200422_harshtrivedi_A1_PartB.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/harsh500/CS220A-Computer-Organisation/blob/main/200422_harshtrivedi_A1_PartB.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WuEfEVo2seo"
      },
      "source": [
        "# EE675A - Assignment 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i29hbFMG2ses"
      },
      "source": [
        "**Name:**  <br />\n",
        "**Roll No:**\n",
        "***\n",
        "## Instructions\n",
        "\n",
        "- **Release Date**: **21st Jan 2024**  \n",
        "- **Deadline**: **Part B : 4th Feb 2024 11:59PM**\n",
        "- Kindly name your submission files as `RollNo_Name_A1_PartB.ipynb`, based on the part you are submitting. Marks will be deducted for all submissions that do not follow the naming guidelines. <br />\n",
        "- You are required to work out your answers and submit only the iPython Notebook. The code should be well commented and easy to understand as there are marks for this. This notebook can be used as a template for assignment submission. <br />\n",
        "- Submissions are to be made through HelloIITK portal. Submissions made through mail will not be graded.<br />\n",
        "- Answers to the theory questions if any should be included in the notebook itself. While using special symbols use the $\\LaTeX$ mode <br />\n",
        "- Make sure your plots are clear and have title, legends and clear lines, etc. <br />\n",
        "- Plagiarism of any form will not be tolerated. If your solutions are found to match with other students or from other uncited sources, there will be heavy penalties and the incident will be reported to the disciplinary authorities. <br />\n",
        "- In case you have any doubts, feel free to reach out to TAs for help. <br />\n",
        "\n",
        "***\n",
        "## Introduction\n",
        "\n",
        "You are free to use parts of the given code but may also choose to write the whole thing on your own.  \n",
        "The illustrations for Part-B are adapted from [Alejandro's blog post](https://medium.com/@alejandro.aristizabal24/understanding-reinforcement-learning-hands-on-part-2-multi-armed-bandits-526592072bdc) on Multi-Armed Bandits. Throughout the course we will be using the [Gymnasium toolkit](https://gymnasium.farama.org/index.html) for the assignements so before starting you may want to go through the [basic usage](https://gymnasium.farama.org/content/basic_usage/) and basics of gymnasium and the installation procedure."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wON2nVET2set",
        "tags": []
      },
      "source": [
        "***\n",
        "# Part B: Multi-armed bandits\n",
        "## Demo and Preliminaries\n",
        "\n",
        "We will begin by getting familiar with the basic problem setup before we dig in to the actual assignement problems. Let's start by loading all the required libraries for this notebook. For generating plots you must have `ipympl` installed and `jupyter-matplotlib` extension installed and enabled."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H89KRE9N2seu",
        "outputId": "4b62c242-4bc7-4316-d7ba-6b76b9145106"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.66.1)\n"
          ]
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "!python.exe -m pip install --upgrade pip\n",
        "!pip install \"gymnasium\"\n",
        "!pip install ipympl\n",
        "!pip install matplotlib\n",
        "!pip install tqdm\n",
        "%matplotlib widget\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.animation import FuncAnimation\n",
        "import gymnasium as gym\n",
        "from tqdm import tqdm\n",
        "import time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9O01qmZN2sev"
      },
      "source": [
        "## Scenario\n",
        "\n",
        "The Multi-Armed Bandit describes a situation in which an agent has only one state, and multiple actions to interact with the environment. Each action gives a random reward, centered on an unkown value. Our agent wants to maximize the reward received, which means it wants to find the action that yields a higher reward. Based on this, let's build the scenario.\n",
        "\n",
        "We're going to implement the multi-armed bandit environment using OpenAI's gym interface. Also, our implementation will be vectorized for the sake of optimization. This allows us to run multiple agents on multiple environments at the same time. The code below is based on [this implementation](https://github.com/diegoalejogm/openai-k-armed-bandits)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Vz6nX5I_2sev"
      },
      "outputs": [],
      "source": [
        "from gymnasium import spaces\n",
        "from gymnasium.utils import seeding\n",
        "import gymnasium as gym\n",
        "\n",
        "class ArmedBanditsEnv(gym.Env):\n",
        "    \"\"\"\n",
        "    The famous k-Armed Bandit Environment, implemented for the gym interface.\n",
        "    Initialization requires an array of length equals to k, where each item is\n",
        "    a function which samples from a specified distribution.\n",
        "    \"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, mean, stddev):\n",
        "        assert len(mean.shape) == 2\n",
        "        assert len(stddev.shape) == 2\n",
        "\n",
        "        super(ArmedBanditsEnv, self).__init__()\n",
        "        # Define action and observation space\n",
        "        self.num_bandits = mean.shape[1]\n",
        "        self.num_experiments = mean.shape[0]\n",
        "        self.action_space = spaces.Discrete(self.num_bandits)\n",
        "\n",
        "        # Theres one state only in the k-armed bandits problem\n",
        "        self.observation_space = spaces.Discrete(1)\n",
        "        self.mean = mean\n",
        "        self.stddev = stddev\n",
        "\n",
        "    def step(self, action):\n",
        "        # Sample from the specified bandit using it's reward distribution\n",
        "        assert (action < self.num_bandits).all()\n",
        "\n",
        "        sampled_means = self.mean[np.arange(self.num_experiments),action]\n",
        "        sampled_stddevs = self.stddev[np.arange(self.num_experiments),action]\n",
        "\n",
        "        reward = np.random.normal(loc=sampled_means, scale=sampled_stddevs, size=(1,self.num_experiments))\n",
        "\n",
        "        # Return a constant state of 0. Our environment has no terminal state\n",
        "        observation, done, info = 0, False, dict()\n",
        "        return observation, reward, done, info\n",
        "\n",
        "    def reset(self):\n",
        "        return 0\n",
        "\n",
        "    def render(self, mode='human', close=False):\n",
        "        pass\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np.random(seed)\n",
        "        return [seed]\n",
        "\n",
        "    def close(self):\n",
        "        pass\n",
        "\n",
        "\n",
        "class ArmedBanditsGaussian(ArmedBanditsEnv):\n",
        "    def __init__(self, num_experiments=1, num_bandits=3):\n",
        "        self.means = np.random.normal(size=(num_experiments, num_bandits))\n",
        "\n",
        "        ArmedBanditsEnv.__init__(self, self.means, np.ones((num_experiments, num_bandits)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWmKZ0uX2sew"
      },
      "source": [
        "Here we're creating two classes, `ArmedBanditsEnv` is the main class, while `ArmedBanditsGaussian` is an auxiliary class that allows us to easily create an environment with random mean rewards for each action. Our environment receives numpy arrays for the means and standard deviations for each action. The dimensions of this arrays are described as `num_experiments`x`num_bandits`. Taking a step requires a numpy vector of size `num_experiments`, where each value specifies which action to take for each experiment. The step functions returns, among other information, a vector of reward obtained for each experiment. Let's see this in action:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "id": "rc9rBv-T2sex",
        "outputId": "344f8e78-cd7f-4c42-bc4a-1dfb59e9f67d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bandit: 0  gave a reward of: [4.09574649]\n",
            "Bandit: 1  gave a reward of: [0.81834762]\n",
            "Bandit: 2  gave a reward of: [-3.14576937]\n",
            "Bandit: 3  gave a reward of: [-9.00090655]\n"
          ]
        }
      ],
      "source": [
        "means = np.array([[5, 1, 0, -10]]) # The mean for a four-armed bandit. Single experiment\n",
        "stdev = np.array([[1, 0.1, 5, 1]]) # The standard deviation for a four-armed bandit.\n",
        "\n",
        "env = ArmedBanditsEnv(means, stdev) # Create the environment\n",
        "\n",
        "for i in range(4):\n",
        "    action = np.array([[i]])\n",
        "    _, reward, _, _ = env.step(action)\n",
        "    print(\"Bandit:\", i, \" gave a reward of:\", reward[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSsziz9u2sey"
      },
      "source": [
        "## Evaluating our actions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8g7dhvZ2sez"
      },
      "source": [
        "In order to learn something from our interaction with the environment, we need to know how exactly we determine the value of our actions, as well as how to keep that value up to date. A simple strategy our agents can take is to calculate the expected return for each action. This can be done through experience by taking the average of previous rewards given by a determined action.\n",
        "\n",
        "For example, we can use the next list of rewards to calculate the expected return of the chosen action:\n",
        "\n",
        "$\\bar{\\mu}_n = \\dfrac{R_1+R_2+\\dots+R_n}{n}$\n",
        "\n",
        "Having to store all the previously seen rewards to calculate the value of an action is cumbersome, inefficient and unnecessary. We can derive another form of average, called *Incremental average update rule*, which only requires us to know the previous average $\\bar{\\mu}_{n-1}$ and the number $n$:\n",
        "\n",
        "$\\bar{\\mu}_n = \\bar{\\mu}_{n-1} + \\dfrac{1}{n}(R_n - \\bar{\\mu}_{n-1})$\n",
        "\n",
        "Here's a small demonstration of this function at work"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hvBWbzZ2sez",
        "outputId": "7c6cb139-83f5-4e25-9530-3d1c86f1b7aa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average obtained from incremental update rule:  4.991666666666666\n",
            "Average obtained from basic average function:   4.991666666666666\n"
          ]
        }
      ],
      "source": [
        "def inc_avg(prev_avg, new_val, n):\n",
        "    return prev_avg + 1/n*(new_val - prev_avg)\n",
        "\n",
        "# Obtain the previous average\n",
        "vals = np.array([4.5, 5.04, 5.32, 4.8, 5.11])\n",
        "prev_avg = vals.mean()\n",
        "\n",
        "# Calculate a new average using the incremental average update function\n",
        "new_val = 5.18\n",
        "new_avg = inc_avg(prev_avg, new_val, 6)\n",
        "\n",
        "# Calculate the same average using all previous values for comparison\n",
        "avg = np.append(vals, new_val).mean()\n",
        "\n",
        "print(\"Average obtained from incremental update rule: \", new_avg)\n",
        "print(\"Average obtained from basic average function:  \", avg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "di0QdX_n2sez"
      },
      "source": [
        "This method will be used on the next strategies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEuDOZ852se0"
      },
      "source": [
        "## The Greedy Agent\n",
        "\n",
        "This strategy is focused on always choosing the best known action at the time. Every time the agent takes an action, it looks at the estimated values for each action, and chooses the one that has a greater score. If more than two values look best, then the agent selects arbitrarily among those best-valued actions. This is called breaking ties arbitrarily\n",
        "\n",
        "Taking the action with the greatest value is equivalent to using the **argmax** function. Although, we need to implement some changes so that the funciton breaks the ties the way we intend it to do:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "IFNJoUd92se0"
      },
      "outputs": [],
      "source": [
        "def argmax(q_values):\n",
        "    \"\"\"\n",
        "    Takes in a matrix of n*k q_values and returns the index\n",
        "    of the item with the highest value for each row.\n",
        "    Breaks ties randomly.\n",
        "    returns: vector of size n, where each item is the index of\n",
        "    the highest value in q_values for each row.\n",
        "    \"\"\"\n",
        "    # Generate a mask of the max values for each row\n",
        "    mask = q_values == q_values.max(axis=1)[:, None]\n",
        "    # Generate noise to be added to the ties\n",
