Sample-based Learning Methods/Week 3/Notebook: Policy Evaluation with Temporal Difference Learning/C2M2-Assignment-v4.ipynb

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    "# Assignment: Policy Evaluation in Cliff Walking Environment\n",
    "\n",
    "Welcome to the Course 2 Module 2 Programming Assignment! In this assignment, you will implement one of the fundamental sample and bootstrapping based model free reinforcement learning agents for prediction. This is namely one that uses one-step temporal difference learning, also known as TD(0). The task is to design an agent for policy evaluation in the Cliff Walking environment. Recall that policy evaluation is the prediction problem where the goal is to accurately estimate the values of states given some policy.\n",
    "\n",
    "### Learning Objectives\n",
    "- Implement parts of the Cliff Walking environment, to get experience specifying MDPs [Section 1].\n",
    "- Implement an agent that uses bootstrapping and, particularly, TD(0) [Section 2].\n",
    "- Apply TD(0) to estimate value functions for different policies, i.e., run policy evaluation experiments [Section 3]."
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    "## The Cliff Walking Environment\n",
    "\n",
    "The Cliff Walking environment is a gridworld with a discrete state space and discrete action space. The agent starts at grid cell S. The agent can move (deterministically) to the four neighboring cells by taking actions Up, Down, Left or Right. Trying to move out of the boundary results in staying in the same location. So, for example, trying to move left when at a cell on the leftmost column results in no movement at all and the agent remains in the same location. The agent receives -1 reward per step in most states, and -100 reward when falling off of the cliff. This is an episodic task; termination occurs when the agent reaches the goal grid cell G. Falling off of the cliff results in resetting to the start state, without termination.\n",
    "\n",
    "The diagram below showcases the description above and also illustrates two of the policies we will be evaluating.\n",
    "\n",
    "<img src=\"cliffwalk.png\" style=\"height:400px\">"
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    "## Packages.\n",
    "\n",
    "We import the following libraries that are required for this assignment. We shall be using the following libraries:\n",
    "1. jdc: Jupyter magic that allows defining classes over multiple jupyter notebook cells.\n",
    "2. numpy: the fundamental package for scientific computing with Python.\n",
    "3. matplotlib: the library for plotting graphs in Python.\n",
    "4. RL-Glue: the library for reinforcement learning experiments.\n",
    "5. BaseEnvironment, BaseAgent: the base classes from which we will inherit when creating the environment and agent classes in order for them to support the RL-Glue framework.\n",
    "6. Manager: the file allowing for visualization and testing.\n",
    "7. itertools.product: the function that can be used easily to compute permutations.\n",
    "8. tqdm.tqdm: Provides progress bars for visualizing the status of loops.\n",
    "\n",
    "**Please do not import other libraries** — this will break the autograder.\n",
    "\n",
    "**NOTE: For this notebook, there is no need to make any calls to methods of random number generators. Spurious or missing calls to random number generators may affect your results.**"
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    "# Do not modify this cell!\n",
    "\n",
    "import jdc\n",
    "# --\n",
    "import numpy as np\n",
    "# --\n",
    "from rl_glue import RLGlue\n",
    "# --\n",
    "from Agent import BaseAgent \n",
    "from Environment import BaseEnvironment  \n",
    "# --\n",
    "from manager import Manager\n",
    "# --\n",
    "from itertools import product\n",
    "# --\n",
    "from tqdm import tqdm"
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    "## Section 1. Environment\n",
    "\n",
    "In the first part of this assignment, you will get to see how the Cliff Walking environment is implemented. You will also get to implement parts of it to aid your understanding of the environment and more generally how MDPs are specified. In particular, you will implement the logic for:\n",
    " 1. Converting 2-dimensional coordinates to a single index for the state,\n",
    " 2. One of the actions (action up), and,\n",
    " 3. Reward and termination.\n",
    " \n",
    "Given below is an annotated diagram of the environment with more details that may help in completing the tasks of this part of the assignment. Note that we will be creating a more general environment where the height and width positions can be variable but the start, goal and cliff grid cells have the same relative positions (bottom left, bottom right and the cells between the start and goal grid cells respectively).\n",
    "\n",
    "<img src=\"cliffwalk-annotated.png\" style=\"height:400px\">\n",
    "\n",
    "Once you have gone through the code and begun implementing solutions, it may be a good idea to come back here and see if you can convince yourself that the diagram above is an accurate representation of the code given and the code you have written."
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    "# Do not modify this cell!\n",
    "\n",
    "# Create empty CliffWalkEnvironment class.\n",
    "# These methods will be filled in later cells.\n",
    "class CliffWalkEnvironment(BaseEnvironment):\n",
    "    def env_init(self, agent_info={}):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def env_start(self, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def env_step(self, reward, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def env_end(self, reward):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def env_cleanup(self, reward):\n",
    "        raise NotImplementedError\n",
    "    \n",
    "    # helper method\n",
    "    def state(self, loc):\n",
    "        raise NotImplementedError"
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    "## env_init()\n",
    "\n",
    "The first function we add to the environment is the initialization function which is called once when an environment object is created. In this function, the grid dimensions and special locations (start and goal locations and the cliff locations) are stored for easy use later."
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    "%%add_to CliffWalkEnvironment\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def env_init(self, env_info={}):\n",
    "        \"\"\"Setup for the environment called when the experiment first starts.\n",
    "        Note:\n",
    "            Initialize a tuple with the reward, first state, boolean\n",
    "            indicating if it's terminal.\n",
    "        \"\"\"\n",
    "        \n",
    "        # Note, we can setup the following variables later, in env_start() as it is equivalent. \n",
    "        # Code is left here to adhere to the note above, but these variables are initialized once more\n",
    "        # in env_start() [See the env_start() function below.]\n",
    "        \n",
    "        reward = None\n",
    "        state = None # See Aside\n",
    "        termination = None\n",
    "        self.reward_state_term = (reward, state, termination)\n",
    "        \n",
    "        # AN ASIDE: Observation is a general term used in the RL-Glue files that can be interachangeably \n",
    "        # used with the term \"state\" for our purposes and for this assignment in particular. \n",
    "        # A difference arises in the use of the terms when we have what is called Partial Observability where \n",
    "        # the environment may return states that may not fully represent all the information needed to \n",
    "        # predict values or make decisions (i.e., the environment is non-Markovian.)\n",
    "        \n",
    "        # Set the default height to 4 and width to 12 (as in the diagram given above)\n",
    "        self.grid_h = env_info.get(\"grid_height\", 4) \n",
    "        self.grid_w = env_info.get(\"grid_width\", 12)\n",
    "        \n",
    "        # Now, we can define a frame of reference. Let positive x be towards the direction down and \n",
    "        # positive y be towards the direction right (following the row-major NumPy convention.)\n",
    "        # Then, keeping with the usual convention that arrays are 0-indexed, max x is then grid_h - 1 \n",
    "        # and max y is then grid_w - 1. So, we have:\n",
    "        # Starting location of agent is the bottom-left corner, (max x, min y). \n",
    "        self.start_loc = (self.grid_h - 1, 0)\n",
    "        # Goal location is the bottom-right corner. (max x, max y).\n",
    "        self.goal_loc = (self.grid_h - 1, self.grid_w - 1)\n",
    "        \n",
    "        # The cliff will contain all the cells between the start_loc and goal_loc.\n",
    "        self.cliff = [(self.grid_h - 1, i) for i in range(1, (self.grid_w - 1))]\n",
    "        \n",
    "        # Take a look at the annotated environment diagram given in the above Jupyter Notebook cell to \n",
    "        # verify that your understanding of the above code is correct for the default case, i.e., where \n",
    "        # height = 4 and width = 12."
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    "## *Implement* state()\n",
    "    \n",
    "The agent location can be described as a two-tuple or coordinate (x, y) describing the agent’s position. \n",
    "However, we can convert the (x, y) tuple into a single index and provide agents with just this integer.\n",
    "One reason for this choice is that the spatial aspect of the problem is secondary and there is no need \n",
    "for the agent to know about the exact dimensions of the environment. \n",
    "From the agent’s viewpoint, it is just perceiving some states, accessing their corresponding values \n",
    "in a table, and updating them. Both the coordinate (x, y) state representation and the converted coordinate representation are thus equivalent in this sense.\n",
    "\n",
    "Given a grid cell location, the state() function should return the state; a single index corresponding to the location in the grid.\n",
    "\n",
    "\n",
    "```\n",
    "Example: Suppose grid_h is 2 and grid_w is 2. Then, we can write the grid cell two-tuple or coordinate\n",
    "states as follows (following the usual 0-index convention):\n",
    "|(0, 0) (0, 1)| |0 1|\n",
    "|(1, 0) (1, 1)| |2 3|\n",
    "Assuming row-major order as NumPy does,  we can flatten the latter to get a vector [0 1 2 3].\n",
    "So, if loc = (0, 0) we return 0. While, for loc = (1, 1) we return 3.\n",
    "```"
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    "%%add_to CliffWalkEnvironment\n",
    "#GRADED FUNCTION: [state]\n",
    "\n",
    "# Work Required: Yes. Modify the return statement of this function to return a correct single index as \n",
    "# the state (see the logic for this in the previous cell.)\n",
    "# Lines: 1\n",
    "def state(self, loc):\n",
    "    ### START CODE HERE ###\n",
    "    i, j = loc\n",
    "    return i * self.grid_w + j\n",
    "    ### END CODE HERE ###"
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    "### AUTOGRADER TESTS FOR STATE (5 POINTS)\n",
    "# NOTE: The test below corresponds to the annotated diagram for the environment\n",
    "#       given previously and is limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_state():\n",
    "    env = CliffWalkEnvironment()\n",
    "    env.env_init({\"grid_height\": 4, \"grid_width\": 12})\n",
    "    coords_to_test = [(0, 0), (0, 11), (1, 5), (3, 0), (3, 9), (3, 11)]\n",
    "    true_states = [0, 11, 17, 36, 45, 47]\n",
    "    output_states = [env.state(coords) for coords in coords_to_test]\n",
    "    assert(output_states == true_states)\n",
    "test_state()"
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    "## env_start()\n",
    "\n",
    "In env_start(), we initialize the agent location to be the start location and return the state corresponding to it as the first state for the agent to act upon. Additionally, we also set the reward and termination terms to be 0 and False respectively as they are consistent with the notion that there is no reward nor termination before the first action is even taken."
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    "%%add_to CliffWalkEnvironment\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def env_start(self):\n",
    "    \"\"\"The first method called when the episode starts, called before the\n",
    "    agent starts.\n",
    "\n",
    "    Returns:\n",
    "        The first state from the environment.\n",
    "    \"\"\"\n",
    "    reward = 0\n",
    "    # agent_loc will hold the current location of the agent\n",
    "    self.agent_loc = self.start_loc\n",
    "    # state is the one dimensional state representation of the agent location.\n",
    "    state = self.state(self.agent_loc)\n",
    "    termination = False\n",
    "    self.reward_state_term = (reward, state, termination)\n",
    "\n",
    "    return self.reward_state_term[1]"
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    "## *Implement* env_step()\n",
    "\n",
    "Once an action is taken by the agent, the environment must provide a new state, reward and termination signal. \n",
    "\n",
    "In the Cliff Walking environment, agents move around using a 4-cell neighborhood called the Von Neumann neighborhood (https://en.wikipedia.org/wiki/Von_Neumann_neighborhood). Thus, the agent has 4 available actions at each state. Three of the actions have been implemented for you and your first task is to implement the logic for the fourth action (Action UP).\n",
    "\n",
    "Your second task for this function is to implement the reward logic. Look over the environment description given earlier in this notebook if you need a refresher for how the reward signal is defined."
