DifferentialMLTF2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialMLTF2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working paper: https://arxiv.org/abs/2005.02347 \n",
    "GitHub: https://github.com/differential-machine-learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differential Deep Learning\n",
    "\n",
    "---\n",
    "Updated for TensorFlow 2\n",
    "---\n",
    "\n",
    "---\n",
    "Antoine Savine, January 2021\n",
    "---\n",
    "\n",
    "This notebook implements the novel ideas of *twin networks* and *differential training* from the working paper [Differential Machine Learning](https://arxiv.org/abs/2005.02347) by Brian Huge and [Antoine Savine](https://antoinesavine.com) (2020), and applies them in a few simple contexts, including the reproduction of some results from the paper. In the article, we presented the ideas and discussed the algorithms, but skipped important implementation details best covered in a notebook with code. Those details are dicussed here.    \n",
    "\n",
    "Although this is a *demonstration* notebook, the algorithms are implemented in self contained, modular and extensible blocks of code. They may be easily extracted, extended and integrated into production systems. Some advanced extensions particularly important in a production context are discussed in the [appendices](https://github.com/differential-machine-learning/appendices/).\n",
    "\n",
    "The code is based on TensorFlow 1.x. You may run the notebook in a local TensorFlow 1.x enviroment *with GPU support*, also with common libraries like numpy and matplotlib. On Google Colab, the correct version of TensorFlow is automatically selected, and all the necessary libraries are preinstalled. All you need is *enable GPU support*: menu runtime/change runtime type and select hardware accelerator GPU.\n",
    "\n",
    "We do not further discuss the algorithms covered in the article or fundamentals of TensorFlow (see classic resources like [Geron's textbook](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291), first edition for TensorFlow 1.x). For modern deep learning and best practices, we refer e.g. to [Goodfellow and al's Deep Learning Book](https://www.deeplearningbook.org/) or [Andrew Ng's deep learning lectures](https://www.coursera.org/specializations/deep-learning). \n",
    "\n",
    "This notebook was ported on TensorFlow 2 in January 2021. The original TensorFlow 1 notebook will no longer be updated. For now, the notebook runs TensorFlow 1 code in a TensorFlow 2 environment. An upgrade to idiomatic TensorFlow 2 code will be available shortly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF version = 2.6.0\n",
      "GPU support =  True\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    %matplotlib notebook\n",
    "except Exception:\n",
    "    pass\n",
    "\n",
    "# import and test\n",
    "import tensorflow as tf2\n",
    "print(\"TF version =\", tf2.__version__)\n",
    "\n",
    "# we want TF 2.x\n",
    "assert tf2.__version__ >= \"2.0\"\n",
    "\n",
    "# disable eager execution etc\n",
    "tf = tf2.compat.v1\n",
    "tf.disable_eager_execution()\n",
    "\n",
    "# disable annoying warnings\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# make sure we have GPU support\n",
    "print(\"GPU support = \", tf.test.is_gpu_available())\n",
    "\n",
    "# import other useful libs\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "# representation of real numbers in TF, change here for 32/64 bits\n",
    "real_type = tf.float32\n",
    "# real_type = tf.float64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part I : Core Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feedforward neural network in TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function builds a classic feedforward neural network in TensorFlow, implementing the set of equations $(3)$ from the article. This is classic deep learning code and a direct translation of equations $(3)$ in Python. We tried to keep notations consistent with the paper.\n",
    "\n",
    "The weights are initialized with TensorFlow's  [variance_scaling_initializer](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/initializers/variance_scaling), implementing the particularly effective [Xavier/Glorot](https://www.deeplearning.ai/ai-notes/initialization/)  initialization strategy, a best practice in modern deep learning. A correct initialization is a key ingredient in an effective practical implementation of deep learning. \n",
    "\n",
    "Since the network is initialized randomly, we also implement seed in the interest of reproducibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vanilla_net(\n",
    "    input_dim,      # dimension of inputs, e.g. 10\n",
    "    hidden_units,   # units in hidden layers, assumed constant, e.g. 20\n",
    "    hidden_layers,  # number of hidden layers, e.g. 4\n",
    "    seed):          # seed for initialization or None for random\n",
    "    \n",
    "    # set seed\n",
    "    tf.set_random_seed(seed)\n",
    "    \n",
    "    # input layer\n",
    "    xs = tf.placeholder(shape=[None, input_dim], dtype=real_type)\n",
    "    \n",
    "    # connection weights and biases of hidden layers\n",
    "    ws = [None]\n",
    "    bs = [None]\n",
    "    # layer 0 (input) has no parameters\n",
    "    \n",
    "    # layer 0 = input layer\n",
    "    zs = [xs] # eq.3, l=0\n",
    "    \n",
    "    # first hidden layer (index 1)\n",
    "    # weight matrix\n",
    "    ws.append(tf.get_variable(\"w1\", [input_dim, hidden_units], \\\n",
    "        initializer = tf.variance_scaling_initializer(), dtype=real_type))\n",
    "    # bias vector\n",
    "    bs.append(tf.get_variable(\"b1\", [hidden_units], \\\n",
    "        initializer = tf.zeros_initializer(), dtype=real_type))\n",
    "    # graph\n",
    "    zs.append(zs[0] @ ws[1] + bs[1]) # eq. 3, l=1\n",
    "    \n",
    "    # second hidden layer (index 2) to last (index hidden_layers)\n",
    "    for l in range(1, hidden_layers): \n",
    "        ws.append(tf.get_variable(\"w%d\"%(l+1), [hidden_units, hidden_units], \\\n",
    "            initializer = tf.variance_scaling_initializer(), dtype=real_type))\n",
    "        bs.append(tf.get_variable(\"b%d\"%(l+1), [hidden_units], \\\n",
    "            initializer = tf.zeros_initializer(), dtype=real_type))\n",
    "        zs.append(tf.nn.softplus(zs[l]) @ ws[l+1] + bs[l+1]) # eq. 3, l=2..L-1\n",
    "\n",
    "    # output layer (index hidden_layers+1)\n",
    "    ws.append(tf.get_variable(\"w\"+str(hidden_layers+1), [hidden_units, 1], \\\n",
    "            initializer = tf.variance_scaling_initializer(), dtype=real_type))\n",
    "    bs.append(tf.get_variable(\"b\"+str(hidden_layers+1), [1], \\\n",
    "        initializer = tf.zeros_initializer(), dtype=real_type))\n",
    "    # eq. 3, l=L\n",
    "    zs.append(tf.nn.softplus(zs[hidden_layers]) @ ws[hidden_layers+1] + bs[hidden_layers+1]) \n",
    "    \n",
    "    # result = output layer\n",
    "    ys = zs[hidden_layers+1]\n",
    "    \n",
    "    # return input layer, (parameters = weight matrices and bias vectors), \n",
    "    # [all layers] and output layer\n",
    "    return xs, (ws, bs), zs, ys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explicit backpropagation and twin network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These functions construct the twin network by explicit backpropagation (eq. $(4)$ in the article). The twin network simultaneously and efficiently predicts values and their differentials to inputs, allowing to train on datasets augmented with differential labels (i.e. gradients of labels to inputs, as explained in the working paper). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute d_output/d_inputs by (explicit) backprop in vanilla net\n",
    "def backprop(\n",
    "    weights_and_biases, # 2nd output from vanilla_net() \n",
    "    zs):                # 3rd output from vanilla_net()\n",
    "    \n",
    "    ws, bs = weights_and_biases\n",
    "    L = len(zs) - 1\n",
    "    \n",
    "    # backpropagation, eq. 4, l=L..1\n",
    "    zbar = tf.ones_like(zs[L]) # zbar_L = 1\n",
    "    for l in range(L-1, 0, -1):\n",
    "        zbar = (zbar @ tf.transpose(ws[l+1])) * tf.nn.sigmoid(zs[l]) # eq. 4\n",
    "    # for l=0\n",
    "    zbar = zbar @ tf.transpose(ws[1]) # eq. 4\n",
    "    \n",
