PY580_Notebook12_2D_Ising_Tensorflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 12: Identifying Phases in the 2D Ising Model with TensorFlow\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning Goal\n",
    "The goal of this notebook is to familiarize the reader with the nuts and bolts on using the TensorFlow package for building Deep Neural Networks.\n",
    "\n",
    "## Overview\n",
    "\n",
    "In this notebook, we show how one can use deep neural nets to classify the states of the 2D Ising model according to their phase. This should be compared with the use of logistic-regression, Random Forests and XG Boost on the same dataset in the previous Notebooks 6 and 9.\n",
    "\n",
    "The Hamiltonian for the classical Ising model is given by\n",
    "\n",
    "$$ H = -J\\sum_{\\langle ij\\rangle}S_{i}S_j,\\qquad \\qquad S_j\\in\\{\\pm 1\\} $$\n",
    "\n",
    "where the lattice site indices $i,j$ run over all nearest neighbors of a 2D square lattice, and $J$ is some arbitrary interaction energy scale. We adopt periodic boundary conditions. Onsager proved that this model undergoes a phase transition in the thermodynamic limit from an ordered ferromagnet with all spins aligned to a disordered phase at the critical temperature $T_c/J=2/\\log(1+\\sqrt{2})\\approx 2.26$. For any finite system size, this critical point is expanded to a critical region around $T_c$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from __future__ import absolute_import, division, print_function\n",
    "import numpy as np\n",
    "seed=12\n",
    "np.random.seed(seed)\n",
    "import sys, os, argparse\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.framework import dtypes\n",
    "# suppress tflow compilation warnings\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "\n",
    "tf.set_random_seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure of the Procedure\n",
    "\n",
    "Constructing a Deep Neural Network to solve ML problems is a multiple-stage process. Quite generally, one can identify the key steps as follows:\n",
    "\n",
    "* ***step 1:*** Load and process the data\n",
    "* ***step 2:*** Define the model and its architecture\n",
    "* ***step 3:*** Choose the optimizer and the cost function\n",
    "* ***step 4:*** Train the model \n",
    "* ***step 5:*** Evaluate the model performance on the *unseen* test data\n",
    "* ***step 6:*** Modify the hyperparameters to optimise performance for the specific data set\n",
    "\n",
    "Below, we sometimes combine some of these steps together for convenience.\n",
    "\n",
    "Notice that we take a rather different approach, compared to the simpler MNIST Keras notebook. We first define a set of classes and functions and run the actual computation only in the very end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Load and Process the Data\n",
    "\n",
    "We begin by writing a `DataSet` class and two functions `read_data_sets` and `load_data` to process the 2D Ising data. \n",
    "\n",
    "The `DataSet` class performs checks on the data shape and casts the data into the correct data type for the calculation. It contains a function method called `next_batch` which shuffles the data and returns a mini-batch of a pre-defined size. This structure is particularly useful for the training procedure in TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataSet(object):\n",
    "\n",
    "    def __init__(self, data_X, data_Y, dtype=dtypes.float32):\n",
    "        \"\"\"Checks data and casts it into correct data type. \"\"\"\n",
    "\n",
    "        dtype = dtypes.as_dtype(dtype).base_dtype\n",
    "        if dtype not in (dtypes.uint8, dtypes.float32):\n",
    "            raise TypeError('Invalid dtype %r, expected uint8 or float32' % dtype)\n",
    "\n",
    "        assert data_X.shape[0] == data_Y.shape[0], ('data_X.shape: %s data_Y.shape: %s' % (data_X.shape, data_Y.shape))\n",
    "        self.num_examples = data_X.shape[0]\n",
    "\n",
    "        if dtype == dtypes.float32:\n",
    "            data_X = data_X.astype(np.float32)\n",
    "        self.data_X = data_X\n",
    "        self.data_Y = data_Y \n",
    "\n",
    "        self.epochs_completed = 0\n",
    "        self.index_in_epoch = 0\n",
    "\n",
    "    def next_batch(self, batch_size, seed=None):\n",
    "        \"\"\"Return the next `batch_size` examples from this data set.\"\"\"\n",
    "\n",
    "        if seed:\n",
    "            np.random.seed(seed)\n",
    "\n",
    "        start = self.index_in_epoch\n",
    "        self.index_in_epoch += batch_size\n",