        "    r_noise = 1e-6*np.random.random(q_values.shape)\n",
        "    # Get the argmax of the noisy masked values\n",
        "    return np.argmax(r_noise*mask,axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfXWUrgO2se0"
      },
      "source": [
        "Here we're generating a mask of all the values in the input that are equal to the maximum value. Then ,we generate some noise and multiply it with the mask. Taking the argmax of this new list will be equivalent to the desired argmax with ties broken arbitrarily.\n",
        "\n",
        "Now that we have specified the argmax function, we can declare our class for the Greedy Agent, which is capable of acting upon the environment, and updating its estimates after receiving a reward."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "dUjX3Vz02se1"
      },
      "outputs": [],
      "source": [
        "class GreedyAgent:\n",
        "    def __init__(self, reward_estimates):\n",
        "        \"\"\"\n",
        "        Our agent takes as input the initial reward estimates.\n",
        "        This estimates will be updated incrementally after each\n",
        "        interaction with the environment.\n",
        "        \"\"\"\n",
        "        assert len(reward_estimates.shape) == 2\n",
        "\n",
        "        self.num_bandits = reward_estimates.shape[1]\n",
        "        self.num_experiments = reward_estimates.shape[0]\n",
        "        self.reward_estimates = reward_estimates.astype(np.float64)\n",
        "        self.action_count = np.zeros(reward_estimates.shape)\n",
        "\n",
        "    def get_action(self):\n",
        "        # Our agent is greedy, so there's no need for exploration.\n",
        "        # Our argmax will do just fine for this situation\n",
        "        action = argmax(self.reward_estimates)\n",
        "\n",
        "        # Add a 1 to each action selected in the action count\n",
        "        self.action_count[np.arange(self.num_experiments), action] += 1\n",
        "\n",
        "        return action\n",
        "\n",
        "    def update_estimates(self, reward, action):\n",
        "        # rew is a matrix with the obtained rewards from our previuos\n",
        "        # action. Use this to update our estimates incrementally\n",
        "        n = self.action_count[np.arange(self.num_experiments), action]\n",
        "        prev_reward_estimates = self.reward_estimates[np.arange(self.num_experiments), action]\n",
        "\n",
        "        # Update the reward estimates incementally\n",
        "        self.reward_estimates[np.arange(self.num_experiments), action] = inc_avg(prev_reward_estimates,reward,n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkLDTxGg2se1"
      },
      "source": [
        "The greedy agent contains a matrix for the estimates, as well as for the number of times each action has been taken. This is necessary for using the incremental average update rule, used inside the `update_estimates` function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sQmJ4qtp2se1"
      },
      "source": [
        "### Testing the agent\n",
        "\n",
        "Let's see how the Greedy Agent behaves on the environment. For this, we're going to generate some animations where we're able to see how the agent estimates the values for each action, as well as the real values provided by the environment. Remember that the agent doesn't have access to that information."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "aad3011722cc47918a484f479a580475",
            "1e3f86f8f66a4c7c9d611b75158d0b8e",
            "e8a4efe462124422a87177d4bdbea29b",
            "a35bccc901494a9ca17e22c7447d09ca"
          ]
        },
        "id": "Z_jM_1_W2se1",
        "outputId": "a934be56-4f44-4451-ac8f-1501605a49e1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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traYeu1tbWxe/fuEa/5xS9+EU8++WTs3LnzpL9Pf39/9Pf3F77u7e09rXkBAAAYW+6gjxNHjx6NRYsWxbp162LSpEknfV1ra2vU1NQUHvl8fgynBAAA4HS5g14ikyZNioqKiuju7h623t3dHVOmTDlu/69//evYt29fzJ8/v7A2NDQUERETJkyIPXv2xCWXXHLcdStWrIjm5ubC1729vSIdAAAgQQK9RCorK2P27NnR0dFR+FFpQ0ND0dHREV/4wheO23/ZZZfFK6+8Mmzt/vvvj6NHj8a3vvWtE0Z3LpeLXC436vMDAAAwugR6CTU3N8eSJUtizpw5MXfu3FizZk309fXF0qVLIyJi8eLFMX369GhtbY2qqqq44oorhl1/wQUXREQctw4AAMD4I9BLqKmpKQ4dOhQrV66Mrq6umDVrVrS3txc+OG7//v1RXu5jAgAAAM4GZVmWZaUeguLp7e2Nmpqa6Onpierq6lKPAwAAlIg2SI/bswAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIEOAAAACRDoAAAAkACBDgAAAAkQ6AAAAJAAgQ4AAAAJEOgAAACQAIFeYm1tbTFjxoyoqqqKurq62Lp16wn3rlu3LubNmxcTJ06MiRMnRkNDwzvuBwAAYPwQ6CW0YcOGaG5ujpaWlti+fXvMnDkzGhsb4+DBgyPu37JlSyxcuDBeeuml6OzsjHw+HzfccEO88cYbRZ4cAACA0VaWZVlW6iHOVnV1dXHNNdfEY489FhERQ0NDkc/n44477ojly5f/zesHBwdj4sSJ8dhjj8XixYtP6nv29vZGTU1N9PT0RHV19d81PwAAMH5pg/S4g14iAwMDsW3btmhoaCislZeXR0NDQ3R2dp7Ur/Hmm2/GW2+9FRdeeOEJ9/T390dvb++wBwAAAOkR6CVy+PDhGBwcjNra2mHrtbW10dXVdVK/xj333BPTpk0bFvl/rbW1NWpqagqPfD7/d80NAADA2BDo49Tq1atj/fr18eyzz0ZVVdUJ961YsSJ6enoKjwMHDhRxSgAAAE7WhFIPcLaaNGlSVFRURHd397D17u7umDJlyjte+/DDD8fq1avjxRdfjKuuuuod9+Zyucjlcn/3vAAAAIwtd9BLpLKyMmbPnh0dHR2FtaGhoejo6Ij6+voTXveNb3wjHnrooWhvb485c+YUY1QAAACKwB30Empubo4lS5bEnDlzYu7cubFmzZro6+uLpUuXRkTE4sWLY/r06dHa2hoREf/6r/8aK1eujKeffjpmzJhReK/6u971rnjXu95VsucBAADA30+gl1BTU1McOnQoVq5cGV1dXTFr1qxob28vfHDc/v37o7z8Ly9y+Pa3vx0DAwPx6U9/etiv09LSEl/5yleKOToAAACjzM9BP8v4WYcAAECENkiR96ADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEeom1tbXFjBkzoqqqKurq6mLr1q3vuP/HP/5xXHbZZVFVVRVXXnllbN68uUiTAgAAMJYEeglt2LAhmpubo6WlJbZv3x4zZ86MxsbGOHjw4Ij7X3755Vi4cGHccsstsWPHjliwYEEsWLAgXn311SJPDgAAwGgry7IsK/UQZ6u6urq45ppr4rHHHouIiKGhocjn83HHHXfE8uXLj9vf1NQUfX198dOf/rSw9pGPfCRmzZoVa9euPanv2dvbGzU1NdHT0xPV1dWj80QAAIBxRxukZ0KpBzhbDQwMxLZt22LFihWFtfLy8mhoaIjOzs4Rr+ns7Izm5uZha42NjfHcc8+d8Pv09/dHf39/4euenp6I+PNvRgAA4Oz1dhO4Z5sOgV4ihw8fjsHBwaitrR22XltbG7t37x7xmq6urhH3d3V1nfD7tLa2xoMPPnjcej6fP42pAQCAM81///d/R01NTanHIAT6GW/FihXD7rofOXIk3vOe98T+/fv9JmRM9fb2Rj6fjwMHDnjJFGPKWaNYnDWKxVmjWHp6euKiiy6KCy+8sNSj8H8EeolMmjQpKioqoru7e9h6d3d3TJkyZcRrpkyZckr7IyJyuVzkcrnj1mtqavyBT1FUV1c7axSFs0axOGsUi7NGsZSX++zwVPg3USKVlZUxe/bs6OjoKKwNDQ1FR0dH1NfXj3hNfX39sP0RES+88MIJ9wMAADB+uINeQs3NzbFkyZKYM2dOzJ07N9asWRN9fX2xdOnSiIhYvHhxTJ8+PVpbWyMi4s4774zrr78+Hnnkkbjxxhtj/fr18atf/SqeeOKJUj4NAAAARoFAL6GmpqY4dOhQrFy5Mrq6umLWrFnR3t5e+CC4/fv3D3u5ybXXXhtPP/103H///XHvvffG+9///njuuefiiiuuOOnvmcvloqWlZcSXvcNoctYoFmeNYnHWKBZnjWJx1tLj56ADAABAArwHHQAAABIg0AEAACABAh0AAAASINABAAAgAQL9DNTW1hYzZsyIqqqqqKuri61bt77j/h//+Mdx2WWXRVVVVVx55ZWxefPmIk3KeHcqZ23dunUxb968mDhxYkycODEaGhr+5tmEt53qn2tvW79+fZSVlcWCBQvGdkDOGKd61o4cORLLli2LqVOnRi6Xi0svvdR/Rzkpp3rW1qxZEx/4wAfi3HPPjXw+H3fffXf86U9/KtK0jFc///nPY/78+TFt2rQoKyuL55577m9es2XLlvjwhz8cuVwu3ve+98VTTz015nPyFwL9DLNhw4Zobm6OlpaW2L59e8ycOTMaGxvj4MGDI+5/+eWXY+HChXHLLbfEjh07YsGCBbFgwYJ49dVXizw5482pnrUtW7bEwoUL46WXXorOzs7I5/Nxww03xBtvvFHkyRlvTvWsvW3fvn3xxS9+MebNm1ekSRnvTvWsDQwMxMc//vHYt29fbNy4Mfbs2RPr1q2L6dOnF3lyxptTPWtPP/10LF++PFpaWmLXrl3x5JNPxoYNG+Lee+8t8uSMN319fTFz5sxoa2s7qf2/+c1v4sYbb4yPfexjsXPnzrjrrrvi1ltvjeeff36MJ6Ug44wyd+7cbNmyZYWvBwcHs2nTpmWtra0j7r/pppuyG2+8cdhaXV1d9i//8i9jOifj36metb927Nix7Pzzz8++//3vj9WInCFO56wdO3Ysu/baa7PvfOc72ZIlS7JPfepTRZiU8e5Uz9q3v/3t7OKLL84GBgaKNSJniFM9a8uWLcv+6Z/+adhac3Nzdt11143pnJxZIiJ79tln33HPl7/85exDH/rQsLWmpqassbFxDCfj/3MH/QwyMDAQ27Zti4aGhsJaeXl5NDQ0RGdn54jXdHZ2DtsfEdHY2HjC/RBxemftr7355pvx1ltvxYUXXjhWY3IGON2z9tWvfjUmT54ct9xySzHG5AxwOmftJz/5SdTX18eyZcuitrY2rrjiili1alUMDg4Wa2zGodM5a9dee21s27at8DL4vXv3xubNm+MTn/hEUWbm7KENSm9CqQdg9Bw+fDgGBwejtrZ22HptbW3s3r17xGu6urpG3N/V1TVmczL+nc5Z+2v33HNPTJs27bj/CMD/dzpn7Re/+EU8+eSTsXPnziJMyJnidM7a3r1742c/+1l85jOfic2bN8frr78en//85+Ott96KlpaWYozNOHQ6Z+3mm2+Ow4cPx0c/+tHIsiyOHTsWt99+u5e4M+pO1Aa9vb3xxz/+Mc4999wSTXb2cAcdKLrVq1fH+vXr49lnn42qqqpSj8MZ5OjRo7Fo0aJYt25dTJo0qdTjcIYbGhqKyZMnxxNPPBGzZ8+OpqamuO+++2Lt2rWlHo0zzJYtW2LVqlXx+OOPx/bt2+OZZ56JTZs2xUMPPVTq0YBR5g76GWTSpElRUVER3d3dw9a7u7tjypQpI14zZcqUU9oPEad31t728MMPx+rVq+PFF1+Mq666aizH5Axwqmft17/+dezbty/mz59fWBsaGoqIiAkTJsSePXvikksuGduhGZdO58+1qVOnxjnnnBMVFRWFtcsvvzy6urpiYGAgKisrx3RmxqfTOWsPPPBALFq0KG699daIiLjyyiujr68vbrvttrjvvvuivNw9N0bHidqgurra3fMi8bv5DFJZWRmzZ8+Ojo6OwtrQ0FB0dHREfX39iNfU19cP2x8R8cILL5xwP0Sc3lmLiPjGN74RDz30ULS3t8ecOXOKMSrj3KmetcsuuyxeeeWV2LlzZ+HxyU9+svBptPl8vpjjM46czp9r1113Xbz++uuFvwSKiHjttddi6tSp4pwTOp2z9uabbx4X4W//xVCWZWM3LGcdbZCAUn9KHaNr/fr1WS6Xy5566qnsv/7rv7Lbbrstu+CCC7Kurq4sy7Js0aJF2fLlywv7f/nLX2YTJkzIHn744WzXrl1ZS0tLds4552SvvPJKqZ4C48SpnrXVq1dnlZWV2caNG7Pf/e53hcfRo0dL9RQYJ071rP01n+LOyTrVs7Z///7s/PPPz77whS9ke/bsyX76059mkydPzr72ta+V6ikwTpzqWWtpacnOP//87Ic//GG2d+/e7D/+4z+ySy65JLvppptK9RQYJ44ePZrt2LEj27FjRxYR2aOPPprt2LEj++1vf5tlWZYtX748W7RoUWH/3r17s/POOy/70pe+lO3atStra2vLKioqsvb29lI9hbOOQD8D/du//Vt20UUXZZWVldncuXOz//zP/yz8s+uvvz5bsmTJsP0/+tGPsksvvTSrrKzMPvShD2WbNm0q8sSMV6dy1t7znvdkEXHco6WlpfiDM+6c6p9r/59A51Sc6ll7+eWXs7q6uiyXy2UXX3xx9vWvfz07duxYkadmPDqVs/bWW29lX/nKV7JLLrkkq6qqyvL5fPb5z38++5//+Z/iD8648tJLL434/19vn68lS5Zk119//XHXzJo1K6usrMwuvvji7Hvf+17R5z6blWWZ18UAAABAqXkPOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAgQ6AAAAJECgAwAAQAIEOgAAACRAoAMAAEACBDoAAAAkQKADAABAAv4XsC2T8n7JSBkAAAAASUVORK5CYII=' width=1000.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aad3011722cc47918a484f479a580475"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.animation:MovieWriter imagemagick unavailable; using Pillow instead.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "list index out of range",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1089\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0manim\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mall_anim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1090\u001b[0;31m                 \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Clear the initial frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1091\u001b[0m             \u001b[0mframe_number\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1750\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1751\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-e566ccee3583>\u001b[0m in \u001b[0;36minit\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_experiments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0minit_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;31m# axa.append(init_ax(i))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-e566ccee3583>\u001b[0m in \u001b[0;36minit_ax\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minit_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'Axes' object is not subscriptable",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-e566ccee3583>\u001b[0m in \u001b[0;36m<cell line: 58>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;31m# Uncomment the next line if you wish to store the animations as a gif\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0manim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./greedy-agent.gif'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagemagick'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filename, writer, fps, dpi, codec, bitrate, extra_args, metadata, extra_anim, savefig_kwargs, progress_callback)\u001b[0m\n\u001b[1;32m   1083\u001b[0m         \u001b[0;31m# callback a no-op; canvas.manager = None prevents resizing the GUI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m         \u001b[0;31m# widget (both are likewise done in savefig()).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1085\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrc_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'savefig.bbox'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1086\u001b[0m              \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaving\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1087\u001b[0m              cbook._setattr_cm(self._fig.canvas,\n",