    "    xbar = zbar # xbar = zbar_0\n",
    "    \n",
    "    # dz[L] / dx\n",
    "    return xbar    \n",
    "\n",
    "# combined graph for valuation and differentiation\n",
    "def twin_net(input_dim, hidden_units, hidden_layers, seed):\n",
    "    \n",
    "    # first, build the feedforward net\n",
    "    xs, (ws, bs), zs, ys = vanilla_net(input_dim, hidden_units, hidden_layers, seed)\n",
    "    \n",
    "    # then, build its differentiation by backprop\n",
    "    xbar = backprop((ws, bs), zs)\n",
    "    \n",
    "    # return input x, output y and differentials d_y/d_z\n",
    "    return xs, ys, xbar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vanilla training loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are classic training loops for the feedforward neural network. As customary in modern deep learning, the training set is traversed in mini-batches, where the cost function is minimized with the best practice [ADAM](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/train/AdamOptimizer) algorithm. Andrew Ng's deeplearning.ai web site has an [intuitive presentation of various optimization algorithms in deep learning](https://www.deeplearning.ai/ai-notes/optimization/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vanilla_training_graph(input_dim, hidden_units, hidden_layers, seed):\n",
    "    \n",
    "    # net\n",
    "    inputs, weights_and_biases, layers, predictions = \\\n",
    "        vanilla_net(input_dim, hidden_units, hidden_layers, seed)\n",
    "    \n",
    "    # backprop even though we are not USING differentials for training\n",
    "    # we still need them to predict derivatives dy_dx \n",
    "    derivs_predictions = backprop(weights_and_biases, layers)\n",
    "    \n",
    "    # placeholder for labels\n",
    "    labels = tf.placeholder(shape=[None, 1], dtype=real_type)\n",
    "    \n",
    "    # loss \n",
    "    loss = tf.losses.mean_squared_error(labels, predictions)\n",
    "    \n",
    "    # optimizer\n",
    "    learning_rate = tf.placeholder(real_type)\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)\n",
    "    \n",
    "    # return all necessary \n",
    "    return inputs, labels, predictions, derivs_predictions, learning_rate, loss, optimizer.minimize(loss)\n",
    "\n",
    "# training loop for one epoch\n",
    "def vanilla_train_one_epoch(# training graph from vanilla_training_graph()\n",
    "                            inputs, labels, lr_placeholder, minimizer,   \n",
    "                            # training set \n",
    "                            x_train, y_train,                           \n",
    "                            # params, left to client code\n",
    "                            learning_rate, batch_size, session):        \n",
    "    \n",
    "    m, n = x_train.shape\n",
    "    \n",
    "    # minimization loop over mini-batches\n",
    "    first = 0\n",
    "    last = min(batch_size, m)\n",
    "    while first < m:\n",
    "        session.run(minimizer, feed_dict = {\n",
    "            inputs: x_train[first:last], \n",
    "            labels: y_train[first:last],\n",
    "            lr_placeholder: learning_rate\n",
    "        })\n",
    "        first = last\n",
    "        last = min(first + batch_size, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential training loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The apparently similar *differential* training loop implements the main idea from the paper, to train twin networks on datasets augmented with differentials of labels to inputs, by minimization of a combined cost function reflecting errors in both predicted values *and predicted derivatives*:\n",
    "\n",
    "$$\n",
    "    c = \\alpha c_{\\text{val}} + \\beta c_{\\text{diff}}\n",
    "$$\n",
    "\n",
    "where $c_{\\text{val}}$ is the classic mean square error (MSE) of predictions to labels and $c_{\\text{diff}}$ is the cost of wrong derivatives:\n",
    "\n",
    "$$\n",
    "    c_{\\text{diff}} = \\frac{\\sum_{\\text{inputs j}} \\lambda_j^2 {mse}_j}{\\text{num inputs}}\n",
    "$$\n",
    "\n",
    "where ${mse}_j$ is the mean squared error of derivatives to input $j$ and the weights $\\lambda_j$ normalize derivatives so all the components of the cost have similar magnitudes, as explained in the article. We provide a basic normalization formula for the weights a few cells below, and a complete review of data preparation and weighting in [this appendix](https://github.com/differential-machine-learning/appendices/blob/master/App2-Preprocessing.pdf).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diff_training_graph(\n",
    "    # same as vanilla\n",
    "    input_dim, \n",
    "    hidden_units, \n",
    "    hidden_layers, \n",
    "    seed, \n",
    "    # balance relative weight of values and differentials \n",
    "    # loss = alpha * MSE(values) + beta * MSE(greeks, lambda_j) \n",
    "    # see online appendix\n",
    "    alpha, \n",
    "    beta,\n",
    "    lambda_j):\n",
    "    \n",
    "    # net, now a twin\n",
    "    inputs, predictions, derivs_predictions = twin_net(input_dim, hidden_units, hidden_layers, seed)\n",
    "    \n",
    "    # placeholder for labels, now also derivs labels\n",
    "    labels = tf.placeholder(shape=[None, 1], dtype=real_type)\n",
    "    derivs_labels = tf.placeholder(shape=[None, derivs_predictions.shape[1]], dtype=real_type)\n",
    "    \n",
    "    # loss, now combined values + derivatives\n",
    "    loss = alpha * tf.losses.mean_squared_error(labels, predictions) \\\n",
    "    + beta * tf. losses.mean_squared_error(derivs_labels * lambda_j, derivs_predictions * lambda_j)\n",
    "    \n",
    "    # optimizer, as vanilla\n",
    "    learning_rate = tf.placeholder(real_type)\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)\n",
    "    \n",
    "    # return all necessary tensors, including derivatives\n",
    "    # predictions and labels\n",
    "    return inputs, labels, derivs_labels, predictions, derivs_predictions, \\\n",
    "            learning_rate, loss, optimizer.minimize(loss)\n",
    "\n",
    "def diff_train_one_epoch(inputs, labels, derivs_labels, \n",
    "                         # graph\n",
    "                         lr_placeholder, minimizer,             \n",
    "                         # training set, extended\n",
    "                         x_train, y_train, dydx_train,          \n",
    "                         # params\n",
    "                         learning_rate, batch_size, session):   \n",
    "    \n",
    "    m, n = x_train.shape\n",
    "    \n",
    "    # minimization loop, now with Greeks\n",
    "    first = 0\n",
    "    last = min(batch_size, m)\n",
    "    while first < m:\n",
    "        session.run(minimizer, feed_dict = {\n",
    "            inputs: x_train[first:last], \n",
    "            labels: y_train[first:last],\n",
    "            derivs_labels: dydx_train[first:last],\n",
    "            lr_placeholder: learning_rate\n",
    "        })\n",
    "        first = last\n",
    "        last = min(first + batch_size, m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combined outer training loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The outer training loop optimizes the weights of neural approximators for a number of epochs (complete sweeps of the training set). We applied the recent [one-cycle learning rate schedule](https://arxiv.org/abs/1803.09820) of Leslie Smith and found that it considerably accelerates and stabilizes the training of neural networks. 100 epochs is more than sufficient in most practical cases. A convergence and/or cross-validation test may be included for early stopping. Typical training takes around a second on a decent GPU (longer on Colab's shared GPUs). The approximator class, defined next, holds all the necessary data and parameters, along with the TensorFlow graph and computing session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(description,\n",
    "          # neural approximator\n",
    "          approximator,              \n",
    "          # training params\n",
    "          reinit=True, \n",
    "          epochs=100, \n",
    "          # one-cycle learning rate schedule\n",
    "          learning_rate_schedule=[    (0.0, 1.0e-8), \\\n",
    "                                      (0.2, 0.1),    \\\n",