    "        if self.index_in_epoch > self.num_examples:\n",
    "            # Finished epoch\n",
    "            self.epochs_completed += 1\n",
    "            # Shuffle the data\n",
    "            perm = np.arange(self.num_examples)\n",
    "            np.random.shuffle(perm)\n",
    "            self.data_X = self.data_X[perm]\n",
    "            self.data_Y = self.data_Y[perm]\n",
    "            # Start next epoch\n",
    "            start = 0\n",
    "            self.index_in_epoch = batch_size\n",
    "            assert batch_size <= self.num_examples\n",
    "        end = self.index_in_epoch\n",
    "\n",
    "        return self.data_X[start:end], self.data_Y[start:end]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data itself are being processed in the function `read_data_sets`, which loads the Ising dataset, and splits it into three subsets: ordered, critical and disordered, depending on the temperature which sets the distribution they are drawn from. Once again, we use the ordered and disordered data to create a training and a test data set for the problem. Classifying the states in the critical region is expected to be harder and we only use this data to test the performance of our model in the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle, os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "from urllib.request import urlopen \n",
    "\n",
    "def load_data():\n",
    "\n",
    "    # path to data directory (for testing)\n",
    "    #path_to_data=os.path.expanduser('~')+'/Dropbox/MachineLearningReview/Datasets/isingMC/'\n",
    "\n",
    "    url_main = 'https://physics.bu.edu/~pankajm/ML-Review-Datasets/isingMC/';\n",
    "\n",
    "    ######### LOAD DATA\n",
    "    # The data consists of 16*10000 samples taken in T=np.arange(0.25,4.0001,0.25):\n",
    "    data_file_name = \"Ising2DFM_reSample_L40_T=All.pkl\" \n",
    "    # The labels are obtained from the following file:\n",
    "    label_file_name = \"Ising2DFM_reSample_L40_T=All_labels.pkl\"\n",
    "\n",
    "    #DATA\n",
    "    data = pickle.load(urlopen(url_main + data_file_name)) # pickle reads the file and returns the Python object (1D array, compressed bits)\n",
    "    data = np.unpackbits(data).reshape(-1, 1600) # Decompress array and reshape for convenience\n",
    "    data=data.astype('int')\n",
    "    data[np.where(data==0)]=-1 # map 0 state to -1 (Ising variable can take values +/-1)\n",
    "\n",
    "    #LABELS (convention is 1 for ordered states and 0 for disordered states)\n",
    "    labels = pickle.load(urlopen(url_main + label_file_name)) # pickle reads the file and returns the Python object (here just a 1D array with the binary labels)\n",
    "    \n",
    "    print(\"Finished loading data\")\n",
    "    return data, labels\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "def prepare_data(data, labels, dtype=dtypes.float32, test_size=0.2, validation_size=5000):\n",
    "    \n",
    "    L=40 # linear system size\n",
    "\n",
    "    # divide data into ordered, critical and disordered\n",
    "    X_ordered=data[:70000,:]\n",
    "    Y_ordered=labels[:70000]\n",
    "\n",
    "    X_critical=data[70000:100000,:]\n",
    "    Y_critical=labels[70000:100000]\n",
    "\n",
    "    X_disordered=data[100000:,:]\n",
    "    Y_disordered=labels[100000:]\n",
    "\n",
    "    # define training and test data sets\n",
    "    X=np.concatenate((X_ordered,X_disordered)) #np.concatenate((X_ordered,X_critical,X_disordered))\n",
    "    Y=np.concatenate((Y_ordered,Y_disordered)) #np.concatenate((Y_ordered,Y_critical,Y_disordered))\n",
    "\n",
    "    # pick random data points from ordered and disordered states to create the training and test sets\n",
    "    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, train_size=1.0-test_size)\n",
    "\n",
    "    # make data categorical (i.e [0,1] or [1,0])\n",
    "    Y_train=to_categorical(Y_train)\n",
    "    Y_test=to_categorical(Y_test)\n",
    "    Y_critical=to_categorical(Y_critical)\n",
    "\n",
    "\n",
    "    if not 0 <= validation_size <= len(X_train):\n",
    "        raise ValueError('Validation size should be between 0 and {}. Received: {}.'.format(len(X_train), validation_size))\n",
    "\n",
    "    X_validation = X_train[:validation_size]\n",
    "    Y_validation = Y_train[:validation_size]\n",
    "    X_train = X_train[validation_size:]\n",
    "    Y_train = Y_train[validation_size:]\n",
    "\n",
    "    # create data sets\n",
    "    dataset = {\n",
    "        'train':DataSet(X_train, Y_train),\n",