            "\u001b[0;32m/usr/lib/python3.10/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m    151\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtyp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0;31m# Suppress StopIteration *unless* it's the same exception that\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36msaving\u001b[0;34m(self, fig, outfile, dpi, *args, **kwargs)\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36mfinish\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfinish\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m         self._frames[0].save(\n\u001b[0m\u001b[1;32m    502\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_all\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mappend_images\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_frames\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m             duration=int(1000 / self.fps), loop=0)\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'Axes' object is not subscriptable",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipympl/backend_nbagg.py\u001b[0m in \u001b[0;36m_handle_message\u001b[0;34m(self, object, content, buffers)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msend_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_webagg_core.py\u001b[0m in \u001b[0;36mhandle_json\u001b[0;34m(self, content)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhandle_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 462\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    464\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrefresh_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_webagg_core.py\u001b[0m in \u001b[0;36mhandle_event\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m    264\u001b[0m         handler = getattr(self, 'handle_{0}'.format(e_type),\n\u001b[1;32m    265\u001b[0m                           self.handle_unknown_event)\n\u001b[0;32m--> 266\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhandle_unknown_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_webagg_core.py\u001b[0m in \u001b[0;36mhandle_draw\u001b[0;34m(self, event)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhandle_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 282\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_handle_mouse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_webagg_core.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_png_is_old\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Swap the frames.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3148\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3150\u001b[0;31m         \u001b[0mDrawEvent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"draw_event\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3152\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_without_rendering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_process\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1261\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1262\u001b[0m         \u001b[0;34m\"\"\"Generate an event with name ``self.name`` on ``self.canvas``.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1263\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    310\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m                         \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_exception_printer\u001b[0;34m(exc)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_exception_printer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_get_running_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"headless\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0mtraceback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, s, *args, **kwargs)\u001b[0m\n\u001b[1;32m    302\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m                     \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m                 \u001b[0;31m# this does not capture KeyboardInterrupt, SystemExit,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 \u001b[0;31m# and GeneratorExit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_start\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    902\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    903\u001b[0m         \u001b[0;31m# Now do any initial draw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 904\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_draw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    905\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m         \u001b[0;31m# Add our callback for stepping the animation and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/animation.py\u001b[0m in \u001b[0;36m_init_draw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1748\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframe_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1750\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1751\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1752\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drawn_artists\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-e566ccee3583>\u001b[0m in \u001b[0;36minit\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0maxa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_experiments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m         \u001b[0minit_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;31m# axa.append(init_ax(i))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;31m# return axa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-e566ccee3583>\u001b[0m in \u001b[0;36minit_ax\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minit_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m     \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'Axes' object is not subscriptable"
          ]
        }
      ],
      "source": [
        "# Initialize the environment of our multi-armed bandit problem\n",
        "num_experiments = 1\n",
        "num_bandits = 8\n",
        "num_steps = 100\n",
        "means = np.random.normal(size=(num_experiments, num_bandits))\n",
        "stdev = np.ones((num_experiments, num_bandits))\n",
        "\n",
        "env = ArmedBanditsEnv(means, stdev)\n",
        "\n",
        "# Initialize the agent\n",
        "agent = GreedyAgent(np.zeros((num_experiments,num_bandits)))\n",
        "\n",
        "# Code for plotting the interaction\n",
        "fig, axs = plt.subplots(1, num_experiments, figsize=(10, 4))\n",
        "x_pos = np.arange(num_bandits)\n",
        "\n",
        "def init():\n",
        "    axa = []\n",
        "    for i in range(num_experiments):\n",
        "        init_ax(i)\n",
        "        # axa.append(init_ax(i))\n",
        "    # return axa\n",
        "\n",
        "def init_ax(i):\n",
        "    ax = axs[i]\n",
        "    ax.clear()\n",
        "    ax.set_ylim(-4, 4)\n",
        "    ax.set_xlim(-0.5, num_bandits-.5)\n",
        "    ax.set_xlabel('Actions', fontsize=14)\n",
        "    ax.set_ylabel('Value', fontsize=14)\n",
        "    ax.set_title(label='Estimated Values vs. Real values', fontsize=15)\n",
        "    ax.plot(x_pos, env.mean[i], marker='D', linestyle='', alpha=0.8, color='r', label='Real Values')\n",
        "    ax.axhline(0, color='black', lw=1)\n",
        "    # return ax\n",
        "\n",
        "\n",
        "# Implement a step, which involves the agent acting upon the\n",
        "# environment and learning from the received reward.\n",
        "def step(g):\n",
        "    action = agent.get_action()\n",
        "    _, reward, _, _ = env.step(action)\n",
        "    agent.update_estimates(reward, action)\n",
        "    axa = []\n",
        "    for i in range(num_experiments):\n",
        "        ax = axs[i]\n",
        "        # Plot the estimated values from the agent compared to the real values\n",
        "        estimates = agent.reward_estimates[i]\n",
        "        init_ax(i)\n",
        "        values = ax.bar(x_pos, estimates, align='center', color='blue', alpha=0.4, label='Estimated Values')\n",
        "        ax.legend()\n",
        "        # axa.append(ax)\n",
        "    # return axa\n",
        "\n",
        "anim = FuncAnimation(fig, func=step, frames=num_steps, init_func=init, interval=10, repeat=False, blit=True)\n",
        "plt.show()\n",
        "\n",
        "# Uncomment the next line if you wish to store the animations as a gif\n",
        "anim.save('./greedy-agent.gif', writer='imagemagick', fps=60)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import output\n",
        "output.enable_custom_widget_manager()"
      ],
      "metadata": {
        "id": "bYIfZTRw1t7d"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support for third party widgets will remain active for the duration of the session. To disable support:"
      ],
      "metadata": {
        "id": "6TLS8pBh1t7l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import output\n",
        "output.disable_custom_widget_manager()"
      ],
      "metadata": {
        "id": "eMVyFYzw1t7m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liI8-lBU2se2"
      },
      "source": [
        "you may try the previous block of code multiple times to see multiple animations. In general, you may realize that most of the actions are not explored by the agent, and that it will mostly stick to the one action that gave it some positive reward. The agent is good at evading negative values, but it will only land on the optimal action by pure chance.\n",
        "\n",
        "We can plot the average behavior by doing more experiments. The next plot displays the percentage of times the agent chose the optimal action over an average of 10000 experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586,
          "referenced_widgets": [
            "2c9363a23a9842d0a0cd461e7838ac08",
            "1f2bfdca47404519b78f2d8453de5bdb",
            "d1de42eb23a54270b092c2cee7e38e54",
            "3457e2172fc44237afb47d89c0d96e42"
          ]
        },
        "id": "mBAlPJxg2se2",
        "outputId": "bb15712a-755d-4864-e6ce-4aa2858c50db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:05<00:00, 174.10it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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zN2zYoFatWqlp06aqW7euZs2apY4dO2ru3LkaMmSIq12ZMmUkSR9//LErFF/p6lBw9Rf9AwcOaOvWrZozZ06GHrTk5OQM7SpVqpTlbV+u7oUqU6aMunTpon/+859Z7nd2mjVrphIlSujHH3/UM888k2n++fPntXLlSrVt2zZD79rJkycztT1x4oSrVz2/fP/997r77rs1fvx417SVK1fm6TaaN2+upk2b6quvvlLr1q2VkJCQaWClq/n6+iomJkbGmAzv/fHjx+VwOLIcyConHTp00JQpU7R8+XL17NnTNb1hw4aSlO2teK4+7sqUKaMGDRpkO9hW+fLllZSUJDc3N73wwguZenclqWTJkpLSjs3U1FSdOnUqQ9A8ceJEpmVu5jU8fPiw1qxZI2NMpjAqpf1bTj8jo1KlSlkek1f+OylduvR17RcAIO8RgAHAAgkJCZo3b566d++ul156KcO8y5cv695779XMmTPVqlUrSWmju06YMEE9e/bUqVOnXF+2Jalu3boKCAjQ/v37NWLEiBuuJX2k3PReJSntvqILFy7M0K5Vq1b6+uuvtW/fPtdp0GfOnNGqVasyhI5u3bpp6tSpCgoKko+Pz3XXUbRoUQ0bNkwffvihlixZkmkk6Pfee0/nz5/Xc889l2H6pUuXtHTpUtdp0AkJCZluo3R1b+2tEBcXl6nH8Vq3A8rKtXr3hg4dqueee05btmzRPffco4oVK+a4vrZt22rcuHFavHixK7AaY/T999+rSZMmGU7NvR4PP/ywXn31Vb300ku68847bzhAp+vWrZsWLVqkChUqqGrVqlm2KVKkiEJCQrRz5069//772a6rZcuWcjgcmjt3roYNG+aaPm/evCzb3+hrOHPmTBljNHv27Extp0+frpkzZyomJkbly5dXq1attGjRIn300UeuP9T8/PPPGUakLl68+HXt1/WiRxgArh8BGAAssHDhQl24cEHDhw/P8trDe++9V999950mTJggDw8P9evXT2+++aZGjBghp9Op+vXru9o6HA598skneuihhxQXF6devXrJx8dHUVFR+uWXXzRkyBDXtahZqVevnqpWrarRo0e7rkccO3asypQpo6NHj7rahYaGauzYserZs6feffddeXl5udq5uf3vippRo0bp22+/Vbt27fTMM8+oZs2aOnPmjLZu3arU1FS999572dbyzjvvaMOGDerdu7eeffZZdenSRQkJCZo1a5Zmzpypl19+OcP1uVJaD+eTTz6pN998U+XLl9fEiRN19uxZjR49OsM+Tps2TQsXLlTlypVdj7zUvXt3jRs3TuPHj1ejRo30448/6vfff7/h9VSsWFGlS5fWt99+q9q1a8vLy0uNGjVyhZx+/frp+eefV1hYmN59991rru+ee+5Ry5YtNWDAAP3zn/9U1apVNWXKFIWFhWW4FdL18vLy0ty5c9WjRw81adJETz/9tJxOp6S009cnT54sLy8veXp65riexx9/XFOmTFGHDh303HPPqV69erp48aJ27NihQ4cO6YsvvpAkjR8/Xh06dNADDzyg/v37q1y5coqOjtaqVavUqVMn9enTR/Xq1dNDDz2k559/XpcvX1adOnX0zTffZDh+r3Sjr+HMmTPVrFkzPfLII5nmlStXTl9//bW+/fZbjRgxQi+//LKaN2+uXr166R//+IdiYmL0zjvvyNfXN8O/k+vZr+t1rWMGAHAFCwfgAgDbuvfee03FihUzjBR7pZ9++slIMosXL3ZNSx/R9oMPPshymZUrV5ouXbqYkiVLmqJFi5o6deqYp556ykRFRRljch4pdsuWLaZ169amaNGiplq1amb8+PHmzTffNF5eXhna7d6923To0MF4eXkZf39/M27cOPPoo48ap9OZoV1MTIx58sknTZUqVYynp6epXLmy6dmzZ4bRmrOTkJBgPvjgA9OoUSNTtGhRU7JkSdOuXTszb968TG3TR3FetmyZCQ4ONkWKFDENGjTIdGuaqKgoc/fdd5vSpUsbSebNN9/MsHy6q0dkTufl5eVaJt0zzzxjKlSo4Hp+6dIlM2jQIFO2bFnj4+Nj+vTpYzZs2JDpNb/WKNDGpN3Wql69esbLyyvLkZkffPBBU6VKFZOcnJzzi/lfp0+fNgMHDjTlypUzRYoUMU2aNDE//PBDhjbXOwp0uiNHjpgRI0aYwMBA4+XlZYoVK2aCg4PNc889Zw4ePOhqlz4K9Lp16zKtIzY21owePdrUqFHDeHp6mvLly5vOnTubGTNmZGgXFhZm7rvvPuPr62u8vLxMzZo1TWhoqNm1a5erzYULF0xoaKgpWbKkKV26tBk8eLCZOnVqtvt0va/hli1bjCTz6aefZtumWbNmGUZMX7hwoalXr54pUqSICQ4ONsuWLTOVKlUyI0eOvOH9kmT++c9/Zlguq/fqWscMACCNwxhjLMjdAIDbQGJiooKCgtSpUydNmzYt37cfGhqq9evXa8+ePfm+bavExcWpatWqeuqpp66r9xKZ5fdruHv3btWvX1/Tp0/XgAEDbvn2AADZ4xRoAMB1++CDD1SuXDnVrFlTp06d0meffaZjx45lOYgX8taFCxe0fft2ffnll4qLi8twrSuuT369hv/4xz/UoUMH+fn5KTw83HXqeVYDXgEA8hcBGABw3Tw9PfX++++77n/btGlTLV26VE2bNrW4stvf1q1b1bFjR1WpUkVTp07N82uY7SC/XsOLFy9q5MiROnnypEqUKKGOHTvqgw8+uOEBxwAAeY9ToAEAAAAAtuB27SYAAAAAABR+BGAAAAAAgC0QgAEAAAAAtsAgWLc5Ly8v+fn5WV0GAAAAAEknT55UYmKi1WXYFgH4Nufn5+carRUAAACAtfz9/a0uwdY4BRoAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCALTZixAhVr15dDodDYWFh2babMmWKateurVq1amnw4MFKSkrKvyIBAAAA4DZAALbYQw89pDVr1qhatWrZtomIiNDrr7+uP/74Q+Hh4Tpx4oS++OKLfKwSAAAAAAo/ArDF2rVrJ39//xzbzJ07V7169VLFihXlcDj05JNPavbs2flUIQAAAADcHjysLgDXFhkZmaGHuHr16oqMjLSwohvXdcLqLKd/+bfmql6uuA6duqTB32zOss2vo9pLklbvO6kxi3Zlml+tbDF9NaCFJOmbdYf073WHM7VpW9tPb9xbX5L0zk+79Mf+k5naPN6mmv7WprokadDXm3T4dFymNq/1rK/2QX7sE/vEPrFP7BP7xD6xT+xTIdun9G3A3ugBvs1MmDBB/v7+rkdsbKzVJQEAAABAgeAwxhiri0Bar+6CBQvkdDozzfvwww914MABTZo0SZK0ZMkSjR07VmvWrLnmev39/RUVFZXX5QIAAAC4CXw/txY9wIVA7969tXDhQh0/flzGGE2aNEmPPPKI1WUBAAAAQKFCALbY0KFDXX8F6t69uwIDAyVJgwYN0sKFCyVJNWvW1Ntvv6077rhDgYGB8vPz09ChQ60sGwAAAAAKHU6Bvs1xigUAAABQcPD93Fr0AAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAW279/v0JCQhQUFKQWLVpo586dmdqkpqZq1KhRql+/vho1aqSOHTsqPDzcgmoBAAAAoPAiAFts6NChGjJkiPbt26fRo0crNDQ0U5uFCxfqP//5j/7880/99ddf6ty5s1555ZX8LxYAAAAACjECsIViYmK0efNm9e/fX5LUu3dvHTlyJFPvrsPhUGJiohISEmSM0YULF+Tv729FyQAAAABQaHlYXYCdHTlyRJUqVZKHR9rb4HA4FBAQoMjISAUGBrra3XvvvVq5cqUqVqyokiVLqkqVKlq9erVVZQMAAABAoUQPcCGwefNm7dixQ0ePHtWxY8fUuXNnPfnkk1m2nTBhgvz9/V2P2NjYfK4WAAAAAAomArCFqlatqujoaCUnJ0uSjDGKjIxUQEBAhnbffPONOnXqpNKlS8