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    "%%add_to CliffWalkEnvironment\n",
    "#GRADED FUNCTION: [env_step]\n",
    "\n",
    "# Work Required: Yes. Fill in the code for action UP and implement the logic for reward and termination.\n",
    "# Lines: ~7.\n",
    "def env_step(self, action):\n",
    "    \"\"\"A step taken by the environment.\n",
    "\n",
    "    Args:\n",
    "        action: The action taken by the agent\n",
    "\n",
    "    Returns:\n",
    "        (float, state, Boolean): a tuple of the reward, state,\n",
    "            and boolean indicating if it's terminal.\n",
    "    \"\"\"\n",
    "\n",
    "    if action == 0: # UP (Task 1)\n",
    "        ### START CODE HERE ###\n",
    "        # Hint: Look at the code given for the other actions and think about the logic in them.\n",
    "        possible_next_loc = (self.agent_loc[0] - 1, self.agent_loc[1])\n",
    "        if possible_next_loc[0] >= 0: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "        ### END CODE HERE ###\n",
    "    elif action == 1: # LEFT\n",
    "        possible_next_loc = (self.agent_loc[0], self.agent_loc[1] - 1)\n",
    "        if possible_next_loc[1] >= 0: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "    elif action == 2: # DOWN\n",
    "        possible_next_loc = (self.agent_loc[0] + 1, self.agent_loc[1])\n",
    "        if possible_next_loc[0] < self.grid_h: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "    elif action == 3: # RIGHT\n",
    "        possible_next_loc = (self.agent_loc[0], self.agent_loc[1] + 1)\n",
    "        if possible_next_loc[1] < self.grid_w: # Within Bounds?\n",
    "            self.agent_loc = possible_next_loc\n",
    "        else:\n",
    "            pass # Stay.\n",
    "    else: \n",
    "        raise Exception(str(action) + \" not in recognized actions [0: Up, 1: Left, 2: Down, 3: Right]!\")\n",
    "\n",
    "    reward = -1\n",
    "    terminal = False\n",
    "\n",
    "    ### START CODE HERE ###\n",
    "    # Hint: Consider the initialization of reward and terminal variables above. Then, note the \n",
    "    # conditional statements and comments given below and carefully ensure to set the variables reward \n",
    "    # and terminal correctly for each case.\n",
    "    if self.agent_loc == self.goal_loc: # Reached Goal!\n",
    "        reward = -1\n",
    "        terminal = True\n",
    "    elif self.agent_loc in self.cliff: # Fell into the cliff!\n",
    "        reward = -100\n",
    "        terminal = False\n",
    "        self.agent_loc = self.start_loc\n",
    "    else: \n",
    "        reward = -1\n",
    "        terminal = False\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    self.reward_state_term = (reward, self.state(self.agent_loc), terminal)\n",
    "    return self.reward_state_term"
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    "### AUTOGRADER TESTS FOR ACTION UP (5 POINTS)\n",
    "# NOTE: The test below is again limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_action_up():\n",
    "    env = CliffWalkEnvironment()\n",
    "    env.env_init({\"grid_height\": 4, \"grid_width\": 12})\n",
    "    env.agent_loc = (0, 0)\n",
    "    env.env_step(0)\n",
    "    assert(env.agent_loc == (0, 0))\n",
    "    \n",
    "    env.agent_loc = (1, 0)\n",
    "    env.env_step(0)\n",
    "    assert(env.agent_loc == (0, 0))\n",
    "test_action_up()"
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   "source": [
    "### AUTOGRADER TESTS FOR REWARD & TERMINATION (10 POINTS)\n",
    "# NOTE: The test below is limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_reward():\n",
    "    env = CliffWalkEnvironment()\n",
    "    env.env_init({\"grid_height\": 4, \"grid_width\": 12})\n",
    "    env.agent_loc = (0, 0)\n",
    "    reward_state_term = env.env_step(0)\n",
    "    assert(reward_state_term[0] == -1 and reward_state_term[1] == env.state((0, 0)) and\n",
    "           reward_state_term[2] == False)\n",
    "    \n",
    "    env.agent_loc = (3, 1)\n",
    "    reward_state_term = env.env_step(2)\n",
    "    assert(reward_state_term[0] == -100 and reward_state_term[1] == env.state((3, 0)) and\n",
    "           reward_state_term[2] == False)\n",
    "    \n",
    "    env.agent_loc = (2, 11)\n",
    "    reward_state_term = env.env_step(2)\n",
    "    assert(reward_state_term[0] == -1 and reward_state_term[1] == env.state((3, 11)) and\n",
    "           reward_state_term[2] == True)\n",
    "test_reward()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "4a499bb0871653446d6b303c2625df09",
     "grade": false,
     "grade_id": "cell-88082a3dab94c4a8",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## env_cleanup()\n",
    "\n",
    "There is not much cleanup to do for the Cliff Walking environment. Here, we simply reset the agent location to be the start location in this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "ef29fa83791e5f064a53af742f2859a2",
     "grade": false,
     "grade_id": "cell-4c235eb2667f9f0d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
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   "outputs": [],
   "source": [
    "%%add_to CliffWalkEnvironment\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def env_cleanup(self):\n",
    "    \"\"\"Cleanup done after the environment ends\"\"\"\n",
    "    self.agent_loc = self.start_loc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "180b31b525fb94442eb79f07d7097700",
     "grade": false,
     "grade_id": "cell-ff4e65eb47d735c9",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Section 2. Agent\n",
    "\n",
    "In this second part of the assignment, you will be implementing the key updates for Temporal Difference Learning. There are two cases to consider depending on whether an action leads to a terminal state or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "d4eaa5e48e54f9c5e55149325b215d47",
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   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "# Create empty TDAgent class.\n",
    "# These methods will be filled in later cells.\n",
    "\n",
    "class TDAgent(BaseAgent):\n",
    "    def agent_init(self, agent_info={}):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def agent_start(self, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def agent_step(self, reward, state):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def agent_end(self, reward):\n",
    "        raise NotImplementedError\n",
    "\n",
    "    def agent_cleanup(self):        \n",
    "        raise NotImplementedError\n",
    "        \n",
    "    def agent_message(self, message):\n",
    "        raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "83d69efb64983138188a2bad513622b1",
     "grade": false,
     "grade_id": "cell-edd826505e77e70a",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## agent_init()\n",
    "\n",
    "As we did with the environment, we first initialize the agent once when a TDAgent object is created. In this function, we create a random number generator, seeded with the seed provided in the agent_info dictionary to get reproducible results. We also set the policy, discount and step size based on the agent_info dictionary. Finally, with a convention that the policy is always specified as a mapping from states to actions and so is an array of size (# States, # Actions), we initialize a values array of shape (# States,) to zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "24d451072e0b43f3ad7980887c6f5861",
     "grade": false,
     "grade_id": "cell-077135deef2f8fd9",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_init(self, agent_info={}):\n",
    "    \"\"\"Setup for the agent called when the experiment first starts.\"\"\"\n",
    "\n",
    "    # Create a random number generator with the provided seed to seed the agent for reproducibility.\n",
    "    self.rand_generator = np.random.RandomState(agent_info.get(\"seed\"))\n",
    "\n",
    "    # Policy will be given, recall that the goal is to accurately estimate its corresponding value function. \n",
    "    self.policy = agent_info.get(\"policy\")\n",
    "    # Discount factor (gamma) to use in the updates.\n",
    "    self.discount = agent_info.get(\"discount\")\n",
    "    # The learning rate or step size parameter (alpha) to use in updates.\n",
    "    self.step_size = agent_info.get(\"step_size\")\n",
    "\n",
    "    # Initialize an array of zeros that will hold the values.\n",
    "    # Recall that the policy can be represented as a (# States, # Actions) array. With the \n",
    "    # assumption that this is the case, we can use the first dimension of the policy to\n",
    "    # initialize the array for values.\n",
    "    self.values = np.zeros((self.policy.shape[0],))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "a4f9d05505f09198739674b1b330b9cd",
     "grade": false,
     "grade_id": "cell-99e8e59cd1f7a5ef",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "# agent_start()\n",
    "\n",
    "In agent_start(), we choose an action based on the initial state and policy we are evaluating. We also cache the state so that we can later update its value when we perform a Temporal Difference update. Finally, we return the action chosen so that the RL loop can continue and the environment can execute this action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8c5490c3d5fa88339b02cdf933f7f106",
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     "grade_id": "cell-1b6dd05f7f49c1fc",
     "locked": true,
     "schema_version": 1,