    "        'test':DataSet(X_test, Y_test),\n",
    "        'critical':DataSet(X_critical, Y_critical),\n",
    "        'validation':DataSet(X_validation, Y_validation)\n",
    "    }\n",
    "\n",
    "    return dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `DataSet` class and the `read_data_sets` function are wrapped in another function: `load_data`. To call the latter, one specifies the sizes for the training, test and validation data sets. This function also contains the local path to the file with the Ising data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_Ising_DNN():\n",
    "    data, labels = load_data()\n",
    "    return prepare_data(data, labels, test_size=0.2, validation_size=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps 2+3: Define the Neural Net and its Architecture, Choose the Optimizer and the Cost Function\n",
    "\n",
    "We can now move on to construct our deep neural net using TensorFlow. To do this, we create a class called `model`. This class contains many useful function methods which break down the construction of the DNN. Unique for TensorFlow is creating placeholders for the variables of the model, such as the feed-in data `self.X` and `self.Y` or the dropout probability `self.dropout_keepprob` (which has to be set to unity explicitly during testing). Another peculiarity is using the `with` scope to give names to the most important operators. While we do not discuss this here, TensorFlow also allows one to visualise the computational graph for the model (see package documentation on [https://www.tensorflow.org/](https://www.tensorflow.org/)).\n",
    "\n",
    "To classify whether a given spin configuration is in the ordered or disordered phase, we construct a minimalistic model for a DNN with a single hidden layer containing $N_\\mathrm{neurons}$ (which is kept variable so we can try out the performance of different sizes for the hidden layer). \n",
    "\n",
    "First, we define two private functions: `_weight_variable` and `_bias_variable`, which we use to set up the precise DNN architecture in the function `create_DNN`. The network architecture thus includes a ReLU-activated input layer, the hidden layer, and the softmax output layer. Notice that the softmax layer is _not_ part of the `create_DNN` function.\n",
    "\n",
    "Instead, the softmax layer is part of the function `create_loss` which, as the name suggests, defines the cross entropy loss function, predefined in TensorFlow's `nn` module. We minimize the cost function using the `SGD` optimizer from the `train` module in the function `create_optimiser`. The latter accepts a dictionary `opt_kwargs` with optimizer arguments to be set externally when defining the DNN.\n",
    "\n",
    "Last, the function `create_accuracy` evaluates the model performance.\n",
    "\n",
    "All these function are called in the `__init__` of our `model` class which sets up the DNN. It accepts the number of hidden neurons $N_\\mathrm{neurons}$ and a dictionary with the optimizer arguments as input, as we shall study the performance of the DNN as a function of these parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class model(object):\n",
    "    def __init__(self, N_neurons, opt_kwargs):\n",
    "        \"\"\"Builds the TFlow graph for the DNN.\n",
    "\n",
    "        N_neurons: number of neurons in the hidden layer\n",
    "        opt_kwargs: optimizer's arguments\n",
    "\n",
    "        \"\"\" \n",
    "\n",
    "        # define global step for checkpointing\n",
    "        self.global_step=tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')\n",
    "\n",
    "        self.L=40 # system linear size\n",
    "        self.n_feats=self.L**2 # 40x40 square lattice\n",
    "        self.n_categories=2 # 2 Ising phases: ordered and disordered\n",
    "\n",
    "\n",
    "        # create placeholders for input X and label Y\n",
    "        self.create_placeholders()\n",
    "        # create weight and bias, initialized to 0 and construct DNN to predict Y from X\n",
    "        self.deep_layer_neurons=N_neurons\n",
    "        self.create_DNN()\n",
    "        # define loss function\n",
    "        self.create_loss()\n",
    "        # use gradient descent to minimize loss\n",
    "        self.create_optimiser(opt_kwargs)\n",
    "        #  create accuracy\n",
    "        self.create_accuracy()\n",
    "\n",
    "\n",
    "    def create_placeholders(self):\n",
    "        with tf.name_scope('data'):\n",
    "            # input layer\n",
    "            self.X=tf.placeholder(tf.float32, shape=(None, self.n_feats), name=\"X_data\")\n",