    "                                      (0.6, 0.01),   \\\n",
    "                                      (0.9, 1.0e-6), \\\n",
    "                                      (1.0, 1.0e-8)  ], \n",
    "          batches_per_epoch=16,\n",
    "          min_batch_size=256,\n",
    "          # callback function and when to call it\n",
    "          callback=None,           # arbitrary callable\n",
    "          callback_epochs=[]):     # call after what epochs, e.g. [5, 20]\n",
    "              \n",
    "    # batching\n",
    "    batch_size = max(min_batch_size, approximator.m // batches_per_epoch)\n",
    "    \n",
    "    # one-cycle learning rate sechedule\n",
    "    lr_schedule_epochs, lr_schedule_rates = zip(*learning_rate_schedule)\n",
    "            \n",
    "    # reset\n",
    "    if reinit:\n",
    "        approximator.session.run(approximator.initializer)\n",
    "    \n",
    "    # callback on epoch 0, if requested\n",
    "    if callback and 0 in callback_epochs:\n",
    "        callback(approximator, 0)\n",
    "        \n",
    "    # loop on epochs, with progress bar (tqdm)\n",
    "    for epoch in tqdm_notebook(range(epochs), desc=description):\n",
    "        \n",
    "        # interpolate learning rate in cycle\n",
    "        learning_rate = np.interp(epoch / epochs, lr_schedule_epochs, lr_schedule_rates)\n",
    "        \n",
    "        # train one epoch\n",
    "        \n",
    "        if not approximator.differential:\n",
    "        \n",
    "            vanilla_train_one_epoch(\n",
    "                approximator.inputs, \n",
    "                approximator.labels, \n",
    "                approximator.learning_rate, \n",
    "                approximator.minimizer, \n",
    "                approximator.x, \n",
    "                approximator.y, \n",
    "                learning_rate, \n",
    "                batch_size, \n",
    "                approximator.session)\n",
    "        \n",
    "        else:\n",
    "        \n",
    "            diff_train_one_epoch(\n",
    "                approximator.inputs, \n",
    "                approximator.labels, \n",
    "                approximator.derivs_labels,\n",
    "                approximator.learning_rate, \n",
    "                approximator.minimizer, \n",
    "                approximator.x, \n",
    "                approximator.y, \n",
    "                approximator.dy_dx,\n",
    "                learning_rate, \n",
    "                batch_size, \n",
    "                approximator.session)\n",
    "        \n",
    "        # callback, if requested\n",
    "        if callback and epoch in callback_epochs:\n",
    "            callback(approximator, epoch)\n",
    "\n",
    "    # final callback, if requested\n",
    "    if callback and epochs in callback_epochs:\n",
    "        callback(approximator, epochs)        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The practical performance of neural networks strongly depends on implementation details, like weight initialization and optimization. Another crucial practicality is the *normalization of training data*. We refer to deep learning texbooks for a discussion of the importance of normalization. One reason is that we need hyperparameters like the learning rate schedule to remain constant over datasets. If notional was to be increased by factor 1M all things equal, gradients would be multiplied by 1M too and learning rates would have to be divided by 1M to keep things similar. Normalizing data avoids manual tinkering of hyperparameters for different datasets. All the examples in the paper: the Gaussian basket, the autocallable trade and the netting set, were all approximated with the exact same hyperparameters. This is only possible with normalized datasets. \n",
    "\n",
    "We implement below a *basic* normalization strategy, where the training inputs and labels are normalized by mean and standard deviation, with differentials normalized accordingly. The differential weights in the cost function $\\lambda_j$ divide costs by the norm of the normalized differentials, keeping similar the magnitude of all the components of the cost.\n",
    "\n",
    "We can do a lot better, especially with differential labels. We could perform successive changes of basis, combined with filtering strategies, to feed neural networks with data orthonormal in terms of inputs *and* in terms of differentials, highlighting the most significant risk factors and filtering out the irrelevant ones. This is covered in detail in the [this appendix](https://github.com/differential-machine-learning/appendices/blob/master/App2-Preprocessing.pdf), where we discuss novel, and extremely effective data preparation algorithms, exploiting information contained in differential labels to considerably alleviate the load of training neural networks and subsequently, improve training performance and reduce sensitivity to hyperparameters. \n",
    "\n",
    "Although the basic normalization implemented here is sufficient for textbook examples, a reliable implementation in production for arbitrary schedules of cash-flows, resilient to very high dimensionality, necessitatesthe more complete data preparation.\n",
    "\n",
    "Note that the prediction of values and derivatives must be adjusted accordingly: prediction inputs must be normalized, and resulting predictions must be 'un-normalized'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic data preparation\n",
    "epsilon = 1.0e-08\n",
    "def normalize_data(x_raw, y_raw, dydx_raw=None, crop=None):\n",
    "    \n",
    "    # crop dataset\n",
    "    m = crop if crop is not None else x_raw.shape[0]\n",
    "    x_cropped = x_raw[:m]\n",
    "    y_cropped = y_raw[:m]\n",
    "    dycropped_dxcropped = dydx_raw[:m] if dydx_raw is not None else None\n",
    "    \n",
    "    # normalize dataset\n",
    "    x_mean = x_cropped.mean(axis=0)\n",
    "    x_std = x_cropped.std(axis=0) + epsilon\n",
    "    x = (x_cropped- x_mean) / x_std\n",
    "    y_mean = y_cropped.mean(axis=0)\n",
    "    y_std = y_cropped.std(axis=0) + epsilon\n",
    "    y = (y_cropped-y_mean) / y_std\n",
    "    \n",
    "    # normalize derivatives too\n",
    "    if dycropped_dxcropped is not None:\n",
    "        dy_dx = dycropped_dxcropped / y_std * x_std \n",
    "        # weights of derivatives in cost function = (quad) mean size\n",
    "        lambda_j = 1.0 / np.sqrt((dy_dx ** 2).mean(axis=0)).reshape(1, -1)\n",
    "    else:\n",
    "        dy_dx = None\n",
    "        lambda_j = None\n",
    "    \n",
    "    return x_mean, x_std, x, y_mean, y_std, y, dy_dx, lambda_j"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting it all together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we put it all together in a *Neural_Approximator* class. Most of the code should be self explanatory. \n",
    "\n",
    "Note that we compute the coefficients $\\alpha$ and $\\beta$ for balancing cost between values and derivatives in a straightforward manner:\n",
    "\n",
    "$$\n",
    "    \\alpha = \\frac{1}{1 + \\lambda n} \\text{ and }  \\beta = \\frac{\\lambda n}{1 + \\lambda n}\n",
    "$$\n",
    "\n",
    "where $n$ is the number of inputs, so an error on a derivative has a weight similar to a value error, and $\\lambda$ is a hyperparameter without significant effect, as explained in the paper, and left to 1, safe for debugging.\n",
    "\n",
    "We implement a simple, feedforward architecture with 4 hidden layers of 20 units, throughout our tests. We kept the same architecture in production, with the addition of a *wide and deep* layer, inspired by [Google](https://ai.googleblog.com/2016/06/wide-deep-learning-better-together-with.html) and discussed in the [this appendix](https://github.com/differential-machine-learning/appendices/blob/master/App4-UnsupervisedTraining.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Neural_Approximator():\n",
    "    \n",
    "    def __init__(self, x_raw, y_raw, \n",
    "                 dydx_raw=None):      # derivatives labels, \n",
    "       \n",
    "        self.x_raw = x_raw\n",
    "        self.y_raw = y_raw\n",
    "        self.dydx_raw = dydx_raw\n",
    "        \n",
    "        # tensorflow logic\n",
    "        self.graph = None\n",
    "        self.session = None\n",
    "                        \n",
    "    def __del__(self):\n",