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   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_start(self, state):\n",
    "    \"\"\"The first method called when the episode starts, called after\n",
    "    the environment starts.\n",
    "    Args:\n",
    "        state (Numpy array): the state from the environment's env_start function.\n",
    "    Returns:\n",
    "        The first action the agent takes.\n",
    "    \"\"\"\n",
    "    # The policy can be represented as a (# States, # Actions) array. So, we can use \n",
    "    # the second dimension here when choosing an action.\n",
    "    action = self.rand_generator.choice(range(self.policy.shape[1]), p=self.policy[state])\n",
    "    self.last_state = state\n",
    "    return action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "fd9be2a7be0a8fbd3aee6b7c14f4ec37",
     "grade": false,
     "grade_id": "cell-a472a21b4a57b885",
     "locked": true,
     "schema_version": 1,
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    }
   },
   "source": [
    "## *Implement* agent_step()\n",
    "\n",
    "In agent_step(), the agent must:\n",
    "\n",
    "- Perform an update to improve the value estimate of the previously visited state, and\n",
    "- Act based on the state provided by the environment.\n",
    "\n",
    "The latter of the two steps above has been implemented for you. Implement the former. Note that, unlike later in agent_end(), the episode has not yet ended in agent_step(). in other words, the previously observed state was not a terminal state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "4c68b1daf60c5fbf3c75d4329048af15",
     "grade": false,
     "grade_id": "cell-2bec3235783127e8",
     "locked": false,
     "schema_version": 1,
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   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "#[GRADED] FUNCTION: [agent_step]\n",
    "\n",
    "# Work Required: Yes. Fill in the TD-target and update.\n",
    "# Lines: ~2.\n",
    "def agent_step(self, reward, state):\n",
    "    \"\"\"A step taken by the agent.\n",
    "    Args:\n",
    "        reward (float): the reward received for taking the last action taken\n",
    "        state (Numpy array): the state from the\n",
    "            environment's step after the last action, i.e., where the agent ended up after the\n",
    "            last action\n",
    "    Returns:\n",
    "        The action the agent is taking.\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ###\n",
    "    # Hint: We should perform an update with the last state given that we now have the reward and\n",
    "    # next state. We break this into two steps. Recall for example that the Monte-Carlo update \n",
    "    # had the form: V[S_t] = V[S_t] + alpha * (target - V[S_t]), where the target was the return, G_t.\n",
    "    target = reward + self.discount * self.values[state]\n",
    "    self.values[self.last_state] = self.values[self.last_state] + self.step_size * (target - self.values[self.last_state])\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Having updated the value for the last state, we now act based on the current \n",
    "    # state, and set the last state to be current one as we will next be making an \n",
    "    # update with it when agent_step is called next once the action we return from this function \n",
    "    # is executed in the environment.\n",
    "\n",
    "    action = self.rand_generator.choice(range(self.policy.shape[1]), p=self.policy[state])\n",
    "    self.last_state = state\n",
    "\n",
    "    return action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8f516e1bdb6ca3f1a1ce385cf2c56765",
     "grade": false,
     "grade_id": "cell-f5ed31e224e22cda",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## *Implement* agent_end() \n",
    "\n",
    "Implement the TD update for the case where an action leads to a terminal state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "65b123f954dd8176f089d389bc2e9735",
     "grade": false,
     "grade_id": "cell-08c5ac56c1a0a841",
     "locked": false,
     "schema_version": 1,
     "solution": true
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   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "#[GRADED] FUNCTION: [agent_end]\n",
    "\n",
    "# Work Required: Yes. Fill in the TD-target and update.\n",
    "# Lines: ~2.\n",
    "def agent_end(self, reward):\n",
    "    \"\"\"Run when the agent terminates.\n",
    "    Args:\n",
    "        reward (float): the reward the agent received for entering the terminal state.\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ###\n",
    "    # Hint: Here too, we should perform an update with the last state given that we now have the \n",
    "    # reward. Note that in this case, the action led to termination. Once more, we break this into \n",
    "    # two steps, computing the target and the update itself that uses the target and the \n",
    "    # current value estimate for the state whose value we are updating.\n",
    "    target = reward\n",
    "    self.values[self.last_state] = self.values[self.last_state] + self.step_size * (target - self.values[self.last_state])\n",
    "    ### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "8e12df5724ecfa704bc67e2577c2ea34",
     "grade": false,
     "grade_id": "cell-06cbc6053aa156bc",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## agent_cleanup()\n",
    "\n",
    "In cleanup, we simply reset the last state to be None to ensure that we are not storing any states past an episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "540f808d320ce14b29ee7e8ddcda6ae7",
     "grade": false,
     "grade_id": "cell-c39f860970dfe3a7",
     "locked": true,
     "schema_version": 1,
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   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_cleanup(self):\n",
    "    \"\"\"Cleanup done after the agent ends.\"\"\"\n",
    "    self.last_state = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "f62a4cb327c230937f3bc459f305e6f6",
     "grade": false,
     "grade_id": "cell-aadbaa36598224e6",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## agent_message()\n",
    "\n",
    "agent_message() can generally be used to get different kinds of information about an RLGlue agent in the interaction loop of RLGlue. Here, we conditonally check for a message matching \"get_values\" and use it to retrieve the values table the agent has been updating over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "9e222e9cc5396065dafe6e39a2052097",
     "grade": false,
     "grade_id": "cell-a440254b4f0573e2",
     "locked": true,
     "schema_version": 1,
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    }
   },
   "outputs": [],
   "source": [
    "%%add_to TDAgent\n",
    "\n",
    "# Do not modify this cell!\n",
    "\n",
    "# Work Required: No.\n",
    "def agent_message(self, message):\n",
    "    \"\"\"A function used to pass information from the agent to the experiment.\n",
    "    Args:\n",
    "        message: The message passed to the agent.\n",
    "    Returns:\n",
    "        The response (or answer) to the message.\n",
    "    \"\"\"\n",
    "    if message == \"get_values\":\n",
    "        return self.values\n",
    "    else:\n",
    "        raise Exception(\"TDAgent.agent_message(): Message not understood!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "6830d90d89beea893034ebe0d7b2373c",
     "grade": true,
     "grade_id": "td_updates",
     "locked": true,
     "points": 20,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "### AUTOGRADER TESTS FOR TD-UPDATES (20 POINTS)\n",
    "# NOTE: The test belows serve as a good check in debugging your code for the TD updates. However, \n",
    "#       as with the other tests, it is limited in scope. Hidden tests are used in the autograder.\n",
    "#       You may wish to run other tests to check your implementation.\n",
    "def test_td_updates():\n",
    "    # The following test checks that the TD check works for a case where the transition \n",
    "    # garners reward -1 and does not lead to a terminal state. This is in a simple two state setting \n",
    "    # where there is only one action. The first state's current value estimate is 0 while the second is 1.\n",
    "    # Note the discount and step size if you are debugging this test.\n",
    "    agent = TDAgent()\n",
    "    policy_list = np.array([[1.], [1.]])\n",
    "    agent.agent_init({\"policy\": np.array(policy_list), \"discount\": 0.99, \"step_size\": 0.1})\n",
    "    agent.values = np.array([0., 1.])\n",
    "    agent.agent_start(0)\n",
    "    reward = -1\n",
    "    next_state = 1\n",
    "    agent.agent_step(reward, next_state)\n",
    "    assert(np.isclose(agent.values[0], -0.001) and np.isclose(agent.values[1], 1.))\n",
    "    \n",
    "    # The following test checks that the TD check works for a case where the transition \n",
    "    # garners reward -100 and lead to a terminal state. This is in a simple one state setting \n",
    "    # where there is only one action. The state's current value estimate is 0.\n",
    "    # Note the discount and step size if you are debugging this test.\n",
    "    agent = TDAgent()\n",
    "    policy_list = np.array([[1.]])\n",
    "    agent.agent_init({\"policy\": np.array(policy_list), \"discount\": 0.99, \"step_size\": 0.1})\n",
    "    agent.values = np.array([0.])\n",
    "    agent.agent_start(0)\n",
    "    reward = -100\n",
    "    next_state = 0\n",
    "    agent.agent_end(reward)\n",
    "    assert(np.isclose(agent.values[0], -10))\n",
    "    \n",