    "            # target\n",
    "            self.Y=tf.placeholder(tf.float32, shape=(None, self.n_categories), name=\"Y_data\")\n",
    "            # p\n",
    "            self.dropout_keepprob=tf.placeholder(tf.float32, name=\"keep_probability\")\n",
    "\n",
    "\n",
    "    def _weight_variable(self, shape, name='', dtype=tf.float32):\n",
    "        \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n",
    "        # weights are drawn from a normal distribution with std 0.1 and mean 0.\n",
    "        initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "        return tf.Variable(initial, dtype=dtype, name=name)\n",
    "\n",
    "\n",
    "    def _bias_variable(self, shape, name='', dtype=tf.float32):\n",
    "        \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n",
    "        initial = tf.constant(0.1, shape=shape) \n",
    "        return tf.Variable(initial, dtype=dtype, name=name)\n",
    "\n",
    "    \n",
    "    def create_DNN(self):\n",
    "        with tf.name_scope('DNN'):\n",
    "\n",
    "            # Fully connected layer\n",
    "            W_fc1 = self._weight_variable([self.n_feats, self.deep_layer_neurons],name='fc1',dtype=tf.float32)\n",
    "            b_fc1 = self._bias_variable([self.deep_layer_neurons],name='fc1',dtype=tf.float32)\n",
    "\n",
    "            a_fc1 = tf.nn.relu(tf.matmul(self.X, W_fc1) + b_fc1)\n",
    "\n",
    "            # Softmax layer (see loss function)\n",
    "            W_fc2 = self._weight_variable([self.deep_layer_neurons, self.n_categories],name='fc2',dtype=tf.float32)\n",
    "            b_fc2 = self._bias_variable([self.n_categories],name='fc2',dtype=tf.float32)\n",
    "        \n",
    "            self.Y_predicted = tf.matmul(a_fc1, W_fc2) + b_fc2\n",
    "\n",
    "            \n",
    "    def create_loss(self):\n",
    "        with tf.name_scope('loss'):\n",
    "            self.loss = tf.reduce_mean(\n",
    "                            tf.nn.softmax_cross_entropy_with_logits_v2(labels=self.Y, logits=self.Y_predicted)\n",
    "                        )\n",
    "            # no need to use tf.stop_gradient() on labels because labels are placeholders and contain no params\n",
    "            # to be optimized. Backprop will be applied only to the logits. \n",
    "\n",
    "    def create_optimiser(self,opt_kwargs):\n",
    "        with tf.name_scope('optimiser'):\n",
    "            self.optimizer = tf.train.GradientDescentOptimizer(**opt_kwargs).minimize(self.loss,global_step=self.global_step) \n",
    "            #self.optimizer = tf.train.AdamOptimizer(**kwargs).minimize(self.loss,global_step=self.global_step)\n",
    "\n",
    "    def create_accuracy(self):\n",
    "        with tf.name_scope('accuracy'):\n",
    "            correct_prediction = tf.equal(tf.argmax(self.Y, 1), tf.argmax(self.Y_predicted, 1))\n",
    "            correct_prediction = tf.cast(correct_prediction, tf.float64) # change data type\n",
    "            self.accuracy = tf.reduce_mean(correct_prediction)\n",
    "            \n",
    "            \n",
    "            \n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps 4+5: Train the Model and Evaluate its Performance\n",
    "\n",
    "We want to evaluate the performance of our model over a set of different learning rates, and a set of different hidden neurons, i.e. we consider a variable size of the hidden layer. Therefore, we create a function `evaluate_model` which trains and evaluates the performance of our DNN for a fixed number of hidden `neurons` and a fixed SGD learning rate `lr`, and returns the final loss and accuracy for the three data sets of interest.\n",
    "\n",
    "Apart from the number of `neurons` and the learning rate `lr`, `evaluate_model` accepts the data `Ising_Data`. This is done for convenience: loading the data is computationally expensive and we only need to do this once.\n",
    "\n",
    "We train our DNN using mini-batches of size $100$ over a total of $100$ epochs, which we define first. We then set up the optimizer parameter dictionary `opt_params`, and use it to create a DNN model. \n",
    "\n",
    "Running TensorFlow requires opening up a `Session` which we abbreviate as `sess` for short. All operations are performed in this session by calling the `run` method. First, we initialize the global variables in TensorFlow's computational graph by running the `global_variables_initializer`. To train the DNN, we loop over the number of epochs. In each fix epoch, we use the `next_batch` function of the `DataSet` class we defined above to create a mini-batch. The forward and backward passes through the weights are performed by running the `DNN.loss` and `DNN.optimizer` methods. To pass the mini-batch as well as any other external parameters, we use the `feed_dict` dictionary. Similarly, we evaluate the model performance, by running the `DNN.accuracy` function on the same minibatch data. Note that the dropout probability for testing is set to unity. \n",