vNzU0DBgzQypUrs1znqFGjFBUV5XqUKFHilu8HAAAAABQGBGALlS9fXk2bNtWMGTMkSfPmzZO/v3+G058lqWbNmlqxYoUuX74sSVq0aJGCg4PzvV4AAAAAKMy4BthikydPVmhoqMaOHSsfHx9NmzZNkjRo0CD16tVLvXr10j/+8Q/t3r1bjRs3lqenpypWrKhJkyZZXDkAAAAAFC4OY4yxugjcOv7+/oqKirK6DAAAAADi+7nVOAUaAAAAAGALBGAAAAAAgC0QgAEAAAAAtkAABgAAAADYAgEYAAAAAGALBGAAAAAAgC0QgAEAAAAAtkAABgAAAADYAgEYAAAAAGALBGAAAAAAgC0QgAEAAAAAtkAABgAAAADYAgEYAAAAAGALBGAAAAAAgC0QgAEAAAAAtkAABgAAAADYAgEYAAAAAGALBGAAAAAAgC0QgAEAAAAAtkAABgAAAADYAgEYAAAAAGALBGAAAAAAgC0QgAEAAAAAtkAABgAAAADYgofVBdwuoqOjFRERoeTkZNe0du3aWVgRAAAAAOBKBOA88N577+nDDz9UzZo15e7uLklyOBzauHGjxZUBAAAAANIRgPPA1KlTdeDAAZUtW9bqUgAAAAAA2eAa4DxQoUIFwi8AAAAAFHD0AOeBrl27auTIkerXr5+8vb1d0xs1amRhVQAAAACAKzmMMcbqIgq7GjVqZJrmcDh08OBBC6rJyN/fX1FRUVaXAQAAAEB8P7caPcB5ICIiwuoSAAAAAADXwDXAeWTevHkaO3asJOnYsWPavn27xRUBAAAAAK5EAM4Db7zxhr766itNnz5dUtrpz0OHDrW2KAAAAABABgTgPPDjjz9q0aJFKl68uCSpUqVKio2NtbgqAAAAAMCVCMB5oGjRonJ3d88wjbHFAAAAAKBgYRCsPFCtWjX98ccfcjgcSkpK0tixY+V0Oq0uCwAAAABwBQJwHvjkk080YMAAbd++XcWLF1fHjh01c+ZMq8sCAAAAAFyBAJwHPDw89PPPPysuLk7GGBUvXlz79u1TuXLlrC4NAAAAAPBfXAOcB+6//35dvnxZxYoVU/HixXX48GHde++9VpcFAAAAALgCATgP9OnTR48++qgkKTo6Wj169NDEiRMtrgoAAAAAcCVOgc4DTz/9tA4dOqShQ4dqw4YNGjNmjO6++26rywIAAAAAXIEe4Fz466+/XI/+/ftr69at6tq1q2rXrq2//vrL6vIAAAAAAFdwGG5Ye9Nq1KiR7TyHw6GDBw/mYzVZ8/f3V1RUlNVlAAAAABDfz63GKdC5EBERYXUJAAAAAIDrRADOQ4mJiUpMTHQ99/HxsbAaAAAAAMCVuAY4D6xfv1716tVTsWLF5Ovr63oAAAAAAAoOeoDzwDPPPKPp06frySef1O+//65PPvlE3t7eVpcFAAAAALgCPcB5ICkpSa1atVJycrJKliypV199Vd9++63VZQEAAAAArkAAzoX9+/dLkjw80jrSy5Ytq61bt+rkyZM6efKklaUBAAAAAK5CAM6Fvn37SpIeffRRnT59Wq+88orat2+vqlWravjw4RZXBwAAAAC4EtcA50L6LZSfffZZSVK3bt105swZJSQkqGTJklaWBgAAAAC4CgE4F86fP6+ffvrJFYSv1qtXr3yuCAAAAACQHQJwLpw8eVITJ07MMgA7HA4CMAAAAAAUIATgXAgMDNSKFSusLgMAAAAAcB0YBAsAAAAAYAsE4FzgFGcAAAAAKDwcJrsRnHBb8Pf3V1RUlNVlAAAAABDfz61GDzAAAAAAwBYIwBbbv3+/QkJCFBQUpBYtWmjnzp1Zttu+fbs6dOigevXqqV69epo/f34+VwoAAAAAhRujQFts6NChGjJkiEJDQzV37lyFhoZq06ZNGdrExcXpvvvu0zfffKM777xTKSkpOnPmjEUVAwAAAEDhRA9wHti6dat69OihoKAg1axZ0/W4lpiYGG3evFn9+/eXJPXu3VtHjhxReHh4hnazZs1S69atdeedd0qS3N3d5efnl/c7AgAAAAC3MXqA88CAAQM0fPhwtWnTRu7u7te93JEjR1SpUiV5eKS9DQ6HQwEBAYqMjFRgYKCr3a5du+Tl5aWePXsqKipKjRo10vjx4wnBAAAAAHADCMB5wN3dXUOHDr1l609OTtby5cu1fv16Va5cWa+88oqGDRumuXPnZmo7YcIETZgwwfU8Njb2ltUFAAAAAIUJp0DngTvuuEObN2++4eWqVq2q6OhoJScnS5KMMYqMjFRAQECGdgEBAerYsaOqVKkih8Oh/v37a/369Vmuc9SoUYqKinI9SpQoceM7BAAAAAC3IQJwHvj9998VEhKi+vXrq2nTpq7HtZQvX15NmzbVjBkzJEnz5s2Tv79/htOfJalPnz7atGmTLly4IElasmSJGjdunPc7AgAAAAC3MU6BzgP/93//d9PLTp48WaGhoRo7dqx8fHw0bdo0SdKgQYPUq1cv9erVSwEBAXrllVcUEhIiNzc3ValSRV988UVelQ8AAAAAtuAwxhiri7hdHDt2TJJUuXJliyv5H39/f0VFRVldBgAAAADx/dxqnAKdB3bv3q0GDRq4Hg0bNtSePXusLgsAAAAAcAUCcB546qmn9Oqrr+rs2bM6e/asXn31VQ0bNszqsgAAAAAAV+AU6DzgdDoVFhZ2zWlW4BQLAACAgi81NVV8Lb89OBwOubll38/I93NrMQhWHnB3d9euXbtUv359SdKuXbvk7u5ucVUAAAAo6M6ePauTJ08qJSXF6lKQh7y9vVWtWrUcgzCsQQDOA2PHjlW7du3UqFEjSdL27ds1c+ZMi6sCAABAQXb27FnFxMSoSpUq8vb2lsPhsLok5AFjjI4ePaqYmBhVrFjR6nJwFQJwHujevbt27dqljRs3SpJat26tcuXKWVwVAAAACrKTJ0+qSpUqKlGihNWlII9VqFBBhw4dUoUKFfjDRgFDAM4j5cuXV8+ePa0uAwAAAIVAamqqUlJS5O3tbXUpuAU8PT1ljJExhgBcwBCAc6F9+/ZavXq1fH19MxzY6Qf6mTNnLKwOAAAABVX6gFeEo9sbA5sVPFyVnQvffvutJCksLEzbtm1zPdKfAwAAAIVJ9erVVadOHTmdTtWrV0/9+vXTpUuXbnp906dP1549e7Kd36FDB9WoUUNOp1N16tTRa6+9dtPbysrmzZvVt29fSdKhQ4dUunTpLNvlNA+3FwJwLlSqVEmS9O9//1vVqlXL8Pj3v/9tcXUAAADAjfvuu+8UFhamnTt36vz585o+ffpNr+taAViSJk6cqLCwMK1fv14zZszQTz/9dNPbu1rz5s313Xff5dn6UPgRgPPA/Pnzr2saAAAAUFhcvnxZcXFx8vX1dU0bN26cWrZsqaZNm6pHjx46fPiwJOmnn35So0aN5HQ6FRwcrB9//FFfffWVNm/erGeffVZOp1NLlizJcXu+vr5q2bKl9u7dq9jYWD3xxBMKDg5WcHCw3n77bVe7MWPGqF69enI6nXI6nTp8+LDi4+PVt29f1a9fX40bN1a3bt0kSatWrZLT6cywneeff16NGjVSgwYNtHz58ixr2bRpkzp16qTmzZurSZMmmjNnzs28hCiAuAY4F5YtW6aff/5ZR48e1ahRo1zTz58/b2FVAAAAKKy6Tlid5fQv/9Zc1csV16FTlzT4m81Ztvl1VHtJ0up9JzVm0a4s512Pvn37qmjRojp06JCaNWumPn36SJJmzZqlvXv3at26dXJ3d9e///1vPfXUU1q8eLFee+01TZ48WW3atFFqaqouXLig0qVLa8aMGRo5cqTuv//+a243KipKa9as0bBhw/Tuu+8qMTFRf/31l+Lj43XnnXeqbt266tatm8aNG6fo6GgVLVpUcXFxcnNz09KlS3Xu3Dnt2pW239mNxXP+/HnVq1dP48aN0/r169WrVy8dOHAgQ5tz585pyJAhWrJkiSpVqqRTp06padOmCgkJUZUqVa77dUTBRA9wLnh7e6t06dJyc3NTqVKlXI/g4GB6gAEAAFAopZ8CferUKVWvXl2jR4+WJC1YsEDLly9Xs2bN5HQ69cEHHygyMlKS1LlzZz3zzDP64IMP9Ndff93Q9bTpPcQPPPCAXn/9dXXs2FHLly/X4MGD5ebmpuLFi+tvf/ubfv31V/n4+Kh27drq37+/Jk+erDNnzsjb21uNGzfW7t279dRTT+m7776Tp6dnltvy8PBQaGiopLRbl1auXDnT2D1r167VwYMHddddd8npdKpLly6SpL17997gK4mCiB7gXGjfvr3at2+v+++/X40bN7a6HAAAABRy1+qprV6u+DXbtA/yU/sb6PHNjoeHh3r37q0XXnhB48ePlzFGL7/8soYMGZKp7YQJE7Rz506tXLlSAwYM0GOPPaYXX3zxurYzceLEa/YQp4+W7e7urvXr12vt2rVatWqVWrdurdmzZ6tt27batWuXVqxYoeXLl+vFF19UWFjYdW3/6pG4jTFq0KCB1q5de13Lo3ChBzgPfPrppzp9+rTr+alTpzR06FALKwIAAAByb8WKFapTp44k6f7779ekSZNcpxcnJSW5ek/37NmjBg0aaPjw4Ro2bJjWr18vSfLx8bmpywO7dOmiKVOmyBijS5cu6d///re6deumixcv6sSJE2rbtq1ef/113Xnnndq2bZuioqLkcDjUq1cvjRs3TsYYHTlyJNN6k5OTXYPVbty4UceOHct0jXBISIgiIiIyXB8cFhamy5cv3/B+oOChBzgPbNmyRWXLlnU9L1eunDZt2mRhRQAAAMDNSb8GODk5WdWqVdOkSZMkSY899phOnz6tjh07SkoLk0888YSaNGmiV155RXv37lWRIkVUrFgxff7555KkIUOG6LnnntPEiRM1duxY3X333ddVw+uvv64RI0aoYcOGkqSHH35Yffr0UVRUlB566CFdunRJDodDtWvX1oABA7R27Vq9/PLLMsYoOTlZjz/+uBo1aqRVq1ZlWG+pUqW0Y8cONW7cWMnJyZo1a5ZKliyZoTPL19dXixcv1vPPP6/nnntOSUlJCggI0IIFC3L5yqIgcBjuzpxrDRs21Pbt213PjTFq2LChduzYYWFVafz9/RUVFWV1GQAAALhCSkqK9u3bp6CgILm7u1tdDvJYTu8v38+txSnQeaB169YaPny4Dh8+rEOHDunpp59W69atrS4LAAAAAHAFAnAeGD9+vOLi4tSiRQu1bNlSCQkJ+uijj6wuCwAAAABwBa4BzgM+Pj6aOnWq63lKSop++umn67rfGQAAAAAgf9ADnIf27t2rF198UVWqVNGYMWOsLgcAAAAAcAV6gHMpLi5O3333nb766itFREQoPj5e69atU926da0uDQAAAABwBXqAc2Hw4MGqWrWqFi5cqJdeekmRkZEqXbo04RcAAAAACiACcC58++23atSokYYOHaqePXvKw8NDDofD6rIAAACAG/b2229r0KBBrudr1qyRw+HIcC/dJ598Uq+//nqebG/VqlX6+eefs53/1ltvyc/PT06nU/Xq1VPfvn119uzZPNl2OqfTqYsXL0qSqlevrrCwsCzb5TQPhQsBOBeio6PVv39/vfPOO6pWrZpee+01JSUlWV0WAAAAcMM6duyYIeyuXLlSrVq1yjStU6dO173O5OTkbOddKwBL0mOPPaawsDDt2LFDSUlJeT7OTlhYmEqWLJmn60TBRgDOhRIlSmjgwIFau3atfv75ZyUkJOjy5csKCQnRZ599ZnV5AAAAwHVr3bq1jh07pqioKElpAfWNN95wBeDo6GhFRkaqTZs2iomJ0YMPPqiGDRsqODhYkydPdq2nevXqGj16tFq2bKkBAwZo//79uuOOO9S4cWM1bNhQr732msLCwjRp0iTNnDlTTqdT77zzTo61ubu7q0uXLtq7d68k6cMPP1SDBg3UsGFDPfbYYzp//rwk6aefflKjRo3kdDoVHBysH3/8UZI0ZswY1atXT06nU06nU4cPH5YkORwOnTt3zrWdmTNnqlmzZgoMDNSHH36YZS3Hjx9Xnz591LJlS9f+oPBgEKw8Ur9+fY0bN07vv/++fvzxR02dOlVPPfWU1WUBAAAA16VIkSIKCQnRypUr1adPH0VEROjuu+/WiBEjlJCQoJUrV6pNmzby9vbWgAEDVKdOHc2fP18xMTFq1qyZGjdurNatW0uSTp8+rQ0bNsjhcOiZZ55Rz5499fLLL0uSzpw5ozJlyujJJ5/UuXPn9NFHH12ztvj4eC1YsEBt2rTR0qVLNXXqVK1bt06lS5fWkCFD9NJLL+nzzz/Xa6+9psmTJ6tNmzZKTU3VhQsXdPbsWY0bN07R0dEqWrSo4uLi5OaWdT/giRMntHnzZp0+fVpNmzbVHXfcoZCQkAxtBgwYoFdeeUXt27dXcnKyevbsqTlz5ujhhx/O3RuAfEEAzmMeHh7q3bu3evfubXUpAAAAKGz+X6uspz8ySypbSzp9QPq2X9Zt/rEh7f/hy6Vlr2Y97xrST4OuVq2aWrZsKSmtZ3jdunVatWqVOnbsKElavny5tmzZIkkqX768HnzwQS1fvtwVgENDQ11j47Rr104vvPCCYmNj1b59e3Xp0uW6apHSemRXr14tSWrfvr1eeuklvfbaa+rbt69Kly4tSRo2bJgrfHbu3FnPPPOMHnroIXXr1k1Op1MpKSmqXbu2+vfvr27duumee+6Rv79/ltsbOHCgHA6HypUr59qnKwPwpUuX9Ntvv+nEiROuabGxsa6eaRR8BGAAAAAAktIC8JQpUxQQEKAOHTpISgueK1eu1MqVKzV9+vQsl7t6INgSJUq4fu7du7dCQkL066+/6v/+7//00UcfacmSJddVz2OPPXbNHuIrtz1hwgTt3LlTK1eu1IABA/TYY4/pxRdf1Pr167V27VqtWrVKrVu31uzZs9W2bdtrbv/q/TLGSJLWr18vb2/v69oHFCwEYAAAAKCguFZPbdla124T2CXtcRNatGihmJgYzZw5UwsXLpSUFoB79uyp6OhoV69wly5d9OWXX+q9997TyZMnNX/+fM2ZMyfLde7fv1+1atXS3/72N7Vs2dLVo+rj4+O6FvdGdOnSRc8995xGjRolHx8fTZ48Wd26dZMk7dmzRw0aNFCDBg3k4eGhX375RRcvXtTFixfVtm1btW3bVjt37tS2bduyDMDTp09X+/btdebMGf3www+aPXt2hvklSpRQx44d9f777+utt96SJB07dkypqanZ9iqjYCEAAwAAAJAkeXp66s4779Sff/6punXrSpKCgoJ08eJF3XnnnfL09JQkffLJJxo2bJgaNmwoY4xeffVVtWqV9enbc+fO1YwZM1SkSBGlpqZq0qRJkqQHHnhA//73v+V0OvXggw/qjTfeuK4a77rrLu3YsUNt2rSRm5ubGjVq5BqA9pVXXtHevXtVpEgRFStWTJ9//rnOnz+vhx56SJcuXZLD4VDt2rU1YMCALNft5+enZs2a6fz58xo+fHim63+ltNOyR40apeDgYDkcDhUvXlyTJ08mABcSDpPej48b9sknn+Q4f8SIEflUSfb8/f1dI/kBAACgYEhJSdG+ffsUFBQkd3d3q8tBHsvp/eX7ubXoAc6Fbdu2ZTvv6usFAAAAAADWIgDnwrRp06wuAQAAAABwnQjAeeTYsWPasWOHEhISXNN69eplYUUAAAAAgCsRgPPA1KlT9c477+jMmTOqXbu2/vzzT7Vu3ZoADAAAAAAFiJvVBdwOJk6cqG3btqlWrVrasmWLVqxYoaCgIKvLAgAAAABcgQCcB4oUKSJfX18lJydLktq1a6ewsDBriwIAAAAAZMAp0HnAy8tLxhgFBQXpo48+UrVq1RQbG2t1WQAAAACAK9ADnAfGjBmjCxcu6IMPPtDixYs1duxY1824AQAAgMIiKSlJb7/9turWrasGDRqoSZMmuv/++2/p2Y07duxQ9erVb9n6gSvRA5wHOnXqJEkqVaqUfv31V4urAQAAAG7O3//+d8XGxmrdunXy9fWVJC1fvlx79+6V0+nM0DYlJUXu7u4WVAncPAJwHoiLi9PXX3+t/fv3u64DlqRPPvnEwqoAAACA67d//3798MMPOnLkiCv8SlKXLl0kSdOnT9fXX3+tMmXKaN++ffriiy/k4eGh0aNH68KFC0pJSdErr7yihx9+WJK0bNkyvfvuu4qPj5e7u7v+9a9/qWPHjpKkt956SzNnzpSPj4/uuusu17aGDx+uypUr65VXXpEk7d27V126dFFERIQ8PIguyD2Oojzw4IMPytPTUy1atOCvYAAAALgpT//2tI5cPHJL1l21ZFV92vnTHNts27ZNgYGBKlOmTLZtNmzYoG3btqlOnTo6d+6cOnbsqCVLlqhSpUo6deqUmjZtqpCQECUmJuqtt97SsmXL5OPjo/DwcLVt21aHDh3S8uXLNWfOHG3ZskUlS5bU448/7lr/008/re7du2v06NFyd3fXZ599piFDhhB+kWc4kvJAZGSkdu3aZXUZAAAAQJ45cOCAevfurfj4eIWEhKh9+/YKCQlRnTp1JElr167VwYMHM/TgSmm9tnv27FF4eLjatWvnmu7m5qbIyEj99ttv6tOnj3x8fCRJQ4cO1Zo1ayRJderUUf369fXjjz+qe/fumj17trZv355Peww7IADngbp16+rUqVMqV66c1aUAAACgkLpWD+2t1qRJE4WHh+vs2bPy9fVVrVq1FBYWpunTp2vBggWSpBIlSrjaG2PUoEEDrV27NtO6du/era5du2rWrFnX3K7D4cjw/JlnntG//vUvnTx5Ul27dlWFChVyt2PAFRgFOg+89957atOmjfr166cnnnjC9QAAAAAKi9q1a+u+++7TwIEDde7cOdf0S5cuZdk+JCREERERWr58uWtaWFiYLl++rO7du2v58uX666+/XPM2btwoKe2a4jlz5ujixYsyxuiLL77IsN5u3brp+PHjGjNmjIYPH56HewjQA5wnBg8erDZt2qh58+ZcAwwAAIBCa/r06XrvvffUqlUreXh4yNfXV35+fho9erT27t2boa2vr68WL16s559/Xs8995ySkpIUEBCgBQsWKDAwULNmzdLQoUMVFxeny5cvq0mTJpo1a5buvvtubdy4UU2bNs00CJaU1iM8cOBAzZo1S23atMnP3YcNOIwxxuoiCrsGDRpo586dVpeRJX9/f0VFRVldBgAAAK6QkpKiffv2KSgoiA6ULPTs2VN9+/bNMEBWYZLT+8v3c2txCnQeaNy4sY4ePWp1GQAAAEChtnnzZgUGBsrNzU39+vWzuhzchjgFOg+cPHlSwcHBatOmjby9vV3T58+fb2FVAAAAQOHSvHlzhYeHW10GbmME4DzQv39/9e/f3+oyAAAAAAA5IADngQEDBlhdAgAAAAqR9Fv/MBzP7Sn9fb36Fk+wHgE4F8aPH6/nnntOo0aNynL+hAkT8rkiAAAAFAZubm7y9vbW0aNHVaFCBXl6elpdEvKIMUanT5+Wp6en3NwYcqmgIQDnQvqNwEuVKmVxJQAAAChsqlWrppiYGB06dIie4NuMp6enAgICrC4DWeA2SHngr7/+UqNGja45zQoMsw4AAFCwGWNcDxR+Docjx55fvp9bix7gPBAaGqqtW7decxoAAABwNYfDwbWiQD4hAOdCTEyMjh8/rvj4eG3fvt31V7vz58/r0qVLFlcHAAAAALgSATgXZs+erY8++kjHjh1Tr169XNNLlSqlF1980cLKAAAAAABX4xrgPPDuu+/q9ddfv6ll9+/frwEDBujUqVMqVaqUpk+frgYNGmTZ1hijzp07a+vWrTp37tx1rZ9rDAAAAICCg+/n1mJc7jzw+uuva/369RozZozee+89bdiw4bqXHTp0qIYMGaJ9+/Zp9OjRCg0NzbbtxIkTVatWrTyoGAAAAADshwCcB8aPH6++ffvq9OnTOnXqlPr27Xtd9wCOiYnR5s2b1b9/f0lS7969deTIEYWHh2dqu3PnTi1YsEAvvfRSntcPAAAAAHbANcB5YPLkydq6davKli0rKa1HuHXr1ho1alSOyx05ckSVKlWSh0fa2+BwOBQQEKDIyEgFBga62iUlJWnw4MGaMmWK3N3db92OAAAAAMBtjB7gPODj4+MKv5JUpkwZ+fj45Nn63377bT344IOqV6/eNdtOmDBB/v7+rkdsbGye1QEAAAAAhRmDYOWBF198UTExMRo4cKAkafr06fLz81O/fv0kSY0aNcpyuZiYGAUGBurMmTPy8PCQMUaVKlXSmjVrMvQAt23bVpGRkXI4HEpOTtaxY8cUEBCgTZs2yc/PL8fauMgeAAAAKDj4fm4tAnAeqFGjRrbzHA6HDh48mO38Dh06KDQ0VKGhoZo7d67ef/99bd68Odv2hw4dktPpZBRoAAAAoBDi+7m1uAY4D0RERNz0spMnT1ZoaKjGjh0rHx8fTZs2TZI0aNAg9erVK8P9hQEAAAAAN48e4DywatUqbdmyRZLUrFkzdejQwdqCrsBfmAAAAICCg+/n1qIHOBcuXryonj176uDBg2rWrJkk6aOPPlKNGjW0ePFilSxZ0uIKAQAAAADpGAU6F0aPHq3GjRvr4MGDWrBggRYsWKADBw6ocePGeuGFF6wuDwAAAABwBXqAc+GXX37Rvn375Ob2v78jFClSRBMnTlTdunUtrAwAAAAAcDV6gHPB09MzQ/hN5+HhIU9PTwsqAgAAAABkhwCcCyVLltRff/2Vafqff/6p4sWLW1ARAAAAACA7nAKdC2+88Ybuuecevfbaa2rdurUkad26dRozZow+//xzi6sDAAAAAFyJAJwLPXv2lIeHh9577z2NGjVKUtptkL788kvdddddFlcHAAAAALgS9wG+zXGfMQAAAKDg4Pu5tegBBmwg1aQqxaRIRjLp//33b19Z/Zz+n6T/zbtievrP6VzzTOblklKTFJ8cn6H9lctlJ6v2OU3/78wbXia7GnJcJrvabuLviXle200sc6Pryml9eV7bzbynN/H+GBmlpKYo1aRmPLavWldW68jU5urtZ3qa8zqvtb5rHWc3uv5bsc4brvkG1yf993Mti/fMKgWihgLSp1EQXouCoiC8JwXl/SgIr4UkDW40WG4OhkGyMwIwcA3pH9gJKQm6ePmiEpITFJ8cr4SUhP/9fNW0hJQEXU65rMSURF1OuaxLSZcUnxyvlNQUJZvk//0yMmlf4lJNqlKV9n9jzP+ep6b9P31acmqykk2yklKS0tZjjFJMyv/W8d+ge+X0FJNi4asHAABQcAxsOJAAbHME4Fw4e/asfH19rS4DN8gYo1PxpxR5MVKXki4pLjlOB84dUNTFKJ1NPKvzCed1LvGcElIS5CY3nU08KymtJzM3HHLI3c1dHg4PORwO13R3h7scDofcHe5yc7iltfvvNDeHm+vhkEMebh7ycvdScc/i8nB4ZJifvrybm5vc9L/n7m7/nS43yZFWh8PhUPp/klz1ZJjn+N/89Dau/65on2Gew5Fpmoebh4p6FM32l82V28g0z5H9vJxe5xtdV7bLFOTa8nD7Oa0vx2XysrZ8en9yWsbD7X//pnJcJotVXN0m03PHNeZfYxu5Xv91vCe3vOYbXN+1ak7/THV9vlngZo71PNluDsfxLd6wBZu012tsu2PKAu4Od6tLgMUIwLnQuXNnbd26VX369NH3339vdTm4QmJKog6cO6DElETFJcXpz5N/amvMVkXHRutk/EnFJ8dnuZy3u7dKeZVSaa/SKle0nFJNquqWrStjjHy8fFTaq7S83b3l7eGtoh5F5e3uraKeRTNN8/Lwkpd72qOIexEV8ygmL3cvy36xAQAAACAA50p8fLw2bNig7du3a/v27ZmubWjUqJFFldlHcmqywmLC9PvR33U+8bz2ndmnqNgonUs8l6ltUY+iqlKiipx+TlUtWVUBPgHyKeIjbw9vBfgEqGapmirqUTT/dwIAAABAviAA58LIkSP197//XREREerVq1eGeQ6HQwcPHrSosttbUkqStsZs1dKIpfot8rcMYdfXy1e1SteSX1E/VS9VXSU9S8rL3Uv1ytZTvbL15OnmaV3hAAAAACzFbZDywMMPP6w5c+ZYXUaWbqdh1o9fOq6pO6Zqzt45SjbJkqTA0oHqHNBZHat2VMXiFVXGuwynGQMAAKDAup2+nxdG9ADngTlz5iguLk5hYWGSJKfTqWLFillb1G3kUtIlzd8/X5//+bkuXr6oemXqqWPVjupWvZtqla5ldXkAAAAACgkCcB5Yt26dHnzwQVWoUEEOh0MnTpzQvHnz1KZNG6tLK9SMMVp2eJk+3PShYuJiVMa7jCZ0mKDOAZ0Zvh4AAADADSMA54Fnn31Wc+fO1R133CFJWrt2rZ599lmtX7/e4soKr5TUFH2w6QPN2jNLxT2L6+WWL6t3UG95uXtZXRoAAACAQooAnAfi4+Nd4VeSQkJClJCQYGFFhVtSapJeWP2Cfov8TS0qttD49uPl6839lgEAAADkDueR5oESJUpo+fLlrue//fabihcvbmFFhdv/bfs//Rb5m+6qfpcmdZlE+AUAAACQJ+gBzgMff/yxevfuLXd3d0lSamqq5s+fb3FVhdPao2s1dcdUNSzXUO+1fY/bFgEAAADIMwTgPNC8eXOFh4dr7969kqQ6derI05PgdqNi4mL08pqXVcKzhP7V7l+EXwAAAAB5igCcRzw9PRUcHGx1GYXaG2vf0JmEMxrffryqlqxqdTkAAAAAbjNcA4wCYf/Z/frP0f+oR/Ue6la9m9XlAAAAALgNEYBRIHy/93tJ0mP1HrO4EgAAAAC3KwIwLBeXFKefDv6kOr511NivsdXlAAAAALhNEYDz0MqVKxUcHKyaNWtq+vTpVpdTaCw6uEiXki6pT50+cjgcVpcDAAAA4DZFAM6F8+fPZ3j+2WefacOGDdq+fbs+/PBDi6oqXIwx+m7vdyruWVw9a/a0uhwAAAAAtzECcC706tVLc+fOdT0vUqSItm7dqq1bt3IbpOv058k/te/sPvWs2VPFPItZXQ4AAACA2xgBOBd++eUXhYWF6eGHH9axY8c0duxYffnll/r444/15ZdfWl1eofDd3u8kSX3r9LW4EgAAAAC3O+4DnAteXl4aM2aMtm/frgEDBuiBBx7QN998Y3VZhcbZhLNadmiZmpZvqtq+ta0uBwAAAMBtjh7gXEhNTdWSJUt04sQJ/fzzz0pKSlKPHj20Z88eq0srFFZErlBSapJ6B/W2uhQAAAAANkAPcC706dNHpUqVUlxcnObPn6/PPvtM999/v0aOHCmn06k333zT6hILtFVHVsnd4a72/u2tLgUAAACADRCAcyE8PFxhYWGSJKfTKUmqVq2afvjhB82ePdu6wgqBlNQUbTi+Qc7yTpXyKmV1OQAAAABsgACcCzVq1NCgQYMUFxenli1bZpj36KOPWlRV4XD4wmHFJ8erUblGVpcCAAAAwCYIwLkwZ84cLVu2TJ6enuratavV5RQqu87skiTVK1vP4koAAAAA2AUBOBc8PDx0zz33WF1GobT79G5JUr0yBGAAAAAA+YNRoGGJ3Wd2q5hHMQX4BFhdCgAAAACbIAAj36WaVO0+vVt1y9SVm4NDEAAAAED+IH0g3x29eFSxSbGqX7a+1aUAAAAAsBECMPLd7jNp1//WLVPX4koAAAAA2AkBGPku/Fy4JKm2b22LKwEAAABgJwRg5Lv9Z/fLzeGmmqVqWl0KAAAAABshACPfhZ8LV0DJAHl7eFtdCgAAAAAbIQAjXyUkJyjyYiSnPwMAAADIdwRg5KuD5w8q1aSqdmkCMAAAAID8RQBGvkofACvQN9DiSgAAAADYDQEY+Wr/2f2SRA8wAAAAgHxHAEa+OnLxiNwd7vIv6W91KQAAAABshgCMfHXi0gmVK1pOHm4eVpcCAAAAwGYIwMhXMXExqlC8gtVlAAAAALAhAjDyTVJqkk7Gn1SFYgRgAAAAAPmPAIx8czr+tIwMARgAAACAJQjAyDcn4k5IksoXK29xJQAAAADsiACMfHPiUloApgcYAAAAgBUIwMg39AADAAAAsBIBGPkmJi5GkhgFGgAAAIAlCMDIN+mnQNMDDAAAAMAKBGCL7d+/XyEhIQoKClKLFi20c+fOTG1WrFihli1bqn79+mrQoIFefPFFpaamWlBt7pyIOyFfL195uXtZXQoAAAAAGyIAW2zo0KEaMmSI9u3bp9GjRys0NDRTG19fX3377bfatWuXtmzZorVr1+qbb77J/2Jz6UTcCXp/AQAAAFiGAGyhmJgYbd68Wf3795ck9e7dW0eOHFF4eHiGdk2aNFHNmjUlSd7e3nI6nTp06FB+l5srxhjFxMUQgAEAAABYhgBsoSNHjqhSpUry8PCQJDkcDgUEBCgyMjLbZY4fP665c+eqZ8+e+VVmnohNilVSapLKFi1rdSkAAAAAbIoAXIhcuHBB9957r1588UU1b948yzYTJkyQv7+/6xEbG5vPVWbtXMI5SZKvl6+1hQAAAACwLQKwhapWraro6GglJydLSjtNODIyUgEBAZnaXrx4UT169NB9992nUaNGZbvOUaNGKSoqyvUoUaLELav/RpxNPCtJ8vUmAAMAAACwBgHYQuXLl1fTpk01Y8YMSdK8efPk7++vwMDADO1iY2PVo0cP9ejRQ6+99poVpeba2YS0AFzaq7S1hQAAAACwLQKwxSZPnqzJkycrKChI77//vqZNmyZJGjRokBYuXChJ+vjjj7Vx40bNnz9fTqdTTqdT7733npVl37D0HuAy3mUsrgQAAACAXTmMMcbqInDr+Pv7KyoqyuoyNG3HNE3YMkEz7p6hxn6NrS4HAAAAsERB+X5uV/QAI1+4rgFmECwAAAAAFiEAI1+kXwPMIFgAAAAArEIARr44l3hO7g53lfAsGKNSAwAAALAfAjDyxcXLF+VTxEcOh8PqUgAAAADYFAEY+eLC5QsqWaSk1WUAAAAAsDECMPJFeg8wAAAAAFiFAIx8cSHxgny8CMAAAAAArEMAxi2XlJqkuOQ4ToEGAAAAYCkCMG652MuxksQp0AAAAAAsRQDGLXfh8gVJBGAAAAAA1iIA45a7kJgWgDkFGgAAAICVCMC45S5evihJDIIFAAAAwFIEYNxyF5LoAQYAAABgPQIwbrn4pHhJUnGP4hZXAgAAAMDOCMC45eKS4yRJRT2KWlwJAAAAADsjAOOWi09O6wEu5lnM4koAAAAA2BkBGLdcXBI9wAAAAACsRwDGLefqAfagBxgAAACAdQjAuOVc1wB70gMMAAAAwDoEYNxy6aNAcwo0AAAAACsRgHHLxSfHy9PNU55unlaXAgAAAMDGCMC45eKS4+j9BQAAAGA5AjBuufjkeG6BBAAAAMByBGDccnFJ9AADAAAAsB4BGLdcfHI8t0ACAAAAYDkCMG45rgEGAAAAUBAQgHHLcQ0wAAAAgIKAAIxbKiU1RYkpifQAAwAAALCch9UF4PaWalI1pNEQBZYOtLoUAAAAADZHAMYt5enuqaebPG11GQAAAADAKdAAAAAAAHsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAW279/v0JCQhQUFKQWLVpo586dWbabMmWKateurVq1amnw4MFKSkrK50oBAAAAoHAjAFts6NChGjJkiPbt26fRo0crNDQ0U5uIiAi9/vrr+uOPPxQeHq4TJ07oiy++yP9iAQAAAKAQIwBbKCYmRps3b1b//v0lSb1799aRI0cUHh6eod3cuXPVq1cvVaxYUQ6HQ08++aRmz55tRckAAAAAUGh5WF2AnR05ckSVKlWSh0fa2+BwOBQQEKDIyEgFBga62kVGRqpatWqu59WrV1dkZGS+15sr/69V1tMfmSWVrSWdPiB92y/rNv/YkPb/8OXSslczz/etIfX7Nu3njV9Km77K3KZWJ6nHP9N+/vll6cCKzG1aDJJaDk77edYj0tmIzG26vycFdmGf2Cf2iX1in9gn9ol9Yp8K2z6lbwO2Rg/wbWbChAny9/d3PWJjY60uCQAAAAAKBIcxxlhdhF3FxMQoMDBQZ86ckYeHh4wxqlSpktasWZOhB/jDDz/UgQMHNGnSJEnSkiVLNHbsWK1Zs+aa2/D391dUVNQt2wcAAAAA14/v59aiB9hC5cuXV9OmTTVjxgxJ0rx58+Tv758h/Epp1wYvXLhQx48flzFGkyZN0iOPPGJFyQAAAABQaBGALTZ58mRNnjxZQUFBev/99zVt2jRJ0qBBg7Rw4UJJUs2aNfX222/rjjvuUGBgoPz8/DR06FArywYAAACAQodToG9znGIBAAAAFBx8P7cWPcAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWCMAAAAAAAFsgAAMAAAAAbIEADAAAAACwBQIwAAAAAMAWHMYYY3URuHW8vLzk5+dndRmKjY1ViRIlrC4DBRDHBnLC8YHscGwgOxwbyElBOD5OnjypxMRES2uwMwIw8oW/v7+ioqKsLgMFEMcGcsLxgexwbCA7HBvICccHOAUaAAAAAGALBGAAAAAAgC0QgJEvRo0aZXUJKKA4NpATjg9kh2MD2eHYQE44PsA1wAAAAAAAW6AHGAAAAABgCwRgAAAAAIAtEIBxS+3fv18hISEKCgpSixYttHPnTqtLQj5KSEjQ/fffr6CgIDVu3Fhdu3ZVeHi4JCkmJkY9evRQ7dq1FRwcrN9//921XE7zcPuZNm2aHA6HFixYIIljA1JiYqKGDx+u2rVrq2HDhurfv7+knH+n8PvGPpYsWaKmTZvK6XQqODhYX3/9tSQ+O+xoxIgRql69uhwOh8LCwlzTb/azgs8RmzDALdSxY0czbdo0Y4wxc+bMMc2bN7e2IOSr+Ph4s3jxYpOammqMMebTTz817du3N8YY8/e//928+eabxhhjNm7caKpUqWIuX758zXm4vURERJg2bdqY1q1bmx9++MEYw7EBY0aOHGmGDx/u+uyIjo42xuT8O4XfN/aQmppqfH19zZ9//mmMSfsM8fLyMhcuXOCzw4ZWr15tjhw5YqpVq2a2bdvmmn6znxV8jtgDARi3zIkTJ0zJkiVNUlKSMSbtl1aFChXM/v37La4MVtm0aZOpVq2aMcaY4sWLu77UGmNMixYtzK+//nrNebh9pKSkmM6dO5vNmzeb9u3buwIwx4a9xcbGmpIlS5rz589nmJ7T7xR+39hHamqqKVOmjFm9erUxxpg///zTVK5c2SQmJvLZYWNXBuCb/azgc8Q+OAUat8yRI0dUqVIleXh4SJIcDocCAgIUGRlpcWWwyscff6z77rtPp0+fVlJSkipWrOiaV716dUVGRuY4D7eXCRMm6I477lCzZs1c0zg2cODAAZUpU0Zjx45V8+bN1bZtW/322285/k7h9419OBwOfffdd3rwwQdVrVo13Xnnnfr666918eJFPjsgKefvn3yOQJI8rC4AgD2MHTtW4eHh+u233xQfH291ObDYjh07NG/ePK7DQybJyck6fPiw6tevr/fff1/btm1T165dtXjxYqtLQwGQnJysMWPGaP78+WrXrp02bdqkXr16Zbj+EwByQg8wbpmqVasqOjpaycnJkiRjjCIjIxUQEGBxZchv48aN0/z587V06VIVK1ZMZcuWlYeHh44fP+5qc+jQIQUEBOQ4D7ePP/74Q4cOHVLt2rVVvXp1rV+/XkOGDNH333/PsWFzAQEBcnNz02OPPSZJatKkiWrUqKHDhw9n+zuF3zf2ERYWpmPHjqldu3aSpBYtWsjf319//fUXnx2QlPP3z5udh9sLARi3TPny5dW0aVPNmDFDkjRv3jz5+/srMDDQ4sqQnyZMmKDZs2fr119/VenSpV3TH374YU2aNEmStGnTJh09elTt27e/5jzcHoYNG6bo6GgdOnRIhw4dUuvWrfXFF19o2LBhHBs2V65cOXXu3FnLli2TJEVERCgiIkJ33HFHtr9T+H1jH+khZffu3ZKk8PBwHThwQHXq1OGzA5Jy/v55s/Nwm7HyAmTc/vbs2WNat25tateubZo1a2b++usvq0tCPjpy5IiRZGrWrGkaN25sGjdubFq2bGmMMeb48eOma9euJjAw0NSvX9+sWLHCtVxO83B7unIQLI4NHDhwwHTo0MEEBwebRo0amblz5xpjcv6dwu8b+5g1a5br2AgODjYzZ840xvDZYUdDhgwxVapUMe7u7qZ8+fKmVq1axpib/6zgc8QeHMYYY3UIBwAAAADgVuMUaAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCAAQDIR/Pnz1ezZs3kdDpVt25dderUSampqfroo490/Phxq8sDAOC2xm2QAADIJ9HR0WrYsKG2bNmiatWqSZK2bt2qJk2aqEaNGlqwYIGcTqe1RQIAcBujBxgAgHxy4sQJubu7q0yZMq5pTZs21bvvvqtjx46pb9++cjqdCgsLU1JSkl566SW1bNlSTqdTffr00dmzZyVJoaGheuKJJxQSEqKgoCANGDBA8fHxkqSvvvpK9evXl9PpVMOGDbVhwwZL9hUAgIKIHmAAAPJJamqqHn74Ya1cuVLt27dXSEiI+vXrpypVqqh69eoZeoDHjh2rlJQUvf7665Kkd999V8ePH9f/+3//T6Ghodq0aZPWr1+vYsWK6f7771ebNm30yiuvqFSpUtqzZ48qVaqkpKQkJSYmqkSJEhbuNQAABQc9wAAA5BM3NzfNmzdPa9euVY8ePfSf//xHDRo0UHh4eKa2CxYs0IwZM+R0OuV0OjV79mxFRES45vfp00clS5aUu7u7Bg4cqOXLl0uSOnfurMcff1wff/yxIiIiCL8AAFzBw+oCAACwm7p166pu3boaOnSoevTooYULF2ZqY4zRp59+qm7dul3XOh0OhyRp3rx52rJli1atWqW7775bY8aM0SOPPJKn9QMAUFjRAwwAQD45evSo/vOf/7ienz17VhEREapVq5Z8fHx0/vx517z7779fEydOVFxcnCQpLi5OO3fudM2fO3euYmNjlZKSomnTpqlLly5KTk7WgQMH1Lx5cz3//PN66KGHtHHjxvzbQQAACjh6gAEAyCfJycl65513FBERoWLFiik5OVkDBgzQfffdp5MnT2rw4MEqVqyYpk+frtGjRysxMVGtWrVy9e6OHj1aDRo0kCS1aNFC3bt318mTJ9WmTRuNHDlSKSkpeuKJJ3TmzBl5eHjIz89P06ZNs3KXAQAoUBgECwCAQiY0NFROp1MjR460uhQAAAoVToEGAAAAANgCPcAAAAAAAFugBxgAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANgCARgAAAAAYAsEYAAAAACALRCAAQAAAAC2QAAGAAAAANjC/wcsGXLBNgevPAAAAABJRU5ErkJggg==",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' width=960.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c9363a23a9842d0a0cd461e7838ac08"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "num_experiments = 10000\n",
        "num_steps = 1000\n",
        "num_actions = 10\n",
        "\n",
        "# Initialize the environment\n",
        "env = ArmedBanditsGaussian(num_experiments, num_actions)\n",
        "# Initialize the agent\n",
        "agent = GreedyAgent(np.zeros((num_experiments, num_actions)))\n",
        "\n",
        "# Store the scores and averages for later plotting\n",
        "averages = np.zeros((num_steps))\n",
        "optimality = np.zeros((num_steps))\n",
        "scores = np.zeros((num_experiments, num_steps+1))\n",
        "\n",
        "#Store the optimal actions for later use\n",
        "optimal = np.argmax(env.mean, axis=1)\n",
        "\n",
        "for i in tqdm(range(num_steps)):\n",
        "    # Select an action to execute on the environment\n",
        "    action = agent.get_action()\n",
        "    _, reward, _, _ = env.step(action)\n",
        "\n",
        "    # Update the agent estimates with the previously observed rewards\n",
        "    agent.update_estimates(reward, action)\n",
        "\n",
        "    # Store the average cumulative score and optimality of the current step\n",
        "    scores[:,i+1] = scores[:,i] + reward\n",
        "    avg_score = np.mean(scores[:,i+1]/(i+1))\n",
        "    averages[i] = avg_score\n",
        "\n",
        "    # Get optimal actions from the environment\n",
        "    current_optimality = np.mean(action == optimal)\n",
        "    optimality[i] = current_optimality\n",
        "\n",
        "plt.figure(figsize=(12,6), dpi=80, facecolor='w', edgecolor='k')\n",
        "plt.plot([1.0 for _ in range(num_steps)], linestyle='--')\n",
        "plt.plot([0.0 for _ in range(num_steps)], linestyle='--')\n",
        "plt.plot(optimality)\n",
        "plt.legend([\"Best Possible\", \"Worst Possible\", \"Greedy\"])\n",
        "plt.title(\"Average Optimality of Greedy Agent\", fontsize=14)\n",
        "plt.ylabel(\"% of Optimal Action Taken\")\n",
        "plt.xlabel(\"Steps\")\n",
        "plt.show()\n",
        "greedy_scores = averages\n",
        "greedy_optimality = optimality"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWa-6MVh2se3"
      },
      "source": [
        "The Greedy Agent chooses the best action on average ~40% of the times. Additionally, extra experience won't improve it's score, as it usually lands on a sub-optimal action on the first steps, and stays there for ever. The Greedy Agent is not a good strategy for finding the optimal action on this scenario."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKr4rdEa2se3"
      },
      "source": [
        "## The Epsilon-Greedy Agent\n",
        "\n",
        "Another strategy is the Epsilon Greedy Agent, which adds to the previous strategy. An Epsilon-Greedy Agent allows for some exploratory actions, by every once in a while choosing any action randomly, instead of always acting greedily. The probability of taking an exploratory action is defined by the parameter `epsilon`. An epsilon of `0` is a Greedy Agent. An epsilon of `0.2` means our agent takes a random action 20% of the time. An epsilon of `1` is an agent that behaves randomly. Let's build this new agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Poi42r3d2se3"
      },
      "outputs": [],
      "source": [
        "class EpsilonGreedyAgent(GreedyAgent):\n",
        "    def __init__(self, reward_estimates, epsilon):\n",
        "        GreedyAgent.__init__(self, reward_estimates)\n",
        "        # Store the epsilon value\n",
        "        assert epsilon >= 0 and epsilon <= 1\n",
        "        self.epsilon = epsilon\n",
        "\n",
        "    def get_action(self):\n",
        "        # We need to redefine this function so that it takes an exploratory action with epsilon probability\n",
        "\n",
        "        # One hot encoding: 0 if exploratory, 1 otherwise\n",
        "        action_type = (np.random.random_sample(self.num_experiments) > self.epsilon).astype(int)\n",
        "        # Generate both types of actions for every experiment\n",
        "        exploratory_action = np.random.randint(self.num_bandits, size=self.num_experiments)\n",
        "        greedy_action = argmax(self.reward_estimates)\n",
        "        # Use the one hot encoding to mask the actions for each experiment\n",
        "        action = greedy_action * action_type + exploratory_action * (1 - action_type)\n",
        "\n",
        "        self.action_count[np.arange(self.num_experiments), action] += 1\n",
        "\n",
        "        return action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t1KQFxgD2se3"
      },
      "source": [
        "As may be seen, we're inheriting the `GreedyAgent` implementation, and only changing the `get_action` function. For taking an action, we're using a masking that defines which experiments will choose an action randomly and which will act greedily. Then, we apply that masking between random and greedy actions to obtain the action vector for all of our experiments."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_hRV_Xk2se6"
      },
      "source": [
        "### Testing the Agent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPE2O8AU2se6"
      },
      "source": [
        "Here, we're going to use the same animation as before to observe how this new strategy behaves. Our agents will have a value of epsilon of `0.1`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "8f95bb8dc3ad4ca6acd3b9b9bbc8ec76",
            "a1368739238043cda487ade051075695",
            "acd7a1fc433f40e8a5694f58b7ec2ddd",
            "3c9294aa4a6f453fabcabfdfae6ba260"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "SWWsxxsa2se7",
        "outputId": "2e51430b-87dc-48ee-e412-6bd3d3fad6a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.animation:MovieWriter imagemagick unavailable; using Pillow instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' width=1000.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f95bb8dc3ad4ca6acd3b9b9bbc8ec76"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "# Initialize the environment of our multi-armed bandit problem\n",
        "num_experiments = 2\n",
        "num_bandits = 8\n",
        "num_steps = 200\n",
        "means = np.random.normal(size=(num_experiments, num_bandits))\n",
        "stdev = np.ones((num_experiments, num_bandits))\n",
        "\n",
        "env = ArmedBanditsEnv(means, stdev)\n",
        "\n",