    "test_td_updates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "editable": false,
    "nbgrader": {
     "checksum": "ba2a884124c4e5c073f529f94a2bd580",
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     "grade_id": "cell-3f4e7f85a3f548e9",
     "locked": true,
     "schema_version": 1,
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   "source": [
    "## Section 3. Policy Evaluation Experiments\n",
    "\n",
    "Finally, in this last part of the assignment, you will get to see the TD policy evaluation algorithm in action by looking at the estimated values, the per state value error and after the experiment is complete, the Mean Squared Value Error curve vs. episode number, summarizing how the value error changed over time.\n",
    "\n",
    "The code below runs one run of an experiment given env_info and agent_info dictionaries. A \"manager\" object is created for visualizations and is used in part for the autograder. By default, the run will be for 5000 episodes. The true_values_file is specified to compare the learned value function with the values stored in the true_values_file. Plotting of the learned value  function occurs by default after every 100 episodes. In addition, when true_values_file is specified, the value error per state and the root mean square value error will also be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "d929e2760dd07cbdd0bea18f05639d81",
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     "grade_id": "cell-7b2ee7906789af2d",
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   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# Work Required: No. \n",
    "def run_experiment(env_info, agent_info, \n",
    "                   num_episodes=5000,\n",
    "                   experiment_name=None,\n",
    "                   plot_freq=100,\n",
    "                   true_values_file=None,\n",
    "                   value_error_threshold=1e-8):\n",
    "    env = CliffWalkEnvironment\n",
    "    agent = TDAgent\n",
    "    rl_glue = RLGlue(env, agent)\n",
    "\n",
    "    rl_glue.rl_init(agent_info, env_info)\n",
    "\n",
    "    manager = Manager(env_info, agent_info, true_values_file=true_values_file, experiment_name=experiment_name)\n",
    "    for episode in range(1, num_episodes + 1):\n",
    "        rl_glue.rl_episode(0) # no step limit\n",
    "        if episode % plot_freq == 0:\n",
    "            values = rl_glue.agent.agent_message(\"get_values\")\n",
    "            manager.visualize(values, episode)\n",
    "\n",
    "    values = rl_glue.agent.agent_message(\"get_values\")\n",
    "    if true_values_file is not None:\n",
    "        # Grading: The Manager will check that the values computed using your TD agent match \n",
    "        # the true values (within some small allowance) across the states. In addition, it also\n",
    "        # checks whether the root mean squared value error is close to 0.\n",
    "        manager.run_tests(values, value_error_threshold)\n",
    "    \n",
    "    return values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "nbgrader": {
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     "grade_id": "cell-17e3e97164635860",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "The cell below just runs a policy evaluation experiment with the determinstic optimal policy that strides just above the cliff. You should observe that the per state value error and RMSVE curve asymptotically go towards 0. The arrows in the four directions denote the probabilities of taking each action. This experiment is ungraded but should serve as a good test for the later experiments. The true values file provided for this experiment may help with debugging as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "5f6345cb14ab92ac88ba1653695a1cec",
     "grade": false,
     "grade_id": "cell-70b3816dea097209",
     "locked": true,
     "schema_version": 1,
     "solution": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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W73LIS1GfUNjnDRkfBbd75+9tOhJAAAAQMnpRORFcKeqbr311kgpxTHHHBONjY17fP1FF10UKaX44Q9/2OTzN910U6SU4rvf/W6rtzmkxfnWH5/Jgvsldz9X9BwAAACg5HQi8iK40ypDhw6NmpqaOO+887JfdPrud787pk2b1qz39+nTJ1JKccMNNzT5/D333JN9v3trOaTFufPphVlw/6dfPBiVSqXoSQAAAECJ6UTkRXCnVc4+++xIKWWPww8/PB555JFmv/9jH/tYpJRixIgRTT4/ceLESCnFOeec0+zP3HYwd3x069bNIS3I8vWbs+DeY2BtzF22vuhJAAAAQIkJ7uRFcKcq1qxZE5MmTYqPfvSjkVKKK664olnv2/b6kSNHNvn8hAkTBPcO4hM3TMqC+78/+krRcwAAAIASE9zJi+BOVdXV1cUpp5wSnTp1iqeeemqPr/eVMuUxdPycLLh/acTjRc8BAAAASkwnIi+CO1U3dOjQSCnFpZdeusfX+qWp5fHUq6uy4N5r8Lh4c0t90ZMAAACAktKJyIvgTtWNGjUqUkrxzW9+s9mvPfvss5t8vl+/fpFSilGjRrV6l0NarPqGxjhpyPgsuk94flnRkwAAAICS0onIi+BO1dXU1ERKKa6++uo9vnbJkiXRuXPn2H///WP58uXbPbdly5Y45JBDonPnzrF06dJW73JIi/etPzyTBffBdz9X9BwAAACgpHQi8iK402KTJk2K22+/Perrt/9KkLq6urjxxhujc+fOceCBB8bChQuz54YNGxbHHntsDBo0aKfP+/KXvxwppfjc5z633WcOGDAgUkpxwQUXVGW3Q1q8O55emAX303/5YFQqlaInAQAAACWkE5EXwZ0WGz16dKSU4l3velf07t07vvSlL8U555wThx12WKSU4oADDog77rhju/cMGTIkUkpRU1Oz0+etWLEievbsGSml6NmzZ/Tt2zdOPPHE7H+vWLGiKrsd0uItX7c5C+49BtbGvOUbip4EAAAAlJBORF4Ed1ps/vz5MXjw4Dj99NPjsMMOi/322y/e9ra3xQknnBD9+/ePefPm7fSe3QX3iIjVq1dH//794/DDD4/9998/Dj/88PjOd74Tq1atqtpuh7Rt+N/XT8qC+4hJrxQ9BwAAACghnYi8CO6UhkPaNlx135wsuF8w8omi5wAAAAAlpBORF8Gd0nBI24Yn56/KgnuvweNi49b6Pb8JAAAAoIp0IvIiuFMaDmnbUN/QGCcOGZ9F9wdeWFb0JAAAAKBkdCLyIrhTGg5p23HhH6Zlwf3HY2YVPQcAAAAoGZ2IvAjulIZD2na/OP5jAAAgAElEQVTc8dTCLLj/81UPRqVSKXoSAAAAUCI6EXkR3CkNh7TtWLZucxbcewysjZff2FD0JAAAAKBEdCLyIrhTGg5p2/Lx6ydlwX3kY/OLngMAAACUiE5EXgR3SsMhbVt+ed+cLLhfMPKJoucAAAAAJaITkRfBndJwSNuWJ15ZmQX3Xj8aF5u2NhQ9CQAAACgJnYi8CO6UhkPattQ1NMaJl43PovuDc5YVPQkAAAAoCZ2IvAjulIZD2vZ88/fTsuB+6T2zip4DAAAAlIRORF4Ed0rDIW17bn9qQRbcP3TVQ1GpVIqeBAAAAJSATkReBHdKwyFte5au3ZwF9x4Da+OVNzYUPQkAAAAoAZ2IvAjulIZD2jb1vu7RLLj/9rH5Rc8BAAAASkAnIi+CO6XhkLZNvxg3JwvuX/ntk0XPAQAAAEpAJyIvgjul4ZC2TY+/sjIL7r1+NC42bW0oehIAAADQwelE5EVwpzQc0raprqExTrxsfBbdH5qzvOhJAAAAQAenE5EXwZ3ScEjbrm/8bloW3C+7Z1bRcwAAAIAOTiciL4I7peGQtl23PbkgC+4fHvpQ0XMAAACADk4nIi+CO6XhkLZdS9ZuyoJ7j4G1MX/Fm0VPAgAAADownYi8CO6UhkPatvW+7tEsuI+aPL/oOQAAAEAHphORF8Gd0nBI27Yrx72QBfev/vbJoucAAAAAHZhORF4Ed0rDIW3bpr68Mgvux/xoXGyuayh6EgAAANBB6UTkRXCnNBzStq2uoTFOuGx8Ft0fenF50ZMAAACADkonIi+CO6XhkLZ9//q7p7PgPuTPs4ueAwAAAHRQOhF5EdwpDYe07fvPJxdkwf0jQx8qeg4AAADQQelE5EVwpzQc0rbv9TWbsuDeY2BtvLrizaInAQAAAB2QTkReBHdKwyFtHz527SNZcB89eX7RcwAAAIAOSCciL4I7peGQtg8/H/tCFtxrRj1Z9BwAAACgA9KJyIvgTmk4pO3DlHkrsuB+zI/Gxea6hqInAQAAAB2MTkReBHdKwyFtH7bWN8b/uvS+LLo//OLyoicBAAAAHYxORF4Ed0rDIW0/vn7r01lwH/Ln2UXPAQAAADoYnYi8CO6UhkPafvzhidey4H7G1Q8XPQcAAADoYHQi8iK4UxoOafuxeM2mLLj3GFgbr618s+hJAAAAQAeiE5EXwZ3ScEjbl49e80gW3P9jyqtFzwEAAAA6EJ2IvAjulIZD2r5cUft8Ftz/76gni54DAAAAdCA6EXkR3CkNh7R9mTxvRRbcj/3xuNhc11D0JAAAAKCD0InIi+BOaTik7cuW+oY4/tL7suj+yEtvFD0JAAAA6CB0IvIiuFMaDmn78y//8XQW3H/yl9lFzwEAAAA6CJ2IvAjulIZD2v78/vHXsuB+5tUPFz0HAAAA6CB0IvIiuFMaDmn7s2j1xiy49xhYGwtWbix6EgAAANAB6ETkRXCnNBzS9unsax7JgvutU18teg4AAADQAehE5EVwpzQc0vbpZ/c+nwX3r41+qug5AAAAQAegE5EXwZ3ScEjbp8fmrsiC+7E/Hheb6xqKngQAAAC0czoReRHcKQ2HtH3aUt8Qx196XxbdH33pjaInAQAAAO2cTkReBHdKwyFtv/7lP57Kgvvlf3m+6DkAAABAO6cTkRfBndJwSNuv3z3+Whbcz/zVw0XPAQAAANo5nYi8CO6UhkPafi1ctTEL7j0G1sbCVRuLngQAAAC0YzoReRHcKQ2HtH0761cPZ8H9d1NfLXoOAAAA0I7pRORFcKc0HNL27af3Pp8F936jnyp6DgAAANCO6UTkRXCnNBzS9m3S3Dey4H7cj++LzXUNRU8CAAAA2imdiLwI7pSGQ9q+balviON+fF8W3SfNfaPoSQAAAEA7pRORF8Gd0nBI279+o5/KgvtP732+6DkAAABAO6UTkRfBndJwSNu/3019NQvuZ/3q4aLnAAAAAO2UTkReBHdKwyFt/xau2pgF9x4Da2Phqo1FTwIAAADaIZ2IvAjulIZD2jGc+auHs+D+u8dfK3oOAAAA0A7pRORFcKc0HNKO4fK/PJ8F93/5j6eKngMAAAC0QzoReRHcKQ2HtGN49KU3suB+/KX3xZb6hqInAQAAAO2MTkReBHdKwyHtGDbXNcRxP74vi+6PzV1R9CQAAACgndGJyIvgTmk4pB3H10Y/lQX3K2qfL3oOAAAA0M7oRORFcKc0HNKO49apr2bB/aPXPFL0HAAAAKCd0YnIi+BOi23cuDHGjBkT/fr1i5NOOikOOuig6N69e7z3ve+Nyy+/PDZs2NCiz+vRo0eklHb5mDNnTlV2O6Qdx4KVG7Pg3mNgbSxes6noSQAAAEA7ohORF8GdFhsxYkQWw0844YT4/Oc/H717946DDjooUkpx3HHHxfLly5v9eduCe01NTZOPJUuWVGW3Q9qxnHn1w1lw/8MTrxU9BwAAAGhHdCLyIrjTYrfeemtceOGFMXfu3O1+vmTJkjj55JMjpRTnn39+sz9vW3DPm0PasfzkL7Oz4P71W58ueg4AAADQjuhE5EVwp6qmTp0aKaXo1q1bbN26tVnvEdzZG4+89EYW3P/XpffF1vrGoicBAAAA7YRORF4Ed6pq48aN2dfNNPerYAR39sbmuoY49sfjsug+Zd6KoicBAAAA7YRORF4Ed6pq1qxZkVKK/fbbL7Zs2dKs92wL7kOHDo1vfOMbMWDAgBg+fHi88cYbVd3mkHY8/3fUk1lw//nYF4qeAwAAALQTOhF5Edypqq9//euRUopPfepTzX7PtuC+46N79+4xcuTIqm1zSDue/5jyahbcP3btI0XPAQAAANoJnYi8CO5UzdixY6NTp06x3377xYwZM5r9vv79+8fdd98dCxYsiE2bNsXs2bPj4osvji5dukRKKcaMGdOiHdsO5o6Pbt26OaQdzGsr38yCe4+BtfH6mk1FTwIAAADaAcGdvAjuVMULL7wQ73jHOyKlFNdff31VPnP48OGRUopjjjmmRe8T3MvljKsfzoL7H59YUPQcAAAAoB0Q3MmL4E6rLVq0KI444ohIKcXFF19ctc9tbGyMQw89NFJKMX/+/FZ/nkPaMQ358+wsuP9/tz5d9BwAAACgHdCJyIvgTqusWLEijjvuuEgpxde+9rWoVCpV/fwPfvCDkVKKKVOmtPqzHNKO6eEXl2fB/X9del9srW8sehIAAADQxulE5EVwZ6+tX78+Tj311EgpxbnnnhsNDQ1V/zO2xfyZM2e2+rMc0o5pc11DHPOjcVl0n/LyiqInAQAAAG2cTkReBHf2ypYtW+LMM8+MlFL07t07tm7dWvU/Y/bs2dGpU6fo3r17VT7fIe24akY9mQX3K8e+UPQcAAAAoI3TiciL4E6LNTQ0RJ8+fSKlFB/60Idi48aNe3zPsGHD4thjj41BgwZt9/Px48fHtGnTdnr9zJkz4/jjj4+UUgwYMKAqux3Sjmv05PlZcD/n2keLngMAAAC0cToReRHcabHrr78+UkqRUoo+ffpETU1Nk48VK/77qz2GDBkSKaWoqanZ7rO2/bxHjx5x1llnRd++feO0006Lrl27RkopPvKRjzQr6DeHQ9pxvbrizSy49xhYG6+v2VT0JAAAAKAN04nIi+BOi22L5Ht6vPrqqzu9Z8fgPnXq1OjXr1+cdNJJcfDBB0fXrl3jne98Z5xxxhkxYsSIqn4vvEPasX1k6ENZcP/PJxcUPQcAAABow3Qi8iK4UxoOacc25M+zs+D+r797uug5AAAAQBumE5EXwZ3ScEg7todeXJ4F9xMuGx9b6xuLngQAAAC0UToReRHcKQ2HtGPbXNcQx/xoXBbdp768suhJAAAAQBulE5EXwZ3ScEg7vq/+9sksuF857oWi5wAAAABtlE5EXgR3SsMh7fhGTZ6fBffe1z1a9BwAAACgjdKJyIvgTmk4pB3f/BVvZsG9x8DaWLJ2U9GTAAAAgDZIJyIvgjul4ZCWw4eHPpQF99ueXFD0HAAAAKAN0onIi+BOaTik5XDZPbOy4P6N300reg4AAADQBulE5EVwpzQc0nJ4aM7yLLifeNn4qGtoLHoSAAAA0MboRORFcKc0HNJy2LS1IXr9aFwW3R9/ZWXRkwAAAIA2RiciL4I7peGQlscFI5/Igvsvxs0peg4AAADQxuhE5EVwpzQc0vIY+dj8LLj3vu7RoucAAAAAbYxORF4Ed0rDIS2Pl9/YkAX3HgNrY+nazUVPAgAAANoQnYi8CO6UhkNaHpVKJf75qgez4H77UwuKngQAAAC0IToReRHcKQ2HtFx+PGZWFty/+ftpRc8BAAAA2hCdiLwI7pSGQ1ouD7ywLAvuJ142PuoaGoueBAAAALQROhF5EdwpDYe0XDZurY9eg8dl0f2JV1YWPQkAAABoI3Qi8iK4UxoOaflcMPKJLLj/8r45Rc8BAAAA2gidiLwI7pSGQ1o+Iya9kgX3j18/qeg5AAAAQBuhE5EXwZ3ScEjLZ97yDVlw7zGwNpat21z0JAAAAKAN0InIi+BOaTik5VOpVOL0Xz6YBfc7nlpY9CQAAACgDdCJyIvgTmk4pOX0ozHPZcH9wj9MK3oOAAAA0AboRORFcKc0HNJymvj8siy4nzhkfNQ3NBY9CQAAACiYTkReBHdKwyEtp41b66PX4HFZdH9y/qqiJwEAAAAF04nIi+BOaTik5fXlEU9kwf2q++YUPQcAAAAomE5EXgR3SsMhLa8Rk17Jgvv/vn5S0XMAAACAgulE5EVwpzQc0vKat3x9Ftx7DKyN5es2Fz0JAAAAKJBORF4Ed0rDIS2vSqUS//SLB7PgfsfTC4ueBAAAABRIJyIvgjul4ZCW2+C7n8uC+7f+8EzRcwAAAIAC6UTkRXCnNBzScpvw/LIsuJ80ZHzUNzQWPQkAAAAoiE5EXgR3SsMhLbc3t9RHr8Hjsuj+1Kurip4EAAAAFEQnIi+CO6XhkPKlEY9nwX3o+DlFzwEAAAAKohORF8Gd0nBI+fdHX8mC+ydumFT0HAAAAKAgOhF5EdwpDYeUucvWZ8G9x8DaWL5+c9GTAAAAgALoRORFcKc0HFIqlUr80y8ezIL7nU8vLHoSAAAAUACdiLwI7pSGQ0pExCV3P5cF92/98Zmi5wAAAAAF0InIi+BOaTikRETcP3tpFtxPGjI+6hsai54EAAAA7GM6EXkR3CkNh5SIiA1b6uPowWOz6P70q6uKngQAAADsYzoReRHcKQ2HlG3O//fHs+B+9fgXi54DAAAA7GM6EXkR3CkNh5Rthj/6chbcP3njpKLnAAAAAPuYTkReBHdKwyFlm5eWrc+Ce4+BtbF8/eaiJwEAAAD7kE5EXgR3SsMhZZtKpRIfvPKBLLj/adqioicBAAAA+5BORF4Ed0rDIeWtBt31XBbcv/3HZ4qeAwAAAOxDOhF5EdwpDYeUtxo/e2kW3N/7k/ujvqGx6EkAAADAPqITkRfBndJwSHmrDVvq4+jBY7PoPu211UVPAgAAAPYRnYi8CO6UhkPKjr44/PEsuF9z/4tFzwEAAAD2EZ2IvAjulIZDyo5+88jLWXD/1LDHip4DAAAA7CM6EXkR3CkNh5Qdvbh0fRbcewysjRUbthQ9CQAAANgHdCLyUvrgnlLa7tGpU6d4+9vfHh/4wAfi2muvjbq6uibfV1NTk73nX//1X3f5+Vu3bo3/+T//Z/bahx9+eKfXvPjii/H1r389jjrqqNh///3joIMOip49e8YnP/nJGDp0aCxZsiQiIsaOHRsppejZs2ez/m5HHXVUpJRiwoQJERExevTonf6+TT2a2tgROKTsqFKpxD9e+UAW3O96ZlHRkwAAAIB9QCciL4L7XyNzTU1N1NTUxAUXXBD//M//HJ07d46UUpx99tlRX1+/0/veGtzf8Y53xNatW5v8/Lvuumu3MXvcuHFxwAEHREop/u7v/i4++clPxvnnnx+nn3569vPbbrstIiLq6+vj0EMPjZRSPP7447v9e02dOjVSSnHYYYdFY2NjRPx3cO/Zs2f2923qMWfOnL34L9n2OaQ0ZdBdM7Pg3v8/ny16DgAAALAP6ETkRXD/awjf0RNPPJEF79///vc7Pb8tuJ988smRUooxY8Y0+fl9+vSJLl26xPve976dgvumTZvikEMOiZRS/OQnP9kp7K9fvz5GjhwZjz32398tfdFFF0VKKb797W/v9u/1rW99K1JK8f3vfz/72bbgXlNTs9v3dlQOKU25b9bSLLi/7/L7o6GxUvQkAAAAIGc6EXkR3HcR3CMivvnNb0ZKKS644IKdntsW3K+++uro3LlznHfeeTu9Zs2aNdGtW7c455xzonfv3jsF9wkTJkRKKQ4//PBm7502bVqklOJd73pXk//yPuL//Uv4d73rXZFSihkzZmQ/F9wdUna2fnNd9LxkbBbdn1mwuuhJAAAAQM50IvIiuO8muA8bNixSSnHOOefs9Ny24H7bbbfFmWeeGQcccECsXbt2u9cMHz48Ukrxu9/9rsng/sc//jH7V/Itcfzxx0dKKWpra5t8/t57742UUpx44onb/Vxwd0hpWt/hU7Pgfs2El4qeAwAAAORMJyIvgvtugvuVV165x3/hftttt8XIkSMjpRS//e1vt3vNhz/84ejevXts2LChyeD+6KOPRkopunbtGlOmTGn25m27zj///Caf/+IXvxgppbjqqqu2+7ng7pDStF8/8nIW3D897LE9vwEAAABo13Qi8iK47ya4f/jDH46UUvzhD3/Y6bm3Bve1a9fGAQccEGeeeWb2/IIFC6JTp05ZFG8quDc0NGT/Wr1Lly7xiU98Iq655pqYMmXKLn8Ja0TEwoULo1OnTlnMf6sNGzZE9+7do3PnzrFo0aLtnhPcHVKaNmfpuiy4v2dQbazcsKXoSQAAAECOdCLyIrjvENwbGxvj5Zdfzr6//dOf/nST35X+1uAeEXHeeedF586dY/HixRHx3/8KfezYsRHRdHCPiHjllVfitNNOy3Zsexx44IFx/vnnx0svNf31FmeccUb2dTVvdeutt0ZKKc4666yd3rMtuO/u8T/+x/9o/n+8dsYhZVcqlUp84OcPZNH97mcX7flNAAAAQLulE5EXwX038flf/uVforGxscn37Rjc77nnnkgpxdChQyMi4oQTTohDDz00i/W7Cu7bPPLII/GDH/wgTj/99DjwwAOzDW9729ti0qRJO73+t7/9bZPfL/+xj30sUkoxatSond6zLbj37NkzampqmnxceOGFzf5v1944pOzOwP+amQX3Abc9W/QcAAAAIEc6EXkR3P8atrcF5759+8axxx6b/XzkyJFNvm/H4F5XVxcHH3xwvO9974tnn302UkrRv3//7PV7Cu5vtWnTprjzzjvjiCOOiJRS9OrVKyqVynavWbduXRx44IHRpUuXWLZsWURELF26NLp06RIHHnhgrFu3bqfP9ZUyDim7dt+sJVlwf//l90dDY2XPbwIAAADaJZ2IvAjuu/gO96uuuir7apfXXnttp+d3DO4RkX0Nzcc//vFIKcWTTz6ZPdeS4L7NrFmzsn1NfbVM3759I6UU1113XUREXHvttZFSir59+zb5eYK7Q8qurd9cFz0vGZtF92cXrC56EgAAAJATnYi8CO67+aWp55xzTqSU4mtf+9pOzzUV3CdPnpx9Xq9evbZ7/d4E94iIgw8+OFJKMWXKlJ2eq62tjZRSnHrqqRERccopp0RKKWpra5v8LMHdIWX3vvCbqVlwv3ZC078/AQAAAGj/dCLyIrjvJrhPnz49OnXqFF27dt3pX7k3FdwjIt7//vfHwQcfHL/85S+3+3lTwX3Hr4nZ0erVq6NTp06RUopXX311p+fr6+vj0EMPjZRS9h3yhxxySJO/5DVCcHdI2ZNbHn45C+6fvmly0XMAAACAnOhE5EVw301wj4j47Gc/Gyml+Na3vrXdz3cV3HelqeD+5z//Ob7whS/E1KlTd3r96tWr4zOf+UyklOLkk0/e5ecOGDAgUkrZv4R/6/fG76hawX3jxo0xZsyY6NevX5x00klx0EEHRffu3eO9731vXH755bFhw4YWf+aaNWvioosuiiOOOCL233//OOKII2LAgAGxZs2aVm19K4eUPXlhybosuL9nUG2s3LCl6EkAAABADnQi8iK47yG4z5gxIzp16hQHHHBALF26NPt5NYL7mDFjsj//sMMOi0984hNx/vnnx1lnnRV/8zd/k4X0GTNm7PJzn3766ewzdvze+B1tC+49e/bMfklsU4/7779/t3+XESNGZH/eCSecEJ///Oejd+/ecdBBB0VKKY477rhYvnx5s/67RESsXLkyevXqFSmlOOqoo+ILX/hCnHDCCZFSiqOPPjpWrlzZ7M/aHYeUPalUKvGBnz+QRfcxzy4uehIAAACQA52IvAjuewjuERHnnntupJTihz/8YfazagT3zZs3R21tbXznO9+JU089Nf72b/82unbtGm9/+9vjlFNOicGDBzcrXB9//PGRUopjjjlmt6/bFtz39Nj2S1h35dZbb40LL7ww5s6du93PlyxZEieffHKklOL888/f4+5tvvKVr0RKKc4999ztvg6nf//+kVKKr371q83+rN1xSGmOf/vTzCy4X3Tbs0XPAQAAAHKgE5GX0gd3qmvq1KmRUopu3brF1q1b9/j6pUuXRufOnWO//faLZcuWbffcli1b4pBDDokuXbrs9NzecEhpjnHPLcmC+8k/nRCNjbv/XQsAAABA+6MTkRfBnarauHFj9i/llyxZssfXjxo1KlJKcfbZZzf5fL9+/SKlFKNHj271NoeU5li3uS56XjI2i+7TF1bv9wgAAAAAbYNORF4Ed6pq1qxZkVKK/fbbL7Zs2fMvnLzooot2+rqet7rpppsipRTf/e53W73NIaW5Pv+bqVlwv27iS0XPAQAAAKpMJyIvgjtV9fWvfz1SSvGpT32qWa/v06dPpJTihhtuaPL5e+65J/t+99ZySGmumx+elwX3z9w0ueg5AAAAQJXpRORFcKdqxo4dG506dYr99tsvZsyY0az3fOxjH4uUUowYMaLJ5ydOnBgppTjnnHOavWPbwdzx0a1bN4eUZpn9+tosuL9nUG2senPPv48AAAAAaD8Ed/IiuFMVL7zwQrzjHe+IlFJcf/31zX7fRz/60UgpxciRI5t8fsKECYI7+1ylUol/uGJiFt3vmb646EkAAABAFQnu5EVwp9UWLVoURxxxRKSU4uKLL27Re32lDG3VD+6ckQX3794+veg5AAAAQBXpRORFcKdVVqxYEccdd1yklOJrX/taVCqVFr3fL02lraqduSQL7n//0wnR2Niy/9sGAAAA2i6diLwI7uy19evXx6mnnpr9C/SGhoYWf8aoUaMipRRnn312k8/369cvUkoxatSo1s51SGmRtZvq4qhLxmbRfcbCNUVPAgAAAKpEJyIvgjt7ZcuWLXHmmWdGSil69+4dW7fu3S+VXLJkSXTu3Dn233//WL58+U5/xiGHHBKdO3eOpUuXtnqzQ0pLnffrKVlwv37i3KLnAAAAAFWiE5EXwZ0Wa2hoyL57/UMf+lBs3Lhxj+8ZNmxYHHvssTFo0KCdnvvyl78cKaX43Oc+F/X19dnPBwwYECmluOCCC6qy2yGlpW56aF4W3D978+Si5wAAAABVohORF8GdFrv++usjpRQppejTp0/U1NQ0+VixYkX2niFDhkRKKWpqanb6vBUrVkTPnj0jpRQ9e/aMvn37xoknnpj977d+Tms4pLTUrMVrs+D+nkG1sfrNvfv/5AAAAADaFp2IvAjutNi2eL6nx6uvvrrTe5oK7hERq1evjv79+8fhhx8e+++/fxx++OHxne98J1atWlW13Q4pLVWpVOLUKyZm0f2e6YuLngQAAABUgU5EXgR3SsMhZW98/84ZWXD/3u3Ti54DAAAAVIFORF4Ed0rDIWVv3Dvz9Sy4//1PJ0RjY6XoSQAAAEAr6UTkRXCnNBxS9sbajXVx5KDaLLrPXLSm6EkAAABAK+lE5EVwpzQcUvbW526ZkgX3Gx6YW/QcAAAAoJV0IvIiuFMaDil7a9iDc7Pg3ufmyUXPAQAAAFpJJyIvgjul4ZCyt2YtXpsF9yMH1caajVuLngQAAAC0gk5EXgR3SsMhZW81NlbilJ9NzKL7n2e8XvQkAAAAoBV0IvIiuFMaDimtcfEdM7Lg/r07phc9BwAAAGgFnYi8CO6UhkNKa/xlxutZcD/lZxOisbFS9CQAAABgL+lE5EVwpzQcUlpjzcatceSg2iy6P7dobdGTAAAAgL2kE5EXwZ3ScEhprXNvmZIF9xsfmFv0HAAAAGAv6UTkRXCnNBxSWuvGB+Zmwf3cW6YUPQcAAADYSzoReRHcKQ2HlNZ6btHaLLgfOag21mzcWvQkAAAAYC/oRORFcKc0HFJaq7GxEqf8bEIW3f8y4/WiJwEAAAB7QSciL4I7peGQUg3fu2N6FtwvvmNG0XMAAACAvaATkRfBndJwSKmGP894PQvup/xsYjQ2VoqeBAAAALSQTkReBHdKwyGlGtZs3BpHDqrNovusxWuLngQAAAC0kE5EXgR3SsMhpVr63Dw5C+7DHpxb9BwAAACghXQi8iK4UxoOKdVywwNzs+D+uVumFD0HAAAAaCGdiLwI7pSGQ0q1zFy0JgvuRw6qjbUb64qeBAAAALSATkReBHdKwyGlWhobK3HKzyZk0f3ema8XPQkAAABoAZ2IvAjulIZDSjV97/bpWXD//p0zip4DAAAAtIBORF4Ed0rDIaWa7pm+OAvup14xMRobK0VPAgAAAJpJJyIvgjul4ZBSTavf3BpHDqrNovusxWuLngQAAAA0k05EXgR3SsMhpdo+e/PkLLjf9NC8oucAAAAAzaQTkRfBndJwSKm26yfOzYL7eb+eUvQcAAAAoJl0IslguBYAACAASURBVPIiuFMaDinVNmPhmiy4H3XJ2Fi7qa7oSQAAAEAz6ETkRXCnNBxSqq2xsRJ//9MJWXSvnbmk6EkAAABAM+hE5EVwpzQcUvLw3dunZ8H9B3fOKHoOAAAA0Aw6EXkR3CkNh5Q83DN9cRbc/+GKiVGpVIqeBAAAAOyBTkReBHdKwyElD6ve3BrvGVSbRffnX19X9CQAAABgD3Qi8iK4UxoOKXn5zE2Ts+B+88Pzip4DAAAA7IFORF4Ed0rDISUv1018KQvun//N1KLnAAAAAHugE5EXwZ3ScEjJy/SFa7LgftQlY2Pd5rqiJwEAAAC7oRORF8Gd0nBIyUtjYyVO/umELLqPe25J0ZMAAACA3dCJyIvgTmk4pOTpotuezYL7v/1pZtFzAAAAgN3QiciL4E5pOKTkacyzi7Pg/oGfPxCVSqXoSQAAAMAu6ETkRXCnNBxS8rTqza3xnkG1WXR/Ycm6oicBAAAAu6ATkRfBndJwSMnbp2+anAX3Wx5+ueg5AAAAwC7oRORFcKc0HFLydu2El7Lg/oXfTC16DgAAALALOhF5EdwpDYeUvD27YHUW3HteMjbWb64rehIAAADQBJ2IvAjulIZDSt4aGivx/svvz6L7fbOWFD0JAAAAaIJORF4Ed0rDIWVfGHDbs1lwH/hfM4ueAwAAADRBJyIvgjul4ZCyL9z97KIsuH/g5w9EpVIpehIAAACwA52IvAjulIZDyr6wcsOWeM+g2iy6z1m6ruhJAAAAwA50IvIiuFMaDin7yqeHPZYF918/8nLRcwAAAIAd6ETkRXCnNBxS9pVrJryUBfe+w6cWPQcAAADYgU5EXgR3SsMhZV95ZsHqLLj3vGRsrN9cV/QkAAAA4C10IvIiuFMaDin7SkNjJd5/+f1ZdL9v1tKiJwEAAABvoRORF8Gd0nBI2Zf6/+ezWXAfdNfMoucAAAAAb6ETkRfBndJwSNmX7npmURbc//HKB6JSqRQ9CQAAAPgrnYi8CO6UhkPKvvTG+i1ZcO8xsDZeXLq+6EkAAADAX+lE5EVwpzQcUva1/3PjY1lw/80jLxc9BwAAAPgrnYi8CO6UhkPKvvar+1/MgvsXhz9e9BwAAADgr3Qi8iK4UxoOKfvatNdWZcH96MFjY8OW+qInAQAAAKETkR/BndJwSNnXGhor8d6f3J9F9/GzlxY9CQAAAAidiPwI7pSGQ0oRvv3HZ7LgPuiu54qeAwAAAIRORH4Ed0rDIaUIf5q2KAvuH7zygahUKkVPAgAAgNLTiciL4E5pOKQU4Y31W7Lg3mNgbby0bH3RkwAAAKD0dCLyIrhTGg4pRfnkjZOy4D780ZeLngMAAAClpxORF8Gd0nBIKcrV41/Mgvv5//540XMAAACg9HQi8iK4s1emTZsWv/jFL6JPnz7x7ne/O1JK0a1bt736rB49ekRKaZePOXPmVGWzQ0pRnn51VRbcjx48NjZsqS96EgAAAJSaTkReBHf2ymc+85mdwnhrg3tNTU2TjyVLllRls0NKUeobGuOkIeOz6H7/7KVFTwIAAIBS04nIi+DOXvnlL38Zl112Wdx7772xbNmyqgT3vDmkFOlbf3wmC+6X3P1c0XMAAACg1HQi8iK4UxWCO+zenU8vzIL7P/3iwahUKkVPAgAAgNLSiciL4E5VCO6we8vXb86Ce4+BtTF32fqiJwEAAEBp6UTkRXCnKqoR3IcOHRrf+MY3YsCAATF8+PB44403qrrRIaVon7hhUhbc//3RV4qeAwAAAKWlE5EXwZ2qqEZw3/HRvXv3GDlyZNU2OqQUbej4OVlw/9KIx4ueAwAAAKWlE5EXwZ2qaE1w79+/f9x9992xYMGC2LRpU8yePTsuvvji6NKlS6SUYsyYMS36vG0Hc8dHt27dHFIK9dSrq7Lg3mvwuHhzS33RkwAAAKCUBHfyIrhTFa0J7rsyfPjwSCnFMccc06L3Ce60VfUNjXHSkPFZdJ/w/LKiJwEAAEApCe7kRXCnKvII7o2NjXHooYdGSinmz5/f6s9zSGkLvvWHZ7LgPvju54qeAwAAAKWkE5EXwZ2qyCO4R0R88IMfjJRSTJkypdWf5ZDSFtzx9MIsuP/TLx6MSqVS9CQAAAAoHZ2IvAjuVEVewf24446LlFLMnDmz1Z/lkNIWLF+3OQvuPQbWxrzl64ueBAAAAKWjE5EXwZ2qyCO4z549Ozp16hTdu3ePrVu3tvrzHFLaiv99/aQsuI+Y9ErRcwAAAKB0dCLyIrhTFXsK7sOGDYtjjz02Bg0atN3Px48fH9OmTdvp9TNnzozjjz8+UkoxYMCAqmx0SGkrrrpvThbcvzziiaLnAAAAQOnoRORFcGev1NbWxgc+8IHskVKKTp06bfez2tra7PVDhgyJlFLU1NRs9znbft6jR48466yzom/fvnHaaadF165dI6UUH/nIR2Ljxo1V2eyQ0lY8OX9VFtx7DR4Xb26pL3oSAAAAlIpORF4Ed/bK6NGjI6W028fo0aOz1+8quE+dOjX69esXJ510Uhx88MHRtWvXeOc73xlnnHFGjBgxIhoaGqq22SGlrahvaIwTh4zPovvE55cVPQkAAABKRSciL4I7peGQ0pZc+IdpWXD/0Zjnip4DAAAApaITkRfBndJwSGlL7nhqYRbcT//lg1GpVIqeBAAAAKWhE5EXwZ3ScEhpS5at25wF9x4Da2Pe8g1FTwIAAIDS0InIi+BOaTiktDUfv35SFtxHTHql6DkAAABQGjoReRHcKQ2HlLbml/fNyYL7BSOfKHoOAAAAlIZORF4Ed0rDIaWteeKVlVlw7zV4XGzcWl/0JAAAACgFnYi8CO6UhkNKW1PX0BgnXjY+i+4PvLCs6EkAAABQCjoReRHcKQ2HlLbom7+flgX3H4+ZVfQcAAAAKAWdiLwI7pSGQ0pbdPtTC7Lg/s9XPRiVSqXoSQAAANDh6UTkRXCnNBxS2qKlazdnwb3HwNp4+Y0NRU8CAACADk8nIi+CO6XhkNJW9b7u0Sy4j3xsftFzAAAAoMPTiciL4E5pOKS0Vb8YNycL7heMfKLoOQAAANDh6UTkRXCnNBxS2qrHX1mZBfdePxoXm7Y2FD0JAAAAOjSdiLwI7pSGQ0pbVdfQGCdeNj6L7g/NWV70JAAAAOjQdCLyIrhTGg4pbdk3fjctC+6X3TOr6DkAAADQoelE5EVwpzQcUtqy255ckAX3Dw99qOg5AAAA0KHpRORFcKc0HFLasiVrN2XBvcfA2nhp2fqiJwEAAECHpRORF8Gd0nBIaet6X/doFtw/d8uUaGisFD0JAAAAOiSdiLwI7pSGQ0pbd+/M17f7V+43Pzyv6EkAAADQIelE5EVwpzQcUtqDAbc9mwX3owePjVmL1xY9CQAAADocnYi8CO6UhkNKe7B2U13845UPZNH97Gseic11DUXPAgAAgA5FJyIvgjul4ZDSXkyZt2K7r5b5yV9mFz0JAAAAOhSdiLwI7pSGQ0p78rN7n98uuk+a+0bRkwAAAKDD0InIi+BOaTiktCeb6xrinGsfzYL7aT+fGGs2bi16FgAAAHQIOhF5EdwpDYeU9ub519dFr8Hjsuj+7T8+E5VKpehZAAAA0O7pRORFcKc0HFLao9888vJ2Xy0z5tnFRU8CAACAdk8nIi+CO6XhkNIeNTRW4vO/mZoF9xOHjI/FazYVPQsAAADaNZ2IvAjulIZDSnu1aPXGOPGy8Vl0/+Lwx6Ox0VfLAAAAwN7SiciL4E5pOKS0Z3c9s2i7r5YZMemVoicBAABAu6UTkRfBndJwSGnPKpVKXPiHaVlw7zV4XMxZuq7oWQAAANAu6UTkRXCnNBxS2rvVb26Nf7hiYhbde1/3aGypbyh6FgAAALQ7OhF5EdwpDYeUjuCRl97Y7qtlrhz7QtGTAAAAoN3RiciL4E5pOKR0FJfdMysL7u8ZVBuPv7Ky6EkAAADQruhE5EVwpzQcUjqKTVsb4qxfPZxF93/6xYOxbnNd0bMAAACg3dCJyIvgTmk4pHQkzy1aGz0vGZtF9+/dPr3oSQAAANBu6ETkRXCnNBxSOpphD87d7vvca2cuKXoSAAAAtAs6EXkR3CkNh5SOpr6hMfrcPDkL7u/9yf2xdO3momcBAABAm6cTkRfBndJwSPn/2bvT8Kqqe4/jC4REhoqISLEVFAioIFVRnMWKlaJWBBSqorng0GoFRDswJShOgCI4VgSDKLaiAg6QBAIBwjzPU8IchIQkhCHzcH73BZ5djwkZz84+yf5+nme9cJ+ckxWfu/9Nv3d3pSY6kJqhK8OirOjeb8oqFRZ6nN4WAAAAAAABjU4EuxDc4RoMUtRUX6456HO0zKfL9zu9JQAAAAAAAhqdCHYhuMM1GKSoqTwej574dK0V3NuOiFRC8imntwUAAAAAQMCiE8EuBHe4BoMUNVnK6Rx1emW+Fd3vfTdOufmFTm8LAAAAAICARCeCXQjucA0GKWq6BTuSfI6WeTN6l9NbAgAAAAAgINGJYBeCO1yDQQo3GDpzixXcLxs6R+sOpDm9JQAAAAAAAg6dCHYhuMM1GKRwg4ycfHUZF2tF99vGxup0Tr7T2wIAAAAAIKDQiWAXgjtcg0EKt1h/8LhaDZtrRfd/fr3Z6S0BAAAAABBQ6ESwC8EdrsEghZuMn7/b5zz36G1Hnd4SAAAAAAABg04EuxDc4RoMUrhJXkGh7n9vqRXcrxk9X8dO5Ti9LQAAAAAAAgKdCHYhuMM1GKRwmz3HTqvdyEgruvefukYej8fpbQEAAAAA4Dg6EexCcIdrMEjhRp+tPOBztMz0VQec3hIAAAAAAI6jE8EuBHe4BoMUbuTxeBQasdoK7pePjNK+lAyntwUAAAAAgKPoRLALwR2uwSCFWyWfzNbVL8+zonuP95cpv6DQ6W0BAAAAAOAYOhHsQnCHazBI4WZRW4/4HC0zMSbe6S0BAAAAAOAYOhHsQnCHazBI4XYvfrXJCu6ths3VxkPpTm8JAAAAAABH0IlgF4I7XINBCrc7lZ2nW8YstKL7HW8uUmZuvtPbAgAAAACgytGJYBeCO1yDQQpIq/el6dKh/ztaZsTsLU5vCQAAAACAKkcngl0I7nANBilwxpionT7nucfuTHZ6SwAAAAAAVCk6EexCcIdrMEiBM3LzC9V9YpwV3Du9EqO0jFyntwUAAAAAQJWhE8EuBHe4BoMU+J/dSacUMiLSiu5Pf7ZWHo/H6W0BAAAAAFAl6ESwC8EdrsEgBXx9snSfz9EyM9YecnpLAAAAAABUCToR7EJwh2swSAFfhYUePTJ5pRXcrwyL0qG0TKe3BQAAAACA7ehEsAvBHa7BIAWKOnIiS1eNiraie+8Pl6ugkKNlAAAAAAA1G50IdiG4o0LWrVunN954Qz179tTFF18sY4yCg4Mr/Hnp6ekaPHiwWrRooaCgILVo0UKDBg1Senq63/bMIAWK9/2mH32OlvlgUYLTWwIAAAAAwFZ0ItiF4I4K6dGjh4wxPquiwT01NVUhISEyxqhVq1bq06eP2rdvL2OM2rRpo9TUVL/smUEKnN2g/26wgnub4XO19fAJp7cEAAAAAIBt6ESwC8EdFTJmzBiFh4frhx9+UFJSUqWC+2OPPSZjjHr16qX8/Hzr+sCBA2WM0eOPP+6XPTNIgbM7kZWnG19fYEX3ruMXKzuvwOltAQAAAABgCzoR7EJwh19UNLgfPXpUtWvXVt26dZWUlOTzWk5Ojpo2bapzzjmnyGsVwSAFSrY8IcXnaJmXvt/m9JYAAAAAALAFnQh2IbjDLyoa3CMiImSMUdeuXYt9fcCAATLGaOrUqZXcIYMUKItXftjuE93j4o85vSUAAAAAAPyOTgS7ENzhFxUN7oMHD5YxRv/4xz+Kff3999+XMUbPP/98ZbfIIAXKIDuvQHe/vcQK7p1fi1F6Zq7T2wIAAAAAwK/oRLALwR1+UdHg3rNnTxlj9M477xT7+rfffmud715ZDFKgbLb/eFIhwyOt6P63L9bL4/E4vS0AAAAAAPyGTgS7ENzhFxUN7n/4wx9kjNHkyZOLfT0mJkbGGN19991l/kzvwPzlCg4OZpACZfTR4j0+R8vM3nDY6S0BAAAAAOA3BHfYheAOv6hocL/rrrtkjNGUKVOKfX3+/PkEd8ABBYUePfTRCiu4dxgVrcPpWU5vCwAAAAAAvyC4wy4Ed/gFR8oANU/i8Ux1CI+2ovufJ61UYSFHywAAAAAAqj86EexCcIdf8EdTgZpp5vpEn6NlJsftdXpLAAAAAABUGp0IdiG4wy8qGtwjIiJkjFHXrl2LfX3AgAEyxigiIqKyW2SQAhXg8Xj0zPR1VnAPGR6pnUdPOr0tAAAAAAAqhU4EuxDc4RcVDe5HjhxR7dq1FRQUpOTkZJ/XcnJy1LRpU9WuXVtHjx6t9B4ZpEDFHM/I1fWvxljRvduEJcrJL3B6WwAAAAAAVBidCHYhuMMvSgvu7733ntq1a6ehQ4cWee3RRx+VMUa9e/dWfn6+dX3QoEEyxqhfv35+2SODFKi4xbuP+Rwt8/rcHU5vCQAAAACACqMTwS4Ed1TInDlzdMMNN1jLGKNatWr5XJszZ4719aNGjZIxRqGhoUU+KyUlRa1bt5YxRq1bt1bfvn3VoUMH659TUlL8smcGKVA54d9utYL7pUPnaOXeVKe3BAAAAABAhdCJYBeCOypk6tSpMsaUuKZOnWp9fUnBXZKOHz+ugQMH6pJLLlFQUJAuueQSPffcc0pLS/PbnhmkQOVk5RbozrcWWdH95jcW6mR2ntPbAgAAAACg3OhEsAvBHa7BIAUqb0viCbUeNteK7kO+3Oj0lgAAAAAAKDc6EexCcIdrMEgB/3hvYbzPee5zNh9xeksAAAAAAJQLnQh2IbjDNRikgH/kFxSq5wfLrODe8aV5Onoi2+ltAQAAAABQZnQi2IXgDtdgkAL+cyA1Q1eGRVnRvd+UVSos9Di9LQAAAAAAyoROBLsQ3OEaDFLAv75cc9DnaJmpy/Y5vSUAAAAAAMqETgS7ENzhGgxSwL88Ho+e+HStFdzbjohUQvIpp7cFAAAAAECp6ESwC8EdrsEgBfwv5XSOOr0y34ru974bp9z8Qqe3BQAAAABAiehEsAvBHa7BIAXssWBHks/RMm9G73J6SwAAAAAAlIhOBLsQ3OEaDFLAPkNnbrGC+2VD52jdgTSntwQAAAAAwFnRiWAXgjtcg0EK2CcjJ19dxsVa0f22sbE6nZPv9LYAAAAAACgWnQh2IbjDNRikgL3WHzyuVsPmWtH9n19vdnpLAAAAAAAUi04EuxDc4RoMUsB+4+fv9jnPPXrbUae3BAAAAABAEXQi2IXgDtdgkAL2yyso1P3vLbWC+zWj5+vYqRyntwUAAAAAgA86EexCcIdrMEiBqrHn2Gm1GxlpRff+U9fI4/E4vS0AAAAAACx0ItiF4A7XYJACVeezlQd8jpaZvuqA01sCAAAAAMBCJ4JdCO5wDQYpUHU8Ho9CI1Zbwf3ykVHal5Lh9LYAAAAAAJBEJ4J9CO5wDQYpULWST2br6pfnWdG9x/vLlF9Q6PS2AAAAAACgE8E2BHe4BoMUqHpRW4/4HC0zMSbe6S0BAAAAAEAngm0I7nANBingjBe/2mQF91bD5mrjoXSntwQAAAAAcDk6EexCcIdrMEgBZ5zKztMtYxZa0f2ONxcpMzff6W0BAAAAAFyMTgS7ENzhGgxSwDmr96Xp0qH/O1pmxOwtTm8JAAAAAOBidCLYheAO12CQAs4aE7XT5zz32J3JTm8JAAAAAOBSdCLYheAO12CQAs7KzS9U94lxVnDv9EqM0jJynd4WAAAAAMCF6ESwC8EdrsEgBZy3O+mUQkZEWtH96c/WyuPxOL0tAAAAAIDL0IlgF4I7XINBCgSGT5bu8zlaZsbaQ05vCQAAAADgMnQi2IXgDtdgkAKBobDQo0cmr7SC+5VhUTqUlun0tgAAAAAALkIngl0I7nANBikQOI6cyNJVo6Kt6N77w+UqKORoGQAAAABA1aATwS4Ed7gGgxQILN9v+tHnaJkPFiU4vSUAAAAAgEvQiWAXgjtcg0EKBJ5B/91gBfc2w+dq6+ETTm8JAAAAAOACdCLYheAO12CQAoHnRFaebnx9gRXdu45frOy8Aqe3BQAAAACo4ehEsAvBHa7BIAUC0/KEFJ+jZV76fpvTWwIAAAAA1HB0ItiF4A7XYJACgeuVH7b7RPe4+GNObwkAAAAAUIPRiWAXgjtcg0EKBK7svALd/fYSK7h3fi1G6Zm5Tm8LAAAAAFBD0YlgF4I7XINBCgS27T+eVMjwSCu6P/vFenk8Hqe3BQAAAACogehEsAvBHa7BIAUC30eL9/gcLTN7w2GntwQAAAAAqIHoRLALwR2uwSAFAl9BoUcPfbTCCu4dRkXrcHqW09sCAAAAANQwdCLYheAO12CQAtVD4vFMdQiPtqL7nyetVGEhR8sAAAAAAPyHTgS7ENzhGgxSoPqYuT7R52iZj5fsdXpLAAAAAIAahE4EuxDc4RoMUqD68Hg8emb6Oiu4hwyP1M6jJ53eFgAAAACghqATwS4Ed7gGgxSoXo5n5Or6V2Os6N5twhLl5Bc4vS0AAAAAQA1AJ4JdCO5wDQYpUP0s3n3M52iZ1+fucHpLAAAAAIAagE4EuxDc4RoMUqB6Cv92qxXcLx06Ryv3pjq9JQAAAABANUcngl0I7nANBilQPWXlFujOtxZZ0f3mNxbqZHae09sCAAAAAFRjdCLYheAO12CQAtXXlsQTaj1srhXdh3y50ektAQAAAACqMToR7EJwh2swSIHq7b2F8T7nuc/ZfMTpLQEAAAAAqik6EexCcIdrMEiB6i2/oFA9P1hmBfeOL83T0RPZTm8LAAAAAFAN0YlgF4I7XINBClR/B1IzdGVYlBXd+01ZpcJCj9PbAgAAAABUM3Qi2IXgDtdgkAI1w5drDvocLTN12T6ntwQAAAAAqGboRLALwR2uwSAFagaPx6MnPl1rBfe2IyKVkHzK6W0BAAAAAKoROhHsQnCHazBIgZoj5XSOOr0y34ru974bp9z8Qqe3BQAAAACoJuhEsAvBHa7BIAVqlgU7knyOlnkzepfTWwIAAAAAVBN0ItiF4A7XYJACNc/QmVus4H7Z0DladyDN6S0BAAAAAKoBOhHsQnCHazBIgZonIydfXcbFWtH9trGxOp2T7/S2AAAAAAABjk4EuxDc4RoMUqBmWn/wuFoNm2tF939+vdnpLQEAAAAAAhydCHYhuMM1GKRAzTV+/m6f89yjtx11eksAAAAAgABGJ4JdCO5wDQYpUHPlFRTq/veWWsH9mtHzdexUjtPbAgAAAAAEKDoR7EJwh2swSIGabc+x02o3MtKK7v2nrpHH43F6WwAAAACAAEQngl0I7nANBilQ83228oDP0TLTVx1weksAAAAAgABEJ4JdCO5wDQYpUPN5PB6FRqy2gvvlI6O0LyXD6W0BAAAAAAIMnQh2IbjDNRikgDskn8zW1S/Ps6J7j/eXKb+g0OltAQAAAAACCJ0IdiG4wzUYpIB7RG094nO0zMSYeKe3BAAAAAAIIHQi2IXgjgrLzs5WeHi4QkJCFBwcrObNm6t///5KTEws1+e0bNlSxpizrp07d/plvwxSwF1e/GqTFdxbDZurjYfSnd4SAAAAACBA0IlgF4I7KiQ7O1s333yzjDFq3ry5+vTpo86dO8sYo6ZNm2rPnj1l/ixvcA8NDS12HTlyxC97ZpAC7nIqO0+3jFloRfc73lykzNx8p7cFAAAAAAgAdCLYheCOCgkLC5MxRjfddJNOnz5tXR8/fryMMbr99tvL/Fne4G43BingPqv3penSof87WmbE7C1ObwkAAAAAEADoRLALwR3llpeXp/PPP1/GGG3YsKHI6x07dpQxRuvWrSvT5xHcAdhpTNROn/PcY3cmO70lAAAAAIDD6ESwC8Ed5RYbGytjjFq3bl3s66NHj5YxRqNGjSrT5xHcAdgpN79Q3SfGWcG90ysxSj2d4/S2AAAAAAAOohPBLgR3lNuECRNkjNFDDz1U7Otz5syRMUYPPPBAmT7PG9zHjRunv/zlLxo0aJAmTZqkY8eO+XPbDFLAxXYnnVLIiEgruj81ba08Ho/T2wIAAAAAOIROBLsQ3FFuQ4YMkTFGQ4YMKfb1TZs2yRija6+9tkyf5w3uv1z169fXlClT/LZvBingbp8s3edztMyMtYec3hIAAAAAwCF0ItiF4I5ye+qpp2SM0YgRI4p9PSEhQcYYtW3btkyfN3DgQM2aNUsHDx5UVlaWtm3bphdeeEHnnHOOjDGaPXt2ufbnHZi/XMHBwQxSwMUKCz16ZPJKK7hfGRalQ2mZTm8LAAAAAOAAgjvsQnBHuT355JMyxmjkyJHFvh4fH1+u4H42kyZNqtDnENwBnM2RE1m6alS0Fd17f7hcBYUcLQMAAAAAbkNwh10I7ig3fx8pczaFhYW66KKLZIzRvn37KvVZEoMUwBnfb/rR52iZ92MTnN4SAAAAAKCK0YlgF4I7ys3ffzS1JDfddJOMMVq+fHmlP4tBCsBr0H83WMG99bC52nr4hNNbAgAAAABUIToR7EJwR7nFxsbKGKPWrVsX+/ro0aNljFF4eHilv9fll18uY4w2b95c6c9ikALwOpGVpxtfX2BF967jFys7r8DpbQEAAAAAqgidCHYhuKPccnNz1ahRIxljtGHDhiKvd+zYUcYYrVmzplLfZ9u2bapVq5bq16+v3NzcSn2WxCAF4Gt5QorP0TIvfb/N6S0BAAAAAKoInQh2IbijQkaMGCFjjG6++WZlZGRY18ePHy9jjG699Vafr3/vvffUrl07DR061Od6dHS01q1bV+TzN2/erCuuuELGGA0aNMgve2aQAvilV37Y7hPd4+KPOb0lAAAAAEAVoBPBLgR3qxvOGgAAIABJREFUVEh2drZuuOEGGWPUvHlz9enTx/rnJk2aKCHB948Qjho1SsYYhYaGFnu9ZcuWuvPOO9W3b1917txZderUkTFGXbp0UWZmpl/2zCAF8EvZeQW6++0lVnDv/FqM0jMr/7+oAQAAAAAENjoR7EJwR4VlZWUpLCxMrVu3VlBQkJo1a6bQ0FAdOnSoyNeeLbivWLFCAwYM0FVXXaUmTZqoTp06uuCCC3THHXdo8uTJKijw35nKDFIAxdn+40mFDI+0ovuzX6yXx+NxelsAAAAAABvRiWAXgjtcg0EK4Gw+WrzH52iZ2RsOO70lAAAAAICN6ESwC8EdrsEgBXA2BYUePfTRCiu4dxgVrcPpWU5vCwAAAABgEzoR7EJwh2swSAGUJPF4pjqER1vR/c+TVqqwkKNlAAAAAKAmohPBLgR3uAaDFEBpZq5P9Dla5uMle53eEgAAAADABnQi2IXgDtdgkAIojcfj0TPT11nBPWR4pHYePen0tgAAAAAAfkYngl0I7nANBimAsjiekavrX42xonu3CUuUk1/g9LYAAAAAAH5EJ4JdCO5wDQYpgLJavPuYz9Eyr8/d4fSWAAAAAAB+RCeCXQjucA0GKYDyCP92qxXcLx06Ryv3pjq9JQAAAACAn9CJYBeCO1yDQQqgPLJyC3TnW4us6H7zGwt1MjvP6W0BAAAAAPyATgS7ENzhGgxSAOW1JfGEWg+ba0X3IV9udHpLAAAAAAA/oBPBLgR3uAaDFEBFvLcw3uc89zmbjzi9JQAAAABAJdGJYBeCO1yDQQqgIvILCtXzg2VWcO/40jwdPZHt9LYAAAAAAJVAJ4JdCO5wDQYpgIo6kJqhK8OirOjeb8oqFRZ6nN4WAAAAAKCC6ESwC8EdrsEgBVAZX6456HO0zNRl+5zeEgAAAACgguhEsAvBHa7BIAVQGR6PR098utYK7m1HRCoh+ZTT2wIAAAAAVACdCHYhuMM1GKQAKivldI46vTLfiu73vhun3PxCp7cFAAAAACgnOhHsQnCHazBIAfjDgh1JPkfLvBm9y+ktAQAAAADKiU4EuxDc4RoMUgD+MnTmFiu4XzZ0jtYdSHN6SwAAAACAcqATwS4Ed7gGgxSAv2Tk5KvLuFgrut82Nlanc/Kd3hYAAAAAoIzoRLALwR2uwSAF4E/rDx5Xq2Fzrej+z683O70lAAAAAEAZ0YlgF4I7XINBCsDfxs/f7XOee/S2o05vCQAAAABQBnQi2IXgDtdgkALwt7yCQt3/3lIruF8zer6ST2U7vS0AAAAAQCnoRLALwR2uwSAFYIc9x06r3chIK7r3n7pGHo/H6W0BAAAAAEpAJ4JdCO5wDQYpALt8tvKAz9Ey01cdcHpLAAAAAIAS0IlgF4I7XINBCsAuHo9HoRGrreB++cgo7UvJcHpbAAAAAICzoBPBLgR3uAaDFICdkk9m6+qX51nRvcf7y5RfUOj0tgAAAAAAxaATwS4Ed7gGgxSA3aK2HvE5WmZCzG6ntwQAAAAAKAadCHYhuMM1GKQAqsKLX22ygnurYXO18VC601sCAAAAAPwCnQh2IbjDNRikAKrCqew83TJmoRXd73hzkTJz853eFgAAAADgZ+hEsAvBHa7BIAVQVVbvS9OlQ/93tMyI2Vuc3hIAAAAA4GfoRLALwR2uwSAFUJXGRO30Oc/9X99s1rxtR5WRw9PuAAAAAOA0OhHsQnCHazBIAVSl3PxCdZ8Y5xPdW/5rjkKGR+qRySv18ZK9ik86JY/H4/RWAQAAAMB