    "\n",
    "Once we have exhausted all training epochs, we test the final performance on the entire training, test and critical data sets. This is done in the same way as above.\n",
    "\n",
    "Last, we return the loss and accuracy for each of the training, test and critical data sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(neurons, lr, Ising_Data, verbose):\n",
    "    \"\"\"This function trains a DNN to solve the Ising classification problem\n",
    "\n",
    "    neurons: number of hidden neurons\n",
    "    lr: SGD learning rate\n",
    "    Ising_Data: Ising data set\n",
    "    verbose (bool): toggles output during the calculation \n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    training_epochs=100\n",
    "    batch_size=100\n",
    "\n",
    "    # SGD learning params\n",
    "    opt_params=dict(learning_rate=lr)\n",
    "\n",
    "    # create DNN\n",
    "    DNN=model(neurons,opt_params)\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "\n",
    "        # initialize the necessary variables, in this case, w and b\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "\n",
    "        # train the DNN\n",
    "        for epoch in range(training_epochs): \n",
    "\n",
    "            batch_X, batch_Y = Ising_Data['train'].next_batch(batch_size,seed=seed)\n",
    "\n",
    "            loss_batch, _ = sess.run([DNN.loss,DNN.optimizer], \n",
    "                                    feed_dict={DNN.X: batch_X,\n",
    "                                               DNN.Y: batch_Y, \n",
    "                                               DNN.dropout_keepprob: 0.5} )\n",
    "            accuracy = sess.run(DNN.accuracy, \n",
    "                                feed_dict={DNN.X: batch_X,\n",
    "                                           DNN.Y: batch_Y, \n",
    "                                           DNN.dropout_keepprob: 1.0} )\n",
    "\n",
    "            # count training step\n",
    "            step = sess.run(DNN.global_step)\n",
    "\n",
    "\n",
    "        # test DNN performance on entire train test and critical data sets\n",
    "        train_loss, train_accuracy = sess.run([DNN.loss, DNN.accuracy], \n",
    "                                                    feed_dict={DNN.X: Ising_Data['train'].data_X,\n",
    "                                                               DNN.Y: Ising_Data['train'].data_Y,\n",
    "                                                               DNN.dropout_keepprob: 0.5}\n",
    "                                                                )\n",
    "        if verbose: print(\"train loss/accuracy:\", train_loss, train_accuracy)\n",
    "\n",
    "        test_loss, test_accuracy = sess.run([DNN.loss, DNN.accuracy], \n",
    "                                                    feed_dict={DNN.X: Ising_Data['test'].data_X,\n",
    "                                                               DNN.Y: Ising_Data['test'].data_Y,\n",
    "                                                               DNN.dropout_keepprob: 1.0}\n",
    "                                                               )\n",
    "\n",
    "        if verbose: print(\"test loss/accuracy:\", test_loss, test_accuracy)\n",
    "\n",
    "        critical_loss, critical_accuracy = sess.run([DNN.loss, DNN.accuracy], \n",
    "                                                    feed_dict={DNN.X: Ising_Data['critical'].data_X,\n",
    "                                                               DNN.Y: Ising_Data['critical'].data_Y,\n",
    "                                                               DNN.dropout_keepprob: 1.0}\n",
    "                                                               )\n",
    "        if verbose: print(\"crtitical loss/accuracy:\", critical_loss, critical_accuracy)\n",
    "\n",
    "\n",
    "        return train_loss,train_accuracy,test_loss,test_accuracy,critical_loss,critical_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Modify the Hyperparameters to Optimize Performance of the Model\n",
    "\n",