    "        if self.session is not None:\n",
    "            self.session.close()\n",
    "        \n",
    "    def build_graph(self,\n",
    "                differential,       # differential or not           \n",
    "                lam,                # balance cost between values and derivs  \n",
    "                hidden_units, \n",
    "                hidden_layers, \n",
    "                weight_seed):\n",
    "        \n",
    "        # first, deal with tensorflow logic\n",
    "        if self.session is not None:\n",
    "            self.session.close()\n",
    "\n",
    "        self.graph = tf.Graph()\n",
    "        \n",
    "        with self.graph.as_default():\n",
    "        \n",
    "            # build the graph, either vanilla or differential\n",
    "            self.differential = differential\n",
    "            \n",
    "            if not differential:\n",
    "            # vanilla \n",
    "                \n",
    "                self.inputs, \\\n",
    "                self.labels, \\\n",
    "                self.predictions, \\\n",
    "                self.derivs_predictions, \\\n",
    "                self.learning_rate, \\\n",
    "                self.loss, \\\n",
    "                self.minimizer \\\n",
    "                = vanilla_training_graph(self.n, hidden_units, hidden_layers, weight_seed)\n",
    "                    \n",
    "            else:\n",
    "            # differential\n",
    "            \n",
    "                if self.dy_dx is None:\n",
    "                    raise Exception(\"No differential labels for differential training graph\")\n",
    "            \n",
    "                self.alpha = 1.0 / (1.0 + lam * self.n)\n",
    "                self.beta = 1.0 - self.alpha\n",
    "                \n",
    "                self.inputs, \\\n",
    "                self.labels, \\\n",
    "                self.derivs_labels, \\\n",
    "                self.predictions, \\\n",
    "                self.derivs_predictions, \\\n",
    "                self.learning_rate, \\\n",
    "                self.loss, \\\n",
    "                self.minimizer = diff_training_graph(self.n, hidden_units, \\\n",
    "                                                     hidden_layers, weight_seed, \\\n",
    "                                                     self.alpha, self.beta, self.lambda_j)\n",
    "        \n",
    "            # global initializer\n",
    "            self.initializer = tf.global_variables_initializer()\n",
    "            \n",
    "        # done\n",
    "        self.graph.finalize()\n",
    "        self.session = tf.Session(graph=self.graph)\n",
    "                        \n",
    "    # prepare for training with m examples, standard or differential\n",
    "    def prepare(self, \n",
    "                m, \n",
    "                differential,\n",
    "                lam=1,              # balance cost between values and derivs  \n",
    "                # standard architecture\n",
    "                hidden_units=20, \n",
    "                hidden_layers=4, \n",
    "                weight_seed=None):\n",
    "\n",
    "        # prepare dataset\n",
    "        self.x_mean, self.x_std, self.x, self.y_mean, self.y_std, self.y, self.dy_dx, self.lambda_j = \\\n",
    "            normalize_data(self.x_raw, self.y_raw, self.dydx_raw, m)\n",
    "        \n",
    "        # build graph        \n",
    "        self.m, self.n = self.x.shape        \n",
    "        self.build_graph(differential, lam, hidden_units, hidden_layers, weight_seed)\n",
    "        \n",
    "    def train(self,            \n",
    "              description=\"training\",\n",
    "              # training params\n",
    "              reinit=True, \n",
    "              epochs=100, \n",
    "              # one-cycle learning rate schedule\n",
    "              learning_rate_schedule=[\n",
    "                  (0.0, 1.0e-8), \n",
    "                  (0.2, 0.1), \n",
    "                  (0.6, 0.01), \n",
    "                  (0.9, 1.0e-6), \n",
    "                  (1.0, 1.0e-8)], \n",
    "              batches_per_epoch=16,\n",
    "              min_batch_size=256,\n",
    "              # callback and when to call it\n",
    "              # we don't use callbacks, but this is very useful, e.g. for debugging\n",
    "              callback=None,           # arbitrary callable\n",
    "              callback_epochs=[]):     # call after what epochs, e.g. [5, 20]\n",
    "              \n",
    "        train(description, \n",
    "              self, \n",
    "              reinit, \n",
    "              epochs, \n",
    "              learning_rate_schedule, \n",
    "              batches_per_epoch, \n",
    "              min_batch_size,\n",
    "              callback, \n",
    "              callback_epochs)\n",
    "     \n",
    "    def predict_values(self, x):\n",
    "        # scale\n",
    "        x_scaled = (x-self.x_mean) / self.x_std \n",
    "        # predict scaled\n",
    "        y_scaled = self.session.run(self.predictions, feed_dict = {self.inputs: x_scaled})\n",
    "        # unscale\n",
    "        y = self.y_mean + self.y_std * y_scaled\n",
    "        return y\n",
    "\n",
    "    def predict_values_and_derivs(self, x):\n",
    "        # scale\n",
    "        x_scaled = (x-self.x_mean) / self.x_std\n",
    "        # predict scaled\n",
    "        y_scaled, dyscaled_dxscaled = self.session.run(\n",
    "            [self.predictions, self.derivs_predictions], \n",
    "            feed_dict = {self.inputs: x_scaled})\n",
    "        # unscale\n",
    "        y = self.y_mean + self.y_std * y_scaled\n",
    "        dydx = self.y_std / self.x_std * dyscaled_dxscaled\n",
    "        return y, dydx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part II : Learning Pricing and Risk Functions from LSM samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained in the article, to learn pricing approximations from ground truth labels is not viable for financial applications like regulations or scenario-based risk reports, because the ground truth prices of complex transactions and trading books must be computed with numerical methods like nested Monte-Carlo simulations, where the computation load makes it impossible to simulate training sets in realistic time.\n",
    "\n",
    "Instead, we **learn from samples** *a la* Lonstaff-Schwartz (or **LSM**, from Valuing American Options by Simulation: A Simple Least-Squares Approach, The Review of Financial Studies, 2001). Our inputs are simulated Markov states at some time $T_1$ (called *exposure date* in the context of regulations), labels are *payoffs* (sum of discounted cash-flows paid after $T_1$) picked on the same Monte-Carlo path, and differential labels are **pathwise derivatives** usually computed with automatic adjoint differentiation (AAD, see [textbook](https://amazon.com/Modern-Computational-Finance-Parallel-Simulations-dp-1119539455/dp/1119539455)) (although in the simple cases of Black & Scholes and Bachelier, we easily compute them explicitly).\n",
    "\n",
    "It follows that training sets are produced quickly, for a computation cost similar to *one* Monte-Carlo price, but we now expect the machine to learn pricing and risk functions without having ever seen a price or a delta, only samples. That training still converges to the correct pricing and risk function may be seen somewhat magical. Fortunately, Longstaff & Schwartz' idea of training on samples is backed by solid mathematics and guaranteed to converge to the true pricing function. A sketch of proof is provided [here](https://github.com/differential-machine-learning/appendices/blob/master/App1-LSM.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Black & Scholes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first, and somewhat compulsory example, we learn the pricing and risk function of a European call in Black & Scholes' classic model of 1973, training on simulated LSM samples and verifying performance against the correct pricing function, given by Black & Scholes formula.\n",
    "\n",
    "The simple code below generates a training set of LSM samples, along with pathwise differentials, and a test set of true prices and risks computed in closed form.\n",
    "\n",