16ESwC8EdrsEgBVDV4pNO6fpXY4pE95+vm99YqOGztmj+9iSefgcAAACAKkIngl0I7nANBikAJ5zIzNPX6xL17BfrddWo6BLje8jwSD06eZUmx+1VQvJpnn4HAAAAAJvQiWAXgjtcg0EKwGn5BYVauz9N46J36p53ih4388t1y5iFGjl7qxbsSFJmLk+/AwAAAIC/0IlgF4I7XINBCiDQJJ/M1oy1h/Ts9PXqUNrT7yMi1W/KKk1Zuk97jvH0OwAAAABUBp0IdiG4wzUYpAACWX5BoVbvS9PYqJ3F/rHVX65bxy5U2LdbtXBnkrJyC5zePgAAAABUK3Qi2IXgDtdgkAKoTpJOZmvGmkN6Zvo6dQgv/en3xz5ZrU+W7tO+lAyntw4AAAAAAY9OBLsQ3OEaDFIA1VVeQaFW7U3VmKid6jZhSalPv98+Llbh325V7M5knn4HAAAAgGLQiWAXgjtcg0EKoKY4eiJbX645qL98tk7tS3n6ve2ISD3+yWpFLNun/Tz9DgAAAACS6ESwD8EdrsEgBVAT5RUUauXeVL0euaNMT793GRerUd9t06JdycrO4+l3AAAAAO5EJ4JdCO5wDQYpADc4ciJL/1l9UE9/trbUp9/bjYxUaMRqfbp8vw6k8vQ7AAAAAPegE8EuBHe4BoMUgNvk5hdq+Z4UvT53h+5+u/Sn3+94c5Fe+n6bFu8+xtPvAAAAAGo0OhHsQnCHazBIAbjd4fQsfbHqoJ6atlZXhkWV+vR7/6lrNG3Ffh1MzXR66wAAAADgV3Qi2IXgDtdgkALA/+TmF2p5Qopem7tDd41fXOrT779/a5Fe/n67lvD0OwAAAIAagE4EuxDc4RoMUgA4u8TjmZq+6oCe+HStrijl6ffLR0ZpwNQ1+mzFfh1K4+l3AAAAANUPnQh2IbjDNRikAFA2OfkFWpaQold+2K6uZXj6/c63Fmn0D9u1ND5FOfk8/Q4AAAAg8NGJYBeCO1yDQQoAFXMoLVOfrTygJz5do8tHlvz0+xVhUXri0zX6fOUBJR7n6XcAAAAAgYlOBLsQ3OEaDFIAqLyc/ALFxR/T6B+26863FpX69HvX8Yv16pztWpbA0+8AAAAAAgedCHYhuMM1GKQA4H+H0jL12Yr9GjB1jdqNjCz16fcnp63V9FUHdDg9y+mtAwAAAHAxOhHsQnCHazBIAcBe2XkFWrL7mF7+frt+/2bpT7//4e3Fem3uDi1PSFFufqHT2wcAAADgInQi2IXgDtdgkAJA1TqYmqlpK/br/yJWl/r0+5VhUXpq2lp9seqgfuTpdwAAAAA2oxPBLgR3uAaDFACck51XoMW7j2nUd9t0Rxmefr/77SV6fe4OrdiTqrwCnn4HAAAA4F90ItiF4A7XYJACQODYn5Khqcv2KTRitdqOKPnp9/bh0Xr6s7X67+qDOnoi2+mtAwAAAKgB6ESwC8EdrsEgBYDAlJ1XoNhdyRr13TZ1GRdb6tPv3SYs0RuRO7VyL0+/AwAAAKgYOhHsQnCHazBIAaB62JeSoYhl+/T4J6sVUsrT7x3Co/XXz9fpyzU8/Q4AAACg7OhEsAvBHa7BIAWA6icrt0CxO5MV/u1W3Ta29Kff/zgxTmOidmoVT78DAAAAKAGdCHYhuMM1GKQAUL15PB7tPXZanyzdp35TVpX+9PuoaD0zfZ1mrDmkpJM8/Q4AAADgf+hEsAvBHa7BIAWAmiUzN18LdyZp5OytunXswlKffu8+MU5jo3Zqzf405fP0OwAAAOBqdCLYheAO12CQAkDN5fF4tOfYaU2O23vm6ffhJT/9ftWoaD07fb2+WntIyad4+h0AAABwGzoR7EJwh2swSAHAPTJz8xWzPUkjZm/RLWNKf/r9nnfi9Gb0Lq3l6XcAAADAFehEsAvBHRWWnZ2t8PBwhYSEKDg4WM2bN1f//v2VmJhY7s9KT0/X4MGD1aJFCwUFBalFixYaNGiQ0tPT/bZfBikAuJPH41FC8ilNjturRyeX/vR7x5fm6W9frNfX6xJ17FSO09sHAAAAYAM6EexCcEeFZGdn6+abb5YxRs2bN1efPn3UuXNnGWPUtGlT7dmzp8yflZqaqpCQEBlj1KpVK/Xp00ft27eXMUZt2rRRamqqX/bMIAUASFJGTr7mb0/S8FlbdPMbpT/9ft+7S/XWvF1adyBNBYUep7cPAAAAwA/oRLALwR0VEhYWJmOMbrrpJp0+fdq6Pn78eBljdPvtt5f5sx577DEZY9SrVy/l5+db1wcOHChjjB5//HG/7JlBCgD4JY/Ho/ikU/p4yV49/PFKtRk+t9Sn35/7zwZ9sy5RK/emasPB49r+40ntOXZaicczlXwqWyey8pSdVyCPhzgPAAAABCo6EexCcEe55eXl6fzzz5cxRhs2bCjyeseOHWWM0bp160r9rKNHj6p27dqqW7eukpKSfF7LyclR06ZNdc455xR5rSIYpACA0pzOyde8bUc1bNYW3fT6glKffi9thYyIVIfwaHV6Zb5ufmOhfv/mInWbsET3v7dUD/17hfpNWaUnPl2jZ6ev1/NfbtS/vtms8G+36rW5O/TWvF16d0G8Ji3Zo0+X79d/Vx/UrA2JmrP5iGK2Jyku/phW70vTpkPp2nHkpPYeO63D6VlKOZ2jU9l5yskn+gMAAABnQyeCXQjuKLfY2FgZY9S6detiXx89erSMMRo1alSpnxURESFjjLp27Vrs6wMGDJAxRlOnTq3Ejs9gkAIAysPj8Wh30ilNWrJHf560Uq2Hlfz0e6CutiMiddWoaF33asyZ6P/WIv1xYpx6vL9MfT7yRv+1evaL9RoyY6OGztyiUd9t0+tzd2j8vF16PzZBk+P2atqK/fpyzUHN3nBYkVuOaOHOJC2NT9Ga/WnanJiuXUdPaX9Khn5Mz1Lq6RydzslXXkEh0R8AAAABiU4EuxDcUW4TJkyQMUYPPfRQsa/PmTNHxhg98MADpX7W4MGDZYzRP/7xj2Jff//992WM0fPPP1+pPUsMUgBA5ZzKzlPU1qMaOnOzuo5frBtfX6BrRs9X+/DoUo+icfO6bOgctRsZqY4vzdP1r8bo1rELdedbi9R9Ypwe+GCZ+k5aocc/Wa0np63V375YrxdmbNKwWVv00vfb9HrkDo2fv9uK/p+tPKAZaw7p242HFbX1iGJ3Jmt5QorW7k/TlsQT2p10SgdSM3T0RLbSMnKVkZOv/IJCp/9PBwAAAAGITgS7ENxRbkOGDJExRkOGDCn29U2bNskYo2uvvbbUz+rZs6eMMXrnnXeKff3bb7+1znevLAYpAMBOBYUeZeUWKD0zV8kns3UoLVMJyae09fAJrTtwXCv2pGrRrmRFbzuq7zb9qK/WHtL0VQf0ydJ9+nDRHk2I2a0xUTs1+oftGjF7i/7+1SYN/M8G/eWzdfq/iNV6+OOV6vXhct37bpz+8PZi3T4uVje8tkBXvzxPV4RFVdsn8KtitRo2V1eERel3L89T59didNvYWN01frHufTdOPT9Ypj9PWqnQiNV64tM1euLTtXri07V6ctpaPfXTevqzM+svn63TXz5bp79+fmY9M/3Menb6+jPrizPrbz+t5/6zQc/9Z4MG/rQG/ffMGvzTev7LjXr+y40a4l0zzqwXZmzSCzM26cWvzqy//7T+8fWZ9c+vN+ufX2/Wv745s4bO9K4tGjbrzBr+0xox+8waOXurRs7eqrBvz6zwn9ao77b5rJe+P7Ne/n67Xv5+u0b/cGa98tN6dc6Z9drcHXpt7g697l2RZ9YbkTv1RuROjYk6s8b+tMZFn1lvRu/Sm9G79Na8M2v8vF0aP3+3xs/frbd/WhNizqyJMfGaGBOvdxacWe/+tN5beGa9H5vgsz5YdGZ9uGiPPly0R/9efGZ9FMBr0pLAXR8v2Ruwa3Ici8VisZxakVuOOP1rt9/QiWAXgjvK7amnnpIxRiNGjCj29YSEBBlj1LZt21I/6w9/+IOMMZo8eXKxr8fExMgYo7vvvrvM+/MOzF+u4OBgBikAoEbLLyhUZm6+jmfk6uiJbB1MzVR8kjf6p2n5nhTF7kpW1Naj+nbjYc1Ye0ifrzygKUv36YNFCXp7/m69EblTL3+/XcNnbdGLX23Sc//ZoKemrVVoxGr9edJK9fxgme55J05dxy/WbWNj1fm1GP3u5Xm6fGSULhvqfFxnsVgsFovFYtm3+k1Z5fSvvH5DcIddCO4otyeffFLGGI0cObLY1+Pj48sc3O+66y4ZYzRlypRiX58/fz7BHQCAaiS/oFAZOflKy8jVkRNZOpCaod1Jp7Ql8YTW7k/TsoQUxe5MVtTWI2ei/5pD+mzFfk2O26v3YxM0fv5uvR65Qy99v03DZm3RCzM26W9frNeT09bq8U9Wq++kFXrgg2XqPjFOd761SLeOXajrX41Rx5fmqd3ISF1K9GexWCwWi8WybRHcgdIR3FFuHCkDAAAClcfjUV5BoU7n5Cv1dI5+TM/SvpQM7Tp6SpsT07Vmf5qWxqdo4c4kRW45otkbDuvLNQc1bcV+fbJ0n6b8tH75P5/2HmXhPW7DeySI99gS7zEm3mNNfnncifcYFO+xKN5jUrzHpniPUfEeq+I9ZmX8T8eueI9g8R7J4j2iZWzU/45v8R7n4j3exXvci/f4F+9xMN7jYbzHxXiPj/EeJ/PLY2a8x894j6PxHk/jPa7Ge3yN9ziboTO968xRN96jb7xH4XiPxvn7V/87Nsd7jI73WB3vMTveY3e8x/B4j+XxHtPjPbbHe4yP91gf7zE/3mN/AnF5jyYKxOU9PikQl/eIJxaLxWI5sybGxDv9657f0IlgF4I7yo0/mgoAAAAAAIDqjE4EuxDcUW6xsbEyxqh169bFvj569GgZYxQeHl7qZ0VERMgYo65duxb7+oABA2SMUURERKX2LDFIAQAAAAAAcAadCHYhuKPccnNz1ahRIxljtGHDhiKvd+zYUcYYrVmzptTPOnLkiGrXrq2goCAlJyf7vJaTk6OmTZuqdu3a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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "env_info = {\"grid_height\": 4, \"grid_width\": 12, \"seed\": 0}\n",
    "agent_info = {\"discount\": 1, \"step_size\": 0.01, \"seed\": 0}\n",
    "\n",
    "# The Optimal Policy that strides just along the cliff\n",
    "policy = np.ones(shape=(env_info['grid_width'] * env_info['grid_height'], 4)) * 0.25\n",
    "policy[36] = [1, 0, 0, 0]\n",
    "for i in range(24, 35):\n",
    "    policy[i] = [0, 0, 0, 1]\n",
    "policy[35] = [0, 0, 1, 0]\n",
    "\n",
    "agent_info.update({\"policy\": policy})\n",
    "\n",
    "true_values_file = \"optimal_policy_value_fn.npy\"\n",
    "_ = run_experiment(env_info, agent_info, num_episodes=5000, experiment_name=\"Policy Evaluation on Optimal Policy\",\n",
    "                   plot_freq=500, true_values_file=true_values_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "nbgrader": {
     "checksum": "9b8fd1dd026cb36082aa0d6245763627",
     "grade": false,
     "grade_id": "cell-11c76882b7939d66",
     "locked": false,
     "schema_version": 1,
     "solution": true
    }
   },
   "outputs": [],
   "source": [
    "# The Safe Policy\n",
    "# Hint: Fill in the array below (as done in the previous cell) based on the safe policy illustration \n",
    "# in the environment diagram. This is the policy that strides as far as possible away from the cliff. \n",
    "# We call it a \"safe\" policy because if the environment has any stochasticity, this policy would do a good job in \n",
    "# keeping the agent from falling into the cliff (in contrast to the optimal policy shown before). \n",
    "# BOILERPLATE:\n",
    "policy = np.ones(shape=(env_info['grid_width'] * env_info['grid_height'], 4)) * 0.25\n",
    "### START CODE HERE ###\n",
    "for i in range(env_info['grid_height']-1, 0, -1):\n",
    "    policy[i * env_info['grid_width']] = [1, 0, 0, 0]\n",
    "for j in range(env_info['grid_width']):\n",
    "    policy[j] = [0, 0, 0, 1]\n",
    "for i in range(env_info['grid_height']-1):\n",
    "    policy[i * env_info['grid_width'] + (env_info['grid_width'] - 1)] = [0, 0, 1, 0]\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "b67dc6aaaa345faed7768ab6c07a926c",
     "grade": true,
     "grade_id": "safe_policy",
     "locked": true,
     "points": 20,
     "schema_version": 1,
     "solution": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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/8nJtb3x1r+o7E23vam2k28/sbZTb39jZW/56etre9s/n1DZO/75+NpOv+O4yt5557G1nXXX6srOvut1tb33Z0dX9v6frart3LtXVnbBz4+s7aKfH1HbB37x6souu+fPavvQPYdX9uFfrqjtI79cXtmVvzqktqt/dVBl19x7YG2fvHdpZdf++oDa/vbX+1d23Zp9a7t+zZLa/u6+fSr74n0vr+3v1+5d2ZfXvqS2r/7Liyv7h395UW03/uZPKvvH3yyq7du/2aOy796/sLZ/un9Bbd+/f15lP/rXObX95F9nV3bbb2fV9s+/nVnZXb/dtba7H9ilsnsemFHbvQ9Mr2zNg/X9y4M7V3Z/gz3wUH0PPzS9skcb7H89PKOy3z28S23/++FdK3vy4Zm1Pf3IbpU988js2tY/MqeyjY/Oq+3/PLqgssULewV32kJwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11DcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV32klwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11DcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV32klwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11DcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV32klwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11DcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV32klwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11DcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV32klwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11DcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV32klwp2sI7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+BOOwnudA3BXXAX3AV3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9xpJ8GdriG4C+6Cu+AuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugjvtJLjTNQR3wV1wF9wFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcKedBHe6huAuuAvugrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7rST4E7XENwFd8FdcBfcBXfBXXAX3AV3wV1wF9wFd8FdcBfcBXfBnXYS3OkagrvgLrgL7oK74C64C+6Cu+AuuAvugrvgLrgL7oK74C64006CO11j8eLF0VNGxLgyfuvWMyFnIybmbGR924+cNLiNGrjJORvdyqa0tt7BbMemG9fXxo2Z2r5tl7Wdqhvbvo0d18ZtP5hNa74dBrftBrvxbdqEvI1pZROHcJPat76Bm9zhTRnC7Vhfb7s2tcPbKWejW9m0Id7OHdz0Id6MnTq7XYZwuw7c1Np627mZzdfX0nZsvt2qG5O47WqbUt+s5hs7iI2bNbn5Zte3/SC3w+xJL3jjG23OwE2sbcIgNrHhJjTdpLnNN7nhxjfdlJa2Q2U7Ntq86qY23Pa17TSITZs3rul2brix1c2vb3oLmzF/u6bbpYXtOn/MoDZzQV/T7VZbb22zWtjshhvddHNa2NwFo5puXgubP3AL61swyC1sYbu3sD1a2ujKFrWwxQ3XW1lvb4/gTlsI7nSNI444IhYvXmz/b319fdHX19fxc9jwnTtiW5r7Yc3mjlizuSPWbO6IbWnuhzWbO2LN1tfXF9tvv32ncxXbIMEdutTmbzDwfNwRtsT9oBl3hGbcEZpxR9gS94Nm3BGacUdoF8EdupRvLDTjjrAl7gfNuCM0447QjDvClrgfNOOO0Iw7QrsI7tClfGOhGXeELXE/aMYdoRl3hGbcEbbE/aAZd4Rm3BHaRXCHLuUbC824I2yJ+0Ez7gjNuCM0446wJe4HzbgjNOOO0C6CO3Qp31hoxh1hS9wPmnFHaMYdoRl3hC1xP2jGHaEZd4R2EdyhS/nGQjPuCFviftCMO0Iz7gjNuCNsiftBM+4IzbgjtIvgDgAAAAAACQR3AAAAAABIILgDAAAAAEACwR0AAAAAABII7gAAAAAAkEBwBwAAAACABII7AAAAAAAkENyhizz99NNx/fXXxzve8Y54xSteEb29vVFKiQ9+8IMt/f9/+tOfxvHHHx8zZsyI3t7emDZtWhx44IHx2c9+ts0nZ6hs7R3Z7JZbbomenp4opcRb3vKWNp2WThjMHXnsscfiM5/5TKxcuTIWLFgQY8aMiQkTJsTSpUvjuuuui+eee24IPwPaaWu+hjz44INx0kknxfTp06Ovry8WLFgQ5513Xqxbt24ITs5wsW7durj00ktjr732irFjx0ZfX1/Mnz8/TjvttHj00Uc7fTyGkS996UuxfPnymDJlSvT19cXMmTNj1apV8YMf/KDTR2MYueiii6KUEqWU+MIXvtDp49Bhv/rVr+Lyyy+Pgw8+OGbOnNn/77SrVq2K73//+50+HkNo3bp1cf7558eCBQuir68vpk+fHieddFI8+OCDnT4a2wjBHbrInXfe2f8Xzj9cKyHk6quvjpEjR8bIkSNjv/32i+OOOy4OOuigmDRpUhxyyCFDcHqGwtbckc3Wr18fu+++u+C+jRrMHTnhhBOilBKjR4/u//pxwAEHxIgRI6KUEq973eti06ZNQ/hZ0C6D/Rqydu3amDp1apRSYs8994zVq1fH3Llzo5QS++67b6xfv36IPgM6ad26dbHPPvtEKSUmT54cr371q2PVqlWxyy67RCkldt5557j//vs7fUw6bNOmTXH88cdHKSXGjRsXhx12WBx77LGx7777Rm9vb1xyySWdPiLDxL333ht9fX39fycV3Nn8/WT8+PGxfPnyWL16dey5555RSomenp746Ec/2ukjMgTWrVsX++23X5RSYvr06bF69er+v39MnTo11q5d2+kjsg0Q3KGLrF27Nk4++eT41Kc+FXfccUecc845LYWQG2+8MXp6emLu3Lnxi1/8ovLahg0b4s4772znsRlCg70jf+jcc8+Nnp6eOOWUUwT3bdBg7sjpp58eH/rQh+Lxxx+vfPy2226L8ePHRyklPvWpT7X76AyBwX4NWbp0aZRS4vTTT+//2MaNG2PVqlVRSonzzz+/3UdnGLjqqquilBJLliyJJ598sv/j69evj2OOOSZKKfHGN76xgydkOHjPe94TpZR41ateVfu+8sQTT8SaNWs6dDKGk+eeey6WLl0a06ZNi9e+9rWCOxERsXz58vj85z8fzz77bOXjn/zkJ6OUEiNHjox77rmnQ6djqJx33nn9P9Tx+9//vv/jV1xxRZRSYunSpR08HdsKwR262AUXXNA0hGzatClmz54dI0aMiLvuumsIT8dw0Mod+UP33HNP9Pb2ximnnBKf+9znBPcu8ELvyECXXXZZlFLiwAMPTD4Zw0Er9+O2226LUkrstNNOtZ9kf+yxx2L06NExadKk2LBhQ7uPS4cdffTRUUqJL37xi7XXNv/2xKJFizpwMoaLNWvWxMiRI2O33XaL//iP/+j0cRjGPv3pT0cpJW644YY48cQTBXeaWrFiRZRS4sILL+z0UWijDRs2xMSJE6OUEnfccUft9b322itKKfHTn/60A6djWyK4QxdrJYR885vfjFJKrFixYghPxnDxQmLqc889F/vtt19MnTo1Hn/8ccG9S2xtcL/xxhujlBILFy5MPhnDQSv34/zzz49SSpx88skNXz/44IOjlBLf+9732nRKhovNjwnZUnDff//9O3AyhovNP91+2WWXdfooDGOPPvpoTJw4sf+xl4I7rTj77LOjlBJvfvObO30U2ujmm2+OUkrMmzev4esXX3xxlFLiggsuGNqDsc0R3KGLtRJCzjrrrCilxIc//OH493//9/jEJz4Rb3vb2+KMM86IG264wXN1t3EvJKZec801UUqJ66+/PiJCcO8SWxvcr776ar+6uQ1r5X5s/lX/a665puHr7373u6OUEldeeWW7jskwsfn7xitf+crKI2WeffbZ/kfKfPrTn+7gCem0l73sZVFKidtvvz1+85vfxKWXXhpvfvOb433ve1985zvf6fTxGCaOOeaY6Ovr63+8kOBOKzb/lpXH2G3bPvrRj0YpJY455piGr3/jG9+IUkqsXLlyiE/GtkZwhy7WSgg5/PDDo5QSF110UcyYMaP2Rnhz5szxnLttWKsx9eGHH47x48fHQQcd1P8xwb07bE1w37BhQyxatChKKXHFFVe04XR0Wiv346UvfWmUUuJrX/taw9evvPLKKKXEu971rnYdk2Fi06ZN/WF98uTJ8ZrXvKb/TVPHjx8fl156aaePSIeNGTMmSinx8Y9/PPr6+mp/Lz300EMr/7GG7vP1r3+9/99dNhPcaWbt2rX9X1M8SmTbduaZZ0YpJc4888yGr991111RSom99957iE/GtkZwhy7WSghZsmRJlFJi1KhRMW/evLjpppviqaeeip/97Gdx6KGH9kf3Z555ZghPzlBpNaauXLkyent749577+3/mODeHbYmuL/3ve/t/xriWbzbplbux4IFC6KU8rw/nXrttdf6Fe8usmnTpv7favjDLV26NG655ZZOH48OWrduXf99GDVqVKxYsSLuvvvueOqpp+I73/lOzJkzJ0opceyxx3b6qHTI73//+5g5c2YsXLiw8lu4gjtbsnHjxjjggAN8/egSp556apRS4pxzzmn4+n333edxl6QQ3OGPyNFHHx277777C9qtt976vH9eKyHk5S9/ef87tv/yl7+svLZ+/frYddddo5QS1157bdrnyeB14o58+ctfbvjrl4L78NSJO9LI5z//+ejp6YkxY8bEj3/84639tEjSifsxf/78KKXETTfd1PD1zW98J7gPf1t7f5544olYtmxZjB07Nq666qp46KGH4oknnoivfvWrMXPmzBg1alR85Stf6eBnyNbamjvy9NNP9wf3WbNmxbPPPlv5s+++++4YMWJE9PT09D9KhD8uW/s15LTTTotSSnz3u9+t/LmC+7Yj++8pERFvfetbo5QSc+fOjccff3yIPhM65ZRTTolSSpx77rkNX1+zZo3gTgrBHf6IbH5u5QvZlt5krpUQctBBB23xTco2v3nVCSecsLWfHgmG+o48+eSTMWPGjFiwYEGsW7eu8prgPjx14uvIQN/+9rejt7c3Ro4cKZ4NM524Hx4ps+3Y2vuzOYo1el7/7bffHiNGjIjddtstNm7cOISfFZm29o6MGDFiiz+ZuM8++/hBkD9iW3M/br311hgxYkS84Q1vqP25gvu2I/vvKRdddFGUUmLatGlx3333Dd0nQsd4pAxDRXCHLtZKCDnppJOilBLHH398w9c/8YlPRCklli9f3q5j0kHN7sj3vve9KKXE7NmzY9myZZXtvvvuUUqJ6dOnx7Jly+Lkk08e4tMzFF5ocP/JT34S48aNi56envjsZz/b5tPRad40lVZt2rQpent7o5QSDz30UMN/Zt68eVFKiV//+tdDfDqGi82PjXm+N89dvXp1lFI8778Lbf5+85KXvKT2d9Jp06ZFKSUWLVoUy5Yti6uvvrrTx2UY+PjHPx6llJgwYULceeednT4OQ8SbpjJUBHfoYq2EkI997GNRSonDDz+84euXXHJJlFJi1apV7TomHdRqcG9lL37xi4f49AyFFxLcf/GLX8TkyZOjlBIf+chHhuB0dFor9+P888+PUsrz/ke5gw8+OEopcfPNN7frmAwDjzzySP/3i6eeeqrhP7P5tyE8hqp7HXXUUVFKicsvv7zh64ccckiUUuKqq64a4pPRaZu/37SyM844o9PHpcNuuOGG6OnpibFjx8YPf/jDTh+HIXTzzTdHKSXmzZvX8PWLL744Sqk/LhVeKMEdulgrIeT++++Pnp6emDJlSjz99NO11zf/i80ll1zSzqPSIVvzhpgeKdMdWr0j999/f8yYMSNKKXHhhRcO0enotFbux6233hqllNhpp50qb3IXEfHYY4/F6NGjY8KECbFhw4Z2H5cOWr9+ff9PuDf6jytPPvlkjB07Nkop8cgjj3TghAwH1113XZRS4rDDDqu99tRTT8WkSZOilBI/+MEPOnA6hiuPlOEP3XjjjTFq1Kjo7e2Nb33rW50+DkPs2WefjQkTJkQpJe64447a63vttVeUUuK2227rwOnYlgju0MVaDWXHHXdclFLi1FNPrTw39TOf+UyUUmLMmDHxwAMPtPu4dIDgTjOt3JF/+7d/639jzLPOOmsIT0entfo1ZP/996/91OHGjRv7f5r1+d7Yim3LkUce2f9IiD+M6uvWrYsTTjghSnn+95ShO6xfvz5mz54dpZS47rrr+j++cePG/scg7rnnnvHcc8918JQMN4I7m/3whz+M7bbbzptwd7lzzjknSimx3377VX6o8IorrohSShxwwAEdPB3bCsEduszKlStjyZIlsWTJkthll12ilBIzZ87s/1ijZ5X97nc0vAA+AAAUkUlEQVS/i4ULF0YpJebMmRNHHXVU7L333lFKiZEjR8b111/fgc+EdhnMHWlEcN92vdA7snLlyiilxNixY+PEE09sOCF+2zGYryFr1qyJKVOmRCklXvSiF8Wxxx4bc+fOjVJKLFmypPamzGyb1q5d2/+s5R122CFWrFgRr33ta/t/O2by5Mnx85//vNPHpMN+/OMfx7hx4/rf1O6oo47qj/BTpkyJu+++u9NHZJgR3Nls4sSJ/f9O+3x/J/Wmy9u+devWxZIlS/rfb2z16tX9/3vKlCneQJcUgjt0mVmzZm3xmYazZs1q+P978skn4+yzz465c+dGb29vTJkyJY488sj40Y9+NLSfAG032DsykOC+7Xqhd2TZsmVNn6fa6r1i+Bvs15AHHngg3vSmN8XOO+8cvb29MW/evDj33HPjmWeeGdpPgI567LHH4swzz4w99tgjxowZE319fTF//vx4+9vfHg8++GCnj8cwsWbNmjjhhBNi2rRpMXr06Nh1113j1FNPjd/+9redPhrDkODOZq084//EE0/s9DEZAs8880ycd955MW/evOjt7Y1p06bFiSee6Df3SSO4AwAAAABAAsEdAAAAAAASCO4AAAAAAJBAcAcAAAAAgASCOwAAAAAAJBDcAQAAAAAggeAOAAAAAAAJBHcAAAAAAEgguAMAAAAAQALBHQAAAAAAEgjuAAAAAACQQHAHAAAAAIAEgjsAAAAAACQQ3AEAAAAAIIHgDgAAAAAACQR3AAAAAABIILgDAAAAAEACwR0AAAAAABII7gAAAAAAkEBwBwAAAACABII7AAAAAAAkENwBAAAAACCB4A4AAAAAAAkEdwAAAAAASCC4AwAAAABAAsEdAAAAAAASCO4AAAAAAJBAcAcAAAAAgASCOwAAAAAAJBDcAQAAAAAggeAOAAAAAAAJBHcAAAAAAEgguAMAAAAAQALBHQAAAAAAEgjuAAAAAACQQHAHAAAAAIAEgjsAAAAAACQQ3AEAAAAAIIHgDgAAAAAACQR3AAAAAABIILgDAAAAAEACwR0AAAAAABII7gAAAAAAkEBwBwAAAACABII7AAAAAAAkENwBAAAAACCB4A4AAAAAAAkEdwAAAAAASCC4AwAAAABAAsEdAAAAAAASCO4AAAAAAJBAcAcAAAAAgASCOwAAAAAAJBDcAQAAAAAggeAOAAAAAAAJBHcAAAAAAEgguAMAAAAAQALBHQAAAAAAEgjuAAAAAACQQHAHAAAAAIAEgjsAAAAAACQQ3AEAAAAAIIHgDgAAAAAACQR3AAAAAABIILgDAAAAAEACwR0AAAAAABII7gAAAAAAkEBwBwAAAACABII7AAAAAAAkENwBAAAAACCB4A4AAAAAAAkEdwAAAAAASCC4AwAAAABAAsEdAAAAAAASCO4AAAAAAJBAcAcAAAAAgASCOwAAAAAAJBDcAQAAAAAggeAOAAAAAAAJBHcAAAAA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width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### AUTO-GRADER TESTS FOR POLICY EVALUATION WITH SAFE POLICY\n",
    "agent_info.update({\"policy\": policy})\n",
    "v = run_experiment(env_info, agent_info,\n",
    "               experiment_name=\"Policy Evaluation On Safe Policy\",\n",
    "               num_episodes=5000, plot_freq=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "64c386534d040888bfa2fe3ba58f1370",
     "grade": false,
     "grade_id": "cell-3a8720c5685c8716",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [],
   "source": [
    "# Do not modify this cell!\n",
    "\n",
    "# A Near Optimal Stochastic Policy\n",
    "# Now, we try a stochastic policy that deviates a little from the optimal policy seen above. \n",
    "# This means we can get different results due to randomness.\n",
    "# We will thus average the value function estimates we get over multiple runs. \n",
    "# This can take some time, upto about 5 minutes from previous testing. \n",
    "# NOTE: The autograder will compare . Re-run this cell upon making any changes.\n",
    "\n",
    "env_info = {\"grid_height\": 4, \"grid_width\": 12}\n",
    "agent_info = {\"discount\": 1, \"step_size\": 0.01}\n",
    "\n",
    "policy = np.ones(shape=(env_info['grid_width'] * env_info['grid_height'], 4)) * 0.25\n",
    "policy[36] = [0.9, 0.1/3., 0.1/3., 0.1/3.]\n",
    "for i in range(24, 35):\n",
    "    policy[i] = [0.1/3., 0.1/3., 0.1/3., 0.9]\n",
    "policy[35] = [0.1/3., 0.1/3., 0.9, 0.1/3.]\n",
    "agent_info.update({\"policy\": policy})\n",
    "agent_info.update({\"step_size\": 0.01})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "c900a0e55cf506cdae6e3607890bcdbd",
     "grade": true,
     "grade_id": "near_optimal_stochastic_policy",
     "locked": true,
     "points": 40,
     "schema_version": 1,
     "solution": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [01:47<00:00,  3.58s/it]\n"
     ]
    }
   ],
   "source": [
    "### AUTO-GRADER TESTS FOR POLICY EVALUATION WITH NEAR OPTIMAL STOCHASTIC POLICY (40 POINTS)\n",
    "arr = []\n",
    "from tqdm import tqdm\n",
    "for i in tqdm(range(30)):\n",
    "    env_info['seed'] = i\n",
    "    agent_info['seed'] = i\n",
    "    v = run_experiment(env_info, agent_info,\n",
    "                   experiment_name=\"Policy Evaluation On Optimal Policy\",\n",
    "                   num_episodes=5000, plot_freq=10000)\n",
    "    arr.append(v)\n",
    "average_v = np.array(arr).mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "checksum": "74d80436e59bbf99333411fd5ac1675b",
     "grade": false,
     "grade_id": "cell-0276c863cb27066d",
     "locked": true,
     "schema_version": 1,
     "solution": false
    }
   },
   "source": [
    "## Wrapping Up\n",
    "Congratulations, you have completed assignment 2! In this assignment, we investigated a very useful concept for sample-based online learning: temporal difference. We particularly looked at the prediction problem where the goal is to find the value function corresponding to a given policy. In the next assignment, by learning the action-value function instead of the state-value function, you will get to see how temporal difference learning can be used in control as well."
   ]
  }
 ],
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  "coursera": {
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   "display_name": "Python 3",
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