    "To study the dependence of our DNN on some of the hyperparameters, we do a grid search over the number of neurons in the hidden layer, and different SGD learning rates. As we explained in Sec. IX, these searches are best done over logarithmically-spaced points. \n",
    "\n",
    "Since we created the `evaluate_model` function with this in hindsight, below we simply loop over the grid values and call `evaluate_model`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_search(verbose):\n",
    "    \"\"\"This function performs a grid search over a set of different learning rates \n",
    "    and a number of hidden layer neurons.\"\"\"\n",
    "\n",
    "    # load Ising data\n",
    "    Ising_Data = prepare_Ising_DNN()\n",
    "    #Ising_Data=load_data()\n",
    "\n",
    "    # perform grid search over learnign rate and number of hidden neurons\n",
    "    N_neurons=np.logspace(0,3,4).astype('int') # check number of neurons over multiple decades\n",
    "    learning_rates=np.logspace(-6,-1,6)\n",
    "\n",
    "    # pre-alocate variables to store accuracy and loss data\n",
    "    train_loss=np.zeros((len(N_neurons),len(learning_rates)),dtype=np.float64)\n",
    "    train_accuracy=np.zeros_like(train_loss)\n",
    "    test_loss=np.zeros_like(train_loss)\n",
    "    test_accuracy=np.zeros_like(train_loss)\n",
    "    critical_loss=np.zeros_like(train_loss)\n",
    "    critical_accuracy=np.zeros_like(train_loss)\n",
    "\n",
    "    # do grid search\n",
    "    for i, neurons in enumerate(N_neurons):\n",
    "        for j, lr in enumerate(learning_rates):\n",
    "\n",
    "            print(\"training DNN with %4d neurons and SGD lr=%0.6f.\" %(neurons,lr) )\n",
    "\n",
    "            train_loss[i,j],train_accuracy[i,j],\\\n",
    "            test_loss[i,j],test_accuracy[i,j],\\\n",
    "            critical_loss[i,j],critical_accuracy[i,j] = evaluate_model(neurons,lr,Ising_Data,verbose)\n",
    "\n",
    "\n",
    "    plot_data(learning_rates,N_neurons,train_accuracy, 'training')\n",
    "    plot_data(learning_rates,N_neurons,test_accuracy, 'testing')\n",
    "    plot_data(learning_rates,N_neurons,critical_accuracy, 'critical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the data, we used the function `plot_data`, defined below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_data(x,y,data,title=None):\n",
    "\n",
    "    # plot results\n",
    "    fontsize=16\n",
    "\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(data, interpolation='nearest', vmin=0, vmax=1)\n",
    "    \n",
    "    cbar=fig.colorbar(cax)\n",
    "    cbar.ax.set_ylabel('accuracy (%)',rotation=90,fontsize=fontsize)\n",
    "    cbar.set_ticks([0,.2,.4,0.6,0.8,1.0])\n",
    "    cbar.set_ticklabels(['0%','20%','40%','60%','80%','100%'])\n",
    "\n",
    "    # put text on matrix elements\n",
    "    for i, x_val in enumerate(np.arange(len(x))):\n",
    "        for j, y_val in enumerate(np.arange(len(y))):\n",
    "            c = \"${0:.1f}\\\\%$\".format( 100*data[j,i])  \n",
    "            ax.text(x_val, y_val, c, va='center', ha='center')\n",
    "\n",
    "    # convert axis vaues to to string labels\n",
    "    x=[str(i) for i in x]\n",
    "    y=[str(i) for i in y]\n",
    "\n",
    "\n",
    "    ax.set_xticklabels(['']+x)\n",
    "    ax.set_yticklabels(['']+y)\n",
    "\n",
    "    ax.set_xlabel('$\\\\mathrm{learning\\\\ rate}$',fontsize=fontsize)\n",
    "    ax.set_ylabel('$\\\\mathrm{hidden\\\\ neurons}$',fontsize=fontsize)\n",
    "    if title is not None:\n",
    "        ax.set_title(title)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Code\n",
    "\n",
    "As we mentioned in the beginning of the notebook, all functions and classes discussed above only specify the procedure but do not actually perform any computations. This allows us to re-use them for different problems. \n",
    "\n",
    "Actually running the training and testing for every point in the grid search is done below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loading data\n",
      "training DNN with    1 neurons and SGD lr=0.000001.\n",
      "training DNN with    1 neurons and SGD lr=0.000010.\n",
      "training DNN with    1 neurons and SGD lr=0.000100.\n",
      "training DNN with    1 neurons and SGD lr=0.001000.\n",
      "training DNN with    1 neurons and SGD lr=0.010000.\n",
      "training DNN with    1 neurons and SGD lr=0.100000.\n",
      "training DNN with   10 neurons and SGD lr=0.000001.\n",
      "training DNN with   10 neurons and SGD lr=0.000010.\n",
      "training DNN with   10 neurons and SGD lr=0.000100.\n",
      "training DNN with   10 neurons and SGD lr=0.001000.\n",