        "# Initialize the agent\n",
        "agent = EpsilonGreedyAgent(np.zeros((num_experiments,num_bandits)), 0.1)\n",
        "\n",
        "# Code for plotting the interaction\n",
        "fig, axs = plt.subplots(1, num_experiments, figsize=(10, 4))\n",
        "x_pos = np.arange(num_bandits)\n",
        "\n",
        "def init():\n",
        "    for i in range(num_experiments):\n",
        "        init_ax(i)\n",
        "\n",
        "\n",
        "def init_ax(i):\n",
        "    ax = axs[i]\n",
        "    ax.clear()\n",
        "    ax.set_ylim(-4, 4)\n",
        "    ax.set_xlim(-0.5, num_bandits-.5)\n",
        "    ax.set_xlabel('Actions', fontsize=14)\n",
        "    ax.set_ylabel('Value', fontsize=14)\n",
        "    ax.set_title(label='Estimated Values vs. Real values', fontsize=15)\n",
        "    ax.plot(x_pos, env.mean[i], marker='D', linestyle='', alpha=0.8, color='r', label='Real Values')\n",
        "    ax.axhline(0, color='black', lw=1)\n",
        "\n",
        "# Implement a step, which involves the agent acting upon the\n",
        "# environment and learning from the received reward.\n",
        "def step(g):\n",
        "    action = agent.get_action()\n",
        "    _, reward, _, _ = env.step(action)\n",
        "    agent.update_estimates(reward, action)\n",
        "    for i in range(num_experiments):\n",
        "        ax = axs[i]\n",
        "        # Plot the estimated values from the agent compared to the real values\n",
        "        estimates = agent.reward_estimates[i]\n",
        "        init_ax(i)\n",
        "        values = ax.bar(x_pos, estimates, align='center', color='blue', alpha=0.4, label='Estimated Values')\n",
        "        ax.legend()\n",
        "\n",
        "anim = FuncAnimation(fig, func=step, frames=np.arange(num_steps), init_func=init, interval=10, repeat=False, blit=True)\n",
        "# plt.show()\n",
        "\n",
        "# Uncomment the next line if you wish to store the animations as a gif\n",
        "anim.save('./epsilon-greedy-agent.gif', writer='imagemagick', fps=60)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQBszQNX2se7"
      },
      "source": [
        "Again, you may run the previous block multiple times to see different runs. As can be observed, the epsilon-greedy agent not only finds the optimal solution most of the times, but it is also capable of finding close estimated values for all the actions! How does our new strategy compares to the greedy agent on average?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "gUk7GyL22se7"
      },
      "outputs": [],
      "source": [
        "def run_experiment(num_experiments=1000, num_steps=1000, num_actions=10,epsilon=0.1):\n",
        "    # Initialize the environment\n",
        "    env = ArmedBanditsGaussian(num_experiments, num_actions)\n",
        "    # Initialize the agent\n",
        "    agent = EpsilonGreedyAgent(np.zeros((num_experiments, num_actions)), epsilon)\n",
        "\n",
        "    # Store the scores and averages for later plotting\n",
        "    averages = np.zeros((num_steps))\n",
        "    optimality = np.zeros((num_steps))\n",
        "    scores = np.zeros((num_experiments, num_steps+1))\n",
        "\n",
        "    #Store the optimal actions for later use\n",
        "    optimal = np.argmax(env.mean, axis=1)\n",
        "\n",
        "    for i in tqdm(range(num_steps)):\n",
        "        # Select an action to execute on the environment\n",
        "        action = agent.get_action()\n",
        "        _, reward, _, _ = env.step(action)\n",
        "\n",
        "        # Update the agent estimates with the previously observed rewards\n",
        "        agent.update_estimates(reward, action)\n",
        "\n",
        "        # Store the average cumulative score and optimality of the current step\n",
        "        scores[:,i+1] = scores[:,i] + reward\n",
        "        avg_score = np.mean(scores[:,i+1]/(i+1))\n",
        "        averages[i] = avg_score\n",
        "\n",
        "        # Get optimal actions from the environment\n",
        "        current_optimality = np.mean(action == optimal)\n",
        "        optimality[i] = current_optimality\n",
        "    return optimality, averages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "637d740eabed432ba4b19ca5268270f2",
            "5bd79d5860184b97b63c69726d4143fb",
            "2b967548f54747dfa9101a459b7f5ca2",
            "e030289b2cc54cc2a19c904375b78547"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "RLNOrcM82se7",
        "outputId": "e788568a-9c7d-4e62-9b3c-30472140c95a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:06<00:00, 163.54it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' 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              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "637d740eabed432ba4b19ca5268270f2"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "num_experiments = 10000\n",
        "num_steps = 1000\n",
        "num_actions = 10\n",
        "epsilon = 0.1\n",
        "\n",
        "balanced_eps_optimality, balanced_eps_scores = run_experiment(num_experiments, num_steps, num_actions, epsilon)\n",
        "\n",
        "plt.figure(figsize=(12,6), dpi=80, facecolor='w', edgecolor='k')\n",
        "plt.plot([1.0 for _ in range(num_steps)], linestyle='--')\n",
        "plt.plot([0.0 for _ in range(num_steps)], linestyle='--')\n",
        "plt.plot(balanced_eps_optimality)\n",
        "plt.plot(greedy_optimality)\n",
        "plt.legend([\"Best Possible\", \"Worst Possible\", \"0.1 $\\epsilon$ Greedy\", \"Greedy\"])\n",
        "plt.title(\"Average Optimality of Greedy Agent vs Epsilon-Greedy Agent\")\n",
        "plt.ylabel(\"% of Optimal Action Taken\")\n",
        "plt.xlabel(\"Steps\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6yXM3Rw2se8"
      },
      "source": [
        "After 1000 steps, the Epsilon-Greedy Agent is capable of reaching an 80% average optimality. This means that on average, they choose the best possible action 80% of the times after that amount of iterations. We can also see how our agent is capable of improving from experience, contrary to the Greedy Agent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOGGfJRm2se8"
      },
      "source": [
        "## Values for epsilon"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zx_9bwy2se8"
      },
      "source": [
        "On the previous example, we used a value for `epsilon` of `0.1`. This means that the agent chose an exploratory action 10% of the times. What would have happened if we chose another action? Like with almost all hyper-parameters in Machine Learning, there's a range of values which behave better than others. Let's make an actual experiment using different values for `epsilon`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "f2112f92326140cdb9628771749f0927",
            "f4bc1f5957a9448a9443a638760f931a",
            "eeba42189aa644f4b86ef42fb73f2997",
            "b72f8a30bef2461ba887048c61b83cc7"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "G-OedWQ72se8",
        "outputId": "12b6bcfa-76dd-4070-a1b7-216c4b7013b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:00<00:00, 1373.19it/s]\n",
            "100%|██████████| 1000/1000 [00:00<00:00, 1386.90it/s]\n",
            "100%|██████████| 1000/1000 [00:00<00:00, 1456.74it/s]\n",
            "100%|██████████| 1000/1000 [00:00<00:00, 1366.90it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
            ],
            "image/png": 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",
            "text/html": [
              "\n",
              "            <div style=\"display: inline-block;\">\n",
              "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
              "                    Figure\n",
              "                </div>\n",
              "                <img 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' width=960.0/>\n",
              "            </div>\n",
              "        "
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2112f92326140cdb9628771749f0927"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "num_experiments = 1000\n",
        "num_steps = 1000\n",
        "num_actions = 10\n",
        "epsilons = np.array([0.0, 0.01, 0.1, 0.4])\n",
        "\n",
        "plt.figure(figsize=(12,6), dpi=80, facecolor='w', edgecolor='k')\n",
        "# plt.plot([1.6 for _ in range(num_steps)], linestyle='--') # why 1.6?\n",
        "\n",
        "\n",
        "for epsilon in epsilons:\n",
        "    _, reward = run_experiment(num_experiments, num_steps, num_actions, epsilon)\n",
        "\n",
        "    plt.plot(reward)\n",
        "\n",
        "# plt.legend([\"Best Possible\"] + epsilons.tolist())\n",
        "plt.legend(epsilons.tolist())\n",
        "plt.title(\"Average reward for multiple values of epsilon\")\n",
        "plt.xlabel(\"Steps\")\n",
        "plt.ylabel(\"Average Reward\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2NwECSH2se8"
      },
      "source": [
        "Here, we're plotting the average amount of reward received by the agent. This is because plotting the optimality on this many tests gets too crowded and noisy. As can be seen, the agent with an epsilon of `0.1` was the one that received the most amount of reward. Values of epsilon that are too small take very few exploratory actions, and therefore take a long time to explore and find the most optimal value. On the other hand, values of `epsilon` too high will obstruct the capacity of the agent of acting optimally. Too much exploration gets in the way of exploitation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32BlCjd3HvId"
      },
      "source": [
        "***\n",
        "## Part B questions\n",
        "\n",
        "Now that we have familiarized ourselves with the basic setup let us test a few more stratergies as part of the assignment problem.\n",
        "\n",
        "Consider a two-armed Bernoulli bandit scenario with true means given by $\\mu_1 = \\frac{1}{2}, \\mu_2= \\frac{1}{2}+\\Delta$, for some $\\Delta < \\frac{1}{2}$. Let the time horizon be $T=10000$. `[20 Marks]`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4V1r2un0-T9"
      },
      "source": [
        "### B1\n",
        "Take $\\Delta=\\frac{1}{4}$ and run the Monte Carlo simulations to estimate the expected regret of the ETC algorithm which explores each arm $m = T^{2/3} (\\log T)^{1/3}$ times before committing. Specifically, you run the ETC algorithm to compute the sample regret\n",
        "$$ \\mu_2 * T - \\sum_{t=1}^T R_t, $$\n",
        "where $R_t$ is the reward obtained in time step $t$.\n",
        "\n",
        "Repeat this experiment 500 times and estimate the expected regret by taking the average of the sample regrets you obtained in all those 500 experiments. `[5 Marks]`"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math"
      ],
      "metadata": {
        "id": "uA59McS8IZ8K"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "nkZWU7G40-T9"
      },
      "outputs": [],
      "source": [
        "class ETCAgent:\n",
        "    def __init__(self, reward_estimates,Total_rounds):\n",
        "        \"\"\"\n",
        "        Our agent takes as input the initial reward estimates.\n",
        "        This estimates will be updated incrementally after each\n",
        "        interaction with the environment.\n",
        "        \"\"\"\n",
        "        assert len(reward_estimates.shape) == 2\n",
        "\n",
        "        self.num_bandits = reward_estimates.shape[1]\n",
        "        self.num_experiments = reward_estimates.shape[0]\n",
        "        self.reward_estimates = reward_estimates.astype(np.float64)\n",
        "        self.action_count = np.zeros(reward_estimates.shape)\n",
        "        self.next_arm = np.zeros(self.num_experiments,dtype=int)\n",
        "        self.total_action = 0\n",
        "        self.T = Total_rounds\n",
        "\n",
        "    def get_action(self):\n",
        "        # Our agent is greedy, so there's no need for exploration.\n",
        "        # Our argmax will do just fine for this situation\n",
        "        action = 0\n",
        "        if self.total_action < (self.T**(2/3))*(math.log(self.T**(1/3))):\n",
        "          action = self.next_arm\n",
        "          self.next_arm = (self.next_arm + 1)%self.num_bandits\n",
        "        else :\n",
        "          action = argmax(self.reward_estimates)\n",
        "        self.total_action=self.total_action+1\n",
        "        # Add a 1 to each action selected in the action count\n",
        "        self.action_count[np.arange(self.num_experiments), action] += 1\n",
        "        return action\n",
        "\n",
        "    def update_estimates(self, reward, action):\n",
        "        # rew is a matrix with the obtained rewards from our previuos\n",
        "        # action. Use this to update our estimates incrementally\n",
        "        n = self.action_count[np.arange(self.num_experiments), action]\n",