    "The implementation is a fast, vectorized Monte-Carlo simulation in numpy. Code should be self explanatory, safe for two maybe unusual features. We allow raising volatility by a factor *volMult* between now (time $0$) and the pricing date $T_1$ to get more samples on the wings and better learn asymptotics. Our labels may also represent *the average of two antithetic samples* instead of one, which reduces variance for an acceptable cost in this simple case. \n",
    "\n",
    "Pathwise differentials are easily computed in Black & Scholes, where:\n",
    "\n",
    "$$\n",
    "    S_{T_2} = S_{T_1} exp \\left[ - \\frac{\\sigma^2}{2} \\left( {T_2} - {T_1} \\right) + \\sigma \\left( W_{T_2} - W_{T_1} \\right)  \\right]\n",
    "$$\n",
    "\n",
    "so\n",
    "\n",
    "$$\n",
    "    \\frac { \\partial \\left( S_{T_2} - K  \\right)^+} {S_{T_1}} = 1_{\\left\\{S_{T_2} > K\\right\\}} \\frac{S_{T_2}}{S_{T_1}}\n",
    "$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper analytics    \n",
    "def bsPrice(spot, strike, vol, T):\n",
    "    d1 = (np.log(spot/strike) + 0.5 * vol * vol * T) / vol / np.sqrt(T)\n",
    "    d2 = d1 - vol * np.sqrt(T)\n",
    "    return spot * norm.cdf(d1) - strike * norm.cdf(d2)\n",
    "\n",
    "def bsDelta(spot, strike, vol, T):\n",
    "    d1 = (np.log(spot/strike) + 0.5 * vol * vol * T) / vol / np.sqrt(T)\n",
    "    return norm.cdf(d1)\n",
    "\n",
    "def bsVega(spot, strike, vol, T):\n",
    "    d1 = (np.log(spot/strike) + 0.5 * vol * vol * T) / vol / np.sqrt(T)\n",
    "    return spot * np.sqrt(T) * norm.pdf(d1)\n",
    "#\n",
    "    \n",
    "# main class\n",
    "class BlackScholes:\n",
    "    \n",
    "    def __init__(self, \n",
    "                 vol=0.2,\n",
    "                 T1=1, \n",
    "                 T2=2, \n",
    "                 K=1.10,\n",
    "                 volMult=1.5):\n",
    "        \n",
    "        self.spot = 1\n",
    "        self.vol = vol\n",
    "        self.T1 = T1\n",
    "        self.T2 = T2\n",
    "        self.K = K\n",
    "        self.volMult = volMult\n",
    "                        \n",
    "    # training set: returns S1 (mx1), C2 (mx1) and dC2/dS1 (mx1)\n",
    "    def trainingSet(self, m, anti=True, seed=None):\n",
    "    \n",
    "        np.random.seed(seed)\n",
    "        \n",
    "        # 2 sets of normal returns\n",
    "        returns = np.random.normal(size=[m, 2])\n",
    "\n",
    "        # SDE\n",
    "        vol0 = self.vol * self.volMult\n",
    "        R1 = np.exp(-0.5*vol0*vol0*self.T1 + vol0*np.sqrt(self.T1)*returns[:,0])\n",
    "        R2 = np.exp(-0.5*self.vol*self.vol*(self.T2-self.T1) \\\n",
    "                    + self.vol*np.sqrt(self.T2-self.T1)*returns[:,1])\n",
    "        S1 = self.spot * R1\n",
    "        S2 = S1 * R2 \n",
    "\n",
    "        # payoff\n",
    "        pay = np.maximum(0, S2 - self.K)\n",
    "        \n",
    "        # two antithetic paths\n",
    "        if anti:\n",
    "            \n",
    "            R2a = np.exp(-0.5*self.vol*self.vol*(self.T2-self.T1) \\\n",
    "                    - self.vol*np.sqrt(self.T2-self.T1)*returns[:,1])\n",
    "            S2a = S1 * R2a             \n",
    "            paya = np.maximum(0, S2a - self.K)\n",
    "            \n",
    "            X = S1\n",
    "            Y = 0.5 * (pay + paya)\n",
    "    \n",
    "            # differentials\n",
    "            Z1 =  np.where(S2 > self.K, R2, 0.0).reshape((-1,1)) \n",
    "            Z2 =  np.where(S2a > self.K, R2a, 0.0).reshape((-1,1)) \n",
    "            Z = 0.5 * (Z1 + Z2)\n",
    "                    \n",
    "        # standard\n",
    "        else:\n",
    "        \n",
    "            X = S1\n",
    "            Y = pay\n",
    "            \n",
    "            # differentials\n",
    "            Z =  np.where(S2 > self.K, R2, 0.0).reshape((-1,1)) \n",
    "        \n",
    "        return X.reshape([-1,1]), Y.reshape([-1,1]), Z.reshape([-1,1])\n",
    "    \n",
    "    # test set: returns a grid of uniform spots \n",
    "    # with corresponding ground true prices, deltas and vegas\n",
    "    def testSet(self, lower=0.35, upper=1.65, num=100, seed=None):\n",
    "        \n",
    "        spots = np.linspace(lower, upper, num).reshape((-1, 1))\n",
    "        # compute prices, deltas and vegas\n",
    "        prices = bsPrice(spots, self.K, self.vol, self.T2 - self.T1).reshape((-1, 1))\n",
    "        deltas = bsDelta(spots, self.K, self.vol, self.T2 - self.T1).reshape((-1, 1))\n",
    "        vegas = bsVega(spots, self.K, self.vol, self.T2 - self.T1).reshape((-1, 1))\n",
    "        return spots, spots, prices, deltas, vegas   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Black & Scholes example is not particularly interesting, but it allows to fix ideas in a simple context. The reason why it is not so interesting is its dimension 1. The only state variable is the spot price $S_{T_1}$. In low dimension, function approximation is an easy problem, effectively resolved by standard deep learning or classic regression or interpolation models. \n",
    "\n",
    "The test functions below train classical and differential networks and display test results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(generator, \n",
    "         sizes, \n",
    "         nTest, \n",
    "         simulSeed=None, \n",
    "         testSeed=None, \n",
    "         weightSeed=None, \n",
    "         deltidx=0):\n",
    "\n",
    "    # simulation\n",
    "    print(\"simulating training, valid and test sets\")\n",
    "    xTrain, yTrain, dydxTrain = generator.trainingSet(max(sizes), seed=simulSeed)\n",
    "    xTest, xAxis, yTest, dydxTest, vegas = generator.testSet(num=nTest, seed=testSeed)\n",
    "    print(\"done\")\n",
    "\n",
    "    # neural approximator\n",
    "    print(\"initializing neural appropximator\")\n",
    "    regressor = Neural_Approximator(xTrain, yTrain, dydxTrain)\n",
    "    print(\"done\")\n",
    "    \n",
    "    predvalues = {}    \n",
    "    preddeltas = {}\n",
    "    for size in sizes:        \n",
    "            \n",
    "        print(\"\\nsize %d\" % size)\n",
    "        regressor.prepare(size, False, weight_seed=weightSeed)\n",
    "            \n",
    "        t0 = time.time()\n",
    "        regressor.train(\"standard training\")\n",
    "        predictions, deltas = regressor.predict_values_and_derivs(xTest)\n",
    "        predvalues[(\"standard\", size)] = predictions\n",
    "        preddeltas[(\"standard\", size)] = deltas[:, deltidx]\n",
    "        t1 = time.time()\n",
    "        \n",
    "        regressor.prepare(size, True, weight_seed=weightSeed)\n",
    "            \n",
    "        t0 = time.time()\n",
    "        regressor.train(\"differential training\")\n",
    "        predictions, deltas = regressor.predict_values_and_derivs(xTest)\n",
    "        predvalues[(\"differential\", size)] = predictions\n",
    "        preddeltas[(\"differential\", size)] = deltas[:, deltidx]\n",
    "        t1 = time.time()\n",
    "        \n",
    "    return xAxis, yTest, dydxTest[:, deltidx], vegas, predvalues, preddeltas\n",
    "\n",
    "def graph(title, \n",
    "          predictions, \n",
    "          xAxis, \n",
    "          xAxisName, \n",
    "          yAxisName, \n",
    "          targets, \n",
    "          sizes, \n",
    "          computeRmse=False, \n",
    "          weights=None):\n",
    "    \n",
    "    numRows = len(sizes)\n",
    "    numCols = 2\n",
    "\n",
    "    fig, ax = plt.subplots(numRows, numCols, squeeze=False)\n",
    "    fig.set_size_inches(4 * numCols + 1.5, 4 * numRows)\n",
    "\n",
    "    for i, size in enumerate(sizes):\n",
    "        ax[i,0].annotate(\"size %d\" % size, xy=(0, 0.5), \n",
    "          xytext=(-ax[i,0].yaxis.labelpad-5, 0),\n",
    "          xycoords=ax[i,0].yaxis.label, textcoords='offset points',\n",
    "          ha='right', va='center')\n",
    "  \n",
    "    ax[0,0].set_title(\"standard\")\n",
    "    ax[0,1].set_title(\"differential\")\n",
    "    \n",
    "    for i, size in enumerate(sizes):        \n",
    "        for j, regType, in enumerate([\"standard\", \"differential\"]):\n",
    "\n",
    "            if computeRmse:\n",
    "                errors = 100 * (predictions[(regType, size)] - targets)\n",
    "                if weights is not None:\n",
    "                    errors /= weights\n",
    "                rmse = np.sqrt((errors ** 2).mean(axis=0))\n",