      "training DNN with   10 neurons and SGD lr=0.010000.\n",
      "training DNN with   10 neurons and SGD lr=0.100000.\n",
      "training DNN with  100 neurons and SGD lr=0.000001.\n",
      "training DNN with  100 neurons and SGD lr=0.000010.\n",
      "training DNN with  100 neurons and SGD lr=0.000100.\n",
      "training DNN with  100 neurons and SGD lr=0.001000.\n",
      "training DNN with  100 neurons and SGD lr=0.010000.\n",
      "training DNN with  100 neurons and SGD lr=0.100000.\n",
      "training DNN with 1000 neurons and SGD lr=0.000001.\n",
      "training DNN with 1000 neurons and SGD lr=0.000010.\n",
      "training DNN with 1000 neurons and SGD lr=0.000100.\n",
      "training DNN with 1000 neurons and SGD lr=0.001000.\n",
      "training DNN with 1000 neurons and SGD lr=0.010000.\n",
      "training DNN with 1000 neurons and SGD lr=0.100000.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "verbose=False\n",
    "grid_search(verbose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Convolutional Neural Nets with TensorFlow\n",
    "\n",
    "We have so far considered each 2D-Ising state as a $(40\\times 40,)$-long 1D vector. This approach neglects any spatial structure of the spin configuration. On the other hand, we do know that in every one of the 2D-Ising states there are *local* spatial correlations between the spins, which we would like to take advantage of to improve the accuracy of our classification model.\n",
    "\n",
    "To this end, all we need to do is modify the class `model` to include convolutional layers. The code below is analogous to the DNN `model` class we discussed in detail above, except for the following noticeable discrepancies:\n",
    "\n",
    "* note the auxiliary variable `X_reshaped` in the function `create_CNN` which casts the 2D-Ising configuration data as a 2D array, in order to bring out the spatial correlations.\n",
    "* new below are the `conv2d` and `max_pool_2x2` functions which help create the convolutional structures of the network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class model(object):\n",
    "    # build the graph for the CNN\n",
    "    def __init__(self,opt_kwargs):\n",
    "\n",
    "        # define global step for checkpointing\n",
    "        self.global_step=tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')\n",
    "\n",
    "        self.L=40\n",
    "        self.n_feats= self.L**2 \n",
    "        self.n_categories=2\n",
    "\n",
    "        # create placeholders for input X and label Y\n",
    "        self.create_placeholders()\n",
    "        # create weight and bias, initialized to 0 and construct CNN to predict Y from X\n",
    "        self.create_CNN()\n",
    "        # define loss function\n",
    "        self.create_loss()\n",
    "        # use gradient descent to minimize loss\n",
    "        self.create_optimiser(opt_kwargs)\n",
    "        # create accuracy\n",
    "        self.create_accuracy()\n",
    "\n",
    "\n",
    "        print(\"finished creating CNN\")\n",
    "\n",
    "    def create_placeholders(self):\n",
    "        with tf.name_scope('data'):\n",
    "            self.X=tf.placeholder(tf.float32, shape=(None,self.n_feats), name=\"X_data\")\n",
    "            self.Y=tf.placeholder(tf.float32, shape=(None,self.n_categories), name=\"Y_data\")\n",
    "            self.dropout_keepprob=tf.placeholder(tf.float32, name=\"keep_probability\")\n",
    "\n",
    "\n",
    "    def create_CNN(self, N_filters=10):\n",
    "        with tf.name_scope('CNN'):\n",
    "            # conv layer 1, 5x5 kernel, 1 input 10 output channels\n",
    "            W_conv1 = self.weight_variable([5, 5, 1, N_filters],name='conv1',dtype=tf.float32) \n",
    "            b_conv1 = self.bias_variable([N_filters],name='conv1',dtype=tf.float32)\n",
    "            X_reshaped = tf.reshape(self.X, [-1, self.L, self.L, 1])\n",
    "            h_conv1 = tf.nn.relu(self.conv2d(X_reshaped, W_conv1, name='conv1') + b_conv1)\n",
    "\n",
    "            # Pooling layer - downsamples by 2X.\n",
    "            h_pool1 = self.max_pool_2x2(h_conv1,name='pool1')\n",
    "            # conv layer 2, 5x5 kernel, 10 input 20 output channels\n",
    "            W_conv2 = self.weight_variable([5, 5, 10, 20],name='conv2',dtype=tf.float32)\n",
    "            b_conv2 = self.bias_variable([20],name='conv2',dtype=tf.float32)\n",
    "            h_conv2 = tf.nn.relu(self.conv2d(h_pool1, W_conv2, name='conv2') + b_conv2)\n",
    "\n",
    "            # Dropout - controls the complexity of the CNN, prevents co-adaptation of features.\n",