        "        prev_reward_estimates = self.reward_estimates[np.arange(self.num_experiments), action]\n",
        "\n",
        "        # Update the reward estimates incementally\n",
        "        self.reward_estimates[np.arange(self.num_experiments), action] = inc_avg(prev_reward_estimates,reward,n)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def run_experiment(num_experiments=1, num_steps=1000, num_actions=2,epsilon=0.1):\n",
        "    # Initialize the environment\n",
        "    means = np.array([[0.5,0.5+0.25]]) # The mean for a four-armed bandit. Single experiment\n",
        "    stdev = np.array([[1,1]]) # The standard deviation for a four-armed bandit.\n",
        "\n",
        "    env = ArmedBanditsEnv(means, stdev) # Create the environment\n",
        "    # Initialize the agent\n",
        "    agent = ETCAgent(np.zeros((num_experiments, num_actions)), num_steps)\n",
        "\n",
        "    # Store the scores and averages for later plotting\n",
        "    averages = np.zeros((num_steps))\n",
        "    optimality = np.zeros((num_steps))\n",
        "    scores = np.zeros((num_experiments, num_steps+1))\n",
        "\n",
        "    #Store the optimal actions for later use\n",
        "    optimal = np.argmax(env.mean, axis=1)\n",
        "\n",
        "    for i in tqdm(range(num_steps)):\n",
        "        # Select an action to execute on the environment\n",
        "        action = agent.get_action()\n",
        "        _, reward, _, _ = env.step(action)\n",
        "\n",
        "        # Update the agent estimates with the previously observed rewards\n",
        "        agent.update_estimates(reward, action)\n",
        "\n",
        "        # Store the average cumulative score and optimality of the current step\n",
        "        scores[:,i+1] = scores[:,i] + reward\n",
        "        avg_score = np.mean(scores[:,i+1]/(i+1))\n",
        "        averages[i] = avg_score\n",
        "\n",
        "        # Get optimal actions from the environment\n",
        "        current_optimality = np.mean(action == optimal)\n",
        "        optimality[i] = current_optimality\n",
        "    return 0\n",
        "run_experiment()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "-R4hkCkSA-RQ",
        "outputId": "cc3357e9-0601-4619-cd3b-d36d97936535"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1000/1000 [00:00<00:00, 8928.08it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwbupELS0-T9"
      },
      "source": [
        "### B2\n",
        "\n",
        "Repeat the above for various values of $\\Delta \\in \\{0.05, 0.1, 0.2, 0.3, 0.4, 0.45\\}$ and plot the estimated regret as a function of $\\Delta$ and verify whether it satisfies the regret upper bound we derived in class. `[5 Marks]`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hlDs1K2h0-T9"
      },
      "outputs": [],
      "source": [
        "# write your code for the above part here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ZGV1iP00-T-"
      },
      "source": [
        "### B3\n",
        "Repeat the experiment with the UCB algorithm and plot the comparison with ETC. `[10 Marks]`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ulpAglpy0-T-"
      },
      "outputs": [],
      "source": [
        "# write your code for the above part here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ca2LJcHT0-T-"
      },
      "source": [
        "### B4\n",
        "(**Bonus**) In the ETC algorithm, assume that we know $\\Delta$, and choose a better $m$ as function of $\\Delta$ and repeat the experiments and compare with UCB. What did you observe?\n",
        "\n",
        "Hint: Check how many samples of exploration are required to make $\\epsilon < \\frac{\\Delta}{2}$ with a high probability of $1-\\frac{1}{T}$. `[5 Marks]`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IbzHv-Ch0-T-"
      },
      "outputs": [],
      "source": [
        "# write your code for the above part here"
      ]
    }
  ],
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GvXrtWWLVv09ddf6/nnn9fy5cvVrl27QnkuZ3/Jzs98uwMHDjjOcPvqq6+qR48eqlq1qgIDAyVJ7dq10+bNm2WMcbk2+19Tu3XrluclYsLDw11e59W67777NHXqVH388cd6/fXX5eXlpfnz5zvm2RljdPfdd2v//v0aOXKkRo4cqVq1aik4OFg2m00TJkxQfHx8vt6H3NjfH2fTOnTooNq1a7u0nsL8vN17773aunWrPvzwQ8XHx+vSpUtauHCh/P391bdv32zLhoaGau3atdq0aZO++OILrV+/XmvXrtWqVasUHx+vr7/+WnXr1nXpea/k79eaXbBggQYOHKhnn31W3bp1U5s2bST97/2rVKlSthEIZ+wb2sX5M7/33nv18ccf68MPP3RsBOW2kfntt9/qscceU1hYmF577TV16tRJlSpVkr+/vySpSpUqhXY24rw+i3mNzkpyvPeSFB0drV27dmn58uX66quvtH79ei1cuFALFy5U27ZttX79evn5+RVKzcg/elMWelPe6E2uozfRm1CM3LVvPaxJ+TwRjzOnT592HF/XqlUrx/SCHueX2+PyOgbJfrzRpEmTHNP+/e9/G0nm8ccfd7q+iIiIfB/HFhMTYySZTz75JNfX9nedOnUyksxXX33ldP5dd92V7+P87Fq3bu04Hm7ZsmVGyjrD6+V27dplJJmWLVs6XUf//v2dPr+z9/vQoUNGkrnuuuucrmvo0KE51hUXF2eUy8lt8iO3z9uVHDlyxHh7e5vq1aubzMxMs3TpUiPJ3HXXXS49/ujRo2bgwIFGkunXr9/Vlu+Q14l47Cej6dy5s2PawYMHjf7vJDyuKsyf+ZVcuHDBlCtXzgQFBZkzZ86Yn3/+2UgyzZs3z7Hsk08+aSSZf/3rXznmnT171thsNpe/F5577rlcj9m7dOmS8fX1zbGu2rVrG0kmOTnZ5dfnzM6dOx1nfp4xY0aB1oXs6E30Jjt6U97oTXmjN9GbPAHXQUehCw0NVXx8vGw2m3bu3OmYbv+L3aVLl9xVmiTpr7/+kiRVq1Ytx7wNGzbo6NGjOaZfqfabb75ZkrRo0SKX67Af+7dw4cIc806ePOk49uhqXH7d2b9fX9Yur/fhr7/+0qpVq1x+vvDwcPn4+Gjfvn053qOLFy8qISEhx2Ou5j1zJrfP25VERESoa9euOnDggDZt2pTrX9BzU7FiRU2ePFmS8vW8V2Py5MkKCAjQunXr9M0330jK+rk1aNBAu3btcvnaxIX5M78SX19f9evXT2fPntXixYvzfH/zquvjjz/O16hJ5cqVJcnpe7Ju3TqnuykW1mexUaNGGjVqlKSi/0wg/+hNrqE30ZtcRW+iN6FoENBRIP/5z3+c/rIvW7ZMxphsJzMJDw+Xr6+v9u7d6zjpjTvYT+bz/vvvKy0tzTH90KFDGjlypNPHXKn2Pn36qGHDhvrggw8UFxeX47hCY4w2bdqkTZs2OaYNGzZM/v7++uCDD7R69WrH9IsXLyo2NjZbbfk1YMAA+fj46IsvvtCiRYvk5eWle+65J9syderUkZeXl9auXZvtJCfnz5/XyJEjdfLkSZefz8/PT23bttXJkyc1Y8YMx/RLly7pscce0759+3I8pk2bNrr55pu1adMmjRo1SikpKTmW+fHHH7V8+XLH/fx83lxh31h8++23tWTJEoWFhen222/Psdybb77p9DV89dVXkpTjebt06aIGDRpkO4lQQVSuXNnx2Xzuuecc0ydOnKjMzEz16dNHiYmJOR534sQJvfPOO477hfkzd8XlG+MfffSRvLy8NHDgwBzL2X8n33333WwbKbt27dJTTz2Vr+e88cYbJWX9fl9+LOO+ffs0ZswYp4957LHHFBgYqMcff1yfffZZjvnp6en65JNP9N///leSdPbsWb3++us6depUtuUyMzMdn9f8fhZRuOhNWehN9KbL0Zuy0Jtgee4bvIcV6f92IxwyZEiut4kTJzqWv/POO40kU7t2bdOrVy8zcOBAc/311xubzWa8vLzMwoULs62/Z8+eRpJp1KiRue+++0xMTIyZPXu2Y35x7EaYnp5uGjVqZCSZSpUqmT59+pgePXqYoKAg065dO9OuXTun67tS7b/99pupWbOmkWQqVqxounbtau655x5zyy23OK55O3369Gzr/Ne//mWkrMv0dOrUyQwYMMDUqFHDhIWFOS4rcjW7ERpjzO233+74ed58881Ol7n//vuNJBMYGGh69Ohh+vbtayIiIkx4eLjTXf+Myf39XrVqlfHy8jKSTNu2bc1dd91loqKiTHh4uBkyZIjTdR09etQ0b97cSDJly5Y1nTp1Mvfcc4/p0aOHiYyMNFLWdWjt8vt5u5IzZ844LhkkycTExDhdzn7JmIYNG5o+ffqY/v37O6YFBASYjRs3Zlve/jnOzzVH89qN0BhjDh8+bAIDA40k88MPPzimT5gwwfEZatGihenXr5/p27evad68ufH29jZhYWHZ1lOYP/MryczMdLwuSaZLly5Ol0tOTjaVKlUyUtZlmO6++27TtWtX4+vra/r165fv7wX7tXnDwsJMz549TZcuXUyZMmVyXZcxxixevNjxWahTp47p2bOnGTBggLnhhhtMmTJlsr3vf/31l5FkfH19zfXXX28GDBhgevfu7fjM1qhRo8C7JCI7ehO9id6UE72J3kRvKpkI6MjG/mWV161p06aO5RMSEsyoUaNMs2bNTPny5U1AQICpVauWGTBggNm6dWuO9R89etTcd999plKlSsbb29uxwWVXHBtBxhhz8uRJ89BDD5kaNWoYf39/U6tWLfPUU0+ZtLS0XNd3pdqNMebUqVNm6tSppkWLFiY4ONgEBASYGjVqmFtvvdXMmDHD6TU/Fy1aZNq0aeO4duWdd95pfvnllyteM/NKFixY4PiZzZs3z+kyly5dMq+88opp2LChCQgIMBEREWbQoEFm//79uT5/Xu/3l19+aVq1amX8/f1NuXLlzN1332327duX52s5d+6cef311027du1MWFiY8fPzM5GRkaZjx47mpZdeMgcPHnQsm9/Pmyvsx+pJMmvXrnW6zOeff26GDx9uGjVqZMqWLWuCgoJMvXr1zIgRI8yvv/6aY/mi2AgyxpixY8caKedxhQkJCaZfv36mSpUqxtfX15QvX95ER0eb0aNHm4SEhGzLFvbP/ErGjx/veH8vDw1/d/DgQXPPPfeYqlWrmoCAAHPttdea559/3ly6dCnf3wvp6elm3LhxJjIy0vj5+ZnatWubqVOn5rouu99//908/PDDpm7duiYgIMCEhISY+vXrmwEDBpiFCxea9PR0Y4wxFy9eNDNmzDC9e/c2tWvXNkFBQaZs2bImOjraTJkyxZw4cSLf7xPyRm+iN9GbcqI30ZvoTSWTzZhCOCUiAAAAAAAoEI5BBwAAAADAAgjoAAAAAABYAAEdAAAAAAALIKB7sOeff142m02PPvqou0sBAEASvQkAgIIgoHuorVu36q233lJ0dLS7SwEAQBK9CQCAgiKge6DU1FQNGjRI77zzjq655hp3lwMAAL0JAIBC4OPuApB/o0aNUo8ePdS1a1dNnTo1z2XT09OVnp7uuJ+ZmamTJ0+qfPnystlsRV0qAOD/GGN05swZValSRV5eJe/v4/QmAPA8Jb03eSICuoeZP3++tm/frq1bt7q0fHx8vKZMmVLEVQEAXHXw4EFVq1bN3WUUKnoTAHi2ktibPBUB3YMcPHhQjzzyiFatWqWAgACXHjN+/HiNHTvWcf/06dOKiorSwYMHFRoaWlSlAgD+JiUlRZGRkQoJCXF3KYWK3gQAnquk9iZPZjPGGHcXAdcsXrxYd911l7y9vR3TMjIyZLPZ5OXlpfT09GzznElJSVFYWJhOnz7NRhAAFKOS+v1LbwIAz8X3r/Uwgu5BunTpoh07dmSbNmzYMDVo0EBPPfXUFTeAAAAobPQmAAAKDwHdg4SEhKhx48bZppUpU0bly5fPMR0AgOJAbwIAoPBwqj4AAAAAACyAEXQPt379eneXABS7jIwMXbx40d1lADn4+flxmRrRm1A60ZtgRb6+vhxq5GEI6AA8hjFGR44c0alTp9xdCuCUl5eXatasKT8/P3eXAqCY0JtgdWXLllWlSpVks9ncXQpcQEAH4DHsG0AVK1ZUUFAQjQaWkpmZqT///FOHDx9WVFQUn0+glKA3waqMMTp79qyOHTsmSapcubKbK4IrCOgAPEJGRoZjA6h8+fLuLgdwqkKFCvrzzz916dIl+fr6urscAEWM3gSrCwwMlCQdO3ZMFStWZHd3D8CBcgA8gv24vqCgIDdXAuTOvmt7RkaGmysBUBzoTfAE9s8n50jwDAR0AB6FXQdhZXw+gdKJ331YGZ9Pz0JABwAAAADAAgjoAEqnWbOkRo2y/i0hhg4dql69ehXpc8ydO1dly5Yt0ucAgFKL3nRV6E0oSQjoAEqfWbOkuDjp+PGsf4t4Q2jo0KGy2Wyy2Wzy9fVVzZo19eSTT+r8+fNF+ryX+/TTT+Xt7a1Dhw45nV+3bl2NHTu22OoBAPwNvSkHehNKIwI6gNLFvgFks0lRUVn/FsOGULdu3XT48GH98ccfmj59ut566y1NmjSpSJ/zcnfccYfKly+vefPm5Zi3YcMG/f7774qJiSm2egAAl6E35ZhHb0JpRUAHUHpcvgEUHp41LTy8WDaE/P39ValSJUVGRqpXr17q2rWrVq1a5ZifmZmp+Ph41axZU4GBgWratKk++eQTx/yMjAzFxMQ45tevX1+vvfaay8/v6+ur++67T3Pnzs0xb/bs2WrTpo0aNWqkadOmqUmTJipTpowiIyP18MMPKzU1Ndf1Ott18dFHH1WnTp1cfm1//fWXBg0apAoVKigwMFB169bVnDlzXH5tAODR6E30JuAyBHQApYOzDSC7YtoQstu5c6e++eYbxyW5JCk+Pl7vvfee3nzzTf3888+KjY3Vvffeq4SEBElZGxLVqlXTxx9/rF27dumZZ57RhAkTtHDhQpefNyYmRnv27NGGDRsc01JTU/XJJ584Rii8vLz0+uuv6+eff9a8efO0du1aPfnkkwV6vVd6bRMnTtSuXbu0bNky/fLLL5o5c6bC//4zAoCSiN5EbwL+xsfdBQBAkctrA8guPFxKTs5aTpJGjCjUEr788ksFBwfr0qVLSk9Pl5eXl/71r39JktLT0/XPf/5Tq1evVtu2bSVJtWrV0saNG/XWW2+pY8eO8vX11ZQpUxzrq1mzpjZv3qyFCxfq7rvvdqmGhg0b6vrrr9fs2bN14403SpIWLlwoY4wGDBggKWuEwa5GjRqaOnWqRo4cqX//+99X9bpdeW1JSUlq3ry5WrZs6XheACjx6E2S6E3A3xHQAZR806dL585lHdeXl/BwKSkpa/lC3gjq3LmzZs6cqbS0NE2fPl0+Pj7q06ePJOn333/X2bNndfPNN2d7zIULF9S8eXPH/RkzZmj27NlKSkrSuXPndOHCBTVr1ixfdQwfPlyxsbF64403FBISotmzZ6tfv34KCQmRJK1evVrx8fH69ddflZKSokuXLun8+fM6e/asgoKC8v26XXltDz30kPr06aPt27frlltuUa9evdSuXbt8PxcAeBR6kwO9CfgfdnEHUPLFxkqBgVmjEHlJTs5aLja20EsoU6aM6tSpo6ZNm2r27NnasmWL3n33XUlyHEe3dOlSJSYmOm67du1yHA83f/58Pf7444qJidHKlSuVmJioYcOG6cKFC/mqwz4asXDhQu3Zs0ebNm1y7EK4f/9+3X777YqOjtann36qbdu2acaMGZKU6/N4eXnJGJNt2sWLFx3/d+W1de/eXQcOHFBsbKz+/PNPdenSRY8//ni+XhcAeBx6kwO9CfgfRtABlHz2EYe4uKwNHWe7EiYnS8ZIEycW+gjF33l5eWnChAkaO3as7rnnHjVs2FD+/v5KSkpSx44dnT5m06ZNateunR5++GHHtL179+b7uUNCQtSvXz/Nnj1be/fuVb169XTDDTdIkrZt26bMzEy98sor8vLK+vvtlY4jrFChgnbu3JltWmJionx9fSXJpddmX8+QIUM0ZMgQ3XDDDXriiSf08ssv5/v1AYDHoDc50JuA/2EEHUDpMGJE1gaOMTlHK4pxA8iuX79+8vb21owZMxQSEqLHH39csbGxmjdvnvbu3avt27frjTfecFx6pm7duvr++++1YsUK/fbbb5o4caK2bt16Vc8dExOjb775Rm+++aaGDx/umF6nTh1dvHhRb7zxhv744w/95z//0Ztvvpnnum666SZ9//33eu+997Rnzx5NmjQp20aRK6/tmWee0ZIlS/T777/r559/1pdffqlrr732ql4bAHgUepMDvQnIQkAHUHo42xBywwaQJPn4+Gj06NF68cUXlZaWpri4OE2cOFHx8fG69tpr1a1bNy1dulQ1a9aUJD344IPq3bu3+vfvrzZt2ujEiRPZRizyo0OHDqpfv75SUlI0ePBgx/SmTZtq2rRpeuGFF9S4cWN98MEHio+Pz3Ndt956qyZOnKgnn3xSrVq10pkzZ7KtU9IVX5ufn5/Gjx+v6Oho3XjjjfL29tb8+fOv6rUBgMehN0miNwF2NvP3AzRQoqWkpCgsLEynT59WaGiou8sBXHb+/Hnt27dPNWvWVEBAQMFWZj9z7rlzWcf1FfMGEEquvD6nfP/mjvcGnoreBE9Ab/IsHIMOoPSxb/BMn5510h02gAAA7kZvAiACOoDSasQINn4AANZCbwJKPY5BBwAAAADAAgjoAAAAAABYAAEdAAAAAAALIKADAAAAAGABBHQAAAAAACyAgA4AAAAAgAUQ0AHAA82dO1dly5Z1dxn5Uhw179+/XzabTYmJiUX6PACAnOhNztGbkB8EdA8zc+ZMRUdHKzQ0VKGhoWrbtq2WLVvm7rIA5GHo0KGy2Ww5bt26dXPp8TVq1NCrr76abVr//v3122+/FUG12RXXxtbRo0fl6+ur+fPnO50fExOjFi1aFHkdyD/6EuCZ6E1XRm+CO/i4uwDkT7Vq1fT888+rbt26MsZo3rx5uvPOO/XDDz+oUaNG7i4PcIu33y7e53vggfw/plu3bpozZ062af7+/lddQ2BgoAIDA6/68VYTERGhHj16aPbs2RowYEC2eWlpaVq4cKGef/55N1WHvNCXAOfoTZ6P3gR3YATdw/Ts2VO33Xab6tatq3r16um5555TcHCwvv32W3eXBiAP/v7+qlSpUrbbNddcI0kyxmjy5MmKioqSv7+/qlSpojFjxkiSOnXqpAMHDig2NtYxuiHlHD2YPHmymjVrptmzZysqKkrBwcF6+OGHlZGRoRdffFGVKlVSxYoV9dxzz2Wra9q0aWrSpInKlCmjyMhIPfzww0pNTZUkrV+/XsOGDdPp06cdzz158mRJUnp6uh5//HFVrVpVZcqUUZs2bbR+/fps6547d66ioqIUFBSku+66SydOnMjzPYqJidGaNWuUlJSUbfrHH3+sS5cuadCgQVq+fLk6dOigsmXLqnz58rr99tu1d+/eXNfpbJRl8eLFjvfRbsmSJWrRooUCAgJUq1YtTZkyRZcuXZKU988H9CXAk9Gb6E2wHgK6B8vIyND8+fOVlpamtm3burscAFfp008/1fTp0/XWW29pz549Wrx4sZo0aSJJ+uyzz1StWjU9++yzOnz4sA4fPpzrevbu3atly5Zp+fLl+uijj/Tuu++qR48e+u9//6uEhAS98MILevrpp7VlyxbHY7y8vPT666/r559/1rx587R27Vo9+eSTkqR27drp1VdfVWhoqOO5H3/8cUnS6NGjtXnzZs2fP18//fST+vXrp27dumnPnj2SpC1btigmJkajR49WYmKiOnfurKlTp+b5Ptx2222KiIjQ3Llzs02fM2eOevfurbJlyyotLU1jx47V999/rzVr1sjLy0t33XWXMjMz8/2+23399dcaPHiwHnnkEe3atUtvvfWW5s6d69hgzOvng+zoS0DJQW/KQm9CcWMXdw+0Y8cOtW3bVufPn1dwcLAWLVqkhg0bOl02PT1d6enpjvspKSnFVSaAy3z55ZcKDg7ONm3ChAmaMGGCkpKSVKlSJXXt2lW+vr6KiopS69atJUnlypWTt7e3QkJCVKlSpTyfIzMzU7Nnz1ZISIgaNmyozp07a/fu3frqq6/k5eWl+vXr64UXXtC6devUpk0bSdKjjz7qeHyNGjU0depUjRw5Uv/+97/l5+ensLAw2Wy2bM+dlJSkOXPmKCkpSVWqVJEkPf7441q+fLnmzJmjf/7zn3rttdfUrVs3xwZVvXr19M0332j58uW51u/t7a0hQ4Zo7ty5mjhxomw2m/bu3auvv/5aq1atkiT16dMn22Nmz56tChUqaNeuXWrcuHGe709upkyZonHjxmnIkCGSpFq1aikuLk5PPvmkJk2alOfPB1ny05ckehNgFfQmehOshxF0D1S/fn0lJiZqy5YteuihhzRkyBDt2rXL6bLx8fEKCwtz3CIjI4u5WgCS1LlzZyUmJma7jRw5UpLUr18/nTt3TrVq1dL999+vRYsWOXZhy48aNWooJCTEcT8iIkINGzaUl5dXtmnHjh1z3F+9erW6dOmiqlWrKiQkRPfdd59OnDihs2fP5vo8O3bsUEZGhurVq6fg4GDHLSEhwbFL3y+//OLY0LJzZUR1+PDh2rdvn9atWycpa4SiRo0auummmyRJe/bs0cCBA1WrVi2FhoaqRo0akpRj18P8+PHHH/Xss89mey3333+/Dh8+rLNnzxbaz6cky09fkuhNgFXQm+hNsB5G0D2Qn5+f6tSpI0m67rrrtHXrVr322mt66623ciw7fvx4jR071nE/JSWFDSHADcqUKeP4vf27yMhI7d69W6tXr9aqVav08MMP66WXXlJCQoJ8fX1dfo6/L2uz2ZxOs+9yt3//ft1+++166KGH9Nxzz6lcuXLauHGjYmJidOHCBQUFBTl9ntTUVHl7e2vbtm3y9vbONu/vIzH5VbduXd1www2aM2eOOnXqpPfee0/333+/47i8nj17qnr16nrnnXdUpUoVZWZmqnHjxrpw4YLT9Xl5eckYk23axYsXc7yeKVOmqHfv3jkeHxAQUGg/n5IsP31JojcBVkFvcg29CcWJgF4CZGZmZttV8HL+/v4FOhsngOIRGBionj17qmfPnho1apQaNGigHTt2qEWLFvLz81NGRkahP+e2bduUmZmpV155xTGSsXDhwmzLOHvu5s2bKyMjQ8eOHdMNN9zgdN3XXntttuMJJbl80rCYmBg99NBDuuOOO3To0CENHTpUknTixAnt3r1b77zzjuN5N27cmOe6KlSooDNnzigtLU1lypSRpBzXoW3RooV2796d60aqlPfPBznl1ZckehPgKehN/0NvQnEhoHuY8ePHq3v37oqKitKZM2f04Ycfav369VqxYoW7SwOQh/T0dB05ciTbNB8fH4WHh2vu3LnKyMhQmzZtFBQUpPfff1+BgYGqXr26pKzdAzds2KABAwbI399f4eHhhVJTnTp1dPHiRb3xxhvq2bOnNm3apDfffDPbMjVq1FBqaqrWrFmjpk2bKigoSPXq1dOgQYM0ePBgvfLKK2revLmOHz+uNWvWKDo6Wj169NCYMWPUvn17vfzyy7rzzju1YsWKPI/xu1y/fv00ZswYPfjgg7rlllscI6vXXHONypcvr7fffluVK1dWUlKSxo0bl+e67O/phAkTNGbMGG3ZsiXHiX6eeeYZ3X777YqKilLfvn3l5eWlH3/8UTt37tTUqVOv+PMp7ehLgOeiN9GbYD0cg+5hjh07psGDB6t+/frq0qWLtm7dqhUrVujmm292d2kA8rB8+XJVrlw5261Dhw6SpLJly+qdd95R+/btFR0drdWrV+uLL75Q+fLlJUnPPvus9u/fr9q1a6tChQqFVlPTpk01bdo0vfDCC2rcuLE++OADxcfHZ1umXbt2GjlypPr3768KFSroxRdflJR1/N3gwYP12GOPqX79+urVq5e2bt2qqKgoSdL111+vd955R6+99pqaNm2qlStX6umnn3aprqCgIA0YMEB//fWXhg8f7pju5eWl+fPna9u2bWrcuLFiY2P10ksv5bmucuXK6f3339dXX32lJk2a6KOPPnJcjsfu1ltv1ZdffqmVK1eqVatWuv766zV9+nTHRs6Vfj6lHX0J8Fz0JnoTrMdm/n4ABEq0lJQUhYWF6fTp0woNDXV3OYDLzp8/r3379qlmzZoKCAhwdzmAU3l9Tvn+zR3vDTwVvQmegN7kWRhBBwAAAADAAgjoAAAAAABYAAEdAAAAAAALIKADAAAAAGABBHQAAAAAACyAgA7Ao3DhCVgZn0+gdOJ3H1bG59OzENABeARfX19J0tmzZ91cCZC7CxcuSJK8vb3dXAmA4kBvgiewfz7tn1dYm4+7CwAAV3h7e6ts2bI6duyYJCkoKEg2m83NVQH/k5mZqePHjysoKEg+PrRXoDSgN8HKjDE6e/asjh07prJly/LHYw/BFgQAj1GpUiVJcmwIAVbj5eWlqKgoNtCBUoTeBKsrW7as43MK6yOgA/AYNptNlStXVsWKFXXx4kV3lwPk4OfnJy8vjh4DShN6E6zM19eXkXMPQ0AH4HG8vb1pNgAAS6E3ASgM/JkfAAAAAAALIKADAAAAAGABBHQAAAAAACyAgA4AAAAAgAUQ0AEAAAAAsAACOgAAAAAAFkBABwAAAADAAgjoAAAAAABYAAEdAAAAAAALIKADAAAAAGABBHQAAAAAACyAgA4AAAAAgAUQ0AEAAAAAsAACOgAAAAAAFkBABwAAAADAAgjoAAAAAABYAAHdw8THx6tVq1YKCQlRxYoV1atXL+3evdvdZQEASin6EgAAhYeA7mESEhI0atQoffvtt1q1apUuXryoW265RWlpae4uDQBQCtGXAAAoPDZjjHF3Ebh6x48fV8WKFZWQkKAbb7zxisunpKQoLCxMp0+fVmhoaDFUCACQSs/3b377klR63hsAsBq+f62HEXQPd/r0aUlSuXLl3FwJAAD0JQAACsLH3QXg6mVmZurRRx9V+/bt1bhxY6fLpKenKz093XE/JSWluMoDAJQyrvQlid4EAEBuGEH3YKNGjdLOnTs1f/78XJeJj49XWFiY4xYZGVmMFQIAShNX+pJEbwIAIDccg+6hRo8erSVLlmjDhg2qWbNmrss5G6WIjIzkOBMAKGYl/Tg/V/uSRG8CAKso6b3JE7GLu4cxxuj//b//p0WLFmn9+vVX3Ajy9/eXv79/MVUHACht8tuXJHoTAAC5IaB7mFGjRunDDz/UkiVLFBISoiNHjkiSwsLCFBgY6ObqAAClDX0JAIDCwy7uHsZmszmdPmfOHA0dOvSKj2c3FgBwj5L6/VvQviSV3PcGAKyO71/rYQTdw/D3FACAldCXAAAoPJzFHQAAAAAACyCgAwAAAABgAQR0AAAAAAAsgIAOAAAAAIAFENABAAAAALAAAjoAAAAAABZAQAcAAAAAwAII6AAAAAAAWAABHQAAAAAACyCgAwAAAABgAQR0AAAAAAAsgIAOAAAAAIAFENABAAAAALAAAjoAAACAgpk1S2rUKOtfAFfNx90FAAAAAPBgs2ZJcXHSuXNZ/0rSiBHurQnwUIygAwAAALg69nBus0lRUVn/xsUxkg5cJQI6AAAAgPy7PJyHh2dNCw8npAMFQEAHAAAAkD/OwrkdIR24ahyDDgAAAJRib7+dv+Xrb5ylFkvjJNl0PjhcOu5sqXAFpCZLT8Vp+wZpdwfXj0l/4IH81QOUJIygAwAAAHBZ9Orp8rlwLiuc5+F8cLh8LpxT9OrpxVQZ4PkI6AAAAABc9lPXWF3yC8waIc9DQGqyLvkF6qeuscVUGeD5COgAAAAAXLa7wwht7zFRksk1pGdNN9reY2K+dm8HSjsCOgCgaM2aJTVqxImCAKAEySukE86Bq0dABwAUHftZfo8f52y+AFDCOAvphHOgYAjoAICicfkleKKiuOQOAJRAl4f04JNJIpwDBcNl1gAAhc/Z9XHDw6Xk5KzpkjSCjTcAKAnsYTx69XT91DWWcA4UAAEdAFC4nIVzO0I6AJRIuzuMIJgDhYBd3AEAhSevcG4XHs7u7gAAAE4Q0D3Mhg0b1LNnT1WpUkU2m02LFy92d0kA8D/Tp0vnzuUezu3Cw7OWmz69eOpCkaI3AQBQONjF3cOkpaWpadOmGj58uHr37u3uclBcZs3KCjKxsewSjGL39tuuL1v/uli1WBon7UvW+eDcQ3rWWX4Dtf26WO3Ox/ofeMD1ZVF86E0AABQOArqH6d69u7p37+7uMlCc7LsMnzvHcbuwPPvxhy2Wxikg1XlI5xI8JQ+9CQCAwsEu7oCVcZkqeCBn18W1I5wDAADkjhH0Ei49PV3p6emO+ykpKW6sBvnCZargwZyNpBPOYUdvAgDAOUbQS7j4+HiFhYU5bpGRke4uCa640mWqGEmHB7h8JD34ZJII57CjNwEA4BwBvYQbP368Tp8+7bgdPHjQ3SXhSrhMFUoQe0g/H1KBcA4HehMAAM6xi3sJ5+/vL39/f3eXgfywX6YqKirv5cLDpaSkrOXZ1R0WtrvDCII5sqE3AQDgHCPoHiY1NVWJiYlKTEyUJO3bt0+JiYlKSkpyb2EoPLGxUmBg1rHmeUlOzlouNrZ46gKAXNCbAAAoHIyge5jvv/9enTt3dtwfO3asJGnIkCGaO3eum6pCobKPhsfFZYVwZ7u5JydLxkgTJzJ6DsDt6E0AABQOArqH6dSpk4wx7i4DRS2vkE44B2Ax9CYAAAoHu7ijdJo1S2rUyNonWBsxIiuEG/O/3d0J5wAAAECJxQg6PM7bbxfs8fU3zlKLpXHyuXBOl56K0/YNKtAJrB54oGD15OnykfSkpKxjzgnnAAAAQIlEQEepYg/nkk2p5aIUkJr8f/cLFtKLlD2MT5+edUI4wjkAAABQIhHQUWpcHs7PB2cd030+ONxzQjrBHAAAACjROAYdpYKzcG6Xdd+mFkvjVH+jhY9JBwAAAFCiEdBR4uUVzu0I6QAAAADcjYCOEi969XT5XDiXazi3Ox8cLp8L5xS9enoxVQYAAAAA/0NAR+Gw8GXLfuoaq0t+gQpITc5zuYDUZF3yC9RPXWOLqTIAAAAA+B9OEpcPFy5c0OrVq/Xrr78qLS1NEydOlCSdP39eKSkpCg8Pl5dXKfybx6xZWZcBO3cu61/JUic0s5/4rcXSOAWkJjsdSc8K70bbe0y07oniAMAJehMAACUHHdtFn3/+uaKiotSzZ089/vjjmjx5smPeTz/9pMqVK2v+/PnuK9Bd7OHcZpOiorL+jYuz3Ej67g4jtL3HREkmx0g64Rwex8J7rKB40ZsAAChZCOgu2LRpk/r27St/f3+99tpruueee7LNb926terUqaNPP/3UTRW6yeXhPPz/RqXDwz0qpBPO4XHsv3fHj1vy9wzFh94EAEDJwy7uLoiLi1PZsmW1bds2hYeH68SJEzmWadmypbZs2eKG6tzEWTi3Cw+XkpMtv7t78MkkXfILJJzDc/x9jxWL/p6heNCbAAAoeRhBd8GWLVt05513KvzvQfQykZGROnLkSDFW5UZ5hXM7DxhJPx9SgXAOz+Fhe6yg6NGbAAAoeQjoLkhPT1doaGiey5w6dar0nIRn+vSsE8LlsVEoKWv+uXNZy1vM7g4j9PHknwnn8AxX2mOFkF4q0ZsAACh56NouqFWrlrZu3ZrnMps3b1aDBg2KqSI3i42VAgOzdq/NS3Jy1nKxXLYMuGoevscKig69yQWedkJFT6sXAFDoCOgu6NOnjzZt2qQ5c+Y4nf/yyy9r586d6t+/fzFX5iYjRkgTJ0rG5B7Sk5Oz5k+cyLGxQEGUgD1WUDToTVfgaSdU9LR6AQBFgoDugieeeELXXnutRowYoZtvvllr1qyRJD355JO64YYb9NRTT6lZs2YaPXq0mystRnmFdMI5UHjYYwW5oDflwUMuAergafUCAIoMZ3F3QXBwsL7++muNHj1aCxcuVEZGhqSs0Qmbzaa7775b//73v+Xv7+/mSouZPXzHxWWFA/vZ2wnnQOFx9nv2d/zelUr0plzkdkJFq171wNPqBQAUKQK6i6655hp98MEHev3117V161adPHlSoaGhatWqlSIiItxdnvtcHh6SkrJG8AgJQOHKK6QTzks1etPfeNolQD2tXgBAkSOg51P58uXVrVs3d5dhLfaNhunTs3avZSMCKHzssYI80Jvk+gkVrRJ6Pa1eAECx4Bh0FI4RI6Sff2bjAShKl5/7ISmJcA5cztNOqOhp9QIAigUj6C646aabXFrOZrM5TtIDAEWCPVbwf+hNfxMbm/e5GuysckJFT6sXAFAsCOguWL9+fZ7zbTabjDGy2WzFUxCA0m3ECII56E1/52knVPS0egEAxYKA7oLMzEyn01NSUrR9+3ZNmDBB1apV00cffVTMlQEASit6kxNuPKHi229fzaNGqH5HqcXSOOlMss4H/6/egNRkSUbbe0zU7swRUj7W/8ADV1MLAMAKCOgFEBoaqk6dOmnFihVq0qSJnnvuOT3zzDPuLgsWc3UbbUWHDTegZCv1vcnDTqi4u0NWPS2WxikgNSukZwvnHaxVLwCgaHGSuEIQEhKi7t27a86cOe4uBQAASaW8N3nYCRV3dxih7T0mSjIKPpkkwjkAlF6MoBcSLy8vHT582N1lAADgUKp7k4edUNEexqNXT9dPXWMJ5wBQShHQC8Eff/yhjz/+WDVq1HB3KQAASKI3SfK4Eyru7jCCYJ4LKx0uxqFiAIoSAd0Fw4cPdzr90qVLOnTokDZu3KiLFy/q2WefLZZ6ZsyYoZdeeklHjhxR06ZN9cYbb6h169bF8twAAGugNwEAUPIQ0F0wd+7cPOfXr19fjz32mEYUw1/pFyxYoLFjx+rNN99UmzZt9Oqrr+rWW2/V7t27VbFixSJ/fgCANdCbAAAoeQjoLti3b5/T6V5eXipbtqxCQkKKrZZp06bp/vvv17BhwyRJb775ppYuXarZs2dr3LhxxVYHAMC96E0AAJQ8BHQXVK9e3d0lSJIuXLigbdu2afz48Y5pXl5e6tq1qzZv3uz0Menp6UpPT3fcT0lJkSQlJiYqODi4aAsuIklJ7q4gu+3b857vafUCf2elz7Anf35TU1MLdX30Jmvh96Ro8f4WLSu9v1LJfI+tqrB7EwqOgO5BkpOTlZGRoYiIiGzTIyIi9Ouvvzp9THx8vKZMmZJjeseOHYukxtLouefcXUH+eFq9wOX4/FoPvcl6+D0pWry/RY/3GKUZAd2J995776ofO3jw4EKspODGjx+vsWPHOu6npKQoMjJSCQkJjlGKzz5zV3XO9e7t7gpgpc+EK58HT6sXRcuqn4fU1NQCBVB6k/uUxN9r3t+ixftbtKz0/kpXfo+tXG9BexMKHwHdiaFDh8pms+XrMcYY2Wy2It0ICg8Pl7e3t44ePZpt+tGjR1WpUiWnj/H395e/v3+O6c2aNVNoaKgk6fvvC7/WgmjRwt0VwEqfCVc+D55WL4pWQT8P9TfOKrRrUV/+ebDvxn216E3uUxJ/r3l/ixbvb9Gy0vsrXfk9tnK9Be1NKHwEdCfmzJnj7hKc8vPz03XXXac1a9aoV69ekqTMzEytWbNGo0ePdm9xAFAC1N84Sy2Wxsnnwjm1WBonSZa5LjW9CQCAko+A7sSQIUPcXUKuxo4dqyFDhqhly5Zq3bq1Xn31VaWlpTnOnHs1HnigEAsEAA9lD+eSTanlohSQmmypkF7aehMAAKURAd3D9O/fX8ePH9czzzyjI0eOqFmzZlq+fHmOk/MAAFx3eTg/HxwuSTofHG65kG5V9CYAAAoHAd0DjR49mt0GAaCQOAvndoR019GbAAAoOC93F+ApDh48qAcffFC1a9dWYGCgvL29c9x8fPh7BwB4krzCuV3WdJtaLI1T/Y2zirW+K6E3AQBQstC1XfDHH3+oTZs2+uuvv9SoUSOlp6erevXqCggI0B9//KGLFy+qadOmKlu2rLtLBQDkQ/Tq6fK5cE6p5aLyXO58cLiCTyYpevV0y4yi05sAACh5GEF3wZQpU3T69GmtWbNGP/74oyRp2LBh+uWXX7R//37dcccdSktL0yeffOLmSgEA+fFT11hd8gtUQGpynssFpCbrkl+gfuoaW0yVXRm9CQCAkoeA7oLVq1frtttuU8eOHR3TjDGSpMqVK2vBggWSpAkTJrilPgDA1dndYYS295goyeQa0rOmG23vMdEyo+cSvQkAgJKIgO6C5ORkNWjQwHHfx8dHZ8+eddz39/fXzTffrC+//NId5QEACiCvkG7VcC7RmwAAKIkI6C4IDw9XWlpatvv79+/PtoyPj49OnTpVvIUBAAqFs5Bu5XAu0ZsAACiJCOguqFu3rvbu3eu437p1a61YsUJ//PGHJOn48eP65JNPVLt2bXeVCAAooMtDevDJJFk5nEv0JiDfZs2SGjXK+hcALIqAnov09HTH/7t37661a9c6RiEeffRRnTlzRtHR0WrVqpXq1aunI0eO6P/9v//npmoBAIXBHtLPh1SwZDinNwFXadYsKS5OOn48619COgCLIqDnonLlyho9erS2b9+uhx9+WAkJCfL29pYkderUSfPnz1f16tW1c+dORURE6PXXX9f999/v5qoBAAW1u8MIfTz5Z8uFc4neBFwVezi32aSoqKx/CekALIrroOfi/Pnz+ve//62ZM2eqadOmiomJUf369R3z+/Xrp379+rmxQgBAaUNvgpXU3zhL0aun66eusZb8g5ak7OE8PDxrWni4lJycNV2SRli0dpQYDzzg7grgSQjouTh69Kg+/PBDzZ49W1u3btWYMWP0xBNPqFevXoqJiVGXLl3cXSIAoJQpTb2JDVprq79xllosjZPPhXNqsTQr6FoupDsL53aEdAAWxS7uuQgJCdGDDz6oLVu2aOfOnYqNjVVYWJjmz5+vW265RTVr1lRcXJwOHjzo7lIBFFD9jbPUb3Ij1d/I7o6wNnoTrMAeziWbUstFSbKpxdI4a32H5hXO7cLD2d0dgOUQ0F3QsGFDvfzyy/rvf/+rzz77TD169NChQ4c0adIk1axZU927d9cnn3yiixcvurtUAPlk39AMOHPcehuYQB7oTXCHy8P5+eCs4Jv1r8VC+vTp0rlzuYdzu/DwrOWmTy+eugDgCgjo+eDt7a1evXrp888/18GDB/XCCy+oXr16WrFihfr376+qVau6u0QA+eARo0DAFdCbUFychXM7y4X02FgpMDBrN/a8JCdnLRcbWzx1AcAVENCvUkREhJ544gktWLBA7du3lzFGJ06ccHdZAFzkMaNAQD7Qm1BU8grndpb6Dh0xQpo4UTIm95CenJw1f+JEjkEHYBmcJO4qnDlzRh9++KHeffddbdu2TcYYlSlTRnfffbe7SwPggiuNAgWkJlv3pEe4otJ6cjF6E4pS9Orp8rlw7v/2Nsrd+eBwBZ9MUvTq6e7//rSH7ri4rDB++e7uhHMAFkVAz4d169Zp9uzZWrRokc6dOydjjK6//nrFxMSof//+Cg4OdneJAK7A1VEgQjo8Bb0JxeGnrrFZ5+tITc71u1OSAlKTdckvUD91tcgu485COuEcgIUR0K/gv//9r+bMmaO5c+dq//79MsaoQoUKGjlypGJiYnTttde6u0QA+eCRo0DA39CbUNzs34N5hfSA1GRJRtt7TLTW9+blIT0pKeuYc8I5AIsioOdiwYIFmj17ttauXauMjAx5eXnp1ltvVUxMjO688075+PDWAZ7IY0eBANGb4F55hXTLhnM7exifPj3rhHCEcwAWRSfPxcCBAyVJNWvW1LBhwzR06FBVq1bNzVUBKCiPHgVCqUdvgrs5+w71mO/MESMI5gAsj4Cei4EDByomJkY33XSTu0sBUMg8ehQIpRq9CVZw+Xdo8MkkXfIL5DsTAAoJAT0XH3zwgbtLAOCiqzpr9wMjpFmS4uIUYrvsxEHBWScO6jhihDoWdqFAAdGbYBX2MB69erp+6hpLOAeAQkJAB1B6ceIgALhquzuMIJgDQCEjoAMo3ThxEAAAACyCgA4AnDgIAAAAFuDl7gIAAAAAAAABHQAAAAAAS2AXdw/y3HPPaenSpUpMTJSfn59OnTrl7pIAAKUcvQlAcbuqq7cAHoIRdA9y4cIF9evXTw899JC7SwEAQBK9CYCHmjVLatQo61/AQhhB9yBTpkyRJM2dO9e9hQAA8H/oTQA8zqxZWZdYPXcu61+Jk8XCMhhBBwAAAFA62MO5zSZFRWX9GxfHSDosgxH0Ei49PV3p6emO+ykpKW6sBgAAehMAN7k8nIeHZ00LD5eSkxlJh2Uwgu5m48aNk81my/P266+/XvX64+PjFRYW5rhFRkYWYvUAgJKI3gSgxHEWzu3CwxlJh2Uwgu5mjz32mIYOHZrnMrVq1brq9Y8fP15jx4513E9JSWFDCACQJ3oT4H71N85S9Orp+qlrrHZ3YFS3QPIK53aMpMMiCOhuVqFCBVWoUKHI1u/v7y9/f/8iWz8AoOShNwHuVX/jLLVYGiefC+fUYmlWYCSkF8D06VknhIuKynu58HApKSlreQI63IRd3D1IUlKSEhMTlZSUpIyMDCUmJioxMVGpqanuLg3Ipv7GWeo3uZHqb2Q3MaCkozcBhcseziWbUstFSbKpxdI4empBxMZKgYFZI+R5SU7OWi42tnjqApxgBN2DPPPMM5o3b57jfvPmzSVJ69atU6dOndxUFZAdf/UHShd6E1B4Lg/n54OzdsU+HxyugNRkempB2EfD4+KyQriz3dyTkyVjpIkTGT2HWzGC7kHmzp0rY0yOGxtAsAr+6g+UPvQmoHA4C+d2WffpqQUyYkRW+DYm50g64RwWwgg6gELBX/0BAMjywAP5fMCsWVJCnBSSdRKzEGfLVMg6iVnHhDh1vFEEyavhbCSdcA6LYQQdQIHxV38AAArAfhKz3M4wbhcenrXc9OnFU1dJdPlIelIS4RyWQ0AHUCB5hXM7QjoAAHngJGbFyx7SK1QgnMNyCOgACiR69XT5XDiXazi3Ox8cLp8L5xS9mr/6AwCQTV7HR9uxK3bhGjFC+vln3ktYDgEdQIH81DVWl/wCFZCa91/9A1KTdckvUD915a/+AADkwEnMAIiADqCAdncYoe09JkoyuYb0rOlG23tM5ERxAADkxllIJ5wDpQoBHUCB5RXSCecAAOQDJzEDSjUuswagUNjDd4ulcQpITXZcYo1wDgBAPtnD+PTpWSeEI5wDpQYBHUChuTykB59M0iW/QMI5AABXY8QIgjlQChHQARQqexiPXj1dP3WNJZwDAAAALiKgAyh0uzuMIJgDAAAA+cRJ4gAAAAAAsAACOgAAAAAAFkBABwAAAADAAgjoAAAAAABYAAEdAAAAAAALIKADAAAAAGABXGYNAACglHngAXdXAABwhhF0AAAAALCyWbOkRo2y/kWJxgg6AAAAAFjVrFlSXJx07lzWv5I0YoR7a0KRYQQdAAAAAKzIHs5tNikqKuvfuDhG0kswAjoAAAAAWM3l4Tw8PGtaeDghvYQjoAMAAACAlTgL53aE9BKNgA4AAAAAVpFXOLcjpJdYBHQAAAAAsIrp07NOCJdbOLcLD89abvr04qkLxYKzuAPIgevjAgAAuElsbNbIeHJy3iE9OVkKDMxaHiUGI+gAAAAAYBUjRkgTJ0rGZIVwZ5KTs+ZPnMgl10oYArqH2L9/v2JiYlSzZk0FBgaqdu3amjRpki5cuODu0gAApRS9CQCKSF4hnXBeorGLu4f49ddflZmZqbfeekt16tTRzp07df/99ystLU0vv/yyu8sDAJRC9CYAKEL28H357u6E8xLPZowx7i4CV+ell17SzJkz9ccff7j8mJSUFIWFhen06dMKDQ0tw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",
            "_dom_classes": [],
            "_figure_label": "Figure 10",
            "_image_mode": "full",
            "_message": "",
            "_model_module": "jupyter-matplotlib",
            "_model_module_version": "^0.11",
            "_model_name": "MPLCanvasModel",
            "_rubberband_height": 0,
            "_rubberband_width": 0,
            "_rubberband_x": 0,
            "_rubberband_y": 0,
            "_size": [
              960,
              480
            ],
            "_view_count": null,
            "_view_module": "jupyter-matplotlib",
            "_view_module_version": "^0.11",
            "_view_name": "MPLCanvasView",
            "capture_scroll": false,
            "footer_visible": true,
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",
            "_dom_classes": [],
            "_figure_label": "Figure 11",
            "_image_mode": "full",
            "_message": "",
            "_model_module": "jupyter-matplotlib",
            "_model_module_version": "^0.11",
            "_model_name": "MPLCanvasModel",
            "_rubberband_height": 0,
            "_rubberband_width": 0,
            "_rubberband_x": 0,
            "_rubberband_y": 0,
            "_size": [
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            "_view_count": null,
            "_view_module": "jupyter-matplotlib",
            "_view_module_version": "^0.11",
            "_view_name": "MPLCanvasView",
            "capture_scroll": false,
            "footer_visible": true,
            "header_visible": true,
            "layout": "IPY_MODEL_f4bc1f5957a9448a9443a638760f931a",
            "pan_zoom_throttle": 33,
            "resizable": true,
            "toolbar": "IPY_MODEL_eeba42189aa644f4b86ef42fb73f2997",
            "toolbar_position": "left",
            "toolbar_visible": "fade-in-fade-out"
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        },
        "f4bc1f5957a9448a9443a638760f931a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
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