    "                t = \"rmse %.2f\" % rmse\n",
    "            else:\n",
    "                t = xAxisName\n",
    "                \n",
    "            ax[i,j].set_xlabel(t)            \n",
    "            ax[i,j].set_ylabel(yAxisName)\n",
    "\n",
    "            ax[i,j].plot(xAxis*100, predictions[(regType, size)]*100, 'co', \\\n",
    "                         markersize=2, markerfacecolor='white', label=\"predicted\")\n",
    "            ax[i,j].plot(xAxis*100, targets*100, 'r.', markersize=0.5, label='targets')\n",
    "\n",
    "            ax[i,j].legend(prop={'size': 8}, loc='upper left')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.subplots_adjust(top=0.9)\n",
    "    plt.suptitle(\"% s -- %s\" % (title, yAxisName), fontsize=16)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below trains approximators on LSM samples simulated by our *BlackScholes* class. Classical deep learning is able to learn very accurate approximations in this simple case, so differential learning doesn't make much difference, although it improves the learned shape and differentials on small datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using seed 8923\n",
      "simulating training, valid and test sets\n",
      "done\n",
      "initializing neural appropximator\n",
      "done\n",
      "\n",
      "size 1024\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70753113235648f988859ac026d71579",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ebaaa4c2a0541e095cf7460cc852721",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "size 8192\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efcdb52b8f3d46ceb65cd176ba7b18e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8e39327a0e74964bcf6f48236f1f8a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simulation set sizes to perform\n",
    "sizes = [1024, 8192]\n",
    "\n",
    "# show delta?\n",
    "showDeltas = True\n",
    "\n",
    "# seed\n",
    "# simulSeed = 1234\n",
    "simulSeed = np.random.randint(0, 10000) \n",
    "print(\"using seed %d\" % simulSeed)\n",
    "weightSeed = None\n",
    "\n",
    "# number of test scenarios\n",
    "nTest = 100    \n",
    "\n",
    "# go\n",
    "generator = BlackScholes()\n",
    "xAxis, yTest, dydxTest, vegas, values, deltas = \\\n",
    "    test(generator, sizes, nTest, simulSeed, None, weightSeed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show predicitions\n",
    "graph(\"Black & Scholes\", values, xAxis, \"\", \"values\", yTest, sizes, True)\n",
    "\n",
    "# show deltas\n",
    "if showDeltas:\n",
    "    graph(\"Black & Scholes\", deltas, xAxis, \"\", \"deltas\", dydxTest, sizes, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian basket options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The recent success of deep learning is largely due to their ability to break the long standing *curse of dimensionality* that breaks classic regression models in high dimension. Contrarily to classic linear models, neural networks don't regress on a fixed set of basis functions. They *learn from data* a relevant regression basis in their hidden layers, embedding a powerful dimension reduction capability in their structure. This is why deep learning succeeded in such high dimensional tasks as computer vision, where the dimension of the inputs is the number of pixels in a picture. Convolutional nets effectively learn the low dimensional features that matter to e.g. image recognition, something a classic regression model couldn't do.\n",
    "\n",
    "*Differential* machine learning also shines in high dimension, where differential labels help identify relevant features more effectively. In fact, the additional performance from differential training exponentially increases with dimension. This is why twin networks are particularly effective for learning values of complex transactions or trading books as functions of a high dimensional state. \n",
    "\n",
    "To illustrate this ability in a simple context, we extend the Black & Scholes example to a basket option written on $n$ correlated stocks. In place of the Black & Scholes model, the stocks are simulated in Bachelier's Gaussian model, where the true price of the basket option is known in closed form, and given by Bachelier's formula applied to the basket at $T_1$. Therefore, we can monitor the performance of our approximators by comparison to the correct, analytic  prices and deltas.\n",
    "\n",
    "In addition, the example is particularly interesting because *the price is really a non-linear function of the one-dimensional basket*. We expect the machine to learn that from data, and twin networks achieve this a lot better than feedforward networks. The stellar results of the section 3.1 of the article demonstrate the power of differential learning. \n",
    "\n",
    "The code below follows the exact same logic as the *BlackScholes* simulator to simulate LSM samples for a basket option in a $n$-dimensional, correlated Bachlier model. The correlations, volatility and basket weights are re-generated randomly on every run, allowing to verify performance in multiple configurations. It is a direct extension of the *BlackScholes* simulator and, as such, such be rather self explanatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper analytics\n",
    "def bachPrice(spot, strike, vol, T):\n",
    "    d = (spot - strike) / vol / np.sqrt(T)\n",
    "    return  vol * np.sqrt(T) * (d * norm.cdf(d) + norm.pdf(d))\n",
    "\n",
    "def bachDelta(spot, strike, vol, T):\n",
    "    d = (spot - strike) / vol / np.sqrt(T)\n",
    "    return norm.cdf(d)\n",
    "\n",
    "def bachVega(spot, strike, vol, T):\n",
    "    d = (spot - strike) / vol / np.sqrt(T)\n",
    "    return np.sqrt(T) * norm.pdf(d)\n",
    "#\n",
    "    \n",
    "# generates a random correlation matrix\n",
    "def genCorrel(n):\n",
    "    randoms = np.random.uniform(low=-1., high=1., size=(2*n, n))\n",
    "    cov = randoms.T @ randoms\n",
    "    invvols = np.diag(1. / np.sqrt(np.diagonal(cov)))\n",
    "    return np.linalg.multi_dot([invvols, cov, invvols])\n",
    "#\n",
    "\n",
    "class Bachelier:\n",
    "    \n",
    "    def __init__(self, \n",
    "                 n,\n",
    "                 T1=1, \n",
    "                 T2=2, \n",
    "                 K=1.10,\n",
    "                 volMult=1.5):\n",
    "        \n",
    "        self.n = n\n",
    "        self.T1 = T1\n",
    "        self.T2 = T2\n",
    "        self.K = K\n",
    "        self.volMult = volMult\n",
    "                \n",
    "    # training set: returns S1 (mxn), C2 (mx1) and dC2/dS1 (mxn)\n",
    "    def trainingSet(self, m, anti=True, seed=None, bktVol=0.2):\n",
    "    \n",
    "        np.random.seed(seed)\n",
    "\n",
    "        # spots all currently 1, without loss of generality\n",
    "        self.S0 = np.repeat(1., self.n)\n",
    "        # random correl\n",
    "        self.corr = genCorrel(self.n)\n",
    "\n",
    "        # random weights\n",
    "        self.a = np.random.uniform(low=1., high=10., size=self.n)\n",
    "        self.a /= np.sum(self.a)\n",
    "        # random vols\n",
    "        vols = np.random.uniform(low=5., high = 50., size = self.n)\n",
    "        # normalize vols for a given volatility of basket, \n",
    "        # helps with charts without loss of generality\n",
    "        avols = (self.a * vols).reshape((-1,1))\n",
    "        v = np.sqrt(np.linalg.multi_dot([avols.T, self.corr, avols]).reshape(1))\n",
    "        self.vols = vols * bktVol / v\n",
    "        self.bktVol = bktVol\n",
    "\n",
    "        # Choleski etc. for simulation\n",
    "        diagv = np.diag(self.vols)\n",
    "        self.cov = np.linalg.multi_dot([diagv, self.corr, diagv])\n",
    "        self.chol = np.linalg.cholesky(self.cov) * np.sqrt(self.T2 - self.T1)\n",
    "        # increase vols for simulation of X so we have more samples in the wings\n",
    "        self.chol0 = self.chol * self.volMult * np.sqrt(self.T1 / (self.T2 - self.T1))\n",
    "        # simulations\n",
    "        normals = np.random.normal(size=[2, m, self.n])\n",
    "        inc0 = normals[0, :, :] @ self.chol0.T\n",
    "        inc1 = normals[1, :, :] @ self.chol.T\n",
    "    \n",
    "        S1 = self.S0 + inc0\n",
    "        \n",
    "        S2 = S1 + inc1\n",
