWO+6Pw4SI3Af8JvB2a22jJ/24iDj5+jngVuCZ4Vi5d2xzvX8euF9EAhE5S1b+/7ff9u0DPwc8Ya19vpNwGOu+37uOUbr/hz1qYpQXshEaPyD7pfDhYduzD+X9KbKm2u8BD+fLW4H/BjySp38eODVsW/eo/OfIRmSdBy506hyYBv4aeDL/nBq2rXtU/hIwD0z0pB3auicToZeBiOyX8fv61TWZW+M/5c+CR4B7hm3/HpX/KbK+NZ37/6N53nfn98R54LvA24Zt/x6Vv+/1Dnw4r//vA28Ztv27XfY8/b8C/2RD3sNY9/3edSNz/2uEAEVRFEVRlBFC3ZqKoiiKoigjhIozRVEURVGUEULFmaIoiqIoygih4kxRFEVRFGWEUHGmKIqiKIoyQqg4UxRFyRGRqoj86rDtUBTlaKPiTFEUZY0qoOJMUZShouJMURRljd8FbhaRh0XkI1ZhnU0AAAC1SURBVMM2RlGUo4lOQqsoipIjIjcBX7DWvnzIpiiKcoTRljNFURRFUZQRQsWZoiiKoijKCKHiTFEUZY0VYHzYRiiKcrRRcaYoipJjrZ0HviEij+qAAEVRhoUOCFAURVEURRkhtOVMURRFURRlhFBxpiiKoiiKMkKoOFMURVEURRkhVJwpiqIoiqKMECrOFEVRFEVRRggVZ4qiKIqiKCOEijNFURRFUZQRQsWZoiiKoijKCPH/AcWD2e/Thn7mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the result\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.style.context('ggplot')\n",
    "\n",
    "plt.plot(range(len(mean_corner)), mean_corner, '-', color='#df4d19', label=\"$Mean, p_s=0.8,p_a=0.9$\")\n",
    "plt.fill_between(range(len(mean_corner)), mean_corner - std_corner, mean_corner + std_corner,\n",
    "                 alpha=0.2, color='#a43737', label=\"Std, p_s=0.8, p_a=0.9\")\n",
    "\n",
    "plt.plot(range(len(mean_p51)), mean_p51, '-', color='#421b9b', label=\"Mean, $p_s=0.5, p_a=0.01$\")\n",
    "plt.fill_between(range(len(mean_p51)), mean_p51 - std_p51, mean_p51 + std_p51,\n",
    "                 alpha=0.2, color='#a06ee1', label=\"Std, $p_a=0.01, p_s=0.5$\")\n",
    "\n",
    "plt.plot(range(len(mean_pm51)), mean_pm51, '-', color='#616f39', label=\"Mean, $p_s=0.01, p_a=0.5$\")\n",
    "plt.fill_between(range(len(mean_pm51)), mean_pm51 - std_pm51, mean_pm51 + std_pm51,\n",
    "                 alpha=0.2, color='#a7d129', label=\"Std, $p_s=0.01, p_a=0.5$\")\n",
    "\n",
    "plt.legend( prop={'size': 12})\n",
    "plt.title(\"MSE (for %i repetitions using strategy 'corner')\" % len(mean_p51))\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"error\")\n",
    "plt.ylim(bottom=0)\n",
    "plt.savefig(\"imgs/mse_different_strategies.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#errors_p99999 = copy.deepcopy(errors)\n",
    "#mean_p99999 = np.mean(errors_p99999,axis=0)\n",
    "#std_p99999  = np.std(errors_p99999,axis=0)\n",
    "#del errors_p99999, errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#errors_p5 = copy.deepcopy(errors)\n",
    "#mean_p5 = np.mean(errors_p5,axis=0)\n",
    "#std_p5  = np.std(errors_p5,axis=0)\n",
    "#del errors_p5, errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors_pnormal = copy.deepcopy(errors)\n",
    "mean_pnormal = np.mean(errors_pnormal,axis=0)\n",
    "std_pnormal  = np.std(errors_pnormal,axis=0)\n",
    "del errors_pnormal, errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the result\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.style.context('ggplot')\n",
    "\n",
    "plt.plot(range(len(mean_p5)), mean_p5, '-', color='#df4d19', label=\"$Mean, p_s=p_a=0.5$\")\n",
    "plt.fill_between(range(len(mean_p5)), mean_p5 - std_p5, mean_p5 + std_p5,\n",
    "                 alpha=0.2, color='#a43737', label=\"Std, p_s=p_a=0.5\")\n",
    "\n",
    "plt.plot(range(len(mean_pnormal)), mean_pnormal, '-', color='#421b9b', label='Mean, $p_s=0.8, p_a=0.9$')\n",
    "plt.fill_between(range(len(mean_pnormal)), mean_pnormal - std_pnormal, mean_pnormal + std_pnormal,\n",
    "                 alpha=0.2, color='#a06ee1', label='Std, $p_s=0.8, p_a=0.9$')\n",
    "\n",
    "plt.plot(range(len(mean_p99999)), mean_p99999, '-', color='#616f39', label='Mean, $p_s=p_a=0.999$')\n",
    "plt.fill_between(range(len(mean_p99999)), mean_p99999 - std_p99999, mean_p99999 + std_p99999,\n",
    "                 alpha=0.2, color='#a7d129', label='Std, $p_s=p_a=0.999$')\n",
    "\n",
    "plt.legend( prop={'size': 12})\n",
    "plt.title(\"MSE (for %i repetitions)\" % len(mean_p5))\n",
    "plt.xlabel(\"t\")\n",
    "plt.ylabel(\"error\")\n",
    "plt.ylim(bottom=0)\n",
    "plt.savefig(\"imgs/mse_different_p.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(1,3,1)\n",
    "plt.cla()\n",
    "plt.imshow(vals[3][0][-1][3])\n",
    "plt.title('Posterior belief ($p_s=p_a=0.5$)')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.cla()\n",
    "plt.imshow(vals[4][0][-6][3])\n",
    "plt.title('Posterior belief ($p_s=0.8,p_a=0.9$)')\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.cla()\n",
    "plt.imshow(vals[1][0][-1][3])\n",
    "plt.title('Posterior belief ($p_s=p_a=0.999$)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"imgs/different_p.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}