    "            h_conv2_drop = tf.nn.dropout(h_conv2, self.dropout_keepprob,name='conv2_dropout')\n",
    "\n",
    "            # Second pooling layer.\n",
    "            h_pool2 = self.max_pool_2x2(h_conv2_drop,name='pool2')\n",
    "\n",
    "            # Fully connected layer 1 -- after second round of downsampling, our 40x40 image\n",
    "            # is down to 7x7x20 feature maps -- maps this to 50 features.\n",
    "            h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*20])\n",
    "\n",
    "            W_fc1 = self.weight_variable([7*7*20, 50],name='fc1',dtype=tf.float32)\n",
    "            b_fc1 = self.bias_variable([50],name='fc1',dtype=tf.float32)\n",
    "\n",
    "            h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n",
    "\n",
    "            # Dropout - controls the complexity of the CNN, prevents co-adaptation of features.\n",
    "            h_fc1_drop = tf.nn.dropout(h_fc1, self.dropout_keepprob,name='fc1_dropout')\n",
    "\n",
    "            # Map the 50 features to 2 classes, one for each phase\n",
    "            W_fc2 = self.weight_variable([50, self.n_categories],name='fc12',dtype=tf.float32)\n",
    "            b_fc2 = self.bias_variable([self.n_categories],name='fc12',dtype=tf.float32)\n",
    "\n",
    "            self.Y_predicted = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n",
    "\n",
    "    def weight_variable(self, shape, name='', dtype=tf.float32):\n",
    "        \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n",
    "        initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "        return tf.Variable(initial,dtype=dtype,name=name)\n",
    "\n",
    "\n",
    "    def bias_variable(self, shape, name='', dtype=tf.float32):\n",
    "        \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n",
    "        initial = tf.constant(0.1, shape=shape)\n",
    "        return tf.Variable(initial,dtype=dtype,name=name)\n",
    "\n",
    "\n",
    "    def conv2d(self, x, W, name=''):\n",
    "        \"\"\"conv2d returns a 2d convolution layer with full stride.\"\"\"\n",
    "        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID', name=name)\n",
    "\n",
    "\n",
    "    def max_pool_2x2(self, x,name=''):\n",
    "        \"\"\"max_pool_2x2 downsamples a feature map by 2X.\"\"\"\n",
    "        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n",
    "                                strides=[1, 2, 2, 1], \n",
    "                                padding='VALID',\n",
    "                                name=name\n",
    "                                )\n",
    "\n",
    "\n",
    "    def create_loss(self):\n",
    "        with tf.name_scope('loss'):\n",
    "            self.loss = tf.reduce_mean(\n",
    "                            tf.nn.softmax_cross_entropy_with_logits(labels=self.Y,logits=self.Y_predicted)\n",
    "                        ) \n",
    "\n",
    "    def create_optimiser(self,kwargs):\n",
    "        with tf.name_scope('optimiser'):\n",
    "            self.optimizer = tf.train.GradientDescentOptimizer(**kwargs).minimize(self.loss,global_step=self.global_step) \n",
    "            #self.optimizer = tf.train.AdamOptimizer(**kwargs).minimize(self.loss,global_step=self.global_step)\n",
    "\n",
    "    def create_accuracy(self):\n",
    "        with tf.name_scope('accuracy'):\n",
    "            correct_prediction = tf.equal(tf.argmax(self.Y, 1), tf.argmax(self.Y_predicted, 1))\n",
    "            correct_prediction = tf.cast(correct_prediction, tf.float64)\n",
    "            self.accuracy = tf.reduce_mean(correct_prediction)"
   ]
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   "source": [
    "## Exercises\n",
    "\n",
    "* TensorFlow allows one to visualize the model as a graph. Read the [TensorBoard](https://www.tensorflow.org/get_started/graph_viz) documentation and implement this in the code above. \n",
    "* Read about using [TensorBoard](https://www.tensorflow.org/get_started/graph_viz) to monitor the training and test errors.\n",
    "* Use the CNN `model` defined above to run the Ising classification analysis. Choose the optimal network architecture to improve on the results of the non-convolutional layer. To test the performance of the CNN, use the critical data set. \n",
    "* Does using the `Adam` optimizer improve on the performance on the critical data? Modify the CNN `model` class to do a grid search over the parameters of the `Adam` optimizer."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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