    "        bkt2 = np.dot(S2, self.a)\n",
    "        pay = np.maximum(0, bkt2 - self.K)\n",
    "\n",
    "        # two antithetic paths\n",
    "        if anti:\n",
    "            \n",
    "            S2a = S1 - inc1\n",
    "            bkt2a = np.dot(S2a, self.a)\n",
    "            paya = np.maximum(0, bkt2a - self.K)\n",
    "            \n",
    "            X = S1\n",
    "            Y = 0.5 * (pay + paya)\n",
    "    \n",
    "            # differentials\n",
    "            Z1 =  np.where(bkt2 > self.K, 1.0, 0.0).reshape((-1,1)) * self.a.reshape((1,-1))\n",
    "            Z2 =  np.where(bkt2a > self.K, 1.0, 0.0).reshape((-1,1)) * self.a.reshape((1,-1))\n",
    "            Z = 0.5 * (Z1 + Z2)\n",
    "                    \n",
    "        # standard\n",
    "        else:\n",
    "        \n",
    "            X = S1\n",
    "            Y = pay\n",
    "            \n",
    "            # differentials\n",
    "            Z =  np.where(bkt2 > self.K, 1.0, 0.0).reshape((-1,1)) * self.a.reshape((1,-1))\n",
    "            \n",
    "        return X, Y.reshape(-1,1), Z\n",
    "    \n",
    "    # test set: returns an array of independent, uniformly random spots \n",
    "    # with corresponding baskets, ground true prices, deltas and vegas\n",
    "    def testSet(self, lower=0.5, upper=1.50, num=4096, seed=None):\n",
    "        \n",
    "        np.random.seed(seed)\n",
    "        # adjust lower and upper for dimension\n",
    "        adj = 1 + 0.5 * np.sqrt((self.n-1)*(upper-lower)/12)\n",
    "        adj_lower = 1.0 - (1.0-lower) * adj\n",
    "        adj_upper = 1.0 + (upper - 1.0) * adj\n",
    "        # draw spots\n",
    "        spots = np.random.uniform(low=adj_lower, high = adj_upper, size=(num, self.n))\n",
    "        # compute baskets, prices, deltas and vegas\n",
    "        baskets = np.dot(spots, self.a).reshape((-1, 1))\n",
    "        prices = bachPrice(baskets, self.K, self.bktVol, self.T2 - self.T1).reshape((-1, 1))\n",
    "        deltas = bachDelta(baskets, self.K, self.bktVol, self.T2 - self.T1) @ self.a.reshape((1, -1))\n",
    "        vegas = bachVega(baskets, self.K, self.bktVol, self.T2 - self.T1) \n",
    "        return spots, baskets, prices, deltas, vegas    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reuse our testing functions without modification, starting with dimension 1, where the basket is a European call, priced in the Bachelier model, very similar to Black & Scholes. Unsurprisingly, the results are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using seed 4652\n",
      "simulating training, valid and test sets\n",
      "done\n",
      "initializing neural appropximator\n",
      "done\n",
      "\n",
      "size 1024\n"
     ]
    },
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      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
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      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
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     "metadata": {},
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "size 8192\n"
     ]
    },
    {
     "data": {
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       "model_id": "3d72f5ad74cf49a39a98501421df94dc",
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       "version_minor": 0
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      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
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      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# basket / bachelier dimension\n",
    "basketDim = 1\n",
    "\n",
    "# simulation set sizes to perform\n",
    "sizes = [1024, 8192]\n",
    "\n",
    "# show delta?\n",
    "showDeltas = True\n",
    "deltidx = 0 # show delta to first stock\n",
    "\n",
    "# seed\n",
    "# simulSeed = 1234\n",
    "simulSeed = np.random.randint(0, 10000) \n",
    "print(\"using seed %d\" % simulSeed)\n",
    "testSeed = None\n",
    "weightSeed = None\n",
    "    \n",
    "# number of test scenarios\n",
    "nTest = 4096    \n",
    "\n",
    "# go\n",
    "generator = Bachelier(basketDim)\n",
    "xAxis, yTest, dydxTest, vegas, values, deltas = \\\n",
    "    test(generator, sizes, nTest, simulSeed, None, weightSeed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show predicitions\n",
    "graph(\"Bachelier dimension %d\" % basketDim, values, xAxis, \"\", \"values\", yTest, sizes, True)\n",
    "\n",
    "# show deltas\n",
    "if showDeltas:\n",
    "    graph(\"Bachelier dimension %d\" % basketDim, deltas, xAxis, \"\", \"deltas\", dydxTest, sizes, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving on to the dimension 7 of the article, we display predictions and correct values as a function of the underlying basket at $T_1$, for 4096 independent examples. The thickness of the plot measures the ability to learn from data that the value is a fixed function of the current undelying basket. A thin curve reflects that this property is correctly learned. A thick line means that the approximator predicts different values for different sets of stocks corresponding to the same basket, hence, failing to learn the pricing function correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using seed 281\n",
      "simulating training, valid and test sets\n",
      "done\n",
      "initializing neural appropximator\n",
      "done\n",
      "\n",
      "size 4096\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "380433cfd9c44ec4b73fb7eb8ade96df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99907e7ef8704155985d3c1b6ae48253",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "size 8192\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c48963bac9d4be4be3b4466fa248120",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0265bee1b6624b9a95f4688565a47f03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "size 16384\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a5425400fda48e8962be261add56290",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "60a0c00f27cf466eb70fd68cd1efc40d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basket / bachelier dimension\n",
    "basketDim = 7\n",
    "\n",
    "# simulation set sizes to perform\n",
    "sizes = [4096, 8192, 16384]\n",
    "\n",
    "# show delta?\n",
    "showDeltas = True\n",
    "deltidx = 0 # show delta to first stock\n",
    "\n",
    "# seed\n",
    "# simulSeed = 1234\n",
    "simulSeed = np.random.randint(0, 10000) \n",
    "print(\"using seed %d\" % simulSeed)\n",
    "testSeed = None\n",
    "weightSeed = None\n",
    "    \n",
    "# number of test scenarios\n",
    "nTest = 4096    \n",
    "\n",
    "# go\n",
    "generator = Bachelier(basketDim)\n",
    "xAxis, yTest, dydxTest, vegas, values, deltas = \\\n",
    "    test(generator, sizes, nTest, simulSeed, None, weightSeed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show predicitions\n",
    "graph(\"Bachelier dimension %d\" % basketDim, values, xAxis, \"\", \"values\", yTest, sizes, True)\n",
    "\n",
    "# show deltas\n",
    "if showDeltas:\n",
    "    graph(\"Bachelier dimension %d\" % basketDim, deltas, xAxis, \"\", \"deltas\", dydxTest, sizes, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reproduce the result of the article where both networks converge to a correct approximation, but differential training gets there orders of magnitude faster, and especially outperforms on smaller training sets. This is what makes it so particularly relevant for the risk management of financial Derivatives.\n",
    "\n",
    "Below, we test dimension 20. Notice that learning time is virtually unaffected by dimension, and that the performance of the twin network is resilient to high dimensionality, where the standard network starts to struggle. \n",
    "\n",
    "Our simple implementation correctly works in dimension up to 30 something. The more thorough data preparation discussed in [this appendix](https://github.com/differential-machine-learning/appendices/blob/master/App2-Preprocessing.pdf) in necessary for higher dimensions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using seed 3518\n",
      "simulating training, valid and test sets\n",
      "done\n",
      "initializing neural appropximator\n",
      "done\n",
      "\n",
      "size 16384\n"
     ]
    },
    {
     "data": {
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       "model_id": "2c2a785f9a8d4a7fbd5cb5338ea1c0a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82bc3a1ba0934f5ab3469384fc64adac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "size 65536\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c25aaa395fe84b4eadf4b3cad385a1dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f37b113b199f497b944dd987a2d5ee9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "size 131072\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "90446563b3194f9e82ed1d5439e8e8ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "standard training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3394dae5a1b48a8a6f190dca88fa108",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "differential training:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# basket / bachelier dimension\n",
    "basketDim = 50\n",
    "\n",
    "# simulation set sizes to perform\n",
    "sizes = [16384, 4*16384, 8*16384]\n",
    "\n",
    "# show delta?\n",
    "showDeltas = True\n",
    "deltidx = 0 # show delta to first stock\n",
    "\n",
    "# seed\n",
    "# simulSeed = 1234\n",
    "simulSeed = np.random.randint(0, 10000) \n",
    "print(\"using seed %d\" % simulSeed)\n",
    "testSeed = None\n",
    "weightSeed = None\n",
    "    \n",
    "# number of test scenarios\n",
    "nTest = 4096    \n",
    "\n",
    "# go\n",
    "generator = Bachelier(basketDim)\n",
    "xAxis, yTest, dydxTest, vegas, values, deltas = \\\n",
    "    test(generator, sizes, nTest, simulSeed, None, weightSeed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    } else {\n",
       "        alert(\n",
       "            '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",
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       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
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       "    this.context = undefined;\n",
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       "\n",
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       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
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       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
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       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"950\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\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(\n",
       "            '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",
       "\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 = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(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 (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.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 = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\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 toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\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",
       "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",
       "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], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.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,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\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",
       "                img.type = 'image/png';\n",
       "            }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, 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",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "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.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\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",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\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.innerHTML =\n",
       "        '<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 / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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   "source": [
    "# show predicitions\n",
    "graph(\"Bachelier dimension %d\" % basketDim, values, xAxis, \"\", \"values\", yTest, sizes, True)\n",
    "\n",
    "# show deltas\n",
    "if showDeltas:\n",
    "    graph(\"Bachelier dimension %d\" % basketDim, deltas, xAxis, \"\", \"deltas\", dydxTest, sizes, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "# Conclusion: Automatic Adjoint Differentiation (AAD)\n",
    "---\n",
    "\n",
    "We implemented the main ideas of the working paper and verified their unreasonable effectiveness for the approximation of pricing and risk functions in finance. Everything relies on *differential labels*, the gradients of training labels to training inputs, fed to the machine learning model in an augmented dataset. We have seen that training on differentials offers a massive performance improvement, but, of course, the differential labels must be computed first.\n",
    "\n",
    "In particularly simple textbook contexts, like a European call in Black & Scholes or a basket option in multi-dimensional Bachelier, differential labels are easily computed in explicit form. In low dimension, they could be computed by finite differences. In a general case with an arbitrary schedule of complex cash-flows simulated in an arbitrarily sophisticated model, closed form differentials are not available and finite differences are far too slow. In dimension 100, every training example must be computed 101 times to estimate differentials by finite differences. In addition, differentials approximated by finite differences may not be accurate enough for the purpose of training: we don't want the optimizer chasing imprecise differentials.\n",
    "\n",
    "Introduced to finance by the ground breaking *Smoking Adjoints* (Giles and Glasserman, Risk 2006), AAD is a game changing technology allowing to compute differentials of arbitrary computations, automatically, with analytic precision, and for a computation cost of around 2 to 5 times one evaluation, depending on implementation, and *independently on the dimension of the gradient*. \n",
    "\n",
    "AAD arguably constitutes the most significant progress in computational finance of the past 20 years. It gave us real-time risk reports for complex Derivatives trading books and regulations like XVA, as well as instantaneous calibrations. It made differentials massively available for research and development in finance. Quoting the conclusion of our Wilmott piece *Computation graphs for AAD and Machine Learning parts 1, 2 and 3* (Savine, Wilmott Magazine, 2019-2020):\n",
    "\n",
    "*New implementations of AAD are pushing the limits of its efficiency, while quantitative analysts are leveraging them in unexpected ways, besides the evident application to risk sensitivities or calibration.*\n",
    "\n",
    "To a large extent, differential machine learning is another strong application of AAD. It is AAD that gave us the massive number of accurate differentials necessary to implement it, for a very cheap computation cost, and is ultimately responsible for the spectacular performance improvement. The real-world examples in the Risk paper, sections 3.2 and 3.3, were trained on AAD differential labels.\n",
    "\n",
    "The working paper or the complements do not cover AAD. Readers are referred to the (stellar) founding paper. [This textbook](https://www.amazon.com/Modern-Computational-Finance-Parallel-Simulations-dp-1119539455/dp/1119539455) provides a complete, up to date overview of AAD, its applications in finance, and a complete, professional implementation in modern C++.\n",
    "\n",
    "The video tutorial below introduces the core ideas in 15 minutes. Click on the picture to play. \n",
    "\n",
    "[<img src=\"https://miro.medium.com/max/1400/1*bHsIA1jy08p71uZcg8HiIQ.png\" />](https://towardsdatascience.com/automatic-differentiation-15min-video-tutorial-with-application-in-machine-learning-and-finance-333e18c0ecbb)[Towards Data Science: AAD Explained in 15min](https://towardsdatascience.com/automatic-differentiation-15min-video-tutorial-with-application-in-machine-learning-and-finance-333e18c0ecbb)\n",
    "\n"
   ]
  },
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   "execution_count": null,
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   "source": []
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